How can you hire enough people to scale that while making the economics work?

Why would they join you rather than founding their own company?

> How can you hire enough people to scale that while making the economics work?

Once you (as in you the person) have the expertise, what you need all the people for exactly? To fine-tuning you need to figure out the architecture, how to train, how to infer, pick together the dataset and then run the training (optionally setup a pipeline so the customer can run the "add more data -> train" process themselves). What in this process you need to hire so many people for?

> Why would they join you rather than founding their own company?

Same as always, in any industry, not everyone wants to lead and not everyone wants to follow.

llm.finetune(data) is a leaky abstraction

Read Andrej’s blog that I linked earlier in the thread if you want to understand why.

If it works it works? :shrug:

The problem is that it doesn’t always work and when it does fail it fails silently.

Debugging requires knowing some small detail about your data distribution or how you did gradient clipping which take time and painstakingly detailed experiments to uncover.

> The problem is that it doesn’t always work and when it does fail it fails silently.

Right, but why does that mean you need more employees? You need to figure out how to surface failures, rather than just adding more meat to the problem.

> How can you hire enough people to scale that while making the economics work?

Pick the right customers.

> Why would they join you rather than founding their own company?

The network effects of having enough resources in one place. For having other teams deal with the training data, infrastructure, deployment, etc.

I think you are saying to go after the very high end of the market.

That’s fair, one market segment of this is sometimes called sovereign compute.

Another common model that I have seen is to become the deepmind for one very large and important customer.

I think this works.