Why Owning Your Data Is the New Moat | Primary Venture's Emily Man

Emily Man (0:00)

My framework is The Roadkill, The Cockroaches and The Cheetahs. The Roadkill is old school software companies. Maybe they were point solutions, maybe they were low value, that are ultimately just gonna get run over by AI. The Cockroaches is the second category of things that can survive a nuclear blast, right? And the nuclear blast being the AI wave that is happening, but they might not have specific AI related tailwinds, payroll technology, things that move money. The last category being the Cheetahs are folks who have a real opportunity to become AI native and to embed the way AI works into each one of their work clothes and their offerings. I think the best example here is Intercom, which started in 2013 as a company that pre-AI generation and did a sort of refounding moment. Some companies will make up the curve here, but there is others that I think are at a real risk.

CJ Gustafson (0:57)

Is this thing on?

SPEAKER_3 (1:00)

Yesterday's price is not today's price.

CJ Gustafson (1:12)

Welcome back to Run the Numbers. My name is CJ. I'm a recovering tech CFO, and I talk to the world's top CFOs as well as the investors who fund them. On today's show, I have Emily Man of Primary Ventures on, and she is kind enough to take us through the five layers of the AI stack and explain where value accrues. We then touch on the changing P&L and why it is perhaps the most exciting time ever to be a CFO as AI works its way into every line and department throughout the P&L. We talk about the race to capture context, and by context, I mean organizational alpha that is maybe sitting somewhere out there that needs to be read by AI, and then put into workflows.

Finally, we talk about things like MCP, going headless, and harnesses, what those terms mean and what it means for your data. All this and much more coming up. Emily, thank you so much for joining me on the show today.

Emily Man (2:02)

CJ, I am so excited to be here.

CJ Gustafson (2:04)

It's a different scenario than when we first met.

Emily Man (2:07)

I know.

I think the last time we chatted, you were CFO of Carts Tech and I was a growth investor. So, lots has changed.

CJ Gustafson (2:15)

And you're at Primary now. What's most exciting that you're working on today?

Emily Man (2:20)

Gosh, what isn't exciting at this point in time, right? As you can imagine, we are at the forefront of taking intelligence and bundling it into useful capabilities and the types of things that we're seeing on the ground on a day-to-day basis. I don't know, it's just been the most exciting in my entire career to be an investor.

CJ Gustafson (2:37)

You have such a succinct way of taking complicated things and packaging them like you just did into something that's easier to understand. Like you said, we have all this intelligence. Now we're trying to package it into stuff that's useful. One of the things that you've taught me is that there are different layers of the AI stack. Now I work with a lot of finance people, a lot of CFOs, and we pay all these different bills for stuff, but we don't always understand the technical underpinnings of it. I'm hoping just set the table for us here. What are the five different layers of the AI stack?

Emily Man (3:13)

Maybe just to start, this is all stolen from Jensen, but I think he has a very distinct way of talking about the layer cake that is underpinning the AI era. I think about it as a supply chain, from what do you need to get from raw material all the way to finished product. On one end, you have energy. That's power, that's electricity, those are the inputs into the hardware itself. The second layer being chips. From there, you take the chips and you put them into infrastructure.

That's where you wire everything all together and make it useful for a set of models that are built on top of them. Now, the models are probably something that we can all easily understand. They have dominated the news cycle for the last probably two years at this point. But at the very highest piece of that, it's the application layer. The applications that connect what the models can do with the end human output or the human outcome.

CJ Gustafson (4:08)

Got it. Where has the attention and funding focused on so far?

Emily Man (4:12)

For the first era of the AI boom, we've been in this massive CapEx wave where everyone is focused on building the infrastructure. Call it the layers two through four, the chips, the infrastructure, and the models.