Interesting article - agree that AI isn't really a moat rather than something that can be expanded on once the core product brings in data. Confused on why many founders don't recognize this - why do you think so?
Also, do you really think data/AI can be a moat for these big companies? Data is becoming increasingly commoditized, and data itself has marginal diminishing returns. After a certain point, the data collected is very similar to prior data collected and doesn't enhance the predicative capabilities of the model. Even if you take a company like Cloudfare/Crowdstrike, as competitors chase them in these fields, they gain access to similar data that supposedly gave those providers a moat. I found this article from a16z to be interesting on this topic: https://a16z.com/2019/05/09/data-network-effects-moats/
This resonates a lot actually, thanks for sharing!
A lot of data startups I talk to like to highlight their AI/ML prowess even before serving a dozen customers successfully. In my view, there's a growing tendency amongst founders to make assumptions that are often based on second-hand knowledge (customer discovery etc) and turn those assumptions into algorithms. In fact, technical prowess alone can never help build a real moat I believe, let alone in the early days.
Also, I really don't see how startups relying on publicly available AI (GPT-3, DALL-E) can ever have a moat.
largely true but there are some exceptions. In some cases, AI is the product itself(DALL-E?). Also scenarios where the customer onboarding involves ingesting large volume of customer data (CDP with predictive modeling & clustering)
I witnessed first hand the challenges of acquiring data for AI products in the healthcare sector. Not only is getting the initial data hard (and expensive), but you will also face deployment challenges, at least while your model is still nascent, meaning hasn't generalized on the entire (or a good subset) of the population it encounters. This, in turn reduces the margins of AI companies; they become a blend of software + services. Shameless plug on this here -> https://karimfanous.substack.com/p/ai-software-services
Interesting article - agree that AI isn't really a moat rather than something that can be expanded on once the core product brings in data. Confused on why many founders don't recognize this - why do you think so?
Also, do you really think data/AI can be a moat for these big companies? Data is becoming increasingly commoditized, and data itself has marginal diminishing returns. After a certain point, the data collected is very similar to prior data collected and doesn't enhance the predicative capabilities of the model. Even if you take a company like Cloudfare/Crowdstrike, as competitors chase them in these fields, they gain access to similar data that supposedly gave those providers a moat. I found this article from a16z to be interesting on this topic: https://a16z.com/2019/05/09/data-network-effects-moats/
Hey! Just to let you know I linked this post here:
https://www.libertyrpf.com/i/72573854/why-ai-is-not-a-moat
Cheers! 💚 🥃
This resonates a lot actually, thanks for sharing!
A lot of data startups I talk to like to highlight their AI/ML prowess even before serving a dozen customers successfully. In my view, there's a growing tendency amongst founders to make assumptions that are often based on second-hand knowledge (customer discovery etc) and turn those assumptions into algorithms. In fact, technical prowess alone can never help build a real moat I believe, let alone in the early days.
Also, I really don't see how startups relying on publicly available AI (GPT-3, DALL-E) can ever have a moat.
So well said! Thank you for writing this piece.
largely true but there are some exceptions. In some cases, AI is the product itself(DALL-E?). Also scenarios where the customer onboarding involves ingesting large volume of customer data (CDP with predictive modeling & clustering)
Testify!
100% spot on. It's the data that's the moat.
I witnessed first hand the challenges of acquiring data for AI products in the healthcare sector. Not only is getting the initial data hard (and expensive), but you will also face deployment challenges, at least while your model is still nascent, meaning hasn't generalized on the entire (or a good subset) of the population it encounters. This, in turn reduces the margins of AI companies; they become a blend of software + services. Shameless plug on this here -> https://karimfanous.substack.com/p/ai-software-services