Both OpenAI and Anthropic have accelerated AI-assisted research and engineering, with Anthropic reporting in September 2026 that its Claude models now lead 26% of internal AI R&D tasks under human supervision and collaborate on over 90%, up sharply from early 2026 levels, while writing more than 80% of merged production code. OpenAI researchers recently confirmed recursive self-improvement as the top training priority for models like GPT-6, noting agents now handle data auditing at 100x human efficiency and that the company has met its automated research intern milestone. Neither lab has announced full autonomous successor-building capabilities, however, and executives at both have publicly urged caution, voluntary slowdowns, and safety coordination to manage risks from rapid capability gains. Upcoming internal benchmarks and any model releases could shift timelines for a formal announcement.
基於Polymarket數據的AI實驗性摘要。這不是交易建議,也不影響該市場的結算方式。 · 更新於2026 年 10 月 31 日
10%
2026 年 12 月 31 日
16%
$483 交易量
2026 年 10 月 31 日
10%
2026 年 12 月 31 日
16%
Recursive self-improvement means a capability, as described in the company's announcement, in which the company's AI autonomously conducts the research and development needed to materially improve the general capabilities of its most capable or near-frontier AI systems, or to produce a more capable successor, with the improved system able to drive further such improvements. Humans may set high-level goals and provide resources, oversight, and approval, but must not supply the substantive research choices or the technical solution. Improvements that are toy, narrow, sandboxed, proof-of-concept, or limited to prompts, skills, or benchmark scores will not qualify.
A qualifying announcement must describe the capability as currently achieved in an official capacity. Statements that AI is assisting, accelerating, or writing code for the company's research, statements of future plans or expectations, speculation, interviewer characterizations, leaks, model outputs, and third-party claims will not qualify. A public release, a named model, a technical demonstration, and independent verification are not required.
The primary resolution source for this market will be official information from OpenAI and Anthropic or their official representatives; however, a consensus of credible reporting may also be used.
市場開放時間: Sep 18, 2026, 7:59 PM ET
Recursive self-improvement means a capability, as described in the company's announcement, in which the company's AI autonomously conducts the research and development needed to materially improve the general capabilities of its most capable or near-frontier AI systems, or to produce a more capable successor, with the improved system able to drive further such improvements. Humans may set high-level goals and provide resources, oversight, and approval, but must not supply the substantive research choices or the technical solution. Improvements that are toy, narrow, sandboxed, proof-of-concept, or limited to prompts, skills, or benchmark scores will not qualify.
A qualifying announcement must describe the capability as currently achieved in an official capacity. Statements that AI is assisting, accelerating, or writing code for the company's research, statements of future plans or expectations, speculation, interviewer characterizations, leaks, model outputs, and third-party claims will not qualify. A public release, a named model, a technical demonstration, and independent verification are not required.
The primary resolution source for this market will be official information from OpenAI and Anthropic or their official representatives; however, a consensus of credible reporting may also be used.
Both OpenAI and Anthropic have accelerated AI-assisted research and engineering, with Anthropic reporting in September 2026 that its Claude models now lead 26% of internal AI R&D tasks under human supervision and collaborate on over 90%, up sharply from early 2026 levels, while writing more than 80% of merged production code. OpenAI researchers recently confirmed recursive self-improvement as the top training priority for models like GPT-6, noting agents now handle data auditing at 100x human efficiency and that the company has met its automated research intern milestone. Neither lab has announced full autonomous successor-building capabilities, however, and executives at both have publicly urged caution, voluntary slowdowns, and safety coordination to manage risks from rapid capability gains. Upcoming internal benchmarks and any model releases could shift timelines for a formal announcement.
基於Polymarket數據的AI實驗性摘要。這不是交易建議,也不影響該市場的結算方式。 · 更新於


警惕外部連結哦。
警惕外部連結哦。
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