Tarek Reslan

In April, Google’s Marvin Chow posted on LinkedIn that “marketing engineers are the hire of 2026.”

By September, the Wall Street Journal was writing about the role.

We’ve seen this before. Remember when everyone was a “growth hacker”? Marketing has a habit of inventing new titles for work people already do.

So is “marketing engineer” another buzzword, or is something changing? We looked at job postings, LinkedIn discussions, and Reddit threads to find out.

Short answer: a marketing engineer is a marketer who builds AI workflows that handle repetitive work, so they spend more of their time on strategy and decisions. The title is new and still rare. The skill behind it is spreading much faster than the title.

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At the beginning of each quarter, we scrape hundreds of marketing job descriptions on LinkedIn.
The goal is to track how AI is changing marketing hiring and what companies expect from new hires.

We did it in Q1. Again in Q2. And now we have the Q3 numbers.

This time, we analyzed 878 US marketing job descriptions and compared them with our previous datasets.

The direction is becoming pretty hard to ignore.

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During July and August 2026, Reddit was one of the most visible sources inside AI search.

Then, almost overnight, something changed.

According to data from Promptwatch, Reddit represented an average 3.83% of citations in ChatGPT Search between July 18 and August 7.

Between August 14 and 17? 0.52%.
That’s an 86.4% drop in a matter of days.

Promptwatch’s data suggests this coincided with a broader change in how ChatGPT searches the web. Alongside broad searches, ChatGPT dramatically increased its use of domain-specific site: searches.

In other words, this may not be ChatGPT deciding that “Reddit is bad.”
It may simply be changing how it decides which sources deserve a visible citation.

And that distinction matters. Especially if you’ve spent the last year hearing that your GEO strategy needs Reddit.

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AI can speed up CRO research. But which parts should you actually trust it with?

That’s the question we set out to answer in one of our AI Native Marketer workshops, hosted by CXL President Hesh Fekry and CRO consultant Ruben de Boer, who has spent more than 15 years running experimentation programs.

The goal was to move beyond the theory and see what happens when you actually apply AI to a proven CRO research process. We built an AI-powered workflow that automates three parts of ResearchXL: heuristic review, thematic analysis, and test idea generation.

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In July 2026, as part of our AI Native Marketer program, we ran a live workshop on building an AI-powered AEO and GEO research pipeline.

There’s increasing pressure on marketers to keep up with AI search. Follow the right people, read the latest studies, monitor LinkedIn, subscribe to every newsletter, and spot the next tactic before everyone else does.

But here’s the problem: marketers are also expected to ship more than ever before. How are you supposed to move faster when you’re still spending hours every week manually scanning articles, case studies, and social posts just to figure out what’s actually worth paying attention to?

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A second brain is a folder of plain text files that gives your AI assistant persistent memory of your projects, your voice, and how you work, so it stops starting from zero in every new conversation.

Marketers are being asked to do more with the same headcount. One marketer with AI is increasingly expected to ship the output of three.

We ran a hands-on workshop inside our AI Native Marketer program to address this new reality: a structured knowledge base, built in the free note-taking app Obsidian and operated by Claude Code, that any AI can read at the start of every session.

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B2B marketing workflow automation works when you redesign the workflow before you touch a tool, not after.
The common failure mode is opening Claude, ChatGPT, or a workflow platform and handing over an entire task, hoping the output holds up. It rarely does.

The process for building genuine automation hasn’t changed since before AI existed: you still have to know each step of the existing workflow in detail before you can decide what an AI or automation should replace.

What gets inserted into each step has changed; the discipline of mapping the step itself hasn’t.

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The biggest limitation of large language models today isn’t intelligence. It’s memory.

Open a new chat, switch to a different project, or kick off a new workflow, and you’re back to square one. You re-explain who you are, how you work, what your goals are, your tone of voice, your systems, your active projects, and the dozens of small details that actually make an AI’s output usable instead of generic.

Multiply that across every chat, every project, and every AI agent or automation you build, and the problem compounds fast.

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Most marketers will tell you they use AI every day. What the data shows is a different story.

We launched an AI skills assessment to help marketers identify where they actually stand, whether they’re AI-assisted, AI-integrated, or truly AI-native. The assessment was built to measure real competency across five areas: workflow, production, research, analytics, and AI operations.

Once the results came in, one pattern stood out immediately: the gap between how frequently marketers use AI and how advanced their actual skills are.

So we turned the data into a benchmark report. Here’s what we found and what it means for where the market is heading.

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