METR is a non-profit research organization. We prioritise independence and trustworthiness, which shapes both our research process and our funding options. To date, we have not accepted payment from frontier AI labs for running evaluations. ^[1]
Part of METR's role is to independently assess the arguments that frontier AI labs put forward about the safety of their models. These arguments are becoming increasingly complex and dependent on nuances of how models are trained and how mitigations were developed.
For this reason, it's important that METR has its finger on the pulse of frontier AI safety research. This means hiring and paying for staff that might otherwise work at frontier AI labs, requiring us to compete with labs directly for talent.
The central constraint to our publishing more and better research, and scaling up our work aimed at monitoring the AI industry for catastrophic risk, is growing our team with excellent new researchers and engineers.
And our recruiting is, to some degree, constrained by our fundraising - especially given the skyrocketing comp that AI companies are offering.
To donate to METR, click here: https://metr.org/donate
If you’d like to discuss giving with us first, or receive more information about our work for the purpose of informing a donation, reach out to giving@metr.org
However, we are definitely not immune from conflicting incentives. Some examples: - We are open to taking donations from individual lab employees (subject to some constraints, e.g. excluding senior decision-makers, constituting <50% of our funding) - Labs provide us with free model access for conducting our evaluations, and several labs also provide us ongoing free access for research even if we're not conducting a specific evaluation.
Various grantmaking orgs are limited in to only or mainly funding 501c3s, so for individual donors who are able to donate to c4s, there is some reason to think those opportunities would be more neglected and higher value.
That said, for anyone thinking about donating to METR, that still seems like a great (though maybe not optimal) choice!
FYI: METR is actively fundraising!
METR is a non-profit research organization. We prioritise independence and trustworthiness, which shapes both our research process and our funding options. To date, we have not accepted payment from frontier AI labs for running evaluations. ^[1]
Part of METR's role is to independently assess the arguments that frontier AI labs put forward about the safety of their models. These arguments are becoming increasingly complex and dependent on nuances of how models are trained and how mitigations were developed.
For this reason, it's important that METR has its finger on the pulse of frontier AI safety research. This means hiring and paying for staff that might otherwise work at frontier AI labs, requiring us to compete with labs directly for talent.
The central constraint to our publishing more and better research, and scaling up our work aimed at monitoring the AI industry for catastrophic risk, is growing our team with excellent new researchers and engineers.
And our recruiting is, to some degree, constrained by our fundraising - especially given the skyrocketing comp that AI companies are offering.
To donate to METR, click here: https://metr.org/donate
If you’d like to discuss giving with us first, or receive more information about our work for the purpose of informing a donation, reach out to giving@metr.org
However, we are definitely not immune from conflicting incentives. Some examples:
- We are open to taking donations from individual lab employees (subject to some constraints, e.g. excluding senior decision-makers, constituting <50% of our funding)
- Labs provide us with free model access for conducting our evaluations, and several labs also provide us ongoing free access for research even if we're not conducting a specific evaluation.
I agree METR does great work! But I tentatively think it won't be the right donation choice for individual EAs:
That said, for anyone thinking about donating to METR, that still seems like a great (though maybe not optimal) choice!