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. 👇🏻

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

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.

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. 👇🏻

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

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.”

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.

The money still has to come from somewhere. You take out credit, delay something else or, as I’ve done before, reach into your own savings to cover the gap.

The bigger the business, the more money you have to find. Getting caught once is painful. When it happens again, you can end up frighteningly close to the edge. I’ve been there too.

, the CFO behind The Profit Lens, has seen this pattern up close before. In her first Millennial Masters guest piece, she looked at the cuts that save money now and cause damage later.

This time, she explains which warning signs appear first and the five numbers worth checking every month.👇🏻

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 warning comes before the balance

You watch the bank balance for signs of trouble. By the time it starts looking uncomfortable, the cause may already be several weeks old.

Profit has weakened, invoices have gone out late or clients have quietly stretched their payment terms.

The balance still matters. It tells you what has already happened. The useful warning signs appear earlier.

Early signs you notice too late

One of the first numbers I watch is monthly profit. When it falls below 5% and keeps moving towards breakeven, cash pressure may already be building.

You can miss the change when profit isn’t reviewed monthly. Revenue and the bank balance get more attention, while the full numbers may only appear at quarter end. By then, the cash impact can already be several weeks old.

Profit erosion is only one signal.

🧾 The invoice hasn’t gone out

A project is completed and nobody chases the paperwork. Every day between delivery and invoicing adds another day to the wait.

⏳ The client keeps stretching payment

A client who used to pay in 30 days starts paying in 45, then 60. Nobody renegotiated, but the gap between delivery and collection widened.

🤝 Work started before the money was agreed

Work starts on a handshake and the contract follows later. The delivery costs are already real, while the cash protection remains uncertain.

A full pipeline can still hide a cash problem when the money arrives too late. A healthy revenue forecast doesn’t mean cash will arrive when you need it.

I’ve seen businesses with money in the bank discover that very little of it was genuinely available once the payments arrived and the invoices were still outstanding, while the profit had been eroding for longer than anyone had noticed.

A monthly review catches these changes while there’s still time to respond.

Five numbers to check every month

These are the five checks I use when cash feels tight. Together, they show what your bank balance leaves out and where it may be heading next.

1️⃣ Cash left after commitments

The bank balance becomes more useful once you understand what’s already committed against it.

List every payment due in the next 30 days, including salaries, rent, supplier invoices, contractor payments and tax obligations. Subtract that total from the balance.

What remains is the cash you can genuinely use.

2️⃣ One month of outgoings

Calculate the total cash that must leave the business each month, including fixed and variable costs.

This tells you how much runway you have if revenue slows and whether the balance is healthy or temporarily inflated.

3️⃣ Profit this month

Is your business profitable right now, and at what level?

Profit below 5% and moving towards breakeven is one of the clearest signs that cash pressure may be building. Catching it here gives you more options than waiting for the balance to fall.

4️⃣ Overdue client payments

Open your receivables and focus on what should already have arrived.

Overdue receivables are cash the business has earned but hasn’t collected. Following them up promptly is often the fastest way to improve the cash position without cutting costs.

5️⃣ Tax due in the next 90 days

Corporation or income tax, VAT or sales tax, and payroll taxes accumulate quietly before arriving on fixed dates.

Map every tax payment due in the next 90 days before deciding how much cash you genuinely have available.

Growth widens the cash gap

Your business needs working capital to cover suppliers, salaries and delivery costs before clients pay.

The cycle is healthier when client money arrives early enough to cover the costs of delivery. When it doesn’t, the business has to fund the gap from somewhere else.

Payment terms are a common cause. Clients may pay in 60 or 90 days while suppliers are due in 30. You deliver the work, cover the costs and then wait.

Growth increases the pressure because a business growing quickly needs more working capital. Extra cash may be required simply to fund delivery while the company waits to be paid.

The business model needs to account for that gap. Client payment terms should support the cost of delivery, margins must leave enough cash inside the company, and owner withdrawals can’t drain the capital required to keep operating.

Why you keep putting it off

You’re busy selling, delivering and managing everything else. Cash checks can feel like a finance task to revisit when there’s more time. That moment usually arrives when the bank balance forces it.

You may hesitate to chase a client because the relationship feels more important than the invoice. Growth can also feel like the answer to the timing problem.

Growth may bring in more cash later, but the bills due this month still need paying. Slow-paying clients rarely speed up because you need the money, and a margin that weakened over six months won’t recover immediately.

Reviewing these numbers every month gives you a better chance of finding the problem while it’s still manageable.

Check before the balance forces you

Review profit, committed payments, overdue invoices and tax obligations every month.

The bank balance can tell you that cash is tight. These numbers can tell you why, while you still have time to do something about it.

👤 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

Millennial Masters asked something different of me. I had to put my face on camera and publish ideas and experiences I’d previously kept private.

I’m not shy and I don’t lack confidence. New formats and more personal posts can still feel uncomfortable, although repetition means the cringe no longer makes every decision for me.

Lauren Currie gave me the best description of what this feels like:

“Making your dreams come true is embarrassing. It is uncomfortable, it’s sweaty, ugly, you pitch bad, you cry in toilets, you post things that make you cringe later.”

The cost starts when you keep avoiding it. 👇🏻

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

Nobody’s coming to discover you

The myth is that good work eventually gets found. Keep your head down and serve customers well, then wait for someone to notice.

Lauren calls that belief what it is:

“The lie that good work speaks for itself and that someone will eventually find you and call you an unsung hero is… a lie.”

When you’ve built your confidence by doing the work properly, talking about it can feel less worthy than getting on with it.

Yota Trom recognised where some of this comes from: “We grew up with this idea of work hard and your work will be recognised,” she said. “Well, that’s not true anymore.”

That belief came from a world where employers controlled most routes to opportunity. Now the people who might buy from you, back you or join you can hear directly from the person building the company.

Staying quiet limits how far the work can travel.

Modesty can become an excuse

The resistance usually sounds respectable. You want the product and the team to get the credit, and putting yourself forward can feel self-important.

Jess Jensen regularly hears leaders use modesty to justify staying out of sight. They say, “I don’t want this to be about me,” because they don’t want to look “puffed up and big”.

The instinct may be genuine, although the business still ends up without a recognisable human voice.

There’s also the discomfort of watching yourself learn in public. Early videos feel wooden, and speaking in public can bring back an identity you thought you’d outgrown.

Joe Averill remembers his hands bouncing up and down before a panel because he was so nervous. He connected it to the identity he had carried since childhood as “the shy one”.

An old version of you can still limit what you’re willing to do, even after you’ve proved it wrong.

Tom Hutchinson-Smith found the camera brought out the same problem. “The moment that camera turned on, I couldn’t string two words together,” he said. “It was embarrassing. I honestly didn’t want to do it.”

That’s why knowing visibility matters rarely solves the problem. The hard part is being watched while you’re still learning.

The business pays for your absence

Visibility can look like a branding extra, which makes delaying it feel harmless. The cost shows up across the business.

When you stay invisible, everyone outside the company has less context. Buyers struggle to place the business, and potential hires can’t see who they’d actually be working for.

When Yota is asked how companies attract top talent, her answer is to make the leadership visible. Candidates want to understand the person behind the job description and how the company is actually run.

Being visible gives buyers more reason to trust you before the sales conversation starts. A buyer who has followed your thinking for months arrives with far more context than somebody seeing a cold sales page.

Lauren took the argument back to the place founders can’t ignore: the bank balance. That’s the commercial cost of avoiding embarrassment.

Each post you avoid or appearance you turn down looks small in isolation. Over time, they become missed routes into the business.

AI makes hiding more expensive

The cost of hiding is rising because the internet is filling up with competent-looking work that nobody can place.

Gary Das said trust is at an all-time low because AI has made it possible for almost anyone to create a convincing video. His response was to build a stronger brand by showing more of himself as a person alongside the products.

AI can produce credible-looking work in seconds. The question is why anybody should trust whoever published it.

Anjeanette Carter called a personal brand “business insurance” in the age of AI. Her regret is that she didn’t start building hers earlier.

That connection can survive changes in the product or market, so you aren’t starting from zero every time the business changes.

You don’t need to perform

The mistake is assuming visibility requires a performance. That leads to content so polished that the person disappears from it.

Jess said leaders still feel pressure to look perfect. Her view is that imperfection can become “your magic” because it shows humanity and makes you relatable.

People only need enough of your real thinking to understand what sits behind the company. You can still keep boundaries and publish work you’re proud of.

You can explain a decision or share what changed your mind. Lauren described it as “sharing to learn rather than trying to shine”.

Your face and voice make the thinking behind the company easier to understand and remember. The business needs that connection more than it needs another perfect company update.

Pay the cringe tax

There’s no clean route around the awkward stage.

Tom’s advice is to get the first 100 videos out of the way because they’re probably not going to be very good. Repetition makes the camera feel more normal and helps you sound like yourself.

The same thing happens whenever you put your work in public. Your early attempts feel overly self-conscious, but doing more of them teaches you what sounds like you and what belongs in public. Waiting until you feel ready only delays the practice you need.

That’s the cringe tax: being publicly average at something until you become good at it.

Staying hidden leaves you dependent on people finding the company by accident. They need to see who’s behind the work before they have a reason to remember it.

Paying the cringe tax is cheaper than making the business pay for your absence.

That feeling is dangerous once the money lands and problems are easier to ignore.

I’ve learned the hard way that funding doesn’t mean the business will survive.

That’s why I invited to write this guest post. He’s a four-time founder and investor who also writes the ScaleSignals newsletter.

His argument is uncomfortable: the money can hide the problems that still need fixing. 👇🏻

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 cash is hiding the cracks

Nobody wants to say this at the celebration dinner, but a company can raise brilliantly and still struggle afterwards.

I’ve watched it happen from a few angles, first as a four-time founder and later from the investor side of the table. Funding helps, but the raise itself tells you very little about whether the business will work.

It took me a long time to accept that raising well can leave a company in worse shape. I now pay far more attention to what founders do during the 18 months after the money arrives.

Money changes how you behave

A raise changes the pressure inside the company in ways that can hurt you.

When you have very little money, you’re forced to focus on one thing for a clear customer because you can’t afford to chase every opportunity. That pressure keeps the company disciplined.

After the raise, five ideas suddenly look affordable. The company starts spreading itself across them, and every individual decision looks like progress at the time.

The risk begins when the story needed for the next round starts pulling the product away from what current customers use it for.

Investors also become another group whose expectations shape your decisions. The raise commits you to the future performance you sold to them.

Then the raise changes how you feel about the company. After months spent closing the round, it’s easy to feel like you’ve finished something.

But the day the money lands is when the real test begins.

The runway hides more than it fixes

A company that raises a big round can appear to be winning. Press coverage and hiring create visible signs of progress.

For a while, the money papers over problems that would otherwise have killed the company, or at least forced it to fix them.

Runway can hide weak unit economics for longer. Marketing spend can cover unclear positioning while new acquisition replaces the users who keep leaving.

The money postpones the point when the company has to deal with them, which gives those problems more time to grow.

The company that would have been forced to confront its weak retention at $200K in the bank gets to ignore it until it has burned $8M, at which point the same problem is much bigger and much harder to fix, and the runway that was hiding it is gone.

This is why “they raised a huge round” tells you almost nothing about whether a company is healthy. The money can accelerate a business that already works.

On raise day, a strong business and a weak one can look equally healthy because both have cash.

Keep the discipline that got you funded

The founders I’ve watched come through this well all do a version of the same thing. They keep the discipline that scarcity used to force on them.

They keep doing one thing well, even though they can now afford to pursue five. They use the extra runway to deepen what already works and stay honest about what the money may be hiding.

The week after the round closes, disciplined founders look for the issue most likely to hurt the company later and deal with it while they can still afford to.

Customer needs still have to guide the product after the raise. The next round can become so visible that customer signals get pushed aside.

A company that keeps improving for customers gives itself a stronger case when the next round comes.

The round only buys you time

A raise only proves that someone believes the company can work. The business still has to justify the cheque.

The 18 months after the round show whether they were right or whether you simply became very good at raising.

If you’ve just closed a round, celebrate it. Then use the next year and a half to prove the business deserved the money.

👤 Ben Botes is an active GP, 4x exited founder, and the author of Unstoppable Growth. He writes about capital, growth, and what actually happens after the raise at ScaleSignals. Connect with Ben on LinkedIn.

ScaleSignals

How capital actually moves — from an active GP and 4x exited founder, for founders and investors in markets where the standard playbook does not apply.

By Ben Botes | GP & 4x Founder

It usually came wrapped in internet bro nonsense. Work from anywhere, automate everything, run a tiny empire from your laptop in Thailand while the tools do the hard bit in the background.

That version still sounds unlikely. The useful version is harder to ignore now.

This keeps coming up in Millennial Masters conversations with founders building around AI. Nobody seriously thinks it has fully arrived, but the shape is becoming clearer.

AI lets one person do more, that’s already real. The bit people keep skipping is that the person has to be good. 👇🏻

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

Hiring pressure comes first

Anjeanette Carter described AI as the thing that turned her into “an army of copywriters”. She made the “very, very tough decision” to stop working with writers because she could now take that work back in-house.

AI made that possible because she already knew the craft. It didn’t give a beginner the same judgement overnight.

Nick Holzherr is seeing something similar. He said firms are “not hiring junior marketing people anymore” in the same way. They’re hiring more senior people who can use AI, which means “they just need less people.”

Thibault Louis-Lucas is already organising work differently because of it. He says he doesn’t have employees. He has several products and partners with “one very skilled maker” for each one. His view is that we’re moving towards “less management” because the person being managed can increasingly be replaced by AI.

Skill does the heavy lifting

This is the part most AI hype gets wrong. The new one-person company raises the standard.

Anjeanette makes the point better than anyone: “AI is an amazing tool. But it’s only an amazing tool if you can marry your expertise with it. If you don’t have expertise in a specific area, AI isn’t going to make it better. It’s only going to amplify your limited knowledge and expertise.”

If you already have taste, AI can help you move faster. Without it, the tool mostly helps you produce more work that still needs fixing.

People with real skill get more from AI because they can brief it properly and spot when the answer is off.

The work that needs you

AI can clear away some of the work that eats up the day, giving you more room for the decisions you still need to make yourself.

James Augustin said humans should be doing things that are “innately human”, especially the work where another person still needs to believe you. In his words, “that’s the highest leveraged thing a human can be doing.”

You shouldn’t be spending all day dragging work through admin and early drafts. AI can take more of that weight, while you stay close enough to make the calls that actually shape the business.

Anjeanette’s warning matters here too. “I think the biggest thing right now is judgment,” she said. AI still needs that human expertise.

The fantasy version still breaks

There’s a lazy version of this story that needs shutting down.

It’s the one where AI agents run the whole business while the founder disappears, and companies switch off whole teams because the software can apparently handle everything now. For most real companies, that’s still fantasy.

Josh Payne said he once thought we were close to fully self-improving agents that could do everything without human involvement. His view changed. “Human plus AI is currently still better than AI only in most cases.”

Ben Tasker’s example shows what happens when companies pretend otherwise. He described an organisation that tried to replace a call centre with AI, laid off the employees, then realised the AI was horrible and had to hire everyone back a few weeks later.

The one-person company works when the founder knows how far the tool can go before human judgement has to take over.

Building is cheaper, demand is not

AI has made building cheaper and faster. Demand doesn’t arrive just because the product exists.

If one capable founder can now make more with fewer people, building itself becomes less rare. The hard part becomes getting the right people to care.

Tibo asked the right question: “Imagine if you do not need developers at all to create products… where is the value then if it’s not in the product itself?” His answer is distribution and storytelling.

Jason Tan showed what this looks like in practice in his guest post. With Engage AI, code didn’t just help him build the product. It helped him find the first users by building small useful things that reached the right people before they knew the main product existed.

A small team can now make more things, which means the market gets noisier faster. You still have to know who it’s for and how it gets to them.

Simon Jenner’s point makes this more interesting for small markets. When tech costs drop, markets that were too small before can start to make sense. A tiny audience that couldn’t support a traditional build cost may be enough for a leaner niche product if the numbers work.

The one-person company can work in smaller sectors when the founder understands them well enough to reach it.

Taste is the limit

AI can help you carry more before you hire. It can also help you produce a lot of average work when you don’t know enough to see what’s missing.

This works when you already know what you’re doing. You need enough judgement to know how far the tool can take the work before your own taste has to take over.

AI can make one person more powerful. It can’t give them taste.

Building software is getting easier. Getting the right people to care is getting harder.

writes Behind the Scenes and created Engage AI, which he grew to 100,000 users without VC funding or a marketing team.

In this guest post, he explains how he stopped treating distribution like a viral lottery and used code itself as a route to demand. 👇🏻

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 viral myth

A founder told me recently that his startup’s growth plan was to keep posting until something worked.

Since early 2026, I’ve been mentoring startups at the Founder Institute and River City Labs. About two months ago, I was in a mentoring session with a founder named Mark. After we finished talking about his product, I asked about growth.

“What are your key distribution channels? What are you doing to market the startup?”

“We post a lot of content on LinkedIn and Instagram,” Mark said. “We’re also thinking about repurposing that content for TikTok.”

“Why LinkedIn, Instagram, and TikTok? They have completely different audiences.”

“Maybe, but our target audience is on all these platforms, so it makes sense to be everywhere.”

“Which one is working so far?”

Mark paused. “We haven’t had anything go viral yet. We’re still trying to figure out what will go viral.”

He sounded exactly like the old me.

Back in my consulting days, I was on LinkedIn, hosted a podcast, made YouTube videos, and worked hard to become a thought leader. Some posts did go viral, and they brought views, but they didn’t bring many paying customers.

A lot of founders treat distribution like a lottery. They build the product, post everywhere and hope the algorithm takes over. It almost never works like that.

Demand comes from going narrow

You need to know who the product is really for, where those people already spend attention, and how to reach them in a way that feels useful.

With Engage AI, we ignored most people on LinkedIn. The average employee comments occasionally and can get by with copying from ChatGPT when they want to sound informed online. That wasn’t our customer.

We went after a very specific group: small business owners in professional services, usually with teams of five to twenty, and usually at least a couple of years into operating. They relied on relationships to win work and stayed visible by leaving thoughtful comments on prospects’ posts every day.

The problem was the volume. Doing that 50 to 150 times a day could take up most of the working day. Some were paying virtual assistants in the Philippines around $1,500 a month to do it. Even then, the comments were often generic because the assistants didn’t have the subject knowledge to engage properly. It still took hours.

That was the problem we built for. A small group with a painful, repetitive job and a reason to pay to make it easier.

Code can be a distribution channel

When I built Engage AI, I knew who it was for. The harder part was getting it into their hands. I had no VC funding, no marketing team, and no real ad budget. I tried a lot of things, but only a few really worked.

The shift for me was realising that my technical skill could help distribute the product, not just build it. Code could be a route to demand.

One of the strongest examples was a free Chrome extension called LinkedIn Hashtag Analytics. When a user hovered over a hashtag on LinkedIn, it showed the exact follower count. It solved a small but real problem, and we gave it away for free.

It put us in front of LinkedIn power users, and the effect compounded in ways I didn’t expect. Hootsuite and HubSpot shared it and linked back to us because it solved something for their audiences. LinkedIn creators did the same. They were happy to recommend it because it was helpful, not because we paid them.

That helped from an SEO point of view, but the bigger point was trust. The extension became something people used repeatedly, which made the paid product feel more credible when they came across it later.

That single free tool brought in 35,000 users in two and a half years.

Build something useful first

A lot of founders try to introduce the product before they have earned any attention.

A better route is to build something small and useful that solves a narrow problem for the same audience.

You’re giving them a reason to notice you before asking them to trust the main product.

Go where trust already exists

The second thing that worked was borrowing an audience rather than trying to build one from scratch.

I used to run a podcast called The Analytics Show, so I already knew how much work it takes to build and keep an audience. I also knew my old audience was wrong for Engage AI. They were mostly enterprise executives.

So instead of starting over, I pitched myself as a guest on hundreds of sales podcasts.

One of my team members spent an hour every day pitching hosts and following up. Like most outbound work, the first message rarely did much. Most of the results came through follow-up.

It worked because those hosts had already built trust with the people I wanted to reach. I didn’t need to build that trust from zero. I needed to show up in a place where it already existed.

That strategy brought in 25,000 users.

The same logic applied elsewhere. We integrated Engage AI into CRMs like Pipedrive, Zoho CRM, and HubSpot because our customers were already spending part of their day there.

We wanted the product to fit naturally into software they already used and trusted. That meant working with an existing habit instead of trying to create one from scratch.

Automate reach carefully

The third thing that worked was using our technical skills to automate parts of distribution. We built AI agents and scripts to extend our reach, and some of that brought in another 25,000 users.

A few examples worked especially well at the time. We published a book on Amazon through one of those systems. We used automation to comment on journalists’ LinkedIn posts, which helped us land earned media.

Some of those experiments were strange, and some only worked because AI still felt new. That matters because I wouldn’t present those tactics as a template to copy now. The better lesson is narrower.

If you’re a technical founder, you can automate repeatable parts of distribution in the same way you automate repeatable parts of operations.

You can remove friction from research, follow-up, outreach support, media tracking, and other work that usually gets done inconsistently.

The specific tactics aged quickly. The lesson didn’t.

What didn’t work

We also tried plenty of things that went nowhere.

Affiliate marketing didn’t work the way people often talk about it. You can’t set up a programme and assume partners will come running. You still have to recruit them, and in B2B you usually need a stronger reason than a commission.

LinkedIn automation spam was a waste of time. We sent at least 10,000 automated messages to business owners we knew were active on the platform. It barely brought in any paying customers. Mostly, people were annoyed.

Product Hunt also did very little for us. We launched there, won Product of the Day, got the traffic spike, and watched it fade. The audience was wrong. Most Product Hunt users were not the sort of LinkedIn-heavy relationship builders we were targeting.

The things that worked all had the same shape. They got us in front of the right people in places those people already trusted.

The developer’s advantage

There is a limit to this approach. Eventually, side projects and technical shortcuts stop being enough. At some point, you need a more formal marketing engine as well.

But getting off the ground is a different phase.

Building software is easier than it used to be, which makes distribution harder.

If you’re technical, that doesn’t mean you’re stuck. It means you may already have a useful advantage if you use it properly.

Technical skill can become distribution skill.

It can help you build a free tool that earns attention, create integrations that put your product inside someone else’s workflow, and automate the repeatable parts of getting in front of the right people.

The job is to understand who your buyer is, where they already spend attention, and what you can build that gets you in front of that exact group.

Code didn’t just help me build the product. It helped me get the first users.

👤 Jason Tan writes Behind the Scenes, where he shares practical lessons on building AI products and scaling adoption without VC funding or a marketing team. He created Engage AI, grew it to 100,000 users worldwide with a lean team, and now works as Fractional CAIO at Ascendnce, helping SMEs use AI inside their operations. He writes about bootstrapped AI products, growth loops, user adoption and building for niches big tech is unlikely to reach. Connect with Jason on LinkedIn.

Behind the Scenes

Surviving and thriving as a bootstrapped solo founder and first-generation immigrant. My unfiltered lessons to shorten your path to success.

By Jason Tan

You have the first call, the buyer seems interested. Then the proposal goes in and everything suddenly turns cold.

That’s because buyers and procurement teams are starting to use AI tools to review vendors, flag vague claims and compare options before the next call is booked.

, who writes Eng Sales for technical founders who have become their own first sales rep, started digging into this after one of his own deals was rejected before the demo.

What he found and shared in this guest post is uncomfortable and worth taking seriously. If AI is helping you write faster, there’s a good chance someone on the other side is using it to filter faster too. 👇🏻

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

AI judges your business before people do

You trained AI to write your outreach emails, personalise at scale, draft follow-ups and summarise sales calls. Your buyers are training AI to delete them.

Procurement teams are increasingly using AI tools to evaluate vendors before a human gets involved. They’re parsing, scoring and ranking your proposal before anyone on the buying team reads it closely.

Most founders have no idea it’s happening. I didn’t either, until a deal I was certain would close got rejected before the demo.

When I asked why, the procurement lead said: “We flagged your proposal as low-priority. We moved forward with vendors who scored higher.”

That sent me down a rabbit hole. What I found was that “we” wasn’t really a person. In that process, it was an AI screening tool.

If your messaging was built only for human readers, with emotional hooks, narrative flow and relationship cues, it may now be getting filtered before anyone properly reads it.

Figure this out early and you’ll have an advantage, because the buying process is changing quickly.

Here’s what AI tools are starting to filter out, what actually gets through, and how to pass the gate.

The deal died before the demo

The sales conversation used to start when a human read your email or took your call. That window is getting smaller.

Research from Forrester and Gartner shows that B2B buyers often complete a large part of their evaluation before speaking to a vendor. That was already true before AI tools made it easier to sort, score and compare proposals even faster.

In some buying processes, your proposal may now be fed into AI tools before a human review. The buyer can ask whether you match the requirements, where the risks are, and how you compare with the alternatives already under consideration.

That can happen before anyone has properly read what you sent.

The story I mentioned in the intro wasn’t spray-and-pray or cold outreach. We’d already had a strong first conversation. There was a budget, they wanted to improve their sales process, and the sales engineer who was my internal advocate recommended sending an exec summary and key outcomes to procurement.

They were a small manufacturing company with a solid product but inconsistent revenue. I was proposing a fractional revenue officer retainer to help them build a more consistent sales process and support any sales hires they needed.

So I sent what I thought was a solid “exec summary + scope + outcomes” deck. Three months of meetings and prep led to that moment. I’d done similar work for manufacturing companies, in my day job and on the side, and the sales engineer I knew felt good about it too.

Then it went quiet. When procurement finally replied, it was procedural: “Your response didn’t score high enough to move forward.”

I asked the question you don’t usually ask: “Score? Against what?” They answered: “We run all vendor responses through a tool that checks for specificity, missing requirements, and outcome clarity before scheduling any demos.”

I pressed for more details, but was met with: “Sorry, we don’t share the rubric with any vendors at this time.” I was caught off guard because I felt confident I’d at least get to the demo. It was clear the procurement lead was on to the next project, so I followed up with the engineer.

When I grabbed a coffee with her, the first thing she asked was: “Did you ever submit anything?” I told her what I’d put together and shared the response from procurement.

That’s when she told me the “tool” was a new AI agent the procurement team was using to reduce the time it took to review proposals.

Through my deep dive, I talked to different procurement leads and business owners. A procurement manager at a mid-market software company told me their team now runs vendor responses through Claude before scheduling calls.

The prompt was direct: “Evaluate this proposal against our requirements doc. Flag any missing mandatory items, vague outcome language, or pricing that doesn’t match our budget range. Score it 1-10 on specificity.” Vendors scoring below 7 were unlikely to get a proper human review.

That matters because AI doesn’t read between the lines. It doesn’t pick up on confidence, credibility signals, or the warmth of your narrative in the same way a human might. It scans for evidence, specificity and measurable outcomes.

If your pitch buries those things, or doesn’t include them at all, it may get filtered before anyone hears your voice.

Millennial Masters

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

By Daniel Ionescu

Why weak proposals get filtered

After that rejection, I started asking questions. This took some time, but that’s why I wanted to share it here.

It turned out the procurement team was using AI to rank every vendor response against a scoring rubric.

I asked what triggered a low score. In my conversations, I noticed four patterns I had missed, and most founders are still missing them too.

1️⃣ Vague benefit language

Phrases like “we help companies improve efficiency” or “we drive growth” don’t give AI anything useful to evaluate.

They’re adjectives without anchors. AI screening tools can flag language that can’t be quantified and deprioritise vendors using it.

“We help sales teams work smarter” gets filtered.

“We reduce sales cycle time from 90 days to 45 days by fixing discovery call structure” gives the system something to score.

The second one names a problem and an outcome. A few more examples:

Before: “We offer white-glove onboarding.”
After: “We migrate your data in 10 business days, set up three core workflows, and train your team in two 60-minute sessions.”

Before: “Our platform is easy to implement.”
After: “Implementation takes two weeks for a standard rollout, including SSO and one CRM integration. No custom engineering required.”

Before: “We have strong security.”
After: “SOC 2 Type II, SSO, least-privilege access, and audit logs. Security questionnaire turnaround in 48 hours.”

2️⃣ Feature-first structure

Like most founders, I wrote proposals in the order I thought about my product. First, here’s what it is. Then, here’s how it works. Finally, here’s what you get.

Buyer AI is looking for a clearer order. It needs the problem statement, the outcome and the mechanism.

Diagnose their problem first, show that you have delivered similar outcomes, then touch on features. That way the AI and the human get the impact before they stop reading.

3️⃣ Trust signals without proof

“We’ve worked with companies like yours.” Which companies?

“Our clients see significant ROI.” What’s the number?

“We’re a trusted partner in the industry.” According to who?

AI tools are often being used against procurement rubrics that weight evidence over assertion.

If you claim trust without naming a verifiable proof point, you may get flagged as low-credibility. That proof point could be a customer, a metric, or a case study with a clear outcome.

To be fair, procurement teams would have flagged this before AI. The difference now is that weak proof can stop the proposal before anyone sees the rest of it.

Assertion without evidence scores at the bottom.

4️⃣ Nothing to compare

Buyers using AI to evaluate vendors are often generating comparison matrices.

The AI needs something to slot you into. That might be a category, a differentiation point, or a specific problem you solve better than the alternatives.

If your messaging doesn’t give it a hook, you get dropped into a generic bucket and ranked by price. If price is the only differentiator, you’ve already lost.

What stronger proposals prove faster

Here’s what changed when I redesigned my proposals for AI-first evaluation. The better versions were structured for machines and written for humans.

1️⃣ Lead with the outcome

I stopped leading with what we do and started leading with what they get.

Instead of “we help sales teams improve their process”,

I wrote: “We help technical founders close their first 10 B2B customers without hiring a sales team.”

AI can parse that. A human can remember it. It passes the relevance screen much faster.

2️⃣ Attach proof to every claim

Every assertion now has a number, a company type, or a timeframe attached.

“I helped a mid-market HVAC company build their commercial sales cycle to 20% year-on-year growth by fixing their CRM flows.”

AI can compare and score that. It also gives the human buyer something to verify.

3️⃣ Use the buyer’s own words

The best proposals I’ve submitted used the same words the buyer used in their internal documents.

When I’m diagnosing a problem in the first meeting, I write down the exact phrases they use.

“Our pipeline is clogged with unqualified leads.”

“We can’t tell which deals are real.”

Then I use those exact phrases in the proposal.

When your language matches their problem description, you score higher on relevance. The AI sees the match and the human feels understood.

4️⃣ Make the next step clear

I close each proposal with a clear next step.

“Here’s a 20-minute diagnostic call where I’ll identify the one leak in your current sales process.”

The buyer’s AI may be checking ease of progression too. A specific ask signals confidence in the value. A vague next step leaves more work for the buyer.

The 10-minute AI audit

I wanted to make a nice, easy checklist for the Millennial Masters community, pulling these shifts all together.

Before you send your next proposal, run this 10-minute AI audit:

Paste your executive summary into Claude or ChatGPT and ask:

“Summarise this vendor’s offering in two sentences. What problem do they solve and for whom?”

If the AI’s summary doesn’t match what you intended, your message isn’t passing the screen.

Fix it now. Because your buyer may be running a similar test, and you may not get a second chance.

👤 David Roy writes Eng Sales, a weekly newsletter for technical founders who have become their own first sales rep. He writes about sales, pipeline ownership and the revenue systems founders need before they build a bigger team. Connect with David on LinkedIn.

ENG Sales

Eng Sales is a weekly sales system converastion for technical-founders & small B2B leaders who are still closing deals themselves. I’ve done this work and built sales teams. Now I’m sharing my experiences for people who are still their own 1st sale rep.

By David Roy

It’s easier to stay in building mode because the product gives you something controllable to hold, while sales and demand feel messier and expose you faster.

You tell yourself that if you create something good enough and keep improving it, the right people will eventually notice.

Joshua Dziabiak built a billion-dollar business and said the quiet part plainly: “you could build the best product in the world. And if you don’t have a good strategy to tell the world about it, it doesn’t really matter.”

A lot of early-stage frustration gets explained as a product problem. Joshua wasn’t the first founder to say this to me. He was just one of the clearest.

Across more than 70 Millennial Masters interviews, this has become one of the lessons I keep hearing in different forms: the product can be strong and still lose if the market never understands why it matters. 👇🏻

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

Good products still need demand

The product can be good and solve something real. That still doesn’t make it a business.

Mike Jones learned that too. “When we start our businesses, especially when we’re naive,” he said, you think “as long as we just build a good thing, that it will sell itself because there’s need for it, and there’s demand for it, and it will just work.”

Need can exist, people can show interest, and the business can still fail to move because need on its own is not a sales engine.

You can confuse product quality with traction for longer than you think.

Every improvement gives you another reason to delay the harder question: does the market understand the pain well enough to care?

This question is less comfortable than another feature release. It’s also much closer to the business.

AI changed what’s scarce

AI has lowered the cost of getting something functional into the world, and more people can turn an idea into something usable.

“Imagine if you don’t need developers at all to create products. Where’s the value then if it’s not in the product itself?” For Thibault Louis-Lucas, the answer is distribution and the ability to tell the story around the product.

Simon Jenner put numbers on the table. A product that once cost around £100,000 to build could fall to £10,000 or £15,000 with no-code, and in some cases closer to £3,000 or £4,000 with AI-assisted coding.

Building gets cheaper. The mistake is thinking the business does too. Simon’s warning is that if you spend £10,000 on the product, you’ll still need £10,000 or £20,000 to get it into the market.

Matt Watson saw the same trap from the builder side. A lot of startup founders “like to build things” and “don’t like to sell things.” “You can build anything, but you’ve got to know how to sell it.”

AI makes product quality harder to defend as the only reason someone should pay attention.

Building feels safer than selling

Building protects you from the parts of the job that feel more exposed. Selling means asking, and marketing means explaining yourself before everyone understands.

“Making your dreams come true is embarrassing,” Lauren Currie said. “It’s uncomfortable, it’s sweaty, ugly, you pitch bad, you cry in toilets, you post things that make you cringe later.”

Visibility is “showing yourself and your work to the world before you’re ready.”

Matt saw the same problem with builders spending months with AI tools, writing huge amounts of code without revenue, customers, or proof that anyone wants the thing.

“Claude Code was the needle and every prompt is the drug,” he said. Building gives you momentum you can feel. Selling gives you feedback you might not like.

You can keep calling it product refinement long after it has become avoidance.

Meanwhile, someone with a weaker product gets the market’s attention because they’re willing to explain the problem better.

Lauren also called out “the lie that good work speaks for itself.” Nobody’s coming to find you and tell you it’s finally time to take up space.

The pain has to be clear first

Distribution starts with understanding what people need to hear before they can buy. Traffic and posting matter later.

Melissa Kwan ran into this with eWebinar. People weren’t ready to hear the benefits because they still didn’t understand the pain.

“I can’t sell benefits if they don’t understand the pain,” she said. “I first have to educate them that they have pain to begin with.”

You’re moving people from not seeing the problem to understanding what the current way is costing them.

Matt asks builders a simple question: “How many customers have you talked to about this?” If the answer is none, “then you don’t even know what to build.”

Those conversations shape the product and tell you what has to be obvious before the thing makes sense.

Melissa’s other lesson matters too. She kept hiring agencies to do the hard work she didn’t want to do because she didn’t know the basics of marketing. She put “the most important revenue generating role” in someone else’s hands.

Polish won’t save a weak offer

A better landing page or a tighter deck won’t help much if the offer is weak or unclear.

Freddie Pullen believes “entrepreneurs that do well have incredible offers. You can have a terrible landing page. If you’ve got a really good offer, you’ll still make sales.”

A good offer makes the problem feel real enough to act on. It gives the buyer a reason to care now. If the offer doesn’t solve an issue, “it’s just not clear enough.”

Matt’s own business, FullScale, built an inbound engine through podcasts, newsletters and LinkedIn while many competitors were still relying on cold outbound. That created relationships and leads, and became part of how the business grew.

The product isn’t the whole job

You don’t need to become an influencer.

The correction is less flattering. You have to stop treating visibility and demand like lower-status work.

AI has made building cheaper and faster, so more products are getting into the market. That makes this harder to hide from.

If nobody understands the pain or trusts the offer, the product doesn’t get a fair shot. If they don’t remember you exist, it disappears completely.

You can still care deeply about the product. Just don’t pretend it’s the whole job.

Can the business actually handle more speed?

When everything still runs through a few people, or one very tired founder, AI doesn’t remove the problem. It puts more weight on it.

In this guest piece, , founder of Orby and writer of The Adaptive Organization, explains why AI exposes the weak spots the business was already working around. 👇🏻

AI will find the cracks

It’s six months since you signed the AI contract. The tool is still being paid for, but no one on your team has opened it in three weeks. You still haven’t decided whether to call it a failure, so for now it stays “in pilot”.

You probably blame the tool, or you blame the people around it. Some of that may be true, but it still misses the bigger problem. The business underneath it was never designed to absorb change quickly.

AI added pressure, and that pressure exposed cracks that were already there. You just hadn’t seen them yet because nothing had pushed hard enough.

AI pushes harder than anything your business had to absorb before.

Most failed AI rollouts get described as tooling failures, whether the tool was a bad fit, the data was poor, or adoption never really happened.

The algorithm is only a small part of whether AI works. More of it sits in the data, the tools already in place, and the way the work actually happens. Most businesses haven’t changed any of that.

That is why AI adoption stalls so often between pilot and production. The tool gets bolted onto the existing process, and the bottleneck stays exactly where it was.

What a fragile business looks like

Fragility sits in the boring parts of the business. It shows up after the contract is signed, when no one can tell you who owns the customer.

It is there in the hiring decision that still needs four people in a room, in the task that gets done three times because ownership is unclear, and in every meaningful decision that routes back through you because you’re still the only person who knows the full picture.

Ownership lives in people’s heads, so when someone leaves, the knowledge and the process go with them. Teams “work it out” every time because no one ever decided what done looks like.

People don’t know what they’re allowed to decide, so they escalate. Senior people end up carrying outcomes they can’t really influence.

You’re still the operating system the business quietly depends on to make things happen.

A business can survive like this when work moves slowly and people are still covering the gaps. It starts to matter when you ask that same system to take on AI.

Millennial Masters

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

By Daniel Ionescu

Why AI makes the cracks visible

You’ve absorbed other changes before, whether that was a hire, a CRM rollout, or a pricing change. You got through them by working harder.

AI is different because it moves work faster than your team can handle cleanly. If your handoffs were already messy, it doesn’t wait for clarity. It generates, and the mess compounds.

It also cuts across teams. Real use cases rarely sit neatly inside one function. They move between sales and ops, product and support, finance and delivery. If those boundaries are unclear, AI magnifies the confusion.

It pushes decisions closer to the edge too. The value of AI sits in routine decisions moving faster than a weekly leadership meeting can authorise.

If your business still runs on one or two people reviewing everything, AI doesn’t solve that. It exposes how expensive that delay really is.

The work gets worse before it gets better

Adoption often looks worse before it gets better because the work in the middle is slow and unglamorous.

You have to clean up the process, decide who owns what, train people properly, and stop pretending the tool will fix the mess on its own. It costs time and money before it looks useful.

Businesses judging AI too quickly often cut the work that would have helped it succeed. The initiative gets written off and the conclusion is that AI is not for them yet.

That’s usually the wrong conclusion. The business just isn’t ready for what it is asking AI to do.

Ask what would break first

If you doubled volume next quarter, what would break first? Whatever breaks first is already fragile. You just haven’t had to face it yet.

AI brings that pressure faster than most teams are ready for. It speeds up the work and leaves less room to stop and fix what is going wrong.

Wherever your answer lands, that is where the work has to start. If the business is still running on you, or on a few people holding everything together, AI will find that quickly.

Better to find out before you’ve signed the contract. Better still to solve it now, before the contract turns the weakness into an expensive lesson.

The businesses using AI well tend to have the basics in place. People know what they own. Decisions happen closer to the work. The founder is no longer the operating system.

Their advantage comes from the structure underneath. The tool you’re about to buy will only ever be as good as the system you put it into.

👤 Sam Rees is a registered psychologist and the founder of Orby, an Organisational Effectiveness System that helps leaders design businesses that can absorb change rather than just react to it. She writes The Adaptive Organization on Substack. Connect with Samantha on LinkedIn.

The Adaptive Organization

Clear, pragmatic thinking on organizational design, change, and capability in an era of constant disruption; grounded in judgment, not hype. AI covered where it matters.

By Sam Rees, Reg.Psych

You tell yourself the company will become easier to hold together once there are other people helping. It should stop depending so much on you and your ability to keep dragging things forward.

Across Millennial Masters interviews, I’ve spoken to founders who got there, with hundreds of staff and revenues running into the hundreds of millions, and sometimes beyond. Then the worries changed.

The old business started failing before the new one was fully built. The job changed, and they moved further away from the work that made building it exciting in the first place. 👇🏻

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 old company breaks first

Joshua Western said going from 50 people to 60 was harder than going from 2 to 50. Around 50 people, “the things that you set up, the ways of working, what little processes you try and have as a startup, all fall over.”

Growth puts more pressure on the company and shows how much of it was being held together by people who knew what to do without being told.

A lot of what looked solid was really people filling gaps, remembering what mattered, and sorting things out because the team was still small enough for that to work.

Then the company reaches a size where those old shortcuts stop working, and suddenly simple jobs need more chasing than they used to.

Emma Mills described this break point. Once a company reaches seven figures, what got it there doesn’t get it to the next stage.

The early version relies on people doing whatever needs doing. The next one needs clearer roles and fewer important calls stuck in one person’s head.

That can seem daft when you built the business on speed and instinct. If you ignore it, the problems show up somewhere else.

Headcount can lie

Noel Andrews dealt with one of the easiest founder vanities: headcount.

He scaled to 17 people and thought that meant the business was moving forward. From the outside, it looked like a bigger, more serious business.

Inside, it was chaotic. The company looked like “a graceful swan on the surface,” while underneath there was too much paddling and too little control.

Then he went back down to eight people. The smaller team did twice as much work and made three times the profit.

More people can make the business look more real. They can also create work that only exists because more people are there.

Add people without rebuilding how the company works, and you can make the business look bigger from the outside while it gets worse inside.

The job changes on you

Tyler Dunagin looked back on his own journey and described the founder role as a move from all-star NBA player to coach, and later towards the owner’s box.

At the start, you’re on the field. You’re doing the work, solving all the problems. Then scale starts pulling you away from that closeness.

It sounds sensible until you’re the one having to make the swap. Some founders are good at the early fight and much less comfortable in the role the company needs later.

Nick Telson-Sillett recalled meeting people by the coffee machine in his own office and realising he didn’t know them because he hadn’t been involved in hiring them. They would ask what he did, and he would say he was the founder.

You can become a stranger inside the company you built. That’s the part founders don’t always see coming.

The job changes from being close to the work to building a company that can keep moving without you touching everything. Yet you can still miss the version of the business where you felt useful in a more immediate way.

Millennial Masters

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

By Daniel Ionescu

Early heroes can turn blockers

Simon Penson said it’s the growth stage where someone in the room feels so important that, if they left, the business would be in trouble.

Startups depend on people like that. They stay late and help the company survive when there’s no real structure around them.

Then the business grows, and the person who was essential at the start can become a blocker in the next stage, because that step needs something different.

A practical generalist can struggle once the business needs a specialist. The person who did three jobs may not be the right person to lead one of them at scale.

That’s hard because the role has history attached to it. You remember who helped when the business was still fragile and the future wasn’t always clear.

Their contribution can still matter. The problem starts when you keep them in the same role because you feel you owe them.

Then everyone else has to work around it. People cover the gaps, standards slip, and the business starts fitting around the person instead of the work.

That’s how loyalty becomes expensive.

Clarity becomes part of the work

In the early stage, people sit near each other and things keep moving because whoever needs to fix the problem is close by. You can get away with a lot when everyone knows the backstory.

Nick Holzherr remembers the gap between 30 people and 120 people. At that size, you have to think much harder about how things are said and done, so they still make sense when you’re not there for every decision.

More time goes into writing things down so people can act without you explaining it each time. It’s necessary work, and it’s less fun than a quick conversation with those who already understand the business.

Andrew Steele understood a business with 100 people shouldn’t feel like a startup with five. You can’t just run it over WhatsApp any more.

You can keep trying to hold on to the startup feeling long after you’ve outgrown it. Eventually, people need to know what good looks like without asking you every time.

That may feel slower, but it’s just what happens when the business is no longer small.

The numbers get heavier

From the outside, scale can make a company look safer. More revenue and a bigger team suggest the fragile early stage is over.

James Fleming looked back at the early years and said that if he’d seen the later outgoings, he would have cried. The business reached around £135,000 a month just to keep running.

That kind of number changes the company. You can have more revenue and feel less free because the cost of being wrong grows with the business.

Kevin de Patoul lived the hypergrowth version. His company went from 15 to 60 people, then after raising $70 million, from 60 to 160 in 12 months. His warning was that if you keep pushing forward without looking at what’s breaking behind you, things can start imploding.

That’s the danger of scale. The business can look stronger from the outside while small problems are getting more expensive inside it.

The job you wanted disappears

Joshua Dziabiak loved the stage where the job was still about making something. You could take an idea from nothing and turn it into a real business.

Then the company scaled, and the work changed. He described it as “financial engineering.” The big moves became smaller, more careful ones. The work became about keeping a larger machine on track.

The business had succeeded. The work that made it feel alive started to disappear.

Joshua also described the strain of carrying that stage. You put on a brave face, tell the story, get people fired up, then go to bed wondering whether you’re losing your mind. He joked that building the business took 10 years off his life.

You can be exhausted. The company can take too much from the person leading it. The role can become one you want, without wanting to perform it every day.

Joshua eventually recognised that he was a zero-to-100 founder. He wasn’t the person who wanted to take a company from $100 million to $10 billion.

That kind of self-knowledge is rare because founder culture rewards the opposite instinct. You keep going and keep telling yourself that stepping aside would look like defeat.

Sometimes the honest answer is that the company has outgrown the work you’re best at.

Scale is a different company

That doesn’t mean the founder failed. It means the company changed.

At some point, you have to ask whether you still want the job that comes with the next stage.

The old company may have to break for the next one to exist. The harder part is that it can break something in you too.

You can chase scale as the reward for building well, then realise it’s given you a company that needs a version of you that was never really yours.

That’s the cost you don’t always see until you’ve already built the thing you thought you wanted.

Millennial Masters

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

By Daniel Ionescu

Your profile and posts are becoming part of how professional authority gets found and described beyond the platform.

, writer of The Link Tank, has spent more than 15 years helping professionals build credibility on LinkedIn. In this guest piece, she explains what you need to fix now. 👇🏻

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

AI search is reading your LinkedIn

Your next prospect may not Google you. They may ask an AI assistant, and LinkedIn is becoming one of the sources those systems lean on when answering professional queries.

Many founders still aren’t thinking about LinkedIn this way. But what they put there, across their profile and posts, is starting to do more than support human search.

It’s becoming part of the evidence layer AI tools use when they decide how to describe someone professionally.

The old rules of SEO and keyword-optimised profiles still matter. There’s now another layer on top.

The numbers have changed

LinkedIn is now the most-cited domain for professional queries across six major AI platforms. ChatGPT, Gemini, Google AI Overviews, Google AI Mode, Microsoft Copilot, and Perplexity all cite LinkedIn more than any other professional source.

The growth has been fast. Between November 2025 and February 2026, LinkedIn moved from outside the top twenty to among the most-cited sources on ChatGPT alone, with citation frequency more than doubling in three months.

A Semrush study of 89,000 LinkedIn URLs cited in AI responses found that LinkedIn was the second-most-cited domain overall, ahead of Wikipedia, YouTube, and every major news publisher. Across AI responses, 11% referenced LinkedIn. On ChatGPT Search specifically, that figure was 14.3%.

Meanwhile, more buyers are using AI tools to research people, companies, and services before making decisions. Your profile is increasingly being evaluated by something before a human sees it.

AI is building a picture of you

AI tools don’t just send people towards you the way Google used to. They can describe you inside the answer itself.

When someone asks ChatGPT, “Who are the best advisers for fintech compliance?” or asks Perplexity, “Who should I follow for startup fundraising advice?”, those tools pull from public sources, including LinkedIn content and profile data, to construct an answer.

This is where the shift becomes more important for founders. AI tools are increasingly indexing and citing the content people create on LinkedIn, not just their profiles.

Profound’s longitudinal data shows that citations to LinkedIn feed posts and long-form articles grew from 26.9% to 34.9% between November 2025 and February 2026, while citations to profile pages fell from 33.9% to 14.5%.

A well-optimised static profile is no longer sufficient. What you publish is now part of your AI footprint. Your headline, About section, content, and consistency together form the signal AI reads.

One further Semrush finding is worth holding onto: 95% of the LinkedIn content cited by AI was original content rather than reshares.

LinkedIn’s own AI matters too

LinkedIn has been moving towards AI-led ranking and recommendation systems. One public research paper describes 360Brew, a 150B-parameter model built to handle ranking and recommendation tasks across LinkedIn.

Whether or not every feed decision runs through that exact model, the direction is clear: LinkedIn is moving away from simple engagement hacks and towards richer signals around relevance, identity, and expertise.

That makes coherence matter more. When your headline says one thing and your posts say another, the public signal gets weaker. When your profile, expertise, and posting topics reinforce each other over time, the pattern becomes easier to recognise.

That matters for AI visibility beyond LinkedIn too. The platform has been improving its structured headings, content architecture, and emphasis on time-stamped, expert-authored, conversational content. Those are the kinds of signals retrieval systems use when deciding what to cite.

5 things to fix now

1️⃣ Treat your headline as a keyword field

LinkedIn uses your headline as one of the primary signals that helps determine where your profile surfaces in search, including within LinkedIn’s own AI-powered search and in external AI tools. A headline that reads “CEO at [Company]” is searchable only for those two terms. A more specific headline that names your niche, your buyer’s problem, and the outcome you deliver creates more useful signals.

2️⃣ Align your About section with the topics you want to own

Your About section needs to reinforce the same expertise your posts are building. Write it for a search query, not as a biography. Use clear language about who you help, what problem you solve, and what changes as a result. Then check that the first 200 to 300 characters, the text visible before “see more”, answer the question: is this person for me?

3️⃣ Post original long-form content on your main topics

Posts in the 500 to 2,000 word range give you enough room to explain something useful, show how you think, and create original material AI tools can cite. Consistently sharing practical advice in mid-length posts gives both humans and machines more to work with.

4️⃣ Post consistently on the same two or three subjects

360Brew’s broader logic suggests topic consistency matters. Posting repeatedly on the same niche subjects helps signal expertise to both LinkedIn’s algorithm and AI search systems. Coherence matters more than volume.

5️⃣ Think beyond the profile page

LinkedIn’s AI-powered people search has expanded the role of natural language in how people are surfaced. A founder whose profile consistently frames them as someone who helps Series A fintech startups build investor relations can appear in search using exactly those terms, provided that signal is coherent and sustained over time. A list of credentials tells the algorithm very little about whether you are the right person for a specific query. A clear description of who you help and what changes as a result gives it something more useful to cite.

The question has changed

The shift from being found to being cited is becoming one of the most important visibility changes in AI search.

Even if LinkedIn still feels like a professional network you maintain, it is increasingly part of the professional knowledge base AI tools use when they decide how to describe and recommend someone.

If you build a consistent, credible, content-rich LinkedIn presence, you’re shaping the AI footprint that influences how you are discovered and described across the platforms your prospects use.

The question is no longer only: does my LinkedIn look good?

It is: what does AI say about me when someone asks?

👤 Melanie Goodman is a former Magic Circle lawyer turned LinkedIn strategist and founder of Trevisan. She helps professionals in law, finance, HR and other regulated industries build trust, attract clients, and get seen on LinkedIn without turning into an influencer cliché. She writes The Link Tank, a practical LinkedIn publication for consultants and service-based business owners. Connect with Melanie on LinkedIn.

The Link Tank™️

A warm welcome. The LinkedIn strategies I use to build authority, grow a newsletter & convert connections into paying clients. Every week, get practical, evidence-led LinkedIn tactics and know-how to credibly grow your LinkedIn presence.

By Melanie Goodman

By the end of the week, you’ve dealt with a lot and still feel like the real work never got touched. The thinking that moves the business forward got crowded out again.

, who writes All the Margins, has led through exactly that kind of pressure.

In this guest piece, she writes about what reactive leadership does to your judgement, your calendar, and the quality of the decisions your business gets from you. 👇🏻

Urgency takes the week before strategy touches it

You can lose a whole week to other people’s urgency and barely notice it happening. Someone is always awake, constantly working, and usually needs something right now.

By Friday, you realise most of your time went on dealing with whatever reached you first, not on the work that actually needed your judgement.

That matters more now because AI is making execution cheaper and judgement more important. If your week is filled by whatever feels urgent, you lose the part of the job that still needs you most.

I learned the hard way, leading large international teams, that without protected time every day brought its own urgent crisis and I lost the thread of what I was actually there to do.

Each one pulled me further from the work that actually needed me. A client fire drill on Monday. A team member falling behind on Tuesday. A data cleansing crisis. A third-party supplier failing again.

The cost hit in ways I didn’t see quickly enough. Two members of my leadership team were pulling their teams in opposite directions. I was so consumed by the daily fire drills that I spotted the problem too late, and the damage was already done.

By the end of some weeks I was running on empty. My decisions were reactive, shaped by someone else’s emergency rather than a direction I’d chosen.

On Monday mornings, when we sat down to set priorities for the week, I was carrying the residue of everyone else’s week into that meeting. The tail was wagging the dog.

The problem looked like a workload problem. It ran much deeper.

When you haven’t had space to step back, you stop thinking ahead and start managing whatever is right in front of you.

Reactive mode cuts off the kind of thinking that moves things forward. You deal with what’s in front of you and lose sight of where things are heading. That space isn’t going to appear on its own.

Once I realised the system is designed to consume your attention, everything changed. I became much stricter about where my time goes and less willing to give it away just because something feels urgent.

Research found that unfolding events consume around 36% of a CEO’s time. The real skill is knowing which events deserve your attention.

Here’s what that looks like in practice.

Protect the time first

No-meeting Fridays are a default for me, not an absolute rule. There are always exceptions.

Protecting Friday changes the whole week. It creates room for proper thinking and for conversations that are not squeezed between back-to-back calls. Walking helps too. Indoors or outdoors, it’s better for creative thinking than sitting.

This year I introduced office hours. It’s a recurring slot for the smaller asks that would otherwise arrive as mid-day interruptions: a ten-minute conversation, a yes or no, a sign-off on a logo design or a client response.

It takes around 23 minutes to regain full focus after an interruption. Putting smaller asks into a predictable slot gives the team a reliable rhythm and gives me back time to concentrate.

I also request agendas before meetings. Not to control the conversation, but to give myself time to reflect, think through my questions and the points I want to make.

Writing them down means I won’t get pulled into someone else’s agenda. My thinking stays clearer and I’m less likely to lose the point.

See where your focus is

A colour-coded diary sounds too simple to matter. It isn’t. When I look at my week in colour, I can see where my attention’s really going. That’s often not where I thought it was. A week that feels balanced rarely looks that way when you map it honestly.

My gym time goes in the diary too, even though it sits outside office hours. If it’s not scheduled, it won’t happen. I repeat that to my team regularly. The week will fill, and it’ll be the first thing to go.

If you don’t protect it, the week usually pushes it out.

Track your attention

Quarterly reviews, client deadlines, and team issues will always be more pressing than learning time. Without blocked time, the calendar fills before learning gets a look in.

I’ve found a way to protect it, though not yet where I want it. For now, it sits on Saturday mornings. It’s in the diary, it’s visible, and I’ve arranged other personal and family commitments around it to make it real.

That matters because it signals to everyone, including me, that this isn’t optional. I’m working to bring it into the working week, where it belongs. But it only gets done if you start where it fits.

The week won’t give you the space to think. You have to make it. Start with the calendar. Look at this week’s diary. Is it shaped by what matters, or by whatever got to you first?

👤 Daphne Mavroudi-Chocholi 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

Then a few months later, you realise it was doing more work than you thought.

Pressure makes bad cuts feel rational, and a bad cut rarely looks bad at the time you make it.

, the CFO who writes The Profit Lens, has seen that pattern up close more than once.

In this guest piece, she explains the difference between costs that protect the business and cuts that bleed later.

This is how to tell which is which before you find out the hard way. 👇🏻

There’s a mistake I’ve watched businesses make in every crisis I’ve lived through professionally. They cut without understanding what they’re actually cutting.

When pressure hits, most founders open the P&L, look for costs, and start cutting. It feels like action. In the short term, the numbers improve. EBIT recovers, cash extends, and the board is satisfied. Then, quietly, something else begins to break.

The cost that saves your business and the cost that damages it can look identical on a P&L. Both are line items with a number attached. Neither comes with a label that says “don’t touch this one.”

Understanding the difference matters before a crisis forces the issue.

The cut that looked rational

During a cost optimisation process I observed at close range, a leadership team made a decision that looked entirely rational on paper.

The business was under pressure because revenue was uncertain. The instinct was to reduce costs and protect margin, so the leadership team restructured the sales bonus scheme.

The new scheme was harder to achieve, rewarding growth rather than managed business. The logic was sound. What happened next wasn’t in the model.

The sales team, already navigating a difficult market, read the new scheme as a pay cut disguised as a restructure, and motivation collapsed.

The people who had the deepest client relationships, the ones who’d built the revenue base the business was trying to protect, started disengaging. Some left, some stayed but stopped trying.

In one quarter, revenue fell by half. It wasn’t the bonus scheme alone. It was what the bonus scheme communicated: that in a moment of pressure, the business chose its own margin over its people.

The signal travelled fast, and behaviour changed. Client relationships weakened and revenue followed. The cost of the old bonus scheme was visible. The cost of the new one didn’t show up until it was catastrophic.

The damage shows up later

This isn’t a professional services problem. I’ve seen the same dynamic in technology companies, retail, manufacturing, and early-stage startups. The business changes, but the mistake doesn’t.

A founder I worked with decided to cut a sales team that wasn’t delivering results. The logic was straightforward: three quarters of missed targets, no visible pipeline activity. The cost was significant and the return appeared to be zero.

What nobody measured was the conversion cycle in their industry. Six months after the team left, several of the prospects they’d been nurturing signed contracts with competitors.

The relationships had been built. The trust had been established. The work of eighteen months had been done. But the people who had done it were gone, and the business that benefited wasn’t the one that paid for the work.

The founder had cut a cost that looked like overhead. It was a driver. The damage didn’t appear on the P&L for two quarters. By then the connection between the decision and the consequence was lost.

The cut looked sensible that month. The revenue damage appeared later, when contracts that should have landed did not.

That’s how driver costs work. The saving shows up first. The damage arrives later, after the decision has already been made and explained away.

Not every cost is overhead

In twenty years of finance, I’ve come to think about costs in two categories that don’t appear anywhere in standard accounting.

Overhead costs support the business without directly generating revenue or client relationships. Tools, subscriptions, office resources, administrative functions, non-client-facing processes.

These costs matter. The business couldn’t function without them. But if you reduce or remove them, the revenue engine keeps running. Clients don’t notice. Relationships don’t break, and the pipeline doesn’t shrink.

Driver costs are directly connected to the business’s ability to generate and retain revenue: senior consultants who know client preferences, sales teams who hold relationship capital, marketing that fills the pipeline, and the specialist whose expertise is the reason a client renews.

These costs don’t just support the business. They are the business. When they’re cut, the damage shows up in revenue and retention, and the pipeline.

A driver cost is almost never obvious from the P&L line alone. A “consultant fee” could be overhead or the single most important relationship in your client base. A “sales incentive” could be a discretionary reward or the mechanism keeping your best revenue generators in the room.

The label tells you nothing. You have to ask the question.

Where cuts bleed

Across different industries and business models, certain categories appear again and again on the wrong side of the cut.

Sales support and business development activity

These feel discretionary when revenue is uncertain. They aren’t. Conferences, events, and relationship-building spend often show no visible return for months or years, and then a conversation from eighteen months ago becomes a signed contract. Cut this category and the pipeline doesn’t collapse immediately. It shrinks quietly over time.

Retention roles and people with institutional knowledge

The senior person who knows how every client thinks, what every internal process looks like, and where every relationship stands isn’t easily replaced. Their cost is visible, but their value usually isn’t. The moment they leave, the loss begins, but it often takes a year to fully appear.

Training and development spend

Cutting it feels safe because nothing breaks immediately. But a team that stops developing stops improving. In knowledge-based businesses, the quality of thinking is the product. When thinking goes stale, the work does too, and clients feel it.

Delivery quality investments

The tools, resources, and time that allow a team to do excellent work rather than adequate work. Cutting here cuts quality as well as cost. In markets where reputation drives referrals, quality is a driver cost.

Brand and trust-building activity

Content, thought leadership, community presence. These feel like nice-to-haves when cash is tight. But they’re often how future clients find the business in the first place. Cut them and the top of the funnel narrows. The effect appears six to twelve months later, when new business slows.

Ask these four questions first

When I’m working with a founder through a cost review, I ask four questions about every significant line item before I recommend anything.

Does this cost help us win work?

If the answer is yes, it’s almost certainly a driver. Anything connected to pipeline, business development, brand, or sales capacity belongs here.

Does this cost help us keep work?

Client retention in professional services is built on relationships, expertise, and consistent quality. Any cost connected to the people, tools, or processes that deliver these things is a driver.

Does this cost protect trust, speed, or quality?

Those three things are what reputation rests on in any service business. A cost that protects any of them is worth examining carefully before removing.

Would losing this cost show up later rather than now?

If the effect would appear in six months rather than six weeks, that’s a signal you’re looking at a driver, not overhead.

Start with the bank statement

The most reliable place to start a cost review isn’t the P&L. Open your bank statement and filter recurring payments, subscriptions, licences, and monthly commitments. List them.

You’ll almost always find costs you forgot you were paying for: tools adopted during a growth phase and never reviewed, licences bought for a team that has since changed, subscriptions that made sense eighteen months ago and no longer do.

These are almost always overhead. Start here. Reduce what’s not being used. Check licence volumes against actual usage. Ask vendors for discounts. In a difficult market, most will negotiate rather than lose the contract.

Then work through the remaining costs with the four questions above. Start with fixed costs, the ones that arrive regardless of revenue, then move to variable ones.

For each one, ask what would actually happen if it disappeared tomorrow, not what the model says. What would actually happen to clients, pipeline, or the people who deliver the work.

The answers will tell you what to cut and what to protect.

The spreadsheet won’t warn you

The founders who recover fastest from a difficult period usually know which costs can be cut and which ones will hurt the business if slashed.

Before you cut anything, ask what it’s actually doing for your business.

Overhead can usually be reduced without damaging the engine.

Driver costs can look similar on paper, but cutting them weakens the part of the business that brings work in and keeps clients.

The difference isn’t always obvious, but the cost of finding out the hard way is almost always higher than the saving that prompted the decision.

👤 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

The best AI business may already exist

Wrapping AI around an existing workflow and charging a subscription is one opportunity. It’s visible, and in some cases it can work. It’s also often less durable than it looks. The model providers can add the feature themselves. Bigger products can fold it in. What looked like a business can get squeezed fast.

The more interesting opportunity is what happens when founders use AI to change the economics of work they already understand. That came through when I went back through 66 Millennial Masters interviews with founders already using AI in one way or another.

The money is often in keeping more revenue and making parts of the business viable that were too expensive to touch before. Smaller teams are part of that story, but not the whole thing. 👇🏻

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Millennial Masters

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

By Daniel Ionescu

Margin comes first

The clearest gains often show up in margin before they show up in fresh revenue.

A founder who already knows how to write, sell, analyse, research, or build can now cut a surprising amount of junior work out of the cost base.

The output still needs direction, judgement, and standards. The middle layer around it can get much thinner.

That’s one of the clearest ways founders are making money with AI right now: by keeping more of the revenue they were already capable of generating.

A business can hold the same top line and still get much stronger if delivery cost drops, outside suppliers matter less, and the same work gets done with less payroll around it.

That changes how much cash the business keeps, how flexibly it can price, and how long it can grow before it needs more people.

Anjeanette Carter described becoming “an army of copywriters” and deciding to stop working with writers, which cut a six-figure payroll.

That’s a story about protecting the revenue from a business she already understood by changing how the delivery gets done.

Hiring gets pushed back

Once margin improves, the next shift is headcount.

Some teams are still growing in the old way. A lot of others are holding the line on hiring because AI has changed what one capable operator can carry.

The business can do more before it needs another hire. Some roles do not disappear, but they arrive later.

If one strong operator can now carry work that once needed two or three people around them, the company can stay leaner for longer, delay hiring, and grow with a different fixed-cost base.

That changes when hiring makes sense and how much revenue the business needs before it can carry the next person.

Nick Holzherr said firms are no longer hiring marketing juniors in the same way. They’re keeping more senior people who can use AI well, which means they simply need fewer staff.

Carly Meyers believes huge companies will be built with teams of one, two, or three, with AI carrying a lot of the extra load those teams used to hire for.

Judgement still decides

This only works when the founder or operator already knows what good looks like.

AI can extend expertise. It does very little for people who did not know what good looked like before they touched it. Faster average work is still average work.

Anjeanette Carter was very clear that “if you don’t have expertise in a specific area, AI isn’t going to make it better. It’s only going to amplify your limited knowledge.”

That’s the point more founders need to sit with. AI can help when you already know what good looks like. It does far less when you don’t.

That matters commercially because it’s where the savings can turn false.

If founders mistake output speed for decision quality, they can ship more bad work, make more bad calls, and damage the business faster.

AI lowers the cost of production, but being wrong is still expensive.

Josh Payne said “human plus AI is currently still better than AI only in most cases,” and Ben Tasker described how an organisation replaced a call centre with AI, fired the staff, then hired them back a few weeks later because the system was awful.

The founders getting real value from AI are keeping human judgement on the parts that still need it. They’re using AI to absorb more of the repetitive work and the junior-heavy execution.

The human still decides whether the work is commercially safe and worth shipping.

Product is easier, winning isn’t

Once building gets cheaper, other parts of the business start to matter more.

Technical founders can now get further with less capital in less time. That opens useful opportunities. It also weakens the product as a moat when the product is the only thing the business has.

If more people can build something usable with AI tools, functionality alone becomes a thinner defence. Thibault Louis-Lucas asked where the value sits if you no longer need developers in the old way to create products. His answer is distribution and storytelling.

Simon Jenner explained that when build costs fall hard enough, niches that were once too small to matter start becoming viable. A business that made no sense at one cost structure can look very healthy at another.

When building gets cheaper, more founders can get to a working product. What becomes scarce is the ability to earn attention, build trust, and turn a functional product into something people actually choose.

The moat moves away from pure build difficulty and towards distribution.

The graveyard has money in it

Some of the strongest AI opportunities are sitting inside neglected assets that humans stopped touching because the economics were too weak before.

AI lowers the cost of revisiting things that were already there but no longer worth a person’s time.

Ryan Carruthers’ example was sales. He said AI is the best place to start there because you can test it on dead leads. If the process fails, the leads were dead anyway.

In his case, those systems had hundreds of conversations, booked sales calls, and did it in a place where the brand risk was low because the asset had already been abandoned.

It’s an operator looking at wasted value and realising the cost of revisiting it has changed. Yehong Zhu showed the same commercial instinct in a different market through licensing. She’s building a business around the way AI changes the value of media rights and data access.

The tool creates a new buyer. The founder changes what can be sold.

The premium moves elsewhere

The smartest founders are using AI to change the economics of work they already understand. They’re cutting waste from familiar work and reopening parts of the business that were too expensive to touch before.

If a product gets cheaper to build, the premium shifts toward trust, distribution, and judgement. Trust matters more when polished slop is cheap. Distribution matters more when a product is easier to copy.

Judgement still decides what is worth shipping, and domain understanding still decides what anyone will pay for.

Gary Das pointed to collapsing trust. Freddie Pullen pointed to the value of long-form human formats that still feel unmistakably real. Those are clues about where price and loyalty can still hold when AI has made the underlying production cheaper.

Founders who already know how to create value can keep more of it, do more with fewer people, and make businesses work that would not have made sense under the old cost structure.

The lasting opportunity sits inside work that already has a business underneath it. That’s where more of the value stays.

Related conversations:

The harder part is what people learn from you in the smaller moments. It’s what I learned on my own the hard way.

A bad reaction after one difficult update can teach the team more than any leadership principle you talk about.

, co-founder of Kestryl Edge, works in that space between behaviour, pressure, and team dynamics.

His guest piece gets into what emotional intelligence looks like when it leaves the leadership vocabulary and shows up in how people actually behave around you. 👇🏻

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

Your behaviour trains the team

Low-trust leadership usually shows up fast.

It creates dependency and makes people think twice before raising problems. Very quickly, the team learns that self-protection matters more than honesty.

People stop speaking plainly and start managing the boss instead of the work. They protect themselves before they protect standards, and problems get softened before they reach the surface.

Trust is set from the top

Trust matters because it shapes whether people feel safe enough to do the work properly.

Low trust doesn’t stay contained in one difficult interaction. It changes the whole environment around a team. People become hyper-vigilant. They start reading tone into emails and second-guessing how to say things safely.

Soon the day fills with small exhausting questions: How much do I say? Is this safe to admit? Will this be used against me? Over time, that wears people down. Judgement gets suppressed, creativity narrows, and the body carries the cost as well as the work.

A bad boss can have more influence over your health than people want to admit. That sounds extreme until you’ve worked for someone who keeps you in a permanent state of tension.

The body doesn’t always know the difference between a real threat and a workplace one. It just responds.

A team can’t do its best work while it’s constantly scanning for danger.

Power matters, and teams adapt around the person who sets the tone. That’s what makes emotional intelligence practical in everyday work.

Where trust is built

Leaders who know themselves understand how their mood changes a room.

They can feel the moment when they’re reacting rather than responding, which makes them less likely to turn defensiveness into policy or tension into tone.

People feel the difference between a leader who knows themselves and one who doesn’t, even when they can’t name it.

That’s where self-regulation starts to matter. Can you receive hard news without punishing the person who brought it? Can you stay steady in a tense meeting? Can you pause before turning irritation into damage?

Teams trust leaders they can anticipate. If your reactions are volatile, people start editing themselves around you. If your reactions are measured, they start bringing you the truth faster.

This is one reason I write while people talk. Notes slow me down and keep me listening a little longer than my instincts want to. It is a small thing, but trust is often built in small things.

When leaders regulate themselves well, teams learn that bad news can be raised without explosion and that mistakes will be examined rather than used for blame. Those moments do not just make people feel better, they make the work better.

Empathy matters for the same reason. It’s a form of attention, the discipline of not rushing past someone’s actual experience because you’re more comfortable with your own interpretation of it.

People trust leaders who try to understand their reality before judging it, who notice what isn’t being said, and who respond to the person in front of them rather than just the role they happen to occupy.

But empathy on its own is still not enough.

Behaviour teaches the team

That’s what emotional intelligence looks like in practice.

It isn’t jargon, posture, “clarity”, “alignment”, or any of the other polished words leadership culture likes to hide behind.

It means knowing yourself well enough not to let your own volatility run the room and regulating your response so other people don’t have to manage it for you.

It also means listening closely enough to understand what is actually happening for someone else and following through in ways that prove your behaviour can be trusted.

Low-trust leaders create low-trust teams because people learn from behaviour, not declared values.

When your behaviour teaches people to stay quiet, protect themselves, and hide the truth, that is what the team becomes.

When it teaches them that they can speak honestly, think clearly, disagree safely, and recover from mistakes without fear, that becomes the team too.

That’s why emotional intelligence matters: leaders decide, day after day, whether people around them feel safe enough to think and work properly.

What looks like high standards and hard work can easily become the reason your business stops growing.

When you build from scratch, being in the middle of everything feels normal. It’s part of why the business exists.

Then the company grows, the work changes, and you stay just as central because it still feels like the safe, responsible thing to do.

You might think the answer is more people or better systems. Often, it’s how much still runs through you.

After a while, that stops being a habit and starts becoming identity. The more Millennial Masters interviews I’ve done, the more I’ve recognised parts of this in myself.

Gary Das realised he was the problem.

“I was the bottleneck. The business had almost outgrown me. I hadn’t grown with the business as a person, in my mindset, my behaviours, and my habits.”

Your company can outgrow your old way of operating before it outgrows your ambition.👇🏻

You stay too central

This usually builds slowly, as you stay close to everything. Harry Sanders said it’s a symptom of founders who want to “know everything, touch everything, feel everything.”

Businesses like that stay around the same size because the founder never stops acting like the centre of gravity. Every decision routes back to you. The team waits for your reaction before moving.

You drop new ideas into work that’s already moving because being involved still feels like your job. That’s why the habit lasts longer than it should.

When you hold on to the version of yourself the early business needed, the strain shows up before the systems problem is even obvious.

Standards are just a cover story

This is where you can hide. You tell yourself the issue is quality or standards.

Gary’s attitude was that “no one can do it as good as me,” which made it easier to step back in instead of training people properly.

That can sound sensible when you’re the one doing it. But the harder part is letting someone else not do it your way.

Damon Flowers said founders often want staff to do things exactly the way they would do them, and they struggle to accept handing over a task and getting 60% of the result back while someone learns.

Founders often can’t tolerate that stage, so they step back in because fixing the problem still feels like their job. All that does is teach the team to hand problems back to you.

You teach the wrong habit

Damon’s point is what your behaviour teaches the team. If you take the work back every time it comes back imperfect, you train people in exactly the wrong way.

They learn they don’t need to finish the hard part because they know it can always come back to you. After a while, the whole company starts working like that.

Then you complain that nobody can take anything off your plate, while teaching the business that it still cannot function without you.

You call it high standards, but the team experiences a system where ownership never settles anywhere because you keep pulling it back in.

That’s one of the easiest ways to build a company full of capable people who never become fully capable inside your business.

The role changed, you didn’t

Tyler Dunagin uses an American football analogy: you start as the all-star player, move into coaching, and end up in the owner’s box.

That’s a tough change when your identity was built around being the only one who could move things forward through effort.

Harry Sanders said realising he wasn’t a good manager was an ego hit. “You can either be rich, or you can be king. You can’t be both. The king is the person that everyone loves, that comes in, says hi. Being rich is building the organisation that doesn’t need you.”

Delegation is only part of it because your role changed. The business needed you to build the structure around the work and let other people carry more of it.

Part of you still wanted the emotional reward of being the all-star player. That’s normal. It also keeps the company smaller than it should be.

Then you resent the business

Joy Zarine’s example is painfully recognisable. You’re on holiday, laptop by the pool, answering emails, annoyed, thinking this isn’t why you built a business.

You keep yourself central because it feels safer, then you resent the business for still needing you. You complain that you can’t switch off while still holding onto the habits that make switching off impossible.

Joy’s point was that this is on you. The frustration is real, but the structure underneath it is self-created. You need people around you, and you have to let them mess up, as painful as that is.

Emma Mills was told more directly that she was stuck in her ways. She runs a personal assistants business, but didn’t have a PA of her own.

Her mastermind facilitator pointed out she was coaching other owners on leverage while still being the bottleneck in her own business.

You can usually see the pattern in other people long before you see it in yourself.

When pushing harder stops working

Mike Jones lived the darkest version of this in his CrossFit business. He tried to push through scaling, but the way he was running it started taking a real toll on his life.

He worked more and more, the business got on top of him, and in the end his brain couldn’t cope. The pressure built until he shut down a good business.

That strips away the idea that this is just a productivity problem. You can carry the wrong structure for a while and still keep the business alive.

There’s still a cost, and it can be big enough to take the whole thing down.

What changes when you step back properly

James Fleming admitted he “was getting frustrated, and the excuse I was making was… it’s your fault,” pointing fingers at the team.

He realised “I had to change” when one of his senior leaders called him out for pushing too hard.

He started stepping back and saying, “I don’t know, but I know you guys do,” passing ownership back to the people he’d hired.

That matters because the role changed. Your job is no longer to fix everything yourself. It’s to set direction, hire well, and trust people to do the work to the standard you expect.

Without this identity shift, the business still runs through you, even after you’ve built the team.

If you keep stepping back in every time something is imperfect, you stay the bottleneck. You’re teaching the company that it still can’t move without you.

If that never changes, the business stays stuck with you at your limit.

Related conversations:

That sounds like an advantage until you realise it also makes it easier to waste time and money on the wrong idea.

, partner at MGV Capital and founder of Alpha Bytes, where he writes about startups, investing, technology, and building companies, has seen that from both the operator and the investor side.

In this guest piece, he writes about why founders need better filters now that building is cheap and exploration matters more than ever. 👇🏻

The real skill is choosing what to build

We’re idea machines by nature. That’s never really been an issue.

The problem is that most ideas, even the good ones, aren’t always worth building.

They could be worth exploring. That distinction matters more now than it used to.

The filter is gone

In the old world, scarcity did the filtering for you.

If an idea needed six months and serious capital to prototype, you stress-tested it before writing the first line of code.

You asked hard questions early because the cost of being wrong was enormous. Time, capital, and effort were brutal filters, but honest ones.

That world is disappearing quickly. AI has collapsed the cost of execution in ways that would’ve sounded absurd even a few years ago.

You can spin up an MVP in a weekend and get something usable in front of people almost immediately. The friction that used to kill bad ideas early has gone.

That sounds like progress, and in many ways it is. But it also makes it easier to waste time building the wrong thing.

The bottleneck has changed. It’s no longer “can we build this?” It’s “should we build this, and should we build it now?”

Exploring is not building

After being on both sides of the fence as an entrepreneur and an investor, I’ve noticed there’s a stage between having an idea and shipping it.

That stage is exploration. It’s where you test an idea before putting real resources behind it. No code. No hires. No capital. Just the cheapest due diligence you can do.

The problem is that exploration is messy by nature. Ideas sit in your head, in random notes, in voice memos that make no sense the next morning.

That’s where a system helps. Instead of relying on gut instinct, it gives you a repeatable way to capture, evaluate, and either park or promote ideas.

The goal is to sequence ideas and finding the ones that are actually right for you.

Some ideas are good on paper and still wrong for the person trying to build them because they don’t match their experience, skills, or ambition.

A simple way to do it

I’ve been running a version of this for a while, and it’s changed how I think about new projects. Here’s the framework stripped back to the essentials:

1️⃣ Capture everything. Write every idea down. No filtering. A note, a voice memo, whatever works. The point is to get it out of your head. Unsorted ideas create cognitive load. A log helps with that.

2️⃣ Let it sit. I usually do 72 hours. Some people do 48 hours, some a week. What matters is that you don’t act on it within 24 hours. Excitement is a bad filter. The ideas that still feel interesting after the dopamine fades are the ones worth exploring.

3️⃣ Score, but do not judge. For the ideas that survive, run them through four questions. Does this solve a real problem I can explain in one sentence? Do I have a unique angle or unfair advantage? Is there any sign of demand, even anecdotal? Could I explain this to a stranger in 60 seconds? Anything below your threshold gets parked.

4️⃣ Explore on paper. For ideas that pass the filter, do a focused two- to four-hour research sprint before you build anything. Map the competitive landscape, look at existing solutions, and find communities already talking about the problem. AI can help you synthesise information, but it shouldn’t make the decision for you.

5️⃣ Define the kill criteria. Before you go any further, write down the specific conditions under which you’d stop pursuing the idea. If you can’t articulate what would make you walk away, you’re probably not being honest about the opportunity.

6️⃣ Keep a live repository. The ideas that survive aren’t a to-do list. Revisit them monthly. Look for recurring themes, overlapping audiences, and complementary concepts. Sometimes the best product isn’t a single idea in the log, but the overlap between several of them.

7️⃣ Promote one. Park the rest. When you’re ready to commit real resources, pick one idea and move it into formal validation. Build a landing page, set up interviews, create a waitlist, or talk about it publicly. The rest stay parked.

The trap you need to see coming

This system has a failure mode, and it’s a seductive one. Too much exploration without commitment is just procrastination.

The system is there to help you decide. If you’ve been exploring the same idea for six months without moving it into validation, that’s probably your answer.

There’s also the temptation to get attached to having a rich idea log without ever acting on it.

The log is there to help you move. To stop it turning into a trophy case, set a cadence, quarterly at minimum, where you force a promote-or-park decision on your top ideas.

AI can make this trap worse. Because it can generate research, competitive analysis, and prototype mockups on demand, it’s easy to confuse activity with progress. A lot of output can still mean very little progress.

The real advantage now

Humans will keep generating ideas. That part isn’t changing.

What’s changed is the cost of acting on them, and with it, the cost of acting on the wrong ones.

In the AI economy, speed alone won’t be enough. The difference is knowing what deserves real commitment and what should stay in exploration.

👤 Barry Winata is a partner at MGV Capital and writes Alpha Bytes. Before moving into investing, he spent nearly a decade as an operator across engineering, growth, recruiting, product, operations, and sales, helping startups grow into successful companies, including businesses acquired by Verizon and others backed by investors such as SoftBank, Intel, and Samsung. Connect with Barry on LinkedIn.

Alpha Bytes

The pragmatist's anti-hype guide on AI and its impact on you, your career, business, culture & society.

By Barry Winata

I’ve seen it and I’ve done it, usually when you’re stretched and trying to keep too many things moving at once.

Marketing feels vague and product feels too technical, so you bring in outside help to “handle it”.

That can seem sensible, like you’re finally out of the weeds. Sometimes it is.

A lot of the time, though, you’re using outside help to avoid learning a function you still need to understand.

That’s where the trouble starts. You hand away part of the business before you understand it well enough to judge it or spot when it’s going wrong. 👇🏻

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

You skipped the hard bit

Melissa Kwan ran into this twice. She kept hiring agencies to do the hard work she didn’t want to do, then got poor results because she didn’t even know the basics of marketing.

She never really took that on because she told herself it wasn’t for her. In the end, she put “the most important revenue role” in someone else’s hands.

If the function is still a black box to you, you don’t really know what you’re buying.

You don’t know how to write the brief, how to interview for the role, or whether the person in front of you is excellent, average, or just saying the right words.

Melissa realised that “you cannot hire someone to do a good job if you don’t even know how to interview for them… It’s not that they’re not good. It’s that I don’t know how to direct them.”

You think you’re fixing a capability gap. What you often create is a visibility gap.

A product doesn’t stay finished

Melissa ran into the same problem again when it came to product. The issue was never just agencies. It was handing away a core function.

She hired a dev shop that was meant to cover everything: PM, developers, technical lead, the lot. The deal was milestone-based. It sounded sensible, but the product didn’t really work, the shop kept burning time, and the moment real users arrived the gaps started opening everywhere.

Once a product is live, it keeps breaking, changing, and needs fast decisions. A dev shop isn’t built for that kind of urgency. “A dev shop will never be able to fix that. They’ll be like, yeah, maybe next week.”

A project can be outsourced. A live product usually cannot be treated like one.

If what you’re handing away needs constant judgement, fast iteration, and real ownership, you cannot afford to have all of that understanding sitting outside the company.

Agencies optimise for the incentive

Gary Das found a similar issue in his marketing spend. He was paying marketing companies to get leads for his mortgage business, and all they cared about was getting leads because that was the metric they were paid on.

They weren’t bothered if anyone answered the phone, if those leads became appointments, or if they ever turned into clients.

That isn’t a moral failure. It’s an incentive structure. Outside help usually optimises for the metric it gets paid on, and that metric can sit one step away from what actually matters.

If you don’t understand the function well enough to see that gap, you can burn a lot of time and money while everyone involved still claims the work is performing.

You can outsource execution, but you still have to own the result.

You still need a clear view

Damon Flowers gets to the practical fix. If you’re hiring for something technical and you don’t have the background to judge it, bring in someone independent who does.

Tap your network and get somebody to shadow the work, review it, or tell you whether what’s being built is what should be built.

You don’t need to become the world expert in every function. You just need enough understanding to know what good looks like. If you don’t have that yet, you need someone close enough to the business who does.

The damage starts when you lose visibility. When something gets blindly handed off, you start losing sight of the pipeline, the cost, and the key metrics that tell you whether the engine is healthy or leaking.

The problem is outside help plus founder blindness. Melissa found the fix almost by accident. The outsourced dev team kept burning through time and money without getting the product working, so David, who had been her boyfriend for seven years, stepped in pro bono to help her get unstuck.

Once she saw what he could do, it made no sense to keep paying an external agency for poor results. She brought him in as CTO and co-founder, sorted the equity, and now he runs the remote engineering team in Vietnam.

That also gave Melissa room to focus on what she needed to own herself. She could bring marketing in-house, rework the messaging and positioning, and stop outsourcing a critical part of the business she had already spent years avoiding.

Some knowledge needs to stay inside

“Do you want to have your own army, or do you want to pay mercenaries?”

Yannik Schrade’s point is that you need to think hard about what your moat actually is. If the team’s skill set and knowledge of how to solve the problem are part of that moat, building that expertise internally matters.

That’s why he’s in favour of having your own team, where the capability compounds inside the business instead of leaking out through outsourced relationships.

A lot of founders miss this because they treat everything as a resourcing problem. Some tasks are just execution. Others are where your edge starts to form.

If the function shapes revenue, product quality, or how the business learns, handing it away too early can do real damage. It can stop the company from building its own judgement.

Where outsourcing actually works

Liam White is a good example of where outsourcing does make sense. He realised that if his sauces business wanted to stay lean, it made more sense to find a manufacturer and keep the internal focus on sales, marketing, and product. The manufacturer had expertise it would’ve taken years to build in house, and outsourcing let them scale faster.

Harvey Armstrong and Sam Holmes are beer lovers, not beer makers. They work with professionals on producing Prime Time, keep control of the brand, and use outside expertise to scale without pretending they need to own every technical detail from day one.

Joe Averill makes the same point with fractional talent. A startup may not have the money for a full-time CFO. Fractional support gave him access to higher-level capability without the full-time cost.

So this is not an anti-outsourcing argument. It works when you are buying specialist help or capacity you do not need to keep inside the business. It breaks down when you use it to avoid understanding a core function.

The rule that matters

If the work is core to revenue, product quality, customer understanding, or your future moat, you need enough understanding to judge it.

That doesn’t mean doing everything yourself forever. It means not handing away the part of the business you still don’t know how to see clearly.

The expensive mistake isn’t paying too much. It’s handing away a function before you know how to see it clearly.