[emanuer]

Here is the perspective of a serial founder, exploring fields which I might be able to disrupt:

- The regulatory moat is immediately intimidating.

- The data moat, often, is quite surmountable as long as LLMs can generate high-quality synthetic data (e.g., user preferences). On this I disagree with the author, to some extend.

- The "distribution moat" is another significant barrier. Even if I have a superior product, if the marketing and sales demands are so high that neither I nor an army of bots can manage it alone, the business becomes nonviable (e.g., enterprise sales).

- "Switching costs" form the next moat. The higher these costs, the greater the value per dollar I must offer over the incumbents (e.g., software for dentists).

- Another key barrier is the “business rules” moat. Achieving 80% of the required features may be easy, but as customers demand 90% or 95%, the complexity and cost of reverse engineering grow exponentially. The more mature the market, the higher these demands (e.g., Jira).

With the power of LLMs at my disposal, I have reaffirmed two core beliefs:

1. I must focus on a niche small enough, so that I am the only provider. (e.g., accounting software for gym owners in the north of France)

2. I must offer a value proposition different from that of the incumbents, where competing with me, would harm their business. (e.g., image editing app where you pay per hour used)

So my search continues…

[mritchie712]

You're likely tossing out a random example on #1, but if that were a real idea, you'd need a good answer for: why can't gym owners in the north of France just use quickbooks or xero.

[emanuer]

You are correct, it was just a random example.

And I share your observation, if there is no clear answer to your question, the idea must be disregarded.

[whiplash451]

I like your train of thoughts. I think you're missing the network effect. It is often an overplayed classic, but I do think that it matters in an AI world.