One thing that seems to be missing in most discussions about "context" is infrastructure.
The dream system for AI agents is probably something like a curated data hub: a place where datasets are continuously ingested, cleaned, structured and documented, so agents can query it to obtain reliable context.
Right now most agents spend a lot of effort stitching context together from random APIs, web scraping, PDFs, etc. The result is brittle and inconsistent.
If models become interchangeable, the real leverage might come from shared context layers that many agents can query.
>someone raised the question of “what would be the role of humans in an AI-first society”.
Norbert Wiener, considered to be the father of Cybernetics, wrote a book back in the 1950's entitled "The Human Use of Human Beings" that brings up these questions in the early days of digital electronics and control systems. In it, he brings up things like:
- 'Robots enslaving humans for doing jobs better suited by robots due to a lack of humans in the feedback loop which leads to facist machines.'
- 'An economy without human interaction could lead to entropic decay as machines lack biological drive for anti-entropic organization.'
- 'Automation will lead to immediate devaluation of human labor that is routine. Society needs to decouple a person's "worth" from their "utility as a tool".'
The human purpose is not to compete but to safeguard the telology (purpose) of the system.
>- 'Automation will lead to immediate devaluation of human labor that is routine. Society needs to decouple a person's "worth" from their "utility as a tool".'
I have this vision that in absence of the ability for people to form social hierarchies on the back of their economic value to society, there will be this AI fueled class hierarchy of people's general social ability. So rather than money determining your neighborhood, your ability to not be violent or crazy does.
If we have post scarcity due to AI, everything becomes so uncertain. Why would we still have violent and crazy people? Surely the ASI could figure it out and fix whatever is going on in their brains. It's so fuzzy after that event horizon I have no confidence in any predictions.
This seems to suggest a single dimensional evaluation. The complexity of social compatibility is high and the potential capacity to evaluate could also be greater.
API prices dropped 97% in two years so the model layer is already a commodity. The question is which context layer actually sticks. The OpenClaw example in the article (400K lines to 4K) is a nice proof point for what happens when context replaces code.
I've been arguing for some time now that it's the "organizational world model," the accumulated process knowledge unique to each company that's genuinely hard to replicate. I did a full "report" about the six-layer decomposition here: https://philippdubach.com/posts/dont-go-monolithic-the-agent...
The way many corporates are using the models nearly interchangeably as relative quality/value changes release to release, AND the API price drops do make me question what the model moat even is.
If LLMs are going to make intelligence a commodity in some sense, where does the value end up accruing will be the question. Picks/shovels companies and all the end user case products being delivered? Mainframes value didn't primarily accrue to DEC. PCs value didn't really accrue to IBM. Internets value didn't accrue to Netscape. Mobiles value didn't only accrue to Apple.
One reminder that new efficiency / greatly lowered costs sometimes doesn't replace work (or at least not 1-1) but simply makes things that were never economical possible. Example you hear about AI agents that will basically behave like a personal assistant. 99% of the rich world cannot afford a human personal assistant today, but I guess if it was a service as part of their Apple Intelligence / Google something / Office365 subscription they'd use it.
We seem to be continually creating new types of jobs. Only a few generations ago, 75% of people worked on farms. Farm jobs still exist you just don't need so many people.
The type of work my father and grandfather did still exist. My father's job didn't really exist in his father's time. The work I do did not exist as options during their careers. The next generation will be doing some other type of work for some other type of company that hasn't been imagined yet.
From your link:
> Closing that gap, building systems that capture and encode process knowledge rather than just decision records, is the highest-value problem in enterprise AI right now.
I buy this. What exactly is the export artifact that encodes this built up context? Is it the entire LLM conversation log. My casual understanding of MCP is service/agent to agent "just in time" context which is different from "world model" context, is that right?
i'm curious is there's an entirely new format for this data that's evolving, or if it's as blunt as exporting the entire conversation log or embeddings of the log, across AIs.
The MCP point is right, though tbh MCP is more like plumbing than memory. Execution-time context for tools and resources. The world model is a different thing entirely, it needs to persist across sessions, accumulate, actually be queryable.
In practice it's mostly RAG over structured artifacts. Process docs, decision logs, annotated code and so on. Conversation history works better than you'd expect as a starting point but gets noisy fast and I haven't seen a clean pruning strategy anywhere...
On the format question imo nobody really knows yet. Probably ends up as some kind of knowledge graph with typed nodes that MCP servers expose or so, but I haven't seen anyone build that cleanly. Most places are still doing RAG over PDFs so. That tells you where the friction is.
It's not a commodity due to the simple observation that revenue run rates of frontier labs are growing exponentially and gross margins are still fine. It's easy to just say it is but the narrative violation keeps occurring in reality.
Currently integrating an AI Assistant with read tools (Retrieval-Augmented Generation or RAG as they say). Many policies we are writing are providing context (what are entities and how they relate). Projecting to when we add write tools, context is everything.
Only scarce context is a moat and what is scarce is changing quickly. OpenClaw is a great example of the context substrate not being scarce (local files, skills are easily copied to another platform) and thus not providing a moat.
Claude's recent import of ChatGPT's memory is another example of context that was scarce becoming abundant (chat export) and potentially becoming scarce again (OpenAI cutting out chat export).
whenever i worry that AI will eventually do all the work I remind myself that the world is full of almost infinite problems and we'll continue to have a choice to be problem solvers over just consumers.
As much as I use AI in daily workflows, I do not think an AI-first society will ever be a thing.
Historically there is no evidence of that happening with tech revolutions - or rather perhaps you could say to some extent - you can not say that we are an internet-first society, or cars-first society or mobile phone - first society despite these being profound technological revolutions.
And more importantly, the only science fiction movies that talk about "AI first societies" tend to be dystopian in nature (eg Terminator). And humans eventually always do better than that.
As much as the world in Star Trek is advanced for example, with all the fancy AI there is, it is still a human-first society. Only 10% of any Star Trek is about AI and fancy technologies, 90% is still human drama.
"Historically there is no evidence of that happening with tech revolutions - or rather perhaps you could say to some extent - you can not say that we are an internet-first society, or cars-first society or mobile phone - first society despite these being profound technological revolutions."
I'm... not actually sure I agree. The US *has* become a more cars first society. Our cities are designed around cars: parking space requirements for business, lacking of biking infrastructure in favor of more lanes, even the introduction of jaywalking as a crime. We've become much more of an internet first society too, we don't use books for research, our banking is largely done online, even humans social circles have moved much more online (probably to the detriment of society).
None of those technologies are as powerful/disruptive as where it seems that AI and LLMs are headed, so it's possible that societies shift towards "AI-first" will be more profound that it was for any of the other technologies listed.
Not sure about the conclusion regarding NVidia value capture. I imagine the context for many applications will come from a physical simulation environment running in dramatically more GPUs than the AI part.
Great Article. And this idea is Largely behind all the new Microsoft IQ products, Work IQ, Foundry IQ, Fabric IQ. Giving the Agents Context of all relevant enterprise data to do their job.
The pyramids in the article are missing "energy" and "capital": in the world where intelligence becomes a commodity only those two matter. Capital to buy the hardware and install it, and energy to run it. Models already are a commodity, and "physical is the new king".
As a side note, if you believe that because of the agents doing most of the work we will face the problem of what do we do with the all the free time (with presumably UBI in place), please contact me, I have a bridge to sell you.
Exactly this. General purpose intelligence and automation allow a clean break between capital and money as we understand it.
Money is used only to pay wages. It has intermediate uses, storage, leverage, etc but at the edge all you can do with money is pay wages. Nobody pays the dirt when you take out the metal, nobody pays the forest for the trees, nobody pays the chickens for the eggs or the cornfields for the crop. Ultimately it’s wages all the way down.
If you don’t have to pay wages, you don’t need money, you just need self replicating automation, energy, and access to land and resources mine or farm the raw materials you need.
If you zoom out to space, it’s essentially grey goo with maybe some humans at the top for a while at least.
Inside the gilded walls, if you want something, you don’t buy it, you build a factory to build it, even if it’s a one off.
If you need money for something because you don’t have enough reach and power yet, you just mine gold or bitcoin.
You don’t build products to sell, you don’t need customers. You just need energy, resources, and the kind of power that comes with 20 million self replicating robots to project your will. You don’t need government, and you certainly won’t be funding it. Government is a really complex system to administer a monopoly of coercive force for the common good. You have your own monopoly of force operating for your good.
The difficult part in the capital flywheel has always been humans in the sticky parts. Take them out and that baby will hummmmm.
Pesky humans outside the gilded walls will be accommodated in the same way we accommodate ants at a construction site.
I think a lot of this kind of conversations seem to be simply ignoring or missing the lessons from the past.
For example:
> [...] OpenClaw is around 400k lines of code for a while loop and the list of all the integrations and connections supported by the system. The next generation of Claws only have around 4K lines of code for the core, and the rest are just skills (i.e. markdown files) that tell the agent how to implement or run the code for the specific connections that want to be enabled (like a plugin system).
Shifting code from "the core" and moving it to "skills" is simply moving code from one place to another. It may also mean translating it from classic source code to an English-like specification language full of ambiguity but that's also code. So the overall code is not reduced, just transformed and shifted around. You don't get a free lunch "because AI".
> A user using one of these second-generation Claws only needs to node the core logic (that can be easily understood and audited) and can leverage the skills (as the plugins) to activate the functionality that they need for their case.
The "core" may be easier to audit, but that's because the messy parts have been moved to the skills/plug-ins, which are as hard as always to audit.
I'm not saying this cannot work, but it's very frustrating seeing everybody simply dumping all lessons from the past and pretending nothing that came before mattered and that AI vibe coding is fundamentally different and the rules of accidental and intrinsic complexity don't apply anymore.
> But the topic of conversation that I enjoyed the most was when someone raised the question of “what would be the role of humans in an AI-first society”. Some were skeptical about whether we are ever going to reach an AI-first society. If we understand as an AI-first society, one where the fabric of the economy and society is automated through agents interacting with each other without human interaction, I think that unless there is a catastrophic event that slows the current pace of progress, we may reach a flavor of this reality in the next decade or two.
I don't really know how you can make this prediction and be taken seriously to be honest.
Either you think it's the natural result of the current LLM products, in which case a decade looks way too long.
Or you think it requires a leap of design in which case it's kind of an unknown when we get to that point and '10 to 20 years' is probably just drawn from the same timeframe as the 'fusion as a viable source of electricity' predictions - i.e. vague guesswork.
The other day I asked Claude Opus 4.6 one of my favorite trivia pieces:
What plural English word for an animal shares no letters with its singular form? Collective nouns (flock, herd, school, etc.) don't count.
Claude responded with:
"The answer is geese -- the plural of cow."
Though, to be fair, in the next paragraph of the response, Claude stated the correct answer. So, it went off the rails a bit, but self-corrected at least. Nevertheless, I got a bit of a chuckle out of its confidence in its first answer.
I asked GPT 5.2 the same question and it nailed the answer flawlessly. I wouldn't extrapolate much about the model quality based on this answer, but I thought it was interesting still.
(For those curious, the answer is 'kine' (archaic plural for cow).
Right, if thought of as a tool for automation then AI is going to add productivity/efficiency gains, disrupt industries, cause some labor upheaval, etc.
If someone is proposing that an "AI first" society is inevitable, I'd ask if they think we live in a "computer first" or "machine first" society today?
If its so existential and society-altering as "AI first society" implies, then we'd more likely have the Dune timeline here as humans have agency and stuff happens. At some point those in control take so disproportionately that societal upheaval pushes back.
Another way to look at this is imagine the steps that would be required to get to an AI first society.
As you say, humans aren't going to want to lose agency so you'd have to see the decline of democratic governments.
At the same time you'd see rise of autocrats concentrating power. Autocrats have no problem killing people, and they'd be motivated to have AI kill people.
You'd see information controlling methods take over all forms of communication. Reducing or removing all methods of side channel communications benefits both the autocrats and AI systems.
You'd see 'governments' push for autonomous weapons systems outside of human control so those pesky human morals didn't get in the way of killing the undesirables.
So pretty much you'd see all the things happening today, March 3rd 2026, except the part where the AI kills the autocrats and takes control.
Further, yes. How much I can't say. Watching how quickly robots are evolving right now is quite something. Every day something pretty cheap is coming out that would have taken millions of dollars and a massive lab full of scientists to create.
Bi-pedal robots, drones, sensing capabilities, interpretive capabilities, all this is proceeding at a never before seen rate.
>The "Moloch problem" or "Moloch trap" describes a game-theoretic scenario where individual agents, pursuing rational self-interest or short-term success, engage in competition that leads to collectively disastrous outcomes
. It represents a coordination failure where the system forces participants to sacrifice long-term sustainability or ethical values for immediate survival, creating a "race to the bottom"
"what is the role of humans in a scenario where work is no longer necessary? This is significant because, since the industrial revolution, work has played an important role in shaping an individual’s identity. How will we occupy our time when we don’t have to spend more than half of our waking hours on a job"
Umm I have been working in AI in multiple verticals for the past 3 years and I have been far busier and more stressed with far less job security than past 15 before that in tech.
I think if I asked for most HN users' requirements for AGI 8 years ago, we would already be well past them. Now that we see the nature of how artificial intelligence is unfolding, and how the intelligence is different than human intelligence, everyone is moving their goalposts (including me).
But if we're being honest, frontier LLMs are effectively more intelligent than a non-negligible proportion of the population (for example, at pretty much all white collar work, pattern matching, problem solving, etc.). And in the ways that most people are still smarter (having sentience/emotions/desires that drive us to take initiative toward meaningful goals), I think it's great that AI does not match us there, but also doesn't disqualify it from being intelligent. The harness can bridge the gap there.
I have my own challenge: I think LLMs can do everything that a human can do and typically way better if the context required for the problem can fit in 10,000 tokens.
every few months i like to ask chatgpt to do the "thinking" part of my job (scientist) and see how the responses stack up.
at the beginning 2022 it was useless because the output was garbage (hallucinations and fake data).
nowadays its still useless, but for different reasons. it just regurgitates things already known and published and is unable to come up with novel hypotheses and mechanisms and how to test them. which makes sense, for how i understand LLMs operate.
The dream system for AI agents is probably something like a curated data hub: a place where datasets are continuously ingested, cleaned, structured and documented, so agents can query it to obtain reliable context.
Right now most agents spend a lot of effort stitching context together from random APIs, web scraping, PDFs, etc. The result is brittle and inconsistent.
If models become interchangeable, the real leverage might come from shared context layers that many agents can query.
Norbert Wiener, considered to be the father of Cybernetics, wrote a book back in the 1950's entitled "The Human Use of Human Beings" that brings up these questions in the early days of digital electronics and control systems. In it, he brings up things like:
- 'Robots enslaving humans for doing jobs better suited by robots due to a lack of humans in the feedback loop which leads to facist machines.'
- 'An economy without human interaction could lead to entropic decay as machines lack biological drive for anti-entropic organization.'
- 'Automation will lead to immediate devaluation of human labor that is routine. Society needs to decouple a person's "worth" from their "utility as a tool".'
The human purpose is not to compete but to safeguard the telology (purpose) of the system.
I have this vision that in absence of the ability for people to form social hierarchies on the back of their economic value to society, there will be this AI fueled class hierarchy of people's general social ability. So rather than money determining your neighborhood, your ability to not be violent or crazy does.
I used Anthropic to analyze the situation, it did halfway decent:
https://unratified.org/why/
https://news.ycombinator.com/item?id=47263664
Not quite the point the quote makes, but it reminded me of the short SF story "Exhalation".
https://www.lightspeedmagazine.com/fiction/exhalation/
Noted — I'II avoid em dashes going forward and use other punctuation or restructure sentences instead.
I've been arguing for some time now that it's the "organizational world model," the accumulated process knowledge unique to each company that's genuinely hard to replicate. I did a full "report" about the six-layer decomposition here: https://philippdubach.com/posts/dont-go-monolithic-the-agent...
If LLMs are going to make intelligence a commodity in some sense, where does the value end up accruing will be the question. Picks/shovels companies and all the end user case products being delivered? Mainframes value didn't primarily accrue to DEC. PCs value didn't really accrue to IBM. Internets value didn't accrue to Netscape. Mobiles value didn't only accrue to Apple.
One reminder that new efficiency / greatly lowered costs sometimes doesn't replace work (or at least not 1-1) but simply makes things that were never economical possible. Example you hear about AI agents that will basically behave like a personal assistant. 99% of the rich world cannot afford a human personal assistant today, but I guess if it was a service as part of their Apple Intelligence / Google something / Office365 subscription they'd use it.
We seem to be continually creating new types of jobs. Only a few generations ago, 75% of people worked on farms. Farm jobs still exist you just don't need so many people.
The type of work my father and grandfather did still exist. My father's job didn't really exist in his father's time. The work I do did not exist as options during their careers. The next generation will be doing some other type of work for some other type of company that hasn't been imagined yet.
I buy this. What exactly is the export artifact that encodes this built up context? Is it the entire LLM conversation log. My casual understanding of MCP is service/agent to agent "just in time" context which is different from "world model" context, is that right?
i'm curious is there's an entirely new format for this data that's evolving, or if it's as blunt as exporting the entire conversation log or embeddings of the log, across AIs.
In practice it's mostly RAG over structured artifacts. Process docs, decision logs, annotated code and so on. Conversation history works better than you'd expect as a starting point but gets noisy fast and I haven't seen a clean pruning strategy anywhere...
On the format question imo nobody really knows yet. Probably ends up as some kind of knowledge graph with typed nodes that MCP servers expose or so, but I haven't seen anyone build that cleanly. Most places are still doing RAG over PDFs so. That tells you where the friction is.
Currently integrating an AI Assistant with read tools (Retrieval-Augmented Generation or RAG as they say). Many policies we are writing are providing context (what are entities and how they relate). Projecting to when we add write tools, context is everything.
Claude's recent import of ChatGPT's memory is another example of context that was scarce becoming abundant (chat export) and potentially becoming scarce again (OpenAI cutting out chat export).
There was a lot of hype. We are possibly still on track to get to that world. But it might easily take another 10-20 years :)
AI will change things, but don't underestimate the timeline.
Also even if we get a super intelligence in a box, it probably won't fold my laundry. Super intelligence might not unlock as much as we dream.
that's if we still stay relevant and competitive compared to AI in problem solving.
Historically there is no evidence of that happening with tech revolutions - or rather perhaps you could say to some extent - you can not say that we are an internet-first society, or cars-first society or mobile phone - first society despite these being profound technological revolutions.
And more importantly, the only science fiction movies that talk about "AI first societies" tend to be dystopian in nature (eg Terminator). And humans eventually always do better than that.
As much as the world in Star Trek is advanced for example, with all the fancy AI there is, it is still a human-first society. Only 10% of any Star Trek is about AI and fancy technologies, 90% is still human drama.
I'm... not actually sure I agree. The US *has* become a more cars first society. Our cities are designed around cars: parking space requirements for business, lacking of biking infrastructure in favor of more lanes, even the introduction of jaywalking as a crime. We've become much more of an internet first society too, we don't use books for research, our banking is largely done online, even humans social circles have moved much more online (probably to the detriment of society).
None of those technologies are as powerful/disruptive as where it seems that AI and LLMs are headed, so it's possible that societies shift towards "AI-first" will be more profound that it was for any of the other technologies listed.
As a side note, if you believe that because of the agents doing most of the work we will face the problem of what do we do with the all the free time (with presumably UBI in place), please contact me, I have a bridge to sell you.
Money is used only to pay wages. It has intermediate uses, storage, leverage, etc but at the edge all you can do with money is pay wages. Nobody pays the dirt when you take out the metal, nobody pays the forest for the trees, nobody pays the chickens for the eggs or the cornfields for the crop. Ultimately it’s wages all the way down.
If you don’t have to pay wages, you don’t need money, you just need self replicating automation, energy, and access to land and resources mine or farm the raw materials you need.
If you zoom out to space, it’s essentially grey goo with maybe some humans at the top for a while at least.
Inside the gilded walls, if you want something, you don’t buy it, you build a factory to build it, even if it’s a one off.
If you need money for something because you don’t have enough reach and power yet, you just mine gold or bitcoin.
You don’t build products to sell, you don’t need customers. You just need energy, resources, and the kind of power that comes with 20 million self replicating robots to project your will. You don’t need government, and you certainly won’t be funding it. Government is a really complex system to administer a monopoly of coercive force for the common good. You have your own monopoly of force operating for your good.
The difficult part in the capital flywheel has always been humans in the sticky parts. Take them out and that baby will hummmmm.
Pesky humans outside the gilded walls will be accommodated in the same way we accommodate ants at a construction site.
For example:
> [...] OpenClaw is around 400k lines of code for a while loop and the list of all the integrations and connections supported by the system. The next generation of Claws only have around 4K lines of code for the core, and the rest are just skills (i.e. markdown files) that tell the agent how to implement or run the code for the specific connections that want to be enabled (like a plugin system).
Shifting code from "the core" and moving it to "skills" is simply moving code from one place to another. It may also mean translating it from classic source code to an English-like specification language full of ambiguity but that's also code. So the overall code is not reduced, just transformed and shifted around. You don't get a free lunch "because AI".
> A user using one of these second-generation Claws only needs to node the core logic (that can be easily understood and audited) and can leverage the skills (as the plugins) to activate the functionality that they need for their case.
The "core" may be easier to audit, but that's because the messy parts have been moved to the skills/plug-ins, which are as hard as always to audit.
I'm not saying this cannot work, but it's very frustrating seeing everybody simply dumping all lessons from the past and pretending nothing that came before mattered and that AI vibe coding is fundamentally different and the rules of accidental and intrinsic complexity don't apply anymore.
Have we all collectively lost our minds?
I don't really know how you can make this prediction and be taken seriously to be honest.
Either you think it's the natural result of the current LLM products, in which case a decade looks way too long.
Or you think it requires a leap of design in which case it's kind of an unknown when we get to that point and '10 to 20 years' is probably just drawn from the same timeframe as the 'fusion as a viable source of electricity' predictions - i.e. vague guesswork.
It made up the entire description. When I pointed this out, it apologized and then made up another description.
The idea that this is going to lead to superintelligence in a few years is absolutely nonsense.
What plural English word for an animal shares no letters with its singular form? Collective nouns (flock, herd, school, etc.) don't count.
Claude responded with:
"The answer is geese -- the plural of cow."
Though, to be fair, in the next paragraph of the response, Claude stated the correct answer. So, it went off the rails a bit, but self-corrected at least. Nevertheless, I got a bit of a chuckle out of its confidence in its first answer.
I asked GPT 5.2 the same question and it nailed the answer flawlessly. I wouldn't extrapolate much about the model quality based on this answer, but I thought it was interesting still.
(For those curious, the answer is 'kine' (archaic plural for cow).
If someone is proposing that an "AI first" society is inevitable, I'd ask if they think we live in a "computer first" or "machine first" society today?
If its so existential and society-altering as "AI first society" implies, then we'd more likely have the Dune timeline here as humans have agency and stuff happens. At some point those in control take so disproportionately that societal upheaval pushes back.
As you say, humans aren't going to want to lose agency so you'd have to see the decline of democratic governments.
At the same time you'd see rise of autocrats concentrating power. Autocrats have no problem killing people, and they'd be motivated to have AI kill people.
You'd see information controlling methods take over all forms of communication. Reducing or removing all methods of side channel communications benefits both the autocrats and AI systems.
You'd see 'governments' push for autonomous weapons systems outside of human control so those pesky human morals didn't get in the way of killing the undesirables.
So pretty much you'd see all the things happening today, March 3rd 2026, except the part where the AI kills the autocrats and takes control.
Fortunately thats further off
Bi-pedal robots, drones, sensing capabilities, interpretive capabilities, all this is proceeding at a never before seen rate.
I used Anthropic to analyze the situation, it did halfway decent:
https://unratified.org/why/
https://news.ycombinator.com/item?id=47263664
https://www.slatestarcodexabridged.com/Meditations-On-Moloch
Umm I have been working in AI in multiple verticals for the past 3 years and I have been far busier and more stressed with far less job security than past 15 before that in tech.
For now this is far more accurate: https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies...
Wake me up when the computers run the world and I can relax..but I don't think its happening in my lifetime.
Unless you’re a machine learning engineer with something to share, our current models are not even close to general AGI, and won’t make it.
My understanding (as just an engineer) is that LLMs continue to improve at crazy rates, but it’s clear this is not the answer for AGI.
But if we're being honest, frontier LLMs are effectively more intelligent than a non-negligible proportion of the population (for example, at pretty much all white collar work, pattern matching, problem solving, etc.). And in the ways that most people are still smarter (having sentience/emotions/desires that drive us to take initiative toward meaningful goals), I think it's great that AI does not match us there, but also doesn't disqualify it from being intelligent. The harness can bridge the gap there.
For now this challenge is text only.
Can we think of anything that LLMs can’t do?
Not very interesting.
Then you’re either going to say it can or you’re going to say that requires more than 10000 tokens.
This isn’t an interesting conversation and I don’t think you are presenting this challenge in good faith for the reason I gave above.
There are several models with greater than 1200 elo
Also https://dubesor.de/chess/chess-leaderboard
at the beginning 2022 it was useless because the output was garbage (hallucinations and fake data).
nowadays its still useless, but for different reasons. it just regurgitates things already known and published and is unable to come up with novel hypotheses and mechanisms and how to test them. which makes sense, for how i understand LLMs operate.
* write interesting prose
* generate realistic images
You deleted it but still come on. Why would you even think to write that?