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That’s what your VC investment would be buying; the model of “pay experts to create a private training set for fine tuning” is an obvious new business model that is probably under-appreciated.

If that’s the biggest gap, then YC is correct that it’s a good area for a startup to tackle.

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adrian_b 8 days ago | parent [-]

It would be hard to find any experts that could be paid "to create a private training set for fine tuning".

The reason is that those experts do not own the code that they have written.

The code is owned by big companies like NVIDIA, AMD, Intel, Samsung and so on.

It is unlikely that these companies would be willing to provide the code for training, except for some custom LLM to be used internally by them, in which case the amount of code that they could provide for training might not be very impressive.

Even a designer who works in those companies may have great difficulties to see significant quantities of archived Verilog/VHDL code, though it can be hoped that it still exists somewhere.

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theptip 8 days ago | parent [-]

When I say “pay to create” I generally mean authoring new material, distilling your career’s expertise.

Not my field of expertise but there seem to be experts founding startups etc in the ASIC space, and Bitcoin miners were designed and built without any of the big companies participating. So I’m not following why we need Intel to be involved.

An obvious way to set up the flywheel here is to hire experts to do professional services or consulting on customer-submitted designs while you build up your corpus. While I said “fine-tuning”, there is probably a lot of agent scaffolding to be built too, which disproportionately helps bigger companies with more work throughput. (You can also acquire a company with the expertise and tooling, as Apple did with PA Semi in ~2008, though obviously $100m order of magnitude is out of reach for a startup. https://www.forbes.com/2008/04/23/apple-buys-pasemi-tech-ebi...)

	▲	adrian_b 7 days ago \| parent [-]
		I doubt any real expert would be tempted by an offer to author new material, because that cannot be done in a good way. One could author some projects that can be implemented in FPGAs, but those do not provide good training material for generating code that could be used to implement a project in an ASIC, because the constraints of the design are very different. Designing an ASIC is a year-long process and it is never completed before testing some prototypes, whose manufacture may cost millions. Authoring some Verilog or VHDL code for an imaginary product that cannot be tested on real hardware prototypes could result only in garbage training material, like the code of a program that has never been tested to see if it actually works as intended. Learning to design an ASIC is not very difficult for a human, because a human does not need a huge number of examples, like ML/AI. Humans learn the rules and a few examples are enough for them. I have worked in a few companies at designing ASICs. While those companies had some internal training courses for their designers, those courses only taught their design methodologies, but with practically no code examples from older projects, so very unlikely to how a LLM would have to be trained.