Human Internet

Maker vs Mover Work

Thu, 26 Feb 2026 00:00:00 +0000

In March 2008, as the financial crisis loomed, author and academic Clay Shirky published a piece about newspapers that went what we’d describe today as “going viral”: “Newspapers and Thinking the Unthinkable”.

The piece argued something that was blasphemous in the moment: newspapers were already dead. The internet disrupted them and it was time, Shirky argued, for the industry to acknowledge this. He writes:

When reality is labeled unthinkable, it creates a kind of sickness in an industry. Leadership becomes faith-based, while employees who have the temerity to suggest that what seems to be happening is in fact happening are herded into Innovation Departments, where they can be ignored en masse. This shunting aside of the realists in favor of the fabulists has different effects on different industries at different times. One of the effects on the newspapers is that many of their most passionate defenders are unable, even now, to plan for a world in which the industry they knew is visibly going away.

In our last issue, we covered how companies should think about the AI age. For this edition, let’s focus on the individual. Let’s focus on you.

And let’s focus on the unthinkable: Your job is going to be unrecognizable soon.

We’re all asking the same questions:

Is my company’s product at risk of being disrupted?
Is what I do each day the best use of my time in the age of AI?
Most importantly, is what I’m doing as an individual in my career even going to be a thing in 5 years?

I’m not going to write about this with the kind of unearned and false authority that seems to do numbers on social media. But I spend each day working with marketing and executive teams and I see some themes:

Make sure you’re a Maker, not a Mover

A Maker is someone who creates something new. A new idea, a new story, a new product.

A Mover is someone who takes what exists and optimizes, reformats, or otherwise adjusts it depending on the context.

The reporter who conducts interviews is the Maker.
The act of taking those interviews and writing them is Mover work.
Repackaging that written article for social is Mover work.

AI will eat all of the “Mover” work by taking the raw ingredients produced by the Makers and brute-force its way to producing its optimal arrangement. For what it lacks in humanity and emotional context, it can make up for in sheer volume.

We’ve seen this kind of disruption before. It used to be someone’s job to meticulously typeset a newspaper or magazine. Then word processors came around. It used to be someone’s job to hand deliver the news to your doorstep. Now we have browsers and email.

All Maker work is not created equal

When I tell people this theory they usually push back right away pointing out that the cost of creation has gone to zero. So why, pray tell, do Makers thrive in the AI age?

To explain, let’s take another AI-level disruption: the creation of the smartphone. When everyone has a professional quality camera in their pocket, one would think that we’d need fewer photographers. It’s easier to make, so Maker work would be first to go, right?

That never happened. The number of photographers has remained flat and, depending on the year, slightly increased. Wages, meanwhile, kept pace with or slightly exceeded inflation ¹. The efficiency did not remove demand for Maker work — at least not in terms of employment.

Photographers in the U.S. - at a five year average to smooth out volatility

(Btw you can do the same for accountants, Makers in their own right, they held steady in the age of QuickBooks. Same with software engineers in the age of Claude Code.)

So what happened?

A flat line (like the one in the chart above) can appear to reflect the status quo. But a more accurate explanation is that photography jobs that leverage the new tech (in this case, the iPhone, photo editing tools, and the internet) replace the photography jobs that were in the “old world” — think darkrooms and selling news photos for thousands of dollars to profitable media publications.

Yes, Maker work is more durable, but the nature of Maker work changes. To survive, you’ll need to capture the benefits of AI.

Dumb vs smart Maker work

There are people using AI for “Maker” work such as business writing. This is an ineffective use of the tool. It’s dumb Maker work.

Sure, it saves the individual a few minutes or even hours, but unless the user is giving it new and novel inputs, the output will by definition be mediocre. This is ok for, say, smoothing over an important email. Less so for your company’s manifesto. Or your Q2 analysis.

A Feb 22nd article in the Economist highlighted that only 13% of U.S. working-age adults use AI every day. And the ones that do mostly use it for “discrete tasks,” not fancy automations or workflows. The same article notes that we haven’t seen macroeconomic productivity gains from AI usage yet.

I often see busy executives just throw AI vomit at their teams and expect them to use it productively. Or I see marketers take a brief and generate a blog post, shrug, and then hit publish. More dumb Maker work. This kind of AI usage saves the creator time but does not effectively do what writing is supposed to do: communicate information to another person.

The AI-enabled Maker will spend as much time crafting the inputs as producing the outputs. And the outputs will produce benefits for both the creator and the reader. The smartest people I know have rich wells of context that inform anything they produce with AI. This allows them to build on what they’ve done before to produce something new and of value quickly. What is creativity if not connecting dots and remixing? ²

Distribution as Mover work

Another common refrain is that the edge is in “distribution.” In a world where I can generate 1,000 novels with a prompt all that matters is getting it in front of the right people. That’s the skill to hone.

To be blunt: that’s growth hacker cope.

Pre-AI, mastering online “distribution” required a knowledge of human psychology and an obsessive monitoring of the given distribution channels. The former because humans are the ones consuming this, after all. The latter because the preferences of the platforms would change, and if you were the first to notice you could reap the rewards — almost like a day trader.

Many growth marketers benefit from this obsessive monitoring and the rest of us pay them for their knowledge because we don’t want to get lost in the minutiae of Facebook News Feed tweaks.

Take sports media. It’s common for a reporter to do “Maker” work and uncover a scoop or a new story. Reporters and journalists stink at promoting their work (it’s ok, it’s a separate skill set) and “aggregators” often brute force a bunch of approaches to capture the interest of the market. This is Mover work. Below, one of them finds the juiciest detail in a story about Deandre Ayton and gets more engagement on X than the Maker/reporter, and thus more revenue from the platform’s creator payout program³.

Via WorldWideWob

Post AI, we still have the shady network of aggregators floating around stealing and profiting off the work of the Maker. But now, we also have AI agents who can take any piece of “content” and create endless versions, post them, record the results, and adjust. In the future, that reporter could give his story to AI to create endless posts and graphics. You can see this on LinkedIn as AI comments, posts, and direct messages are rampant. An AI can comment on more things than you ever could, even if you think that is a good distribution channel.

There is no “taste” or creativity in finding a distribution edge in algorithmically powered distribution. It’s a matter of information asymmetry and brute force. The AI will find it before you ever do. This is Mover work and you should work to automate it. (I’m reminded of the scourge of AI-created music to flood Spotify.)

Now, this is not to say being strategic about distribution is not worth your time. But it’s better to focus on the paths and channels that an AI can’t outrun you. Anything where an algorithm can serve as an intermediate is a good thing to outsource to an AI. Focus on the other stuff.

Or better yet, don’t be dependent on the platforms to build your audience.

Using 2025 dollars: In 2012 the median photographer’s wage was $39,708. In 2022? $43,923. Interestingly, the bottom 25% of photographers saw the biggest wage growth. (via BLS.) ↩
For example, for a client I was tasked with producing what was effectively an online book. It was a microsite with 10+ busy CEOs as sources.

The AI could transcribe, tag quotes, and as I was writing it would recommend quotes to include and videos to produce. It could do research to make sure I covered new ground when interviewing. It would also connect dots I missed. This system still needs human intervention (taste and intuition still matter!) but I no longer have a need for “Mover” jobs like researcher, “content ops” or content designer. I owned the entire creative stack.

In the big picture, I am doing the same content writer “job” as a peer who doesn’t use this approach. But the tasks that make up the job have been expanded. Now, I need to write, research, and build AI workflows. ↩
This incentive structure is terrible for the Makers and for thoughtful posting, and I wish X.com had different dynamics. Alas. ↩

AEO and Horseless Carriages

Thu, 05 Feb 2026 00:00:00 +0000

We love seeing a new thing and applying the rules of the old thing to it. Here’s what I tell my clients.

A car is not a “horseless carriage,” it’s a new category of thing. And when a truly new thing comes along, its gravitational pull affects seemingly unrelated variables.

The invention of air conditioning unlocked the economic potential of tropical cities. The automobile led to the creation of the suburbs. The shipping container led to globalization.

A truly revolutionary technology produces second- and third-order effects that are nearly impossible to predict, but must be accounted for. When this happens, you can’t extend the old rules into the new world.

Too many companies are taking a “horseless carriage” approach to AI and AEO¹. After seeing SEO traffic ebb away, they are throwing equal resources into being mentioned in Gemini, Claude, and ChatGPT (what I’ll call “The AI Agents”).

Search is dying, so AI is its 1:1 replacement.

Great strategy, let’s get lunch.

Reframing AEO

These companies were handed an automobile and asked how the horses fit in.

Instead, look at the distribution factors of AI agents from first principles and not compared to SEO or search:

The AI Agents are volatile. They are feverishly and constantly updating their models, how they work, and what data they are pulling from. Good luck guessing what’s working today that will also work tomorrow. Remember when GPT boosted and then nuked Reddit mentions?
The AI Agents are popular. People are using them and are using them more each month. Even “normies” know what ChatGPT is. This is where people are going to answer high purchase intent queries that used to go to Google. Like it or not, your audience is here.
The AI Agents obfuscate their sources. Even if you ask, “Where did you get this?” they will often name the place or category of the source vs directly linking to an asset. And even if you do get cited, the click-through rates are abysmal.
AI agent prompts are not 1:1 to search queries. Prompts are often much longer, in plain language, and one of many moments in conversation. Good luck trying to track the right phrases.

AI agents are exploding in popularity, but they won’t directly send people to your owned properties. Which means you may be able to game the AI to use your content and ideas, but will likely not get direct “credit.”

What to do instead

Treat your AEO strategy like advertising. AEO is the best way in the history of media to insert a seed of an idea into the market. This is a brand play, not a funnel motion.

Think of your “intellectual property”. What is the idea that, if the market understood, would position your company as the obvious solution? “If only every target customer thought X, we would have more inbound interest.” What is “X” for you?

Some examples:

At Crossbeam, we created the category and term “Ecosystem-Led Growth.” It’s how we framed our product, its education, and the brand. But now, when I ask GPT for GTM approaches, it suggests ELG to me (see below). It doesn’t mention Crossbeam. But like an ad, the moment I ask GPT for software to help, or Google it, or ask peers, Crossbeam would be the only and obvious answer.

Oh weird, GPT, where did you learn that?

Clay created the “GTM Engineer” role and backed it with education, content, and community. Ask an AI agent how to scale outbound, or who you should hire to scale your growth, and it’ll describe the workflow Clay enables, the position it’s created, and cite the education Clay has been delivering.

Clay's Head of Growth, Davide Grieco, on rethinking the playbook.

dbt Labs created “Analytics Engineering” as a role, wrote the manifesto, built a certification program and a 7,000-person conference. Further, its open source community is a content-generating machine for the LLMs to source.

Note that in all examples, the companies are playing offense. They are staking out some sort of opinion on the way the world should be and framing their product and marketing around that opinion.

They are defining the market in their terms—literally and figuratively. When you get the people using the AI agents and the agents themselves using your language, concepts, and IP, you’ve bent reality in your direction.

Compare that to the SEO approach: create articles that match specific search queries, get the traffic, and convert that into a purchase. Most companies are not picky about the SEO phrases they target. Whatever gives them traffic is good enough. AEO isn’t that smooth. It’s a billboard or a magazine article: a way to plant an idea.

Here’s what I tell my clients:

Have a POV/opinion that, when cited outside the context of your company, is in your company’s interests.
Readjust your top-of-funnel expectations. The numbers will be lower but you shouldn’t see massive disruption in your bottom-of-the-funnel metrics if you navigate this transition properly.
If you want to have an AI-created content motion to seed the AI agents, I’ve seen results there - but expect a high amount of volatility. Only do this after you get the basics of brand and content down and “fork” it from your brand content. AI agents do read less popular pages, so you could even format them to be hidden from Google or your website navigation and stand them up exclusively as AI agent sources.
“How did you hear about us” needs to be a step in your funnel as attribution from AI agents will be fuzzy.
AEO is another part of your marketing toolkit, and shouldn’t be the primary motion. Traffic from AI agents does convert better. After all, the user is far more educated when they arrive on your owned channels. I’ve found this to be true, but most companies will net out behind if they only shift SEO resources to AEO with no other strategy.
A generation of marketers has learned to optimize for the machine layer (search) and not the human layer (interest, attention, and purchasing). Build a team that is 30% aware and optimizing for the machines and 70% producing new and creative work.

If you’re looking for the ultra-measurable specific funnel of SEO days past, you are going to be frustrated. Build your POV, extend into human-made content channels, augment with machine-friendly materials to feed LLMs, and track brand and conversion metrics — not direct referrals from AI agents.

There’s a bit of a battle between calling this “AEO” (Answer Engine Optimization) or “GEO” (Generative Engine Optimization). I’m sticking with AEO for now. ↩