1. Summary of overall Gen AI Status
2. Several key breakthroughs are suggested as necessary for achieving Artificial General Intelligence (AGI)
3. Interdisciplinary collaboration plays a crucial role in advancing Artificial General Intelligence (AGI)
4. Productivity growth levels comparable to those seen during the Industrial Revolution through AGI development will likely require extensive interdisciplinary collaboration across multiple fields
5. Analysis, conclusion and some informed speculation

Here are some key areas of collaboration that seem necessary, with speculative examples drawing parallels to Industrial Revolution innovations:

1. Computer Science and Neuroscience:
   Similar to how steam power revolutionized manufacturing, the integration of advanced machine learning algorithms with deeper understanding of human cognition could lead to AGI systems that can adapt and learn across domains. This collaboration could produce AI that mimics the brain’s ability to generalize knowledge, potentially automating complex cognitive tasks across industries.
2. Engineering and Materials Science:
   Just as iron machinery transformed production capabilities, new hardware architectures optimized for AI workloads, combined with novel materials (e.g. neuromorphic chips), could dramatically increase computational power and efficiency. This could enable AGI systems to process vast amounts of data and make decisions at speeds far beyond human capability.
3. Robotics and Biomechanics:
   Analogous to the replacement of horses with mechanical power, advanced robotics informed by human and animal biomechanics could lead to highly adaptable physical AI systems. These could revolutionize manufacturing, construction, and logistics by performing a wide range of physical tasks with human-like dexterity but machine-like endurance.
4. Psychology and Ethics:
   As factories changed social structures, widespread AGI adoption will likely transform human-machine interactions. Collaboration between psychologists and ethicists could help develop AGI systems that not only perform tasks efficiently but also interact with humans in socially and ethically appropriate ways, potentially reshaping workplace dynamics and social norms.
5. Economics and Complex Systems Theory:
   The shift to mechanical methods during the Industrial Revolution reshaped economic systems. Similarly, economists working with complex systems theorists could model and predict the large-scale economic impacts of AGI, helping to develop policies and strategies to manage the transition and maximize productivity gains while minimizing disruption.
6. Energy Science and Environmental Studies:
   Just as steam power required new energy sources, AGI systems will likely have significant energy demands. Collaboration between energy scientists and environmental researchers could lead to breakthroughs in efficient, sustainable computing technologies, ensuring that AGI-driven productivity doesn’t come at an unsustainable environmental cost.
7. Education and Cognitive Science:
   As the Industrial Revolution necessitated new forms of education and training, the AGI era will likely require novel approaches to human learning and skill development. Educators collaborating with cognitive scientists could develop new pedagogical methods that help humans effectively leverage AGI tools, enhancing overall workforce productivity.
8. Law and Philosophy:
   The Industrial Revolution prompted new legal frameworks around labor and intellectual property. Similarly, legal scholars and philosophers working together could develop new paradigms for AGI governance, addressing issues like AI rights, liability for AI actions, and the ethical use of AGI in decision-making processes.

Speculating further, we might see the emergence of entirely new interdisciplinary fields, such as “AGI-Human Symbiosis Studies” or “Cognitive Economics,” which could drive innovations we can hardly imagine today. The key to achieving Industrial Revolution-level productivity growth will likely lie in fostering a collaborative ecosystem where these diverse disciplines can cross-pollinate ideas and rapidly translate theoretical breakthroughs into practical applications.

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Appendix – Sources used in development of this preliminary list

[1] Generative Al https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/7715488/6b52fa2c-7d10-4e62-873c-72b33a093504/TSGWhitepaperQ32023GenerativeAI-2.pdf
[2] [PDF] Factor prices and productivity growth during the British industrial … https://scholar.harvard.edu/files/antras/files/dualrevind.pdf
[3] [PDF] Understanding productivity growth in the industrial revolution – Free http://tankona.free.fr/crafts21.pdf
[4] The Power of Collaborative Research: Fostering Productivity and … https://www.econtentpro.com/blog/collaborative-research/285
[5] Industrial Revolution and Technology https://education.nationalgeographic.org/resource/industrial-revolution-and-technology/
[6] Industrial Revolution – Wikipedia https://en.wikipedia.org/wiki/Industrial_Revolution
[7] Chapter 2. The future of artificial intelligence – OECD iLibrary https://www.oecd-ilibrary.org/sites/a1689dc5-en/1/3/2/index.html?csp=5cbbea11094afe4b75c96b4a3ec0bcd2&itemContentType=book&itemIGO=oecd&itemId=%2Fcontent%2Fpublication%2Fa1689dc5-en
[8] Evolution of artificial intelligence research in Technological … https://www.sciencedirect.com/science/article/pii/S0040162523002640
[9] The Future of Intelligence: Unlocking Artificial General Intelligence (AGI) – Elnion https://elnion.com/2023/08/03/the-future-of-intelligence-unlocking-artificial-general-intelligence-agi/

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