Keeping the story behind what we build
When AI writes code faster than people can read it, teams can end up with systems that run but that nobody understands. We propose keeping a checkable record that links each change to the goal it serves, the trade-off it made and the test that shows it works.
AI coding assistants can now change a whole codebase overnight. The code runs, but the reasons behind each change are lost: which requirement it served, what alternative was rejected, what it might break. When something fails a week later, nobody on the team knows where to look.
Speed without a map
That is the risk facing software teams today. AI coding assistants can now draft hundreds of lines in seconds. They fix typos, wire up connections, and write tests before you finish your morning coffee. A single developer can now produce the volume of code that used to take an entire department.
Yet as code pours in, something vital begins to slip away. Nobody on the team can explain why a particular choice was made. Nobody remembers which trade-offs were weighed or why an earlier design was rejected. The machine generates answers, but human understanding gets left behind.
The trail of reasons
To keep our footing, we need more than raw speed. We need the machine to produce understanding as an explicit deliverable alongside its code.1
Think of it like blazing a trail through a dense forest. A machine can clear trees at blinding speed. But if it leaves no markers on the trunks, anyone following behind will quickly get lost. A good trail keeps clear waypoints that answer three simple questions.
1. What human problem were we trying to solve? Every change begins with a need. Linking code back to that original need keeps the project anchored in reality.
2. What other choices were weighed? Good engineering is about trade-offs. Recording which options were rejected prevents teams from repeating old dead ends months later.
3. What test proves it still works? Passing tests should verify the original intent, not just confirm that the code runs without crashing.
Why productive friction matters
When tools make generation effortless, the temptation is to accept every suggestion on faith. Clicking confirm is easy. Stopping to understand what changed takes patience.
Yet preserving that small pause is what keeps human teams in control. Software researchers describe this pause as productive friction.1 It is the deliberate habit of slowing down just enough to verify the reasons behind a change.
An assistant should help organize the map, flag stale notes, or point out unverified assumptions. But the human maintainer must still walk the trail and confirm that the path makes sense.
Shared records nobody can erase
This discipline becomes even more critical when software assistants coordinate across different organizations. When an assistant at a clinic schedules lab work with an assistant at an insurer, decisions happen in milliseconds. If an appointment is cancelled, who explains why?
A shared system cannot rely on one company's private database to settle disputes. We need shared records where every change points directly to the permission that authorized it.2
Each shared record forms an immutable tree of actions that any party can independently check. When every step leaves a verifiable receipt, truth no longer depends on whichever platform is biggest.3
What this means
You do not have to choose between fast development and clear understanding. By requiring software assistants to leave checkable waypoints, teams keep their systems inspectable and resilient over the long haul.
Where this leaves us
Generative tools will only become faster and more capable. That speed can either bury us under opaque software or free us to focus on higher craft. The difference comes down to whether we demand understanding alongside our output.
Before accepting large automated changes into your codebase or organization, ask a simple question. Can someone new to the project read the reasons behind this change six months from now? Keeping the story behind what we build is how we make sure technology serves human understanding, rather than replacing it.
1. Margaret-Anne Storey, "Cognitive Tours: Making Understanding a Deliverable in AI-Assisted Development" (2026), arXiv:2611.08456 (introducing cognitive tours, waypoints, and productive friction to combat cognitive debt and intent debt in automated software development).
2. David Hyland-Wood and Rob Ewin, "CoordX Protocol: Peer to Peer Cryptographically Verified Shared Records" (2026), arXiv:2611.06034 (specifying serverless, cryptographically verified shared records structured as causal trees of signed immutable entries).
3. Jack B. Dennis and Earl C. Van Horn, "Programming Semantics for Multiprogrammed Computations," Communications of the ACM 9, no. 3 (1966): 143–155, doi:10.1145/365230.365252; and George C. Necula, "Proof-Carrying Code," POPL '97 (1997): 106–119, doi:10.1145/263699.263712.
