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AI economics

AI economics

Tech aienterpriseinfrastructureeconomics Sriram Krishnan
Despite 900M users at OpenAI and enterprise deals at Anthropic, no AI lab generates enough profit to self-fund its next cluster.
What: Sriram Krishnan analyzes AI lab economics: OpenAI (900M+ MAU, consumer-heavy, low revenue per token), Anthropic (300K+ businesses, 1,000+ spending >$1M/year, GPU-constrained), xAI (massive Colossus GPU supply, zero demand, partnered with Cursor). Introduces metrics: tokens per watt-year (supply efficiency), revenue per token (demand quality), revenue per watt-year (profitability). No lab has achieved self-funding threshold.
Why it matters: Reveals the AI industry's fundamental profitability problem: despite billions in investment, every lab depends on external capital because inference costs scale indefinitely while revenue per token remains low. Cloud providers (Microsoft, Amazon) win regardless of which model succeeds.
Deep dive
  • OpenAI faces a usage mix problem: 900M+ monthly active users (mostly consumers) generate high volume but low revenue per token, with many free and paid users sending low-value prompts that compound inference costs
  • Anthropic has the opposite problem: 300K+ businesses (1,000+ spending >$1M/year) create high revenue per token and predictable usage, but demand exceeds GPU capacity, causing constant rate-limiting
  • xAI owns massive GPU supply through Colossus but has zero model utilization or developer demand, leading to partnerships like Cursor and leasing spare capacity to Anthropic
  • Google owns the full stack (TPUs, data centers, cloud, applications) with low AI cost structure, playing defensive via Gemini to protect Search ads and offensive via TPUs/Cloud
  • Meta generates $200B in ad revenue immune to AI disruption; open-sourced Llama (one-time training cost, zero inference cost) to force OpenAI and Anthropic's prices down, trading researcher appeal for market pressure
  • Microsoft invested billions in OpenAI (now worth hundreds of billions), locked in multi-billion Azure commitments, and wins regardless of model race outcomes through cloud infrastructure revenue
  • Amazon invested in both OpenAI and Anthropic simultaneously with multi-billion AWS deals, building Titanium chips with Anthropic, completely indifferent to model race winner
  • Training is a one-time capital investment (build the factory once); inference runs 24/7 and compounds with scale, consuming compute on every prompt and response
  • Tokens per watt-year measures supply efficiency (output per unit of sustained power per year through better hardware, batching, and inference stacks)
  • Revenue per token measures demand quality (price × utilization × usage mix across consumer, business, enterprise, and API tiers)
  • Revenue per watt-year combines both to determine whether a lab can self-fund its next cluster, balancing against cost per watt-year
  • Jensen Huang introduced 'Tokens per Watt × Available Watt' at GTC 2026, but this only addresses supply without guaranteeing revenue follows
  • No AI lab has crossed the self-funding threshold yet; all still depend on outside capital from investors, IPOs, cloud partners, or sovereign funds
Decoder
  • Colossus: xAI's massive GPU cluster that currently has excess capacity and is being leased to other labs
  • Titanium chips: Amazon's custom AI accelerator chips co-developed with Anthropic as an alternative to NVIDIA GPUs
Original article

AI economics

The Players

Every AI lab has its own identity/profile:

  • OpenAI - 900M+ monthly active users, mostly consumers
  • Anthropic - 300K+ businesses, 1000+ spending >$1M/year
  • xAI - massive GPU supply via Colossus but zero demand

This creates different problems:

  • OpenAI - usage mix problem. High consumer volume and a mix of free and paid users mean low revenue per token and high inference costs to serve. Many users like you and me sending way too many low value prompts compounds fast
  • Anthropic - GPU supply problem. An enterprise heavy customer base means a great usage mix. High revenue per token and predictable usage patterns mean demand exceeds capacity. Constant rate-limiting
  • xAI - demand problem. Huge supply with Colossus without model utilization

tldr: Anthropic needs more GPUs. OpenAI needs better quality demand. xAI has supply and no demand.

xAI figured it was easier to generate developer demand through the Cursor partnership/acquisition and monetize extra capacity by leasing some Colossus GPUs to Anthropic.

Meta, Google, Microsoft and Amazon are different beasts:

  • Google - owns the full stack: TPUs, data centers, cloud and applications (Search, YouTube). Low AI cost structure because it does everything in-house. But every query that goes to ChatGPT or Claude is a lost ad impression. Strategy: (1) defensive via Gemini to protect Search ads (2) offensive via TPUs and Cloud to monetize AI infra demand
  • Meta - ad revenue ($200B) on platforms AI doesn't threaten. Invested a lot in GPU supply but used for internal ad ranking and recommendations. Open-sourced its own Llama model: one-time training cost, zero ongoing inference cost. The bet: if open-source Llama is 60-90% as good, it creates market pressure that forces OpenAI and Anthropic's inference prices down. The tradeoff is talent- the best researchers want frontier compute not boring ad optimization. Hence the outsized comp to attract talent
  • Microsoft - cloud infra provider. Bet early by investing billions in OpenAI now worth hundreds of billions. OpenAI is committed to purchasing billions of cloud (Azure) services so Microsoft gets paid regardless of who wins the model race. Strategy: (1) defensive via Copilot embedded across Office, Teams and Windows to protect enterprise software (2) offensive via Azure (Cloud) to monetize infra demand
  • Amazon - cloud infra provider. Same playbook as Microsoft but bet on both AI models simultaneously. Invested in OpenAI and Anthropic and struck multi-billion AWS cloud deals with both. Indifferent to who wins the model race. Building its own Titanium chips with co-development from Anthropic. Focus is on cloud infrastructure usage. No search, no enterprise or consumer applications to defend or worry about.

The Economics

Training vs Inference

Training is a one-time capital investment. You build the factory once and it's a sunk cost. Inference is the factory running 24/7. Every user prompt and model response consumes compute. This compounds with scale and never stops.

The core metrics:

  • Tokens per watt-year is a supply side metric. It tells you how much output you're squeezing from physical infrastructure, eg how much output per unit of sustained power per year. Better hardware, smarter batching, and efficient inference stacks all show up here
  • Revenue per token is a demand side metric. Price × utilization × mix. This is (1) price per million tokens, eg consumer, businesses, API prices, (2) usage mix, eg individual user vs free user vs paid user vs enterprise user vs API calls, (3) % utilization of compute (eg like hotel occupancy but for GPUs, servers etc)

Revenue per watt-year results from both. What you actually earn per unit of physical infrastructure. This is one number that determines whether a lab can self-fund its next cluster. The other number is cost per watt-year. It matters because each model generation requires more compute than the last.

Jensen from NVIDIA introduced Tokens per Watt x Available Watt at GTC 2026. This only applies to the supply. Revenue per token addresses the demand. Just because you can build more efficient infrastructure and secure more watts, revenue won't automatically follow. Look at xAI.

Every lab is playing this same game: squeeze more tokens per watt, price highly and acquire more power.

The question is who funds the next cluster - outside capital (investors, IPO, cloud partners, sovereign funds) or self fund in-house through operating profit. No lab has crossed the self-funding threshold yet.

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