Budgeting for AI isn't hard

A couple days after publishing last week’s post – AI is not a line item – we saw this fascinating tweet from Eric Glyman (CEO of Ramp) making effectively the same point.

Eric Glyman@eglyman

As I wrote this, I saw X go into meltdown over tokens. You've seen the headlines: “Uber blows yearly AI budget in just one quarter.” “Meta employee burns 281 billion tokens in April.” But, the problem isn't spending. Spending works. Since 2023, the top quartile of our AI

Eric Glyman @eglyman

Today, Ramp raised $750M at a $44B valuation. Last time we grew this fast, we were 1/20th the size. For 2000 years, business was built on two pillars. Today, a third: intelligence. It’s your least governed cost. It’s also your single greatest opportunity.

3:35 PM · Jun 5, 2026 · 362K Views

69 Replies · 73 Reposts · 837 Likes

This line in particular stuck out to us:

Finance says, “half the budget,” engineering says, “double it” and you don’t know who’s right because there is no shared language of value. There’s no attribution, and no attribution means no allocation.

This got us thinking about what it actually means to allocate budget in AI and how enterprises should be thinking about it. What’s interesting about this challenge is that there has rarely been a truly universal resource in the enterprise. Marketing never asked for budget to run cloud infrastructure, and even if engineering built an internal tool for marketing, that was typically still considered engineering’s responsibility.

Platform plays are the closest we’ve historically gotten – plenty of companies will centralize on Salesforce, but from an invoicing perspective, you could very clearly break apart seat costs for Sales Cloud, Marketing Cloud, and Service Cloud. Now, you get a single invoice at the end of the month that shows a large number of tokens used. Who used them, to what end, and was there an ROI? Who the hell knows!

We have genuine sympathy for finance teams who are facing these challenges for the first time – and for the engineering leaders who are getting their “AI budgets” blown up because, well, everything is AI now. And that’s the most interesting thing to realize: Everything is AI now. There can’t be a single line item for AI because AI is in every part of your budget. So what do you do instead?

The funny thing is that it’s not all that interesting. AI budgeting is a solved problem – the industry collectively learned how to account for variable costs from cloud infrastructure, and budgeting for AI spend is the same. What needs to catch up is the infrastructure to make this happen.

This might sound trivially obvious, but it seems to get muddled more often than we might think. It’s the most obvious place to start. The X discourse is all about tokens for obvious reasons: Software is no longer zero-marginal cost, and that means that your bills genuinely are climbing faster than you’d realize with the use of AI. But your large monthly Anthropic invoice is different from an agent that’s built for a particular application area. Spend on verticalized agents should obviously go into the budget for the specific organization that is using that agent, and the team that’s buying that agent – just like with any other software! – should be precise and empirical with evaluating what the potential ROI is. You’d be shocked how often we talk to customers who seem to have given almost no thought to how they would measure the success of the agent. If there isn’t an ROI, there isn’t going to be a budget.

The interesting thing here is that application spend is getting harder to predict. Most agents are adopting token-based billing (or some abstraction on top of tokens) with some occasional seat-based access layered on top. For most of us, the token cost dominates the seat cost by at least an order of magnitude. How do you budget when your spend can vary so much? Again, history gives us clear answers. Lest we forget, this was the exact concern with (and criticism of) serverless computing ~8-10 years ago: There was no way for enterprises to know how much a workload would cost. But companies found a way around this challenge, allocating a bucket of spend to cloud providers and then burning down that spend through their own usage or through marketplace purchases. The same model will very likely emerge for AI spend.

Budgeting for “raw” intelligence is a much harder problem, and that’s where the infrastructure needs to catch up.

Engineering teams have historically had to develop good abstractions for resource management, and the rest of the enterprise is going to have to catch up. Developers don’t run tests on the production cluster, and the staging and production databases have to be isolated. That makes it dramatically easier for a finance team to understand COGS, development spend, and potential sources of waste.

That same level of visibility is going to need to be adopted to meter AI spend accordingly. Today, tools like Claude and Cursor give you visibility into how many tokens each user used, but that’s just the starting point. You have no idea if those tokens were spent on useful work, new experiments, or totally useless things. On one hand, you don’t want to be metering so closely that you discourage use – it’s fine if someone spends a couple bucks looking up where to eat lunch today – but visibility matters.

This is where products like Entire are particularly interesting to us. Creating a clear audit trail to help teams understand what work was done, which agents were responsible for it, and how that ties back to the ultimate goals that the company sets is a critical component in understanding what AI spend is actually worth – and where the waste is happening. While it’s very early, we have a hunch that spend management and cost optimization might end up being an underrated application area for tools like Entire.

The interesting question, though, is how that same discipline extends into other areas. Again, engineering by its nature is structured. Coding agent spend can be tracked as a part of a git commit. Some areas – e.g., customer support – have natural analogs, and you can easily imagine tracking how much was spent in resolving a particular ticket. Others like marketing and sales are dramatically less amenable to precise tracking in their current structure. While we’re not experts in exactly how you should empiricize those functional areas, we think it’s somewhat inevitable that good visibility and structure on token spend will become integral to how those areas are run.

None of this is going to be tidy in the interim. Teams will experiment, budgets will be disorganized, and spend will run over what you expect. That’s fine – that’s what the early years of cloud spend looked like too! The mistake is responding to that fuzziness by painting with a broad brush. You absolutely should not be sticking all of your AI spend into one budget and saddling the engineering team with it. That might feel like an okay short-term fix, but it’ll hold your whole company back.

None of this requires inventing a new discipline. The accounting model already exists and can be copied from cloud infrastructure. What’s missing is the visibily layer, and that’s being built right now. Eric Glyman is right that no attribution means no allocation, but attribution is an engineering problem – and engineering problems get solved incredibly fast nowadays. The finance teams that handle this transition well will be the ones who recognize that AI should be treated like the next phase of infrastructure.