Discussion

• @jackclarksf Jack Clark on x

  I've spent the past few weeks reading 100s of public data sources about AI development. I now believe that recursive self-improvement has a 60% chance of happening by the end of 2028. In other words, AI systems might soon be capable of building themselves.

• @binarybits Timothy B. Lee on x

  I sincerely don't understand what people mean when they say this. On the one hand, every AI researcher is already using Claude Code (or its competitors) to help them develop new architectures. OTOH, AI models do not have bodies so they can't build data centers

• @jackclarksf Jack Clark on x

  @karinanguyen There's also MLE-Bench, which is ecologically valid (tasks come from real kaggle competitions) and involves building a very diverse set of ML apps to solve specific problems. The same progress shows up here. [image]

• @danwilliamsphil Dan Williams on x

  Very interesting/worrying/exciting. Some half-baked thoughts: It's certainly true that AI is rapidly advancing when it comes to many aspects of AI R&D. But I'm more sceptical because: - We don't really understand the psychology and sociology of research “creativity” (a

• @_nathancalvin Nathan Calvin on x

  “a likely chance (60%+) that no-human involved AI R&D - an AI system powerful enough that it could plausibly autonomously build it's own successor - happens by the end of 2028” Not always appreciated that Jack and other folks at the labs really truly sincerely believe this

• @emollick Ethan Mollick on x

  Co-founder of Anthropic, interesting that he refers to public sources when he is also obviously privy to lots of internal sources that he cannot discuss. I assume he sees the same thing at Anthropic.

• @ahall_research Andy Hall on x

  As AI systems scale and become self-improving they'll become increasingly autonomous, and we'll have to figure out how to govern them. Ironically, the most logical solution will involve a separation of powers among AI systems, with independent “AI auditors” checking the actions […

• @jackclarksf Jack Clark on x

  @karinanguyen My whole experience doing this project was finding endless “up and to the right” graphs at all resolutions of AI R&D, from the well known (e.g., SWE-Bench) to more niche (like those above). It's a fractal, but at all the resolutions you see the same trend of meaning…

• @mariushobbhahn Marius Hobbhahn on x

  I, unfortunately, have similarly short timelines. Note that this means clearly superhuman AI capabilities since probably no **single** human today could build a frontier model start-to-finish by themselves anymore.

• @andymasley Andy Masley on x

  For people who are extremely skeptical of AI being able to automate AI R&D, what specifically is the magic spark that humans have that AI will never have that's relevant to AI research? If you imagine the typical AI researcher today, what knowledge or abilities are in their brain

• @petermccrory Peter McCrory on x

  A nice overview from @jackclarkSF The most important economic question is when & to what extent AI automates innovation, the engine of long-run growth/prosperity. And automating AI R&D is a path to rapid innovation elsewhere and in turn faster growth: https://www.nber.org/...

• @sjgadler Steven Adler on x

  There is a mounting economic pressure coming, to make people obsolete in the training of stronger AI systems. At that point, boy had we better hope that the AI ‘wants’ what we want, and that we've built oversight systems that don't buckle under the pressure

• @ilex_ulmus @ilex_ulmus on x

  Why is he acting like he didn't know this internally from Anthropic, as the company has been saying for like a year now?

• @jason @jason on x

  Feels material and realistic

• @krishnanrohit Rohit on x

  One of Jack's best essays. I think he's definitely right in the specific sense, AI *will* make AI researchers more productive, probably 10x more than its doing today, and fairly likely to be wrong in the more important sense, where “attention is all you need” level discoveries

• @sudoraohacker Arun Rao on x

  I think there's a lot more uncertainty about AI recursive self improvement happening than what Jack suggests. There are 3-4 good benchmarks on generic AI research that suggest progress, but the ultimate gating factor is whether software will be enough for AGI or if we need

• @jackclarksf Jack Clark on x

  Another nice example is PostTrainBench from @karinanguyen et al, where you need to autonomously have powerful models (e.g, Opus 4.6) finetune weaker open weight models to improve perf on some benchmarks. This is an important subset of the overall task of AI R&D. [image]

• @jackclarksf Jack Clark on x

  A lot of the conclusion comes from assembling a mosaic out of many distinct data sources. Some examples - progress on CORE-Bench, where the task is implementing other research papers (huge amounts of AI research comes from interpreting and replicating results) [image]

• @jackclarksf Jack Clark on x

  Major essay in Import AI 455, just published online. [image]

• @alexolegimas Alex Imas on x

  In today's newsletter @jackclarkSF predicted that full no-human-involved AI R&D will happen by the end of 2028. Much of the pushback against RSI has been that AI has not yet shown the capacity to generate fully new ideas. This is the key part from Jack's post: the majority of [im…

• r/singularity r on reddit

  Anthropic co-founder Jack Clark says AI is nearing the point where it can automate AI research

• @zavaindar Zavain Dar on x

  “... because the marginal value of spending more on AI versus human labor will be constantly growing as a consequence of the sustained capability expansion of the AI systems ...” h/t @jackclarkSF [image]