What Most Startups Get Wrong About AI Content In 2026

—Hassan Rashid is a managing editor at GrowthX. All opinions expressed are solely those of the author—

Almost every startup I’ve worked with or spoken to in the past two years has shipped AI-generated content in some form. Publishing became nearly free so teams published. Blogs that used to produce two posts a month started producing twenty and for a while, the numbers looked encouraging.

Then Google’s helpful content updates caught up. Google itself estimates that its March 2024 update cut low-quality, unoriginal content in search results by 45% and a lot of what startups had shipped fell into that category. I build AI content systems for venture-backed B2B companies, and I’ve spent a lot of time looking at which pages survived.

The difference has very little to do with which model a team uses. It comes down to three mistakes and most startups are still making all of them.

Mistake 1: Mistaking Volume For Strategy

The first mistake is treating AI as a publish-more machine. It makes sense on paper: content used to be the expensive part of marketing, now it’s cheap, so more of it should mean more traffic.

Google has been explicit about this. Its spam policies now name “scaled content abuse” directly, which covers producing large numbers of pages to capture rankings rather than to help readers, whatever tool was used to make them. Sites that scaled thin content watched their traffic collapse through 2024 and 2025, and plenty of them never recovered.

It’s worth asking honestly whether each page says something your customers actually need, in a way a competitor couldn’t copy by prompting the same model. If the answer is no, publishing more only makes the problem bigger.

Mistake 2: Treating AEO As A New Discipline

The second mistake runs the other way. Founders watch ChatGPT and Google’s AI Overviews answer their customers’ questions and conclude they need an entirely new playbook, usually under the label of answer engine optimisation or AEO. There’s no shortage of agencies willing to sell it to them as a separate service.

In the accounts I work on, the pages that AI assistants cite are mostly the same pages that already perform well in ordinary Google searches. The fundamentals haven’t changed: original information, clear structure and a site that has earned some authority. SEO specialists made a similar point to TechRound recently, describing how AI search exposes weak SEO rather than replacing the discipline.

To be fair, some things really are different. AI assistants draw on a wider set of sources than your own website, so reviews, industry forums and third-party coverage matter more than they used to. But that’s an extension of building a credible brand, which was always the job. Chasing AI-visibility tricks before the fundamentals are in place doesn’t work because the assistants reward the same authority that search does.

Mistake 3: Outsourcing Expertise To The Model

This one does the most damage. When content is handed entirely to a model or to a junior marketer with a ChatGPT subscription, the output can only be a remix of what’s already on the internet. It usually reads fine but it can’t say anything new because the model has no experience of your customers, your market’s bad advice or the problems your product was built to solve.

Google’s quality guidance leans heavily on what it calls E-E-A-T: experience, expertise, authoritativeness and trust. Fully generic AI output carries none of those signals.

The frustrating part is that the missing ingredient is usually sitting in the founder’s head. After years in a market, you know what customers misunderstand, which objections come up in every sales call, and where the standard advice in your industry falls short. That knowledge is exactly what generic content lacks and in most startups, none of it ever reaches the blog.

What Actually Ranks Now

The content that performs in 2026, in search and in AI answers, starts from primary material. That means your own data, your own customer conversations and your own experience of doing the work, with a named person behind it who can actually back it up. First-hand experience has become a visible quality signal and it’s the one thing a competitor with the same AI tools can’t reproduce.

The second ingredient is human judgement. Someone experienced has to decide which claims need evidence, what the draft gets wrong, and whether the piece genuinely helps the reader or just matches a keyword. A model can produce the draft but it can’t tell which parts of its own output are generic or mistaken. That still takes a person with real standards.

What The Winners Do Differently

The startups getting real results treat content as a system built around their expertise. The strongest pattern I’ve seen is interview-led: someone sits down with the founder or the technical team, captures how they actually think about the problems customers bring them, and that material becomes the backbone of everything published. AI then does what it’s genuinely good at, which is scaling production around that expertise rather than substituting for it.

The other habits follow from there. Drafts get reviewed by someone who knows the subject before they go out. Articles carry named authors and where it makes sense, named expert reviewers. And the team invests in the system itself, meaning the standards, the review step and the sources of original data, so every new piece inherits the quality of the setup rather than starting from scratch.

The Takeaway For Founders

If content is part of your plan (it should be!), measure it the way Google and your buyers do. Track whether it demonstrates real expertise, whether it answers the questions your customers actually ask, and whether it converts, rather than counting how many pieces shipped this month.

And when you make a content hire, prioritise someone who can build and run that kind of system and can tell a genuinely good draft from one that just sounds fine, over someone whose main skill is producing a lot of copy. Production is cheap now, so the judgement around it is what you’re really hiring for.

Hassan Rashid is a managing editor at GrowthX ($12M Series A), where he builds AI-powered content systems for B2B technology companies including Ramp and Vercel. He has also led SEO for Alpaca Health, an AI-powered healthcare platform. He previously spent two years as an associate product manager at Addepar, the wealth management platform used to manage and advise on more than $9 trillion in assets. All opinions expressed are solely those of the author.