The hype is extreme right now and everyone is still trying to figure out how to use the tools. People who are further along the bleeding edge are trying to tear down all the process that we used to have to further improve velocity. After people go all the way to "dark factories" most companies will realize that they don't actually have any good ideas for what to build, and honestly never did. They've been coasting for years, someone else can replicate their product now and its just a race to the bottom. At that point, token budgets are going to collapse.

I'm staunchly pro-AI as a technology, but I do think the bubble is going to pop in the next year or two just because the business value won't materialize for most companies fast enough.

There are some improvements on coding and speed of developers, but more broadly in the enterprise AI is just producing a lot of slop that folks are getting fed up with.

AI content has a look and feel people sense immediately.

It’s amazing to see how quickly things shifted from “wow this is so cool, AI is going to change everything” to folks calling out “you lazy bum, this just looks like some slop you threw together with AI… let’s get some real thinking please.”

We are firmly heading into “trough of disillusionment” territory on the hype cycle.

One more point I noticed: since AI adoption is being promoted by companies, collaboration between developers could suffer. Why wait for a more experienced developer to have the time to explain some aspect of the codebase to you (and at the same time confess your ignorance), when AI can do it right away in a competent-sounding way (and most of the time it will probably be right, too)?

I think if these companies first adopted local models with fewer token outs and the learners got to watch the tokens get made, there'd be a lot more understanding.

The Hub captures decisions humans made. It can't see the ones they didn't, which is most of what AI ships. You instrument the deliberate half and inherit the undeliberate one.

> The company may still learn almost nothing.

Not a problem if the hired "AI" now does that job. /i

Path dependencies between invention and utilization... are complicated and hard to fathom.

Our mental models of developments like the industrial revolution, literacy, printing or suchlike tend to be a lot more straightforward than how things play out in practice.

When a bottleneck is eliminated... you tend to shortly find the next bottleneck.

Meanwhile, there is an underlying assumption everyone seems to make that "more software, more value" is the basic reality. But... I'm skeptical.

To do lists, wishlists, buglists and road maps may be full of stuff but...

Visa or Salesforce have already exploited all their immediate "more software, more money" opportunities.

The ones in a position to easily leverage AI are upstarts. They're starting with nothing. No code. No features. No software. With Ai, presumably, they can produce more software and make value.

Also... I think overextended market rationalism leads people to see everything as an industrial revolution...which irl is much more of an exception.

The networked personal computing revokution put a pc one every desk. It digitized everything. Do we have way better administration for less cost? Not really. Most administrations have grown.

Did law fundamentally change dues to dugital efficiency? No. Not really.

If you work on a terrible enterprise codebase... it's very possible that software quality/quantity isn't actually that important to your organization.

Link is dead.

In the current "big co" I'm working for it's a bit ridiculous.

While AI tools have been provided pretty quickly (over a year ago, I initially used gemini cli, then copilot once it added anthropic models) the management is absolutely clueless about it.

The top wants agents. Every team is asked few times a week "what autonomous agents will you build next" and answers the current AI lacks agency required not to mess up critical long running tasks and generate even more work are falling on deaf ears.

(also ideas such as, why don't we setup a wiki page were teams can post their repetitive tasks and we can use AI to script them, are considered "not fast enough" - just build it... but we are the automation team, we automated everything we do years ago :-)

Middle managers on the other hand suddenly started giving juniors senior's work and asking seniors "tell them (juniors) how to prompt it".

Seriously? How about I prompt it myself instead? Oh, but it makes a shit load of architectural errors and boobie traps the junior will fail to find... So now instead of a cursory glance I have to spend an hour reviewing a small PR from them.

And any questions about "why are you creating a new X for this instead of extending the existing one?" are met with blank panicked stares...

The essence of this BS is contained in my description of the recent "Copilot Review" incident.

We sometimes merge the same Github workflow files (10 line files) to dozens of repos, we have to obtain approvals for the PRs from a bunch of teams working in different timezones, but the merge has to be done everywhere at once and it has to be coordinated with other work.

As we were on a day of such task some "helpful hand" enabled Copilot PR reviews for the whole org.

Copilot helpfully opened 7 or 8 discussions on each PR giving us such precious advice as "your concurrency group uses the commit sha as a differentiating factor, this will allow multiple runs to proceed concurrently" to which one is tempted to answer "no shit sherlock".

We suddenly had almost 200 conversations to "resolve" an hour before the merge and a bunch of approvers didn't give their approvals because "there is a discussion".

Thankfully we had copilot that wrote us a script in 5 minutes to resolve the problem caused by itself...

Maybe our next overnight agent can go over all our open PRs and close Copilot Review conversations with appropriate messages?