they are lying to you about AI development

Quick Overview

The narrative that AI progress is either wildly overhyped or completely stalled is false; the reality, supported by experts like Scott Aronson and Julian Schitweiser, is that AI like GPT-5 is accelerating scientific discovery by providing key technical steps in complex proofs and doubling task completion abilities every four to seven months, fundamentally shifting economic value toward human judgment and opportunity spotting rather than mere implementation skills.

Key Points: Scott Aronson, a computer science professor, utilized GPT-5 to provide a key technical step in a proof for a paper on QMA (quantum Merlin Arthur), accelerating a process that he believed he and a colleague could have done in weeks down to half an hour of back-and-forth iteration. Aronson emphasizes that GPT-5 did not produce a solution humans could never find, but it helped him get 'unstuck' by suggesting an insight that filled in gaps, stating, 'The point is, anyone engaged in mathematical research knows that an AI that can merely fill in the insights that should have been obvious to you is a really huge freaking deal.' Julian Schitweiser, citing exponential growth patterns similar to the early COVID-19 pandemic, notes that AI capabilities are doubling the length of tasks they can complete every seven months, or every four months when tracking only recent 2024 releases. Meter Research charts show that models like GPT-4o and Claude Opus are rapidly approaching human expert performance, with Claude Opus 4.1 achieving 47.6% win/tie rate against industry experts on a benchmark where 50% is parity. Julian Schitweiser projects that models will be able to autonomously work for full days by mid-2026 and frequently outperform experts on many tasks by the end of 2027. Research discussed by Carlos E. Perez suggests that while computers historically widened wage gaps, current AI iterations are benefiting struggling workers more than experts by compensating for skill differences in 'implementation' tasks, effectively leveling the playing field in Phase One. The long-term economic outlook (Phase Two) suggests that as implementation becomes free, the skill of 'judgment' or 'opportunity judgment'—knowing which designs or applications matter—becomes the primary differentiator and the most valuable skill, making better AI less likely to cause full automation because automated systems lack flexible judgment.