Unfortunately it will take longer for our bosses to walk it back. I feel like I'm fighting the battle daily, telling execs what kind of work LLMs do not replace... it's very slippery, they keep on doing the rhetorical texas two-step - I don't think they even realize they're doing it. We communicate that LLM is amplifying, they hear it can replace. "No, we need humans to help with specs" "But AI can help with that." "But only help, they can't come up with the idea." "Sure they can, we can just ask them."

It's also amazing how hidden some of these realities were before. Like, you assign a ticket to a developer - in the past they just wanted to know the developer was working on it and didn't care so much which work was what. They'd probably be so surprised to find out that a large percentage of implementation was deriving exactly what was meant by the jira ticket or the specification or the product person's intent. Which is all the stuff you have to work on before you can type in a prompt to an LLM. But now there's this pressure to believe that the developers only do the implementation part that the LLMs do, so they can pretend there will be major efficiency improvements. And it's really hard to explain to them what it is that developers even do.

I know I'm not saying anything new here, but at least where I'm working all of these matters feel much more present than they did months ago.

Companies only want to spend money on AI in order to save more money somewhere else. So if LLMs make some tasks easier but overall don't make a big dent on shipping dates because of all the friction points you mentioned, and more, then it will be difficult to justify buying all these tokens. Even if the shipping timelines are the same but the quality goes up that still could be hard to justify token spend too.

It tells you a lot about your execs and how little they care, either for their employees or their customers. The quarterly profits are their God and they will worship at the altar of the stock price.

Instead of finding ways to make AI enhance their employees and make them more productive, they immediately jump to ways to eliminate employees. It's the opposite of a growth mentallity.

I'd love for these executives to show me a time when investing in people was the wrong choice. I've never seen a company punished for doing the right thing, caring for humans and providing a good work environment. This suicidal tendency in the corporate world to constantly decimate your workforce every cycle is just mind boggling and the fact the stock market responds to it so positively is horrifying.

That's exactly right.

I hav set up a system where customer success and sales can drop in artifacts of customers talking about what they value (emails, transcripts, etc) and skills analyS them and then use them to add context to issues in the backlog.

The idea is that everything in the backlog is tied to an explanation of who it benefits and how it benefits them. We're using AI to merge multiple sources and automate the writing of it. The hope is it streamlines that communication. Our backlog issues now are 3-4 pages that explain very clearly why the issue matters, what it's higher level goal is, etc.

At first engineering was like "woa that's a lot of text" but after reading it was then "that's the best written issue I've ever seen".

Okay, so cool we are streamlining product management and setting ourselves up to automate customer feedback to development pipeline, dramatically cutting down on that issue discernment bottleneck you're pointing at...

..except today I found an issue with critical hallucinations in it. It mixed up what the customer said and what the cs rep said, to the extent that the issue was just straight up incorrect. This was with Opus 3.7 extended thinking. (Mind you it was a big transcript and pushing the limits of context window, loading multiple skills, etc)

So there's some serious potential, but it's just not there yet. Even if all this works flawlessly, the context these models can hold at once is like 0.1% of what a human can (if not less). So we will still need the humans for quite a while to make the harder decisions.

This is in a very leading edge startup pushing the limits of what LLMs can do... And even in this context optimized for LLM success it's still no where close to replacing people. We get a ton of value out of LLMs, but let me clarify that the hold up isn't just fact checking, it goes way beyond that.

In some ways I keep thinking it comes down to context management. Humans can hold so many orders of magnitude more context. Context is the bottleneck. The tech is a long way off being capable enough, and even when it is, there will be lots of operational and cultural obstacles to getting the right context into the AI.

And then there is the jevons paradox consideration...

It feels like we are a long way off. It seems plausible a generation from now employment will look very different, and I can kind of grasp how we get there, but I'm extremely skeptical of any unemployment apocalypse on a 5 year time horizon being triggered by AI. Maybe an unrelated economic shock, but not AI.

> "But only help, they can't come up with the idea." "Sure they can, we can just ask them."

I've had multiple instances now where AI left to it's own devices has solved a tricky problem that I honestly didn't think it was capable of. I routinely have them design their own experiment loops, learn from each round and iterate on the process. Multiple times it has lead to a needle moving change with no need for human intervention.

There are, of course, many cases where this is not true, but they're certainly more capable than I had previously thought and can solve an increasingly large range of problems on their own.

Reading the comments here is like glimpsing in to either the past or an alternate timeline.

There's tons of inertia in the system so don't expect change to happen over night, but reading "AI won't replace jobs" today feels a lot like when I used to hear "nobody will purchase things online!" back in the mid 1990s.