Don't Prompt Me
We're handing off big ideas and make-or-break decisions. And just --dangerously-skip[ing]-permissions
What You Can’t Prompt
Is there anything wrong with hand and off complete ideas? Is there anything wrong with allowing computers to make big decisions that have real effects? Perhaps it's not as bad as you think.
Here’s the history of how we got here. Memory stored on clay tablets. Spread across populations through reading. Calculation to mathematics. Then to machines that did it faster than any human could. Then to networks that scaled all of it across the entire planet.
Every step was the same story with different tools. The printing press didn’t kill writers, it made literacy the baseline and pushed writers toward deeper meaning. The spreadsheet didn’t kill bookkeepers, it killed the need for rooms full of people doing arithmetic by hand and created the field of financial analysis. GPS didn’t kill navigators, it allowed precision as a survival skill and freed up cognitive bandwidth for everything else.
The pattern was always: tool handles the rote, human handles the judgment.
What’s breaking now is that we’re outsourcing the judgment itself because the tool is impressive, the pressures are real, and some bean counter said “we should be using AI for this.”
The human remainder
Here’s what every cognitive outsourcing event in history has in common: it created a premium on whatever was left. It commodities tasks that were once dedicated to skilled tradesmen.
When writing took over sheer brain memory, the premium moved to interpretation. What does this (the writing) mean and what do we do about it. When calculation moved to machines, the premium moved to knowing which calculation to run. When search engines indexed all human knowledge, the premium moved to knowing which question to ask.
AI is compressing code generation, document drafting, data synthesis, and pattern recognition. So the premium is moving again. It’s moving to judgment, relationships, and the ability to define what actually matters before anyone writes a line of code or drafts a single requirement.
If you’re a program manager in a federal agency right now and your response to AI is, “Let’s automate the reporting,” you may be optimizing for the thing that is becoming worthless: the artifact. In some cases, the artifact may never have had much value in the first place.
Think about what these artifacts usually are: documentation proving something was done according to regulation. And by “proving,” I mean it was written down, stored somewhere, and almost nobody will ever audit whether the paper matches reality. That should force a basic question: What value does this actually provide in practice?
If the answer is none, then the target should not be automation. The target should be removal. Get rid of the process. Get rid of the artifact. I know federal environments are not that simple. You cannot always delete a requirement because it annoys you. But you can challenge it, negotiate it, ask why it exists, or document that a section is not applicable instead of pretending it adds value. Try it out. Ask the receiving end why they need it, or if you can get by without it.
Where AI is worth it
AI is good for things that humans waste time on, of course.
It’s good for developers. GitHub’s research found that developers using AI coding assistants completed tasks roughly 55 percent faster than those working without one. 1Engineers can focus on the architecture, and less on the actual code. That’s a real trade with real productivity. It’s not much different than using C++ over assembly or machine language in computer science.
It’s good for the compliance artifact stack (EPLC, IT Boards, etc.), too. Almost nobody is reading that risk register. Nobody is scrutinizing the configuration management plan before they check the box. It’s going to live in a SharePoint folder until the contract ends.
When I say “nobody,” I do not mean literally no one. I mean very few people, if any, will ever read this material closely enough for it to matter. That is a larger problem, and probably a separate newsletter article. Not here.
If AI can generate a FISMA-compliant system security plan or a FedRAMP-ready continuous monitoring strategy in a fraction of the time it used to take, take the time back. Use it for something that matters until you can rid the entire process.
It’s good for getting something real in front of users faster. A working prototype in two weeks, two days, or even two hours, instead of six months means you kill bad ideas before they become programs of record. We all know management hates to admit sunk costs. In a space where programs have consumed hundreds of millions (or billions, ECSS2) of dollars before a real user ever touched the product, faster feedback loops are genuinely transformative.
These are real wins. They’re just not the wins the systems integrators are selling you.
The checkbox, good in theory, terrible in practice
Most compliance artifacts exist because a regulation says they must, not because anyone will use them. Trust me, I want to believe in the system. I want to believe that having everything “checked” guarantees a successful product. But the reality is different. Checkboxes are the last thing the product needs and the last task completed before submission. Look at the nearest public restroom. Check the cleaning log by the exit door. It may show that someone checked the box. But when was it actually cleaned?
The federal government now spends more than $100 billion on IT every year covering everything from legacy system maintenance to new acquisitions.3 That scale of investment has always attracted scrutiny: the GAO has flagged IT-related problems in its High Risk program since the early 1990s, and formally designated "IT Acquisitions and Operations" as its own High Risk area in 2015, a designation the office has renewed in every update since.4
If an artifact is never read, never used to catch a real problem, never changes a single decision, then it’s just theater. The box gets checked. The program moves forward. The document rots in a folder no one can find.
AI is perfect for theater. Generate it. Check the box. Move on. The real cost was never the artifact, it was the human spending three weeks on something no one reads. The only thing reading AI is more AI, turtles all the way down (get it?).
The acquisition cycle is its own trap
Here’s a problem that isn’t getting nearly enough attention: AI is moving at software speed, and federal acquisition is moving at regulatory speed.
The Federal Acquisition Regulation runs to thousands of pages. A competitive procurement from solicitation to award routinely takes twelve to eighteen months. A major IDIQ or GWACs contract vehicle can lock a program into a technology approach for five to ten years. An Authority to Operate (ATO), the security approval required before most federal systems can go live, can take six to eighteen months on its own.
AI capabilities are advancing on a timescale measured in months, even weeks. By the time you finish an ATO for a specific model version, that version has a handful of successors.
This is not an AI problem. And AI won’t fix it alone, because the constraint isn’t cognitive, it’s regulatory. It’s the protest timeline. “Other Transaction Authorities” exist to bypass some of the regulations. However, it takes takes someone with the knowledge, relationships, and institutional credibility to make the case for an exception (despite the justification written using AI, too). You still need to know who to call on.
That someone is a human. Specifically, one who has been around long enough to know when the rules bend and who needs to sign off on the bending of the rules.
What is the price tag
The pre-AI default was to acquire first, define later, lock in a contract, then spend years negotiating what the system should actually do. Agile was supposed to change that, and it's been gospel in federal IT circles for a decade. But genuine agile (iterative, user-driven, willing to kill bad ideas early) never really took hold in government. What took hold was the vocabulary and buzzwords.
Now: deploy the AI, then figure out what you need it to do (again backwards thinking).
Same trap. Faster, more expensive, with a better slide deck created by AI.
Requirements gathering can still be human work. Sitting across from someone and asking what they actually need. Finding the constraint they forgot to mention: the API that hasn’t been updated since 2007, the policy that blocks the entire workflow, the workaround that became standard practice three administrations ago and is now baked into every downstream system.
Healthcare.gov launched on October 1, 2013, and collapsed under load on day one.5 The technical failures were real, but the root cause was a requirements and governance failure. Dozens of contractors, no single integrator accountable for the end-to-end system, and a launch deadline that had become a political fixed point that no one was willing to move regardless of readiness. No AI tooling fixes a governance structure where nobody owns the outcome. That decision to hold the date, to accept the fragmentation, to not escalate, was a human failure at the leadership level.
You can’t prompt your way to what your users forgot to tell you. And you definitely can’t prompt your way to the hard call nobody wanted to make.
What you can’t prompt
When you have a real problem, you don’t run a query. You call the person who has done this before.
The one who reads the org chart the way Robert Caro reads a political biography, looking not for who holds the title, but for who holds the power (I loved the LBJ Series by Robert Caro). The one who turned a hard no into a yes in a meeting two years ago, on the strength of a relationship built over a decade.
Federal agencies are not flat organizations with clean reporting lines and transparent decision rights. They are layered institutions with career staff who have survived six administrations, political appointees who have eighteen months to make their mark, informal power structures built on years of working the same committees, and institutional memory that doesn’t live in any document. Knowing how to navigate that, who to brief before the formal meeting, who needs to feel included before they’ll say yes, which deputy’s chief of staff is actually running the budget process regardless of title, that is the real product. It took years to build. It doesn’t transfer to a model. In other words, the soft skills.
Persuasion is human. Getting the people in the room who aren’t decision-makers to pull in the same direction anyway, that’s the job underneath the job. No model has skin in that game.
The right measure
“I need a chatbot for constituent services” is not a requirement. It’s already a solution. Someone decided the answer before they understood the question.
The outcomes worth fighting for don’t sound like technology. They sound like: we caught $40 million in fraud before the money left the account. We cut veterans’ benefits processing time from nine months to six weeks. We gave case workers enough time back to actually see their clients instead of updating a database.
AI can contribute to all of those, and in some cases already is. The IRS has used machine learning to flag high-risk returns for audit selection. CBP uses AI to screen cargo manifests for anomalies that would take human analysts days to find. The Social Security Administration has been piloting tools to help process disability determinations faster, a backlog that has real human cost for people waiting on benefits they’re owed.
These work not because someone deployed AI at the problem, but because someone first defined what “working” actually means in each context. The outcome was specified before the tool was selected (so rare in the public space). That sequence matters. Reverse it, and you get a very impressive demo that solves a problem no one had.
You can’t prompt your way to knowing which outcome actually matters. You have to have been in the room. Talked to the caseworker. Understood what nine months actually costs a veteran waiting on a decision. AI can get you there faster once you know where you’re going. It has no idea what’s worth going there for.
What’s next
We outsourced memory. Calculation. Scale. Now we’re outsourcing whole ideas.
What’s left?
The model wasn’t in the room when that trust was built. It didn’t sit through the failed pilots, the budget kills, the vendor who promised a platform and delivered a PowerPoint.
You did. Knowing who to call, why the last approach failed, and which office will kill this if you don’t bring them in early, that’s more than institutional knowledge. That’s survival knowledge. It lives in people who stayed, who paid attention, who built something over time that outlasted the last reorganization.
Every organization that hollows out that function to save money on a headcount line while expanding an AI license is going to rediscover, the hard way, that the tool doesn’t know what it’s for. It will generate artifacts. It will check boxes. It will produce a demo that impresses.
And then someone will ask what problem it solved. And the room will go quiet.
The people trying to replace judgment with a prompt are going to find out the hard way.
The views expressed here are my own and do not represent any federal agency.
GitHub, “Research: Quantifying GitHub Copilot’s Impact on Developer Productivity and Happiness,” September 7, 2022. GitHub reported that developers using GitHub Copilot completed the task 55 percent faster than developers who did not use Copilot. https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/
The Air Force’s Expeditionary Combat Support System, or ECSS, is the obvious warning label. It was supposed to modernize Air Force logistics through a unified ERP system. Instead, after roughly $1 billion and years of work, the program was cancelled with “negligible” value delivered. https://centreforpublicimpact.org/public-impact-fundamentals/the-us-air-forces-expeditionary-combat-support-system-ecss/?utm_source=chatgpt.com
U.S. Government Accountability Office, “Federal Efforts to Update Old IT Are Years Behind Schedule,” GAO WatchBlog, March 2025, gao.gov. The FY2025 federal IT budget totaled approximately $102 billion government-wide, per OMB data reported by the Congressional Research Service (Information Technology Spending in the President’s Budget Submission for FY2025, CRS Report R48049, 2024).
GAO, High Risk List, gao.gov/high-risk-list. The program began in 1990; IT system modernization projects appeared among flagged concerns as early as 1992. GAO formally added “Improving the Management of IT Acquisitions and Operations” as a standalone High Risk area in 2015 (GAO-15-290) and has retained it through its most recent 2025 update (GAO-25-107743).
HHS Office of Inspector General, HealthCare.gov: Case Study of CMS Management of the Federal Marketplace, OEI-06-14-00350 (2016), oig.hhs.gov.