Smaller, cheaper language models running locally on regular computers now match frontier AI models on most tasks while costing 50-85% less to run, threatening the business model of OpenAI and Anthropic just as they face massive compute contracts coming due in 2027-2028.

• Chinese AI models are 4-6x cheaper than US alternatives (Kimi K3 at $12 vs Claude at $49) while delivering nearly identical performance, and have already captured 60% of global market share on OpenRouter.
• Small language models achieve 88.7% accuracy on real-world queries and now match frontier models on 81.2% of typical mixed workloads, with performance improving 5.3x between 2023-2025 as local hardware accelerators advance.
• OpenAI and Anthropic face $852B in compute payments due in 2027-2028 from take-or-pay contracts, but need to extract $400B+ annually from enterprise customers to break even—a hard sell when companies can run equivalent models in-house for a fraction of the cost.

This post announces the top three winners from a 600+ submission essay contest on major AI challenges. Jassi Pannu outlines a state-scale plan to end airborne pathogens, Ege Erdil advises growth-focused policies for countries outside the AI supply chain, and Michael Li compares AI labs’ economics to Hong Kong’s MTR model. Each full essay follows the brief winner descriptions.

• Far-UVC lighting infrastructure could cut seasonal flu mortality 60% and add $1T+ to global GDP for a $40-60B/10-year investment, while also blunting engineered pandemic risk.
• Erdil argues countries outside the AI supply chain should pursue standard growth policies (property rights, low capital taxes, light regulation) rather than geopolitical maneuvering to stay economically relevant.
• Li compares AI labs to Hong Kong's MTR, suggesting labs could offset massive compute costs by owning complementary "adjacent" assets rather than relying solely on core product revenue.

The article explores startups like Polsia and Thomas that use swarms of AI agents to launch and run businesses with almost no human employees. It shows how most of these AI-created ventures will fail but a small percentage will succeed, mirroring Shopify’s model, and argues investors are banking on that 5% of winners.

• Polsia claims ~$10M annualized revenue and 7,600 customers within five months using AI agents instead of employees, despite a 2.0 Trustpilot score suggesting "zero employees" is partly marketing spin
• YC-backed startups (Thomas and others) are building AI systems whose product is literally spinning up more companies automatically, in insurance, DTC brands, consulting, and beyond
• The model mirrors Shopify's economics: most AI-spawned ventures will fail or stall, but investors are betting that if just 5% become real winners, that's enough to justify the whole platform
• AI has made the cheap, mechanical startup grunt work (paperwork, landing pages, outreach) free and instant, so the real differentiator left is human obsession, insight into customer problems, and toughness—the 5% that agents haven't cracked