AI Policy / March 27, 2026
Five Companies Control AI.
The Government Just Said That’s Fine.
NVIDIA controls hardware. OpenAI, Anthropic, Google control frontier models. Microsoft controls distribution. The White House AI Framework addresses copyright and child safety. It does not address concentration. Here is the power map and why the silence matters.
Sources: White House National AI Policy Framework March 2026; FTC AI market structure report 2025; Epoch AI compute concentration analysis; March 2026.
Five companies control the AI infrastructure that every other company, government, and researcher depends on. OpenAI, Google DeepMind, Anthropic, Meta, and Microsoft build the frontier models. NVIDIA builds the hardware they all run on. AWS, Azure, and Google Cloud provide the compute infrastructure. The U.S. government acknowledged this concentration in its 2026 AI framework and did nothing about it. The White House framework calls for “maintaining open access to AI resources” and “preventing anti-competitive practices” without proposing structural remedies for a market that is already concentrated beyond the point where voluntary commitments change anything.
The concentration is not accidental. It is the result of three compounding advantages: capital requirements (training a frontier model costs $100M to $1B+), data advantages (the companies with the most users generate the most training data), and talent concentration (the researchers who know how to train frontier models number in the low thousands globally, and most of them work for these five companies or their close affiliates). These advantages compound: more capital enables better models, better models attract more users, more users generate more data, more data enables better models, and the cycle repeats. New entrants face the compounding disadvantage of starting without any of these assets.
The Hardware Monoculture
NVIDIA controls approximately 80 to 90% of the AI training and inference GPU market. Every major AI lab trains on NVIDIA hardware (H100, H200, B100, B200 series). The software ecosystem (CUDA, cuDNN, TensorRT, NCCL) is proprietary to NVIDIA. Migrating away from NVIDIA requires rewriting the entire software stack, which no company can afford to do while simultaneously competing in the model market. This is the classic lock-in pattern: the hardware vendor’s software ecosystem becomes the industry standard, and switching costs exceed the cost of staying.
AMD’s MI300X and Intel’s Gaudi series are technically competitive on some benchmarks but lack the software ecosystem maturity. Google’s TPUs are used internally and by Google Cloud customers but are not available for purchase. Amazon’s Trainium chips are AWS-exclusive. The alternative hardware exists. The alternative software ecosystem does not. Until an open-source CUDA alternative achieves feature parity (AMD’s ROCm is progressing but still behind), NVIDIA’s position is structurally secure. The AI industry’s dependence on a single hardware vendor is a systemic risk that no one has a plan to mitigate.
The Cloud Compute Bottleneck
Three companies (AWS, Azure, Google Cloud) control the cloud infrastructure that most AI applications run on. Together they hold approximately 65% of the global cloud market. For AI workloads specifically, the concentration is higher because GPU availability is constrained and the hyperscalers have the purchasing power to secure allocation from NVIDIA ahead of smaller providers. An enterprise that wants to deploy AI at scale has three realistic options for GPU compute. If any of the three experiences an outage, a pricing change, or a policy change, a significant portion of the world’s AI infrastructure is affected.
The cloud providers are also model providers (Azure hosts OpenAI’s models, Google Cloud hosts Gemini, AWS hosts Anthropic’s Claude through Amazon Bedrock). This vertical integration means the same company that provides your compute also competes with you in the model market. Microsoft invests $13 billion in OpenAI and hosts its models on Azure. Google builds Gemini and hosts it on Google Cloud. Amazon invests $4 billion in Anthropic and hosts Claude on AWS. The platform providers have a structural information advantage: they can see which models their customers use, how they use them, and where the demand is growing, and they can use that information to compete in the model layer.
What Concentration Risk Looks Like
Open Source as Partial Mitigation
The open-weight model movement (Meta’s Llama, Alibaba’s Qwen, Mistral, DeepSeek) partially mitigates model-layer concentration. If OpenAI raises prices or changes terms, enterprises can migrate to an open-weight alternative. But open-weight models still require NVIDIA hardware and cloud compute to run. The model layer is diversifying. The hardware and infrastructure layers are not. Open-weight models reduce dependence on model providers. They do not reduce dependence on NVIDIA or the hyperscalers.
The structural solution would require either: breaking up the vertical integration (preventing cloud providers from also being model providers), creating alternative hardware ecosystems (public investment in open-source GPU alternatives), or mandating interoperability standards (so applications can move between cloud providers and hardware vendors without rewriting). None of these are on any government’s agenda. The DOJ antitrust case against Google addresses search market concentration, not AI infrastructure concentration. No equivalent case targets AI-specific market structure.
Why This Matters for Everyone Building with AI
If you build an AI application in 2026, you depend on at least two of the five companies for your core infrastructure. Your model comes from OpenAI, Anthropic, or Google (or an open-weight model that runs on NVIDIA hardware). Your compute comes from AWS, Azure, or Google Cloud. Your GPU was manufactured by NVIDIA using a TSMC process. At every layer of the stack, you are a customer of a company that could change its pricing, terms, or availability at any time with limited alternatives available.
The practical response for builders: multi-model architecture (so you can switch between model providers), multi-cloud deployment (so you are not locked to one compute provider), and investment in open-weight model capabilities (so you have a fallback if API terms change). These strategies reduce concentration risk at the application level. They do not eliminate it at the infrastructure level. As long as NVIDIA controls the hardware and three hyperscalers control the compute, the AI industry’s supply chain has single points of failure that no application-level architecture can fully mitigate.
The government said this is fine. The market structure says it is a risk. The question is whether the risk materializes before anyone acts on it. History suggests it will. Concentration risk in technology supply chains has produced crises before (the 2020 semiconductor shortage, the 2021 cloud outages, the ongoing TSMC geopolitical risk). The AI supply chain is more concentrated than any of those. The only question is timing.
Sources: White House AI Framework (2026); NVIDIA market share data (Mercury Research, Jon Peddie Research); AWS/Azure/Google Cloud market share (alignment Research Group); OpenAI/Microsoft investment terms; Amazon/Anthropic investment terms; DOJ v. Google antitrust ruling (2024); TSMC fabrication data; OpenSecrets (AI lobbying expenditures); Gartner AI spending projections.