5 Contrarian AI Theses For Early Stage Investors

Ideas that challenge conventional wisdom

Happy Sunday and welcome to Investing in AI. I’m Rob May, CEO at Nova, the company behind Brandguard. I also host the AI Innovators Podcast. Let me know if you have suggestions for interesting guests.

As an angel investor, I constantly shape and re-shape my theses. If you’ve followed my writing on AI (since 2016!) I’ve shifted my points of view on many things over time. This week, I want to explore 5 AI theses I’m mulling over that are less common. The goal is to prime your mind to watch for the evidence that these are true or false, so that you can shift your perspectives with the evidence.

1. Horizontal LLMs will lose. This thesis comes in two flavors. The first is, LLMs generally will lose when they get displaced by a new and better technology. Many of the people I know who work at the cutting edge of AI believe LLMs will not get us to AGI, and that the next leaps will come from something new, not just more data. The second version of this thesis is that LLMs will verticalize to the extent it probably makes more sense to use multiple versions of vertical LLMs than one horizontal one, for most applications. The latest wave of AI has been all about training data and compute, and if these two trends (new architectures, verticalization) make those less powerful, it has interesting implications for who wins in certain markets.

2. AI won’t impact full markets the way previous tech waves have - but instead will only impact specific companies. This thesis is a bit nuanced, but I will try my best to explain what I mean. Most of the time, VCs have these ideas like “cloud is going to cause XYZ changes in enterprise software” or “mobile is going to mean ABC for fintech.” They address tech changes in terms of how they impact markets, not individual companies. This thesis says that AI won’t have a consistent impact across the companies in a specific market. The way to think about AI isn’t “here’s what it will mean for this industry.” The reason this could happen is that AI fundamentally applies intelligence and learning to steps in the corporate value chain, and some companies have value chains that are much much more amenable to taking advantage of that than others. In fact, most companies try to build their workflows so you need as little intelligence and learning as possible. Best practice is to systematize everything you can. So one possibility here is that the companies that benefit most from AI may have more in common by having similar steps in their value chain rather than in terms of the product they make or customer they serve.

3. AI will kill most forms of competitive advantage. Some strategic thinkers have been arguing that as tech moves faster and faster, long term competitive advantages go away, and all you have is a series of short term fleeting competitive advantages. Google’s “we have no moat” memo is a good example of where this thinking currently is on AI. If intelligence and execution both eventually become commoditized from AI, where will competitive advantage accrue? What will it mean for early stage investing?

4. AI will bifurcate the economy into real and AI worlds. I’ve written about this before but, I’m not sure that many people agree so I’m including it as a non-popular thesis. When I speak on this topic, I always point out that at some level you run up against the laws of physics, and AI can’t change those. Concrete dries at the speed concrete dries. AI isn’t going to help us build new cities in days instead of years. Some things still take time. Investors are pouring money into industries where, at best the benefit of AI might be 20-30%, not 20-30x.

5. Customer acquisition channels will collapse into agents. Like many of you I’ve been following AutoGPT closely. The first order effect, if agents become the norm, is simply that we use them more for more tasks and maybe we use fewer software application interfaces. But I think not enough people are thinking about the second order effect of that which could be - the agents become a major customer acquisition channel. Why go to G2 or answer a cold sales email or click on an ad when I can just ask my agent what I should do? It may not happen because the companies building agents have a lot of issues to solve and won’t be thinking about that yet, and the companies selling you stuff have every incentive to exploit new channels, including agents. It will be interesting to see how it plays out but as an investor, you have to think about this long term. If you like a company because of their PLG or community driven GTM, it’s possible those are irrelevant in a few years.

There you have it. I’m always looking for theses that aren’t popular and evaluating them against the evidence I see in the world. These are five currently under evaluation, and they may or may not end up panning out. But in the search for success as a builder and investor at early stage companies, these are important things to think about.

Thanks for reading.