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💯 Your company is about to grade your AI use

💯 Your company is about to grade your AI use

They called a real Monet "AI slop", what good AI looks like, the most underrated AI job, and what happens to everyone who never got a fair run at getting good.

Max Haining
May 17, 2026

Someone posted a real Monet this week, told everyone it was AI, and asked the internet to explain why it was inferior. Dozens of people replied with detailed confident critiques of a real Monet saying it’s "AI slop."

It's a good prank because of what it exposes. Alberto Romero wrote this week that the strange thing about AI slop is that it doesn't really look like anything, it just reads like ordinary work. So spotting the machine was never really the problem. The problem is the rest of us. Tell people something is AI and they'll find the flaws, real or not.

Last week I wrote about AI landing inside the tools you already use. That settles one question and opens a bigger one.

The settled question is access and who's got it. Ramp's latest numbers show more than half of all companies now use AI, and for the first time more businesses are paying for Claude than for ChatGPT. I saw the same thing up close last week. I ran two roundtables (around twenty L&D, HR and ops leaders) and we were done talking about tools in about five minutes. Everyone already has the big ones. The harder question is now that everyone has the tools, is anyone any good with them?

Window into the Future

So if the tools were settled in five minutes, what filled the rest of the room? The culture. And under all of it, one question nobody could really answer: what does good AI use actually look like here? Some treat heavy AI use as a kind of cheating. Others treat it as survival. Often it's the same company and sometimes even in the same team.

Zapier did something about that. They made AI fluency a requirement for every new hire, then published an update to the rubric they score people against and the telling part is what they added. The original test had three parts: mindset, strategy, building. The new fourth part is accountability.

Their line for it: with AI you can hand off the work, but not the responsibility for it. "Capable" (you use AI with purpose and can show real impact) used to be a decent score but now it's the minimum. And the tier below it, the one they call unacceptable, sounds almost exactly like Romero on slop: work that reads like obvious AI, and looks the same before AI as after.

So who owns this? I argued recently that the most underrated AI job in most companies sits in HR. Not because HR is technical, but because it already touches every employee and every review cycle. Zapier seems to agree. Their Chief People Officer, Brandon Sammut, is now Chief People and AI Transformation Officer, and that rubric runs through every hire.

McKinsey's new Rewired makes the same case in consultant language. Every digital transformation is really a talent transformation and adds the uncomfortable part: as AI takes on more, teams get smaller, and someone has to plan for that honestly. You have to decide what good looks like then build it into how you hire and promote.

The good news: a rising bar is progress. For years "I used ChatGPT" counted as an achievement. The bad news is a bar also sorts people.

Deedy Das wrote a much-shared post on the mood in San Francisco where a small group got rich fast and everyone else is wondering if the ladder they're climbing is even the right one. Jasmine Sun was circling the same thing on the fear of a "permanent underclass". So if "good at AI" becomes a hiring filter, it matters who got a fair shot at getting good and who didn't.

And maybe not everyone needs to be good at AI at everything. Eleanor Warnock makes the point that enjoyment counts for something. The baker doesn't want to vibe-code an inventory tool, they want to make a great croissant. Claude for Small Business will take that kind of work off their plate, and that's genuinely useful. But Warnock's instinct is right: being able to do something and wanting to do it are not the same thing.

(Every team I talk to now have the tools and the budget but are missing a shared answer to "what does good look like here". Ianos, in delivery management, finished our 15 Days of AI this week and posted that the surprise was how much of it showed up in his actual day. So yes buying the tools is easy but the capability and the measuring are what we keep building 15 Days of AI around.)

Which brings me back to that fake Monet. The crowd were handed a label and confidently picked apart flaws that weren't there. Companies are about to do a version of that. Deciding what counts as good AI use before anyone's agreed what they're even looking at.

Join a 5-day Build Sprint 🛠️

If you've been meaning to actually build something, this is your sign. Harold and Ash are running a five-day vibe coding sprint (May 27–31) made for non-technical people, the whole point being that you can ship a working app without writing code.

There are live workshops along the way too, then a demo day to show what you made. Sign up here to join The Vibe Coding Games.

Alongside the sprint, the team will also host live workshops full of practical tips, AI workflows, and live demos.

🗓️ 27 May | Kickoff: Build Sprint Starts
🗓️ 29 May | Workshops
🗓️ 1 June | Build Sprint Deadline
🗓️ 9 June | Final Demo Day

How to AI 🤖

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

Before you go ✌️

Has anyone where you work actually said out loud what good AI use looks like? Or is everyone just guessing? Hit reply, I read every one.

See you next Sunday!

Max

P.S. Want to make your team & company AI-first? Let us help here.