20VC Ash Fontana on The 5 Core Characteristics That Make Data Valuable, What VCs Can Learn From Italian Craftsmen and Howard Marks & The Importance of Vertical Integration In Scaling Today

Summary Notes


In this insightful conversation, host Harry Stebbings interviews Ash Fontana, the managing director at Zetta Venture Partners, a firm specializing in AI-first companies with B2B business models. Ash shares his journey from founding Top Guest and working with AngelList to his current role at Zetta, highlighting the importance of research, proprietary data, and the concept of AI-first companies. He emphasizes the difference between AI-first and traditional software companies, with the former focusing on predictive models and quantitative feedback to automatically iterate on product features. Ash also discusses the challenges incumbents face when integrating AI-first acquisitions, the significance of vertical integration, and the unique business models that emerge from AI-first strategies. The conversation also touches on the nuances of venture capital as a craft, drawing parallels with Italian craftsmanship and the quest for self-optimization, all while acknowledging the competitive landscape of seed funds in AI.

Summary Notes

Pre-Interview Acknowledgments

  • Harry Stebbings expresses gratitude to eight individuals who contributed to the preparation of the interview with Ash Fontana.
  • Harry highlights the meticulous research process involving conversations with people close to the guest.
  • Ash Fontana is introduced as a managing director at Zetta Venture Partners, a fund specializing in AI-first companies with B2B business models.
  • Ash's background includes launching online investing on AngelList, creating the first startup index fund, and expanding AngelList into Europe and the UK.
  • Ash has made investments in companies such as Canva and Mixmax and co-founded Top Guest, which sold for an eight-figure sum.

"This episode is a result of eight conversations with eight different people close to this guest."

This quote demonstrates the depth of research and preparation that goes into the podcast episodes, ensuring a rich and informative discussion.

"Ash Fontana, managing director at Zetta Venture Partners, the fund that invests in AI first companies with b to b business models."

This quote provides an introduction to Ash Fontana, highlighting his current role and the focus of Zetta Venture Partners.

Sponsorship Acknowledgments

  • Harry Stebbings promotes Carter, a platform simplifying equity management for startups and investors.
  • Brex is introduced as a corporate card solution for startups, with fast growth and tailored rewards.
  • RankScience is mentioned as a tool to enhance organic traffic and SEO for companies.

"Go to carter.com two Zerovc to get 10% off and more than 800,000 employees and shareholders use Carter to manage hundreds of billions of dollars of equity."

This quote is part of the sponsorship acknowledgment, describing the benefits and reach of Carter's platform.

"Brex founders Henry K. And Pedro built a payments business in Brazil but kept getting rejected for a corporate card in the states."

This quote tells the story behind Brex's founding, emphasizing the founders' motivation to create a solution based on their personal experiences.

"RankScience is the easiest way to grow organic traffic and get your content ranking higher in Google."

This quote introduces RankScience as a service for improving web content's SEO and organic search performance.

Ash Fontana's Background and Journey

  • Ash describes his childhood interests in computers and businesses, and his path to venture capital in the US.
  • His experiences in Australia, including working in his family's vertically integrated farming business, shaped his understanding of business operations and value capture.
  • Ash's journey through travel, study, love, and professional opportunities led him to settle in New York and enter the world of venture capital.

"I liked pulling apart computers and pulling apart companies."

This quote reflects Ash's early interests, which eventually led him to a career in venture capital.

"I just set my sights on the US and found my way here at every opportunity, first for travel and then for study and then for love."

Ash explains his gradual and determined move to the US, which was motivated by various personal and professional reasons.

Impact of Family Business Experience

  • Ash discusses the impact of working in his family's farming business on his approach to business and investment.
  • The principle of vertical integration, controlling all aspects of production, is emphasized as a key learning point.
  • Ash applies the concept of vertical integration to other businesses, recognizing its value in capturing improvements in subprocesses.

"Controlling every part of that process allows you to capture all the value you create by improving each subprocess."

This quote underlines the importance of vertical integration in business strategy, a principle Ash learned from his family's business.

Lessons from AngelList

  • Ash shares insights gained from his time at AngelList, including investment heuristics learned from Naval Ravikant.
  • He learned the importance of early signal discovery and the practicalities of running a venture fund at scale.
  • AngelList's experience with semi-automated, high-volume screening influenced Zetta's approach to identifying investment opportunities.

"I just learned a lot about investing in startups from Naval."

Ash credits Naval Ravikant for teaching him valuable lessons about startup investment.

"We found companies like Uber, Xymogen and Opendoor before anyone else or before many others."

Ash highlights the success of AngelList in identifying high-potential startups early on.

AI-Driven Sourcing in Venture Capital

  • Ash discusses the limitations and potential of AI in sourcing venture investments.
  • He believes that while AI can augment the sourcing process, relying solely on algorithmic analysis for early-stage companies is ineffective.
  • Zetta Venture Partners uses multiple data streams and a combination of human and automated processes to discover investment opportunities.

"I think like a lot of what people call AI, it's more about augmentation than automation."

Ash emphasizes that AI is more effective as a tool to augment human decision-making rather than replace it.

"We're finding 1000 companies a month through this system and then taking 100 of those through diligence and bringing one term sheet stage every month or two."

This quote illustrates the scale and efficiency of Zetta's AI-augmented sourcing process.

Defining AI-First Companies

  • Ash takes responsibility for popularizing the term "AI-first" and explains the concept.
  • AI-first companies focus on building predictive models and iterating based on quantitative feedback from machine agents, contrasting with traditional software companies that rely on qualitative feedback from human customers.
  • AI-first companies have a strategic advantage over incumbents due to their data-centric approach and the ability to improve predictive models continuously.

"AI first companies on the other hand, they focus on building predictive models, not product features."

Ash defines AI-first companies by their focus on predictive modeling rather than traditional product feature development.

Acquiring AI-First Companies

  • Ash addresses the feasibility of incumbents acquiring AI-first companies to bypass the transition to AI-centric operations.
  • He suggests that while integrating AI-first companies is possible, incumbents face challenges in adapting their business models and data practices to leverage AI effectively.

"That's a really good question. I think often it's actually relatively straightforward."

Ash acknowledges the potential for incumbents to acquire AI-first companies, indicating that integration can be manageable.

Integration Methods and Incumbent Companies

  • Incumbent companies often excel in product and delivery but lack predictive capabilities.
  • The predictive component is valuable as it aids in decision-making rather than just performing calculations.
  • Successful acquisitions occur when incumbent companies identify AI-first companies that complement their product offerings.

"Incumbent companies usually have the product part down pat and maybe the delivery or the services part down pat, but they don't have the model part... And incumbent companies have at least part of that solution. They just don't have the really valuable part, which is the predictive part, the part that sort of helps you make a decision, not just calculate something for you."

This quote emphasizes the gap in incumbent companies' capabilities—they have mastered product development and delivery but lack in the area of predictive modeling, which is crucial for decision-making. This gap presents an opportunity for AI-first companies to be acquired and integrated into the incumbents' solutions.

The Importance of Proprietary Data

  • Proprietary data isn't always necessary but often required to build superior models.
  • Access to unique data can create a competitive advantage by producing better outcomes than those using common data and models.
  • Evaluating data involves considering accessibility, fungibility, dimensionality, breadth, and perishability.

"You need proprietary data to build a model that generates results that are far better than what someone else can generate by throwing commodity data into an openly available model... So if the playing field is level in that regard, and then the data is the same, then you'll probably end up with a very similar result."

Ash Fontana articulates the necessity of proprietary data in developing AI models that outperform those using widely available data and tools. Proprietary data can be the differentiator that leads to unique and valuable results.

Data Perishability

  • Perishability's importance varies based on the context of the model.
  • Financial markets require up-to-date data due to constant changes, while modeling disease development can rely on older data.
  • The value of perishable data depends on the modeled system's dynamics and evolution.

"If you're trying to model financial markets, which are changing every microsecond, you have to work with perishable data... However, if you're trying to model something different... like a complex system... that's not going to completely change over a very short period of time... So it really depends on what you're trying to model."

Ash Fontana discusses how the necessity of fresh data depends on the subject of the model. In fast-paced environments like financial markets, data must be current, while in more stable systems, such as disease progression, older data may still be relevant.

Data Fungibility

  • Fungibility refers to the interchangeability of data sets for similar outcomes.
  • Data that is hard to acquire can sometimes be replaced with more accessible data that yields the same insights.
  • Understanding data fungibility is crucial for evaluating its unique value proposition.

"Fungibility is just very similar data going to get you to the same outcome as you feed it through a machine learning world."

Ash Fontana defines data fungibility as the ability to substitute one data set with another without compromising the result. This concept is important when assessing the exclusivity and competitive advantage of a data set.

Systems of Record vs. Systems of Intelligence

  • Systems of record involve manual, structured data entry and serve as databases.
  • Systems of intelligence automatically collect unstructured data and are integral to decision-making processes.
  • The evolution from systems of record to systems of intelligence indicates a shift towards proactive, data-driven strategies.

"A system of record just sort of really is just a database with a UI on it. A system of intelligence is something that is a core part of your decision making process and makes suggestions to you as you're trying to make a decision."

Ash Fontana differentiates between systems of record, which are essentially databases, and systems of intelligence, which actively contribute to strategy and decision-making by providing automated suggestions based on data.

Business Models for AI-First Companies

  • AI-first companies differ from SaaS companies in value proposition and business models.
  • AI-first companies tend to offer revenue-generating predictions or decision-making assistance rather than just workflow improvements.
  • Competitive advantages in AI-first companies are built through a virtuous loop of data collection, model improvement, and customer acquisition.

"An AI first company tends to pitch based on earning someone revenue or helping you make a decision you couldn't otherwise make... And AI first companies tend to have a little bit more of a shared benefit model or a percentage of the increase in revenue model."

Ash Fontana outlines how AI-first companies focus on directly impacting revenue or enhancing decision-making capabilities, which often leads to business models that share in the benefits generated by their predictive models.

Identifying the Value Inflection Point

  • Determining the value inflection point involves assessing model accuracy, stability, and the potential payoff for customers.
  • Startups may need to improve feature engineering, gather more data, or refine their predictions to enhance customer value.
  • The goal is to help startups progress from their current state to a point where they deliver significant value.

"So that's how we sort of work through it with startups and figure out where are you today and how close are you to delivering that customer value."

Ash Fontana explains the process of working with startups to identify their current status and the steps needed to reach a point where their AI models provide substantial value to customers.

Business Model Quality

  • Business model quality is driven by compounding competitive advantage and vertical integration.
  • Vertically integrated businesses, like Apple and Amazon, are seen as producing superior products due to their control over the entire process from production to delivery.
  • A focus on creating a virtuous loop of data and model improvement is key to building a strong business model.

"I just believe the best products in the world are made by vertically integrated businesses."

Ash Fontana expresses his belief that vertical integration leads to the creation of the best products, as it allows for comprehensive control and optimization throughout the production process.

Vertical Integration in AI First Companies

  • Vertical integration is essential for AI first companies to capture the full value they create.
  • It involves owning the entire process from data collection to delivering predictions and integrating with customer business processes.
  • This approach is more challenging than using third-party data, models, or platforms, but it ensures complete control and value capture.

"For an AI first company, this means doing everything from collecting your own proprietary data, building your own models, delivering those predictions to customers through your own products, then offering your own services to integrate with a customer's business process."

This quote explains the comprehensive approach that AI first companies must take to be vertically integrated, emphasizing the importance of controlling the entire value chain from data collection to service integration.

Market Size and Total Addressable Market (TAM)

  • Ash Fontana dismisses top-down TAM models in favor of a bottom-up approach based on product roadmaps and customer ROI.
  • He uses a tool to estimate the potential market cap of a company, heavily discounting for execution risk to arrive at a present value.
  • Ash argues that early-stage TAM estimations are not very useful and prefers to focus on what can go right for the company.

"So then I've got that average revenue per user times number of potential users in the world. And then I apply a margin. So what's the business's margin? To figure out how the revenue flows to the bottom line."

Ash describes his method of calculating potential revenue and profitability by assessing the value of product features and estimating the number of potential customers.

Valuing AI First Companies vs. SaaS Companies

  • Valuing AI companies is based on the quality of predictions they can offer customers.
  • AI companies should have pricing power due to their unique predictive capabilities.
  • They start with lower margins but have the potential for much higher margins than SaaS companies, due to fixed initial costs and data generation.

"With an AI company, you're valuing it based on how close they are to offering a valuable prediction to customers."

This quote highlights the core difference between valuing AI companies and SaaS companies, with AI valuations focusing on the ability to provide valuable predictions.

Capital Intensity and Margins in AI First Companies

  • AI first companies require significant initial investment to build models and integrate with customer data.
  • The initial costs are fixed, and once the model reaches superhuman performance, it generates its own data, reducing costs.
  • Gross margins can initially be low but have the potential to reach very high percentages as the company matures.

"So you tend to start at 40 or 50% gross margin, but then you get to 95, 98%."

Ash explains that although AI companies start with lower gross margins, they can achieve extremely high margins once their models are sufficiently developed and self-sustaining.

Appreciation for Howard Marks

  • Ash Fontana admires Howard Marks for his focus on pricing risks that others cannot or will not price.
  • He emphasizes the importance of learning from various investing fields to apply those principles to startup investing.
  • Ash uses experiences from different areas of investing to inform his decisions in venture capital.

"And it was sort of very intuitive or like my liking of his work is really just because it resonates with my pathological allergy to competition."

Ash expresses his affinity for Howard Marks' investment philosophy, which aligns with his own aversion to competition and his focus on unique investment opportunities.

Concerns Over Current Tech Valuations

  • Ash acknowledges that some companies are overvalued and may price themselves out of future investment opportunities.
  • He is not overly concerned as successful companies can generate significant value, making early valuations less relevant.
  • Ash sees more reasonable valuation practices in parts of the world outside the tech hubs.

"I'm concerned inasmuch as people sort of price themselves out of ever even having a shot."

Ash expresses concern that high initial valuations can hinder a company's ability to raise follow-on capital, which is crucial in the early stages of building a company.

Lessons from Italian Craftsmanship

  • Ash Fontana compares investing to craftsmanship, emphasizing the need for lifelong commitment and continuous improvement.
  • Successful investing, like craftsmanship, requires building and refining one's own tools and methods.
  • Teaching and apprenticeship are valuable for both understanding and improving one's craft.

"The second is investing tools. I mean, the best craftsmen don't just use their tools better than anyone else, they actually end up building their own tools to do certain things."

Ash draws parallels between the tools used by craftsmen and the analytical tools developed by investors to make better and more consistent investment decisions.

Attention to Detail in Craftsmanship and Investing

  • The importance of meticulous attention to detail in both craftsmanship and investing.
  • The analogy between Italian craftsmanship and investment analysis.
  • Identifying overlooked aspects in investments can lead to better risk pricing.

Pay attention to details. Pay attention to that dovetail joint, or the way a certain corner is rounded, or the angle of approach on a handle or something like that. And investing. This means pay attention to the algorithm in the footnote of the research paper that the founder wrote, or a formula in a model or a certain clause in a legal contract, because this is where you learn how to analyze things that others just can't analyze or aren't analyzing.

The quote emphasizes the parallel between the detailed work of Italian craftsmen and the granular analysis required in investment. It suggests that success in investing comes from scrutinizing the minutiae that others may overlook, such as specific algorithms or clauses in contracts.

Self-Optimization and Environmental Impact

  • Ash Fontana's shift from self-optimization to global optimization.
  • Systematic efforts to reduce environmental impact.
  • Weekly routine of replacing household products with more eco-friendly alternatives.

I guess I've moved to a sort of global optimization, rather like local optimization or self optimization. And over the last couple of years, I've just been very systematically reducing my impact on the environment.

Ash Fontana describes a transition from focusing on personal betterment to considering the broader environmental impact of his actions, detailing systematic changes to reduce his ecological footprint.

The Problem with "Life Hacks"

  • Critique of the term "life hack" due to its short-term implication.
  • Emphasis on consistent, simple daily practices for long-term benefits.
  • Suggestion to look at a post with 100 ways to get more done for those interested in productivity tips.

I sort of reject this term life hack because it's sort of like an oxymoron, right? Like your life is long, but a hack is short. And really, the things that work over time are those that are consistently done.

Ash Fontana challenges the concept of life hacks, arguing that meaningful improvements come from consistent daily practices rather than quick fixes.

Relationship Building with Other Investors

  • Preference for inclusive activities like long walks for building work relationships.
  • Acknowledgement of the potential discriminatory nature of certain bonding activities.
  • Open invitation for physical challenges as a means of connecting with others.

Just go on long walks with people because that doesn't really sort of leave anyone out.

Ash Fontana suggests long walks as a non-discriminatory way of building relationships with other investors, emphasizing inclusivity in professional networking.

Reading Habits and Epistemology

  • Current reading interests in epistemology and the history of German biology.
  • The value of reductionism in understanding concepts over purely experimental approaches.
  • Relevance of historical scientific movements to modern machine learning and epistemology.

I'm reading a book, and this is a curveball called the Strategy of Life. And it's an epistemology book, and it's specifically about teleology as manifested in early 19th century german biologists.

Ash Fontana shares his current reading on epistemology, highlighting the importance of deep thinking and reductionism in understanding complex subjects, which he finds relevant to machine learning.

Career Advice: Strengths and Weaknesses

  • The impact of focusing on strengths early in one's career.
  • The necessity of addressing weaknesses to avoid slowing down later on.
  • The concept of sequencing in personal development and career progression.

Look, people go fast early in their career because of their strengths, but they slow down if they don't work on their weaknesses.

Ash Fontana relays advice on the importance of leveraging strengths to advance quickly in one's career while also recognizing the need to work on weaknesses to maintain momentum.

Investment Strategy and Thesis

  • The challenge of saying no to deals that don't align with an investment thesis.
  • The importance of focusing on a small number of high-quality investments.

We don't have to do everything, we just have to do a small number of incredibly good things.

Ash Fontana discusses the discipline required in investment strategy, emphasizing the need to stay focused on deals that fit within the firm's thesis rather than pursuing every opportunity.

Investing in European AI and Research Discrepancy

  • Recognition of Europe as a leader in fundamental AI research.
  • The disparity in funding between European and US AI entrepreneurs.
  • Zetta's investment strategy that leverages this discrepancy.

Fundamental AI research in Europe, in certain areas at least, is the best in the world.

Ash Fontana explains Zetta's investment approach, which capitalizes on Europe's strong AI research output despite the funding imbalance compared to the US.

Advantages of Being a Foreigner in Silicon Valley

  • The perceived benefits of having an accent and being memorable in professional settings.
  • The light-hearted view of standing out as a foreigner in the tech industry.

Having an accent is like playing life on easy mode. People at least remember who you are.

Ash Fontana humorously comments on the advantages of being a foreigner in Silicon Valley, suggesting that it can be beneficial in making a lasting impression.

Addressing the WeWork Controversy

  • Acknowledgment of the challenges in creating something from nothing.
  • Recognition of WeWork's accomplishments despite controversies.

Not totally creating something from nothing is very, very hard. And they've definitely done that.

Ash Fontana provides a measured response to the question about WeWork, acknowledging the difficulty of building a company and recognizing WeWork's ability to create value.

Challenges of Competition in Venture Capital

  • Personal aversion to competition in the investment landscape.
  • The influx of seed funds and self-proclaimed AI experts in the industry.
  • The strategy of staying the course amidst increasing competition.

My pathological allergy to competition. And there's an influx of seed funds, all sorts of AI experts, so to speak, and think that is very difficult for me to deal with on a daily basis, given I'm so allergic to competition.

Ash Fontana expresses his discomfort with the competitive nature of the venture capital industry and the challenge of differentiating in a crowded market.

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