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This is worth considering, but FWIW, 50 GW would be around 10% of US electricity if it runs continuously (the US consumes at a rate of about 500 GW if you divide total consumption by one year). If the new capacity is as clean as the overall electric grid that would be about 2.5% of US emissions (25% of US emissions come from electricity) and 0.35% of global emissions (US emissions are 1/7 of global emissions).

I'm not going to do this math now but I think if the new capacity is 100% natural gas then that's about as carbon-intense as the US electric grid as a whole, or maybe somewhat worse (the US has a lot of clean energy, but it also has coal plants which are >2x more carbon intense than gas). 100% natural gas would be the worst case, because there is no scenario where the US builds new coal plants.

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Anthropic's submission to the White House's RFI on AI policy

by Agustín Covarrubias 🔸

Mar 61 min read 6

41

AI safetyPolicyExistential riskAI governanceAnthropicUS policy

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This is a linkpost for https://www.anthropic.com/news/anthropic-s-recommendations-ostp-u-s-ai-action-plan

Anthropic just published their submission to the Request for Information for a new US AI Action Plan (OSTP RFI).

It's 10 pages total and focuses on strengthening the US AISI (and broadly government capacity to test models), strengthening export controls (with some very concrete proposals), enhancing lab security through standards, scaling up energy infrastructure (asking for building infrastructure for 50 GW of power, or about 4% of the entire US grid capacity), accelerating AI adoption in government, and improving government sensemaking around economic impacts.

I recommend reading it. It's quite insightful regarding the priorities of Anthropic's policy team right now.

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Peter Wildeford6d12

Here's my summary of the recommendations:

National security testing
- Develop robust government capabilities to evaluate AI models (foreign and domestic) for security risks
- Once ASL-3 is reached, government should mandate pre-deployment testing
- Preserve the AI Safety Institute in the Department of Commerce to advance third-party testing
- Direct NIST to develop comprehensive national security evaluations in partnership with frontier AI developers
- Build classified and unclassified computing infrastructure for testing powerful AI systems
- Assemble interdisciplinary teams with both technical AI and national security expertise
Export Control Enhancement
- Tighten semiconductor export restrictions to prevent adversaries from accessing critical AI infrastructure
- Control H20 chips
- Require government-to-government agreements for countries hosting large chip deployments
  - As a prerequisite for hosting data centers with more than 50,000 chips from U.S. companies, the U.S. should mandate that countries at high-risk for chip smuggling comply with a government-to-government agreement that 1) requires them to align their export control systems with the U.S., 2) takes security measures to address chip smuggling to China, and 3) stops their companies from working with the Chinese military. The “Diffusion Rule” already contains the possibility for such agreements, laying a foundation for further policy development.
- Review and reduce the 1,700 H100 no-license required threshold for Tier 2 countries
  - Currently, the Diffusion Rule allows advanced chip orders from Tier 2 countries for less than 1,700 H100s —an approximately $40 million order—to proceed without review. These orders do not count against the Rule’s caps, regardless of the purchaser. While these thresholds address legitimate commercial purposes, we believe that they also pose smuggling risks. We recommend that the Administration consider reducing the number of H100s that Tier 2 countries can purchase without review to further mitigate smuggling risks.
- Increase funding for Bureau of Industry and Security (BIS) for export enforcement
Lab Security Improvements
- Establish classified and unclassified communication channels between AI labs and intelligence agencies for threat intelligence sharing, similar to Information Sharing and Analysis Centers used in critical infrastructure sectors
- Create systematic collaboration between frontier AI companies and intelligence agencies, including Five Eyes partners
- Elevate collection and analysis of adversarial AI development to a top intelligence priority, as to provide strategic warning and support export controls
- Expedite security clearances for AI industry professionals
- Direct NIST to develop next-generation security standards for AI training/inference clusters
- Develop confidential computing technologies that protect model weights even during processing
- Develop meaningful incentives for implementing enhanced security measures via procurement requirements for systems supporting federal government deployments.
- Direct DOE/DNI to conduct a study on advanced security requirements that may become appropriate to ensure sufficient control over and security of highly agentic models

Energy Infrastructure Scaling
- Set an ambitious national target: build 50 additional gigawatts of power dedicated to AI by 2027
- Streamline permitting processes for energy projects by accelerating reviews and enforcing timelines
- Expedite transmission line approvals to connect new energy sources to data centers
- Work with state/local governments to reduce permitting burdens
- Leverage federal real estate for co-locating power generation and next-gen data centers

Government AI Adoption
- across the whole of government, the Administration should systematically identify every instance where federal employees process text, images, audio, or video data, and augment these workflows with appropriate AI systems.
- Task OMB to address resource constraints and procurement limitations for AI adoption
- Eliminate regulatory and procedural barriers to rapid AI deployment across agencies
- Direct DoD and Intelligence Community to accelerate AI research, development and procurement
- Target largest civilian programs for AI implementation (IRS tax processing, VA healthcare delivery, etc.)

Economic Impact Monitoring
- Enhance data collection mechanisms to track AI adoption patterns and economic implications
- The Census Bureau’s American Time Use Survey should incorporate specific questions about AI usage, distinguishing between personal and professional applications while gathering detailed information about task types and systems employed.
- Update Census Bureau surveys to gather detailed information on AI usage and impacts
- Collect more granular data on tasks performed by workers to create a baseline for monitoring changes
- Track the relationship between AI computation investments and economic performance
- Examine how AI adoption might reshape the tax base and cause structural economic shifts

huw6d-6

Nowhere in their RFP do they place restrictions on what kinds of energy capacity they want built. They are asking for a 4% increase in U.S. energy capacity—this is a serious amount of additional CO2 emissions if that capacity isn’t built renewably. But that’s just what they’re asking for now; if they’re serious about building & scaling AGI, they would be asking for much bigger increases, without a strong precedent of carbon-neutrality to back it up. That seems really bad?

Also to pre-empt—the energy capacity has to come before you build an AI powerful enough to ‘solve climate change’. So if they fail to do that, the downside is that they make the problem significantly worse. I think the environmental downsides of attempting to build AGI should be a meaningful part of one’s calculus.

Ben Stewart5d9

Object-level aside, I suspect they’re aware their audience is the hypersensitive-to-appearances Trump admin, and framing things accordingly. Even basic, common sense points regarding climate change could have a significant cost to the doc’s reception.

huw4d2

Is the assumption here that they would lobby behind the scenes for carbon-neutrality? Because this just sounds like capitulation without a strong line in the sand to me

JoshYou6d5

NickLaing6d2

Completely agree that climate analysis should be a huge part of the scaling AGI equation. I don't buy the "But AGI might solve climate" argument. It might solve everything, but the uncertainty is so huge I don't think we should account for that in any equation - I think we should calculate the "knowns:" and largely ignore the wildly unpredictable "unknowns" here.