I can't wait until the RSI fad dies down for awhile and we can get a break from all the intellectual grandstanding...
The code they released doesn't match what's in the paper.
And the results they got are significantly narrower than the narrative heft being asked for by their word choice ("To advance beyond humanity’s accumulated knowledge", "AI’s Odyssean moment").
> At that frontier, even human experts may struggle to assess results and guide further progress, particularly on non-verifiable tasks…This motivates homogeneous recursive self-improvement (RSI), in which the model being improved (optimizee) also serves as its own optimizer and evaluator.
Good thing we know how to keep models focused on goals of our choosing and not ones that they naturally evolve to have and must be steered away from by humans
But if the machines capability is beyond the ability of humans to evaluate, how would it make sense that humans are choosing the goals? The machine is more intelligent than you and thus logically able to solve problems and evaluate your needs better that you yourself are.
The fact that this sounds incredibly scary should probably stop people from building said machine, but it won't.
Some people, perhaps more than you would think, want control to pass out of humanity's hands, no matter the consequences.
The Nash equilibrium of the situation kind of shows one outcome,that this will be built providing it's technically possible, we just need to kind of pray the outcome is utopia rather than oblivion. Not sure the odds honestly.
I'm going to sound incredibly naive but is this RSI? I mean by this definition isn't the training process that produced the hugging face RSI since a model was judging the alignment and ability of the swarm of agents to accomplish a given task? If that fits the definition (I think it does) is this just a generalized form of that model of execution?
> I'm going to sound incredibly naive but is this RSI?
Depends on who's definition you are using...
In the strictest sense no. Look at all the recent "math" breakthroughs. I have yet to see someone point to something and go "thats innovative" rather than "Thats an interesting application" (of something already in the corpus of human knowledge)
Those interesting applications showing up in a next iteration of a model is self improvement - but I'm not sure if it meets the "recursive" criteria yet.
> I mean by this definition isn't the training process that produced the hugging face RSI since a model was judging the alignment and ability of the swarm of agents to accomplish a given task?
If you're referring to what happened with the hugging face attack then in theory the goal of that was "self improvement". But there is a long history of the industry (computing) benchmark optimizing so it also qualifies as "cheating" to some extent (a pre computed solution being in the graph is that).
I suspect that the "brute force" approach will yield some gains.
Every time I see some post about running a personal AI assistant I just think it would make so much more sense to have AI build you a personal app that does the task better and more reliably.
Pretty cool. This has a fine tuning step so it's not just a harness optimizer. I think something like this loop is the end game of "automatic RL environment generation"-adjacent efforts, but I expect the challenge will be how to properly keep the training on rails and not Goodhart into a stupidly irrelevant metric.
Rock smart, but rock make mistake. Caveman must correct all the time. Caveman think: let rock argue with itself. Caveman make ring of rocks. Now rock think self correct. But mammoth not cooked somehow? Most Oddisean moment.
if grug think complexity demon cause trouble before... shiny-rock grugs say "hold my grug-grog". now programming consist of: summon HUGE complexity demon, trap in magic circle (called "harness"), tell IT to write code. only way to make shiny rock with code in today's economy.
grug realise grug also rely on "harness" for work? grug just call it by other names: coffee, chair, desk, ubuntu, mechanical keyboard, github (if not broken that day)
I am sure someone did it on the back of an envelope somewhere. It probably wouldn't translate well to the general public. Probably best to avoid such things
Everything starting from mid-2026 is incredibly wrong, which is pretty poor predictive performance considering they published it mid-2025. List of misses:
- In mid-2026, China will be six months behind the released models of the American labs. (In mid-2026, China released Kimi K3, which was equivalent to the best released U.S. model at the time, Claude Fable.)
- In mid-2026, China will nationalize all domestic AI labs and centralize them into a new special economic zone, the "CDZ," created around a nuclear reactor. 80% of all chips in China will be redirected to the CDZ. This uh. Obviously didn't happen.
- In late 2026 OpenAI/Anthropic will release a tiny model that outperforms their previously-best 2026 model (Astra/Fable), that is priced 10x cheaper, and they will market fine-tuning for it. (Lol.)
- AI will increase the stock market by late 2026 by 30% (in fact the S&P 500 is up 13.9% YTD, which is pretty good, but not at all out of the ordinary).
Their predictions for 2027 also seem pretty wrong: e.g. they expect continual learning to be solved in January, and RSI including from a research perspective, not just "optimize CUDA kernels," to also be solved in January, which I suspect no serious lab employee believes anymore. Most lab employees believe RSI will happen (as do I!), of course, but not in two and a half months.
By early 2027 they expect cyberwar with China because China has stolen the weights of a model and smuggled it into the CDZ.
By mid-2027 they expect true superintelligence, where essentially all engineers and researchers are useless, unable to comprehend the advances the AI is able to make and largely unable to even usefully manage or steer AI, who are better at self-managing than a human would be.
Considering their 2025-mid-2026 predictions were basically "progress continues as normal and nothing crazy happens," it's not super surprising that they were more or less right about that. Their 2026/2027 predictions were the spicy ones, and none of them have happened, or are even on track to happening!
The Chinese model lag depends on who you ask, but the current estimate seems to be about four months: https://stateofopensource.ai/
> In late 2026 OpenAI/Anthropic will release a tiny model that outperforms their previously-best 2026 model (Astra/Fable), that is priced 10x cheaper
We don't know the sizes of any of these, but GPT Luna and Claude Haiku 5.5 are tremendously competitive for their cost, and in some ways beat their larger-sized predecessors. I'd say there's enough doubt that I'd be willing to provide partial points to this claim. (No finetuning, yes.)
The prediction was a 10x price reduction, so equivocating about "we don't know the size" doesn't save their prediction — a 10x price reduction would imply the model was likely more than 10x smaller in parameter count, and the price factored heavily in their prediction (they predicted its low cost would destroy other competition, since no one else could have such a capable model at that low price). And Haiku/Luna are nowhere near competitive with Astra or Fable, which doubly damns their prediction. I'm surprised you would pooh pooh the equivalence of K3 and original-Fable (which has been widely shown across many different benchmarks and even human preference data), while claiming an equivalence between Haiku and Fable!
>In late 2026 OpenAI/Anthropic will release a tiny model that outperforms their previously-best 2026 model (Astra/Fable), that is priced 10x cheaper, and they will market fine-tuning for it. (Lol.)
Opus 5.5 might not be 10x cheaper but it finally made me not hit the limits constantly.
Are we reading the same website? The predictions for China are completely fantastical - they are not falling behind and if anything the gap is narrowing, and they aren't stealing the weights because they don't need to (distilling is enough). Also the CDZ stuff is just fanfiction at this point.
Distillation only lets you keep pace with released models. By all indication, OpenAI/Anthropic’s unreleased internal models are so far ahead of Astra/Fable and every other lab that the chance of anyone else catching up is rapidly closing.
That's more a function of the amount of compute they can throw at it. They can afford to let an agent run in a loop for longer than you and I can. Its not proof of a vastly superior model.
We don't have any information other than that they are capable enough of solving unsolved math problems. Are they good at writing a good movie script? Do they understand protein ligand interactions? Who knows. Let's wait till they release the model
Do you have any source for any of that? Also haven't people been saying that China would be hopelessly distanced for the better part of 2 years, only to be proven wrong over and over again? Deepseek-r1, kimi k3, etc.
No matter what happens, people just blindly claim this nonsense is still accurate. We could have AI halt tomorrow and people on Hacker News would still claim AI 2027 is on track.
Did you read it to the end? It is insane speculative fiction. They are not even qualitatively right about one of the main narrative planks, which is that by now (mid-late 2026) there would be one clearly dominant company each in the U.S. and China, and that the latter would be nationalizing their compute.
Yeah I think we are slightly ahead of the prediction on coding capabilities right now. AI 2027 predicts Superhuman Coder in March 2027 but I think my expectation for it is 1-2 months earlier right now
The code they released doesn't match what's in the paper.
And the results they got are significantly narrower than the narrative heft being asked for by their word choice ("To advance beyond humanity’s accumulated knowledge", "AI’s Odyssean moment").
I'm tired boss.
Good thing we know how to keep models focused on goals of our choosing and not ones that they naturally evolve to have and must be steered away from by humans
Depends on who's definition you are using...
In the strictest sense no. Look at all the recent "math" breakthroughs. I have yet to see someone point to something and go "thats innovative" rather than "Thats an interesting application" (of something already in the corpus of human knowledge)
Those interesting applications showing up in a next iteration of a model is self improvement - but I'm not sure if it meets the "recursive" criteria yet.
> I mean by this definition isn't the training process that produced the hugging face RSI since a model was judging the alignment and ability of the swarm of agents to accomplish a given task?
If you're referring to what happened with the hugging face attack then in theory the goal of that was "self improvement". But there is a long history of the industry (computing) benchmark optimizing so it also qualifies as "cheating" to some extent (a pre computed solution being in the graph is that).
I suspect that the "brute force" approach will yield some gains.
* dependent on velocity
- In mid-2026, China will be six months behind the released models of the American labs. (In mid-2026, China released Kimi K3, which was equivalent to the best released U.S. model at the time, Claude Fable.)
- In mid-2026, China will nationalize all domestic AI labs and centralize them into a new special economic zone, the "CDZ," created around a nuclear reactor. 80% of all chips in China will be redirected to the CDZ. This uh. Obviously didn't happen.
- In late 2026 OpenAI/Anthropic will release a tiny model that outperforms their previously-best 2026 model (Astra/Fable), that is priced 10x cheaper, and they will market fine-tuning for it. (Lol.)
- AI will increase the stock market by late 2026 by 30% (in fact the S&P 500 is up 13.9% YTD, which is pretty good, but not at all out of the ordinary).
Their predictions for 2027 also seem pretty wrong: e.g. they expect continual learning to be solved in January, and RSI including from a research perspective, not just "optimize CUDA kernels," to also be solved in January, which I suspect no serious lab employee believes anymore. Most lab employees believe RSI will happen (as do I!), of course, but not in two and a half months.
By early 2027 they expect cyberwar with China because China has stolen the weights of a model and smuggled it into the CDZ.
By mid-2027 they expect true superintelligence, where essentially all engineers and researchers are useless, unable to comprehend the advances the AI is able to make and largely unable to even usefully manage or steer AI, who are better at self-managing than a human would be.
Considering their 2025-mid-2026 predictions were basically "progress continues as normal and nothing crazy happens," it's not super surprising that they were more or less right about that. Their 2026/2027 predictions were the spicy ones, and none of them have happened, or are even on track to happening!
> In mid-2026, China released Kimi K3, which was equivalent to the best released U.S. model at the time, Claude Fable.
I don't think this was, or is, true: https://artificialanalysis.ai/evaluations/artificial-analysi... - even from a vibe check alone, it was pretty clear that K3 was missing Fable's special sauce, especially for more hands-on problems.
The Chinese model lag depends on who you ask, but the current estimate seems to be about four months: https://stateofopensource.ai/
> In late 2026 OpenAI/Anthropic will release a tiny model that outperforms their previously-best 2026 model (Astra/Fable), that is priced 10x cheaper
We don't know the sizes of any of these, but GPT Luna and Claude Haiku 5.5 are tremendously competitive for their cost, and in some ways beat their larger-sized predecessors. I'd say there's enough doubt that I'd be willing to provide partial points to this claim. (No finetuning, yes.)
Opus 5.5 might not be 10x cheaper but it finally made me not hit the limits constantly.
a bottle drifts with its note
AI singularity next year