I winced when the third presentation of the morning had the exact same purple to blue gradient as the first two. Same rounded-corner icons, and the same slide titled “Key Objectives” with a verb, a noun, and a phrase about needing “community engagement”. I was in the back of the room at an in-person convening in late August trying to follow the substance and realizing there wasn’t much to follow. Literally some of the presenters did not seem to know what was on their slides.

Separately, a team had submitted a proposal that described, end to end, how they would run the logistics of an event: venue management, transport coordination, participant communications, vendor contracting. On paper it looked super thorough and used all the right terms. At the time I suspected it was AI generated, but even if it was, I figured they had matched the pitch against what they could actually do. I hoped it was a beautiful presentation of their vision but still their own vision of the event.

They were given the go ahead. What they could actually do day to day, with the staff and systems they had, turned out to be much thinner. Four weeks before the event I was stepping into threads I wasn’t supposed to be in and joining calls that were supposed to have been led by them. I’ve seen plenty of events that didn’t go perfectly, but what felt different this time was the distance between the proposal and the team who had supposedly written it.

I’ve been chewing on what to call that gap, between the picture-perfect (generic) decks and what the presenters actually knew and could say, and between the plan for the event and the event we got. I still don’t have a name for it, and I haven’t heard it named in the dominant critiques of AI, so I’d love to hear if you do.

This isn’t about these people specifically. They worked hard with what they had and I appreciate what they tried to do. As someone who works with a tiny team of 2.5 people managing a network of hundreds, I understand the pull of using technology to stretch capacity and manage all the work that needs to get done.

What keeps me up is what AI now does for organizations trying to sound fundable in 2026, in a world where philanthropy is shifting under our feet. A small team can generate a proposal that reads as if it has a medium-sized operations unit, and a volunteer network can produce a deck that looks like it came from a communications department. The tools are free and fast, and they write in exactly the language funders expect, so at least at first the veneer can hold. But human judgment still has to enter the loop somewhere, whether or not an AI tool wrote the proposal or answered the emails. At 6am when the van doesn’t show up, someone has to call the driver and decide what to tell thirty people waiting in a lobby. Someone has to have held the overall vision in their head, gathered an organizing team around it, delegated, kept track of timelines and details, and made sure all the small small things were thought of. If that person doesn’t have the capacity, time, or standing inside their own organization to say “this isn’t working”, the gap becomes visible eventually.

For years, and I say this having both written and reviewed proposals, we treated a well-written document as useful evidence of capacity. It might only mean you were a strong writer or had one on your team, but if you could describe operational detail, we assumed you probably had some operational experience. If you could name risks, you had probably run into them. In 2026 that assumption is breaking down quickly. An AI system can name risks someone has never encountered and describe mitigations they have never tried. A well-written text is no longer evidence that anyone has done the work or knows how to carry out what is being proposed.

Some people might wave this off as unethical or poor use of these platforms. But non-profits are struggling to secure funding to continue and are trying to make the most of what they have access to, so I think this is a growing phenomenon we need to pay attention to. The gap between what organizations are proposing and what they can deliver is wider than I’ve ever seen it. I think it will change how much we trust new collaborative partners, and I see funders already beginning to lean back on referrals from people they know.

What does it mean for how we select partners when the application can no longer be trusted as a capacity signal? From the funder side, it suggests discounting the proposal and weighting other things more: past delivery shown with receipts rather than reports, more live conversations and video calls to confirm a group is who it says it is, and a smaller first grant that grows if the work materializes. But what happens to open calls? The OSV Fund received 11,000+ applications earlier this year and offered 13 scholarships. That ratio had no precedent before this year, and I expect it to get more extreme.

So many of the dominant critiques of AI ask whether it will take our jobs or how much more productive it will make us. What I’m noticing is a bit more nuanced than that. AI can indeed raise how organizations present, especially on text-based things like email management, slides and proposals. It doesn’t raise what the humans can do at 6am when something breaks. That comes from practice, from having done it before, and from a team that trusts each other enough to say “this isn’t working”.

It’s showing up in my own work on a global network where I’ve been facilitating discussions about what exit-to-community looks like and trying to sort out what is ready to hand off to community partners and what is risky due to low capacity. That assessment gets harder when the artifacts of readiness, proposals and decks and plans, can be generated in an instant without the readiness actually being there.

I don’t know how to fix this. We all need more capacity, but is AI actually providing it, or is it shifting the need for capacity into other forms? If you run a network, fund small organizations, or review applications, I’d love to hear how you’re navigating it!