Got it. What's Next?
I can tell within about five minutes of talking to someone which type of marketer they are. Not because I’m particularly insightful. Because the pattern has played out the same way across every technology shift I’ve worked through.
There are two types. One adapts. One doesn’t.
Type A built their career on execution. They became the person who knew how to do the thing nobody else could figure out.
Continue readingMany marketing interviews are terrible at predicting job performance.
You ask about their experience. They tell you about campaigns they worked on. You ask behavioral questions. They’ve rehearsed responses since college.
None of it tells you what you actually need to know. Can this person tell good from bad? Do they have judgment?
I’ve started using a different approach. I give them AI-generated work and ask them to fix it.
Before the interview, I use ChatGPT or Claude to create something relevant to the role. An email campaign. A blog post. Product copy. Whatever matches what they’d actually be doing.
I don’t use a perfect prompt. I use a mediocre one. The kind a busy manager might actually write at 4pm on a Friday.
The AI output is usually pretty good. Sometimes it’s 80% there. Sometimes it’s impressively wrong in subtle ways. That’s the point.
In the interview, I hand it to them and say, “I had AI generate this. Take 10 minutes and tell me what you’d change and why.”
Then I shut up and watch.
The responses split into three categories pretty quickly.
Type 1 makes surface-level edits. They fix a typo. Maybe adjust some formatting. They can spot obvious errors but can’t evaluate strategic quality.
Type 2 tears it apart and rebuilds it. They explain what’s missing, what’s off-brand, where the logic breaks down. They can articulate why the tone is wrong for the audience or why the call-to-action won’t work. These are the people you want.
Type 3 is rarer but interesting. They say, “Actually, this is pretty good for [specific use case], but it completely misses the mark for [the actual objective].” They’ve identified a fundamental strategic problem before getting into tactical fixes.
Type 2 and Type 3 are showing you judgment. Type 1 is showing you someone who can follow instructions but can’t think critically.
When I worked with companies navigating social media in the early 2010s, the best interview question wasn’t “what’s your engagement rate?” It was showing them three competitor posts and asking which one they’d model and why.
Same principle here. You’re testing whether they can recognize quality, articulate problems, and think strategically about solutions.
Research from the lending industry proves the point. When humans and AI evaluated information together, default rates dropped to 3.1% MarTech compared to either working alone. But that only works if the human knows how to evaluate what the AI is producing.
“I’d make it more creative.” (Translation: I have opinions but can’t articulate reasoning.)
“I’d run it through Grammarly first.” (Translation: I’m focused on mechanics, not strategy.)
“It’s fine, I’d probably just use it.” (Translation: I can’t evaluate quality or I’m afraid to critique anything.)
“The headline is clickbait-y but the article doesn’t deliver on the promise. I’d either change the headline to match the content or rewrite the intro to fulfill what the headline sets up.”
“This reads like it was written for a general audience, but our buyers are technical. I’d cut the explainer paragraphs and get to the specifications faster.”
“The tone is way too formal for our brand. We sound like a law firm, not a startup. I’d rewrite this entire section to sound more conversational.”
See the difference? They’re not just pointing out problems. They’re explaining the why behind the problem and proposing a fix that connects back to strategy or audience.
This also reveals something important: Are they comfortable saying “this isn’t good enough”? Because in six months, their job will involve telling AI to try again. A lot. If they can’t critique a piece of copy in an interview, they probably won’t manage AI output effectively on the job.
Next time you’re hiring, spend 5 minutes generating something with AI. Hand it to your candidate. Give them 10 minutes.
You’ll learn more than you would from an hour of behavioral questions.
I review a lot of marketing job postings. Most of them look like they were written six years ago.
“Proficient in Adobe Creative Suite.” “Experience with video editing software.” “Knowledge of HTML/CSS a plus.” “Familiarity with marketing automation platforms.”
These aren’t bad skills. They’re just increasingly irrelevant.
It’s like posting a job in 2010 that required “expert knowledge of the Dewey Decimal System” when Google already existed. Sure, librarians still needed to understand information architecture. But the actual skill that mattered had shifted completely.
Continue readingI’ve been doing this long enough to spot the pattern.
In the late 1990s, I worked at Progressive Networks, which changed its name to RealNetworks. We were pioneers and created ways for companies to navigate the shift to video on the internet. Not everyone survived. The ones who did weren’t necessarily the best videographers. They were people who understood what video meant for storytelling and user behavior and how the internet was going ot expand that audience.
Then came mobile in the mid-2000s. iPhone. Android. Suddenly everyone needed an app. I helped clients figure out what that meant for their business. Again, success didn’t go to the best coders. It went to people who understood what a pocket-sized screen changed about how people consumed content.
Social media followed in the early 2010s. Facebook ads. Instagram. Twitter. I saw the same pattern repeat itself. The tool-masters struggled while strategic thinkers adapted to what it meant to let customers suddenly have access to your executives, and the way to broadcast to millions without your PR team being involved.
Now we’re in shift number four: AI.
And honestly? It’s the same movie playing again. Just different special effects.
Continue readingAs we enter a new year, many of us feel the pull to set resolutions. The pattern is familiar. We make a list. We write down goals like eating better, exercising more, organizing our homes, or spending less time on our phones. These are fine intentions, but often they become just another layer of pressure. Another set of expectations stacked on top of the ones we already carry. The word “resolution” ends up feeling like a January version of a task list. But that is not what the word means. Not really.
Continue readingI’ve struggled with some form of ADD throughout different phases of my life. It hasn’t always been diagnosed, and it hasn’t always looked the same, but it’s been there. Some days it’s just a low-level fog. Other days, corralling my thoughts is like chasing 37 cats around the room.
Over time, I’ve tried a number of approaches to get things done. Some worked for a while. Others just added to the noise. But recently I’ve been trying something that’s surprisingly effective: pick three tasks. Only three.
Write them down. Do them. Then pick three more.
Continue readingI’d love to know the story why a high value url like Email.com sits unused. Surely some company has the capital to buy the url and redirect it to their own email platform. Gmail? Heck, it seems like just the thing Microsoft would do to boost traffic to Bing. Anyone know the story?
What would Dave Niehaus said about this season? We can only imagine it might have sounded something like this.
While the changing leaves of October might dazzle tourists, for many Seattle residents, the tail end of the month marks the beginning of a mental health dip. A recent survey of 100 clinical psychologists and mental health researchers sheds light on why late October, in particular, tends to be a mood sinkhole in the Emerald City.
1. The Light Switch Effect
Dr. Maria Klein, a seasonal affective disorder (SAD) specialist, notes that around October 25, Seattle sees a sharp decline in sunlight, often losing 2–3 minutes of daylight per day. “It’s not just gradual darkness,” she explains. “It’s the suddenness that jolts the brain’s serotonin production.”
You know the future is strange when your best shot at a stable career is majoring in “Robot Psychology” or “Data Plumber.” Thanks to the AI revolution which, for those keeping track, promised to free us from work but instead retrained us to make ChatGPT write emails, we now face a curious inversion of the job market.
Here, then, are the top 10 college degrees predicted for the Class of 2035:
1. Prompt Engineering
Because writing good AI prompts is harder than writing haikus. Future students will spend four years studying the subtle difference between “generate an image” and “create a vibey aesthetic.”
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