Import AI 455: AI systems are about to start building themselves.
A credible AI researcher's public argument that autonomous AI self-improvement is likely within two years demands strategic attention from agencies with AI governance mandates.
Key points
- Jack Clark argues there is a 60%+ chance of end-to-end automated AI R&D occurring by 2028.
- Benchmark evidence cited spans coding, scientific replication, kernel optimisation, and alignment research automation.
- Directly APS-relevant operational detail is thin; this is a strategic-horizon framing piece, not actionable guidance.
Implications for Australian agencies
- Monitor AI strategy and governance teams may want to monitor Clark's framing alongside METR task-horizon data as a leading indicator of when AI capability assumptions in current policy frameworks may need revision.
- Consider Agencies developing AI risk assessments could consider whether existing frameworks account for recursive self-improvement scenarios and the alignment degradation risks Clark describes.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Weekly digest, 4 May 2026
"Import AI 455: AI systems are about to start building themselves."
Source: Import AI – Substack (Jack Clark)
Published: 4 May 2026
URL: https://importai.substack.com/p/import-ai-455-automating-ai-research
Jack Clark, co-founder of Anthropic, publishes a detailed essay arguing that all technical prerequisites for end-to-end automated AI R&D are now in place, and assigns a 60%+ probability to a fully human-free AI development loop existing by 2028. He marshals benchmark data across coding (SWE-Bench near-saturation), task time horizons (METR data showing AI autonomy extending to ~12 hours), scientific reproducibility, ML engineering, and a proof-of-concept automated alignment research result from Anthropic. Clark identifies alignment under recursive self-improvement, inequality of AI access, economic disruption, and compounding alignment error as key risks. The piece is a strategic-horizon argument rather than a technical specification, but its author and the volume of evidence cited make it notable for AI governance and strategy readers.
Implications for Australian agencies:
- [Monitor] AI strategy and governance teams may want to monitor Clark's framing alongside METR task-horizon data as a leading indicator of when AI capability assumptions in current policy frameworks may need revision.
- [Consider] Agencies developing AI risk assessments could consider whether existing frameworks account for recursive self-improvement scenarios and the alignment degradation risks Clark describes.
Retrieved from SIMS, 18 July 2026.