Will Anthropic/OpenAI really hire anyone who can fine-tune an LLM?
Otherwise, you should just use gpt5
Preparing a few thousands training examples and pressing fine tune can improve the base LLM in a few situations, but it also can make the LLM worse at other tasks in hard to understand ways that only show up in production because you didn’t build evals that are good enough to catch them. It also has all of the failure modes of deep learning. There is a reason why deep learning training never took off like LLMs did despite many attempts at building startups around it.
Andrej karpathy has a rant about it that captures some of the failure modes of fine tuning - https://karpathy.github.io/2019/04/25/recipe/
Then try the same thing using fine tuning. See which one wins. In ML class we have labeled datasets with breeds of dogs hand labeled by experts like Andrej, in real life users don’t have specific, clearly defined, and high quality labeled data like that.
I’d be interested to be proven wrong
I think it is easy for strong ML teams to fall into this trap because they themselves can get fine tuning to work well. Trying to scale it to a broader market is where it fell apart for us.
This is not to say that no one can do it. There were users who produced good models. The problem we had was where to consistently find these users who were willing to pay for infrastructure.
I’m glad we tried it, but I personally think it is beating a dead horse/llama to try it today
Why would they join you rather than founding their own company?
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.
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.
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.
Read Andrej’s blog that I linked earlier in the thread if you want to understand why.
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.
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.
Depends on what you want to achieve, of course, but I see fine-tuning at the current point in time primarily as a cost-saving measure: Transfer GPT5-levels of skill onto a smaller model, where inference is then faster/cheaper to run. This of course slows down your innovation cycle, which is why generally this is imo not advisable.
But a recent trend that cut into the cost savings is that foundation model companies have started releasing small models. So you can build a use case with qwen 235B, then shrink down to 30B, or even all the way down to 0.6B if you really want to.
The smaller models lose some accuracy, but some use cases are solvable even by these smaller and much more efficient models.
The problem is easily avoided by not using it for other tasks.
This is a reason why general purpose models shine. You don’t have to carefully characterize a task and put guard rails around it.
However, I personally think that this intuition applies to products and interfaces, not to AI.
Intelligence and learning is general. Intelligence without generalization is memorization, which seems to be less useful in practice.
They'll pay for anyone that can personalize models to be meaningfully diverse.
Training an LLM from scratch is trivial - training a good one is difficult. Fine tuning is trivial - doing a good job is difficult. Hitting a golf ball is trivial - hitting a 300 yard drive down the middle of the fairway is difficult.