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What those organizations miss is that they drown their teams in organizational red tape. The way to the future isn't tokenmaxxing - it's cutting back on process noise.

You could kind of use that line of thinking to justify spending on anything.

I agree. We don't need a nondeterministic 10quadrillion vector model. We need an deterministic expert on our narrow business. Something small, that can be run on the 2026 version of the spare PC under the CTOs desk.

It will always be worse compared to a centralized approach where hardware utilization can remain high. Except in case which demand low latency which most development things do not need. It's okay if it takes an extra 100ms for a code review to take place.

> insane

Not sure if you mean this in a good or bad way.

It's a tradeoff.

Generating a feature that is 90% correct in a tenth of the time is a reasonable tradeoff if you're trying to gain traction.

Generating a feature that is 90% correct in a tenth of the time, risking a multi-billion-dollar business, is a terrible tradeoff.

To be clear I meant it's good to be small team on the assumption that we're all using AI... I honestly can't imagine not using AI in May 2026.

You don't use AI at all? What is your main justification for not?

I think the jury’s still out on how big an impact AI will have on overall, average productivity. But it’s definitely a productivity boost for someone who’s capable of writing code without it. If you want to be super conservative, don’t even have it write any code. Use it to search through existing codebases, review your code, find the root cause of bug reports, evaluate pros and cons of alternative approaches, etc. etc. You’re really missing out by not using it at all.

Here’s a concrete example of conservative AI usage: I use Claude to vibe code my nvim config. Now, who cares if my nvim config is AI slop? What’s the worst that can happen? Nvim works for me now way better than it ever did when I was limited by the time I was willing to spend configuring it manually.

I see these kinds of stories here a lot, and I'm curious whether they reflect a steady stream of need for AI coding, or whether a lot of companies have a burst of AI-appropriate coding work now that the technology is available and then will have a smaller need going forward.

Is it like the stereotypical dad who rents a power washer, powerwashes every exposed surface on his property, and then doesn't need to do any powerwashing for a few years; his neighbor who gets an Instant Pot and uses it for every meal for a month, then sees it gathering dust when the family gets tired of pressure-cooked stews; or like their neighbor who gets a microwave oven and uses it multiple times a day for decades?

I guess only time will tell.

  > I'm not sure how it would show up in quarterly results.

Over here our CTO replaced Intercom with an internal equivalent that costs less than $20 / month to run, haiku and sonnet support agent costs included. In less than a few weeks, in his spare time.

In my limited experience with using agents to create tests it tends to code the tests to the existing code instead of ensuring the correctness from a spec. Great for regression testing but still limited in effectiveness for catching existing issues.

It wouldn't, at least not directly. That's why it wasn't done pre-AI.

> Even if Uber really did double developer productivity, would it translate to quarterly results?

It would if it meant they then fired half their software engineers, which is the ultimate goal.

Lowering costs to run the infra would show up as increased profits without any change in rides.

They have to make it easier for ubereats orders to have parts of their bill attached to a corporate card and half attached to someone's personal card and be able to split the invoices!

Uber also has to maintain thousands of region specific rules and features to be able to operate globally, and they do it all in the same app instead of having specific regional versions (which would be a terrible user experience for frequent travelers). That alone is a ton of work the end user will never see but is core to their operation.

This is not just me saying it. The Uber president himself says it in the article.

> so what did all those millions spent on developer salaries get them?

There was no doubt about what these developer salaries got them. It was to keep Uber stable and running in thousands of jurisdictions with varying rules/regulations.

The idea of using AI was (I hope) not just to replace developers for this purpose but to also ship features/products beyond what was already being offered. It has however not panned out as these CEOs/execs thought it would.

> They can also justify spending time and money on new features that only 0.1% of customers might use because 0.1% of their customers is a very large number.

And what are those features exactly? Because even the President of Uber doesn't seem to know:

"“That link is not there yet, right? I think maybe implicitly there is more that is getting shipped, but it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25 percent more useful consumer features,’” said Macdonald."

The budget allocated to AI for the year has been wiped out in 4 months.

> Eats for the way - your driver picks up a takeaway for you to eat while they drive you to your destination.

This seems like the kind of terrible idea that an LLM might have come up with. I'm pretty sure most drivers do not want people eating (especially a whole meal) in their car, and I can't imagine a lot of instances where you're calling an Uber and don't have time to get yourself food, but don't mind waiting an extra 10 minutes for the driver to detour, find parking, and wait for your food.

Holy fuck, aside from the voice bookings, that's some useless shit to spend money building as far as both tokens and salaries go.

Are they profitable yet lol

For context, this is an interview where Uber CEO discussed these ideas:

https://www.theverge.com/podcast/922909/dara-khosrowshahi-ub...

Can't say I am convinced.

The logic is quite simple. Management thinks that AI can improve productivity, but knows that there will be some resistance and some learning curve. So they force people to use it so that people can 1. develop their skills and workflow and 2. find out where it is useful 3. find out what needs to be improved to make it useful.

As a more obvious example consider that cars were just invented and the post office management thinks that they could improve performance of letter carriers. But right now cars are slow, break down a lot and there isn't much infrastructure for them. Lots of letter carriers will (rightly) think that it is a waste of time because they need to get in, stop, park between every house and they break down so often it isn't worth it and half of their route is unsuitable for a car anyways. But if cars are forced for a while they will find out what routes work well for cars and which don't, improve the cars and related infrastructure to make cars more effective and other improvements to unlock more productivity.

So yes, right now management is wasting money on cars and gas for no increased productivity. And yes, measuring how much gas each employee uses and encouraging to use more is obviously stupid in isolation. But the idea is to force adoption to iron out the kinks and find out where it can improve productivity. It is basically funding a research project.

Nothing that C-execs and management advocates for has made any sense for a long time now. If this is the first you're starting to question it all, I must ask what rock you're sleeping under because I desperately need a really good nap...

I surely remember everyone does SOA, everyone does NoSQL, everyone does Hadoop, everyone does microservices, everyone does kubernetes,....

Not with the same pressure as everyone in the company (literally everyone, regardless of the job role) has to burn AI tokens, and attend forced AI workshops, still it is always running after the next new shinny.

Nobody wanted to admit that they had no idea how "AI" was going to help but nobody wanted to get left off the hype train...so they tasked their engineers to figure something out...by just asking them to spend as much as possible (As I explain this it just sounds stupider and stupider). Of course, spending willy-nilly is not a good way to find a profitable (or smart) idea, but that's a problem for future company bottom line.

this is the reason I refuse to budge from my index portfolio besides small 'play money' ventures. My investing philosophy is basically by the time it hits wire you dont know what portion of it factored in. especially in age of AI and automation IMO alpha will vanish faster as anyone can code up eqv of a bloomberg terminal themselves. So all thats left is how do you manage downturns, when market heads down 30% but your handpicked stocks go down 60% you need to have enough wherewithal to hold through bad times. this is where true test of faith comes, I believe I would be cowardly and sell out at wrong time. So its best to just hold index and market sort the bloodbath out itself.