AI has produced tangible operational ROI at Uber — cutting processes from hours to minutes — but precisely quantifying that efficiency is inherently difficult because freed-up time gets absorbed by higher-value work.
After Uber's CTO publicly disclosed blowing through the annual AI budget in four months, Mac clarifies the issue was usage growing faster than expected, not runaway spending. He points to concrete wins — capital allocation cut from 15 hours to 2, forecasting from 8 to 2, marketing QA from 2 weeks to 2 days — but acknowledges that drawing a direct line to headcount reduction is nearly impossible because freed time fills with other valuable work. ✦ AI generated
Andrew Macdonald · 20VC · 2026-08-17 · original ↗
starts at this moment · 37:07
“You blew through what was it? A year's budget for AI in 4 months. Is that evidence of incredibly effective tools, or is that evidence of a desperate need for guardrails?”
If you can take like a capital allocation process, like every week we're allocating pricing dollars across thousands of markets globally, and I can take that from being a 15-hour process to a 2-hour process, which is what we've done. That is tremendous tangible ROI, cuz now you get 2 days of someone's time back. Um if you're able to take a forecasting process, which our finance team is constantly reforecasting every inch of our business, uh and you're able to turn that from 8 hours of work into 2 hours of work, you're able to now do that not only with more precision, cuz you can put an additional layer of nuance into those forecasts, but you're just able to have your folks do other stuff. There's clear ROI there. If you're able to take marketing QA from 2 weeks to 2 days, like there's so many examples of that that we see.
verbatim transcript · starts at 37:07
36:47want to get new and cool stuff built, and we are seeing examples of that every single day. We have stood up a pod of 30 of our best AI engineers that are partnered with business people, or partnered with folks in the G&A functions, to go in and go process by process, and start sort of ground up with AI. How do you improve that process? And if you can take like a
37:11capital allocation process, like every week we're allocating pricing dollars across thousands of markets globally, and I can take that from being a 15-hour process to a 2-hour process, which is what we've done. That is tremendous tangible ROI, cuz now you get 2 days of someone's time back. Um if you're able to take a forecasting process, which our finance team is constantly reforecasting every inch of our business,
37:40uh and you're able to turn that from uh 8 hours of work into 2 hours of work, you're able to now do that not only with more precision, cuz you can put an additional layer of nuance into those forecasts, but you're just able to have your folks do other stuff. There's clear ROI there. If you're able to take marketing QA from 2 weeks to 2 days, like there's so many examples of that um
38:03that we see. And and the way we've done that again is by pairing the business folks with the AI engineers. The the second thing I think that >> Are you actually seeing that today? Because Alex Karp came on CNBC or CNN and and said like, "No, the the ROI question is still there." Like, to to validate what you said, to be clear. Outside of coding and customer support
38:23with the greatest of respect, I think anyone who runs a budget in a large enterprise state would say, "Yes, it's still not material at best." >> I think it's just hard to know. Like, these things are just hard to quantify and so you do have to be a bit top-down um and belief-based about it, right? I I think three examples I just gave there. Assume there are dozens of more of