While high capex is often seen as a negative, for giants like Alphabet and Microsoft, it functions as a powerful moat in the AI race. The sheer scale of spending—tens of billions annually—is something most companies cannot afford, effectively limiting the field of viable competitors.
Amadeus reinvests heavily in R&D, with a spend equivalent to its #3 competitor's total revenue. This creates a widening technology and product gap that smaller players cannot bridge, fortifying its market leadership and making it increasingly difficult for others to keep up.
The AI race has been a prisoner's dilemma where companies spend massively, fearing competitors will pull ahead. As the cost of next-gen systems like Blackwell and Rubin becomes astronomical, the sheer economics will force a shift. Decision-making will be dominated by ROI calculations rather than the existential dread of slowing down.
In the fast-evolving AI space, traditional moats are less relevant. The new defensibility comes from momentum—a combination of rapid product shipment velocity and effective distribution. Teams that can build and distribute faster than competitors will win, as the underlying technology layer is constantly shifting.
Unlike mobile or internet shifts that created openings for startups, AI is an "accelerating technology." Large companies can integrate it quickly, closing the competitive window for new entrants much faster than in previous platform shifts. The moat is no longer product execution but customer insight.
The enduring moat in the AI stack lies in what is hardest to replicate. Since building foundation models is significantly more difficult than building applications on top of them, the model layer is inherently more defensible and will naturally capture more value over time.
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.
As the current low-cost producer of AI tokens via its custom TPUs, Google's rational strategy is to operate at low or even negative margins. This "sucks the economic oxygen out of the AI ecosystem," making it difficult for capital-dependent competitors to justify their high costs and raise new funding rounds.
Google can dedicate nearly all its resources to AI product development because its core business handles infrastructure and funding. In contrast, OpenAI must constantly focus on fundraising and infrastructure build-out. This mirrors the dynamic where a focused Facebook outmaneuvered a distracted MySpace, highlighting a critical incumbent advantage.
While OpenAI leads in AI buzz, Google's true advantage is its established ecosystem of Chrome, Search, Android, and Cloud. Newcomers like OpenAI aspire to build this integrated powerhouse, but Google already is one, making its business far more resilient even if its own AI stumbles.
While competitors like OpenAI must buy GPUs from NVIDIA, Google trains its frontier AI models (like Gemini) on its own custom Tensor Processing Units (TPUs). This vertical integration gives Google a significant, often overlooked, strategic advantage in cost, efficiency, and long-term innovation in the AI race.