Broadsheet

It’s almost hard to remember, but just a few years ago, a lot of very smart people thought that science was in terminal decline. Respected researchers were writing papers with titles like “Are Ideas Getting Harder to Find?”, “Papers and patents are becoming less disruptive over time”, and “Slowed canonical progress in large fields of science”. In 1996, science writer John Horgan even published a book called The End Of Science: Facing The Limits Of Knowledge In The Twilight Of The Scientific Age.

The fundamental idea was that the Universe only has so many insights to discover, and that we’ve picked the low-hanging fruit. It was the idea that Joseph-Louis Lagrange expressed when he said that “we cannot find more than once a system of the world to establish.” As the big ideas and the simple ideas get mined out, we’re forced to throw ever more brains at the mysteries of nature just to keep scientific progress going at a fraction of its old pace:

It looked like science was hitting a wall. Then along came AI.

It’s far too early to tell how much AI will eventually speed up the growth of total factor productivity. But already we can see signs of a speedup in science. Anthropic Founder Jack Clark assembled some of the hopeful data points in a recent lecture:

Anthropic is accelerating its investment into drug discovery, and is already announcing some impressive preliminary results:

Claude also appears to be enabling rapid progress toward a treatment for food allergies. OpenAI, meanwhile, announced a possible breakthrough treatment for neurodegenerative disease.

Meanwhile, Google Cloud Chief Scientist Pushmeet Kohli has a list of recent scientific advances that have been enabled by its new Gemini model, including breakthroughs in nuclear fusion reactor design, rare disease research, and quantum computing:

But as cool as these stories sound, they’re just very minor, preliminary efforts. There are a number of reasons to think that AI is going to propel us into a golden age of scientific discovery. That age might not last forever — AI might still run into diminishing returns eventually. But the next few decades are probably going to be an absolutely amazing time for human science.

(Note: This post is written under the assumption that human civilization survives. A golden age of science could also easily turn into a golden age of bioterrorism, in which case all bets are off.)

The number of “effective researchers” is no longer limited

The most important input into science is scientists themselves. Until this year, that meant human brains — talented, highly trained human brains. The number of trained human brains could be increased by sending more people to grad school, but only to a point — the smartest people tend to be sent into the hardest research fields first, and if you 10x the number of researchers, the average quality will go down. This constraint was already starting to bite, as Ekerdt and Wu argued in 2024:

We argue that a substantial portion of the downward historical trend in U.S. research productivity is a composition effect resulting from self-selection in researcher ability and the dramatic expansion of the research sector…[W]e find that this composition effect has lowered average researcher ability by between 37% and 53%[.]

And of course, low fertility was making the problem worse, because fewer children meant fewer people who were able to become future scientists.

AI just blows up this constraint completely. If an AI agent can function as an autonomous researcher, there’s no constraint on the number of researchers in the world — you can just build more of them, by building more computer chips and data centers that enable the running of more AI agents. Whether or not massive hordes of agents can collaborate to solve individual problems much faster, increasing the number of agents allows them to tackle many different research questions in parallel.

What’s more, these AI “researchers” can be trained incredibly quickly compared to human beings. A human scientist takes decades to train, and more years to reach the frontier of their research field by diligent reading and conversation. This is the “burden of knowledge”, and it’s commonly believed to be one reason ideas have been getting harder to find — by the time human scientists can get up to speed, they’re often so old that their ideas are no longer novel.

AI just annihilates this constraint — it can assimilate the entire literature and reach the frontier of research extremely quickly, and it can also tell human scientists where the frontier lies without forcing them to discover that for themselves. It can also pivot to new research directions more easily than humans can.

And there’s also a self-reinforcing economic cycle here. Because scientific advances enable higher GDP, they enable more resources to be invested into the creation of yet more electronic “scientists”.

Now that doesn’t mean the amount of scientific research goes to infinity — there may still be some essential bottleneck task that human researchers are still needed to perform. There are some indications that AI’s progress on open-ended conceptual research tasks is slower than in other dimensions, and it may be that humans are still needed to provide some of this long-term conceptual thinking. I’d bet against this being true for much longer, but it might be true for a long time.

Even in this most “pessimistic” case, though, the amount of effective capital that scientists can use to leverage their work just went up by a lot.

AI eliminates the theory bottleneck

Science proceeds by an iterative process of theory and experiment. The theory part of that cycle is typically short, but non-trivial — it took a couple of years after the blackbody radiation spectrum was observed for Max Planck to come up with the quantum hypothesis to explain it, and decades longer before quantum theory as we now know it was created. It took about a year for Watson and Crick to figure out the shape of DNA after the requisite data was gathered.

AI will basically eliminate the theory bottleneck entirely — or at least shorten the turnaround time between experiment and theory by an order of magnitude. AI is an absolute beast at math. Recently, an OpenAI model solved some of the most important open problems in computer science, math, and theoretical physics, and no one even batted an eyebrow because that sort of triumph is becoming increasingly commonplace. One of those solutions was a proof that the Standard Model of Particle Physics could be mathematically well-defined — a problem that human theorists had so far been unable to tackle.

Not all research relies equally on theory, but understanding novel experimental results almost always involves some important component of either mathematical or spatial reasoning. That part of the task has now been made much, much faster. And on top of that, science is now open to a greater number of humans who aren’t naturally good at theorizing.

AI can read the whole literature

Much of the work of human scientists involves reading and understanding endless amounts of papers and going to endless conferences and seminars. The purpose of that work is to understand the existing scientific literature. Understanding the literature allows scientists to do the following things:

Come up with new ideas for hypotheses to test
Avoid doing research that someone else has already done
Synthesize approaches from different areas or fields

AI can reduce this effort to almost zero. Consider this excerpt from OpenAI’s blog post about their new research into neurodegenerative disease:

The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning. The agents examined a whole-brain CRISPR atlas…a dataset showing how gene disruptions affect different types of cells across the mouse brain. Each agent investigated a gene disruption, analyzed cellular responses, searched scientific literature, and proposed explanations for the results. Their reports were combined into a reasoning atlas linking genes, cellular effects, relevant publications, and possible biological mechanisms.
The research team estimated that comparable human analysis and literature review, conducted one perturbation at a time, would take months to a year. The agents reproduced findings from human analyses while uncovering additional leads. Their assessments of novelty and scientific value also aligned with judgments from blinded human experts. These findings help prioritize further experiments needed to test the proposed biological mechanisms.
Some of these insights point to new leads for understanding brain disease. [emphasis mine]

This is one case in which parallel work by a large number of agents really does make a huge difference:

Not only does instantaneous literature review vastly speed up the research process, but it allows AI to make novel conceptual leaps from combining approaches from wildly different fields of research. AI regularly does this in math research, combining results, approaches, and theorems from far-flung places that no human mathematician would have thought to combine.

It can do it in science, too. Much of scientific innovation, historically, has been “recombinant” innovation — scientists applying insights from one field to another. With AI, essentially all research is now cross-disciplinary — AI can read not just the literature on a particular subject, but the literature on a million other subjects that might have produced useful techniques or insights for the problem at hand.

Superscience

So far, all of the factors I’ve listed are ways that AI can speed up science as it’s already done. I’ve basically described how AI can send gigantic hordes of superhumanly fast, superhumanly well-read, superhumanly mathematically gifted researchers to re-accelerate scientific discovery.

But it’s also possible that AI will enable types of discovery — and ways of understanding the natural world — that no network of humans, no matter how large or how brilliant it was or how long it had to work, would ever be able to discover. In a post back in July, I wrote about how that might work:

What if there are lots of…aspects of the Universe that [are] too complex to understand in terms of simple laws, but not so complex that they just dissolve into unknowable chaos? It’s possible that we can reliably control these complex phenomena with AI, even if we never reduce them to the kind of principles that we can teach a grad student in a textbook…
Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate…
Perhaps social sciences like economics, sociology, and political science obey similarly complex regularities, and AI can help us find them. Perhaps there are physical processes — plasma, or topological materials, or aerial turbulence, etc. — that obey cloud laws instead of chaos?…
[H]uman collaboration is bottlenecked — it’s limited by our ability to intuit patterns at an individual level, and to communicate these patterns from one individual to another. AI, being a computer, just doesn’t have this sort of limitation; it can work with vast, diffuse patterns without having to break them into pieces or simplify them in order to compress them into the tiny pipelines of person-to-person explanation…
If much of the Universe turns out to be ruled by cloud laws, we could be on the precipice of a scientific renaissance.

In order to discover cloud laws, AI will need huge amounts of data about the natural world. AI figured out human speech by assimilating essentially all of the text humans have ever written. If it’s going to figure out hidden patterns in biology, neuroscience, materials science, etc., it’s going to need similarly massive amounts of data from physical instruments.

That data will be far, far more than humans — individually or collectively — would be able to parse, label, characterize, or model. But AI was built to do this. If scientists simply build huge arrays of sensors to measure complex phenomena like the brain, the cell, the economy, etc., they may be able to feed this flood of data to AI directly and bypass human cognitive intermediation entirely. AI would then learn about the world directly, and it might learn things that humans never could.

This “superscience”, if you want to call it that, would actually go beyond the kind of science humans have done heretofore. Many of the technologies it yielded would be beyond human comprehension — indistinguishable from magic. But to the AIs, they would make perfect scientific sense.

In any case, it seems clear that the scientific slowdown that threatened human progress will now be delayed — at least for a while. Unless humans have somehow found most of the actual exploitable regularities in our Universe, we’re about to enter a new golden age of science.

Subscribe now

I've written before about how November 2025 was an inflection point for coding agents - the point where Opus 4.5 and GPT-5.1 crossed a tipping point that meant their respective coding agents became reliable daily drivers for software engineers. I think we've reached a new inflection point in October 2026, and this time it's for vibe coding computer games.

Claude Opus 5.5 in particular exhibits meaningful improvements in several areas needed to build a compelling game:

3D modeling
Music generation (see Scrimshaw Jukebox)
Game design (!)

That last one is particularly important. Models have been able to create something that looks like a game for quite a while now, as seen in my previous experiments. The challenge has been getting to a game that's actually fun to play.

Game design is a deep subject area. I won't pretend to be well versed in it - I know enough to have respect for it as a craft. Creating a novel, compelling gameplay loop which challenges players at the right level and provides a satisfying learning curve is a fine art. I didn't think the models could do it.

Then I tried telling Claude Opus 5.5 to "Make it a LOT more fun"... and it did.

In the past few weeks I've vibe coded (in the classic sense of the term - no looking at the code at all) four games that I've genuinely enjoyed playing.

I attended Foo Camp in Berkeley over the weekend and met several people who were having similar experiences. Building games that play well is shockingly easy now. The new challenge is coming up with concepts for games that are novel and delightful and that wouldn't exist without AI assistance.

I haven't done an exhaustive study, but my current impression is that GPT-6 Astra isn't quite there yet. It can absolutely build a game, but its sense of fun doesn't match that of Opus 5.5. I expect OpenAI will get there by the end of the year.

Here are the four game projects that have convinced me that the models are now capable of producing fun games with very little guidance. All four are built in HTML and JavaScript and run entirely in the browser.

Tree Appreciator

The smallest game I found to be genuinely delightful is Tree Appreciator. I used the regular Claude chat interface for this - it didn't even need Claude Code.

I built this on a whim while we were driving along Skyline Boulevard looking at trees. My prompt was:

Build a three js game called "Tree appreciator" - it shows you two algorithmically generated trees and the game mechanics is about appreciating the differences between them. Think hard about how to make sure it is both fun and beautiful.

I've found that telling models to use three.js works really well. Claude doesn't have its own image generation model but is capable of building full-color 3D models that look and feel excellent.

This game required no iteration or follow-up prompting at all - I got the game in a single shot. It gave me back a charming little mobile-friendly spot-the-difference game. You can play that here. Here's the chat transcript that built it.

It's pleasingly relaxing. The graphics are fun, and collecting trees that you have completed in a "herbarium" provides a nice, cosy sense of accomplishment.

Emberwick

This was a more ambitious project that I again built using regular Claude chat. I started out by building an experimental prototype of an engine for a top-down fantasy RPG:

Build a demo game engine three.js which is a top down (but at an angle) RPG - the player gets to move through the world with one or two companions. Create a fantasy village setting that demonstrates walking around outside and then shows how when the player enters a building the wall between the player and the camera goes transparent so the user can see what is happening

Add some NPCs and plenty of furniture and different types of building some with an upstairs - be creative, make it look great

Here's that first version. It worked really well!

I was inspired to increase my ambitions:

Make it not so flat - the buildings are great but the landscape itself is completely flat at the moment, fix that

Add basements to two of the buildings with a tunnel system joining them below the village

Add the start of a rocky mountain on one side of the village containing a cave with a sleeping troll

Here's that version.

When that produced a good result, I decided to push it all the way:

Now turn this into an actual game - quadruple the size of the playable area, introduce fun game mechanics, add a plot that isn't too cliche, some twists, a magic system, make it really good

We're back to prompts like "make it really good" here! Asking for a plot that "isn't too cliche" seemed to work too - it's a bit cliché, but still novel enough that I enjoyed following the story.

Unlike Tree Appreciator, this took quite a bit of additional playtesting and prompting to get to a finished version. This was mainly due to gameplay problems - things like a character not being in the right place to complete a quest because a different story element had caused them to move to another location.

Here's that final game. It has a full story that takes about 15 minutes to play through.

There's even an experimental level editor if you want to drop in your own houses, furniture, and landscape features.

Once it was all working (and I'd iterated through some gameplay bugs), I played it through from start to finish and found myself emotionally affected by the ending! I'm somewhat embarrassed by this, because everything in this game - the graphics, the plot, the characters, the music, the dialog - came from the model. My creative input was pretty much just "it's a fantasy village and there's a troll asleep in a cave".

Here's the full shared Claude chat in which I built this game.

I also fed that same initial sequence of prompts through ChatGPT GPT-6 Pro. Here's the transcript and the finished game for comparison. It looks great, but I don't think it plays nearly as well.

Battle Drones

This is by far my most successful project, which I wrote about last week. It's a Command & Conquer-style Real-Time Strategy game where two robot armies (or four as of Saturday) build units, harvest scrap, and battle over territory.

This is also the project that convinced me that Opus 5.5 knows what "fun" is.

The first versions were built back in July using Claude Fable 5. It got me to a workable 2D Command & Conquer-style game, but it wasn't great to play.

I prompted Opus 5.5 to "Make it a LOT more fun" and then later to "Port this game to three.js" and the game was transformed. There's a blow-by-blow account (prompts included) in my write-up.

Today, Battle Drones has 2-4 player multiplayer (via a vibe coded WebSockets relay server), medals, replays, seven maps, a random map generator, and a set of "mutators" that add variants of the rules.

It also lets different LLMs play against each other, using a mode where the game advances five seconds and then pauses to give different models time to interpret the new game state and line up their next set of commands. Here's a replay of Claude Haiku 5.5 vs. GPT-6 Luna:

Battle Drones is a particularly good example of Opus 5.5's ability to create 3D models. This page lets you browse all of the models in the game.

I released Battle Drones under the Apache 2.0 License last Tuesday. On Wednesday Gabriel Turpin released Panache (screenshots here), a fork of Battle Drones that reimagined the game for steampunk 1900s Belle Époque France, with new models and music and an animated intro sequence. Open source at work!

Pumpkin Farmer, aka Quarter Sun Cove

I live in Half Moon Bay, California, home of the annual Half Moon Bay Art and Pumpkin Festival, which includes a giant pumpkin weigh-off competition.

I decided to try building a game that wasn't just a cheap imitation of something that already existed, so I set out to create a game about farming pumpkins, growing giant pumpkins, and handling the rush of the October pumpkin season.

I called it "Quarter Sun Cove".

I'm still iterating on this project, but it's shaping up quite nicely. It's a cosy farming simulator where you spend the year tending to your giant pumpkin and planting out regular-sized pumpkins for sale in October, then sell pumpkins during pumpkin season and enter your giant pumpkin in the weigh-off.

Every character in it is a (vegetarian) animal. The mayor of the town is a pelican. You can be a pelican, skunk, raccoon, possum, beaver, coyote, fox, owl, sea otter, or harbor seal. Those species all later show up (accompanied by their children) to buy pumpkins from you.

It's absurdly cute.

You can play the latest version here. I'm not convinced by the game mechanics yet - it's actually quite complicated: you have to decide when to plant pumpkins such that they will become ripe at the weekends in October with the most visitors.

Here's the initial brainstorming session, which resulted in this spec file. Then I had Opus 5.5 Ultracode burn millions of tokens and nearly two days building it.

I told Claude to "surprise and delight the user frequently" and the result is so many little details and easter eggs. There are mischievous raccoons, an elephant seal that runs the weigh-off, harbor seals that roll their pumpkins back into the ocean, a wandering goat, and superstitions about growing giant pumpkins that sometimes come true.

This isn't my best game yet - it's not as fun to play as Battle Drones - but it represents the kind of game I want more of: weird, fun, uniquely personal - something that wouldn't have been built in the before-times.

Four times as insulting

If you are offended by AI-generated art, AI-generated music, AI-generated writing, and AI-generated code, I have bad news for you: vibe coded games combine all of those at once.

I respect those opinions, even though I don’t strongly hold them myself.

The wider games community already has an aversion to AI-generated assets in games. This has been a growing issue over the past few years as tools like Midjourney have enabled developers to generate assets for their projects. This IGN story covering complaints about slop in Valve's Steam Next Fest is a good recent example.

We now have games where everything - code, assets, dialog, and even the game design itself - can be outsourced to the models. And the results don't suck!

Building a computer game used to be one of the most challenging projects in all of software development, combining a diverse range of skills and requiring intense focus and effort over many months or even years. And now here I am spitting out a playable multiplayer RTS by prompting on my phone!

I expect that, as we've already seen with programming, this will result in a split. Some players will refuse to have anything to do with games that were built in this way. Others may embrace this as opening up game development to a much wider audience. I lean in that direction!

I certainly won't be entering any of my vibe coded games in traditional game jams or competitions - I don't feel like I've earned that right. We'll likely see new avenues emerge for vibe game developers to compare their projects.

What happens if someone actually puts the work in?

The thing I find both exciting and shameful about this is that I have got interesting results from so little creative input. The projects I've described here are loosely ordered by how much effort I've put into them. Not a single project represents more than four hours of my work (plus many more hours of Claude Code churning away by itself).

Now imagine what could be achieved by anyone who takes this seriously, invests significant creative time, applies better taste in game design, and then playtests and iterates on the result until it's as good as they can make it.

I want to see games that wouldn't have existed without these new tools.

You should build a game!

Building games in this way is so much fun. It's the perfect application for vibe coding because it's so low stakes: how much damage can you cause with a bug in a personal vibe coded game project?

(OK, you can do some damage... my attempt at audio-based multiplayer gave my wife Natalie a nasty headache!)

All of my games so far have been HTML and JavaScript using static hosting (generally GitHub Pages or Cloudflare Pages). Modern browsers - including mobile browsers - are incredibly capable. Frontier models already know libraries like three.js and Phaser. I'm finding these mobile web games to be indistinguishable from the native experience, at least for the kinds of games I'm trying to build.

I suggest starting with a prompt like this:

Build a mobile-friendly three.js game where...

This works on both chatgpt.com and claude.ai - you don't even need to fire up a full coding agent. As one last example, here's "Build a mobile-friendly three.js game where a mouse has to hide from a cat" by Claude Opus 5.5 Extra and by GPT-6 Extra High.

Switch to Codex or Claude Code once your project passes the proof-of-concept phase.

And if the game isn't fun enough, add this:

Make it a LOT more fun

Tags: game-design, games, ai, generative-ai, chatgpt, llms, claude, vibe-coding, claude-code

Related Stories

Comments

Related Stories

Comments

Related Stories

The number of “effective researchers” is no longer limited

AI eliminates the theory bottleneck

AI can read the whole literature

Superscience

Spacewalk officially underway

IROSA roll-out begins

"Essentially flawless" spacewalk

There’s nothing these people hate more than paying taxes

Comments

Related Stories

Comments

Early views on unequal gains and abundance

Rising confidence in AI capabilities from 2020 to 2025

Economic transformation and the pace of adoption

Growing attention to safety and institutional limits

The September 2026 exchange about AI leaders

Consciousness and Tabarrok’s warning about infant pain

Comments

Related Stories

Comments

Related Stories

Comments

Tree Appreciator

Emberwick

Battle Drones

Pumpkin Farmer, aka Quarter Sun Cove

Four times as insulting

What happens if someone actually puts the work in?

You should build a game!

Comments

Related Stories

Comments

Related Stories

Comments

Comments

Related Stories

Comments

Related Stories

Created by Claude with light editing by Tim O’Reilly

The Clothesline Paradox

AI should be thought of as infrastructure

In conclusion