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Prediction market on manifold. In my blog post on what I learned in a year of building a coding agent, I list several forecasts, which I've added to this market. Read my post: https://jamesgrugett.com/p/what-i-learned-building-an-ai-coding I considered assigning my own percentage forecasts to each of them in the article, but it seemed a little cluttered. I'll add them here: 80% - The multi-agent paradigm will win 60% - “Live learning” will be standard 70% - Coding agents will flip the initiative 80% - Coding agents will close the loop 50% - Recursively improving coding agents will succeed in the market 50% - xAI will gain a sizable lead in model quality 60% - The specific model will not matter as much as today; the network of agents will be important See also: https://manifold.markets/JamesGrugett/will-ai-agents-be-able-to-code-a-sm Update 2025-07-05 (PST) (AI summary of creator comment): For the answer 'xAI will gain a sizable lead in model quality', the creator has specified that model quality will be judged based on performance on benchmarks. Update 2025-09-25 (PST) (AI summary of creator comment): - For "Recursively improving coding agents will succeed in the market": the agent must be able to spend lots of time autonomously improving itself beyond direct human instructions; 100% self-modification is not required (human involvement is allowed); merely following human-directed tasks does not qualify. The agent must autonomously find and tackle issues to improve itself. Human involvement is allowed, but a human orchestrating each change with the agent as a tool does not qualify. Autonomous self-improvement must be an important mode of improvement, beyond direct human instructions. Update 2025-09-25 (PST) (AI summary of creator comment): - For “Live learning” will be standard: "Live learning" means agents learn across runs without users explicitly telling them what to learn, akin to continual learning. Example: an agent gets better in a codebase by learning from previous failures, not just by following new user instructions. Must be more than simple memory/config edits (e.g., just updating agents.md or a memory file is not sufficient). The agent’s autonomous learning across runs should be an important contributor to its good results.
24h Volume: $900. Liquidity: $10,000. Resolves: 7/4/2026.