There is no agreed answer to "how much should a developer spend on AI tokens per month," but in mid-2026 a lot of people started saying their number out loud. The range is absurd: it runs from companies that have banned AI coding tools entirely (and report productivity went up) to a single OpenAI employee burning $1.3 million of tokens in a month.
Below is a roundup of every credible data point I could find, sorted from lowest to highest, normalised to USD per person per month wherever the source allows. A few caveats first: most individual "spend" figures are heavily subsidised by flat-rate subscription plans, so the raw token value consumed is usually far higher than the cash that changed hands. Where a source gives a daily figure I've assumed ~21 working days to get a monthly number. Treat everything as order-of-magnitude, not accounting.
The spectrum, low to high
$0/month — Companies that banned AI coding tools
- Anonymous VP of Engineering (via Devrim Ozcay) — banned GitHub Copilot, Cursor and Claude Code company-wide in October 2025 over security/IP concerns. Six months later he reports productivity up 20%, fewer production bugs, and real code review returning. Spend on tokens: zero. (Level Up Coding)
- Culture Amp (Doug English, CTO) — not a full ban, but hard guardrails: AI coding is kept out of complex brownfield work where technical debt risk outweighs the speed-up. (MIT Sloan Management Review)
- This is the genuine "AI is bad, we buy no tokens" floor of the distribution. The thesis: the benefits (faster shipping) are immediate and visible; the costs (comprehension gaps, maintenance, knowledge silos) are slow and hidden.
~$0/month marginal — Run the model locally (antirez / DwarfStar DS4)
- Salvatore Sanfilippo (antirez) built DwarfStar 4 (DS4), a local inference engine that runs DeepSeek V4 on a single 96–128GB Mac using asymmetric 2-bit/8-bit quantization. Per-token API cost: effectively nothing beyond electricity.
- His framing is the whole point: "AI is too critical to be just a provided service." If you can run a near-frontier model on hardware you already own, your "token budget" collapses to a one-time hardware cost. (antirez.com)
- Slower than Claude, but HN testers repeatedly noted it feels surprisingly close — the local-inference escape hatch from token bills is now real.
~$100–200/month out of pocket — Simon Willison (individual power user)
- Willison pays roughly $100 per provider to Anthropic and OpenAI on their subsidised individual plans — call it $100–200/month in actual cash.
- The catch: his real token consumption is about $1,000/month against each provider (~$2,000/month of value) — and he once calculated that $200 of subscriptions had consumed $2,180.16 of tokens at API rates. The labs are eating the difference. (Simon Willison, Threads)
- He calls Uber's $1,500/tool cap "rational" and notes those subsidised individual plans are not available to large companies — which is why corporate numbers are so much higher.
Low hundreds/month per dev — Typical mid-size enterprise
- Anonymous 500-developer company — AI tooling invoice of $87,000/quarter (~$340k/year projected), i.e. roughly $57/developer/month, with 85% daily adoption. The CFO now wants ROI nobody can cleanly prove. (Reddit r/EngineeringManagers)
- This is where most "normal" companies actually sit — until agentic workflows kick in and the number jumps an order of magnitude.
Mandate-driven (no public per-head $) — Sentry / David Cramer
- Sentry co-founder David Cramer sent an internal memo making AI usage effectively mandatory — "it is quickly becoming a required skill" — and built dashboards to measure adoption. No clean per-employee dollar figure is public, but the direction of travel is "use it or fall behind." (LinkedIn)
~$2,000–4,000/month per employee — Hudson River Trading (Iain Dunning)
- On the Odd Lots live show, HRT's head of AI Iain Dunning gave the most candid real numbers anyone has: average token spend is "on the order of $100–200 a day, per employee" on his team — roughly $2,000–4,000/month. (YouTube / Odd Lots)
- Heavy users run "$1,000 a day range, bursty" (~$20,000/month in surges).
- His "token-rich vs token-poor" framing is the key quote of the whole topic: "I just don't understand how people who are token poor could keep up with someone who's token rich… all they have to do to get [a 50% boost] is essentially spend money. It creates a have / have-nots dynamic that possibly compounds."
- On ROI: "I talked to someone who said their team is 50% more productive… you'd have to — we pay $100 a day for that."
$500–2,000/month per engineer (capped at $1,500/tool) — Uber
- Uber deployed Claude Code in December 2025 and burned its entire 2026 AI budget in four months. ~95% of engineers now use AI monthly; ~70% of committed code originates from AI. (Reddit, Fortune)
- Running costs landed at $500–$2,000 per engineer per month. The response: a flat $1,500/month cap per tool (Cursor and Claude Code counted separately). (Yahoo Finance / Bloomberg)
- Simon Willison's math: two tools at the cap = ~$36,000/engineer/year, about 11% of the $330k median Uber engineer comp package — a revealing implied value for the tools. (Simon Willison)
The VC thesis: fire people, buy tokens (Lemkin & O'Driscoll)
- This is the "fire some employees to pay for tokens for others" idea made explicit. Scale VP's Rory O'Driscoll and SaaStr's Jason Lemkin argue companies will soon give engineering leaders one pooled budget covering both humans and tokens, forcing a direct trade. (BigGo Finance)
- Lemkin: "By the end of the year we're going to choose tokens over humans for engineering and product… Why do we want to empower mediocre engineers? Let's get rid of them." He'd cut 20–40% of an inherited team to fund tokens for top performers.
- The number everyone is now hunting for is the token-to-salary ratio: O'Driscoll pegs Uber's cap at ~10% of salary; Lemkin assumes 33%. If it's ~$18,000/year of tokens to make a $200k engineer 3x more productive, the math gets brutal.
~$10,000+/month per employee — Amazon & Meta "tokenmaxxing"
- Both ran internal leaderboards (Meta's "Claudeonomics" and "Session Immortal", Amazon's "KiroRank") gamifying token consumption — then dismantled them after employees gamed the system, assigning pointless tasks to agents to climb the rankings. (Fortune – Amazon, Fortune – Meta)
- The eye-watering leaked figure: Meta's board reportedly showed 85,000 employees burning 60.2 trillion tokens in 30 days — ~$900M at Anthropic API pricing, or roughly $10,500/employee/month (estimate, from leaderboard screenshots). (BigGo Finance)
- Context for why this scales so fast: Palo Alto Networks burned through $1M of tokens "very quickly" running Claude over its codebase, and one corporate client reportedly spent $500M on Claude in a single month because no usage limits were set. (Financial Post)
~$10,000+/month for one developer — Steve Yegge & Gas Town (orchestrate a fleet)
- This is what it costs an individual to operate at Yegge's "Level 8" — building your own orchestrator. His Gas Town (a.k.a. Gastown) runs 12–30 parallel Claude Code workers, and he's open that it needs multiple Claude Code accounts at $200/month each plus heavy API billing on top. (Welcome to Gas Town)
- The cleanest measured number comes from DoltHub's Tim Sehn, who ran it: a single 60-minute Gas Town session cost ~$100 in Claude tokens — "about 10X the cost of a normal Claude Code session per unit time." Run that for a working month and you're well into five figures. (DoltHub)
- Yegge himself titled a devlog "What $10k in AI Tokens Actually Produced" — so the per-project burn is real, not hypothetical. (LinkedIn)
- OpenClaw's Peter Steinberger calls Gas Town the "ultimate token burner." Yegge's own framing in Software Survival 3.0 is that tokens, energy and money are interchangeable, and software survives only if it saves cognition — i.e. saves tokens. He's simultaneously the biggest spender and the loudest voice on token efficiency.
~$30,000–40,000/month per person — Heaviest individual heavy users
- HRT's "bursty" $1,000/day users (~$20k/month) sit here, and OpenAI's internal numbers go further: the top spender reportedly uses 100 billion tokens/month, and one employee burned 210 billion tokens in a single week. (SmarterX)
~$1.3M/month ($20,000/day) — Peter Steinberger (OpenClaw / OpenAI)
- The ceiling. OpenClaw creator and OpenAI employee Peter Steinberger posted a near-$20,000/day token bill: $1.3M of API tokens in 30 days — 603 billion tokens across 7.6 million requests and ~100 coding agents. (Tom's Hardware)
- Heavy caveats: OpenAI foots the bill, it was run in expensive "fast mode" (without it the raw cost would be ~$300k), and even commenters call the workflow — agents writing code for agents to review for agents to security-audit — close to satire. (Hacker News)
So what should you spend?
$0 ──────────────────────────────────────────────────────────────▶ $1.3M/mo
│ │ │ │ │ │ │
banned local/ individual mid-size Uber/ Amazon/ Steinberger
AI antirez (Simon~$100) (~$57/dev) HRT Meta (OpenAI)
(~$0) $500–4k $10k+ ▲
Yegge/Gas Town
(~$10k+ solo)
A few honest conclusions from the data:
- Per-seat thinking is dead. The cost driver is adoption intensity, not seat count. One engineer doing multi-step agentic work can out-spend a whole team doing autocomplete by orders of magnitude.
- The "right" number depends on subsidies. Individuals on flat-rate plans (Simon at ~$100/provider) get token value worth 10–20x their cash outlay. Companies billed by raw token (Uber, Meta) don't — which is why their per-head numbers explode.
- A defensible benchmark today is roughly $100–$1,500 per developer per month. Below that you're probably under-investing or relying on subsidised personal plans; above $1,500 you're in Uber territory and should be measuring ROI hard or capping per tool.
- The real metric is revenue per employee, not % of salary on tokens. A flat percentage rule rewards heavy users regardless of whether they create value — which is exactly how leaderboards got gamed.
- And there's always the antirez option: if the bills get silly, a near-frontier model now runs on a laptop for the price of electricity.