Two economists argue that while AI capabilities are exploding, the jump to 10%+ annual GDP growth in the next 15 years is extremely unlikely—not because of the technology, but because of economic frictions like unautomatable jobs, scarce resources, and measurement gaps. They show the math: doubling wealth once in 15 years only requires 4.7% annual growth, which would already be massive.

• Thinking in wealth levels rather than growth rates reveals how extreme double-digit predictions actually are: 16.6% annual growth means being 100x richer in 30 years, not just "a bit better off"
• While standard growth models can theoretically produce explosive AI-driven growth by eliminating labor as a bottleneck, this requires five unrealistic assumptions to all hold simultaneously—including complete economy-wide automation and zero disruptions
• A more reasonable baseline for the next 10-15 years is 4-5% growth, and the authors have bet money that US per capita real GDP growth stays below 15% annually through 2033

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.

AI model costs and rapid obsolescence are eating into margins—each new generation demands more compute, serves less time, and recoups less revenue. The only way to capture lasting value is by using closed-door models to drive high-value discoveries (like drug design) and own the instruments that generate proprietary data.

• GPT-4.5 only recouped 0.7× its $5.3B cost in five months before being surpassed, versus GPT-3's 3.6× return over 30 months—AI model economics are getting worse, not better, as inference costs now exceed training costs
• Labs are shifting from selling API tokens to using models for proprietary discovery (drugs, materials) whose value outlasts the model itself
• AI drug discovery cuts R&D costs 25-40% and timelines 30-40%, potentially saving $1B per drug against a backdrop of $6.7B average lifetime drug revenue
• Pairing closed models with owned physical instruments (like DeepMind's materials lab or OpenAI/Ginkgo's protein synthesis work) creates proprietary data flywheels competitors can't replicate