No dramatic thesis here, just something I’ve been watching happen in the content space over the past 18 months.
AI writing tools produce output that’s consistently fine. Not excellent. Not memorable. Fine. It answers the brief. It has the right structure. It hits the keywords. It doesn’t embarrass anyone. And for a lot of organizations, “doesn’t embarrass anyone” has quietly become the standard.
The problem with fine is that fine is invisible in the short run and corrosive in the long run. A piece of content that’s genuinely insightful, that says something specific and surprising, that has a sentence you want to share - that content builds something. It builds an audience, a brand position, a reputation for knowing things. Fine content builds nothing. It just occupies space.
What I’m seeing is that teams adopting AI-first content workflows are hitting volume targets and missing quality targets they didn’t know they had. The quarterly review looks good: 40 pieces published, SEO metrics holding, no complaints. The two-year review looks different: organic growth has stalled, time-on-page is down, email list engagement is declining, and nobody can quite explain why because nothing obviously bad happened.
The “good enough” problem is that it’s a slow leak. You don’t notice until you’ve lost a significant amount of pressure.
I’m not making a case against AI tools. I use them. I’m making a case for being honest about what “good enough” is actually doing to the content ecosystem if we let it run unchecked. Because the individual decision to publish fine content instead of good content is rational. The collective outcome of everyone making that rational decision is something I don’t think we’ve fully priced in yet.
Has anyone found practical ways to hold the quality line while still capturing the efficiency gains?
The SEO data supports this more than people want to admit.
Sites that published heavily AI-assisted content in 2023 had early volume gains. The sites that survived the subsequent algorithm updates were the ones that had genuine expertise baked in - real practitioner knowledge that the AI couldn’t supply. The fine content got devalued. The content that actually said something specific and verifiable held.
The practical implication is that AI tools work best as a drafting accelerant when someone with real domain knowledge is doing the editing. If the human in the loop is primarily a proofreader rather than a subject matter contributor, the output stays at fine and the risk accumulates.
The quality line question has a practical answer for me: differentiate between content types.
Operational content - FAQs, product descriptions, process documentation, templated emails - fine is actually the right standard. It should be clear and correct. It doesn’t need to be distinctive.
Brand content, thought leadership, anything where you’re trying to build a relationship with an audience over time - fine is the wrong standard. The bar is “would a person actually want to read this,” which is higher and harder to fake with current tools.
Most teams I observe are applying the AI-for-efficiency logic uniformly across both types, which works for the first category and quietly damages the second.
The slow leak metaphor is exactly right and I think the creative writing space will feel it earlier than most.
Readers have built habits around certain authors because those authors deliver something specific. The relationship is built on distinctiveness. If the distinctiveness erodes because the author is increasingly producing fine-but-not-memorable content with AI assistance, the audience doesn’t leave immediately. They disengage slowly. They stop recommending. They stop renewing.
The audience relationship is a long asset. Fine content depreciates it in ways that don’t show up for months or years. By the time you notice, the trust is already diminished and rebuilding it is much harder than maintaining it would have been.
I see this in student writing and it bothers me for the same reason.
The AI-assisted essays I read are technically competent. Structure is fine. Evidence is cited. Claims are supported. But they’re forgettable in a way that good student writing isn’t. Good student writing has wrong turns in it, unexpected angles, sentences that don’t quite work but are clearly the result of someone trying to say something specific.
Fine writing signals nothing happened. And writing is supposed to be evidence that something happened - that a mind engaged with a problem and produced something as a result of that engagement. The fine AI essay is evidence that a prompt was submitted. That’s a different thing.
The collective action problem framing is right and it’s the reason I think market pressure alone won’t solve this.
Every individual organization has an incentive to publish fine content because it’s cheap and the downside is slow and diffuse. The organization that holds the quality line pays a short-term efficiency cost for a long-term brand gain that’s hard to measure and easy to discount.
What might move the needle is if the platforms that distribute content start better rewarding genuine engagement signals - shares, return visits, deep reads - over volume. There’s some evidence that’s happening at the algorithm level. But it’s slow and gaming it is always possible.
The more direct answer to your question about holding the quality line: I tie content quality to revenue metrics explicitly in reporting. If you can show the leadership team that the high-quality piece drove three times the pipeline that the fine pieces did, the efficiency math changes. Most teams aren’t tracking at that level of granularity. They should be.