Episode summary
Who Will Actually Make Money From AI?
How I Invest With David Weisburd
· · Hosts: David Weisburd · Guests: Lucas Swisher
Summary
Lucas Swisher argues that the biggest AI winners will not be found by betting on a single model or a single application, but by understanding where durable advantages compound across the stack. He says the application layer is not destined to disappear, because some use cases sit close to the model while others depend on deep integrations, compliance, unique data, and the ability to use multiple models. Coding is the clearest example of a model-tethered use case, while legal, finance, and other enterprise workflows may preserve more room for independent apps.
He is most constructive on the language model layer and data infrastructure. Swisher says frontier models are still seeing strong demand, with enterprise users especially willing to pay for better performance, and he argues that talent and capital create a self-reinforcing moat at the top. On the infrastructure side, he highlights Databricks as a durable bet because databases and context layers tend to outlast technology waves. He also thinks AI is moving from copilots to agents that can do real work, which expands the opportunity beyond software budgets into labor spend.
Swisher also sketches the next AI winners in public markets: an AI-reshaped version of the Mag 7, stronger returns from firms above $10 billion in market cap, and major upside in power, chips, and custom silicon. At the same time, he warns that investors can lose money in pre-revenue companies at high valuations and in fragile businesses sitting between adjacent AI layers.
Key takeaways
- Swisher says application companies can still win if they have unique data, deep integrations, or multi-model workflows, especially in legal and enterprise software.
- He believes the highest-ROI AI layers five years out are data infrastructure and foundation models, not chips or data centers.
- Swisher says talent is the main moat in AI, because the best people cluster around the best-funded frontier model companies and reinforce their lead.
- He thinks the next market leaders could look more like an AI 8 than the old Mag 7, with OpenAI, Anthropic, Nvidia, Google, Amazon, TSMC, SpaceX, and ByteDance among the names to watch.
- He warns investors against pre-revenue companies with very high valuations and against businesses trapped between the chip, model, and infrastructure layers.
Notable quotes
“we're very large investors in Anthropic, OpenAI and also some of the application companies, right? Cursor”
“We're not sort of doomsdayers on the application layer. How I think about it is it's almost like a bar chart”
“The primary mistake that I think folks are making at the application layer is just trying to drive to revenue too fast”
How the episode opens
Today's guest is Lucas Swisser, co-head of growth investing at CO2, investors in OpenAI, Anthropic, Datab Brooks, Curser, and Harvey. Today, the biggest question in AI isn't whether the technology works, it's who captures the value. Lucas explains why the biggest opportunities in AI may not be where investors are looking.
Without further ado, here's my conversation with Lucas. So if two years from now GPT7 comes out and it's dramatically better than GPT today, are application companies cooked? I think this is a great question.
It's obviously something we think about a lot. I mean we're very large investors in Anthropic, OpenAI and also some of the application companies, right? Cursor, which was just announced that it's acquired by SpaceX.
Thank you.
As well as other companies like Harvey and Open Evidence.
And I think we think about the world that it's it's not black and white like this, right? that if anthropics models or open models continue to get incredibly better and you continue to see improvements over time that all of a sudden the application layer vanishes. We're not sort of doomsdayers on the application layer.
How I think about it is it's almost like a bar chart. If you were to imagine like the x-axis are all the use cases, right? So you think like legal, medical, HR, finance all the way out to like some operating system for car washes.
Obviously the language models are going to have some advantages in some and some advantage and not really have other advantages in others. And how do we sort of think about like where that bar chart fills up? Where does anthropic and open AI get 100% and where do they get 30% of a market?
What's your framework on figuring out which application verticals will get disrupted versus those that won't?
From the episode’s captions: roughly the first two minutes, lightly cleaned. Keep watching on YouTube →
Topics
- ai durable advantages
- ai infrastructure layers
- openai vs anthropic
- ai data bottlenecks
- talent as competitive moat
Companies and tickers mentioned
- NVDA
- OpenAI
- Anthropic
- Databricks
- SpaceX
- NVIDIA
- Coatue Management
Watch the episode
Watch “Who Will Actually Make Money From AI?” on YouTube
Summary, takeaways and quote selection by Octagon. Quotes are short excerpts; the full conversation is in the episode.
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How I Invest With David Weisburd, this episode and its artwork belong to David Weisburd. Octagon is not affiliated with the show; this page is an independent summary.
