How the Battle for Control Could Crush AI’s Promise
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Bibliographic details
- Authors: CARL BENEDIKT FREY
- Published: September 3, 2025
Thesis and central argument
- A shift toward centralization and concentration could snuff out technology’s productive potential.
- True breakthroughs emerge from decentralized exploration and widening the arena of experimentation; centralized scale and concentration risk stagnation.
- Sustaining a policy regime that safeguards competitive arenas, not the fortunes of particular firms, is essential for AI to deliver productivity gains.
Historical evidence and analogies
- Soviet Union
- Despite early technological successes (Sputnik, Yuri Gagarin), the USSR collapsed as the computer revolution took off.
- Zelenograd’s centrally controlled innovation contrasted with Silicon Valley’s decentralized experimentation; institutionally inhospitable environments limited exploration.
- Friedrich Hayek: central planners lacked essential local knowledge, could manage standardized operations but floundered during technological uncertainty.
- Japan
- Japanese autoworkers were 17 percent more productive than their US counterparts by 1980.
- Japan focused on process improvements: Edwin Mansfield found roughly two-thirds of Japanese R&D targeted process improvements.
- Japanese coordination and keiretsu structures favored incremental refinement over frontier product innovation, contributing to a stall when the center of innovation shifted to software.
- Western Europe
- Postwar coordinated capitalism supported catch-up growth but became an obstacle when the economy needed to pivot to frontier innovation.
- France’s indicative planning and Italy’s state-owned enterprises were effective for incremental, predictable progress but ill-suited to rapid, uncertain technological change.
- Southern European nations experienced prolonged stagnation during the computer revolution, often described as “two lost decades.”
- United States in the computer era
- US antitrust policy (rooted in the 1890 Sherman Antitrust Act) pried open markets (e.g., unbundling IBM, breaking up AT&T), enabling entrepreneurial dynamism that powered the internet and software-led gains.
- Regions organized around fierce competition (Silicon Valley) outperformed more hierarchical clusters (Route 128).
Frontier innovation and AI-specific findings
- Scaling is insufficient for frontier breakthroughs; exploration matters more than perfecting formalized systems.
- Large language models (LLMs)
- Grew 10,000-fold in scale between 2019 and 2024 yet still scored only about 5 percent on the ARC reasoning benchmark.
- Leaner approaches such as program search have topped 20 percent on the same benchmark.
- Newer in-context learning methods are “racing ahead.”
- Embodied knowledge and limitations of centralized models
- Language models trained on the entire internet still lack sensorimotor experience comparable to any four-year-old.
- Hans Moravec’s observation: what is effortless for humans (e.g., walking a trail) remains hard for machines, and vice versa.
- Historical examples illustrate statistical-consensus failure modes: an LLM trained in 1633 would uphold geocentrism; given 19th century literature, it would deny human flight.
- Demis Hassabis (Google DeepMind) concedes true artificial general intelligence may need “several more innovations.”
Risks from centralization and concentration today
- China
- Recentralization: licenses, credit, and contracts favor politically reliable conglomerates; antitrust law is wielded selectively; anti-corruption campaigns make loyalty prerequisite for survival.
- Provincial experimentation has withered; officials chase crude indicators (e.g., patent counts), flooding registries with low-value filings.
- Patronage and loyalty are displacing competence, eroding the state’s capacity to nurture frontier-level innovation and pushing growth toward slower, less-innovation-driven paths.
- Private or foreign-backed firms remain the most dynamic sectors; state-owned enterprises lag.
- Firms lacking strong political connections (example: DeepSeek) tend to be most innovative but remain vulnerable without robust legal protections.
- United States and incumbent concentration
- Labor mobility is hampered by a web of noncompete clauses, curbing tacit-knowledge flows and discouraging new firm creation.
- Incumbent lobbying hard-codes regulatory advantages (patent extensions, sector-specific licensing hurdles) reducing competitive dynamism.
- Market concentration in AI-related inputs and platforms:
- Microsoft’s alliance with OpenAI controls about 70 percent of the commercial LLM market.
- Nvidia provides about 92 percent of the specialized graphics-processing units (GPUs) used to train these models.
- Alphabet, Amazon, and Meta, alongside Microsoft and Nvidia, have been acquiring stakes in promising AI start-ups, consolidating influence.
Key statistics and exact figures drawn from the text
- LLM scale increase: 10,000-fold between 2019 and 2024.
- LLM performance: about 5 percent on the ARC reasoning benchmark.
- Program search performance: topped 20 percent (on ARC).
- Human sensorimotor benchmark: any four-year-old (qualitative comparison).
- Historical training examples: 1633; 19th century (qualitative).
- Japanese autoworker productivity advantage: 17 percent by 1980.
- US antitrust origin: 1890 Sherman Antitrust Act (historical reference).
- Market concentration:
- Microsoft/OpenAI alliance: about 70 percent of the commercial LLM market.
- Nvidia share of specialized GPUs: about 92 percent.
Policy implications and recommendations
- Protect and expand competitive arenas rather than protecting incumbent firms:
- Prevent regulatory capture that hard-codes advantages for incumbents (patent extensions, licensing hurdles).
- Limit practices that impede labor mobility (e.g., overly broad noncompete clauses) to preserve tacit-knowledge flows essential for startup-driven innovation.
- Lower barriers to entry and widen experimentation:
- Reduce centralization that channels licenses, credit, and contracts toward politically reliable conglomerates.
- Encourage decentralized experimentation and startup formation to explore uncharted technological frontiers.
- Preserve institutional flexibility:
- Avoid overreliance on crude performance indicators (e.g., patent counts) that encourage low-value filings and gaming.
- Design policies that reward competence and experimentation rather than patronage and loyalty.
- Safeguard legal protections for innovators:
- Ensure legal frameworks protect firms from sudden political shifts that could divert resources toward building political alliances instead of innovation.
Content adapted from "How the Battle for Control Could Crush AI’s Promise" by Carl Benedikt Frey, F&D Magazine, September 2025.
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