GitHub flipped the switch on June 1. Copilot went from flat-rate subscription to metered billing — AI Credits at $0.01 each, consumed by the token, tracked to the model, billed to your org. But the billing change is the least interesting thing that happened.
The backlash was immediate. On the GitHub Community forums, developers on Copilot Pro+ (the $39/month plan) reported burning through 8% of their monthly credit allotment in two hours. One engineer ran the math and estimated his quota would be gone in less than two days. The threads filled up with people threatening to cancel, switch to Cursor, or go back to writing code by hand.
Here's what nobody is saying out loud: that burn rate is information.
The flat-rate era hid something important.
When AI tools cost the same regardless of how much you used them, usage became invisible. You couldn't tell whether your team was getting real gains from Copilot or just tab-completing import statements. You couldn't tell if the 10x engineer was actually 10x because of the tool, or if she was 10x before the tool and the tool just made her faster. You couldn't tell which of your senior engineers had actually changed how they work versus which ones installed the plugin and mostly ignored it.
The metered model blows that cover. Now you can see exactly how many tokens each engineer is consuming, through which models, at what output rate. You've got a signal you didn't have before.
The problem is most engineering leaders don't know what to do with it.
This is not a cost problem. It's a composition problem.
The instinct right now is to treat metered Copilot billing like a SaaS spend issue — add it to the budget, set a monthly cap, maybe throttle the high spenders. That's the wrong frame.
The right question is: what is the relationship between AI credit consumption and shipped output on your team?
Some engineers will burn $200 a month in credits and ship features faster than anyone else on your roster. That's a multiplier. You want more of it. You want to understand what they're doing differently and whether you can hire for it.
Some engineers will burn $200 a month in credits and have nothing meaningful to show for it. That's not a Copilot problem. That's a signal that the engineer doesn't know how to work with AI tools effectively — or that they're using them as a crutch in a way that's generating churn rather than output.
That second category is where your staffing problem lives.
The new engineering role has an AI coefficient.
The question in technical hiring has shifted from "can this person write code" to "can this person use AI tools to write better code faster." The metered billing era makes that measurable in a way it wasn't before.
What you're now paying for is not Copilot. You're paying for each engineer's ability to convert AI compute into working software. Some people have a high conversion rate. Most people have a rate that has never been measured because the tools were too cheap to meter.
When you're hiring now — especially at senior and staff levels — you need to understand what someone's AI-assisted output looks like at cost. Not "do you use AI tools" (everyone says yes), not "what tools do you use" (everyone lists the same five). Ask what their actual workflow looks like. Ask about ratios: how much of their working code starts from AI completion versus from scratch? How do they validate model output? What do they do when the model confidently generates something wrong?
These are not trick questions. They're the questions that predict whether someone is going to make your Copilot bill look like an investment or an expense.
The engineering leaders who run the most aggressive AI shops already screen for this. Farhan Thawar, who runs engineering at Shopify, put the interview reality bluntly when describing candidates who won't touch the tools:
"If they don't use a copilot, they usually get creamed by someone who does."
— Farhan Thawar, Head of Engineering, Shopify, in The Pragmatic Engineer
Cursor just got acquired for $60 billion.
I mention this because it's not unrelated. SpaceX agreed to acquire Cursor for $60 billion — an AI coding assistant — less than a week after its own IPO. That's the largest acquisition of a venture-backed startup on record.
The bet is that AI coding infrastructure is the most valuable layer of the software development stack going forward. Maybe they're right. Maybe in three years every serious engineering org is on some version of AI-native development tooling with metered, per-token pricing baked in.
If that's where this goes, then your ability to hire engineers who use these tools well — who have a high AI-output conversion rate — is not a nice-to-have. It's the core of your competitive position.
The Copilot bill isn't the problem. The Copilot bill is the diagnostic.
VC5 Consulting helps technology companies build and scale engineering teams. We work with CTOs and technical founders on staffing strategy, compensation benchmarking, and talent pipeline development. If you're rethinking what your senior engineering bench looks like in an AI-native development environment, let's talk.