I’m a researcher at RAND where I lead the compute research in the Technology and Security Policy Center within RAND Global and Emerging Risks. I’m also a Professor of Policy Analysis at the Pardee RAND Graduate School. I’m based in Washington, DC.

My research focuses on the role of compute for advanced AI systems and how compute can be leveraged as an instrument for AI governance, with an emphasis on policy development and security implications. I regularly provide policy and technical advice to leading tech companies and governments, primarily in the US.

My publications cover the impacts and governance of advanced AI systems and empirical trends in machine learning, such as training compute, data, and AI hardware. Furthermore, I’m researching the role that compute/cloud providers play in AI Governance and how society can adapt to increasingly advanced AI systems.

Previously, I was a Senior Research Fellow at the Centre for the Governance of AI (GovAI), with whom I remain affiliated as an Adjunct Fellow. I’m also a member of the OECD.AI Expert Group on AI Compute and Climate, a Pro Forecaster at INFER Pub, and a strategy specialist at Epoch on AI governance strategy. I have a background in computer engineering and studied at ETH Zürich and RWTH Aachen.

You can find more about my professional background on LinkedIn, my publications on Google Scholar, musings on my blog, and my tweets here. You can contact me using the contact details mentioned in the footer.

Research

You can find most of my publications on my research page and Google Scholar. I also summarized some of my research in 2023 here.

Some selected recent publications:

• Training Compute Thresholds: Features and Functions in AI Regulation (Twitter thread): Regulators in the US and EU are using training compute thresholds to identify general-purpose AI models that may pose risks. But why are they using them if training compute is only a crude proxy of risk?

• Societal Adaptation to Advanced AI (Twitter thread): Increasingly advanced AI systems will diffuse into society. How do we manage the accompanying risks?

• Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation (Twitter thread): We propose compute providers as an important node for AI safety, both in providing secure infrastructure and acting in an intermediary role for AI regulation, leveraging their unique relationships with AI developers and deployers.

• Computing Power and the Governance of AI (Twitter thread and blog post summary): Recent AI progress has largely been driven by increases in the amount of computing power used to train new models. Governing compute could be an effective way to achieve AI policy goals, but could also introduce new societal risks.

• Increased Compute Efficiency and the Diffusion of AI Capabilities (Twitter thread and blog post summary): Falling development costs allow more and more groups to reproduce existing AI capabilities. But falling costs also benefit large compute investors, helping them maintain their leads by pushing forward the frontier.

• What Should the Global Summit on AI Safety Try to Accomplish?: The summit could produce a range of valuable outcomes. It may also be a critical and fleeting opportunity to bring China into global AI governance.

Previously

I’m usually pretty good at keeping myself busy. In the past, I’ve done research in neuroscience, explored wireless and embedded systems, helped people to use evidence and reason to achieve their altruistic goals, explored Amazon from the inside, became a whale watching guide (and subsequently saved the whales), deployed ML to resource-constrained devices, fixed windshields on Kauai, measured radiated spurious emissions of 5G, investigated charities with legacies.now, cuddled with a bunch of dogs, and, lastly, failed a start-up on particulate sensors with friends of mine.

That’s not the correct order, and some of those things happened simultaneously—I’ll leave this to you.