Executive summary: The author argues that technically skilled people concerned about AI governance should focus on building measurement and cost-reducing technologies that shift incentives and enable regulation, because governance bottlenecks are fundamentally technical and this path is currently more leveraged than either pure alignment research or direct policy work.
Key points:
The author claims that internal technical safety work does little to shift broader incentives and that switching to policy often abandons one’s comparative advantage in a crowded domain.
Across climate change, food safety, and COVID-19, governance was driven by two mechanisms: improved measurement that created visibility and accountability, and cost reductions that made good behavior economically practical.
For AI, measurement can orient strategy through metrics like METR’s agent time horizons, which have been doubling roughly every seven months since 2019, and Epoch’s reporting that training compute has grown roughly 4–5x per year.
The author argues that public behavioral benchmarks for sycophancy, deception, and related issues could shift incentives by creating competitive pressure, analogous to standardized fuel efficiency ratings.
Standardized evaluation suites and compute accounting are needed to make regulatory requirements—such as those in the EU AI Act and California’s SB 53—enforceable and comparable across developers.
Driving down the cost of oversight, including through automated evaluation tools and privacy-preserving audit technologies like secure enclaves and cryptographic proofs, could make rigorous oversight standard practice and dissolve trade-offs between transparency and IP protection.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: The author argues that technically skilled people concerned about AI governance should focus on building measurement and cost-reducing technologies that shift incentives and enable regulation, because governance bottlenecks are fundamentally technical and this path is currently more leveraged than either pure alignment research or direct policy work.
Key points:
The author claims that internal technical safety work does little to shift broader incentives and that switching to policy often abandons one’s comparative advantage in a crowded domain.
Across climate change, food safety, and COVID-19, governance was driven by two mechanisms: improved measurement that created visibility and accountability, and cost reductions that made good behavior economically practical.
For AI, measurement can orient strategy through metrics like METR’s agent time horizons, which have been doubling roughly every seven months since 2019, and Epoch’s reporting that training compute has grown roughly 4–5x per year.
The author argues that public behavioral benchmarks for sycophancy, deception, and related issues could shift incentives by creating competitive pressure, analogous to standardized fuel efficiency ratings.
Standardized evaluation suites and compute accounting are needed to make regulatory requirements—such as those in the EU AI Act and California’s SB 53—enforceable and comparable across developers.
Driving down the cost of oversight, including through automated evaluation tools and privacy-preserving audit technologies like secure enclaves and cryptographic proofs, could make rigorous oversight standard practice and dissolve trade-offs between transparency and IP protection.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.