Main Point
A polished document does not establish that the person presenting it understands the argument. AI can make writing easier, but accepting a finished answer may also bypass the work through which people develop a position of their own. The Economist’s discussion brings this distinction into focus: writing is an output, a process of thinking and a relationship with a reader. The challenge for universities and businesses is to obtain better text without removing learning, judgment and responsibility from the work.
Consider an illustrative meeting at a Georgian service company. An employee presents a report proposing a new service. The writing is clear, the account of customer needs sounds persuasive and the conclusion leads neatly toward expanding the offering. Then a colleague asks what makes the team believe customers need this particular service. Why was improving the existing service ruled out? What evidence would change the recommendation? The report appears to contain answers, but its presenter struggles to explain them. Sentence quality is no longer the central issue.
The document in this scenario might even be factually correct. What remains uncertain is whether the employee can distinguish an observation from an assumption, or an assumption from a recommendation. AI may have helped the employee reason more effectively; alternatively, the employee may simply have accepted an argument already assembled. The finished document cannot, by itself, distinguish those processes. The question in the title is therefore not a verdict on machine consciousness. It concerns something more practical: what does a person understand, and what can that person take responsibility for, when presenting a document under their own name?
Some of the difficulty of writing is the difficulty of deciding what you think
In its editorial of September 26, 2026, The Economist locates the value of writing beyond elegant sentences. Its argument is that a substantial part of the difficulty lies in clarifying thought: turning a vague impression into a statement, justifying a conclusion and discovering where the logic stops working. In a companion article, literary scholar Emma Smith describes how writing can reveal what a person actually thinks. This is an argument about the purpose of writing, rather than simply the mechanics of producing it. [1,2]
At the service company, for example, “customers want a better experience” would be a starting point, not a completed analysis. Making that statement precise requires asking whether customers want speed, lower prices, simplicity or more attentive staff. The team might discover that different customers face different problems, and that a concern compressed into one sentence actually requires several decisions. Finding that contradiction while writing is not an interruption to the task of formatting a report. It is part of the analysis, even when the difficulty that produced it disappears from the final version.
AI could assist with this work by asking questions, exposing an unclear connection or helping formulate a counterargument. Commissioning a finished position and adopting the answer as one’s own conclusion is a different task. In the first case, the person still has choices to make. In the second, the basis for those choices may remain unexamined. The distinction cannot be measured by the number of words generated or the list of tools used. It depends on what the person does after receiving an answer, including what they are able to reconsider.
The evidence should not be overstated. The Economist’s warning that reliance on completed text may weaken the habit of reasoning is an editorial argument. It does not establish that every use of AI diminishes thinking, or that the effect is identical across ages and tasks. The risk examined here is the possibility of skipping practice that contributes to judgment. Whether that is happening in a particular workplace or learning environment requires a separate assessment. [1,2]
Better writing and a more capable writer are different outcomes
A weak argument against using AI would insist that machines always write badly. The Economist itself acknowledges that human prose is often awkward, and that improving the technical quality of a document has genuine value. It distinguishes situations in which precision and clarity are the main objectives from those in which personal voice, judgment and the author’s involvement are part of the meaning. Research assistance, proofreading and feedback are among the useful applications it identifies. [1,2]
That distinction also matters to someone who understands a subject but finds it difficult to write in another language. Polishing an existing argument in a second language is not the same as delegating the argument itself. The companion article explicitly treats such help as an acceptable use. For an employee at a Georgian company, improving an English-language message could allow a partner to understand an already developed position more clearly. The concern arises when language editing also introduces an unnoticed change in substance, leaving the employee unable to identify a new commitment or conclusion. [2]
Evaluating the work therefore requires two different questions. Is the document clear, supported and appropriate to its purpose? Can the accountable person explain why it says what it says? The first question examines the output; the second examines human involvement. Excellent editing does not automatically answer the second question, just as awkward prose does not necessarily reveal weak thinking. Useful assistance should improve both sides, rather than allow success on one to conceal a problem on the other.
Readers look for responsibility as well as information
We do not always read solely to obtain information. Sometimes we want to understand what a particular person believes, whether they acknowledge a mistake and how seriously they take our experience. The Economist illustrates this distinction through an organizational announcement and an apology. Explaining an ordinary rule is one task; accepting responsibility for a problematic decision is another. Efficient wording may be enough for the former, while the latter also involves the reader’s expectations of human concern. [2]
This does not mean an apology edited with AI must be insincere. The more useful questions are whether the organization has understood the problem, decided how to address it and committed to fulfilling its promise or merely produced a message in an appropriate tone. Manually typed words cannot guarantee sincerity either. But persuasive language cannot substitute for the position and action that consequential communication is supposed to express. Assistance with editing and an imitation of accountability are not the same thing.
The editorial also considers trust from the reader’s perspective. Human writing can function as a signal of effort: someone spent time deciding that something was worth saying. That does not guarantee quality, but it forms part of the relationship between author and reader. In the publication’s assessment, uncertainty about the origins of a text can spread suspicion to work that a person actually wrote. Transparency about the process therefore serves more than a technical purpose. It helps readers understand what kind of communication they are receiving. [1]
A possible workplace response is to define responsibility more clearly. AI might assist with language editing, while named individuals remain responsible for checking facts, approving commitments and deciding what to publish. For an important document, “I read it and liked it” may be an insufficient form of approval. It would also be useful to know which claims the person checked, what they changed and what they still regard as uncertain. This is a proposed working practice, not an organizational standard tested in the source articles.
Universities need to see the work, not just the answer
Consider another illustrative scenario: two students writing essays on the same question. One reads the sources, develops an initial position, asks AI to identify contradictions and revises the argument. The other obtains a completed essay and changes a few words. The finished texts might be equally fluent. Reading them alone would not establish that the students learned equally well how to frame a question, select evidence or defend a conclusion. This is an assessment problem, not automatically an argument for banning the technology.
One possible response is to design assignments that make the development of a position visible. What did the student think initially? Which source changed that view? Which argument did they reject, and why? A brief oral discussion or an opportunity to apply the same reasoning to a different example could provide additional information. Assessment would then examine more than the provenance of the text. It would reveal what the student can explain, revise and defend. This is a proposal for educational design; the source material does not provide a quantitative estimate of its effectiveness.
AI’s role could be defined differently in such an environment. Instead of supplying a finished conclusion, it could ask the student to find missing evidence, explain an assumption or compare alternative answers. Finishing an assignment faster would not be the only relevant outcome. Evaluation would also consider whether the student identified a mistake and understood why a revision was needed. There should still be work that makes independent capability visible: succeeding with assistance and acquiring a skill that can be used independently are different outcomes.
None of this requires one rule for every assignment. If the objective is language editing, assistance may be a natural part of the work. If the objective is constructing an argument independently, delegating the initial reasoning may skip the very experience the assignment was designed to provide. The educator’s starting question should concern the capability being developed. That makes it easier to decide where AI belongs and what the student needs to do personally.
BTU Researchers’ Assessment
The central proposition of this analysis is that the quality of AI-assisted work should not be judged solely by the resulting document. Universities and organizations also have reason to consider the capability that remains with the person after the task. If an employee prepares a report faster but cannot examine its assumptions, document production has accelerated; professional development has not yet been demonstrated. If assistance helps the employee recognize a contradiction and reconsider a position, the process has created an opportunity for learning alongside the output.
The assessment distinguishes the quality of the text, the person’s understanding of the issue and the clarity of responsibility. This is not a validated index or the result of an experiment conducted for this article. It is an analytical proposal that translates The Economist’s argument into questions about education and work. Its practical test is straightforward: can the person explain the conclusion in their own words, identify what supports it and describe the circumstances under which they would change their view?
Proportionality matters. Not every email requires an extensive justification, and collaboration does not mean that a person must repeat all the work performed with AI. The depth of review should reflect the significance of the task. Editing a routine notice and preparing an important promise to a customer need not follow identical processes. Recognizing that distinction would help preserve the benefits of saving time while preventing accountability from being reduced to a ceremonial signature.
Key Findings
- A polished document does not, by itself, demonstrate that its presenter understands the argument.
- Developing a position through writing is different from editing the language of a position already formed.
- Assistance with research, proofreading and feedback does not require surrendering human authorship or responsibility.
- Assessment can be designed to include explanation, the history of revision and the application of reasoning to a new example.
- The proposed applications in Georgia are conceptual; no representative local study was conducted for this analysis.
Why This Matters for Georgia
For a Georgian university or company, access to technology is only part of the question. The other part concerns what people learn while using that access. Improving the English expression of a problem already understood in Georgian could be useful assistance. Transferring a generic answer written for an international setting into local practice without checking its relevance would be a different matter. The illustrative company needs to understand its own customers, not simply obtain prose that could describe almost any service in any country.
A practical opportunity is to design assignments and workflows in which local examples, source evaluation and an explanation of one’s own decision remain essential. That matters particularly in the transition from university to work: an employer needs an understanding of the issue behind a document, not just the document itself. The source material does not show how widespread this problem is in Georgia. It provides questions with which to examine local educational and organizational practice, without inventing data or treating international commentary as a direct description of the country.
Conclusion
Delegating part of the writing process to AI can be sensible. What matters is the human involvement that remains: framing the question, assessing evidence, reconsidering a position and accepting responsibility for the result. Not every stage requires manually typing words, but none can be replaced simply by the presence of fluent sentences. A useful standard for authors, students and managers is therefore not just whether they can produce a document. It is whether they can explain and defend what it says, especially when no one is supplying the answer.
Data and Main Sources
[1] The Economist. “The perils of AI writing” – Machine-made prose, Leaders. September 26, 2026, p. 10. An editorial discussion of writing, thinking, authorship and reader trust.
[2] The Economist. “AI writes and wrongs” – English literature, Britain. September 26, 2026, pp. 28–29. An analytical article on writing quality, the development of thought and the context of communication.
The analysis uses the publication’s articles, not the primary data from studies mentioned within them. The business and university scenarios are illustrative. The proposed educational and working practices are analytical contributions to this article. The material is educational and does not constitute organization-specific professional advice.
Prepared by the academic team of Business and Technology University and the BTUAI Research Team, Tbilisi, Georgia.
Full Georgian and English versions have been prepared for BTUAI.ge.