Claude-3 Opus on using advance market committments to incentivize automated AI safety R&D:
’Advance Market Commitments (AMCs) could be a powerful tool to incentivize AI labs to invest in and scale up automated AI safety R&D. Here’s a concrete proposal for how AMCs could be structured in this context:
Government Commitment: The US government, likely through an agency like DARPA or NSF, would commit to purchasing a certain volume of AI safety tools and technologies that meet pre-specified criteria, at a guaranteed price, if and when they are developed. This commitment would be legally binding.
Funding Allocation: A significant pool of funds, on the order of several billion dollars, would be allocated to back these AMCs. The exact amount would be determined based on estimates of the funding needed to meaningfully accelerate AI safety R&D over a 5-10 year time horizon.
Clear Technical Criteria: The government would work with AI safety experts and leading labs to establish clear, measurable technical criteria that AI safety tools and technologies must meet to qualify for the AMC. These could include:
Ability to automatically detect and mitigate certain classes of AI risks (e.g. robustness issues, unexpected behaviors, reward hacking, scalable oversight failures)
Formal verifiability and interpretability of AI systems’ reasoning and decision-making
Automated generation of comprehensive test suites and monitoring for AI systems
Performance on benchmark tasks that evaluate AI safety and robustness
Seamless integration with common AI development frameworks and workflows
Pricing and Volume Guarantees: For each category of AI safety tech that meets the specified criteria, the government would guarantee a certain price per unit and commit to purchasing a minimum volume. These numbers would be set at levels that meaningfully derisk and subsidize the substantial R&D investment required.
Contractual Arrangements: AI labs would enter into binding contracts with the government to develop qualifying technologies. Upon successful development, the government would be obligated to purchase the technologies at the predetermined prices. IP and usage rights would be negotiated upfront.
Staged Releases and Readjustment: As AI capabilities advance rapidly, the AMC criteria would be staged over time to incentivize continual progress. Initial qualifying thresholds would target nearer-term safety challenges, with subsequent releases focused on more advanced issues. Pricing and volumes could be readjusted periodically as the technology matures.
Open Collaboration and Access: To maximize the public benefit, AMC contracts would stipulate that a substantial portion of the safety IP developed must be made freely available for research use and external validation. Certain core components could be open-sourced. Purchased tools would also be made available to developers and labs beyond the initial partners.
International Coordination: While led by the US, efforts would be made to coordinate and collaborate with governments and labs globally. Shared standards and compatible AMCs across regions would amplify the impact. Joint commitments could pool resources and expertise.
The goal of this AMC structure is to create a clear, long-term market pull that shifts the incentives for AI labs and makes large investments in automated AI safety R&D economically attractive and even necessary for competitive positioning. The guaranteed demand would provide greater certainty and help crowd in private capital. Staged, iterative AMCs would support sustained progress and adapt to evolving challenges. Broad access to the resulting safety IP and tools would uplift capabilities across the AI ecosystem.
Alongside other supporting policies and funding mechanisms, AMCs for automated AI safety technology could be a centerpiece of public-private collaboration to ensure advanced AI systems are developed responsibly. Robust market incentives, combined with technical leadership from top labs, could dramatically accelerate the development and deployment of scalable AI safety solutions.′
Claude-3 Opus on using advance market committments to incentivize automated AI safety R&D:
’Advance Market Commitments (AMCs) could be a powerful tool to incentivize AI labs to invest in and scale up automated AI safety R&D. Here’s a concrete proposal for how AMCs could be structured in this context:
Government Commitment: The US government, likely through an agency like DARPA or NSF, would commit to purchasing a certain volume of AI safety tools and technologies that meet pre-specified criteria, at a guaranteed price, if and when they are developed. This commitment would be legally binding.
Funding Allocation: A significant pool of funds, on the order of several billion dollars, would be allocated to back these AMCs. The exact amount would be determined based on estimates of the funding needed to meaningfully accelerate AI safety R&D over a 5-10 year time horizon.
Clear Technical Criteria: The government would work with AI safety experts and leading labs to establish clear, measurable technical criteria that AI safety tools and technologies must meet to qualify for the AMC. These could include:
Ability to automatically detect and mitigate certain classes of AI risks (e.g. robustness issues, unexpected behaviors, reward hacking, scalable oversight failures)
Formal verifiability and interpretability of AI systems’ reasoning and decision-making
Automated generation of comprehensive test suites and monitoring for AI systems
Performance on benchmark tasks that evaluate AI safety and robustness
Seamless integration with common AI development frameworks and workflows
Pricing and Volume Guarantees: For each category of AI safety tech that meets the specified criteria, the government would guarantee a certain price per unit and commit to purchasing a minimum volume. These numbers would be set at levels that meaningfully derisk and subsidize the substantial R&D investment required.
Contractual Arrangements: AI labs would enter into binding contracts with the government to develop qualifying technologies. Upon successful development, the government would be obligated to purchase the technologies at the predetermined prices. IP and usage rights would be negotiated upfront.
Staged Releases and Readjustment: As AI capabilities advance rapidly, the AMC criteria would be staged over time to incentivize continual progress. Initial qualifying thresholds would target nearer-term safety challenges, with subsequent releases focused on more advanced issues. Pricing and volumes could be readjusted periodically as the technology matures.
Open Collaboration and Access: To maximize the public benefit, AMC contracts would stipulate that a substantial portion of the safety IP developed must be made freely available for research use and external validation. Certain core components could be open-sourced. Purchased tools would also be made available to developers and labs beyond the initial partners.
International Coordination: While led by the US, efforts would be made to coordinate and collaborate with governments and labs globally. Shared standards and compatible AMCs across regions would amplify the impact. Joint commitments could pool resources and expertise.
The goal of this AMC structure is to create a clear, long-term market pull that shifts the incentives for AI labs and makes large investments in automated AI safety R&D economically attractive and even necessary for competitive positioning. The guaranteed demand would provide greater certainty and help crowd in private capital. Staged, iterative AMCs would support sustained progress and adapt to evolving challenges. Broad access to the resulting safety IP and tools would uplift capabilities across the AI ecosystem.
Alongside other supporting policies and funding mechanisms, AMCs for automated AI safety technology could be a centerpiece of public-private collaboration to ensure advanced AI systems are developed responsibly. Robust market incentives, combined with technical leadership from top labs, could dramatically accelerate the development and deployment of scalable AI safety solutions.′