Oct 28, 2019 · 1h 12m · capital-allocators
Ash Fontana – Investing in Artificial Intelligence at Zetta Ventures (First Meeting, EP.11)
0:00 / 0:00
gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this interview with Ted Seides, Ash Fontana discusses the founding thesis and operational methodology of Zetta Ventures, an early-stage venture firm dedicated to B2B artificial intelligence. Fontana details the evolution of intelligent computing, rigorous technical and data diligence frameworks, disciplined fund construction, and how specialized startups establish durable enterprise moats.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Ted holds 17.5% of the talking time here. How this is scored →
Ted as informed peer 2.6 Guest teaching 4.3 Guest disagreement 0.2 Ted pushing back 0.2
speaking balance: gold is Ted, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 1:01:45 Rejecting simplistic data volume tropes
Ash firmly rejects common market tropes like 'data is the new oil,' explaining that volume without dimensionality or freshness is useless.
Hardest push from Ted ▶ 23:34 Probing the real risk of autonomous decision makingTed presses Ash on how delegating actual operational decisions to AI increases foundational investment risk.
Biggest teaching moment ▶ 30:50 Equating seed AI diligence to margin expansion value investingAsh educates Ted on how early AI investing parallels value investing by pricing the margin expansion resulting from raising algorithmic accuracy from 30% to commercial grade.
Ted holds their own ▶ 54:28 Drilling into portfolio math and return hurdlesTed methodically walks Ash through fund sizing math, dilution requirements, and exit distributions to unpack the exact mechanics of their venture model.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Ted as informed peer | Guest teaching | Guest disagreement | Ted pushing back | Why |
|---|---|---|---|---|---|---|
| Early Entrepreneurship and Founding TopGuest | 1 | 1 | 0 | 0 | Ted opens with broad biographical questions about how Ash got into tech and his early startup TopGuest. Ash shares his personal journey and the founder empathy he gained. | |
| Scaling AngelList and Online Fund Infrastructure | 2 | 3 | 0 | 0 | Ted asks open-ended questions about Ash's time scaling AngelList. Ash explains building the online investment infrastructure and running syndicates from scratch. | |
| Transition to Concentrated Investing and Launching Zetta | 2 | 3 | 0 | 0 | Ted asks why Ash decided to leave AngelList for concentrated venture. Ash details meeting Mark and forming the core thesis that all software will become intelligent software. | |
| Team Composition and the Value of Complementarity | 2 | 2 | 0 | 0 | Ted asks about team structure. Ash explains hiring for complementarity and how diverse operating backgrounds sharpen decision-making. | |
| The AI Risk Curve: Personalization to Autonomous Creation | 2 | 6 | 1 | 0 | Ted asks about the general understanding of AI. Ash provides an educational breakdown of the AI risk curve from personalization to automation and autonomous creation. | |
| Mitigating Prediction Risk and Customer Validation | 4 | 5 | 0 | 1 | Ted pushes on how higher algorithmic risk translates to investment risk. Ash explains how Zetta validates customer willingness to pay and avoids catastrophic probabilistic risk like clinical healthcare. | |
| Evaluating Technical Feasibility and Margin Expansion | 3 | 6 | 0 | 0 | Ted asks about technical evaluation. Ash frames early AI venture investing as a form of value investing where underwriting model accuracy directly drives gross margin expansion. | |
| Anatomy of a Seed-Stage AI Startup and Insurance Case Study | 3 | 5 | 0 | 1 | Ted asks about the anatomy of early-stage AI startups and whether the model outweighs team capability. Ash uses an auto insurance claim example to explain why deep technical expertise is required to tune models. | |
| Data Evaluation Frameworks and Minimum Algorithmic Performance | 3 | 6 | 0 | 0 | Ted inquires into diligence frameworks for improving model accuracy. Ash breaks down data evaluation criteria and the concept of minimum algorithmic performance. | |
| Convex Payoff Profiles and B2B Investment Focus | 2 | 5 | 0 | 0 | Ash outlines convex versus concave payoff profiles across sectors and explains his focus on B2B software over consumer applications due to intellectual integrity. | |
| Internal Decision-Making and Blank-Page Investment Memos | 3 | 4 | 0 | 0 | Ted asks about team decision-making. Ash outlines their strict first-meeting gating rule and why Zetta forces blank-page memos rather than templates. | |
| Intellectual Honesty and Walking Away from Diligence | 3 | 4 | 0 | 0 | Ted asks whether writing memos ever breaks confirmation bias. Ash explains walking away frequently after testing technical assumptions and margin ceilings. | |
| Managing Competitive Deals and Evaluating Founder Fit | 3 | 4 | 1 | 1 | Ted probes how Zetta handles competitive bidding and founder trade-offs. Ash states that competing means losing and highlights Zetta's hardline requirement on customer diligence. | |
| Post-Investment Execution and Proprietary Data Playbooks | 4 | 5 | 0 | 1 | Ted walks through fund math and portfolio construction. Ash details power law exposure, check sizing, dilution targets, and fund sizing. | |
| Portfolio Examples: Passive Health Monitoring and Cloud Kitchens | 2 | 4 | 0 | 0 | Ted asks for portfolio examples. Ash describes Maya's passive heart monitoring and an intelligent software platform optimizing ghost kitchen supply chains. | |
| AI Opportunities Across Financial Services, Banking, and Insurance | 3 | 5 | 0 | 0 | Ted connects the topic to institutional financial services. Ash explores simulation modeling for hedge funds, credit underwriting data, and actuarial disaster models. | |
| Common Misconceptions in Artificial Intelligence and Big Tech Talent | 3 | 7 | 2 | 0 | Ash debunks common industry misconceptions, explaining why 'data is the new oil' is an erroneous analogy and dispelling myths around big tech talent monopolies. | |
| The Long-Term Vision for Zetta and Global AI Creation | 2 | 2 | 0 | 0 | Ted transitions through closing questions regarding long-term vision, bike design hobbies, information diets, and foundational life lessons. |
Statements from this episode (29)
Fontana: Founder Empathy Outlasts Tactical Knowledge in Venture Investing
“The one thing that you take away from that, or I took away from that, that I apply to venture is not like, this is how you hire your first salesperson, or this is how you like integrate with this platform, because all that knowledge changes, best practice is a…”
Fontana: All Software Will Become Intelligent Software
“And we met, and we just had this mind meld around, like, this next era of computing, and that is, all systems, all software will become intelligent software.”
Fontana: The 2013 Convergence That Sparked the Neural Network Revolution
“That was a really interesting time, 2013, 14, because that's when this neural network revolution started, and that is, we were finally at the point where we'd had some research breakthroughs into how neural networks work. We had enough data to feed these, like…”
Fontana: Future AI ensembles will discover knowledge beyond human comprehension
“And what's really gonna happen in that era, which we're sort of starting now, is You get an ensemble of AI techniques, so you might have like a Bayesian system and a neural network and a bunch of other stuff that work in concert together to understand a system…”
Fontana: Why Zetta Ventures Avoids Healthcare AI Investments
“We've stayed away from a lot of applications of current AI technologies to healthcare, because when it goes wrong, it's really catastrophic, and it will go wrong. But we've moved towards a lot of applications to it in industry, like, again, in manufacturing an…”
Fontana: Europe produces more AI research than the US or China
“There's more AI research coming out of Europe than anywhere in the world. More than the US, more than China.”
Fontana: Why Physical Presence in AI Hubs Still Provides an Edge
“I used to think that geographic advantage in sourcing is like one of the most unsustainable things, because anyone can just get on a plane and be somewhere, but it just constantly surprises me that just being in these centers and being able to have conversatio…”
Fontana: Early-Stage AI Investing Is Effectively Value Investing
“You could actually think of what we do as value investing in a way, because what we do is we price the risk that a company can actually generate this really valuable prediction. And when we meet a company, they're probably at 30 to 50% accuracy on a model, whi…”
Fontana: Seed-stage AI startups typically have five to ten people
“They're usually a couple of people, so the founders, usually one comes from the research world, and another comes from the domain they're applying their research to, and then a couple of engineers, and that's it. They're usually five to 10 people, something li…”
Fontana: Off-the-shelf AI models fail on specific industry problems without tuning
“Once you get to like a specific industry problem, like
Is the bottle cap too big for the bottle on this production line?
Like, did the plastic extrusion process not work properly?
You can't sort of use off-the-shelf models to do that, and so, what you need …”
Fontana: AI Startups Must Hit Minimum Algorithmic Performance to Deliver Value
“And so working out what that minimum, we call it the minimum algorithmic performance, but essentially like what accuracy a customer needs for that to be valuable to them is one thing. And then how far away is the company from achieving that?”
Fontana: Why AI Startups Should Target Industries With Convex Payoff Curves
“You want to work in industries where the payoff is really high. So I think of like convex and concave payoffs, where if you get it right, it's really valuable to you. But if you get it wrong, it doesn't really matter. So again, going back to medical use cases,…”
Fontana: Most AI systems are simple statistical extrapolation models
“The reality is, as much as we're excited about this era of AI creating new knowledge and ensembles of models, understanding complex systems, for the most part, we're pretty far away from that, and the reality is a lot of AIs are, like, fairly simple statistica…”
Fontana: Zetta Requires Partner Pass-or-Advance Decisions After One Meeting
“And so at Zeta, if you meet a company, you have to decide whether to have them meet someone else on the team or pass on the opportunity after the first meeting.”
Fontana: Why Investment Memos Should Avoid Templates
“I think the interesting thing, and this has been something that I'm a bit of a stickler for, is our memos start as a blank page, and we try not to use templates, because I think templates form your thinking, and so you start with a blank page, and you just sta…”
Fontana: Zetta memos always analyze compounding machine learning data loops
“Invariably, we always have a section on the competitive advantage the company could build and compound by looping customer feedback data through the machine learning system to generate a prediction that gets better and better over time.”
Fontana: Zetta Abandons Deals Mid-Memo Almost More Often Than It Invests
“Oh, we have, Almost more of them than actually making the investment.”
Fontana: A Lack of Memo-Writing Energy Predicts Bad Board Members
“Firstly, do I even have the activation energy to write this memo? Because if I don't have the activation energy to write the memo, am I going to want to turn up to board meetings on like a six hour flight away every month? Am I going to want to get on the phon…”
Fontana: If You Are Competing For a Venture Deal, You Are Losing
“I sort of say something internally, which is not often the most helpful thing to say, which is as soon as you're competing, you're losing.”
Fontana: Average Returns Rise With More Exposure in Power Law Distributions
“And the unintuitive thing about power law distributions is that your average return goes up as your points of exposure go up.”
Fontana: Smaller fund sizes don't require hitting extreme power-law tail winners
“And so, our investors don't have to hold a belief that we're extraordinary pickers that can predictably find companies in their very, very long tail of the power law distribution. We hope we do that, but they don't have to believe that up front if your fund si…”
Fontana: Society Is Entering an Era Where No One Cooks
“It pains me to think that we're going into an era where no one cooks their own food, but we are, and delivery and various other technologies are getting so good that you click a button and you get food in 10 minutes, and people love that, of course.”
Fontana: Simulation Tools Offer Greater Value Than Decaying Hedge Fund Data
“What I'm excited by there is not necessarily like the next best data source, because they tend to be very perishable. I'm excited about different tools for those hedge funds that are trying to make predictions in complex systems.”
Fontana: Why 'Data is the New Oil' is a Useless Analogy
“I think also people think about data volume rather than the dimensionality of data or whatnot, and often you solve really important problems with really small amounts of data. And so, I think people think about volume, and data is the new oil, and all this sor…”
Fontana: AI talent is not overly concentrated in big tech
“I also see every day people breaking off from these companies and just applying their knowledge to all sorts of other industries day-to-day and starting their own companies, and so Talent is like not that concentrated, actually, and there are all sorts of grea…”
Fontana: 2019 Data Science Tooling Resembles 1980s Software Engineering
“The state of tooling for a data scientist in, so what, 2019 is what it was for a software engineer in like the late eighties, early nineties, There's still so much that's so hard about building these things that could be made a lot easier with the right tools”
Fontana: Google will never build AI solutions for niche vertical markets
“They're not really structured, and their businesses are so big such that it's not really worth it for them to go in and solve problems in a lot of vertical markets, so markets where the total opportunity is a couple hundred million dollars. This just doesn't w…”
Fontana: Pitch Jargon Signals a Lack of Fundamental Understanding
“I tend to switch off in pitches where the presenters sort of use like a word cloud of popular terms in their pitch, and it's also a pretty strong sign to me that they haven't come to a fundamental understanding from something new. Like, to explain something bo…”