Thanks for the post, Jack! 

I like the idea of more rigorously measuring the work done among AI governance organisations. Below are some example indicators people may consider, depending on the types of work. 

[I used AI to brainstorm these based on orgs in https://aisafety.com/map. So apologies for any misclassifications.]

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Policy research and analysis (GovAI, IAPS, CSET, LawAI, RAND, CLTR)

• Uptake:
  • Cited in a named government consultation response, committee report, agency RFI or impact assessment
  • Invited to give formal evidence to a named legislature or agency
  • Count of unsolicited briefing requests from officials above a defined seniority
• Adoption:
  • A definition, threshold or framing traceable to a named publication appears in draft or enacted text
  • Staff placed into government or standards roles, destination named

Direct advocacy (Encode, Americans for Responsible Innovation, Secure AI Project, ControlAI, Good Ancestors)

• Uptake:
  • Bill introduced containing text the organisation drafted or substantially shaped
  • Co-sponsors added following engagement, or legislators publicly crediting the organisation
  • Coalition sign-ons, weighted by whether signatories were new to the issue
• Outcome:
  • Bill progression through defined stages, each stage separately probability-weighted
  • Provision survives conference, amendment or reconciliation intact

Standards and technical governance (AI Standards Lab, SaferAI, Fathom, AVERI, METR, Apollo)

• Uptake:
  • Submitted text accepted into a working draft at a named standards body
• Adoption:
  • Evaluation protocol appears in a frontier lab's published system card or safety framework
  • Count of labs voluntarily commissioning a third-party evaluation
  • A standard referencing the work is incorporated by reference into regulation

International convening and track II (SAIF, Simon Institute, Concordia AI, IASEAI)

• Output:
  • Attendance by participant class rather than headcount: sitting regulators, frontier lab decision-makers, state-affiliated institutions
• Uptake:
  • Bilateral follow-on engagements initiated within six months, reportable in aggregate
• Outcome:
  • Joint statement or shared framework published by participants
  • Participants' subsequent public positions move in the intended direction

Talent and field-building (MATS, BlueDot, ML4Good, Kairos, Talos, ERA)

• Output:
  • Cohort size and completion rate
• Outcome:
  • Placement into named target institutions at 12 and 24 months
  • Proportion reporting they would not otherwise have entered the field
  • Retention in the field at 24 months, and seniority progression

Watchdog and transparency (AI Lab Watch, The Midas Project, Palisade)

• Uptake:
  • Documented response, correction or commitment from the named target
• Outcome:
  • Verified policy change at a lab following publication
  • Finding cited in regulatory or litigation proceedings

Forecasting and epistemic infrastructure (Epoch, FRI, AI Futures Project, Metaculus)

• Uptake:
  • Dataset or forecast cited in official documents or by named decision-makers
• Outcome:
  • Published calibration score on the organisation's own forecasts

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Hi Jesse, I think the information about this may not be available yet since the standard is still in its early stage. However, you may refer to the Country Champions, and Supporters page of the INPRF website for more information.

Hi Abraham, thanks for sharing your thoughts! I really appreciate you taking the time to read through the implementation guidance.

I'm especially curious about your point on the modified cash basis. I'm not familiar with this approach, and I can see the appeal of simplicity of this basis compared to the full accrual basis. My concern is whether this would create comparability problems:

• Lack of standardisation: I don't know if there is a uniform way to implement modified cash basis - each organisation could choose which items to treat as cash vs. accrual, making cross-organisation comparisons difficult.
• Timing artifacts: Organisations with different payment cycles could show different expense patterns even with identical underlying activities. For example, an org that pays invoices immediately vs. one that batches payments quarterly could look artificially different.
• Missing obligations: Without capturing accrued expenses or other provisions, we wouldn't be able to see the org's outstanding commitments, which seems important for understanding the true financial position.

You mentioned you'd be excited to see a standard that supports modified cash for audit purposes - which I think would be really interesting! Creating such a standard would presumably need to address these comparability challenges by defining exactly which modifications are acceptable and how to apply them consistently.

Hi Siobhan, thanks for your question!

I'm writing from my personal perspective here, so this doesn't represent CEA's official position. However, I think this is worth discussing internally, and I appreciate you raising it.

I'd expect that for any organisation considering adopting INPAS, there would need to be some time to properly understand the specific requirements - including how they relate to existing local regulatory reporting obligations and what the implementation process would look like.

This was a really great read. Thank you for sharing!

Data is here, in both human and machine-readable formats.

Thanks for sharing this! Flagging that the link can't be opened: "Sorry, unable to open the file at present. Please check the address and try again."
 

This is amazing to see, George! As a local who needs to try very hard to find authentic juicy tofu in Yunnan, I can attest to how delicious it is and feel excited about the possibility of popularising it elsewhere!

As someone who had never been to the UK and moved to Oxford for the first time a month ago, I can testify that Trajan House made me immediately feel at home. Jonathan warmly reached out to me offering help on personal life ops things such as bank accounts, SIM cards, and accommodations. Working in the same office room with Jonathan, I have no doubt that he is a lovely person to work with and a great friend to know. I'd be excited to see the office reaching its next level and lots of impacts to follow!