Import AI 437: Co-improving AI; RL dreams; AI labels might be annoying
The labelling-complexity discussion offers a grounded caution for APS policy designers — well-intentioned AI disclosure requirements can generate substantial compliance costs.
Key points
- Import AI's issue 437 covers four distinct topics: co-improving AI, AI labelling policy complexity, SimWorld simulator, and DeepMind's SIMA 2 agent.
- The AI labelling section directly illustrates why simple-sounding AI policy can impose significant compliance burdens on industry.
- Coverage is research-forward and internationally focused; limited direct APS operational relevance but useful as a frontier signal.
Implications for Australian agencies
- Monitor Policy teams working on AI transparency or labelling requirements may want to monitor how EU labelling compliance burdens evolve as a reference case for proportionality design.
- Consider Agencies tracking frontier AI capability could consider the SIMA 2 self-improvement findings as early signal for how autonomous agent capability is advancing beyond controlled game environments.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Weekly digest, 8 December 2025
"Import AI 437: Co-improving AI; RL dreams; AI labels might be annoying"
Source: Import AI – Substack (Jack Clark)
Published: 8 December 2025
URL: https://importai.substack.com/p/import-ai-437-co-improving-ai-rl
Import AI issue 437 covers four topics. First, a Facebook AI Research paper argues for 'co-improving' AI — humans and machines collaboratively developing superintelligence — rather than autonomous self-improvement, framed as safer but acknowledged as aspirational. Second, a discussion of EU product labelling complexity illustrates how apparently simple AI labelling mandates can impose heavy compliance costs, using IKEA's experience as an analogy. Third, researchers from multiple US universities have released SimWorld, an Unreal Engine 5 simulator for training and evaluating AI agents at scale. Fourth, DeepMind details SIMA 2, a Gemini-based game-playing agent that demonstrates strong generalisation and a self-improvement scaffold, with implications for robotics.
Implications for Australian agencies:
- [Monitor] Policy teams working on AI transparency or labelling requirements may want to monitor how EU labelling compliance burdens evolve as a reference case for proportionality design.
- [Consider] Agencies tracking frontier AI capability could consider the SIMA 2 self-improvement findings as early signal for how autonomous agent capability is advancing beyond controlled game environments.
Retrieved from SIMS, 18 July 2026.