The AI industry is not a winner-take-all market. Instead, it's a dynamic "leapfrogging" race where competitors like OpenAI, Google, and Anthropic constantly surpass each other with new models. This prevents a single monopoly and encourages specialization, with different models excelling in areas like coding or current events.
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When evaluating AI startups, don't just consider the current product landscape. Instead, visualize the future state of giants like OpenAI as multi-trillion dollar companies. Their "sphere of influence" will be vast. The best opportunities are "second-order" companies operating in niches these giants are unlikely to touch.
The current AI market is like hot, moving fat in a skillet—fluid and competitive. The key strategic question is predicting when "the heat comes off and then everything's fixed." This "congealing" moment will lock in market leaders and make disruption much harder, marking the end of the wild early phase.
OpenAI, the initial leader in generative AI, is now on the defensive as competitors like Google and Anthropic copy and improve upon its core features. This race demonstrates that being first offers no lasting moat; in fact, it provides a roadmap for followers to surpass the leader, creating a first-mover disadvantage.
Michael Burry's comparison of OpenAI to Netscape is apt regarding market share erosion due to intense competition. However, the AI market is expanding exponentially. Unlike the browser market of the 90s, OpenAI can lose market share percentage yet still see massive absolute revenue and usage growth.
Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.
AI favors incumbents more than startups. While everyone builds on similar models, true network effects come from proprietary data and consumer distribution, both of which incumbents own. Startups are left with narrow problems, but high-quality incumbents are moving fast enough to capture these opportunities.
Initially, even OpenAI believed a single, ultimate 'model to rule them all' would emerge. This thinking has completely changed to favor a proliferation of specialized models, creating a healthier, less winner-take-all ecosystem where different models serve different needs.
Despite its early dominance, OpenAI's internal "Code Red" in response to competitors like Google's Gemini and Anthropic demonstrates a critical business lesson. An early market lead is not a guarantee of long-term success, especially in a rapidly evolving field like artificial intelligence.
Conventional venture capital wisdom of 'winner-take-all' may not apply to AI applications. The market is expanding so rapidly that it can sustain multiple, fast-growing, highly valuable companies, each capturing a significant niche. For VCs, this means huge returns don't necessarily require backing a monopoly.
Don't underestimate the size of AI opportunities. Verticals like "AI for code" or "AI for legal" are not niche markets that will be dominated by a few players. They are entire new industries that will support dozens of large, successful companies, much like the broader software industry.