A Hammer Looking for a Nail: Anthropic’s Nonprofit Upskilling Program Repeats International Development's Mistake
Anthropic is spending one hundred fifty million dollars to put a thousand early-career workers inside nonprofits. The program is called Claude Corps. Each fellow gets a year, a salary of eighty-five thousand dollars, training in Claude, and a placement inside a mission-driven organization where their job is to help that organization put AI to work. The first cohort starts in October. More than four hundred nonprofits have signed up to host.
I read the announcement twice. The second time, I recognized it. Not from anything in technology. From international development.
I have watched this approach fail before
For most of the last century, international development ran on a particular assumption. “Expertise” lived in the capital, or the donor country, or the consulting firm, and it traveled outward to places that had problems but not answers. The expert arrived with a solution that had worked somewhere else. The local context was treated as a setting for the solution rather than a source of one.
The results are well documented. Schemes that ignored how things were actually done on the ground. Tools nobody adopted. Programs built around problems the community would not have named first. The failures were rarely failures of the tool. They were failures of a direction of travel. Knowledge was assumed to move one way, from the people who held the method to the people who supposedly just had the need.
The field has spent decades trying to unlearn this. Simply put, participatory development was the answer to that problem. It started from a simple reordering: the people who live inside a problem understand it in ways an outsider cannot, and any solution that does not begin with their knowledge will ride roughshod over the parts that matter. Outside expertise still had a place. It just had to earn that place on local terms, in service of knowledge that was already there.
I spent more than a decade inside that world. I know how hard the lesson is to learn and how easily it gets forgotten.
Who has the expertise?
Using AI well is a real expertise. It takes time to build and it does not come free. I am not saying the tool side is easy. I am saying there are two expertises here, the tool and the mission, and the program is built as if there is only one.
But, it does not even fund that one well. The fellow is a young professional with under two years of work experience, selected not for tool expertise but for comfort and judgment from daily use of Claude. The base camp training is a reasonable on-ramp for a newcomer. It is a one week pre-placement training covers prompt design, building with the API, and running an AI evaluation and about five hours a week after. There is no training in the work of the organizations these fellows will enter. It is not the making of an expert in anything. Anthropic is explicit that it is not looking for sector expertise, only comfort and judgment from daily use of Claude. The fellow arrives as fluent as one can get in Claude in a one-week training and learns the mission on the job. So the program does not actually deliver deep tool expertise either. It delivers a junior person with a week of orientation, positioned as the AI authority in the room.
There is a deeper asymmetry underneath the training gap. The tool side, thin as it is, still gets a dedicated person, full-time, for a year, with a salary behind them. The mission side gets no new hire at all. It stays with a staff member who already had a full job, and who now has to add managing that newcomer on top of it. And the fellow will take what they learn and move on when the year ends. The program is not just under-resourcing the domain side. It is treating the domain knowledge as something a smart outsider absorbs on the way to the real work, while the tool gets treated as the expertise worth a salary and a year.
This is familiar. It is the exact assumption international development is still grappling with. The outsider arrives certified in the method, and the place they land is treated as the setting where the method gets applied rather than as knowledge that should shape what gets done in the first place. The method is the expertise. The community is the backdrop. Claude Corps rebuilds that same hierarchy.
The inversion shows up again in who decides what the fellow works on. To be eligible to host, an organization does not even need a project in mind. Anthropic’s own guidance says that if something is slowing your team down but you have not figured out the AI use yet, that is fine, because Anthropic helps with project discovery before the fellowship begins. The whole arrangement runs on an assumption that is never tested: that AI is the answer. Maybe it is, for a given problem. Maybe it is not. But a program that places a Claude-trained fellow first and looks for the problem second has already decided the question that domain knowledge exists to ask. It is a hammer looking for a nail.
The program is built around the model, not the nonprofit
There is an obvious way to build this if the goal is the host’s mission. Take someone the organization already trusts, someone who knows the work, and train them in the tool. That person exists. The program calls them the supervisor. Instead the program imports a newcomer trained in Claude and asks the supervisor to direct them in the margins of an already full job.
The reason it cannot take the obvious path is in the eligibility rules. Anyone over eighteen with under two years of full-time work experience can apply, regardless of educational background. The hard filter is a workforce filter, early-career and new to the workforce, not a mission filter. The program screens out the people best positioned to bridge a tool to a sector, the ones who already carry a sector in their hands, because the fellow being a new entrant to the workforce is not incidental. It is the design.
That points to what the program is actually organized around. Read the structure as the answer to a single question, how does Claude take root in four hundred nonprofits, and every feature falls out cleanly. Fellows trained only in Claude. Hosts required to be paying Claude for Nonprofits customers before they can receive a fellow at all. Workflows built around Claude that keep running after the fellow leaves. A fresh entrant rather than a sector veteran who might conclude the tool fits in a smaller way than hoped. None of these follow from what would most help this organization. All of them follow from how the model spreads.
I have watched this structure before, pointed at a different group. Last fall I wrote about Microsoft, OpenAI, and Anthropic launching an academy to train hundreds of thousands of teachers in AI. I said then that training is often product onboarding in disguise, and that the effort was honestly about two things at once, preparing students and expanding markets. Swap teachers for fellows and classrooms for nonprofits and the shape is the same. The people being trained are real, the help is real, and underneath it the model gets a generation fluent in it as the default way the work is done.
This is not the design of a program whose top level goals are aimed at the intern nor the nonprofit. So the honest version of the question is not whether the fellow benefits, or whether the host benefits. Both can be true. It is whether anyone designing this started from them. The fellow’s career and the host’s mission read like outcomes the program would be glad to produce on the way to the outcome it was built for. When a workforce program is genuinely for its workers, or a capacity program genuinely for the organizations, you can see it in what the design refuses to compromise. Here the thing held constant is the model. Everything else bends around it.
The correction development already found
Participatory development doesn’t tackle this by rejecting outside expertise. It tackles it by changing the direction of travel. The knowledge that organizes the work belongs to the people inside it. Whatever comes from outside has to bend toward that knowledge, not the reverse.
Applied here, the reordering is straightforward. The person bridging AI and a nonprofit’s work should start from deep knowledge of that work: what the organization is trying to do, who it serves, what its data actually represents, where the hours really go, what would break if you automated the wrong thing. From there, they reach toward the tool, learning enough to judge where it fits and where it does not. That judgment cannot be installed in a base camp. It is the accumulated knowledge of the domain, and the program treats it as the part that takes care of itself.
That is not the harder version of Claude Corps. It is the inverse of it. Same two expertises, opposite ordering. I have written before that AI is most useful when you manage it the way you would manage a junior staffer, where your judgment, your framing, and your knowledge of your own context are the things that do not automate. That only works when the judgment comes first and the tool answers to it. Claude Corps runs the relationship the other way.
What it looks like to build from this side
The other ordering is not hypothetical. It is what I am building Anthralytic toward. This piece is not about that, and if you want to read more you can go to anthralytic.com. The point is only that the reverse direction can be built: a platform whose methods a practitioner designed, with a human expert kept in the loop where field knowledge matters most, so the domain knowledge is the thing the tool is built out of rather than something bolted on afterward. The mission knowledge sets the terms and the technology is shaped to serve it. That is the opposite ordering from a fellow trained in a model and sent to find a use for it.
I am not neutral here. I’m building something that argues against the direction Claude Corps travels. And I’m building it on the same models Claude Corps would deploy. The complicity is real and I would rather name it than pretend to stand outside it. But the argument does not rest on my positionality. It rests on a lesson international development paid for over decades and is now positioned to relearn through AI.
If Anthropic wanted to build it the other way
There is a version of this program that starts from the nonprofits. Here is what it would change.
Instead of recruiting newcomers and training them in Claude, borrow the people who already know the work. Every host has staff who understand the mission, the data, the constraints, and the communities. Take one of them out of their regular load for the year, full-time or half-time, and train them in the tool. Have the fellowship pay for the share of their salary the organization is no longer covering. They are already embedded. They already passed the hard test, the one no base camp can teach, which is knowing the work. Add the tool to the expertise rather than adding the expertise to the tool.
Then evaluate it honestly. The current plan puts measurement and evaluation in the hands of the program’s own partners, scoring whether host organizations advanced their missions. An honest design would hold out a control group, a comparable set of organizations that receive no fellow, so the gains can be told apart from what the organizations would have managed anyway. And it would measure more than Claude usage. How much Claude gets used is the easy number, and it is the wrong one. The question is whether outcomes improved for the people the organizations serve, which is the measurement the sector keeps mistaking velocity for. Counting tool adoption and calling it impact is the same error one layer up.
Anthropic can keep its adoption goal. The whole structure is built to drive usage, which is plain enough that I was able to reverse engineer the strategy from the eligibility rules and the host requirements alone. There is nothing hidden about it. But a program cannot serve two masters and measure for only one of them. If helping nonprofits is a real goal alongside spreading the model, then nonprofit outcomes have to be in the measurement as a priority, not as a line Social Finance reports at the end. What gets measured is what the program is actually for. Right now that is adoption.
None of this is more expensive than what is already committed. It is the same money, pointed at the sector instead of the model. It would be harder to scale into a clean replicable unit, and it would be slower, because the people who know the work are not interchangeable the way a base camp cohort is. That difficulty is the tell. The friction is exactly the sector’s real texture, the thing the current design smooths away by importing a standard newcomer instead.
The nonprofit sector does not need the old mistake in a new coat. It needs the bridge built from the side that already understands the work. If Anthropic wants to help the nonprofit world, that is where to start: with the knowledge that is already there, and the honesty to measure whether anything actually changed for the people it was all supposed to be for.
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