High margins create stability but also invite competition. The ideal strategy is to operate with margins low enough to build customer loyalty and a competitive moat, while retaining the *ability* to raise prices when necessary. This balances long-term growth with short-term financial resilience.
Tech giants often initiate custom chip projects not with the primary goal of mass deployment, but to create negotiating power against incumbents like NVIDIA. The threat of a viable alternative is enough to secure better pricing and allocation, making the R&D cost a strategic investment.
Model providers like Anthropic should open-source previous-generation models to establish 'prompt compatibility.' This creates an ecosystem where developers build applications on the free model, making it seamless to later upgrade to the premium, proprietary version as their needs and budgets grow.
Tech giants like Google and Microsoft are spending billions on AI not just for ROI, but because failing to do so means being locked out of future leadership. The motivation is to maintain their 'Mag 7' status, which is an existential necessity rather than a purely economic calculation.
While NVIDIA's CUDA software provides a powerful lock-in for AI training, its advantage is much weaker in the rapidly growing inference market. New platforms are demonstrating that developers can and will adopt alternative software stacks for deployment, challenging the notion of an insurmountable software moat.
New AI models are designed to perform well on available, dominant hardware like NVIDIA's GPUs. This creates a self-reinforcing cycle where the incumbent hardware dictates which model architectures succeed, making it difficult for superior but incompatible chip designs to gain traction.
For a hyperscaler, the main benefit of designing a custom AI chip isn't necessarily superior performance, but gaining control. It allows them to escape the supply allocations dictated by NVIDIA and chart their own course, even if their chip is slightly less performant or more expensive to deploy.
Unlike prior technological inputs like energy, which required machinery to be useful, AI compute can be added directly to the economy to strengthen it. Simply increasing compute improves product quality and expands user access simultaneously, acting as a direct economic force multiplier without traditional bottlenecks.
In five years, NVIDIA may still command over 50% of AI chip revenue while shipping a minority of total chips. Its powerful brand will allow it to charge premium prices that few competitors can match, maintaining financial dominance even as the market diversifies with lower-cost alternatives.
Contrary to fears of mass unemployment, AI will create massive deflationary pressure, making goods and services cheaper. This will allow people to support their lifestyles by working fewer hours and retiring earlier, leading to a labor shortage as new AI-driven industries simultaneously create new jobs.
The importance of speed in AI is deeply psychological. Similar to consumer packaged goods where faster-acting ingredients create higher margins and brand affinity, low-latency AI creates a powerful dopamine cycle. This visceral response builds brand loyalty that slower competitors cannot replicate.
For Europe to compete in AI, it must overcome its aversion to large-scale energy projects. The winning strategy is to co-locate massive compute infrastructure in areas with cheap, abundant energy, like Norwegian wind farms. Without this, Europe risks becoming a 'tourist economy' built on past glories.
A key component of NVIDIA's market dominance is its status as the single largest buyer (a monopsony) for High-Bandwidth Memory (HBM), a critical part of modern GPUs. This control over a finite supply chain resource creates a major bottleneck for any potential competitor, including hyperscalers.