Who Is All This AI-Generated Text For?

English is not my first language. Lately, a large part of my work as a software engineer has involved meetings, followed by AI-generated summaries, backlog items, pull request descriptions, and security plans.

I started discussing this with Gemini because of the language barrier: producing more polished English does not necessarily make it easier for teammates to read. But that led to a bigger question: who is all this text actually for?

The work moves to the reader

AI can help someone organise their thoughts or express an idea in another language. That is useful. But “make this sound professional” can turn a simple question into a paragraph that takes longer to understand.

If one person expands their notes with AI and the next person uses AI to summarise them, what have we gained? For someone working in another language, there may be extra effort just to work out whether a sentence is a requirement, a suggestion, or a polite disagreement.

Some documents are also input to agents or formal approval processes. They may need information beyond a teammate’s immediate question. That does not remove the need to make the human decision clear.

Consider a hypothetical discussion about retrying a failed data import:

We have not checked whether retrying after a partial write creates duplicates.

Now imagine the summary says:

The pipeline supports resilient retries and automated recovery.

The uncertainty has disappeared without the underlying problem being solved. If that summary becomes an implementation task, the next person or agent may never see the warning.

The useful document would keep the question open and point to the evidence needed: fail an import after some records are written, retry it, and check for duplicates. Plain language should make that technical detail easier to find, not replace it with reassuring words.

Whose contribution survives?

When live discussions become the main input to generated plans, participation depends partly on how easily people can put their thoughts into spoken English. Someone who needs more time may understand the technical problem perfectly well and still struggle to get their concern into the record.

I would like teams to share important questions before meetings and leave room for written responses afterwards. Diagrams and small examples can help too. But offering a chat box is not enough: the person responsible for the decision needs to account for those contributions, whether or not the summarising tool captures them.

People should also be able to correct the record before it becomes work. A proposed option must not quietly become an accepted decision. Silence after an automated summary should not count as agreement.

For me, inclusion means that a teammate’s slightly awkward explanation of a failure case can change the plan. They should not have to produce more polished English before the team takes the concern seriously.

An approval needs to mean something

Keeping a human in the loop sounds reassuring. But what are they actually being asked to verify?

For the import example, I want someone to inspect the retry behaviour and the test for partial writes. They need the evidence, time to examine it, and the authority to say it is not ready. Clicking approve on a general claim about resilience tells us much less.

Automated checks can cover known constraints. Another model can help find contradictions. Neither removes the need to understand the change, and two models agreeing is not proof that the implementation behaves as described.

If the generated material exceeds what the team can reasonably review, the scope or process needs to change. “Please review carefully” does not create more time.

I do not think software engineering in 2026 means that we all stop writing code and become proofreaders. It still involves understanding what we are building and helping other people make sound decisions about it.

For the next generated document I review, I would like to know which decision needs my attention, what supports it, and how I can challenge it before it becomes somebody else’s task. That seems a more useful measure of collaboration than how much text we managed to produce.