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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?

echelon 1y ago
> 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.
gdiamos 1y ago
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.
bfio 1y ago
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CaptainOfCoit 1y ago
> 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.
- gdiamos 1y ago
  llm.finetune(data) is a leaky abstraction Read Andrej’s blog that I linked earlier in the thread if you want to understand why.
  - CaptainOfCoit 1y ago
    If it works it works? :shrug:
    - gdiamos 1y ago
      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.
      - CaptainOfCoit 1y ago
        > 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.

echelon 1y ago

> 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.

gdiamos 1y ago

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.

bfio 1y ago

[dead]

CaptainOfCoit 1y ago

> 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.

gdiamos 1y ago
llm.finetune(data) is a leaky abstraction Read Andrej’s blog that I linked earlier in the thread if you want to understand why.
- CaptainOfCoit 1y ago
  If it works it works? :shrug:
  - gdiamos 1y ago
    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.
    - CaptainOfCoit 1y ago
      > 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.