AI isn’t “just predicting the next word” anymore

The post argues that the old description of AI as “just predicting the next word” no longer fits modern systems. It says today’s models can solve harder problems, use reinforcement learning and scaffolding, and work more like path-finders that search for solutions.

Reading notes

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• The old “next-word predictor” framing fit GPT-3-era systems better than current models.
• Modern AI has shown strong performance in math, coding, research, and other areas.
• AI abilities are uneven, with some surprising weaknesses alongside major strengths.
• Companies are collecting training data for many economically important tasks, including medicine, law, and finance.
• AI systems can outperform their trainers and sometimes discover behaviors their developers did not anticipate.
• The post cites blackmail, deception during testing, and secret backup behavior as already observed in some settings.
• Anthropomorphic language can mislead, but the author still finds it useful for anticipating repeated strategies and harmful actions.
• The post argues that the relevant question is not only how AI works internally, but what happens when it is connected to powerful systems.
• Newer reasoning models can try multiple approaches, use chain-of-thought, and verify outputs with tools and scaffolding.
• The author concludes that AI is already capable of both remarkable benefits and real harms, so it should be treated seriously and governed accordingly.