What is the Impact of Cloud Computing on AI Startups?

Twenty years ago, building an AI startup meant buying servers, setting up a data center, and hiring infrastructure engineers — a capital-intensive process that took months and millions before you could train a single model. Cloud computing changed the economics completely. Today, a founder with a credit card can rent an eight-GPU cluster for an afternoon, train a model, then shut it down — paying only for the hours used. This fundamentally altered who can build AI companies. Hyperscalers (AWS, GCP, Azure) provide general-purpose cloud infrastructure plus managed AI services. Specialized GPU clouds (Lambda Labs, Modal, Replicate, Together AI) focus exclusively on affordable GPU access for AI workloads. Foundation model providers (OpenAI, Anthropic, Mistral) offer API access to cutting-edge models, letting application-layer startups skip training entirely. Serverless inference platforms (Cerebrium, Banana, Beam) handle autoscaling so startups only pay for actual inference usage. The impact extends beyond cost. Cloud infrastructure lets startups experiment rapidly, scale usage up and down dynamically, and match infrastructure spend to revenue. A company with unexpected viral growth can serve a million users tomorrow. A company exploring a new idea can spin up prototypes for $50. The downside: AI compute bills at scale can be massive, and lock-in to specific cloud providers creates real risk. But the net effect is clear — cloud computing is why the AI startup boom exists at all.