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Google VP warns that two types of AI startups may not survive
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Publication date: 28 February 2026，推荐阅读safew官方下载获取更多信息
Super Bowl LX was a two-score game with less than five minutes remaining. New England had the ball on the Seahawks’ 44-yard line and – after reaching the end zone in the fourth quarter, finally – that familiar sense of possibility. But that quickly vaporized when Devon Witherspoon knifed in on a corner blitz and jarred the ball loose from the Patriots quarterback, Drake Maye, mid-throw. Uchenna Nwosu snatched it in stride and rumbled 45 yards to the end zone, sealing Seattle’s 29‑13 victory.，详情可参考搜狗输入法2026
I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.