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

Eventually he asked him a much less enjoyable question:

How much revenue had all that work produced? None.

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

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

Procrastination can look impressive

Nate Littlewood recognised the same pattern while running Urban Leaf.

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

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

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

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

Work you know how to score

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

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

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

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

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

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

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

AI has made the hiding place bigger

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

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

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

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

What problem am I learning this to solve?

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

“I just need another week”

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

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

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

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

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

Two years and £5,000 later

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

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

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

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

Find the work you keep walking around

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

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

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

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

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

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

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

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

What did the work actually change?

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

The work you’re avoiding probably won’t become easier while you perfect everything around it. Eventually, somebody else gets a vote.

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

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

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

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By Daniel Ionescu

A clothing retailer asked us to build a promotion analyser.

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

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

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

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

What the system couldn’t see

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

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

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

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

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

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

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

What left with the manager

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

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

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

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

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

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

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

Where the work went

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

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

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

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

What to capture before you automate

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

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

AI forces some of that knowledge into the open.

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

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

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

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

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

Who still knows when this recommendation is wrong?

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

Between Roles

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

By Om Prakash Pant

I’ve been thinking about that as AI moves beyond drafts and starts making decisions for us. Sending an email is different when nobody checks it first. So is recommending something to a customer or deciding what another person gets to see.

A year ago, I was more interested in whether AI sounded like AI. There were obvious giveaways everywhere. Certain words appeared too often and sentences had the same polished rhythm. Entire paragraphs could say surprisingly little while sounding as though something important just happened.

That became the first version of AI slop for me. You could spot it from the language alone. People kept banned-word lists and told models to stop saying things like ‘delve’ and ‘tapestry’.

The models learnt quickly. With decent instructions, many of those giveaways disappear. You can remove every obvious AI phrase and still end up with something bland or detached from the decision it’s supposed to help you make.

I started asking if I agreed with the thinking behind the words, even when they sounded human. Once the language became harder to catch, the judgement underneath stood out more. 👇🏻

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By Daniel Ionescu

Slop starts before the draft

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

You still need to understand what the customer cares about and what you don’t know yet. What’s missing may be exactly what makes the recommendation wrong.

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

The manager never disappeared

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

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

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

Mistakes get more expensive

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

With agents, a mistake can reach someone before you see it. A sales agent can follow up with dozens of leads while you’re doing something else. Poor judgement can leave dozens of people with the same bad impression of your company.

In customer support, an agent can answer the question correctly and handle the person badly. The customer may have needed an apology. A reply can follow policy perfectly and make a £20 problem feel like a reason to leave.

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

Exceptions are harder to hand over

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

A long-standing customer paying you £20,000 a year may need a different response from somebody who signed up yesterday and ignored three attempts to help. A missed payment from a normally reliable client means something different from a new customer who has broken every term since day one. Those distinctions rarely fit neatly into one rule.

You can see the same thing in how you already manage people. The best employees learn the exceptions by watching you approve one refund and refuse another, or turn down a large client because the work would wreck the team. Over time, they understand more than the written process.

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

Your judgement can’t stay in your head

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

Customers often give you half the information, and an apparently perfect sales lead may turn out unable to afford you. Something that saves one team two hours can create six hours of clean-up somewhere else. That’s where you find out whether the rule actually works outside the clean demo.

When the judgement is sound, AI can apply it far more widely than you could on your own, whether that means answering a customer at midnight or helping your team when the usual person isn’t available.

The customer only sees what happened

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

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

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

I care more about the call now

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

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

I’ll happily automate decisions where little is at stake and the standard is clear. Anything with too much history behind it or too much at stake for the customer still needs someone involved.

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

Melissa Kwan had built eWebinar into a proven product. She called it ‘quite overbuilt’, yet the starting price was still $99.

Bigger companies saw that figure and questioned whether the company was serious enough to support them.

“Our starting point is still $99. But there are big companies that look at us and think we’re not serious enough,” she said.

“People look at the pricing and they think we’re a tiny company. So it doesn’t inspire confidence. We’re not an expensive bottle of wine.”

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

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

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

New here? Millennial Masters shares 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.

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

By Daniel Ionescu

Price becomes part of the evidence

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

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

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

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

eWebinar had outgrown its price.

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

The wrong customers arrived

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

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

“They want everything and then they can’t afford $49,” Melissa said. “Within two weeks, they churn.”

The low price brought in customers with little reason to stay.

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

“It was drowning our support,” Melissa said. “It was a huge distraction for David because then he couldn’t code.”

The price was now deciding how the founders spent their time.

When eWebinar doubled its starting price to $99, Melissa said the low-value support burden disappeared almost immediately.

The new price filtered out buyers who were unlikely to use the product properly.

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

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

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

The price follows the work inside

The pricing model gets tested when the work gets faster.

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

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

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

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

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

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

Price isn’t always the problem

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

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

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

The service worked. The market didn’t.

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

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

What kind of business is the price creating?

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

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

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

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

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

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

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

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

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

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

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

Why buyers stop

It isn’t always the price or scepticism.

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

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

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

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

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

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

Where proof gets buried

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

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

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

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

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

Before & after: The same claim, easier to trust

Nothing changed except where the proof appeared.

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

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

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

Run the test before you hit send

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

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

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

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

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

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

Empathy Elevated

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

By Colette Molteni

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

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

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

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

By Daniel Ionescu

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

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

It didn’t start with the invoice

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

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

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

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

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

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

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

Every extra day adds up

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

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

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

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

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

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

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

Who makes the call?

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

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

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

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

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

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

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

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

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

Where the delay really starts

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

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

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

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

The Profit Lens

By Nadia Codreanu

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

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

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

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

New here? Millennial Masters shares practical lessons from founders on building better companies and 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.

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

By Daniel Ionescu

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

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

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

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

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

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

More tools won’t fix it

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

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

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

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

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

The work between the tools is still yours

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

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

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

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

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

Three habits that survive the switch

This is where you stop starting from scratch every time.

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

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

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

These habits let you change tools without rebuilding the work.

Find out where you’re stuck

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

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

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

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

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

Fix the habits before adding tools

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

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

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

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

Cash & Cache

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

By Raghav Mehra

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

The candidate everyone liked

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

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

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

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

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

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

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

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

When smooth becomes the signal

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

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

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

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

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

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

What your process is actually testing

The process was thorough and still had a blind spot.

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

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

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

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

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

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

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

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

The cost arrived later

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

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

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

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

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

Add a step they can’t rehearse

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

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

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

More stages only help when each one tests something different.

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

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

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

The Leverage Point

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

By Daryna Karuna

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

The business hits your limit first

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

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

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

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

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

Your head is not a system

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

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

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

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

Hiring exposes the mess

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

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

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

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

Stop hiring helpers

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

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

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

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

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

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

Let the work come back imperfect

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

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

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

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

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

Write down what keeps repeating

The practical work is turning your judgement into company memory.

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

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

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

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

What worked small will crack later

The work is never finished because the business keeps changing.

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

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

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

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

Buyers don’t want your job

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

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

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

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

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

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

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

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

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

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

New here? Millennial Masters shares lessons from founders who’ve built, grown and led companies. Subscribe for ideas on AI, growth, leadership and the decisions that rarely come with a playbook.

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