Uber Just Exposed AI’s Biggest Cost Problem

0:00Eric Siu:So Uber gave 5 ,000 engineers access to cloud code in December. By February, usage had nearly doubled. By April, the CTO told the company they burned through the entire annual AI budget. So the adoption curve tells you everything about what happened. In December 2024, 32 % of Uber's engineers were using cloud code. By February 2026, that number was 63%. That seems a little slow to me. But anyway, that's not a gradual rollout. That's a product so useful that engineers pull it into the workflow faster than finance could model the spend. Okay, that's a lot of employees though. Uber has about 34 ,000 employees and your younger is roughly 15 % of that.

0:34Eric Siu:Okay, so all this to say, Neil, is this CTO, if I go back over here, so look, AI-related costs at Uber are up 6x since 2024. This is what we're talking about. And we're seeing this in our organization, yours too. CTO Praveen Nepali Naga's quote was, I'm back to the drawing board. That's the CTO of a$144 billion company admitting that the tools work so well that his team can't afford to keep using them at this rate. So here's the thing. So he has to go back to his CFO and ask for a larger budget now. But to Neil's point, if this is working for you so well, you should be spending more. And here's the other thing.

1:07Eric Siu:You might be thinking, oh, I can put this all on open source tokens. But if you're actively building things and shipping code, you can't put on an open source tokens because you might need to rework it a couple of times. And the time you spent there is not worth it. Chances are, if you're shipping a lot of this stuff, a lot of it's going to have to be on frontier models.

1:25Neil Patel:And there's an important thing here. they rolled this out in December and by February they had good adoption. By April they went through all their tokens. I bet you there's a lot of terrible usage of cloud code within this organization. I don't mean in a bad way. It's actually in almost all organizations where they're doing a lot of things inefficiently and they're not optimizing for cost savings. That'll start coming out soon where companies start thinking about it. And then his budget from April will last the whole year and he won't need to go back to the CFO. We'll see.

1:55Eric Siu:I mean, so it says the CFO problem here is now the bottleneck of AI adoption at enterprise level. The technology works, productivity gains are real. Uber's own data says 75 % of code review comments are marked helpful by engineers. Okay, there's your thing, Niels. 75 % are helpful. The constraint is that the traditional annual budgeting was designed for tools with predictable per seat costs and AI coding agents have usage curves that look like cloud compute bills from 2015, exponential until someone notices. Every enterprise CTO is about to have the same meeting. The tools are too good to pull back.

2:26Eric Siu:The costs are too unpredictable to ignore. And the companies that figure out token cost optimization first will have a structural advantage over every competitor. Still running annual budget cycles. I'll tell you something, Neil. We've been playing around with token optimization this week just on my own stuff. I'm trying all the models. Gemma 4, Quinn, which is from Alibaba. I'm trying all these other ones. It's like they're decent, but there's still nothing close to what you get with Claude.

2:49Neil Patel:Well, on our end, so let's look at it from a business standpoint. We tend to create a lot of tools for marketing-related purposes, all right, for ourselves, our clients, or just putting out there because tools get you more brand mentions, build more links. So we use Claude quite a bit. What we found is our outputs are similar, and I don't know exactly what my team is using, but I saw a report on this. We're down in 21 % in cost because we've been optimizing for efficiencies. So 21 % savings over the last 30 days is huge. And I bet you as time goes forward, not only will the models get cheaper, but people will come up with solutions for optimization because this is a big problem.

3:30Neil Patel:And I bet you our bill a year from now will be maybe like 25 % of what it is today on a monthly basis.

3:37Eric Siu:So I'll give you guys some practical things you can do and I'll give you my thoughts on this, Neil. So you guys can all use Open Router. So Open Router will help you route to the right models and they'll save, it'll help you save a lot of money. They take 5 % of the, basically the spend because you're routing everything through them, right? That's one thing you can do. You can buy a local infrastructure. So the reason why I bought these DGX Sparks is to see kind of how local inference will work. And then we're going to consider buying more and more. The thing is, Neil, I think we're going to token optimize.

4:06Eric Siu:I think I mentioned it last time, maybe 75, 80 % goes through it, maybe for open models and then 15, 20 % on Frontier, which is like, when I say Frontier, I mean, you know, you're using Claude, Opus, like the latest version, right? We're using Mythos when it comes out. I think, Neil, the costs are going to come down, but what's going to happen is usage is going to skyrocket even more. And so what ends up happening is costs still keep going up because the usage is still unpredictable. Because I see my team using it now. The ones that are getting pilled by it, they're like, we need more, we need more.

4:35Eric Siu:Because they see how I'm using it and they start to use it that way. They're like, oh my God, right? I'm like, oh my God. If they even start using it even like 25 % in the way I use it right now, my costs still go up exponentially.

4:46Neil Patel:Yeah, but I don't look at costs going up exponentially because at the same time, even though the models are becoming cheaper and cheaper, you're going to have it to a certain point where some of the older models are good enough for a lot of tasks and you won't be using as many frontier models. So that, I think, will save a lot. B, and this is the big one, no business really cares about the cost if the work is actually producing a direct ROI. So if they're able to, if they grow their cost by 10x for that department, but their profitability and their revenue grows in total, a business is happy. Look, if you're publicly traded and you spent too much on AI credit, but you can show, hey, we're predicting 20 % growth in revenue and we actually hit 28%.

5:31Neil Patel:And I know our profit for the quarter was supposed to be a billion dollars, but we have the same amount of people. Yes, we use AI more. And due to the revenue increase, our profitability was actually$1.2 billion. The market will reward you and your stock goes up. No one really cares if you're spending more, if you can produce more.

5:49Eric Siu:I just think this, and this actually, I have a thing that ties in with this. I just think we're all going to be producing a lot more. Just looking at how I work, you might say you don't work that hard. I still think you work really hard right now. But I think the way we, I think we're just going to end up doing more. So let me share this with you real quick because I've kind of said this before, Neil, on this podcast. I think there's going to be net new – there's going to be more entrepreneurs in the world because you can do so much now. So this guy from Deal, remember Deal, the one that did the spy with Rippling?

6:20Neil Patel:Oh, yeah. The company that you can use to – I think it's like pay employees internationally or something? Yeah, yeah, yeah.

6:26Eric Siu:I think they're a good company, but they did that whole spy thing. So he said P. Marca. So P. Marca is Marc Andreessen. Oh, look. Hey, look. I'm cool with these guys. I'm homies. So look at this. So when a company becomes more productive, this is from Alex Boaz. I don't know how to pronounce it. So when a company becomes more productive, it doesn't sit still. It goes after more customers, enters new markets, builds new products. Productivity gives you leverage and leverage makes you want to do more, not less. Exactly what I just said. So no CEO in history has looked at a more productive team and said, great, let's shriek.

6:57Eric Siu:They hire more. That's what we're seeing in our data. So we also tend to forget that new tech creates new jobs and industries. A job title that didn't exist now demands 70K people, 283 % year on year. For jobs that directly experience the productivity gains, demand should surge. As CEOs, watch how productive these people are and how much more they could be doing. So if you see this over here, Neil, you want to read it?

7:21Neil Patel:So AI trainers outpacing every job in 2025 at 283 % growth. I can read 2025. Yeah, I'll call out the rest, Neil.

7:30Eric Siu:So thank you for reading that part. So 2025 starts, you have 18 ,000 AI trainers employed globally. Now that's 70 ,000. I never even thought about this. Like you're going to need more AI trainers, obviously, right? AI engineers and PM roles are actually up 400%. So you have 15 ,000 open AI engineering jobs, not the company, just open AI jobs. And 1135 open AI product manager jobs. So it's going up, right? So I think, Neil, we're going to see more entrepreneurs. And I think people are going to realize that creation gives them a sense of meaning and purpose, and they're going to want to do more of it.

8:04Eric Siu:And I think we're just going to want more, more, more, Neil. That being said, I don't disagree with you that for more basic tasks like doing one plus one, maybe that's going to be free or close to free. But I think we're always like maybe 10 % to 20 % of the time, you're still always trying to build new stuff. And that still eats up a lot of budget because you still want the smartest models.

8:22Neil Patel:Yeah, so I'm in Brazil right now as we're recording this. and yesterday I was at a really large publicly traded company's office in Brazil. They're global. To give you perspective, they own three floors of an office space. They're not the biggest floors, they're normal size, like, you know, just good size buildings, but they have three floors or that's what they take up. I don't know how much they spend on their new office. If I had to guess, 50-ish million dollars, okay, which is really, that's a lot of money for like decorations and technology and all that kind of stuff right and i'm in brazil so if you take the currency exchanger that's roughly like 250 million usd equivalent that's a lot of money because remember people get paid less in brazil as well so for a company to spend around 250 million in their currency and employees get paid you know proportionally like u.s employee getting 100 grand they get 100 rei so that would be like 20 000 us dollars um and i was talking to the whole marketing team and we were talking about strategy for Brazil as well as global strategy.

9:25Neil Patel:And the one thing that's consistent with them and almost every other company I talk to, no one's thinking about shrinking or laying off employees in marketing or anywhere else. Everyone's talking about, hey, everyone's using AI. It's causing companies to move faster. We need to move faster. How do we adopt these tools in the correct way to be more effective? How do we focus on the right KPIs so that way we actually see revenue growth and not just increased costs in AI and no change in the top line. But everyone has this mentality that we've been seeing recently because if you go back a year, Eric, I think you'll agree with this.

10:01Neil Patel:Everyone's like, oh my God, doom and gloom. Everyone's going to start getting laid off. Tons of jobs are going to be displaced, at least in the corporate world. We're not talking about flipping burgers or stuff like that. Even in the corporate world, people are afraid of that. But we're seeing the opposite. We're not seeing sales reps get canceled, marketing employees get canceled. we're seeing them stay roughly the same sometimes even increase but everyone's like how do you get 2x the output and productive output productive output doesn't mean oh they did 2x more work it's more so they did 2x more stuff that actually moves the bottom line or the top line and that's what we're seeing and if you don't keep up with the competitors you're gonna lose so you can't actually cut you just got to figure out how to move faster with what you have you're

10:43Eric Siu:not use your microphone i'm not that's right okay anyway neil's gonna get cut so okay so um

10:51Neil Patel:you want to know what happened what so i had a on my last trip come back from krona i keep it in my bag so i have protein powder in my bag okay like literally i i you know travel with protein powder

11:04Eric Siu:yeah it's vegan show the brand that's legion right yeah it's leaf we should we should give a shout out to Legion. There you go.

11:10Neil Patel:Great product. Okay. So I didn't know that they had to check it. Like they checked to see if there's powder. Yeah, yeah, yeah. I had my mic on top. The rep pulled it out and then boom, it hit the ground and it got messed up. So I ordered a new one from Amazon, but I was pissed. But what can I do? You know, it's like you can't be like, hey, TSA rep,

11:31Eric Siu:you owe me. You know, you know what's funny? Sometimes I would get pulled aside from TSA and they'd take the mic out. I get stopped from the mic Because they think it's a grenade. Yeah.

11:40Neil Patel:I don't get the grenade. They would tell me, because you know when they say take all electronic devices out, I would say maybe like 30, 40 % of the time, they stop my bag and they're like, you didn't take out your technology. And I think it's a microphone. You just said laptops. But electric toothbrush, they never make me take out. But for some reason, the mic, it gets flagged. Yeah, the mic gets flagged quite often.

12:02Eric Siu:Anyway, real quick, if you want to acquire customers faster and more efficiently this year with the latest strategies and tactics, then check out singlegrain.com. That is my ad agency. Again, www.singlegrain.com. Check it out. And if it seems like a fit, we'll get in touch and help you with a free marketing plan. So Neil, I think I have one point to bring up, but it'll come back to me. So I want to bring up these charts over here. So these charts are from Code 2. We've kind of covered them before, but I think it's worth bringing it up because the key thing here is, will AI lead to mass unemployment?

12:34Eric Siu:Oh, I remember what I was going to say. So the way I think we might measure, this is going to be an experiment for us, Neil. So the way we're going to measure AI adoption is we have cloud teams right now. We can see usage in cloud code. We can see usage in cloud cowork. So we want to see, one, we want to see usage, but we also want to see the pull request coming from GitHub. Now, those two things can be gamed. If you're just shipping a bunch of pull requests and you're just spending a lot, it could be stupid spend, right, to Neil's point. So the thing that you saw earlier from Uber, where they're saying 75 % of engineers said the code was helpful code, we might have something like that where the engineers or maybe the people, the managers are checking and saying, hey, like you did all this, you're engaged, but what have you actually built?

13:15Eric Siu:Can you screen share with us? Right? So my point of saying this is that you can have these metrics that are maybe easy to record, but they might be easy to game. You need to have a pairing metric next to it to make sure that what you're doing is actually quality. And it's not just a bunch of slop, which is what my homepage got called, even though it converts. So, yeah.

13:32Neil Patel:Anyway. Eric's homepage was not slopped. That's just what, again, what someone else thinks. I'm neutral. If his homepage was crap, I would tell him. Yes, is it perfect from a design perspective? No. But get it out there. Find out if the model works, if the marketing message, you know, converts. And then go and adjust it and fine-tune it later on once you've figured out how to really make the funnel.

13:52Eric Siu:You know what I've learned from this, Neil, right now? Like, you and I, we grew up during the internet era. but we weren't exactly working at the time just yet. And what I've learned is when the demand is there, it doesn't matter. Like you're going to get pulled. The demand is going to pull you and like, who cares? Right. So, um, okay, check this out. So this is a done from code two. So literally they had a post about a month ago saying, will AI lead to mass unemployment? Okay. And so I just want to call out here the ROI that we want to look at instead of return on investment, we want to look at return on displacement, ROP, R-O-P.

14:26Eric Siu:Okay. So when you look at agriculture in 1900, 12 million agricultural workers, 41 % of the workforce. You go all the way to 1970, it's 2 % of the workforce, 3.5 million workers. But net-net, what happened was agriculture lost 8 million jobs from 1910 to 1970. And then new industries gained 46 million jobs. So 46 divided by 12 over there, 5x.

14:50Neil Patel:So there you have it. I see the same thing with engineering. If you go back to the Uber example.

14:56Eric Siu:Sorry, it's 46 divided by eight. Yeah, go ahead.

14:58Neil Patel:Still, it's amazing, right? But if you look at like the Uber example and how they're talking about 75 % efficiencies or 75 % approval, like they found it being helpful, the first thing that comes to my mind is outside of cost, when you're using it, yes, you're releasing more, but is that releasing more, improving LTV customer, you know, repeat visits or repeat usage, you know, conversion rates, new potential onboarding or revenue, like all those kinds of things. And what I believe is you'll end up seeing is you'll see some displacement because people are like, you're using all this A stuff. Do we actually need all these people?

15:37Neil Patel:And B, I think you'll also see, in addition to costs going down, people being like, you're using A for all this stuff and this is great. And you did 50 things this month versus the normal 10. But out of the 50 things you did, Only six of them actually help drive a return. Well, normally when you do 10, four of them drive a return. So you did 50 % more things on that end, but you did five times, 5x the amount of output. So then they start fine tuning what things are being used for. And you're going to start seeing not as much usage in certain areas, more usage in others, employees getting displaced because they realize they don't need them all.

16:16Neil Patel:And then some of those employees go on to other sectors, other fields, other jobs. And it's the same thing in marketing. Dude, the amount of customers that are asking us to build them agents right now is ridiculous. And it just goes to show people are like, oh, you're going to have to get rid of people. Well, we're at the same time, we're getting new requests that are requiring us to hire people or reshift. We're replacing different people.