It is something to sneeze at if you are 10-15% more expensive to employ due to the cost of the LLM tools. The total cost of production should always be considered, not just throughput.
Claude Max is $200/month, or ~2% of the salary of an average software engineer.
https://llm-stats.com/models/compare/gpt-3.5-turbo-0125-vs-q...
So they'll probably find a reasonable cost/value ratio.
As long as the courts don't shut down Meta over IP issues with LLama training data, that is.
I can't stress that enough: "open source" models are what can stop the "real costs" for the customers from growing. Despite popular belief, inference isn't that expensive. This isn't Uber - stopping isn't going to make LLMs infeasible; at worst, it's just going to make people pay API prices instead of subscription prices. As long as there are "open source" models that are legally available and track SOTA, anyone with access to some cloud GPUs can provide "SOTA of 6-12 months ago" for the price of inference, which puts a hard limit on how high OpenAI, et al. can hike the prices.
But that's only as long as there are open models. If Meta loses and LLama goes away, the chilling effect will just let OpenAI, Microsoft, Anthropic and Google to set whatever prices they want.
EDIT:
I mean LLama legally going away. Of course the cat is now out of the bag, the Pandora's box has been opened; the weights are out there and you can't untrain or uninvent them. But keeping the commercial LLM offerings' prices down requires a steady supply of improved open models, and the ability for smaller companies to make a legal business out of hosting them.
If these companies plan to stay afloat, they have to actually pay for the tens of billions they've spent at some point. That's what the parent comment meant by "free AI"
Training is expensive, but it's not that expensive either. It takes just one of those super-rich players to pay the training costs and then release the weights, to deny other players a moat.
All the 100s of billions of $ put into the models so far were not donations. They either make it back to the investors or the show stops at some point.
And with a major chunk of proponent's arguments being "it will keep getting better", if you lose that what you got? "This thing can spit out boilerplate code, re-arrange documents and sometimes corrupts data silently and in hard to detect ways but hey you can run it locally and cheaply"?
Which, again, leads to a future where we're stuck with local models corrupting data about half the time.
Short-term, it's a normal dynamics for a growing/evolving market. Long-term, the Sun will burn out and consume the Earth.
The R&D is running on hopes that increasing the magnitude (yes, actual magnitudes) of their models will eventually hit a miracle that makes their company explode in value and power. They can't explain what that could even look like... but they NEED evermore exorbitant amounts of funding flowing in.
This truly isn't a normal ratio of research-to-return.
Luckily, what we do have already is kinda useful and condensing models does show promise. In 5 years I doubt we'll have the post-labor dys/utopia we're being hyped up for. But we may have some truly badass models that can run directly on our phones.
Like you said, Llama and local inference is cheap. So that's the most logical direction all of this is taking us.
There's risk to that assumption, but it's also a reasonable one - let's not forget the whole field is both new and has seen stupid amounts of money being pumped into it over the last few years; this is an inflationary period, there's tons of people researching every possible angle, but that research takes time. It's a safe bet that there are still major breakthroughs ahead us, to be achieved within the next couple years.
The risky part for the vendors is whether they'll happen soon enough so they can capitalize on them and keep their lead (and profits) for another year or so until the next breakthrough hits, and so on.
How is one spending anywhere close to 10% of total compensation on LLMs?