Scott examines the rise of AI superforecasters that now match or exceed top human forecasters, explores how they work and their current performance, and analyzes implications for decision-making, prediction markets, and the future role of AI opinions. Longer summary

Scott discusses the emergence of AI superforecasters that are now matching or slightly exceeding top human forecasters in prediction accuracy. He describes how these AI systems work (using scaffolding around frontier models like GPT/Claude), demonstrates their use with examples from FutureSearch and Preseen, and analyzes their performance on platforms like Metaculus where they're competing in tournaments against humans. The post explores both near-term implications (AI forecasters being easier to access than human superforecasters, potentially influencing policy and business decisions) and longer-term possibilities (AI forecasters serving as an 'opinion layer' for AI systems, transformation of prediction markets into AI-vs-AI competitions). Scott argues these developments could be genuinely beneficial, giving people access to superhuman forecasting ability just as AI threatens other aspects of society. Shorter summary

Scott Alexander's April 2026 links roundup covers diverse topics including Venn diagram complexity, flag desecration laws, AI developments, political analysis, scientific studies, and various cultural curiosities. Longer summary

This monthly links post compiles interesting articles, studies, and observations from across the internet in April 2026. Major themes include AI progress and policy (including discussions of AI alignment, capabilities, and regulation), political developments (Trump administration actions, election analysis), scientific findings (from evolutionary psychology to medical treatments), and various cultural oddities. Scott provides brief commentary on each link while noting that he hasn't independently verified all claims and that commenters typically find errors in a few links per post. Shorter summary

Scott reviews the state of prediction markets after explosive growth, finding that most volume is degenerate sports gambling rather than useful forecasting, and proposes both technical solutions and two potential futures for the field. Longer summary

Scott Alexander examines the current state of prediction markets after their recent explosion in popularity, noting that while volume has grown from millions to billions per month, most of it comes from sports betting rather than the epistemic improvement he'd hoped for. He explores several problems: markets aren't asking the most important questions society needs answered; resolution criteria disputes ("rulescucking") create controversy; and there are concerns about insider trading and manipulation. He proposes solutions including a novel approach to conditional markets and suggests two paths forward: either creating user-generated, subjectively-resolved real-money markets (the "Siskind Cube"), or accepting that prediction markets' main value may be as training data for AI forecasters that could make the markets themselves obsolete by late 2026. Shorter summary