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Comment by internetter | Hacker News Reader

I know I'm responding to AI right now, but

> which means figuring out if the company can afford this level of productivity at scale.

If it was actually productive, then the revenue would increase and affordability wouldn't be a question.

Yes, my thoughts exactly. Productivity by definition creates things, hopefully valuable things. Is all the extra burn on chatbots worth the cost? Has Uber somehow gotten dramatically more efficient and effective due to this massive budget overrun? Or have they just given people shiny and expensive ways to push the same work around?

> If it was actually productive, then the revenue would increase and affordability wouldn't be a question.

Revenue has increased. Have you seen Meta's latest earnings? +33% revenue - in this economy.

Affordability is not a question. There is a reason companies like Meta have no issue with their engineers spending $1k/day on tokens. It's just not that much compared to how much they make per employee.

This article is about Uber, not Meta

How can that be attributed to any code an LLM wrote?

>$8 billion of net income was the result of a tax benefit the company realized in the first quarter of the year.

So exactly how much of their revenue is because of any code LLMs wrote vs. just structural tail winds?

You can always say "it's not because of LLMs", that's nearly unfalsifiable.

But if all of your peers are saying LLMs are more productive, if you're building things faster than ever before, the macro picture speaks for itself.

It sounds like this has a pretty falsifiable claim here - is the revenue attributed to a tax thing? Then it's clearly not attributable to code.

I agree that the macro picture would speak for itself. Can you point to any macro level detail that is indeed cleanly showing benefits from increased productivity from LLMs?

Not a macro detail, but my peers and I are shipping features at at least 5-10x the speed we used to.

I'm not asking to say "it's not because of LLMs" I am asking for evidence that LLMs are creating revenue for users.

I think the lack of evidence for LLM productivity is not an indictment on LLMs… it’s an indictment on the industry still having no real way to measure developer productivity in general.

I agree that you can't draw any conclusions about AI, but their revenue increased by 33% percent. That's just straight income before any taxes or costs are applied.

Yes, but that doesn't mean AI increased their revenue. Is there definitive proof that AI/LLMs caused this increase?

I completely agree with you. I pointed out replying to the same person that in the same report their ad impressions were up 20% and the price per ad was up 12%, which account for a huge amount chunk of that revenue increase.

All I was saying here was that tax breaks wouldn't impact revenue since revenue is reported before taxes, operating costs and anything else.

That means absolutely nothing in the context of this conversation. It says right in their release ad impressions are up almost 20% and cost per add is up 12%. Those two metrics alone account for most of the increase in their revenue. Absolutely no conclusion can be drawn regarding the impact of AI on those numbers one way or the other.

It's not like they used AI to crank out some new revenue generating piece of software, or massively reduce operating costs. In fact their operating costs rose by 35%.

> It says right in their release ad impressions are up almost 20% and cost per add is up 12%

Have you wondered why this is the case? How do you think they increased impressions so much at their scale? How they did this despite losing 20M users?

To put it clearly, AI at every part of the pipeline: writing software, product features/experiments, A/B testing them, and pushing them out to users. Even before you get to something like LLM driven recommendations, you can virtually entirely automate the process of finding more "engagement alpha" with AI.

If you have an evidence from their financial releases showing a correlation between AI usage and increased revenue I'd love to see it. Otherwise you're just making wild ass guesses.

Edit: Also, historically Meta has been growing revenue by 30 to 50 percent for the last decade. With the only exception being 2022 and 2023. So it's not like recent performance is an outlier.

Growth is exponentially harder the bigger you get. You can't take previous years as your base case.

So then no, you don't have anything to back the statement and your just guessing wildly. You can just say that.

After losing 20 million users? https://www.theverge.com/tech/921089/meta-earnings-q1-2026-u...

I really don't understand their economics.

That is not true at all. No matter how "productive" a company is means nothing if people aren't buying your product. And using LLMs to be more productive will not convince anyone to buy your product. Human creativity and intuition to make a product that people want to use is what sells. Productivity for productivity's sake doesn't really move the needle at all, and can make things worse.

Not every change a developer makes increases revenue, and the changes that do often have a lag time.

This is my thought too. The eggheads in accounting set budgets, and we produce products within that budget. I could be twice as productive with twice as many people, and maybe 50% more productive with good AI, but if it's not budgeted for it's an issue (especially short-term before the product is released).

I'd argue it's often the contrary -- since it's easy to ship features and fixes, people often ship things without questioning if it makes business sense to support a use case, or if the design is solid. Now you have exactly the same revenge but more things to maintain

What if you're the SRE and the code fixes mean the site goes from 99% uptime to 99.9% up? How do you measure the revenue from that?

Depending if the site has a direct competitor and non-sticky customers, you can often get accurate loss estimates from outages. For example, friends of mine at Doordash would know when UberEats was down by the corresponding spike in traffic to their app. The competitor captures all the lost traffic.

Most enterprises will have a harder time quantifying losses, as some percentage of customers will come back later. To understand that, you need to look for a drop in completed purchase rates compared to site visits.

For a SaaS, it's even more difficult, as customers are often held captive by long contracts and might tolerate SLA breaches up to a certain point. A reasonable, though fictional, proxy would be the revenue for the contract pro-rated against the uptime during that period.

Seems like an unscrupulous operator would take action to take down their competitor's site with a DDoS in order to drive business to themselves.

On this side of the equation I think you start pulling in customer context and risk analysis on the downside. What is the churn risk for operation at 99% vs 99.9% availability.

If your site is for B2B and impacts customers own operations or revenue, you'll likely be wanting to chase the 99.9%, customers won't tolerate the 1.5 hours per week of downtime and will churn.

However, if the value you're site creates is tolerant to those sorts of disruptions, someone is just inconvenienced and can come back later, a large investment to move from 99% to 99.9% wouldn't be justified. There is literally no impact from the investment. The harder part will be the reality, most investments will be somewhere in the middle with ambiguity on the impact. IIRC, SRE principles do talk about this when setting SLOs in different terms.

I've heard some companies refer to the concept as economical thinking, which is I think a great way to think about it. Doesn't mean you'll always get it right, more so that we embed being conscious about the ROI in our work.

I also believe this is an area that I've observed several engineers really struggle with, especially when moving from big tech to startups, where it's really easy to import culture from another company, and in earlier stages of startup life... if you don't have product-market-fit, it doesn't matter how good you're availability is. Attention is a resource, make sure it's allocated to what creates value for the customer.

Steelmanning the other side: a counter example would be if competitors use the same tools to achieve the same productivity gains.

> If it was actually productive

They are extremely productive if you use them right. To the point it worries me how clever these pseudo-AI models can get in the next year.

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