Why could the same AI rebuild a 16,000-line program without seeing its code, yet fail to customize a library for METR? The answer shapes what you hand it.

The common expectation is that AI advancements in one difficult area, like mathematics, will translate to broader competence. However, the release of OpenAI’s mathematical research manuscripts, generated by an internal model, highlights the possibility of ‘narrow superintelligence’ where AI excels in specific tasks but may lack generalized judgment. This uneven development could lead to a future with increasingly astonishing successes alongside harder-to-explain failures, making the term ‘smarter’ less informative.

• AI’s progress might not be uniform, with capabilities becoming ‘spikier’ and gaps widening.
• Extraordinary performance in one domain, like mathematics, does not automatically imply equal progress in others, such as engineering judgment.
• OpenAI’s internal model producing mathematical research with machine-checked proofs exemplifies narrow superintelligence.
• The inference from mathematical breakthroughs to broad competence is a key assumption in public AI narratives that needs closer examination.
• Uneven AI development could result in increasingly astonishing successes and harder-to-reconcile failures.
• Training feedback concentration, where useful feedback is plentiful, can accelerate progress in specific abilities faster than others.
• A system that notices requirement conflicts with project purpose without prompting would be evidence against uneven development.
  https://foxvector.com/articles/0c3ef238-81ff-466f-bee6-50dc87b6b7fb