“AI bubble” is the headline everyone can write. It sounds smart, it sells, and it sidesteps the harder question: what value is actually getting built right now? Yes, there’s froth. There are decks with more screenshots than product. Some of this will go to zero. That’s not profound; that’s the cost of a fast frontier.Nvidia and OpenAI announced a plan for up to $100B in investment/compute capacity, targeting at least 10 gigawatts of Nvidia systems, with initial delivery windows beginning in 2026. Multiple outlets confirm the contours: chips plus non-controlling equity, large staged tranches, and a data-center buildout measured in gigawatts.Compute is the bottleneck. Whoever secures chips, power, and datacenter throughput owns pacing for the next model cycles. ReutersInfra capital is arriving in waves. This follows earlier mega-initiatives like Stargate (OpenAI + partners) and other hyperscaler alignments. AxiosWhat “bubble” talk missesMarkets don’t just mint overvalued startups—they stress-test the entire stack. The sharper questions investors should be asking now:Infra math: Do training/inference costs, latency targets, power constraints, and delivery timelines line up? (A 10-GW roadmap is an energy story as much as it is a chips story.)Workflow depth: Is the “wow” just a demo—or does it lock into daily use?Moats that matter: Proprietary data, embedded distribution, switching costs, regulatory leverage.Unit economics with teeth: Can usage scale as costs fall—or does every new user inflate burn?The dot-com era torched paper wealth and left behind the internet’s spine. Crypto’s manic cycles produced primitives that now move real value at internet speed. AI will rhyme: the noise will fade; the compounding infrastructure, methods, and embedded workflows won’t. That’s how frontiers mature.The edge now is fast, disciplined filtration.Run disciplined AI diligence. Pull comparable outcomes, cohort behavior, inference budgets, and failure modes before a partner meeting.Scor