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You're Competing Wrong in AI (Do This Instead)

Nate B. Jones outlines five levels of AI builders and argues that durable advantage comes from customer depth, distribution, and anticipating future…

Nate B. Jones offers a practical framework for AI founders who feel discouraged every time OpenAI or Anthropic ships something new. His point is not that builders should ignore the labs; it is that they should stop competing with them on generic capability and instead win through domain-specific insight.

The five levels of AI builders

Level one is raw enthusiasm: a builder loves an idea and wants to use AI to make it real. That can start a project, but it often collapses when the founder has not thought through customers, go-to-market, the broader problem space, or how model releases may change the product.

Level two adds customer learning. The original idea still matters, but the builder is willing to adjust after speaking with users. Jones notes that this level can already create meaningful side businesses when the product is tied to a concrete pain.

Level three brings distribution into the picture. AI is not only a product or coding tool; it can also amplify outbound, storytelling, personalized LinkedIn messages, voice calls, generated podcasts, and social channels such as TikTok.

Where the advantage becomes defensible

Level four builders deeply understand a problem space and hold a specific thesis about how to attack it. Jones uses WhisperFlow as an example: the deeper belief is not simply better transcription, but that voice is becoming a new computing paradigm.

Level five builders go further by understanding the trajectory of AI in their own domain. They anticipate longer-running agents, better tool use, larger context windows, or other coming capabilities, then build for what will become possible in six to twelve months.

The takeaway

AI labs are not automatic black holes for entrepreneurship. They move quickly, but they cannot know every market’s details as deeply as domain specialists do. The strongest founders use lab progress as a tailwind while building around customer knowledge, distribution, a durable thesis, and a specific view of where AI is heading.

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