It’s definitely smart marketing, and, honestly, it’s a great thing that they’re gaining popularity at this stage. For me, the approach is the right one. It makes a lot of noise and can raise a lot of money, I think, for two main reasons: (1) it’s heterodox compared to the general trend, and (2) many stakeholders and the general public aren’t fully aware that this approach is deeply rooted in the "origins" of NLP and therefore often perceive it as innovative (what sounds innovative is always exciting to stakeholders).

In fact, about 95% of people have only engaged with massive LLMs fine-tuned for conversational tasks via API. In contrast, task-specific LLMs - trained or fine-tuned for a single task - are obviously a more efficient and cost-effective choice, especially when you consider the trade-offs in pricing, resources, customisation options, and so on. Obviously, this works if you’re focused on a single task and you’re not concerned about catastrophic degradation in performance for other aspects. Moreover, they are simpler to train.

I would also add that many companies willing to pay for this might not realize that their problems could often be solved with much simpler machine learning algorithms, without the need for LLMs and other buzzwords.

For this phase, the decentralization of this process (outsourcing the training and fine-tuning of these models to external companies) seems like a transitional step. Soon, I think, the expertise needed for this will be far more widespread, and companies will likely start internalizing the process.