This is part two of 🚪 The Exit Files, my three-part Millennial Masters series following ten founders through the years around a business sale.

In part one, When growth takes over, I explored the pressure of building towards a payout nobody could guarantee.

This time, I’m looking at the months or years of work that can follow a sale, and how the deal shapes the handover. 👇🏻

An hour after the money landed

The money arrived in Luke Tobin’s account on a Monday afternoon. About an hour later, he had a big presentation with the team. They didn’t know he’d sold Digital Ethos, and he couldn’t tell them yet.

The sale had taken about fourteen or fifteen months. Luke had spent so long thinking about the figure that seeing it land in his account felt strange and anticlimactic. He still had a meeting to get through.

You can complete a sale and spend the next two years working in the business. The deal determines how much money you still have to earn and when you can leave.

The Millennial Masters I spoke to agreed to very different terms. Their experiences show why it’s worth looking closely at the job you’ll have after the sale, including who gets to make decisions about the business you built.

Another two years on the job

Thibault Louis-Lucas, known as Tibo, and his co-founder sold Tweet Hunter and Taplio in a deal that could reach $10 million. He had expected to start building something new almost immediately, but buyers wanted him to commit another two years.

“Something that we didn’t anticipate is that all the buyers out there were asking us to stay two years in business with the acquisition.”

Staying meant spending two more years growing businesses he no longer owned, with much of his payment depending on how well he did.

The buyer paid $2 million upfront, with up to $8 million more tied to revenue milestones over the following two years. A headline price can include money you’ll only receive if the business keeps growing.

Tibo knew the buyer’s founder from school. The team was relatively small, and he felt they understood how he liked to work. In our conversation, he credited the freedom to keep building independently with helping the arrangement run smoothly.

They hit five of the six revenue milestones, so the total sale amount came to $8 million. The teams stayed quite separate, though, and Tibo thought closer collaboration could have helped them grow further. The independence he’d wanted also limited how much they worked together.

You’ll be working with the buyer while pursuing those targets. How much control you keep over the work deserves as much attention as how long you agree to stay.

Back at work on Monday

Nick Telson and his co-founder Andrew sold DesignMyNight to The Access Group for over £25 million. They closed the deal on a Friday and returned to work on Monday. Nick felt proud of the sale, but they had another two years and revenue targets to hit before they could leave.

The earn-out gave them time to get used to handing over the business. With about six months left, they’d put management in place and handed much of the work to the buyer. They could start thinking about what came next.

“And then suddenly this new acquirer is telling you how to run your own business,” Nick said. By the later stages, he and Andrew were ready to leave. The two years had helped them get used to letting go.

You may feel inseparable from the business when you sign. Eighteen months of working for its new owner can change that.

Staying for the money

Nick Holzherr sold his recipe app Whisk to Samsung in what he later described as a multimillion-pound deal. Part of his payment was held in escrow for three years. Leaving before then meant forfeiting that money. His payment depended on staying, rather than the revenue targets in Tibo’s deal.

Samsung asked Nick to grow Whisk’s team from about 30 to 120 people in nine months. The app became Samsung Food, and he coordinated its integration across the company’s devices and global teams. “I worked more in those six months or nine months than I probably have ever in my life, which I did not expect.”

At the end of the three years, Nick received the remaining money. He was free to leave, but liked the team and the mission. He worked with a coach and kept diaries to understand what he wanted from work. His role had become more about securing budgets and aligning people across Samsung, and he increasingly missed building things himself.

Digital Ethos sold for a reported £8 million. Luke Tobin had no earn-out, taking most of his payment in cash and the rest in shares in the wider group. For the first few months, he worried that if he stopped holding on so tightly, “the wheels on the bus might come off”. He felt he had to justify the sale, a pressure he now thinks was probably misplaced.

He stayed in a co-CEO role and sat on the larger group’s board. His work shifted from the agency’s day-to-day problems towards strategy and acquisitions. He enjoyed working with the other CEO and seeing how a business with around 1,300 people operated.

Leaving when the business is ready

Before accepting an offer, think through an ordinary working week after completion. You’ll need to know who you report to and how much freedom you’ll have to run the business while earning the rest of the payment.

Gavin Bell agreed to a nine-month earn-out when he sold his paid advertising agency Yatter to Velstar in what he describes as a seven-figure deal. At month eight, he told the buyers he no longer felt he was contributing much.

“It doesn’t need me.” He left a month early. The handover had worked: the business could carry on without him.

You spend years making decisions that keep the business going. A successful handover means other people can make those decisions without you.

Gavin was ready to leave when he saw that happen. Working out what he wanted to do afterwards would take longer.

Next Friday on 🚪 The Exit Files

The handover ends, and the founders begin to find out what life looks like without the businesses they built. Part three gets into the routines they struggle to leave behind and why some choose to build again.

Subscribe to get it in your inbox 📧

Millennial Masters

Helping founders make better business decisions

By Daniel Ionescu

I can catch a bad edit or an automation misbehaving because I know how it should work and when the output looks wrong. But when I use AI in an area I don’t know that well, there’s no safety net.

I’m relying on an answer I’m not really qualified to challenge, especially when ChatGPT or Claude can sound so reassuringly confident and convincing.

spent years working in AI in Silicon Valley and on Wall Street before launching her podcast, Artificial Reality. Now she asks operators across industries about the AI they use and what they’d pay for that nobody has built yet.

Because nobody on her show sells AI, they answer honestly. And in those conversations, she spotted the cost nobody puts on the AI invoice: you still need enough expertise to catch a bad answer. 👇🏻

A husband-and-wife team walked into an M&A meeting holding a $5 million valuation of their business. It had made $125,000 in net profit that year, with no intellectual property or real estate. Claude had produced the number, and they believed it because nothing in the report suggested they shouldn’t.

Christine McDannell, who runs the sell-side M&A firm The Magnolia Firm, knew it was wrong before she finished reading. She told them the multiple for that business was three to four times net profit, emailing them before the call because some things shouldn’t be said to someone’s face on Zoom.

Then she read the report behind the number. Claude had assigned a dollar value to every department in the company based on payroll. It valued the accounting department at $200,000. It had counted expenses as assets. That’s how it arrived at $5 million.

The real problem is who can catch it

Christine caught it in seconds because she values businesses for a living. The sellers couldn’t, because they don’t. They’d used a capable tool, presumably prompted it well, and received a confident, well-formatted document about the most consequential financial decision of their lives.

They’d bought precisely the thing they had no way to check. It points to a cost of this technology almost nobody prices in:

The expertise you need to check an AI’s work is roughly the expertise you were hoping it would replace.

Follow that where it goes. The safest uses are the Tuesday admin you could’ve done yourself, because you’d spot an error instantly.

The risk rises when AI moves into a once-a-decade decision in a field you don’t practise, where you may never know it got something wrong.

Millennial Masters

Helping founders make better business decisions

By Daniel Ionescu

The failures rhyme

A charter client told his captain it was safe to anchor in 25-knot winds. He was certain because ChatGPT had said so. He’d screenshotted the answer and circled it in pen before the conversation started.

Jennifer Kerum, who runs a luxury yacht charter in Croatia, was the one who had to repair that relationship afterwards, not the model. Her line: “I don’t care what ChatGPT says. You need to trust the captain.”

Why did the model say yes? Because whether it’s safe to anchor depends on the seabed, the shelter of that particular bay, the boat, the crew and the forecast at four in the morning. That knowledge lives in captains. It isn’t written down anywhere on the internet.

Nikki Siso opened a restaurant in Austin. She ran a 45-page lease through ChatGPT, got eight risk flags in three minutes, and had her lawyer review it in 10 minutes for free instead of billing her $1,000. Steal that one, it works.

But nothing told her the sequence: that the mechanical, electrical and plumbing engineer has to come before the permit application. That cost her a month and $30,000. The sequence isn’t written down either. It lives in the head of the inspector, the contractor and whoever opened a restaurant in that town last year.

Sofiane Ghorbel, with 25 years in luxury hospitality, gave me the sentence I’ve quoted in every talk since: “We don’t sell rooms, we sell trust.” I asked him about Marriott’s RENAI virtual concierge, which struggled with a guest’s request for a nearby Italian restaurant with bar seating, so the guest was sent back to the human concierge. Sofiane said AI was already proving more useful behind the scenes in forecasting, revenue management and operations.

Line the cases up and the shape is hard to miss:

Across the seven industries I looked at, every AI win was invisible to the customer. Every failure was visible.

The knowledge AI doesn’t have

I spent two and a half years deep in Silicon Valley. The engineers I worked with were exceptional, but they’d never done the jobs they were building for. They hadn’t walked a factory floor or sat with someone being told what their life’s work is worth.

So the tools ship generic answers. They can cover a huge amount of ground without having the experience that comes from doing the job.

Those legacy industry firms doing twenty million a year on sticky notes have looked at the offer and decided it isn’t good enough.

Ask them what the years around the sale felt like and you’ll hear about exhaustion, or going back to work on Monday with the millions already in the bank.

Over the next three Fridays, I’m bringing those stories together in 🚪The Exit Files.

I’m following the experiences of ten founders, from hypergrowth, through to the sale and into life after. First, the years spent building towards a payout nobody could guarantee, and what they really cost. 👇🏻

The sale is still years away

Luke Tobin was winning work faster than his agency could look after it. Digital Ethos was doubling in size each year, heading towards almost 100 people in five years. He was trying to hire enough people and put systems in place to deliver the work he’d already sold.

“Honestly, it was always a bit unstable.”

Every new client creates work that somebody has to deliver, and hiring someone to do it takes time. When the business doubles again, whatever worked last year is already stretched.

He eventually sold the business for a reported £8 million. Knowing that makes the pressure easier to justify in hindsight. He had to keep running the business without knowing whether the sale would go through. The strain can start years before you negotiate a sale.

Keeping up is hard work

Matt Watson lived it at VinSolutions, which made software for car dealerships. During the financial crisis, dealers were looking for ways to cut costs and sell cars online. That created demand for his business at a time when others were struggling.

“We were growing way faster than we could even keep up.”

VinSolutions reached $30 million in annual revenue without outside investment. But it didn’t have enough servers or support people. Matt could see how a cash injection would help the team hire ahead of demand. Eventually, profits grew enough that they no longer needed outside investment.

Revenue makes a company look further along than it may feel when you’re running it. A customer can sign today and need help tomorrow. Recruiting and training someone to provide that help takes longer. Until then, you and your existing team take the hit.

Luke’s agency and Matt’s software company were different businesses, yet both had customers arriving faster than they could serve them. Keeping up was exhausting, and every new sale added more work to an already stretched team.

Getting bigger doesn’t guarantee this will ease. More demand puts more pressure on your team while customers still expect everything on time.

Doubt follows you home

Joshua Dziabiak wanted The Zebra to improve how people bought insurance. Getting established meant using a lead generation model similar to the one he wanted to change.

He had to convince investors and colleagues that this approach would help him get there. After getting people excited about the business, he found himself in bed wondering, “is this really how I should be building this company?”

Joshua felt his instinct was ultimately right. The route worked, and he found the experience rewarding. He also joked that building the business took ten years off his life.

You can spend the day defending a decision and still doubt it when you get home. Your team needs enough confidence to keep working, so you have to decide how much of that uncertainty to share.

Protect yourself and the team

Luke suffered burnout while building the agency. He said the leadership team he’d already put in place kept the business ready to sell. If everything had still depended on him, burnout could have crippled it.

Two employees walked into Luke’s office in tears after a client shouted at them and refused to listen. The client paid £10,000 a month and had only worked with the agency for four months. Luke called them and ended the contract.

He gave up £120,000 in annual revenue to protect his team. A couple of years earlier, he said, he wouldn’t have dared. By then, he’d seen enough of the damage the client was doing.

If growth is wearing you down, look at the work you’re agreeing to take on as well as the people you need to hire. Luke spent years learning how to win clients. Letting this one go became part of learning how to run the company he’d built.

The years won’t come back

Nick Telson and his co-founder Andrew had a sale in mind when they built DesignMyNight. They worked backwards from the amount they wanted to make. By the time they sold in 2017 for over £25 million, they’d spent seven years working relentlessly.

Building through his twenties meant Nick missed some of the freedom his friends enjoyed as they started earning. He felt he hadn’t always been the friend or son he wanted to be. The people close to him understood, and he’s since been more deliberate about those relationships.

He doesn’t regret building DesignMyNight. He loved doing it and values the financial freedom it gave him. There’s no reason to turn that into a confession that success wasn’t worth it. He can be pleased with the choice and still recognise what those years asked of him and the people around him.

Melissa Kwan remembers making excuses to meet friends after dinner because she couldn’t afford to join them. Before selling Spacio for a seven-figure sum, she missed weddings and lost friendships as business trips took priority. She recalls the years before building eWebinar as “almost 10 years of pure suffering”.

She still feels she gained more than she lost, including financial freedom and meeting her husband along the way. But she couldn’t know that while she was making those sacrifices. “It didn’t feel like I was going anywhere.”

If you’re building towards a sale, be frank with the people close to you about what you’re asking of them. A payout won’t give you those years back.

Hundreds of emails later… and the replies don’t come. So you send more because there are always more names waiting.

The list keeps you busy enough to avoid asking why any of those businesses would want to hear from you today.

That’s why I was intrigued when , who’s obsessed with finding useful things to do with Claude, explained how he finds the names on that list with an actual reason to buy now. 👇🏻

In July, a client rang me. He sells scheduling software and pays for one of those B2B data tools that does what the demo says it will.

He got the business to seven figures without a sales team. He shared his screen. There were 500 companies sitting in a spreadsheet, every one matching his filters.

I asked him who he was emailing first. He went quiet for a second. “I’m working through them alphabetically.”

His tool did its job and handed him the names. He had no way to pick between them, so he sorted by column A and started typing.

A match is not a reason

A match tells you a company looks like your customer. To prioritise it, you need to know what changed there recently.

Software finds matches brilliantly. Spotting useful signals takes more work.

Your data tool knows a company has 80 staff, sells software and is based in Manchester. It has no idea the operations lead walked out six weeks ago and nobody replaced her.

The operations lead leaving gives you something useful to open an email with.

The same filters produce the same list

You pick an industry, headcount band, region and maybe a tech tag. So does your competitor.

You run the query on Tuesday. He runs it on Wednesday. You both get the same 500 companies, and then you both email them.

Your advantage is the reason behind the list.

That reason is in data no vendor holds: your closed deals, call recordings, the customers who bought inside a fortnight and the ones who took nine months.

Your competitor cannot see any of it.

What your sales tool can’t decide

Build the list from your own history

Put your last ten to twenty closed deals in one place: call transcripts, CRM exports, email threads and whatever notes you kept.

I used Claude Code to read a local folder containing eighteen deals in one go.

Then ask one narrow question: For each of these companies, what changed in the 90 days before they first replied to us?

Use the deal material to find the first clues, then check them against public sources. Tell it to look for hires, launches, new sites, leadership moves and tooling switches.

Ask which changes appear most often. You’re looking for what was happening inside those companies before they bought. Build the pool using the data tool you already pay for.

Use broad filters at this stage. You want a few hundred companies because this is only the starting pool. Take the pool and score every company against the changes you found in your closed deals. Ask for one line explaining each score. Only award a score when you can show the evidence behind it.

The evidence should come from places you can check, such as job boards for hires, local press and planning notices for new sites, or the company’s careers page for the volume of open roles.

That one line does the heavy lifting. It becomes the opening of your email, and it answers the question he couldn’t answer in July.

Weak buying signals

Some changes create more noise than buying intent.

Funding rounds attract sellers quickly, so the buyer’s inbox fills up at the same time. A raise shows that money has arrived. It doesn’t reveal a problem your product can solve.

Headcount growth is too broad to tell you much. Look for the hire that points to a specific gap.

Check the age of the evidence too. A role posted in March is filled or forgotten by July. Signals go stale, so set a window of about six weeks and bin the rest.

What happened when we rebuilt the list

For the scheduling software company, we rebuilt the list around three signals tied directly to the problem it fixes.

• A new Operations Manager or Regional Director, because new leaders pull apart old systems in their first 90 days.

• A second site announced or leased, which is the point where spreadsheet shift planning stops working.

• Five or more part-time or hourly roles posted at once, which means the workforce is growing faster than anyone can schedule it.

About 4,000 companies fit the broad filters. In any month, a few hundred showed one of those changes. Those few hundred became the list, and for the first time we had something useful to write about.

Each email opened on the change found at that company. The volume barely changed: roughly 1,000 emails in June and another 1,000 in August.

August is the worst outbound month of the year in Europe, which makes the increase harder to dismiss. His reply rate went from 0.9% to 7.6%. We filtered out bounces and out-of-office responses, so every reply was written by a person.

Cleaner addresses will have helped the reply rate. The copy got better because the list gave him something to say.

Find your reason to write

Pick one company from the list your data tool gave you this morning and say out loud why it needs you this month.

If you can’t answer, open your last ten won deals and look for what changed before those customers bought. Find twenty companies where that same change is happening now.

👤 Jasper Vanuytrecht is a fractional GTM lead who helps B2B founding teams use Claude Code to research prospects and build outbound systems they can run themselves. He has spent a decade working in sales and writes Founder’s GTM, where he shares practical ways to win new clients without hiring more sellers. Connect with Jasper on LinkedIn.

Founder's GTM

The Claude GTM skills founders use to win new clients without hiring more sellers. [+ Claude Code Starter Guide upon sign-up] ⬇

By Jasper Vanu | Claude for GTM

has seen that problem from the inside. She built and sold an agency, later ran the company that acquired it to £14m revenue, and now writes The Humans in the Loop and runs The AI Edit.

In this guest post, she looks at what happens after AI gets switched on, when the model changes, the business moves on or the system simply stops without anybody noticing. 👇🏻

If you hired someone and never looked at their work, you’d call that negligence.

It’s surprisingly easy to do the same thing with AI. Something gets set up, tested and switched on. It works, so everyone moves on.

Three months later, the model may be different and the process around it may have moved on. The information it relies on may no longer be current, or the workflow might have stopped without producing the kind of error that gets anybody’s attention.

You only find out when somebody eventually notices. We’re putting more AI systems into businesses without always deciding who owns them once they’re live.

New here? Millennial Masters shares practical lessons from founders on building better companies and dealing with the parts of the job nobody prepares you for. Subscribe for the next one 👇🏻

Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

The model can change underneath you

When you build an AI workflow, you test what comes out, decide it’s good enough and move on. But not every model name points to something fixed forever. OpenAI’s chat-latest model, for example, points to the latest Instant model used in ChatGPT. OpenAI says the underlying model snapshot is regularly updated.

We saw another version of this when OpenAI retired GPT-4o from ChatGPT in February 2026. GPTs using retired models were automatically moved to newer equivalents. OpenAI was clear that its API wasn’t changing at that point, so this wasn’t a case of every Make or Zapier automation being silently switched.

Something you tested and approved can still end up running on a different model or version later. The output may be perfectly fine. Unless somebody checks it again, you don’t know.

The technology doesn’t have to change for the same problem to appear. Sometimes your business changes instead.

Your process changed, your AI didn’t

The production process for my Insider’s AI Briefing ran through a series of automations inside Notion. They took me half a day to build and were worth every minute, until I changed the thing they were automating.

I moved the session from Zoom to Substack Live. The way I set it up changed, but the automations didn’t. They carried on asking me for Zoom links when what I needed was a Substack Live link. They were doing exactly what I’d told them to do. The instruction had simply expired, and nothing in the system could tell me that.

I know they need updating. Fixing them takes time I haven’t found yet, so I’m running on substandard automations and I’m the person whose job it is to notice. There’s a harder version of this problem when the workflow stops doing anything at all.

The workflow stopped and nobody knew

I run a Zap that watches a folder in Outlook and turns anything I drop in there into a task. Then I migrated my email, which reset the Zaps attached to it, and that one stopped.

I didn’t know. There was no error message because, from Zapier’s point of view, nothing had failed. It simply wasn’t being asked to do anything anymore.

Three weeks later, someone chased me about a guest post I owed him. I went looking and found the task sitting in the Outlook folder where I’d left it. There were nine others behind it.

This is one of the hardest failures to catch because there’s nothing obvious to investigate. A wrong answer will usually reveal itself eventually. Missing work can sit there until the person waiting for it asks where it is.

A system can also keep working while the thing it knows slowly goes out of date.

Nobody was keeping it current

A client had a custom GPT trained on the company’s tone of voice. It was good, and the team used it every day for client-facing work. Then the person who built it left.

The GPT still worked and sounded roughly right. The problem was that the company kept changing while the GPT stayed where it was.

Nobody had decided who was responsible for keeping it current because they’d treated it as something that could be set up once and left alone. It never stopped answering, so there was no obvious moment when somebody had to question it.

What sounded exactly right in January was merely adequate by August, but it still sounded just as confident. Keeping a system current still doesn’t guarantee the output is right.

Plausible isn’t the same as right

AI produces work that often looks finished before anybody has properly checked it. The language is fluent, the formatting looks clean and there may be nothing obvious telling you to stop. That makes it easy to trust too quickly.

Even Deloitte got caught by this. It partially refunded the Australian government after a report produced using an Azure OpenAI model contained fabricated citations and a made-up court quote. The problems weren’t caught before publication. A University of Sydney researcher spotted them afterwards.

If work like that can get through Deloitte’s checks, looking finished clearly isn’t enough. And when it goes wrong, the AI doesn’t get embarrassed. You do.

So who owns it?

For most small businesses, this doesn’t need to start with another hire. It needs a name against each system.

Somebody should know what it was built to do, check what it produces often enough to notice when that changes and have the authority to switch it off.

For every AI system running in your business, ask:

• Who owns this?

• When did they last look at what it produced?

• What would have to happen for them to notice it had stopped being good?

If nobody’s name is against it, it isn’t a system you’re running. It’s a system that’s still switched on.

👤 Heather Baker is the creator of The Humans in the Loop and founder of The AI Edit, helping leaders understand and use AI in their businesses. She previously built and sold an agency before becoming CEO of the company that acquired it and growing the business to £14m revenue.

The Humans in the Loop

Helping leaders think clearly about AI.

By Heather Baker

The trouble arrives a few years later, when you’re hiring experienced people after giving fewer juniors the chance to learn the job.

Jordan Stachini sees the incentive clearly in a creative agency. If AI can generate ideas quickly, the obvious cost-cutting question is whether the agency still needs ten junior creatives.

The best people she’s worked with became good through trial and error, moving from junior roles towards becoming middleweight designers, lead designers and creative directors.

“How do you then get more senior creatives coming through if you’re stopping it right at the start?” she asks.

If you remove more of the work people used to learn on, you need another way to build the experience you’ll still be hiring for later. 👇🏻

Junior work is already changing

Nick Holzherr says companies are already changing who they employ. “They’re not hiring junior marketing people anymore,” he says. “They are hiring more senior people that can use AI. So they just need fewer people.”

The appeal is obvious: an experienced marketer who knows how to use AI needs less supervision and is better placed to judge what AI produces. You get useful work sooner without paying to teach several people from scratch.

The problem can also appear when the job survives. Konstantin Klyagin’s software agencies use AI to take boring work away from existing developers and leave them extra time for more interesting work. He doesn’t frame it as a way to replace staff.

Konstantin says building larger, reliable software still requires somebody who understands architecture, infrastructure and how the system fits together. If AI removes some of the repetition that helped people develop that knowledge, your company must decide how a junior developer is going to learn it.

The boring work was teaching you something

Nobody needs to spend years copying numbers between spreadsheets because boredom supposedly builds character. Some tasks deserve to disappear.

Early work also puts somebody in front of hundreds of small decisions. A junior writer starts noticing why one draft is weak. Somebody handling clients learns to recognise the moment confidence begins to slip. Those lessons add up before the person can fully explain what they’ve learnt.

Anjeanette Carter argues that AI is far more useful when somebody can combine it with knowledge of the subject. Without that, a junior can get a better first draft before they understand why it’s good or recognise when it’s poor. They then learn by reviewing the work and seeing how it performs for a real client or user.

Millennial Masters

Helping founders make better business decisions

By Daniel Ionescu

You used to learn by overhearing

Michael Buckworth saw what happened to trainee lawyers who worked remotely during the pandemic. Junior lawyers struggled, stopped developing and produced poorer work. He says less contact with senior lawyers put pandemic trainees at a disadvantage. He called it the loss of “osmosis”: hearing senior people speak to clients and listening to conversations around the team.

Nick Telson-Sillett noticed the same mechanism in sales: junior employees sitting nearby picked up his intonation and small techniques simply by hearing his calls. Recordings preserve the call, but not all of that everyday exposure.

With AI handling more of the practice work, a remote junior will spend less time around experienced colleagues. If your team is remote, shared documents and a weekly call won’t recreate all the learning that once happened around experienced people.

The senior hire is the easier choice

Gavin Bell’s first hire shows what close apprenticeship can produce. Keir arrived raw. They sat side by side every day handling clients, giving Gavin plenty of chances to explain his approach and correct the work as it happened. Keir developed through the experience.

Gavin now has the money to hire senior people who can work independently sooner. Teaching another inexperienced employee would cost time and attention that the business no longer has to spend in the same way.

There’s nothing hypocritical in that decision. The same choice becomes more attractive once you can afford experienced people. The problem grows when companies across the market make the same decision and give fewer juniors the years of work needed to become the senior hire everyone wants.

AI can help rebuild the practice

Ben Tasker found a useful role for AI inside training: using it for roleplay so somebody can build “muscle memory” and learn common cues before they face a live client.

AI can give juniors more chances to practise when the job itself gives them fewer. They can rehearse a difficult conversation, try another response and discuss what happened with an experienced colleague. Asking AI for a finished answer teaches less than requiring the person to choose an approach and explain it.

That’s why AI makes practice cheaper and easier to repeat. But experienced colleagues still define what good looks like.

Training becomes somebody’s job

Jordan already treats this as a cost the company has to absorb. She hires 19-year-old apprentices, pays them a professional wage and thinks of it as a bigger investment because the employer has an obligation to teach, mentor and support them.

That matters more as accidental training disappears. Universities can prepare people for work, but the judgement specific to your company has to grow inside the business. If you use AI and run a distributed team, juniors already get less of that everyday practice.

Look at the experience disappearing from a role as AI takes over more tasks. Remove the genuine waste. Before it goes, check whether any of that work was also giving people useful practice.

Once you know what still matters, put it back into the role on purpose. Give a junior real responsibility with AI assistance and keep them close to the consequences. Let them hear experienced colleagues handle clients before you expect them to lead those conversations themselves.

AI can help you cut entry-level jobs and reduce how much junior work you need today. Five years from now, you’ll still be looking for experienced people. The senior hire you want then has to learn somewhere first.

It often comes back as something somebody still has to review or correct. Models have improved, but in my experience, they still need checking.

That’s why , the entrepreneur behind Taking My Time Back, uses a simple three-question test before handing work to AI.

In this guest piece, he explains where human judgement still matters when AI is doing more of the work. 👇🏻

“The AI told me to do this.”

I hear that from clients and my own team more often than I’d like. My first response is usually: did you think about it? Did you actually read it?

The problem isn’t always the answer AI gave them. Sometimes the mistake is asking AI to make the judgement for them.

If you don’t know what good looks like, almost anything AI gives you can seem good enough. You need to understand the work well enough to tell it what you want and spot when it gets something wrong.

Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

The three-question test

I use AI every day. For repetitive work, it makes sense because it gives me more time to think. But there’s a phrase I keep coming back to: cheap delegation gets expensive.

The cost usually shows up later, when somebody has to fix the output or a customer notices the difference.

So before I delegate a task, whether it’s to AI, a virtual assistant or somebody else, I think about three things.

What I hand over

AI isn’t going to do my podcasts, but it can handle plenty of the work around them.

Across the team, I’d estimate we get back an hour and a half to two hours a day from repetitive work that didn’t need much thought in the first place.

After one of my calls, I want the transcript saved in Notion, turned into notes, summarised and used to draft a follow-up email.

A person used to have to know the call had ended and work through those jobs one by one. Now the workflow runs automatically, without me having to kick off each step.

The problem is that a bad automation can keep producing poor work every time it runs, and somebody still has to catch it.

I’ve seen this in outsourcing too. When somebody spends every day doing the same mindless task, they’re not learning much from it. That’s the sort of work I’m happy to hand over. I’m more careful with work built on years of experience.

Ford had been relying more heavily on automated quality systems, but executives weren’t getting the results they wanted. It brought in 350 veteran engineers to help find problems and improve the AI itself.

The review bill

At what point does reviewing work that AI created cheaply and quickly take longer than doing it properly in the first place?

It’s easy to skip that question until the review work starts piling up.

AI can save a lot of time, but some of that saving gets swallowed by keeping things working. A model update can break an automation or suddenly make old instructions less reliable.

That rework adds up. In one survey, 85% of employees said AI saved them 1–7 hours a week, but 37% of the time saved went back into correcting, rewriting or adding missing context. Often, the clean-up falls to whoever notices the problem and has to sort it out.

Saving time doesn’t necessarily mean less work either.

I can take more calls because I’m not writing every summary myself, but those calls create more follow-up and give my team more clients to support.

AI lets the business take on more, but the workload doesn’t shrink on its own.

The time you save is easy to fill again, so you have to be deliberate if you want any of it to stay free.

Your name’s still on it

If something goes out under your name, it’s still your responsibility, whether AI touched it or not. You still have to verify the information and decide whether the work meets your standard.

Air Canada learned that the hard way when its chatbot gave a customer incorrect fare information. A tribunal held the airline responsible for what the bot had told him.

With writing, I often start from a transcript because it keeps me close to how I actually speak. Then I might use AI to pull the information together or show me where 1,200 words can become 900.

If it helps me say the same thing more clearly and saves somebody time, great. I still decide whether the suggestion sounds right, and plenty of the time the original language is better.

Whatever AI does along the way, the person whose name is on the work still has to make the call. That’s why I think judgement matters more as AI takes on a bigger share of the work.

When people become the premium option

I can already see it in content. Once it becomes cheap to produce something passable, a lot of it starts sounding the same. Work with a clear human voice stands out more.

I think customer service is going the same way. 87% of customers now say companies using generative AI for support still need to give them access to a human.

Klarna’s experience shows why that matters. Its AI assistant was doing the work of around 700 agents in 2024. A year later, its CEO admitted the cost focus had hurt quality. The company started hiring people again, with him describing human service as a VIP option.

When bots become standard, getting an actual person who understands the situation starts to feel more valuable.

I’m happy to automate aggressively, but I’m careful when the work depends on human judgement because that’s where cheap delegation tends to get expensive.

👤 Cory Blumenfeld is a five-time founder with two exits and the writer behind Taking My Time Back, where he writes about building businesses that don’t depend on the founder for everything. He’s built teams of 70+ and now focuses on helping founders win back their time through better systems, people and AI. Connect with Cory on LinkedIn.

Taking My Time Back

Helping founders, CEOs, and leaders take back 1,000+ hours a year with systems, teams, and AI 👊🏻

By Cory Blumenfeld

Maybe the story explains a decision or gives context about the person behind the business. At the right moment, it can make someone feel less alone. But I still finish plenty of these posts wondering why I should know this about a stranger.

Kamal Ellis-Hyman is a motivational speaker who has built a business around putting himself in front of people. Even he told me, “This idea that we’ve got to get up and use our audience as our therapist is a crazy one.”

He thinks something gets lost when the personal story becomes the whole point. You know what happened to somebody and how badly it affected them, but still have very little idea what they’re good at or why you’d want to work with them.

That sounds harsh until you think about the advice founders have heard for the last few years: show more of the person behind the business and stop sounding so corporate. It caught on for a reason. 👇🏻

You were told to be more human

Jess Jensen prefers the term “professional branding” because she thinks visibility should ultimately serve your company. She says polished corporate comms are easy to tune out, and that “your imperfection is actually your magic because it shows your humanity.”

And now that AI can make anyone sound polished in seconds, Gary Das thinks it matters even more that people can see who they’re dealing with. ChatGPT or Claude can churn out a month’s worth of motivational LinkedIn posts almost instantly, so the real person behind the work matters more when a potential customer is deciding who to trust.

I buy that. I’ve built Millennial Masters around conversations with people, and I write under my own name. Being visible has created opportunities that wouldn’t have existed if I’d stayed behind a company logo.

So what are you sharing for? Once you accept that visibility matters, you still have to decide what belongs in public. Showing more of yourself can easily turn into feeling that more of your life should become content.

Not every story needs publishing

Melissa Kwan calls LinkedIn “quite toxic” because of the constant “look at me” culture around revenue screenshots and public declarations of success.

It’s not really about whether those posts are tasteful. Does any of it get customers, or is it mostly feeding someone’s ego?

The same applies when what you’re sharing is much more personal: why does somebody else need to know this? David Homan had to make that call during a fundraising round while his father was in hospice.

He told prospective investors what was going on and made clear he wasn’t trying to make anyone feel guilty about investing. He gave them honest context while they decided whether to back him.

That’s deeply personal, but it belonged in the conversation because it helped them understand what he was dealing with. It didn’t need to end with “five lessons I learned while my dad was dying”.

Millennial Masters

Helping founders make better business decisions

By Daniel Ionescu

When your face becomes the company

The boundary can matter to the business too. Gavin Bell built a business around himself, with his own name doing a lot of the work. Videos and speaking brought customers, but over time he wanted a company that could grow without his face attached to it.

By then, his visibility was part of the company’s identity. He stopped the personal-brand work and built his agency as a separate brand that could stand on its own.

When customers mainly discover a company through its founder, separating the two later can get difficult, especially if you’re looking to sell the business.

You don’t have to solve that problem on day one, but it’s still worth knowing you’re creating it.

You’re allowed to keep some of your life

Kate Assaraf said something that has stayed with me: “The older I get, the more I realise how small I want my world to be and how private I’d like my world to be.”

She doesn’t think the measure of an entrepreneur is growth. That might sound unfashionable when you work online, where greater reach can make it seem normal for strangers to know more about your life too.

Building a public profile doesn’t mean giving people access to what happens in your relationships or the harder parts of life away from the business. Keeping some of that private can simply be a boundary you choose.

Decide before the algorithm does

I don’t think there’s a universal line for how much of yourself you should share. Some people love the attention and will happily talk about their lives in public. Plenty of others hate the limelight.

After 15 years behind a keyboard and a camera, I’m now used to putting more of myself into the work and, increasingly, in front of it. Pretty much every feature I write includes a note about something I’ve got wrong or learned the hard way, and I do the same with most guest posts I preface.

I still feel awkward about some of it. I’ve written before about the cringe tax of putting yourself out there before you feel ready. Repetition stops that discomfort making every decision for me, and I see the discomfort as simply part of my own personal development and growth.

Personal material is already part of how I write. I still want to know why this particular detail of my life needs to be public before I publish it. If the best answer is that it might perform well, that’s probably not enough.

Before I post something personal, I try to picture it lingering there with no likes underneath and somebody finding it five years from now. If I’m still comfortable with that, I’m usually happy to publish.

That makes it harder to share something just because vulnerability happens to perform well.

There are parts of a life that don’t become more valuable because more people can see them. Being visible online doesn’t require turning your whole life into public property.

You can let people know who you are without giving them everything that’s happened to you. Some stories can stay yours.

“The worst person to compete with is someone having a brilliant time.

That kind of energy is annoying because it doesn’t need permission.”

It appeared in my feed on the same day ⁠Rebecca Spilter published a post about building income from her fractional work, including the jobs that simply need doing even when they’re no fun.

That got me wondering whether fun really is a competitive advantage, or just something we like saying before turning it into an inspirational LinkedIn post.

I should admit where I started: I desperately wanted “you cannot compete with someone who’s having fun” to be true. Who wouldn’t want enjoying yourself to put you ahead?

There’s privilege baked into the question too. For plenty of people, work is about survival. Enjoyment barely enters the calculation. So I went looking at the research. 👇🏻

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Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

Fun at work isn’t one thing

Research into fun at work is surprisingly new. Karl and Peluchette’s 2006 study was an early attempt to define workplace fun. They found that employees who experienced more fun reported greater job satisfaction, and that satisfied employees rated their organisation’s customer service more positively.

⁠Tews, Michel and Stafford found a messier picture. In a study of 195 restaurant servers, organised fun was linked with stronger sales performance, particularly among older employees. Manager support for fun was linked with lower turnover among younger employees, but also with poorer sales performance regardless of age.

So fun doesn’t simply equal better performance.

The results also depend heavily on where the research takes place. Studies tend to examine particular workplaces such as restaurants, hospitality businesses or call centres. We don’t have decades of evidence across every kind of work showing that making work more fun automatically improves performance.

What matters more is where the enjoyment comes from.

The work itself matters more

In 2008, ⁠Adam Grant studied what happens when people enjoy their work and care about who benefits from it. Across two field studies, people who experienced both showed greater persistence, performance and productivity.

Grant has since talked about “shallow fun” and “deep fun”. Shallow fun is the foosball table or office party. Deep fun comes from getting absorbed in difficult work that matters to you. It’s the kind of work where you can lose track of time because the problem itself has your attention.

That gets much closer to what interested me about Daniel’s original line. Having fun doesn’t mean making the work easier. Sometimes the difficulty is part of why you enjoy it.

When enjoyment becomes an advantage

Enjoyment can become an advantage, especially in work where judgement matters. Think about the work where there isn’t an obvious answer. You have to make decisions, solve unfamiliar problems and keep going when your first attempt fails.

Alysa Liu retired from competitive figure skating at 16, returned in 2024 on her own terms, won the 2025 World Championship and then Olympic gold in 2026. Liu has spoken openly about rediscovering how much she enjoyed skating once the pressure around it changed.

That doesn’t mean every training session suddenly became enjoyable. Elite sport still involves repetitive practice and days when nothing works. She came back wanting to skate again.

Fun can’t replace skill, effort, fair treatment or well-designed work. Enjoyment can help people persist and learn. If you enjoy difficult work, you’re more likely to stay with it long enough to get better at it.

You don’t need another games cupboard

Trying to manufacture fun for other people only gets you so far. A drink after an all-hands or a Eurovision sweepstake might help people connect. It can’t compensate for badly designed work. People need room to develop their own humour and rituals without a manager engineering every moment of enjoyment.

What matters more is the work people actually get to do. Look at who gets difficult problems they can genuinely own. If your most capable people spend most of the week doing predictable work, another social won’t change much. The same applies to you.

Instead of asking how to make your week more fun, look at the problems you genuinely enjoy getting stuck into. Which ones hold your attention? Where does your judgement actually matter? Then check whether you’re protecting enough time for that work or letting admin swallow it.

Keep some work worth enjoying

Most jobs will always contain work you’d happily avoid. Rebecca was right about that. Sometimes the invoice needs sending or the awkward email needs writing. No amount of research into motivation changes it.

The question is what sits alongside those jobs. The research gives us a reason to notice the difficult work we genuinely enjoy and protect some room for it.

Enjoyment can keep you interested long enough to get better. Over time, that becomes hard to compete with.

👤 writes ⁠All the Margins, for people who are accomplished, stretched, and quietly asking themselves whether this is really as good as it gets. She has built and turned around businesses across neuroscience, AI, accessibility, and pet care, led teams, managed boards, and made difficult decisions without a manual. ⁠Connect with Daphne on LinkedIn.

All The Margins

All the Margins is for people with full lives who want more from them — without adding anything, overhauling anything, or pretending the answer is a morning routine. Twice a week on the small moves that change how your life feels from the inside.

By Daphne Mavroudi-Chocholi

He produced millions of lines of code. Every prompt gave him something else to work on and a little hit of satisfaction.

Eventually he asked him a much less enjoyable question:

How much revenue had all that work produced? None.

He’d spent half a year doing difficult work while avoiding what made him uncomfortable. He wasn’t talking to customers or asking anybody to pay for it.

It can look a lot like being excellent at your job. 👇🏻

Procrastination can look impressive

Nate Littlewood recognised the same pattern while running Urban Leaf.

His background was in engineering and financial analysis, so numbers and operations felt familiar. Marketing and sales were far less certain, and he drifted back towards work where he felt capable.

He built what he later described as “amazing spreadsheets and databases,” while the company needed his attention elsewhere more urgently.

Nate also became fascinated by everything he suddenly had the chance to learn. He was getting smarter, then realised much of it wasn’t useful to what the business needed from him at the time.

Competence can hide avoidance surprisingly well. You can be doing genuinely useful work and doing it well. Meanwhile, the business may need you somewhere else.

Work you know how to score

Damon Flowers noticed himself drifting towards the areas he already knew in the early days of his business.

He stuck close to what he understood while the jobs he was less comfortable with kept getting pushed back. He sees people obsess over details such as fonts and logos while harder jobs stay untouched.

That kind of work gives you clearer feedback. A spreadsheet can be right or wrong. A design can be improved. Code usually gives you something obvious to fix.

Emma Mills points to an even more ordinary version: disappearing into email for an hour because it feels productive when you’re unclear about what to do next. An inbox gives you a visible finish line too.

Sales doesn’t give you that certainty. You can prepare properly and still hear no.

Put something you’re proud of in front of customers and you may find out they don’t care, or that the last month of work was based on a bad assumption.

That uncertainty is what makes familiar work so easy to return to.

AI has made the hiding place bigger

Matt thinks AI coding tools have made an old habit much easier to indulge.

If you love making things, you can now keep going without anyone else needing to get involved. There’s always a new tool to try or a process you could spend an afternoon rebuilding.

And you don’t have to be building software to do it. Carly Meyers sees business owners pile up AI subscriptions because they’re frightened of falling behind, without knowing what they’re getting back.

After decades building software, Konstantin Klyagin calls AI a multiplier. If you haven’t thought the product through or understood what users need, you can simply produce more of the wrong work faster.

What problem am I learning this to solve?

If you can’t answer that, you may have found another way to stay busy.

Millennial Masters

Helping founders make better business decisions

By Daniel Ionescu

“I just need another week”

Simon Jenner sees it when you keep delaying a launch. The technology gets blamed first. One more feature needs adding, or the marketing isn’t quite ready. “I just need another week.” Then another.

The feedback starts once real people see it. Until then, you’re still working mostly from assumptions about what they want.

Zain Peer ran into it while building London Nootropics. The recipes took around two years to develop. Even then, the packaging could’ve been better.

Zain says they could easily have spent another year perfecting everything. Eventually they learned to put something live once it was “good enough to get the data,” then improve it from there.

The difficult part is recognising when polishing has stopped reducing risk and started delaying judgement.

Two years and £5,000 later

Jack Good found out what waiting could cost. Early on, he inherited £5,000 and decided to build a marketplace connecting removal and storage companies.

He spent two years building and customising the site. By the time he released it, the £5,000 was gone.

Then the product failed. He hadn’t properly tested the idea before committing the time and money.

Once you’ve spent months building something, walking away means confronting the time and money already sunk into it.

Find the work you keep walking around

I’ve found one question more useful than asking whether I’ve had a productive week:

What important work do I keep finding a reasonable excuse not to do?

Maybe you keep improving the product because calling customers can wait until tomorrow. Or you rebuild the spreadsheet while the price stays where it is. Research can stretch for weeks when choosing a direction feels harder.

Sometimes the work you’re avoiding isn’t sales at all. It might be telling someone they aren’t working out. Maybe you keep putting off asking for money because another decision feels easier.

Learning is especially easy to justify because nobody can really accuse you of wasting your time.

Kate Assaraf realised bookkeeping was one of those areas for her. She didn’t understand it and had no desire to become good at it, so she hired someone who already was.

“I need to learn more first” sounds perfectly reasonable. Sometimes it’s simply how you buy yourself another week.

Matt asked about revenue because that was the test his friend had postponed for six months. The same test can be adapted to almost any kind of busy work:

What did the work actually change?

If weeks keep disappearing while the same awkward conversation goes untouched, pay attention to what keeps replacing it.

A lot of that comes from experience that was never written down in the first place. Sometimes you only realise how much one person knew until they’re gone.

writes Between Roles, drawing on his experience across AI, technology, consulting, products and retail.

His guest post looks at what happens when the system keeps running after the person who understood all the quirks has moved on. 👇🏻

A clothing retailer asked us to build a promotion analyser.

The job sounded straightforward: scrape competitor websites, pull out their discounts and help the pricing team understand what was happening in the market.

Their pricing manager had been doing much of that manually. Browsing competitor sites, checking prices and watching seasonal promotions took two or three hours every week. We built a system to handle it.

The pricing manager helped shape the POC for three or four weeks, then worked with us for another two or three months as we refined the system. He could see which recommendations made sense and which needed another look.

Then he moved to another store. That was when we discovered how much of the system still depended on what he knew.

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Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

What the system couldn’t see

The system could see competitor prices. The pricing manager knew what those prices meant for this particular business.

He knew the seasonal baseline. If the store sold a certain number of winter coats last year at a particular margin, he had a feel for what a realistic increase might look like this year.

He also knew where the data could mislead us. At one point, the system recommended buying 1,500 units of a particular T-shirt colour for the next 15 days.

The confidence score was high. The manager wasn’t convinced. He had already seen the recent orders and knew the recommendation didn’t fit.

The same happened with women’s office wear. The system saw strong demand and suggested increasing quantity.

When the manager looked closer, the spike came from a promotion that bundled the product with other items. It didn’t mean underlying demand had increased.

That judgement had been built through experience. Much of it had never been written down because one person already knew it.

What left with the manager

The replacement manager came from a different region, with different weather and buying patterns.

He didn’t automatically trust the system. He wanted its recommendations checked against historical data before trusting them.

That mattered because the original manager had spent months working with the POC. He had seen where it behaved well and where it needed another look. The new manager inherited the output without having gone through that same learning process.

Their approach to margin showed the difference. The original manager had become comfortable testing some promotions at profit margins of around 1–3%.

The new manager kept profit margins closer to 3–6% while the recommendations were being validated. He was more cautious because he didn’t yet trust the POC in the same way.

The system itself also had limits. It included some procurement-cost data, but it didn’t yet contain the full cost picture. Delivery costs and wider sourcing inputs were due to be added later.

The recommendation could still help, but someone needed to understand what wasn’t in it yet.

Where the work went

The visible task had largely disappeared. Someone no longer had to spend two or three hours every week browsing competitor websites and manually collecting prices.

The new manager had to compare recommendations with historical data. Finance still needed to understand the effect on margin. Inventory still had to judge whether the quantities made sense.

Somebody still had to decide whether a recommendation that looked reasonable on screen actually made sense for that store.

The research was largely automated. People still had to judge whether the recommendations made sense for that store. That distinction only became obvious when the original manager moved.

What to capture before you automate

When someone has done the same job for years, plenty of their knowledge never appears in a process document.

They recognise when a spike looks odd because they remember what happened after the last promotion. They can also tell when a number looks plausible on screen but still deserves another look.

AI forces some of that knowledge into the open.

If the person teaching the system leaves before you’ve captured enough of it, the task may still run. The next person then has to work out how much to trust the answer.

That’s what happened here. The scraper worked and the competitor research took far less time.

What didn’t transfer as easily was the confidence the original manager had built over months of testing the recommendations against what he already knew about the store.

When he moved, the next manager had to rebuild some of that confidence for himself.

The task was easier to hand over than the judgement around it.

Who still knows when this recommendation is wrong?

👤 Om Prakash Pant writes Between Roles, where he explores AI, technology, consulting, products and retail through the experience of someone who’s worked across them. His writing focuses on what changes when new technology meets the messy reality of how work actually gets done. Connect with Om on LinkedIn.

Between Roles

Technology changes fast. Good decisions age well. I write about AI, retail, architecture, consulting, products and the changing roles around them, connecting ideas with the realities of building and delivering.

By Om Prakash Pant

I’ve been thinking about that as AI moves beyond drafts and starts making decisions for us. When an unchecked email or recommendation can now leave the business before anyone sees it, there’s far more at stake.

A year ago, I was interested in whether AI sounded like AI. There were obvious giveaways everywhere and I called that the first version of AI slop. Better instructions got rid of most of them.

But you can remove every obvious AI phrase and still end up with something bland or detached from the decision it’s supposed to help you make.

I started paying more attention to the thinking behind the words. Once AI stopped giving itself away so easily, bad judgement became harder to ignore. 👇🏻

Slop starts before the draft

You can ask AI to draft a proposal and get something that looks ready to send. AI can summarise a customer problem or tell you what to do next in seconds, and you stop noticing how many decisions are hiding behind the request.

You still need to understand what the customer cares about and what you don’t know yet. The recommendation can be wrong because important context never made it into the prompt.

You used to have to stop and make more of those decisions yourself. When the result arrives in seconds, they’re easier to skip. Getting the task off your list tells you very little about whether you made a better decision.

The manager never disappeared

If somebody joined your company tomorrow, you wouldn’t throw a vague task at them and assume they understood how you think. You’d explain the job properly, then correct their work until they understood your standard.

A meeting summary is fairly forgiving and a follow-up email is easy to review. Choosing who gets a discount or how to handle an unhappy client is much harder to leave to AI.

With AI, you still set what it’s allowed to do and when it needs you. You’re still the manager, except now it can make the call without waiting for you.

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Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

Mistakes get more expensive

For a long time, bad AI mostly meant clean-up. You’d generate something weak, spot the problem and fix it. Maybe you wasted twenty minutes, or an employee spent an afternoon rewriting work that was supposed to save time.

A sales agent can follow up with dozens of leads while you’re doing something else. The same bad call can reach every one of them before you notice.

In customer support, following the policy isn’t enough. Someone angry over a £20 problem may need an apology before an answer, and an automated reply that misses that can give them a reason to leave.

I now notice another kind of AI slop when weak thinking gets repeated automatically. A perfectly normal sentence can hide a decision that’s wrong for the company. At that point, whether the AI sounds convincing matters far less to me. I want to know if I would have made the same call.

Exceptions are harder to hand over

You know when a customer needs a phone call instead of another email. Experience also teaches you when a refund makes sense beyond the policy, or when an attractive sales opportunity is likely to become painful later. Much of that judgement comes from years of doing the work without writing the reasoning down.

A long-standing customer paying you £20,000 a year may need a different response from somebody who signed up yesterday and ignored three attempts to help. That kind of judgement rarely fits neatly into one rule.

You can see the same thing in how you already manage people. Good employees learn the exceptions by watching you make calls no policy can cover, like refusing a refund or turning down a large client because the work would wreck the team. Over time, they pick up judgement that was never written down.

An AI agent doesn’t get that history by sitting in the office for six months. Trying to teach that judgement to an agent exposes all the places where your business relies on decisions nobody has properly explained.

Your judgement can’t stay in your head

You have to spell out which decisions the agent can make on its own and when it needs to stop and ask you.

Customers give you half the information, and an apparently perfect sales lead can turn out unable to afford you. Those messy cases show whether the rule actually survives outside a clean demo.

When the judgement is sound, AI can make those calls when you’re not there, whether that means answering a customer at midnight or helping your team when the usual person isn’t available.

The customer only sees what happened

If an automated recommendation pushes somebody towards something they don’t need, that decision comes from your company and becomes part of what they think about you.

AI still stands out because it feels new. I expect it to disappear into the background like the CRM your salesperson uses or the software that calculates an invoice.

Every decision you hand over to AI becomes another way somebody experiences your company.

I care more about the call now

I now ask if I’d have made the recommendation myself and whether I could explain it if a customer challenged me.

If the agent makes the same call a hundred times this month, I want to be comfortable with the pattern it creates.

I’ll happily automate routine decisions with a clear standard. Customer history and higher-stakes calls still need a person involved.

The moment an AI starts acting in my name, its mistakes become my reputation.

Melissa Kwan had built eWebinar into a proven product. She called it ‘quite overbuilt’, yet the starting price was still $99. Bigger companies saw that figure and questioned whether the company was serious enough to support them.

“Our starting point is still $99. But there are big companies that look at us and think we’re not serious enough,” she said. “People look at the pricing and they think we’re a tiny company. So it doesn’t inspire confidence. We’re not an expensive bottle of wine.”

For larger buyers, it introduced doubt before they had enough evidence to judge it.

That makes underpricing more than a margin problem. The number helps buyers decide what kind of company they’re dealing with before they’ve seen much else.

Choosing a lower figure because it feels safer can make the actual buyer you want more cautious. 👇🏻

New here? Millennial Masters shares lessons from founders on building better companies and handling the parts of the job nobody prepares you for. Subscribe for ideas on growth, AI, leadership and the decisions that shape your business.

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Helping founders make better business decisions

By Daniel Ionescu

Price becomes part of the evidence

Before buying, customers rarely know enough to judge a company properly.

They can’t see the systems behind the product or the problems the team has solved, so they judge what they can see.

An unusually low figure can make a larger customer question whether the business is established enough to support them. They may never say why and simply decide the business isn’t ready for them.

A deliberately low-cost company can work when the whole business is built for that position.

eWebinar had outgrown its price. The number was still sending an earlier version of the company into the market, making the business look less capable than it was.

The wrong customers arrived

Melissa kept hearing the same explanation: ‘We signed up because you were the cheapest.’ The product was worth far more than the price suggested.

Many lacked a clear need for the product. Some had signed up without understanding how it fitted their company, then struggled to use it and left within weeks.

“They want everything and then they can’t afford $49,” Melissa said. “Within two weeks, they churn.” The low price brought in customers with little reason to stay.

Melissa, David and their chief operating officer were handling support themselves because the company couldn’t yet hire a support team.

“It was drowning our support,” Melissa said. “It was a huge distraction for David because then he couldn’t code.” The price was now deciding how the founders spent their time.

When eWebinar doubled its starting price to $99, Melissa said the low-value support burden disappeared almost immediately. The new price filtered out buyers who were unlikely to use the product properly.

The cheapest customer can become the most expensive one to keep.

Brad Mac saw the same pattern with discounts. A 50% discount can make the ads look healthier on paper and pull in impulse buyers who disappear fast.

Paying less to win a customer means little when they buy once or create more work than the sale pays for. Those tidy numbers can hide a worse customer mix.

The price follows the work inside

The pricing model gets tested when the work gets faster.

Better processes shorten delivery. AI is speeding that up even more. Where that gain ends up depends on how you charge.

Joy Zarine saw the problem in time-based pricing. “Being better at your job when you charge time for money actually means that you’re faster, you’re more efficient and you earn less,” she said.

The customer gets the benefit of that experience. Charging by time means faster delivery reduces the bill, even when the result hasn’t become less valuable.

Luke Tobin saw AI making this more urgent. “The efficiency gains allow the consultant or the team to do more work,” he said.

“It doesn’t mean that they should be paid less for that work because they’re just able to do more in the time that they have.”

Ask who benefits when the work gets faster. A weak pricing model can turn better work into a smaller invoice.

Price isn’t always the problem

Raising the price works when the market can carry it. It won’t fix every business.

Noel Andrews ran into that with an interview coaching company. “When people did pay, they got tremendous value,” he said.

“But getting people to pay a reasonable amount of money, I don’t think there’s almost anything I could have done to the offer. I think it was just a market problem for me there.”

The service worked. The market didn’t.

A low price doesn’t always reflect weak confidence or poor judgement. Sometimes customers simply won’t pay enough to cover what the service costs to deliver.

A price increase can’t repair a market that won’t support the business. Changing the number can make the real problem harder to see.

What kind of business is the price creating?

The price you choose keeps shaping the business long after launch. It affects who comes in and how much work those customers create.

It decides who you serve and who benefits when the work gets better. Sometimes the number exposes a market that can’t support the company at all.

Being inexpensive can work when the whole business is designed for those customers. Underpricing can pull the business into a shape that makes it weaker.

Before lowering the price to win another customer, ask what that number will bring into the business. If it isn’t working, ask what it’s already changing.

What kind of business is that number quietly building around you?

looks at what happens next. The buyer opens it, but the proof sits too far from the claim and they’re left to connect it themselves.

That extra work creates doubt. The longer they search, the easier it becomes to stop.

Colette spent eight years in sales and revenue operations analysing where deals slowed down and why buyers disengaged.

She writes Empathy Elevated, a newsletter on clearer communication and better decision-making. Her guest post shows why putting the proof where the buyer needs it changes everything. 👇🏻

New here? Millennial Masters shares practical lessons from founders on building better companies and handling the parts of the job nobody prepares you for. Subscribe for honest ideas on growth, AI, leadership and the decisions that shape your business.

Millennial Masters

Business, growth, and AI. The newsletter and podcast for founders and leaders.

By Daniel Ionescu

It was a tough conversation with Brice. A deal had gone quiet. It started with a DM asking if I had a few minutes to chat. I did, so we got straight into the account.

There were several opportunities on the table, including one large enough to put him over quota for the entire year in Q2.

But he wasn’t calling to celebrate. “She just went cold.”

I needed to understand what had happened, if anything. It wasn’t the first time I’d heard the story. Different rep, different company, different product, but the same commercial problem.

Every deal has its own story. The pattern underneath is often much more familiar.

Why buyers stop

It isn’t always the price or scepticism.

The easy answer is that the buyer ghosted you, or the timing wasn’t right. That may be true. The bigger problem is usually the work required to connect your claim to proof that their pain will actually go away.

Every time a buyer has to hunt for evidence, flip through pages or take your word for it, you’re asking them to do more work. Eventually, the buyer stops and steps away.

Looking at enough stalled deals, the same pattern kept appearing. Once an opportunity spends longer than average in a stage, it’s often close to dead.

The number of days isn’t the cause. It usually means the buyer has been left searching for answers.

The document has been sent. The follow-up notes are in their inbox. But there’s no response.

The problem is the distance between the claim and the evidence. That distance is measured in seconds, pages and scrolls. The more work it takes to understand why they should believe you, the easier it becomes to stop.

Where proof gets buried

The claim and the evidence often live in different places. The value proposition comes first, while the metrics and testimonials sit somewhere else.

The buyer is left to connect them. If they don’t, uncertainty fills the gap.

They’re looking for a solution. But when they have to do the research themselves, confidence starts to fall.

For sales, this can look like a messaging or communication issue. Brice certainly thought it was.

The issue is usually placement. The message may be fine. The buyer simply reaches the proof too late.

Before & after: The same claim, easier to trust

Nothing changed except where the proof appeared.

The claim didn’t change. In the first version, it appeared where you would expect it, but the proof sat much further down the page, beyond the few seconds most people give before deciding whether to keep reading.

In the second version, the same proof moved up beside the claim. Nothing else changed.

Now the buyer sees the evidence at the exact point they need it. They don’t have to search for it or make the connection themselves. The claim becomes easier to trust because the proof is right there.

Run the test before you hit send

For every claim in your proposal, deck, or page, ask yourself: can the buyer find the proof within a glance? If not, move it.

What happened with Brice that day was not unique. It’s a pattern I have seen many times, and in many cases it could have been prevented.

This is about catching the problem before the document leaves your hands, rather than diagnosing it after the deal has gone quiet.

Most buyers don’t walk away because they disagree with you. They walk away because understanding your case became work.

The easier you make the proof to find, the less work the buyer has to do to say yes.

👤 Colette Molteni writes Empathy Elevated, a newsletter about clearer communication and better decisions. She spent eight years in sales and revenue operations analysing where deals slowed down and why buyers disengaged. Connect with Colette on LinkedIn.

Empathy Elevated

Human skills for high-intellect professionals navigating work in the age of AI. Free weekly. Paid Deeper.

By Colette Molteni

Plenty of businesses are running close enough to the line that a single payment arriving one day late can throw off payroll, delay suppliers or force a scramble to move money around.

I’ve lived it and seen it first-hand. In tougher periods, having months of cash in the bank is a luxury few businesses can afford.

, CFO and writer of The Profit Lens, looks at where those delays really begin, and why the problem often starts long before the invoice is overdue.

New here? Millennial Masters shares practical lessons from founders on building better companies and handling the parts of the job nobody prepares you for. Subscribe for honest ideas on growth, AI, leadership and the decisions that shape your business.

Millennial Masters

Business, growth, and AI. The newsletter and podcast for founders and leaders.

By Daniel Ionescu

Late payments come up again and again during my consulting work with founders.

What surprised me was how often the delay began weeks before the invoice became overdue, through missed approvals, unclear delivery terms or an invoice that never reached the right person.

It didn’t start with the invoice

Before you begin any work in a service or project-based business, you need a clear delivery process.

Start with a contract that defines the scope, timeline and deliverables. Confirm the payment details upfront too.

Understand the client’s procurement process, particularly with larger buyers. If they require a purchase order, make sure it’s approved before you start the work.

There’s another rule that’s easy to overlook. In services, the work is only delivered once the client formally accepts it. Sending it doesn’t mean the job is finished.

That confirmation, even in a simple email, should trigger the invoice immediately. Every day between acceptance and invoicing delays payment.

The invoice also needs to reach whoever actually processes payments, which may not be the person who ordered the work.

Staff change, especially in larger businesses. Lose track of the right contact and payment can stall for weeks.

Every extra day adds up

Imagine two agencies delivering the same kind of project. One waits three days after acceptance to raise the invoice. The other sends it that afternoon.

Neither client necessarily pays late, but one agency gets paid three days earlier on every project. Across a year, those days add up.

Net 45 or Net 60 can feel generous when you’re trying to win the business. It feels different when you’re still waiting two months after finishing the work.

For many service businesses, 14 to 30 days is enough. Apply those terms consistently rather than renegotiating them with every client.

Asking for 30 to 50% upfront means you’re not carrying all the financial risk until the client pays. It also confirms that both sides are committed.

Some businesses pay late regardless of what the contract says. A quick online search or credit check can reveal that pattern before it becomes your problem.

Make your client and supplier terms work together. When suppliers expect immediate payment but clients pay in 60 days, your business finances the difference.

Who makes the call?

Even with a good process, you can still leave it too late to follow up.

You may delay chasing because you don’t want to damage the relationship, but clients usually expect a reminder. A polite, consistent follow-up is better than silence.

At the end of each month, pull a report showing overdue invoices by age and give it to whoever actually speaks to the client.

One founder told me the conversation stressed him out because he worried about annoying the client or losing them.

A sales colleague once gave me advice that changed how we handled this.

Payment is part of the client relationship, and the person the client already knows usually has the best chance of speeding it up.

Start by asking how things are going, whether the work met their expectations and whether they need anything else.

Then mention the payment: “By the way, we noticed the last invoice hasn’t been paid yet. Could you check whether it’s been picked up on your side?”

Give one person responsibility for following up, whether they feel comfortable doing it or not.

Where the delay really starts

You don’t need a crisis to face a cash crunch. A few late payments can make payroll feel heavy.

Monitor collections as closely as sales. A sale won’t pay the bills until the cash reaches your account.

By the time an invoice becomes overdue, the missed approval, late sign-off or wrong contact may already have cost you weeks.

👤 Nadia Codreanu is a CFO with 20 years’ experience across corporate and founder-owned businesses. She writes at The Profit Lens about financial clarity, leadership under pressure, and the decisions that matter most for professional services founders. Connect with Nadia on LinkedIn.

The Profit Lens

By Nadia Codreanu

That’s when you find out whether you’ve built a useful way of working or just got very good at using one model.

If the prompts, context and checks only make sense inside that tool, changing models means rebuilding far more than it should.

is a tech consultant and AI practitioner based in Spain, who writes the Cash & Cache newsletter.

In this guest piece, he looks at the simple habits that keep the work moving when the model changes. 👇🏻

New here? Millennial Masters shares practical lessons from founders on building better companies and dealing with the parts of the job nobody prepares you for. Subscribe for the next one.

Millennial Masters

Business, growth, and AI. The newsletter and podcast for founders and leaders.

By Daniel Ionescu

One AI model went offline for three weeks. Some of the work disappeared with it.

Anthropic launched Fable 5 and Mythos 5 on 9 June. Three days later, both were suspended under US export controls. Access returned on 1 July.

The interruption exposed a problem that had been there all along. Too much of the work depended on one model’s prompts and quirks.

When the tool vanished, people had to rebuild the context and find another way to check the results.

The tool you rely on can change or disappear with little warning.

When changing models means starting again, too much of the work belongs to the tool.

More tools won’t fix it

When a model disappears for three weeks, the obvious reaction is to add more tools.

You spread the work around and tell yourself you’re safer. Often, you’ve only created more places for things to get lost.

Adding another tool takes ten minutes and a credit card, which makes it feel like progress.

Learning a smaller number of tools properly, then cutting the ones you don’t need, takes more work.

Four subscriptions won’t save you if none of the work can move cleanly between them. You’re still stuck, only now you’re paying four bills instead of one.

The work between the tools is still yours

Models keep getting better at drafting, coding and checking work.

Someone still has to decide which tool does what, pass the right context between them and catch the mistakes.

Take a normal sales process. One tool writes an email while another scores the lead and summarises the call.

Things can get lost between the score, the summary and what actually happened.

The tools can take on more of the tasks. You still have to join the work up and decide what to trust.

Three habits that survive the switch

This is where you stop starting from scratch every time.

First, can you give a new tool the context it needs in under five minutes? If everything still lives in your head or an old chat, changing tools means rebuilding the work.

Second, do you decide what the tool needs to ask before it starts? “Analyse this” leaves too much open. Asking it to show its assumptions first gives you a better chance of catching a bad answer early.

Third, how do you check the result? Asking the same model whether it’s confident isn’t a proper check. You need to check it against evidence or have someone who understands the work review it.

These habits let you change tools without rebuilding the work.

Find out where you’re stuck

You don’t need another tool for this. Take five minutes and answer four questions.

Context: Could you give a new tool enough information to pick up the work today? If not, too much still lives in your head or an old chat.

Before it starts: What should the AI ask first? If you don’t know, you’re still giving it loose instructions and hoping for the best.

Checking: Think about the last AI answer you used for something important. How did you know it was right? Asking the same tool if it’s sure doesn’t count.

Subscriptions: List every AI tool you’re paying for and write down the last useful thing each one produced. If a tool has done nothing useful for 30 days, cancel it.

Fix the habits before adding tools

Before you add another subscription, sort out how you use the tools you already have.

Keep the context somewhere you can move, and decide how you’ll check the answer before the tool starts.

Then a tool going dark becomes an annoyance you can work around.

👤 Raghav Mehra is a tech consultant and AI implementation practitioner based in Spain. He writes Cash & Cache, a newsletter on AI strategy and implementation, where 2,000+ subscribers get the actual workflows behind how he builds and runs things with AI, not just the parts that make it into a highlight reel. Connect with Raghav on LinkedIn.

Cash & Cache

Where strategy meets execution — exploring how AI is transforming businesses, markets, and value.

By Raghav Mehra

A bad hire hurts at any level. Put the wrong person near important decisions and the damage can spread before the probation period has even caught up.

The trap is that a polished interview can make the risk harder to see. Clean answers, neat stories and the right values can feel like proof, when they may only show that someone prepared well.

Daryna Karuna writes The Leverage Point, a field guide to hiring and senior decision risk. Her work draws on investigative journalism and behavioural risk analysis.

Her guest post is about the trap of the polished candidate, and why key hires need to be tested beyond the answers they’ve prepared. 👇🏻

New here? Millennial Masters shares practical lessons from founders on building better companies and handling the parts of the job nobody prepares you for. Subscribe for honest ideas on growth, AI, leadership and the decisions that shape your business.

Millennial Masters

Business, growth, and AI. The newsletter and podcast for founders and leaders.

By Daniel Ionescu

The candidate everyone liked

A consultant I work with was embedded inside the hiring team of a mid-sized European digital agency specialising in SEO, AEO and reputation management.

The company needed a senior operational leader, and internal HR ran the process: interviews, a personality questionnaire and panel conversations. At one stage, the CEO sat in personally.

The candidate looked strong on paper and in the room. She had a clear track record, gave structured answers and came across as confident without overreach.

She spoke fluently about the company’s operating model and gave the room what it wanted to hear. HR approved, the CEO approved, and the test scores were clean.

The consultant was the only person who objected. There was no evidence, no clear data point and no formal flag in the report. His concern was that everything was too smooth.

The answers had no friction. She never searched for a word, and none of the stories carried real personal cost.

Every response landed where the interviewer wanted it. The values she articulated were the company’s published values, available to anyone who’d spent twenty minutes preparing.

He told me later: “If I’d stayed silent, I would’ve had a reputation problem.”

When smooth becomes the signal

He raised the objection behind closed doors. The CEO disagreed, it escalated, and for a time they stopped working together.

The candidate was hired. Two months later, she requested leave because a close relative had fallen seriously ill.

The company responded with understanding, and the leave became indefinite, then permanent.

A year later, through an industry contact, the agency found out that the former hire was working for a direct competitor. The role appeared to have been arranged before she arrived.

The agency treated it as a suspected industrial espionage case. The objective was the company’s proprietary technology stack, including its internal tooling, ranking methods and client delivery setup.

That’s the kind of operating knowledge that can take years to build and very little time to copy once someone’s inside.

What your process is actually testing

The process was thorough and still had a blind spot.

Interviews test what a candidate can present. Personality questionnaires measure what a candidate is willing to say about themselves. References confirm what a network will vouch for.

Every step evaluates the same thing: the version of the person prepared for you.

A trained candidate can perform that prepared version very well. They only need to give the process what it rewards: fluency and the right values in the right order.

Your interview has to separate a genuine values fit from someone who studied your About page forty minutes before walking in.

Your personality questionnaire struggles with strategic answers because it depends on the person choosing to answer truthfully.

The process had several steps and several people involved, while every checkpoint tested the same surface.

A better process should make the candidate work through something harder to polish: a trade-off, a messy handover or a decision with incomplete information.

Senior people can prepare polished answers. Judgement is harder to fake when the situation has no clean script.

The cost arrived later

Insider risk reaches smaller technology-driven services too. The asset worth extracting is often the way the work is built rather than a client list.

The agency believed it had lost proprietary methodology, and work built over years had been copied in weeks.

The damage showed up gradually, through a competitor using familiar-looking approaches and moving faster in areas where the agency had been ahead.

The consultant was right. Without a method to back it up, that judgement was hard to defend in a boardroom.

He kept the relationship with the CEO, but only after months of broken trust. Being vindicated twelve months later didn’t fix what the disagreement had cost in the meantime.

Add a step they can’t rehearse

That consultant’s gut feeling was real. Gut feelings are hard to repeat, teach or explain to a board.

What was missing was one step that tested something the candidate couldn’t rehearse.

That could mean making them work through a messy scenario, calling references who weren’t handed to you, or using an assessment that gets beneath rehearsed answers.

More stages only help when each one tests something different.

At senior level, one hire can access your systems and working methods within weeks. Hiring has to test more than fluency.

A thorough process still needs one moment the candidate can’t polish.

👤 Daryna Karuna writes The Leverage Point, a field guide to hiring and senior decision risk. Her work focuses on clear thinking tools, practical diagnostics and how to understand what polished processes can miss. Connect with Daryna on LinkedIn.

The Leverage Point

A calm field guide to psychometrics, hiring, and decision risk at the senior level — for founders, CEOs, and consultants navigating the gap between assessment methods and how decisions actually get made.

By Daryna Karuna

That’s useful early, until the business starts needing you for too much.

Then you have more people, customers and moving parts. Your memory and willingness to step in are still holding too much together.

Gary Das had to admit this in his own business. “I realised that I was the problem. I was the bottleneck. The business had almost outgrown me.”

The old survival habits can become the scaling problem. 👇🏻

New here? Millennial Masters shares practical lessons from founders on building better companies and handling the parts of the job nobody prepares you for. Subscribe for honest ideas on growth, AI, leadership and the decisions that shape your business.

Millennial Masters

Business, growth, and AI. The newsletter and podcast for founders and leaders.

By Daniel Ionescu

The business hits your limit first

It happens because you keep making yourself useful. You keep fixing things because it’s quicker than teaching the business how to fix them.

Harry Sanders has seen companies stay the same size for years because “the founder wants to be involved in everything. The founder wants to know everything, touch everything, feel everything.”

Emma Mills had the uncomfortable version of this. She was helping other business owners get out of their own way while missing it inside her own company.

Someone in a mastermind told her she was helping everyone else delegate while becoming “the bottleneck to the business”.

The business keeps moving because you keep rescuing it. Eventually, it grows until it reaches the limit of one person’s attention.

Your head is not a system

Joe Averill hit this when he tried to hire his first employee. There was “no infrastructure in the business”. Everything was in a pen and pad, on pieces of paper, or stuck in his head.

You can’t delegate instinct. People need to know what good looks like before they can reach it.

Damon Flowers points to the information “solidly in your head that nobody else knows about”, including where the business is going and who it’s really for.

What feels obvious to you may still be private to everyone else. If your team has to ask you every time, the standard hasn’t left your head.

Hiring exposes the mess

You’re tired, stretched and doing too much, so you bring someone in expecting the load to reduce. Often, it reveals how unclear the work was.

Tyler Dunagin has seen hiring make the problem bigger. You expect someone to take over half the problems, but “if you don’t have a system or KPIs or know what you’re delegating, you just doubled your problems.”

You have a picture of the result, then fail to explain it properly at the start.

The team member does the work, but the standard was never shared.

Stop hiring helpers

The danger is building a team of people who still need you for every meaningful decision.

Gavin Bell calls these people helpers. You hire a virtual assistant or someone from Upwork, and for a while it looks like progress.

Matt Watson saw this from the product side. You tell people exactly what to do, then wonder why the business doesn’t scale. “You have to stop giving them tasks and start giving them goals.”

Will Polston has a simple rule: “I delegate the outcome, not the activity.”

A helper can take a task off your list. An owner takes responsibility for a result when they have enough authority and a shared standard.

If someone needs your instruction every time, the work is still passing through you.

Let the work come back imperfect

Damon Flowers says founders have to be okay handing something over and getting 60% of the result at first. Many owners want 95% or 100%, so they step back in and take the work over again.

You think you are protecting the standard, but you are teaching the team to hand difficult work back to you.

Lydia Snape has lived both sides. She hired managers, then still jumped in and did the work. Later, she moved too far out and watched things slip. Her lesson was that people need you to set and explain the standard before they can reach it.

William Stokes treats wrong decisions as process problems. When someone makes the wrong call, his team asks why the decision was made, then looks for the missing system that allowed it to happen.

Scaling means other people learning how to fix the work without handing it back to you.

Write down what keeps repeating

The practical work is turning your judgement into company memory.

Luke Tobin got there by creating operating manuals that explain the work clearly enough for other people to repeat it.

Nick Holzherr turned that into a habit. When someone asks a question, avoid jumping on a call and answering it live. Write the answer down and share that instead.

A written answer becomes company memory every time a question returns. Over time, the team learns to check what already exists before asking again.

Documentation sounds like admin until it removes the need for your memory.

What worked small will crack later

The work is never finished because the business keeps changing.

Joshua Western felt that break between 50 and 60 people. That jump was harder than going from two to 50, because the early ways of working and small processes started falling over.

The same structure that made you fast early can create confusion later.

Harry Sanders found the truth by leaving the building. He’d told himself the business ran without him, then physically left during a UK expansion and spotted the cracks.

Distance exposes dependency. You see what still waits for you and who was following instructions rather than owning outcomes.

Buyers don’t want your job

That’s where the problem becomes commercial. A business that still depends on you may produce revenue, but it’s harder to sell and harder to trust from the outside.

Luke Tobin says too much founder dependency is one of the biggest risks for a buyer. It’s “probably the biggest turn off”. Buyers want to see specialists in each function, not you holding every part together.

Joy Zarine takes it back to the holiday laptop. You’re by the pool, answering emails and feeling frustrated that this isn’t why you built a business. “That’s on us as the founder.”

You can start as the operating system. You can’t stay there forever.

At some point, the job is to move your judgement out of your head and into the way the business works.

The business grows beyond you only when it stops needing you to hold every answer.

Every new tool adds another thing someone has to check and clean up.

writes The Humanizing and works with banks, fintechs and private equity-backed businesses on making AI work inside complex organisations.

Her piece is about the gap between a good demo and the business strain that follows.

The next discipline is deciding what deserves trust and what should be cut. 👇🏻

AI made building cheap. You can now build and test ideas far faster than you could three years ago. That was supposed to remove the hard part. It mostly moved it.

Uber reportedly burned through its annual Claude Code budget in months. Engineers were ranked on adoption leaderboards. The productivity gains were real, but so was the budget overrun.

That’s the new AI problem in miniature. Creation now scales faster than the business can manage it. The bottleneck moved into judgement and how well the business underneath actually works.

This is where AI slop starts inside a company: tools, dashboards and agents stacked on top of work nobody has properly fixed. It can look productive and still leave someone with more work to check and clean up.

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Millennial Masters

Business, growth, and AI. The newsletter and podcast for founders and leaders.

By Daniel Ionescu

Task tools are fragile

A founder building only at the task layer is easy to copy. The kind of tool that writes meeting notes or drafts emails can be absorbed into the base model or bundled into larger tools.

If that’s the whole product, the company is always waiting for the next model update to eat its margin.

Jobs carry a different kind of value. Underwriting or customer servicing depends on judgement and accountability you can’t copy by automating the pieces.

The team that owns the job knows when AI is useful and when it is quietly producing garbage at scale. That judgement is much harder to copy.

Adoption tells you less than trust

Adoption can tell you who clicked. Absorption shows whether the tool has become part of how the work gets done. That means redesigning the workflow and making clear who owns it.

If you’re selling AI into a company, deployment doesn’t prove much. The product has to earn enough trust that people keep using it after the first mistake.

A tool with awkward integration and unclear ownership is easy to replace, especially when the improvement is hard to prove.

AI exposes the mess underneath

Humans know when a policy is out of date, an approval step is ignored or an exception only exists in someone’s head. Agents don’t have that context, so they follow the mess exactly.

When an agent follows a policy that is three years out of date, or routes a decision to a role that no longer exists, the failure is immediate. It can’t politely work around the ambiguity. It reveals the work the business has been avoiding.

The organisational debt was already there. Humans had been quietly working around it, and AI made it much harder to defer.

Every tool becomes a claim on attention

Every AI system you keep creates a weekly claim on someone’s attention. Someone has to check it and decide whether it’s still worth having.

The problem rarely arrives as one big failure. It starts when people stop trusting the tool.

A bank can launch a copilot for relationship managers and see 60% adoption in month one. By month three, usage may collapse because the summaries work for internal meetings and still fail the higher bar of client preparation. The useful question is whether people come back after the first wrong output.

A PE-backed company can deploy a contract review agent and find that 40% of contracts need manual exception handling. The agent only exposed the real problem: the exception process lived in one senior person’s head.

The work required to make AI useful inside a real organisation keeps rising. Twelve AI tools across three teams means twelve things that need checking and justifying every week, whether they’re working or not.

Subtraction is the next advantage

The next advantage belongs to teams that can kill the tools that don’t improve the work.

Subtraction is hard because every tool has people and politics attached to it. Killing one can mean admitting the bet was wrong or accepting that you can’t chase every possible win.

A simple test helps. Remove tools that don’t directly improve a job your customer runs. When two tools do the same thing, keep the one that needs less checking.

If an experiment hasn’t produced a measurable result in ninety days, it’s consuming attention without returning it.

AI made building cheap. AI slop is coming for your business too, unless you can subtract.

👤 Neha Kabra works with banks, fintechs and private equity-backed businesses on making AI work inside complex organisations. Her 18-year career spans banking and consulting, including a decade at McKinsey, and she writes about AI, work and business change at The Humanizing. Connect with Neha on LinkedIn.

Neha Kabra

AI is changing technology, competition, organisations, incentives, and power. I write about the second-order effects and strategic shifts leaders can’t afford to miss.

Who has the final say when you disagree? What happens if one person works harder, the business can’t pay you or you want different exits?

When the company has no value and both of you are excited, those conversations can feel unnecessary. You trust each other and assume you’ll work out the difficult parts later. That’s the cheapest moment you’ll ever have to discuss them.

Once money, equity, staff and years of your life are tied up in the company, a disagreement can stop the business moving and leave you building something you no longer fully control. By then, the relationship may already be badly damaged.

I’ve lived through at least four versions of this, and the tension looked different every time. My conversations on Millennial Masters have shown me how much can be agreed early, before a disagreement becomes a breakup.

That’s why you need the co-founder version of a prenup. It sets out what happens when trust is strained and you can no longer agree. 👇🏻

Do you really need a co-founder?

The first question is whether you need a partner at all. A gap in your skills doesn’t automatically mean someone else should own part of the company.

Kate Assaraf learnt that after a painful split. “You can’t transfer your passion for something to someone else,” she says. When she rebuilt, Kate hired a bookkeeper instead of bringing in another partner.

Equity can feel cheaper than salary when cash is tight, so you give away ownership to fill a role you can’t yet afford to hire for. The cost appears later if that person stops adding value while keeping a large part of the company.

James Church has seen how badly that can end. You bring in someone to handle development, find out they’re not as good as expected, then have to deal with “this 50% shareholder who’s no longer adding any value”.

A missing skill might call for an employee, contractor or adviser. Giving away part of the company needs to solve more than a hiring problem.

Date before you commit

Nick Telson-Sillett recommends spending at least three months “dating each other” before committing. That means time together beyond talking excitedly about the idea.

You need to know what drives the other person, what their life allows and how they work under pressure.

Those differences are difficult to spot while everything is hypothetical. Work on something real together and see how you both react to deadlines, money and problems.

Skills matter too. Joshua Dziabiak compares choosing a co-founder to “putting a ring on it” and says the person should complement you. “If they’re good at the same things you’re good at, they’re probably not the right partner.”

Millennial Masters

Helping founders make better business decisions

By Daniel Ionescu

Agree what you’re building

You can start a company together with completely different ideas about the deal.

Michael Buckworth sees it happen when one person expects profitability to take five years and a sale to take a decade, while the other expects a million pounds in profit by year two and a huge exit in year three.

Both believe the plan is obvious. Those private assumptions shape almost every important decision later. Discuss the future before you both become attached to different versions of it.

Agree how much time you’ll each give the company, whether outside work is allowed, when you can start paying yourselves and how long you can manage without profit.

You also need to know whether you’re building to sell and what a good exit means to each of you.

Equal shares, unequal reality

A 50/50 split feels like the cleanest expression of trust. You’re starting together, so you divide the company equally and avoid an awkward negotiation. The problem starts when the contribution stops feeling equal.

Kate doesn’t believe responsibility can ever stay perfectly 50/50. Once the balance feels wrong, it starts eating away at the partnership.

The balance shifts when one person carries more of the work or risk, or life changes what the other can give. The same split can also leave the company stuck when neither of you can break the tie.

Peter Watson objects to automatic 50/50 splits. “Someone has to have the 51%,” he says. “The amount of businesses that go to zero is because two people just can’t agree. Deadlocks. Then what do you do? It’s over.”

An equal split can work if you agree who breaks a deadlock. Before dividing the company, agree what each of you will contribute and what happens if the balance changes.

Who gets the final call?

Gus van Rijckevorsel is particularly blunt about companies with two CEOs. To an investor, it can signal that nobody has settled who’s in charge.

You need to know who owns each part of the business and who gets the final call.

Kevin de Patoul argues openly with his co-founders and sees it as part of working together. After one difficult conversation lasting an hour and a half, he accepted that the other person was right.

The damage starts when nobody knows who decides and the argument goes nowhere.

Be clear about who is CEO, what each of you owns and which major decisions you must make together. Write down where one person has authority to decide.

Plan how either of you leaves

Discussing what happens if one of you leaves before the company has properly started can sound disloyal, as though you are already preparing for failure.

Michael compares a founders’ agreement to a prenup. You agree the terms while the questions are still theoretical, put the document away and hope you never need it.

Kate describes a business-partner split as feeling like a marriage ending. Once that happens, neither of you is likely to negotiate calmly.

Reverse vesting means you can receive shares at the start and earn the right to keep them over time. If one of you leaves early, the agreement can allow the unearned shares to be returned.

The agreement should also cover leaving, long-term illness, buyouts and ownership of the intellectual property.

Get qualified legal advice in the country where your company is registered. A solicitor can document the deal. They can’t decide what you both actually want.

Check the deal still works

A founders’ agreement captures what you both wanted when you signed it. The company and your lives will change.

Joshua Western checks in with his co-founder roughly every quarter. They ask whether they still want the same thing from the company and what an exit could look like.

One of you may become more cautious while the other wants to take bigger risks, and the roles you started with may stop fitting the company.

Tom Wallace-Smith calls this “alignment”. If there’s a problem, they take it straight to each other before resentment builds. The paperwork can’t do that for you.

The best time to discuss a breakup is while the relationship still feels secure.

A founders’ agreement can’t rescue the wrong partnership. It can bring hidden expectations into the open and stop a departure from paralysing the company.

Before dividing the equity, agree who makes the big calls, what you both owe the business and what happens if one of you leaves.