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I love to model and simulate. As the dead economy theory[0] (discussion [1]) was submitted here, I decided to simulate it. It was really hard to figure out a path "good for the humanity", in the sense of a balanced system, not a winner take all situation, etc.

I think this is the reason why you have the tendency to propose some freeze-all policies, full control or similar. If you want to find the equilibrium, you need to accept that it will be a controlled equilibrium, most likely on a saddle point, with underlying process changing all the time, requiring fast changes in regulations. Our democratic systems, laws, etc. are not built to do that, they are built on the idea of intrinsic stability of our world where incremental improvements do not need cutting through what was decided before.

[0]: https://www.owenmcgrann.com/p/the-dead-economy-theory

[1]: https://news.ycombinator.com/item?id=48324712

Saddle point is a nice way to put it.

One counterpoint is that the "labor as TAM" argument is far larger than it needs to be. Only a fraction of it needs to be captured to justify all the capex and make 5 new companies displace FAANG, and this does not have to translate to unemployment to succeed.

https://jodavaho.io/posts/ai-jobpocolypse.html

The difference in the unemployment vs efficient employment model is mostly user driven adoption vs company mandated adoption, or centaurs vs reverse centaurs.

https://pluralistic.net/2026/07/02/canonization/#operate-ite...

Thanks for the links, I had missed those. Also:

> Our democratic systems, laws, etc. are not built to do that, they are built on the idea of intrinsic stability of our world where incremental improvements do not need cutting through what was decided before.

Without totally derailing the thread, this is also obviously why climate and biosphere collapse is not (and likely will continue not) to be addressed, e.g. Timothy Morton's Hyperobjects

> to me this article feels like an exercise in creative writing rather

because it is. Previously: https://news.ycombinator.com/item?id=43571851 / https://ai-2027.com/

Hasn't the AI 2027 "creative writing exercise" held up not-so-bad thus far?

This incredibly critical part of "early 2026" doesn't seem to hold up very well:

  Overall, they are making algorithmic progress 50% faster than they would without AI assistants

Yes the AI labs are using LLM-assisted development, but I am not aware of LLM-assisted research into better deep learning algorithms. "50% faster algorithmic progress" is a strange thing to predict about neural networks: historically it seems like algorithmic progress is very infrequent, and tends to be disruptive across the field. Likewise with the underlying algorithms in Codex and Claude Code - they aren't that sophisticated in the first place, and vibe coding an implementation 50% fasfer doesn't count as AI-assisted algorithmic progress. Maybe they mean cost?

And ironically they didn't predict the actual 2026 reality that Mythos sucks as an agent but commanded global attention as a cybersecurity tool.

Also this just seems childish and clearly hasn't really panned out:

  But China is falling behind on AI algorithms due to their weaker models. The Chinese intelligence agencies—among the best in the world—double down on their plans to steal OpenBrain’s weights.

China has the exact same "algorithms"! The "algorithms" are on arXiv and the specific architectures are typically public. What China lacks is compute, and the US labs had a big head start on training data + RLHF.

The article hinges on AI automating R&D and finding something fundamentally more powerful and reliable than the current transformer LLMs. But that hasn't panned out at all. What has panned out is better scaffolding around running the LLMs in an iterative loop.

It actually seems to me AI 2027 holds up badly, unless your only takeaway is "AI gets better."

To me, this feels like a last ditch effort to revive the AGI narrative to reject the coming and current commoditisation of these models, contrary to all current evidence. https://artificialanalysis.ai/

How is commoditisation of models incompatible with AGI?

The "AGI narrative" is distinct from the existence of AGI.

Most of the discussion around AGI is highly speculative. I am not saying AGI could not exist, and it is a term that has historically been loosely defined. Decades of coming science and research will tell.

If we can solve 99% of the world's problems with current non-AGI models then nobody besides a select few will care about AGI

Most of the world's problems are fundamentally social, political, and religious. These cannot be solved with current non-AGI models. Probably not with AGI either.

They will if the remainder use AGI to empower themselves at everybody's expense.

> How is commoditisation of models incompatible with AGI?

A recursively self-improving AI has strong first-mover effects. That isn’t fundamentally incompatible with commoditisation if there is literally only one path to super-intelligence and you can have AIs at different rings on that ladder co-existing. (Not technically commoditised at that point. There are still different rings. But close enough.)

But the existence of commoditised AI implies model selection isn’t a huge deal, which in turn implies the models are about the same, which strongly implies there is no recursive self-improvement. Depending on your definition, you may still have AGI. But you don’t have superintelligence.

> But the existence of commoditised AI implies model selection isn’t a huge deal, which in turn implies the models are about the same, which strongly implies there is no recursive self-improvement. Depending on your definition, you may still have AGI. But you don’t have superintelligence.

This is only true at a given AI capability level, no? e.g., if AI at the GLM-5.2 level is commoditized, all that suggests is that there's no recursive self-improvement easily possible at the capability level of GLM-5.2. (And with the harnesses for it that exist so far, etc etc.)

If I observe commoditization of a given tier of model capabilities at a given point in time, this seems to say little about what's possible with models six months later, or models that are undergoing proprietary deployments at that very moment inside the major labs, or even models that are notionally available for public use but have had recursive self-improvement adjacent capabilities intentionally nerfed (e.g., Fable).

(I might be misinterpreting your comment tbc - if you mean observing commoditization implies there is no existing, ambient superintelligence at the moment of that observation, then I don't disagree.)

> if you mean observing commoditization implies there is no existing, ambient superintelligence at the moment of that observation, then I don't disagree

This is a better way to put it, thank you. More precisely, I'd say commoditisation implies there is no existing self-improving AI on the market.

The moment someone gets exponential self-improvement, model fungibility breaks and the first mover wins. This is the Bostrom singularity the rationalists flip out about.

Yes. Anyone who doesn't acknowledge the efficiency difference between pretraining vs RL and assume that since we've run out of data for the former, we have to do the latter, is not making a serious attempt at modelling the future:

https://www.tobyord.com/writing/inefficiency-of-reinforcemen...

This is similar to that other exponential, which happened with CPUs - we ran out of true geometric scaling in the mid 2000s, and everything else supporting Moore's Law has been cleverness that arrived in the nick of time, supported by a bit of marketing, and very optimizable benchmarks, far from guaranteed gains coming from making a single physical metric better.

>Because AI represents the distillation of the very best human knowledge and expertise. AI is compression of human capabilities, the very best ones.

I'm confused if this is satire, sarcasm, or genuine belief. If this was the case, then AI companies should absolutely remove the "it may make mistakes", because doing mistakes would imply that "the very best human knowledge and expertise" is what actually fails, and not the AI.

With that being said, I'll still urge people to visit a professional therapist for health problems and I generally still trust human knowledge workers for critical scenarios. I will reconsider your claim when chatGPT can effectively play Yu-Gi-Oh! (or at the very least respond with the correct rules appropriately), which is a significantly lower stakes scenario than betting your entire company on its aptitude.

My framing may have been confusing there. “distillation of the very best human knowledge of expertise”. Distillation is different from outright capability or reliability. It is not directly adjacent.

For anything health related all AI models show high levels of anchoring bias. I would not use it as a confidant, and be skeptical of claims. Even so, human doctors are also fallible and prone to cognitive bias.

I think the obfuscation is because human intelligence has been projected onto AI model capability. AI models only have a limited dimension of human intelligence, and in some axes orthogonal, and when I say distillation I refer to this.

> Because AI represents the distillation of the very best human knowledge and expertise.

You say it like it's a fact, but in reality everyone sees the phenomenon of AI slop.

P.S. Information search and retrieval if the best and most direct way to use LLMs.

An even more cynical take than me!

I agree with your last statement.

> everyone sees the phenomenon of AI slop

Just purely organic YouTube Comments circa early '20s alone surely outslop any "AI" by a giant margin.

Everyone sees the markers, and it's a hot topic. There are maybe a thousand from-scratch trained models, and just few mainstream ones produce most of human-targeted content. In today's world, no surprise everyone knows the common patterns of those. That sloppy landscape is not just load-bearing em-dashes — it's a humble testament to their reinforcement learning.

Humans produce tons of texts, with all sorts of nonsense in it, without thinking it through. Our slop is just a lot more diverse. And mostly just spoken out loud.

> P.S. Information search and retrieval if the best and most direct way to use LLMs.

Yes, but not directly, if they don't know something they tend to hallucinate like mad, even today. YMMV, but in my experience they work best as actual "cheap" reasoning for building queries and checking out search engine results. Even if they misinterpret some result, more and more results will still steer it towards correct conclusions and it can point at some results that relate well enough to be useful.