Ash Fontana – Investing in Artificial Intelligence

**Patrick O'Shaughnessy** (0:00)
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Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com.

**SPEAKER_2** (0:59)
Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaughnessy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.
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**Patrick O'Shaughnessy** (1:22)
My guest this week is Ash Fontana, a managing partner at venture capital firm Zetta. Zetta invests in companies which build software that use artificial intelligence methods like machine learning to predict and prescribe outcomes. Ash's combined experience as a founder, entrepreneur and investor give him the perfect background to discuss with us one of the hottest topics in business and investing. This conversation is useful for anyone trying to evolve their own way of dealing with data. Of particular interest are the ways in which Ash and his team evaluate datasets and how they think about competitive advantage in this new world, where Ash advocates a new term to replace the concept of moat, something he calls loops.
If we can use data to do things better than humans, or if we can supercharge our intuition with predictive models, we can harness the power of this new technology. What Ash has taught me is that data itself is dumb, but great datasets can represent the fuel for incredible companies. Let's dive in to how that may be. Please enjoy this conversation on how AI is changing business and how we might profit from that change.
So, Ash, you run one of the more focused and specific strategies of anybody that I've come across. A good place to start would be a quick description of the strategy itself, the types of businesses that you're looking for. And then we will dive into the very interesting topic that is AI.

**Ash Fontana** (2:41)
Yeah. So essentially, we're investing in what we think is a fundamental shift in computing. And therefore, a shift in the technology industry, and therefore a shift in how you invest in the technology industry. And that is investing in things that don't just give you calculations quickly, or put things in and out of a database quickly, but investing in things that give you predictions. And those predictions are super relevant to your business or create like real value for your business.
So we invest in those sorts of companies. Now, what does that mean? From the bottom up, it means they're collecting unique data, and then they're compounding the value of that data with some sort of intelligent system. Usually, that's machine learning. Sometimes it's something more simple than that. So that's what we invest in. In terms of stage, pre-traction is when the company has...
For us, it's when we can see that the data is going to be predictive of something really valuable, and we can talk to customers and say, would you find that valuable, how valuable? And then we go from there, and we help them actually put it into market.

**Patrick O'Shaughnessy** (3:36)
One of the things that I'd like to focus on today is how this new kind of way of thinking about things may disrupt old models. And we went back and forth a little bit ahead of time talking about SaaS businesses, or to software businesses, more generally speaking, which dominate today. They've had some of the most attractive economics. You've got some of the most incredible companies like Vista that have rolled these things together, or Constellation Software. And there's this incredible honeymoon right now with the software business model, and it's union economics. So I think you're a bit of a contrarian on the future of that model, relative to data-driven or AI-driven business models.
So maybe give us your perspective on that.