161. Why SaaS is not a fit for VC and How AI Compounds Competitive Advantage (Ash Fontana)

Ash Fontana The Full Ratchet

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Ash Fontana of Zetta Venture Partners joins Nick to discuss Why SaaS is not a fit for VC and How AI Compounds Competitive Advantage. In this episode, we cover:

  • Categories of AI that Ash is most interested in
  • The difference between real AI and AI-enabled companies
  • Why SaaS will cease to be investable by VCs
  • The current AI stage of adoption
  • How he times the market
  • The four phases of AI
  • What phase of AI they invest in
  • How AI is and will be affected by limited data
  • How startups can compete for talent w/ GAFA (Google, Amazon, Facebook, Apple)
  • The moats being created by their AI-first portcos
  • How they think about metrics and milestones for AI-backed companies
  • If AI should be feared
  • and finally we wrap up w/ Ash’s thoughts on Chris Dixon’s position that we will see a movement from centralization back to decentralization in tech– and the role that AI will play


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Quick Takeaways:

  1. The price of doing startup investment deals, prior to AngelList syndicates, was about 10x what it is today w/ syndicates.
  2. Having specific investment focus helps with investment process, operational expertise, talent networks, identifying common problems, customer networking, gaining intelligence, differentiation and, ultimately, drives better returns (16% higher multiples, 33% higher IRRs and have lower failure rates).
  3. Tech is shifting from making humans more efficient to completing activities for humans– and this is why AI is the next revolution.
  4. They consider what type of problem is trying to be solved by AI– and if sufficient, data, tools and technology exists to drive prediction in that area.
  5. They invest in AI that creates the core value for the customer/user.  They do not invest in “AI-enhanced” companies where the key differentiation is not AI.
  6. In Venture Capital, one should invest in something that has a competitive advantage for decades– the moats must be durable over long periods.
  7. Because of the importance of long-term, durable moats, SaaS will cease to be a category for Venture Capital investors.
  8. 4 phases of AI– we are currently in Phase 3:
    1. Phase 1 (low risk): AI applied to consumer applications (Google and Amazon giving recommendations)
    2. Phase 2 (slightly higher risk):  AI applied to enterprise SaaS (CRMs suggesting leads)
    3. Phase 3 (high financial risk):  AI-centric applications that completely replace a workflow (AI tech to estimate damage on a car)
    4. Phase 4: (very high financial risk): Applications we never considered before (AI to optimize data center use or energy flow across an electricity grid or making medical diagnoses)
  9. 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.
  10. In AI, data is the moat and machine learning is a way to compound the value of that moat.
  11. Questions Ash asks about data:  Is the dataset really hard to get, is it fungible does it have high dimensionality, does quantity provide quality, is it perishable
  12. Is there a virtuous cycle w/ the data: the data feeds an algorithm that predicts something for a customer, the customer uses the product more and more, that adds more data to the system which makes the system better and better.
  13. The two key questions he asks of startups:  Is there significant value in the data and do they have a way to compound that value.
  14. The key metric for phase 3 AI is:  Is the efficacy better than a human?
  15. AI itself shouldn’t be feared but AI can create monopolistic power, held by a few companies– and that is something to be concerned about.
  16. 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.