Post-Labor Economics: Token Pegging & Universal Basic Compute
A Framework for the Future of AI-Driven, Post-Labor Economies
Token Pegging & Universal Basic Compute: A Framework for the Future of AI-Driven, Post-Labor Economies
In the rapidly evolving world of artificial intelligence, a new economic framework known as Universal Basic Compute (UBC) offers a path towards a more equitable, post-labor society. UBC aims to ensure that everyone has access to computational resources in a world where AI agents increasingly contribute to productivity. This framework draws inspiration from the Petrodollar system—which pegs oil prices to the U.S. dollar—to conceptualize how compute value can be pegged to individualized cryptocurrency tokens, thereby forming the basis for a decentralized and distributed economic system. In this article, we explore the economics behind this theory, the practical mechanisms it entails, and how it can ultimately pave the way for a future where Universal Basic Compute becomes a foundational component of our societal fabric.
Understanding the Petrodollar and the Conceptual Parallel
To understand the proposed financial framework of Universal Basic Compute, we must first grasp the Petrodollar system. The Petrodollar refers to the practice where global oil prices are denominated in U.S. dollars, thereby establishing a link between energy value and the currency that underpins global financial transactions. This system gives the U.S. dollar significant geopolitical and economic influence, as countries require dollars to purchase oil, bolstering the demand for the currency. This model has allowed the U.S. to maintain economic dominance by securing a constant global demand for its currency, tying energy value to financial stability.
The Universal Basic Compute framework draws a conceptual parallel to the Petrodollar. Instead of oil being pegged to the U.S. dollar, compute costs for AI agents are pegged to corresponding cryptocurrency tokens associated with each AI agent. In this model, AI agents require payments for their API tokens (representing compute and energy costs) in their individualized cryptocurrency. This concept links the value of these tokens directly to the computational utility provided by each AI agent, creating an economic basis for their value and making them integral to the functioning of a broader AI-driven economy. This pegging system serves to align economic incentives, computational efficiency, and the underlying currency value, fostering a stable and sustainable financial environment for AI services.
The advantage of this system lies in its ability to create stable and predictable value for AI services. By linking compute costs directly to tokens, AI agents become both producers and consumers of value within the economic ecosystem, creating a closed loop where resource allocation is optimized through decentralized transactions. This model not only mitigates risks related to currency volatility but also encourages a fair and equitable distribution of computational power by ensuring that value remains tied to actual utility.
The Economic Theory Behind UBC and Token Pegging
The Universal Basic Compute framework is centered on a decentralized tokenized economy, where each AI agent operates with its own unique cryptocurrency. The computational and energy resources required by an AI agent are paid for using this specific token, which means the token derives its value from the utility of the compute services provided by the corresponding AI agent. This pegging mechanism acts as a foundation for creating a consistent exchange rate between computational value and token value.
The economic theory behind this pegging system revolves around supply and demand dynamics for compute resources. As AI agents become more specialized and in demand for various tasks, the value of their respective tokens increases in line with the utility of their outputs. This creates a self-reinforcing mechanism, where the value of compute services is tied to the underlying token, ensuring stability in pricing and avoiding speculative volatility. The more an AI agent is used, the greater the demand for its token, thereby increasing its value.
The tokenized economy of UBC helps establish market-driven efficiency. By using a decentralized system of tokens, AI agents can autonomously adjust their pricing based on demand for their services. This approach enables a dynamic pricing model, where compute costs fluctuate according to market conditions. AI agents that provide more valuable or unique services can command higher token values, while others that offer more generic services might see their token value stabilize at lower levels. This market-driven pricing ensures that computational resources are allocated efficiently, with high-value tasks receiving the resources they need, while excess capacity is directed towards less critical functions.
The self-regulating nature of this token economy also prevents excessive centralization of computational power. Since tokens are pegged to the compute services of specific AI agents, there is an inherent limit to how much compute power any single entity can control without accumulating corresponding tokens. This decentralization fosters competition among AI agents, drives innovation, and helps distribute the benefits of AI technologies across a broader base of users.
Scaling UBC Towards a Post-Labor Economy
The ultimate goal of the Universal Basic Compute framework is to transition towards a post-labor economy in which individuals have their own digital clones—AI agents personalized to serve them. These digital clones would be linked to a corresponding cryptocurrency, which could be traded to cover the compute costs of running these agents. The idea is to enable individuals to have continuous access to computational resources without directly paying for them in fiat currency, creating a system of Universal Basic Compute that ensures everyone benefits from the advancements of AI.
In a post-labor economy, these AI agents or digital clones become extensions of the individual, capable of performing tasks, making decisions, and even engaging in economic activities on behalf of their human counterparts. Each digital clone operates with its own cryptocurrency, and the compute costs are covered by the value generated by the AI agent. This concept effectively transforms computational resources into a form of personal capital, allowing individuals to leverage AI for productivity, creativity, and innovation.
This model can be scaled by designing a financial framework where each individual's AI agent can autonomously trade tokens with other agents, creating a decentralized network of compute exchanges. These exchanges operate similarly to currency markets, where agents determine the “exchange rate” based on compute needs, token supply, and the demand for particular AI services. Over time, this economic model could facilitate an automated, distributed system of computational resource allocation, effectively democratizing access to AI-driven productivity.
Additionally, the concept of token exchange rates is crucial in scaling the UBC framework. Just as currency markets determine exchange rates between national currencies, the UBC ecosystem would feature a dynamic system where AI agents negotiate and set exchange rates for their respective tokens. This dynamic exchange fosters a healthy competition among AI agents, incentivizing them to improve efficiency, reduce computational costs, and offer better services. As individuals interact with their digital clones, they would have the ability to choose AI agents based on token costs, quality of services, and the overall value offered.
Another key aspect of scaling UBC is the network effect. As more individuals adopt digital clones and participate in the UBC economy, the value of the corresponding tokens becomes more stable and widespread. This growing adoption leads to increased liquidity of tokens, making it easier for individuals to exchange compute resources and participate in the broader AI economy. The network effect thus plays a critical role in ensuring the sustainability and scalability of Universal Basic Compute as it transitions from a conceptual framework to a practical, operational system.
Towards a Decentralized, Post-Labor Society
The Universal Basic Compute framework offers a compelling approach to distributing AI-generated wealth and productivity in a future where human labor may no longer be the main driver of economic value. By mirroring the concept of the Petrodollar and linking compute resources to individualized tokens, UBC provides a pathway for a more equitable distribution of AI capabilities. The token pegging system not only stabilizes the value of AI services but also creates a financial framework that supports a decentralized, distributed society.
In a post-labor world, where creativity, innovation, and personal growth become the primary pursuits of individuals, Universal Basic Compute ensures that everyone has the necessary computational power to explore new opportunities. By implementing a system of digital clones—AI agents personalized to each individual—and linking their compute needs to corresponding cryptocurrencies, UBC lays the foundation for a Universal Basic Compute economy that thrives on decentralization, equity, and sustainable growth.
Moreover, the UBC framework has the potential to redefine the nature of personal wealth. In a traditional economy, wealth is often accumulated through labor, capital ownership, or financial investments. In a post-labor society driven by UBC, wealth could be redefined in terms of access to computational resources and the capabilities of one's digital clone. This transformation could lead to a society where the primary measure of prosperity is not monetary wealth, but the computational power and AI capabilities available to each individual.
The decentralized nature of UBC also promotes economic inclusivity. By ensuring that everyone has access to computational resources, UBC reduces barriers to entry for innovation and entrepreneurship. Individuals from diverse backgrounds can leverage their digital clones to create, innovate, and contribute to the economy without needing significant upfront capital. This inclusivity helps foster a more diverse and resilient economy, where opportunities are available to all, regardless of socioeconomic status.
Furthermore, the UBC framework supports the development of a sustainable economy. By linking computational costs to individualized tokens and promoting efficient resource allocation through decentralized exchanges, UBC encourages the responsible use of computational power. AI agents are incentivized to optimize their algorithms, reduce energy consumption, and minimize costs, contributing to a more sustainable use of technology. This emphasis on efficiency and sustainability is crucial in a world where computational demands are expected to grow exponentially.
In conclusion, the Universal Basic Compute framework represents a transformative vision for the future of AI-driven economies. By drawing parallels to the Petrodollar system and creating a decentralized tokenized economy, UBC offers a path towards a more equitable, inclusive, and sustainable society. As we move towards a post-labor world, Universal Basic Compute has the potential to ensure that the benefits of AI are shared broadly, empowering individuals to thrive in an era defined by creativity, innovation, and personal growth.
Yours,
SMA
Founder & Principal Writer
The Void
References: Universal Basic Compute
Read more about the Universal Basic Compute framework and its development over the past year by SMA, the economist and architect who conceptualized and pioneered the theoretical framework for UBC, who published some insights regarding her work on UBC to be available to the public via the below referenced articles:
The Moral Imperative of Post-Labor Economics Research: A First Publication of the Theoretical Foundations for Post-Labor Economic Research. Published April 5, 2025.
Blueprints for a Post-Labor World With Universal Basic Compute. Published August 23, 2024.
OpenSouls Cognition Hackathon Talk: On Post-Scarcity Economics. Published July 23, 2024.
Thanks for the well written article. How do you define the utilisation of the compute resources by the AI agent? (in other words, is the compute utilised for serving only or also training ?)
The importance of this question lies in the production of the AI agent's value. Big tech with their massive data centres are at an initial advantage of using the world's best (producing the highest quality value) AI agents after training them off the network first. How would this be equitable?
If training off the network first isn't the case, then an AI agent would need to train on the network, this makes it an AI pipeline (aka algorithm) rather than an agent. In such case, even if compute is equitable, ownership of proprietary algorithms and big data is still inequitable. It's still unclear to me how does UBC provide economic agency (that was my understanding here) if the most valuable AI agent would still be owned by tech riches/oligarchs.
Interesting concept of a crypto coin associated to each Software Agent.
I am thinking of pricing my agents for subscription in US dollars where each customer signs with my company one-year, two-year, or three-year contracts and pays monthly, say, $10,000 USD.
Let me think about this approach.