Building A Stronger AI Safety Authority
CAISI has the makings of a world-first safety agency
Hi
Welcome (back) to The Prompt. AI isn’t well understood, but we learn a lot in our work that can help:
A blueprint for a stronger CAISI
Within Codex growth, we see knowledge work changing
Plus, our AI policy positions all in one place
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[Policy] Building a world-leading AI safety agency
As frontier AI systems become more capable, government needs a trusted institution that can independently evaluate emerging capabilities, assess whether safeguards are keeping pace, and provide policymakers with clear technical insight.
That’s why, in our new frontier safety blueprint, we’re calling for strengthening the US-based Center for AI Standards and Innovation, or CAISI, as the federal government’s primary authority for frontier AI safety.
The first priority should be beefing up the institution itself. Before CAISI can take on significant new responsibilities, it needs the resources, expertise, authorities, and infrastructure necessary to succeed. That means permanent authorization and funding, flexible hiring authorities to attract top technical talent, access to classified computing environments, and stronger coordination across government.
Once that foundation is in place, CAISI should serve as an independent evaluator of the most capable frontier AI systems. The goal is not to create a regulator that approves or blocks deployments – developers should remain responsible for deployment decisions. Instead, CAISI should provide rigorous technical evaluations that help policymakers, companies, and the public better understand capabilities, safeguards, and emerging risks.
Over time, policymakers should also support a broader ecosystem of independent technical assessors. CAISI can help establish standards, certify qualified evaluators, and coordinate assessments that provide ongoing visibility into frontier AI progress. One area of focus should be understanding progress toward recursive self-improvement (RSI), where AI development itself is accelerated by AI. That’s a potential future capability which could have significant implications for AI governance and national security.
Policymakers in the US – and the rest of the world, too – should not be forced to make decisions in the dark when it comes to AI safety. As AI becomes increasingly consequential, building trusted institutions that can evaluate and understand frontier systems may prove just as important as building the technology itself.
[Policy] Our policy positions, in one place
Calling for a stronger CAISI is also listed on our new summary of our public policy agenda.
This summary outlines the principles that guide our work, including democratization, or resisting the potential of this technology to consolidate power in the hands of the few; and resilience. It also details the policy priorities we’re actively engaging on today – from frontier AI safety and youth protections, to workforce transition, AI literacy, content provenance, cybersecurity, and infrastructure.
Many of the ideas we’ve discussed here in The Prompt are reflected there, including proposals to support stronger protections for young people online, expand access to AI literacy and workforce training, and build the infrastructure needed to power the Intelligence Age.
AI policy is evolving rapidly, and we expect our views will continue to evolve as well. But we believe it’s important to make clear to all AI stakeholders, meaning effectively anyone, to understand where we stand, what we support, and why. Our goal is to make our policy thinking more transparent, more accessible, and easier for anyone to engage with as we work toward ensuring artificial general intelligence benefits all of humanity.
[Data] Knowledge work gets its factory redesign
Codex now has over 5 million weekly active users, up more than 6x since the launch of the desktop app four months ago. Developers remain the biggest user group, but knowledge workers now account for about 20%, their share is growing more than 3x as fast. And within that data, we see a bigger shift happening.
Knowledge work now dominates advanced economies. More than 40% of US workers – roughly 72 million people – spend their days working with information: code, documents, analyses, designs, decisions, and communication. Yet despite decades of digitization, much of that work remains surprisingly inefficient.
Modern workers can create information faster than ever, but they still spend enormous amounts of time searching for context, tracking down colleagues, reconciling versions, and moving information between systems. McKinsey found that knowledge workers spend nearly half their week managing email or looking for information and expertise.
This is the legacy of piecemeal digitization. Email, documents, spreadsheets, dashboards, CRMs, chat tools, and SaaS applications each solved specific problems, but together they created fragmented workflows. The office became digital without becoming integrated.
Three frictions now define the cost of knowledge work:
Search: finding the right file, dataset, message, precedent, or expert across sprawling systems.
Coordination: moving information and decisions through teams, tools, and approval chains.
Verification: ensuring work is accurate, accepted, and survives contact with reality through reviews, testing, validation, and signoff.
These frictions help explain the productivity paradox. As Erik Brynjolfsson observed, information technologies generate their largest gains only when organizations redesign workflows around them. Electricity followed the same pattern: factories did not realize its full benefits until they rebuilt production around distributed electric motors rather than simply replacing steam engines.
Knowledge work is still waiting for its equivalent redesign. Previous generations of software reduced the cost of producing documents and information, but not the attention required to consume and act on them. The result has been more artifacts, more tools, and scarcer attention.
Codex is that redesign. Just as factories put electric motors next to every machine, Codex places AI closer to every problem. It can find inputs, coordinate workflows, produce deliverables, check quality, and help move work through approval processes. By removing the bottlenecks around information, organizations can move faster and allow people to focus on their highest-impact work. – Chris Nicholson, Field Correspondent
Watch Chris explain the shift in the video below, and check out more Codex data here.
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Graphics created by Base Three using ChatGPT.