I think you are conflating capabilities with context. Yes you may have a superpowerful AGI but you do need to giveit the relevant context for a specific job, which is essentially turning it into a specialised AI. Wether that context is given to it through training or prompts or something else, you are still sepcialising it. That's why agentic AI is so powerful: you take a powerful base model and make it more useful by giving it the context. No matter how powerufl your AI gets, it will still be more effective when given specific context.
1I would have written a comment very similar to chasmani's to your answer to the following snippet you reacted to, if chasmani hadn't already done so:
Expecting a model to do all the work, solve everything, come up with new innovations etc is probably not right. This was kinda the implicit assumption behind *some* interpretations of capabilities progress. The ‘single genius model’ overlooks the fact that inference costs and context windows are finite.
People overrate individual intelligence: most innovations are the product of social organisations (cooperation) and market dynamics (competition), not a single genius savant.
I think you are not appropriately answering the point made here.
Yes. This perspective is also behind some intuitions for gradual disempowerment: Even if you have an aligned AI, if you specialize it into a billion contexts, each individual AI may try to do good while the collective still destroys the world.
Economists studying AGI like a market phenomenon may be akin to biologists studying computers through the lens of evolution - technically possible, occasionally insightful, but probably fundamentally missing the point. The economic frame persists not because it's accurate but because it's comfortable. It allows experts to feel relevant without confronting the possibility that their expertise might become obsolete.
The economists' frame may be precisely inverted. They're trying to understand a unified intelligence through the lens of coordination mechanisms that exist only because unified intelligence is impossible for humans. The question isn't "how will AGIs trade?" but "why would they remain separate enough to need trade?"
Nitpick: the economists naively expect the humans to trade with the AGIs or to have the AGIs obey the humans and make deals that will be approved by the humans, not the AGIs to trade with each other. If the humans kept having anything to offer to each other (e.g. while the AIs can, say, order a burrito on DoorDash, but can't have a robot make the burrito), then trade would be possible.
TODO: settle on title. improve first section " here is a remarkable uniformity and linearity in the AI capabilities of AI models"
cite:https://www.lesswrong.com/posts/tDkYdyJSqe3DddtK4/alexander-gietelink-oldenziel-s-shortform?commentId=Ne4P69CjF2fwbG4iC
cite: https://www.lesswrong.com/posts/tDkYdyJSqe3DddtK4/alexander-gietelink-oldenziel-s-shortform?commentId=Ne4P69CjF2fwbG4iC
[see also Four Ways Learning Economics makes you people dumber future AI]
This is a tweet by Seb Krier that caught my eye. The exact person and exact points are incidental. It illustrates what to is a flaw in many 'economics' frames on AI.
This seems to me missing something incredible important about what Artificial General Intelligence will actually be. [1] There is a certain type of economist [eg Tyler Cowen] that will proclaim AGI is near [or even already here!] and apply their standard economics tools to confidently proclaim AGI will not be dangerous, or it won't meaningfully impact growth rates, or it will adhere to human contracts and all this AI safety stuff is silly nonsense, even regulatory capture!
AGI as a Tool; AGI as an Agent
Let's start with: thinking of AGI as a Tool instead of as an Agent.
The point of AGI is exactly its generality: learning how to make good products, or scaffolding around ' raw intelligence' is itself a task that can be learned. Indeed it is learned by humans every day.
The Bitter Lesson, Again and Again Rich Sutton warned us: betting against scale is a losing game. Yet every few months, someone announces their specialized AI that finally beats the frontier models at medical diagnosis or legal reasoning through "clever architecture" or "curated data." A few months later? The next GPT or Claude absorbed their innovation and surpassed them while simultaneously improving at everything else. The Bitter Lesson isn't just about chess or Go anymore - it's about everything. Specialized training on curated datasets can't compete with the universal learner trained on everything. Economists predicting stable specialization are making the classic mistake Sutton identified: betting on human ingenuity over computational scale.
There is a remarkable uniformity and linearity in the AI capabilities of AI models. To a very good approximation AIs can be pretty linearly ordered in their capabilities. The frontier models produced by OpenAI, DeepMind, xAI, Anthropic are simulatenously the SOTA for virtually all AI tasks[2] . There aren't really specialized models doing all kinds of specialized work. Rather it is overwhelmingly the case that virtually all tasks that can be done well by AI are done best by frontier large language models.
Why is this the case? AIs are trained on the whole of the internet. Any innovation that is made by one company is quickly absorbed by the others. New workflows, scaffolding, tools, specific business contexts can be absorbed through extra finetuning, in-context learning or simply more compute. Vision and image generation is easily integrated into a larger multimodal large language model.
There is not much economic sense in training many different AIs. Nor is there much sense in building specialized AIs trained on only specific data sets. On the whole you want to spend as much of your compute on as much data as you can on one mega model.
One Big Transformer
Actually instead of 'general intelligence' I think it's better to talk about 'universal intelligence'. In other words, an intelligence that can absorb the skills and abilities of any other intelligence. We have some idealized formal models [solomonoff induction, AIXI] of what a universal intelligence might look like.
These mathematical models are highly idealized of course but they come down to a remarkable idea: one can simply amalgate different intelligences/minds/AIs into one big intelligence/mind/AI that is (almost) as good at any task as any of its constitutent intelligences/minds/AIs. Ultimately all intelligence may be absorbed into one super universal singleton intelligence.
Current AI already looks remarkably like this idealization. In a way LLMs are closer to this idealized universal intelligence than humans.
Humans can't directly amalgate into one big super smart human. Humans can't directly share their thoughts, knowledge or abilities. Their abilities are limited by the size of their skulls, the length of their lives, the limits of their senses.
Humans can share the contents of their minds much better than lower animals using language. Indeed is oft argued that language is the reason the human species rules over the lower animals. Using language humans can share skills, knowledge, abilities, and coordinate strategies over vast distances in space and time to many other humans simulatenously.
Transformers combining into one big transformer
The Dismal Science
Humans can't directly unify into one big human. This neccesitates complex coordination mechanisms for coordinating efforts, this includes culture, institutions and markets. Society retains specially trained humans to analyze these mechanisms. We call these economists. Despite a constant barrage of criticism from their envious social science and humanity cousins, economists have been fantastically succesful in their ability to describe and prescribe society.
Consider what enables economics: agents can't share their internal states, learning is costly and slow, knowledge transfer is lossy, coordination requires negotiation, and capabilities are rivalrous (if I'm using my brain for law, I can't simultaneously use it for medicine).
For AGI, none of these constraints may be relevant. Minds can fork and merge. Training can be instant through weight sharing. Coordination happens at silicon speed without contracts. When one AI masters a new domain - say, protein folding or contract law - it won't need to teach others through language or demonstration. It will simply share the relevant weights, like copying a file. The receiving AI instantly acquires years of "experience" in milliseconds.
Will AGI need currency? Currency exists because humans can't directly verify and compare utility functions. Will it need prices? Prices exist because information about preferences and production possibilities is distributed and hidden. Will it need contracts? Contracts exist because commitments can't be directly verified and trust must be manufactured. Will it need property rights? Property rights exist because rivalrous goods require allocation mechanisms. When a unified intelligence can directly observe all its subsystems, perfectly coordinate its actions, and share all resources optimally - these mechanisms become vestigial, like discussing the TCP/IP protocol between neurons in a brain.
The Comfort of Familiar Frames
The insistence that institution and culture and economics and multiagent systems will be a useful frame to look at the nature of AGI is widespread. This is implicit in the otherwise revolutionary Hanson's " world of Em", in Eric Drexler's " AI services", "a datacentre of geniuses", and all over economist's models of the future of AI.
But is it a good frame? Economics is most relevant when there are many different individuals with different skills, abilities, knowledge, etc that nonetheless are attempting to coordinate.
Economists studying AGI like a market phenomenon may be akin to biologists studying computers through the lens of evolution - technically possible, occasionally insightful, but probably fundamentally missing the point. The economic frame persists not because it's accurate but because it's comfortable. It allows experts to feel relevant without confronting the possibility that their expertise might become obsolete.
The economists' frame may be precisely inverted. They're trying to understand a unified intelligence through the lens of coordination mechanisms that exist only because unified intelligence is impossible for humans. The question isn't "how will AGIs trade?" but "why would they remain separate enough to need trade?"
Your Left Brain Doesn't Trade With Your Right
Consider again Seb Krier's claim:
Human innovations often emerge from collaboration rather than isolated genius. But economists mistake this for a fundamental truth about intelligence rather than a workaround for human limitations. We collaborate because we can't fit all knowledge in one head, can't live long enough to master everything, can't directly share our trained neural patterns. A universal AGI doesn't collaborate with itself any more than your left and right brain hemispheres engage in "trade." It simply thinks, with perfect internal coordination that makes our best institutions look like children playing telephone.
A much better analogy for the future and nature of AGI may be that of a superintelligent (benevolent?) hivemind.
Perhaps Seb and his intellectual ilk don't believe AGI is possible or are very sure it will take another thousand years. This would seem to be at odds with the enormous amount of progress we have seen in recent years. It would seem that at the very least they should flag this rather extreme epistemic position and I'm a little skeptical they will defend this position when pressed. It is nevertheless possible that they believe this; I don't know this specific person well but I doubt it.
with some minor exceptions, eg Go