After a particularly tiring vibe coding session with Claude, I shared some of the resulting grumpiness in a few posts to the Write the Docs slack. I’ll confess. I’m probably not AI’s (as the term is currently bandied about) biggest fan. But I’m not a hater. I’m just, well, disappointed with it. It’s just not living up to its purported potential (a.k.a. hype, these days).

I’d been writing code (i.e. prompting Claude to write code for me, a.k.a. vibe coding) and Claude’s code was getting buggier and buggier. I’ve seen that happen before after it’s written a lot of code. It acts like it’s tired, but I think it’s due to having too many things to keep track of in the conversation, so it loses its place (I’m not an AI psychologist…yet).

In any case, I was beginning to wonder if it would have been faster to just start typing my own code (it wouldn’t), but I wanted to see how it played out. Eventually, after Claude had gotten stuck, again, I was troubleshooting in parallel and suggested a fix. Lo and behold, Claude agreed (as always). With that experience still fresh in my head, I went to the Write the Docs slack to see how others were faring in their AI journeys.

Thinking of past bubbles

In one post, I compared current AI hype to the hype I recall when PCs (as in IBM PC) came out in the early 80s. They promised the moon and in microscopically fine print, mentioned that “some assembly was required.” Sometimes some C, as well. (If you know, you know.)

In the 80s, it’s not that PCs weren’t amazing pieces of technology that could fit on your desk and still have room for your phone and desk blotter. Remember, this was a time when computer hardware had to have its own air-conditioned office. It’s that they lacked the “killer app” (the application that solves a high-value problem for a large audience), until Lotus 1-2-3 & Multiplan, two of the first spreadsheet apps), came out and ran on the PC.

Those killer apps transformed the PC from a geeky novelty to an absolute necessity. They enabled regular people to see the value that these machines could provide. Fast-forward to today, that’s how AI seems to be positioned: A novel (to most non-AI researchers and developers) technology waiting for its “killer app.” Just like the PC could do anything, but nobody cared until it did something useful. It’s hard to tell what AI’s killer app will be until we see it. If I knew what it was, I wouldn’t be here writing another blog post; I’d be working on AI’s killer app!

“95% of AI projects fail!”

YouTube’s AI must have read my mind, because it suggested a video titled,  MIT Shows 95% of AI Projects Fail — Artificial Intelligence Might Be Stupid. The speaker took a rather pragmatic view of AI and expressed a lack of surprise at the 95% failure rate. Hardly flattering towards AI, but it seemed honest.

After publishing my last post, I asked Claude what it thought about it, using the “reflective blog post” prompt I’ve been developing. It appears that we have some more work to do, indeed.

Claude reviewed my post in terms of:

• Authenticity
• Professional stakes and relevance
• Voice and intellectual honesty
• Process documentation quality
• Factual accuracy
• Overall assessment

Basically, I got a report card from my mechanical English teacher. Let’s see how I fared…

Authenticity

Evaluates whether the uncertainty and professional challenges feel genuine rather than manufactured for content purposes.

Let’s see what my strengths in Authenticity are, according to Claude:

• Genuine uncertainty: The author demonstrates real ambivalence about AI collaboration without rushing toward resolution (“I’m not ready to abandon my AI-overlord, er, I mean co-writer, just yet”)
• Honest stakes: Clear professional and personal impact – less satisfaction, feeling like work, questioning writing identity
• Specific observations: Concrete details about the back-and-forth editing process, watching drafts transform, losing sense of accomplishment
• Temporal authenticity: Uses present-tense investigation language (“What I think this really means…”) and frames conclusions as provisional

OK. So far, so good. What does Claude think I need to work on?

• Limited process transparency: Doesn’t show systematic methodology for investigating this challenge beyond informal reflection
• Missing specifics: Could benefit from more concrete examples of the “soul removal” process or specific editing exchanges

Perhaps. However, I’m not convinced those are appropriate in a reflection paper. It could be that we have a difference of opinion on what a reflection paper should include. Or, perhaps, adding more of this information would make it stronger. In retrospect, I think I’m still figuring out those aspects of my writing, so I’m still not quite sure what to say about them.

I’ll give Claude credit for identifying some inner workings that I need to process.

Professional stakes and relevance

Examines how clearly the article demonstrates real workflow impact and broader applicability to others in the field.

I’m terrible at spotting flaws in my own work. As I iterate through multiple drafts and careful editing, I still tend to lose myself in the details and focus more on the trees than the forest. It’s not uncommon for me to miss weak arguments, unclear explanations, and assumptions that need defending—until after I publish, of course.

While preparing my previous post about AI writing workflows, I decided to test something: asking Claude to actively look for problems with my draft from an antagonistic perspective. Not just gentle feedback, but the kind of opposition you’d face from readers with strong opposing viewpoints.

The results were more brutal than I’ve been used to from Claude, yet they were more useful, than I expected. Here’s what I learned by using adversarial review to stress-test arguments, and why this technique shows promise despite some significant limitations.

The experiment setup

Close to publishing, I gave Claude this prompt:

Review the most recent draft as an antagonistic reader who would like to find any and all problems with the article (whether constructive or just to be antagonistic). This reader might have a pro-AI agenda to push or an anti-AI (as in no AI can be good) agenda.

I was looking for the kind of opposition that finds every possible angle of attack—the readers who come to your work already convinced you’re wrong and looking for ammunition to prove it.

Claude responded by creating and applying three distinct antagonistic personas:

• The ideological opponent (challenges core assumptions and claims bias)
• The pedantic critic (nitpicks definitions and demands citations)
• The bad faith reader (misrepresents positions for rhetorical advantage)

Then it proceeded to tear into my article from both pro-AI and anti-AI perspectives. I had asked for this, and the responses were deliciously brutal. They were antagonistic in exactly the way I’d requested, and to be honest, genuinely useful.

What different types of opposition reveal

Claude organized the feedback by its perspective on AI. What follows is a summary of the feedback. You can see all the feedback in the transcript of the chat.

After six months of diving into AI tools, I’m still figuring out how to work with them without compromising my professional integrity. The old boundaries between my words and borrowed words don’t map cleanly onto AI assistance. When AI helps craft my prose, am I still the author? When my students use GPTs (a generative, pre-trained transformer, built on a large-language model, or LLM and more commonly known as an AI chat tool) to generate sophisticated responses, how do I know if they actually understand the material? The underlying tension in both contexts is the same: Where does human skill end and tool dependency begin?

I’ve been wrestling with this question through direct experience, using AI tools in my own writing and as I prepare to teach students about AI applications in technical writing scenarios. The uncertainty isn’t comfortable, but it’s productive. It’s forcing a clarity about professional standards that I previously took for granted.

The attribution gap

Traditional models for crediting intellectual work assume clear human sources. Plagiarism involves stealing and passing off ideas or words as one’s own without crediting the source. That creates a property line between what’s “my work” and what isn’t. The property line is fuzzy, but it’s recognized.

Work-for-hire contracts handle using another’s writing by transferring ownership. I might write the words, but my employer becomes “the writer” through legal assignment. Editorial assistance operates on a continuum: the greater the influence of dictionaries, grammar checkers, or human editors on the final product, the more they should be credited.

Search engines provide access to enormous amounts of knowledge while making attribution relatively straightforward. This makes it easy to build on others’ genius, and cite the sources to avoid plagiarism, while maintaining a clear distinction between your words and those of others.

But what’s a GPT in this context?

• Is it a sophisticated grammar checker? Definitely.
• Is it a mechanical editor? It can be.
• Is it your personal writer-for-hire? It could be.
• Is it a source of original content? Unclear.
• Is it an industrial-strength plagiarism machine? That’s still a topic of heated discussion.

AI doesn’t fit into existing categories while somehow fitting into all of them. The academic press is still debating this. Stances range from “do what makes sense” to “no way, no how” depending on the field and editorial board. Most of my academic papers were guided by ACM and IEEE standards, so the ACM’s more flexible approach feels familiar and reasonable while maintaining academic transparency.

The competence question

As a writer, the integrity question centers on authorship: “Am I still the writer if AI helps structure my arguments?” As an instructor, it’s about learning: “Do my students understand the material if they’re using AI to generate responses?”

Google Cloud recently published “Smarter Authoring, Better Code: How AI is Reshaping Google Cloud’s Developer Experience,” describing how they’re applying AI to documentation challenges that every technical writing team recognizes: keeping content accurate, current, and useful for developers working with constantly evolving services.

Because AI tools consistently claim to excel at content analysis and summarization, I put that claim to the test on this article. I’ve been working with these tools for about six months now and wanted to test what I’d learned. So, I fed the Google article to Claude and spent some time exploring what it could tell me about the content, the claims, and the gaps.

Spoiler alert: the questions I asked mattered more than the answers I got.

The thought leadership gap: Style over substance

The Google article describes two main AI applications in technical writing:

• Integrating Gemini into writers’ authoring environments for productivity tasks like generating tables and applying style guides, and
• An automated testing system that uses Gemini to read documentation steps and generate Playwright scripts for validation.

The article also describes a multi-agent system for code sample generation that uses Protobuf definitions as source of truth, with generator agents creating samples and evaluator agents scoring them against rubrics.

Claude initially provided a summary that was cleaner and better organized than my mental notes after reading the same article. However, when I pushed for evidence supporting the claims, Claude articulated what I’d been sensing: the article provides limited concrete evidence beyond “over 100 tests daily” and references to “tens of thousands of idiomatic samples.” There was no mention of metrics on accuracy improvements, time savings, before/after comparisons, or validation of claims about preventing “factual drift.” You can see the whole conversation in the chat transcript.

When I questioned Claude about why Google would publish such a detail-light article, Claude offered a perspective that troubled me:

For thought leadership aimed at developer relations leaders and engineering executives, too much evidence could backfire. Publishing specific metrics creates comparison benchmarks that competitors can target. On the other hand, a claim like, “Over 100 tests daily” suggests impressive scale without revealing whether those tests actually pass.

That bothered me. We’re talking about a business communication norm where impressive-sounding claims matter more than verifiable results. Claude put it bluntly: “There’s an ethical gap between presenting a technical approach and implying proven success.”

This weekend, I woke my LinkedIn profile from its hibernation and spent some time bringing it up to date and dusting off the cobwebs. The good news is that it gave me something to blog about!

I sent my profile into hibernation last fall for a variety of reasons. The main reason was just to come up for air as I found the constant barrage of “inspirational accomplishments” and “incredible journeys” overwhelming. Sprinkled with incredibly sad stories of people having to sell their houses after months of unsuccessful job hunting, made it just too much of an emotional roller coaster ride.

No offense to anyone who’s managed to accomplish anything during these difficult times. And my heart goes out to everyone who needs a job and is still struggling to find one. But I needed to step away from the notifications for a while.

Well, times up! I’m back online and I’m here to write this, let’s call it a, celebratory post.

Back online

To kick things off, I’d need some content for LinkedIn (as is the nature of the site), so I asked Claude (AI) to write a blog post about the style of LinkedIn posts in the style of a LinkedIn post. This is a task for AI because it’s not natural for me to write in, or about, the genre.

Unfortunately, I might have confused it. The AI was working in the context of my blogging-style prompt and you can see how it tries to follow my blog-post style while analyzing LinkedIn accomplishment theater, and while trying to make the post itself LinkedIn-worthy.

What follows is the AI-generated post that I wished I could have written.

I just published a blog post, and you won’t believe what happened next

Yesterday, I sat down to write a simple blog post about content strategy. What started as 800 words of practical advice somehow transformed into a LinkedIn-worthy tale of professional triumph that would make a motivational speaker weep with pride.

Here’s how it happened (and why we should probably talk about it).