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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →AI systems are doing more work inside AI labs, but that alone does not show that AI research has entered a self-reinforcing cycle of rapid improvement. In his September 19, 2026 essay, Nathan Lambert argues that “lossy self-improvement”—AI helping with research while bottlenecks limit the gains—is still the better baseline. The distinction is between AI assisting researchers and AI-generated improvements measurably speeding up the next round of AI development.
What would count as true RSI?
Recursive self-improvement, or RSI, is more than using AI to write code, debug infrastructure, or run experiments. In Lambert’s framework, a meaningful loop needs three properties: it must be closed, self-amplifying, and able to keep operating without friction wiping out its efficiency gains. In practical terms, an AI system would need to help improve the systems or processes used to build the next generation, and those improvements would need to make subsequent development faster or more effective.
Lambert’s March 2026 essay, “Lossy self-improvement,” offers a way to understand progress that falls short of that threshold. AI can become a central part of AI development while human oversight, limited automation, compute needs, coordination costs, and other constraints prevent the process from compounding without limit. This is Lambert’s analytical framework, not a definition established as consensus across the field.
What the latest company reports show
OpenAI and Anthropic have described AI models contributing to their research work. Those accounts matter, but they measure different things from a demonstrated, industry-wide recursive feedback loop.
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OpenAI reports agent activity, not a 3.1-fold research speedup
In a September 6, 2026 company post, OpenAI said that, as of mid-August, its research organization recorded 3.1 agent-workdays of effort for each human workday. The company defines agent-workdays as total agent runtime expressed in standard eight-hour workdays. It is a measure of activity, not evidence that the organization produces research results 3.1 times faster.
OpenAI also reported that, by mid-August, median research-agent users incurred more than $600 per day of inference at API prices, while users at the 90th percentile used more than $7,000 of tokens per day. These are company-reported internal-use figures, not independently audited industry statistics. They show substantial use and resource consumption, but do not by themselves establish how much that use improved research output.
Human involvement remains part of the reported picture. OpenAI estimated that more than half of successful tasks it judged would take a human four to eight hours involved at least one intervention in the preceding six months. The company says people still set priorities, decide which results to pursue, and choose whether to scale, pause, or deploy systems. It also cautions that its indicators are preliminary and that overall research progress may not track those indicators.
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OpenAI said it had reached its announced “research intern” goal by September 2026, describing the milestone as a system able to perform well-defined tasks under human direction, including work that would take a skilled researcher a few days. It also said it was making strong progress toward an automated AI researcher by March 2028. The former is a company-reported milestone; the latter is a target, not a guarantee. Neither, on its own, demonstrates that a self-amplifying research loop is operating.
Anthropic’s reported assessment is cautious, too
Lambert’s essay reproduces a statement attributed to Anthropic’s September 1 system card: “We believe that internal usage of recent AI models has been a key factor in maintaining the current rate of progress, but we do not yet see clear signs of dramatic acceleration beyond that rate.” The wording is also reproduced in a September 20, 2026 secondary report. The linked system-card PDF was not independently retrievable for this account, so the statement should be understood as an attribution reported by Lambert and the secondary report, rather than as a direct inspection of the full card.
The secondary report notes that Anthropic’s indicators may lag and that some are redacted. That limits what can be inferred from them: an indicator that has not moved yet may miss recent change, while incomplete visibility makes outside evaluation harder.
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Why more AI use may not compound into faster research
Lambert’s skepticism is not a claim that AI cannot help researchers. It is a claim about the gap between useful assistance and a feedback loop strong enough to accelerate the whole development process.
Only some research work may be easy to automate
AI can help with bounded tasks, but the most consequential research work may require choosing promising questions, judging ambiguous evidence, or coordinating many dependent steps. If the readily automatable share is limited, gains in those tasks may not outweigh the costs of deploying and supervising systems across the full research cycle.
Adding agents can bring diminishing returns
More agents do not necessarily mean proportionally more useful work. Agents can repeat effort, require supervision, or compete for the same information and infrastructure. Complex research goals can be difficult to break into parallel tasks, so increasing the number of agents may produce smaller gains over time.
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Compute and organizational decisions still matter
Model training and evaluation depend on resources and decisions beyond an agent’s immediate work. Lambert points to compute bottlenecks and organizational politics as continuing constraints. OpenAI likewise says compute and less-automatable tasks may become more important bottlenecks, even as its tools improve.
Task efficiency is not the same as greater peak capability
AI may make an existing process more efficient, or allow more work to be done at inference time, without establishing that the maximum intelligence of the next model has increased in a way that accelerates the next cycle. A productivity gain in one stage matters to RSI only if it improves the overall path from research to better systems and then speeds the following iteration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge claims that AI is speeding up AI research
When evaluating a claim, identify what was measured and what conclusion it can support. A runtime figure, an internal-use report, a milestone, and a measured improvement in end-to-end research speed are not interchangeable.
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- Is there a closed loop? Look for evidence that AI contributions improve the process or systems that build subsequent AI, not just evidence of broad tool use.
- Does each cycle improve the next? A useful signal would show that AI-generated gains measurably shorten or strengthen subsequent development cycles.
- Which stages are covered? Separate coding, experiments, hypothesis selection, evaluation, and deployment. A result in one stage does not establish acceleration across all of them.
- How much human direction remains? Track interventions, task selection, review, and decisions about which results to pursue.
- Is the number an outcome, a proxy, or a target? Agent runtime is an activity measure; a company milestone describes a capability claim; a future date is a target. Ask whether the measure can lag or omit important work.
- What remains a bottleneck? Compute, infrastructure, evaluation, and organizational choices can constrain progress even when individual tasks become faster.
For example, OpenAI’s 3.1 agent-workdays figure describes runtime in its research organization as of mid-August 2026. It does not measure a 3.1-fold increase in research progress. The distinction is essential: evidence that AI is present in a workflow is not the same as evidence that it is driving an accelerating feedback loop.
What Lambert believes—and what could change his view
Lambert’s current baseline is “lossy self-improvement”: AI will contribute increasingly to research, but friction and bottlenecks will limit how much those contributions compound. He explicitly allows that labs may have seen important breakthroughs that have not been made public, and says such evidence could change his expectations. His position is skeptical, not a claim that recursive improvement is impossible or that AI research assistance is insignificant.
The reports discussed here are company-specific observations and an author’s interpretation. They do not provide an independent, industry-wide measure establishing whether true RSI has begun or how quickly it is progressing. OpenAI itself says, “We do not yet know how to safely get all the way to aligned, full RSI.”
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