Executive summary: The author argues that Anthropic’s Responsible Scaling Policy v3.0 is a principled upgrade—not a capitulation—because it replaces implied unilateral “bind ourselves to the mast” commitments (which they think were distorting incentives and planning) with a clearer three-part structure (industry-wide recommendations, Risk Reports, and a Roadmap) that they expect to drive more achievable, higher-leverage risk mitigation work over time.
Key points:
The author expects backlash to the move away from “hard commitments,” but says they pushed for the change for ~a year and are “affirmatively excited” because it fixes design flaws rather than responding to “catastrophic risk from today’s AI systems” being high.
They frame original RSP goals as: (1) creating “forcing functions” to make companies urgently implement mitigations, (2) serving as a testbed that can feed into regulation, and (3) building consensus/common knowledge about risks and mitigations—while “not a core goal” was achieving a substantial voluntary pause.
They argue “binding commitments” are a double-edged sword in fast-changing AI: they can prevent motivated reasoning, but can also lock companies into bad priorities, create Goodharting, and produce backlash when costs are high for modest safety benefit.
As evidence RSPs can work, they cite ASL-3 deployment work improving robustness to jailbreaks for specific “uses of concern,” enabled by company-wide coordination and prioritization pressure (including work on “Constitutional Classifiers”).
They describe mixed outcomes on security: the RSP increased capacity and focus (e.g., egress bandwidth controls, weight protection) but may have pulled effort away from “unsexy” baseline security and created confusion about what “ASL-3 security” meant.
They claim the old RSP created “wrong incentives” for ASL-4/5 preparation because meeting implied standards (e.g., against state-backed attackers) seems infeasible on ~2-year timelines without years-long slowdown, which they don’t think is good unilaterally and which pressures risk assessments toward minimizing perceived capability thresholds.
They present v3 as separating three functions: “recommendations for industry-wide safety” (explicitly non-unilateral), “Risk Reports” (aimed at more honest characterization with movement toward external review), and a “Roadmap” (ambitious-but-achievable commitments designed to be a better forcing function).
They argue unilateral pausing can be good in some futures but is hard to operationalize and, in today’s environment, could look like “crying wolf” and advantage competitors; they prefer flexibility plus transparency requirements about competitor context and advocacy steps if proceeding with higher-risk systems.
They acknowledge v3’s mechanism relies on real follow-through—Risk Reports and Roadmaps could be perfunctory—but they expect comparative public scrutiny (and a “race to the top” on visible artifacts) to pressure quality more than rigid policy text would.
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Executive summary: The author argues that Anthropic’s Responsible Scaling Policy v3.0 is a principled upgrade—not a capitulation—because it replaces implied unilateral “bind ourselves to the mast” commitments (which they think were distorting incentives and planning) with a clearer three-part structure (industry-wide recommendations, Risk Reports, and a Roadmap) that they expect to drive more achievable, higher-leverage risk mitigation work over time.
Key points:
The author expects backlash to the move away from “hard commitments,” but says they pushed for the change for ~a year and are “affirmatively excited” because it fixes design flaws rather than responding to “catastrophic risk from today’s AI systems” being high.
They frame original RSP goals as: (1) creating “forcing functions” to make companies urgently implement mitigations, (2) serving as a testbed that can feed into regulation, and (3) building consensus/common knowledge about risks and mitigations—while “not a core goal” was achieving a substantial voluntary pause.
They argue “binding commitments” are a double-edged sword in fast-changing AI: they can prevent motivated reasoning, but can also lock companies into bad priorities, create Goodharting, and produce backlash when costs are high for modest safety benefit.
As evidence RSPs can work, they cite ASL-3 deployment work improving robustness to jailbreaks for specific “uses of concern,” enabled by company-wide coordination and prioritization pressure (including work on “Constitutional Classifiers”).
They describe mixed outcomes on security: the RSP increased capacity and focus (e.g., egress bandwidth controls, weight protection) but may have pulled effort away from “unsexy” baseline security and created confusion about what “ASL-3 security” meant.
They claim the old RSP created “wrong incentives” for ASL-4/5 preparation because meeting implied standards (e.g., against state-backed attackers) seems infeasible on ~2-year timelines without years-long slowdown, which they don’t think is good unilaterally and which pressures risk assessments toward minimizing perceived capability thresholds.
They present v3 as separating three functions: “recommendations for industry-wide safety” (explicitly non-unilateral), “Risk Reports” (aimed at more honest characterization with movement toward external review), and a “Roadmap” (ambitious-but-achievable commitments designed to be a better forcing function).
They argue unilateral pausing can be good in some futures but is hard to operationalize and, in today’s environment, could look like “crying wolf” and advantage competitors; they prefer flexibility plus transparency requirements about competitor context and advocacy steps if proceeding with higher-risk systems.
They acknowledge v3’s mechanism relies on real follow-through—Risk Reports and Roadmaps could be perfunctory—but they expect comparative public scrutiny (and a “race to the top” on visible artifacts) to pressure quality more than rigid policy text would.
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.