Scott argues that dismissing AI as 'just a next-token predictor' is like dismissing humans as 'just reproduction machines' - both confuse the optimization process that shaped an entity with how that entity actually thinks. Longer summary

Scott argues that dismissing AI as 'just a next-token predictor' confuses levels of optimization. He draws an analogy to humans: just as humans were shaped by evolution optimizing for reproduction but don't think about sex when doing math, AIs were shaped by next-token prediction but don't simply predict tokens when thinking. Scott explains that human brains use predictive coding (predicting next sense-data) to build world-models, while AIs use next-token prediction to build their own world-models. Both processes create complex internal representations - like helical manifolds in 6D space for AIs, or toroidal attractors in human hippocampi - that operate far above the level of simple prediction. The post concludes that both humans and AIs perform 'real thought' using structures created by their respective optimization processes. Shorter summary

Scott Alexander shows how he won his 2022 bet about AI image generation capabilities, tracking the progress from early failures to complete success in 2025, using this to argue against AI skeptics. Longer summary

Scott Alexander describes the resolution of a bet he made in June 2022 about AI image generation capabilities. The bet claimed that by June 2025, AI would master image compositionality and be able to accurately generate specific complex scenes. The post shows the progression of AI image generation from 2022 to 2025, starting with early failures by DALL-E2, through various partial successes with Google Imagen and DALL-E3, and ending with ChatGPT 4o's complete success in May-June 2025. Scott uses this to argue against critics who claimed AI was just a 'stochastic parrot' that couldn't achieve true understanding, though he acknowledges some remaining limitations with very complex prompts. Shorter summary

Scott Alexander bets that AI models will quickly overcome current limitations, based on how GPT-3 improved on GPT-2's shortcomings identified by Gary Marcus. Longer summary

Scott Alexander discusses his prediction that AI models will quickly overcome current limitations, using examples of how GPT-3 improved on GPT-2's shortcomings. He analyzes Gary Marcus's critiques of AI capabilities, showing how many issues Marcus pointed out with GPT-2 and GPT-3 were resolved in subsequent versions. While acknowledging Marcus's expertise, Scott argues that the pattern of AI rapidly improving suggests current flaws will likely be fixed soon, though this doesn't necessarily disprove Marcus's deeper concerns about AI's true intelligence. Shorter summary

Scott Alexander critiques Daron Acemoglu's Washington Post article on AI risks, highlighting flawed logic and unsupported claims about AI's current impacts. Longer summary

Scott Alexander critiques an article by Daron Acemoglu in the Washington Post about AI risks. He identifies the main flaw as Acemoglu's argument that because AI is dangerous now, it can't be dangerous in the future. Scott argues this logic is flawed and that present and future AI risks are not mutually exclusive. He also criticizes Acemoglu's claims about AI's current negative impacts, particularly on employment, as not well-supported by evidence. Scott discusses the challenges of evaluating new technologies' impacts and argues that superintelligent AI poses unique risks different from narrow AI. He concludes by criticizing the tendency of respected figures to dismiss AI risk concerns without proper engagement with the arguments. Shorter summary