AI is Finally Solving the Most Hated Task in Software Development

Developers loves to code. Even those who barely know coding can now vibe code, and enjoy the thrill of building something new instantly. But one task cuts across every person, every team, and every organization: keeping software documentation up to date. It is the one job almost no one wants to do and never gets prioritised.

Keeping documentation up-to-date is probably the most hated task in software development, more than unnecessary meetings.

WHY DOCUMENTATION IS SO WIDELY HATED

A recent empirical study [https://www.researchgate.net/publication/391698852_Software_Developers'_Resistance_Against_Documentation_Writing_Comparing_Agile_and_Non-Agile_Practitioners] of 89 practitioners, comparing agile and non-agile teams, found that although 96 percent recognize the value of documentation, 70 percent admit they dislike writing it, especially those working in agile environments. Developers across settings echo the same frustration. Once the code is working and features ship, documentation often becomes something to do later, or never.

In fact this isn't just anecdotal. Engineers in fast-moving teams regularly deprioritize anything that does not deliver immediate features. A 2025 survey found that despite developers wanting to spend time building features, they actually spend only 16 percent of their week writing new code. The rest is swallowed by maintenance, bug fixing, and overhead.

Documentation, in such an environment, becomes a burden that offers no tangible reward at the moment.

THE HIDDEN COST: TIME, MONEY, PRODUCTIVITY, AND RISK

Calling documentation boring is one thing, but calling it a business liability is another. Companies that neglect documentation often pay dearly.

Poor or outdated documentation creates knowledge silos and single points of failure. When key team members leave or systems evolve, teams are forced into time-consuming reverse engineering and firefighting. That increases support costs, slows down development cycles, and introduces risk of downtime or defects.

Poor documentation also harms onboarding. It stretches the ramp-up time for new engineers and makes handoffs painful. The problem becomes more stark if your users are also customers, as this leads to instant frustration, distrust and eventually churn. There is a reason why developer tool companies like Stripe spend a enormous time and resources keeping user-facing documentation up-to-date

There is another consequence that did not exist a few years ago. Most documentation is now read by coding agents such as Claude 4.5, Cursor, and other AI pair programmers. These agents rely heavily on written documentation to understand a system. When teams let their docs drift, these agents generate dysfunctional code, amplify outdated patterns, or introduces subtle bugs.

WHY CONTINUOUS DOCUMENTATION REMAINS A MYTH

Teams have talked for years about treating documentation like code. Ideas such as docs-as-code and doc-ops became popular because developers wanted documentation to follow the same workflows as source files. In practice this rarely worked. The intention was solid, but the supporting tooling never reached the level needed for true continuous documentation [https://deepdocs.dev/why-ci-cd-still-doesnt-include-continuous-documentation/].

Documentation generators exist, but most of them focus on API signatures or low level structures. They do not handle the higher level documentation that explains system behavior, architecture, workflows, reasoning, or constraints.

Building a reliable continuous documentation pipeline has historically required teams to glue together static site generators, custom scripts, CI hooks, markdown linters, and manual review processes. A few teams with strong platform engineering muscles pulled it off. Most teams did not have the time or staff to maintain such a system.

This is why continuous documentation never became a real industry practice. The ambition was there, the tools were not.

HOW AI IS FINALLY FIXING THE DOCUMENTATION PROBLEM

AI has already reshaped how teams write and review code, and it has lowered the barrier to producing documentation. Modern AI code editors can explain complex logic, summarise modules, and draft missing sections with little friction.

While AI editors has helped reduce the friction of writing documentation from scratch, tools like DeepDocs [https://deepdocs.dev/] have emerged to address the maintenance aspect on keeping existing documentation aligned with an evolving codebase. When the code changes, these tool analyses the diff, and updates the relevant docs automatically. This removes the disconnect between fast-moving code and slow-moving documentation.

The result is a workflow where documentation stays in sync without requiring developers to stop and rewrite pages manually. Teams get clearer onboarding paths, fewer knowledge gaps, and far less risk of outdated information misleading developers or AI coding agents.

CONCLUSION

Software documentation is universally disliked, neglected, and often pushed aside, yet teams and now coding agents rely on it every day to build, review, and maintain software. The clearest path forward is to let AI handle the parts of documentation that humans consistently avoid.

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