I think the push from management for us to use AI has made it so we don’t have to be efficient with our consumption, so now we write md files which we feed to Claude in a loop instead of python and bash scripts to do routine tasks.

A Chinese factory can train sweatshop workers in two weeks on a new pillow design. A dedicated machine costs millions and can't pivot. Human labor wins not on capability. The machines exist. It wins on flexibility per dollar. And the ratio still favors humans by an order of magnitude in most categories.

Agent replacements are the dedicated machine. Their real cost isn't tokens. It's tokens plus the engineer wrapping them, plus orchestration, plus the supervisor, plus the eval pipeline, plus the rebuild every time a model version subtly changes behavior. The team you replaced could pivot in two weeks. The agent stack can't.

Flexibility per dollar is the gap.

because companies will need

“proof of productivity gains or metrics that show a clear return for all this AI investment.”

which in my opinion is simply not true. I haven’t seen any good study that showed AI to actually improve productivity overall. It massively helps in some areas, but then promptly gets stuck in others. So you still need an expert to guide it.

I think its the same disease that makes people make shitty, unoptimized, bloated apps because modern client machines ahve so much ram. But that wont work AI agents. Not until tokens become dirt cheap anyway. Until then we'll need apps with more efficient usage patterns

People are willing to accept the fact that the token price will come down or efficiency will go up even more! Meanwhile, they are sure of the cost of human workers from decades of data we’ve had.

So... if you spend $3m to replace a $1m team... you are betting on that $3m cost coming down. It's a proof of concept. The first step is to find out if agents can do the job at all. At this point you are hoping future versions will get more efficient.

Trying to make something efficient before you know that it is even possible is hard.

Drop-in, profitable on day-1 isn't what the frontier looks like.

Using human workers: monthly subscription but rate limited due to biological nature.

1. Build-out and Competition: (current phase) Multiple AI companies write down massive debt while building data centres and offering sweetheart deals to customers in an attempt to dominate the market. The financial numbers will be silly by design in this phase because it's all predicated on obliterating/outlasting the competition so you can move on to...

2. Enshittification and Exploitation: With most competition wiped out, the survivors will have to pay their debts. A chainsaw will be applied to every corner that can be cut (and many that shouldn't). Prices will be jacked up mercilessly.

3. Maturity: Eventually, once debts are paid down, the technology will reach the point where it's cheap and omnipresent. It might be good. It might not be. e.g. Web search is "mature", but it kind of sucks right now.

AI users are going to become more efficient in how they use it and they're also going to learn when AI is appropriate to use and when it isn't. AI itself will likely improve long-term, but it may get worse at times. It's definitely going to get much more expensive. The math is going to change during each of these phases. Businesses who torch their human capabilities and become dependent on AI during Phase 1 are headed for rough sailing in Phase 2.

The best part for their AI argument lowering fees - the AI is crap, it can help with QA, but still 98% of reports are false positive and can't really do almost any task.

So told them to feel free to replace me with AI if they think AI can do my job and send me only tasks AI can't do, but keep my rates same (the reality is AI by itself can't do any of my tasks) and still didn't warn them about introducing new rush/holiday fees I am not charging yet and are included in the rate + new AI fee for tasks AI simply can't do. Only result will be, maybe I will get less tasks, but I will make sure to charge more for those AI can't do.

It gets worse when you look at LLM (or even any other kind of AI) benchmarks, they tend to cap out around 110% of human performance.

The more that LLM services try and creep towards profitability, the more features they are paywalling behind higher tiers, the more some lazy junior dev is going to look like a better value proposition.

And when some of the CTO's they have pushed LLMs on to go looking for cost savings, some of them are going to look at opex instead of capex and in house the LLMs using open models.

The only real question to my mind is whether the air will be let out of the AI balloon slowly, or if it escapes in one big pop.