The AI Startup “Goldrush” Feels A Lot Like Business as Usual
Artificial Intelligence has been around for decades but in the past nine months has it become a panacea to every VC backed investment strategy. Going from something stuck behind the scenes, designed to get us to click more ads – into something humans might mistake for actual intelligence and personality.
The real utility is upon us, and so is the hype.
No one has taken more notice than the startup VC ecosystem. It’s not just that this technology really does look like it’s going to change the world in many ways, it’s that investors were hungry. The heyday of new Social Media companies of the 2000’s, the Gig Economies of the early 2010’s, and the “AirBNB for yet-something-else” became mature (and over-saturated) businesses. Investors have spent the last half-decade looking for the next big thing. When it came to new startups pitching for investment, the quickest change we saw was that it became mandatory for every startup pitch to include an AI angle. Even if the core of their business had no particular need for it, they had to have a story about it. At first, this felt a bit forced, but now it feels expected. At a minimum, it shows you’re aware of the startup world you’re entering and are planning for the future.
AI represents more than just a new technology. It’s poised to become a new platform. It belongs in the list of things like Mobile, Cloud, or even the Internet itself. We surely live in extraordinary times to have witnessed the birth of so many world-changing technological advancements. As a platform, it has some unique properties that allow it to move even faster than previous platforms.
There are many places for companies to be born within the AI ecosystem, but the explosion of most new companies are those building on top of someone else’s AI system. A company that provides a seemingly new capability can be built in weeks or days. Because everything is so new, many of these companies can provide consumers with new superpowers.
The value proposition is easy to understand and often quite compelling.
At the same time, you need to consider that if a company can be built in days, mostly as a layer on top of someone else’s system, are they providing enough value to be a good investment?
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Interestingly, these companies are also potentially training the system that they’re built upon on how to do what they do. So by building a legal document analysis system on top of someone’s AI system, you may be inadvertently training that AI system to analyze legal documents without you. It’s quite interesting to see a new platform that absorbs capabilities from things built upon it.
The term “AI Startup” has already become broad and vague.
Although the hype generates a great deal of fear-of-missing-out, a good deal of scrutiny needs to be applied to evaluating new AI companies and their long-term value proposition.
Companies with proprietary data to train their own models have a built-in moat (which then makes the company’s value about its data, and less about the AI wrapped around it). Companies that provide the tools built around the AI ecosystem can also have a strong and defensible story.
Building the foundational pieces, like the LLMs themselves, was an insurmountable task just a few years ago, but it’s been surprising how fast new players are entering this arena. According to the Stanford Institute for Human Centered Artificial Intelligence 2022 AI Index Report, training an AI image-classification system cost only $4.60 in 2021, compared to over $1,000 for a similar system in 2017, pointing to the overall trend of lowering training costs with faster training time. It’ll be fascinating to watch which companies, if any, take the lead in this space as technology advances rapidly.
Right now the investing space is a balance between the incredible speed it’s advancing and separating the wheat from the chaff. On top of that, investing in AI startups follows a familiar playbook of a company with a good market, a superlative team, and a convincing argument of execution. In other words, while the AI ecosystem might be moving at double the normal speed – the investment thesis feels a lot like business as usual.
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