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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Yes, for some bounded writing and production tasks. In a controlled online experiment published in Science in July 2023, professionals given ChatGPT finished midlevel writing tasks about 40% faster and produced output that scored 18% higher on quality. That result is useful, but it measures something narrower than “content work.” It says little about whether a fast draft is accurate, properly sourced, or right for its audience. Those checks still rest on human research and editorial judgment, which is why speed and reliability should be evaluated separately.
Does AI actually make writing faster?
The clearest direct evidence comes from Shakked Noy and Whitney Zhang, whose paper “Experimental evidence on the productivity effects of generative artificial intelligence” appeared in Science on July 13, 2023. The study was a preregistered online experiment with 453 college-educated professionals. Half were randomly given access to ChatGPT, and all completed occupation-specific, incentivized midlevel writing tasks. The average time taken fell by 40%, and assessed output quality rose by 18%.
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Those two numbers are the strongest quantitative support for the “speeds up” half of the headline, but they need their context attached. The tasks were short, bounded, and designed for the experiment. The study did not test investigative reporting, specialized technical research, long-running projects, or every AI system available today. A 40% gain in that setting is a reasonable expectation to test in your own workflow, not a guaranteed gain for every writer or publication.
What the 40% figure does and does not cover
- Covered: time to complete a defined writing task, measured in a controlled setting, with and without a specific tool.
- Covered: a quality score assessed for the output of those tasks.
- Not covered: whether the factual claims in the output were verified, whether sources were traced, or whether the piece held up after publication.
- Not covered: time spent on research before the task, on editing afterward, or on managing the tool itself.
Where the time savings show up in content production
Evidence from journalism practice points to the same pattern: AI helps most with work that is repetitive, well defined, and easy to check against a source. The Associated Press and University of Chicago report Generative AI in Journalism: The Evolution of Newswork and Ethics in a Generative Information Ecosystem, published in April 2024, records newsroom uses across text and multimedia production, information gathering, translation, transcription, and other areas.
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Drafting and routine production
Text production was the most common category in that report. Of the task mentions recorded from its 181 respondents, 69.6% were classified as content production: text. That figure describes how often tasks were mentioned, not the share of all journalists who use AI for writing, so it should not be read as a adoption rate.
Translation, transcription and idea generation
In a peer-reviewed study published in PLOS ONE on June 18, 2024, Anne M. Dijkstra, Anouk de Jong, and Marco Boscolo combined an analysis of AI news reporting from September 1, 2022 to February 28, 2023 with interviews in Belgium, Italy, Portugal, and Spain. Interviewees expected AI to help with repetitive work such as translation. The OECD’s 2025 review of generative AI and productivity also notes that less-experienced creators may find AI useful for generating ideas. These are areas where a machine-produced first pass can save effort, provided a person checks the output against the original material.
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Why a faster draft is not automatically a better result
Speed and quality are separate measurements. A draft produced in half the time still needs its facts, sources, relevance, and context checked before anyone publishes it. The AP and University of Chicago report states plainly that it remains uncertain how much productivity generative AI yields in newsrooms. It also notes that prompting and editing generate work of their own, which means the time saved on the first draft can be partly reinvested in revision.
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Where journalists locate the risk
The same report asked respondents about their ethical concerns. Among the 174 who answered that question, 21.8% (48 of 174) named lack of human supervision, 16.4% (36 of 174) named inaccurate information, and 9.5% (21 of 174) named bias. These percentages describe the respondents to that report, not all journalists, but they show where practitioners see the danger: unsupervised output and factual error rank well above bias as concerns.
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The report describes journalists as editors and checkers of AI output. They need to know where information came from and whether it can be verified. Dijkstra and colleagues found a similar pattern in the four-country interviews: journalists raised concerns about rigor, integrity, and transparency about sources, and described editorial oversight, internal discussions, and professional associations as ways to protect quality.
What human research still contributes
When a draft is generated quickly, the expensive part of the job shifts from typing to judgment. Human research is more than proofreading. In practice it includes:
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- Choosing the question. Deciding which question the piece must answer, and which claims it can leave out, depends on knowing the audience and the stakes.
- Finding and evaluating evidence. A writer or editor decides which primary sources, datasets, and expert voices are credible enough to rely on, and what they actually say.
- Resolving context. A statistic may be accurate and still misleading without its denominator, date, region, or conditions. Only someone who knows the subject can tell.
- Tracing provenance. Being able to say where each claim came from is what allows a correction, an update, or a reader’s challenge to be handled properly.
- Taking responsibility. A named editor or publication stands behind what is published, and that accountability cannot be delegated to a tool.
The studies support human checking and oversight as current professional practice. They do not, by themselves, prove that a human-led research process always produces a better article than an AI-assisted one. That is a practical position about editorial risk, and it is the reason to keep the research step with people.
Expertise changes what AI is good for
The OECD’s 2025 review, The effects of generative AI on productivity, innovation and entrepreneurship, summarizes studies of creative work and finds that results depend on who is using the tool. AI idea generation may help less-experienced creators, while suggestions can be less helpful to experienced creators who already have strong judgment. The review also reports a study of 600 university students in which AI-assisted idea generation negatively affected measured creative writing ability. These individual findings are described as summarized by the OECD; they come from the underlying studies, not from experiments the OECD ran itself.
The review argues that human expertise and oversight should complement AI. It warns that using AI exclusively may fail to produce unique and valuable work. For writers and editors, the practical implication is that the more you already know about a subject, the more carefully you should weigh whether a generated suggestion adds anything beyond what you would have written.
A decision framework for splitting the work
A marketing framework by Grewal and coauthors, published in the Journal of the Academy of Marketing Science (first published December 14, 2024), proposes choosing and implementing generative AI based on the nature of the inputs and the level of human augmentation a task needs. It is a conceptual model, not a productivity experiment, but it supports a simple rule: the more consequential and less repeatable the task, the more human involvement it needs. The table below applies that logic using dimensions drawn from the sources. It is a practical starting point, not a validated scoring tool, and it does not show that one workflow wins in every setting.
Quick Recap
| Task type | Factual stakes | Verification needed | Suggested division of work |
|---|---|---|---|
| Brainstorming angles or headline options | Low | Check originality and fit with the brief | AI generates options; a human selects, rejects, and reworks them |
| Routine, repeatable copy in a fixed format | Low to moderate | Spot-check facts and figures against sources | AI produces a first draft; an editor verifies every factual element |
| Translation or transcription | Moderate | Compare against the original, especially names, numbers, and quotations | AI produces the output; a bilingual or trained reviewer checks it |
| Explainers or articles built on factual claims | High | Trace each claim to a source and confirm its context | Human research comes first; AI support is limited to bounded tasks such as structuring or summarizing sources the writer has already read |
| Investigative, specialized, or long-form analysis | High | Full source verification, with provenance for every key claim | Human-led throughout; AI used only for narrow, checkable subtasks |
Limits of the evidence
- The controlled experiment covered short writing tasks performed by college-educated professionals. Results for other tasks, levels of expertise, or AI systems may differ.
- The journalism findings come from one newsroom-focused report and one four-country study with a fixed time window. Neither is a representative global survey of content professionals.
- The ethical-concern percentages are based on respondents to the AP and University of Chicago report and should not be applied to all journalists.
- No source cited here measures whether a human-led research process produces more accurate or valuable content than an AI-assisted one across all settings. The claim that human research drives better results is a practical editorial position supported by the evidence on risk, verification, and oversight.
A checklist before you publish an AI-assisted draft
- Confirm that the question the piece answers was set by a person who knows the audience.
- Trace every statistic and factual claim to a named source, with its date, region, and denominator.
- Check names, numbers, and quotations against the original material, especially after translation or transcription.
- Log the time spent on prompting and editing, so the real productivity gain is measured rather than assumed.
- Assign a named editor who takes responsibility for the final text.
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