There were two doormen at the entrance of the Stephen’s Green Hotel in Dublin. Top hats, long coats, the whole thing. They greeted people coming in, hailed cabs, made small talk with someone wearing a welsh rugby jersey who’d clearly stayed there before. The lobby behind them had a sound to it that hotels with sliding glass doors don’t have.

It is rare now. Most hotels at that level removed their doormen years ago. The math to replace them always made sense. Around $50k a year per door, give or take. A sensor never calls in sick. Nobody wrote a memo about what was being lost, because on paper nothing was being lost. A salary line went to zero and the spreadsheet looked great.

What the doorman was actually doing was reading the room. He registered the suitcase, the pace, the look on someone’s face after a delayed flight, and adjusted. He remembered the guest from three visits ago. He produced a tiny social signal, you are in the kind of place that notices you, that the building itself could not produce. Rory Sutherland wrote about this years ago. I have been noticing it run, in different clothes, for most of my career.

The cut always makes sense on the model. But what if the model is measuring the wrong thing?

The first time I watched it happen, I was working on Ireland’s first digital bank. Two-factor authentication then meant a small device that looked like a calculator. From a product point of view it was friction. I hated it. It was added cost and I wanted it gone. Then we asked customers, and they told us, clearly and repeatedly, that they liked it being annoying. This was their savings account. Most of them put money in at the start of the month and pulled it out by the end. The device gave them a few seconds to think. Many even lost it and never looked for replacement because it meant they could never access their savings, so their savings kept accumulating.

We ended up running marketing campaigns that called the friction a feature, money at arm’s length. The thing the spreadsheet wanted to delete was the thing customers were paying us for.

Years later, in fintech in MENA, the line item under pressure was events. Sponsorships, exhibitions, the cost of being in the room. The attribution was awful and never straight forward to measure. Almost no deal could be tracked back to a stand without some serious pseudoscience. Easy cut, on the model. What we kept finding was that businesses we’d met at events came back six months later, after a chain of conversations that started because they’d seen us there. The act of paying budget to be present was itself the trust signal. It build credibility.

The spreadsheet saw a cost with no leads closed in the first month post the event. What was actually being bought was the right to be considered credible in a market where credibility is the entire product.

A different version of the same pattern, running in reverse, in real estate in Dubai. We tried to build a platform to sell property fully online. Cut the real estate agent, cut the showroom visit, cut the trust gap, cut the cost. It sounded amazing and music to many ears. Also looked good on a slide. It was dumb and it didn’t work at full ticket sizes. People did not click “buy” on a house. The version that did work was a hybrid, a deposit online, with a window to come and see the place in person before committing. We had to put friction back in. The friction was not in the way of the trust. The friction was the trust.

I am watching the same conversation happen now in almost every company I talk to. The new voice AI bot with a generic voice will replace the friendly human onboarding call. The generic writing automation will take care of new marketing blog posts. I’ve even sat in a meeting where someone proposed AI to interview all new candidates. The numbers make sense again. The thing being removed is, again, hard to put on a slide. Some of those teams are going to find out, in 18 months, what was actually keeping the customer there or what was attracting top talent.

Some of what builds a business has never made it onto a dashboard, and probably never will. The doorman, the calculator device, the event stand, the showroom visit, none of them survive a financial review on their own merits, because their merits are not the kind of thing a financial review can see. The people making these cuts are usually smart. And in a meeting, only the model gets to speak. So the cut goes through, every time, until the trust is gone and nobody can say exactly when it left.

The companies that kept the doorman looked stupid at the time. Now they’re the ones that feel premium.

See you out there.

Martin

P.S. My writing soundtrack The Doors - Break On Through

The most useful thing my AI assistant has done in the last two months wasn’t a fintech update or a last minute Liverpool injury alert before an FPL deadline.

It told me Project Hail Mary was in cinemas that weekend and to book before it sold out. I booked and it was full. It recommended it because it already knew I was reading the book. That’s what OpenClaw does at its best. It runs in the background, picks up on what you care about, and shows up with things you didn’t think to ask for.

I’ve been running it since February. Long enough to realize I was wrong about what it was.

Subscribe now

OpenClaw started as a side project by Peter Steinberger, got dragged through a trademark fight, renamed twice, then exploded.

I set it up on an EC2 server in February because I thought it would be an interesting chatbot. I called it Frank. I spent the next month realizing I’d misunderstood what it was.

I’m not an engineer. Which is probably why this clicked later for me than it should have. I thought AI was a better chatbot. It turned out to be something closer to infrastructure.

My relationship with APIs is the same as my relationship with plumbing. I understand how it works until it doesn’t. Every AI tool I’d used before had one thing in common. You go to them. You open a tab, type something, get an answer. Start a new chat and they forget you exist.

OpenClaw is built differently. It has full access to my computer. It reads files, writes files, runs code, calls APIs, browses the web, sends messages, takes actions. It doesn’t wait in a tab for instructions. It works in the background. Once that clicked, everything followed.

I thought I was building a chatbot. I wasn’t. The chat turned out to be the least interesting part. Two months in, I have 19 cron jobs running on Frank.

Every morning at 8am a briefing lands in my Telegram. Liverpool fixtures, injury news, MENA fintech headlines, what happened in AI overnight, and an FPL deadline reminder if one’s coming.

My previous morning routine was finding out about things I’d already missed.

The ones I didn’t expect to value are weirder. Once a week Frank reads an article and sends me the key points. Once a month it runs a roast, reads the gap between what I said mattered and what I actually did, then writes something specific enough that I want to forward it to someone. Once a month it invites five guests to a fictional dinner party and writes the first five minutes as dialogue. The guests are chosen based on what I’m wrestling with, not what’s on my task list.

Underneath the scheduled jobs there’s a second layer most people skip. Frank has a vector database with everything I’ve fed it, daily notes, knowledge files, project states. When I ask about something with history, it searches that first. A few weeks ago it connected a forgotten video transcript to a project I was working on and named a data opportunity I’d missed. That’s when it stopped feeling like storage and started feeling like memory.

Frank also has named failure modes, all written into a file called SOUL.md. Sycophantic Frank agrees with everything. Essay Frank gives me a thousand words when I wanted forty. Hedge Frank qualifies a strong opinion until there’s nothing left of it. There’s one rule across all of it. Be the assistant you’d actually want to hang out with for a beer.

The mistake people make with AI is assuming better tools mean less human work. Usually it means more of the work becomes worth doing.

That pattern is old. In 1865, William Stanley Jevons noticed that when coal became more efficient, demand didn’t fall. It exploded. Cheaper and easier didn’t reduce usage. It made more things worth doing.

I think AI works the same way. Once it stops living in a tab and starts becoming persistent, contextual, and embedded in daily work, you don’t use it less. You use it everywhere. The constraint stops being access and starts becoming imagination.

Spreadsheets didn’t eliminate accountants. They made accounting faster, which meant more analysis got done, which created more demand for people who knew what to do with it.

That’s the part most people miss. The question isn’t just whether AI replaces tasks. It’s whether you understand what this version of AI makes newly possible.

You don’t need to be an engineer to run this. But you do need to think like one. Things break. One of my cron jobs hallucinated a Liverpool vs Bayern Munich fixture for three straight days. A Google Calendar integration never properly worked. What does work is debugging with another AI in parallel. Paste the error into Claude, ask what it means, try the fix, repeat.

Week one is frustration. By month one you’re experimenting. By the end of month two you know what you actually want this to be. I set up an AI agent called Frank. It roasts me, sends me a morning briefing, and acts like a second brain.

I’m bad at remembering birthdays, and Frank reminds me. That tiny use case tells you more about the future of AI than most product demos do.

The setup will be one click eventually. The people doing it the hard way now are learning what they actually want it to do. That part no product can automate for you.

The useful version of AI won’t feel like software you visit. It’ll feel more like a collaborator that remembers, notices, and occasionally interrupts you for a good reason.

See you out there.

Martin

P.S. My writing soundtrack Madonna Into The Groove (Live Aid 1985)

Most knowledge workers haven’t worked out why that matters yet.

To be fair, neither did the housemaids.

Alvin Toffler coined “prosumer” in 1980.

He was watching people on factory floors start designing their own products. The line between who makes things and who buys them was blurring. He thought it would change everything. It did. Just not for everyone.

The internet did the same thing to media.

Suddenly anyone could publish. Anyone could have an audience. The gatekeepers in journalism, music, film they panicked. Record labels held emergency meetings. Newspapers hired consultants to explain the internet to them.

The consultants charged a lot. Then they wrote a book about it.

But knowledge workers didn’t care.

Consultants got bigger audiences, lawyers got more clients, and the internet turned out to be the best thing that ever happened to credentialed experts. More distribution, same monopoly on the output.

A lawyer with 40k LinkedIn followers still charges £500 an hour. Possibly more, because now you’ve heard of them.

You could reach a million people from your messy bedroom and still couldn’t draft a legal contract or build a financial model. The output required a decade of training.

The credential.

Distribution got democratised but expertise didn’t.

Two waves of disruption. Knowledge work survived both.

Probably felt pretty good about itself.

Then AI arrived.

AI is not another distribution change. It doesn’t help experts reach more people. It helps more people skip the expert entirely.

A junior analyst can now build a complex model. A founder can draft the contract. A one person startup can produce work that would have required a 6 person specialist team 12 months ago.

The internet gave everyone distribution but AI gives everyone the output.

Most knowledge workers are still processing this through the wrong frame.

Right now, thousands of consultants are updating their LinkedIn headlines to say “AI-enabled.” Firms are adding AI to their pitch decks. Analysts are using ChatGPT to write faster. They’re optimising the credential instead of questioning whether it still exists.

It’s the same mistake the housemaids made.

At the turn of the century, domestic service was one of the largest employment categories in the world. Cooks, laundresses, housemaids. An entire economy built around tasks that required a person. Then the washing machine arrived. The servants didn’t lose their jobs to cheaper servants. The category just stopped requiring people.

Nobody saw it coming because the threat didn’t look like competition.

It looked like convenience.

That’s what AI looks like right now to most knowledge workers. Convenient. Useful. A bit of a time-saver. Not “you can do this faster.” But “someone else can do this now.”

What survives isn’t the credential. It’s the judgment behind it. But judgment isn’t something you decide to have. It’s built from being wrong. Slowly. Expensively. The pattern recognition a senior lawyer has isn’t there because they studied hard. It’s there because they missed something in a contract at 25 and spent 3 months fixing it.

You cannot shortcut that loop. Trust me, I’ve tried.

For the generation entering work now, that loop will never exist.

They’ll be more productive at 25 than any analyst in history. The credential will be irrelevant before they even start. Knowledge, in the traditional sense, is just on tap. And they’re going to be brilliant. That’s what makes it complicated.

But tap water and a sommelier are not the same thing. The difference isn’t vocabulary. It’s the ten thousand bottles you drank badly before you understood what good meant.

The experienced knowledge worker loses their credential. That’s survivable if the judgment is real. The next generation will never need the credential and will never build the judgment.

That’s a different problem entirely.

The credential itself. The thing that said you were allowed to produce this output. That’s what just got disrupted.

The washing machine didn’t negotiate.

It just turned up and got plugged in.

In the last year, LinkedIn lost 60% of its B2B traffic to AI search.

Email open rates climbed but click-throughs fell. The emails your team spent hours on are being opened, summarised, and sorted by AI before a human reads them.

Some marketing teams looked at the open rate, called it a win, and moved on. Others are still celebrating.

That’s the small version of the problem.

AI has changed how people find information. When someone asks ChatGPT or Perplexity which vendor to use, certain names come back. The brands that surface tend to be the ones AI can read and verify clearly. Ad spend doesn’t factor in much.

You’re no longer competing for a click. You’re competing to be the answer when no one’s searching.

Most marketing teams know this version of the story, even if they haven’t updated their approach for it yet. What they’re less aware of is the second game.

It’s not just how people find things that’s changing. It’s who decides.

Google now runs shopping ads inside its AI search interface, with 75m daily users. OpenAI is building ad campaigns that set their own budgets, create their own assets, and optimise without anyone making a call.

Sometimes the buyer isn’t a person anymore. It’s an agent or system, operating on someone’s behalf, checking criteria they set up once and largely forgot about.

In B2B, that changes something fundamental.

When a business evaluates a vendor today, a person does it. They read case studies, sit through demos, call references, form impressions in meetings. Human buyers respond to story, to warmth, to the sense that you understand their problem. You can have a rough first conversation and recover in the second one.

A system doing that same evaluation has none of that. It reads data. It checks whether evidence can be verified. If there are two vendors with similar pricing and one has cleaner documentation, structured case studies with verified numbers, and consistent third-party mentions, and the other has a better looking website and a slick sales rep, the system picks the first one. Gut feel doesn’t factor in and rapport doesn’t carry over.

Most B2B marketing is built around the sales rep and the slick website. Built to impress humans in rooms and build trust. Which is fine, right up until the room has no humans in it.

Nobody knows exactly when AI buying agents become the dominant mode. But the direction is already clear.

In B2B payments in MENA, this has already moved from theory to roadmap. The conversation isn’t about AI evaluating vendors anymore. It’s about AI executing the transaction. The buyer and the payer are becoming the same thing, and neither of them is human.

Gartner surveyed 402 senior marketing leaders last year. 65% said AI will fundamentally change their work. 20% said they personally need zero skills updates to deal with it. Only 15% of CEOs believe their CMO is AI savvy right now.

By 2027, Gartner predicts AI illiteracy will be one of the top three reasons large-enterprise CMOs get replaced. If not sooner.

The easy response is that marketing leaders need to learn the tools. That’s true but it’s the smaller problem. The harder one is leaders who understand the tools just fine and still haven’t grasped that the job changed. They’re treating AI as efficiency software. Something that does the same things faster.

It isn’t. AI is changing who finds you and who chooses you. Running the same strategy at higher speed doesn’t help when the race moved.

The gap in that Gartner data is worth sitting with. The same leaders who expect AI to fundamentally change their industry don’t think they personally need to change. Somewhere between “this transforms marketing” and “I probably don’t need to upskill much,” the logic fell apart. Most of them are thinking about AI as a better version of what they already had. A faster horse.

What marketing needs to become is genuinely different from what got most people into senior roles in the function. Being cited by AI systems requires different thinking from being ranked on Google. Being selected by a buying agent requires different trust signals and proof from persuading a procurement lead. And doing both simultaneously, with the same team and budget, while the original game keeps running in parallel, is the actual problem. Nobody’s written that job description yet.

The CMOs who work this out first won’t be the ones who learned the tools earliest. They’ll be the ones who understood the job changed and adjusted their thinking, not just their software.

That’s a harder thing to see from inside a function that’s been doing roughly the same thing for 20 years, and largely gotten away with it.

Founders spend weeks polishing a pitch.

What they almost never see is what happens when they leave the room.

The side conversations.
The doubt.
The quiet “let’s park this.”
The Slack message that just says “thoughts?” with no follow-up.

Loretta lived in that room for ten years at Google evaluating vendor partnerships. She saw over 100 pitches. Maybe 10 survived. The other 90 probably still think the demo went well.

When I read her piece, it felt familiar. It maps onto the same patterns I keep coming back to here.

Can they repeat what you do.
Can they prove it.
What happens when it breaks.
And what happens after the meeting.

There’s a story in here about a vendor who bypassed the actual decision maker three times to reach someone more senior. It went about as well as you’d expect. Don’t skim that part.

I’ll hand it over to Loretta.

Subscribe now

For years, I was the gatekeeper.

Startups would pitch me their SaaS tools, creative agencies, measurement platforms, all hoping to get access to Google’s app developer ecosystem. I decided whether they got to work with our customers or got politely archived.

Out of 100+ partnership pitches I reviewed, maybe 10 made it through. The gap between success and failure? It wasn’t the product. It was how they approached the sale.

If you’re trying to crack an enterprise deal with Google or similar corporations, here’s what actually works.

The org chart problem

One of the biggest challenges shared with me by my partners is how complex it is to navigate the organisation.

Unlike startups, where you have a very flat culture and people can easily connect you with the person in charge, at corporations like Google, it can be difficult to navigate, even internally.

To give a very simple example, at Google, there are entirely different organisations for Google Cloud, Google Play, YouTube, Google Ads, etc.; and within each organisation, they have their own sales, analysts, research, partnerships, marketing, and finance teams.

In my own role, I could easily have 100+ stakeholders just internally. Instead of talking to whoever is working in the company and hoping that they can connect you with a person who could help, what you need to do is really map your product into the organisation and see which teams or departments are going to be your best allies.

Easier said than done.

Here are some quick tips:

• Read the company’s statement to understand their OpEx and organisational structure

• Research whom your competitors interact with through press releases, LinkedIn updates, etc.

• Check with your industry connections to see if they can share insights

Your goal is to gather enough information about the organisation and how it’s structured, so that you can identify which teams are most interested in your products. Reach out to one team at a time, because with each interaction, the knowledge you have gained might actually influence your research afterwards.

• Research LinkedIn to find people in certain positions or teams. Note their locations, identify the reporting line, and try to map out the org chart.

• Review LinkedIn and job descriptions to gain more context about what each role does.

• Finally, reach out and talk to people, a lot of them. See if they can help you paint the remaining picture.

Finding the real decision maker

After narrowing your target to the correct teams, the next step is to identify the decision makers. Note that it’s plural, because in a lot of situations, signing an enterprise deal requires several teams’ collaboration:

• Users: who are actually going to use the product

• Budget owner (POC): the person owning all these external deals, subscriptions, and partnerships

• Budget approver: the person and team that advise on the budget of the team as a whole, controlling the budget cycle, and approving budget requests, etc.

In a very typical scenario, you will need to speak to all the parties above. Connect with the users on different occasions to gauge their demand for the product, and get an ally who can then help you make a case to the person-in-charge; who, hopefully, will be keen to collaborate, and help you fast-track the necessary administrative processes with the pocket owner.

A quick tip because a lot of people get this wrong: the decision maker is not always at headquarters.

I had a vendor bypass me repeatedly to reach my director in Americas, when I am actually the budget owner for global partnerships. After the third bypass, despite positive product feedback from our teams, we never signed.

If you can’t navigate our organisation respectfully during sales, how could you handle the partnership?

Understand who owns the decision, and work with them, not around them.

What the winning pitches did differently

Identifying the owners is not going to guarantee you the deal unless you have the right pitch, and nailing that is a lot more challenging than you might have imagined.

I’ve interacted with different vendors, and here’s how the ones that got the deals did it:

• Start with questions: understand the team (users’) goals, challenges, and day-to-day, the tools (ie competitors) they use, and team size & organisation chart, etc.

• Explain how the product could solve the users’ problems: how it could save their time, improve workflow, etc. You are only able to make concrete examples when you have learnt enough about your users

• Resolve their concerns: even when a product is demonstrating proven value to the team, the POC often has some concerns about its credibility, or its experience in working with big corporations. This is essential for the partner to ease these concerns before they are asked: your existing relationships, if any, with the company; enterprise customer cases; experience working with their competitors (extremely important); whether you have international exposure (offices, teams, customers)

• Put together a tailored trial for the users: very rarely can a corporation sign an enterprise deal without trying. Propose a trial, get help from the POC to share and communicate it to the power users. Host training sessions for power users to help them understand why your product is important. Monitor trial activity metrics and identify insights, reach out to the users so you can provide support, answer feedback, and gauge interest. Your goal is to prove how your product can deliver the value you promise, make a case, and have the users advocate for you.

Why timing kills more deals than product

Okay, let’s say you’ve nailed all of the above. It doesn’t mean that you can sign the deal right now. The thing is, every organisation or team has a very different finance cycle.

For my team, we determined the ultimate budgets for the broader team at the beginning of the year. That often meant we might not have a lot of leeway to make in-year adjustments. In your communication with the POC, understand the budget cycle. This can give you a lot of clarity regarding what budget they can give and when they can give it.

Ideally, you want to come in at the right timing so you can help the POC put together a budget request to the budget owner (such as the finance or sales operations teams) before the next annual budget cycle is approved.

However, if the conversation happens mid-year, you can explore whether you could start with a smaller deal by tapping into unused budget from the current year. This allows you to establish a footprint with the hope of expanding into a larger deal for the following year. This can be a good strategy because for most enterprises, renewing a deal requires significantly fewer layers of approval than approving an entirely new deal.

All these circumstances will then influence the product proposal which you put together for the POC and the subsequent decision and budget approval.

As you might have realised, the company POC needs to be your best friend because they’re the person that determines whether they want to go through the additional administrative processes for you, which often takes up extra time for them.

After the deal (or the rejection)

Let’s say you have done everything you can. Chances are either the company will happily sign the deal with you and bring you on board as their first enterprise partner, or there is a possibility that they like the product but not enough to actually pay for the services right now. So, what do you do next?

If you have signed the deal, maintain regular contact with the POC, even if the relationship is officially passed over to the account management team. The POC is very likely to provide updates, such as changes in team structure, layoffs, etc., which you want to be the first to know so that you have extra time to prepare for the next renewal cycle.

Also, if your product has helped the POC successfully prove their work, reach out to them and see if they can connect you with other users in the company. An internal advocate with a pre-existing deal makes it much easier to expand your product subscription to new teams or departments.

However, if your POC is not able to sign the deal with you right now, ask questions to understand the real reason behind it. Is it because of current budget constraints, product limitations, lack of enterprise customer endorsement, or is it just not the right team? Continue to warm the lead and wait for a time when they might be able to convert. Meanwhile, continue to explore other teams that could be a better fit given the new information you’ve obtained about this company.

If the issue is about the budget cycle, help identify the right time to re-engage so you can ensure they put you in the first request. The goal is to continue cultivating relationships with enterprises through different touchpoints. You need to ensure your product remains top of mind, while slowly helping them develop the required trust before they are finally ready to onboard.

Enterprise sales is hard.

For each company, you’re dealing with 10+ stakeholders, 6-12 month cycles, and layers of bureaucracy. Most vendors give up because they underestimate the time investment or overestimate their product’s appeal.

But here’s what I learned watching the ones who succeeded: they treated it like a partnership from day one. They genuinely helped our teams solve problems, respected our processes, and played the long game.

The companies that cracked Google weren’t necessarily the ones with the best product. They were the ones who built trust, understood our constraints, and made our lives easier.

About Loretta

I spent a decade at Google working across digital marketing, strategic partnerships, and app development with thousands of companies worldwide. I helped thousands of companies globally from scrappy startups to Fortune 500s grow their businesses, and I am a passionate mentor at Google for Startups, Techstars etc.

Now I help bootstrap founders and marketers cut through the noise with practical, proven strategies through my newsletterMarketing Proven. I also offer mentorship sessions for founders growing their businesses, navigating enterprise sales, or simply workers struggling with their careers.

The meeting went well. Then nothing happened.

Early in my career, I ran a digital consultancy. We pitched for a large tender with an international company. Two meetings. They loved us. Loved our ideas, our approach, our strategy. The vibe in the room was electric. We were already spending the money in our heads.

A week later, we found out we didn’t get the work.

When I asked why, they told me, they loved what we did. They loved our strategy. But they worried we were too small. Too young. Might not be around long enough to support them. Might not have the experience to handle the challenges of an account their size.

Every one of those concerns could have been addressed in the room. But we never got the chance. The meetings were going so well, we didn’t think we needed to.

The decision happened somewhere else.

This is the part that sales books skip over. The part between “great meeting” and “closed deal.” Most founders obsess over the pitch. The demo. The deck. What happens in the room where they’re presenting.

But most complex deals don’t die in that room.

They die in the meeting you’re not in. The one where your champion has to explain to their boss, or their boss’s boss, why they’re recommending you. The one where someone asks a question your champion can’t answer. The one where the person who could kill it quietly decides to stay silent instead of speak up.

Gartner found that 74% of B2B buying teams experience “unhealthy conflict” during decisions. That’s conflicting objectives, disagreements about the best path forward, people getting overruled by decision-makers who weren’t even in your meeting. Three quarters of all buying committees. Not the exceptions. The norm.

(I wrote about why trust networks matter more than funnels here, this is what happens after you've built that trust and still lose.)

And here’s the part that stings. 51% of customers who are willing to buy are not willing to advocate for you internally. Liking you is free. Fighting for you costs political capital.

Half of your champions won’t fight for you. They like you and they want you to win. But when the room gets heated, they stay quiet.

I’ve been on the other side of this too.

At one company, I was building a digital arm inside a traditional business. The tech stack was legacy. Really old. I wanted to move to cloud-based tools. Salesforce Marketing Cloud, HubSpot, Mutiny. Tools that would let us do sophisticated lead generation, data analysis, proper funnel optimization.

The IT team wanted to keep the non-cloud version of Salesforce. It was next to impossible to integrate any third party tools with. Hard to get the data we needed. But it was what they knew. They had vested interests. They wanted to keep doing things the way they’d always done them. They didn’t want extra work.

To change anything, I needed CEO level approval. And I was stuck between a rock and a hard place. The company wanted results on day one. Showing results on day two. But the strategic work of upgrading the stack needed time. Time I didn’t have. Political capital I hadn’t built.

In hindsight, I should have been more aggressive. More persuasive in showing the benefit of migrating faster. But I was the champion for those tools, and I couldn’t close the internal sale. Many of the third-party vendors I was speaking to couldn’t understand why. It’s hard to understand unless you’re on the inside.

That’s what it feels like to be your own champion who can’t sell internally. And if you’re selling to enterprises, that’s what your champion feels like too.

Here’s what most people get wrong about champions.

A champion who "loves your product" and a champion who'll risk their neck for it are two very different people.

Champions don’t fight for you. They fight for themselves. They need to believe that getting you on board is in their best interest. That your solution protects their job. Advances their career. Makes them look smart.

It’s not charity. It’s not about how much they liked your demo.

They need to believe: “If I don’t get this vendor, my job is at risk.”

That’s a much higher bar than “I think these folks are great.”

Matthew Dixon’s work on the “JOLT Effect” found that 40-60% of B2B deals end in no decision. Not lost to a competitor. Just... nothing. The deal slowly dies.

And only 44% of those no-decisions are because the buyer prefers the status quo. The other 56%? Indecision. Fear. The buyer wants to change, but can’t pull the trigger.

They’re not scared of missing out. Nobody ever got promoted for taking a risk that worked. But plenty got fired for one that didn't.

So the safe choice is no choice. The path of least resistance is silence.

Your deal dies in a meeting you never saw. Killed by a fear story you never addressed. Defended by a champion who didn’t have the ammunition to fight.

These are the questions I ask myself now before I leave the room:

Can they repeat what you do?

In one sentence. Without jargon.

If your champion can’t explain it clearly, they won’t try.

Do they have proof they can point to?

Not your deck. Something they can show someone else.

A case study. A specific result.

Can they answer the fear question?

When someone asks “What happens if this goes wrong?” what does your champion say? If you don’t know, they don’t either.

Do they know who else needs to agree?

And what those people care about? The decision rarely rests with the person you pitched. Who’s in the room you’re not in?

The deal closes somewhere else. With people you’ve never met and based on conversations you’ll never hear.

The question isn’t whether you won the meeting.

It’s whether you armed your champion to win the one meeting you’re not in.

See you out there.

Martin

P.S.

If you've had a deal go dark after a great meeting, tell me what you think went wrong. I'm collecting autopsy reports.

My writing soundtrack “End of the Beginning“ by Djo.

This is uncharted.

A newsletter about going places before there’s a map.

I didn’t know that when I started it. It took me over a year to figure out what I was doing. Honestly, I almost quit twice.

uncharted is simple. I write about what happens when technology changes faster than people do and what that means for people trying to build, sell, and decide in the real world. What’s working. What isn’t. What nobody wants to say out loud.

Don't expect polished takes, those are fairytales. This is something else.

Thank you for showing up. For the replies that made me rethink things. For forwarding the uncomfortable ones to people who needed to hear them. For not unsubscribing when I wrote something dumb.

Agentic Vibe Marketing Is Already Here was my biggest post this year. One smart marketer can now outwork a team of five mids. Most companies still haven’t noticed.

Compound Engineering With Claude Code showed what AI-native engineering looks like in the hands of a pro. Two engineers, six agents, features from voice commands. Very few know how to hire or manage for this yet.

Every Startup Needs a Designated Hater remains a favorite. Someone who rips bad ideas before they escape. Every team needs one.

If My Work Looks Simple, I’m Replaceable hit on something nobody says out loud. People make work look complicated to stay important. This is job security through controlled complexity.

When to Hire Humans vs Hand It to AI touched a nerve I wasn’t expecting. Big Tech cut grad hiring by 25%. Banks slashed junior roles. Nobody has a clean answer. Including me.

You Can’t Prompt Your Way to Production said what the AI hype machine won’t. If you don’t understand your AI-generated code, you’re building something that breaks when paying customers show up.

AI is Stealing Everyone’s Voice was about staying human when it’s getting easier not to. Still the thing I care about most.

Next year won’t be any clearer. Good. That’s where I work best.

See you out there.

Martin

P.S. If there’s a topic you want me to tackle in 2026, reply to this email. I read everything.

I’ve watched startups blow through six figures on marketing before they could explain what they sold. The budgets weren’t wrong. The priorities were.

Gregory Kennedy has one of the cleanest frameworks I’ve seen for fixing this. He’s a fractional CMO in the SF startup scene (inMobi, Nextroll, Sojern), and we’ve been trading notes on messy GTM for a while. He also claims Irish roots. I’ll believe it when I see the paperwork.

Over to Gregory.

A founder’s guide to marketing budgeting

Founders always ask me:

“How much should I spend on marketing?” But that’s the wrong question.

The right question is: “What actually matters at your stage?”

(Click here to get the Google sheet of Sample B2B Startup Marketing Budgets. Don’t request access. Make a copy and go to town on this version.)

Because I’ve seen pre-seed startups blow $5,000/month on Google Ads when they don’t even have a functional signup page. I’ve watched seed-stage companies hire a full-time content person before they’ve figured out what content they need, let alone what converts. And I’ve seen a Series A startup run an expensive OOH (Out Of Home, think billboards and bus shelters) brand campaign targeting developers (I guess they take the bus) when they couldn’t effectively explain the product in a 30-minute-long Zoom call.

Your marketing budget isn’t just about how much you spend. It’s about what works, and where you should focus at each stage.

Here’s the breakdown that most other marketers won’t give you.

Pre-seed $10,000/month (or get to gefault alive)

The Reality: At pre-seed, you’re not “doing marketing.” You’re running science experiments with a credit card. Your entire job is to figure out if anyone actually wants what you’re building before you run out of money.

Focus: Content and your core messaging.

Here’s where your $10K goes

Content Marketing: 40% ($4,000/month)

• AI automation tools ($1,500)

• Actual human copywriting ($1,500)

• Social media management ($700)

• Design and visuals ($300)

Digital Foundation: 25% ($2,500/month)

• Website development and hosting ($1,500)

• Email marketing tools ($500)

• Analytics and tracking ($500)

Lead Gen & Events: 20% ($2,000/month)

• Events and trade shows ($1,000)

• Social media / newsletter ads ($800)

• List building ($200)

Brand Development: 15% ($1,500/month)

• PR and outreach ($800)

• Marketing collateral ($400)

• Brand identity ($300)

Seed stage $35,000/month (or time to figure out what works)

The Reality: You’ve raised some money. Congratulations. Now comes the hard part: spending it so it has a big impact. At seed, you have just enough traction to be dangerous. Now you need to build a growth marketing engine.

Focus: Amplifying your message with paid.

Here’s your $35K breakdown

Growth Marketing: 45% ($15,700/month)

• Paid ads / Paid social ($4,000)

• Events and trade shows ($4,000)

• Newsletters ($2,500)

• Influencer partnerships ($2,000)

• Referral program incentives ($1,500)

• Growth experiments ($1,000)

• Community building ($500)

• List building ($200)

Content Marketing: 21% ($7,300/month)

• AI automation tools ($2,500)

• Content and copywriting ($3,500)

• Video content production ($1,000)

• Design and visuals ($300)

Marketing Technology: 13% ($4,500/month)

• CRM and marketing automation ($2,000)

• Analytics and attribution tools ($1,500)

• A/B testing tools ($400)

• Social media management platforms ($600)

Digital Foundation: 11% ($4,000/month)

• Website development and hosting ($2,500)

• Analytics and tracking tools ($1,000)

• Email marketing tools ($500)

Brand Development: 10% ($3,500/month)

• PR agency or consultant ($2,000)

• Marketing collateral ($700)

• Brand identity and design ($500)

• Brand partnerships ($300)

Series A $100K/month (or building the machine)

The Reality: You’ve raised more money. You have real traction. Now you need to operationalize go-to-market. Series A isn’t about scrappiness anymore. It’s about systems, processes, and predictability.

Focus: Creating go-to-market SOPs (Standards Operating Procedures).

Here’s how you spend $100K without lighting It on fire

Growth Marketing: 45% ($45,000/month)

• Google Ads and search marketing ($15,000)

• Social media advertising ($12,000)

• Display and programmatic ($6,000)

• Retargeting and remarketing ($4,000)

• Testing emerging channels ($3,000)

• AI automation ($2,000)

• Growth experiments ($1,000)

• Email marketing tools ($1,000)

• Community building ($1,000)

Brand Development: 20% ($22,000/month)

• PR agency retainer ($9,000)

• Website development and hosting ($5,000)

• Brand campaigns ($2,500)

• Design and creative tools ($1,000)

• Thought leadership ($1,000)

• Brand identity and design ($1,500)

Content Marketing: 20% ($20,000/month)

• Content team salaries/contractors ($10,000)

• AI automation tools ($4,000)

• Video/audio production ($3,500)

• Graphic design and visuals ($2,500)

Marketing Operations: 15% ($15,000/month)

• AI and marketing automation ($4,000)

• CRM and sales tools ($3,500):

• Analytics and BI tools ($3,000)

• Testing and optimization tools ($2,500)

• Analytics and tracking tools ($2,000)

Remember these budgets aren’t rigid rules

The best founders I work with know that there is no prize for following this precisely. They use them as a starting point, then adapt based on what’s actually working for their business. Maybe you need to plow every dollar into paid ads? Or maybe it’s events and hackathons?

It’s about understanding what matters at your stage, investing in the fundamentals, and making every dollar you spend count.

Click here to get the Google sheet of Sample B2B Startup Marketing Budgets that this edition of the newsletter was based on.

The frameworks are Gregory’s. The underlying truth that most early-stage companies have a clarity problem dressed up as a budget problem that’s what I keep seeing too.

Big thanks to Gregory for putting this together.

If you want more from him, follow him on X or subscribe to Vibe Your SaaS.

Complexity looks like progress, but it’s actually self-preservation.

I can spot it in the first 10 minutes of the dreaded Monday morning standup. Someone’s waffling about why something simple took three days. Honestly, they’re not stupid. They’re scared.

Most roles don’t have clear metrics, if any metrics at all. So people deconstruct existing processes, rebuild them in more complex ways, and present that complexity as proof of value. What’s worse is managers buy it.

If my work looks simple, I’m replaceable. If it looks complicated, I’m essential.

So people add unnecessary steps. They create dependencies. They become the one-eyed visionary in the land of the blind, the only person who understands the complex system they just built. This is job security through controlled complexity.

I’ve watched it destroy momentum and the will to live over and over.

It’s not malice. It’s fear. Nobody wants to be the person whose job looks easy because easy jobs get cut first. So they make it look hard.

The marketing manager could update the site in 10 minutes but writes a three-page brief. The PM schedules a workshop instead of making a call. The analyst builds a model to prove what they already know. All symptoms of the same disease.

The problem isn’t people. It’s what the system rewards.

Performance reviews celebrate completed projects, not killed ones. More dashboards. More process. More noise. Complexity becomes career insurance.

So people build layers. More process. More dashboards. More meetings. Not to make things better, but to make themselves harder to remove. They are products of the system.

Clarity is dangerous. It pinpoints who’s actually moving the needle and who’s just orbiting around it. Complexity keeps everyone safe.

In most companies, complexity equals job security and that’s rational behavior in a broken system.

Once you see it, you can’t unsee it.

How to spot it in your own company

Marketing gives a tour of the stack. Slides, logos, arrows everywhere. It looks like a nuclear control room. Then you ask which tool created revenue last month?

Silence.

There’s probably no right answer, but the silence highlights the thought didn’t even cross their mind. It was never the objective.

The stack keeps growing. The results don’t. But at least, the integrations map looks impressive in the deck.

Then there’s the dashboard nobody opens. You know the one. The attribution model nobody understands. Months of work that change nothing.

I’ve watched a fintech startup spend four months building dashboards with 200+ metrics. Interactive filters. Color codes. Drill-downs. The execs stood up and clapped.

Nobody opened it again.

They needed 3 numbers, revenue, churn, CAC. Four months of work. Zero decisions made. The analytics team still got promoted. In most companies, effort is the product. The actual product is just proof you were busy.

Ask someone to explain their impact in one sentence. If they need a slide deck, they don’t have impact. They have activity. Activity looks like a job. Impact looks like you did less than you did. So people choose activity.

Same story in product.

We tested 40+ button shades. We never fixed onboarding. This is perfect optimisation of the wrong problem.

Everyone’s working hard but nobody’s making progress.

Simplicity doesn’t look sophisticated. And nobody gets fired for looking busy, they get fired for looking idle. So you get dashboards. Reports. Meetings. A full calendar that hides an empty impact.

Does any of it change what we do tomorrow? Of course not.

I’ve seen companies with 3 revenue streams and 15 ways to measure them. The measurement becomes the work.

Complexity is how companies hide failure. The more broken the system, the fancier the charts. Every mess hides behind a prettier dashboard.

The saddest part is most of the people doing this mean well. They think showing how simple it could be makes them disposable.

But in startups, the people who make things simpler are the ones everyone remembers.

Why complexity wins, even when it loses

Complexity is visible.

You can show the org chart. The process map. The dashboard. Look at all this work.

Simplicity is invisible. “We track 3 metrics now instead of 50.” Sounds like you did less work. Even if those 3 metrics actually drive decisions and the 50 didn’t.

Busy is far safer.

Knowing what matters requires being right.

So how do you spot the people who’ve figured this out?

Mastery is knowing what to ignore

The data team that shows 3 numbers instead of 50. The marketer who kills 10 campaigns to focus on one channel. The analyst who matches emails instead of building multi-touch attribution.

Mastery is making it look easy. Which means people undervalue it.

That’s the tax on being good at your job, you make it look like there was no job to do.

But here’s what separates good companies from the rest, is they recognise this tax and refuse to charge it. They promote the person who deleted useless metrics, not the one who added them. They reward the designer who removed three steps from onboarding, not the one who redesigned the same page four times.

Your north star

If your work is easy to explain, people assume it’s easy to do. If your work is complex to explain, people assume it’s valuable.

This is totally backwards. But it’s also reality and it’s why teams default to complexity.

The hardest part about simplicity is it leaves you nowhere to hide. No dashboards to point to. No process maps to walk through. No busy work to mistake for progress.

Just results. Or the lack of them. Most people can’t handle that level of clarity.

Which is exactly why the people who can are the ones you want to keep.

The people who make things simpler are rare.

Don’t punish them for it.

Martin

P.S. If you’ve seen this pattern in your own team (or caught yourself doing it), reply and tell me. I’m curious how common this actually is.

Find me on X

This week’s track is Experience by Ludovico Einaudi.

When Roman generals paraded through the streets after a victory, someone walked behind them whispering

“Respice post te. Hominem te esse memento. Memento mori.”

meaning

“Look behind you. Remember you are a man. Remember you will die”

2,000 years ago they already understood that success makes people stupid.

We’ve had all that time. Most companies still haven’t worked it out.

In 1628, Sweden launched the most expensive warship ever built. The Vasa. It sank in 20 minutes. Too many cannons, too tall, and every single person involved in the build knew the design was unstable. Nobody told the king.

Nearly four hundred years later, McDonald’s CEO shot a video about the Big Arch that was so bad it felt like a parody. Sam Altman took a public jab at Anthropic that made him look rattled, not confident.

In both cases, someone in the building knew. They just didn’t say anything.

This is possibly the oldest problem in management.

And we keep trying to fix it with the wrong thing. More process. More approvals. More committees. None of that works because the problem was never process. The problem is that telling your boss their idea is bad feels like a career decision.

Most companies die from the inside out. From bad judgment disguised as creativity. From the “clever” ad nobody asked for. From the rebrand that made customers wonder if you’d lost the plot. Not from competitors. Not from the market. From the room going quiet when it shouldn’t have.

I’ve been in rooms like that and the pattern is always the same. The boss talks. Everyone nods because disagreeing doesn’t feel like an option.

That’s how dumb ideas survive long enough to go live.

Sometimes it’s not even fear.

You’ve spent three weeks on something. You hate it now, but killing it means admitting you were wrong. So you push it out anyway and hope nobody notices. Or it’s just conformity. You don’t want to be the difficult one. So you smile, pretend to like it, hit publish, and watch something you knew was bad land in front of customers.

Most of what gets called “shipping fast” is actually just nobody having the guts to kill something slow.

The higher you climb as a founder, the less truth you hear. And most companies have no structural fix for that.

You end up trapped in your own certainty, mistaking polite nods for consensus.

In 1970, Avis CEO Robert Townsend proposed a simple idea.

Hire someone whose entire job is to tell the CEO what is actually broken. Not a committee. A person.

He was half joking. He was also completely right.

Every company needs what I’d call a designated hater.

One person with explicit permission to say “this is bad, please stop” before it ships.

Not a “brand guardian” with a 100 slide deck. One person who says what the room is thinking and then goes back to sipping their flat white.

I’ve worked in companies that had this person, whether the title existed or not. The ones where they left or got managed out shipped noticeably worse work within a few months.

The person who says “wait, prove it” before you waste 3 months on something nobody wanted.

People love talking about psychological safety. Usually what they mean is everyone being nice to each other. That’s comfort. Actual safety is knowing you can say “this doesn’t work” without it showing up in your performance review.

Every founder or CEO thinks they want honesty until they get it.

People assume the person who says no kills morale.

I’ve watched the opposite happen.

Watching something stupid go live when everyone in the room knew it was stupid, and knowing you could have stopped it. That’s what actually kills morale.

The Romans gave someone the job of saying “you’re not as good as you think.”

Most companies still haven’t.

This week’s track is Fashion by David Bowie.

It’s midsummer and I need a break.

Stepping away for a couple of weeks to recharge. Back soon with some unfiltered, possibly unhinged ideas. Until then, here are a few of my most popular posts this year.

See you in a few weeks!

Subscribe now

Most job ads are fiction. Not malicious fiction. Aspirational fiction. Founders write about the company they’re building, not the one they’re running. Then someone joins, hits the reality in week two, and starts quietly looking elsewhere.

It all started with the most legendary founder-led recruitment message of all time.

Legend says Sir Ernest Shackleton posted this ad in a London paper before his 1914 Imperial Trans-Antarctic Expedition.

“Men wanted for hazardous journey. Low wages, bitter cold, long months of complete darkness, constant danger, safe return doubtful. Honour and recognition in case of success.”

He didn’t sugarcoat anything.

He didn’t try to “sell the vision.”He sold the pain. And 5k men applied. He didn’t hide the constraint, he featured it.

And that did three things:

1. Filtered for resilience: No one stumbled into that expedition by accident. You had to want the hardship.

2. Increased commitment: The harder the entry, the stronger the identity.

3. Bonded the team: Everyone joined eyes wide open, ready to suffer together.

This was psychology.

And Shackleton intuitively understood what modern hiring still forgets.

People who volunteer for a hard path are wired for it. When you choose hardship with eyes open you stick. The tougher the entry the stronger the post rationalisation and loyalty.

In other words the constraint is the feature.

The way Shackleton picked candidates sounds insane by modern standards.

• Asked physicist Reginald James if he could sing (he could, he was hired).

• Took men “on sight” based on gut feel.

• Chose those who made him laugh.

• Let a 19-year-old stow away stay onboard after he was discovered mid voyage.

But when the endurance was crushed by ice and they were stranded for two years?

No one died.

Every early-stage founder today is Shackleton.

You’ve got:

• Not enough funding

• Uncertain roadmap

• High risk of failure

• Zero perks

Stop pretending it’s something else.

Let’s call it what it is.

You’re asking people to join your Antarctic expedition.

But instead of:

“Hazardous journey. Bitter cold. Safe return doubtful…”

You’re writing job posts like:

“We’re a dynamic team building the future of AI infrastructure…”

No one believes that.

And worse you're attracting the wrong people.

Write the pain first. Do not bury it in bullet 14.

Example:

You will ship with incomplete specs. You will own things you have never done before. There is no safety net.

Tell them why the pain is worth it. Equity with numbers. Direct access to the founder. Code to production in hours not quarters.

Get them to walk you through the hardest thing they shipped with no clear brief. Ask what they did when production went down at 3am. Ask what they taught themselves last year and how they proved it worked.

Day one you restate the constraints.

You show the cap table. You map the first ninety days with real deliverables.

No surprises later.

High autonomy. High coaching. If you ask for pain you owe support.

Avoid edgy posturing instead of honest clarity.

Copy this JD template prompt

Role

Founding engineer

Reality first

You will ship fast with incomplete information. You will work on weekends when production breaks. You will own things you have never done before. There is no safety net.

Why it is worth it

You will design and build the core system that the company scales on. You will own real equity. You will work directly with the founder. 
Your decisions will show up in the product in days not quarters.

What you must be

Calm when things break
Teach yourself anything fast
Thrive in chaos, not handbooks
Write it down so others can move fast

What the first 90 days look like

Week 1: ship X
Month 1: own Y
Month 3: redesign Z

Compensation

Lower cash than market. Real equity. Transparent vesting. We share the cap table and model the likely outcomes in the first call.

Apply if reading the pain section made your pulse quicken. Skip this if you need specs, process, or backup dancers. 

Shackleton showed that brutal honesty can create the strongest crew. He didn’t hide the suffering. He used it to build trust.

And when everything fell apart, that meaning is what kept the team alive.

No ping pong tables. Just clarity, commitment, and the kind of people willing to follow a brutally honest leader into the ice.

See you out there

Martin

I’ve been using Claude Code for months. Running multiple agents, voice input, the whole stack. Thought I had a handle on it. Then I watched Every’s team work. Two engineers. Six agents. Features materializing from voice commands. And I realized I’d been thinking about it all wrong.

Dan Shipper, co-founder of Every called it “compound engineering.”

This isn’t advanced vibe prompting and it’s also not some clever hacks. It’s a new way of programming and it’s rapidly changing how devs create web and mobile apps.

Every is one of the first truly AI-native teams, built from the ground up to work with agents. They publish a daily AI newsletter, ship multiple AI products, and run a million-dollar consulting arm. And their engineers write almost zero code.

Kieran Klaassen, a senior engineer at Every working on Cora, coined the term “compound engineering.” His Claude Code workflow looks like a different discipline entirely. What I saw wasn’t just faster coding. It was a different relationship between engineer and machine. One I hadn’t fully understood until I watched it in action.

Subscribe now

The question nobody’s asking

The debate about whether AI replaces engineers is already outdated.

The real question is how do you hire for this? How do you evaluate an engineer who orchestrates six agents versus one who writes clean code by hand? How do you manage a team where output varies 100x based on how well someone conducts AI?

Nobody has a playbook for that yet. And that’s where it gets interesting.

We went from Assembly to C to Python. Each abstraction made developers more powerful, not unemployed as predicted. Now we're going from typing code to conducting AI agent orchestras.

The Every team proves it. Two engineers producing the output of fifteen. Multiple Claude terminals running at once. Voice in, features out. Routine work fully delegated to agents. They’re spinning up features in hours.

This is what software engineering looks like in an AI native startup. A new abstraction layer where the best engineers become conductors, not typists.

How Every pioneered compound engineering

The journey was messy.

They started with Cursor and Copilot.

Hit walls. Switched to Windsurf.

Hit more walls. Then Claude Code dropped.

1. Discovered: Claude Code could act as autonomous team members

2. Breakthrough: Running multiple instances like a distributed team

Their Compound Engineering Workflow in Action:

Morning: Kieran speaks a feature idea into Claude Code (voice, not typing)

Research Phase: Agent searches web for best practices, analyses codebase

Planning: Creates detailed GitHub issue with implementation steps

Execution: Multiple agents work in parallel, frontend, backend, tests, docs

Review: Human orchestrators check architecture, not syntax

What makes Claude Code different

The gap between Claude Code and everything before it is architectural.

Copilot and ChatGPT live in your IDE as suggestions. They see what you show them. They can’t do anything you don’t do first.

Claude Code takes over your terminal. It reads your whole codebase, edits files directly, runs commands, manages git. You stop being the coder. You become the person who decides what gets built.

Claude Code wasn’t built for you. It was built for Anthropic’s engineers to ship faster then released as a product. Here’s a great guide as to how Anthropic team use Claude code for everything from Growth Marketing to Data Science.

The compound engineering playbook

1. Build Prompts That Build Prompts

The Every team created a prompt that generates research documents, which become prompts for implementation.

"This is part of the compounding effect," Kieran explained. "Having an idea that has a lot of outcomes."

One voice command → Research document → GitHub issue → Multiple implementation agents

2. Fix Problems at the "Lowest Value Stage"

Don't let AI implement a flawed plan.

Review the architecture early when changes are cheap.

3. Parallel Agent Execution

The Every team runs multiple terminals. This is something I want to spend more time researching myself.

Terminal 1: Frontend agent building UI
Terminal 2: Backend agent creating APIs  
Terminal 3: Test agent writing specs
Terminal 4: Docs agent updating guides
You: Orchestrating and reviewing

What agent orchestration actually means

This isn’t “prompt engineering” where you feed one big query and pray. This is agent orchestration, where one Claude agent can spawn other agents to handle subtasks.

You can literally split a project into frontend, backend, test, and docs and then have separate agents work on each in parallel, across different terminals. One agent writes UI. Another builds the API. A third writes test specs.

It’s like running a distributed team, except no one sleeps, complains, or stops to eat.

The 100x engineer stack

Here’s what the new breed of builders actually use:

• Claude Code Max ($200/month)
  Access to Opus 4 in your terminal. Your autonomous engineering team, on demand.

• Multiple terminals
  Run agents in parallel, frontend, backend, testing, docs.

• Voice input (e.g. Whispr Flow or Monologue)
  Speak features into existence. No typing. Just talk.

• Pre-trained prompt libraries
  Build prompts that build systems. This is compounding in action.

• GitHub automation
  Claude files PRs, writes tests, merges code. No junior dev required.

Must-watch

The Every demo. It’s the closest thing I’ve seen to real AI orchestration in action.

Reality check for engineers and marketers

Kent Beck said

90% of traditional programming is becoming commoditised.
The other 10%? It's now worth 1000x more.

The same change is coming for everyone whose work used to mean “doing the thing.” Marketers who wrote campaigns by hand. Designers who built every screen from scratch. Analysts who manually pulled reports. The job description is changing underneath them.

What matters now is system thinking over syntax. Orchestration over implementation. Taste and architecture over raw technical skill. The ability to manage AI agents like a tech lead manages humans.

The engineers who figure this out will be worth 100x. The ones who don’t will be competing with tools that cost $200 a month.

But nobody’s built the system for finding, hiring, or managing these people yet. The orchestration layer is new. The organisational layer hasn’t caught up.

That gap is where the interesting problems are.

No fairytales required.

Martin

Marketers talk about attribution like it’s a science.

It’s closer to astrology.

I’ve run $20M+ in performance marketing campaigns where every click had to earn its keep. And I’ve run brand campaigns where you’re flying blind and hoping for signs.

The stuff I couldn’t measure often worked the best.

But most marketers still worship dashboards. We’ve built million-dollar strategies off metrics that wouldn’t pass a school science project.

Dashboards don’t lie. They just leave out nearly everything that matters.

The fairytale is simple, track every click and you’ll know what works. If it’s not in the data, it didn’t happen.

Most sharing is invisible.

Dark social accounts for roughly 77% of all content sharing, and none of it shows up in your analytics. iOS 14.5 gutted tracking so badly that the attribution numbers most marketers rely on are essentially made up. Your dashboard shows you what’s easy to count, not what’s true.

And that might be the best thing that’s happened to marketing in a decade.

Because once obsessive attribution dies, you have to ask better questions. Do people remember you? Are they talking about you? Are they coming back on their own, without a discount code dragging them in?

That’s marketing. The dashboard just showed you the part that was easy to measure.

I believe most attribution is fiction.

But pretending you don’t need any numbers is stupid too.

Try walking into a board meeting with no data, just vibes.

Let me know how that goes.

You need numbers.

The mistake is treating them as truth instead of what they are: a rough signal. A hunch dressed up in a spreadsheet.

If you starve your brand just because it’s hard to measure, you’re killing demand. I’ve seen this happen. A campaign is working, people are talking, pipeline is filling up, but the dashboard can’t prove the connection. So someone pulls the budget. Six months later the pipeline dries up and nobody connects the dots.

Ryanair gets this. Their TikTok content is chaotic, unfiltered, and impossible to tie directly to bookings. But they estimate earned media value and compare it against paid spend. The CFO gets a number. The number is imperfect. The brand keeps growing.

Attribution’s a hunch. Treat it like one.

The honest approach is incrementality testing. Instead of asking “which channel gets credit,” you ask “did this channel actually change anything?” Run a channel for two weeks. Pause it for two weeks. Measure the difference. That’s your real impact. It doesn’t need cookies, pixels, or creepy tracking. It just needs patience and a willingness to find out that some of your spend is doing nothing.

Most marketers would rather not know. The dashboard lets you tell a story. Incrementality testing might wreck it.

First-party data is the only data that matters now. If you don’t own the relationship, you don’t own the result. Attribution models built on third-party data are dead. Email lists, direct traffic, customer accounts, branded search. These are your actual signals. They’re messy and incomplete, but at least they’re yours.

Tracking is worse. Budgets are tighter. Every founder I talk to secretly knows that half their attribution report is a well-designed lie.

I still use dashboards. Of course I do. But I’ve stopped treating them like gospel. The problem was never the dashboards. It was how much we believed them.

The best marketing I’ve done couldn’t be measured at the time. It showed up later. Repeat customers. Word of mouth. Deals that closed because someone remembered something they couldn’t trace back to a campaign.

If you need a pixel to prove you did your job, you probably didn’t.

Until next time

Martin

P.S. this week’s track is Enjoy The Silence by Depeche mode

Cash is like makeup.

It hides the blemishes until it runs out.

I’ve spent money both ways. $20M+ in performance campaigns where every click had to earn its keep. And brand campaigns where you’re flying blind and hoping for signs. The stuff I couldn’t measure often worked the best. But the stuff that taught me the most was the times when there was no money to waste.

The fairytale is that more money means better decisions.

Proper market research before you ship. A/B testing every variation. Hiring the perfect cultural fit. Building the brand that authentically represents your values.

Sounds responsible.

In practice, it’s expensive procrastination.

When you’ve got $5k left, you can’t A/B test your feelings. User research gets very simple very fast. Build for who’ll pay you tomorrow. Not some imaginary persona named Ryan who “likes artisanal coffee and SaaS dashboards.”

You ship the ugly thing that works instead of perfecting something nobody wants. You hire whoever ships fast, not whoever nails the culture fit interview. Payroll’s a hell of a project manager.

Constraints kill the BS.

CB Insights looked at 76 startups that raised at least $50 million before failing. The average total funding was $180.7 million. Most of that money was burned right before collapse. They failed because the cash let them avoid finding out what was real.

This tracks with what researchers have found about constraints and creativity. Too few constraints and you get waste. Too many and you stifle action. Somewhere in the middle, where you’re forced to be honest about what matters, that’s where the good work happens.

I’m not romanticising being broke. Money is better than no money. Obviously.

But constraints don’t hold you back from building a real business. They stop you from pretending you’re building one when it’s actually an expensive hobby.

Before spending a dollar, there’s one question worth asking does this directly solve a customer problem someone will pay for? If the answer isn’t an immediate yes, cut it. No brand guidelines. No team retreats. No perfect office setup. Just customer problems and solutions.

When money is tight, you talk to customers. Not in a “let’s schedule some user research” way. In a “talk to ten people this week or we’re dead” way. Funded teams schedule research. Broke teams are already in the room, because they have no choice. There’s a difference.

Perfection is just procrastination wearing designer clothes. If you can’t ship a working version in 48 to 72 hours, you’re overthinking it. The version that ships and gets feedback beats the version that’s still being polished in a Figma file nobody asked for.

A funded startup can spend six months debating product direction. A bootstrapped team has six weeks or they’re done. One of them is going to find out much sooner whether anyone actually wants what they’re making.

Next time you wish for more funding, ask yourself would it buy progress, or just buy you more time to avoid the hard questions?

No fairytales required.

Martin

“You need full time execs who live and breathe your mission 24/7”

This is the fairytale you’ve been sold.

But in reality full time execs spend days in meetings that should’ve been Looms. Full-time execs build empires just to justify their existence. They expand their remit, create dependencies, and suddenly your CMO is running an internal podcast nobody asked for.

Fractionals can’t do that. They’re forced to focus only on outcomes. Fractional’s do the serious work, and leave before your HR system remembers their birthday.

Don’t think of fractionals as second-rate. They’re not.

I’ve met many ex-Fortune 500 leaders who now rent their brains to startups for a fraction of what they used to earn. Big companies just can’t compete on meaning. The fractionals I’ve worked with say they’d rather fix complex problems at startups than sit through another brand alignment meeting.

I know fractional leadership has been hyped before and mostly ignored.

But this time I believe it’s different.

We’re seeing AI agents wipe out half of teams overnight. Just look at Salesforce’s layoffs as they restructure for AI efficiency.

Layoffs have flooded the market with top talent who’d rather freelance than rot in corporate meetings all day.

As vibe coding makes launching SaaS startups easier, we'll see more founders bootstrapping instead of raising. They can’t afford full-time Fortune 500 execs. But fractional CTO’s, CFOs, and CMOs are suddenly within reach.

By 2026, over a third of US companies will use fractional execs.

Ask any VC today, they don’t care about your org chart. They care about your burn and your runway. That’s it..

I’ve seen founders collect execs like Pokémon cards “Gotta catch ‘em all!”

Then they wonder why they’re burning $200K a month and getting nowhere.

Fractional execs don’t care about your team building offsite karaoke.

They care about results.

• Paddle used a fractional CFO to untangle international tax before hiring one full time.

• Brex hacked new markets with external marketing brains so their founders could keep sprinting.

Outcomes > Hours

What fractional won’t fix

• Culture: They won’t drink the Kool-Aid daily.

• Deep domain knowledge: Full timers spot patterns because they’re in the weeds.

• Speed pivots: They’re not sitting in your Slack 24/7.

Know these trade-offs before you commit.

Here’s what to consder.

Pre-Seed to Seed ($0-2M)

• CFO: Model your runway before you run out (5-10 hrs/week)

• CMO: PMF experiments, GTM tests (10-15 hrs/week)

• Legal: Incorporation, early contracts (as needed)

Series A ($2-10M)

• COO: Stop the chaos before it eats your team alive (10-20 hrs/week)

• CTO: Architect your stack properly so it doesn’t implode

• VP Sales: Build a playbook so you’re not founder-selling forever

Consider a full time Head of Product (own the roadmap),

CFO if growth is bigger than 20% monthly.

Series B+ ($10M+)

• Time to get serious. Full time C-suite.

• Keep fractionals for niche roles: board advisors, new market entries, security audits.

If you want to outsmart big companies, fractional hiring is how you do it. You get their best people for a fraction of the cost. But startups are marriages without honeymoons.

Late nights, stupid fights, wondering why you signed up.Fractional lets you date before committing equity. Execs test you too. No one wants to marry a crazy founder.

Sure, fractional execs can leave fast. But full timers disappear too and cost more when they do.

Fractional means immediate strategy, zero long-term baggage.

Elite leadership no longer needs a $2M war chest.

See you out there.

Martin

I started out as a web dev, but that feels like another life.

Then I became the marketer waiting three weeks for a button change. “Urgent” meant nothing. Every A/B test? Blocked. Every landing page tweak? Delayed.

So I jumped to no-code. Built everything myself. Zapier, Make, Webflow, all of it.

Then Replit dropped AI agents. Cursor followed. Suddenly my dusty 10% of coding knowledge actually mattered again.

But I also hit wall after wall of bugs and crashes. After burning weeks debugging AI-generated spaghetti code, that’s when it hit me.

You can't prompt your way to production.

Vibe coding works. But it’s not magic.

Everyone’s selling the same story “Just describe your idea and AI will build it. No skills needed. Anyone can launch a SaaS in a weekend.”

What they don’t mention is that 82% of developers now use AI for writing code, but 45% say AI is “bad or very bad” at handling complex tasks. GitHub reports 88% productivity gains for developers using AI tools, but those gains are “unevenly distributed.” The biggest winners? Developers who already understood how systems work before they asked AI to write anything.

Cursor hit $100M ARR in under two years. Proof that vibe coding works. But they had 60 people who understood systems. Replit can build a 2D game from a single prompt, but it takes someone who knows debugging to make it production-ready.

The pattern is consistent across every case study I’ve seen. AI amplifies what you already know.

If you understand system architecture, AI becomes a superpower. If you don’t, you’ll spend weeks rebuilding what you thought was done.

Most people building with AI can’t fix their own products when they break. Your customers don’t care that “AI got it wrong.” They see a broken app and a builder who can’t fix it.

Think like a systems architect for your AI built app

You don’t need to become a full-stack developer. But you do need to understand how the pieces fit.

1. Frontend: what users see
   Buttons, pages, screens. What they click, tap, scroll.

2. Backend: the brain
   Handles logic, processes requests, runs the show.

3. Database: the memory
   Where all your user data, settings, and content live.

Vibe coding tools can now write all three. But if you don’t know how they connect? You’ll burn weeks fixing things that never worked properly in the first place.

Don’t learn syntax. Learn how the machine works.

Think in components.
Every feature is just smaller blocks stacked together. A signup flow is a form plus input fields plus submit logic plus error messages.

State management.
How data moves and where it lives. Click a button, update profile, change state, re-render UI.

API basics.
GET reads data. POST creates it. PUT updates it. DELETE removes it.

Debugging.
Stuff breaks. Paste the error into ChatGPT and ask what’s wrong.

Data relationships.
Users have accounts. Accounts store settings. Products connect to orders.

Fast start stack

Once you get these core concepts, here’s the stack I recommend.

Coding Platform (Where You Build)

• Start with: Replit or Lovable (no setup, in-browser)

• Level up with: Cursor (AI + real dev power)

• Others: Lovable, v0.dev

Frontend

• Use = Next.js (React-based, easy to scale)

• Styling = Tailwind + Shadcn (Professional UI, no custom CSS hell)

Authentication

• Use: Clerk (Free to start, $25/month for 10K users)

• Covers everything from logins to social sign-in without the setup headache.

Backend & Database

• Go-to pick: Supabase

• Everything you need without juggling five different services

• Pricing: Free for 500MB, $25/month for production

Email

• Use: Resend (Free for 3k & $20/month for 100K emails)

• Confirmations, Notifications, Invites)

• Simple email setup that just works.

Hosting = where your app lives

• Use: Vercel

• Works great with Next.js & One-click deploy

• Pricing: Free for hobby projects, $20/month for production

Version Control (Save + Track Code)

• Use: Git + GitHub (free)

• Save versions, rollback mistakes, collaborate

Vibe coding = fast iteration + smart systems thinking.

Understand the parts. Use the right tools and you’ll ship something useful.

Learning these fundamentals takes weeks, not minutes.

You'll hit walls. You'll feel like giving up when a "simple" feature breaks everything.

You'll wonder if it's worth it.

The question isn’t whether you can build something with AI. It’s whether you can keep it running when your first hundred customers show up.

No fairytales required.

Martin

Half my content team left recently.

Normally I'd panic and start posting job listings. But something made me pause. With all these AI tools floating around, what if I just... didn't replace them yet?

Sounds crazy, right? But here's what I discovered.

AI crushes the first draft stuff. Research that used to take hours now takes minutes. Rough copy that used to drain junior writers gets handled. Simple analysis that used to clog calendars just happens. That's the easy 80%. Time consuming but not rocket science.

The remaining 20%?

Strategy, taste, trust. That's still human territory.

Google's research backs this up. AI tools help developers work 21% faster, but senior developers see much bigger gains because they know which buttons to push.

Same principle applies whether you're building products, crafting campaigns, or running strategy. For example a senior marketer costs $120K plus benefits plus management overhead. My AI stack costs $60 a month.

Everyone's selling the same fairytale.

"AI will just make us all more productive! We'll focus on higher-value work!"

Meanwhile, those same consultants are quietly replacing their own teams with ChatGPT subscriptions or Claude skills.

AI’s eating its young. It’s making juniors irrelevant.

Researchers at SignalFire found that Big Tech companies reduced the hiring of new graduates by 25%. Instead they actually increased experienced hiring by 27%. This means they want people who manage AI, not get replaced by it.

Goldman and Morgan Stanley are testing AI tools to replace junior analysts. "Testing" is corporate speak for "as soon as Legal approves it."

Even software engineers are being replaced. Unemployment in IT jumped from 3.9 to 5.7% in one month.

Geoffrey Hinton won a Nobel Prize for his AI work. When asked on the Diary of a CEO what advice he'd give his own kids about preparing for AI's impact. His answer might surprise you.

It’s like a quiet massacre.

Klarna replaced 700 customer service agents with chatbots. The CEO bragged about being OpenAI’s guinea pig. Then quietly hired people back when customers got annoyed. This time? Lower pay.

BlueFocus, a Chinese agency, fired all its writers and designers two days after getting Azure access. AI writes their ads now.

IBM plans to scrap 30% of its back-office staff over the next five years. That’s 7,800 roles gone.

Turnitin’s CEO said they’ll cut 20% of staff. Future hires can come straight out of high school. The only skill required? Knowing how to use AI.

One senior orchestrating an AI stack outputs more than a team of five. The maths isn’t subtle: $150k plus $60 a month versus $270k for the same output. Probably more.

Google found AI makes senior devs 22% faster. Juniors? Barely 4%. Same logic applies across the board. The more experience you bring, the more AI amplifies it. Junior developers are using AI as a crutch instead of learning how code actually works.

Stop asking "Should I hire someone for this role?"

Start asking what Julia from Aligned Recruitment asks:

"Do we need to hire headcount for this role or can this be done with one of the AI tools on the market?"

Stop hiring people to do what AI already does better.

Start hiring people who know what good looks like. Taste isn't something you can prompt into existence.

Think about your marketing team. You’ve got one strategist who knows what good copy looks like, plus three junior writers cranking out first drafts. Now that strategist hands the first drafts to Claude, keeps the strategy, and you’ve just saved two salaries.

Same pattern everywhere. One person with taste plus AI beats a room full of people without it.

What AI still can’t do is read a room, close a seven-figure deal with a handshake, or know what good looks like before it exists. Production scales. Judgement doesn’t. That’s the line.

The grad job reality is not looking good.

A survey of 9,000 software engineers found that 90% believe finding a job is significantly harder than it was in 2020.

Fresh graduates are stuck in that classic trap. Can't get hired without experience. Can't get experience without getting hired. Those entry-level jobs that used to train people? They're not coming back.

The skills you learned early in your career? Half of them are already useless. By 2030, 70% of the skills used in most jobs will change.

If you're hiring right now, ask yourself one question: "Can AI do 80% of this job?" If yes, hire someone who knows how to use AI, not someone who'll compete with it.

Three months ago, using AI for core work was still “experimental.” Today, YC’s newest batch has an average team size of 2.3 people. The last batch? 4.1. The builders who win in the next 1-3 years will be the ones who understood this change first

I'm not saying fire your junior team or stop hiring young talent. That's not the point.

But if you're bootstrapping a startup with three months of runway, this is your new reality. The market isn't waiting for anyone to catch up.

The fairytale says we'll all adapt and find new roles. Maybe we will. I hope so.

But right now, today, while we're all figuring this out, early-stage founders have to make tough choices with limited cash.

One senior operator who knows how to use AI can often outproduce a traditional team. Not because they're smarter, but because they know which problems to hand off and which to own.

See you out there.

Martin

uncharted is for people who have to figure it out before they’re ready.

What’s real and what’s noise. New technology, new markets, and the strange human stuff that usually decides what works before the technology does.

Subscribe now

What are you dealing with?

Why did that deal go quiet?

• Great meeting. No reply.

• Your pitch was fine. Your org chart was wrong.

• Why trust, not funnels, closes enterprise deals

What is AI actually doing to my job?

• The chat is the least interesting part of AI

• The washing machine didn’t negotiate with the housemaid

• Marketing is splitting into two jobs

• When to hire humans vs hand it to AI

How do I operate when there’s no playbook?

• Every startup needs a designated hater

• If my work looks simple, I’m replaceable

• Why your job ads attract the wrong people

• Why being broke makes you smarter

What am I actually being sold?

• Burn the dashboard

• You can’t prompt your way to production

• Everyone looks good now

P.S. I do this work with teams too. New product, no category, trust needs building. DM me on X or find me on LinkedIn.

Here’s what most people will take from it, the AI race is complex, fast-moving, and you’d better understand all of it or get left behind.

Here’s what the slides actually prove, the winners understood maybe 10% and used that 10% to solve one boring problem in one boring industry. Then they charged money for it.

“AI for everyone” is yesterday’s news. “AI for dentists” will print money.

Subscribe now

The 10 slides that will change how you build:

1. AI just became dirt cheap (slide 137)

Training an AI model is still expensive. But the good news is using AI is getting cheaper and cheaper. That’s great for startups focused on AI-powered apps, not building the models themselves.

2. All AI models are basically the same (slide 142)

At the start of 2024, there was a big gap between models.

By early 2025, all 3 are scoring within 23 points of each other. That means the "best" AI model is only 2-3% better than the others. They all do the same thing.

Therefore, use whatever model is fastest and cheapest and switch whenever you find a better deal.

3. The vertical AI gold rush is real (slides 233-243)

• Cursor (helps programmers code): $300M revenue in 2 years

• Harvey (helps lawyers write): $70M revenue in 15 months

• Abridge (helps doctors take notes): $117M revenue in 5 months

"AI for everyone" is yesterdays news.

Pick one job in one industry.

4. OpenAI is losing money (Slide 173)

OpenAI made $3.7B but spent $5B on compute alone. They're bleeding cash.

Even the AI leaders aren't profitable. "Build it and they'll come" doesn't work in AI.

5. Open source won (slide 268)

Meta’s free AI model was downloaded 1.2 billion times. It’s almost as good as paid options. The free stuff is good enough now.

I even wrote about this here

The teams building moats aren’t renting AI

6. Developers already use AI (slide 147)

63% of developers use AI daily. Last year it was 44%. Next year it’ll be 80%. If your developers aren’t using AI yet, they’re working at half speed.

7. China's building fast and cheap (slide 286)

Chinese AI models match US performance at 1/10th the cost.

They're moving scary fast.

Your technical moat is temporary. Someone will clone your AI features for pennies. So build moats that aren’t technical. Customer relationships, data, brand, network effects.

8. Big tech spent $212 Billion on infrastructure (slide 97)

Amazon, Google, Microsoft etc. spent $212B building AI infrastructure in 2024.

They built the highways and you should build the gas stations.

9. AI jobs are exploding (slide 332)

AI job postings up 448%. Traditional IT jobs down 9%. The skill shift is happening.

Everyone's hiring AI people. You won’t win this talent war on salary alone. Train your existing team. Hire for potential, not experience. Build a learning culture.

10. ChatGPT hit 800M users in 17 months (slides 55-56)

ChatGPT reached 800M weekly active users faster than any product in history. 90% of users are outside North America.

Speed and global reach matter more than perfect AI. ChatGPT won by being first and everywhere.

Launch fast, go global immediately.

But not before you are ready: Why Startups Fail at Global Expansion

The other 330 slides? Energy consumption, robot stats, philosophical debates. Interesting for academics. Useless for builders.

340 slides prove one thing: the people winning picked one boring problem and solved it. The AI is incidental. The problem is the product.

The best AI company is the one that picked the right problem.

What problem are you solving?

See you out there.

Martin