It's not just salary, but also safety/labor regulation, legal risk, vacations, sick time, personal conflicts, HR, benefits.
Even when automation is more expensive on paper, it's generally still cheaper
You underestimate what these models cost. Uber's budget is $1,500/dev/month. I gather that was put in place because the dev's were going through $6,000/dev/month, which Uber decided could not be cost justified.
Fable costs at least twice as much, or $12,000/dev/month.
Fable can apparently work for hours without supervision, which means a skilled engineer can now have it working on many tasks concurrently. I would not be at all surprised if they can put a nought or two on that number. If you do that, you are well out of "what a human costs" territory.
People keep saying this and it keeps not happening.
ChatGPT Pro was $200/mo when it launched in '23 for a ~100B class model with 8k context. Claude Max is now the same price for practically unlimited access to a ~1T class model with 1M context.
Moore's Law never died, it just switched architectures.
As far as I can tell this part of the job isn't really on anyone's radar anymore.
I can't help thinking there might be some kind of strategic issue here.
Perhaps someone should ask Mythos about it.
If you get $100,000 per year as a SWE, and Anthropic offers a coding model for $100,000 per year (but working 24/7), then you'll have to give up all of those addons that make the fully burdened cost of the employee. Say goodbye to vacation, sick time, benefits, etc.
> "They're slaves."
> "Well, what the heck," said Buck. "I mean, they aren't people. They don't suffer. They don't mind working."
> "No. But they compete with people."
> "That's a pretty good thing, isn't it--considering what a sloppy job most people do of anything?"
> "Anybody that competes with slaves becomes a slave," said Harrison thickly, and he left.
Kurt Vonnegut, Player Piano
Do you not believe in running tests, evaluations, or experiments at all to better understand your environment?
The ROI in the case of a positive outcome is the reduced time needed to inspect the results in the future (the entire point of AI is to know what you can trust it on, so you can delegate everything at that level with less oversight). The ROI in the negative case is the tokens not wasted on tasks to ambitious for the model.
We know this model will be cheaper and faster with time.
And we have not even reached the timespan/timeframe were we have ASIC style models.
OpenAI has to do something which will beat Fable otherwise Anthropic won. China currently overtakes cars, pv, batteries and very soon silicon chip making, it has all the incentive to also take over AI.
Why? Demand for AI compute seems to be increasing faster than new production is due to come online for the foreseeable future, particularly if more-intensive models induce demand.
I find it good for code reviews.