IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2605.04522.html

DAO-enabled decentralized physical AI: A new paradigm for human-machine collaboration

Author

Listed:
  • Mark C. Ballandies
  • Florian Spychiger
  • Uwe Serdult
  • Claudio J. Tessone

Abstract

We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems. We (1) synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics; (2) connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other; (3) position DAO-governed decentralized physical infrastructure networks (DePIN) within a vertically integrated stack that links energy and sensing to connectivity, storage/compute, models, and robots; (4) show how these elements specify workflows that couple machine execution with human oversight, enabling enhanced self-organization of techno-socio-economic systems, which we call DePAI; and (5) analyze risks, including security, centralization, incentive failure, legal exposure, and the crowding-out of intrinsic motivation, and argue for value-sensitive design and continuously adaptive governance. DePAI offers a path to scalable, resilient self-organization that integrates physical infrastructure, AI, and community ownership under transparent rules, on-chain incentives, and permissionless participation, aiming to preserve human autonomy.

Suggested Citation

  • Mark C. Ballandies & Florian Spychiger & Uwe Serdult & Claudio J. Tessone, 2026. "DAO-enabled decentralized physical AI: A new paradigm for human-machine collaboration," Papers 2605.04522, arXiv.org.
    • Handle: RePEc:arx:papers:2605.04522
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2605.04522
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas Liebig & Patrick A. Puhani & Alfonso Sousa‐Poza, 2007. "Taxation And Internal Migration—Evidence From The Swiss Census Using Community‐Level Variation In Income Tax Rates," Journal of Regional Science, Wiley Blackwell, vol. 47(4), pages 807-836, October.
    2. Michael D. König & Claudio J. Tessone & Yves Zenou, 2010. "From Assortative To Dissortative Networks: The Role Of Capacity Constraints," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 483-499.
    3. Joshua C. Yang & Fynn Bachmann, 2025. "Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee," Papers 2502.05017, arXiv.org, revised Jun 2025.
    4. D'aniel Kondor & M'arton P'osfai & Istv'an Csabai & G'abor Vattay, 2013. "Do the rich get richer? An empirical analysis of the BitCoin transaction network," Papers 1308.3892, arXiv.org, revised Mar 2014.
    5. Michael Lustenberger & Florian Spychiger & Lukas Küng & Jens Martignoni, 2025. "DAOs as property owners: a conceptual exploration from the perspective of organizational system theory," Journal of Organization Design, Springer;Organizational Design Community, vol. 14(2), pages 127-143, June.
    6. Ying-Ying Hsieh & Jean-Philippe Vergne & Philip Anderson & Karim Lakhani & Markus Reitzig, 2019. "Correction to: Bitcoin and the rise of decentralized autonomous organizations," Journal of Organization Design, Springer;Organizational Design Community, vol. 8(1), pages 1-1, December.
    7. Dániel Kondor & Márton Pósfai & István Csabai & Gábor Vattay, 2014. "Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    8. Voshmgir, Shermin & Zargham, Michael, 2020. "Foundations of Cryptoeconomic Systems," Working Paper Series/Institute for Cryptoeconomics/Interdisciplinary Research 1, WU Vienna University of Economics and Business.
    9. , D. & Tessone, Claudio J. & ,, 2014. "Nestedness in networks: A theoretical model and some applications," Theoretical Economics, Econometric Society, vol. 9(3), September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ayana T Aspembitova & Ling Feng & Lock Yue Chew, 2021. "Behavioral structure of users in cryptocurrency market," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-19, January.
    2. Alexandre Bovet & Carlo Campajola & Jorge F. Lazo & Francesco Mottes & Iacopo Pozzana & Valerio Restocchi & Pietro Saggese & Nicol'o Vallarano & Tiziano Squartini & Claudio J. Tessone, 2018. "Network-based indicators of Bitcoin bubbles," Papers 1805.04460, arXiv.org.
    3. Carlo Campajola & Marco D'Errico & Claudio J. Tessone, 2022. "MicroVelocity: rethinking the Velocity of Money for digital currencies," Papers 2201.13416, arXiv.org, revised May 2023.
    4. Mario V. Tomasello & Mauro Napoletano & Antonios Garas & Frank Schweitzer, 2017. "The rise and fall of R&D networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 617-646.
    5. Ke Wu & Spencer Wheatley & Didier Sornette, 2018. "Classification of cryptocurrency coins and tokens by the dynamics of their market capitalisations," Papers 1803.03088, arXiv.org, revised May 2018.
    6. Ladislav Kristoufek, 2015. "What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    7. Kristoufek, Ladislav, 2018. "On Bitcoin markets (in)efficiency and its evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 257-262.
    8. Martins, Francisco Leonardo Bezerra & do Nascimento, José Cláudio, 2022. "Power law dynamics in genealogical graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    9. Serdar Neslihanoglu, 2021. "Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    10. Nick James & Kevin Chin, 2021. "On the systemic nature of global inflation, its association with equity markets and financial portfolio implications," Papers 2111.11022, arXiv.org, revised Jan 2022.
    11. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-18, August.
    12. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," Papers 1808.08585, arXiv.org.
    13. Espinoza-Licona, David R. & Pérez-Sosa, Felipe A., 2019. "El bitcoin, ¿una burbuja especulativa? Análisis de la estabilidad paramétrica de series de tiempo para el periodo 2009-2018," eseconomía, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 14(51), pages 45-60, Segundo s.
    14. Massimiliano Zanin & David Papo & Miguel Romance & Regino Criado & Santiago Moral, 2016. "The topology of card transaction money flows," Papers 1605.04938, arXiv.org.
    15. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    16. Lars Steinert & Christian Herff, 2018. "Predicting altcoin returns using social media," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-12, December.
    17. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    18. Young Bin Kim & Sang Hyeok Lee & Shin Jin Kang & Myung Jin Choi & Jung Lee & Chang Hun Kim, 2015. "Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    19. Jackson, Matthew O. & Zenou, Yves, 2015. "Games on Networks," Handbook of Game Theory with Economic Applications,, Elsevier.
    20. Yu Zhang & Claudio Tessone, 2024. "Bitcoin Transaction Behavior Modeling Based on Balance Data," Papers 2409.10407, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2605.04522. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.