[cmiles8]

Companies are slamming the brakes on AI in a massive reversal that’s unlike anything I’ve seen in the last 25 years in tech.

6 months ago it was use AI all the time go! Now companies are putting use limitations in place, strict budget controls, and the wagons are circling around various “AI labs” teams that cost a ton and have shown little to no ROI.

It was all fun and games until the bill arrived. Now it seems there’s a mad rush for AI companies to IPO before the music truly stops.

[procgen]

Demand is high and will remain so. Supply just needs to catch up.

[bpt3]

Demand for a magic box that solves your problems at a low cost will always remain extraordinarily high. Supply is the hard part, because it will never catch up.

Some people believed LLMs were that magic box for a time, and that time is coming to an end if the parent poster is correct.

[pseudohadamard]

Just had to deal with this with a company that had outsourced its support to "AI". Probably saved them a ton of money not having to employ those annoying humans. Problem is that for this particular company once you get to the point where you have to contact support you're almost certainly in a situation that no stochastic parrot has any hope of comprehending, let alone solving. I spend about an hour going round in circles with the parrot until I finally figured out what to tell it to get it to give up and connect me to a human, who fixed the problem in about five minutes.

The scary thing here is that I know how the parrots work, what they can't do, and how to get around them. The typical person calling will assume they've been helped by the parrot, which is just going through the motions without comprehending anything or fixing anything.

[KolibriFly]

Well, the history of cloud computing shows that infrastructure usually becomes cheaper over time. But it’s still unclear whether this rule applies to reasoning models.

[cmiles8]

Honestly that’s the trap that’s increasingly looking like it will blow up this whole thing. Nobody can point to any viable revenue pathway that justifies the amount of capital investment underway, all while folks are increasingly slamming the brakes on things.

Theres an extremely ugly financial picture developing that those with full blown AI psychosis appear unable, or simply are unwilling, to see.

[treis]

Of course they can. They're going to sell ads and subscriptions. Both of which are going to make bank. That their service is wildly oversubscribed and hence expensive is not an indication that they're in economic trouble.

[cmiles8]

No.

Ads are a zero sum game where there’s only so much ad money to go around. AI doesn’t grow the pot. Google isn’t going to lose the ad game, it would destroy them. Google got scooped early on with AI search but is roaring back now.

Also consumers won’t pay high amounts for subscriptions, that’s enterprise territory which doesn’t tolerate ads. And these are the folks now slamming the brakes on spending.

Net, “ad revenue” is not even close to a viable plan to save the present train from spectacularly flying off the tracks.

[treis]

ChatGPT has like a billion weekly users that are giving them a massive amount of data. Everyone is going to want to advertise with them.

Enterprise isn't slamming the breaks on spending. At worst they've transitioned from spending like drunken sailors to spending like mildly inebriated sailors. Every single white collar worker is still going to have an AI subscription. And for people like programmers they'll still spend $1k on them.

[Insanity]

Yeah, there's just this massive wave of AI delusion turning into disillusion. Writing code was never the slow part of enterprise development. We've made the slow part _somewhat_ faster, trading off quality in turn all while burning hundreds of thousands of dollars in tokens.

It's no surprise that when ROI remains elusive (it's hard to measure for any knowledge work) and costs are skyrocketing that the C-suite wants to slam the brakes.

[alain94040]

Not my experience at all. One slow part was coding. AI takes care of that. But more importantly, the slow part was iterating through concepts, ideas, and prototypes. I thought people on this site embraced lean startups and agile development. AI really helps make that feedback loop 10X faster. I can do an experiment, show it to coworkers and get feedback in a morning, for something that would have taken me almost a week in the past. So now we can try a lot more options, whereas before, we kept getting hit by the sunk cost fallacy: I spent a week on this, I really don't want to start again from scratch with this other approach that may or may not be better.

[swatcoder]

The lean startup "feedback loop" was with customers (not coworkers). The idea was that you iterate on your viable product (not vibe prototype) with the market that derives value from it.

The slow part is finding those customers, syncing your deliveries with their processes, giving them time to meaningfully assess new workflows and features in the course of their business operations, collating the feedback you receive from all of them, and merging that feedback with your organization's long term growth objectives to drive new ideas into development. Well-developed organizations layer this inescapably slow flow across numerous parallel channels so engineering utilization can stay high since healthy engineering already cycled much faster than these market-engaged flows can.

Neither coding nor internal prototypes were the slow part. Market engagement and market-informed product planning were the slow part. And still are.

You may not realize it yet, and maybe you've just misrepresented it, but most of what you seem to be describing is usually considered wheel-spinning and navel-gazing. You may have made your internal process cycle faster, but you very likely just turned a wasteful busywork churn into a more efficiently wasteful busywork churn.

[alain94040]

Neither coding nor internal prototypes were the slow part

That is not my experience mentoring 100+ startup founders. Building a prototype, the gateway to serious customer engagement, used to take months and many startups would die before finishing their first one.

[skydhash]

Aren't those startups the ones wanting a google style infrastructure based on kubernetes with database sharding, an event-source architecture,... And when you told them a few VPS with postgres would have sufficed, they absolutely insisted that unless it's a next.js app backed by a serveless ecosystem and tens SaaS, they couldn't build their products?

[Insanity]

Fair enough, experiences do differ. But how are you evaluating those POCs? Just based on 'visually what looks better', or architecturally etc?

In my experience, the slow parts are around making sure you're aligning on a long-term vision, understanding the domain and customer problem well enough, balancing the technical aspects/speed today with quality down the line, etc.

This probably does depend on what kind of tech problems you work on. If you're purely doing frontend development I'm sure you'll be faster. If you work on complexer systems with e.g robotics/hardware interaction, I can't see it being significantly faster. YMMV :)

[rickydroll]

There are multiple points of iteration. for me, it's user interface and core algorithms. Because the cost of creating an iteration was so high before, I would think about the problem for a long time and then implement the one that seems best maybe kind of?? I was always wondering that maybe I could have found a better solution. Now with AI, I can iterate through two or three solutions that I'm trying to decide between and see which one works best in a much shorter time frame.

[mrhottakes]

You're not gaining any knowledge, insight, or experience from all of your iteration. You're churning for the sake of churn and pretending you're benefiting from it.

[skydhash]

> I can do an experiment, show it to coworkers and get feedback in a morning, for something that would have taken me almost a week in the past.

That argument always rings hollow to me. What systems were you prototyping that took that long? I don't need to build a complete MVP to present a design. Or understand an API.

In the visual art industry, there are thumbnails and storyboards that are the first iteration of any project. They are quick to produce, and can serve as the basis for brainstorming. No one wants a finished picture, because it restrain your thinking. Too much details and you start bike-shedding.

Only when you've solved higher concerns and have a concrete direction that you start to invest physical efforts. But that does require someone to have the capacity to discern higher concerns from crude sketches. If you don't and rely on "I'll know it when I see it", then you sure need finished products to clarify your thinking.

[russelldjimmy]

Other commenters have made points similar to what I had in mind. The one thing I’d ask is this:

> AI really helps make that feedback loop 10X faster

Why be imprecise or exaggerate? Give the exact multiplier. Words mean something.

[lazide]

The slow part was making money.

Iterating and prototyping can certainly help there, but at the end of the day if you launch a non-working (or non-reliable) prototype, you’re going to just have angry customers, not happy ones.

And that rarely works out well long (or even medium) term.

And most of the value from iterating and prototyping is from learning, something the AI kinda screws with.

[scotty79]

That's because AI went from something they needed to internally promote to something you'd have to pry from cold dead hands of the trailblazers.