# Trends in Economic Inputs to AI

Introduction
============

Frontier AI companies have seen rapid increases in the economic resources they have available to pursue AI progress. At some companies, the number of employees is at least doubling every year, and the amount of capital received is tripling every year. It is unclear whether this growth is sustainable. Is the revenue growing faster than the capital requirements? If it is, will revenue catch up before the capital available for AI investments runs out?

I do not think that there is enough publicly available data to answer these questions. It is plausible that frontier AI companies will run into significant economic limitations before [Metaculus’s forecast](https://www.metaculus.com/questions/5121/date-of-general-ai/) for AGI in July 2033.

Similar Work
------------

*   Epoch has [an estimate](https://epoch.ai/blog/can-ai-scaling-continue-through-2030) for how various inputs to AI training runs could scale through 2030. Their work is distinct from this post because they focus on technical inputs (electric power, chip manufacturing, data, and latency), while this post focuses on economic inputs (labor and capital).
*   Epoch also has [an estimate](https://epoch.ai/data-insights/ai-companies-revenue) for revenue trends during 2023-2024. This post looks at a longer time scale and looks at other inputs in addition to revenue.
*   Parker Whitfill and Cheryl Wu have published [an estimate](https://forum.effectivealtruism.org/posts/xoX936hEvpxToeuLw/estimating-the-substitutability-between-compute-and#Trends) for the number of employees at frontier companies over time.

Limitations
-----------

None of the frontier AI companies are independent, publicly traded companies. They are either independent companies that are not publicly traded (OpenAI, Anthropic, and xAI[^r622dw0nx59]), portions of a larger company (DeepMind and MetaAI), or not based somewhere with strong public reporting requirements (DeepSeek).

These companies are not required to make the data I am interested in publicly available. Instead, the information comes mostly from public statements from these companies. These are unlikely to be inaccurate (since then the companies could be sued for fraud), but they can be very imprecise or only selectively reported.

The quality of the data varies widely depending on what is being measured, and between the frontier AI companies. I think that enough of the data is high enough quality to draw some interesting conclusions from it.

All of the data described in this post can be found here: [Growth Trends for AI Labs](https://docs.google.com/spreadsheets/d/1GX8EajYxhKrSiaVIGQJtlW1Ig856-fuEdSFBqCIR6Aw/edit?usp=sharing).

A key assumption that I am making is that the amount of capital received is proportional to the current capital requirements at the frontier AI companies. Equivalently, these companies are spending most of their capital on furthering AI capabilities, rather than holding on to it for the future. If the amount of available capital slows down, this would translate into frontier AI companies spending fewer resources on developing AI.

I am not predicting what would happen if capital does run out. Does AI progress transition to a new exponential trend with a slower growth rate or is there an AI winter? Do the frontier AI companies continue unscathed or do some of them fail when the bubble pops? This post attempts to estimate whether the current trends are sustainable, not what would happen if they are not.

Employees
=========

The growth rate for the number of employees at OpenAI is 2x per year, at Anthropic is 2.4x per year, and at DeepMind is 1.4x per year.

I do not predict that the growth in the number of employees will run into hard limits before 2033.

Sources
-------

There are two main sources of data for the number of employees at the frontier AI companies.

The first source is when the companies publish the number of their employees themselves, or tell journalists who publish it. For example, during the conflict between OpenAI’s former Board of Directors and Sam Altman, [news](https://news.bloomberglaw.com/us-law-week/openai-staff-threaten-to-go-to-microsoft-if-board-doesnt-quit) [organizations](https://www.forbes.com/sites/tylerroush/2023/11/20/more-than-500-openai-employees-threaten-to-quit-over-sam-altmans-removal/) [reported](https://www.wired.com/story/openai-staff-walk-protest-sam-altman/) that over 700 out of OpenAI’s 770 employees threatened to leave with Sam Altman.

The other main source of data is from market research organizations like [RocketReach](https://rocketreach.co/anthropic-profile_b7e9da6fc2a8683b), [PitchBook](https://pitchbook.com/profiles/company/462805-93#overview), or [LeadIQ](https://leadiq.com/c/openai/5a1d9d1e2300005c008cfc05/employee-directory). They regularly create estimates for the number of employees at lots of companies (among other data) and sell that data as part of their consulting service. The process by which they collect data is not always public, but it can include scraping LinkedIn and surveys sent to companies, in addition to the news reports. Most of my data comes from market research organizations through intermediaries who have made the data public. An example is the blog posts by SEO AI about [OpenAI](https://seo.ai/blog/how-many-people-work-at-openai) and [Anthropic](https://seo.ai/blog/how-many-people-work-at-anthropic).

Some of these sources only say the month or year of their estimates, rather than the exact day. I have arbitrarily assigned these numbers to the middle of the year (July 1) and the beginning of the month. Changing when these are assigned can change the growth rate by 0.4x per year, with the strongest effect when half of the data is annual estimates.

Data
----

OpenAI has the most extensive data available, with multiple time series from market research organizations going back as far as 2015, in addition to a scattering of media or self-reported estimates. They currently have about 7400 employees, and are growing at a rate of 2.0x per year.

Anthropic has a similar density of data, but has existed for a much shorter amount of time. They currently have about 2300 employees, and are growing at a rate of 2.4x per year.

DeepMind has existed for even longer than OpenAI, but has sparser data. Half of my data points come from a single source,[^jtg7t0914tl] with the others coming from media reports. They currently have about 6700 employees and are growing at a rate of 1.4x per year.

MetaAI, xAI, and DeepSeek do not have enough data for me to be comfortable estimating a trend. The most recent estimate for each of them has fewer employees than OpenAI, Anthropic, or DeepMind today.

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/5ea750ab4038b8f4b82811d73fb8b30fa18ef70efa350676.png)

**Figure 1: **Estimates for the number of employees at frontier AI companies vs time. All of the companies have some estimates, but only OpenAI, Anthropic, and DeepMind have enough data for me to include a trendline. DeepMind has long had the most employees, but they have recently been passed by OpenAI.

Projections
-----------

I will now recklessly extrapolate these trends forward in time. 

The target dates for this extrapolation are: July 2027, when [AI 2027](https://ai-2027.com/) predicts that AGI will be announced, July 2030, a target date for an [Epoch investigation](https://epoch.ai/blog/can-ai-scaling-continue-through-2030), July 2033, when [Metaculus predicts](https://www.metaculus.com/questions/5121/date-of-general-ai/) general AI, and July 2047, the aggregate forecast for 50% chance of HLMI from the 2023 [AI Impacts survey](https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdf).

In July 2027, these trends predict that OpenAI will have about 26,000 employees, and Anthropic and DeepMind will each have about 11,000 employees.

In July 2030, these trends predict that OpenAI will have about 210,000 employees, Anthropic will have about 160,000 employees, and DeepMind will have about 30,000 employees.

In July 2033, these trends predict that OpenAI will have 1.8 million employees, Anthropic will have 2.4 million employees, and DeepMind will have 80,000 employees. Changing the methodology can change these projections by roughly a factor of 2.

These projections are large, but not completely absurd. OpenAI & Anthropic would be larger than the largest software companies today: Microsoft at 228,000 and Google at 186,000 employees. They are similar to the [largest companies today](https://companiesmarketcap.com/largest-companies-by-number-of-employees/): Walmart at 2.10 million and Amazon at 1.56 million employees. The [tech industry currently employs](https://comptiacdn.azureedge.net/webcontent/docs/default-source/research-reports/comptia-state-of-the-tech-workforce-2024.pdf?sfvrsn=a8aa5246_2) 6.4 million people, so these 3 companies would employ about 2/3 of the current US tech workforce.

While I expect that this growth will cause shortages of people with particular skills, I do not think that there are hard limits for frontier AI companies finding new employees before 2033.

In July 2047, these trends predict that OpenAI will employ 32 billion people, Anthropic will employ 600 billion people, and DeepMind will employ 8 million people. These numbers are ridiculous. If AGI is first developed in 2047, we should not expect that the number of employees at frontier AI companies will have continued to grow at the rate they are growing today.

Capital
=======

Frontier AI companies are receiving a large and rapidly increasing amount of capital. OpenAI, Anthropic, and xAI have received tens of billions of dollars, and this is growing at between 2.2x and 3.5x per year.

The availability of capital could be a significant constraint for these companies in the near future.

Sources
-------

The available sources for capital received are better than the sources for the number of employees, for some of the frontier AI companies.

Most of the funding rounds for the private companies have been made public. They are conveniently conglomerated by organizations like [Tracxn](https://tracxn.com/d/companies/openai/__kElhSG7uVGeFk1i71Co9-nwFtmtyMVT7f-YHMn4TFBg/funding-and-investors#funding-rounds). Funding rounds are also discrete events, so I do not have to worry about when an estimate was made. I am more confident in my estimates for capital trends for OpenAI, Anthropic, and xAI than in my estimates for employee trends.

DeepMind and MetaAI are parts of larger companies and have received most of their capital internally. These financial transfers are not publicly available, so I cannot do this analysis for them.

DeepSeek is in China, and so has different reporting requirements. A [US Congressional report](https://selectcommitteeontheccp.house.gov/sites/evo-subsites/selectcommitteeontheccp.house.gov/files/evo-media-document/DeepSeek%20Final.pdf) estimated that they received $420 million from the hedge fund that owns them, as of April 2025. I have found no other sources with data about DeepSeek’s capital.

Data
----

OpenAI has received about $62 billion in total capital. This number is growing at a rate of 2.2x per year.  Their [most recent funding round](https://www.cnbc.com/2025/03/31/openai-closes-40-billion-in-funding-the-largest-private-fundraise-in-history-softbank-chatgpt.html), $40 billion on March 31, 2025, looks to be above the trend. However, this capital has not yet all been received: $10 billion was received when this was announced, and $30 billion will be received by the end of the year.[^ot0safuwdm] If you split the capital up accordingly, it is on trend.

Anthropic has received about $30 billion in total capital. This number is growing at a rate of 3.5x per year. They also have the [most recent funding round](https://www.anthropic.com/news/anthropic-raises-series-f-at-usd183b-post-money-valuation): $17 billion on September 2, 2025.

xAI has received about $22 billion in total capital. This number is growing at a rate of 3.3x per year. The trends for Anthropic and xAI look remarkably similar to each other.

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/ab246292c7d62b3a5c1ffc81ff76ced5cd01889109de646a.png)

**Figure 2: **Estimates for the total capital received by three of the frontier AI companies vs time. OpenAI has more capital, but Anthropic and xAI are growing faster.

Projections
-----------

I will now recklessly extrapolate these trends forward in time.

In July 2027, these trends predict that OpenAI will have received about $280 billion in capital, Anthropic will have received about $270 billion, and xAI will have received about $230 billion. The total amount of [venture capital](https://nvca.org/nvca-yearbook/) under management in the US is currently about $1.2 trillion. These three companies would account for over 60% of US venture capital. The current pool of venture capital is rapidly running out. Frontier AI companies have already begun looking for other sources of capital. They have sold conventional debt and are receiving investments from sovereign wealth funds.

In July 2030, these trends predict that OpenAI will have received about $3 trillion in capital, Anthropic will have received about $12 trillion, and xAI will have received about $8 trillion. For comparison, the total amount of capital in [sovereign wealth funds](https://globalswf.com/) is currently $14.3 trillion, the total size of the [US bond market](https://www.pewresearch.org/short-reads/2025/08/12/what-to-know-about-the-bond-market/) is currently $58.2 trillion, and the total size of the [US stock market](https://data.worldbank.org/indicator/CM.MKT.LCAP.CD) is currently $62.2 trillion. These three companies would account for more than all of the capital in sovereign wealth funds, or more than a third of the US bond market or the US stock market. Figuring out whether this is an unreasonable amount of capital would require trying to figure out how much of the US bond & stock markets are potentially divertible into AI companies, which is beyond the scope of this post.

In July 2033, these trends predict that OpenAI will have received about $34 trillion in capital, Anthropic will have received about $520 trillion, and xAI will have received about $310 trillion. For comparison, the [total global wealth](https://www.ubs.com/global/en/wealthmanagement/insights/global-wealth-report.html) is about $500 trillion. These three companies would account for more than all of the current total global wealth. 

There is no point in extrapolating these trends to July 2047 other than to gawk at how many zeros there are.

It seems important to emphasize that these are trends in inputs to AI, not outputs from AI. This is an estimate of the amount of capital received by frontier AI companies, which I assume tracks the amount spent to produce and deploy AI systems at scale.[^vv51c2gzzof] It is not an estimate of the amount of value produced by AI systems.

In order for these trends to continue through 2033, most of the wealth required to continue AI development has to be newly generated. If frontier AI companies are not generating a significant fraction of global GDP,[^tbjm7zmq23] they will have run out of money before 2033.  Determining when exactly this will occur would require estimating how much of global wealth is available to be invested in AI, which is beyond the scope of this post. Most global wealth is not liquid or is otherwise unavailable for investing in AI. I turn instead to trends in the revenue generated by AI.

Revenue
=======

The revenue data publicly available for most frontier AI companies is terrible. A growth rate of 3x per year is maybe reasonable.

Some frontier AI companies have also made public projections for their future revenue, and these projections are lower than what the trends would suggest.

Sources
-------

None of the frontier AI companies are legally required to publish revenue data. OpenAI, Anthropic, and xAI are privately owned (not publicly traded on the stock market). DeepMind and MetaAI are both part of larger companies. DeepSeek is in China. These data are only public if companies choose to share them.

There are multiple ways of reporting revenue. I am focusing on annual revenue: the actual amount of revenue generated over the course of a year. To give it a specific date, I assign it at the middle of the year (July 1). Another common thing to report is annualized revenue: the revenue generated in a month (or quarter), multiplied by 12 (or 4). This can be helpful for tracking trends that might be changing rapidly. I am not using it because companies report annualized revenue for some months but not others, and I expect that there is selection bias.

OpenAI
------

OpenAI is the only frontier AI company that has published decent revenue data. Their nonprofit is legally required to publish their precise revenue, although that is a small part of their total organization. Their for profit company has also published annual revenue from 2020-2024, and has a reasonable looking projection for 2025.

OpenAI generated $3.7 billion in revenue in 2024 and projects $12.7 billion in 2025. It is growing at a rate of 3.2x per year.

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/e486c5147dad2541662547d8b0a24b408132940289b8acd8.png)

**Figure 3: **Revenue of OpenAI’s nonprofit and for profit entities vs time. OpenAI’s projections for 2029 are substantially below the current trend.

OpenAI has made revenue projections for 2029: either [$100 billion](https://www.nytimes.com/2024/09/27/technology/openai-chatgpt-investors-funding.html) or [$125 billion](https://www.saastr.com/bloomberg-openai-to-hit-12-7-billion-this-year-but-wont-be-profitable-until-125-billion/) and it will be cash flow positive by then. These projections include a chart, which I have used to estimate their revenue projections for each year from 2026-2030.

This is a surprising projection. If the current trend continues, OpenAI would generate $1.4 trillion in revenue in 2029. This would also be the first year when projected revenue would cover projected capital requirements.

OpenAI is publicly projecting that its revenue growth will slow. They are projecting a revenue growth rate of 1.5x per year, not 3.2x per year. 

Either OpenAI's projections are accurate and their revenue growth will be slower than the trends described above, or their public projections are inaccurate.

1.  Accurate prediction:  
      
    In this case, OpenAI's current revenue growth will not continue. Since OpenAI is also claiming that it will be cash flow positive, it is predicting that the growth rate in capital it receives will slow.  
      
    This would have a significant impact on AI forecasts. In particular, [many](https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/) [forecasts](https://epoch.ai/blog/trends-in-ai-supercomputers) [involve](https://www.lesswrong.com/posts/LczkzW4uPaQS3joj8/forecasting-ai-forecasting) [extrapolating](https://epoch.ai/blog/power-demands-of-frontier-ai-training) exponential trends.[^4w1oz0txpud] If current trends rely on maintaining this exponential growth of inputs, then those forecasts seem dubious.  
     
2.  Inaccurate prediction:  
      
    OpenAI might also continue their current exponential growth rate (or grow faster). This is consistent with a qualitative prediction they made: that projected revenue would surpass projected capital requirements in 2029. It is inconsistent with their numerical predictions.

Anthropic
---------

Anthropic’s data is terrible. I have found multiple sources with very different estimates. The revenue for 2024 seems to be between $200 million and $1 billion, and is growing at a rate of between 2.6x and 11.5x per year. This is not a small range. In particular, it includes the growth rate for capital received (3.5x per year), so it is unclear whether these projections suggest that Anthropic will ever be profitable.

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/dd85b6bd123b901fe4f495495407686f07374d839729bb26.png)

**Figure 4: **Revenue of Anthropic vs time, according to three different sources. There is huge uncertainty.

Anthropic has also [made projections](https://www.reuters.com/technology/anthropic-projects-soaring-growth-345-billion-2027-revenue-information-reports-2025-02-13/) for 2025 and 2027. Their 2025 projection is $2.2 billion in revenue. Their “base case” projection for 2027 is $12 billion, and they say that revenue “could reach as high as” $34.5 billion. If I assume exponential growth between 2025 and 2027, they are projecting an exponential growth rate of between 2.7x and 7.8x per year.

Other Frontier AI Companies
---------------------------

DeepMind has not released revenue data since 2020. [That data](https://www.artificialintelligence-news.com/news/deepmind-sees-revenue-jump-and-turns-first-ever-profit/) included “how much Alphabet pays for internal services, and that can be completely arbitrary.”

I don’t know of any revenue data that has been released for MetaAI.

xAI has only released rapidly changing projections for annual revenue. In February of this year, [they projected](https://markets.businessinsider.com/news/stocks/elon-musk-s-xai-is-seeking-to-raise-10b-at-75b-valuation-1034364455) annual revenue for 2025 to be $100 million. In June, after generating $50 million in revenue in the first quarter, [they projected](https://www.reuters.com/business/musks-xai-expects-annual-earnings-over-13-billion-by-2029-bloomberg-reports-2025-06-06/) annual revenue for 2025 to be $1 billion, and $14 billion in 2029.[^aaz8rmopkdt]

DeepSeek has released a [theoretical calculation](https://www.reuters.com/technology/chinas-deepseek-claims-theoretical-cost-profit-ratio-545-per-day-2025-03-01/) of what their revenue could be: $200 million, along with a theoretical cost-profit ratio of 545%.

I distrust all of these numbers.

Epoch’s Estimates
-----------------

Epoch [has also estimated](https://epoch.ai/data-insights/ai-companies-revenue) the growth rate in revenue for the frontier AI companies. They focused on the years 2023-2024. They estimate that Anthropic and DeepMind[^4pd0mahn2u] had revenue of single digit billions per year and OpenAI had revenue of about $10 billion per year, in April 2025. The growth rate for each of these three companies’ revenue is estimated to be about 3x per year. They also argue that no other AI company had revenue exceeding $100 million in 2024.

The biggest difference in methodology is that Epoch uses reported annualized revenue. This allows them to focus on a shorter time period, although I don’t think that there is a solution for the selection bias in reporting. DeepMind doesn’t even report annualized revenue, so Epoch created a proxy based on the number of users.

I think that Epoch’s estimates are reasonable, and that they adequately express uncertainty. A growth rate of 3x per year is consistent with my estimate for OpenAI, and is within my large uncertainty for Anthropic and DeepMind.

Conclusion
==========

The economic resources being used for AI are growing rapidly.

The number of employees at OpenAI and Anthropic is at least doubling every year, and increasing by 40% every year at DeepMind. The amount of capital received by OpenAI is doubling every year, and more than tripling every year for Anthropic and xAI. The growth of revenue generated is highly uncertain, but might be tripling every year.

It is unclear how sustainable this is. Frontier AI companies could run out of US venture capital in mid 2027 and could run out of all global wealth in 2033. Substantial new wealth generated by AI is necessary to maintain exponential trends. Revenue might catch up to capital requirements for OpenAI in 2029, although they project a much lower revenue then. For Anthropic, neither Epoch’s revenue estimate nor their own baseline projections are growing as fast as their capital received (although other estimates exist). Other frontier AI companies have too little data to make the comparison.

Either frontier AI companies will have to generate more revenue than their own projections suggest, or the amount of capital available to be invested in AI will not be able to continue its current exponential growth for much longer.

Acknowledgements
================

This post was produced by the [ML Alignment & Theory Scholars Program](https://www.matsprogram.org/). [Jeffrey Heninger](https://www.lesswrong.com/users/jeffrey-heninger) was the primary author of this post and [Ryan Kidd](https://www.lesswrong.com/users/ryankidd44) scoped, managed, and edited the project. Thank you also to [Cameron Holmes](https://www.lesswrong.com/users/cameron-holmes?mention=user) and John Teichman for comments on a draft.

Thank you to the many people who volunteered as mentors for scholars at MATS! We would also like to thank our 2025 donors, without whom MATS would not be possible.

If you are interested in becoming a MATS scholar, [applications](https://www.matsprogram.org/apply) for the Winter 2026 cohort are now open until October 2.

[^r622dw0nx59]: xAI merged with the social media company X (formerly Twitter) in March 2025, so it could also be considered to be in the second category. In that merger, xAI had a higher valuation. 

[^jtg7t0914tl]: Whitfill’s and Wu’s post. 

[^ot0safuwdm]: $20 billion of that is conditional on OpenAI restructuring. 

[^vv51c2gzzof]: Epoch estimates that frontier AI companies should spend roughly equal amounts on training and inference. If frontier AI companies are using most of the capital they acquire on training and inference, then each would account for somewhat less than half of these companies’ current budget. 

[^tbjm7zmq23]: The annual capital requirements for these three companies would be greater than the current global GDP of $111 trillion. I do not know what the rest of the economy would look like in order for this to work. 

[^4w1oz0txpud]: There are also forecasts that do superexponential extrapolation. 

[^aaz8rmopkdt]: This projected revenue is two orders of magnitude lower than their projected capital received by 2029. 

[^4pd0mahn2u]: This estimate excludes revenue gained by integration of DeepMind’s products within other Google products, like including Gemini with Google search results.