AI coding assistants have moved from experiment to infrastructure. The median software engineer now generates $213 per week in AI token spend—roughly $11,000 annually per seat. High-volume users hit $911 per week. That's real budget, and for early-stage teams it's a line item that demands the same scrutiny as cloud hosting or SaaS tooling.
But here's the part that matters: more spend does not automatically mean more output.
Larridin, an a16z-backed observability platform for AI development tools, recently published benchmark data that should change how founders think about AI tooling ROI. The numbers reveal a stark reality—teams that treat AI coding tools as magic productivity dust are leaving money and velocity on the table.
The 10x Spend Spread—and the 2x Output Gap
Larridin's data shows a 10x spread in weekly AI token spend between the median engineer ($213) and the 90th percentile ($911). On the surface, you'd assume the high spenders are shipping proportionally more code, features, or fixes.
They're not.
Deeply AI-fluent engineers—those who understand prompting, context windows, and tool limitations—shipped roughly 2x the output of partial adopters at comparable spend levels. Meanwhile, low-adoption engineers saw no measurable output gains even as their token spend increased 20x through wasteful or poorly-targeted usage.
The takeaway: fluency, not budget, drives returns. Throwing more tokens at a problem without understanding how to use the tools effectively is like buying faster servers without profiling your database queries. You're spending, but you're not optimizing.
What This Means for Founders Building MVPs
If you're building a product—especially under time and budget constraints—this data has three direct implications:
1. AI Tooling Spend Is Now a Material Line Item
At $11,000 per engineer per year, AI coding tools are no longer rounding errors. For a five-person engineering team, that's $55,000 annually. If you're fundraising or managing burn, investors will ask how you're measuring ROI on that spend. "We use Copilot" is not a strategy.
2. Train for Fluency, Not Just Access
Buying seats for GitHub Copilot, Cursor, or Codeium is table stakes. The teams winning on velocity are the ones investing in fluency training—teaching engineers how to prompt effectively, when to trust AI-generated code, and how to use AI tools as co-pilots rather than autopilots. Larridin's data proves that the same spend level produces radically different output depending on how well the engineer uses the tool.
3. Instrument and Optimize Early
Set spend review triggers. If token costs spike without corresponding velocity gains, something's broken—either in how the tools are being used, or in whether they're the right tools for your stack. Platforms like Larridin exist because you can't optimize what you don't measure. If you're running lean, you need visibility into whether your AI tooling is accelerating your roadmap or just accelerating your burn rate.
Key Takeaways
- The median engineer now spends $213/week on AI coding tokens—over $11,000/year per seat.
- High spenders hit $911/week, but spend alone doesn't correlate with output.
- AI-fluent engineers ship ~2x the output of partial adopters at the same spend level.
- Low-adoption engineers see no output gains even with 20x higher spend.
- Fluency, not budget, drives velocity. Training and instrumentation matter more than seat licenses.
- Founders should treat AI tooling spend like any other infrastructure cost: measure it, optimize it, and be ready to defend the ROI.
The TechAhir Approach: Speed With Discipline
At TechAhir, we've integrated AI tooling into our development workflow—but we don't vibe-code. Every project is led by a senior developer who acts as the human guardrail, ensuring AI-generated code is production-grade, secure, and maintainable. We run customized-model QA to catch regressions and edge cases, resulting in virtually zero defects at handoff.
That discipline is why we can ship full, working, sellable MVPs in 3 days—not throwaway prototypes. Speed without rigor is just technical debt with a shorter fuse.
If you're looking to validate your idea, get to market, or prove traction before a fundraise, AI tooling is part of the stack—but it's the process, fluency, and quality controls that determine whether you ship fast or just burn fast.