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Silicon Valley Girl · 3 min · Oct 6, 2026

The money in AI is in data and applications, not models

DA

Daron Acemoğlu

Institute Professor, MIT

Nobel-winning MIT economist who studies how AI and automation reshape work, productivity, inequality and economic growth.

  • Marina MogilkoHost · Host, Silicon Valley Girl

    Co-founded LinguaTrip and built Linguamarina, an English-learning YouTube channel with almost 9M subscribers.

About this segment

Acemoğlu doubts foundation models will earn trillions because of open source and diffusion limits. He says the edge is in proprietary data and niche applications built with workers' tacit knowledge.

From “Nobel Economist: Where He Sees the Real Money in AI”, Oct 6, 2026. Watch the full episode

“I really don't see how you're going to make trillions of dollars from foundation models because of potential bottlenecks, either in the development of the models or in their diffusion.”
DADaron Acemoğluat 40:42 · Silicon Valley Girl40:42

Takeaways

  1. Open source models limit how much foundation models can monetize
  2. Cultivate your own data and pay the workers who generate it
  3. Build applications by talking to workers, who hold tacit knowledge
Watch 2:35 from 40:08

In this episode

Full episode

  1. 40:08The money in AI is in data and applications, not models2:35

  2. 25:52Making workers 5% more productive beats automating 5% of jobs2:09Up next

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 40:20

    What would be your advice for someone who sees this amazing wealth being created by AI? At least what we see in companies' valuations. We still have to see the ROI on the actual economy. But when you say, you know, we want to participate in this goods creation, how can we do that practically in the next... Look, I actually don't see how foundation models are going to make a lot of money, despite all of the investment going into Frontier Labs.

  2. 40:48

    So we'll see some failed IPOs? Not failed IPOs right away, because I think the enthusiasm is there at the moment. But I really don't see how you're going to make trillions of dollars from foundation models because of potential bottlenecks, either in the development of the models or in their diffusion. But even if those don't happen, the next layer of model, open source models, are going to be very good as well.

  3. 41:11

    So just not the monetization opportunities are going to be limited. So I think the money is in high-quality data and in applications. That's where I see the edge. If your company, your line of business has a lot of data, cultivate it, use it. Even build better data. Find ways of capturing that data while also compensating your workers who are generating that data. That's fairness. That's also a good business decision.

  4. 41:40

    Think of applications. That's where the edge is going to be. Applications are going to be much harder to replicate by open source if they're really creative and fill a niche. So I think those are the places. And as a worker, have that flexibility, have that understanding of AI, but maintain your creativity and maintain your base. Yeah, also for a worker, I think it's also very important to realize that they are the source of data.

  5. 42:05

    They've been working with customers and product for years. And by the way, this is another important thing if you're going to develop applications. A lot of the tacit knowledge is with the workers. You can't just sit in your office in front of a computer and put a very powerful AI and come up with an application. You need to go there, get your hands dirty and talk to workers. Well, hopefully this conversation sparks some bright ideas and entrepreneurs who are watching who want to make practical applications of AI.

  6. 42:30

    I want to finish with the last question.