Ash Fontana: Why SaaS is not a fit for VC and How AI Compounds Competitive Advantage – The Full Ratchet

Key Takeaways

  • Most VC firms are not “focused funds” – they’re more general (they invest in a wide range of companies across many different fields). This is different from Zetta – they only invest in AI & machine learning companies.
    • Focused funds get a 16% higher multiple on invested capital and a 33% higher ROI compared to generalist funds 
  • The 4 Phases of AI:
    • Phase 1 (low risk): AI applied to consumer applications
      • Ex. – Google and Amazon giving recommendations
    • Phase 2 (slightly higher risk): AI applied to enterprise SaaS companies
      • Ex. – CRMs suggesting leads
    • Phase 3 (high financial risk): AI-centric applications that completely replace a workflow
      • Ex. – Using AI tech to estimate damage on a car, computer vision in autonomous cars, etc.
      • We are currently in Phase 3
      • The key metric for phase 3 AI: Is the efficacy better than a human?
    • Phase 4: (very high financial risk): Applications we haven’t considered before
      • Ex. – Using AI to optimize data center use or energy flow across an electricity grid, making medical diagnoses, etc.
    • Note – In many cases, even if an AI tech has better efficacy than it’s human counterpart, it will still incur adoption risk. Many people are not ready to trust AI as a total replacement for human judgment.
  • AI itself shouldn’t be feared. However, AI can create monopolistic power held by a few companies– this is something to be concerned about.

Intro

  • Host – Nick Moran (@TheFullRatchet)
  • Ash Fontana (@ashfontana) is a managing partner at the venture capital firm Zetta
    • They invest in companies that build software using artificial intelligence methods to predict and prescribe outcomes

Ash‘s Background

  • As a kid, Ash was interested in two things: Pulling apart companies (looking at their balance sheets, stock prices, etc.) and pulling apart computers
    • As an adult, Ash managed to combine these two interests and now invests in tech companies for a living
      • “Realizing that was a job was a very satisfying moment in my life”
  • Ash ended up going to law school and then on to work in the investment banking industry (in order to gain the necessary skills needed to be a VC)

The Advantages of a Focused VC Fund

  • Most VC firms are not “focused funds” – they’re more general (they invest in a wide range of companies across many different fields). Why did Zetta decide to focus on one specific area?
    • “We’ve taken the view that extreme focus is much better for the development and investment process, much better in terms of operational expertise, and as a plus – you see similar problems”
      • It also helps with marketing – Zetta is able to easily differentiate itself among all other VC firms
    • “It’s empirically proven that focused funds outperform general funds”
      • Focused funds get a 16% higher multiple on invested capital and a 33% higher ROI compared to generalist funds 
  • Why focus on AI and machine learning?
    • “We think this is going to affect every area of technology and industry”
      • Tech is shifting away from making humans more efficient to better completing activities for humans – this is why AI is the next revolution
        • But that being said, we’re still in the very early days of AI
  • Zetta invests in AI that creates the core value for the customer/user – they do not invest in “AI-enhanced” companies where the key differentiation is the AI
    • In venture capital, one should invest in something that has a competitive advantage for decades – the moats must be durable over long periods
      • Because of the importance of long-term, durable moats, SaaS (software as a service) companies will cease to be a category for venture capital investors since it’s no longer all that challenging to build good software
        • “Just building software is not going to keep your head in the game anymore”

The 4 Phases of Artificial Intelligence

  • Phase 1 (low risk): AI applied to consumer applications
    • Ex. – Google and Amazon giving recommendations
  • Phase 2 (slightly higher risk): AI applied to enterprise SaaS companies
    • Ex. – CRMs suggesting leads
  • Phase 3 (high financial risk): AI-centric applications that completely replace a workflow
    • Ex. – Using AI tech to estimate damage on a car, computer vision in autonomous cars, etc.
    • We are currently in Phase 3
    • The key metric for phase 3 AI: Is the efficacy better than a human?
  • Phase 4: (very high financial risk): Applications we haven’t considered before
    • Ex. – Using AI to optimize data center use or energy flow across an electricity grid, making medical diagnoses, etc.
  • Note – In many cases, even if an AI tech has better efficacy than it’s human counterpart, it will still incur adoption risk. Many people are not ready to trust AI as a total replacement for human judgment.
  • Zetta mostly invests in startups in phases 3/4

Thoughts On Data

  • The most obvious source of data is your customer’s data
  • An AI company might have a great algorithm and solution in theory, but if it lacks the data needed to prove itself, Zetta may pass on investing
  • There is a virtuous loop with data:
    • The data feeds an algorithm that predicts something for a customer, the customer uses the product more and more, this adds more data to the system which makes the system better and better
  • The two key questions to ask startups: Is there significant value in the data and do they have a way to compound that value?
    • Step 1: Analyze the data and figure out if it’s a unique asset 
    • Step 2: Figure out whether there is some way to use an intelligence system to compound the value of that data

Additional Notes

  • Ash has an AngelList syndicate which allows anyone to invest in a startup alongside him
  • The risk level of AI varies depending on the field
    • In medicine, you want the AI to be 100% accurate because people’s lives are at stake
    • In something like inventory management, if the AI isn’t perfect you’ll have some excess inventory which isn’t all that big of a big deal
      • The main question here: Is the AI better than a human?
  • AI itself shouldn’t be feared. However, AI can create monopolistic power held by a few companies– this is something to be concerned about.
    • Companies are using AI to develop advanced competitive advantages
      • This may result in a handful of companies dominating certain industries, thus gathering an extreme amount of money in the hands of only a few organizations
  • AI is still very human-intensive – there won’t be a runaway AI for quite sometime 
  • While investors like Chris Dixon see a future of a decentralized web, Ash cites the significant expense of decentralized applications and how the economics and speed don’t work for many applications
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