Enterprise Field Notes · Issue #6
Dear Andrew, COO of Uber
Uber burned through its AI budget in four months. This is an open letter to the COO who said so out loud, on what we actually owe.
New here? Subscribe to Enterprise Field Notes, one new issue every week.
By Ben Pickett
Andrew,
I had this week’s issue half-written. It was about redesigning workflows. Then I read your interview in Fortune, and I set the other one down.
You said the thing most people in your seat will only say in private. You cannot draw a straight line between what Uber spends on AI and what it actually produces. Here is how you put it.
That link is not there yet. Maybe implicitly there’s 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 like 25% more useful consumer features.”
In plain terms: we are spending a fortune on AI, and almost nobody can prove it is working. That is the most honest sentence an operator has said out loud this year, and you said it on the record.
This letter is what I owe you back, as one COO to another. I changed my plan for the week because this is the question sitting in every operating review in the country right now, and it deserves an answer.
You burned through Uber’s 2026 AI budget in four months. Your engineering organization went from 32% Claude Code adoption to 84% in the same window. Per-engineer API costs ran $500 to $2,000 a month. At the top of that range across 5,000 engineers, that annualizes to nine figures on a single tool, before counting the rest of your AI stack.
And when you look at the output, you cannot point to a 25% lift in consumer features that justifies the curve.
You are not alone in this. Enterprise AI spending jumped 108% in a year. The average company’s AI bill went from a little over a million dollars to seven million. Seventy-eight percent of IT leaders say they are seeing charges they never budgeted for. This is not an Uber problem. It is everywhere. You are just the one who said it with your name attached.
You are not running it wrong. The industry’s defaults are running you wrong. The answer is not less AI. The answer is stewardship. The reason you cannot prove your AI is working is that the way enterprise AI is deployed in 2026 fails three different kinds of stewardship at once.
Money. Natural resources. People.
Each one alone would be a problem. Together they compound, and the compound is what your budget is paying for.
Money is the one your finance team feels first.
The single biggest decision driving your token spend is happening implicitly, in the background, every minute of every day. Your engineers are defaulting to the most capable model available for every task they touch. Some of those tasks need a frontier model. Most of them do not.
A query that summarizes a Slack thread does not need Claude Opus. A query that drafts a short Jira ticket does not need GPT-5. A query that pulls a snippet from internal documentation does not need the most expensive model in the room. But when the default presents a frontier model as the option, the frontier model is what gets used.
Per-seat pricing makes this worse, not better. The seat does not predict the spend. Token volume does. And token volume runs on model selection. Two engineers on identical seat licenses can produce a tenfold difference in monthly cost depending on which model they reach for and how often the model gets retried because the first answer was wrong.
Here is the math at scale that bothers me most. If the per-engineer spend in your reported range averages around $1,000 a month, and your engineering organization is roughly 5,000 strong, the annualized run rate for a single coding tool sits at about $60 million. That is one tool, on one cohort. You have other cohorts. You have other tools. Across an enterprise AI stack, this is a budget category that did not exist three years ago and now competes for share with payroll. The CFO conversation in 2027 will not be about whether AI is in the budget. It will be about whether AI is the budget.
This is not theoretical. At Swa, the company I co-founded, we run our own platform on our own work. Over the last 90 days, our internal compute cost dropped 51%. We did not change the work. We changed one thing. Instead of sending every task to the most expensive model, an orchestration layer routed each task to the right model for the job. Same work, same people, half the compute cost. That is what an orchestrator does. It is the single highest-leverage decision in enterprise AI right now, and most companies have not made it.
If your engineers had been defaulting correctly across model tiers, your Q1 burn would not have ended in four months.
Money is the visible cost. There is a second cost we are not pricing in.
Recent benchmark research across 155 model architectures found that frontier models with 70 billion or more parameters consume roughly two orders of magnitude more energy per token than smaller open-source models. That is not a 20% gap. That is a 100x gap on the same task.
Each 100-word AI prompt uses roughly one bottle of water (about 519 milliliters) on the data-center cooling side. A typical AI data center draws electricity equivalent to 100,000 households. By 2028, US data centers are projected to consume 12% of national electricity, up from 1.9% in 2018. The IEA forecasts global data-center electricity demand to roughly double by 2030. AI workloads account for most of that growth.
Consider the timeline. The IEA projects global data center electricity demand to double by 2030, with AI accelerator workloads driving most of the growth. That demand has to be met by something. Part of it will be met by new renewable capacity. Part of it will be met by extending the life of fossil-fueled generation that was scheduled to retire. The default-to-premium choice in your engineering team’s prompt window is connected, by a chain of consequence, to that retirement decision being deferred. We do not see the chain because each link sits in a different organization. The chain is still there.
This is not a separate issue from the money story. It is the same decision viewed from a different axis. Defaulting a routine query to a frontier model is a stewardship failure on two pillars at once. Joules and dollars. The same routing decision that cuts the budget cuts the energy footprint by something close to it, because routing means a smaller model carries the load the frontier model did not need to.
Pricing has not caught up to the externality yet. It will. The companies that figure this out first will be the ones that built stewardship into the architecture before they had to.
The third pillar is the one the industry talks about the least. People.
When 84% of your engineering organization adopts a coding agent, that is a headline. Inside the organization it is also a story about cognitive load. Somebody has to validate every output the agent produces. Somebody has to catch the hallucination that slips past. Somebody has to repair the trust deficit when the answer was wrong. Those somebodies are your engineers, and the validation work shows up nowhere in the productivity number you are being asked to defend.
Stack Overflow’s 2025 developer survey found that 66% of developers cite “answers that are almost right, but not quite” as their biggest frustration with AI tools. 45% report that debugging AI-generated code takes longer than debugging their own. Trust in AI output declined year over year while adoption rose. The two numbers are not in tension. They are the same story.
WalkMe’s 2026 study found that workers lose 51 working days per year to technology friction, up 42% from 2025. 54% of workers bypassed AI tools in the last 30 days to do the task manually. The tools are deployed. People work around them anyway. That is not a training problem. That is a stewardship debt being paid in the attention and patience of the people you are asking to make the tools work.
There is also a structural piece nobody has named yet. The validation work that AI offloads onto your engineers is asymmetric. The model produces in seconds what would have taken a junior engineer hours. The senior engineer who reviews and corrects the model output now spends those hours doing review work that used to be distributed across multiple junior engineers learning the craft. The short-term productivity curve looks good. The pipeline of engineers who learn by writing the code in the first place is being quietly hollowed out. Three years from now, when the model is better but the senior engineers have started to retire, the organization will have a depth problem nobody saw coming.
Adoption is easy to measure. Trust is not. Depth on the bench is not. Your 84% is the number that comes easy. The ones that matter do not show up on a dashboard.
Three pillars. One architectural cause underneath all three.
I learned to recognize that cause a long way from enterprise AI. I served six years on the City Council of Jefferson, Oregon, three of them as Mayor. Most of that work was infrastructure. We built a new wastewater treatment plant. We built a new city hall. We paved roads that had not been paved in decades. We dealt with water rights, drainage, and the boring backbone that lets a town function. Good management is what makes infrastructure invisible. Water that just runs when you turn the tap. Lights that just come on. Roads that just hold. People only notice it when it fails.
AI should be infrastructure. Flushing your toilet should just work. AI should just be a utility. But it is not one yet. Right now it is a tap with no plumbing behind it. The water comes out, but nothing routes it, meters it, or controls the pressure. That is why you cannot prove your AI is working.
The industry deployed AI without an orchestration layer. Without routing. Without governance. Without cost attribution at the user level. Without observability at the model level. The companies running enterprise AI in 2026 are running it the way the early internet was deployed: directly, defaulting to the most powerful resource available, hoping the bill works out.
That is not a vendor problem. It is a discipline problem. And the discipline has a name. Stewardship.
Money breaks the budget. Joules break the planet. People-neglect breaks the org. Each pillar fails by itself. Together they fail faster, because the same architectural gap is causing all three.
Andrew, since you have read this far, I want to tell you why I am building what I am building, and then I will stop.
I left Nike to co-found Swa because I watched too many enterprise AI deployments fail at exactly this architectural layer. The model was never the problem. The architecture around the model was the problem. Swa is that architecture. It lives where people already work, runs every major model, governs them, attributes every dollar, and connects to the systems the work already depends on. Routing is one piece. The 51% is one outcome of the whole thing working together, not a single trick. How we get there is more than I will put in a public letter.
I am not writing this to sell you anything. I am writing because these are the problems I get out of bed to solve. AI is here to stay. The only question left is what it costs us to keep it, in money, in energy, and in people. The next eighteen months decide whether we treat AI as infrastructure with real stewardship, or whether we keep chasing the most aggressive adoption number while all three quietly bleed. I care more about getting that answer right than about who gets credit for it.
If you are not Andrew but you are running a similar operation, the same diagnosis applies. The link you are looking for between AI spend and AI output does not arrive naturally. It arrives only when stewardship becomes an architectural decision rather than a moral one. The discipline is the same whether the company is 5,000 engineers or fifty. The order of operations is the same. Money. Energy. People. Routing, governance, observability, attribution. Boring infrastructure work that nobody pitched you when AI was the headline. The work that turns AI from a line item into a return.
Andrew, you said you cannot draw the line between what you spend on AI and what it produces. I believe you. With the architecture most companies are running, that line does not exist to be drawn. It is not a measurement failure. It is an architecture failure. The line appears when the layer underneath your AI is built to make it appear, and most companies have not built it yet.
If you ever want to compare notes, one COO to another, I am one of the people who has built this from the other side. The offer is real, with no pitch attached.
Either way, thank you for saying what you said on the record. The honesty is what makes this conversation possible. The rest of us owe you the same honesty in return.
Ben Pickett
Co-Founder and COO, Swa
Former Global Director of Site Reliability Engineering, Nike
Ben Pickett is Co-Founder and Chief Operating Officer of Swa and former Global Director of Site Reliability Engineering at Nike. Swa delivers enterprise AI inside Slack, Teams, WhatsApp, Zoom, SMS, and more channels coming, integrated into the systems behind them through user-scoped auth. One interface across many models. Zero data retention by default. Flat-rate pricing. Deployment in under three minutes. Swa Solo, our first version for individuals and small teams to try Swa before bringing it to the enterprise, ships soon. swa-ai.com
Header artwork generated with Swa.
References
- Uber burned through its entire 2026 AI budget in four months. Now its COO is questioning whether it’s worth it. Fortune, May 26 2026. Andrew Macdonald, President and COO, on record about the productivity-to-spend link. Source of the Q1 2026 R&D figure ($951M, up 17%) and the four-month budget burn.
- AI Costs Surge 108%: Why Your 2026 Budget Is Already Outdated. Beri.net, 2026. Enterprise AI spending up 108% year over year; average annual spend grew from $1.2M (2024) to $7M (2026); 78% of IT leaders report unexpected AI charges they did not budget for.
- AI Inference Cost Crisis 2026: Why Your AI Bill Is Exploding. Oplexa Research, 2026. Documents the Claude Code adoption curve at Uber (32% to 84% across 5,000 engineers) and the $500 to $2,000 per-engineer monthly API range. 80 to 85% of enterprises miss their AI infrastructure forecasts by more than 25%.
- Microsoft Doubles Copilot Pricing to $60 Per Seat. WebProNews, 2026. Microsoft 365 Copilot Enterprise raised from $30 to $60 per seat per month. GitHub Copilot transitioned all plans to usage-based billing effective June 1, 2026.
- Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models. arXiv 2502.05610, 2026. Comprehensive measurements across 155 model architectures: frontier models with 70B+ parameters consume approximately two orders of magnitude more energy per token than smaller open-source models.
- Energy and AI. International Energy Agency, 2025. Global data center electricity demand projected to rise from 415 TWh (2024) to approximately 945 TWh by 2030. US data centers at 4.4% of national electricity in 2025, up from 1.9% in 2018; projected to reach as high as 12% by 2028.
- AI’s Cooling Problem: How Data Centers Are Transforming Water Use. Environmental Law Institute, 2026. Each ~100-word AI prompt uses approximately 519 milliliters of water on the data-center cooling side. US AI servers are projected to drive 200 to 300 billion gallons of additional annual water consumption and add 24 to 44 million metric tons of CO2-equivalent emissions by 2030.
- 2025 Stack Overflow Developer Survey: AI. Stack Overflow, December 2025. 84% of developers use or plan to use AI tools; 51% of professional developers use AI daily; 66% cite “AI solutions that are almost right, but not quite” as their biggest frustration; 45% report debugging AI-generated code takes longer than debugging their own. Trust in AI output declined year over year.
- Enterprises Lose 51 Workdays Per Employee to Technology Friction Annually. WalkMe Global Study, 2026. Survey of 3,750 respondents at enterprises with 1,000+ employees across 14 countries. 51 workdays lost per year per employee, up 42% from 2025. 54% of workers bypassed AI tools and completed tasks manually in the past 30 days.
- The Definitions Gap. The Enterprise Field Notes Issue #5, May 19 2026. Companion piece on workflow alignment as the upstream input every other AI initiative depends on.
- The Productivity Disconnect. The Enterprise Field Notes Issue #4, May 14 2026. Companion piece on the gap between AI spend and measurable enterprise output.
About the author
I'm Ben. I write Enterprise Field Notes, and by day I'm COO at Swa, after years running reliability, data protection, and database operations at Nike. The lesson that keeps proving itself: anything you cannot run without, and cannot walk away from, is a risk you have not priced yet. What is yours?
Header artwork generated with Swa.
Read more of Ben's Enterprise Field Notes at benpickett.com.
© 2026 Ben Pickett · Enterprise Field Notes