I hope it's ok if I press a bit here on a key point that weighs on questions of inevitability, strong and weak.
Can you give a more specific description of the deeper cognition you were able to do about the grant once you were freed from the distracting detail work like compiling the budget?
These are kind of claims that I see from some people who have been making use of generative AI and I so far have not had that experience. Perhaps for two reasons--the first that is for me the detail work often illuminates the "deep thinking" work. Working with a group a while back on a grant application, I felt that the question of what we needed money for and exactly how we would try to spend it was a substantive problem. Writing the top-level narrative wasn't all that hard--it was a set of ideas that we were all comfortable with and committed to. Figuring out how the grant would let us enact those ideas in new ways was where all the "deep thinking" had to happen. So I feel as if automating would have given us a budget that looked "typical" when in fact we needed to think atypically about it. The second reason is that if the "deep thinking" is hard, it's not because I don't have the time, it's because there's something in there that I don't know how to think about, that I'm uncertain about, and a lack of time isn't producing that ambivalence.
But maybe this is just me in my domains, where I'm not sure what I'm meant to be freed from or what I might be liberated into. What specifically did you feel you were able to think about better once the cognitive load of preparing a budget was off your plate?
Thanks for the comment and pressing on this a bit. My experience writing this proposal and budget was that I was able to build out a picture of the specific activities that I was thinking about, and then let AI help with translating that into the appropriate format to match what the funding organization wanted to see. After mapping out the big picture activities, I used Claude to help generate a draft budget in line with those activities and the budget guidelines (just uploaded the guidelines doc into a project where Claude could access it). Claude then built out a spreadsheet (with working formulas, a detailed and summary budget, and a budget justification) and provided some estimates for the costs associated with each line item. Then I reviewed those costs and made more edits, mostly by just telling Claude and having it directly edit the docs.
In this case, I was still in the middle of the detailed activities (although I think AI can help to sharpen and challenge those assumptions there, too), but the biggest help was acting as an interface with the budget spreadsheet and articulating a budget narrative from the activities and my description of them. It was also helpful to translate between the budget format that the funding organization wants and the internal budget template that our grants team wanted to use. This sort of mechanical translation work feels like drudgery to me. I'm happy to have AI automate this away.
What felt nice about this approach is that it allowed me to get into the details whenever I wanted and adjust specific amounts of categories, but allowed me to step back and flexibly adjust the categories using natural language. The ability to check the budget against the provided budget guidelines to suggest if there was anything I missed in my review was helpful too.
I'm certainly not arguing that this is some sort of revolutionary new way to work or that given the right prompt I could generate the whole project proposal with AI, but it certainly felt helpful in allowing me to stay out of the minutiae and operate at a higher level of abstraction, reasoning about the activities and outsourcing some of the more extended prose that is often quite boilerplate and templated in these grants anyway. At the very least, it made it more enjoyable to write the proposal and focus on the ideas and avoid getting bogged down in the minor details in spreadsheets and budgets, which often feel draining to me.
I hope it's ok if I press a bit here on a key point that weighs on questions of inevitability, strong and weak.
Can you give a more specific description of the deeper cognition you were able to do about the grant once you were freed from the distracting detail work like compiling the budget?
These are kind of claims that I see from some people who have been making use of generative AI and I so far have not had that experience. Perhaps for two reasons--the first that is for me the detail work often illuminates the "deep thinking" work. Working with a group a while back on a grant application, I felt that the question of what we needed money for and exactly how we would try to spend it was a substantive problem. Writing the top-level narrative wasn't all that hard--it was a set of ideas that we were all comfortable with and committed to. Figuring out how the grant would let us enact those ideas in new ways was where all the "deep thinking" had to happen. So I feel as if automating would have given us a budget that looked "typical" when in fact we needed to think atypically about it. The second reason is that if the "deep thinking" is hard, it's not because I don't have the time, it's because there's something in there that I don't know how to think about, that I'm uncertain about, and a lack of time isn't producing that ambivalence.
But maybe this is just me in my domains, where I'm not sure what I'm meant to be freed from or what I might be liberated into. What specifically did you feel you were able to think about better once the cognitive load of preparing a budget was off your plate?
Thanks for the comment and pressing on this a bit. My experience writing this proposal and budget was that I was able to build out a picture of the specific activities that I was thinking about, and then let AI help with translating that into the appropriate format to match what the funding organization wanted to see. After mapping out the big picture activities, I used Claude to help generate a draft budget in line with those activities and the budget guidelines (just uploaded the guidelines doc into a project where Claude could access it). Claude then built out a spreadsheet (with working formulas, a detailed and summary budget, and a budget justification) and provided some estimates for the costs associated with each line item. Then I reviewed those costs and made more edits, mostly by just telling Claude and having it directly edit the docs.
In this case, I was still in the middle of the detailed activities (although I think AI can help to sharpen and challenge those assumptions there, too), but the biggest help was acting as an interface with the budget spreadsheet and articulating a budget narrative from the activities and my description of them. It was also helpful to translate between the budget format that the funding organization wants and the internal budget template that our grants team wanted to use. This sort of mechanical translation work feels like drudgery to me. I'm happy to have AI automate this away.
What felt nice about this approach is that it allowed me to get into the details whenever I wanted and adjust specific amounts of categories, but allowed me to step back and flexibly adjust the categories using natural language. The ability to check the budget against the provided budget guidelines to suggest if there was anything I missed in my review was helpful too.
I'm certainly not arguing that this is some sort of revolutionary new way to work or that given the right prompt I could generate the whole project proposal with AI, but it certainly felt helpful in allowing me to stay out of the minutiae and operate at a higher level of abstraction, reasoning about the activities and outsourcing some of the more extended prose that is often quite boilerplate and templated in these grants anyway. At the very least, it made it more enjoyable to write the proposal and focus on the ideas and avoid getting bogged down in the minor details in spreadsheets and budgets, which often feel draining to me.