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I Sold My AI Startup Before Revenue: Here’s What Investors Missed — And Founders Shouldn’t

Nobody is talking about this: the best AI returns aren't coming from ChatGPT wrappers—they're coming from founders solving model and infrastructure problems.

Paper-cut illustration of amber paths carrying capital toward a small coral research venture between larger buildings.
Capital and the next generation of AI ventures.AI illustration by KeyNews
The KeyNews take

Why it matters

A contrarian take on AI startup investability from an angel investor who exited pre-revenue. The piece argues that foundational AI (models, infrastructure) will create more long-term value than application-layer products, offering a strategic framework for how founders and investors should think about positioning in the AI economy.

The key facts

9 to know
  1. Author: Alexander Kardos-Nyheim, angel investor

  2. Thesis: Deep technical challenges (model/infrastructure level) > application-layer products

  3. Context: Author sold AI startup before revenue

  4. Type: Guest commentary on investability criteria for AI startups

  5. Source: Crunchbase News

  6. Author: Alexander Kardos-Nyheim (angel investor)

  7. Core thesis: Long-term value in foundational AI (models, infrastructure) > application-layer products

  8. Context: Author sold AI startup pre-revenue

  9. Focus: Investment evaluation framework and due diligence questions for AI founders/investors

Go to the source

Crunchbase Newsnews.crunchbase.com

Publisher excerpt: The greatest long-term value in AI will come from companies solving deep technical challenges at the model and infrastructure level rather than application-layer products built on existing AI platforms, writes angel investor Alexander Kardos-Nyheim. In this guest commentary he shares processes and…
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