Token taxes as a mechanism for reducing AI-driven power concentration

The rapid development of AI threatens to erode government revenues, resulting in stark inequality, and an extreme concentration of economic and geopolitical power. To prevent this, governments require effective mechanisms for capturing the economic value of AI by shifting taxation away from labour and towards capital. Token taxes – surcharges on model inference applied at the point of sale – are a promising contender for such a mechanism as they are more likely to be enforceable via compute governance infrastructure.

While token taxes have been proposed, many technical, economic, and legal questions associated with implementation remain unanswered. Building on my ICML position paper (https://arxiv.org/abs/2603.04555), we will work to investigate four open research questions (RQs):

RQ1: Can compute governance infrastructure be leveraged to reliably audit token taxes? RQ2: What are the legal challenges associated with implementing token taxes? RQ3: What are the advantages and disadvantages of token taxes compared to alternative taxation mechanisms such as compute taxes, VAT, and digital services taxes? RQ4: Can we model the impact of a token tax on the UK economy using LLM-powered agent-based modelling?

Our output will be a policy memo with answers to RQ1-RQ4 above, co-authored with the Institute for Public Policy Research (IPPR) who have expressed interest. Based on these findings, we will re-evaluate the desirability of token taxes among the policy options for taxing AI capital.

Token taxes promise to be more enforceable than alternative forms of taxation. Unlike corporation tax (which the European Commission estimates at 9.5% for digital services companies compared to 23.2% for traditional firms), token taxes take advantage of the unique properties of AI to prevent tax evasion. In particular, they can leverage existing compute governance infrastructure for auditing and enforcement. In this way, token taxes can mitigate the concentration of power by allowing governments to capture AI-generated value.

The token tax paper has been mentioned in an interview by US Congressman Greg Casar, proposed as a policy in California gubernatorial candidate, Tom Steyer's manifesto, and we have received a letter of interest from a UK MP, Anneliese Dodds, expressing interest in a token taxes memo with further letters of interest from MPs expected. The Overton window for implementation has therefore shifted rapidly, directly influencing the timing of this project.

Extreme concentration of power threatens to disempower citizens and engender government fiscal crises by reducing labour tax revenues. Left unchecked, this concentration will degrade the very democratic institutions that societies rely on to avoid catastrophic outcomes, including great power conflict and nuclear war. Governments' ability to reliably tax AI capital and redistribute the windfalls will be central to mitigating this destabilising inequality. While there are excellent organisations (The Windfall Trust, Convergence Analysis) producing high-quality work on policy interventions, few are focused on translating this research into timely memos for policymakers.

The token taxes project will bridge the gap between research and policy implementation by resolving open technical, economic, and legal questions standing in the way of a token tax bill. The Overton window for implementation has shifted, and the policy is rapidly gaining attention from policymakers in the US and the UK. Our existing relationships with these policymakers will allow us to stress-test whether a dedicated team of AI safety technical governance and economics researchers can strengthen democratic institutions’ capacity to reduce economic and geopolitical power concentration. If successful, we will seek to raise further funding to set up further work streams.

Mentees will work on the RQs above. They will have autonomy in completing their research, and I will check in on their progress on a weekly basis. The RQs mentees work on will be determined by their background (see the prerequisites section below).

For RQ1:

• Degree in Engineering, Computer Science, Applied Maths.
• Experience with compute governance research.
• Experience with black-box auditing methods and formal verification.
• Highly proficient in Python.
• Spent substantial time working with transformers and tokenizers.
• Strong grounding in mathematics.

For RQ2:

• PhD/Master's in Law, Political Science.
• Legal research experience.
• Worked at an international organisation.

For RQ3:

• PhD/Masters in Economics.
• Tax policy experience.
• Very good writer.

For RQ4:

• PhD/Masters/Bachelors in Engineering, Computer Science.
• Highly proficient in Python and ideally at least one of C, C++, Java, Typescript.
• Experience/interest in agent-based modelling.