A thought-provoking read on the challenges of building a true strategic moat for companies that are applying—rather than developing—AI models.

My take? We’re still in the MS-DOS era of AI commercialisation. Just like MS-DOS, today’s AI largely relies on a command-line interface (aka chat). The real breakthrough will come when we move beyond this into something more intuitive, productive, and user-friendly.

The companies that solve this UI challenge won’t just improve usability—they’ll build strong brands, drive mass adoption, and unlock powerful network effects that could form the foundation of a lasting strategic moat.

But for now? We’re still in the early days.

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In short, unless you’re building or hosting foundation models, saying that AI is a differentiating factor is sort of like saying that your choice of database is what differentiated your SaaS product: No one cares.

As a baseline assumption, better models are a rising tide that lifts all boats. It’s generally a good thing for AI applications when more powerful models come out because it gives you a more powerful engine to build on, but this isn’t the basis on which you’re going to build a moat. In the immediate frenzy after a new model is released, one team or another might figure out a better way to use that model, but that advantage is going to evaporate extremely quickly. There’s far too much useful content out on the internet for those advantages to last. This is basically the same as the prompt engineering moat we discussed above. In other words, if you’re relying on that as your differentiation, you’re going to get quickly steamrolled by the competition.
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