The farther into the future we look, the less certain we should be that the current technical paradigm (large language models) will remain the dominant AGI paradigm, and it would be surprising if in 10 years LLMs remain the technology being discussed if AGI arrives and succeeds, requiring policy flexibility regarding technical specifics while maintaining assumptions about enduring factors like compute's importance.

AI proofing a network is a distinct policy question from quantum security, and if cyber warriors can move at machine speed and scale with full automation, this privileges the defenders (rather than attackers) because defenders can use automated capabilities to red team and patch their own systems, assuming a finite number of patchable vulnerabilities exists in the system.

What is currently being observed is not a monopoly situation in frontier model development; instead, there are multiple actors in the frontier model development layer, though other layers (hyperscalers, chips, machines used to make chips) have different market dynamics, with future monopolies in frontier model development dependent on whether scaling requirements continue and whether frontier model pre-training remains easily transferable across tech stacks.

A moratorium on state AI laws passed the House but has only approximately 10% probability of passing through the Senate via budget reconciliation due to Byrd Rule constraints, according to Metaculus forecasts.

Effective technology policy requires physics-informed understanding of individual technologies rather than simply invoking tech buzzwords (AI, quantum, blockchain), specifying what each technology does and doesn't do both today and tomorrow to enable effective policies tailored to technical realities.

Regarding quantum computing and machine learning, there is currently not a single tracked quantum algorithm that would be meaningfully accelerated by quantum computers for machine learning applications, despite quantum computers being useful for decryption and simulating quantum phenomena, so quantum computers are not currently on a path to aid AI development.

Post-quantum or quantum-resistant encryption techniques exist today and are less pleasant to implement than legacy encryption methods, but there is insufficient reason not to implement them now because files and data being transmitted today could still exist and require decryption in the future when quantum computers arrive, making quantum-resistant encryption critical for national security purposes where classified information may retain relevance across decades.

Policy decisions about where inference compute proliferates and under what conditions are critical governance points, with technical solutions including hardware-enabled mechanisms such as chips with location attestation that signal when chips are being moved to unauthorized locations, enabling technical governance solutions embedded in the chips themselves.

RAND's 1946 publication 'Preliminary Design of an Experimental World-Circling Spaceship' anticipated satellites 11 years before Sputnik in 1957 and before ICBMs, demonstrating that important technology futures can be reasoned about in advance despite uncertainty, and that anticipatory analysis about futures like nuclear weapons on missiles (when bombs were 1,000 times too heavy) informed subsequent U.S. policy on bioweapons and other technologies.

U.S. policy on some technologies like bioweapons was anticipatory, motivated by recognizing that bioweapons technology would get cheaper and more accessible, making it strategically preferable for the U.S. to prevent proliferation through the Bioweapons Convention rather than allowing universal access to mass death capabilities, which is why the U.S. unilaterally dropped bioweapons and encouraged other nations to do the same.

Casey Dugan leads intellectual efforts at RAND's TASP and is based in Cambridge, making her available for follow-up conversations with Harvard audiences about AGI research.

Computing power requires energy, so energy policy is part of the story for AI control and development, because powerful AI systems require substantial electrical power.

RAND publishes reports regularly on various technology and security topics; researchers can be followed on social media (X); a newsletter is available providing regular updates on RAND's work, with new pieces released frequently.

RAND distinguishes between realistic and speculative concerns about AGI autonomy by relying on technical analysis and historical patterns of technological development that can be reasoned about in advance, not by waiting for technologies to fully emerge before analyzing them.

AI, as software, has the ability to propagate very quickly over the internet, meaning that even very large language models can be readily transmitted over internet infrastructure.

The computing power required to run trained AI models (inference) is considerably smaller than that required to train them, measured in thousands or tens/hundreds of thousands of dollars rather than millions or billions—making inference proliferation a distinct policy problem from training proliferation.

The classical computing community has historically retained competitive advantage over quantum computing by developing better classical algorithms to solve the same problems, so while quantum algorithms are proposed, classical computer scientists develop competing classical solutions that prove more practical.

The problem of full automation of labor (where no economically relevant tasks remain for humans) is distinct from the transitional period where humans still have economically relevant work, and U.S., China, and other societies will face significant domestic issues when all labor is automated, with multiple different pathways through that transition—some rosier than others.

The likelihood of AGI-related state AI moratorium legislation passing through the U.S. Senate via budget reconciliation (which requires overcoming the Byrd Rule) is currently estimated at 10% on Metaculus.

Effective tech policy requires physics-informed understanding of each technology—what it does and doesn't do today and tomorrow—rather than simply invoking technology buzzwords (AI, quantum, blockchain) and claiming things will be different in the future.