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
Guest Links:
Quick Takeaways:
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The price of doing startup investment deals, prior to AngelList syndicates, was about 10x what it is today w/ syndicates.
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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).
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Tech is shifting from making humans more efficient to completing activities for humans– and this is why AI is the next revolution.
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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.
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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.
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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 will cease to be a category for Venture Capital investors.
- 4 phases of AI– we are currently in Phase 3:
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Phase 1 (low risk): AI applied to consumer applications (Google and Amazon giving recommendations)
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Phase 2 (slightly higher risk): AI applied to enterprise SaaS (CRMs suggesting leads)
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Phase 3 (high financial risk): AI-centric applications that completely replace a workflow (AI tech to estimate damage on a car)
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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)
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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.
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In AI, data is the moat and machine learning is a way to compound the value of that moat.
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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
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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.
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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.
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The key metric for phase 3 AI is: Is the efficacy better than a human?
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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.
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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.