Is AI
Profitable
Yet?

Tracking the spend and revenue of frontier AI companies (May 2026).

NO.

Everyone's Broke.
$1.4T
Total Industry Spend
$613B
Total Industry Revenue
$282,307
$ Spent on AI since page load
Amazon logo
Amazon
Full AI capex est. since 2022
$313B
$22B
-$291B
lost since page open
-$54,579
Alphabet (Google) logo
Alphabet (Google)
Full AI capex est. since 2022
$287B
$25B
-$262B
lost since page open
-$48,619
Microsoft logo
Microsoft
Full AI capex est. since 2022
$266B
$31B
-$235B
lost since page open
-$47,992
Meta logo
Meta
Full AI capex est. since 2022
$230B
$3B
-$227B
lost since page open
-$44,541
Oracle logo
Oracle
Cumulative est. since 2023
$57B
$18B
-$39B
lost since page open
-$9,410
OpenAI logo
OpenAI
Cumulative est. since 2020
$55B
$28B
-$27B
lost since page open
-$4,391
Anthropic logo
Anthropic
Cumulative est. since 2021
$33B
$6.5B
-$26.5B
lost since page open
-$2,728
xAI logo
xAI
Cumulative est. since 2023
$20B
$0.8B
-$19.2B
lost since page open
-$3,764
Mistral AI logo
Mistral AI
Cumulative est. since 2023
$1B
$0.4B
-$0.6B
lost since page open
-$94
Cohere AI logo
Cohere AI
Cumulative est. since 2020
$0.7B
$0.4B
-$0.3B
lost since page open
-$31
DeepSeek logo
DeepSeek
Cumulative est. since 2023
$0.3B
$0.1B
-$0.2B
lost since page open
-$62
Nvidia logo
Nvidia
Cumulative est. since 2023
$225B
$478B
+$253B
earned since page open
+$37,640
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WHY I BUILT THIS
  • Many industry experts and companies claim AI profitability by 2030 is possible, so I wanted to see how close we really are. This site tracks cumulative spend versus revenue across most major AI companies in one place, allowing you to see exactly how much money is flowing into the industry and how far it is from breaking even.

    I'll be updating these numbers monthly as new reports and financials drop. Perhaps one day, the big "NO" will become a "YES," and the question will finally be answered :). So far? The big winner is NVIDIA, who is receiving huge profits from the AI boom by positioning itself as the primary chip supplier to the AI sector.
HOW THE NUMBERS WORK
  • All figures are estimated cumulative totals (all-time). Because most of these companies are private, the numbers aren't exact; instead, they are built from leaked financials, SEC filings, earnings calls, and industry estimates from sources like Bloomberg, the WSJ, The Information, and Epoch AI (all referenced at the bottom). The punchline 'EVERYONE IS BROKE' is intentionally punchy, but shouldn't be taken absolutely literally.

    The site includes both, big tech infrastructure spend and pure lab spending, hence why companies like Amazon and Google have huge spend figures compared to the pure labs like OpenAI or Anthropic (big AI investments, not much direct AI revenue yet). It's important to note that the site tracks whether AI investment specifically has broken even yet, not company-wide profitability, hence why companies such as Amazon and Google look so far in the red despite being hugely profitable companies as a whole.

    Spend numbers include direct R&D costs, compute, and capital expenditure on AI infrastructure (data centres, chips, and networking). Capex is treated as spend despite having long-term asset value; this is intentional. The framing shows the sheer scale of capital being committed to AI before returns materialise, rather than smoothing it across a depreciation schedule. Indirect AI revenue (E.g. Google Search performance boosted by AI Overviews, or Microsoft Office revenue lifted by Copilot) is excluded because there is no reliable way to attribute what share of those gains AI is actually responsible for. The $/sec counters use current annual burn rates rather than historical averages, to reflect what's happening right now.

    Revenue numbers are the trickiest to estimate due to a lot less information on them being readily available. Thus, the revenue figures here are mostly estimated and extrapolated off of ARR figures. Currently, I'd say these numbers are more optimistic than anything, but I will be refining this over time as more information comes out.
DISCLAIMERS
  • The AI economy is circular: Google funds Anthropic, Anthropic runs on Google Cloud, Amazon funds Anthropic, Microsoft co-invests with OpenAI. This means aggregate industry figures double-count some revenue flows. This site is one person's best effort at an honest picture, not a financial audit. If you have better sources, please reach out, I want to improve this site as much as possible every day :).
CONTRIBUTING
  • If you have good sources for any of these numbers or want to contribute, please reach out at: isaiprofitable@outlook.com
  • Built and maintained by one developer. If you find this useful, a coffee goes a long way! Buy me a coffee
SOURCES & REFERENCES
Alphabet (Google) References
Cumulative capex 2022–2024 from SEC 10-K filings: https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=GOOGL
Google Cloud AI revenue est. ~40% of Cloud total: https://epoch.ai/data-insights/ai-companies-revenue
Meta References
Cumulative capex 2022–2024 from Meta SEC 10-K filings: https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=META
Llama open source, Meta AI free — no direct AI product revenue: https://ai.meta.com/llama/
Nvidia References
$130.5B revenue, $72.9B net income (Jan 2025): https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=NVDA
Mistral AI References
$400M+ by Jan 2026: https://sacra.com/c/mistral/
$3.3B funding | loss loosely estimated from funding + industry burn ratios: https://research.contrary.com/company/mistral-ai
Cohere AI References
~$1B total funding | loss loosely estimated from funding + industry burn ratios: https://sacra.com/c/cohere/
DeepSeek References
R1 trained for $294K — excludes $6M base model (V3) cost. Published in Nature.: https://mlq.ai/news/deepseek-reveals-r1-model-training-cost-just-294000-in-peer-reviewed-nature-publication/
$220M ARR by mid-2025 from API + enterprise services: https://electroiq.com/stats/deepseek-ai-statistics/