I think this is largely right. I look a this space as somebody that plugs together bits and pieces of software components for a living for a few decades. I don't need to deeply understand how each of those things work to be effective. I just need to know how to use them.

From that point of view, AI is more of the same. I've done a few things with the OpenAI apis. Easy work. There's not much to it. Scarily simple actually. With the right tools and frameworks we are talking a few lines of code mostly. The rest is just the usual window dressing you need to turn that into an app or service. And LLMs can generate a lot of that these days.

The worry for VC funded companies in this space is that a lot of stuff is becoming a commodity. For example, the llama and phi models are pretty decent. And you can run them yourself. Claude and OpenAI are a bit better and larger so you can't run them yourself. But increasingly those cheap models that you can run yourself are actually good enough for a lot of things. Model quality is a really hard to defend moat long term. Mostly the advantage is temporary. And most use cases don't actually need a best in class LLM.

So, I'm not a believer in the classic winner takes all approach here where one company turns into this trillion dollar behemoth and the rest of the industry pays the tax to that one company in perpetuity. I don't see that happening. The reality already is that the richest company in this space is selling hardware, not models. Nvidia has a nice (temporary) moat. The point of selling hardware is that you want many customers. Not just a few. And training requires more hardware than inference. So, Nvidia is rich because there are a lot of companies busy training models.