Professor Steve Wessling

So they know who they are, they know what the scores are, and they know what the comments are, and they have an opportunity if one of them makes a really good comment about perhaps a fatal flaw in a grant, they can actually take that on board and adjust their scores.

So

That sense that this is sort of anonymous and unaccountable is actually now incorrect.

I think we are.

And just that's a really important point, Norman.

When we look around the world and when I talk to the heads of funding agencies, the NIH, the MRC, funding agencies from around the world, they are all challenged by increasing application load.

and challenged by decreasing success rates.

And success rates are purely a function of the number of applications and the amount of money you have to allocate to that grant call.

And so all over the world, they're being challenged by this increase in application rate, which we think may be in part driven by AI.

And they are being challenged on how to assess grants appropriately in that environment.

And they are looking very closely at the application-centric process that we've developed.

I think it's the existential challenge that the funding environment has to deal with across the world because people can utilise AI to write a grant much quicker.

They can write grants in a day instead of a month.

And that will mean that people will put in more grants and

And we're also seeing the grant quality coming closer together in that we're seeing clumping of the quality of grants.

So we're seeing higher quality all clumping together, making it much more difficult for us as grants.

peer review organisations to choose the very best grants.

And, you know, we're currently having success rates of between 10% and 15%.

Our average success rate is 12.5%.

But that is exactly the same range that welcome the National Institutes of Health of the United States, the