Communications of the ACM
Tokenomy of Tomorrow: Envisioning an AI-Driven World
Recently, Sam Altman commented at Davos that future AI depends on energy breakthrough, in this article I would like to expand on this concept and explore how AI would revolutionize our economy.
AI tokens, distinct from cryptocurrency tokens, are fundamental textual units used in ChatGPT and similar language models. These tokens can be conceptualized as fragments of words. In the language model's processing, inputs are segmented into these tokens. AI tokens are crucial in determining the pricing models for the usage of core AI technologies.
This post explores the concept of "tokenomy," a term coined to describe the role of AI tokens, such as those in ChatGPT, as a central unit of exchange in a society increasingly intertwined with AI. These tokens are central to a future where AI permeates all aspects of life, from enhancing personal assistant functions to optimizing urban traffic and essential services. The rapid progress in generative AI technologies is transforming what once seemed purely speculative into tangible reality.
We examine the significant influence that AI is expected to have on our economic frameworks, guiding us towards a 'tokenomy' – an economy fundamentally driven and characterized by AI tokens.
AI Tokens as Currency
In our projected AI-centric future, AI services become integral to nearly every aspect of our lives, with AI tokens emerging as the primary medium for these services. As such, AI tokens naturally assume the role of a currency.
This development is driven by our increasing reliance on AI across all facets of life and the essential role of foundation AI models in facilitating our interactions with these sophisticated systems. Originally designed for measuring and managing computational tasks, AI tokens gain substantial value due to their critical role in enabling access to AI services.
These tokens are set to become a universal standard in transactions, transcending AI-specific applications to cover a broad spectrum of goods and services, akin to the historical functions of gold and fiat currencies.
Supported by a strong and ever-expanding AI infrastructure, AI tokens are likely to offer a viable alternative to the traditional and often unstable financial systems. As our world gravitates towards a digital and AI-enriched landscape, AI tokens are on course to evolve into a borderless currency, central to international economic transactions. This marks a significant paradigm shift in financial systems, paving the way for an economy deeply integrated with and driven by artificial intelligence.
Economic Efficiency measured by FLOPs per joule
In the burgeoning era of AI, Foundation Models emerge as the pinnacle of productivity tools, outstripping all other technologies in terms of impact and prevalence. Exemplified by innovations like ChatGPT and its advanced successors, these AI models form the cornerstone of various sectors, including business, education, and personal development.
Essentially, foundation models are engines converting energy into intelligence, and energy-to-intelligence conversion efficiency is the core efficiency measure for foundation models. A natural deduction is that if AI tokens are the currency, energy is the anchor of such currency.
As we venture deeper into an AI-driven future, the standard for gauging economic efficiency is poised for a revolutionary shift. Moving beyond traditional industrial indicators, the focus turns to the proficiency of AI systems in converting energy into intelligent output, quantified as computational efficiency in FLOPs (Floating Point Operations) per joule. This shift, grounded in the realization that the new economy's worth stems from the advanced insights and enhancements offered by AI, marks a significant transformation in economic valuation.
The essence of this evolved economic model centers on the energy-to-computational efficiency of AI systems. The more adept an AI system is at utilizing energy for computational tasks, the more value it contributes, creating a direct linkage between energy-efficient computing and economic growth.
This pursuit of heightened computational efficiency in AI becomes a cornerstone of economic strategy in this future landscape. The capacity to process vast data volumes swiftly and energy-efficiently is no longer just a goal; it becomes the bedrock of economic competitiveness and expansion. The global economy's evolution is thus inextricably linked to advancements in AI and computing technologies, making FLOPs per joule an indispensable metric and a gauge of economic health and potential.
Foundation Models as Central Banks
Companies which own the Foundation Models control the supply of AI tokens, and thus they attain a level of influence and power akin to, or even surpassing, that of central banks. This analogy stems from their control over the AI-driven economic infrastructure, much like how central banks regulate monetary supply and influence economic policy.
These tech giants, wielding Foundation Models, effectively become the gatekeepers of the digital economy, controlling the flow of information, the execution of large-scale computational tasks, and the allocation of AI-driven services. Particularly, their role in managing the AI Tokens further solidifies their position, paralleling the monetary control exercised by central banks.
The concentration of such immense control and influence in a handful of corporations raises significant questions about economic sovereignty, market dynamics, and the need for robust regulatory frameworks to ensure fair and equitable AI access and to prevent the monopolistic control of critical AI infrastructure.
Shaoshan Liu's background is a unique combination of technology, entrepreneurship, and public policy. He is currently a member of the ACM U.S. Technology Policy Committee, and a member of U.S. National Academy of Public Administration's Technology Leadership Panel Advisory Group. His educational background includes a Ph.D. in Computer Engineering from U.C. Irvine, and a Master of Public Administration (MPA) from Harvard Kennedy School.
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