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Comment by htrp | Hacker News Reader

> 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.

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I did expect that too, but it isn't happening reliably.

But small customized models seem to perform close to as well as large general ones.

No they wont. Any model you train now will be beat by GPT5 easily

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

Network availability, latency, privacy, etc. many qualities to consider beyond model size and performance for applications.

And cost-efficiency, if I'm using an LLM as an Siri-like assistant on my phone, most of the tasks I'll want it to do won't be that complicated and it would be a waste to send them to some SOTA LLM in the cloud, which I'll have to pay for by a monthly subscription or on a per-token basis.

Except nobody but groq has such type of machines, and the economics of cloud AI is very hard to make it works in practice. Offloading the capital cost (which is the hardest kind of cost to swallow for a company) to customers is very compelling business-wise.

What they are doing is not very special soon a lot of companies will do it. Lets call it end to end LLM hardware

And they will be in everyone's laptop, because that's pretty much the only economically viable way way to amortize the development cost ;)

Not for most human language, or anything that requires business-specific context where what's publicly available lags behind the state of the art your business cares about.

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.

Past performance is not a predictor of future performance.

While it's possible the gap between GPT5 and 4 is as big as between 4 and 3, it's unlikely. The gap between 2 and 3 was much larger as the one between 3 and 4 (and similarly between 1 and 2).

Also, it's not clear that GPT5 will do this in an *economical way* once the spigot of investor money stops.

Tell more more about the data moat.

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