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Harsh Truth About Building With AI... • Your “AI startup” isn’t AI 90% just wrap an API call to OpenAI/Anthropic/Claude and call it innovation. You’re a feature, not a company. • Models change, your product breaks Base models update silently. Prompts that worked yesterday may fail tomorrow. If your moat is a prompt, you don’t have a moat. • Infra bills scale faster than revenue Running LLMs is expensive. Every “free trial” user burns cash. Margins get ugly before growth kicks in. • Data > model Everyone can call the same GPT API. What makes you unique is proprietary, structured, painful-to-collect data. Most skip this hard part. • UX beats intelligence Smarter answers don’t win. Seamless workflows do. Users care more about how well your tool fits their job than which model powers it. • Regulation is a time bomb AI compliance (privacy, copyright, bias) will catch up. Most AI startups don’t even log decisions for audit — they’ll pay later. • Distribution still eats tech Even the best AI app dies if no one knows it exists. OpenAI could clone you overnight and ship to millions. Moat = distribution, not model. • AI ≠ instant scale AI tools feel magical in demos but often break in messy, real-world use cases. Scaling = endless human-in-the-loop patchwork. • Talent scarcity Real ML engineers who can fine-tune, optimize, and deploy models are rare. Most startups rely on generalists faking depth. • Most “AI startups” are just waiting rooms A huge % are features that Big Tech hasn’t gotten around to building yet. Once they do, you’re done. 👉 The harsh truth: AI isn’t a cheat code. It’s just another tool. What still matters? Distribution, data, execution, and customer obsession.