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 argues against Gary Marcus's critique of AI scaling, discussing the potential for future AI capabilities and the nature of human intelligence. Longer summary

Scott Alexander responds to Gary Marcus's critique of AI scaling, arguing that current AI limitations don't necessarily prove statistical AI is a dead end. He discusses the scaling hypothesis, compares AI development to human cognitive development, and suggests that 'world-modeling' may emerge from pattern-matching abilities rather than being a distinct, hard-coded function. Alexander also considers the potential capabilities of future AI systems, even if they don't achieve human-like general intelligence. 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