Who Built AI?A data story
PublicPrivate

Who Built AI?

September 2026

Private companies?
Public investment?
Let's follow the threads.

Five judgment calls move that number a lot, so you can make them yourself at the end.

scroll
c. 1700
Public share so far
–
Pull the braid straight and count
0%

of today's AI industry traces back to public inputs. The rest is private capital, engineering and product.

Both have been necessary, neither sufficient.

Draw your own line

Five judgment calls do a lot of the work. Try changing them and watching the total.

60.0%
public share · at baseline
60.0%
public share of valueat baseline

Weights are not renormalized, so they can sum past 100. That is on purpose.

What 60% means

It is an attribution claim

It answers "who built this?" and goes no further. The public did the foundation and the raw material. The private sector did the products and the business.

It is not a legal claim

Whether the public is owed 60% of the money AI produces is a separate, much harder question.

The policy question it really raises

Should the public have a greater say in how the technology is governed? On these numbers, yes.

How the number was built

Nine inputs, each with a public share and a weight. Multiply, add, done.

Where the estimate is least definitive

  • Public infrastructure: the least confident row. Five points either way is fair.
  • Training data: the most consequential row. Crediting the platforms costs about four points.
  • The transformer: a live argument in the field. 40% or 70% public, both defensible.

The full breakdown

Points = public share × weight. The braid, as a table.

Sources

The papers, records and datasets behind each chapter.

Mathematics & science

  • Newton, I. (1687). Philosophiæ Naturalis Principia Mathematica. London: Royal Society.
  • Fourier, J. (1822). Théorie analytique de la chaleur. Paris: Firmin Didot.
  • Boole, G. (1854). An Investigation of the Laws of Thought. London: Walton & Maberly.
  • Turing, A. M. (1936). "On Computable Numbers, with an Application to the Entscheidungsproblem." Proceedings of the London Mathematical Society, s2-42(1), 230–265.
  • Shannon, C. E. (1948). "A Mathematical Theory of Communication." Bell System Technical Journal, 27(3), 379–423.
  • Bush, V. (1945). Science, The Endless Frontier. Report to the President. Washington: US Government Printing Office.

Hardware

  • Bardeen, J., & Brattain, W. H. (1948). "The Transistor, a Semi-Conductor Triode." Physical Review, 74(2), 230–231.
  • Kilby, J. S. (1976). "Invention of the Integrated Circuit." IEEE Transactions on Electron Devices, 23(7), 648–654.
  • Gertner, J. (2012). The Idea Factory: Bell Labs and the Great Age of American Innovation. New York: Penguin.
  • Mead, C., & Conway, L. (1980). Introduction to VLSI Systems. Reading, MA: Addison-Wesley. (The textbook of the DARPA-funded VLSI design revolution.)
  • National Research Council (1999). Funding a Revolution: Government Support for Computing Research. Washington: National Academies Press.
  • National Research Council (2012). Continuing Innovation in Information Technology. Washington: National Academies Press. (The "tire tracks" chart tracing federal research to billion-dollar industries.)
  • Browning, L. D., & Shetler, J. C. (2000). Sematech: Saving the U.S. Semiconductor Industry. College Station: Texas A&M University Press.
  • Nickolls, J., Buck, I., Garland, M., & Skadron, K. (2008). "Scalable Parallel Programming with CUDA." ACM Queue, 6(2), 40–53.

Internet

  • Cerf, V. G., & Kahn, R. E. (1974). "A Protocol for Packet Network Intercommunication." IEEE Transactions on Communications, 22(5), 637–648.
  • Leiner, B. M., Cerf, V. G., Clark, D. D., Kahn, R. E., Kleinrock, L., Lynch, D. C., Postel, J., Roberts, L. G., & Wolff, S. (2009). "A Brief History of the Internet." ACM SIGCOMM Computer Communication Review, 39(5), 22–31.
  • Abbate, J. (1999). Inventing the Internet. Cambridge, MA: MIT Press.
  • Merit Network (1995). NSFNET: A Partnership for High-Speed Networking. Final Report 1987–1995. Ann Arbor: Merit Network, Inc.
  • Berners-Lee, T. (1989). "Information Management: A Proposal." CERN internal memo, Geneva.

Algorithms

  • Rosenblatt, F. (1957). The Perceptron: A Perceiving and Recognizing Automaton (Project PARA, Report 85-460-1). Buffalo: Cornell Aeronautical Laboratory, for the Office of Naval Research.
  • Rosenblatt, F. (1958). "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain." Psychological Review, 65(6), 386–408.
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). "Learning Representations by Back-Propagating Errors." Nature, 323, 533–536.
  • LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). "Backpropagation Applied to Handwritten Zip Code Recognition." Neural Computation, 1(4), 541–551.
  • LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). "Gradient-Based Learning Applied to Document Recognition." Proceedings of the IEEE, 86(11), 2278–2324.
  • Hochreiter, S., & Schmidhuber, J. (1997). "Long Short-Term Memory." Neural Computation, 9(8), 1735–1780.
  • Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). "A Fast Learning Algorithm for Deep Belief Nets." Neural Computation, 18(7), 1527–1554.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). "ImageNet Classification with Deep Convolutional Neural Networks." Advances in Neural Information Processing Systems 25. (Trained on two NVIDIA GTX 580 GPUs.)
  • Bahdanau, D., Cho, K., & Bengio, Y. (2015). "Neural Machine Translation by Jointly Learning to Align and Translate." ICLR 2015; arXiv:1409.0473 (2014).
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). "Attention Is All You Need." Advances in Neural Information Processing Systems 30.
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). "Improving Language Understanding by Generative Pre-Training." OpenAI.
  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." NAACL-HLT 2019.
  • Brown, T. B., et al. (2020). "Language Models are Few-Shot Learners." Advances in Neural Information Processing Systems 33.
  • Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., et al. (2020). "Scaling Laws for Neural Language Models." arXiv:2001.08361.
  • Ouyang, L., et al. (2022). "Training Language Models to Follow Instructions with Human Feedback." Advances in Neural Information Processing Systems 35.
  • Bai, Y., et al. (2022). "Constitutional AI: Harmlessness from AI Feedback." arXiv:2212.08073.
  • Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023). "Direct Preference Optimization: Your Language Model is Secretly a Reward Model." Advances in Neural Information Processing Systems 36.

Training data

  • Common Crawl Foundation. Common Crawl: Overview and Crawl Archives. commoncrawl.org (founded 2007; first public crawl 2008).
  • Raffel, C., et al. (2020). "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer." Journal of Machine Learning Research, 21(140), 1–67. (Introduces the C4 web corpus.)
  • Gao, L., et al. (2020). "The Pile: An 800GB Dataset of Diverse Text for Language Modeling." arXiv:2101.00027.
  • Touvron, H., et al. (2023). "LLaMA: Open and Efficient Foundation Language Models." arXiv:2302.13971. (Table 1 itemizes the public sources: CommonCrawl, C4, GitHub, Wikipedia, Books, arXiv, Stack Exchange.)
  • Penedo, G., et al. (2024). "The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale." arXiv:2406.17557.
  • Wikimedia Foundation. Wikipedia statistics. stats.wikimedia.org.

Compute, capital and infrastructure

  • Paszke, A., et al. (2019). "PyTorch: An Imperative Style, High-Performance Deep Learning Library." Advances in Neural Information Processing Systems 32.
  • Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022). "Compute Trends Across Three Eras of Machine Learning." IJCNN 2022; arXiv:2202.05924.
  • OpenAI (2025, January 21). "Announcing The Stargate Project." openai.com.
  • Smith, B. (2025, January 3). "The Golden Opportunity for American AI." Microsoft On the Issues. (Roughly $80 billion of AI data-center investment in fiscal 2025.)
  • Meta Platforms (2025, January). Fourth-quarter 2024 results and 2025 capital-expenditure guidance of $60–65 billion. investor.atmeta.com.
  • National Science Foundation Act of 1950, Public Law 81-507.

Public R&D, its returns, and the dollar cross-check

  • Mazzucato, M. (2013). The Entrepreneurial State: Debunking Public vs. Private Sector Myths. London: Anthem Press.
  • Bloom, N., Van Reenen, J., & Williams, H. (2019). "A Toolkit of Policies to Promote Innovation." Journal of Economic Perspectives, 33(3), 163–184.
  • Jones, B. F., & Summers, L. H. (2020). "A Calculation of the Social Returns to Innovation." NBER Working Paper 27863.
  • Fieldhouse, A. J., & Mertens, K. (2023). "The Returns to Government R&D: Evidence from U.S. Appropriations Shocks." Federal Reserve Bank of Dallas Working Paper 2305.
  • OECD. Main Science and Technology Indicators database; Research and Development Statistics, 1950–2020.
  • Office of Management and Budget. Historical Tables, Budget of the United States Government.
  • National Center for Science and Engineering Statistics (NSF). Federal R&D Funding, by Budget Function; Science and Engineering Indicators.
  • National Center for Education Statistics. Digest of Education Statistics, expenditure tables for elementary, secondary and postsecondary education.