Anthropic co-founder explains why there's a 60%+ chance of AI systems autonomously building their successors by 2029 and the consequences of automated AI R&D
The first step towards recursive self improvement — Welcome to Import AI, a newsletter about AI research.
Import AIJack Clark
Context & Ripple Effects
This sits at the start of a cluster of coverage on frontier labs trying to reduce human involvement in model development. Subsequent reporting describes OpenAI and Anthropic pursuing recursive self-improvement, while Anthropic later said Claude was authoring a large share of code merged into its codebase.
The story also extends Anthropic's existing public emphasis on listening to concerns and pursuing tougher AI-safety rules. It makes automated research progress a central part of that safety and governance argument, rather than treating it only as a productivity tool.
First-order effects
Anthropic's warning raises the operational importance of evaluating and governing AI systems used in research and model-development workflows, not just customer-facing deployments.
The forecast puts recursive self-improvement on the agenda for investors, policymakers, and enterprise users as a material frontier-lab capability scenario, while remaining a stated probability rather than a demonstrated outcome.
Second-order effects
OpenAI, Anthropic, and other frontier labs face stronger incentives to show both research-automation progress and credible controls around it; Anthropic's later disclosure about Claude-written code gives that competition a concrete internal-workflow dimension.
Debates over AI safety laws are likely to focus more on development-stage safeguards, because systems that contribute to their successors could compress the interval between research iterations.
Third-order effects
If AI takes on a growing share of AI R&D, advantage may concentrate further in organizations that combine frontier models, compute, evaluation infrastructure, and the ability to deploy those systems safely in their own development loops.
The key governance boundary could shift from regulating finished models alone toward oversight of automated research processes; how quickly that happens depends on whether labs can substantiate autonomous capabilities beyond coding-assistance metrics.
The trend: Frontier AI is moving from automating downstream knowledge work toward automating parts of the model-development process itself, making recursive self-improvement both a competitive objective and a governance concern.
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.
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
@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]
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
“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
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.
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 […
@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…
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.
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
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/...
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
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
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
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]
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]
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…
“... 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]