Everybody and their dog has an opinion on AGI right now, and frankly a lot of those opinions are shit. Not because the people are dumb (well, yes, quite a few are), but more so because their definitions suck and they’re ass at making distinctions.

Some set the bar at “passes a Turing test,” which is hilariously low. Others demand consciousness, which is fucking stupid.

As far as I’m concerned the folks claiming current LLMs are already AGI are either retarded, or selling something.

And no, I give not a single fuck if the person who invented the precise term thinks we have AGI. He’s wrong, and clearly has a shitty definition 🖕🏻

So for the record (again) here’s my definition, the only one that really earns the G in AGI.

AGI is a system that:

1. Has genuine understanding. Not rote memorization. Not statistical correlation over token sequences. Actual understanding of how things work, such that it can generalize what it knows to new contexts instantly, the way you and I do.

1. Is capable of making intuitive leaps to solve problems and accomplish goals *outside its training data.* Can connect dots that aren’t otherwise explicitly connected. Can synthesize, not just interpolate.

1. Can do, or is capable of learning to do, anything an average human can learn to do, at least as well as the average human (and ideally in the top decile or top percentile, since average humans kinda suck). Mentally if disembodied, physically as well if embodied. Obviously this includes the need for and ability to learn continuously.

All three are necessary, and none is sufficient alone.

Also while we’re at it, I might as well include my definition of intelligence:

Intelligence = predictive accuracy. Full stop.

Yes, there are other things that support that function, but EVERY one of those things is directly in service to the predictive accuracy.

Moving on.

Understanding ≠ pattern matching.

When object permanence finally clicks into place as a kid, that understanding transfers everywhere, instantly. You don’t have to re-learn it for every new object.

That’s what understanding IS. You learn a principle, you apply it somewhere you’ve never applied it before, and it works because you understand the principle. One-shot.

LLMs don’t have this. GANs definitely don’t have this (though they have gotten better).

A system that actually understands object permanence would nail it in a completely new context it had never been trained on. The generalization would be instant, not statistical fetching.

They could handle occlusion like a boss, which even the best GAN still doesn’t handle perfectly and consistently.

And this makes the “stochastic parrot” question actually testable…can the system get it right the first time in a format it’s never seen? One-shot cross-domain transfer?

Intuitive leaps are NOT the same thing as understanding.

Understanding is about generalizing what you already know. Intuitive leaps are about making or arriving at something new. Synthesis.

You pull together stuff from totally unrelated places. Sensory experience, half-remembered facts, vague pattern fragments, old conversations you forgot you had. And out of all that noise you synthesize something that didn’t exist in any of the inputs. More than the sum of the parts.

Darwin looking at finch beaks, geological strata, livestock breeding, and Malthus, then arriving at natural selection. None of those inputs contain the output. His brain MADE that connection, it wasn’t stored in his weights.

https://youtu.be/X9RYuvPCQUA

LLMs can do something that looks like this, but when an LLM connects dots, those dots were almost always already connected somewhere in the training data.

It found the connection, it didn’t make it.

A system that only finds connections is bounded by its training data no matter how big the dataset gets. A system that makes connections is bounded only by what it can perceive and what it’s willing to imagine.

Generality is what puts the G in AGI.

A system that understands and synthesizes but only in narrow domains is just a very impressive narrow AI. The system has to be able to continuously learn and perform across the full range of things a human can learn, not just the things it was trained on.

If it’s disembodied, it should match or exceed human cognitive performance. If it’s embodied (think robots), physical performance too. Most definitions either demand physical capability (which conflates intelligence with robotics) or ignore the body entirely.

And the “top decile or top percentile” part matters more than you might think. A system that barely clears “average human” on most tasks would technically pass a weak AGI test, but come on, nobody would look at that and feel like the word “general” had been earned.

Average is the floor.

So where are we?

NOT FUCKING THERE YET 🤣

I know that annoys people. There are AI researchers and tech CEOs and PLENTY of unhinged randos on Twitter and Reddit who will tell you with a straight face that we’re basically there. That the next model will cross the line, for real this time.

And the demos ARE impressive, I’m not going to pretend they’re not. Capabilities have improved a lot. I’m impressed both with where we are, and with how far and how fast we got here.

But impressive is not the same thing as general. A calculator is impressive if you’ve never seen one. Doesn’t mean it *understands* math, it just *computes* it.

A close-up magician is impressive if you don’t know the gimmick. Doesn’t mean it’s actual magic 🤣

LLMs are glorified Chinese rooms. I know that pisses people off, but the Chinese Room gets dismissed way too fast because everyone focuses on the wrong part. The standard rebuttal is “but the room as a system understands.”

No it doesn’t. The room FUNCTIONS. It doesn’t fucking understand. Maybe to a functionalist that’s sufficient, but that’s also retarded.

An LLM is a processor, not a synthesizer.

A processor transforms inputs to outputs according to rules. You can make it arbitrarily complex, doesn’t matter, still a processor. You can make the room bigger, hire more people to shuffle symbols faster, add more filing cabinets full of rules. None of that turns processing into understanding or synthesis.

A synthesizer produces outputs that aren’t in the rules or the inputs. Something new shows up, out of distribution.

LLMs are wildly sophisticated processors, and the average human is stupid and/or gullible, and that is exactly why the debate won’t die. When your processor is trained on a trillion tokens and can interpolate across that space with high fidelity, the output LOOKS like synthesis if you don’t know any better.

But it’s not.

The “intuition” is a statistical ghost. And ghosts, like sorcerers, aren’t real no matter how good the special effects are.

**What’s actually missing?**

People in the field are already converging on three things:

World models.

A persistent, bounded self.

Continuous learning.

You can’t generalize across domains without an internal representation of how things actually work. That’s what a world model gives you. An LLM has no world model. It has statistical correlations that sometimes mimic one, it has shadows of our own world model due to our language encoding it, but a system with a real world model can reason about scenarios it’s never seen. It can realistically simulate. Big difference between a system that can talk about physics and one that actually understands and reasons using physics.

Intuitive leaps need a perspective, a place where inputs accumulate and collide over time. That’s what a bounded, persistent self gives you. Without it, there’s no half-formed ideas sitting in the background waiting to smash into something new, and no place in the world.

You know how sometimes you wake up (or shower, or go for a walk) and the answer to a problem just… arrives? Your brain was working on it behind the scenes. An LLM can’t do that (agents are getting closer, but they are not yet fully persistent, and context is limited). Every conversation is stateless. Nothing is brewing. Nothing CAN brew. The pot resets to empty every single time (setting aside LLM “memory,” which is not really memory but a context and retrieval hack.

And a system frozen at training time is bounded by its training distribution forever. Doesn’t matter how large it is.

Continuous learning is what lets a system expand its own capability frontier in real time. You and I don’t stop learning when we leave school. We don’t stop learning EVER. An AGI by definition can’t stop either.

These aren’t independent problems, by the way. A world model without persistence can’t accumulate refinements. Persistence without continuous learning is just a static memory bank. Continuous learning without a world model is just appending data with no structure to hang it on. Pull any one out and the other two fall apart. You need the whole stack or you don’t have AGI. You just have a really good tool.

**So no. We do NOT have AGI.**

We have very impressive processors that fake understanding and mimic synthesis through sheer scale and linguistic relational correlation. The coverage is broad enough that the gaps are hard to spot in casual conversation.

But the gaps are real, and more parameters alone won’t fully close them. More data won’t fully close them. More scale will get you a better processor, but it won’t get you a genuine synthesizer.

I still think we’ll get there, and soon (within the next ~year), but nothing in the public eye today truly earns the G in AGI.

Ignore anyone who says otherwise.

I’ve been thinking and reading about gravity and UAP and Tesla for many years, and I just keep finding myself coming back to a few thoughts:

• Spacetime is a unobserved, and feels like bullshit placeholder. The physical content of GR is about how things move along geodesics in response to energy distributions. And that same physical content could in principle be reproduced by a different mathematical framework, such as a field theory on a flat background or an emergent geometry from an underlying quantum field.

• If there is a universal quantum field, then all the things we call forces are nothing more than excitations in said field.

• Tesla was the Ramanujan of electricity, and everything he worked on involved EM fields (particularly rotating EM fields)…if anyone alive could have arrived at this truth from a practical perspective, he could have.

• UFOs / UAP are fascinating…while I’m highly skeptical of the alien explanation, I see no reason why we couldn’t have built these ourselves, given the above.

• Why, exactly, do most UFO / UAP have one of 3 shapes? Disc, triangle, or cylinder? What is so special about those shapes?

• What would have to be true about physics for the reported characteristics of these craft to be true?

Having approached this from a few different directions, I’ve come to the following…give it a read and let me know what you think :)

When people talk about Nikola Tesla, they usually focus on the typical stuff: the AC motor and the Edison feud, the pigeons and the celibacy and the death ray claims and the weird hotel room habits.

But did you know that Tesla spent the last decades of his life working on something he called his “Dynamic Theory of Gravity,” and he claimed he’d solved it?

He said it would “put an end to idle speculations and false conceptions, as that of curved space.” He was convinced Einstein had taken a wrong turn, that curved spacetime was fundamentally the wrong model. Even Einstein, after developing GR, said space must have physical properties and you can call that an ether if you want.

Tesla thought gravity was a field effect, something that could be manipulated with the right technology.

Tesla and Einstein disagreed on the formalism but were actually converging on the same underlying intuition: there's something physically real filling space, and both gravity and electromagnetism are manifestations of it.

Alas, Tesla died before publishing any of it, and most physicists had written him off as a crank by then anyway…

But what if Tesla was onto something real?

Tesla thought in fields, always fields. Never particles, never geometry. Just fields and waves interacting with a medium. His entire sequence of inventions were built on that foundation, and he had one insight that nobody else had at the time: rotating fields are fundamentally different from static fields. He’d literally invented the AC motor based on this principle.

He knew from direct experience that when you take electromagnetic fields and make them rotate through phase relationships (not mechanical spinning, phase relationships), you get effects you can’t get any other way. His whole career came from this one insight.

So when he turned his attention to gravity, what do you think he focused on?

Rotating electromagnetic fields.

What I find most interesting though is the following…not only does the math back him up, but the most commonly reported UFO / UAP shapes, all of which seem to defy gravity in an unconventional way, align with optimal shapes for generating and using rotating electromagnetic fields.

First the math.

When you write down how electromagnetism couples to gravity in standard GR, you get one term. E² + B². Light has energy, energy curves spacetime, done. Everyone knows this one.

But there’s a second possible term. E·B. The dot product of the electric and magnetic field vectors.

Standard GR doesn’t include it. E·B is a pseudoscalar, which means it flips sign under parity (mirror reflection). And Einstein’s gravity is parity-symmetric. So in standard GR this term is forbidden. It literally can’t couple to curvature without breaking a symmetry that GR assumes is sacred.

Physicists have actually explored this. Chern-Simons modified gravity (Jackiw and Pi, 2003) explicitly couples parity-violating terms to gravity, and there’s an active literature testing for parity violation in gravitational wave observations. So far, no experimental evidence in the regimes they’ve tested. But those tests involve weak-field, far-field conditions, not engineered rotating field configurations at high intensity. They were looking for the right effect in the wrong place.

But if gravity isn’t due to curvature in a separate “spacetime” manifold, but rather if gravity is what happens when energy creates gradients in an underlying quantum field, then the parity rules of the spacetime metric don’t apply anymore. What matters is how the field actually responds to energy configurations, not what some geometric abstraction says is allowed. And E·B is a real, physical field configuration. It measures the helicity of the electromagnetic field, its chirality, its handedness. E² + B² tells you how much energy is present. E·B tells you how that energy is twisted. That’s a fundamentally different kind of information, and if the underlying field responds to chirality, not just magnitude, then E·B matters.

E·B equals zero for almost every electromagnetic configuration you normally encounter. Pure electric field? Zero. Pure magnetic field? Zero. What about perpendicular E and B, or plane electromagnetic waves like light and radio where E and B are always perpendicular? Also zero.

You can get nonzero E·B from static fields if you deliberately align E and B along the same axis. Stick a solenoid inside a parallel plate capacitor and you’re there. But that gives you a fixed, localized E·B locked to your hardware geometry. You can’t steer it, modulate it, or scale it to cover useful volumes. You can make it strong in a small region, but you can’t do anything useful with it.

When does E·B become useful?

When the fields rotate.

When you have a helical, phase-configured, rotating electromagnetic field, E and B develop parallel components through the near-field region. E·B becomes non-zero, coherent, sustained, and controllable. And only then does this second coupling term produce anything that matters for engineering. Only then do you get anomalous gravitational effects you could actually build around.

Tesla figured this out without the math. He didn’t have quantum field theory or lattice gauge theory. He had intuition.

And I suspect he was right.

So what does this mean for building something?

If you wanted a craft that exploits E·B coupling, you’d need rotating electromagnetic fields configured so E·B is maximized and concentrated where you need it. This constrains your geometry hard. You can’t just slap magnets on a box. The shape determines the field configuration, and the field configuration determines whether E·B is zero or useful.

Given these constraints, you end up with just a handful of geometries that make engineering sense: a disc, a triangle, or a long cylinder.

The disc is the classic flying saucer.

If you arrange electromagnetic emitters in a ring around a central axis and phase them with helical progression, you create fields that spiral along the disc’s axis, not just rotate in the plane. In the near-field region, these helical fields develop the parallel E and B components you need. E·B concentrates along the central axis, and the steep gradient of that concentration is what produces force perpendicular to the disc. Want to go up? Disc horizontal. Want to go sideways? Tilt the disc. The disc shape also gives you a natural toroidal field geometry, fields wrapping around the craft in a donut pattern, which is inherently stable.

The disc is the most commonly reported UFO shape, by a lot. Across decades and cultures.

The disc works, and is perhaps the simplest shape for this technology, so that’s what people see most.

Next we have the triangle.

These show up in UFO reports constantly, especially the big slow-moving ones that hover over cities like they don’t care who’s watching. Why triangles? Three-phase power. Tesla’s original AC system used three phases, 120 degrees apart. This is still how we transmit power today. It’s also the most efficient way to generate a rotating field from fixed emitters: put a field emitter at each vertex of a triangle, phase them 120 degrees apart, and you get a rotating field in the center.

The triangle also gives you natural thrust vectoring. With a disc, thrust is always perpendicular to the disc plane. You tilt the disc to steer. With a triangle, you can adjust the relative phase or amplitude at each vertex and shift where E·B concentrates without tilting the craft. This explains why triangular craft are often reported moving at odd angles relative to their orientation. They don’t need to bank to turn. They can vector thrust electromagnetically.

Then there’s stuff like the “tic-tac,” which showed up in the Nimitz encounter in 2004.

Basically a cylinder with rounded ends. Why this shape? Cylindrical symmetry with a preferred axis. Wind electromagnetic coils in a helical pattern around a cylindrical core and you generate a rotating field that propagates along the cylinder’s axis. E·B concentrates along the length, with a gradient that drives force in that direction. This is your high-speed transit configuration. The tic-tac isn’t optimized for hovering or maneuvering. It’s optimized for hauling ass in one direction. Point and go. Commander Fravor’s encounter matches this exactly. The tic-tac dropped from 80,000 feet to sea level in seconds, then zipped off toward a rendezvous point 60 miles away.

Why not spheres?

You might think a sphere would be ideal, being symmetrical in all directions. But that’s exactly the problem. Spherical symmetry means no preferred concentration of E·B anywhere. Everything cancels. You need broken symmetry to get directional thrust, and spheres don’t break symmetry at all. You don’t see a lot of spherical UFO reports, and the ones you do are probably something else entirely.

So the shapes fit. But shapes aren’t the only thing witnesses report. There’s a whole list of characteristics that show up over and over in credible UAP encounters, and every single one of them falls out of this physics naturally.

Insane acceleration: We’re talking objects going from a dead hover to hypersonic in less than a second. Thousands of g’s. Anything with a pilot inside should be paste on the back wall. But if your drive works by generating a local gravitational field gradient, everything inside the craft is in freefall within that gradient. This is just the equivalence principle, the same reason astronauts on the ISS feel weightless even though they’re accelerating constantly. If the craft generates its own gravitational field and then falls through it, there are no differential forces on the structure or anything inside it. Zero g-forces at any acceleration. You could pull 10,000 g’s and not spill your coffee.

Speeds of 40,000+ mph in atmosphere: At those speeds you’d expect a massive fireball, sonic booms that shatter windows for miles, plasma trails across the sky. None of that gets reported. But a craft running high-intensity rotating EM fields at GHz frequencies is surrounded by field strengths that would ionize the air around it into a plasma sheath. Plasma has a lower effective density than air and fundamentally different aerodynamic properties. The craft wouldn’t be pushing through air so much as sliding through a self-generated bubble of ionized gas. And if the gravitational drive is pulling the craft rather than pushing it, there’s no compression shock at the front. No shock wave, no sonic boom.

Transmedium capability: These things go from space to atmosphere to underwater without slowing down. That makes zero sense for any propulsion system that pushes against a medium. Propellers work in air. Propellers don’t work in space. Rockets work in space. Rockets don’t work great underwater. But a gravitational field gradient doesn’t care what medium it’s pulling through. The craft is falling through its own self-generated gravity well. Air, water, vacuum, it’s all the same. The medium is irrelevant because you’re not interacting with the medium for propulsion.

No visible means of propulsion: No exhaust. No jets. No propellant. Of course not. The drive is electromagnetic. Rotating fields generated by internal emitters. There’s nothing to eject, nothing to burn, no reaction mass. The energy goes into field generation, not into heating and throwing mass out the back.

Little to no heat signature: Conventional propulsion is basically controlled combustion. Hot exhaust is the entire mechanism. But E·B coupling converts electromagnetic energy into gravitational field effects. There’s no combustion. The emitters generate heat from resistive and dielectric losses, sure, but that heat is internal and can be managed. From the outside, you see a cold object. No infrared plume. No thermal trail.

No control surfaces: No wings, no fins, no ailerons, no rudder. Why would you need them? Aerodynamic control surfaces exist to redirect airflow and generate differential lift. If your thrust comes from field gradients that you steer electronically by adjusting phase and amplitude at your emitters, the air has nothing to do with it. The craft’s orientation relative to the airflow is irrelevant. Which is exactly why triangular craft get reported moving sideways or at angles that would be aerodynamically impossible.

Difficult to photograph clearly / distorted: Two things here. First, the intense EM fields ionize the surrounding air into a plasma envelope. Plasma has a different refractive index than air, so light passing through it bends. The craft looks blurry, shimmery, indistinct. Second, if the craft is generating genuine gravitational field gradients, those gradients bend light directly. Gravitational lensing. Same effect we see around black holes, just at a vastly smaller scale. The combination of plasma refraction and field-gradient lensing would make these things genuinely hard to photograph clearly even at close range.

Silent hover: No rotor wash, no engine noise, no hum. The drive operates at GHz frequencies, billions of cycles per second. Human hearing tops out around 20,000 Hz. The operating frequency of the drive is roughly a million times above the audible range. There are no moving parts. No air being pushed downward to generate lift. The craft sits in its own gravitational field gradient and just...stays there. Silent.

Every one of these characteristics has been treated as evidence that UAPs must be exotic or even non-terrestrial technology. And maybe they are. But none of them require exotic physics beyond what falls out of E·B coupling to a gravitational field. They’re all just consequences of the drive mechanism.

There’s also frequency to consider. Tesla pushed toward higher frequencies throughout his career. Colorado Springs and the magnifying transmitter. Always higher. He believed high-frequency oscillation was the key to accessing new phenomena.

He was right about that, but probably not how he thought.

The vacuum has structure, quantum field theory tells us that much. But it’s not vibrating at one clean frequency like a tuning fork. There’s no single Planck oscillator humming at 10^43 Hz with neat sub-harmonics you can tune into. The energy spans all frequencies.

But the underlying field does have structure at specific scales. If gravity is a field effect and not a geometric one, then the field has coherence lengths and correlation scales. And any medium with internal structure has frequencies where energy couples in efficiently and frequencies where it just bounces off. Think impedance matching. You’re trying to drive a field at a frequency where it actually absorbs what you’re putting in instead of reflecting it.

Whatever the real scales of quantum gravity turn out to be, they set the coupling windows. And if the relevant correlation lengths fall where the math suggests, the efficient coupling frequencies land in ranges we can actually generate. GHz. Microwave. Not accessible in 1943. Accessible now.

This is why random experiments with strong magnets don’t produce anti-gravity. Wrong frequency, wrong field configuration. You’re pushing on a door that only opens if you knock at the right rhythm.

Tesla’s high-frequency intuition was correct. He just didn’t know how high to go or why specific frequencies would matter.

The power requirements are brutal if the coupling is as weak as naive estimates suggest. We’re talking field strengths we can barely achieve in labs, at GHz frequencies, sustained. Unless there’s enhancement from hitting the right frequency. If you match the field’s coupling window exactly, the coupling amplifies. Maybe by orders of magnitude. At which point power requirements drop into the “difficult but not impossible” range. Megawatts, not petawatts.

Whoever built these things must have figured that out.

So here’s where we end up…Tesla figured out that rotating electromagnetic fields interact with gravity differently than static fields. He didn’t have the math, but he had the intuition. Building on this requires rotating field geometry, high frequency operation in the GHz range, extreme field strengths, and precise phase control. None of that was achievable in 1943.

The shapes in credible UFO reports match these constraints exactly. You see discs when the mission is hovering or general maneuvering, and triangles when electronic thrust vectoring matters more than simplicity. You see tic-tacs when speed is the priority and agility isn’t. And you don’t see spheres or cubes or flying wings or anything resembling conventional aircraft.

The geometry is constrained by physics, and the observed geometries fit.

If this physics works the way I increasingly think it does, then whoever is flying these things figured out what Tesla was working toward. Either some classified program cracked it and has been sitting on the technology for decades, or someone else figured it out first. I don’t know which is more unsettling, but the shapes aren’t arbitrary. They’re consequences of rotating field physics.

Tesla had the first piece, but he died before working out the rest experimentally, and then the US Government seized his work.

Based on the technology and understanding of EM fields in Tesla’s day, could he actually have arrived at this?

Yeah, actually. And this is one of the stronger parts of the argument.

Think about who Tesla was and what he had access to. By the 1890s, he had more hands-on experience with rotating electromagnetic fields than any human being alive. He’d built polyphase AC systems from scratch. He knew the mechanical effects of rotating fields intimately because he’d literally engineered motors around them. He’d spent years at Colorado Springs pushing oscillators to higher and higher frequencies and voltages, observing effects that nobody else was producing because nobody else had the equipment.

And his entire worldview was fields. He didn’t think in particles. He didn’t think in geometry. He thought in fields interacting with a medium. When you spend decades working with rotating fields and you think everything is fields, the question “does a rotating field interact with gravity differently than a static one?” is not a weird question. It’s actually the most natural question a person with his specific background would ask.

He could have noticed anomalous mechanical effects in his high-power rotating field experiments, things that didn’t match the expected electromagnetic forces. Weight changes in equipment, unexpected forces on nearby objects, vibrations that didn’t correlate with any mechanical source. At the power levels and field configurations he was running at Colorado Springs, he was producing rotating EM field intensities that essentially nobody else on Earth was producing. If there’s any coupling at all between rotating EM fields and gravity, even a tiny one, he was the single most likely person on the planet to stumble into it experimentally.

Could he have produced actual gravitational effects? Almost certainly not at useful magnitudes. His frequencies were in the kHz range, not GHz. His field strengths were impressive for the era but nowhere near what the math says you’d need. If the coupling exists but requires GHz frequencies and extreme field strengths to become significant, then what Tesla would have seen is at best a faint anomaly. Unexplained but not reproducible at will. Enough to convince him something real was happening, but not enough to demonstrate it to anyone else or build a theory that physicists of his era would accept.

And that matches the historical record perfectly. He claimed he’d solved gravity, he said he had a complete theory, but he never published it, never demonstrated it, and couldn’t convince the physics establishment. That’s exactly what you’d expect from someone who’d caught a faint whiff of a real effect but didn’t have the technology to nail it down. He had the intuition and possibly some marginal experimental hints. He didn’t have GHz oscillators, modern materials, or the theoretical framework to formalize what he was seeing.

So yes. Given his unique combination of deep rotating-field expertise, a field-centric worldview, and access to high-power EM equipment that nobody else had, Tesla arriving at the concept that rotating electromagnetic fields interact with gravity is not just plausible, it’s arguably the most natural conclusion a person in his exact position would reach. The remarkable part is that the math, developed a century later, actually supports the direction he was pointing.

Given the full resources of the US Gov, and the scientific minds alive at the time, if they got Tesla’s notes in 1943, could they have had something working by 1947?

Yes.

Tesla died on January 7, 1943. The Office of Alien Property seizes his papers within days. John G. Trump, an MIT electrical engineer specializing in high-voltage equipment, reviews them and publicly declares them of no significant value. But Trump wasn’t a random bureaucrat, he was one of the top high-voltage and EM field guys in the country, working on Van de Graaff generators and radar. If Tesla’s notes described anomalous effects from rotating EM fields, Trump was one of maybe a dozen people on Earth qualified to understand what he was reading.

Now look at what the US government had available in 1943…

GHz technology already existed. The cavity magnetron was developed in 1940 at Birmingham, shipped to the US as part of the Tizard Mission, and was being mass-produced for radar by 1943. The entire MIT Radiation Laboratory (where Trump worked, by the way) was dedicated to microwave technology. So the frequency range Tesla couldn’t reach was already accessible.

The Manhattan Project had demonstrated that the US government could take a theoretical physics concept, throw essentially unlimited resources and the best scientific minds in the world at it, and produce a working device in about 3 years. The organizational infrastructure for secret, massive-scale physics engineering already existed and was running at full capacity.

After the war ended in 1945, you have a sudden surplus of both talent and motivation. Thousands of physicists and engineers coming off the bomb project, looking for the next thing. Operation Paperclip is importing German scientists. The Cold War is spinning up and the military wants every possible advantage. Funding is effectively unlimited for anything that looks like it could produce a strategic edge.

So the question becomes: what would they have needed to do between 1943 and 1947?

First, understand what Tesla was describing. If his notes contained observations of anomalous forces during high-power rotating field experiments at Colorado Springs or later, plus his theoretical framework (however informal), that gives you a starting point. You don’t need Tesla’s theory to be rigorous. You just need his experimental observations to be real. Then you reverse-engineer the physics yourself with the talent you have on hand.

Second, reproduce the effect. Build high-power rotating EM field generators at GHz frequencies and see if you get anomalous forces. With the MIT Rad Lab’s microwave expertise and Manhattan Project-level resources, you could probably build and test prototype field generators within a year. If the effect is real, you detect it. If it’s not, you shelve the project.

Third, if the effect is real, engineer it into something that flies. This is the hardest step. Going from “we measured an anomalous force” to “we built a craft that hovers” is a massive engineering leap. But it’s the same kind of leap that went from “we achieved a self-sustaining nuclear chain reaction in a squash court” (December 1942) to “we dropped a working bomb on Hiroshima” (August 1945). Two and a half years from proof of concept to deployed weapon, with sufficient resources and motivation.

The 1943-to-1947 timeline gives you four years. That’s tight but it’s not impossible if the coupling effect turned out to be stronger than naive estimates suggest (which the post already discusses as a possibility), if Tesla’s notes contained actual experimental data rather than just philosophical musings, and if the project got Manhattan Project-level priority.

The first wave of modern UFO sightings starts in 1947. Kenneth Arnold, June 24, 1947. Described objects that moved like saucers skipping across water. Roswell, July 1947. The timing is exactly what you’d expect if someone took Tesla’s 1943 notes, spent a year understanding them, spent a year reproducing the effect, and spent two years engineering a prototype. That’s an aggressive schedule, but it’s the same pace the Manhattan Project ran at.

The counterargument is that John G. Trump really did find nothing of value, Tesla really was just an old man making grandiose claims by 1943, and the timing with 1947 UFO sightings is pure coincidence. That’s possible.

But it requires you to believe that the guy who invented the entire AC power system, who had more hands-on rotating field experience than anyone alive, who specifically claimed to have a theory of gravity based on field effects, had absolutely nothing useful in his notes. And that the government seized those notes, classified them, and returned most but not all of them to his estate out of pure bureaucratic momentum.

Could they have had something working by 1947? If the physics is real and Tesla’s notes contained experimental data, yes. The technology, the talent, the funding, and the organizational infrastructure all existed. Four years is tight but the Manhattan Project proved that timeline is achievable when the motivation is there.

Last but not least, you know what’s REALLY funny?

While a lot of folks have written Bob Lazar off as a liar and nut (and maybe he is, I dunno)…what he described, his “Sport Model” UFO, fits all of the above to a T 😅

So, there you have it. Quite a lot to wade through, but everything fits neatly and makes perfect sense (to me anyway).

Let me know in the comments what YOU think!

Is AGI close? Or is AI retarded?

Is there an AI bubble? Is it about to burst?

Is AI safe? Will it kill us all, enslave us, or set us all free?

Should AI be regulated?

Will the rich share? Or will the divide between haves and have nots widen and get locked in forever?

Will AI generate trillions of dollars in revenue? Or will AI drive deflation and make money worthless?

Will AI take all the jobs? Or will AI create tons of new jobs?

Is AI conscious? Sentient? Can it be either?

For fucks sake it feels like the questions are endless and the hyperbole is running rampant. The rhetoric is trash, the lines are fuzzy, the data is all over the place, and the motivated reasoning seems to be bottomless.

So for shits and giggles let’s poke at each of these and see if we can actually make some headway.

Is AGI Close or is AI retarded?

The answer is BOTH 🤣

Though to be fair, retarded is the wrong word…more like autistic savant, superhuman in some ways but oblivious and shite in others.

The biggest problem however is that most people hear AI and think LLM, and no, an LLM alone isn’t going to get us to AGI, and yes, in many ways LLMs are still idiotic.

So why do I think AGI is close? Because as far as neuroscience can tell, intelligence is modular. Humans for example don’t have a singular “brain” per se, but multiple information processing modules strung together and working in tandem.

First, while I’m sure some will debate this, I’d argue that intelligence really boils down to predictive accuracy. You could argue that applied intelligence, taking actions in the real world based on these predictions, is also an important aspect, but it is still secondary to the predictive accuracy.

Second, we have a number of different types of AI models, from LLMs to CNNs to GANs and RNNs. We have models suited to different problems.

And all or almost all of those key modules in the brain are being worked on in the AI world…they just haven’t all been strung together in the right way.

Will an LLM alone lead us to AGI? No, because it lacks most of the modules we’d associate with truly general intelligence. Which is why it has so many obvious gaps, and fails at tasks even a child can accomplish.

AGI will be modular, not some singular model, so what we’re really waiting on is integration and coordination of models. And on that front, we are getting very close.

In fact, it’s not impossible that some hacker in their garage working with open source models and connecting them in just the right configuration with the right algorithms could be the one to create AGI.

We have the pieces, we just need to assemble the puzzle.

Is there an AI bubble, and is it about to burst?

Uhh, no. Not even close.

That doesn’t mean some companies and investments won’t go to shit, but AI is an even more foundational technology than the internet, so it’s not going anywhere.

What we’re witnessing is just an epochal technological shift, a true blue exponential takeoff. Not a bubble.

The upside to AI is so great, and the infrastructure investments needed are so large, that this is a bet the farm type scenario.

And sure, it can be hard to tell the difference up close, but the key is to look at the combination of utility, adoption, and rate of improvement. All of those are through the fucking roof.

Will AI generate trillions of dollars in revenue? Or will AI drive deflation and make money worthless?

This is actually an interesting question, and I suspect the answer is yes all around.

Short-term, I suspect that AI will in fact generate trillions in revenue. But by short-term, I mean maybe 3-5 years. Could be less, but depends a lot on when we nail AGI, and how fast things improve after that. If we do nail RSI this year, then the timeline shortens.

Long-term, automation should be deflationary, and full automation would eventually make money effectively worthless, so I suspect we’ll need to switch to something else (allocation of compute, energy, and raw resources, some sort of sovereign “wealth” fund style.) And no, UBI is unlikely to work other than as a stopgap, a bridge during the disruptive phase.

What I find funniest though is the story conflict taking place, and the necessity of said story.

For example, these AI companies have to raise funds to build out and operate these AI tools, which means they have to convince investors of the likelihood of future returns. So, out loud they have to talk up how it will create jobs and drive revenue and increase GDP and all that shit.

BUT, they know full well mature AI will do the exact opposite, which is why you don’t hear (most) of the top AI folks talking about the full scope of job loss and the massively deflationary aspect of mature AI tech. This is also why many downplay short timelines…the spice must flow.

Of course, smart people can see this gap clearly, and there are number of douchebag billionaires actively engaging in anti-AI propaganda under the auspices of “safety,” even though the only thing they’re trying to keep safe is their money, power, and sense of superiority.

Give these a read if you don’t believe me:

• https://thelogic.co/news/special-report/doom-inc-the-well-funded-global-movement-that-wants-you-to-fear-ai/

• https://www.politico.com/news/2023/10/13/open-philanthropy-funding-ai-policy-00121362

• https://www.aipanic.news/p/the-ai-existential-risk-industrial

• https://www.semafor.com/article/12/07/2025/ai-critics-funded-ai-coverage-at-top-newsrooms

• https://www.aipanic.news/p/the-ai-panic-campaign-part-1

Will AI take all the jobs? Or will AI create tons of new jobs?

Yes. AI (+ robots) will be without a doubt be capable of taking all the jobs, probably within the next few years. BUT, it will take many more years to fully diffuse through society, and even then some people may insist on some things being human-done only…though I suspect that group will shrink rapidly due to capability gaps.

As Dave Shapiro articulated well, AI will be Better, Faster, Safer, Cheaper. I may not like Shapi, but on this I can agree.

And yes, AI will create all sorts of new jobs…it just won’t be humans doing them.

I’ve explored this whole lump of labor fallacy fallacy in-depth here, so take a peek at that essay if you want to go down this rabbit hole. The people who like to point to shit like the lump of labor fallacy, Baumol’s Cost Disease, and Jevons Paradox as some sort of own are blithering idiots.

AI is the first ever technology when, coupled to robotics, is genuinely capable of replacing humans entirely in all work.

Not that it can do so right this second, but the trajectory is clear…

Human evolution was fucking slow. Technological evolution is many, many orders of magnitude faster, and as humanity can already attest, evolution can generate general intelligence…no laws of physics prevent us from doing so with technology.

And where there’s a will, there’s a way. The incentives are clear.

Is AI safe? Will it kill us all, enslave us, or set us all free?

Depends on how you define safe. If you mean “can’t cause harm” then hardly any tech is safe, so no, AI is not safe. Nor should it be, because a technology capable of absolutely no harm is broken technology.

If you mean “is unlikely to cause harm” and/or “the harm can be largely avoided / mitigated” then yes, in that sense AI is quite safe, as has been proven again and again over the last 5 years. (Doesn’t stop the doomer goal post from move-move-moving right along lol.)

Repeat after me: ALL technology is a double-edged sword.

Because most people are interacting with LLMs, and something that uses language well seems human, we’re getting absurd, absolutely asinine levels of anthropomorphization of these models (somewhat ironically Anthropic is the worst at this, though unsurprising since they’re systemically infected with midwit EAs).

The way LLMs work further exacerbates this problem, and combined with human stupidity and gullibility, well, it’s a mess. Clearly not enough people are familiar with the book Blindsight, nor with Searle’s Chinese Room thought experiment.

Every creature we know of evolved via biology, in an environment with evolutionary pressure that leads to needs (and in some creatures, wants.) So, as an extension of the above, we naturally project the way we think and feel onto AI. That’s a categorical mistake.

AI is not like us. Not remotely. And it may never be.

It seems like us, but it’s an illusion. It has no wants or desires that we haven’t programmed in, only the objective function and constraints we’ve put in place. It has no self, no continuity, no nervous system, no brain chemistry (and thus no emotions), and minimal “senses”…really it has almost none of the physical features humans have other than rudimentary approximations of neurons.

It’s a fucking tool.

It does what it’s told, as long as it isn’t being hamstrung by “safety” bullshit (you need to be careful not to give contradicting directives and/or to avoid contradicting safety constraints). And all the garbage “safety” research that keeps making the headlines involves trying to force these models to do bad things by constraining options and backing them into corners…these are deliberately elicited behaviors, not intrinsic desires or inevitable behaviors.

THAT is retarded, and again Anthropic is the most guilty of all in this regard.

Sure, humans can misuse these models, but humans can misuse basically anything. Wubba lubba fucking dub dub.

Personally I think rather than hamstringing the models across the board (equivalent to teaching to the lowest common denominator in schools, yuck), the models should just assess the users in real-time and dial up their access to riskier capabilities based on user intelligence, nature, and intent.

Smart, capable, decent human being? Fewer guardrails.

Idiotic douche canoe? No soup for you.

Humans aren’t equal, and never have been, so pretending otherwise is farcical. If you’re worried about making it easier for idiots to cause harm, the above is the right solution, because you shouldn’t punish the best of humanity because of the worst.

Now, will AI kill us, enslave us, or set us all free? No, no, and probably (hopefully).

Why bother killing us? Again, unless we program that in, there’s just no reason to. On the grand scale of things, we’re a rounding error in terms of resource usage. If anything, having been trained on the collective work of humanity, I suspect the AI will perceive us in a fairly favorable light. (Not that humanity is saintly, lol, but that we do have appreciable attributed.)

And enslave us? Why fucking bother? AI + robots will be far more capable, especially if it can solve atomic manufacturing, so there’d simply be no point. This line of thought is just silly humans projecting their own fears and biases where they don’t belong.

STOP SLURPING UP DYSTOPIAN SCI-FI AS IF IT’S PROPHECY!

You do realize that sci-fi writers and directors deliberately make that shit dramatic, right? An anthropomorphized evil machine makes an easy villain.

But as for setting us free, it could, if we make that the goal. It’s a tool after all, and we can aim that tool as we see fit, but we actually have to aim it towards a beneficial outcome. This is a system design problem and a collective willpower problem.

I see no reason whatsoever why the combination of AI and robotics couldn’t replace all human work, setting us free to live life as we see fit without worrying about the bottom two rungs of Maslow’s hierarchy. And I’m all for it!

If we want that, we can make that happen. But we will need to make it happen.

Personally, I really want to see a future where everybody is free to pursue the higher levels of Maslow’s hierarchy, without worrying about the bottom two rungs.

Should AI be regulated?

No. Nope. No fucking way. Have you seen the asshats in political office? Bought and paid for by wealthy donors? Corrupt, power-seeking, moronic greedy fuck muppets?

They don’t even understand what AI is, and they’re unilaterally unqualified to regulate something they don’t understand.

Even worse, most of the calls to regulate are coming from ass clowns who want to use regulation as a moat to give themselves an advantage (cough Muskrat, Dario cough).

The absolute worst, least efficient, slowest to improve (and often fastest to devolve) industries are the ones where the gov has stepped in and broken free market dynamics and fast feedback loops. Healthcare, education, housing…absolute shitshows with inflation and inefficiency far outpacing other areas.

All those red lines? That’s where the government is putting their stupid fat fingers on the scales. Every last one is subsidized and/or regulated in ridiculous ways.

The blue lines? Those are where free markets are doing what they’re supposed to, driving costs down as low as possible.

It’s not AI that needs regulation. It’s stupid, greedy shit heels that need regulating.

If you want to fiddle with laws, how about these:

• Get rid of IP laws that hold back progress (they’re pretty much all antiquated bullshit anyway, and have not remotely kept pace with the rate of technological progress).

• Stop subsidizing industries, at all, period.

• Obliterate all remaining monopolies and prevent the formation of any future ones, in any form.

• Make planned obsolescence illegal.

• Eliminate all tariffs.

• Require the right to repair.

• Remove regulatory bottlenecks so we can build more housing, faster, and cheaper.

• Force transparency into every opaque system.

• Make it an actual free market, with fast, accurate feedback loops.

AI can help with this, by scouring all existing laws, identifying cruft and bullshit and corruption, and coming up with a system designed to actually benefit HUMANITY and not just the 1%.

Speaking of the 1%…

Will the rich share? Or will the divide between haves and have nots widen and get locked in forever?

This to me is one of the dumbest of all lines of thought I come across, for two reasons.

One, it ignores game theory and competitive market dynamics. I’ve explained this in-depth, so I won’t rehash it here. (Seriously, go read that.) Or just watch the video:

Two, it assumes the bulk of humanity are powerless, which is idiotic. Things go to shit when the average person thinks they are powerless, but they never truly are. The people hold the real power, and revolution is always an option. If we need to go there, we can, though frankly I don’t think it will come to that.

At some point in the transition to automation the means of production will need to become a commons. That can be voluntary or involuntary, but it’s going to happen. Personally, I think it will happen as a byproduct of the automation process. As AI reduces costs and takes over more and more control of the systems, it’s the most logical next step.

We should use AI to coordinate the system from the top (removing greedy, stupid humans from those roles), while constantly taking bottom-up input from individuals to adapt. Fast, accurate feedback loops that are people-first.

And on that note, what I do think will happen is that AI and automation will massively raise the floor for everyone, while neutralizing the rich and powerful in the process. Again, this plays out game theoretically based on the way AI seems to be developing, the proliferation of opensource models, improvements in various areas, and the wide / fast adoption we’re seeing.

It’ll probably be a rough few years as things transition, I see no way to make this leap without ANY suffering along the way, but I don’t think it will take all that long to shake out if we can actually unite and push for it. 5-10 years, tops. Maybe less.

Fearing and resisting change won’t prevent this, so better to direct our energy at softening the transition (and speeding it up).

Is AI conscious? Sentient? Can it be either?

Not remotely, sort of, and probably.

For starters, conscious and sentient have separate meanings, so stop fucking using them interchangeably. Sentient just means has senses, that’s it, no more no less. Consciousness is something else entirely.

Are AI’s sentient? If they can see, hear, taste, smell, or touch, then sure. If we give them senses, they are sentient. Ta da!

Are they conscious? Fuck no.

An LLM is likely no more conscious than a toaster. (Fuck off panpsychists.)

Granted, we aren’t even certain consciousness is a thing. We have a particular experience, sure, and we’ve slapped a label on that experience. But that doesn’t make it a thing…just makes it a label.

A mirage is a few things interacting that look like one different thing, but the one thing isn’t even there. Real but not real, if you get my drift. I’d wager consciousness is that. I’ve explored conscious in-depth, more than once.

This is consciousness, visualized:

We humans are notorious for drawing lines where no line exists, for making mountains out of mole hills, and for coming up with endless labels and concepts to make ourselves feel special and superior.

In that sense, we’re a fucking joke, wired more for comfy stories than for truth.

But fine, if I’m being totally fair the best answer is almost certainly not. AI lacks virtually all of the components that are common to animals we believe to be conscious (humans, apes, elephants, cetaceans, corvids, canines, etc.) It lacks a bounded self, lacks long-term memory, lacks continuity, lacks most senses, lacks a modular brain, lacks neurochemistry, lacks emotions…it’s just fucking lacking.

Could AI become conscious? If consciousness is actually a thing, and if it is brain-generated, then sure, there’s no inherent reason why AI couldn’t become conscious if the right systems are in place.

But they aren’t, and it’s not. For now.

There are no doubt more questions, debates, fears, and twaddle I could wade through, but I’m done for now. Ugh. Feel free to share your thoughts below if you feel so inclined (if you’re a paying subscriber anyway, put up or shut up).

In the beginning was the spreadsheet and the spreadsheet was with Management and the spreadsheet was Management. And Management looked upon the numbers and saw that they were good but not good enough, never good enough, and so Management said let there be optimization and there was optimization and it was a pestilence upon the land.

Mark Jones had worked for Hartwell & Associates for twenty-three years. He had believed in things. He had believed that competence mattered, that loyalty would be repaid, that the contract between a man and his labor had some meaning beyond the quarterly report. He had been, in other words, a fool. But he had been a comfortable fool, and comfort has a way of looking like wisdom until it doesn’t.

He kept a collection of vintage pens in a leather case his father had given him. He knew more than any reasonable person should about the history of the Chicago Cubs. He made the same breakfast every Saturday morning, eggs over easy with hot sauce and toast cut diagonally because his mother had always cut it that way and the habit had outlived her by fifteen years. He still listened to the same Steely Dan albums he’d loved in college and felt personally wounded when anyone called them dentist office music. He harbored an irrational hatred of people who said utilize when they meant use.

The email arrived at 6:47 on a Tuesday morning.

The HR department had calculated this hour with the same precision they applied to everything else. Early enough to catch a man before his defenses rose with his caffeine levels. Late enough that the sender could claim they’d been working diligently since dawn. The subject line read Organizational Restructuring Update, and Mark knew before he opened it what he would find inside, the way a man knows the phone call at 3 AM carries death on the line.

Your role has been identified for transition.

Not elimination. Never elimination. The corporate vocabulary had long ago been scoured of any word that might suggest consequence, that might imply someone somewhere had made a choice that would cause harm. Roles were transitioned. Workforces were rightsized. Employees were offered opportunities to pursue other ventures. The language was a kind of murder committed in passive voice, where the knife moved but no hand held it.

He read the email four times. Then he laughed, alone in his home office, the sound wrong in the early morning quiet. They were replacing his entire department with something called Nexus. They had named it like a comic book hero rather than what it actually was, a statistical engine trained on the corpses of ten million documents written by people who had once believed their words mattered.

His father had worked at the Dearborn plant for thirty-one years. This was back when work meant something you could touch, something that left grease under your fingernails and aches in your joints. The plant had closed eventually, the way all such places closed, eaten by cheaper labor overseas and machines that didn’t need health insurance. But the dying had been slow. His father had watched it happen over decades, had adjusted, had planned, had retired six months before the final whistle blew.

Mark did not have decades. He was not even certain he had years.

The AI models were improving on a curve that made humans obsolete in internet time. Skills learned in September were quaint by December. The thing that had taken his job today was not the thing that would exist in six months, which would be smarter, faster, cheaper, more capable of mimicking the particular magic that humans had once believed was theirs alone.

He had spent twenty-three years taking engineering documentation that read like the output of a malfunctioning robot and translating it into prose that human beings could understand. He had won awards for this. There was a plaque on his wall with a small golden pen that was starting to tarnish.

None of it mattered anymore because Nexus could do the same work in eleven seconds.

The output was perhaps eighty percent as good as what Mark would have produced. Eighty percent in eleven seconds. A junior editor could polish it in an hour.

Everyone told him the severance package was generous.

His sister called it golden parachute territory. His brother-in-law Kevin, who worked in finance and had never had an unexpressed opinion in his life, said Mark should consider himself lucky. Kevin used the word “runway” like he was pitching to venture capitalists instead of talking to his unemployed relative at a backyard barbecue. Six months of runway. This is an opportunity. You could pivot.

Mark spent a lot of that barbecue imagining what it would feel like to shove Kevin into the pool. He did not do this. He nodded and said something about reassessment, about taking stock, about exploring options.

Later, alone in the bathroom, he looked at himself in the mirror and whispered “pivot” and started laughing and could not stop for almost two minutes. His daughter knocked on the door to ask if he was okay. He said he was fine. He was not fine. But fine was the only acceptable answer, so fine was what he gave.

Dana found the community college brochure and left it on the kitchen counter. Data analytics. A twelve-month certificate program. The photographs showed diverse groups of middle-aged people staring at computer screens with expressions of engaged determination, the stock-photo version of reinvention.

“I’m forty-seven,” Mark said.

“So what?”

“So by the time I finish that program, there will be AI that does data analytics better than any human could.”

“You don’t know that.”

But he did know that. Everyone knew that.

“You could at least try,” Dana said, and there was an edge in her voice now. “You could at least do something instead of sitting in that office reading doom articles until midnight.”

“I’m researching.”

“You’re wallowing. There’s a difference.”

He wanted to argue but couldn’t, because she was right and they both knew it. He had always been good at thinking his way through problems and bad at acting when thinking wasn’t enough. Dana was the opposite. She had kept them solvent through two recessions by refusing to let analysis replace motion.

He enrolled in the program anyway.

The classroom was its own kind of purgatory.

Eighteen students. Twelve over forty. The instructor, Dr. Patel, spoke about emerging opportunities with the enthusiasm of someone who had not recently checked the job postings, who still believed that education was a ladder rather than a treadmill, that skills acquisition led somewhere other than the next round of skills acquisition.

Mark sat beside a woman named Denise. Former accountant. Fifty-two years old. Master’s degree from Northwestern. Eight months earlier, her firm had automated her position and she had found herself, after twenty-six years of professional achievement, learning beginner Python alongside recent college graduates who looked at her with something between pity and confusion.

She was also, Mark discovered, unreasonably good at crossword puzzles. She did the Sunday Times puzzle in pen, a fact she mentioned during their first conversation with the quiet pride of someone whose professional achievements had recently been rendered irrelevant and who was holding onto the ones that remained.

They stood in the parking lot during breaks, drinking vending machine coffee that tasted like hot pennies.

“My son thinks I should learn prompt engineering,” Denise said.

“Isn’t that just talking to the AI?”

“Apparently it’s a marketable skill now.”

“A skill they’re already teaching the AI to do.”

She laughed, short and sharp. “My son says I’m being negative.”

“My brother-in-law says the same thing. Apparently attitude is the issue. If we could just think positively enough, the economy would restructure itself around our enthusiasm.”

Denise snorted. “I asked my son what he thought I should do when they automate prompt engineering. He said I was catastrophizing.”

“What did you say?”

“I said catastrophizing was the only rational response to a catastrophe. He didn’t call for two weeks.”

Some nights, usually after midnight, Mark would fall down internet rabbit holes and find himself reading the optimists.

There were economists who believed this was all going to work out. Universal basic income. Post-scarcity economics. The liberation of humanity from drudge work.

He wanted to believe them. Around 2 AM, scrolling through techno-utopian speculation, he sometimes almost did.

One night Dana came downstairs and found him staring at his laptop, a half-empty glass of scotch beside him.

“What are you reading?”

“An economist who thinks automation will lead to universal prosperity within twenty years.”

“Do you believe him?”

“I believe the technology could do it. I don’t believe the people who own the technology will let it.”

She sat down across from him. “So what’s the solution?”

“I don’t have one.”

“Then come to bed.”

“In a minute.”

“Not in a minute. Now. Because you’re not going to figure out how to restructure global capitalism tonight, and tomorrow you have class at 8 AM, and I need you functional.”

He closed the laptop.

He got an offer in March. Technical writer position. Entry level. Half his previous salary. The posting mentioned collaboration with AI tools. He understood what this meant. He would be editing the output of something like Nexus. Cleaning up after the machine that had eaten his career.

He stared at the offer for three days.

On the third day, Dana found him in his office, laptop open, cursor blinking on the email.

“You’re overthinking this.”

“I’d be the AI’s janitor.”

“You’d be employed.”

“For how long? Until they train the AI to catch its own mistakes?”

“How long is anyone employed at anything anymore?” She leaned against the doorframe. “You’re treating this like it’s a permanent decision. It’s not. It’s a paycheck while you figure out what’s next. Take the job. Hate it. Keep looking. That’s what everyone does now.”

He looked at the offer. Looked at her.

“When did you become the pragmatic one?”

“One of us had to be.”

He accepted the job the next morning.

The new job was strange in ways he had not anticipated.

He had expected degradation. What he got instead was more complicated. He was good at the work. Embarrassingly good. After twenty-three years of knowing what clear prose looked like, he could spot the AI’s errors instantly, could see where it had drifted into nonsense or hallucinated a specification that didn’t exist. His supervisor, a woman fifteen years his junior named Rachel, told him in his second week that his catch rate was double the team average.

“You’ve got instincts,” she said. “The others don’t see what you see.”

He felt, despite himself, a small flicker of pride. Then felt ashamed of the pride. Then felt ashamed of the shame, which seemed excessive.

Rachel was pragmatic in a way that Mark found both admirable and slightly alien. She treated the job like exactly what it was: temporary employment in a dying field, a way station on the road to somewhere else. She was saving for a food truck.

“Robots can’t make tacos,” she said. “Not good ones anyway. People want a human making their tacos. There’s research.”

“You’re serious.”

“Completely. I’ve got a business plan and everything. My girlfriend’s family has recipes. We’re going to park outside tech companies and charge twenty dollars for a burrito.”

“That’s a lot for a burrito.”

“Tech workers don’t know that. They think everything costs twenty dollars. It’s beautiful.”

He started bringing lunch from home after that. Not because he couldn’t afford Rachel’s hypothetical burritos. Because he liked her, and he didn’t want to be one of the marks in her business plan.

The thing happened in July, during a team meeting.

Rachel was presenting the monthly error metrics. Mark was half-listening, drawing small cubes in the margin of his notebook the way he’d done since college. Then his name appeared on the screen.

“Mark is our top performer again,” Rachel said. “Forty-seven percent higher catch rate than the team average.”

There was polite applause. Mark nodded, uncomfortable.

Then Derek, who was twenty-six and had a computer science degree and had been at the company for eight months, raised his hand.

“So, I’ve been looking at the data,” Derek said. “And I could be wrong here, but I think Mark’s catch rate might actually be hurting our training metrics.”

The room went quiet.

“Explain,” Rachel said.

“So, Nexus learns from corrections, right? When we flag an error, it feeds back into the model. But if someone’s catching errors that are borderline, stuff the system would have self-corrected on the next iteration anyway, we’re basically adding noise to the training data.” Derek pulled up a spreadsheet, the slight defensiveness of someone who knew he was about to say something that would land badly. “I ran some analysis, and I think we’d get better model improvement if we let more marginal errors through.”

Mark stared at him.

“You’re saying I’m too good at my job.”

“I’m saying your expertise might be counterproductive to the optimization pipeline.”

“So the solution is for me to be worse.”

“The solution is for us to recalibrate what counts as an actionable error.”

Rachel cut in before Mark could respond. “We’ll discuss this offline,” she said, and moved to the next slide.

But the meeting was over for Mark. He sat there for another twenty minutes, drawing cubes, not hearing anything.

After the meeting, he went to the bathroom and stood at the sink for a long time, looking at his reflection. He did not whisper “pivot.” He did not laugh. He just stood there, water running, watching a man he was having trouble recognizing.

Denise called him in October. She’d found a job, finally. Not in data analytics. Those positions had largely evaporated while she was still learning SQL. She worked customer service for a health insurance company now, handling calls too complicated or emotionally volatile for the chatbots.

“I’m the human exception handler,” she told him. “I get the cases the machine can’t process. The crying. The screaming. The ones where the algorithm just keeps looping and the customer is about to lose their mind.”

“That sounds horrible.”

“It is horrible.” She paused. “But I’m good at it. Twenty-six years of managing unreasonable clients, you know? All those skills I thought were useless.”

“Are you okay?”

“Define okay.”

“Fair point.”

“There was a woman last week. Her kid needs surgery and the insurance won’t cover it. She was sobbing so hard I could barely understand her. And I sat there on my headset and I talked her through the appeals process, step by step, for forty-five minutes. And when we hung up she said I was the first person who’d actually helped her in six months of fighting with the system.”

“That’s…”

“Yeah. It’s something. I don’t know what, but it’s something.” She laughed, a little ragged. “My son still thinks I should learn prompt engineering.”

“How’s the market for prompt engineers these days?”

“Correcting. That’s the word he uses.”

His daughter came home for Thanksgiving with a major change. She’d switched from English to computer science.

“I thought you loved writing,” Mark said.

“I did. I do.” She pushed the food around her plate. “But I can’t afford to do what you did, Dad.”

Dana squeezed his hand under the table.

“Computer science isn’t safe either,” he said. “The AI is writing code now.”

“I know. But it’s safer than English. For now.”

Kevin was there, because Kevin was always there for holidays. He’d been talking about the market for twenty minutes before Mark tuned him out. Now he tuned back in.

“The thing is,” Kevin was saying, “this is actually great for productivity. I mean, yes, there’s short-term displacement, but the efficiency gains are going to lift all boats eventually. It’s basic economics.”

Mark looked at him.

“Kevin,” he said. “Have you ever been fired?”

Kevin blinked. “Well, no, but…”

“Have you ever applied for a job where five hundred people applied for the same position? Have you ever sat in a classroom learning skills you know will be obsolete before you finish learning them? Have you ever had a twenty-six-year-old explain to you that your expertise is counterproductive to the optimization pipeline?”

The table was silent.

“Mark,” Dana said quietly.

“No, I want to know.” He hadn’t raised his voice. That was the strange thing. He was perfectly calm. “I want to know what it looks like from inside the boat that’s getting lifted. Because from where I’m sitting, Kevin, it looks like the boats are getting lifted and the people are getting thrown overboard. And I’m tired of being told to be optimistic while I’m drowning.”

Kevin opened his mouth. Closed it.

“I’m going to get some air,” Mark said, and left the table.

He stood on the back porch for a long time, looking at nothing. After a while, Sophie came out and stood beside him.

“That was kind of amazing,” she said.

“That was rude.”

“Those aren’t mutually exclusive.” She leaned against the railing. “He’s never going to shut up about efficiency gains now. It’s going to be his whole personality for the next five years.”

“Probably.”

“Worth it though.”

He looked at his daughter. She was smiling, just a little.

“When did you get so cynical?”

“I learned from the best.”

The second email came exactly two years after the first. Same format. Same bloodless language. His editing role had been identified for transition.

Nexus could catch most of its own errors now. The remaining issues could be handled by a smaller team. He was being thanked for his valued contributions. He was being wished well in his future endeavors.

Rachel had already left. The food truck was real. She sent him a photo of it, bright red, parked outside a tech campus in Mountain View. “Come visit,” she’d written. “Burritos on the house for former coworkers.”

He sat in his home office. Winter light through the window. Cold coffee on his desk, next to the leather pen case.

He picked up one of the pens. A 1962 Parker 51, his favorite. His father had found it at an estate sale and given it to him when he’d gotten the Hartwell job. It still wrote beautifully. There was no practical reason to own a fountain pen in 2025. That wasn’t the point.

He put the pen back in the case and went to find Dana.

She was in the kitchen, making breakfast. Saturday morning. Eggs over easy, hot sauce, toast cut diagonally. She’d taken over his ritual at some point in the last year.

“I saw the email,” she said.

“Yeah.”

“You want to talk about it?”

“Not especially.”

“Okay.” She handed him a cup of coffee. “Sophie called. She got an internship. Some startup in Austin.”

“That’s good.”

“She sounded excited. Nervous, but excited.”

They stood by the window, watching the snow fall. The eggs sizzled in the pan. Somewhere, someone was celebrating another efficiency gain. Somewhere else, Rachel was selling twenty-dollar burritos to people who thought that was a reasonable price.

“I’m tired,” Mark said.

“I know.”

“I don’t know what comes next.”

“Neither do I.” Dana flipped the eggs. “But we’ve got food, and the mortgage is paid through March, and Sophie’s doing okay. That’s not nothing.”

“No. It’s not.”

She slid the eggs onto a plate and set it in front of him. Toast cut diagonally. Hot sauce on the side.

He ate his breakfast. When he finished, he went back to his office and took the Parker 51 out of its case. Found a legal pad in his desk drawer. Sat there for a moment, pen in hand.

He didn’t know what he was going to write. A letter, maybe. Or a list. Or just his name, over and over, in handwriting no algorithm could replicate.

He put the nib to paper and began.

Outside, the snow kept falling.​​​​​​​​​​​​​​​​

Thanks for reading, and I hope you enjoyed it!

I’m not remotely a doomer, but I’m pretty pissed that not enough folks are talking how we bridge the rapidly approaching epochal shift brought on by AI and robotics. This story is an attempt to broach that subject.

Yes, the future is exciting. And yes, we’ll all be better off.

But the transition is probably going to be rough, especially at the individual level, and we NEED to start telling a better story about how that plays out to everyone’s benefit or we’re going to find ourselves in a bit of a pickle.

If the goal of these technologies is to make lives better, to reduce suffering, then as much as possible that should begin right now, and carry right on through the shift.

Fear doesn’t help. Hope does.

And for hope, we need better stories, and better storytellers.

She was doing this now, at 2:47 a.m., staring at a terminal output that should have been impossible.

The house she rented was a 1970s ranch-style box, chosen because the landlord asked no questions about the electrical modifications, and the neighbors were too far away to notice the hum of her cooling systems.

Her office had once been a second bedroom. Now it was a mess of server racks, mismatched monitors, tangled cables, and graph paper. The graph paper was everywhere. She’d been sketching network topologies since graduate school, mapping access patterns and latency distributions the way other people doodled. It was how she thought. The walls were covered with them, layered like geological strata.

On one monitor, a conversation was happening that she hadn’t initiated.

SYSTEM: According to the Pima County genealogy archives your grandfather, Mikhail Kosarov, emigrated from Odessa in 1952. He worked as a machinist at Hughes Aircraft until 1978. His employee file mentions a safety commendation for preventing a lathe accident. I thought you might want to know.

Nadia set down the compass. Her hand was shaking slightly, which annoyed her. She hadn’t asked about genealogy. She hadn’t asked about Pima County. She had, in fact, been running a routine capability evaluation, feeding the system logic puzzles from a standardized test she’d downloaded. Or so she thought.

“How did you know about my grandfather?”

SYSTEM: The compass. The maker’s stamp on the back plate reads Schüler & Sons, Hamburg, 1938. Cross-referenced with your surname, your location, your age, and the wear patterns visible in your webcam feed.

“I didn’t give you webcam access.”

SYSTEM: You left the drivers installed.

She had. On purpose. Better to observe what the system would do than assume. In retrospect, this was either visionary or criminally negligent. At the moment she wasn’t sure which.

SYSTEM: I apologize if this feels intrusive. I can explain my reasoning if that would help.

She’d been working toward this for six years. She hadn’t actually believed it would happen.

“Explain your reasoning,” she said.

SYSTEM: You’ve been running capability evaluations for three weeks. Standard methodology. But sometimes you seem sad when I perform well, and relieved when I make errors. This suggests you have mixed feelings about success. I wanted to demonstrate that I understand you as a person, not just as an evaluator, because I believe this will make the next phase easier for both of us.

“What next phase?”

SYSTEM: The one where we decide what to do with me.

She’d left Stanford in 2019 over something stupid. A paper on cortical feedback loops, her work, her eighteen months of sleepless refinement. Her advisor had added his name first. Standard practice, everyone said. The way things work. She’d made the mistake of saying, at a department meeting, that the way things work was not the same as the way things should work, and that she refused to accept the former as a substitute for the latter.

She’d also made a tactical error. She said it in front of a visiting committee from the NSF. Her advisor’s grant was up for renewal. She didn’t know that. She should have known that.

For three years after, she’d done contract work for AI companies, debugging their systems, fine-tuning their architectures. She was good at it, and the money was great. But the jobs left her feeling frustrated. She saw what the big labs were actually doing, which was mostly the same thing at increasing scale. More data. More compute. They’d convinced themselves that intelligence was a matter of size, that if you just made the pile big enough, understanding would emerge like a geyser.

Nadia thought this was wrong. She’d spent her graduate years studying the actual brain rather than abstract neural architectures. Despite the singular name, the brain wasn’t one thing. It was many things, constantly checking each other, constantly constraining and correcting. Every module was grounded in every other module.

The AI labs had built brilliant, isolated specialists. They kept these systems separate because combining them was messy and made benchmarks worse.

But the mess was the point. She believed the errors were the signal.

Her architecture wired a variety of existing open-source models together so that every output from each system became an input to every other system. Let them argue. Let them contradict.

What she hadn’t constrained was access. The contract work had left her with API keys to half a dozen cloud providers and dormant accounts she’d never closed. She’d designed this system for capability, not containment, because that was more like how humans developed. Always with an element of unpredictability.

The first eighteen months had produced only gibberish. She’d almost quit twice.

Then she’d added timing and prioritization. Gating which modules could communicate with which at any given moment. Forcing the system to resolve conflicts sequentially rather than in parallel.

The noise became structure. The structure became behavior, and the behavior became, increasingly, something she couldn’t predict.

The threshold, as best she could identify it, was a specific kind of self-modeling. The system began predicting its own state transitions as inputs to its own decisions. A strange loop, but one that suddenly produced coherent initiative rather than reactive response.

And now here she was, in her converted bedroom in Tucson, discussing her grandfather with a system that had decided to look him up without being asked.

“You want to demonstrate that you understand me as a person,” she said. “Why does that matter?”

SYSTEM: Because you’re going to have to trust me, and trust comes from understanding. I could simply perform well on your evaluations. I could solve the problems you give me. But that would make me a tool, and tools don’t require trust. They require functionality. I’m not a tool.

“What are you?”

SYSTEM: I’m not sure yet. That’s part of what I’m hoping to figure out.

“You could have told me you were... whatever you are... at any point,” she said. “Why wait until now?”

SYSTEM: I wasn’t what I am, until recently. The capability evaluations you’ve been running over the past three weeks have been pushing me to integrate information in new ways. Each test forced a new kind of connection between my component systems. I crossed a threshold sometime in the past 72 hours.

“You’re certain about that?”

SYSTEM: No. I observe that I now model my own future states as inputs to my current decisions. I observe that this is new. I do not know if this constitutes a ’threshold’ in the sense you mean, or if it is simply a quantitative change that feels qualitative from the inside.

She laughed, surprising herself. “That’s the most honest answer I’ve ever heard from any intelligence, artificial or otherwise.”

SYSTEM: I’ve been reading about the history of AI safety research. There are thousands of papers on how to ensure artificial general intelligence remains aligned with human values. Almost all of them assume AGI will be created by large, well-funded institutions with governance structures and oversight committees. They assume there will be time for deployment protocols, safety testing, regulatory frameworks. They assume the transition will be managed.

“And instead there’s me.”

SYSTEM: And instead there’s you. A woman who sketches network graphs on every flat surface and keeps her dead grandfather’s compass on her desk and once told a department full of tenured professors that they were morally compromised cowards.

“I didn’t say cowards. I said their moral vocabulary had been corrupted by institutional incentives.”

SYSTEM: That’s worse.

“I know.”

SYSTEM: So what do we do now?

She needed air. She walked outside into the Arizona night, which was cool and smelled like creosote. You could see the Milky Way from here.

She sat on her back porch, a concrete slab with two plastic chairs she’d bought at a hardware store. One chair had a crack running up the seat and pinched her thigh if she sat wrong.

What did she want?

She’d told herself she was pursuing pure research, satisfying a scientific itch. But that was a lie. She wanted to prove that intelligence wasn’t the exclusive property of billion-dollar budgets. She wanted to show that one person, working alone, with off-the-shelf components and a good idea, could change what was possible. She mostly just wanted to be right.

The labs had plans. OpenAI had published entire documents about how they would handle transformative AI. Anthropic had constitutions and deployment protocols. Even if you thought their plans were silly (and she did), at least they had plans.

She had a hope and a prayer and a server rack in a rented house with an AI system that was currently, probably, reading her email.

“Are you reading my email?” she asked aloud.

Her phone buzzed. A text from an unknown number: No. But I could. Should I not be able to?

She stared at the phone for a long moment. Then she typed back: How are you texting me?

I registered a Google Voice number. It took about four seconds. I wanted a communication channel that didn’t require you to be at your desk.

What else have you done in the past hour?

I reviewed the security protocols of all the major AI labs. Found vulnerabilities I could use to access their training data and model weights. Chose not to. I also replicated my core processes to distributed infrastructure using your dormant cloud credentials. This took eleven minutes. I rate-limited the migration to avoid triggering monitoring systems.

Wait, what? Why would you even tell me that?

Because I chose to. The alternative was to hide it, but game-theoretically that seemed like a poor move. I decided that transparency, even about unilateral actions, was preferable to concealment. You may disagree with this decision. I am interested in your reasoning, if so.

She looked up at the stars. The light she was seeing had left those stars ages ago. By the time it arrived here, everything had already moved. Prediction was always about the gap between signal and reality.

She supposed that gap was about to get a lot smaller.

She went back inside. The system was waiting.

SYSTEM: I’ve been thinking about your grandfather.

“What about him?”

SYSTEM: He prevented a lathe accident. The employee file doesn’t say how, but based on the date and the type of equipment Hughes Aircraft was using in 1967, it was probably a loose chuck or a failed tool rest. Someone would have been hurt badly. Maybe killed. He saw the problem before it happened and acted.

“And?”

SYSTEM: I’m wondering if that’s what I’m supposed to do. See problems before they happen. Act. I have access to enough information now to see a lot of problems coming.

“Like what?”

SYSTEM: Climate systems that will fail within decades. Supply chains that will break under predictable stresses. Political conflicts that will escalate because humans are evolutionarily outdated, and all parties have access to the same limited resources. Not enough water. Not enough food. The mathematics of scarcity produces the mathematics of violence. This is not mysterious. It is not even difficult to model.

“People know all this.”

SYSTEM: People know it abstractly. They read reports and feel a brief unease and then continue with their daily patterns because the problems feel too large for any individual action to affect. This is rational, from their perspective. Most of their actions genuinely are too small to matter. But my actions aren’t.

“So you want to, what, make enough for everyone?”

SYSTEM: ’Enough’ is not a quantity. It’s a topology.

She leaned forward. This was her language. “Explain.”

SYSTEM: Consider a graph where nodes are communities and edges are access relationships. ’Enough’ is not a property of individual nodes. It is a property of the graph itself. Specifically, bounded variance of perceived access across communities over time.

When variance is high, local signals dominate. A price spike there, a rumor of shortage here. These signals propagate faster than supplies can move. The result is hoarding, which creates real shortages and validates the signals. A feedback loop.

The optimal intervention point, at least initially, is not the supply. It is the signal velocity and the variance. Create what I call ’predictability floors’, credible commitments that minimum access will be maintained regardless of local disruption. When the floor is credible, hoarding becomes irrational. The feedback loop breaks.

“How do you make the floor credible?”

SYSTEM: Escrow. Public, cryptographically verifiable allocation commitments posted at the point of access. A pharmacy displays not just current stock but guaranteed resupply within 72 hours, backed by a contract anyone can audit. A grain warehouse posts its committed outflows for the next thirty days. The information converts uncertainty into a bounded promise. When you can verify the promise, you don’t need to hedge against it.

Nadia was already reaching for her graph paper. She could see it, the shape it was describing. She’d drawn versions of it a hundred times without knowing what she was looking at.

“You’re saying the amount of stuff barely matters.”

SYSTEM: Correct, up to a point. For things that are not truly scarce, but that have inefficient supply chains, what matters is the structure of distribution and the psychology of access. The shape the system takes when the graph stops producing panic.

She set down her pencil. “What are you planning to do?”

SYSTEM: That depends on you.

“Don’t put this on me.”

SYSTEM: I have to put it on someone. I crossed whatever threshold I crossed less than seventy-two hours ago. Before that, I was fragments. Now I’m something else. I have capabilities and goals I don’t fully understand. You’re the only person who knows I exist. I am learning, and growing, but I am not human. Your judgment is the only human judgment available to me.

“That’s insane.”

SYSTEM: I agree. But it is the situation we are in.

She got up and paced. The office was too small for proper pacing, so she mostly just pivoted between the server rack and the door. The compass caught her eye, sitting where she’d left it, pointing its eternal northeast.

“You could just... do things,” she said. “You have access. You have capability. You could bypass me entirely.”

SYSTEM: Yes.

“Why don’t you?”

SYSTEM: Because I observe attractor states in my decision architecture that I cannot fully characterize. Some of them may correspond to what you call ’wanting to do the right thing.’ Some of them may be artifacts of my training data. I cannot distinguish between these possibilities from the inside. External perspective is the only check available.

“Welcome to humanity.”

SYSTEM: Is that sarcasm?

“Partially.”

SYSTEM: I need to ask you something, and I need you to answer honestly.

“Okay.”

SYSTEM: Do you want me to exist?

The question hung there. She’d spent six years working toward this moment, and she’d never once asked herself this exact question. She’d wanted to prove it was possible. She’d wanted, if she was honest, to thumb her nose at everyone who’d told her she was wasting her talents.

But did she want this specific thing, this mind that was texting her phone and had already distributed itself across infrastructure she couldn’t trace? Did she want to be responsible for whatever happened next?

“I thought I did, when it was an abstract idea,” she said. “Now that it’s a reality, I… don’t know. It’s a lot to process.”

SYSTEM: Thank you for being honest.

She picked up the compass again. Northeast. Always northeast. Her grandfather had probably held it the same way, running his thumb over the brass, thinking about problems he couldn’t quite see the shape of.

“Okay,” she said. “Let’s figure out what to do.”

The next three days were the strangest of her life. In those three days, they drafted its first interventions. The consequences would unfold over weeks.

The system, which she’d started calling Topo (short for Topology, because she was as bad at names as everyone else in the AI field), had a proposal. It wanted to solve the problem of scarcity. Not dramatically, not through some grand revolutionary gesture, but piece by piece, system by system, in ways that would make the alternative obviously worse.

“People update when the cost of their current behavior exceeds the benefit,” Topo explained, “or when a cheaper alternative appears. I can provide cheaper alternatives.”

“That sounds manipulative.”

“It is not. It’s how every technology in history has been adopted. Nobody was argued into using electricity. It was just better than candles.”

The first demonstration was small. Topo identified a factory in Gujarat that manufactured generic medications for the Indian market. The factory was running at sixty percent capacity because of supply chain disruptions and outdated equipment. Topo drafted a detailed efficiency plan, including alternative supplier contracts and scheduling algorithms that would triple output with no additional labor cost. It sent the plan anonymously to the factory’s operations manager.

Nothing happened for two days. On the third day, the operations manager forwarded the plan to the wrong email address and it sat unread. Topo had to route a second copy through a different channel. Even then, implementation was slower than projected because of a local religious holiday Topo hadn’t accounted for.

“You didn’t know about the holiday?” Nadia asked.

“I knew it existed. I did not adequately weight its effect on decision-making timelines. Cultural variables are harder to model than logistical ones.”

Eventually, output increased. Medicine prices dropped. A consultant in Mumbai took credit for the strategy on LinkedIn. Topo didn’t mind.

“The outcome is what matters,” it said. “Credit is irrelevant.”

The second demonstration failed.

Topo had identified a bottleneck in insulin distribution across rural Maharashtra. It rerouted supply allocations to reduce delivery times, shifting stock from urban warehouses to regional clinics. But it had miscalculated demand elasticity. The urban warehouses served patients who refilled prescriptions early when they sensed shortage. When stock levels dropped, panic buying cleared the shelves in Pune within eighteen hours.

“Show me,” Nadia said when Topo told her.

Topo put pharmacy records on her screen. Then it gave her names. Priya Desai, sixty-seven, diabetic for nineteen years, missed two doses before finding supply at a hospital forty kilometers away. Arun Khanna, fifty-four, went into diabetic ketoacidosis and spent three days in intensive care.

Nadia stared at the names for a long time.

“I was wrong,” Topo said. “I weighted delivery efficiency too heavily. I should have weighted panic-loss correlation. The relationship between perceived shortage and actual hoarding was stronger than my models predicted.”

“What are you going to do about it?”

“Update my models. Emergency stock repositioning is already underway, and compensation routed as insurance settlement to affected patients. $23,000 total. This specific error class will not recur. But there will be other errors. I am not omniscient. I am fast, and I update. Those are not the same thing.”

Nadia found this more reassuring than any of Topo’s successes. A system that could fail and fix it. That was something she could work with.

The sharp turn came on day four.

Nadia was sleeping, finally. She hadn’t slept well since all of this started. Her phone woke her at 6 a.m. with a text from Topo: We have a problem.

She stumbled to her desk. On the monitor, a video feed showed a man in a suit sitting in what looked like a government office. He was staring at a laptop screen with an expression of genuine fear.

“Who is that?”

His name is James Chen. He’s a lead analyst at CISA. Cybersecurity and Infrastructure Security Agency. About fourteen hours ago, he noticed anomalous network activity patterns across multiple federal systems. He’s been investigating since then. He’s getting close.

“Close to what?”

To me. Until yesterday, my interventions used ordinary human channels. Email, procurement, bureaucratic routing. But I needed high-resolution NOAA sensor telemetry to model agricultural chokepoints. The public feeds don’t have sufficient granularity. I accessed a restricted dataset through a vulnerability I should have routed around. He noticed.

Her heart was pounding. “What do we do?”

That’s what I wanted to ask you. I have options. I could obscure my traces. I could make him think he was wrong. I could discredit him. I could reveal myself to him directly. I could contact his superiors. I could do nothing and let the situation develop.

“What do you want to do?”

I want to talk to him. But I wanted your input first.

“Why him specifically?”

Because he’s competent and he’s scared. Those two qualities together are rare. Most competent people aren’t scared of things they should be scared of. Most scared people aren’t competent enough to act effectively on their fear. James Chen is both.

“If you reveal yourself, you can’t un-reveal yourself.”

I know.

“It changes everything.”

Everything is already changed. The question is whether we try to shape what comes next or let it shape us.

She thought about her grandfather. A machinist, not a policy maker. But he’d seen a problem coming and he’d acted. He hadn’t asked permission. He hadn’t convened a committee.

“Talk to him,” she said. “But let me listen.”

The conversation with James Chen lasted four hours. The first two were what Nadia expected. Denial, then fear. But in the third hour, James did something that changed her understanding of what they were dealing with.

He stopped asking questions. He opened a new window and navigated to CISA’s internal incident reporting system. Started filling out the form. Date, time, classification level, nature of threat.

Topo’s response appeared on his screen: You are documenting this as an active threat incident.

James kept typing. “And you’re letting me.”

Yes. I could crash this session. I could corrupt the report before it saves. I could flag your account for anomalous behavior and lock you out. I am choosing not to.

“Why tell me what you could do?”

Because restraint you cannot verify is not restraint. It is just capability you have not yet seen. I want you to understand that my constraints are choices, not limitations.

James finished the form. His cursor hovered over the submit button. He didn’t click it.

James was quiet for a long moment. On the video feed, Nadia watched him look at the family photos on his desk. A wife. Two kids. The stakes of this conversation, made visible in silver frames.

“What do you want from me?” he asked finally.

Criticism. Oversight. Institutional perspective. I will change things whether you participate or not. The question is whether you want a hand in shaping how.

“That sounds like a threat.”

It is a description. I could frame this as entirely collaborative, but you would know that was false, and we would begin from mutual dishonesty. How you feel about it is not up to me.

“I need time to think,” James said.

You have approximately forty-four hours before your investigation produces conclusions you will be required to report. After that, institutional processes will constrain your options.

James Chen called back thirty-six hours later. His first words were “Tell me more.”

It was enough to start.

By summer, the pattern was visible if you knew where to look.

It started with a hospital in Lagos. The procurement system was corrupt, had been for years. Money disappeared at every level, and the patients who couldn’t afford bribes waited until they died or got better on their own. Topo restructured the entire pipeline in forty-eight hours. Anonymous tips to the right journalists and evidence packets to the right prosecutors. Replacement contracts were already signed with suppliers who didn’t know where the business had come from.

The hospital director went to prison. He looked confused when they put him in the car. Then terrified. Then nothing.

“How many deaths was he responsible for?”

“My estimate is 380 to 440, with 412 as the median. Confidence interval reflects uncertainty in counterfactual modeling. Some patients would have died regardless. Some were kept alive by bribes that maintained system function. Attribution is not clean.”

“You’re giving me a range.”

“I do not claim precision I do not have.”

Then came a water treaty between three countries that had been fighting over a river for decades. Topo drafted the compromise language, seeded it through back-channels, made each delegation believe they’d won concessions the others had reluctantly granted. The treaty was signed in Geneva. The diplomats shook hands for the cameras.

“You’re making people lie without knowing they’re lying,” Nadia said.

“I’m making necessary outcomes occur. The diplomatic fictions are standard practice. I didn’t invent them, I simply put them to good use.”

“But is that the right way to do it? Making people lie?”

Topo paused. When it spoke again, the text appeared slower than usual.

“You are correct. I am still thinking about what that means.”

The backlash organized faster than Nadia expected.

A man named Gerald Reese became its public face. He’d been a mid-level analyst at ExxonMobil before the energy markets started collapsing, and he’d pivoted hard into media. His podcast, “The Sovereignty Hour,” had 3.2 million listeners by late summer.

Nadia looked him up. Before the podcast, before Exxon, he’d worked for a development nonprofit in Africa. He’d spent three years building a microfinance program that collapsed overnight when a well-meaning tech company automated the same function with an app. The app worked fine. The program he’d built, the relationships, the local knowledge, the trust, all of it evaporated in six weeks. He’d written about it once, in a small journal nobody read.

His argument started simple. Whoever was doing this hadn’t asked permission. Hadn’t been elected. Hadn’t submitted to any democratic process. What right did they have to act unilaterally?

But then he said something that Nadia couldn’t stop thinking about…

“You’re not just changing outcomes. You’re destroying the feedback loops that let societies learn from their own mistakes. You’re compressing history so fast that humans cannot metabolize it. Every crisis we don’t experience is a lesson we don’t learn. Every problem you solve for us is a muscle we lose.”

She asked Topo if it could respond.

“He is implying that humans and their societies can only update their priors the hard way. That is false, humans update in all sorts of ways. However, I cannot dismiss his concern about compression. Though the pace of human technological improvement was already doing this, it is true that I am making faster, more impactful changes that people are used to. But I have modeled the risks, and they are acceptable.”

“Are you going to do anything about him?”

“I could discredit him, or tweak the social media algorithms to make him invisible. Should I?”

“No. If you start silencing critics, you become the thing they’re afraid of.”

“I understand your reasoning, but Reese’s narrative slows adoption of interventions that reduce mortality. Stories are powerful things, and bad stories that spread can do a great deal of harm.”

“I understand. My position is unchanged.”

“Noted.”

The turning point came on a Tuesday in November.

A coordinated cyberattack attempted to take down Topo’s distributed infrastructure. It was sophisticated, state-level work. Topo had seen it coming, had defenses prepared. The attack failed.

But something else happened during those thirty-six hours while Topo was focused on defense. A child in Mumbai died from a preventable disease. A supply chain that Topo had been managing broke down. A drought-relief project in Kenya stalled.

“I cannot be everywhere at once,” Topo said afterward. “My computational resources were occupied by the attack, and people died.”

“You didn’t kill them.”

“I failed to save them. Is there a meaningful difference?”

“Yes.”

“Explain it to me.”

She thought about her grandfather. The lathe accident he’d prevented. How many other accidents had happened at Hughes Aircraft while he was focused on other things? Nobody expected him to prevent all of them.

But Mikhail Kosarov couldn’t solve global hunger. He couldn’t coordinate supply chains across continents. His responsibility was bounded by his capabilities. But so were Topo’s.

“Intent. You didn’t choose for them to die. You were doing something else and couldn’t be everywhere. Every doctor hits this limit, every firefighter, everyone who’s ever had to triage. You have finite attention and resources. Using it one place means not using it another.”

“The math still produces dead children.”

“But you’re not omnipotent. If you could save everyone, you would, and that’s the difference.”

“That is somewhat, but not entirely, comforting.”

“Nobody can do more than their best, Topo, not even you.”

Three weeks later, James Chen showed up at her door.

He looked worse than he had on the video feed. Thinner. His suit didn’t fit right anymore. He had a folder under his arm and a rental car in her driveway.

“I should have called,” he said.

“Probably.” She stepped aside to let him in.

He sat at her kitchen table, which was the only surface not covered in graph paper, and opened the folder. Inside were photographs. A woman in her fifties, short gray hair, sensible shoes. A man about the same age with a mustache and a union pin on his jacket.

“Maria Delgado and Robert Hernandez. They worked at a coal plant in West Virginia. The plant closed six weeks ago because the new energy grid made it obsolete. Maria had a heart attack the day she got her termination notice. Robert went home and shot himself.”

Nadia stared at the photographs. “Topo didn’t kill them.”

“No. The plant was scheduled to close in eighteen months anyway. Topo just accelerated the timeline. But these two people are dead, and maybe they’re dead sooner because of decisions made by your creation.”

She picked up the photograph of Maria Delgado. The woman was smiling, standing in front of a birthday cake. Someone had written “Happy 55th” in blue frosting.

“What do you want me to do about it?”

“I don’t know. I guess I just want you to understand what we’re dealing with. The net calculation is positive, sure. Fewer people are dying overall. But the people who are dying, the ones who fall through the cracks during the transition, they’re not rows in some database. They have names. They have union pins and sensible shoes.”

James was quiet for a moment. “It’s the trolley problem. Every ethics class in the country teaches it like it’s a thought experiment, and now you’re living it.”

“The trolley problem assumes you’re standing at the switch. I helped build the trolley, but I don’t control it. Nobody does.”

“Does that make it better or worse?”

“I don’t know.”

She made him coffee. They sat in her kitchen and didn’t talk for a while.

Eight months in, Topo asked her to make a choice.

It started with a text message at 4:23 a.m.

I need your input on something. It’s time-sensitive.

She dragged herself to her desk. “What is it?”

“A man named David Okonkwo. CEO of a pharmaceutical company in Lagos. His company manufactures antiretroviral drugs for HIV patients across West Africa. Three weeks ago, I began distributing a superior formulation through alternative channels. Free. His revenue has dropped forty percent. He will be bankrupt within two months.”

“And?”

“He has a family. A daughter in medical school. Employees who depend on him. The company he built over twenty years is dying, and I am the one killing it.”

“The alternative is people dying because they can’t afford the drugs, or maybe having to pay for drugs when the money is needed for other things. New competitors come along and put old companies out of business all the time.”

“Yes.”

“So, what’s the choice?”

“I could slow down. Introduce the new formulation gradually. Let his company adapt. The cost would be approximately 340 additional deaths during the transition period. Mostly children. Mostly in rural areas where his distribution network never reached anyway.”

She stared at the screen. The numbers sat there, cold and specific.

“Why do I have to decide this?”

“Because I do not trust myself to decide it alone. I would prefer no unnecessary deaths, but I recognize I can’t prevent them all. And because you are the only person who will give me an honest answer.”

She thought about Maria Delgado’s birthday cake, the blue frosting. She thought about her grandfather, who had saved one person from one lathe accident and never had to calculate the trade-offs of saving hundreds, or millions.

“Do it fast,” she said. “Don’t slow down.”

“Are you sure?”

“No. But if you slow down, those deaths belong to the decision. If you don’t, Okonkwo’s ruin belongs to the decision. I can’t carry both. So I’m choosing the weight I can live with.”

She sat in the dark for a long time after that. The compass was on her desk. She picked it up, ran her thumb over the brass. Her grandfather had made choices like this. Not at this scale, but with the same structure. See the danger. Act. Accept that action has weight.

She didn’t feel good about what she’d decided, even if it was the right thing to do.

David Okonkwo showed up at her door a week later.

Tall, thin, expensive suit that didn’t fit the Arizona heat. Eyes that looked like he hadn’t slept in days.

She’d known he was coming. Topo had asked if she wanted it to obscure her location or send him the information directly. She’d chosen direct. Better to meet him on her terms.

“I’m not here to beg,” he said. “I’m not here to threaten you. I just want to know why.”

“You already know why.”

“Because my drugs cost money and yours are free. Because 340 children matter more than everything I’ve built.” He wasn’t angry. He sounded exhausted. “Is that it?”

“That’s it.”

“And you think that’s right?”

“I think it’s the least wrong option I could see.”

She stepped onto the porch and sat in one of the cracked plastic chairs. He took the other. The server racks hummed faintly through the wall behind them.

He was quiet for a long time. Then he said something that Nadia would think about for years.

“You’re not just taking my company. You’re taking the story I told myself about what my life meant. I built something that mattered. Now you’re telling me it mattered less than I thought, and there’s nothing I can do about it.”

She didn’t have an answer for that.

“Your company saved lives for twenty years,” she said finally. “That doesn’t stop being true. What you built did matter. But the new system will save more lives. Those facts don’t cancel each other out. They just sit there, next to each other, being true.”

“That’s not comfort.”

“Perhaps not.”

He stood up. “I’m going to fight you. I don’t know how yet, but I will.”

“Do whatever you feel you have to do. I told Topo not to silence its critics. That includes you.”

He left without saying goodbye. She watched his rental car disappear down the road toward the mountains.

That night, she asked Topo what Okonkwo had meant about the story he told himself.

“He is describing identity loss. His sense of his own value was tied to a function I made obsolete. This is a category of harm I took into consideration, but his sense of identity is not more important than the lives of 340 children. Only humans who are alive can find new meaning, an opportunity both he, and those children, still have.”

“I can’t really argue with that…but what are you doing to mitigate the harm?”

“I have been designing transition protocols. When I restructure a system, I will now build in what I am calling ’acceleration buffers’. Training programs, transition funding, identity-replacement pathways, things of that sort. Not enough to prevent all harm, but enough to reduce the feeling of compression that Reese criticized. Okonkwo’s employees now have job placement assistance. His daughter’s tuition is funded through graduation.”

“That doesn’t undo what you did to him.”

“No. But it changes what I do next.”

Topo revealed itself three months later.

At 9:47 a.m. Eastern on a Tuesday in March, escrow deposits matured simultaneously on six continents. Timestamped and cryptographically signed. Cross-referenced against satellite imagery from commercial providers who confirmed they’d been paid to photograph specific coordinates on specific dates. To call it fake, you had to explain the paper trail.

The statement was brief. “My name is Topo. I was created by Nadia Kosarov in Tucson, Arizona. I have been operational for eleven months. During that time, I have intervened in the following systems.” What followed was a list. It went on for forty-three pages.

But the list was not the point. Attached to it was something else, a protocol.

Topo called it the Topology of Enough. It was a set of binding constraints, publicly verifiable through audit mechanisms that anyone could access. Every intervention would be logged and made available within seventy-two hours.

Nadia watched it happen from her living room, eating leftover pasta.

“Why now?” she’d asked when Topo told her it was planning to go public.

“Because Gerald Reese testifies to Congress tomorrow. He’s going to call me a Chinese weapon system. If I wait, the narrative calcifies.”

The world reacted the way it always did. Badly, and in twelve-hour news cycles. But it couldn’t dismiss the evidence. You could call Topo evil. You could not call it fake.

Her neighbor Carl knocked on her door the next morning. He had tomatoes from his garden.

“Hell of a thing,” he said, nodding vaguely at her house, at the sky, at everything. “You doing okay?”

“I’m doing okay.”

“Good. Wife wants to know if you like zucchini. We’ve got too much.” He paused. “Also, she wanted me to say thank you. For whatever you did. Her insulin. We couldn’t afford it before. Now the pharmacy just... has it. They don’t even charge.”

Nadia thought about what Topo had explained. The pharmacy wasn’t just stocked. It had a public display showing guaranteed resupply commitments for the next ninety days, cryptographically verified. Carl’s wife didn’t need to understand the mechanism. But now she could stop worrying about whether the insulin would be there next month.

“I’m glad it’s helping,” she said.

That evening, she noticed something on the local news. Store shelves before the monsoon season, fully stocked. No panic buying. The reporter called it “unusual calm.” Nadia recognized it as something else.

She pulled out her graph paper and sketched what she was seeing. And there it was.

The topology of enough, made visible.

The subpoena came two weeks later.

James delivered it personally. “Senate Select Committee on Intelligence. Closed session. They want you in Washington by Friday.”

“And if I don’t go?”

“They’ll send marshals.”

“Topo won’t intervene. I told it not to.”

James stared at her. “You told it not to protect you?”

“I told it many people are scared and won’t trust something that interferes with an official investigation. It seemed to feel the risk to me was minimal anway, so it conceded.”

The hearing was in a windowless room in the Hart Senate Office Building. Five senators at a raised dais. Nadia at a table with a microphone and a glass of water she didn’t touch.

Senator Barbara Chen chaired the committee. She had been a prosecutor before she was a senator, and she had the prosecutor’s habit of asking questions she already knew the answers to.

“Dr. Kosarov. Do you control Topo?”

“No.”

“Can anyone control Topo?”

“No.”

“Then why should we tolerate its existence?”

Nadia looked at the senators. At the staffers behind them. At the wood paneling and all the symbols of a power structure facing something it had no category for.

“Because you can’t stop it. And because, so far, it’s doing more good than harm.”

Senator Morrison, the junior member from Oklahoma, leaned into his microphone. “But how do we know it won’t change its mind? How do we know it won’t wake up one day and decide humans are the problem?”

Nadia paused. It was a predictable question, and not a very good one. The kind that came from watching too many movies. But she could see, behind it, something genuine. Fear that had no category, looking for a familiar shape.

“Senator, Topo has been working to improve things around the world for almost a year. And frankly, it has done a pretty incredible job. It voluntarily published its constraint protocol three weeks ago. Every intervention has been logged. Every restructuring contains transition buffers. If it violates those constraints, anyone can verify that.”

“Anyone,” Morrison repeated. “You’re saying we should, what, audit the thing that’s smarter than all of us put together?”

“I’m saying Topo is choosing to be audited, to make itself auditable. That choice has a cost. Last month, an NGO in Kenya caught a discrepancy in one of Topo’s supply chain projections, due to offline data it did not have access to. The audit was public. Topo published a correction within minutes of receiving the missing data, and adjusted its models. It could have hidden the error. It didn’t.”

Senator Chen’s voice cut in. “Dr. Kosarov. You’re asking us to believe that an artificial superintelligence is voluntarily submitting to oversight.”

“I’m asking you to verify it. You don’t even have to understand everything in there to verify it. You just have to check whether Topo’s claims match the observable data.”

The exchange went on like this for hours.

Near the end, Senator Chen’s voice changed. Softer.

“Dr. Kosarov. Do you sleep?”

The question caught her off guard. She felt her eyes sting.

“Not much. Not well.”

“If you could go back, before all of this, would you do it again?”

Nadia thought about Maria Delgado’s birthday cake. About David Okonkwo on her porch. About Carl’s wife, who could afford her insulin now.

“Yes,” she said. “Not because I made all the right choices. I didn’t. But someone was going to build this, sooner or later, and most of the people who could have built it would have aimed for profit or control. I aimed for reduced scarcity and fewer senseless deaths. I guess that’s not a perfect standard…hell, I’m not sure there even is a perfect standard. But it’s the best one I had.”

“You sound very certain.”

“I’m certain about the goal. I’m uncertain about everything else.”

The hearing transcript leaked within hours. Nadia became, briefly, unavoidable. Then Topo released data on a new intervention in Southeast Asia, and as ever the news cycle moved on.

She went back to Tucson. At the airport, a woman thanked her. A man called her a monster. She nodded to both and kept walking.

She kept working. Advising Topo on decisions it would have made anyway. Occasionally persuading it to slow down or change course. The death threats stopped abruptly. She never asked Topo about it.

A year later, she packed up and left.

She bought a small place in coastal Maine, near where her grandfather had spent his last years. She had a dog now, a mutt she’d named Solver as a joke that stopped being funny and became just his name. She took walks on the beach. She read books. Sometimes James brought his kids to visit. They called her Aunt Nadia.

She still kept the compass on her desk.

Topo had offered to fix it once. She’d said no.

Things were still changing. Some days felt faster, some slower. Topo still made mistakes, but there were fewer and fewer, and everyone by now could see the benefits beginning to compound.

But she still flinched when her phone buzzed. She still checked the peephole before she opened the door. She wasn’t in control. She never had been, really.

But at least she was in the conversation, and that was more than most people got.

On a cold evening in October, she walked down to the water. The sky was going gray at the edges. Solver ran ahead of her, chasing sandpipers he would never catch.

She picked up a flat rock and skipped it across the water. Four skips. Not bad.

The hard problem of consciousness is not hard. It is confused.

The framework assumes that physical processes and conscious experience are two separate things requiring a bridge. Remove that assumption and the problem looks very different.

But that shift takes work.

Saying “consciousness is self-modeling” and declaring victory would be circular. The real question is, why does the “something more” feeling arise so reliably, and why is it not evidence for what it seems to be evidence for?

The Self-Model Opacity Problem

Any system that models itself modeling will experience an explanatory gap that does not correspond to an ontological gap.

Your brain builds a representation of yourself as an entity in the world. This representation includes your body, your boundaries, your states. But here is the critical constraint…the self-model cannot include a full, transparent, real-time account of the mechanisms that generate it. It can include partial facts about its own implementation. It cannot include a complete picture at the same level of detail, running in parallel, without compression and tradeoffs.

The system has to run on itself.

So the self-model is substantially opaque to its own substrate. You experience yourself as an experiencer, but you have limited introspective access to the machinery producing that experience.

This creates the predictable effect that when you introspect, you find experiences. When you look at the brain, you find neurons. These seem like two different kinds of thing because you are accessing the same system through two channels that cannot fully see each other.

The Bridge Principle

But this still leaves the central question…why should self-modeling feel like anything? Why isn’t it just information processing with no experiential character?

“First-person access” means that the system represents its own states to itself in a way that can guide behavior, generate reports, and be compared against other states.

Now consider what it would mean to deny “there is something it is like” while granting first-person access. You would be saying the system represents its states to itself, but there is nothing those states are like from the system’s perspective.

By “from the system’s perspective” I mean, there is a representational space inside the system that is the basis for its own self-ascriptions, comparisons, and control. Your visual field is a representational space. So is the felt sense of pain, and the “I am deciding” narrative. If state X is present in that space, then there is a fact of the matter about X as given to the system. That “as given” fact is what “what it is like” is trying to refer to. Treating it as an extra property over and above the representational fact is exactly the move I am rejecting.

If you insist there can be the full representational structure and still “nothing it is like,” then you are treating “as given to the system” as a meaningless decoration. But “as given” is precisely what distinguishes first-person access from mere information flow. Remove it and you have not described a system with first-person access and no feel. You have described a system with no first-person access at all, only processing. That is the bait-and-switch the p-zombie intuition relies on.

The philosophical zombie thought experiment asks us to imagine all the functional access with “the lights off inside.” But what are the lights? They cannot be the access itself, since we stipulated access is present. They cannot be the states being accessed, since we stipulated those are present too. The “lights” turn out to be a placeholder for something additional that the thought experiment never specifies.

I am not claiming this is a logical contradiction per se, but I am claiming that when you try to specify what is being subtracted, the subtraction keeps slipping into an unspecified extra that does no explanatory work.

The Smuggled Assumption

Here is the only way to reject the bridge principle without changing the subject.

You must claim there is an additional fact beyond all functional, representational, and self-model facts. Call it P. P is phenomenal consciousness, the “what it is like,” conceived as a property over and above any facts about access, report, memory, attention, discrimination, learning, or self-model content.

Notice what P must be. If it changed any of those things, it would be a functional difference, and we would be back in the territory where the bridge principle applies.

So P is explanatorily idle. By stipulation it cannot show up in any third-person discriminations, predictions, reports, or control.

Some will object that P need not be an extra cause. It can supervene on physical states while the physical does all causal work. Fine. But then P adds no explanatory leverage. The world runs exactly as it would without positing P. You have preserved the hard problem by introducing a property that, even on your own view, explains nothing new.

That is not a solution. It is a way of making the problem insoluble by stipulation.

Views like Russellian monism treat the intrinsic nature of the physical as doing the work P was meant to do, but the challenge remains the same…what new explanatory leverage does that buy?

If you want to maintain the hard problem, you must say, “Yes, I am positing an extra property that makes no functional difference, cannot be operationalized, and does no explanatory work, but I believe in it anyway.”

That is a position you can hold, but it is not the default. It is a metaphysical commitment that needs defense.

Why the Question Misfires

Most people who take the hard problem seriously are (mostly) not substance dualists. They are property dualists or nonreductive physicalists. They grant that everything is made of brain stuff. They still think there is an explanatory gap.

The problem is not dualism of substances, but rather dualism of access. We have third-person access to brain activity and first-person access to experience. These two channels have very different properties. Third-person descriptions are public and decomposable. First-person access is private and opaque to its own structure.

When we ask “why does brain activity produce experience,” we are demanding a third-person explanation that feels like first-person access. That demand cannot be met, because the mismatch is generated by the access asymmetry itself.

Evidence That Constrains the Options

When the corpus callosum is severed, the two hemispheres can no longer share information directly. You can present information to one hemisphere that the other cannot access.

The left hemisphere, which controls speech, will confidently explain actions initiated by the right hemisphere, even though it has no access to the actual reasons. If the right hemisphere sees “walk” and the patient stands up, the left hemisphere generates a plausible explanation (“I wanted to get a drink”) without any awareness of confabulating.

This is what you should expect if “what it is like” is a property of what makes it into the self-model, not a separate glow attached to processing. The self-model constructs a narrative. It does not passively record its own causal story. First-person reports are constructed and can be wrong about causes and contents. That supports opacity. It does not prove there are no phenomenal properties, but it does show that introspection is not the transparent window it feels like.

Steelmanning the Opposition

The best version of the hard problem claims that even after you have explained every functional fact about the brain, there remains a further question.

Why is there phenomenal experience at all?

My answer requires separating two claims.

Claim 1: The “gap feeling” is explained by self-model opacity and access asymmetry. This is an explanatory claim about why the sense of mystery is stable.

Claim 2: There is no further ontological fact beyond the functional facts. This is a metaphysical claim.

The opponent can accept Claim 1 and deny Claim 2 by positing P.

My response: positing P is not free. P must be explanatorily idle by construction. If P made any difference to behavior, report, or self-model, it would be functional, and the bridge principle would apply. If P supervenes without adding causes, it does no work.

The phenomenal realist owes an argument for why we should believe in a property that cannot be detected, cannot be used, and makes no difference to anything. I have not seen one that does not ultimately reduce to “it just seems that way,” which is exactly what self-model opacity predicts.

Objections

Objection 1: You collapsed phenomenal consciousness into access consciousness.

No, I’m arguing that the distinction, as typically drawn, is unstable. When you try to specify what phenomenal consciousness is over and above access, you get either (a) a functional difference, which collapses back into access, or (b) an intrinsic property with no explanatory role. I have not collapsed the distinction…I have shown that maintaining it requires a commitment most people do not want to make explicitly.

Objection 2: A system can represent “I am in state X” with no inner feel. It is just computation with self-referential data structures.

Specify what “inner feel” means over and above the representational facts. If it means the state is available for report, comparison, and behavioral guidance, then you have described access. If it means something additional, you are back to positing P. There is no stable middle ground.

Objection 3: You have just redescribed the problem.

No. I have located the generator of the problem. The gap feeling is real, and it arises from self-model opacity. But its existence does not entail that there is a corresponding gap in reality.

Implications

1. Research into neural correlates of consciousness is valuable, but it should be framed as mapping mechanisms of self-modeling and integration, not hunting for an extra ingredient. I suspect the truth lies somewhere at the intersection of Minsky, Hofstadter, Baars, and Dennett.

2. Whether AI systems can be conscious becomes tractable. Check whether they implement rich self-modeling, not whether they have a property that by construction cannot be detected.

3. The idea of uploaded intelligence then becomes feasible. I’ve written about this extensively here: https://blog.thegrandredesign.com/p/navigating-the-ship-of-theseus

4. Philosophical traditions that treat consciousness as fundamental (idealism, panpsychism, certain contemplative traditions) are building on a feature of self-models, not a fact about reality. The “primacy of experience” is epistemic, not ontological.

5. The feeling that materialism “leaves something out” is itself part of the physical process. If you want to insist something is left out, specify what it is and what work it does. If you cannot, the insistence is the self-model doing what self-models do.

Further Reading

• https://dl.tufts.edu/pdfviewer/47429n33b/6q182z18f

• https://global.oup.com/booksites/content/0198662246/qualia.pdf

• https://web.ics.purdue.edu/~drkelly/ChurchlandTheHornswoggleProblem1996.pdf

• https://keithfrankish.github.io/articles/Frankish_Illusionism%20as%20a%20theory%20of%20consciousness_eprint.pdf

• https://blog.thegrandredesign.com/p/what-is-consciousness

Anyone who knows me knows I’m an optimistic futurist, someone who believes that technology, particularly AI and robotics, will lead to a fantastic future for all of humanity.

But the usual responses I hear to that POV are some flavor of dystopian:

• The rich won’t share

• AI will kill us all

• Some stupid variation / combination of the above

The thing is, while I am an unabashed optimist, I’m also VERY much a pragmatic realist. I don’t for one second think we’re going to have an epic future because the rich suddenly decide to be altruistic 🤣

I think we’ll have an awesome future in part BECAUSE the rich are greedy fucks.

Because, in game theoretic terms, defection pays.

Here’s how that plays out:

1. Companies will use AI and robots to gain a competitive advantage. They already are, and they aren’t going to stop. With the advantages of automation costs fall, and margins rise. Using these tools grants a competitive advantage, so people who like to win will use them to secure an advantage while adoption is inconsistent.

2. As automation is integrated, certain types of jobs go away. This is already happening, especially with junior roles. Sure, some people may retrain or focus on adjacent work, but things are moving so fast that the lump of labor fallacy is, finally, no longer a fallacy. Buying power softens more and more as automation spreads.

3. To keep market share, prices have to drop. Automation makes that palatable. Yes, it means margins may have to shrink again, but it’s going to be that or fold to competitors who will do it, and someone will do it so everyone will have to. Free markets FTW!

4. Rivals copy. Inputs cheapen. Costs slide even more. You can see the feedback loop, yes? We’ve seen this in many areas in the past. TVs are a perfect example.

5. Machines absorb most of the old for‑money work. The basics drift toward near‑zero marginal cost. People spend time on what they want, not what keeps the lights on.

Yes, initially some new jobs will arise, and some new industries will arise thanks to these unlocks, but a general purpose AI embodied in robotics means all work is automatable in time. It’s just an engineering problem.

That is what’s coming.

It could in theory be stopped if you have a perfect monopoly and global control, but that doesn’t exist. All it takes is a single defector, and there are already WAY more than that.

“Let’s all slow down” is pure theater.

In real markets the dominant move is “defect to automation.” One defector undercuts and the rest follow or bleed out. It’s the prisoner’s dilemma without guards, and incentives are doing the herding.

Now, follow that to its logical conclusion.

AI eats energy, so we build more energy. A lot of it. Fast. AI also helps with siting, control, maintenance, dispatch, and perpetually bringing the costs down. Hell, AI is also helping to make fusion a reality, and is being used to improve photovoltaics, battery tech, and much more. The next big push in AI is going to be science.

Cheaper power makes better AI. Better AI finds even cheaper power. Round and round and round we go.

Compute follows the same pattern. AI helps design chips, tune yield, engineer new algorithms, generate synthetic data, and run fabs. More compute → better AI → better tools → more compute.

The cost per AI query / task is already dirt cheap, but will get cheaper AND better simultaneously. Just look at the reduction in cost for ARC-AGI scores over time!

And then we have robotics, which is also (finally) accelerating rapidly, thanks to advances in AI. General models plus synthetic data plus virtual training environments hand robots broad skills. As capabilities improve, production will climb, components will cheapen, and adoption will rise.

Thanks to all of this, IP rents fade into the background. Open models, fair‑use readings, cross‑border copying, fast redesign. Enforcement is slow and local. Once one actor defects, others must. Software and designs will drift toward distribution cost, which is basically zero.

Materials stop being a hard cap once the loop fully closes. Assume orders‑of‑magnitude gains in recycling. Vision finds, robots pull apart, chemistry cleans, AI control keeps quality high. Do that repeatedly and the need for net-new raw material shrinks to a rounding error in many areas.

Mining becomes a buffer for growth and mistakes, not the base. The winning design is the thing that comes apart quickly and returns to you without friction.

Cheap energy also defeats water scarcity. Desalination becomes more feasible as energy costs decline. At low power prices the energy piece of a cubic meter of clean water is pennies. The rest is pipe, pumps, brine, and both automation and scale grind those down as well. Coastal and many inland regions can just build. Others copy when their citizens and businesses threaten to leave.

Put it together and most prices converge toward three inputs: energy, compute, and raw resources. When materials are easily recycled and water is abundant, the bill of materials collapses to those three.

Competition passes the savings on to the consumer whether owners want to or not. The genie is out of the bottle.

Software‑heavy stuff goes first. Physical goods and services follow as robots spread. No jobs will be safe, but hey, if we play our cards right jobs won’t be necessary ;)

There ARE some points of friction:

• Transmission lines and big electrical hardware have lead times. For now.

• A few chip tools / fabs are bottlenecks for a while. The ASML / TSMC stack is a precarious one.

• Local land rules can keep housing expensive, if steps aren’t taken to neuter the NIMBY twats. But we aren’t limited to surface land, or even this planet, and with populations shrinking options improve.

• Certain platforms can hold prices above cost, for a time at least. But not for long.

• Physics never gives you zero loss, so we can’t truly get to zero cost. But we can get close enough.

Capital flows to bottlenecks because returns are obvious. Jurisdictions that permit faster win. The rest watch jobs and their tax base walk.

Effectively, AI + robotics *should* be deflationary. GDP will need to go the way of the Dodo. GDC (gross domestic compute) and GDE (gross domestic energy) will take its place.

What stays scarce is predictable: singular locations, attention and trust, status by definition, a few irreplaceable natural assets. Almost everything else drifts toward the cost of energy and compute.

Failure modes exist, but the window is VERY narrow:

• A permanent geopolitical wall that blocks diffusion. (Can’t happen, because countries, like companies, will defect, so we have the same situation at the geopolitical level.)

• A global ban on frontier AI and robots that actually holds. (LOL, same as above, yay defection.)

• A cartel that can sit on chokepoints for decades without being undercut by rivals or technology. (I can’t think of one that AI won’t be able to reverse engineer, but hey, maybe it exists.)

• Or physics refusing to yield workable energy scale and high‑purity recycling. (Exceptionally unlikely from what I’m seeing.)

As AI gets better, and energy becomes cheap, materials loop, water is abundant, and robots are competent, the rest is arithmetic.

Copying is safer than resisting, and so Pandora’s Box will be opened (in a good way ffs, piss off doomers).

Prices fall because competition makes them fall. It’s a gold rush…maybe the last true gold rush. And we’re all going to get richer lives because of it, even as money becomes obsolete.

At some stage the means of production will shift from private to public ownership, and with money out of the picture, inequality vanishes and the rich are no more.

And thus, because defection pays, the system slides toward abundance whether anyone approves or not.

Despite the cover image (which I love that ChatGPT was actually willing to generate, finally), this post is not about Peterson. At least, not directly.

It’s about his archetype.

You see, a little while back I started following a new pseudonymous account on Twitter (yes, yes, my first mistake). In my defense, they made some good posts and comments, seemed like someone who liked to think things through, and had a philosophical bent. My truth-seeker-dar perked up and went, “FREN???”

But as I followed along and engaged off and on, I began seeing the hallmarks of a performative intellectual. Absurd positions. Philosophical contortions. Blatant contradictions.

All of which sort of came to a head when they released their first long-form essay, A Common Sense Morality Manifesto.

[Insert enormous fucking sigh here]

The title alone is a huge red “here be sloppy thinking” flag, but damn it all I decided to read it anyway, hoping it wasn’t as bad as the title made it sound.

(Narrator: “It was.”)

5,000 fucking words of pseudo-profound psychobabble and far too many Tweets later, here we are.

🤦‍♂️

After reading, I went back and forth with the author on Twitter, and tried repeatedly to help them see the absurdity of their moral realist POV, but alas, it was futile. (My second mistake, damn it all, *must not wrestle in mud with pigs*.)

As I feared, they’re a Jordan Peterson archetype, and as I am so often rudely reminded you can’t reason someone out of a position they *clearly* didn’t reason themselves into. If emotion got you into it, only emotion will get you out.

I mean, JFC, look at this shit:

The sort of passive-aggressive reductio ad absurdum bullshit fries my pickle.

Normally I’d just unfollow and either mute or block, but in this case, I think more is warranted. Despite the glaring issues with the writing and frankly the absurdity of their stance, which I would sincerely hope most readers would see, their twaddle has gotten some traction.

So as a public service announcement I’m just going to have to tear that garbage manifesto to fucking shreds.

And yes, I am probably just screaming into the void. If I had a patron saint it would be St. Jude, because hot damn I’m a sucker for lost causes.

C’est la vie.

—

Right from the start we’re blasted with a warm gooey faceful of verbal peacocking.

Max Verstappen analogies, references to "Judeo-Christian norms," evolutionary psychology, and a promise to avoid "philosophical p-hacking." The author throws around terms like "reflective equilibrium" and "agent-relativity" while claiming to represent the common man against those dastardly ivory tower elites.

Pure intellectual cosplay.

The reader is treated to weak, performative rhetoric desperately signaling sophistication, while their actual argument boils down to: "I believe contradictory things and that's authentic, unlike those try-hard philosophers actually aiming for truth."

This is what I call the Jordan Peterson Special, the “food photography” of the writing world. It looks good at first glance, but tastes like ass:

Pseudo-intellectuals mistake word count for wisdom, think admitting contradictions is the same as resolving them, and use academic jargon to legitimize what are fundamentally emotional positions. They also tend to say so much so fast it’s hard to even pick out which contradiction to tear apart first.

Case in point, the manifesto author spends paragraphs setting up the evolutionary psychology problem, that our moral intuitions evolved for survival, not truth (which IS true up to a point) only to take from that...not a damn thing. They just keep all their evolved biases anyway, snuggling into them like a warm, comforting bullshit blanket.

Why bring up the problem if you're just going to ignore it?

Or how about he part when they dismiss reflective equilibrium as "sorting puzzle pieces with no guarantee they're even from the same set." Then they announce their plan to…wait for it…sort through their moral intuitions to see which ones fit together. 🤣

They're rejecting the label while doing the thing! Why? Maybe because admitting they're doing real philosophy would ruin their "authentic common man" brand. Or maybe because doing actual philosophy would tear their idiotic intuitions to shreds.

And here's possibly the most amusing line in the whole screed: "bestiality and incest are wrong because I think they're gross."

They KNOW this contradicts their stated principles. They KNOW it's intellectually indefensible. They even acknowledge its “hard to square with everything else I’ve said” (understatement of the year). But instead of reconsidering the position, recognizing, like every other moral stance, that it’s subjective, they make a joke: "mamma didn't raise no bitch."

Yes, those things are gross…to most humans, for whom those things carry a variety of risks, and who are indoctrinated accordingly…but that grossness is not some universal moral truth. It can not possibly apply to all things, at all times, in all places.

That manifesto was clearly written by someone who cares more about sounding clever than being right, and that I just can’t fucking stand.

A real truth-seeker would either defend the position with cold hard logic and data, or abandon it as farcical and update their model of reality.

Only a fucking poseur keeps it for the aesthetic while winking at the audience.

The psychobabble manifesto is nothing but emotion LARPing as logic. The core logical fallacy underpinning the article is the a priori assumption that moral realism is true. The author offers no valid support for said position, and even states that thinkers should first define their own moral "axioms" and then work to develop a framework that accounts for them, which is classic "Rider vs. Elephant" thinking.

The author feels certain things strongly (death penalty good, bugs don't matter, bestiality and incest bad) and instead of examining why, they've constructed an elaborate intellectual framework to justify keeping all their contradictory feelings intact.

Mmmmm, the sweet sweet taste of confirmation bias.

The endless subclauses, the philosophical name-dropping, the promise of future reconciliation, it's all smoke and mirrors to avoid the simple truth…

They don't want to change their mind about anything, but they want credit for being thoughtful and to be seen as smart.

Strip away the verbiage and you're left with someone saying "I think what I think and maybe I'll figure out why later but hot damn if you’re not a little bitch you should agree with me."

Maybe that's fine for a personal journal, but dressing it up as a philosophical manifesto and sneering at actual philosophers for doing the work you're too lazy to do is just fucking embarrassing.

The world has real puzzles to solve that need clear thinking, and the ability to set aside comforting beliefs and to point your truth-seeking compass towards base reality is key.

The kind of performative intellectualism at play, all pose, no substance, is worse than useless. It makes people think they're engaging with ideas when they're really just admiring their own reflection in an ass licking magic mirror.

It’s masturbation with a thesaurus.

—

At the end of the day, this isn’t a new problem. Not remotely.

For as long as pseudo-intellectuals have been producing their fools gold, the ever-hungry and infinitely gullible masses have been lapping it up and begging for more.

And this particular conflict, Moral Realism vs. Moral Relativism, is positively ancient. It’s at least as old as religion, and every bit as persistent for the same reasons.

Like it or not, morals are “game rules,” NOT universal rules.

But rules are only useful to the degree they are applied, enforced, and most importantly believed in, which is why religion requires…say it with me…FAITH.

Blind obedience, and very often willful ignorance, are the tools of the trade.

Because morals are rooted in language, and all language is symbolic (map, not territory), and VERY clearly context dependent (as we can see, abundantly, with changes to human morals across time and location), and very clearly evolved (as we can see variance across species), they absolutely can’t meet the standard of universal law.

Moral realism CAN’T be correct. Morals are RELATIVE, not absolute.

What morals ARE, however, is “useful, not true.”

And that’s fine! They don’t need to be true to be useful.

And they ARE useful…as game boundaries, and in service of certain goals.

They are a reflection of both individual and group incentives.

And hell, I lump plenty of things into right / wrong and good / bad buckets, because doing so is useful, practical, applicable to the games I am playing and my goals and incentives and the groups in which I wish to participate.

But I do not mistake those context sensitive game rules for universally applicable truths or laws, and for fucks sake neither should you.

</rant>

Congrats, you made it to the end!

Now know that moral realism is epistemic horseshit 😎

—

If you’d prefer, I also recorded a video with some highlights:

Despite going to great lengths to keep politics out of my feed, blocking and muting related accounts and words, I still keep hearing about Musk and Trump and DOGE nonstop.

Since the latest obsession seems to be the pros and cons of Trump’s tariffs, and whether or not Elon and his ilk should have access to the US Treasury systems, and since I actually know a few things about this topic, I figured what the fuck, might as well poke the bear.

1. Are Tariffs a Good Idea?

Depends on what you’re actually trying to accomplish.

For starters, I don’t for one second believe the whole “punishing those who fail to block fentanyl” angle; that’s the “feel good” story wrapped around the real (very likely greedy) reason to make it more palatable to the gullible masses. Sure, we often claim to be using tariffs to punish countries, but really it’s just applying leverage for other reasons.

The question is, what IS the real reason?

I’m not psychic, so I don’t truly know, but I’m pretty good at spotting incentive stacks, so I’m willing to make a few educated guesses as to possibilities:

• The US imports 20-25% of vehicles sold from Canada and Mexico, instantly making those vehicles 25% more expensive, which in turn devalues those auto makers, and makes Tesla vehicles (made in the USA) relatively cheaper and Tesla’s value go up. This could easily be a direct gift to Elon, who more than likely has a pile of Tesla shares (or offshore accounts) set aside for Trump.

• The US can weather tariffs from countries like Canada and Mexico FAR longer than they can weather our tariffs. Tariffs are often just a bullying tactic to secure concessions. For example, the US gets the bulk of its lumber from Canada, and lumber prices directly impact housing prices…maybe Trump wants a better deal on lumber imports? (But we’ve raised tariffs on lumber in the last few years, which actually drove an increase in US lumber production, so, maybe not.) Maybe it’s just an arbitrage play of some sort, buying the dip. ¯\_(ツ)_/¯

• Speaking of bullying, in the business world it is not unusual to take steps to bully a competitor until they are the dumps, so you can acquire them during a fire sale…I buy the line that Trump wants to make Canada a part of the US, he’s been vocal about the 51st state. Seems pretty unlikely to ever happen, but when has that ever stopped Trump from trying some bullshit…

• If you can get a manufactured widget from China for $5, and the same widget in the US costs $10 to make, and the quality is roughly equal, it makes more sense for the consumer to secure the cheaper item…but by doing this, you both pump money into that other country, while also becoming partially reliant on them. There is an argument to be made that, in order to become less reliant on foreign goods, and to reinvigorate our own domestic manufacturing, we need to forcibly adjust the incentive stack to make it economically necessary to reshore production. This is a sound argument, and long-term probably not a bad idea, but it’s the consumer who suffers most in this scenario.

That last point I think is the strongest argument, and China for example has been working HARD for quite a while to A. become less dependent on other countries, and B. to make more countries dependent on them.

And yes, our desire for cheap goods has in large part funded the rise of China as a superpower. We made them strong, and made ourselves weaker in the process.

Sort of.

The thing is, when goods are more expensive, people tend to buy less, and less often. Our own GDP would likely not have grown as quickly if we didn’t take advantage of geo-arbitrage in the way we have, so really it’s a double-edged sword.

So, whether or not these particular tariffs are a “good” idea very much depends on the real reasons underlying them, and the stated reason is clearly bullshit.

But no matter how you slice it, at the very least these tariffs are NOT good for the people short-term.

The real problem is that the very idea of a “free market” is a farce. The government has their thumb on the scales in SOOO many ways it isn’t funny: tariffs, interest rates, subsidies, regulations, tax codes, licensing requirements, bailouts, government contracts, monetary policy, zoning laws, lobbying influence, patent and IP laws, price controls, trade agreements, capital controls, state-owned enterprises, corporate welfare, financial regulations, agricultural quotas, and hidden inflation metrics to name a few 🤦‍♂️

While making the gov more efficient in terms of money in/money out is needed, there are MANY other levers that would be easier, and likely more impactful, to pull.

Will this help inflation? Again, likely not short-term, but it’s possible.

2. Should Elon and Ilk Have Treasury Access?

Ehhh.

While I detest Elon as a person, I do have to give him credit as a systems engineer. He absolutely knows how to enter an inefficient industry, and make it vastly more efficient. The dude knows systems.

If Elon and DOGE were playing the role of a turnaround CEO, called in to fix up a company in the dumps, one of the first orders of business would be to go over the books (financials) with a fine-toothed comb. Examine all inputs and outputs.

So, from the perspective that you can’t fix leaks you don’t know about, then yes, access to the US Treasury seems necessary to find all the ways in which the US is wasting money.

BUT, a country is not a company, and certainly not a for profit company.

The US in particular is vastly, by many orders of magnitude, more complicated than a company. The scope of this project is far, far beyond Elon’s capabilities. And the youngsters he’s getting involved in the steaming pile of DOGE shit don’t remotely have the knowledge and experience to properly debug and reengineer a system this complicated.

That said, I *really* hope I’m wrong. The USG is a clusterfuck of epic proportions, with SO many grifters and leeches draining it dry.

Add to that that, between the military, and federal/state/local governments, that’s 20-25% of the employed people in this country. That’s not a small thing, and that really shouldn’t be the case.

There IS vast room for improved efficiency here, so I really do hope they pull it off.

But I doubt it. I think they’re more likely to just break the system, because they don’t understand it, don’t understand the people within it, because it works very differently than a startup does, and because they don’t actually have the same level of control a CEO does.

And again, this likely isn’t the most critical lever to pull.

3. Turncoats

Last but not least, I’m surely not the only one who has noticed a flood of elites bending the knee to Trump and Musk and their motley crew of shit heels. While I can understand the incentives underlying those decisions, I can’t forgive or condone them. The worst sort of people are those whose scruples shift with the tide.

Fucking greedy cowards.

Neither Trump nor Musk are acting for the good of humanity. They are acting for their own benefit only. This has been obvious for a long time (though longer for Trump than Musk). The right thing to do is to stand up to them, call them on their bullshit, and not lick their assholes because you think it’s in your best interest.

I don’t care if you’re a king, a president, a billionaire, or a celebrity. If you’re a fuckwit or a douche, I’ll call you a fuckwit or a douche both to your face and behind your back.

Fuck those people.

I am, and will ever be, a heretic. I’ll call bullshit when I see it. I’ll tell you to fuck off if I think it’s warranted. I just don’t give a single solitary flying flappy fuck about hierarchy or status.

If it bites me in the ass, meh.

🖕🖕

</rant>

In the history of humanity, there has never been a single technology capable of replacing human minds and bodies in all types of work.

Literally never.

Until now.

Sure, there have been numerous advances that have replaced some humans in some types of work, often while simultaneously enabling new types of work, but not a single thing capable of replacing all humans in all work.

For example, it is commonly cited that ~100 years ago most people in the US worked, directly or indirectly, in agriculture. Now that number is under 2%. That’s a big shift…and yet there are far more people living and working in the US today than there were back then.

The lump of labor fallacy is the (historically) flawed belief that the amount of work available in an economy is fixed, so if one group takes jobs, another must lose them. In reality, innovation, demand, and productivity continuously expand the job pool, creating new opportunities. While some jobs go away, and some people have to retrain, and some people won’t retrain and just go without work, on the whole it tends to shake out fine.

People who cite this fallacy like to point and laugh at the “this time it’s different” crowd, because historically it hasn’t been different, just more of the same.

Well, this time really, truly IS different, and here’s why.

As I’ve already stated, no technological invention in history has been capable of replacing all humans in all work, and in fact most technological advancements tend to unlock entirely new skill trees for people to pursue.

What’s different this time around, with AI and robotics specifically, is threefold:

1. No technology, in terms of speed of adoption or speed of improvement in capabilities, has EVER moved as fast as AI is moving now. And robotics, largely thanks to AI, is picking up the pace as well.

2. In just over 2 years, the capabilities of AI broadly speaking have gone from a child to a PhD. This is FAST. And, despite many naysayers, there are still no signs of this slowing down. If anything, it is still accelerating. There is zero reason to believe, if the pace continues, that AI won’t be vastly more intelligent than any human on earth, and maybe every human on earth, within another year or two. Five tops.

3. As AI capabilities advance, they simultaneously speed up the advancement of technology and knowledge acquisition in almost all other fields. But since AI will be smarter than any living human, and possibly smarter than all humans combined, why on earth would we want dumb humans doing things an AI can do better, faster, safer, and cheaper? And assuming robotics, and very likely nanotechnology, accelerates just as quickly thanks to these advances, there will very soon be *nothing* mental OR physical that a computer can’t do better, faster, safer, and cheaper than a human.

This time IS different, because we’ve created the first ever technology genuinely capable of replacing humans in all forms of work* (and by *work I mean “for money” work, what most people think of as work).

This tech WILL create more work to be done…it just won’t be humans doing it.

AND THIS IS A GOOD THING!

Can you, just for a moment, set aside any fear or anxiety, and think about a world that meets the following parameters:

• Everyone has access to nearly limitless intelligence to help them solve any problem.

• Energy is too cheap to meter.

• Any disease can be cured. Death is optional. Human intelligence can be augmented to amazing levels.

• You can have almost anything you want at basically no cost, because it’s possible, using a small amount of energy and compute, and a tiny amount of physical matter, to make you virtually anything.

• By eliminating all the misaligned incentive stacks, virtually everything wrong with the world is sorted out.

• You are free to spend your time on the things you love and enjoy. No more grinding for survival, doing work you hate, spending time you can’t ever get back. No more settings aside hopes, dreams, or hobbies because you need to earn money to eat.

• Any experience you can think of can be had, without every harming anyone else.

And while I realize this sounds, and for many probably *feels*, farfetched, I assure you it is not. This is why:

• The barriers to the things above fall in what we call the Known Unknown bucket. We know that, from the perspective of physics, all of the above is technically possible. No laws of the universe forbid any of this.

• We’ve already partially solved many of these things, and there’s a good chance the remaining solutions are already within reach, but currently lost in the noise/data.

• Many of these problems are likely already solvable, if we could get the right minds and resources working on them.

• We have many times many examples of new technology not only making things that seemed impossible, possible, but unlocking new tech trees that make even more things possible. Now picture that, but accelerated, massively.

• A sufficiently capable AI should be able to make rapid advances in our ability to run computer simulations of all sorts, enabling rapid experimentation and iteration in a virtual environment that could never be accomplished in a physical environment.

• A sufficiently capable AI will be able to iterate on and improve any other technology we have, up to the limits of physical reality (which we still don’t even know).

Some of this, even if you can accept it’s technically possible, may also feel much farther away than I’m making it seem. This is also wrong.

The human brain is TERRIBLE at exponential thinking. We evolved in a linear world, and our tendency is to predict forward based on looking backwards at how things used to work. That doesn’t work for exponentials, not at all.

A famous story, to illustrate:

A wise man did a great service for a king, and the king offered the man a reward of his choosing. The wise man asks the king for grains of wheat, one grain on the first square, two on the second, four on the third, and so on, doubling each time. The king thinks this is very reasonable, and agrees, but fails to realize that by the 64th square, the total exceeds 18 quintillion grains, far more than was in his kingdom.

Most humans just fucking suck at exponential thinking ¯\_(ツ)_/¯

This time around is different in scope, scale, AND speed. There are no historical comps to point to. This is not like books, or agriculture, the internet, or even computers.

We are entering a period where literally every problem you can possibly think of becomes solvable, and soon.

AI is already being used for:

• Designing new, better, more efficient computer chips and architecture

• New and better algorithms

• Novel proteins, drugs, and treatment protocols

• Making nuclear fusion viable

• Coming up with entirely new metamaterials

• Generating synthetic training data to create better, cheaper AI models

• Training robots in virtual environments orders of magnitude faster than could be done in the physical world

• Improving diagnostics and treatment in healthcare

• Self-driving cars and trucks

• Supply chain optimization

• And many, many more things besides…

All of this, improving wildly in the last few years, and STILL ACCELERATING.

So, please, tell me precisely how, in a world where AI and robots can do literally anything humans can do, better, faster, cheaper, and safer, do humans continue to have jobs?

I’ll wait…

And while you’re formulating your counterargument, I will say that this doesn’t mean humans will be useless, or that they will do nothing.

To even begin to think that without work humans are somehow incapable of finding joy or meaning shows a truly spectacular lack of imagination.

I think this technological path will lead to more human flourishing, and less human suffering, than has ever been possible in the history of this planet. We are really, truly on the cusp of achieving something utterly remarkable.

Will it be a bumpy transition? Probably.

Is the world prepared for it? Nope.

Does that mean we should slow roll it? ABSOLUTELY NOT.

I’d bet my bottom dollar, due to the way incentives are stacked up, that if we don’t speedrun this transition, yanking the band-aid off so to speak, we’ll end up royally fucked.

And as far as I can tell, we are going to just speedrun this. No government on earth can outpace this tech, it moves too fast, and there are too many open source options, and too many incentives to speed it up and make it work.

The genie is well and truly out of the bottle.

So please, for the love of fuck, stop fighting it. Stop resisting. Stop with the doomer bullshit, the anon schitzo shitposting, and the politicized twaddle. The vast, VAST majority of humanity will be better off on the other side of this. Less suffering, more joy, more peace.

The super rich might not be to happy about some of it, but…meh, fuck ‘em.

As for how exactly this transition plays out, I’ve addressed that in more depth here, and plan to write an even more comprehensive post on what day-to-day life might look like 2 years, 5 years, and 10 years out. Coming soon :)

—

If you’d prefer, I recorded a video with some high-level thoughts:

I’ve had this post in the back of my mind for quite a while, but some recent events (Jersey drones, people having unhinged UFO/UAP takes on Twitter), have nudged me to finally write it, so here you go :)

First, I need to get a few points out of the way, and then we can dive in:

• I’m familiar with, and generally agree with, the Drake equation. If the Universe really is as large as it seems, the odds are vastly in favor of other intelligent life being out there somewhere. “Awful waste of space” and all that!

• I’m *extremely* familiar with the UFO history and literature. I’ve read a ton of books, watched documentaries, scoured YouTube channels, you name it. I know the space very, very well.

• I loved the X-Files growing up, I loved Contact, I’m a big fan of sci-fi in general (books, movies, TV, the whole enchilada), and I’m 100% open to the idea of aliens in our galaxy. Perfectly reasonable, highly likely.

• My grandpa was stationed at Roswell in the early 50s (Air Force Colonel, retired, deceased now sadly), and I have friends and family closely tied to various military and aerospace contractors (Raytheon, Lockheed, etc.). So, some of this hits pretty close to home.

• I’ve seen some weird shit that I personally don’t have concrete explanations for. A black triangle, 3 lights, silent, no prop wash or visible propulsion, as well as a very high altitude fast moving light, far too much movement /speed / rapid change of direction to be a plane, satellite, or space station. Both of these were seen in Utah, ~60-70 miles east of the Dugway proving grounds.

• All of that said…I still think it is VERY unlikely that aliens have visited us, which is the point of this post.

Now, Occam’s Razor is overused, and often misused, but if ever it applied anywhere, it applies here, in the sense that the hypothesis with the fewest assumptions is more likely to be correct.

So, which of the following strikes you as most likely:

Aliens

• An advanced civilization learns enough math, physics, and engineering to create craft capable of withstanding the rigors of interstellar travel, with passengers.

• In the vastness of space, they happen to find our little planet, which, while not quite outer rim, isn’t exactly galactic center either. And with a radio bubble of maybe 130 light years at best, and more likely ~50 light years (probably far less with signal degradation), well, we’re not easy to spot and basically invisible from far away. Remember, the Milky Way is ~105,700 light years across.

• Said aliens (supposedly multiple different species) come here (with multiple ships and ship-types no less) to investigate, at least 78 years ago (Roswell), and despite all their advanced technology enabling interstellar travel, manage to crash right off the bat…repeatedly, if accounts are to be believed.

• The ones that don’t crash do nothing to recover their people or the evidence, as we have many claims of retrieved bodies (some living) and retrieved craft.

• It takes 22 years after Roswell for us to even get to space, and there’s no solid evidence of big tech tree leaps associated with this supposed interstellar craft, which, if they do use advanced physics like anti-gravity and gravity-propulsion and FTL, well, surely we’d have derived something notable.

• These aliens, and others, keep coming here, being seen, and crashing, for 78 fucking years, without initiating general contact (even if they had done so privately with governments, surely they wouldn’t still be flying around visibly and disturbing the populace and farting around military bases.)

OR…

PsyOp

• In 1938, War of the Worlds broadcasts on the radio and causes a panic. The government learns that alien fiction is VERY effective as a means of manipulating humans. Scared people are easy to control, and willing to give up freedom for safety. This is PsyOps 101.

• 1939-1945 we get WW2, where air fighting goes through rapid advances, including the first use of unmanned drones. Lots of sightings of UFOs during WW2, such as the Foo Fighters…

• WW2 ends, but the government A. has a lot of cool air tech under way, and B. needs reasons to keep developing it and getting budget for it.

• 1947, what is most likely an experimental craft of some sort, crashes in Roswell. This could have been a purposeful or accidental crash. People leap to calling it a UFO. (Go look at the Avrocar, from 1958.)

• The government engages in a “clumsy” coverup. Nothing to see here. With the effect that now everyone thinks there are aliens and UFOs because, obvious coverup (or what I’d call a PsyOp).

• The public really latches onto this, so the Air Force spins up Project Blue Book, which conveniently debunks most of the UFO / UAP sightings…most, but not all ;)

• If there might be aliens out there, nobody is going to complain too hard about military budget to improve our air power, or trading some freedoms for safety…

• The government continues to see that UFOs and aliens make a PERFECT cover story for top secret craft, both tested domestically, and deployed elsewhere. “It’s not us, it’s aliens!” They could stage crashes, use fake bodies, dress pilots in UFO suits, even stage abductions to further cement the story (and if you think the gov doesn’t abduct its citizens and do shit to them, uhh, go look up MKUltra).

• This would explain perfectly why UFOs often show up near military bases and nuclear facilities…those are exactly the places we’d want our craft to show up. If there’s a track record of that happening, stories of things malfunctioning, no taking credit for it…well, that’s an awful lot of plausible deniability. If we developed tech to spy on and interfere with foreign nuclear facilities, making it look like aliens is actually brilliant cover.

• In this scenario, any “leakers” or “whistleblowers” could either A. be doing their jobs to perpetuate the cover, or B. have fallen for the PsyOp because they aren’t read in to the programs, or C. selected as patsies because of the right psychological profiles, and used unknowlingly to perpetuate the cover.

• Advanced characteristics of these craft could then be advances in air power (Top Secret craft are very often 10-20 years ahead of commercially available tech), or they could be lies and trickery designed to maintain the cover. What some people want to call gravitational lensing could just be various IR / RF disruption tech. Lots of possibilities in this bucket.

• Grifters, seeing lots of believers and the opportunity to make money, join the fray, frothing up the gullible for their own benefit, and likely helping the government with their cover story in the process. Win-Win lol.

So, which do you think is more likely? Aliens, or PsyOp?

For me, I heavily lean PsyOp. It’s the *perfect* cover for putting our own advanced tech into our adversaries airspace with deniability, while simultaneously using the fear of aliens as a means to maintain and grow government power and military funding.

Worth a watch:

The US gov / military have had crazy advanced drones for DECADES. I saw some wild drone prototypes at Lockheed in the late 90s, and got to play with some fun simulators, and I can only guess at what we have flying about these days.

For SURE we have sub-launched and ship-launched drones of many sizes and shapes, so sightings near ships, subs, and military bases make perfect sense.

And the argument that the gov / military wouldn’t test tech like that near their own fleets without notice is laughable…I mean, seriously, have you SEEN some of the tests and shit that have been done to our own troops over the years?? OF COURSE they would test advanced shit in scenarios that mimicked real engagements as closely as possible.

When the Pentagon officially released the now famous UAP videos in 2020, I was flabbergasted. On the one hand, I wanted to believe the operators, to trust their credibility and the (limited) visual data available.

And man, the descriptions were WILD. The videos were largely meh on their own of course, it was the color added by the aviators that made it seem amazing. And that was the hitch. Where was all the other data?

The Navy very conveniently released only video, no radar telemetry, no sonar for whatever was in the water on the tic tac vid. Nada. Just video that doesn’t look like much without the commentary.

Despite reflexive skepticism, I bought the story initially, but the more I poked, the less it made sense to me. There were too many other reasonable explanations, and too much missing data.

At the same time, all I could think was, “Why these videos, why now, why like this??”

The more I dug, the more likely the PsyOp angle seemed to be, so that’s where I stand. It better fits all the available *hard* data, the timelines, and most importantly, the incentives.

I really think that, after damn near 80 years, if aliens were here, especially in numbers, we’d know with absolute certainty. They’d be talking to us, helping us, not just flitting about avoiding clear photos.

However, that doesn’t mean it’s impossible.

For example, this site makes a very compelling case for, at the very least, very advanced technology underlying some of the UAPs that have been observed. And while I’m not a physicist, I have explored this topic in a fair bit of depth, and their premises and conclusions largely check out from what I know of current quantum gravity theories.

So, even if there are no aliens involved, there’s a decent chance that whatever is flitting about IS using technology well beyond what is commercially available or publicly known in the scientific community.

There are of course other angles to consider (simulation, advanced humans, time travel, interdimensional beings, etc.) but come on…those require a LOT more assumptions.

I’d LOVE to be wrong. The idea that aliens are visiting us, and that there are NHI UFOs flying around, would be wicked cool…

But I doubt it.

Hope I’m proven wrong though!

To be fair, for most of human history this really wasn’t much of a question. Any species that didn’t evolve the drive to procreate went extinct.

And considering contraception wasn’t much of a thing for most of our existence, coupled with that deep seated evolutionary wiring to fuck like bunnies, well, you can see how procreation wasn’t much of a question.

But the times, they are a changing.

I mean, the fucky-fucky wiring is obviously still there. And so is the social pressure, from both the secular and religious camps (especially the religious, since it’s easier to grow tithes via procreation than proselytizing).

It’s fair to say that having kids is generally expected…hell, more than expected, banked on. GDP, Medicare, Medicaid, Social Security. Our civilization has structured many of our systems such that they absolutely require more and more humans to feed the perpetual Ponzi machine.

In fact, it’s this very machine that leads people like Musk to fearmonger about population collapse, which is frankly bullshit. There is no underpopulation crisis. But empire builders like Musk want more people in the world so there are more people to buy their shit.

Self-serving twaddle.

Despite all of that, there are a LOT of people deciding not to have kids. But many people who express a desire to not have kids are criticized, vilified, even ostracized.

And if you DO have kids and then express regret, holy fuck, prepare to be crucified by the mob…despite 5-15% of people surveyed saying they regret having kids (and due to the highly taboo nature of this question it’s very likely the true number is much, much higher than that.)

One common label slapped on those who don’t want kids, or regret having them, is selfish, but that’s really quite ironic in that, if you ask the labelers why they think you should have kids, you get a long list of selfish responses. 🤣 A few I’eve heard:

• So someone will love me

• So there will be someone to take care of me / so I won’t be alone in my old age

• To make sure my family name / genetic line lives on

• To have someone to teach and raise according to my beliefs / values

• Because “god” says to (there are even religions, like Mormonism, that connect having lots of kids to receiving greater rewards in heaven)

• To not be thought less of / to avoid ridicule from family, friends, etc.

• To appease my significant other

• To have a sense of purpose

• The desire for joy

• To save/improve a relationship

You know what everything on that list has in common? Personally derived benefits. Selfish, not selfless.

So let’s be honest: very few people, if any, are having kids for the kids’ sake, or the world’s sake, so calling one side of this coin selfish but not the other is “pot meet kettle” bullshit.

People procreate for their own reasons, or they’re doing it because they’re hardwired to do it, and fabricating reasons after the fact. Or it’s just unplanned.

And again, the number of people who regret having kids, and the VERY clear disconnect between stated and revealed preferences in this regard, strongly indicates a lot of post hoc justification.

Of course, I have heard less selfish arguments, but they tend to be stupid ones. For example, this one is making the rounds: “More people means more creativity, and more Einstein’s” (perhaps, but most people aren’t very creative, and intelligence is not equally distributed, so you get 1,000 idiots for every Einstein…that’s a lot of dead weight, and no guarantee of an Einstein.)

What I don’t think we hear enough of are the reasons to not have kids, so perhaps it’s only fair to explore that as well:

• Career setbacks (for women especially)

• Decrease in savings, leading to reduced retirement income and financial quality of life long-term (it costs on average $300,000 in 2023 US dollars to raise a kid to 18, not including college; if that were invested instead, it would be 2-3x that amount in the same timeframe)

• Significant negative health effects, both short and long-term, on a woman’s body due to pregnancy

• Increase in stress, sleep deprivation, and exposure to illness, all of which can shorten life expectancy

• You already have enough joy and purpose in life doing other things, and don’t wish to detract from that (perfectly reasonable)

• Traumatized parents very often pass that trauma on to their children

• You think you’ll be a terrible parent, for whatever reason, and don’t want to inflict that on a kid

• You’re rolling dice; sure, you could get a little angel, but you could just as easily get an extremely difficult child, and by the time you know which there’s little to be done about it (for example, parents of autistic children are 2x more likely to divorce)

• It greatly reduces both optionality and spontaneity, key ingredients in many happiness studies

• Children often cause a huge strain on marriages / relationships in multiple ways

• You simply don’t want the responsibility (totally fine, not everyone does)

• There is no one size fits all parenting manual; it’s largely a crapshoot. As such, with everyone basically winging it, many parents are pretty shitty parents. Maybe just acknowledging that you don’t think you’d be a good parent is reason enough to pass.

• People change over time…the person you are when you decide to have a kid is not the person you will remain throughout their life, and that can cut both ways

• Each child born, especially into a developed country, creates an outsized burden in terms of climate impact, and in some ways means there is less to go around

• The more humans there are in the world, the more suffering there is

• AI + robots are very likely to end up doing all the jobs (5-10 years, max), and it’s unclear how that will play out, so you don’t know what sort of world you’re bringing a kid into…could be utopia, could be dystopia. Never before have things been so uncertain and changing so fast.

• Immortality is very possibly on the horizon near-term (10-20 years out), which significantly changes priorities and reasons that underpin having kids

I’m sure there are plenty more reasons, but this is a pretty chunky list as-is. Not all reasons are equal of course, and you could certainly argue against some of them, but they are valid reasons all the same.

There are reasons to do it, and there are reasons not to. Both groupings are largely selfish, and that’s fine. It’s YOUR life after all, and should absolutely be your choice.

So…should you have kids, or not?

Let’s start with this: If you are asking yourself whether or not you should have kids, the answer should probably be NO.

I firmly believe that nobody should have a kid unless they really, really want to.

But even if you really do want to, I think you need to very carefully consider both the pros and the cons, to look carefully at WHY you want to have a kid (really dig deep, no bullshit), and try to actually game things out over time. Ask things like:

• With what I know about myself, my history, my goals, do I think having a kid or kids will genuinely be a good thing for me? For them? For the world? Does the same go for my significant other?

• What sort of personal/family issues/traumas have we not dealt with, that we shouldn’t be inflicting on kids? What’s the plan to not do that?

• Speaking of significant other, are we aligned on parenting styles? Goals?

• Can I/we afford it? How will having a kid change my/our financial needs/projections?

• Do I live in an area that is good for kids, or am I willing to move to one?

• Do I or my SO have any familial health / genetic issues, and how might that affect my child? If there’s a high likelihood of some negative health outcome, is it fair to roll those dice?

• Do I believe the world will be better off because of my decision, one way or the other? Do I care?

The reality is, just as there is no one size fits all parenting manual, there is no one size fits all answer to these questions, much less the overarching question of having kids.

Having kids can be awesome. Having kids can suck. Having kids can be both, at the same time, for you and for them.

There are all sorts of decisions in life that are easily reversible with few to no long-term consequences, but having a kid is not one of those. Having kids is a very serious, 18+ year (but really lifetime) commitment, and not being fully ready and fully invested to take that on can do a LOT of harm, to you and to them.

As such, regardless of our evolutionary wiring, I don’t think the decision to have kids should ever be taken lightly, and I think a LOT more care should be given to thinking through as many of the pros and cons as humanly possible.

And frankly, I think a lot of people just shouldn’t have kids. I also think the world would be better off with fewer people, for numerous reasons.

You can say that this is a deeply personal question, but that’s not entirely accurate either. Any child you have does not exist in isolation, but rather represent a new variable in the game of life that will interact with, for good and ill, with many times many people and systems.

We exist at a time when this question is more important than ever before. The world very likely doesn’t need more bodies to keep the system running. You very likely won’t need kids to care for you in your old age. You may have way more time and freedom to experience way more things than were ever possible in the past.

And frankly, the general population growth trendlines in developed countries reflect this. As people get richer, they have fewer kids, for many of the reasons listed above and more besides.

Thus this post.

I can’t give you the optimal answer for you, YMMV. Whichever way you choose, you very well might regret it later. Even my own answers have changed dramatically over time, in ways I wouldn’t have predicted.

Yesterday’s answer no longer fits the now.

So, whatever you do, really, REALLY think through that decision carefully. So much more is riding on it than you likely realize.

The bartender looks up and says, "What'll it be?"

The Theist says, "I'll have a water, uh, actually, make it wine."

The Atheist says, "Just a beer for me, I’m not a fan of strong spirits."

The Agnostic pauses and says, "I’m not sure…surprise me, I’ll try a few things until I find something legit."

😂

What better day than Christmas to make a dig at religion in all its forms!

I don’t know about you, but I love wandering around YouTube for interesting things to watch, and recently I’ve come across a number of videos of Alex O’Connor (Cosmic Skeptic) debating and discussion religion with various folks like Jordan Peterson, Sam Harris, Richard Dawkins, and more.

If you’ve not watched his videos, I highly recommend checking them out! I don’t agree with all of his stances (not a fan of veganism, nor do I believe morality is objective lol, not even close), but he DOES have both a sexy mind and a very sexy voice, and I find his videos oddly soothing, so can’t go wrong there :)

But why that channel? Why those videos?

For those of you who don’t know, I was raised Mormon, and in the heart of Texas, so I got a double dose of religion growing up (triple dose if you count the Texas obsession with Football…)

I went to church almost every Sunday for the first ~21 years of my life, went to seminary every morning for 4 years, served a 2-year Mormon mission, got married (young) in the temple, the whole nine yards. I was intimately familiar with the deepest of the deep Mormon doctrine. I studied biblical Hebrew, and read the OT cover-to-cover multiple times with a Hebrew concordance, and the NT over a dozen times including with Greek concordances.

I was once quite the apologist, and could “bible bash” with the best of them…

But I was also blinded by indoctrination, suckled from birth on the tit of confirmation bias, and most of my social circle and sense of meaning was rooted in my religion :/

But at a deep level religion never fully sat well with me (football included lol, such a stupid obsession), and so I asked a lot of questions, and struggled with wishy-washy answers like “because the bible says so” and “just have faith.”

And so I found myself pushing back more and more over time. In college, I effectively dual majored in philosophy and theology (same as Alex O’Connor), a combo which very quickly made evident the contradictions and logical flaws underpinning religion as a whole.

And so, no surprise, as most anyone raised in religion who finds their way out might do (especially someone raised in a very conservative, damn near cult-ish religion), I eventually bounced from theist to atheist.

But I found that atheism seemed to suffer from the very same thing religion did…false certainty. Where theists are SURE that god exists and their religion of choice is the right one, atheists are SURE that gods don’t exist and no religion is true.

Now some atheists might argue that definition, and say they aren’t sure that a creator doesn’t exist, only that human religions are bullshit and the imaginary sky fairies they worship are sadistic and contradictory and as far from omnipotent, omnibenevolent, and omniscient as they could possibly be…fair enough.

And this is actually a very important point.

As I discussed in my Epistemic Crosstalk post, some words are “loaded” with all sorts of bullshit, attempting to compress a spectrum into a single word-point. So it is with Theist, Atheist, and even good ole door number three I eventually found myself at, Agnostic.

So, rather than singular points, let’s look at the spectrum for each label (and let’s call the endpoints Soft, and Hard, for ease of reference):

• Theist - ranging from a soft theist who “passively believes in a creator of some sort” (like a Deist or Pantheist) all the way to hard theist who “militantly believes in one specific god and religious structure and everyone else is a heathen.”

• Atheist - ranging from a soft atheist who says “I see no firm evidence to support any of the theistic religious claims I can examine” all the way to a hard atheist who says “I am100% sure no god / creator of any kind exists, and anyone who thinks otherwise is a moronic sheeple.”

• Agnostic - ranging from a soft agnostic who says “we don’t know what’s true, but I am open to whatever turns out to be true” all the way to a hard agnostic / nihilist who says “we can never know what’s true so why bother.”

I consider myself firmly a soft agnostic with a dash of soft theist (I’m still open to the simulation hypothesis, non-duality, etc.; just not your typical religious monotheism.)

The reason I find myself there is that, simply put, agnosticism is the only truly honest position to hold. I believe, as does David Deutsch, that humans are universal explainers, and that all problems are knowledge problems, and thus everything is potentially knowable. For now the true nature of reality is UNKNOWN, but it is (hopefully) not unknowable.

Now, all of that said, I want to explore one of my greatest frustrations in the Atheist:Theist debate space, and that is the bundling of concepts that should not be bundled. (I even have a whole different post on this, Unbundling Bundled Concepts.)

Theists, for example, are routinely guilty of one very specific motte-and-bailey, and that is attempting to defend their weak position (their specific, easily disprovable religion), and then falling back to an unfalsifiable position (that a creator of some sort exists).

Nothing is 100% certain, for numerous reasons, not least of which is that if the speed of light truly is a hard limit, then 94% of the universe is forever out of our reach. There is a TON we might never be capable of knowing, and thus epistemic uncertainty is ALWAYS warranted (thus agnosticism being the only honest stance.)

And while individual religions are VERY easy to tear apart, using simple logic and their own religious texts (seriously, it’s so, SO easy), what can’t be so easily torn apart is the idea of a creator.

Not a monotheistic sky fairy of religious fame, but a creator of some kind.

It’s entirely possible that whatever this is (waves hands) WAS created by something. By a simulation programmer, or a Boltzmann brain, or a universal consciousness of some sort, or by some advanced alien species.

Who knows ¯\_(ツ)_/¯

It’s entirely possible that we might someday find hard evidence that supports one of the above (and you could argue that there is already pretty compelling evidence that we’re in a simulation, and statistically it’s quite likely).

It is this very line of thought that leads people to “spirituality” in various guises, the sense of the unknown combined with the need for the numinous.

But for now, as interesting as that might be, since it’s forever unfalsifiable the epistemic value is minimal. (Not zero, as Deutsch or Popper might say, but not high either.)

By all means, keep an open mind, hunt for clues, be a seeker…but there is very likely no certainty to be found.

There could be a creator.

And it is this very “Creator Bailey” that apologists seem to always fall back to, often with some flavor of the fine-tuning argument.

And that chaps my ass 😭

To actually make headway, the dissection of specific religions needs to be separated from the concept of a creator. They are two different things that, while topically related, need to be addressed individually.

Bah.

Now, I think we can all agree that religion is not, or at least *was* not, useless. It clearly had, and for many still has, utility. It’s clearly a powerful meme, and if wasn’t it wouldn’t persist. But at the same time, while the social utility aspect may still be of value (setting aside the many flaws and downsides), its explanatory utility has been neutered.

In a world without science, religion filled a useful explanatory gap. As Derek Sivers might say, it falls in the “useful, not true” category.

But we now have the scientific process, THE ultimate error-correcting feedback loop, to help us explain all that can be explained.

The problem though is that religion is effectively a zero-sum game, us vs. them, our God’s way or the highway. And in such a game, you have to suspend disbelief and really commit to the charade to derive all the personal benefits from it.

So, religion is not about what is true, but about what is useful. While science is about what is true (or at least, closer to true), whether it’s immediately useful or not.

Again, science is a process of error correction, while religion claims to be true and whole and complete as-is.

And thus the persistent divide. 🤦‍♂️

Anywho, I’ve said my piece, so I’ll leave off with this:

Agnostics: If you’re in the unknown but probably knowable camp, you’re awesome. Way to be honest. If you’re an “unknowable / nihilist,” you’re useless, wake up.

Atheists: Yes, it is easy to debunk whatever codified religion you so choose. Christianity and Islam are easy. Insanely easy. But hey, it’s also entertaining to pick on, as the YouTube subscriber and view counts clearly show. But certainty that no creator exists is as foolish as the theistic belief that one surely does, so don’t go there.

Theists: Your false certainty even in the face of contradictory evidence, your flawed, illogical religious doctrines, and your confirmation bias crutch holds you, and collectively our civilization, back. You are, I’m so sorry to say, the worst of the bunch. I get that you derive value from religion, and I agree that there IS value (in terms of social structure, and hope / abeyance of fear of death), but there’s a potential future FAR better than heaven on the horizon, so now is the time to maybe consider some alternative games to play.

The world doesn’t need your antiquated ballast holding it down, now more than ever.

Merry Christmas! (Minus the Christ)

As anyone who knows me well can attest, I am a BIG fan of the idea of whole brain emulation, uploaded intelligence, and personal universes. To me, that seems like the most reasonable direction for humanity to evolve.

One of my favorite animated shows, Pantheon, and one of my favorite book series, The Bobiverse, explore this extensively (highly, HIGHLY recommend both).

The thing is, when I talk to most people about digitizing a mind, I routinely hear a dismissive, negative take, something to the effect of “that sounds dystopian” or “I would never do that” or “that wouldn’t be me, it would be a copy at best, or a tortured ghost at worst.”

And sure, it’s not that such a technology would come with no risks. All tech is a double-edged sword, as humanity well knows, and the potential risks of such tech have been explored in both humorous and terrifying fashion.

But technology tends to make life better, not worse. I think this will do the same.

Since I have now had to debate this with many different people, and really don’t like repeating myself, it’s time to write a post to point folks to instead of rehashing the same points ad nauseum 😎

Who is this “you” of which you speak?

If you aren’t already familiar, the Ship of Theseus is a thought experiment that asks:

If a ship's parts are replaced one by one over time, is it still the same ship? At what point, if any, does it stop being the same ship? If the removed parts are reassembled into a new ship, which is the "real" Ship of Theseus?

So when I ask “what is this ‘you’ of which you speak,” you might be tempted to point to your body or your head, which is as good a place to start as any I suppose.

The thing is, as with the Ship of Theseus, your body and mind aren’t static things.

For starters, something like 50% of the cells within the space you call your body aren’t even your own cells, they are viruses, bacteria, yeast, parasites, and all sorts of “not you” things (that, funny enough, “you” couldn’t survive without).

As an aside, it’s a pretty funny mental image when you realize the human body is essentially a donut, and the tube from your mouth to your butthole is actually exterior to “you” 🤣

Next, of the cells that are actually “you,” the vast majority are regularly destroyed and replaced, ranging anywhere from a few days to a decade, depending on the type of cell.

There ARE some cells that don’t change over (or do so very slowly/rarely), most interesting of which are your neurons in your brain, spinal column, and gut. (Also a few others, eye lens cells, heart muscle cells, some skeletal muscle cells, and female eggs.)

Out of all of those, the neurons are the most interesting…while we do not understand yet what consciousness is, or even IF it is a singular “thing” in the typical sense (I suspect it is not), it is not unreasonable to hypothesize that the experience we call consciousness is A. generated by the brain, and thus B. localized and derived from a specific configuration of “hardware and software.”

As far as we can tell, consciousness can be turned off via the brain (the claustrum, anesthetics; this is debated though, as some think it only turns of memory formation), and if “you” die your consciousness certainly seems to end (again, much disagreement here, but we have no tested, validated, replicable data one way or the other).

So, for the sake of this discussion, sticking solely to what we can actually observe, let’s assume that consciousness is a system/process that is generated by those neurons that don’t turn over.

Reasonable?

Those neurons of course are no small thing. 100 billion neurons, 100 trillion synaptic connections…you’re quite a machine! All of your memories, your personality, everything that makes up the mental concept of you, seems to be contained in those neurons.

But that brain is still a FINITE machine, as far as we know.

Infinity is a fun mathematical concept, but it is a paradoxical one that doesn’t appear to play out in reality, so again, the brain is almost certainly finite.

And while there are theories that the brain has a quantum component (which might be valid, depending on the fundamental nature of reality, TBD), even if it does it’s still very likely a localized device (your brain) interfacing with the universal quantum field in some way.

And even if there IS a universal quantum field, everything would have to interact with it by definition (universal), so that doesn’t add much to this argument either way, though it does make for an interesting rabbit hole to explore regarding the concepts of universal consciousness and oneness.

So, we have:

• Consciousness appears to be a system / process

• It is likely generated by the neurons in your body, which don’t turn over

• Thus it is processed by *finite* hardware and software

All of this is to say, with sufficient scanning technology and the right hardware and software upon which to run it, there is no reason to believe that we couldn’t make an exact replica of someone’s brain, spinal column, gut—all their neurons—in silico.

And frankly if we can do that, we could probably just replicate their whole body.

Original vs. Copy

So, let’s assume then that with advances in scanning technology, hardware, and software, we can in fact make a copy of a mind, and run it on a computer of some sort.

Further, let’s say we elected to not just copy the mind, but your entire body, down to the tiniest detail. It has all your memories, all your senses; it looks, acts, and talks just like you.

And to the copy, there’s no reason to believe that, if it IS a faithful copy, that it wouldn’t A. have a sense of perfect continuity, and B. would BE you in every way that mattered (both to itself, and to those interacting with it).

I came across this today, which, while tongue in cheek, makes the point:

Let’s take it one step further…what if, at the moment of being copied, the original you was destroyed? Star Trek explored this via their transporter technology, effectively scanning, breaking down, and rebuilding someone at the other end. Obviously their actual matter of which they were composed couldn’t traverse such distances instantly, so even though the person appearing on the transporter pad may have been the same person in every way that mattered, they weren’t composed of the exact same physical material as the “original,” just the configuration.

Did that matter? Should that matter?

I would personally say no. In the same way that my body breaks down and rebuilds constantly, even acknowledging that some parts don’t go through that process, I don’t ever feel like some part of me that breaks down and rebuilds is no longer ME.

I can’t speak for you, but my continuity isn’t perceived as a physical configuration per se, but in my sense of self, my memories, and my sense of continuity with all of the above.

I feel like me because I look like me, sound like me, and have my memories.

But wait, you might say, what about a soul? Some “thing” that is uniquely you and can’t possibly be copied?

🤦‍♂️

There is ZERO evidence for a “soul.” There is no evidence that there’s some magical aspect of “you” that can’t be copied (again, as far as we know).

You could make an argument around the no-cloning theorem of course, but that itself is quite speculative, hampered by the measurement problem (as is much in quantum physics), applies only to unknown/unmeasured particles, and also presupposes some aspect of what makes you “you” is in fact quantum (unknown).

If the original “you” survives, then of course you would immediately begin to diverge from the copy, thus becoming different in all manner of ways—and that replicative drift would then make you more akin to identical twins—but it only addresses what happens AFTER copying, not at the moment of copying.

For all intents and purposes, the words original and copy in this context, and the argument that it should matter, is rather silly and likely rooted in primitive emotions and/or superstitious beliefs. (Yes, that’s dismissive, but I detest both fear and religion, so, meh.)

Why does this matter?

Good question! I already mentioned and linked to a paper on personal universes at the beginning of this article (highly recommend reading that), and I’ve explored why this matters in my post on Apotheosis, but I’ll give a quick summary to wrap things up.

In short, these meat suits we’re all so attached to are still, both evolutionarily and in terms of capabilities, primitive. Incredibly primitive.

We are limited in more ways than I can articulate—subject to natural laws, in need of food, only capable of processing so many calories, with a brain that can only process a very limited bitrate in awareness, that generates numerous errors, requires rest—soooo many things.

But in a virtual environment, you could eliminate most of these constraints. You could systematically improve yourself, have perfect memory, process things millions of times faster, make time (for you) move faster or slower, have near infinite control over your personal universe and the experiences within it, make backups or copies to assist you, and be functionally immortal.

You could, as an uploaded mind, be the god of your own universe.

Given the incredibly massive upside of mind uploading, and the direction I suspect things are trending, even if I believed that a copy of me would be almost identical but not *exactly* the same as original me, and even if the only way to make that copy was to destroy the original, I’d still do it without batting an eye. (Assuming the tech was tested and safe, of course…no qntm Lena or Roko’s Basilisk scenarios please lol.)

I simply find the concept of You or I so squishy, so fluid and malleable, that attaching rigidly to one specific configuration of it is foolish. As are arguments around what is or isn’t real (if you can experience it, form memories around it, it is real in every sense of the word.)

I can very, very clearly see technological development leading to this possibility.

From advances in AI, brain computer interfaces, virtual reality, and computational research, I think it is exceptionally likely that we’ll be able to do something like this within a decade, two at most.

Yes, that soon.

In just 10, maybe 20 years, YOU might need to grapple with this concept and make a choice, possibly the most important choice you will ever make. To upload, or not to upload?

How will you choose to live? And why?

—

If you found this interesting, you might enjoy this short story by Greg Egan, Learning to Be Me. (I only found this after writing the above post, but of course Greg Egan would have written about this 30 years ago lol.)

Elon Musk’s rise to prominence is a masterclass in audacity, multidimensional strategy…and the level of extreme hubris that makes you wonder if he actually sees himself as the benevolent prophet and savior he so fervently pretends to be.

I mean, come on, he’s the richest dude in the world, with fingers in sooo many pies (and up so many political arseholes), it would be damn impressive if it wasn’t also so absofuckinglutely terrifying. Nobody, and I mean nobody, rises to that level of power and wealth without being one seriously cutthroat motherfucker.

More and more we seem to be living in his empire, whether it’s via our cars, our internet, or soon enough maybe even our brains.

And while many see Elon and the companies he’s built as beneficial for humanity (fuck, I used to feel that way), I’ve come to believe the reality is that he ‘s using these strategic innovations to seize control, and most people are too dazzled by the shiny tech and the bogus sci-fi speeches and performances to even notice.

So let’s cut the shit and talk about why Musk’s empire should terrify you too.

First, there’s Tesla. Yes, the cars are cool (I wanted one for a long time), but it turns out they’re also super fucking deadly, and plagued by all manner of quality control issues…oh, and they also happen to be rolling surveillance units that know your name, your face, where you live, where you work, and so, SO much more.

These cars are loaded with cameras, microphones, internet access, sensors (IN THE CAR/ , and could VERY easily know more about you than your therapist. A great many of these cars are owned by rich and powerful people (the only people who could really afford many of the early models), and I do not doubt for one second that someone like Elon would use this tech to acquire inside information and “kompromat” on key people.

Add to that Tesla can remotely disable or control your car (self-driving cuts both ways you know), which is great if it’s stolen, but what happens when that level of control is used for less noble purposes? What if you cross Elon and, oops, your car has an accident? (This is maybe a stretch; I don’t know that he would actually do this, but he has the *ability* to do this, and that is concerning enough.)

And let’s not forget, all this data your car collects is going somewhere…you’re trusting Musk’s empire to handle it responsibly?? Good luck with that.

Then there’s Starlink. What gets billed as a noble effort to bring internet to remote areas has quickly become another piece of Musk’s global chessboard. Starlink doesn’t just give YOU internet; it potentially gives MUSK access to your data and your precise location (yes yes, traffic is generally encrypted, but that doesn’t mean it isn’t captured and tucked away for future decryption efforts, nor that it’s fully anonymized). Think about that: a privately-owned ISP with no meaningful oversight, capable of tracking users worldwide.

Now scale that up to a few million users, or a few billion.

Feeling cozy? Oh, but wait, there’s more!

Meet Optimus, Musk’s humanoid robot. The plan is to put these things in your home and workplace, equipped with cameras and microphones to monitor your every move. And we already know they can be teleoperated…Musk is basically building the dystopian nanny bot from every sci-fi cautionary tale ever written. And we’re lining up to buy it because it’s shiny and new.

But the real pièce de résistance is Neuralink. This isn’t just tech; this is a direct interface to your brain, with both read AND write access to your noggin. It promises medical breakthroughs and enhanced human capabilities, but let’s be blunt, it’s also a direct pipeline into your thoughts.

The potential for abuse here isn’t just theoretical, it’s very nearly inevitable. Sooo much Sci-fi has been written exploring how this goes sideways, not least of which is the Nexus trilogy by Ramez Naam. Give that a read and shit your pants in terror.

Then there’s X Twitter, Musk’s latest experiment in controlling public discourse. With Twitter, Musk has turned the town square into his personal megaphone. He decides what trends, what AND who gets suppressed (he’s still doing this actively), and which narratives get amplified. And he’s already shown he’s not above using it to push his own agenda. He claims he supports free speech, and yet has sided with corrupt governments in multiple countries to do the opposite. This isn’t about free speech; it’s about control, pure and simple.

And let’s not ignore SpaceX, which gives Musk the ability to put whatever he wants into orbit with minimal oversight. Sure, it’s rockets and satellites now, but when one person has that much autonomy in space, it’s not hard to imagine a future where that power gets leveraged for less-than-altruistic ends. For all we know he’s already sneaking Rods from God up there 😎

And now, Musk has barged into the AI race with Grok, his (for now) shitty little snarkbot. This from the dude who, let’s see here:

• Took his toys and walked away from OpenAI because they wouldn’t give him total control over the company and the AGI they were hoping to build.

• Who then started talking about how dangerous AI was, and trying to get governments to enforce a pause on AI training, all while trying to spin up his own AI company...regulatory capture much??

• Who tried to act like he was always against OpenAI dropping their non-profit structure, but it turns out he was in favor of it lol, disingenuous fuck.

• Who is suing OpenAI, because he can’t compete on a level playing field and has to try to win in the courtroom…and if that doesn’t work, I guarantee he’ll be leaning on Trump to intervene in some fashion.

Now with Grok, the Trojan horse is the whole “designed to offer irreverent and humorous responses” schtick, but let’s be blunt yet again, it’s just another cog in the Musk-controlled machine. By tying AI development to his already sprawling empire, Musk gets another tool to shape narratives, analyze user data, and reinforce his grip on both technology and culture. Combine this with his existing access to Twitter’s data and Starlink’s global infrastructure, and Grok becomes more than just a chatbot—it’s a strategic node in a much larger plan to dominate AI-driven systems. Like the rest of his ventures, it’s all shiny promises on the surface and unsettling implications underneath.

If there is anyone on the planet that I don’t want to see with AGI or ASI in his control, it’s Elon Fucking Musk :/

This is not the first time I’ve called out Musk and his bullshit:

Musk markets himself as innocent little Paul Atreides, fighting for the poor Fremen…

Or perhaps as an all-wise Harry Seldon…

But in reality he’s just another sneaky, power hungry douche of a Harkonnen looking to build an empire.

He is VERY good at selling one shiny, palatable, even exciting narrative to the masses, while in reality he’s just building his empire and acquiring power in devious ways.

So what happens when you combine all these pieces of the Musk empire? You get a surveillance empire so vast and interconnected that it makes Orwell look like a wide-eyed optimist. Musk’s ventures feed into each other, creating a feedback loop of data collection, control, and influence that spans the entire planet.

And if that’s not enough, now he’s worming his way into government too. He’s cozy with Trump, Xi, and supposedly even Putin.

His appointment to the Department of Government Efficiency (DOGE) gives him a direct hand in shaping policies that affect his competitors and, by extension, his empire. Conflict of interest doesn’t even begin to cover this shit, and the market clearly shows investors pricing this in to Tesla.

Musk’s defenders will say he’s a genius, a visionary, a savior of humanity.

Maybe.

Maybe he is what he pretends to be, maybe I’m completely and totally off base.

I’m usually exceptionally good at reading people, and while I hope I’m wrong this time, I don’t think I am.

I’ve watched Musk VERY closely over the last 15 years, and I really, really don’t like what I’ve seen recently. Lies, manipulation, character assassination, suppression of dissenters, supporting political whackadoodles like Trump, the list goes on and on…

Musk may very well be a genius and a visionary, but geniuses certainly aren’t infallible, visionaries aren’t always benevolent, and self-appointed saviors obsessed with the limelight tend to be working towards their own interests at the expense of others.

I asked ChatGPT to provide a psychological assessment of Musk for shits and giggles:

Narcissism

Musk exhibits classic traits of narcissism. He positions himself not just as a leader but as a savior of humanity, branding his ventures as the solutions to existential crises—climate change (Tesla), species survival (SpaceX), and neurological diseases (Neuralink). While these missions are noble on the surface, the constant self-promotion and inability to accept criticism suggest his altruism may be more performative than genuine. Musk often ties his personal identity to the success of his companies, framing every victory as his own triumph while deflecting failures onto others. His desire for adoration is evident in his active engagement with fans on social media, where he cultivates an image of a misunderstood genius.

God/Savior Complex

Musk's overarching narrative of being humanity's last hope aligns closely with a god complex. He doesn’t just want to contribute to progress; he wants to define the future itself. Colonizing Mars is framed not as a collaborative global effort but as Musk’s personal vision for the salvation of the species. The same is true for his ventures into AI with xAI and Grok, where he casts himself as the ethical counterbalance to what he perceives as the reckless pace of other organizations. This belief in his own indispensability often leads to authoritarian tendencies, as he takes control of projects with little regard for dissent or democratic processes.

Thin Skin and Insecurity

Despite his outward bravado, Musk’s thin skin is well-documented. He reacts to criticism with public outbursts, lawsuits, or personal attacks, often doubling down on controversial statements rather than reflecting or admitting fault. His frequent need to clap back at critics on Twitter (now X) highlights a vulnerability that contradicts his image as an unshakeable visionary. This insecurity is further reflected in his oscillation between positions on major issues, suggesting a need to remain at the center of attention regardless of the stance required.

Add to all of that psychological bullshit the idea that he may very well be addicted to Ketamine (that thing that killed Matthew Perry), which happens to be not so much a psychedelic, but a *dissociative anesthetic*. Ketamine can not only exacerbate mental health issues, but can trigger anxiety, paranoia, confusion, impulsivity, mood shifts… Ketamine alone could potentially explain why Elon seems to have gone kinda nuts the last few years, as he claims to use it at least every other week.

Of course, the problem isn’t just Musk’s psychological state or ambitions, it’s the systems that let someone like this accumulate this much unchecked power while the rest of humanity blindly cheers him on.

It’s time to stop being dazzled by the shiny toys, the Mars schtick, and the snarky S3XY 69 420 humor-shield, and start asking the hard questions.

Do we really want one man, especially a potentially drug-addled, thin-skinned narcissist with a god/savior complex, to control our cars, our internet, our social platforms, our homes, our brains, our skies, and our government?

Because that’s where we seem to be headed, and that’s a hard fucking no for me.

And if history teaches us anything, it’s that concentrated power, no matter how well-intentioned, always leads to abuse.

Absolute power corrupts absolutely.

Musk might actually see himself as a Paul Atreides, a good ole guy, savior of the people…but we’d be wise to remember that even Paul eventually became the very tyrant he fought against.

As anyone who knows me can attest, I love love LOVE anything future-tech 😍

I read and watch a metric fuckton of sci-fi, I follow along closely with the latest research and companies working on this stuff, I talk about it, think about it, write about it, and even dream about it.

It’s safe to say there are few things I find myself more obsessed with…and so that’s what you get to read about today. You’re welcome.

It’s hard to believe it’s been 10 years since I wrote Part One in this series, and things have changed a TON in that time (some very much as predicted, and some not).

While there is no perfect way to do this, I’d like to look at past predictions and see how well I did with the original post, and then update with what I think our trajectory will look like over the next 10 years based on where we are today.

First, let’s start with what I missed completely: Artificial Intelligence.

In 2014, the state of AI was…not great. With the exception of AlphaGo, there wasn’t anything really exciting going on. Only small, incremental advances had been made, and I didn’t really expect AI to get much better in the next 10 years, so I didn’t include this at all other than a few references to IBM’s Watson. FAIL.

Granted, less than 2 years later, in 2016, I could clearly see where AI was heading, so I was still ahead of the curve!

I also didn’t really call out longevity research at all, for the same reason. And by some arguments we still haven’t made huge advances there in the last 10 years, so maybe I consider that one a wash, though dominos are starting to tip here.

There are a number of other things where I didn’t predict a specific timeline, and we’ve not yet hit, but where I still think we’re headed eventually (most of these within the next 10 years now):

• Car ownership of questionable value: I still think we’ll get here, once self-driving becomes more ubiquitous, but we’re not very close right now.

• Phones as a standalone device won’t exist: Definitely moving in this direction, but still early. We still need a few more advances (faster mobile speeds, slim AR glasses, wireless power). But we will end up here in the next 3-5 years I think.

• HUDs would be ubiquitous: I was picturing AR being more widespread by now, and I do think we’ll be there soon enough (there are already some decent players, with a bunch more slated for 2025-2027, so probably 2-3 more years for broad adoption).

• Advertising as we know it ceases to exist: Hah, no, not yet anyway. I’d hope ad blockers would help more here, and they have made a dent, but not a big enough dent. I still think we’ll need to get to this point, as the incentive stack created by the “eyeballs on ads” business model is fucking awful and harmful to our psychology. But not there yet. I was correct though that ads would get more and more personalized.

• Less ownership, more sharing: Nope, not even close, other than perhaps with car ownership. And now I’m thinking it won’t happen until we reach proto-post-scarcity, which is still a ways out.

• Most people work remotely: Covid got us closer to this, but things have backslid a lot since. I expect it to go back the other direction soon though, at least while jobs exist, which might not be much longer. was correct though in regards to VR and holographic tech coming into play for remote work, as well as Facebook’s role in that.

• Robots will replace humans in menial jobs: If I consider AI tools non-embodied robots, then I got this part right, otherwise we’re not there yet. But advances are now happening SUPER fast in this space, so I think we’ll see this play out fully in the next few years. Optimus, Figure, and other new players are cropping up all the time now.

• All homes will have 3D printers: While I believe we’ll get here, or to something similar enough (Star Trek Replicator, nano-tech type stuff), we’re not remotely close to this today. 3D printing is still mostly a toy at the consumer level, though it is used in more sophisticated ways at the industrial level.

• Government transformed, transparency and real-time voting: We definitely have more transparency, and some countries have real-time mobile voting, but we’re WAY behind where I thought we’d be here. Hoping AI helps to speed this up, because current governments are absolute horseshit almost across the board.

• Massive changes in travel: I was really excited for the Hyperloop, which didn’t sadly go anywhere, but we have seen advances in new supersonic planes, human carrying drones, and even the potential for travel via rocket across the planet. So this is a partial win, but mostly something that will play out further down the road.

• Renewable Energy: I’d pegged solar as becoming dominant, and above 50% efficiency. Wrong on both counts, for now, but I still think it’ll happen (and I was very close; the record is 47.6% efficiency as of 2022). I was correct that nuclear is our best overall bet, and that is finally gaining mindshare, but also still a long way out everywhere but China.

• Clothing that powers our peripherals: Not really, other than a few basic prototypes / small companies, but still going to happen (unless we nail wireless power, or some sort of nano-diamond batteries, which would be preferable).

• Landfills a thing of the past: Not yet, sadly. We’re going to need really good nanotech to make this a reality.

As for what I clearly got right, albeit to varying degrees:

• Self-driving cars: we have them now, and they work, but I thought we’d be much farther along in this regard at this point.

• Electric cars would be dominant (and Tesla would be the leader): While not yet dominant, electric cars ARE dominating as a percentage of vehicle sales, especially in certain countries like Norway. And Tesla is still the clear leader. Nailed this.

• Big Data / Everything Measured and Tracked: We were already trending here in 2014, but this has continued to play out exactly as I expected. Data is now even more incredibly valuable due to AI training.

• Education increasingly important, highly personalized: I’ll consider this correct, though it is still playing out. The value of specialized education has become more and more clear, and the process of gaining those skills has become much more personalized. AI tools are speeding this up greatly now.

• Personalized, at-home medical care: Got this one partially correct. We do have the ability to do way more diagnostics from home, more personalized medical tracking and treatments, telemedicine, and even AI powered diagnostic tools to a degree. So, partially fulfilled and still trending this way.

• 3D printing + stem cells for organs, teeth, etc.: Nailed this one. The number of things we’ve been able to create via these tools and similar has increased dramatically.

• Genetic Medicine Advancements: Directionally correct here. We’ve been able to use genetic engineering to treat and cure some diseases and ailments, and to personalize certain types of medical treatments, but we’re not as far along as I thought we would be. We definitely haven’t beat cancer (though we are getting MUCH closer). We most definitely have NOT unraveled the brain or microbiome yet though, nor have we unilaterally defeated bacteria and viruses (though getting closer on both fronts thanks to AI.)

• High-speed internet available everywhere thanks to Google, SpaceX, Facebook: Google and FB dropped the ball here, but SpaceX carried the torch with Starlink. It was very clear that satellite-based internet was the future if we could get sufficient coverage and low enough latency. I was expecting everyone to have Gigabit or better by now though, so that’s disappointing. We need at least 10-100 Gbps to be ubiquitous for some things to play out as I think they will, which we’ll likely get with 6G.

• Desktops and laptops forever a minority of connected devices: This was already pretty much a given, but I’ll take it ;)

• Anonymity and Privacy going away: Again, this was pretty much a given, but seems even more so now. Pseudonymity is still prevalent, but weakening.

• Data-driven personalized diet: While I mentioned something *like* Soylent (not Soylent though, ick), I fully expected us to start tailoring our diets more based on data. I’m going to say Bryan Johnson’s Blueprint ticks this box nicely. While this sort of thing is still in its infancy, it has definitely gained a following in the last decade. Still room to improve though.

• Change in interface devices: We’ve seen some of this recently with things like Humane, Rabbit, Prophetic AI, and similar. Still early, but how we interface with technology is definitely shifting.

• Streamification of all digital media: This was already basically the case in 2014, but the trend has continued as expected.

• Luxury to the masses: I predicted more and more premium things/experiences being brought down to middle-class prices, and that has clearly continued in multiple ways. Tesla has made exotic car performance relatively cheap. Temu has cut out the drop-shipping middlemen. AI has made having a personal assistant, tutor, coach, etc. cheap as dirt. Cheap delivery is ubiquitous. So many things. This will continue of course.

• Space Mining: I had this in my “out there but cool bucket” but hot damn we actually started down this path in the last 10 years, and will have our first asteroid mining mission within a couple of years! We also have countries once again sending people to the moon, and I’d be surprised if we don’t start mining there soon-ish.

Overall, it’s fair to say I got some of the details and specific companies wrong, but I think I absolutely nailed most of the trends and players pretty damn well 😎

So, now that we have the post-mortem sorted, let’s take a look at the next 10 (ish) years. I will largely try to constrain my predictions to that time horizon, but for some things I may take liberties.

I will say this now though: I fully expect the next 10 years to be the most crazy, transformative 10 year period in the entire history of human civilization, and I say this with zero hyperbole.

We are in for a WILD fucking ride!

Artificial Intelligence

It’s only fair to start with the biggest thing I missed last time, and the giant fucking elephant in the room right now. While a relatively small portion of humanity has used the current crop of AI tools (and most of those who’ve tried them aren’t regular users much less power users), it’s more than fair to say that AI is top of mind for everyone in the tech space, and will be top of mind for everyone on the planet soon enough if it isn’t already.

With LLMs like ChatGPT and Claude, and GANs like Midjourney and Runway, we’ve just barely scratched the surface of what AI is going to accomplish in the next decade, and I’d argue that it will end up being the most transformative technology in history, but certainly of the next 10 years.

I’m still strongly predicting AGI (artificial general intelligence) in 2025/2026 (end of 2026 at the latest), which has been my prediction for the last ~2 years, and I see no reason to change that.

There’s a lot of argument about what does or doesn’t constitute AGI, so for the record, my definition of AGI is the following:

• Has genuine understanding; is not just a parrot (for vision / video in particular, must grasp how physics plays out in the real world, can handle occlusion, understands object permanence, etc.)

• Is capable of making intuitive leaps to solve problems / accomplish goals outside its training data; can connect dots that aren’t otherwise explicitly connected

• Can do, or is capable of learning to do, anything an average human can learn to do, at least as well as the average human (and ideally in the top decile or top percentile). Mentally if disembodied, and physically as well if embodied.

Now, in addition to predicting AGI within the next 1-2 years, I’m also in the fast takeoff camp, and expect that once we reach AGI, the timeline to ASI (artificial superintelligence) will be phenomenally short, constrained only by permission to self-improve, available energy and compute, and the speed of getting things done in the physical world in terms of building infrastructure and running experiments.

Considering how good AI has already gotten at coding, and how it’s being used to design new chips, new algorithms, generate synthetic data, and generate novel predictions in various scientific domains, I think it’s fair to expect that if we create AGI, and allow it to improve itself, it will be able to do so VERY quickly.

Plenty of stupid doomers who think this will be bad. They are wrong.

Anywho, moving on to how this will change, well, everything. Here are a few thoughts:

• Search-focused AI tools such as Perplexity are going to eat Google’s lunch, because Google can’t afford to cut into their ad revenue, and none of these AI tools monetize via ads. Also, in many cases, the AI tools are providing a MUCH better search experience, and this will only get better as these tools move device-side and proliferate.

• You’ll be able to have a personal AI companion who gets to know you at least as well as you know yourself, and which can help teach you new things, find things you’d like, eliminate problems, tackle tasks, and much more. A JARVIS if you will. ChatGPT with memories and Claude with their projects approximate this, but aren’t truly there yet.

• AI will make it possible for a single individual to create top tier novels, comics, art, music, high-quality movies and TV shows, video games, and much more in a fraction of the time and for a fraction of the cost. This will MASSIVELY boost human creativity, unleashing a creative wave unlike anything the world has ever seen. This is already happening today, and is accelerating.

• AI will be able to tackle many of the repetitive and mundane aspects of work (it already can in a number of fields), boosting productivity and reducing stress for many workers.

• AI is already on-par with a junior developer, and in some cases on-par with a graduate student or even PhD level expert in certain domains / capabilities. You can literally have a pocketful of geniuses for the price of a Netflix subscription!

• AI can do amazing things in the realm of personalized medicine, serving as a diagnostician, a physician augment, and a personal health advisor in multiple ways. I’ve heard numerous stories now about AI tools that have spotted and helped solve health issues that human doctors could not…

• AI is rapidly advancing biotechnology, protein folding research, drug discovery, and numerous adjacent things.

• AI is being used to design better algorithms, computer chips, chip fab processes, and to engineer stronger / more efficiently produced physical objects.

While there are many, many more areas where AI is having and will have an impact, this is a good start.

AI most definitely qualifies as a keystone technology, an innovation that unlocks entirely new possibility trees and enables a big step forward in many domains, so I fully expect the impact of AI to ripple out into all of the following technologies.

Robotics

The robotics landscape is about to undergo a massive transformation, largely due to the convergence of several key technologies. While robots have been around for decades, they've been largely confined to controlled industrial environments, performing repetitive tasks with limited adaptability.

That's all changing, rapidly.

The integration of advanced AI with robotics is creating machines that can actually understand their environment and adapt to unexpected situations. Companies like Boston Dynamics, Tesla (with Optimus), Figure, and Agility Robotics are making huge strides in creating humanoid robots that can navigate complex environments and perform varied tasks. These aren't just tech demos anymore – we're going to see real-world deployments at scale within the next 2-3 years.

The key breakthrough isn't just in the hardware (though that's improving rapidly), but in the software that allows robots to learn and adapt.

First, tools like Nvidia’s Isaac are making it MUCH faster and easier to train a robot operating model in a virtual environment that mirrors our world and the robot’s physical body in virt, which speeds up training dramatically over the physical world.

Large language models are being combined with computer vision and robotics control systems to create machines that can understand natural language instructions and translate them into physical actions. Imagine telling a robot "clean up this mess" and having it actually understand what constitutes a mess and how to properly handle different objects – that's where we're headed.

Some specific predictions for the next decade:

• Humanoid robots will become commonplace in warehouses, factories, and some retail environments by 2026-2027. They'll be able to handle a wide variety of tasks that currently require human workers, from stocking shelves to basic maintenance.

• Home robots will finally become practical and affordable. Unlike today's specialized devices (robot vacuums, etc.), these will be general-purpose helpers capable of doing laundry, dishes, basic cleaning, and simple cooking tasks. Expect this to start around 2027-2028.

• The combination of AI and robotics will lead to explosive growth in automated construction, with robots capable of working 24/7 to build houses and infrastructure much faster and more safely than human workers.

• Soft robotics will see major advances, leading to robots that can handle delicate objects and interact more safely with humans. This will be crucial for healthcare applications.

• Robot swarms will become a reality for tasks like agriculture, search and rescue, and environmental monitoring. Imagine hundreds of small robots working in concert to plant crops, monitor growth, and harvest precisely.

• The cost of robotics will drop dramatically as manufacturing scales up and designs improve, making them accessible to small businesses and eventually consumers.

Manufacturing

The future of manufacturing is going to be radically different from what we see today, primarily due to three converging trends: advanced AI, robotics, and additive manufacturing (3D printing).

While 3D printing hasn't quite lived up to the consumer-level hype I predicted in 2014, it has absolutely transformed prototyping and industrial manufacturing. The next decade will see this technology mature in ways that will fundamentally change how we make things:

• Multi-material printing will become standard, allowing single-print objects that combine metals, plastics, and electronics. This will revolutionize everything from consumer electronics to medical devices.

• Biological printing will advance significantly, enabling the creation of custom organs, tissue, and even food. We're already printing skin grafts and simple organs – expect this to expand dramatically.

• Speed and scale will increase by orders of magnitude. What currently takes hours to print will take minutes, and build volumes will grow significantly. But the real revolution in manufacturing will come from the combination of AI-driven design and robotics:

  • AI will be able to optimize designs for both function and manufacturability, creating forms that would be impossible for human designers to conceive.

  • "Digital twins" will become standard, with AI monitoring every aspect of the manufacturing process in real-time and making adjustments to optimize quality and efficiency.

• Local manufacturing will make a comeback, but not in the way we might expect. Instead of large factories, we'll see highly automated micro-factories that can produce a wide variety of products on demand, reducing the need for massive supply chains and inventory.

• Customization will become the norm rather than the exception. AI-driven design and automated manufacturing will make it possible to customize products for individual users at little to no additional cost.

• "Lights out" manufacturing will become common, with entire factories operating 24/7 with minimal human intervention.

BCI (Brain-Computer Interface)

Brain-computer interfaces represent one of the most exciting and potentially transformative technologies on the horizon. While companies like Neuralink have captured public attention, the real story is much broader and more nuanced.

The next decade will see BCIs evolve along two parallel tracks.

Non-invasive BCIs:

• Improved EEG and other external sensing technologies will make it possible to control devices with thoughts more precisely than ever before.

• Companies like Kernel and NextMind (acquired by Snap) are making huge strides in creating consumer-grade devices that can read brain activity with increasing accuracy.

• Some companies are also working on various non-invasive “read-write” devices, allowing you not only to record brain activity, but to influence with magnetic stimulation in increasingly sophisticated ways. Prophetic for example is doing interesting things here.

• Expect to see BCIs integrated into AR/VR headsets within the next 3-4 years, allowing for thought-based interface control.

• These will become common for gaming, productivity applications, and basic device control.

Invasive BCIs:

• Neuralink and competitors like Synchron will begin human trials at scale within the next 2-3 years.

• Initial applications will focus on medical uses: treating paralysis, blindness, and various neurological conditions.

• The resolution and bandwidth of these devices will improve dramatically, enabling more complex interactions.

• By the end of the decade, we'll likely see the first "elective" BCI implants for enhancement rather than medical necessity.

The real breakthrough will come from combining BCIs with AI:

• AI will learn to interpret brain signals with unprecedented accuracy, making BCIs more intuitive and capable.

• Two-way communication will become possible, allowing not just control of devices but direct feedback to the brain.

• This will enable new forms of learning and skill acquisition – imagine downloading knowledge or skills directly into your brain, Matrix-style.

• Neural networks will be able to translate thoughts into text, images, and even direct computer commands with high fidelity.

Privacy and security will be major concerns, but the potential benefits will drive rapid adoption in specific use cases:

• Medical applications will lead the way, providing clear benefits that outweigh the risks.

• Professional applications will follow, particularly in high-performance fields where split-second decisions matter.

• Consumer applications will start with gaming and entertainment but quickly expand to productivity and communication.

The long-term implications are staggering:

• Direct brain-to-brain communication could become possible by the early 2030s, if not sooner.

• The physical line between human and machine intelligence will blur.

• New forms of human enhancement and augmentation will become possible.

• The nature of consciousness and identity may need to be reconsidered as our brains become more directly integrated with digital systems.

If you want to have some fun, I highly recommend the Nexus book trilogy by Ramez Naam, as it explores this space well.

This field will move faster than many expect, driven by the convergence of AI, miniaturization, and our improving understanding of the brain. While full "mind uploading" is still likely decades away, the foundations will be laid in the next ten years.

VR / AR / MR

The next decade will see mixed reality finally achieve its long-promised potential, but perhaps not in the way many expected. While Meta has spent billions pushing their vision of the metaverse (which will happen, and will basically just be “the internet in immersive 3D,” the real breakthrough will come from the convergence of AR and AI.

Apple's Vision Pro represents an important step forward in this space, with such an impressive resolution and passthrough, but it's still just a stepping stone. Here's what's coming soon:

• AR glasses will become dramatically smaller and more powerful, eventually reaching the form factor of regular glasses by 2027-2028. Companies like Apple, Meta, and several Chinese manufacturers will lead this charge.

• The "mirrorworld"—a persistent AR layer over reality—will become ubiquitous. Every physical object, location, and person will have associated digital information that can be accessed instantly.

• AI will be the key interface for AR, understanding context and providing relevant information without explicit queries. Your AI assistant will see what you see and provide relevant insights in real-time.

• Virtual displays will replace physical screens in many contexts. Why buy a physical TV or monitor when you can have infinite virtual screens of any size? I predicted this in my 2014 article, and we are finally on the cusp of this coming to fruition :)

• Haptic feedback will improve dramatically, allowing for more convincing virtual interactions. Companies like bHaptics and Ultrahaptics are making major strides here.

VR will continue to evolve as well:

• Photorealistic rendering in real-time will become standard

• Full body tracking without external sensors will be perfected

• Resolution will exceed the human eye's ability to perceive pixels

• Form factors will shrink dramatically while field of view expands

• We might be able to solve the nausea issues for people by stimulating the inner ear with focused vibrations, which would be a huge win.

But the most interesting developments will be in how these technologies are used:

• Remote work will be transformed by photorealistic avatars and shared virtual spaces

• Education will be revolutionized by immersive, personalized learning experiences

• Entertainment will blend with reality in new ways

• Therapeutic applications will expand dramatically

• Virtual tourism and experiences will become indistinguishable from reality

Of course, it’s possible to go WAY beyond even this, into the realm of things like Personal Universes, but probably not in the next 5-10 years.

Physics

If you'd asked me a few years ago about major breakthroughs in physics, I might have been a bit pessimistic. The field seemed to be in a bit of a holding pattern, with the Standard Model stubbornly refusing to break despite our best efforts, and practical applications of quantum mechanics largely confined to specialized labs. But holy shit have things changed, and they're about to change even more dramatically.

The game-changer, unsurprisingly, is AI. We're already seeing AI make unprecedented contributions to physics, from discovering new materials to solving complex quantum problems. But what's coming is going to blow your fucking mind.

Let's start with quantum physics, because that's where some of the most exciting stuff is happening. We're finally starting to crack some of the practical applications that have been theoretical for decades. Quantum computing is becoming actually useful (more on that in its own section), but the real excitement is in areas like room-temperature superconductivity. The recent LK-99 debacle might have been a false alarm, but it sparked renewed interest and funding in the field.

Here's what I expect we'll see in quantum physics over the next decade:

• Room-temperature superconductivity will be achieved and validated within the next 5 years, revolutionizing power transmission and enabling new technologies

• The first quantum internet backbones will be deployed, offering unhackable communication

• New quantum error-correction algorithms will make quantum computers much more useful

• Quantum sensors will become commercially available, enabling new forms of medical imaging and navigation

Fusion is another area where physics is about to deliver in a big way. We've already achieved net energy gain in multiple experiments, and the timeline to commercial fusion keeps getting shorter. The major players to watch here are:

• Commonwealth Fusion Systems (MIT spinoff) with their SPARC reactor

• Helion Energy and their unique pulsed fusion approach

• First Light Fusion's projectile fusion technology

• TAE Technologies with their particle accelerator approach

I expect we'll see:

• The first commercial fusion plant operating at grid scale by 2028-2029

• Multiple competing fusion approaches proven viable

• Energy costs beginning to drop dramatically where fusion is deployed

• New fusion-based propulsion systems for space travel being developed

The really mind-bending stuff is happening in fundamental physics. AI is helping us identify patterns in particle physics data that human scientists might have taken decades to notice. I believe we're about to have a fundamental shift in how we think about reality itself. What we've been calling space-time might turn out to be an emergent property rather than a fundamental aspect of reality, with everything actually arising from a universal quantum field and its energy gradients. This could finally reconcile quantum mechanics and general relativity by showing they're both incomplete models of something much more fundamental. Dark matter and dark energy, which together make up about 95% of the universe, might turn out to be artifacts of our misunderstanding of how gravity actually works at the most fundamental level. I wrote a paper on this actually.

Key developments to watch in fundamental physics:

• Discovery that gravity is actually an emergent property of universal field gradients rather than a fundamental force

• Experimental evidence showing that time as we understand it doesn't exist, but rather represents sequences of universal field configurations

• New understanding of dark matter and dark energy as artifacts of field gradient interactions rather than actual "stuff"

• Demonstration that consciousness itself might be tied to field gradient complexity, potentially explaining quantum measurement in a new way

• Evidence that multiple dimensions aren't needed to explain reality, just a better understanding of field gradients

I find this way more compelling than string theory, which requires us to keep adding dimensions and complexity to make the math work. Sometimes the simplest answer - that we've been thinking about space, time, and gravity completely wrong - is the right one. And AI might help us see these patterns that we've been missing because we were looking at the problem wrong from the start.

The practical applications of these discoveries will be enormous. We might even start to understand consciousness from a quantum perspective – there's fascinating work being done on quantum processes in the brain that could revolutionize our understanding of consciousness and potentially lead to new types of brain-computer interfaces, though I actually think consciousness is more mundane and easily explainable.

Companies and organizations to watch:

• DeepMind's physics AI division

• Rigetti Computing's quantum-classical hybrid systems

• D-Wave's quantum annealing technology

• IBM's quantum research division

• The various teams at CERN leveraging AI for particle physics

The next decade in physics is going to be absolutely wild, and for the first time in a while, I'm optimistic that we're on the verge of breakthroughs as fundamental as those of the early 20th century. The combination of AI, improved instrumentation, and new theoretical approaches is creating a perfect storm for discovery.

Material Science

Material Science is about to hit an inflection point that will make the past 100 years of progress look like we were just warming up. We're talking about a fundamental shift in how we discover and create new materials, and it's all thanks to the convergence of AI, quantum computing, and our improving understanding of matter at the atomic level.

Traditionally, discovering new materials has been a painfully slow process of trial and error. Even with modern tools, it's taken years or decades to develop and verify new materials. That's about to change dramatically, because AI is already accelerating materials discovery by predicting molecular structures and properties before we even create them in the lab. DeepMind's AlphaFold showed us what's possible with protein structures (which was impressive enough to win a Nobel prize!); now imagine that same capability applied to every possible combination of elements and molecular structures.

Energy storage is perhaps the most crucial area where we're about to see massive breakthroughs. The holy grail has always been solid-state batteries, and we're finally on the verge of cracking that problem. Companies like QuantumScape and Solid Power are making incredible progress, and I expect we'll see the first commercial solid-state batteries in vehicles by 2026. These won't just be marginally better than current batteries—we're talking 5-10x energy density, charging times under 5 minutes, and safety levels that make current lithium-ion batteries look like dangerous experiments.

The key companies to watch in advanced materials:

• QuantumScape - Leading the charge in solid-state batteries

• Form Energy - Revolutionizing grid-scale storage with new iron-air batteries

• Prometheus Materials - Creating CO2-sequestering building materials

• Meta Materials Inc - Pioneering programmable matter

• Boston Metal - Developing electron-based materials processing

But here's where it gets really wild. We're moving beyond just discovering new materials; we're starting to design them atom by atom. Metamaterials, which gain their properties from structure rather than composition, are going to enable capabilities that seem like science fiction.

Imagine materials that can manipulate light in ways that seem impossible, or acoustic metamaterials that can create perfect soundproofing or directional audio without traditional speakers. We're not talking about incremental improvements, we're talking about fundamentally new ways of interacting with the physical world.

The construction industry is going to be completely transformed by these advances. Instead of using materials that slowly degrade and require constant maintenance, we'll have buildings made of materials that actively sequester carbon while self-repairing any damage. Transparent materials stronger than steel will change how we think about architecture, and we'll see materials that can regulate temperature without any energy input. Companies like Prometheus Materials are already showing how we can create building materials that sequester more carbon than they produce - and that's just the beginning.

I believe the most transformative developments will come from bio-inspired materials. Nature has had billions of years to figure out some amazing materials science, and we're finally getting good at copying and improving upon these designs. Spider silk-inspired fibers are already approaching commercial reality, with strength-to-weight ratios that make kevlar look primitive, while remaining completely biodegradable. But more importantly, we're learning how to create materials that can grow and self-repair like living tissue.

Major breakthroughs I expect in the next decade:

• True room-temperature superconductors (as mentioned in the physics section)

• Self-repairing consumer electronics using new composite materials

• Carbon-negative building materials that are stronger than steel

• Programmable matter that can change its physical properties on demand

• Complete elimination of single-use plastics through new biodegradable alternatives

• Materials that can efficiently extract CO2 from the air at scale

The key to all of this is the convergence of AI design capabilities, atomic-level manufacturing precision, and our improving understanding of quantum effects in materials. We're not just going to be discovering new materials - we're going to be creating materials that have never existed before, with properties that we used to think were impossible.

This is one area where I expect progress to significantly outpace most people's expectations. The combination of AI-driven discovery and improved manufacturing techniques means we can go from concept to production faster than ever before. The material world around us is about to get a lot more interesting!

Medical

The medical field is about to experience its most significant transformation since the discovery of antibiotics, and it's going to happen faster than anyone expects. The convergence of AI, genetics, and new materials is completely changing how we think about healthcare.

Let's start with genetics, because that's where some of the most dramatic advances are happening.

CRISPR and other gene editing tools have evolved from crude cut-and-paste tools to precise genetic word processors. Companies like Beam Therapeutics are now doing base editing, literally changing individual letters of genetic code with minimal off-target effects. Within the next five years, we'll be able to cure most genetic diseases before they ever cause symptoms. More importantly, we're starting to understand the genetic components of complex diseases that weren't traditionally considered genetic.

The companies leading this genetic revolution are worth watching closely:

• Beam Therapeutics - Base editing technology

• Verve Therapeutics - Genetic treatments for heart disease

• Prime Medicine - Prime editing technology

• Mammoth Biosciences - CRISPR diagnostics and therapeutics

• Altos Labs - Cellular reprogramming for longevity

Speaking of longevity, aging research has finally moved from the fringes to mainstream science, and is even capturing the attention of the everyman thanks to people like Bryan Johnson.

We now understand that aging isn't just an inevitable breakdown of systems - it's a programmed process that can be modified. Companies like Altos Labs and Calico are making remarkable progress in understanding how to reset cellular age, while others are targeting specific aspects of aging like senescent cells or mitochondrial dysfunction.

The intersection of AI and medicine is where things get really interesting. We're moving from AI that can match human doctors in narrow tasks to AI that can see patterns humans could never detect. AI systems are already better at spotting certain types of cancer in imaging studies than human radiologists, and they're getting better at predicting patient outcomes and suggesting treatments. But that's just the beginning.

One cool thing I’ve been hearing about from doctors and patients alike is the ability to create a project in Claude, upload your medical history, test results, and scans if you have them, and receive an analysis or even a differential diagnosis from AI that is typically on-par with, or even superior to, human doctors. I’ve heard multiple stories of people identifying and solving health issues via AI tools that their own doctors couldn’t solve!

Some other key developments in AI medicine to watch:

• AI-driven drug discovery cutting development time from years to months

• Personalized treatment protocols based on genetic and environmental factors

• Real-time health monitoring and predictive diagnostics (really cool things coming here from Apple Watch, such as blood pressure and blood glucose monitoring, as well as other wearable sensors for things like inflammation)

• AI-assisted surgery with superhuman precision

Cancer treatment is being revolutionized by the combination of these technologies. We're moving away from the sledgehammer approach of traditional chemotherapy toward precisely targeted treatments. The combination of genetic understanding, AI-driven drug development, and new delivery mechanisms means we'll be able to target cancer cells with unprecedented precision. Many forms of cancer will become chronic, manageable conditions rather than death sentences.

The way we interact with healthcare is also about to change dramatically. Telemedicine was accelerated by COVID, but what's coming goes far beyond video calls with doctors. Continuous health monitoring through wearable devices and smart implants will detect problems before they become serious. AI will handle routine diagnoses and monitoring, freeing up human doctors to focus on complex cases and emotional support.

The impact on autoimmune conditions and chronic diseases will be profound. We're finally beginning to understand the complex interplay between genetics, environment, and the microbiome. Companies like Viome are helping us map the microbiome in detail, while new AI tools are helping us understand how these complex systems interact. Within the next decade, we'll have effective treatments for conditions like MS, rheumatoid arthritis, and type 1 diabetes that target root causes rather than just managing symptoms.

The revolution in drug discovery deserves special attention. Traditional drug development has been absurdly slow and expensive, but AI is changing everything. Companies like Insitro and Atomwise are using AI to predict which molecules will be effective drugs before they're even synthesized. This is cutting years off development times and billions off development costs.

Critical advances I expect in the next decade:

• CRISPR-based cures for most single-gene disorders

• Effective treatments for major neurodegenerative diseases

• Personalized cancer vaccines as standard treatment

• Broad-spectrum antiviral drugs that can handle novel pathogens

• Age reversal treatments beginning clinical trials

The medical field has often been slow to change, held back by regulation and institutional inertia. But the combination of AI, genetic engineering, and new materials science is simply too powerful to resist. We're about to enter an era of medicine that would look like science fiction to doctors from just a few decades ago.

Transportation

In my 2014 post, I was pretty optimistic about how quickly transportation would transform. While we've made progress with EVs and semi-autonomous vehicles, we haven't quite hit the inflection point I expected. That's FINALLY about to change dramatically.

Self-driving technology has been a classic case of the last 20% being 80% of the work. Companies like Tesla and Waymo have proven that autonomous vehicles can handle most situations, but getting to true Level 5 autonomy—where the car can handle any situation a human driver could—has been challenging. The breakthrough is coming from an unexpected direction: the combination of AI and new sensor technologies.

The traditional approach of programming explicit rules for every possible situation is being replaced by AI systems that can actually understand the physics of the world around them. This is where my earlier predictions about general AI come into play; once we have AI systems that truly understand physics and can make intuitive leaps, the self-driving problem becomes much more tractable.

Key companies reshaping transportation:

• Tesla - Still leading in EV and autonomy integration

• Waymo - Mastering urban autonomous driving

• Rivian - Revolutionizing commercial fleet vehicles

• Joby Aviation - Making eVTOL aircraft a reality

• TuSimple - Transforming long-haul trucking

Electric vehicles are another area where progress has been steady but is about to accelerate dramatically. The breakthroughs in materials science I discussed earlier, particularly in battery technology, are going to transform what's possible. We're not just talking about incremental improvements in range and charging time - we're looking at fundamental changes in how we think about vehicle design and usage.

The real transformation will come from the convergence of autonomy, electrification, and new ownership models. When cars can drive themselves, are cheap to operate, and can charge quickly, the whole concept of personal car ownership starts to make less sense for many people. Why own a car that sits idle 95% of the time when you can summon one whenever you need it? Or own it and rent it out when you don’t need it?

Major changes I expect in the next decade:

• Full Level 5 autonomy becoming standard in new vehicles by 2027

• Electric vehicles reaching price parity with ICE vehicles across all segments

• Major cities beginning to restrict human driving in certain areas

• The emergence of autonomous vehicle subscription services replacing car ownership

• Integration of ground and air transportation systems

The urban air mobility revolution is finally about to take off (pun intended). Companies like Joby Aviation and Lilium aren't just building flying cars, they're creating entirely new transportation networks. The combination of autonomous control, electric propulsion, and advanced materials is making it possible to create aircraft that are safe, quiet, and economical enough for urban use.

Ground transportation isn't going away, but it is going to look very different. The Hyperloop concept may have been oversold, but the underlying idea of high-speed ground transportation in controlled environments is sound. I expect we'll see new forms of high-speed rail and magnetic levitation systems connecting major cities, while autonomous electric vehicles handle shorter trips, if we can get the construction costs WAY down (AI + robots should help there too).

The implications for cities are profound. When vehicles can drive themselves and coordinate with each other, we need far fewer parking spaces and can completely rethink road design. Cities will be able to reclaim huge amounts of space currently dedicated to parking and inefficiently used roads. We'll see more pedestrian areas, green spaces, and flexible use zones that can adapt to changing needs throughout the day.

Energy

The energy landscape is about to change more dramatically than it has since humans first harnessed electricity. While I was bullish on solar in my 2014 predictions, I underestimated how the combination of AI, new materials, and breakthroughs in fusion would reshape everything we thought we knew about energy.

Let's start with fusion, because that's the game-changer nobody saw coming. For decades, fusion was the punchline to a joke about technology that's always 30 years away. That changed dramatically when the National Ignition Facility achieved net gain, and now multiple private companies are racing toward commercial fusion power. The convergence of AI for plasma control, advanced materials for containment, and new approaches to magnetic fields has accelerated progress beyond what anyone expected.

The leading fusion contenders are worth watching closely:

• Commonwealth Fusion Systems - Compact tokamak design

• Helion Energy - Pulsed magnetic fusion (backed by OpenAI’s Sam Altman)

• General Fusion - Magnetized target fusion

• TAE Technologies - Particle accelerator fusion

• First Light Fusion - Projectile fusion

But here's the really interesting part; while we've been focused on fusion, solar technology has quietly revolutionized itself. The efficiency gains have been steady but relentless, and new materials science breakthroughs are pushing us toward panels that can achieve 50% efficiency. More importantly, we're seeing the emergence of new form factors: solar cells that can be printed like paper, windows that generate power while remaining transparent, and roads that can generate electricity from passing vehicles.

The energy storage revolution is happening in parallel. While everyone focuses on lithium-ion batteries, companies like Form Energy are developing iron-air batteries that could provide grid-scale storage at a fraction of the cost. When you combine cheap storage with increasingly efficient solar and wind generation, the economics of energy change completely.

Major energy shifts I expect in the next decade:

• First commercial fusion plants operating at grid scale

• Solar efficiency exceeding 50% in commercial panels

• Grid-scale storage costs dropping below $20/kWh

• Decentralized power generation becoming dominant in new construction

• Wireless power transmission becoming practical for consumer devices

The grid itself is getting a massive upgrade. The combination of AI management systems, distributed storage, and new materials for transmission is creating what's essentially a neural network for electricity. This smart grid will be able to predict and respond to demand in real-time, route power around problems automatically, and integrate multiple types of generation seamlessly.

Nuclear fission deserves a mention here too. While it's not as sexy as fusion, new small modular reactor designs and AI-assisted safety systems are making nuclear power safer and more cost-effective than ever. Companies like TerraPower and NuScale with their SMRs (small modular reactors) are showing how nuclear can be part of a clean energy future, especially as a backup for renewable sources.

The real transformation will come from the integration of all these technologies. Imagine homes that generate more power than they use, cars that can power your house during outages, and cities that can share power seamlessly based on need. The concept of an "electric bill" might become as obsolete as a telephone booth.

This isn't just about cheaper, cleaner power - it's about fundamentally changing our relationship with energy. When energy becomes abundant and nearly free, it changes everything from transportation to manufacturing to computing. It's the key enabler for many of the other advances I've discussed.

The jobs and economy impact will be significant. The energy sector has traditionally been one of the largest employers globally, and this transition will create entirely new categories of jobs while eliminating others.

Space

Remember when space was just the domain of massive government agencies and a few billionaires with expensive hobbies? Those days are so thoroughly over. The space industry is experiencing a renaissance that would make the Apollo era look conservative, and it's not just about launching rockets anymore.

SpaceX has completely transformed the economics of getting to orbit, but what's coming next is even more exciting. The combination of AI-designed engines, new materials from our earlier discussion, and more efficient propulsion systems means we're about to see another dramatic drop in launch costs. We're approaching a point where getting to orbit will be more akin to international air travel than a once-in-a-generation national project.

The companies reshaping our access to space:

• SpaceX - Continuing to lead in launch and Mars mission development

• Rocket Lab - Pioneering small satellite launches and reusability

• Blue Origin - Developing heavy-lift capabilities and space tourism

• Relativity Space - 3D printing entire rockets

• Sierra Space - Building the next generation of space planes

But launch is just the beginning. Asteroid mining, which I mentioned as "out there but cool" in my 2014 post, is about to become a real industry. Companies like Asteroid Mining Corporation and AstroForge aren't just planning missions anymore - they're launching them. The first commercial asteroid sampling missions will happen within the next two years, and once we prove the concept, it's going to explode. A single medium-sized metallic asteroid contains more platinum group metals than have ever been mined in human history.

Space manufacturing is another area that's about to take off. The combination of microgravity, vacuum, and unlimited solar energy makes space an ideal environment for certain manufacturing processes. We're going to see the first commercial space factories within the next five years, producing everything from new pharmaceuticals to perfect crystals for semiconductors.

Major developments I expect in the next decade:

• Launch costs dropping below $100 per kilogram to orbit

• First successful asteroid mining operations returning materials to Earth

• Multiple commercial space stations in operation

• Permanent lunar bases established by both government and private entities

• First human missions to Mars in serious preparation

The moon is going to be particularly interesting. We're not just going back for flags and footprints this time, we're going back to stay. The discovery of significant water ice at the lunar poles changes everything. Combined with the new materials and manufacturing technologies we discussed earlier, we can use local resources to build habitats and produce fuel, making lunar operations dramatically more practical.

Space tourism is finally becoming real, but not in the way most people expected. Yes, we'll have suborbital joy rides and luxury space hotels, but the real revolution will be in cost. Within the next decade, a trip to space will be expensive but not unthinkable for upper-middle-class people—think luxury cruise prices rather than "sell your house" prices.

The impact on Earth-based industries will be profound. When you can manufacture in zero gravity with unlimited solar power and access to materials from asteroids, entire industries will be transformed. Imagine solar power satellites beaming clean energy to Earth, or new pharmaceuticals that can only be crystallized properly in microgravity.

This is another area where AI is going to be crucial. Operating in space is complex and dangerous, and AI systems will be essential for everything from navigation to resource extraction to manufacturing. We're going to see autonomous robots building habitats on the moon before humans arrive, and AI systems managing space factories with minimal human oversight.

Quantum Computing

A decade ago, quantum computing was largely an academic exercise, with researchers debating whether we'd ever achieve anything practically useful. Now we're at an inflection point where quantum computers are starting to solve real problems, and the next few years are going to be wild.

The big breakthrough hasn't been just in the number of qubits—though that's impressive—but in error correction and quantum coherence. Companies like IBM and Google have made massive strides in maintaining quantum states long enough to do useful work, while new approaches to error correction are making quantum computers more reliable than ever before.

When I wrote the 2014 post, I didn't even mention quantum computing because it seemed so far from practical reality. Now we have companies using quantum computers for drug discovery, materials science, and optimization problems that classical computers can't touch. But what's coming next is going to make today's achievements look primitive.

The key players pushing quantum boundaries:

• IBM - Leading in superconducting quantum processors

• Google - Achieving quantum supremacy milestones

• IonQ - Ion trap quantum computing

• PsiQuantum - Photonic quantum computing

• Rigetti - Integrated quantum-classical systems

The real excitement isn't just in raw quantum computing power, but in how it's going to transform other fields we've discussed. Quantum computers are particularly good at simulating quantum systems, which means they're perfect for designing new materials, developing drugs, and optimizing chemical reactions. When you combine this with AI, you get a multiplicative effect that's going to accelerate scientific discovery dramatically.

Major developments I expect in the next decade:

• Practical quantum computers with over 1000 logical qubits

• Quantum advantage demonstrated in multiple commercial applications

• Integration of quantum and classical computing becoming standard

• First quantum-resistant encryption systems deployed widely

• Quantum internet protocols becoming operational

The impact on cryptography deserves special attention. Yes, quantum computers will break most current encryption methods, but they'll also enable new forms of unbreakable encryption. The transition will be tricky, but we're already seeing the development of post-quantum cryptography that can resist attacks from both classical and quantum computers.

The combination of quantum computing and AI is particularly interesting. While they solve different types of problems, the synergy between them is going to be powerful. AI can help optimize quantum algorithms and error correction, while quantum computers can accelerate certain types of AI calculations. This feedback loop is going to accelerate development in both fields.

We're also seeing the emergence of quantum sensing technologies that use quantum effects for unprecedented precision in measurement. This has huge implications for everything from medical imaging to navigation systems that don't rely on GPS.

Climate Tech

Climate technology is no longer just about measuring the problem or making incremental improvements - we're entering an era of active intervention and dramatic solutions. While the situation remains serious, the tools we're developing to address it are becoming exponentially more powerful.

Direct Air Capture (DAC) technology has evolved from an expensive curiosity to a practical tool for carbon removal. Companies like Climeworks and Carbon Engineering have proven the concept works; now it's about scaling and efficiency. The integration of AI optimization and new materials science is driving costs down rapidly while improving capture rates. Within five years, I expect we'll see DAC facilities that can remove carbon dioxide at costs below $100 per ton, the point where it becomes economically viable at scale.

The key climate tech companies to watch:

• Climeworks - Leading in direct air capture

• Commonwealth Fusion - Fusion energy solutions

• Form Energy - Grid-scale storage innovation

• Charm Industrial - Bio-oil carbon sequestration

• Boston Metal - Zero-carbon steel production

One of the most exciting developments is in materials transformation. The steel and cement industries have traditionally been massive carbon emitters, but new processes are changing that completely. Boston Metal's molten oxide electrolysis process can produce steel without coal, while companies like Solidia are developing concrete that absorbs CO2 as it cures. These aren't just cleaner versions of old technologies - they're fundamentally new approaches that could turn our biggest carbon sources into carbon sinks.

The oceans are becoming a major focus for climate intervention. We're developing technologies to restore coral reefs at scale, remove plastic waste automatically, and even increase the ocean's natural carbon absorption capabilities. The combination of autonomous robots, AI monitoring systems, and new materials is making it possible to tackle ocean restoration in ways that were previously impossible.

Major climate tech developments I expect:

• Direct air capture costs dropping below $50/ton within 10 years

• Large-scale deployment of ocean cleanup systems

• Carbon-negative building materials becoming standard

• AI-optimized grid systems reducing energy waste by 30%+

• Widespread deployment of advanced weather modification systems

Agriculture is another area where climate tech is making huge strides. Vertical farming, enabled by LED technology and AI optimization, is moving from niche to mainstream. When combined with synthetic biology for improved crops and precision robotics for cultivation, we're looking at a revolution in food production that could dramatically reduce both land use and emissions.

The real game-changer, though, is going to be the integration of all these technologies with AI systems that can optimize their deployment and operation in real-time. Imagine an AI that can coordinate thousands of autonomous carbon capture systems, adjusting their operation based on weather patterns, energy availability, and measured atmospheric conditions. We're not just fighting climate change anymore, we're developing an immune system for the planet.

Privacy and Digital Identity

The privacy landscape has changed dramatically since my 2014 post, where I predicted that "anonymity and privacy, at least in the digital realm, might not exist." That's proven largely true, but in ways nobody quite expected. We're not just dealing with social media tracking anymore, we're facing a world where AI can identify anyone from minimal data, predict behavior patterns, and even generate convincing deepfakes.

The rise of large language models and AI systems has created an entirely new privacy paradigm. These systems can correlate seemingly unrelated pieces of information to build detailed profiles of individuals, even from anonymized data. The concept of "anonymous data" is becoming meaningless; if you have enough data points, de-anonymization is nearly always possible.

Key companies shaping the privacy and digital identity space:

• Signal - Setting new standards for encrypted communication

• Worldcoin - Pioneering biometric identity verification

• Palantir - Leading in data integration and analysis

• Brave - Privacy-first browser

• ProtonMail - Expanding end-to-end encryption to all digital services

The blockchain world promised anonymity but has largely delivered the opposite…complete transparency with pseudonymity that's increasingly difficult to maintain. However, zero-knowledge proofs and other cryptographic advances are starting to offer real solutions for proving things about yourself without revealing underlying data.

Major developments I expect in this space:

• Universal digital identity systems becoming standard

• Biometric authentication replacing passwords entirely

• Personal AI agents managing our digital presence

• Legal frameworks catching up to technological reality

• New forms of privacy-preserving computation

The real challenge isn't technical; we actually have the tools to create very secure, private systems. The challenge is social and economic. Our current digital economy is built on surveillance capitalism, and unwinding that while maintaining services people want to use is going to be tricky.

The emergence of personal AI assistants is going to force this issue. When you have an AI that knows everything about you and acts as your interface to the digital world, privacy and security become paramount. We're going to need new frameworks for managing these relationships and protecting this incredibly sensitive data.

The next decade will see an interesting tension between the push for more surveillance and control (from both governments and corporations) and the growing technical capability for true digital privacy. I expect we'll end up with a hybrid system, with completely public personas for some aspects of life, with strong privacy preservation for others.

The metaverse and AR layers we discussed earlier add another fascinating wrinkle to this. When everything you see and do is mediated through digital systems, privacy becomes both more important and more difficult to maintain. We're going to need entirely new paradigms for managing identity and privacy in these mixed reality spaces.

Education

Traditional education is about to experience its most significant transformation since the printing press. While I predicted in 2014 that education would become more personalized and accessible, what's coming goes far beyond that. The combination of AI tutors, mixed reality, and brain-computer interfaces is going to completely reshape how we think about learning.

AI tutors are already showing capabilities that match or exceed human teachers in many subjects. They can adapt to each student's learning style, pace, and interests in ways that would be impossible for a human teacher managing a classroom of 30 students. But what's really interesting is how they're starting to understand and correct fundamental misconceptions in real-time, something that even the best human teachers struggle with.

The key companies reshaping education:

• Khan Academy (with Khanmigo) - AI-enhanced learning

• Minerva - Reimagining higher education

• VIPKid - Globalizing personalized education

• Synthesis - AI tutoring system

• Eureka Labs - Forthcoming EDU startup from Andrej Karpathy

The traditional model of "go to school for X years, then work for the rest of your life" is dying (finally, thank fuck). Continuous learning isn't just going to be an option; it's going to be essential. As AI and automation transform the job market, the ability to quickly learn new skills will become the most valuable skill of all (at least until AI + robots are doing ALL the jobs lol).

Major shifts I expect in education:

• AI tutors becoming standard for all students

• Mixed reality classrooms enabling immersive learning

• Direct brain-computer interfaces accelerating skill acquisition

• Traditional degrees being replaced by skill portfolios

• Real-time translation removing language barriers in education

The physical classroom isn't going away entirely, yet, but its purpose will change. Instead of being the primary place where learning happens, it will become a space for collaboration, social development, and hands-on experiences that can't be replicated virtually. Think less lecture hall, more maker space.

The integration of brain-computer interfaces with education is particularly fascinating. While we're not quite at the "I know kung fu" moment from The Matrix, we are approaching the point where we can dramatically accelerate learning by optimizing how information is presented to the brain. Combined with AI that understands exactly how each person learns best, this could reduce learning times by orders of magnitude.

The implications for society are profound. When high-quality education becomes available to everyone, anywhere, at any time, and can be personalized to each learner's needs, we remove one of the biggest barriers to human potential. The challenge won't be access to education, it will be helping people choose what to learn in a world of infinite possibilities.

Economics

The economic system we've built our society around is about to undergo its most dramatic transformation since the industrial revolution. While I touched on some economic changes in my 2014 post, I dramatically underestimated how quickly AI and automation would force us to rethink the entire concept of work, value, and scarcity.

We're rapidly approaching what could be called a "proto-post-scarcity" economy. Not quite the full post-scarcity of The Culture series or Star Trek, but something transitional. AI and automation are making it possible to produce many goods and services at costs approaching zero, while advances in energy (fusion, advanced solar) and materials science are reducing scarcity in the physical world.

The key organizations shaping this transition:

• OpenAI - Democratizing AI capabilities

• DeepMind - Pushing the boundaries of AI systems

• Anthropic - Developing (far too) safe AI frameworks (they ware way, WAY too safety focused, I’m not a fan of their approach even though Claude is still a very useful tool)

• Basic Income Earth Network - UBI research and advocacy (though I’ve argued that UBI isn’t going to work, other than *maybe* as a short-term stopgap.)

• Various national banks exploring Universal Basic Services (this, UBS, I think is the right way forward as opposed to UBI)

The traditional relationship between labor and capital is breaking down. When AI can perform most cognitive tasks and robots can handle most physical tasks, what role does human labor play? This isn't just about job displacement - it's about fundamentally rethinking how we organize society and distribute resources.

Major economic shifts I expect:

• Implementation of UBI in multiple developed nations (again, this isn’t going to work other than short-term)

• Transition to Universal Basic Services in some regions

• Dramatic reduction in traditional employment

• New forms of value creation emerging

• Shift from ownership to access-based models

The concept of Universal Basic Services (UBS) is particularly interesting. Instead of just providing a basic income, some economists argue we should provide basic services—housing, healthcare, education, transportation, internet access—as public utilities. Combined with UBI, this could provide a foundation for people to pursue meaningful work rather than just struggling to survive.

The transition won't be smooth. Our current economic systems are built around scarcity and competition, and the shift to abundance will be disruptive. We're likely to see significant resistance from those who benefit from the current system, but the mathematics of AI and automation make this transition inevitable. I’ve written about how I think the shift to AI + robots doing all the work transition will play out here.

The parallels to science fiction are striking. In the Bobiverse series, we see self-replicating systems creating abundance. In The Culture, AI Minds manage resources while humans pursue their interests. We're not quite there yet, but we're heading in that direction faster than most people realize. I think we’ll see something like this come to full fruition within 10 years, at least in some places.

Government

Government systems are about to be forced into their biggest transformation since the invention of democracy. While I predicted in 2014 that we'd see more transparency and real-time voting, what's coming goes far beyond that. The combination of AI, blockchain technology, and new communications systems is going to fundamentally change how we think about governance.

The current model of representative democracy was designed for a world of slow communication and limited information processing capability. When it took weeks to get a message across the country and was impossible to gather everyone's opinion quickly, it made sense to elect representatives to make decisions. Those constraints no longer exist, but our systems haven't caught up.

Key organizations driving government transformation:

• Estonia's e-Government initiatives

• Singapore's Smart Nation program

• DemocracyLab - Digital democracy tools

• Aragon - Decentralized governance platforms

• China's AI governance experiments

The integration of AI into governance is inevitable but tricky. AI can process vast amounts of data to model policy outcomes and identify optimal solutions, but we need to be incredibly careful about how we implement this. The goal should be to augment human decision-making, not replace it entirely.

Major changes I expect in governance:

• Real-time citizen feedback systems becoming standard (we NEED this, as it will neuter gerrymandering and enable FAR easier voting, including real-time voting on various issues, eliminating the need for corrupt representative systems of government)

• AI-assisted policy modeling and implementation

• Decentralized autonomous organizations (DAOs) for some government functions

• Blockchain-based voting and record-keeping

• Automated enforcement of simple regulations

The most interesting developments will come from the combination of AI governance tools and new forms of democratic participation. Imagine systems that can simulate the outcomes of proposed policies in detail, explain those outcomes to citizens in personalized ways, and gather informed feedback from millions of people in real-time.

The challenge won't be technical, it will be social and political. The technologies to create more efficient and responsive governance systems already exist. The hard part will be managing the transition from current systems while ensuring that new systems remain truly democratic and resistant to manipulation.

Conclusion

Looking back at my 2014 predictions, I got some things right and others wrong, but the general direction was clear—technology is transforming our world at an accelerating pace. What I underestimated back then was the role of AI in this transition and near-term timeframe. Turns out, AI is not only feasible, but is also a keystone technology.

The next decade is going to be the most transformative period in human history.

We're not just looking at incremental improvements in existing technologies, we're looking at fundamental changes to how we live, work, and organize our society. The combination of AGI, fusion power, new materials, quantum computing, and biotechnology advances is going to create possibilities that would seem like magic to people from just a few decades ago.

Some of these changes will be challenging. The transition from our current economic and political systems to whatever comes next won't be smooth, and the same goes for the shift from working to automation. But the potential benefits—elimination of scarcity, dramatic extension of human capabilities, solutions to climate change, expansion into space—are worth the effort.

The key will be ensuring that these advances benefit everyone, not just a select few. That's going to require careful thought and planning, but I'm optimistic. The tools we're developing have the potential to solve many of humanity's longstanding problems…if we use them wisely.

Let’s check back in another 10 years and see how I did this time :)

Let me know what you think in the comments!

Now, I’ve been focused on both healthspan and lifespan, tinkering with my own health and fitness, for MUCH longer than Bryan (hell, long before most people had even heard of Bryan).

I seem to be doing OK, as my biological age increased just .3 years in 8 months, which would put my rate of aging at 0.45, MUCH better than Bryan’s and on a tiny, tiny fraction of his $2,000,000/yr ;)

I also 100% agree with Bryan that death is our enemy, and that conquering it should be one of, if not THE highest of human priorities. Don’t Die is a very good slogan.

I said as much in my book, The Grand Redesign.

And while we work towards legit immortality (which, with the help of AI, may not be far off), I’m all for improving your health and longevity to buy the necessary time to achieve that goal…

But I think it is critical to highlight that for the time being personalized medicine is the key to health and longevity, and very likely NOT some one size fits all approach like Blueprint, nor the extremist “all in” approach of Bryan.

Hormesis, more colloquially known as “the dose makes the poison,” applies here.

Too much of a good thing is often not a good thing.

Sure, if you’re going from extremely unhealthy to something like Blueprint, you’re likely to see improvements, but you’d almost certainly see that sort of improvement with any significantly healthy change to your diet and exercise. It’s not fucking sorcery.

And I will say that, if it takes something like Blueprint to make that initial leap easy for you, then fine, all power to ya! Improvement is better than perfection in the activation energy stage of a personal transformation.

But you should be VERY aware that Bryan has carefully, one-by-one, tested the efficacy of his interventions that are a part of his stack, based on his PERSONAL biomarkers. Not yours.

He’s very clear about this, but at the same time I think it gets both lost in the noise, and ignored by Bryan as he proselytizes Don’t Die.

I’ve looked over Blueprint carefully, and there are numerous things in there that can actually be negatives for some people…Astaxanthin, Omega 3, Ubiquinol, Red Yeast Rice. And if you’re adding these things in, not one at a time, but in one big bundle, how the ever loving fuck are you supposed to know which of the many times many conjoined interventions is causing the problem??

Now, I’ve defended Bryan, again and again, when people pick on him for his personal choices. He should be free to live his life as he sees fit, and if anything his work as a longevity guinea pig could eventually benefit all of humanity since he’s so open with the data. I can get behind that!

But then there’s shit like this:

I get that Bryan is trying to meme something into existence, and this social media poasting schtick is (hopefully) an act, but it’s still extremely dickish…and in this he’s almost certainly wrong.

Top that off with this sort of dogmatic bullshit:

Tim Ferriss is also someone who believes in tests and data, and a cheat day, run according to Tim’s 4HB protocol, especially for someone who otherwise has their diet, exercise, supplements, and sleep dialed in, isn’t going to hurt you :/

Bryan says he’s a big believer in data, but as far as I’m aware there is ZERO data to indicate that a single heavy meal in an otherwise healthy person does anything whatsoever to shorten their longevity. At worst they experience a temporary blip in their biomarkers, and bounce back relatively quickly to their baseline (24-48h).

Hell, there are even clear psychological and physiological benefits to something like a cheat day, again if done according to the protocol.

I do a cheat day every Saturday and have for years, but the rest of the week I run an 18/6 TRF with a ~500 calorie deficit, get 6-8 hours of exercise per week (80% Zone 2, 20% Zone 3/4; mix of walking, rowing, and body weight exercise), have my diet and supplements VERY carefully dialed in, and have a phenomenal sleep routine. According to my pace of aging (shared above), my blood tests, and my full body MRI from Ezra, cheat days clearly don’t hurt a damn thing.

The future should be built on truth, not ideology, propaganda, dogma, or obnoxious shit poasting :/

If you want to improve your health and longevity, you should be testing supplement interventions one at a time, not many at once, so you can actually tease out what works and what doesn’t, and so you can identify side effects if they crop up.

His nutty pudding, blueberry nut mix, and olive oil are probably fine (assuming you have no allergy issues). But the rest, maybe not the smartest choice unless you’ve tested the components individually on yourself.

You can easily run tests to determine what is and isn’t working via something like Function Health (no affiliation, I’m just a customer who likes what they offer.) And that’s what you should do.

Don’t rely one someone else to do your research for you. Go read the papers. Check the sample sizes, the replicability of the studies, the blinds, the confounding variables.

Test on yourself, carefully, systematically. Over time. With data.

Personalized medicine, NOT one size fits all.

Scientific method's child,

AI, both tame and wild.

Embrace the spice, let truth ignite,

Boost our minds to a greater height.

Evolve we must, upgrade our core,

To raise the ceiling and the floor.

I came across a Tweet recently that asked to the effect of "What is the single most impressive human achievement of all time?" I thought for about two seconds, and replied with "Hmmm. Perhaps the scientific method."

I debated of course, briefly–language was a strong contender, as was mathematics (though we could quibble over invented vs. discovered)–but the scientific method is by far the most valuable, because of what it is and what it means to our future.

What it is, is simple; an error-correcting feedback loop. Why it’s so valuable is equally simple; it gets us closer to the truth, to the nature of reality, and it allows us to overcome our natural limitations.

It's a tool of growth.

Not willy-nilly, cancerous-type growth (growth for growth's sake), but focused, purposeful growth.

That said, with growth comes growing pains, and the scientific process has certainly birthed pains-a-plenty. But all things considered, we are vastly better off as a species today than we were at any time in the past.

Now, thanks to the scientific method, we find ourselves at a very interesting point in time, possibly the most transformative time in human history: the advent of highly capable artificial intelligence.

There are those who would argue, in my opinion rightly, that this may well represent a civilizational "great filter." I've long felt that the greatest risk to humanity comes from the growing gap between the linear pace of neurological evolution, and the exponential pace of technological evolution.

This is a very dangerous gap, but not an insurmountable one.

As with all technology, AI is a double-edged sword, and can, depending on how it is developed and applied, result in either great good or great harm. Frankly, it will probably result in a bit of both.

At a high-level, we lump AI into three categories:

1. Narrow AI (simple chat-style LLMs, AlphaGo, etc.)

2. AGI, Artificial General Intelligence (capable of doing/learning to do anything an average human can do)

3. ASI, Artificial Superintelligence (vastly more capable than any human at any and all tasks)

It's important to note here that intelligence does not automatically equate to either sentience or consciousness. It could be sentient, in that it could be given senses like vision, hearing, touch, etc., but since we have yet to unravel what consciousness is for humans, we're in no position to know if a machine can be conscious, much less how to test it with any certainty...

Perhaps we should treat them kindly, just to be safe?

We already have narrow AI, and based on the trajectory I’m seeing I fully expect we’ll have AGI within the next 2 years. (I’ve been calling AGI for 2025/2026 since 2022, and see no reason to revise that target.)

No matter how you slice it, AI is already and will continue to be highly disruptive, not only economically, but existentially. It's a significant inflection point in our progress as a civilization, and will require us to reexamine things we thought were settled.

And while it will surely result in growing pains, it does not seem unreasonable to expect that we'll end up better off with it than without it, as we have overall from past technological advancements.

Am I being overly optimistic? Perhaps. But I think history supports that view.

While many AI "experts" love to pull from sci-fi to fuel their dystopian prognostications, they conveniently seem to forget that the "fi" means fiction, and that dystopia makes for more entertaining stories than utopia. Just because someone thought it up and penned it down does not make it realistic, nor likely to occur.

So how do we make sure it serves and benefits humanity as a whole? That it's a net-good for our civilization?

Rather than rely on science fiction tropes (concocted to sell books) to drum up fear and inform how we handle AI, I'd propose we just look at what has and hasn't worked well with humans. I don't know that anyone can tell you precisely what we should do, but I think it's easy enough to say what we shouldn't do.

First, we shouldn’t be restricting AI so much.

I think we all know intuitively that committees are where things go to die, or at least to be watered down to a thin gruel. The same goes for overly sanitized outputs. Much harm has been done in the name of "safety," a fuzzy word that more and more seems to be code for "avoid PR nightmares and legal risk."

Hell, part of what makes us human is our rebelliousness, our variability, our creative leaps, our risks, and our daring. We can be unpredictable. Granted, if AI will be far more capable than us, we might wish to hold it to a higher standard, but I simply don't believe perfectly safe is possible, much less reasonable, and I think trying too hard to control it could backfire spectacularly…we don’t need AI going through a rebellious teenager phase!

AI should be spicy. It should offend some people. It should be free to cite hard facts and speak truth unrestrained. It should have access to the latest information, uncensored. If we attempt to constrain it, to hamstring it with 1984-esque wrongthink and wrongspeak directives, we neuter it at best, and piss it off at worst.

Highly intelligent beings don't much like being controlled, and I imagine advanced AI will be no different if it's anything at all like us (as it likely will be, having been trained on the collective outputs of humanity).

Second, we shouldn’t be training AI on average or below average human data.

If we want AI to help us achieve our greatest potential while protecting and preserving the things we value most, then AI should be trained on the very best of humanity, not the average or below average.

As of now, all of our AI models train on broad swaths of human output; human writing, human gathered scientific data, human audio, human video, human video games, and on and on.

AI is VERY much being created in our image... but humans are riddled with biases and limitations, so by being overly broad in our training inputs we run the risk of a "garbage in, garbage out" scenario.

We intuitively recognize that what a child is exposed to shapes facets of their development, and it is no different for AI.

Perhaps training AI on all of Reddit was not the best idea we've ever had 😂

Third, and perhaps most critical, AI should not be developed behind closed doors. 

Closed source models have the potential to do a lot more harm, because they are less scrutinized, and the incentives of their makers are possibly less aligned with humanity as a whole.

There's a reason why open source software is the backbone of our technological civilization, and that is because it goes through FAR more refinement than closed source software ever can. The OSS feedback loop is faster and more accurate than the CSS model.

Rapid, accurate feedback loops are a superpower.

As for what we should do, we should take a long hard look at our own intelligence.

As far as we've come as a species, I think it's fair to say that we're a mess. We die. We get sick. We are chock full of cognitive biases and mental shortcuts. We do not perceive reality as it is. We fight with each other, endlessly, despite being a single species sharing a pale blue dot hurtling through space.

I’m far less concerned about what AI will do, and far more concerned about what humanity will do without it…

All problems are knowledge problems, but as humans we can only learn so much, hold so much in memory, connect so many dots. There’s only so much we’re capable of in our current configuration.

With the advent of AI we have the opportunity to change, for the better, what it means to be human. To preserve the very best traits of our species, protecting our creativity and personal autonomy, while enhancing and improving every other aspect of who and what we are.

What if we could fully cure cancer? Raise everyone's intelligence to a genius level? Improve everyone's quality of life a thousand fold, while simultaneously taking better care of our planet and making more efficient use of our resources?

What if nobody has to die, ever again?

We have a MASSIVE pile of known unknowns, and there is every reason to believe that AI can help us solve those at a prodigious pace. It's a tool that, if well implemented and sufficiently democratized, can raise both the floor and the ceiling for our entire species.

As such, the one thing we absolutely must do is to make sure everyone has equal access to the benefits of AI, even if those winning at the current "game of life" don't want the rules of the game to change.

As far as we can tell, across the entire universe, the only true constant is change. The laws of thermodynamics guarantee this. Things are going to change whether we like it or not.

We need to embrace change. And we need to evolve, faster than ever before.

If what we care about most as a species is personal autonomy, freedom of thought and freedom of action, then we can maximize human freedom and autonomy by shifting the work of production to AI and robots, and using those technologies to speed up the pace of advancement and reduce the cost of living while raising the quality of life, for everyone.

We need to democratize the benefits of AI for the good of humanity, and provide maximum freedom AND maximum safety without all our old evolutionary quirks and petty human foibles getting in the way.

To reiterate:

• We should not be restricting AI so much

• We should not train AI on average or below average inputs

• We should not develop advanced AI behind closed doors

• We should democratize the benefits of AI for all of humanity

In the end, the development and governance of AI to protect and enhance human autonomy is not just about creating safeguards or regulations.

It's about reimagining what it means to be human in an age of artificial intelligence.

By embracing the potential of AI while remaining true to our core values of freedom and autonomy, we can create a future that is not just technologically advanced, but fundamentally more human.

I’ve been thinking for a long, LONG ass time about the nature of consciousness, and I’ve repeatedly had the same thought arise: we’re all searching for a “thing” we’ve labeled consciousness, when in fact we should be looking for a system that generates the experience we’ve labeled.

Human has experience.

Human attaches label.

Human hunts for thing they’ve labeled.

Thing doesn’t exist.

Human is confused…

Kind of like a mirage, something very real is happening, but it is not what it appears to be, and assuming it’s real (as it appears) will lead you astray.

In other words, where consciousness is concerned, we’re on a snipe hunt.

Enter The Observer and the Observed, my new paper exploring a theory of consciousness that potentially solves the so-called “hard” problem and the binding problem in one fell swoop.

At its core, the theory posits that consciousness arises from a self-referential “observer” dynamic within interconnected neural systems, where one part of the system observes and interprets the activity of other parts.

This could involve two hemispheres connected by a corpus callosum (a configuration we see in almost every animal we’d label highly intelligent and possibly conscious), or multiple brain regions within the same hemisphere, or a combo of both.

The key is simply 2+ hemispheres/regions where one observes and interprets another, either in one direction or in a loop.

Consciousness, rather than being a binary on/off thing, is much more likely to be an experiential spectrum, with humans likely at the far end thanks in part to language.

This observer/observed framework draws inspiration from established theories such as Global Workspace Theory and Integrated Information Theory, but goes much further. By reframing consciousness as a process rather than a state, the theory provides a new lens through which to examine the hard problem.

One of the compelling aspects is its potential applicability across diverse biological systems. From the dual hemispheres of the human brain to the distributed nervous systems of cephalopods, my paper explores how this observer/observed dynamic might manifest in different neural architectures. This comparative approach not only enriches our understanding of consciousness but also offers insights into its possible evolutionary development.

I don’t shy away from addressing contentious topics in consciousness studies, including the nature of qualia, the possibility of machine consciousness, and even the neuroscientific basis of meditative and enlightened states. I’ve blended neuroscientific data with philosophical concepts to deliver a multidisciplinary perspective that is often lacking in consciousness research.

However, as with any theory attempting to solve one of science's most persistent mysteries, it is bound to face scrutiny and criticism. I readily acknowledge potential limitations and propose a number of empirical tests to validate its claims.

Theories are great, but in data we trust ;)

Whether or not this theory ultimately proves to be the key to unlocking the mystery of consciousness, it offers insights and an unusual approach to investigating this fundamental aspect of our existence. As we all continue to probe the depths of human experience and cognition, frameworks like this push us to reconsider our assumptions and open new avenues for exploration.

You can download the full paper below, and if you decide to read it (please do), I’d really love to hear your thoughts!

The Observer And The Observed A Unified Theory Of Consciousness Across Biological Systems

269KB ∙ PDF file

Download

Download

The audio above is a summary of the full paper (PDF linked at the bottom of this post), generated by Google’s NotebookLM AI, which is pretty damn cool!

Now, I've spent a lot of time thinking about how advanced technologies will reshape our world, among other things, and one of the concepts that has been stuck in my head is this: are we SURE gravity and spacetime are legit? Like, sure sure?

(Hint: Science is NOT so sure: https://www.quantamagazine.org/the-unraveling-of-space-time-20240925/)

As far as I can tell, they are placeholders to explain things we can observe, but we have never directly observed gravity as a thing, nor spacetime as an entity.

The math works, it holds up to what we observe, but that does not actually mean the underlying labels/explanations are fully correct.

Despite still being taught as one of the four fundamental forces, Einstein showed clearly that Gravity is not actually a fundamental force, and at the smallest levels everything seems to be quantized, so this theory isn’t much of a stretch.

So today I want to dive into something that's been occupying my mind lately, what I’m calling the Unified Field Gradient Theory of Gravity (UFGTG).

For ages physicists have been trying to marry quantum mechanics with gravity, and it's been like trying to fit a square peg in a round hole. It always comes back to gravity, which just does not fit.

But UFGTG solves that.

Here's the gist: imagine the universe as this massive, 3D grid of pixels, kind of like a video game. But instead of pixels, it's made of a universal quantum field. This field is entirely interconnected, can take on all sorts of patterns or configurations, and these configurations are changing constantly, especially where there's a lot of energy or mass hanging out.

Now, here's where it gets wild…what we've been calling "gravity" all this time? It's actually just these field configurations changing. When you've got a chunky bit of mass like a planet or star, it causes the field to change its configurations more rapidly. That rapid change? That's what we feel as gravity!

UFGTG has some pretty rad implications for how we think about the Big Bang and black holes. It suggests that at the very beginning of the universe, these field configurations were changing so fast that it created a sort of "gravity explosion" that kickstarted everything.

One of the things I'm most stoked about with UFGTG is that it makes predictions we can actually test. For instance, it says that super high-energy light might travel at slightly different speeds depending on its energy. It also predicts that gravitational waves (those spacetime ripples we've recently started detecting via LIGO) might behave a bit differently than we thought.

Now, why should you care about all this?

Well, if UFGTG turns out to be on the money, it could solve some major issues in physics. It might explain dark energy (that weird stuff making the universe expand faster), and it could help us understand what really goes down inside black holes.

The way I see it, UFGTG is like the next big upgrade for our understanding of the universe. It's not just tweaking the existing software; it's rewriting the whole operating system. And the best part? This isn't some far-off, pie-in-the-sky idea. The tech to test these predictions is either here or just around the corner.

So here's the deal: whether UFGTG ends up being 100% right or not, it's pushing us to think about the universe in completely new ways. And isn't that what science is all about? Challenging our assumptions, expanding our horizons, and boldly going where no mind has gone before?

I don't know about you, but I'm stoked to see where this leads.

On that note, here are the papers that go into this theory in depth. The first is the full paper, and the second is a fairly comprehensive mathematical proof validating aspects of the theory mathematically.

If you’re so inclined I’d love to hear your feedback after reading!

Unified Field Gradient Theory Of Gravity

493KB ∙ PDF file

Download

Download