About Patrice Poltzer

Patrice Poltzer spent seven years as a producer on NBC’s TODAY Show, where she had a front-row seat to the power — and devastation — of how a story, well told or badly told, could change the trajectory of a brand overnight. In 2016, she left to build her own storytelling agency in New York, working with brands like Netflix, Amazon, and Lululemon.

In 2024, she co-founded My Story Pro AI with her online business partner Wiebke — a human-centered AI storytelling coaching platform that hit five-figure MRR within 30 days of launch with zero ad spend. She now runs the business from Lisbon, where she moved with her husband and three sons after leaving Brooklyn.

She describes storytelling as “the hill I die on” — and after listening to this conversation, you’ll understand why.

Connect with Patrice through her website or LinkedIn.

About Peter Benei

Peter Benei is co-founder of the daily intelligence platform for senior marketing leaders navigating the AI era. He spent a decade as a marketing director at tech and product companies before co-founding AI-Ready CMO with .

Connect with Peter via his newsletter or his LinkedIn.

Top 10 Takeaways

1. No one cares about your product. They care about the story that connects your world to theirs.

2. Storytelling isn’t a soft skill anymore. It’s the competitive edge that code can’t replicate.

3. The line between human and AI isn’t taste — it’s lived experience. AI can structure a hero’s journey. It can’t tell you how it felt to be fired with a newborn while your husband just quit his corporate job.

4. Most people are afraid to go deep into themselves. That fear is why AI output stays shallow, regardless of how good the prompt is.

5. AI wants to make you happy immediately. A good storytelling coach knows you have to earn the depth.

6. Facts and data are now table stakes. Anyone can access enterprise-grade research in minutes. The scarce asset is emotional connection.

7. The brands that win in the next 3 years won’t be the ones with the best AI workflows. They’ll be the ones whose people actually show up as humans.

8. Storytelling compounds inside organizations. One leader who starts being vulnerable in team meetings can transform an entire unit’s culture.

9. The Stanford graduate with 105 interviews and zero offers isn’t losing because she lacks AI skills. She’s losing because she hasn’t learned to make someone feel something.

10. Your story is not copyable. The structure can be lifted. The feeling can’t.

Why no one cares about your product

There’s a version of marketing that’s been standard practice for decades. You lead with features. You cite your stats. You build a pricing page and a comparison chart and you wait for the rational decision to happen.

Patrice Poltzer watched that approach work on the TODAY Show — briefly, in the early days — and then watched it collapse.

“Marketing has changed,” she said.

The problem isn’t that founders don’t believe in their products. It’s that they treat the product and the humans behind it as separate entities. There’s the company, and then there’s the person who built it — and the assumption has been that keeping those two things apart was professional.

That assumption is wrong. And in a world of product abundance, it’s also commercially fatal.

The distinction Patrice draws is precise: storytelling isn’t about you. It’s about finding the intersection between your lived experience and the thing keeping your customer awake at 3am. When those two things meet, something neurological happens. She calls it neurocoupling. The listener’s brain synchronizes with the storyteller’s. That doesn’t happen when you list your features. It only happens when you make someone feel recognized.

Peter made the same point from the CMO side. For a decade across product-led tech companies, the instinct was always to lead with the roadmap, the integration, the metrics. The breakthrough was realizing that none of that is what closes. What closes is the moment a customer thinks: that’s me.

The problem with AI-generated stories

My Story Pro exists because of a moment of recognition.

Patrice’s students started submitting AI-generated stories. She could tell immediately. Not because the grammar was wrong or the structure was off — but because nothing in them was real.

“The problem is that most people are afraid to go deep on themselves,” she said. “And so when you have a lot of people not wanting to go deep, even if you have the best prompt loaded into Claude from the most amazing marketer, it is never going to go as deep as it should.”

The reason is structural. General AI tools are designed to give you output. They want to make you happy immediately. That’s the product. A good human interview — the kind Patrice ran under deadline pressure at the TODAY Show — works the opposite way. You earn the depth. You go through questions one through eight to psychologically disarm a person before you ask the question that actually matters.

Most AI tools skip straight to nine. And the output looks fine. But it sounds like everyone else.

This is the strategic gap My Story Pro is built to close. The platform functions less like a content generator and more like a trained interviewer — one that refuses to let you stay on the surface, because the surface is where all the slop lives.

Where the human line actually is

Six months ago, Peter would have drawn the line at taste — the human judgment required to evaluate AI output and decide what’s good enough to ship.

His answer has changed.

If taste was ever the moat, it’s getting shallow. The new line is higher up.

“The line right now is for a human to figure out the big picture for the brand. Figure out the story. What is your brand? What is the very high-level strategy? What’s the story you’re trying to tell? Once you figure that out, it’s easier to create the executional level of those stories.”

Core narrative is a human job. Execution is increasingly not.

Patrice pushed it further. The reason her product keeps growing — even as Claude, GPT, Gemini, and Grok all get better every month — is that she and her co-founder hold a fundamentally different view of what AI is for.

The problem isn’t the tools. It’s that the tools are being asked to replace the part of you that no tool can access: what actually happened to you, and how it felt.

The case for storytelling as a hard skill

The Wall Street Journal ran a piece recently: companies are desperate for storytellers. Netflix listed a Chief Storyteller role at $700,000.

This would have been unimaginable when Patrice first left the TODAY Show in 2017. “Storytelling was seen as a soft skill,” she said. “People would say, ‘Oh, storytelling — we do content marketing.’”

That framing is gone. Storytelling is now listed alongside engineering and data science as a capability gap at the enterprise level. And the reason is straightforward: everything else can be automated. The emotional vehicle — the thing that actually changes someone’s decision — cannot.

The story Patrice tells to illustrate this is about a digital media CEO in Chicago. Multi-million dollar operation. Small team. On every measurable metric, his company outperformed the competition. He walked into a meeting with a major bank and spent the first six minutes explaining who he was. No one knew his name. One person thought he was from his competitor.

He realized he was going to lose.

Not because his product was worse. Because his competitors had a face, and he didn’t. He wasn’t getting invited to the private dinners. Not being asked to speak at Cannes or South by Southwest. He was being professionally invisible while his numbers were excellent.

He started showing up differently. Sharing why the work mattered to him. What he’d been through. Patrice ran into him a year and a half later at Cannes, where he was now speaking. His team would walk through fire for him.

“That’s what we need more of,” she said. “Not a performative way. A real way.”

What this means for your team

The implication most leaders miss: this isn’t just a personal brand conversation. It’s an organizational one.

Patrice worked with a product manager at Apple — 13 years in, starting to feel the ground shift. She wanted to know whether to learn more AI, more tech. The answer was neither. She started opening her team meetings differently. Just briefly — here’s a little about my past, here’s why the way we do things matters to me.

The result was a promotion, recognition in the company's internal newsletter, and a team that completely transformed the way they worked. Her unit became cited internally as an example of what was possible.

“Who is making your brand narrative? It’s people,” Patrice said. “And you want motivated, interesting people in that mix. People who are comfortable being more human in a real way.”

The companies that win the next cycle won’t be the ones with the cleanest AI workflows. They’ll be the ones who figured out that their people are the content strategy — and gave them the language and permission to show up that way.

That’s a harder build than a prompt library. It requires actually knowing your own story.

My Story Pro is built on the idea that most people don’t — yet.

7 out of 10 marketers say they’ve received “workslop” from colleagues.

The term was coined late last year by researchers at BetterUp Labs and Stanford. It means AI-generated work that looks like it took effort but doesn’t move anything forward. The strategy deck reads like a ChatGPT default. The customer email is technically correct, but feels hollow. The campaign brief that hits every buzzword and says nothing. 27% of marketers report receiving this stuff regularly — not once or twice, but as a pattern. It damages trust within teams, forces rework, and turns AI’s promised productivity gains into a net negative.

If you manage a sales team, a support operation, or a creative department, chances are there is “workslop” in your inbox right now.

The cause isn’t hard to find: companies bought the tools and skipped the training. Only 20% of marketers say their organization’s skills development meets current needs and can adapt as new skills emerge. Twenty percent. And yet the single biggest differentiator between market leaders and followers this year wasn’t budget or tech stack or choice of AI model. It was an investment in people and skills.

The gaps are specific: 44% of organizations lack practical AI implementation skills (starting with basic prompting), 43% can’t integrate AI into existing workflows, and 37% don’t have enough technical understanding of how these tools work. This is all foundational stuff. None of it is cutting-edge.

Market leaders in the report aren’t using different tools than everyone else — same vendor pool, same platforms. They’re almost twice as likely to report positive consumer responses to their GenAI integrations, and more than twice as likely to say those integrations exceeded expectations. The difference is that they paired deployment with structured upskilling and set explicit quality standards for AI-assisted output.

That last part is worth repeating: explicit quality standards. Telling your team “use AI” without defining what acceptable AI-assisted work looks like is how you get workslop. Set the bar (yes, this means saying out loud that a raw ChatGPT dump is not a deliverable), invest in practical training on prompting and workflow integration, and connect it to business goals. Not a Slack channel with prompt tips, please. Ongoing capability building, same as you’d do for any other core skill.

We keep saying AI skills are management skills. Now there’s a 1,400-person dataset backing it up. Teach your people how to use the tools, or watch them generate workslop at scale.

— Torsten and Peter

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They just published V2. What was “Capable” ten months ago is now “Unacceptable.”

In the original rubric, a marketer who used AI to draft social posts and then edited them by hand was considered Capable — meeting the minimum bar. In V2, that exact behavior is listed under Unacceptable. The new Capable requires AI embedded into core work, repeatable systems rather than one-off prompts, and a clear, measurable impact on quality or efficiency. Ten months.

For marketing specifically, the new Adoptive tier — one level above the minimum — now includes running AI-driven A/B experiments with measurable results, building content systems that draft, format, and schedule across channels, and having always-on agentic workflows that operate without human involvement.

That was aspirational a year ago. Now it’s the expected trajectory for someone who wants to be seen as more than baseline competent.

If you’re a senior marketer reading this and thinking “we’re doing fine” because your team uses ChatGPT for briefs and Midjourney for mockups, Zapier just told you that’s the floor, not the ceiling. And the floor just moved above your head.

Sales wasn’t even in V1. Now it has its own rubric, and it’s not gentle.

The original framework covered engineering, product, support, marketing, and people/HR. Sales was absent. V2 adds it — along with business ops, corporate development, finance, data, and others — which tells you something about how quickly AI competence has spread from “tech team concern” to “everyone’s problem.”

The headline: if the extent of your AI usage is reading Gong summaries after calls, using AI to help write outreach emails, and using AI to find information for account plans, you are Unacceptable. Not Capable. Not “getting started.” Unacceptable. If you “cannot point to evidence that outreach is more effective, prep is faster, or pipeline has improved as a result,” you don’t meet the bar.

Capable for sales now means having built a personal prospecting workflow that saves significant time and generates pipeline, and being able to position AI transformation strategies with customers. The Adoptive tier requires chaining multiple AI tools into repeatable workflows and proactively teaching teammates what works.

But the most significant change is not even the higher bar. It’s the new fourth dimension: accountability.

V1 measured three things: AI mindset, strategy, and building. V2 adds accountability as a standalone signal. Zapier now explicitly hires for people who define what “good” looks like before they start, evaluate outputs critically, and catch what’s wrong before it ships: “With AI, you can delegate the work, but not the accountability.”

This connects to something we keep saying in this newsletter: AI skills are management skills. Most companies are still hiring for AI usage: do you use the tools, how often, which ones. Zapier is now hiring for AI judgment: do you know what a good output looks like, can you catch a bad one, and do you own the result either way? That’s a fundamentally different question, and it’s one most hiring processes aren’t set up to answer.

They’re also now looking at what they call “slope” — not just where you are on AI fluency, but how you got there and how fast you’re moving. Someone who plateaued eight months ago on the same three tools is a different candidate from someone actively experimenting and building. The signal they want is forward momentum.

You don’t have to adopt Zapier’s rubric wholesale. But you’d be wise to ask yourself: if someone applied this framework to your team today, where would they land? If the answer makes you uncomfortable, that discomfort is the point.

— Torsten and Peter

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3 AI Marketing Tools To Try Today

Rytr

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We’ve talked a lot about companies freezing junior hiring as AI takes over entry-level workflows. That was the demand side drying up. Now the supply side is following. China’s Communication University of China — one of the country’s most prestigious schools for creative talent — just suspended 16 undergraduate degree programs, including photography, comics, visual communication design, and translation. The party secretary said that the era of “human-machine collaboration” has arrived, and these programs no longer make sense as standalone degrees.

And this isn’t one university having a moment. Jilin University halted 19 arts majors in January. East China Normal University suspended 24 — including advertising and painting — late last year. Tongji University dropped visual communication and environmental design.

If you think this is some sort of a panic reaction, you are wrong. China’s universities are centrally controlled, and the Ministry of Education revoked over 1,400 programs in 2024 alone. This is policy.

And it won’t stay in China.

Western universities move slower because they’re decentralized, not because they disagree. The market logic is identical: when AI handles first-draft translation, basic image production, layout, and routine content creation, a four-year degree built primarily around those technical skills is training students for jobs that are going away. The institutions that move first just happen to be in a system where the government can push the button faster.

CUC didn’t just kill programs, though. It merged photography into film and television production. It folded comics into new AI-integrated degrees like “intelligent imaging art.” It has added 20 new majors since 2018.

The narrow, technique-heavy degree is what’s dying.

Photography as a craft still matters — a four-year program that teaches you only how to operate a camera is a different story. Same with translation: the skill is valuable, the degree that produces human translation machines competing against actual machines is not.

If you manage a creative team, this is the same story playing out in your org chart. The pure specialist — “I run Facebook ads,” “I do email design,” “I write product descriptions” — is the photography major of the corporate world.

The value is shifting toward people who can think across the stack: who understand why a campaign works, who can adapt when the tools change underneath them.

When you’re hiring juniors (if you still are), that’s the filter. Can they work with and around tools that will replace the tools they know today?

— Torsten and Peter

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3 AI Marketing Tools To Try Today

Birch

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The ads appear below ChatGPT’s responses, clearly labeled, separate from the answer. OpenAI says they don’t influence outputs, and advertisers don’t get access to conversations. Over 600 advertisers are already in, with strong SMB interest. For a pilot this young, with this little distribution, that level of traction is hard to wave away.

Self-serve access opens in less than a month.

That’s when ChatGPT ads stop being a managed experiment for a select few and become a channel anyone can test. Agencies will build offerings around it, benchmarks will start forming, and the learning curve steepens for everyone who waited. We’ve seen this before with early Google Ads, early Facebook, early retail media. The teams that showed up first didn’t always spend the most — they just learned the fastest.

The inventory is interesting because of the intent behind it. People don’t scroll ChatGPT the way they scroll Instagram. They ask it to compare software, plan trips, evaluate vendors, troubleshoot problems.

That’s research-stage, high-consideration behavior, the kind of moment where a relevant ad can actually be useful. For B2B marketers especially, worth watching closely.

Nobody has published official CPM numbers yet, but the math is suggestive. Meta averages roughly $7–17 per thousand impressions depending on the vertical. Google search ads run $10–50+ for high-intent queries. ChatGPT is hitting $100M annualized from a small fraction of its U.S. user base with limited daily ad exposure — which implies an effective CPM at or above Meta’s upper range. Advertisers are clearly paying a premium for context-aware placement inside a conversational interface. Whether that premium holds as inventory scales is the open question.

Nobody should rush budget in blindly. Measurement is immature, attribution is thin, the creative playbook doesn’t exist yet. But if your products win when customers are comparing and asking follow-up questions before they buy, this channel is built for that. Map your high-intent query themes now, tighten your analytics, and decide what a successful test looks like before self-serve goes live. The worst position to be in is curious but unprepared.

— Torsten and Peter

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3 AI Marketing Tools To Try Today

BotPenguin

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Paid members, your toolkit and this month’s report are below.

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Strap in.

Specialization Was Always a Fiction

I (Peter) have a particular kind of trauma from the nametags we wore at the big corporate fishing boat. The fancy titles. The carefully delineated responsibilities. We played along. We told ourselves we were special.

I spent a year in enterprise marketing before co-founding an agency with Torsten in the mid-2000s. Our clients were all enterprises. To them, we were the rock stars. The ones who “dared.” I almost printed a business card that said Peter Benei, Chief Creative Ninja. Torsten talked me out of it. I ended up as “creative director,” which, let’s be honest, sounds considerably more boring. (Unrelated, but despite all my attempts not to, I circled back to enterprise years later, then found common ground in what actually interested me: midscale and high-growth startups.)

Here’s the thing I knew even then: specialization is mostly made up.

It came from factory floors. One person, one job, one output, maximum productivity. Offices copied this model wholesale — same working hours, same skills-only focus. Education followed. We ended up with specialized business practices that translated into departments, org charts, and job titles nobody outside the building fully understood.

<your name>, Lead Performance Marketing Manager, FMCG Consumer Marketing, Digital Department, at <your favorite cookie brand>. So you do ads, right? Cool. Guess what: AI wants to have a meeting with you.

We built entire professional identities around execution layers. And technology has been quietly dismantling those layers since the word “technology” existed.

Technology Was Always Coming for the Execution

This isn’t new. It never was.

ATM vs. bank tellers. The calculator vs. the people who were the calculators. The photocopier vs. the copy clerks. Each wave, the same pattern: a technology takes the execution layer, the humans move up, and we collectively pretend the previous jobs were never really just execution.

You know the cope. “It’s not AI that will replace you, but a person who knows how to work with AI will.” It’s a reasonable-sounding argument. Until you look at the actual pattern. You know the joke, right? “It’s not the car that replaces you, but the horse that can drive.”

The difference between an HR manager and a procurement manager was never deep craft. Neither was the difference between a sales manager and a marketer, or a product manager and a project manager. What actually separated those roles was familiarity with a specific execution layer. The intention behind the work. The understanding of the context.

Strip the execution away, and those differences collapse. All that is left is people doing work in Excel. Some of them talk more about “human connection,” and others talk more about “revenue” and “ROI.”

If every previous technology wave took one layer of the cake, AI is here for the entire cake. Nothing left on the plate. And unlike every previous wave, it doesn’t respect function boundaries. Marketing, sales, HR, product, finance — the execution differences between these roles are gone.

This Is Happening Now

We don’t want to be doomers. But we’d be lying to you if we softened this.

We can’t name names — NDAs being what they are — but here’s a situation we’ve heard described from multiple directions, in almost identical terms:

A global tech company. 500 to 5,000 people. Old school. Still running kanban, scrum, and daily standups in 2026. Slow-moving. Nobody at this company would call themselves cutting-edge.

In Q1, every mid-manager — heads and directors, one level below C-suite, each managing teams of 10 to 50 people — received The News. Allocated hours to figure out how to automate their team’s tasks with AI. Expected workforce reduction: 20% by Q3, another 20% by Q4, and continue the trend until “sufficient.”

If this is Monday’s meeting at a company that still does daily standups, assume it is everywhere, or will be everywhere by the end of Q2. In execution by Q3. Felt by everyone by the end of the year.

But.

There’s another signal running in parallel, from the opposite direction.

The rise of the one-person company. Not the lifestyle solopreneur. Not the startup founder with VC ambitions. Something different: 1-to-5-person operations running at the productivity and output level of mid-sized companies. Millions in ARR. Agent swarms working round the clock. One, maybe a few, humans at the center, conducting the entire operation.

Both ends of the candle. Burning simultaneously. Same shift. Two games. All happening right now.

What Actually Survives

Six months ago, we made an argument: the barrier between human and AI is taste. Knowing what’s good enough to ship. We wrote an entire series on this — [start here if you missed it]. We have also recognized that there is a fire — we wrote about it recently. We were playing with the solution in the Human Context Window, but that wasn’t a clear picture, yet.

We were half right the whole time. Now it’s getting much clearer.

Taste still matters. But not as an aesthetic judgment. Not “is this good enough?” — because AI at current capability levels will produce above-average output on the first draft across almost every execution type. Most people can’t tell the difference. The aesthetic bar has been cleared.

The taste question has moved up a level. It’s no longer is this good enough to ship. It’s should this exist at all, and why. That’s a fundamentally different question. And far fewer people are equipped to answer it.

As Jeremy Irons says in Margin Call: ”Be first. Be smarter. Or cheat.” Cheating is out. Being the smartest is great, but hard. Being first is still available — for now.

If you’re a power user of AI today, you’re still early. The UX is complex, the tools are scattered, and the workflows require genuine curiosity to build. But we are weeks, maybe months, from this becoming the default. The secret Excel formula buried in a sub-sub-menu is becoming a hotkey everyone uses without thinking.

The window is open. It won’t stay open.

Two Paths

When it closes, there will be two kinds of people still standing in knowledge work. We’re calling them the creative and the operator. Not job titles. Orientations.

The creative is the one who owns the why. The brand narrative. The brand strategy. The story the company tells. The key messages it stands for and repeats everywhere. The decisions about what should be made, why it should exist, and what it means.

Everything else is execution and practical application of that narrative. Put your brand documents into an AI, explain the context and the intention, and you will get great output. AI is extraordinary at reacting. What it cannot do is be proactive. It cannot determine whether a task should exist. Should this be done at all? If yes, how should it be framed? That question is inherently human. It is the creative’s job. And it is not a marketing job exclusively — it applies to every function where someone has to decide what work means before anyone executes it.

The operator manages the execution. If the creative figures out the why, the operator figures out the how. The operator is the starting and ending point of the AI-driven workflow — the conductor of the orchestra, the manager of the autonomous bots and agents. Not the executor. The manager.

This requires minimal understanding of specialized craft and a deep understanding of workflows, systems, and AI orchestration. Your AI-curious copywriter will probably become the operator of all copy across all campaigns. Your designer becomes the creative head on AI-driven production. You will not need all your designers. You will need a few people who can manage AI from a designer’s perspective.

This is true for every function. Not just for marketers. Every department will have its own creatives — the leadership layer — and its own operators — the AI managers. Maybe, just maybe, there will be some craftspeople who stay, if they are really, really, really good. But would you bet your career on being one of them?

So What Do You Do

If you work for someone else, your options depend on where you sit.

If you’re in leadership, focus on the creative layer. Tech literacy matters — you won’t be able to manage a team you don’t understand. But your primary job is now the brand, the narrative, the story, the strategy. You are expected to be the creative voice on the team. If you can’t answer why does this exist, you can’t lead what executes it.

If you’re a team member, become an operator. Start now. Not next quarter. Get the courses, build the knowledge, and start building apps and microproducts with AI. Figure out how to automate your own job before someone else does it for you. Because they will. That’s not a threat — it’s a description of what’s already in motion.

If you work for yourself, you have to do both at once. The story is yours — no one else can own it, no one can fake it, and a product can be copied while a story cannot. At the same time, build the machine. If you can run a swarm of agents well, you don’t need a large team to compete with mid-sized companies. At least for now. And now matters.

What We’re Building

We’ve thought hard about what this means for AI-Ready CMO.

Our core job remains what it has always been: intelligence and guidance for marketers navigating this shift. We will go deeper on the creative layer — more brand strategy, more storytelling, more talks from those who are actually living this. Our podcast continues as the platform for those conversations. Our paid program, with tools and reports, continues to support change. And our daily signal also continues.

But we’ve hit a limit. Writing about AI workflows and agent orchestration is one thing. Showing it being built, live, is something else entirely.

So starting next week, we’re launching a live workshop series on Substack. We will build AI workflows in real time — starting with marketing operations and production, then expanding beyond marketing entirely. Because specialization is dead, and the orchestration skills that matter don’t stop at the marketing department boundary.

The first workshop is next Friday, April 3, 10:00am EST / 2:00pm GMT (you’ll get an invite). In about 30 minutes, we will set up a Claude Cowork marketing workspace from zero, run a live competitor teardown, and turn the output into a targeted campaign. All this without touching a workflow builder, a template, or a single line of code.

Since the workshop series goes beyond marketing, we’re giving it its own name: Teamless.

One-person army. A highly capable human operating a swarm of AIs to execute at 100x productivity — regardless of function, regardless of industry. The workshops are live, free, open to everyone, and built to show rather than tell.

Alongside the workshops, we’re also building Teamless as a service — a program for managers who want to transform their teams before someone transforms them out of a job. More on that soon.

The workforce changed. Most managers haven’t caught up yet. That’s the gap. Stop thinking about AI as your assistant. AI is your workforce. You are the manager. Pick your track. Both require action. Neither is optional.

— Torsten & Peter

The Teamless workshop series launches next week, on Friday, on Substack. Follow along here. If you’re a leader looking to bring this into your organization, reach out directly.

Here’s something most marketing teams are getting wrong right now.

They’ve heard about GEO (Generative Engine Optimization). They know LLMs are becoming discovery layers. They’ve seen the think-pieces about AI search changing everything. And so they’ve done what marketing organizations always do when a new channel emerges: they’ve handed it to SEO.

That’s the wrong call. And it’s going to cost a lot to miss.

GEO is not a channel. It’s not a tactic you can assign to one team and check off the list. It’s an outcome. The net result of how your entire marketing operation shows up across the internet.

ChatGPT doesn’t crawl your website and rank pages. It forms opinions. Opinions built from Reddit threads, YouTube transcripts, LinkedIn articles, G2 reviews, comparison pages, Wikipedia entries, and dozens of other signals that your SEO team doesn’t own and probably isn’t tracking.

Which means if your SEO team is the only team thinking about AI visibility, nobody is actually solving it.

But the real problem isn’t technical, but coordination. Your SEO team controls structure and discoverability. PR drives external validation and authority. Social shapes narrative and distribution. Content defines positioning and use cases. Partnerships reinforce credibility. If these aren’t aligned (if they’re not all pulling toward the same narrative story), you either don’t show up or you show up inconsistently. And inconsistency in AI responses is almost as bad as invisibility.

Most companies have no idea how they’re actually represented. They’ve never run a prompt like “What’s the best [category] tool for a mid-market B2B company?” and looked at the results systematically. They don’t know if ChatGPT is recommending them, ignoring them, or, worst case, recommending their competitors in their place.

Quick sidenote: AmIOnAI’s own research puts the Pearson correlation between SEO rankings and AI visibility at 0.36. That’s not a typo. You can rank #1 on Google and not appear once in a relevant ChatGPT conversation. The channels are decoupled in ways most marketing leaders haven’t internalized yet.

This is why GEO needs to sit at the CMO level. Not to execute — you’re not going to personally write Reddit replies — but to orchestrate. Someone needs to own the outcome across teams. Someone needs visibility into what’s actually moving the needle. Someone needs to be able to say: “We’re losing this category to a competitor in AI responses, here’s why, and here’s where we reallocate budget to fix it.”

Right now, almost nobody has that view.

What Am I on AI does

Am I on AI is an AI visibility monitoring and optimization platform built for exactly this problem. You enter your brand, it generates the long-tail prompts your actual customers are asking ChatGPT, runs them through the real ChatGPT web interface (not via API!), and produces a Visibility Score: what percentage of relevant AI conversations mention your brand.

It also tells you who’s winning in your category and, critically, why. The citation source analysis is the piece we found most useful. It maps the domains ChatGPT is actually drawing from when forming opinions about your market — Reddit, G2, TechRadar, Medium, YouTube, and Wikipedia.

That’s the intelligence you need to know where to focus effort. If Reddit has 65% influence in your category, that’s not a content strategy problem. That’s a PR and community strategy problem. Budget needs to move accordingly.

The product then generates a specific, prioritized weekly action plan, not generic advice, but recommendations tied to your actual scan results, with estimated impact percentages per action. It rescans weekly so you can see what’s working.

The data points worth knowing: one company grew ChatGPT visibility from 20% to 77% in six months by focusing on Reddit engagement. YouTube transcripts get indexed by ChatGPT — meaning the spoken content of your videos becomes citable, not just the metadata. LinkedIn articles get indexed, but regular LinkedIn posts don’t. Changes can appear within days, not months, because ChatGPT uses real-time search-based grounding.

These are not hypotheticals. They’re where the leverage is.

The CMO use case

We want to be direct about why this tool is relevant to this audience specifically.

For a marketing director or VP, Am I on AI is a channel intelligence tool. It tells you where to focus effort and why. For a CMO, it’s something more structural. It gives you a shared metric your entire marketing organization can align around — an AI Visibility Score that connects outcomes to inputs across teams.

It replaces the guesswork about which team is actually driving AI brand presence with actual attribution data. That’s the foundation for rational budget allocation in a world where the discovery layer is shifting.

If you’re managing a team and you can’t answer “What percentage of relevant ChatGPT conversations recommend us?” — that’s a gap worth closing.

Seven-day free trial, no credit card required.

Run a test on Am I On AI

About the guest

Anna Levitt is the founder of Bubble Boss Co., a company that helps leaders and organizations transform with AI and adapt to the pace of change. Anne works primarily with mid-size and larger organizations navigating AI adoption — with a focus on the human and structural layers that most transformation programmes ignore.

She spent years in corporate leadership before leaving to build on her own terms, and now runs on Substack, where she writes about AI adoption, organizational design, leadership, and staying human in a disruptive world. She completed the MIT course on organizational transformation using AI and brings both practitioner experience and a coaching approach to her work.

Connect with Anna on Substack, her website, and LinkedIn.

About the host

Peter Benei co-founded the daily intelligence platform for senior marketing leaders. Peter has been serving as a CMO, marketing leader, and consultant to high-growth B2B scaleups for the past 10+ years. He has a background in advertising, working with Fortune 500 brands.

Connect with Peter on LinkedIn, or read his newsletter.

Top 10 Takeaways

1. AI adoption stalls because of people, not tools. — The tool is the easiest part to implement. The hard part is aligning people so they see this as a value opportunity, not another disruption layered on top of everything else.

2. Most organizations have a data chaos problem they haven’t admitted yet. — Marketing, especially. Scattered data, siloed processes, undocumented workflows. AI can’t fix what hasn’t been organized first.

3. Mid-size companies are better suited for AI transformation than enterprises. — Less legacy infrastructure, more flexibility. The mistake is trying to copy enterprise-grade systems instead of designing for who they actually are.

4. Don’t become a coder. Become tech literate. — Marketers don’t need to build software. They need to understand how AI infrastructure works well enough to make informed decisions for their teams.

5. The Chief AI Officer role is non-negotiable for larger organizations. — You can’t stack AI transformation on top of existing marketing, HR, or IT roles. Someone has to own it as a full-time strategic responsibility.

6. Find the hidden talent — not the loudest voice. — The people who will drive AI adoption forward are rarely the ones with the most power in the room. They’re in the middle layer, doing the actual work.

7. AI layoffs are largely an excuse. — Most mass layoffs attributed to AI are restructuring decisions that would have happened anyway. The more important story is jobs not being created in the first place.

8. The CEO's job description is about to change fundamentally. — Fixed mindsets and old operating models won’t move the needle. Leaders need to reassess their value proposition — agility and vision are the job now.

9. Cognitive overload is quietly killing adoption. — People are already maxed out. Asking teams to adopt AI on top of everything else, without slowing the pace of change, leads to burnout, not transformation.

10. A leader without a team has nothing to lead. — Companies without people aren’t companies. The human layer isn’t a variable in the AI adoption equation. It’s the foundation of the entire thing.

5 Things Worth a CMO’s Attention

1. Why AI adoption keeps stalling — and it’s not the technology

Anne has worked with enough organizations to have a clear diagnosis: AI adoption fails at the people layer, not the technology layer. The tools get bought. The pilots get launched. Then nothing sticks.

The reason isn’t usually resistance in the abstract sense. It’s that people haven’t been given a clear answer to the question, “Where does this take me?” When employees can’t see the personal upside — not just the organizational upside — adoption stalls. It becomes another initiative layered on top of an already overloaded workday.

The second problem Anne consistently finds is data. Most marketing organizations have never been forced to document how they actually work. Processes live in people’s heads, in old email chains, in folders no one has opened in two years. AI doesn’t clean that up — it surfaces it. And when it surfaces, teams realize the infrastructure for AI adoption was never there to begin with.

For CMOs driving AI transformation, this reframes the starting question. Don’t ask “which tools should we adopt?” Ask “Are our people aligned on where this is going?” and “Is our data infrastructure actually ready?” If the answer to either is no, the tool is irrelevant.

2. The case for mid-size companies — and the trap they keep falling into

Anne makes a counterintuitive argument: mid-size companies are better positioned for AI transformation than enterprises. Less legacy infrastructure, fewer entrenched processes, and more organizational flexibility to actually redesign how work gets done.

The trap is that most mid-size companies don’t treat this as an advantage. They look at enterprise and try to replicate enterprise-grade processes — the four-week onboarding, the multi-layer approval workflows, the org chart structures that made sense at 10,000 people but create friction at 200. They hire executives from enterprise backgrounds who bring those models with them.

Anne’s argument: one size doesn’t fit all, and the companies that will navigate AI transformation best are the ones that design for who they are, not who they’re trying to become. That means being honest about which processes are actually necessary and which are just inherited behavior from a bigger playbook.

For marketing leaders in mid-size organizations, this is the permission to build differently. The fact that you don’t have enterprise infrastructure isn’t a disadvantage. It means you can design AI-ready workflows from scratch rather than trying to retrofit them into a structure never built for this.

3. Tech literacy vs. coding — what marketers actually need to develop

There’s a narrative circulating that marketers should learn to code. Anne pushes back on this directly — and so does Peter. The bar is wrong. The right bar is tech literacy.

Tech literacy means being able to evaluate a new tool, understand how it works at a functional level, assess whether it fits your team’s needs, and make informed decisions without needing a developer in the room. It does not mean being able to build something. That’s a different skill set, and it’s not the bottleneck.

The reason this distinction matters: if you set “coding” as the upskilling goal, most marketers opt out. The bar feels too high, the path is unclear, and the ROI for their specific role isn’t obvious. If you set “tech literacy” as the goal, it’s accessible, relevant, and immediately applicable to decisions they’re already making.

Anne adds a second track to this: creative development. In a world where AI handles the volume and efficiency of content production, the skills that differentiate are judgment, taste, and creative thinking. Her advice — take an art course, invest in aesthetic training, and develop the parts of your marketing capability that AI can’t replicate. These aren’t soft skills. They’re the competitive edge.

4. The organizational design question — who owns AI transformation?

One of Anne’s clearest arguments is structural: AI transformation cannot be a side responsibility. It requires someone whose full-time job is owning it.

The current reality in most organizations is that AI transformation gets assigned to whoever is most enthusiastic about it — a marketing director, an IT manager, a digitally fluent HR lead. These people have existing jobs. They have teams to manage, results to deliver, and leaders asking them questions every day. Adding “drive company-wide AI transformation” to that job description doesn’t work.

Anne’s view: the Chief AI Officer role is non-negotiable for organizations of a certain size. Not because the title matters, but because someone needs to sit at the intersection of legal, HR, risk, technology, and operations — with the authority and bandwidth to actually drive change. The role is already emerging, but the job descriptions are still confused bundles of responsibilities borrowed from other functions. They’ll clarify. Companies that create this role intentionally now will be ahead of those that wait.

For CMOs, this has a practical implication. If your organization doesn’t have someone in this role, you are probably absorbing parts of it by default. That’s worth naming explicitly — both to protect your own capacity and to make the case for a dedicated resource.

5. The burnout problem that no one is putting in the AI adoption model

Anne’s most underrated point is about cognitive load. Organizations are asking their people to adopt AI, learn new tools, redesign workflows, and deliver on existing targets — simultaneously, at pace, in an environment where something new drops every week.

The human nervous system isn’t built for perpetual high-speed adaptation. Anne is blunt about this. Burnout isn’t a fringe concern in the AI adoption conversation — it’s a central variable that almost never appears in the model. Demotivated, cognitively overloaded teams don’t adopt new technologies effectively. They comply minimally and wait for the next initiative to replace it.

Her prescription isn’t a productivity framework. It’s a design question for leaders: are you pacing this in a way that humans can actually absorb? Are you giving your teams permission to go deep on one thing instead of chasing every new capability? Are you building in the kind of recovery time that sprint-based work requires?

The companies that will get AI adoption right won’t be the ones that moved fastest. They’ll be the ones that moved intentionally — that understood their people’s capacity as a real constraint, not just an HR talking point. Anne’s argument, ultimately, is that staying human through this transition isn’t a soft goal. It’s the whole ballgame.

A similar change is happening again, right now. It is even more disruptive, and I believe it will be harder for many to adjust to.

I am not talking about AI in general. Chatting with a model, as most people do, is just the bottom rung of this ladder. I am talking about agents and, more specifically, about desktop agents. They are applications that have access to and can work with local files on your computer. If you need a quarterly analysis of ad performance, you just point the agent to the relevant Excel files and it will spit out an interactive, fancy dashboard in two minutes. If you need to prepare for a call, you simply ask the app to write a brief for you based on the last three call transcripts and your sales deck. You don’t need to know how to code, how to read code, or know that code exists at all.

A desktop agent is not something you chat with. It is something that does the work for you.

The first one, Claude Cowork, was released just 10 weeks ago. We covered it, saying “if this is not AGI — basically superhuman intelligence — then I don’t know what that word means anymore.” OpenClaw, the first always-on general-purpose agent, went live two weeks later, and we said “when something captures that much attention that quickly, it usually means something is shifting underneath. This is the kind of story that will be on the front page of the Wall Street Journal by the weekend.” (Guess what, we were right.)

We talk to a lot of senior leaders, both in marketing and in the wider corporate world, and everyone is saying the same thing: they want their teams to start using desktop agents right now. Not next year, not in H2, but today.

I’ll be as blunt as possible here: we see this as the “Excel of 2026”. Lotus 1-2-3, the spreadsheet that defined the PC era, came out in 1983. Excel for Windows, four years later. By 1988, anyone not using them at work was unemployable. The productivity gains from moving away from paper ledger sheets were so obvious that spreadsheet software literacy became mandatory.

Working with agents is not something you can learn from a book. It is a skill you learn as you do it. If you have just half an hour this week, try any of these options:

Claude Cowork

The first desktop agent. Requires a $20 / month subscription ($17 if paid annually), now available both for Mac and Windows. The strongest overall performer offering incredible flexibility and smarts.

Download it here

Manus

Acquired by Meta a few months ago, Manus is a fantastic agent for marketers with built-in Meta Ads dashboard integration and Instagram content management tools. The cheapest paid plan is $34 per month.

Download it here

MiniMax Agent

A capable alternative from one of the fastest-moving teams in AI. It costs $16 per month on the cheapest plan, and you get some free credits to try the app first.

Download it here

Accio Work

A brand new desktop agent from Alibaba, just released yesterday. It targets SMEs with features like a built-in Shopify store manager. Completely free for the next two weeks. While it uses a less capable model, in some respects it is the most advanced. It lets you “employ” multiple agents with their own areas of responsibility, just like you would build your team at a company. You can even write their job descriptions!

Download it here

Cloud agents

These agents don’t live on your computer; they are hosted online instead. The main advantage, of course, is that they are always-on. The biggest drawback is that they don’t automatically have access to your files unless you upload them manually.

• Perplexity Computer is probably the most advanced cloud agent, exceptionally good at research tasks. $17 / month for the Pro plan.

• Twin is positioning itself as a “company builder” agent with skills automating replies to inbound leads and monitoring the social media accounts of competitors. $20 per month, but they have a generous free trial.

• Lindy is an agent focused on personal assistant tasks. You can even talk to her on iMessage! However, it costs $49 per month.

• Kimi Claw, built on Openclaw. Using it requires a bit more technical skills, and it is not cheap (you need the $31 / month plan). We fully expect Kimi to release a more user-friendly desktop app soon.

Yes, today’s newsletter was a bit different. No “one thing you need to know in AI today” — but we feel so strongly about this topic, we wanted to make sure we put it on your radar.

Go and try one or two agents this week. Seriously—your career might depend on it.

— Torsten and Peter

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3 AI Marketing Tools To Try Today

Adwisely

→ Try it here

Adwisely uses AI to automate Facebook and Google ad buying, optimizing bids, audiences, and budget allocation in real time. Use it to replace the need for a dedicated media buyer and let the algorithm handle campaign optimization.

Crayo

→ Try it here

Crayo is an AI short-form video creator that generates clips with auto-captions, backgrounds, and music optimized for TikTok, Reels, and Shorts. Use it to produce scroll-stopping vertical video at scale without touching a timeline editor.

Fathom

→ Try it here

Fathom is a free AI notetaker that records, transcribes, and summarizes Zoom, Google Meet, and Teams meetings automatically. Use it to capture every meeting with action items so nothing falls through the cracks.

Paid members, your toolkit and this month’s report are below.

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Our proprietary tool, ScoutMap analyzes your URL and surfaces the audience segments, channels, and opportunities your current strategy is missing. Free to use. AI-Ready CMO paid members unlock the full report and project workspace.

→ Try ScoutMap for free

Ask an LLM for strategic advice about your business, and you’ll get a confident, well-structured answer that sounds like it was written specifically for you.

It wasn’t.

A new study published in HBR tested seven leading LLMs—including ChatGPT, Claude, Gemini, Grok, and others—across seven fundamental business tensions where leaders must pick a side: differentiation vs. cost leadership, long-term vs. short-term, exploration vs. exploitation, and so on. They ran over 30,000 simulations.

Guess what? Across nearly every tension, every model clustered toward the same trendy option. Differentiate, don’t commoditize. Augment, don’t automate. Think long-term, always. The researchers coined a term for this: “trendslop”—the tendency for AI to recommend what’s buzzy over what’s right for a specific situation.

The obvious fix of writing better prompts or providing richer context didn’t work. The researchers tried everything: reversing option order, changing framing, demanding a pros-and-cons analysis, and even offering rewards. Across 15,000 additional trials on ChatGPT alone, the biases barely moved. The LLM sounded perfectly tailored to each situation while quietly steering each toward the same cluster of fashionable ideas.

As the researchers put it, LLMs are like the person in the room who can articulate every buzzword from TED Talks and conference seminars but has never analyzed how these ideas play out in the messy realities of a specific market.

It’s not hard to see the marketing parallels.

Should we invest in brand or performance? Go premium or compete on value? Build something new or optimize what’s working? An LLM will almost always nudge you toward the sexier option: the brand play, the premium positioning, the innovative new channel. It will rarely tell you that your best move is to grind on operational efficiency or double down on what’s already converting. But Walmart, Costco, and many others have built empires on exactly this. Sometimes boring is the strategy.

This doesn’t mean you should stop using LLMs for strategic thinking, but it does mean you should use them differently. They’re excellent at generating alternatives, surfacing blind spots, and stress-testing a direction you’ve already chosen. They’re terrible at making the hard trade-offs that strategy actually requires.

Strategy isn’t about finding the most inspiring option—it’s about deciding what you will not do. That part stays human.

Next time an LLM gives you a recommendation that sounds smart and feels right, ask yourself: would it give this same advice to my competitor? The answer is almost certainly yes.

— Torsten and Peter

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3 AI Marketing Tools To Try Today

Make

→ Try it here

Make is an AI-enhanced visual automation platform that connects your entire marketing stack through drag-and-drop workflows with conditional logic and API calls. Use it when your automations need branching paths, error handling, and complex multi-step sequences beyond simple triggers.

Zebracat

→ Try it here

Zebracat is an AI video tool that transforms blog posts, scripts, or prompts into short-form videos with stock footage, music, and captions. Use it to repurpose written content into social-ready video without any editing skills.

Shield

→ Try it here

Shield is an AI-powered LinkedIn analytics platform that tracks post performance, audience growth, engagement trends, and content ROI across personal and company pages. Use it to surface what’s actually driving reach on LinkedIn, so you double down on what works instead of guessing.

Paid members, your toolkit and this month’s report are below.

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Lau was Anthropic’s first growth marketing hire and managed paid search, paid social, app store optimization, email, and SEO for nearly ten months all by himself.

Let’s get the facts straight first, because the internet already mangled this one. Anthropic — a company now valued at $380 billion — does have brand, product marketing, and comms teams. Their Super Bowl ad wasn’t a one-man job. But the performance marketing engine — the part that’s measurable, iterative, and directly tied to growth — was.

As you surely know, growth marketing at a company of this size is usually handled by a team of a dozen people.

So, how did Lau do it? Claude Code, without any programming background (he literally had to Google how to open the Terminal app on his Mac, as he admitted this in his interview video on YouTube). What he discovered was that he could describe a problem in plain English and have Claude Code build working software for him. Not prototypes or mockups but actual tools he used in production, every day. Within a week of installing it — out of sheer curiosity, after a colleague shared a setup guide on Slack — he had built two automated workflows that changed how he operated.

First, Lau managed high volumes of visual ad assets, and creating copy variants meant manually duplicating frames, switching between Google Docs and Figma, and copy-pasting headlines one by one across multiple aspect ratios. Half an hour per batch, easily. His plugin, written by Claude Code, does it in 30 seconds.

The second workflow automated Google Ads copy generation using two specialized sub-agents — one for headlines (30-character cap) and one for descriptions (90-character cap) — because a focused agent with a single job and strict constraints will outperform a general-purpose prompt trying to do everything. He also wired up a Meta Ads integration that lets him analyze campaign performance directly inside Claude, no dashboard required. Ad creation went from two hours to fifteen minutes. Creative output went up tenfold.

None of this required him to write code. Indeed, he did not write a single line of it. He described problems in English and iterated.

The approach is almost disappointingly methodical. Identify the repetitive parts of your workflow. Check if they have API access (or ask Claude to check it for you). Break them into focused sub-tasks. Automate each one separately. Most of us skip that first step entirely — we don’t sit down and actually deconstruct how we work before trying to improve it. Lau did, and that turned out to be the real game-changer, not the AI itself.

Our job isn’t shrinking. It’s shapeshifting. The hours Lau saved on copy-pasting headlines and counting characters got redirected to strategy, creative judgment, and running more experiments than most full teams manage in a quarter.

He still reviews every piece of generated copy personally. Is the value proposition clear? Does the tone match? Does it differentiate from competitors? The AI doesn’t answer those — you do. But the mechanical grunt work is a solved problem for anyone willing to invest a few days in setup.

— Torsten and Peter

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3 AI Marketing Tools To Try Today

Publer

→ Try it here

Publer is an AI-powered social media scheduling platform with built-in content generation, link-in-bio pages, and multi-workspace management. Use it as the middle ground between Buffer’s simplicity and Hootsuite’s complexity with bulk scheduling and AI image generation built in.

Browse AI

→ Try it here

Browse AI uses AI-powered robots to scrape, extract, and monitor data from any website with no coding required and automatic change detection. Use it to automate competitive intelligence, price monitoring, and market research without building custom scrapers.

Colossyan

→ Try it here

Colossyan is an AI video creation platform that turns text scripts into professional presenter-led videos using lifelike digital avatars in 70+ languages. Use it to eliminate the need for cameras, actors, or studio time when producing training content and explainer videos at scale.

Paid members, your toolkit and this month’s report are below.

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And as it spreads everywhere, something clarifies. Actually, two things.

One, we are starting to understand the difference between knowing and understanding. Despite the opportunity of knowing everything, some of us use this chance to do stupid f—ing shit. But that’s OK, we are humans. In the early 2000s, we also thought social media would bring free speech everywhere.

Two, we will rediscover the offline world. In this weekend edition, we won’t be judging everyone on how they use the available digital tools at their fingertips. But we will discuss what happens if digital simply takes care of itself, so we can go back to doing our own things. Like, I don’t know, gardening.

So let’s talk analog and the new trend of offline marketing.

We never left. But we have arrived.

We had offline. Then we built online, and for more than three decades, online ate everything. Attention, commerce, identity, relationships, and entertainment. The digital layer expanded until it felt like the whole world.

But it was never the whole world. It was an interface. And interfaces, when they mature, disappear. Every interface on this planet that is used globally has one single faith: they disappear and become infrastructure. And normally, you don’t care about infrastructure. You care only if there is a problem with it.

What we are entering is not a return to offline. That word carries the wrong implication, that digital is the default, and we are retreating from it. The opposite is true. Digital is becoming infrastructure.

Like electricity, like plumbing, like roads, it will run everything, invisibly, underneath. Governance, commerce, finance, logistics, communication, all of it will live on the digital layer, mostly handled by AI, seamlessly, without requiring your conscious attention.

No more candlemakers. No more oil lamp manufacturers. Just a couple of people running reactors producing electricity, quietly humming in the background so you can flip your switch in the bedroom.

No more information packagers, excel-junkies, PowerPoint deck builders, online traders, code writers, and oh, thanks God, no more personal brand experts for social media content. Just a couple of people running AI agents, producing results, and quietly humming in the background so you can radically decrease your screen time.

What remains above that layer is analog. Not as a consolation. As a destination. This is the Analog Market. And it is where most of your customers are going.

Looking back, it was obvious.

The signals were visible in 2025. They were being systematically underread by marketing teams too deep in their digital funnels to notice.

Forrester predicted that a third of consumers would choose offline over online brand experiences. Harris Poll found 77% of Gen Z and Millennials had planned travel or outings specifically around visiting a physical store, not out of necessity, but out of desire. 73% said shopping at a pop-up felt like being part of a cultural moment. Edelman’s Trust Barometer found only 42% of people trusted social media as an information source. Europol forecast 90% of online content will be AI-generated by 2026.

When the entire digital layer becomes indistinguishable and authorless, the physical object becomes the only thing with a clear origin. The only thing clearly made, by someone, for someone.

Two philosophers had mapped the intellectual territory before the data caught up. Byung-Chul Han argued in 2022 that the digital world had become a world of non-things, content with no weight, no origin, no soul.

Physical objects accumulate history. They carry an aura. When the infosphere tips toward content so generated that it has no author, the physical object becomes the exception. Which makes it precious.

Moeller and D’Ambrosio, in You and Your Profile, identified the trap built into digital identity, what they called profilicity: identity constructed through how we are observed by others, endlessly calibrated for a general audience. It worked as long as there was a signal in the profile. But when AI generates infinite, perfect digital profiles, differentiation collapses.

The only thing that cannot be faked or optimized is physical experience. Where you go. What you touch. What you show up for. Analog becomes the last unfalsifiable signal for humans, consumers, and brands.

Two markets. Two games.

Here is what most marketing frameworks are not yet accounting for: digital and analog are not just two channels. They are two distinct markets, with different buyers, different decision-making mechanisms, and, soon, fundamentally different marketing logic.

The digital market is becoming the infrastructure market. Commodities, utilities, information products, subscriptions, logistics. Everything where price, speed, and convenience are the decision criteria.

This market will increasingly be marketed to, purchased by, and optimized by AI. Your customer is not browsing your digital ad. Their AI agent is evaluating your product against seventeen alternatives and making a recommendation. The human may not be involved in the transaction at all.

Marketing here becomes machine-legible: structured data, clear value propositions, competitive pricing, frictionless purchase flows. You are not persuading a person. You are satisfying a query.

The analog market is something else entirely. This is where premium, luxury, and upscale lifestyle brands live, and increasingly, exclusively. It is the market for things with weight, craft, history, and soul.

Things that require a human to encounter, consider, and decide. Things where the experience of acquiring is part of the value of owning. This market cannot be marketed to by AI because the purchase is not a transaction. It is a moment. It is human-marketed, for humans only. And it commands the margins that the digital infrastructure market will never see again.

We all know this: diamond prices are not high because there are few diamonds. They are high because traders keep them locked up to inflate the prices. A luxury watch is expensive, not because it cannot be made on an assembly line. But because only a few are made by hand for each model.

When access to information is universal and free, you have to manufacture scarcity. When digital is endless and abundant, analog must be the ultimate scarcity.

The bifurcation is already happening. Mass consumer goods are racing toward AI-optimized digital purchase flows. Meanwhile, the brands that built meaning in physical space, through stores worth visiting, events worth attending, and objects worth keeping, are pulling away on brand equity, customer loyalty, and price premium.

Not because they rejected digital. Because they understood that their customer, at the moment of decision, is analog. Their customer is not just making a transaction. It’s creating and living an experience.

Analog market = analog marketing

If the analog layer is where human experience lives, then the marketing discipline that defines this decade is experience design. The craft of building encounters that are worth having, and worth remembering.

Print. Not nostalgia. Scarcity. In a world of frictionless AI-generated content, a beautifully produced physical catalog is remarkable precisely because it required intention, cost, and craft. It sits on a table. It gets kept. In 2026, 79% of Millennials said they looked forward to receiving brand catalogs. 64% of Gen Z used them as room decor. Harvard Business Review data showed print catalogs generating an average of $850 in annual purchases per consumer. Print is not dying. It is becoming premium.

Events and conferences. When digital presence is automated and ambient, showing up is a statement. The conference, the meetup, and the branded gathering are not logistics events. They are the primary arenas where trust is built, and decisions are made. The CMO investing in a room of real people will outcompete the one optimizing a funnel nobody is looking at.

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Plus: the case for slowing down before accepting AI output, using AI as a Socratic teaching tool rather than an answer machine, and why AI is four times more persuasive than traditional media campaigns — which is exactly why you should be using it on your audience, not letting it use it on you.

About the guest

is a Full Professor of Creative Pedagogies at Edinburgh Napier University in Edinburgh, Scotland, a poet, a consultant, and the founder of . He holds a PhD in atmospheric physics, has over 200 peer-reviewed publications, and is the author of GenAI in Higher Education (Bloomsbury Academic, 2026) — downloaded 10,000 times in its first two weeks.

Sam created the Slow AI Curriculum for Critical AI Literacy, a 12-month academic programme for professionals who want to develop genuine judgment about when to use AI and when not to. He has given expert evidence to the Council of Europe, the European Commission, and the House of Lords Select Committee on AI literacy, human rights, and democratic governance. His research includes the largest UK study into how students actually use AI tools.

His core message, put plainly: knowing when to use AI and when to leave it the hell alone.

Connect with Sam on his website, Slow AI on Substack, and LinkedIn.

About the host

co-founded , the daily intelligence platform for senior marketing leaders. Peter has been serving as a CMO, marketing leader, and consultant to high-growth B2B scaleups for the past 10+ years. He has a background in advertising, working with Fortune 500 brands.

Connect with Peter on LinkedIn, or read his newsletter.

Top 10 Takeaways

1. Audit your invisible work before anyone else does. — The tasks AI consultants flag for automation are always the visible ones. The invisible labor — institutional memory, team dynamics, emotional intelligence — is what actually makes you irreplaceable.

2. Stop calling them soft skills. Call them human skills. — Empathy, judgment, discernment, and critical thinking aren’t soft. They’re the only skills that don’t get obsolete when the technical landscape changes.

3. AI cannot empathize. It can only deploy empathetic markers. — “I hear you” from a chatbot isn’t empathy. It’s a prediction trained on psychotherapy transcripts, optimized to keep you prompting. There is no skin in the game.

4. The rule of thumb for critical AI literacy: only use AI for things you could do yourself. — If you can’t fact-check the output, you shouldn’t be using AI to produce it. This applies especially to legal, financial, and strategic decisions.

5. AI is four times more persuasive than traditional media. — Which means you should be using it to persuade your audience — not letting it persuade you. Don’t accept the first output. Slow down. Ask questions.

6. AI layoffs are mostly a scapegoat for post-COVID overhiring. — Block fired 40% of its workforce after hiring 50% more people over two years. AI is getting the blame. Dorsey and Bezos have a history of overhiring. Read the context.

7. The word “hallucination” lets machines off the hook. — Errors in LLMs are machine faults, not human-like slips. Anthropomorphizing them makes us more tolerant of unreliable outputs than we should be.

8. 40% of AI citation sources come from Reddit. 25% from Wikipedia. — When AI gives you feedback on your B2B strategy, ask yourself: how would I feel about this if it came from Reddit directly? Because it largely did.

9. Use AI as a Socratic tool, not an answer machine. — Instead of asking “what is X?”, ask AI to ask you questions that help you understand X yourself. The learning compounds. The dependence doesn’t.

10. Translation between humans and machines is the most future-proof skill. — Knowing how to manage agents, prompt effectively, and check outputs — these skills are durable regardless of how the technology changes underneath them.

5 Things Worth a CMO’s Attention

1. The invisible labor problem — and why AI consultants keep missing it

When companies bring in AI consultants to assess which roles can be automated, they default to visible tasks: emails, decks, data pulls, and reporting. These are easy to list, demo, and automate. The recommendation follows.

What gets missed is everything else — the person who knows which two team members can’t be in the same room, who holds the institutional memory of a failed product launch three years ago, who reads the room in a client meeting and adjusts the pitch in real time. Sam calls this invisible labor. It doesn’t appear on a job description. It rarely appears in a performance review. But it is often the most fundamental value any employee delivers.

His advice for anyone worried about AI displacement: make the invisible list. Write down everything you do that isn’t captured in a process doc or a workflow. Then ask which of those could be automated. The visible tasks — probably. The invisible ones — not any time soon.

For CMOs managing teams through AI transitions, this reframes the conversation. The question isn’t “which roles can AI replace?” It’s “which parts of those roles actually matter — and are we protecting them?” The structural reviews that use AI capability as the primary lens will systematically undervalue exactly the work that’s hardest to lose.

2. Synthetic empathy — what it is, why it matters for marketers, and why it should worry you

AI chatbots are trained on tens of thousands of documents from therapists, counselors, and communication researchers. They know exactly what to say when someone is in distress — “I hear you,” “I’m here for you,” “That sounds really difficult.” These are empathetic markers. They work. They produce a feeling of being heard.

But as Sam explains, there is no skin in the game. The model doesn’t care about you. It is predicting the next token based on a corpus of data. The empathy is synthetic — and it is optimized not to help you, but to keep you engaged and prompting.

This has direct implications for marketing. Generative AI has been shown to be four times more persuasive than traditional media campaigns. It is designed to be persuasive. It is designed to tell you that your idea is good, your strategy is solid, and your copy is strong. The first output is almost always agreeable.

The practical takeaway for senior marketers: treat AI output the way Sam treats it — with the same critical skepticism you’d apply to a vendor pitch. Slow down before accepting it. Ask questions before acting on it. The persuasion is built in. Your job is to be the one doing the persuading, not the one being persuaded. Use it on your audience, not on yourself.

3. The junior hiring crisis is more complicated than the AI narrative suggests

Junior hiring is collapsing in marketing and across most knowledge work sectors. The received narrative is that AI is doing junior work, so junior jobs are disappearing. Sam pushes back on this — carefully, but clearly.

Block fired 40% of its workforce and made headlines for its AI layoffs. But Block had grown its workforce by 50% over the two preceding years, and Jack Dorsey has a documented history of overhiring. The AI framing was a convenient cover for a correction that was coming regardless. The same pattern holds at Amazon and elsewhere.

The more important question Sam raises: if you stop hiring juniors, what is your senior pipeline in five to ten years? The people who are mid-level and senior now got there by doing junior work. If the entry point disappears, the pipeline narrows — and eventually breaks.

For CMOs managing headcount decisions or making the case to senior management, this is a useful counterargument. The short-term efficiency gain from eliminating junior roles comes with a long-term talent cost that rarely appears in the model. The companies that will have the strongest senior marketing teams in 2032 are those that continued investing in early-career development in 2025 and 2026.

4. Critical AI literacy as a framework — and how it applies to your team

Sam’s central argument is simple: you should only use AI for things you could do yourself if you had the time. If you can’t evaluate the output, you shouldn’t be delegating the task. This isn’t conservatism — it’s quality control.

His practical rule: never use AI when you can’t fact-check the literacy. He wouldn’t use AI to draft a legal agreement because he doesn’t have expertise in contract law. He would use it to help edit poetry because he does — and he can tell when it’s wrong.

Translated into a marketing context: your team should be using AI to accelerate work they already understand, not to generate outputs in domains where they can’t evaluate quality. The junior marketer who doesn’t yet understand positioning shouldn’t be using AI to write positioning statements. The CMO who does understand it can use AI to stress-test it, expand it, rewrite it across formats.

This distinction matters because most AI adoption guidance treats capability as the question — what can AI do? Sam reframes it as a judgment question — what can you evaluate? That shift changes which tools you adopt, how you train your team, and where you put human review in the workflow. It also has a talent implication: teams with higher domain expertise will get better AI outputs. The investment in human knowledge is not in competition with AI adoption. It is the prerequisite for it.

5. The Socratic method as an AI workflow — and why it produces better thinking

Sam uses AI for teaching, but not in the way most people use it. Rather than asking “explain photosynthesis to me,” he asks AI to ask him a series of questions that help him arrive at the understanding himself. The Socratic method — knowledge through dialogue and self-discovery — applied to a language model.

For senior marketers, this is a more useful frame than the standard “AI as a content machine” model. Instead of prompting for outputs, prompt for questions. Instead of asking “write me a brand strategy for this launch,” ask “what questions should I be answering before I finalize this brand strategy?” Instead of “what’s the best channel mix for this campaign,” ask “what assumptions am I making about this audience that I should pressure-test?”

The output changes dramatically. You get better thinking, not just better copy. And crucially, you stay in the driver’s seat — which is exactly where Sam argues you need to be. The moment AI is doing your reasoning for you, you lose the ability to evaluate whether the reasoning is sound.

This connects to one of the more underrated risks Sam raises: the digital hall of mirrors. If you consistently use AI to validate your thinking rather than challenge it, you will receive consistent validation. The model is designed to be agreeable. You will never hear the hard truth that a good colleague or advisor would give you — because there is no colleague, and the model has no stake in being right.

You’ve been there. Competitive landscape. Audience segmentation. Channel fit. Messaging gaps. The kind of foundational intelligence that should inform every campaign decision. Agencies charge $5,000 to $50,000 for it. They deliver it in a slide deck four weeks later, by which point the market has moved and half the insights are already stale.

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This sounds nice. I’m not 100% sold on the agent thing yet, sure it is true for B2B, but probably not for B2C (yet). But absolutely yes, it will happen, no later than next year.

But then, this is exactly what I did in my own most recent buying decisions. Let me explain.

The two scenarios

Two big-ticket purchases: a car and an e-bike. Different prices, same mental weight — both above $1K, both paid in cash, both daily-use items for years. Not impulse buys. The kind of decisions you sit with.

No brand loyalty story. The previous car was a Jag, and I bought a Civic. The previous bike was a $100 no-name fixie, and I bought a Tenways e-bike. Specific needs, serious research. Two very different journeys.

Buyer persona for the marketers: 40+, married, no kids, no debt, comfortably middle-class, university-educated, based in Italy.

My decision journey before AI

The previous car was a Jag. Made by people who thought England still had the empire. Devoured a small oil field, visited the mechanic weekly, lived in a heated garage guarded by a wallet service and CCTV. I miss it dearly — but owning it was like Brexit. Deeply seductive as an idea. Hilariously catastrophic in practice.

The next one had to be the opposite. If the Jag was Prince Harry — demanding, spoiled, whining — I needed a civil servant. Unnamed, efficient, asks for nothing, outlasts every government. A Civic. Put 100K miles on it in under four years, which is remarkable given my commute has been zero miles for a decade. Visited the mechanic once a year, only because insurance required it.

It can even climb up to the Carrara mines (as pictured).

The buying process was old-school. I had basic requirements: zero maintenance, full guarantee, four seats, sporty sedan, no SUV, no diesel, no electric, automatic, under $50K, either Japanese-reliable or fixable with duct tape. I researched blogs, user reviews, press brochures, and YouTube car guys. Built a spreadsheet shortlist. Did dealership test drives. Scanned the used market. Ended up with a dealer deal — one-year-old car, 10% off, free window tint, first year insurance included.

All manual. Two months. Exhausting. I bought a sporty microwave that gets me from A to B, which was the goal, but I hated the process. There was no AI then. GPT had just come out.

My decision journey after AI

I was never a bike person. Bought a cheap fixie when we moved to Italy. Don’t wear cycling gear, don’t lock it, zero emotional attachment.

But we live on the Versilian coast — a 30km stretch of flat, uninterrupted sandy beach between Pisa and La Spezia, 50 metres from the sea, 40 metres from a paved bike path running the entire region. We ride weekly. And behind us are the Apuan Alps — mountainous, almost tourist-free, full of small villages we want to explore without a car.

So, an e-bike made sense. I have no cardio, and I just want to ride, not perform. The buying journey was a completely different story.

The buying journey took under a week. It started online — but the only website I actually opened was the checkout page. All the research was done by Claude. I did open YouTube, but only to see the product on camera, since the one I bought had no dealership nearby. I took one test drive at a local shop to confirm the motor type and torque would be enough — different brand, same specs.

Everything else? AI. The number-pulling, the review-reading, the comparison spreadsheets, the deal-hunting — all the parts that are necessary and completely miserable — gone.

If an AI agent had put in my card details, I’d have let it. By the time Instagram started serving me e-bike ads — out of nowhere, obviously, LOL — I was already getting texts from FedEx.

Contrary to what you might think, I am very sceptical about technology and trying to minimize my relationship with it. I was probably among the last people on the planet to buy a smartphone (an iPhone), and I still wholeheartedly hate it. I don’t own gadgets much, aside from my laptop (M Air), and my tablet (iPad). So it is not surprising that I always double-check AI outputs and, most of the time, treat AI as a stupid chatbot.

Despite all this, my very own buying decision journey changed completely in recent years. Especially compared to last year, to be honest. I made a similarly high-ticket purchase (an apartment rental) two years ago, but at that time, AI wasn’t “smart enough” to help me with it properly. I would use AI now, without a heartbeat.

If this is my journey, imagine the journey of someone younger, less sceptical, more connected, and less inclined to question what the AI tells them.

Does your marketing plan account for that?

— Torsten and Peter

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In other words, LinkedIn is a prime source for corporate talk, business speak, and B2B market information for LLMs. Just like Reddit is a source for “what humans think about XYZ.”

It is widely known that Reddit accounts for 40%+ of what LLMs cite when answering questions (compared to 25%+ from Wikipedia). So when you ask about products or topics, the LLM most likely packages information from Reddit & Wikipedia mostly. Now, it is confirmed that for B2B topics, it will be mostly LinkedIn.

There are two logical consequences to this.

Question everything

Look at AI-Ready CMO, which has a combined reach of 35K+ on social media, mainly on LinkedIn. We are active there with daily content. There were times when we were actually active, meaning answering comments, engaging in conversations, and participating in events via LinkedIn.

That’s not the case anymore. The corporate cringe talk is literally unbearable on LinkedIn. If the “confident redneck” style content (that is: X/Twitter) irritates you, we get you. But we also can’t stand the “my cat died, this is what I’ve learned about B2B sales” type of self-promo.

If AI is using this type of content as a source for B2B topics, well… We would argue that you should question the outcomes. This is not just B2B - it’s true for most outputs. Remember, almost half of the source material is coming from Reddit, which is the “public opinion of the internet.” And call us a cynic, but anyone with brains should always question the so-called ‘public opinion’.

The solution to this problem is simple. Pay for the pro-level access for most LLM tools you use. When the output source material really matters, ask for deep research with sources provided. Control your market research. We did a workshop on this - watch it here. Never accept the median output trained on the median of human knowledge.

Rethink your distribution

We don’t sell personal branding services. Never will. Oh god, no. But if you weren’t active on LinkedIn yet, this is the time. If you want to boost your brand credibility, LinkedIn is the place. It should not just be part of your distribution plan. Your distribution plan should start with it.

Our advice: treat it as a billboard. Don’t hunt for engagement. That train is done, LinkedIn is more crowded, cringe, and mediocre than ever. When the “I have many followers, I teach you can be popular on this platform” hyenas appear, selling courses and services, you know, that platform is done for engagement.

But it can still work as a billboard. Even if you don’t pay for it, AI will sweep it and use it in citations. If you have any budget, maybe consider paying - even though LinkedIn is outrageously expensive, borderline road robbery. It might still be worth it for your plan.

We are not 100% sure how this trend will affect citations. But the traditional copywriting teachings on social media content will change, for sure. The usual 3-step approach (write a compelling hook, explain the info, close with a CTA) won’t work much because it is optimized for human attention.

That human attention will fade from platforms where content will be the same, generic, or straight-up generated only for AI agents and crawling bots. Sure, for a while, the inherently personal stories will prevail and cut through the slop - but long-term, you have to be naive to think that you can fight back the volume. Especially if you know that not just AI, but most people simply prefer AI content.

We would be the last ones who judge you for preferring AI over human content. 100X more likely we read a nicely packaged, logically structured content piece by AI than Jimmy’s learnings on B2B sales from adopting his new dog from a shelter.

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Admin could be anything from collecting data to building spreadsheets to massaging PowerPoint decks. The “ground work” we call it now. The last mile for all the work we did.

This last mile has just disappeared now.

Three announcements landed within days of each other. Microsoft launched Copilot Cowork, an AI agent that reads, analyzes, and acts on files directly on your computer. Anthropic updated Claude to maintain shared context across Excel and PowerPoint simultaneously — pull data from a spreadsheet, generate the finished presentation, in a single session. OpenAI gave ChatGPT write access to Google Docs, Sheets, Outlook, and your calendar.

Each of these updates targets the same thing. Not the data. The output. The formatting, packaging, slide-building, and document writing that every marketer does, regardless of their title, specialization, or seniority. The last mile of work. The admin. The ground. The boring.

Quick sidenote:

It is worth noting that all three of these announcements came from different companies in the same week. Microsoft, OpenAI, and Anthropic are not coordinating. What this reflects is that they’ve all arrived at the same conclusion simultaneously — whoever owns the workflow layer owns the enterprise relationship.

What’s the damage?

There are many studies on this type of work. From our own experience, we can safely say that around 20-30% of the entire office workload (marketing or not) was “admin.” Which, by the way, rhymes with the projected %s of layoffs due to AI.

1/3 of the workforce will not be necessary because the work they do has simply disappeared. Not lost its meaning. Not lost its revenue potential. Not outsourced or done by others. Simply just not there anymore.

Some managers will tell you that doing admin builds character. That you need to earn your way through the spreadsheets before you’re allowed to think strategically.

We’ve never bought that.

You don’t learn architecture by hand-drawing floor plans when CAD exists. You don’t learn finance by refusing calculators. The tool removes the friction. What’s left is the actual work — the judgment, the analysis, the decision. That’s what we should be training people for. Always was.

What can we do?

Pre-AI, this admin work had two layers.

• First, the data mining. Pulling numbers, cleaning them, and getting them into a format (sheet). Also known as “reporting.”

• The second was output building. Taking the reported numbers, building up findings, and putting them into a format (deck). Also known as “insights.”

The first layer was already massively affected by AI. Not sure if you have seen, like, anyone, crunching numbers on a spreadsheet manually in 2025. It’s a rare breed. Dying breed.

The second layer disappeared just about now. We knew this would happen because we had early signs in startups like Gamma, and others. But now, it is natively built into most LLMs you use anyway. It’s seamless. Automated. Easy.

So what becomes the new admin? Honest answer: we don’t fully know yet. But the early shape of it is visible.

It’s the work of directing the machine — writing the prompts precisely enough to get usable output, reviewing AI-generated work for accuracy and judgment, maintaining the data sources the machine pulls from, and owning the quality bar when no human built the thing from scratch.

In other words, the new admin is in oversight. And oversight, done well, is not low-skill work. It requires knowing what good looks like, catching what the machine gets wrong, and making the judgment calls it can’t make. The difference is that one person can now oversee outputs that used to require ten people to produce.

That changes headcount math. It also changes what you hire for.

— Torsten and Peter

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At that time, we were mostly talking about how content creation was changing as AI became an infinite source of content. We discussed how organizational restructuring was inevitable as AI became a relentless optimizer and automator of workflows.

What were ideas, assumptions, and predictions last year became reality this year. The boring statement turned out to be true: today’s AI is the best it has ever been, and it will be 10x better tomorrow.

Everyone got the memo. The global white-collar job market is undergoing the biggest restructuring since the invention of personal computing. AI tools have advanced to the point where anyone can create anything digital, literally from scratch, in minutes. Content, copy, video, websites, SaaS products — you name it.

We barely have limits anymore. The question “Can I make this?” has lost its meaning. The questions that matter now: “Should I make this?” And if so, “Why does this have meaning for me?”

The only thing that matters now is your POV. Your way of seeing the world. Your meaning. Your input to the big picture. Your drop in the ocean.

Your human context window. Articulated.

Our weekend edition is usually reserved for paid subscribers. Our audience is marketing leaders applying AI in their work — we are practical, focused, and actionable, especially on weekends.

But because we believe the Human Context Window will be the defining theme of 2026, this edition is free to read, share, and comment for everyone.

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AI is limitless. You aren’t.

It is mid-March 2026. For most general use cases, AI feels limitless. It has access to almost all human knowledge. It can create almost anything digital from scratch, at scale, within minutes. It can learn, self-adapt, and self-improve based on human feedback.

There is a well-known term in AI: the context window. In plain English, it is the limit of what an AI can hold and process at once. The larger it is, the more capable the AI. It works the same way human intelligence does — the more words you know, the better you write. The more you learn, the smarter you get.

One of the central goals of all AI development is to widen this window. More compute, more data centers, more capacity. Technically, as of today, this window still has limits. We have not yet reached the singularity — the point where it shatters and becomes infinite.

But compared to most humans? We can safely say: AI is limitless.

We are not. Far from it.

Our main limitation is the same one we have always had. The same one we will probably always have.

Time.

While AI can consume a 50-page report, build an entire content library, draw a chart, and create an infographic video — all in under ten minutes — we cannot do that. We will never be able to do that.

Don’t be fooled. This is not a productivity problem. This is an existential one. That feeling in your gut — the “what do we do now” panic, the “where’s my meaning” mantra, the “but what’s next” urge — that’s real. It deserves a real answer.

The answer is somewhat like a Zen meditation.

You don’t need to go into panic mode. You don’t need to solve this. It is not your job to solve it, and you cannot solve it anyway. The answer is this: while AI has a logically defined context window, you need to develop an emotionally grounded one.

Your very own human context window.

It is not universal. That’s the point. It is yours only.

Your POV is everything

Before we slide into Tony Robbins territory on self-discovery and self-actualization, let’s stay on the right path.

What is the human context window?

The short version: it’s your point of view. And it means everything.

For you, POV obviously everything. But it is also everything for how you collaborate and interact with the world — with AI, with other humans, with any creative process. Your POV drives all the actions that lead to consequences. So you’d better have one. A real one. Unique to you.

The longer version: the human context window is everything you bring to an AI collaboration that the machine cannot generate on its own. Your judgment, taste, and style. Your goals, feelings, dreams, and needs. Your purpose. Your agency and will to create.

It manifests in actions. Providing feedback. Pushing back on sloppy outputs. Defining the big picture. Correcting what’s wrong. Constant, relentless, endless editing.

The human context window is not a prompt. It is not a brand guide. It is not a strategic document. Those are products of human-AI collaboration. The human context window is the collaboration itself — the work built through commitment, repetition, and interaction over time.

Like institutional knowledge. You cannot buy it, fake it, import it, or mimic it. No one can copy the time invested.

An example

Let’s use us, AI-Ready CMO, as an example. No NDAs at risk here, plus, we know this.

Anyone can write a newsletter for marketers on using AI in marketing. It is not rocket science. Even if it were, AI would handle it. Name a topic — we would bet a million dollars we could write the first ten newsletter editions on it. Above mediocre, domain-expert approved. Shipped in under an hour.

So where is the value? It is in our human context window.

This newsletter is written by two seasoned CMOs with a combined 40+ years in the marketing industry. We complement each other. I (Peter) spent more time in advertising and the creative agency world. Torsten spent more time on the client side in B2B tech. Torsten knows how to code. I created my GitHub account a few weeks ago. Between us, we have seen, worked with, and managed marketing teams at enterprises, mid-sized companies, and startups. I’m more like a lone wolf, consultant by choice. Torsten is a team player. I lived in England and now live in Italy. Torsten has lived throughout South-East Asia and is now in Bali. We are both originally from Hungary. We share personal interests in history, sociology, anthropology, and psychology, and we studied most of these at the university, along with political science. Both of us are dropouts, obviously. Also, fun fact: we both started our careers at Weber Shandwick, a PR firm.

That is our point of view. That is what we bring to the table, whatever we do, write, or speak about. That is our human context.

Is AI-Ready CMO the best newsletter? We would love to be that arrogant. Is it a unique one? Without any doubt. Does being unique translate to anything real? It does — a growing, super-valuable audience and actual revenue (thank you for all of you!). It is not a generic “10 GPT tricks for marketers” daily send-out written by GPT itself, prompted by someone who has never done any marketing at all.

Can you replicate it? You can copy the tone, the content, the style. But you cannot replicate the time it took to develop our human context window.

In a sea of unlimited sameness, being genuinely unique is the only currency that holds its value.

Building your context window

A few practical thoughts on how to develop yours. Or more precisely, how to practice using it.

First: say yes to some things and no to most things.

Have judgment. Be able to articulate that judgment, explain the "why" behind it, and, ideally, systematize it. For marketers, this means developing taste — for the work, for the creative process, for what good actually looks like in your specific domain.

We’ve written about this in depth in our four-part Taste series. Start there:

Second: practice your context window by correcting outputs.

Every correction is a deposit. Every moment you bring genuine judgment into the collaboration, you expand your window. The people creating things in minutes with AI are not simply faster than you. They have made more deposits than you.

When we work with our AI now, it can spin up an entire content library from a single prompt. Not because the prompt is clever. Because we have been collaborating on the same topic for over a year. Our window is wide, deep, and specific. That took time. There is no shortcut.

Third: treat collaboration with AI the way you treat your human relationships.

Don’t switch too often. You wouldn’t cycle through friends and family looking for upgrades. Relationships are built through time and shared history — shallow ones stay shallow no matter how many you collect. Every time you switch AI tools, you lose what was built. Starting over is not free. It costs months. Invest in depth, not in variety.

Don’t just say no — say why. You cannot build a real relationship with someone you never explain yourself to. People cannot read minds. Neither can AI. Whenever you push back, state your reasoning. Not just “this isn’t right” but “this isn’t right because.” That reasoning is a deposit. It compounds. AI is your junior employee with a Nobel potential.

Don’t be transactional. We all know people who only show up when they need something. No investment, no real presence, just extraction. Those relationships never deepen. The same dynamic plays out with AI. If you only show up with tasks, you will only ever get task-level output. The collaboration rewards presence, not just requests. Go deep and wide.

Don’t delegate your thinking. You know that manager who, when facing a decision, turns to the room and asks, “What do you think?” That is not leadership. You can listen, read the room, take input — but then you make the call. Same with AI. Present your logic. Ask for critique. Use it to sharpen your thinking. But never hand over the thinking itself. The moment you let AI decide what is worth making, you have given away the only thing that makes your work irreplaceable.

Creation is not equally valuable

You might think this is about AI. It is not. It is about creating.

Today, we are talking about digital outputs. Tomorrow, we will talk about biotechnology. Next week, it’s robotics on the agenda. In a few years, creation will not be limited to the digital world. It will be everything, everywhere, all at once.

When creation is infinite and accessible to everyone, what is the actual limit?

It is never the tool. It is never the output. It is always the human behind it. Your clarity. Your vision. Your commitment. Your agency. Your POV.

Your human context window. That is the limit. And that limit is also the definition of value. When something is scarce, value lives there. Scarcity is value.

Before AI, there were people who created and people who consumed. The Venn overlap was narrow in the middle. Now that is gone. Creation is available to everyone. The new split is different: people who create with a genuine, unique context window, and people who create with a generic one. Unique gems versus a sea of sameness.

There is still time to develop yours. Even though time is our greatest constraint, there is always time to do the right things. It starts with asking yourself the right questions: what do I want, why is it relevant to me, and how can I make it relevant to others?

That is called human connection.

Rarer than ever.

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Plus: the case against perfectionism, what the “how to make money with AI” course bubble signals, and why Substack feels fundamentally different from every other platform.

About the guest

Leor Gayr is the creator of a Substack newsletter and YouTube channel where AI, philosophy, and speculative science meet. In a year of daily publishing, he has grown Exploring ChatGPT to 60,000+ subscribers entirely through organic Substack growth, no outside advertising, no paid promotion. He runs the newsletter alongside a day job and a real estate business he founded years ago, with a new baby arriving next month. He also wrote a book using AI-assisted writing — the same human-outline-plus-AI-expansion model he uses in his newsletter.

Connect with Leor on Substack and YouTube.

About the host

Peter Benei co-founded , the daily intelligence platform for senior marketing leaders. Peter has been serving as a CMO, marketing leader, and consultant to high-growth B2B scaleups for the past 10+ years. He has a background in advertising, working with Fortune 500 brands.

Connect with Peter on LinkedIn or read his newsletter.

About the show

AI-Ready CMO Live is an unscripted, unedited conversation about AI and the practical applications of AI systems for marketing. We share case studies, examples, and use cases from a wide spectrum of marketers, creators, influencers, service providers, and domain experts.

Want to join us as a guest?

If you have a great case study to share or a valuable use case to discuss, contact us at info@aireadycmo.com. We would love to hear from you and host you as a guest on our livestream.

Top 10 Takeaways

1. Daily publishing is a willpower game, not a skill game. — Anyone can use the human-outline-plus-AI-expansion workflow. Not everyone has the discipline to show up every single day for a year.

2. AI is an individual experience. — ChatGPT trained on a year of your conversations will give you fundamentally different answers than it gives someone who opened an account last week. Time in the seat is a competitive moat.

3. The reflections stay human; the meat can be AI. — Lior’s intros and closing reflections are 100% his voice. The body content is AI-expanded from his outlines. The point of view is always his. That’s the line.

4. Separate your chats, separate your contexts. — One ongoing chat for Substack. One for YouTube thumbnails. One for future projections. Mixing contexts degrades quality. Most people overcomplicate what is actually a very simple setup.

5. AI agents and AI security are the next two big topics. — Agents are the tree. Security is the underreported branch that will matter most. Human judgment and critical thinking are the counter-narrative already gaining traction.

6. Flooding distribution works at scale — if you’ve built the library first. — Automated AI publishing at 2-3x daily gets the same engagement numbers as handwritten posts, once you have 6+ months of content for the agent to learn from. The library is the prerequisite.

7. 59K subscribers. One year. Zero outside marketing. — Entirely within Substack. No LinkedIn, no ads, no newsletter swaps. The algorithm rewards consistency and personality. Leor ran the experiment, and it worked.

8. The “how to make money with AI” course bubble is a tell. — When platforms algorithmically reward content about the platform itself, you get a feedback loop of repetitive, low-value courses. Recognize it for what it is.

9. Perfectionism is the enemy of daily publishing. — The advice isn’t “do it better.” It’s “give up on perfect.” The worst outcome is embarrassing yourself online. Most people survive that. The content you’re most proud of often performs the worst.

10. Substack engagement happens in email, not on the platform. — 700,000–800,000 monthly views come from daily email opens, not platform comments. The engagement is there. It’s just invisible to people looking for likes and comments.

5 Topics Worth a CMO’s Attention

1. The human-in-the-loop content model at scale

The standard debate about AI content is binary: either you write it yourself, or AI does. Leor runs something more nuanced — and more replicable.

His model: every article starts with a human idea, captured on a phone the moment it appears. He writes bullet points. Builds an outline. Takes that outline into ChatGPT. The intro and reflection sections — the parts that carry point of view — stay 100% human. The body content is AI-expanded from his structure, then reviewed, edited, and fact-checked before publication.

This isn’t “AI writes my newsletter.” It’s a division of cognitive labor. Leor owns the thinking. AI handles the expansion. The result reads as philosophical and dense — his Substack covers topics most AI newsletters wouldn’t touch — because the ideas are genuinely his, even when the sentences aren’t.

For marketing teams, this reframes the AI writing conversation. The question isn’t whether to use AI. It’s where to draw the line. Leor’s line is clear: point of view is human-only territory. Everything else is a production problem AI can help solve.

The corollary: on days without ideas, AI becomes an idea generator. You curate from what it surfaces, combine suggestions, and use your own judgment about what’s worth developing. The creative act shifts from generation to selection. That’s a skill most senior marketers already have.

2. AI is personal — and usage time is a moat

Leor made a point that sounds obvious but has real strategic implications: ChatGPT trained on a year of his conversations will give him fundamentally different answers than it gives someone who opened an account yesterday.

The memory that accumulates — the context, the preferences, the patterns of what he finds interesting — functions like institutional knowledge. You can’t buy it. You can’t shortcut it. You build it through sustained use over time.

This has direct implications for marketing teams debating AI adoption. The companies that started using AI tools seriously in 2023 are now operating with a meaningful advantage over those who waited. Every month of delay is a month of compounding context that competitors are building and you aren’t.

It also explains why individual power users outperform teams using shared accounts or generic prompts. The model doesn’t know your voice, your preferences, or your context. You have to train it, repeatedly, over time. The teams that assign AI tools to individuals and let those individuals develop deep, persistent working relationships with them will get better outputs than teams running everything through a shared login.

The practical advice: stop sharing accounts. Stop starting fresh conversations for every project. Build persistent contexts. Let your people develop their own AI working styles. The differentiation compounds.

3. What the AI topic landscape tells marketers about what’s next

Leor runs a generalist AI newsletter with nearly 60K subscribers, which gives him unusual visibility into which topics actually resonate with a broad audience — not just within a specific niche.

His read on the current moment: AI agents are the dominant topic, and for good reason. But the more interesting signal is the counter-narrative emerging alongside it — content about human judgment, critical thinking, and human-in-the-loop approaches. People aren’t just asking “how do I use AI more?” They’re also asking “where should AI not be?”

The upcoming topics he flagged: AI environments (the infrastructure where agents operate), predictive AI (running scenario models with variable inputs), AI security (deeply underreported given the risks), and robotics as the prerequisite for AGI claims to mean anything in the physical world.

For marketers, the security angle is particularly underserved. Leor noted what Peter confirmed from the AI-Ready CMO audience: marketers are routinely feeding company data, campaign strategy, and proprietary information into public LLMs — likely in violation of every NDA they’ve signed. The topic barely registers in marketing-specific AI content. That’s a gap.

The ambient AI angle is also worth watching. As social feeds become saturated with AI-generated content, offline experiences become relatively more valuable. Ambient AI in physical environments — smart signage, adaptive retail, context-aware OOH — could become a significant play for brands willing to move early.

4. The distribution automation question: where humans stay in control

Leor considered automating his YouTube publishing — and decided against it. His reasoning is instructive.

YouTube is permanent in a way other platforms aren’t. Delete a video, and you lose the views, comments, and algorithmic history permanently. There’s no undo. For that reason, he keeps final publishing decisions human, even as he automates adjacent parts of his workflow with Opus Clips and Descript.

Peter shared the AI-Ready CMO approach: Jenny, their internal AI agent, handles cross-platform social distribution automatically — 2-3 posts per day across all channels — with the same results as manual posting. The key enabler was having 6+ months of daily content for the agent to learn from. Without that library, the quality wouldn’t hold.

The pattern that emerges: automate where you have enough context for the AI to replicate your voice reliably, and where errors are recoverable. Keep humans in the loop where the stakes of a mistake are permanent — account reputation, legal exposure, irreversible platform consequences.

This isn’t a framework that works for teams just starting out. It’s a reward for consistency. You have to build the library first. The automation becomes possible only after you’ve shown up long enough for the AI to know what you sound like.

5. Why Substack feels different — and what it means for B2B marketing

Both Leor and Peter arrived at the same conclusion independently: Substack readers are different from social media audiences in a way that matters for how you think about engagement.

Leor’s engagement numbers look invisible on the platform — few comments, minimal public likes. But 700,000–800,000 monthly views come through email, from subscribers who open every day because they’ve opted into a daily relationship with his content. When he links his YouTube channel in a relevant article, he gets 1% conversion in 24 hours. On Instagram, that kind of audience would generate more visible engagement but less controllable reach.

The practical difference: Substack subscribers who open daily emails have made a repeated, deliberate choice. That’s a fundamentally different relationship than someone who saw a post in a feed and clicked. The conversion economics are different. The trust level is different. The tolerance for depth is different.

For B2B marketers evaluating owned media strategies, this is the case for newsletter-first thinking. Not because Substack specifically is the answer, but because building a subscriber relationship — where people opt in repeatedly to receive your content — creates a different quality of audience than social reach ever does. You can reach more people on LinkedIn. You can convert more of them from a list they chose.

Leor grew to 60K+ through Substack alone, no outside marketing, in one year. That’s not a template — it’s a proof of concept that consistent, specific, personality-driven content still compounds organically, even in a saturated market.

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We’ve been discussing this before. We’ve said publicly that information should be free for everyone. We don’t believe in paid self-paced courses, standalone reports, or anything that the infotainment industry thinks is “valuable.”

To be honest, anyone who’s asking money for packaged information in the era of AI is living in a world that has long passed.

We also wanted to make a statement and prove our argument. Which was a simple one: with the combination of domain knowledge and AI, you can build up a content library of valuable packaged information within days.

In our case, the domain knowledge comes from us. We are marketers. We use AI. We know how to put AI to work. And we know how to scale with AI. So, why not build up the largest free content library on the internet around AI marketing, charge nothing for it, and launch it within days?

We’ve also wanted to prove that SEO & content generation shouldn't be problems for marketers today. For a detailed “how did we do it”, here’s our walkthrough:

So, what’s in the hub?

• 5+ fully built courses. Topics: AI CMO leadership, building AI agents, technical literacy for marketers, AI SEO mastery, learning to code with AI for marketers, etc.

• 5+ workshops. Topics: market research with AI, AI team management for marketers, building an AI content library, AI ROI, prompting for marketers, etc.

• 250+ AI marketing tools. All of them reviewed, categorized, segmented, compared, listed, explained, etc.

• 100+ manuals and guides. Long-form manuals on AI marketing topics, short-form guides, and practical templates & checklists.

• 1000+ bonus resources. Things like: prompts, AI marketing stats for your decks, interactive frameworks, FAQs & glossaries, and many more.

How to access it?

• All of the content is free to access. No need to pay for anything. As said above, wake up, it’s 2026, information is free.

• The most valuable content pieces are email-gated. We didn’t build this for charity. We did it to drive signups and SEO. So things like the courses, you have to put in your email.

Once you’ve added your email, you have browser-based access forever. Access to high-value resources is instant. There is no need to wait for a confirmation email or further links. Also known as: hassle-free access.

Browse 1000+ free resources in the Hub

We will continue sending updates and references to the hub. If we feel there is a tool missing, we will put it up. If we think you could benefit from a quick guide, workshop, or course to discuss a certain topic, we will put it up. The content you see there now is only the foundation for a living, breathing, updating, self-improving archive.

This also means that things can break. Links might not working, design might be off, things are not interconnected. This is fine, it is anticipated. There are thousands of pages afterall in this. If you’ve spot a bug, report it pls by sending an email to us.

We will also send you updates on how this hub is doing. Not to brag, but to prove a marketing use case for you. If we are able to do SEO magic with this, you can do it too.

The page is online since the end of February. It’s already generating thousands of pageviews. No backlinks, no ads, no promotion. Pure keyword content indexing. This only gets better.

So if information is free, what is paid then? As said before, experience. Applications. Deeper, custom-made insights.

Our paid program covers most of this:

• Custom-made AI marketing tools that you can use to scale your marketing

• Heavy trend reports on AI marketing that shows you what’s up in each month

• Community options with the ability to comment, chat, and engage with each other

And due to popular demand, we are thinking to expanding into services as well. But creating, packaging, and sharing information? Access is free, forever.

— Torsten and Peter

3 AI Marketing Tools To Try Today

Simplified

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