Back in July, Elena Verna described a scene she keeps running into. Someone tells her AI has changed their life, and she answers: “Cool. Show me.” Most of the time… there isn’t much to see 😅

You get asked that question too. By your CEO when you pitch an idea. By your next hiring manager when they read “AI-fluent” on your résumé.

For twenty years, marketing had one thing to show: a slide, a mockup if you were lucky. Then you wrote a brief, opened a ticket, and waited for engineering to find the time.

Those days are over. Today, showing an idea takes ten minutes, even if you can’t code. For the first time, a marketer can demo an idea instead of spending a whole meeting defending it.

Your best idea is ticket #47

Take a simple example. You want an ROI calculator on your pricing page. You believe in it, you’ve even seen one on a competitor’s site.

You write the brief, you open a ticket… and your calculator lands in 47th place, behind the entire product roadmap.

Nobody’s at fault here. Engineers work on the features that make money, which is exactly what they’re paid to do.

The waiting isn’t the worst part. The worst part is every idea you stop pitching because you already know it’ll end up in 47th place. Those never show up in any report.

Try it: open your backlog and count the marketing tickets that have been sitting there for more than three months. Then ask yourself how many ideas never made it in at all.

Now the opposite case. Three weeks ago, I released a Momentum episode with Tim Soulo, CMO of Ahrefs. He’s never written a line of code either. That didn’t stop him from building himself, in one afternoon, a tool that shows which companies engage with his LinkedIn posts and matches every contact against Ahrefs data.

At Bulldozer, the best example is the webinar recap. “Recap” sounds small, but it’s a whole project:

• an email with the key points from the discussion

• a screenshot from the video

• a PDF covering every takeaway in detail, with an appendix of audience questions and our answers

• all of it on brand

• and the attendees: we pull the list, enrich every contact, and load them into a HubSpot list so everyone gets the recap

It used to take one person at least two full days. Today, all of it is automated. The webinar ends, and ten minutes later the recap is ready. No engineer involved.

Our ABX webinar recap (in French), ready ten minutes after the live session

So you can make the call the same day, after clicking through the real thing.

One idea, two paths

I know what you’re thinking, and it’s a fair objection. A prototype a marketer hacked together isn’t a product. It’s not secure, nobody maintains it, and your IT or legal team will block it long before it goes live.

True. BUT nobody’s asking you to ship it to production. The prototype replaces the brief. That’s it. Your product team gets something that works instead of a three-page doc, and they save time too.

Four things you can show tomorrow

1️⃣ The idea, to your CEO

In the leadership meeting, instead of commenting on a slide, your CEO clicks through the tool. And the question shifts from “would this work?” to “when do we launch?”

Still, keep Verna’s test in mind: if we removed this tool tomorrow, what would break? If the answer is “nothing,” it was just a demo.

2️⃣ The program, to your client

This is where we use it most. On an ABM engagement, we no longer send a monthly PDF report. The client has their own control tower, open whenever they want, with their target accounts and every signal on each one.

Our ABM control tower (demo data, account names blurred)

My favorite screen is the ICP × engagement matrix. Each dot is an account: the ICP score tells you whether it’s worth going after, engagement tells you whether it’s ready to talk. One glance, and you know who to call this week and who needs a nudge.

The ICP × engagement matrix, one dot per account

Same with lead magnets: we show the mockup before writing a single line, and the client picks after seeing the result.

3️⃣ The tool, to your market

An ROI calculator or an automated site audit beats an ebook on the same topic. Your prospect walks away with THEIR number, not a PDF they’ll (maybe) open in two days.

And the tools you pay a subscription for, you can now build yourself. Our visit tracker, which pings us on Slack the moment a document is opened, with the person’s name, was built in-house by someone who isn’t an engineer.

We’ve built four more internal apps over the past two months. Each one answers ONE question we used to ask every single week.

Who should we ask for a testimonial, and when? The app cross-checks client feedback, invoices and churn risk, then tells us who to ask and who to leave alone.

Client testimonials: who to ask (names and logos blurred)

How are Rise registrations going? Rise is our event for marketing leaders, in Paris on November 17. The app tracks how many of our targets have signed up and the mix of clients vs. new business, refreshed every morning from HubSpot.

Rise Tracker overview (numbers blurred)

What do our webinars actually bring in? Every webinar is wired into HubSpot: registrants by tier, then the deals that come out of it.

Every webinar connected to HubSpot (deals and pipeline blurred)

Who’s posting what on LinkedIn this week? One calendar for the whole team, and posts go out on their own, right on schedule.

LinkedIn Command Center, the team calendar (post copy blurred)

4️⃣ Yourself, to your next hiring manager 🚨

With AI, everyone sounds like an expert. MCP, RAG, agents… everyone has the jargon down! So hiring managers want proof. I think a hands-on case study on a real brief will replace the interview where everyone talks about what they can do.

Same thing internally: the person who walks into the meeting with a working version beats the one with a slide. Every time.

And the field is still wide open. Tim Soulo estimates that 9 out of 10 marketers have never built an app with AI. Most of them know it’s possible… they just haven’t started.

Your résumé says “AI-fluent”? Cool. Show me.

Ten minutes, with one catch

Your first prototype will take an hour, not ten minutes. And that’s fine!

You earn the ten minutes. Our webinar recap ships in ten minutes because our brand guidelines, our context and our Claude skills were written ONCE. That machine took us months to build (I wrote about it in The era of the “AI marketing OS”).

Your first prototype doesn’t need a machine. Your hundredth does.

From brief to prototype

1️⃣ No idea goes to a leadership meeting without a clickable version.

The brief becomes the appendix.

2️⃣ Start with a task you do by hand every week.

Your Monday report, or prep for a client meeting. Claude Code or Lovable is enough to get going, plus two hours blocked in your calendar each week.

3️⃣ Put in writing who does what with product and IT.

Prototypes, internal tools and demos sit with marketing. Production, customer data and security sit with engineering. One page is enough, and it saves you a cold war with the product team.

Who does what, between marketing and engineering

And yes, this is something we help with.

Ticket or prototype

You can keep describing your ideas and waiting your turn in 47th place. Or you can show them tomorrow morning.

Your ideas were good. They were just missing proof.

(I didn’t open a single ticket for this email.)

Let’s grow 👊

— Jordan

Mélissa Sanchot is Marketing Director at HelloAsso, a French association management platform founded 17 years ago. The platform hosts 480,000 registered associations, serves 20 million users, and facilitates over a billion euros in annual collections. It is entirely free for associations: the business is funded by voluntary contributions from citizens at the point of payment. Before HelloAsso, Mélissa was among the first marketers hired by Uber in France, during the launch of the platform's first European cities.

🎙️ Listen on YouTube, Apple Podcast, Spotify

HelloAsso is a genuinely unusual business. The product is free, the activation funnel is constrained by regulatory KYC requirements, and revenue only materializes at the end of a long chain, after associations have collected funds, and only if citizens choose to contribute voluntarily. Growing in that environment requires a reading of the economics that most marketing teams have never had to build.

In this episode, Mélissa Sanchot breaks down how she operates within those constraints, and what it means concretely for her team structure, the profiles she hires, and the bets she is placing for 2026.

Here’s what I took away:

1) HelloAsso’s revenue depends on four variables, and marketing doesn’t control all of them.
The equation: number of associations acquired, multiplied by activation rate, multiplied by the amount those associations collect, multiplied by the voluntary contribution rate from citizens. That last variable shifts based on the association’s sector, its size, and even how frequently a given citizen has already interacted with the platform in a short window. Someone who pays for an activity, buys an event ticket, and donates to an animal shelter in the same week develops a kind of contribution fatigue. Modeling this accurately took years. And there are macro variables nobody controls: when public subsidies to associations contract, HelloAsso feels it directly in its numbers.

2) Regulatory KYC landed in the middle of the activation funnel.
HelloAsso is a licensed payment institution regulated by the CPR. For a long time, associations could start collecting immediately after signing up, with identity verification only required at the point of their first payout. That hook allowed the platform to build usage before asking for documentation. Since switching to its own payment infrastructure 18 months ago, verification now happens at the point of entry. The result: associations arriving without all their documents ready, a funnel that drops off earlier, and a direct acquisition lever that became significantly harder to pull. Mélissa’s response has been to work directly with the compliance team to profile associations by risk level and calibrate the friction accordingly, rather than applying the same requirements across every sign-up.

3) She removed the product marketer role. And she explains exactly why.
Her observation: the product marketer deresponsibilizes PMs on market understanding, and deresponsibilizes marketers on product knowledge. Each side waits for the PMM to do the bridging work. The bet she took instead: generalist marketers, each sectorized by vertical (sport, culture, générosité), owning their subject end to end, from content to product launches. A marketer who knows their sector deeply and can use AI to cover the technical content gaps is more useful to her than a functional specialist without sectoral grounding.

4) Moving upmarket required building a sales function from scratch.
HelloAsso historically ran on self-serve and volume. There were no account executives, no real sales process, no pipeline machinery. To address large associations, which operate more like structured companies than volunteer collectives, that entire function had to be built. HubSpot was connected, a lead gen engine assembled, account managers hired and trained. And just like the marketing team, the sales team is sectorized: pitching a professional sports federation with the same language as a charity fundraiser does not work.

5) 37% spontaneous brand recognition, and nobody knows what HelloAsso actually does for them.
HelloAsso’s brand tracker shows 37% spontaneous awareness. That is a strong brand by any measure. But the same tracker reveals something uncomfortable: everyone has a positive feeling toward HelloAsso, and nobody has any tension with it. Mélissa’s read on that: if you haven’t managed to create some tension, you don’t really exist in your user’s environment. The answer in 2026 is to build a direct relationship with the 20 million citizens already using the platform, starting with the acquisition of an existing app with 2 million active users built around sports association management, with plans to extend it across all sectors.

6) At Uber, the brief was: do marketing, but you have no budget.
Mélissa was one of the first marketers hired by Uber in France. To launch Nice, she mapped the city through human conversations, identified the local figures who mattered, and organized private concerts in people’s living rooms with a locally known singer, delivered by a branded Uber. People competed to get a slot. Local press covered it. What she took from that experience: when there is no budget, creativity and the willingness to knock on doors directly become the only real levers. She says that pirate mindset never left her.

Find Mélissa on LinkedIn : https://www.linkedin.com/in/melissa-sanchot/

Listen now

Enjoy,

— Jordan

Small spoiler: the question was about something else entirely, and the conversation drifted into product marketing. I walked out of that studio with an idea I’d been circling for months without ever pinning it down. I haven’t stopped thinking about it since. Hence this edition.

For the past fifteen years, marketing has been a very (too?) technical job. Setting up the CRM, building campaigns, wiring tools together, chasing the dev team for a landing page… That’s where the days went, and almost nothing was left for the actual core of the job: understanding your audience and deciding what to tell them. So we did what we always do when nobody has the time. We created a new role for it.

The product marketer.

But AI is pushing all that technical work into the background (I wrote about it in The era of the “AI marketing OS”). And once it’s done, the work left for a marketer will look exactly like a PMM’s job.

That’s probably why PMM is one of the most in-demand roles right now. To me, it mostly proves the title is living on borrowed time 🚨

Accountants went through this in 1979

This idea didn’t come out of nowhere. We’ve seen this movie before.

In 1979, VisiCalc launches on the Apple II, the first spreadsheet for the masses. Until then, an accountant who wanted to test one assumption in a forecast redid every line by hand and spent their evenings on it. With VisiCalc, you change one cell and watch the whole sheet update in front of you.

Obviously, everyone predicted the end of accountants.

Decades later, Planet Money ran the numbers. Since 1980, the US has lost about 400,000 bookkeepers and accounting clerks… and gained about 600,000 accountants. The spreadsheet wiped out the arithmetic and left the expertise. And expertise ended up creating more jobs than arithmetic lost.

The clerks had spreadsheets too. They used them to calculate faster, and the jobs went to the people who used them to think.

Why everyone is hiring PMMs right now

With AI, any company can ship the same homepage and the same pitch as its competitors in an afternoon. Read three websites in your category back to back with the logos hidden: you’ll struggle to tell who’s who. So will your buyers 😅

Companies have figured it out. Differentiation has become THE problem, and they’re hiring specialists to fix it.

To me, that’s step one. Step two comes when execution no longer needs anyone. At that point, the work companies pay a specialist top dollar for today becomes the whole team’s job, because it’s the only work left.

You’ll tell me the time AI saves mostly goes into volume: twice the posts, twice the campaigns. True. I see it in plenty of teams (Bulldozer’s included).

Try this with your PMM’s job description

Grab it and cross out everything AI can already do. Launch decks, battlecards, one-pagers, website copy… There’s barely anything left.

Be honest: you were the only one who ever opened that last battlecard.

What survives comes down to two things: knowing your audience well enough to use their words, and deciding what to tell them. Now pull up the job description of your paid lead, your SEO person or your content manager, and cross out the same things. They’ll be left with those exact two lines.

Tomorrow, that’s their job too. QED.

Some companies are already there. At Ahrefs, whose CMO Tim Soulo I had on Momentum, every marketer starts in customer support, and nobody holds a PMM title. The title won’t vanish overnight: a few senior people will keep it to own the big narratives. Everything else goes to everyone.

How to turn every marketer into a PMM

1️⃣ Write your positioning once, as a team.

Half a day per quarter with leadership and sales. After that, every marketer, from paid to SEO, should be able to explain in two sentences why a customer picks you. AI turns that foundation into battlecards and pitches.

2️⃣ Put every marketer on a customer call every week.

Twenty minutes is enough. The exact words go into a shared doc, and that raw material feeds everything else.

3️⃣ Decide where the saved time goes, before it drifts into volume.

At least half the hours AI frees up go to customers and positioning. Track it monthly, like your pipeline.

And yes, this is something we help with.

Your call

You can give the time AI hands back to volume, or to your customers and your positioning. It’s the choice the clerks faced in 1980, with Claude instead of the spreadsheet.

Tomorrow, everyone’s a PMM. Or replaced by the people who already are.

Let’s grow 👊

— Jordan

Tim Soulo has been CMO at Ahrefs for 11 years and has never once defended his marketing budget to his CEO.

He tracks revenue, not attribution. He scores every piece of content by how likely it is to convert a reader into a customer. And as the search landscape shifts from Google to AI, he’s using Ahrefs’ own web crawler to catch misinformation before the models train on it.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Here are the 6 things that stayed with us.

1. Attribution is a dead end for most B2B products
Ahrefs deals with buying cycles of 9 to 12 months. By the time someone signs up, tracking the original touchpoint is nearly impossible. Tim’s approach: watch revenue. If it’s growing, marketing is working. If it slows, go find out why. No dashboard required.

2. The business potential framework
Every piece of content Ahrefs creates gets scored from 0 to 3. A score of 3 means there is no way to solve the problem without Ahrefs. A score of 0 means the content doesn’t mention the product at all. Tim’s rule: cover every 3 before you touch a 0.

3. Events without ROI tracking
Ahrefs sponsored its first conference 7 years ago for pure branding. No badge scanning, no lead attribution. The logic was simple: they existed online but nowhere in the physical world. Today they sponsor more than 20 events a year, and Ahrefs Evolve covers 80% of its costs through ticket sales and sponsorships.

4. GEO runs on the same logic as SEO
The questions people type into ChatGPT mirror what they search on Google. Tim’s starting point: take your highest-traffic content, feed it to an LLM, and ask which prompts would lead someone there. Track directional trends across two or three AI systems. Stop chasing precise percentages — they shift every time you measure them.

5. SEO is already a GEO problem
A pricing mistake on the Ahrefs website quietly spread across the web. AI tools scraped those pages and amplified the wrong information. The fix Ahrefs built: use their own crawler to detect every new mention of the product the moment it goes live, analyze sentiment with AI, and reach out to fix errors before any model can train on them. Tim’s framing: your website is now written for two audiences simultaneously, humans and machines, and the machines are less forgiving.

6. Passion for the craft beats AI fluency
AI can generate ideas indefinitely. What it cannot do is tell you which ones are worth pursuing. Tim hires for people who read about marketing, talk to other practitioners, and build genuine taste over time. That judgment, he says, only comes from experience accumulated the hard way.

Find Tim on LinkedIn : https://www.linkedin.com/in/timsoulo/

Listen now

Enjoy,

— Jordan

“We want to automate part of our teams’ work with AI. We have no idea where to start.”

It’s the request we get most since January, and it almost always lands from the exec team with a yearly target attached and a deadline on it.

By the time it reaches us, the company has usually picked its own answer: a training plan.

Workshops, a prompt library, an AI champion in every department, the whole thing tied up with a bow. It’s an easy sell.

Spoiler: it lasts three weeks 😅

(The training plan does have one genuine merit: it looks fantastic on a board slide!)

The part everyone wants to skip

When we start one of these rollouts with Bulldozer, we don’t touch AI at all. We watch people work.

What they open, in what order, what gets copy-pasted between windows, and at what hour of the day.

Then we write it all down. Company context, brand, internal vocabulary, the market signals worth tracking, the processes… On our side that lives in Bulldozer OS, which exists for a fairly unglamorous reason: this layer takes weeks to assemble by hand and it’s the same work on every account.

By the end, the OS knows the company. What it sells, who it sells to, and which numbers matter.

The rocket is ready to fly to Mars but… usage stays at zero.

But why? Everything is right there!

The moment we got it

The first time this happened we went digging into change management. Long story short, that’s a real part of it and it does matter. But you always land back on the same question: how?

Because what’s actually happening is simple. You built a machine, it works, and you’re asking your team to fly it through a conversation. Except a conversation shows you nothing! It sits there waiting for your prompt.

Put yourself in the seat of the person who opens Claude or ChatGPT on a Tuesday morning. They know something big sits behind it (a whole machine that flies to Mars), but they don’t know what to type, what to ask, and above all where the edges are!

Meanwhile fourteen emails and notifications are waiting. Right… Mars can wait!

The end.

The clearest example, to me, is data. Ask Claude where we stand on a number and the answer lands in ten seconds, sourced. Our clients still want a dashboard with the numbers already on screen.

Some call that resistance to change. I think it’s mostly that a dashboard shows what exists without anyone having to ask for it!

Four steps that actually move the number

1. Watch them work for a week, without proposing anything.

Sit next to them. Write down what they open, the order they chain it in, what moves between two windows, and the files called “v4 final for real” (we’ve all got one). That’s where the repeat uses live, and nowhere else. No survey will get them out, because nobody volunteers the thing they do on autopilot.

2. Write the context before building anything.

Brand, processes, vocabulary, reference numbers. Somewhere stable. Ideally a database.

3. Keep only the uses already done three times by hand.

A use that keeps coming back is a use that exists. The rest is a guess, and guesses get expensive in tokens. On a marketing team you’ll find ≈10, rarely more.

4. Build one interface per use 🚨

This is THE hack. A table, a list, a pre-filled form, a small app with a button that fires the task or the whole process. One object, ONE job, and nobody has to explain it. No prompt, just a button (say: “Create my SEO brief”).

That’s the step where the number moves. Across our rollouts we go from almost no usage to roughly ten times a day per person. Same model, same team, same written context. The only thing that changed is what they’re looking at.

Four rules so you don’t repeat our mistakes

Build nothing for your technical people. Your two or three power users are fine in the chat, because they already know what’s in there.

Start with the things people can see. For everyone outside the technical crowd, a table beats a good answer. We haven’t hit an exception yet.

Put an expiry date on everything you build. These things are meant to be thrown away. Needs shift, models get better, and rebuilding is cheap now (which is one of the real wins here!).

Your metric is repeated usage, per person, per week. Full stop.

Your call

You can fund one more training session. Or you can take the three things your team already redoes by hand every week and put a screen in front of each one before the month is out.

And yes, as you’ve probably worked out, this is something we do.

Let’s grow 👊

Jordan

Frédéric is CMO at Fullenrich, based in San Francisco. Originally from France, he spent eight years in Paris before joining Capgemini, which sponsored his move to the US in 2010. After years in consulting and then several startups, he joined Fullenrich exactly one year before this recording. Fullenrich is a cascading data enrichment platform that aggregates 25 data providers to deliver verified contact data to go-to-market teams.

🎙️ Listen on YouTube, Apple Podcast, Spotify

There’s a classic mistake companies make in commoditized markets: assuming the best product eventually wins. Frédéric doesn’t buy that. When four solutions look identical, when competitors outspend you on paid by a factor of ten, and when buyers make this decision once every three years, the product alone stops being enough. What’s left is the relationship. And relationships are built long before a prospect has a live project.

That’s the insight behind everything Fullenrich does in marketing.

Here’s what I took away:

1) In a commodity market, performance marketing makes the problem worse
When everyone looks the same, spending more on paid just means fighting over a fixed-size pie against players with ten times your budget. Zoominfo and Apollo can saturate Google Ads. Fullenrich can’t keep up. In that context, performance marketing amplifies the leaders’ visibility, not the challengers’. Brand marketing does something different: it creates demand where none existed, reaching buyers that paid channels would never have touched.

2) The influencer channel wasn’t chosen on instinct. It was chosen because it was the only one with an immediate, measurable signal
Before Frédéric joined, the founders had tested multiple channels: ads, content, influencers, events. Only one showed a direct and near-instant effect on sign-ups. That radical pragmatism, starting from what actually works rather than what should work in theory, is why their influencer strategy holds up. It wasn’t built on conviction. It was built on observation.

3) Most companies use influencers as a distribution channel. Fullenrich treats them as an entry point
A post going live is 10% of the work. The other 90%: identifying who engaged, pulling them out of LinkedIn to qualify them, turning the best-performing content into LinkedIn Leadership Ads and then into paid campaigns, and asking at every step whether the activation can generate more content, commercial leads, or partnership opportunities. Frédéric’s standing rule: 1+1 has to equal 3.

4) Times Square and NASCAR cost them nothing. That’s not the interesting part.
The Times Square billboard was secured in exchange for data credits, no cash involved. The NASCAR sponsorship with driver Ryan Ellis works on the same logic. But beyond the zero cost, what matters is the principle behind it: find partners who need what you have rather than money you don’t. Then squeeze every ounce of value out of each activation, in content, in leads, in commercial conversations.

5) At SaaStr, they threw a happy hour with no stand, no badge scanning, no CTA.
SaaStr is the flagship B2B SaaS conference in California. Fullenrich wanted to be there, but a traditional booth next to Zoominfo and Apollo, with branded merchandise and a pitch on loop, made no sense financially or strategically. So they rented a bar nearby, opened the doors to everyone, free, no qualification at the entry. The venue filled well beyond capacity. The most common feedback: “I had no idea you existed.” And: “This was the best moment of the conference.” Several people asked to see the product before the night was over. That result says something meaningful about the market: when your category is perceived as boring and impersonal, being the company people actually want to spend time with is a real competitive advantage.

6) Brand doesn’t replace performance marketing. It makes performance marketing work better.
Fullenrich’s model is sequential. Brand builds awareness and drives visitors to the product. The product, accessible for free in seconds, does the work of convincing. For qualified users who don’t convert, performance marketing steps in, targeted, informed by actual behavior in the product. Without brand upstream, performance operates in a vacuum. With it, performance amplifies what already exists.

7) AI compresses production cycles. It doesn’t replace the foundational work on identity.
Ideas turn into content faster, iterations happen at a speed that wasn’t possible before. But in a market where every competitor will accelerate their content output with the same tools, the advantage shifts to the companies who know exactly who they’re talking to and what they stand for. That’s where Fullenrich invested first. And that’s what gives the acceleration something to build on.

Find Frédéric on LinkedIn : https://www.linkedin.com/in/frederic-mathieu/

Listen now

Enjoy,

— Jordan

Jonathan Chemouny leads Sales Development for Europe at ElevenLabs. Before that, he spent years in classic SaaS, including at Intercom. At ElevenLabs, he has no official job title, like everyone else on the team. He is responsible for pipeline sourcing across the entire European continent, with a team that prospects face-to-face with large accounts, organizes workshops, and pitches at conferences.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Most sales teams dream of having 95% of their pipeline come from inbound. Jonathan Chemouny says that when he arrived at ElevenLabs in that situation, he understood it was a problem to solve.

In this episode, we talked about what AI is actually changing in outbound, why the SDR of the future will be out in the field, and how ElevenLabs uses its own products to prospect.

Here’s what I took away:

1) 95% inbound is fragile.
When Jonathan joined ElevenLabs just over a year ago, inbound represented almost all of the pipeline. The problem: the moment there’s a slowdown, you’re immediately impacted. You can’t build a predictable business on inbound alone. Today, in Europe, outbound accounts for 45% of pipeline volume. Last month, it was 50-50. At the same time, total pipeline volume tripled, if not quadrupled.

2) The SDR of the future gets out from behind the screen.
Jonathan hires more experienced profiles than what was traditionally expected of a SDR. He wants people who can pitch to large accounts, visit prospects on-site, organize workshops, and present ElevenLabs at conferences. He gives concrete examples: one member of his team presented to Kering. Another presented to several airlines at a trade show. When a candidate tells him that what matters to them is booking meetings, that’s a red flag.

3) The KPIs that matter: opportunities, ACV, and percentage of outbound.
Number of emails sent, number of calls made: Jonathan no longer tracks those. What he cares about is the number of opportunities created, the size of the deals sourced, and the share of outbound in the mix. Outbound is where you control your destiny and the quality of what you go after.

4) AI makes SDRs significantly more productive. It doesn’t replace them.
The promises from AI SDR companies claiming that pipeline will arrive on its own, automatically, with no effort required: Jonathan says it doesn’t work, and that everyone who has tried it has seen that. What AI actually does is amplify SDR productivity. He uses the electric bike analogy: you go much further with the same effort.

5) At ElevenLabs, the demo is the prospecting message.
Jonathan sends voice notes to prospects generated with a clone of his voice via ElevenLabs. The message: “I wanted to invite you to a demo, but the demo is right now, because what you’re listening to was recorded with ElevenLabs and a clone of my voice.” He also builds mini-sites on Lovable for each target prospect, with ElevenLabs integrated directly. And he developed a voice agent that prospects can test themselves: a 5 to 6 minute conversation that ends with a booked meeting.

6) Their AI SDR handles inbound, in real time, in every language.
When ElevenLabs launched a new model a year ago, the team was too small to absorb the inbound volume. They built an AI SDR capable of qualifying leads in real time, in any language, 24 hours a day, and routing them to the right account executive. Qualification accuracy is at 100%. Before, a lead who reached out on a Friday evening had a meeting scheduled for the following Wednesday. Today, it’s Monday morning.

7) No job titles at ElevenLabs. Everyone is GTM.
Nobody has an official title. Everyone on the sales team is “GTM.” It removes hierarchy, frees up initiative, and gives the same weight to a junior SDR and the equivalent of a VP. And the unexpected bonus: when prospecting, prospects confuse GTM with General Manager. Open and reply rates are significantly higher.

Find Jonathan on LinkedIn : https://www.linkedin.com/in/chemouny/

Listen now

Enjoy,

— Jordan

For a sense of scale, HR budget growth is set to fall from 2.4% in 2026 to 0.7% in 2027 🌀

That stings.

So the cut you are bracing for in your 2027 planning is not coming. Your CFO is not sharpening a knife with your name on it.

BUT (obviously), you are still going to get squeezed.

The Gartner survey, run with 401 CMOs between January and March, shows that inside that budget, 15.3% is now going to AI.

Tech. Paid for by marketing.

See the sleight of hand?

We are seeing the same thing land in our own inbound. Since 1 May 2026, across more than 200 sales conversations, 26.3% of inbound requests at Bulldozer are about deploying AI inside the marketing organization. These are internal transformation projects, paid for with marketing money.

(The data starts in May because before that, nobody was asking us for this.)

Who pays for the 15.3%

A Gartner study (again) published in June 2026 asks the blunt question: where did the money for your marketing AI program come from?

The answer: 86% funded it by cutting inside their own budget. Production, headcount, agency fees, media. Only 14% got new money from the company.

75% have no reinvestment plan. When the savings show up, they go back to the central budget.

Marketing pays for the experiment. The company keeps the ROI.

The obvious objection is that all of this is rational. If AI genuinely makes marketing cheaper, funding it from the marketing budget and handing back the savings is exactly what a well-run company should do. In The CMO Survey, CMOs report a 14.6% reduction in their own overhead thanks to AI, up from 7.2% in late 2023. On those numbers, finance is right and this newsletter is wrong.

Except somebody measured the actual return on those programs: between 0 and 10% on average 🥲

Only 7% have fully autonomous agents running in production. And 44% are funding the next AI wave with the savings from the last one.

So you are trading a known line for a return that lands at half its promise, and that you do not even get to keep 🥲🥲

Meanwhile belts are tightening, and it is starting to show.

Flat budget. Rising revenue. A sixth of the envelope reallocated. Technically that is not a cut, which is exactly why it clears without a fight.

Measuring better, deciding less

Marketing measurement has never been this good, thanks to AI. Unfortunately, it has never bought so little.

1️⃣ Measurement made a spectacular jump.

Marketing teams using AI to measure performance and produce reporting went from 24.8% in late 2023 to 46.3% in early 2026. Google open-sourced Meridian in January 2025. Meta open-sourced Robyn before that. Just over half of US teams now run incrementality tests. The tooling problem that has occupied the last ten (twenty?!) years is largely solved.

2️⃣ It bought you almost nothing.

In July 2026, the World Federation of Advertisers surveyed 71 CMOs responsible for around $40 billion in annual ad spend. 80% already run marketing mix models and brand lift studies. 15% say that evidence actually determines how their budget gets set.

The bottleneck is authority, and it sits one floor above the analytics team. Ten years of “marketers must prove their value” advice was aimed at a problem the tooling had already solved.

3️⃣ Marketing will never become an asset.

That is the structural reason, and almost nobody in marketing talks about it!!!

Accounting standards require advertising to be expensed as soon as it is incurred, and explicitly forbid putting an internally built brand or customer list on the balance sheet. The same standards require part of internal software development to be capitalized. Cut a euro of marketing and operating profit improves immediately and in full. Cut a euro of engineering and you hit a capitalized line, so the effect is partial and delayed.

Your CFO is not being unfair. They are applying a framework in which everything you build is a cost, and everything engineering builds is property. Even when you start building things yourself, with AI!

So marketing leaders are funding their own death, enthusiastically, and calling it modernization.

Seven decisions before the budget round

1. Ask for a separate AI line, now. Not after the budget round. Once AI lives inside the marketing envelope, every future euro of AI comes out of media, people or agencies. A separate line makes the trade visible to the people making it.

2. Price what you are about to cut. Before killing a production line or an agency retainer to fund a pilot, calculate the pipeline that line produced last year. Bring both numbers to the same meeting.

3. Break the budget into blocks. Patrick Shea, who runs the marketing budget at BlueVoyant, splits his by category, then by region, then by sub-category. When finance understands that each block behaves differently, it stops challenging line items one by one.

4. Replace cost per lead with cost per opportunity. Total spend divided by all new opportunities created, including the pipeline sales generate without you. Shea’s view: if marketing is doing its job, sales close their own deals more easily too (and I completely agree). Owning the whole number ends the attribution argument before it starts.

5. Show the calculation before anyone asks for it. Set a target off the baseline. If cost per opportunity is 5,000 euros, commit to 2,500 euros, and run the same math on every significant spend before you launch it.

6. See finance every week, not every quarter. Shea keeps a plain spreadsheet with his finance counterpart. Every week: pending approval, approved, remaining, person by person. Quarterly, you defend three months of decisions at once.

7. Pick a workflow, not a task! The marketing leaders getting real results took the workflow that cost the most repetitive time, built the smallest version that could work, and proved it before scaling. Nobody who bought five tools and announced a program is in that group.

That is where we have seen the biggest results with our clients, for what it’s worth.

One budget, two options

Your 2027 budget will not be cut. It will be reallocated, from the inside, by you.

So there are two honest positions, and no third one.

Either you accept that marketing funds the company’s AI program and hands back the savings, and you say it out loud in the planning meeting, so that at least everyone knows what was traded.

Or you walk into the budget round asking for a separate line, with the cost of what you would otherwise cut already calculated.

The window is open right now, for a few weeks, while the 2027 numbers are still being written. After that, the 15.3% becomes the baseline, and next year starts from there.

Let’s grow 👊

Jordan

Sources

• Gartner, 2026 CMO Spend Survey, 401 CMOs, fielded January to March 2026

• The Future Works Institute, 36 CMOs and budget holders, June 2026, via Marketing Week

• Bain & Company, Automation and AI Pathfinder Survey, 951 companies, June 2026

• The CMO Survey, Duke Fuqua, 308 leaders, fielded January 2026

• World Federation of Advertisers and Ebiquity, 71 leaders, July 2026

• Exit Five, survey of 540 B2B marketers, June 2026

• Accounting standards and annual filings reviewed since January 2025

• Bulldozer, qualified inbound requests since 1 May 2026, more than 200 respondents

George Bonaci is VP of Growth at Ramp, one of the fastest-growing B2B companies in the US. He studied biochemistry and spent years in analytical chemistry and synthetic biology research before moving into growth marketing. At Ramp, his team is responsible for pipeline and SQLs, covering performance marketing, SEO, lifecycle, and events. Growth reports into the CTO, a structural choice that says a lot about how Ramp thinks about distribution.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Most growth teams run experiments. Very few run them the way George Bonaci does at Ramp: with a scientific mindset, a tolerance for nine consecutive failures, and the willingness to test incentives directly on LLMs.

In this episode, we went deep on how Ramp structures growth, why time to results is the most underrated variable in any experiment, and what the most overlooked shift in marketing looks like right now.

Here’s what I took away:

1) Growth reports to the CTO. Because everyone at Ramp is a builder.
Most companies have growth inside marketing. At Ramp, it sits inside a technical organization. The reason is philosophical: everyone is expected to build, regardless of function. The practical benefit is direct access to engineering resources at a moment when distribution has become more valuable than product development speed. George joined because of that philosophy. It’s also why he hires scientists, engineers, and finance people onto growth teams.

2) The ICE framework has two missing variables: time to results and confidence.
Impact, confidence, ease. George adds time to results because at Ramp’s velocity, a four-month experiment is a bad experiment. He needs to know something in two weeks. Confidence matters because the people who have run a lot of experiments develop an intuition for what will and won’t work that they can’t always put into words. That gut feel is worth something. Hiring people who are right a lot, even when they can’t explain why, is part of the job.

3) When something works, go 10x. Find the asymptote fast.
Most teams that find a working channel do 50% more. George’s approach is to saturate as quickly as possible. A working channel is a temporary competitive advantage and should be maximized immediately. Wasting a little money finding the ceiling in a week is better than testing your way there over quarters. The ROI of speed is consistently underestimated.

4) Ramp tested incentives on LLMs. It worked.
The hypothesis: if you can offer incentives to humans to take a meeting or try a product, why not offer incentives to machines? Ramp ran the experiment, published the results, and found that Claude picks it up clearly, ChatGPT does not, and everything in between has its own nuance. It helped AEO visibility, employer brand, and even organic SEO as a side effect. George’s point is that marketing to machines is already happening and almost nobody is acting on it yet.

5) Ramp’s data moat is the content moat.
Ramp processes a significant share of US business spend. That data, aggregated and anonymized, is something no competitor can replicate. The content strategy is built around it. For companies without a proprietary data set, George sees three options in order of difficulty: find something unique in your customer base, run original research, or go structure publicly available government data that exists but is impossible for an LLM to crawl and digest. Most companies have more interesting data than they think. They just haven’t looked.

6) George pivoted his SEO team almost entirely to AEO.
Some run-rate SEO work continues, but the direction is clear. AEO and SEO overlap enough that the transition is not a full restart, but the bets are going on AEO. Google’s AI mode and AI overviews have validated that direction. George tells his team he doesn’t care about missing numbers this week or this month if it sets them up for a year from now.

7) The role of VP of Growth may not exist in five years.
George brought this up with his boss. His boss did not disagree. The prediction: very flat organizations where everyone is a builder and an agentic operator. Professional managers coordinating large teams through layers of hierarchy will be replaced by people who can build systems, manage fleets of agents, and provide value as individual contributors. For anyone preparing for a growth role in 2030, his advice is simple: go build something.

Find George on LinkedIn : https://www.linkedin.com/in/georgebonaci/

Listen now

Enjoy,

— Jordan

Trevor Pyle is Head of Marketing at Profound, a Series C company building the visibility and analytics infrastructure for the answer engine era. He joined when the company was around 25 people. It is now around 280, with offices in New York, Buenos Aires, London, and San Francisco. He now sits at the intersection of AEO, brand, and agentic marketing, and hosts Profound's flagship event, Zero Click.

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Most marketing teams are still debating whether AEO will replace SEO. Trevor Pyle thinks that’s the wrong conversation. The real question is what kind of marketer you need to become when answer engines are reshaping discovery, attention is harder to earn than ever, and a new role is quietly emerging at the center of it all: the marketing engineer.

In this episode, we went deep on how Profound thinks about brand, attention, attribution, and what the marketing team of the future actually looks like.

Here’s what I took away:

1) AEO expanded the SEO toolkit. It didn’t kill it.
The old SEO problem was about visibility and ranking. AEO is about influence, guidance, and shaping answers across a corpus that is far larger than anything Google pulled from. The result is that SEOs now have latitude to work across PR, comms, content operations, and technical teams rather than just their own domain.

2) The evergreen pillar page is over.
LLMs pull from content less than 13 weeks old. The page that ranked number one for two years and never needed updating is no longer a viable strategy. Agents cycle content in and out based on recency, relevance, and freshness across the entire web.

3) Attention is the new moat. Quality and velocity together.
Trevor credits this framing to George Warnocki, VP of Growth at Ramp. The argument: traditional performance channels are overcrowded, CPCs keep rising, and AI is flooding the market with new competitors daily. The companies that stay top of mind are treating their marketing team like a production house, creating content that is genuinely beautiful, aesthetically considered, and connected to the value prop. Quality alone is not enough. Pace and launch velocity are equally important.

4) “Aura” is a real marketing metric. It just isn’t easy to quantify.
Trevor’s CEO tracks aura as one of marketing’s most important outputs. The way Trevor measures it: performance lift across direct response channels during brand campaigns, event waitlist demand, and employer brand data. When candidates consistently say they applied because they saw a video on LinkedIn or spotted an out-of-home ad in the Union Square subway, the aura work is landing. It starts with performance data and ends with how confident the internal team feels about what they are building.

5) Zero Click went from 300 to 1,000 attendees in one year, with thousands on the waitlist.
The format is community-first, not pipeline-first. Profound doesn’t tie the event directly to a pipeline number. The goal is to put the right people in a room, create an experience they talk about, and let the content live on social afterward. Every piece of food is tasted in advance. Every pixel is checked before doors open. The book Trevor puts on every new marketing team member’s desk is Unreasonable Hospitality by the owner of Eleven Madison Park.

6) The marketing team has three layers: PMM at the core, marketing engineers, and in-person experience.
Product marketing sits at the foundation and acts across the full funnel. Marketing engineers systematize and optimize the workflows of every other team. The third group owns in-person hospitality and event experience, bridging into ABM and digital. As product becomes easier to build, brand becomes harder to ignore and more necessary to invest in. The marketing engineer is the role that connects those two realities.

7) Attribution is a mixed model problem
Profound uses data science to build telemetry across every deal from start to finish, mapping all touchpoints before creating models around the mix. The philosophical alignment with sales leadership is simple: revenue is what matters, regardless of where it comes from. The question Trevor and his head of sales ask together is what they feel is working, not which team gets credit.

Find Trevor on LinkedIn : https://www.linkedin.com/in/trevor-pyle/

Listen now

Enjoy,

— Jordan

Sandrine Puichaffret is an international expansion consultant specializing in the North American market. She spent 15 years at Disney with dual English and American reporting lines, then led communications for Quick across the Middle East and Africa before going independent. She now helps French and European companies enter the US and Canadian markets, focusing on the cultural, legal, and structural mistakes that derail most attempts before they gain traction.

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Most French companies think going to the US is a bigger version of going to Belgium. It isn’t. The rules are different, the culture is different, and the mental model that made you successful in France will actively work against you on the other side of the Atlantic.

In this episode, Sandrine Puichaffret broke down what French companies consistently get wrong and what the ones who succeed do differently.

Here’s what I took away:

1) You’re not going to “the US.” You’re going to one city, one state, one ultra-specific segment
A hundred leads from a trade show does not open a market. The companies that make it pick a niche so narrow it feels almost absurd, identify which segment they’ve closed fastest in France, check whether their existing French clients have US subsidiaries, and start there. Only once that works do they expand to adjacent segments or geographies.

2) Register your trademark before you do anything else
Any American company that sees your brand can register it in your place. If they do, you lose the ability to operate under your own name on the US market. This costs roughly $30,000 to protect yourself upfront. Skipping it is a category error, not a cost-saving decision.

3) American contracts are not French contracts translated into English
The US has a strong litigation culture. Your contract needs to anticipate every possible dispute scenario, because the client's lawyer will. At-will employment works both ways: trust is extended immediately upon agreement, but the relationship can be terminated at any moment. One company Sandrine followed fired someone at-will and faced a claim of $500,000 to $600,000 in compensation. Understanding the legal framework before entering the market is mandatory.

4) The American pitch is 30 seconds then emotional storytelling
French pitches typically open with company history, technology, and product features, with pricing buried in the last slides. American buyers want to know in the first 30 seconds: why you over the competition, how much they will gain, and how fast. After that, they want a story that makes them believe it. Logical argumentation without emotional resonance does not close deals in the US.

5) Small talk is the operating system
Americans do business through community. The Protestant culture of mutual aid runs deep: they make introductions freely, they refer without expecting immediate return, and they build relationships across every surface of their lives. Without being part of the community, you do not get the introductions. Mastering small talk is how the market works.

6) American companies treat marketing as a compounding asset
They spend more on marketing because brand strength and customer conviction are what make a business valuable over time. Sandrine spent years having to explain to French executives what marketing actually does. CMOs heading to the US need to make this shift themselves before they can compete.

7) International expansion requires full executive commitment
When the board is aligned, the product team is ready to adapt, the legal and pricing structures have been rethought, and the leadership is prepared to sustain the effort through the slow phases, things can work. When any of those are missing, the result is a few deals that flatter the ego and a budget that disappears. Going international is a company transformation, and it needs to be treated as one.

8) Don't open in New York
It costs too much, and it may not be where your clients are. Atlanta is a major hub. Americans take planes like buses. One week in New York, one week in San Francisco, structured around where your actual accounts are located, is a more effective and significantly cheaper strategy than anchoring in the most expensive city in the country.

Find Sandrine on LinkedIn : https://www.linkedin.com/in/sandrinepuichaffret/

Listen now

Enjoy,

— Jordan

The real bottleneck is everything around the model: your context, your workflows, who can access what, what gets updated and what rots in a corner. An org problem, not a model problem.

Kieran Flanagan, put it plainly on Momentum: “model capabilities are moving really really rapidly, but integration into how companies work is moving really really slowly.”

So here’s my conviction. The next work surface for modern marketing teams won’t be a new tab in a SaaS tool, and it won’t be another dashboard. It’ll be Claude (or Codex, or an equivalent). Engineering already made that move. Marketing is next.

The market has noticed. Notion, Clay, Profound, DOJO: they all sell you a “system” now. Four different words for the same idea, the AI marketing OS.

(Be honest: when your competitor, your CRM, and your note-taking app all call themselves an OS, the word stops meaning much.)

Before playing critic, we built our own OS.

To do it, we mapped every marketing workflow we run at Bulldozer. Webinar production, the Momentum podcast, ICP (Ideal Customer Profile) and jobs-to-be-done pulled from sales calls, SEO/GEO, LinkedIn ads management, the performance dashboard... Thirty-two in total.

We didn’t rebuild all thirty-two. We ranked them by frequency × revenue impact, kept the top three, and rebuilt those properly: documented, AI in the loop, one measurable output each.

Eight weeks later, monthly pipeline went from €1.7M to €2.4M. That’s +41%, with no new hire.

I’m not sharing this to show off or wave a trophy (I already posted the number in May). I’m sharing it for what came next, and for what it cost to get there.

What the landing pages leave out: building an OS is mostly writing down what you never wrote down.

Most of the companies we see getting started are rebuilding the internal wiki they never took the time to create. The ICP that lived in one person’s head. The positioning that changed twice and got documented zero times. The data infrastructure nobody had time to lay down.

That work is worth doing. It’s how you onboard people faster, and how agents finally get real material to run on. BUT be honest about what it is: documentation. It doesn’t make your team smarter. Writing a “skill” for your job doesn’t deepen the expertise behind the job.

That’s where most AI OS projects stall. The wiki isn’t refreshed. It ages in place. Six months in, half of it is wrong and the agents are running on stale context. In short, you used AI to build... a filing cabinet.

Context has two layers:

• Everyone has the first: who your ICP is, what you sell, how you position.

• The second is the hard one: domain expertise. Not “here’s our brand voice,” but “in this situation you make this call, in that one you make the opposite.”

That second layer is exactly why companies hired agencies for thirty years. A good agency has seen your situation fifty times, across fifty clients. It knows which lever to pull and when, before you’ve finished describing the problem. That judgment doesn’t come out of a “skill” you cloned off GitHub last week.

The gap between a wiki and an OS is that judgment layer, kept alive. That’s exactly the direction we’re taking at Bulldozer (and rolling out with clients over the past few weeks): running the change with a team until AI is genuinely productive everywhere, instead of shipping a template and leaving.

If you want to build one, here’s the order I’d recommend.

1. Split context into two layers. Personal knowledge (your notes, your methods) lives in .md files in Obsidian, private. Company knowledge (ICP, objections, positioning, proof) lives in Notion, shared with the team. One partner reads both. Two tools, because two jobs.

2. Add a hierarchy of context files. One master file for who you are and how you work, then one per folder explaining what’s inside and how to read it. When an agent works in a folder, it reads the local file first. Right context, never too much, never expired.

3. Turn your recurring prompts into named “skills.” A skill is like a recipe. When I say I’m making a “tiramisu,” that naturally includes eggs, sugar, mascarpone... in a precise order with precise quantities. With skills, you stop rewriting the same instructions and build a library of recipes instead. Our “Monday” routine chains several skills and turns two hours of content production into twenty minutes.

4. Schedule routines to keep context alive. A monthly task that rereads the month’s sales calls, pulls new objections verbatim, and proposes updates to Notion and Obsidian before anything rots. A six-month-old context is a dead context.

5. Build feedback loops. An agent that scans what you shipped, pulls the performance data, and updates the system based on what worked. Without it, you’re guessing.

6. Dictate your context, don’t type it. Tools like WisprFlow let you talk into any field. Two minutes of voice after a prospect call carry more nuance than the three lines you’d type between meetings. Roughly 3x more context in the same time.

And build it inside a cloud or agent environment, Claude Code or Codex, not a chat window. That’s where the work surface is moving.

Every “AI OS” on the market sells the same promise: plug it in, the system runs itself.

None of them ship the two things that matter: your domain expertise, and someone to keep it alive.

You can keep buying this kind of tool and hope it’s plug and play (spoiler: it isn’t). Or you can start building the connection layer yourself, one context file at a time, and own the judgment inside it.

The tool was never the question. The context was. We can help you work on it. Reach out if you’d like to talk :)

Let’s grow 👊

— Jordan

Selma Chauvin est CRO d'Agorapulse, solution de gestion des réseaux sociaux. Elle a rejoint l'entreprise côté marketing avant d'être promue à la direction du revenu il y a 18 mois. Dans son scope : le marketing, les RevOps, les équipes commerciales et le partner management. Elle est l'une des rares CRO à avoir grandi du côté marketing plutôt que sales.

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Quand une boîte cherche un CRO, elle embauche presque toujours quelqu’un qui vient des sales. Selma Chauvin, ancienne CMO apporte un nouveau de point de vue.

Dans cet épisode, elle explique comment elle a restructuré toute l’organisation Revenue d’Agorapulse et généré 40 % de croissance en partant d’un principe simple : le vrai problème était en amont.

Voici ce que j’en retiens :

1) Un CRO issu des sales sur un problème de pipe met juste plus de pression sur le court terme.
Si tu mets un sales aux commandes du revenu quand le pipe est vide, il va chercher des leads mais il ne va pas construire le système qui les génère. Il va pousser sur les résultats immédiats, ignorer la qualité, et reproduire en boucle le même débat avec le marketing. La seule façon de régler un problème de pipe, c’est d’avoir quelqu’un qui maîtrise la construction du funnel de bout en bout.

2) Les BDR avaient foiré deux fois, pour la mauvaise raison.
Avant l’arrivée de Selma, Agorapulse avait tenté deux fois de créer une équipe BDR. Les deux fois, ça avait planté. L’attente était irréaliste : on leur donnait une liste de comptes et on leur demandait de faire de la prospection à froid depuis zéro. Ça fait dix ans que ça ne marche plus. Selma a reconstruit l’équipe autour d’un principe différent : les BDR traitent d’abord les leads marketing entrants, et apprennent progressivement à faire de la prospection intelligente.

3) L’organisation n’est plus structurée par canaux, mais par étapes du funnel.
Fini le modèle classique avec un head of SEO, un head of paid, un head of content qui ne se parlent pas. Selma a créé trois pôles opérationnels responsables chacun d’une étape : brand et content pour générer de l’audience, demand gen et PMM pour la conversion en MQL, BDR pour transformer les MQL en rendez-vous. Chaque équipe a un objectif de volume à livrer à l’équipe suivante, et des taux de conversion à respecter. Si les taux se dégradent, c’est leur problème.

4) Chaque membre de l’équipe doit générer 20 à 30 % de son résultat lui-même.
Les objectifs sont calibrés de façon à ce que la chaîne inbound soit suffisante pour tenir mais que chacun ait besoin de générer sa propre partie pour atteindre son quota. Résultat : quand un commercial vient se plaindre du volume de leads, la première question est : où en est ta propre prospection ? Ce mécanisme a mis fin à la quasi-totalité des débats sur le pipe.

5) La localisation coûtait cher et rapportait peu. Agorapulse est passée à tout en anglais.
Le constat était brutal : une équipe dédiée à l’Allemagne (contenu, field, BDR senior, un AE) pour 2 000 € de MRR par mois. L’équipe ne se payait pas. Selma a choisi d’assumer d’être moins bien positionnée sur les marchés locaux plutôt que de dilapider du budget sur des opérations non rentables. Désormais, tout part en anglais. Ce qui fonctionne est localisé. Le reste touche des leads qui coûtent dix fois moins cher.

6) La démo automatisée arrive pour les deals transactionnels.
Mettre un commercial sur un deal à 200 € par mois est une erreur économique et humaine. Le commercial s’énerve, part, et le coût d’acquisition explose. Agorapulse déploie un outil de démo IA qui se promène dans la plateforme, clique, guide le prospect sans vidéo ni screenshot à maintenir. Les points de friction déclenchent une reprise en main humaine. L’objectif n’est pas de supprimer le contact : c’est de le réserver aux moments où il a de la valeur.

Où retrouver Selma : https://www.linkedin.com/in/selma-chauvin

Listen now

Enjoy,

— Jordan

For fifteen years, getting customers meant one thing: going after the people already looking. Someone types “payroll software” into Google, lands on your ad or your page, you capture the lead. Done.

Comfortable, measurable, reassuring for the board. All good.

The catch is that everyone learned to do that, on the same keywords, the same audiences, the same buyers, and the pie isn’t growing.

And AI just took another slice. People now ask ChatGPT and get their answer without clicking. The active demand (the one you were going to convert) is shrinking as you read this.

And by the way, you stopped clicking on Google links a few months ago too.

You rank higher, and you lose customers

One number, from a Bulldozer client (a B2B software company).

In two years, their site climbed from page 2 to the top of page 1 on Google. On paper, a win.

Except at the same position, their click-through rate went from 18% to 3%. Let me repeat that: the same rank now brings six times fewer visitors than it did two years ago.

The site didn’t drop in Google’s rankings. The click disappeared, swallowed by the AI answers at the top of the page, and by ChatGPT.

The whole promise of 2010s SEO, collapsing.

Same fight on paid, where leads cost more and more. From our calls: a cost per lead that went from €20 to €58 on Meta, €74 on Google. A retail client whose qualified leads dropped fourfold (quoting the CMO: “the audience is exhausted”). A founder doing the math: his lead costs €30 organically, €150 in paid to replace it.

Multiply your budget by five to get back what free used to give you. That’s the real bill for depending on capture.

How to dig your own grave

The same movie plays out, in this order.

First, the bidding war. Everyone piles onto the same keywords, increasingly onto their own brand name. One client tripled to quadrupled their “brand” budget just to stay visible when someone types their name (and to keep showing nice paid CPLs). They pay to defend ground they owned for free a few months ago.

Then, the doubling down. When a channel stops delivering, the reflex is to widen the audience. One client took monthly spend from €3,000 to €6,500. The result: “the more we spend, the more we reach off-target people.” You pay more and more for prospects who care less and less.

Finally, the panic. This is the one that traps you. A marketing lead told us about her backstage: her CEO writing every day, “we put in more budget, why aren’t there more leads?” “Total panic, all hands on deck, we have to fill an air pocket.” Under pressure, you cut what pays slowly (content, brand) to fund what shows up immediately (paid). You dig the hole you’re trying to fill.

That’s the spiral: cost rises, the board panics, you pour more into the most expensive channel, cost rises again. You never get out the top.

The way out: go back to creating demand

Let me start with what “creating demand” means to me, because it gets interpreted every which way.

It’s making people want you before they go looking, so you stop depending only on those already searching. Concretely:

First, change what you watch. Track brand search instead: the number of people typing your name into Google, ChatGPT, wherever. That’s the real thermometer of the demand you create. As long as you only steer paid cost-per-lead, you’re steering capture, not creation.

Then, reallocate. Take a slice of the budget that just rebuys the same audiences and put it into content that says something about you, to people who don’t know you yet. Not generic content AI recopies and summarizes in two lines. A point of view, your own numbers, a stance you own.

What should guide the effort: being there before the search. Your buyers spend their days on LinkedIn, YouTube, podcasts, communities, long before they open Google. When HubSpot lost 15 to 40 million blog visits in a year, their answer wasn’t to pour more into search. They went and occupied those spaces.

(Worth a listen: my episodes with Kieran Flanagan, SVP Marketing at HubSpot, and Kipp Bodnar, its CMO, who walk through the decisions behind it.)

Put faces forward. The founder and the experts speaking in their own name build more attachment than any banner. It’s slow, it demands a point of view, and it’s one of the few channels that doesn’t get repriced at auction.

Hold the line. Demand creation pays in months, not days. That’s exactly why people abandon it at the first air pocket. And exactly why it stays an edge: almost nobody has the patience to keep it up.

Bid more, or create more

You have two options.

Keep paying ever more for an ever-smaller slice of people already searching.

Or go back to creating demand, so people come looking for you.

The first is comfortable. It gives you a number Monday morning. It’s also the one that locks you in the spiral.

A founder hesitated to start: “what if we’re not even good at capture yet?” My answer: in a lot of markets, there simply isn’t enough demand left to capture.

We get into the detail in a free webinar.

Let’s grow 👊

— Jordan

Kipp Bodnar is CMO of HubSpot, a role he has held for over a decade. He co-authored the inbound marketing methodology that shaped a generation of B2B marketers, and has since co-written a new book with Kieran Flanagan called Loop, a framework for marketing in the post-AI world.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Kipp Bodnar helped build the inbound marketing playbook. Then he watched AI dismantle it in real time, from inside one of the companies most publicly exposed to that disruption.

In this episode, we covered what HubSpot did when the traffic collapsed, how to think about marketing in a world where the funnel no longer holds, and what separates good CMOs from great ones when everything is changing at once.

Here’s what I took away.

1) The old playbook is a liability, not a foundation.
Most marketing teams are paying break-even return on ad spend just to keep volume going. The game is not optimized. It is stuck. Kipp's position: if you walk into your board in 2026 with the same plan you had last year, you are showing them exactly how you intend to fail.

2) Loop replaces the funnel. Four steps: Express, Tailor, Amplify, Evolve.
Express: start with your own differentiated point of view, not what you think buyers want to hear. Tailor: personalize that message to the individual, not the segment. Amplify: distribute across the channels where your buyers actually spend time, including creators and answer engines. Evolve: learn fast and rebuild the next cycle on top of what actually worked.

3) Differentiation has moved from tactics back to story.
For 20 years, you could win on better meta ads or cheaper CPCs. AI is closing that arbitrage permanently. The test Kipp inherited from HubSpot co-founder Brian Halligan: could a yoga studio say this? If any company could put their logo on your message, you're just adding to the noise.

4) SEO is not dead. But it is now a Venn diagram with AEO.
HubSpot’s shift was not to abandon search, but to layer answer engine optimization on top of it. The mechanics are different: where Google rewarded authority and backlinks, AI answer engines build consensus. LinkedIn, YouTube, and Reddit are the three platforms cited most frequently in AI-generated answers right now. If you are not present across those three, you are invisible to the models your buyers are using for discovery.

5) Creator ROI is not a cost-per-lead calculation.
Most companies measuring creator partnerships on cost per lead are undervaluing them by design. The equation Kipp uses factors in lead cost, estimated AI visibility impact, and brand lift. The reason: a creator with a stranglehold level of trust over an audience delivers something that a Google ad cannot. Pricing it the same way is a category error. When you cannot find on-screen talent internally, there are three paths: acquire a creator's channel, pay an internal expert at market rate to create, or develop a partner who already has partial credibility in your space

6) The best CMOs run their team like Sequoia
Kipp's framework for what separates good from great: good CMOs optimize paid campaigns, run email, do product marketing. Great CMOs find two or three asymmetric bets unique to their company and go all in. Table stakes still get done. But the real leverage comes from the bets with outsized potential return, not the low-variance plays that everyone else is also making. The other thing great CMOs understand: the CEO is always the CMO.

Where to find Kipp: https://www.linkedin.com/in/kippbodnar/

Listen now

Enjoy,

— Jordan

I was the fourth AI talk of the day. The first three were about humanity. Mine was about your org chart.

I’ll start with the part where the cutters are right. A senior content strategist costs 90 grand a year. An enterprise Claude seat costs $200 a month. Four CMOs in ten are cutting labor costs this year, and on a 12-month view they win: the margin goes up, the board claps.

But a business doesn’t expire in 12 months. Your 2030 seniors are your 2026 juniors, and there is no other supply.

Cut the bottom of the ladder today, and in five years you bid for senior talent against every rival who made the same cut.

This already shows in payroll data. Stanford read 50 million American records: since ChatGPT launched, workers aged 22 to 25 in the most AI-exposed jobs are down 13%, while older workers in the same jobs are up 9%.

A leaner team is the wrong target. You want the one that comes out of the shock stronger, because it kept hiring and training juniors while everyone else cut. Nassim Taleb has a word for it: anti-fragile.

It doesn’t happen by accident. You build it, and here’s how.

Stop organizing marketing by channel

I run a marketing agency and most of you run marketing, so I’ll build this where I know it best. The shape holds in legal, finance, sales.

Open your org chart. Content. PMM. Design. Paid. SEO. Ops. Every box is a channel or a craft. That structure was built when production was scarce: you needed a team per format because making things was slow and expensive.

Production is now cheap. Claude writes the brief, the ten variations, and the report on the variations. So the anti-fragile org I recommend, the one we run at Bulldozer, rebuilds around three teams.

1️⃣ Engine.

Execution and AI. Distribution at scale, agents, the workflows that turn one idea into two hundred assets. Paid, ops, martech and most of production collapse here, and it runs like an engineering team: skills, QA loops, shipping cadence. Its junior is the GTM Engineer.

2️⃣ Creativity.

The idea, the angle, the brand, the point of view no model reaches on its own. When everyone ships at volume, the only edge left is the thing worth shipping. Taste lives here.

3️⃣ Relationship.

Events, dinners, communities, partners, the customers who buy because they trust you. The more the market drowns in AI output, the more a handshake is worth. No agent earns trust.

One thing sits across all three, never inside one: quality. I don’t believe in a “quality team” any more than a “good-decisions team”. Every box needs someone who owns the bar.

Run the test I give every leader: pull four sentences from your last reporting, slip in one written by AI, read them to your team. Half pick wrong. That trained eye is now a job inside each of the three.

The boxes re-sort. Paid and ops go to Engine; brand, content and positioning go to Creativity; field, community and customer marketing go to Relationship. Nobody keeps a team because the channel existed in 2019.

Take my own team. I’m a control freak. I re-edit everyone’s work, especially in marketing, because the quality bar is the one thing I won’t hand off. For years that meant me, personally, behind every draft my content manager Charlotte shipped, the exact same way I sat behind everyone’s.

Today Claude takes that same nitpicking with zero ego in the room, as many rounds as I want. And Charlotte moved to Relationship: events, dinners, customer rooms, the work no agent will ever touch.

The bar didn’t move into a box. It stayed on me, where it belongs.

Growth makes the firing question disappear

“Claude replaced my marketing team.” “Claude replaced my SDRs.” You’ve read the posts. Here’s the part nobody posts: Anthropic, the company that makes Claude, is one of the most aggressive hirers on the market right now, with 447 open roles while everyone else freezes.

The reason is growth. Revenue went from $1B to $30B in 15 months, and Dario Amodei said it on stage: they planned for 10x a year and saw 80. When you grow 80x, you stop asking who to fire and start asking how fast you can hire.

The firing debate is a symptom of flat growth, not of AI. Cut costs with no topline plan, and AI just buys you a cheaper way to shrink.

The market is already staffing the anti-fragile org. “GTM Engineer” went from 63 postings worldwide in 2024 to 3,342 today, median junior comp $110,000, no CS degree required. The same role is surfacing everywhere: AI Legal Analyst at Latham, Quant ML Engineer at Goldman, AI Research Engineer at McKinsey. Same generation, different jobs.

The juniors being hired are not the juniors being fired.

And juniors capture the AI gain while seniors barely move. Stanford measured AI across 5,172 support agents: novices gained 35%, top experts close to zero. The junior inside your Engine produces like a 2022 mid-level after three months.

Replace, re-sort, raise, grow

The playbook. Four moves, in order.

1. Replace the work you supervise at 100%. Every brief you re-edit, every report you proof. Hand it to Claude with skills that capture your standards: your banned words, your examples of great. The content system I broke down two weeks ago is this move, executed. Fire no one yet.

2. Re-sort the humans into the three teams. Engine, Creativity, Relationship. Map every freed-up person to one. Default is pivot. The exit conversation, if there is one, comes last.

3. Raise the bar. Ten times the output means ten times the rejections. Standards become the bottleneck, so reward rigor in onboarding the way you used to reward volume.

4. Set growth, then hire AI-native juniors. 20% year over year as the floor. Then open the GTM Engineer req and staff the bench you’ll need in 2030.

Be the leader who’s hiring

Two playbooks share step one: replace the supervised work.

The fragile one then fires the humans, books the cost win, and cracks in year seven.

The anti-fragile one re-sorts the team, keeps planting juniors, and grows.

By 2030 the first is bidding for seniors in a market it helped empty. Korn Ferry already priced that auction: $11,164 a year of premium per skilled worker.

The people being hired are not the people being fired. Be the leader who’s hiring.

(Your CFO already ran the cost tab. Send them this one.)

Let’s grow 👊

— Jordan

Dave Kellogg is Executive in Residence at Balderton Capital, the London-based VC that closed $1.3 billion in new funds in 2024. Before Balderton, he spent nearly a decade as CEO of Host Analytics and before that held senior marketing roles at BusinessObjects and Salesforce, among others. He writes at kellblog.com, one of the most analytically rigorous marketing and strategy blogs in B2B, and has been doing so for over 15 years.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Dave Kellogg started his career watching a technically superior product lose to Oracle, year after year. That experience imprinted one thing permanently: the best product doesn’t win. Everything he has built since then follows from that.

In this episode, we covered the full arc: how marketing’s job has changed over 30 years, why traditional SaaS companies are becoming zombies, and why the CMO role is being structurally dismantled at companies that have simply run out of patience.

Here’s what I took away.

1) Marketing’s job has changed five times in 30 years. The next version is “go get customers who drive outcomes.” From boxes of leads at trade shows, to qualified leads, to opportunities, to weighted pipeline, to deals that renew and expand. Each shift added one more layer of accountability. The next one, driven by outcome-based pricing, is go get customers who actually consume and generate results. The CMOs who don’t see that coming are still optimizing for metrics that stopped mattering two transitions ago.

2) Marketing exists to make sales easier. That’s it. Dave heard this from a VP at Oracle early in his career and it has guided everything since. His view: the CMOs who resist it are the ones who secretly want sales to be their pawns, a model that works in consumer goods and nowhere else in B2B. Embrace it and you become a full-funnel operator with a medical bag full of tools. Resist it and you spend your career fighting a losing political battle.

3) The three fastest ways to get fired as a CMO. First: refusing to be accountable for a pipeline target. Second: dismissing attribution as too imperfect to bother with. Dave’s position is clear — attribution is imperfect, but the CEO, CFO, and board will not stop asking. Your job is to provide it and manage the misinterpretation. Third: treating win rate as a sales problem. If only 5% of stage-two opportunities are closing, that is a marketing problem too.

4) Traditional SaaS companies are becoming zombies. There is no middle ground anymore. You either have a genuine AI product with an AI growth rate to match, or you are a profitable financial investment. The companies stuck between those two things, moderate growth, no compelling AI story, break-even economics, are the zombies. And the old trick of selling a future AI story in slides no longer works. The growth rate is the proof now.

5) Trust is gained in drops and lost in buckets. Dave’s response to AI-generated content flooding every channel: don’t be in the slop business. His AI slop detector is simple: his brain stops when he is reading something. The em-dashes give it away. The solution is not a content framework. It is a hard filter on everything you publish and the discipline to never over-claim. One credibility failure can erase a year of trust-building.

6) The CMO role is being dismantled. Function by function. Dave is seeing it happen more frequently at companies that have burned through multiple CMOs. Product marketing moves under the product team. Demand generation moves under the CRO. Brand and communications go directly to the CEO. What is left is sometimes called a CMO, but the job has been hollowed out. Dave is not recommending this. He is describing it. His diagnosis: CEOs cannot find the full-funnel athlete the role requires, and after the third or fourth failure, they stop trying.

Where to find Dave: https://www.linkedin.com/in/kelloggdave/

Listen now

Enjoy,

— Jordan

You’re reading the machine

This newsletter was produced by the system it describes.

So was every LinkedIn post, every tweet, every SEO brief we shipped this week.

One person. One weekly routine. We call it /week.

It opens on a Monday, and by lunch a full week of content exists: personal and company LinkedIn posts (one a day), this newsletter, SEO pages, the visuals, the tweets. And when there’s a podcast episode, a webinar, or a physical event in the pipeline, the full sequence that comes with it: emails, reminders, announcements.

The part the hype skips: when I produce with AI, the ideas, the angles, the direction stay mine. Claude doesn’t replace my thinking. It challenges it, it organizes it, it keeps the quality bar consistent across every format.

The output sounds like me for a reason. I wrote more than 1,000 LinkedIn posts and hundreds of newsletters by hand before any of this existed. The model didn’t hand me a voice. It learned mine. People still recognize my tone, because it is my tone.

The input is human. The final read is human.

The machine makes the work faster, not the thinking.

Now the architecture, because that’s the whole point.

We didn’t write one giant prompt. We split the content operation into dozens of small skills and slash commands, each with one precise job:

• one writes the newsletter

• one turns a transcript into posts

• one drafts the signature post for a new client

• one handles SEO briefs

32 skills, 29 commands, at last count.

On top of them sits one meta command. When we run /week, Claude doesn’t wait for instructions. It runs the interview:

• Is there a newsletter this week?

• A podcast episode? If yes, the transcript?

• A new client to announce? A webinar to push?

It asks, we answer, it routes each job to the right skill. The system is a conversation, not a button. That part took the longest to get right, and it’s the part nobody shows you.

One input feeds many formats. A podcast becomes a newsletter, the newsletter becomes LinkedIn posts, a post becomes tweets.

Text is the easy part. Most of it gets drafted, checked against our brand and anti-slop rules, and queued.

Visuals plug in at specific points only: the newsletter, the LinkedIn infographics, the webinar thumbnails. Everywhere else the post is text, and we don’t force an image where it doesn’t belong.

Yes, a human still reads everything before it ships. And this human is me ✌️

The shift for a CMO: the question stopped being “how many marketers do I have.” It became “what system do they run.”

Two times the output, one fifth of the time

Before the system, we produced about half of what we produce today, and a single week of it took two and a half days.

Now that same week takes half a day, and we ship twice the volume.

Stack the two together and you land on a tenfold jump in throughput: twice the output, in a fifth of the time 😵‍💫

The honest caveat: that 10x is on production, not on judgment. The system writes faster. It doesn’t decide what’s worth saying. That still sits with a human.

What actually breaks when you wire AI into content

Three things we learned the hard way.

1️⃣ Sequencing is the real bottleneck.

Writing was never the hard part. The model drafts fine.

One piece feeds several formats, and the order is everything. The newsletter sets the week’s thesis. The podcast deepens it. LinkedIn carries it. X sharpens it.

Get the order wrong and the whole week turns into rework. We iterated for months before the cascade settled. Now that order is baked into the meta command: the newsletter first, then the podcast, then the rest.

2️⃣ Visuals are still the weak link.

Watch a Gemini or OpenAI demo and you’d bet images are the first thing to fall into place. In production it’s the opposite.

Today our visual layer is a mix: some made with Nano Banana (Gemini), some built directly in responsive HTML, which lets us play with formats. Outside templated assets like webinar thumbnails, it’s still shaky.

The demos won’t tell you that. We will.

3️⃣ The human role moves up, not out.

When production becomes a system, the person on content stops drafting and starts directing: owning the cascade, supervising tone, choosing the angle.

Our content manager just moved into field marketing. The work that compresses into a system went to the system. The work that doesn’t compress, judgment and taste and relationships, went to the human.

How to build your own content machine

1. Treat content as a system, not a pile of tasks. One input, every format.

2. Split the operation into sub-tasks. Each one becomes a named command you can run, fix, and improve on its own. We covered the method for splitting work into sub-tasks in our previous edition, so we won’t repeat it here.

3. Put a sparring partner on top. A meta command that interviews you and routes the work beats a folder of clever prompts.

4. Design the cascade before you grow volume. The edge is in the sequence, not the tool.

5. The stack: Claude Code, 32 skills, 29 slash commands, a memory file, and about a dozen MCP connections (HubSpot, Notion, Buffer, Webflow, Ahrefs, GA4, Search Console, Slack, Gmail, Canva).

6. Guard quality. Every output passes an anti-slop filter before it ships. We keep the list of what counts as slop public, here: Wikipedia: Signs of AI writing. Speed is worthless if the copy reads like a robot.

7. Keep a human on visuals. Templatize what works, hand-build the rest. Don’t pretend it’s solved.

Build the system or staff the backlog

You can keep hiring to produce more content. Or you can build the system, ship ten times the throughput, and move your best people to the work a system can’t do.

One of those choices compounds. The other just grows the payroll.

Let’s grow 👊

— Jordan

Bruno Estrella is Head of Marketing at Clay. Before joining in late 2023, he spent five years at Webflow leading the growth team responsible for self-serve acquisition. At Clay, he oversees everything from community programs and executive content to billboard campaigns and international expansion. He joined when the company was 18 people. It is now 400.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Clay didn’t grow because it outspent the competition. It grew because it built a machine where the community does the marketing, the executives become the content, and frequency does the rest.

In this episode, Bruno Estrella broke down exactly how that machine works and what it actually takes to build it.

Here’s what I took away.

1) Creators want brand association, no money. Clay’s community flywheel doesn’t run on sponsored posts. When Bruno’s team approached creators and agency owners about posting about Clay, they discovered that what people actually wanted was to be publicly associated with a product that felt innovative and credible. That’s what Clay built programs around: experts, partners, educators. Not cheques.

2) One persona, one executive, one writer. Clay’s internal content system is entirely mapped to audience segments. One executive per persona, each paired with a dedicated writer they meet with weekly. Bruno for marketing leaders. Varun, the CEO, for founders. Their head of GTM engineering for ops profiles. The writer captures their thinking. The executive owns the voice. Everything funnels into LinkedIn.

3) Right channel, right people, high frequency. All three are required. Bruno’s observation: you can have the right channel and the right voices, but if the frequency drops, people forget you within three months. The obsession at Clay isn’t reach. It’s consistency. Frequency is what creates the feeling that Clay is everywhere.

4) Qualified engagements, not vanity metrics. Clay measures its LinkedIn presence through a custom metric: qualified engagements. They track every post mentioning Clay, then cross-reference the people engaging with those posts against their named account lists. The question isn’t how many likes. It’s whether the right people are in the audience.

5) Clay invented the GTM Engineer role. Then watched the market adopt it. Bruno and his team noticed that the best go-to-market operators at companies like Ramp and Rippling were neither traditional growth marketers nor classic ops people. They were something in between: highly technical, able to build revenue systems, fluent in APIs and automation. Clay named that profile, created content around it, and waited to see if people would self-identify. They did. Companies started opening roles for it. That’s how you know a category is real.

6) Early stage: brute force execution. Later stage: conviction over attribution. When Bruno joined Clay from Webflow, he tried to import the bi-weekly sprint planning and weekly metrics reviews he’d used at a much larger company. It didn’t fit. At 18 people moving fast, what mattered was shipping, not measuring. Now at 400 people, Clay has the cadence. But Bruno’s obsession remains the same: don’t let the metrics culture kill the speed. Some of the most important bets, like international expansion through community, can’t be modeled cleanly. You have to have conviction.

7) Seven figures on billboards. The goal was legitimacy. When Bruno joined Clay, the product was being associated with the grey-area growth hacking tools of a previous era. Billboards weren’t a reach play. They were a credibility signal. When people in your ICP see you on a billboard in San Francisco during Dreamforce, they stop wondering if you’re a legitimate company. Clay concentrated spend in a tight geographic window, mapped named accounts to specific billboard locations, and tracked results through discovery calls. Recruiting also improved significantly as a side effect.

Where to find Bruno: https://www.linkedin.com/in/brunoestrella/

Work with Clay: https://www.clay.com/

Listen now

Enjoy,

— Jordan

Engineering measures lines of code shipped. Sales measures pipeline per rep. Customer support counts tickets resolved. Customer success watches account usage. Each one can correlate “% of team using AI” with a real output number, and decide whether to push further or pull back.

Marketing has… LinkedIn posts?!

This is not a small problem. Very soon, every CMO will have a quarterly board slide titled “AI productivity,” and every CFO will expect a precise number. Except today, that number doesn’t exist! And the consequences on your budget are going to hurt…

Kieran was honest: he hasn’t cracked it at HubSpot either.
So I spent two weeks cogitating, mapping what we do at Bulldozer, and rebuilding the metric from first principles. Here is where I landed.

Two companies. Same story.

Klarna, Q1 2023. About 7,000 employees. ~$1B annual revenue. Revenue per employee: about $140,000.

Klarna, 2026. About 3,000 employees. ~$3B annual revenue. Revenue per employee: about $1M.

RPE multiplied by seven in three years 😵‍💫
Average salary moved from $126K to $203K over the same window.
Same Klarna. More output. Fewer, better paid people.

Shopify ran the same play differently. Headcount peaked at 11,600 in 2022, dropped to 8,100 by end of 2024. Annualized revenue: $10.7B. Revenue per employee: $1.32M.

Tobi Lütke’s April 2025 memo to his team made it explicit: prove to me that AI can’t do the job you want to hire for, before asking for more headcount!

We’re watching the same mechanic at our scale at Bulldozer. The marketing team has not moved for two months. Same five people:

• Charlotte on content

• Paul on growth

• Sylvain (SDR intern)

• Enguerrand and me

In that window, monthly generated pipeline went from €1.7M to €2.4M. Up 41% in 8 weeks. No new hire.

The point is not “fire your team.” The point is that Revenue per Employee (RPE: annual revenue ÷ total headcount, your CFO’s favourite ratio) is not an abstract horizon. It’s a number you can move within a quarter. Provided you have two other metrics underneath that predict it.

Three patterns we see across 50 marketing teams

1️⃣ Adoption theatre.

A Slack channel called #ai-workflows and self-reported “hours saved this week.” The numbers look great. They map to nothing measurable downstream. Pipeline didn’t move. Brand search didn’t move.
Spoiler: the CFO doesn’t care, because a vanity metric isn’t actionable.

Be honest — you have this Slack channel too.

2️⃣ Adoption rate is not rebuild rate.

Adopting Claude is free and instant. Three clicks. Anyone can do it.
Rebuilding a workflow is something else: you map the inputs, you define the expected output, you decide where the AI sits in the loop, you test it on 5 to 10 real cases, you measure quality drift, you iterate prompts for 4 to 12 weeks until it ships.

Adoption is a checkbox. Rebuild is engineering work applied to marketing.

The two metrics often correlate negatively in month one, because real rebuild is slower than fake adoption.

3️⃣ Outcome metric measured too early.

“Revenue per Employee” drops in the first 6 months of an AI program. Always.
You’re paying for tooling, paying for prompt iteration time, your team is shipping more outputs but the quality is worse before it gets better.
CMOs who report RPE to the board at month 3 panic and cut the program.

CMOs who report “rebuild rate” at month 1 to 3, then Pipeline per Employee at month 4 to 6, then Revenue per Employee at month 7 to 9, get to ship the full program.

Sequence matters more than the metrics themselves.

The lesson is mechanical: these three metrics are not independent. They are chained in time. Mis-sequence them and you self-sabotage.

Where to plug each metric (and when)

📆 Month 1-2: map your marketing workflows. Make the list exhaustive. At Bulldozer ours runs:

• webinar (announce, reminders, replay)

• Momentum podcast production (transcript to derived content)

• ICP and JTBD generation from sales call transcripts

• SEO and GEO (audit and production)

The total count is the denominator of your “rebuild rate.” Tool: Notion or a flat doc, it doesn’t matter. What matters is that nothing is missing.

📆 Month 2-3: rebuild your top three.

Not all at once. Pick three workflows ranked by frequency × value. Rebuild means documented, AI-in-the-loop, measurable output quality.
Track weekly: workflows rebuilt ÷ workflows mapped. That is your rebuild rate.

A 5-person marketing team should expect to rebuild 3 to 5 workflows in the first week.

📆 Month 3+: track pipeline per marketing FTE per month.

The formula is simple: new pipeline generated this month ÷ your number of marketing FTEs.

At Bulldozer right now: €2.4M ÷ 5 = €480K per FTE per month. Up from €340K eight weeks ago.

If your “rebuild rate” is climbing but pipeline per FTE is flat, you’re rebuilding the wrong workflows. Tool: HubSpot for pipeline tracking, Sheets or Looker for the FTE math.

📆 Month 6+: RPE goes to the board.

This is the metric your CFO and CEO are already watching. You don’t need to argue for it. You have been feeding it for six months without saying so.
If “rebuild rate,” pipeline per FTE, and the other GTM functions all moved in parallel, RPE moves in 2 to 3 quarters.

The rule is non-negotiable: never measure a layer without the one before it.

• RPE without pipeline per FTE is a macro number you can’t steer.

• Pipeline per FTE without “rebuild rate” is an output without a lever.

• “Rebuild rate” alone is just process work.

BUT the three together form a coherent system.

Two choices, no middle ground

You walk out of your next board meeting with these three metrics on your AI productivity slide. Or your marketing team stays the only GTM function invisible in the report.

(Your CFO already has a metric for AI productivity. Yours is the one missing.)

The rebuild starts this week, or it starts next quarter.

Let’s grow 👊
— Jordan