AI

I expect rapid progress but not towards general superintelligence

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 14/10/2026, 12:18:24 UTC.

Briefings

The article argues that AI progress will accelerate rapidly due to massive infrastructure improvements, specifically parallelized AI-assisted language modeling, rather than fundamental breakthroughs in model capabilities. This shift will make experimentation easier and potentially trigger a Jevons paradox where demand increases as efficiency improves, leading to more diverse scientific discoveries. However, the author explicitly states that this acceleration will not result in general superintelligence or make models dramatically different in nature.

Key points

• AI progress will accelerate rapidly due to massive infrastructure improvements, specifically parallelized AI-assisted language modeling, making experimentation easier.
• The article predicts a Jevons paradox where demand for AI agents will increase as inference efficiency improves, potentially leading to more diverse scientific discoveries.

Context

The article discusses a shift from research to engineering in AI, where infrastructure improvements like parallelized language modeling will drive rapid progress. It notes that while models may become superhuman at distributed GPU engineering tasks, they will not fundamentally change in nature. The author highlights the potential for AI to accelerate scientific discovery by finding cross-sub-field connections, contrasting this with the current low-quality RL environments.

Limitations

The evidence is limited to the author's personal observations and predictions about infrastructure acceleration and potential scientific impacts. It does not provide specific dates for the acceleration, nor does it offer quantitative data on the speed of progress or the exact nature of future discoveries beyond general scientific discovery.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 24/10/2026, 11:09:00 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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