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{"success":true,"source":{"id":"68dfaad8-7ec0-419b-b8a1-091b9568277d","url":"https://www.anthropic.com/institute/recursive-self-improvement","title":"When AI builds itself","source_type":"report"},"origin":"stored","total_chars":34128,"offset":24000,"content":" that improves over time, and it may become the most important skill for any organization.\n\nAI systems themselves become capable of full recursive self-improvement, and begin building their successors. If technical trends in advancing capabilities continue, and AI systems are able to develop the capabilities inherent to transformative human ingenuity, then it is plausible that AI systems could design and refine themselves.\n\nIn this world, the pace of progress in AI development becomes determined entirely by the availability of compute (or the speed of discovering various efficiencies in algorithmic training or inference) for AI systems. Humans play a substantially diminished role in their development, likely moving most of our effort towards oversight, validation, and verification of an expanding “virtual lab” run by AI systems. We expect that systems capable of automated AI research and development would have skills that would transfer to the rest of science, allowing them to begin to revolutionize other fields.\n\nHow the alignment problem gets solved—or not—in this future is something we are least certain about. Models could prove to be sufficiently aligned and capable enough of research taste that they discover and implement novel solutions that we have not yet reached. They could also be sufficiently wise to halt development if not. Alternatively, the rare occurrences of misalignment present in today’s models could compound as the models build their successors, growing more frequent but less understood until we lose control of them. It’s possible that we can’t build, integrate, and verify the tools that we’d need to understand which trendline we are actually on.\n\nWe do not have good intuitions for what this world would look like, because our economy is currently driven by humans and human-built tools. By its nature, a world driven by fast recursive self-improvement could become dominated by the self-improving model as its capabilities fully eclipse those of humans and the model proliferates across the broader economy. It is difficult to predict what the economy looks like if human labor stops being competitive.\n\nEven if model development became fully automated and recursive, we can’t predict what that would mean for most humans’ daily lives. Amdahl’s law applies here as well. Recursive intelligence could lead to achieving many of the benefits outlined in Machines of Loving Grace , quickly in some domains. We expect that embodied intelligence (i.e., robotics) might quickly follow recursive intelligence, and follow a similar path of increasing returns at decreasing cost. More powerful intelligence might help us build things in the physical world more quickly, run more productive clinical trials of lifesaving drugs, and develop novel forms of coordination.\n\nBut achieving recursive improvement alone does not suggest an immediate change in how industrial production occurs, societies organize, or markets function. More intelligence can’t learn what a drug does over decades of use, can’t hold elections sooner than a constitution dictates, and can’t turn a stranger into an old friend in a weekend. For most people, the felt pace of this future will still be set by the bottlenecks, even if the laboratory upstream runs at the speed of compute. That collision, where recursive intelligence building itself ever faster meets the world of humans, relationships, and governance, is another part of this future we can’t predict.\n\nWhat should we do?\n\nIf it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe. Without a global coordination mechanism, companies and governments will have to make difficult decisions about safety while under competitive and geopolitical pressures.\n\nWe believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology. The Anthropic Institute will conduct research —in collaboration with many others—and take actions to help build the systems that a credible slowdown or pause would require. These systems would enable frontier AI developers to verify that others globally have actually stopped or slowed, and that a bad actor could not use the auspices of a coordinated slowdown to jump ahead in secret. If such systems existed, we expect that we would slow down or temporarily pause, if other developers at or near the frontier also did so in a verifiable manner.\n\nA meaningful slowdown or pause would require multiple well-resourced labs at or near the frontier, in multiple countries, agreeing to stop under the same conditions. It would also require that each can verify that the others have actually stopped. Due to the unique characteristics of AI systems, the detectability (a lower standard than verifiability) element of this arms control problem is much more challenging than with other technologies. Training runs are far easier to conceal than missile silos, their inputs are general-purpose, and the incentive to defect quietly is enormous, because whoever continues while others pause could inherit the lead. A credible pause also has to specify what triggers it, what lifts it, and who adjudicates.\n\nNone of this is necessarily impossible in principle—the world has built verification regimes for other complex technologies (e.g., the Intermediate-Range Nuclear Forces Treaty)—but those regimes took decades to build both the infrastructure and the trust. We don’t have that long. A unilateral pause by one lab, by contrast, is achievable immediately, but accomplishes much less: it would change who the front-runner is, but it would not create the wider deliberative process that is currently missing.\n\nIn the coming months, we will organize conversations where policymakers, researchers, civil society, and other AI companies can help answer some of the questions this piece raises, especially around full recursive self-improvement and how to create better options for coordination and deliberation. We’ll publish what comes out of it. The window to investigate the questions together is here, and people outside AI companies should be involved in this deliberation.\n\nMarina Favaro and Jack Clark co-authored this piece, with editorial support from Santi Ruiz. Shan Carter, Romello Goodman, and Nikki Makagiansar created the visuals from data collected by Brian Calvert and Jun Shern Chan. Daniel Freeman, Jim Baker, Max Young, Sarah Pollack, Francesco Mosconi, Holden Karnofsky, Andy Jones, Kevin Troy, Chloe Lubinski, Anton Korinek, Meg Tong, Andrew Ho, Dan Altman, Drake Thomas, Jack Shen, Sasha de Marigny, and Avital Balwit provided feedback.\n\nUpdate 9/18/2026\n\nHow to read this: Session success is determined by a Claude judge; a session is deemed successful if the Claude Code agent clearly succeeded at the user’s tasks without requiring corrections. Changes in workloads can lead to short-term fluctuations in success rates.\n\nFootnotes\n\nMETR’s key measure tells you the time horizon over which AI systems can be 50% reliable at a basket of tasks, though the trendline looks the same at 80% reliability.\n\nEspecially as they shift toward more open-ended formats and more difficult tasks (e.g., Olympiad-level mathematics), benchmarks often saturate below 100% due to errors in the question and answer sets like ambiguous problem statements and unsolvable questions.\n\nAnthropic leadership have publicly estimated that 90% or more of our code is written by Claude, including scripts and experimental code. Our >80% figure measures the share of lines merged to production that can be attributed to Claude. This is a more conservative measurement in two ways: our attribution pipeline has gaps, and the lines not attributed to Claude include auto-generated code and other artifacts that were not hand-written by humans either.\n\nThis surge in code production is straining the infrastructure everyone shares. GitHub—the platform most of the world’s software is built on—saw roughly one billion code commits in all of 2025; by mid-2026 it saw 275 million a week, on pace for roughly 14 billion over the year. The company’s COO has said that it is “pushing incredibly hard” on capacity just to keep up.\n\nAdditional details on the methodology of this survey are discussed in section 2.3.5 of the Claude Opus 4.7 System Card .\n\nMany respondents may not have thought carefully about how to account for various biases or subtleties in the question definition, and recent research by METR shows that developer estimates of AI productivity uplift can be overestimated.\n\nHow large the speedup gets depends heavily on how much room for improvement the starting code leaves, and it should not be read as a real-world training speedup. So the absolute multiple is not the figure to anchor on here. What is more informative is the like-for-like comparison that this experimental setup makes possible, both across models (~3x to ~52x over the past year) and against a skilled human (~4x in four to eight hours on the same task).\n\nAs a check on judge bias, we ran the same test on a separate set of 127 moments where the human’s next move was already strong (as opposed to the original set, where the human’s direction had room for improvement). There, the models’ suggestions were judged better only about 20% of the time.\n\n* Quotes from Anthropic employees throughout this article are drawn from internal discussions and used with permission. They reflect individual views as of May 2026, not official company positions.\n\nResearch\n\nPolicy\n\nCommitments\n\nCompany\n\nProducts\n\nModels\n\nSolutions\n\nHelp and security\n\nTerms and policies\n\nOur mission\n\nAnthropic is a Public Benefit Corporation. Our mission is the responsible development and maintenance of advanced AI for the long-term benefit of humanity.\n\nLog in\n\nClaude.ai\n\nClaude console\n\n© 2026 Anthropic PBC","truncated":false,"next_offset":null,"note":"This is the end of the stored text."}