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This post seems to misunderstand what a "moat" is in a business sense (and unfortunately a lot of AI hypesters on social media do as well). The fact that LLMs are becoming a commodity was the point of the original "OpenAI has no moat" memo by Google, which has proven to be accurate.
And yet, over and over, I see products with output that clearly comes from simple and frankly lazy prompting. You can do a lot with prompting, but engineerings are not putting in the work! (If an engineer is even the right person... probably not, given any specific application of an LLM.)
Prompting also isn't so reductive that you just write an evaluation and then iterate on the prompt until you satisfy the evaluation. Prompting is a co-creative exercise between the LLM, the domain expert, the product, and the user. And sure "data" fits in there, as well as relationships, comprehensibility, workflows, etc etc... the AI component is just a small piece of any full application.
> We firmly believe the moat for AI application is in the data and the data engineering today. At some point, the process of building custom LLMs might get so fast and easy that we’ll all return to building our own models. That simply isn’t the case today. Customized small models will outperform larger general models for your specific use case.
No they wont. Any model you train now will be beat by GPT5 easily
And of course, not if you care about token throughput more than fancy abilities. Or price for that matter.
So for many if not most businesses needs GPT-4 isn't the best tool out there, and GPT-5 is the canonical example of a vaporware right now.
Earlier quoted context omitted.
I think the real power of customized small models will be running things on local hardware, except that we're in an awkward phase where the local hardware isn't quite beefy enough to run anything really useful yet. Maybe Apple will do something interesting in that space at WWDC.
Also not feasible. A network request to groq type machines will outperform your local hardware by such a huge amount that it wont make sense other than some very niche tasks
Earlier quoted context omitted.
Also not feasible. A network request to groq type machines will outperform your local hardware by such a huge amount that it wont make sense other than some very niche tasks
Network availability, latency, privacy, etc. many qualities to consider beyond model size and performance for applications.
Earlier quoted context omitted.
I think the real power of customized small models will be running things on local hardware, except that we're in an awkward phase where the local hardware isn't quite beefy enough to run anything really useful yet. Maybe Apple will do something interesting in that space at WWDC.
Also not feasible. A network request to groq type machines will outperform your local hardware by such a huge amount that it wont make sense other than some very niche tasks