Your team shipped more content last quarter than in any quarter before it, and the pipeline numbers did not move. That gap has a measured shape. In the Content Marketing Institute’s 2026 B2B research, 87% of marketers using AI for content creation report improved productivity and 80% report improved operational efficiency. Only 39% report improved content performance.
Nearly everyone got faster. Roughly a third got better results.
If you are weighing a technical writing service against an AI content tool right now, you are probably building a comparison table. Cost per word on one axis, turnaround time on the other, maybe a column for revisions. On that table the tool wins by roughly two orders of magnitude, because published technical writing rates start around 87.5¢ per word and a model will produce a draft for a fraction of a cent.
That table is a reasonable thing to build and it will not tell you which option works, because the two numbers that decide the outcome are not on it.
The Two Numbers That Belong on Every Comparison Table
The first number is the one above: 87 versus 39. The second is 12, as in the 12% of B2B marketers who report that AI-assisted content creation decreased their content quality, alongside the 22% who saw no change in quality at all.
Set those against what marketers say their actual problems are. The top three challenges in the same research are creating conversion-focused content (40%), resource constraints across time, people and budget (39%), and measuring content effectiveness (33%).
Raw production volume does not appear on that list. (If the one you recognise is the third, measurement, that is a different problem with a different fix.)
This matters because volume is the thing an AI content tool unambiguously solves, and a technical writing service unambiguously does not. If you buy on the axis where the tool wins, you will get exactly what you paid for: more drafts, faster, aimed at a problem your peers do not report having. The question underneath the purchase is not how much content you can originate. It is what happens when a piece of it is wrong.
What a Wrong Claim Costs, and Who Pays for It
There is a clean way to measure how often a language model invents something, and it comes from code rather than prose. When a model recommends a software package, you can check whether that package exists on PyPI or npm. No editorial judgement, no taste, just a master list.
Run that test on the 2026 frontier cohort and the models hallucinate non-existent package names at rates between 4.62% for Claude Haiku 4.5 and 6.10% for GPT-5.4-mini, measured across 199,845 paired Python and JavaScript prompts, according to a 2026 replication study. The 2024 generation, evaluated across 576,000 code samples from 16 models, averaged at least 5.2% on commercial models and 21.7% on open-source ones, producing 205,474 unique invented package names.
Read those two ranges next to each other. The spread between the best and worst model compressed by an order of magnitude. The floor barely moved.
The models converged on each other, not on being right.
This measures code, not prose, so treat it as a proxy. But it is a proxy that errs toward caution, because it is the friendly version of the test: the one where a fabrication gets caught by a script checking a master list. In technical prose there is no master list. A confidently stated integration limit, a plausible compliance threshold, a version number that sounds right, all of them read exactly like the true ones.
Your buyers will look. In TrustRadius’s survey of 1,862 technology buyers, 63% used AI during their purchase journey, and 94% of those buyers said they fact-check its responses at least some of the time. Verification is now a step in the buying process rather than an optional courtesy.
So the fabrication rate is not an abstract model-safety statistic. It is a number about how often someone with budget authority finds the seam in your documentation.
Why AI for Technical Writing Can’t Check Its Own Work
The obvious mitigation is to run a second pass. Generate the draft, then ask the model to verify it, or ask a different model to check the first one. It costs almost nothing and it feels like diligence.
It does not work the way you would hope, and the same package-hallucination research explains why. When researchers re-ran identical prompts ten times each, 43% of hallucinated packages were repeated in all ten queries, and 58% of the time a hallucinated package recurred more than once across the ten. These are not random slips. Most of them are stable, reproducible outputs of the same statistical process.
Which means regenerating a draft and comparing the two versions is a weaker check than it looks. The error most likely to matter is the error most likely to survive the second run.
Here is what the alternative costs, using this article as the worked example.
Researching it, we collected five statistics that circulate constantly in writing about AI content: that 67% of buyers can spot unedited AI writing, that unedited AI content scores 23% lower on engagement, that 61% of sites publishing at scale lost 40 to 90% of their traffic after a core update. Each appears across dozens of marketing blogs, usually with confident phrasing and no link. Not one traces back to a named study, a sample size, or a publication date. Two are attributed to the Content Marketing Institute and appear nowhere in CMI’s published research.
All five were cut. That is why the number this article opens on is 39% rather than something with more teeth.
The figure that did survive, the 43%, was the most expensive thing here. The conference page returned a 403, the PDF would not parse, and confirming it meant fetching the paper’s HTML and reading section 5.3 to check that the number said what three secondary sources claimed it said. Three attempts, for eleven words of finished copy.
That is the work. It does not scale, which is exactly why the comparison table struggles to price it.
It is also why the decision is not really “tool or service.” Every option produces text and then needs something outside the generating system to confirm it is true. You are choosing who does that, how well, and who carries the consequence when it is missed. The mechanics are written up separately as a repeatable process for fact-checking AI content.
The Line Item That Survives Every Option
The Editorial Freelancers Association’s 2026 rate chart collects rates from working editorial professionals. In it, technical ghostwriting runs 87.5¢ to $1.08 per word, or $75 to $126 per hour. Technical editing runs 3.4¢ to 4.5¢ per word, or $55 to $75 per hour.
That is roughly a twenty-five-fold gap between originating technical content and checking it.
That gap is the most useful thing on your comparison table, because AI collapses the expensive number to near zero and leaves the cheap one untouched. A model writes a 2,000-word integration guide for the price of a coffee. Confirming its API limits, version constraints and error codes are real costs what it always did, because it is the same work done by the same kind of person.
So the honest comparison is not $0.03 per word against $1.00. It is a subscription plus verification against a service that includes verification. Buyers who forget the second half do not save the money. They defer it, usually onto someone harder to book than a writer.
SME Hours Are the Currency You’re Actually Spending
That someone is your subject matter expert, and they are the constraint the whole category is built around.
The structural problem in technical content is that the people who know enough to write it are the people too busy to write it. As Scott Abel put it in July 2026, SMEs are “often busy, hard to schedule, not trained as writers, and not rewarded for documentation work,” and documentation loses the priority fight against “work that affects release dates, customer commitments, operational issues, and performance evaluations.” The result is content that arrives “late, incomplete, or wrong”.
Now look at what each option does to that person’s calendar.
A good technical writing service front-loads their time: a structured interview, a scoped set of questions, a draft that comes back close enough to correct that the review is a review rather than a rescue. An AI tool with no verification layer back-loads it. The draft arrives instantly and lands on the same engineer’s desk, except now they are hunting errors distributed invisibly through 2,000 fluent words instead of answering ten questions in an hour.
Same person. Same scarce hours. Very different spend, and only one of the two shows up on the invoice. The extraction side of that, getting real expertise out of a busy specialist, is its own discipline and the part most suppliers quietly skip.
Does Google Penalise AI Content? No, and the Real Answer Is Worse
Somebody in the buying conversation will raise search risk, framed as a penalty. Worth settling, because the myth is distorting otherwise sensible decisions.
Google’s spam policies define scaled content abuse as producing many pages primarily to manipulate rankings rather than help users, and state that this applies regardless of how the content was produced. Generative AI appears in the policy only inside that framing: using such tools “to generate many pages without adding value for users”.
There is no penalty for machine assistance. There is a penalty for publishing worthless pages at volume, which humans have been doing enthusiastically since long before any of this.
That is worse news than a penalty. A penalty would be a rule you could comply with. Instead the standard is whether the page is worth anything to a reader, which is exactly the judgement your comparison table was built to avoid making.
When AI for Technical Writing Is the Right Call
None of this makes AI-assisted content categorically worse, and any comparison that concludes otherwise is selling something.
The evidence genuinely cuts both ways. A study by Taboola with Columbia University and other collaborators found AI-generated ads averaged a 0.76% click-through rate against 0.65% for human-made ads, as reported by Digiday. Research by VML Intelligence found only 21% of consumers say they would dislike a campaign on discovering it used AI.
Use AI for technical writing where the failure mode is cheap and detectable: internal drafts, structural outlines, first-pass summaries of material you already know cold, repurposing a verified asset into six formats. In those jobs the fabrication rate is an inconvenience rather than a liability, because you are the one reading it and you would notice. That is the shape of AI-assisted technical writing done deliberately: the model does origination, a human owns the claim.
The calculation inverts when the artefact is external, technical, and load-bearing. Documentation an engineer will implement against. A comparison page a buying committee will check. Anything where a plausible wrong number survives to the reader. For that tier, the verification has to be built into the production process rather than bolted on afterwards, which is the whole argument behind our technical article content engine.
This is a question about what to buy, not about whether the job survives. Will AI replace technical writers answers that one separately.
One thing worth knowing before you make this call quietly: 95% of marketers report their organisation uses AI-powered applications, yet only 8% describe their implementation as Advanced or Leading. Everyone has the tools. Almost nobody has an advantage from them. Vendors conflate those two sentences constantly.
What to Ask a Supplier Before You Sign
Whether the supplier is an agency, a freelancer, or a software vendor, five questions separate the ones who have thought about this from the ones who have not.
What happens when a claim is wrong? Not whether it happens. What the process does when it does, and who is accountable.
How many SME hours does this consume, and at which stage? Front-loaded extraction and back-loaded error-hunting cost the same person the same hours and produce very different content.
What is your verification step, and who performs it? If the answer is another model, refer to the 43% figure above.
Can you show me performance data, not production data? Everyone can show you volume. Ask for the 39% number.
What do you refuse to write without an expert in the room? A supplier with no such boundary has not found the edge of their competence yet.
One more consideration, for the strategy conversation rather than the procurement one. Gartner projects that by 2027, 20% of brands will differentiate themselves on the absence of AI in their output. If that holds, the verification cost you have been treating as overhead becomes a positioning asset, and the calculation you run today has a shelf life measured in quarters.
You can run a version of this comparison this week without changing suppliers. Take the last technical piece you published, pick the ten most checkable claims in it, and verify them against primary sources. Count how many hold, and time how long it took. That number is your real cost per word, and it is the only one on the table that has not already been quoted to you.