تحذير أنثروبيك: كلود يكتب 80% من شفرته البرمجية الآن، وتسارع التحسين الذاتي للذكاء الاصطناعي يبلغ ذروته

This is not just an efficiency gain; it represents a structural shift in the development pipeline. The consequence is that Anthropic engineers are now shipping eight times more code per quarter compared to the 2021–2025 baseline . The bottleneck in software creation is shifting from writing and testing to higher-level goal-setting, architecture, and judgment.

Benchmark performance is saturating at an unprecedented pace

The report provides a timeline of breakneck acceleration across several standard measures of engineering and research competence :

• SWE-bench (real-world software engineering): In late 2023, models scored around 2%. By the time of the report, Claude was scoring 93.9%, effectively saturating a benchmark designed to test human-level bug-fixing on real GitHub issues .
• CORE-Bench (reproducing published research): AI success rates jumped from around 20% in 2024 to saturating the benchmark just fifteen months later .
• AI training code optimization: This internal benchmark, which asks a model to optimize code that trains a small language model, saw a staggering leap. In May 2025, the mean speedup was 2.9 times. By April 2026, with the unreleased Claude Mythos Preview, it was 52 times . A skilled human working alone would need four to eight hours to achieve a 4x improvement; the current generation of Claude models exceeds 50x .

The clock speed of autonomy is doubling every four months

Perhaps the most impactful metric for predicting recursive self-improvement is the duration of autonomous tasks. Research from METR tracks how long an AI can work independently at a 50% success rate. This task horizon has expanded from roughly 30 seconds in 2022 to 12 hours with Claude Opus 4.6 by April 2026, a 1,440-fold increase . Claude Mythos Preview can work for at least 16 hours, which is noted as being near the upper limit of what METR can currently measure . The doubling rate for this horizon has accelerated from every seven months to every four months .

A third of AI-assisted work was previously impractical

The quantitative data on code and benchmarks is coupled with internal polling on human productivity. Analysis of 200,000 internal Claude transcripts and 53 in-depth interviews found that 27% of AI-assisted tasks were jobs employees simply would not have attempted without AI, because the time cost previously made them impractical . This is not automation of existing work but an expansion of what is possible to attempt at all. In a separate November 2025 internal study, employees reported using Claude in 60% of their work and estimated a 50% productivity boost, up from 20% the prior year .

Slowing down or containing the risk: a growing rift

Anthropic's position is explicit. The company states, "We are not there yet, and recursive self-improvement is not inevitable. But it could come sooner than most institutions are prepared for" . It argues that a global ability to pause or slow AI development would "likely be a good thing" and is directly urging other labs to consider it .

The same week, OpenAI's actions painted a contrasting picture. On June 3, OpenAI published a public policy agenda calling for a federal frontier AI safety framework with mandatory model evaluations and whistleblower protections, but also with a critical clause: the preemption of state-level safety laws . It explicitly asks the federal AI safety institute CAISI to prioritize monitoring progress toward recursive self-improvement . Simultaneously, OpenAI is staffing up for this exact risk, creating a "Researcher, Recursive Self-Improvement Preparedness" role within its safety team with a posted compensation of $295,000 to $445,000 . The job is framed as a loss-of-control containment problem, a "tasteful and strategic" effort to mitigate risks that "might exist in the future, but might not exist now" .

Both labs see the same wave approaching, but Anthropic is urging the fleet to slow down, while OpenAI is hiring lifeguards and arguing against any single state issuing a swimming ban.

The three steps toward a self-building AI

The blog post outlines a three-phase trajectory toward the ultimate risk :

1. Human-directed acceleration (now): AI assists human engineers, writing most code, but humans set goals and exercise judgment.
2. Autonomous research and engineering: AI can execute well-specified experiments end-to-end without human guidance on methods; a gap remains on high-level goal-setting and judgment.
3. Full recursive self-improvement ("closing the loop"): AI autonomously designs, builds, and trains its own successor with minimal or no human oversight.

Anthropic co-founder Jack Clark has separately estimated a 60% probability that this "loop" closes by the end of 2028 . The internal data in the June 4th post provides the factual underpinning for why that estimate is not a distant hypothetical but a projection from a curve that is already visibly bending upward.