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

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

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

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

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

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

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

Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

The model can change underneath you

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

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

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

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

Your process changed, your AI didn’t

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

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

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

The workflow stopped and nobody knew

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

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

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

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

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

Nobody was keeping it current

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

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

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

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

Plausible isn’t the same as right

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

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

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

So who owns it?

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

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

For every AI system running in your business, ask:

• Who owns this?

• When did they last look at what it produced?

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

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

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

The Humans in the Loop

Helping leaders think clearly about AI.

By Heather Baker

Sophia Matveeva, founder of Tech for Non-Techies, learned that after raising money for her first startup and hiring a technical team. She was the CEO, but when a developer started talking about building the back end on Ruby on Rails, she realised she had no idea what he meant.

Her first instinct was to learn to code. She took computer science and Python courses before seeing she was spending hours learning skills that wouldn’t help her do her actual job. She needed enough technical understanding to make decisions, ask useful questions and work effectively with the team.

AI has lowered the barrier again. A non-technical founder can now make a convincing prototype in an afternoon, which makes it easier to start before finding out whether people understand the product or would pay for it.

Sophia argues that this is where founders need more discipline. You can use AI to get an idea in front of people much earlier and learn from what they actually do with it. Once you’re making something people will pay for, you still need people who understand the technology well enough to know what’s happening underneath.

In our conversation, Sophia gets into what non-technical founders need to understand about tech, how to test an idea before spending heavily on it and where vibe coding stops being enough.

🔗 Find Sophia on LinkedIn

Got an idea you want to build? Subscribe for more on finding out if people actually want it first 👇🏻

Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

Key takeaways

1️⃣ You need to speak tech before you lead it

You don’t need to become a developer, but you need enough understanding to work with one properly. That means knowing what’s being built, why something takes time and what you’re giving up when you choose one feature over another.

2️⃣ Test the idea before you spend money building it

Sophia recommends putting a prototype in front of potential customers before writing production code. Watching how people actually use it can expose problems that are much cheaper to fix at that stage.

3️⃣ Don’t ask customers to validate your idea for you

People often tell you something sounds good because they want to be helpful. Sophia recommends approaching user interviews with curiosity, giving as little guidance as possible and making people comfortable telling you what they don’t understand.

4️⃣ Vibe coding is great for testing, not the finished product

AI lets non-technical founders create convincing prototypes much faster and cheaper. Sophia warns against assuming that means you can safely build an entire commercial product yourself when you can’t judge the quality of the underlying code.

5️⃣ A better product can still lose

Customers already have tools they know and systems built around them. Getting them to switch can require much more than improving the product, particularly when an established competitor is deeply embedded in how a company works.

In this episode

00:00 Introduction to Sophia Matveeva

02:08 Is vibe coding improving product testing?

05:34 Why founders need to speak tech

12:54 How to manage developers

15:31 Test your product before building it

18:47 How to get honest user feedback

22:47 Will people actually pay?

25:57 How to challenge your technical team

30:38 Choosing which features to build

32:57 Why Sophia shut down Enty

35:22 Funding versus bootstrapping

37:23 When to stop funding your own business

39:19 Why better products still lose

42:58 What AI changes for non-technical founders

45:47 Building is easier, finding customers isn’t

Jordan Stachini sees the incentive clearly in a creative agency. If AI can generate ideas quickly, the obvious cost-cutting question is whether the agency still needs ten junior creatives. The best people she’s worked with became good through trial and error, moving from junior roles towards becoming middleweight designers, lead designers and creative directors.

“How do you then get more senior creatives coming through if you’re stopping it right at the start?” she asks. If you remove more of the work people used to learn on, you need another way to build the experience you’ll still be hiring for later. 👇🏻

Junior work is already changing

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

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

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

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

The boring work was teaching you something

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

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

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

You used to learn by overhearing

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

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

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

The senior hire is the easier choice

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

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

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

AI can help rebuild the practice

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

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

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

Training becomes somebody’s job

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

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

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

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

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

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

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

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

“The AI told me to do this.”

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

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

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

New here? Subscribe to Millennial Masters for more on using AI without creating extra work, and knowing when human judgement still matters.

Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

The three-question test

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

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

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

What I hand over

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

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

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

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

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

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

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

The review bill

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

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

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

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

Saving time doesn’t necessarily mean less work either.

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

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

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

Your name’s still on it

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

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

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

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

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

When people become the premium option

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

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

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

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

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

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

Taking My Time Back

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

By Cory Blumenfeld

Through his agencies Redwerk and QAwerk, he sees what happens after the prototype looks convincing and real users start doing things you didn’t expect. Something that worked perfectly in a demo suddenly starts failing in ways you didn’t expect, from payments to duplicated bookings.

That’s why Konstantin sees AI as a multiplier. It can help you build more, but it can also lead you astray with a bad decision before you realise you’ve misunderstood what people actually need.

For founders, that shifts more of the work towards deciding what deserves to be built in the first place. Konstantin thinks talking to users matters even more now, especially when almost anyone can turn an idea into working software without spending months getting there.

In our conversation, Konstantin explains where vibe-coded products start to break, what still needs proper technical experience and how he thinks about building a software business when AI is available to everyone.

We also get into how he’s run the business over 21 years without outside funding, why he avoids hiring ahead of demand and what COVID and the war in Ukraine taught him about adapting when circumstances change underneath you.

🔗 Find Konstantin on LinkedIn

Key takeaways

1️⃣ AI makes bad decisions faster

Konstantin sees AI as an accelerator for whatever is already happening inside the business. If you understand the problem and know what users need, it helps you move further. If the thinking is weak, it helps you create more problems before you notice.

2️⃣ The demo can lie

Vibe coding can get you surprisingly far, especially for demos and early testing. Konstantin has seen products that look finished on screen while basic workflows fail once real users arrive.

3️⃣ Knowing what to build is the edge

When almost anyone can ship software, the bigger advantage comes from understanding your users well enough to know which problems are worth solving. More features don’t help if nobody needs them.

4️⃣ Your prototype still needs proper engineering

AI can move the point where you need a CTO or experienced developer much later. Once the product has real users and needs to work reliably, you still need people who understand the architecture and what’s happening underneath.

5️⃣ Don’t build for a business you don’t have yet

Konstantin has spent 21 years growing without outside funding and avoids hiring before the work is there. That same thinking applies to software: solve the problems you actually have instead of spending heavily on a future version of the business that may never arrive.

In this episode

00:00 Introduction to Konstantin Klyagin

01:59 DataForSEO sponsor message

03:21 More code doesn’t mean more progress

05:27 Build what users actually need

07:20 A prototype can look finished and still fail

10:45 AI multiplies bad decisions too

13:02 When you still need a CTO

17:52 Don’t hire ahead of demand

21:48 Understanding the customer becomes the edge

25:51 DataForSEO sponsor message

26:06 AI doesn’t belong everywhere

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

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

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

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

You were told to be more human

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

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

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

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

Not every story needs publishing

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

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

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

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

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

When your face becomes the company

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

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

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

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

You’re allowed to keep some of your life

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

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

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

Decide before the algorithm does

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

Fun at work isn’t one thing

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

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

So fun doesn’t simply equal better performance.

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

What matters more is where the enjoyment comes from.

The work itself matters more

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

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

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

When enjoyment becomes an advantage

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

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

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

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

You don’t need another games cupboard

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

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

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

Keep some work worth enjoying

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

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

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

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

All The Margins

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

By Daphne Mavroudi-Chocholi

We’d gone live to talk about how much of our work AI should actually do, while all four of us were already giving it jobs that would have taken up our time a year or two ago. What became more interesting was how differently we treated those jobs once AI started doing them without us. 👇🏻

Start with the work you can check

Philip runs much of his business through Notion and now uses AI to help build pages and set up automations without learning every click himself. Cory uses it for repetitive work throughout his day, documenting conversations, updating his CRM, preparing emails and finding people he’s forgotten to reply to.

Cory is comfortable handing over work where he can quickly tell whether it’s been done properly. Summaries and moving information around are easy for him to check. Once the job needs his judgement, he gets involved.

Joel pushed further with some jobs. When he publishes on Substack, AI can turn the original piece into a LinkedIn post and carousel, then publish both without him seeing them first. He spent months testing the process and adjusting the rules before he was comfortable letting it run alone.

Trust has to be earned

Joel wrote the original material, knows what he expects from the LinkedIn version and has spent enough time testing the process to recognise where it can go wrong.

Yet when he was short on time with another post and let AI write the introduction, he published it, read it again and deleted the whole post about five minutes later. It had dealt with the SEO and keywords perfectly well, but it didn’t sound like him.

He understands the first job well enough to tell when AI has done it properly. That becomes much harder when you’re handing over work you don’t understand particularly well yourself.

Your name is still attached

Philip tried letting AI reply to his Substack messages early on and stopped within a week. Nobody complained. He simply didn’t like people believing they were having a conversation with him when they weren’t.

Joel draws a line around email and comments. AI can read the background, prepare a reply in Gmail and leave it there, but he still checks what goes out. He pointed out that you can spend years building trust and lose it quickly after a bad post or piece of advice goes out under your name.

The model isn’t dealing with the customer afterwards if an email gets something wrong, and it isn’t explaining bad advice to a client. You’re still the one who has to deal with it.

AI changes who you can hire

Philip raised an idea I hadn’t given enough thought to. He’d spent most of his time asking what AI could do for him, until hiring another virtual assistant made him consider what somebody else could now handle with AI helping them.

Previously, they’d struggled to give a VA a wider range of work because the company had too many moving parts to pick up quickly. With good documentation and the AI tools they’ve built, a new VA can take on more much sooner without knowing every corner of the business first.

That raised the question I kept coming back to during the conversation: what happens when AI helps you do work before you’ve learnt enough to judge the result?

The polished answer can still be wrong

If you don’t understand good writing, you’ll struggle to spot a weak draft. The same problem follows when you ask AI to communicate with customers or make decisions for you, especially now that the output is polished enough for a mistake in judgement to sit underneath perfectly reasonable wording.

Cory disagreed with part of that. Some of his early LinkedIn writing with AI was awful, he said, but using it got him writing at all. Once he’d started, he paid more attention to other writers, asked better questions and gradually improved. AI helped him try work he might never have taken on otherwise.

AI can help you try something before you’re particularly good at it, and that can become part of how you learn. The problem comes if you never develop enough judgement to stop accepting whatever it gives you.

That matters as you remove yourself from more of the work. I’m already giving AI jobs I wouldn’t have handed over a year ago, and I expect that list will keep growing.

Before I stop checking another one, I want to know I’d still recognise the bad answer.

Zain Peer found that out when London Nootropics appeared on Dragons’ Den (UK’s Shark Tank). The website crashed, orders surged and a business that had been manageable the week before suddenly had to cope with a completely different level of demand.

With no employees at the time, Zain and his co-founder Shez Shaikh were doing everything themselves. Three months later, he says he still hadn’t properly slept.

It was the kind of attention they’d been trying to create from the beginning, and it exposed how much of the business still had to catch up. You stop wondering how to get noticed and start worrying about whether you can actually fulfil what you’ve sold without draining the cash you need elsewhere.

In our conversation, Zain gets into what happened after Dragons’ Den, why he eventually turned down the investment offered on the show, and what he’s learned from building a physical product where every jump in demand has to be funded and handled before the money comes back.

🔗 Find Zain on LinkedIn

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Millennial Masters

Helping founders make better business decisions.

By Daniel Ionescu

Key takeaways

1️⃣ More orders can create a new problem overnight

Demand feels like the goal until the business has to cope with it. After Dragons’ Den, London Nootropics saw its website crash and orders surge while Zain and his co-founder were still doing everything themselves. Growth exposed the parts of the business that hadn’t caught up yet.

2️⃣ Cash gets tight before growth feels comfortable

A physical product business has to spend before the money comes back. Bigger production runs mean more cash tied up in stock, which leaves less available for everything else. Zain has stayed bootstrapped, so every jump in demand has had to be funded carefully.

3️⃣ Repeat customers can change the business

London Nootropics didn’t offer subscriptions for its first two and a half years. Once they introduced them, Zain says they became a game changer, and some customers started buying far larger quantities than he expected. Retention ended up mattering far more than simply chasing the next order.

4️⃣ Retail creates work you don’t see from the shelf

Getting into major retailers sounds like the win, but the work starts before the product ever gets displayed. London Nootropics had to rethink packaging and sort warehousing while also funding more stock in advance. The glamorous part is only the front end of a much bigger job.

5️⃣ You can waste a lot of time trying to make things perfect

Zain says he used to want every detail right before launching. Over time, he learned to get something good enough into the world, watch what customers actually did and improve from there. That shift helped them move faster without waiting for every part of the business to feel finished.

In this episode

00:00 Zain Peer and the start of London Nootropics

02:48 Finding the product people wanted

05:27 Bootstrapping the first launch

07:00 Building trust in the wellness market

10:48 What happened after Dragons’ Den

15:24 Funding stock as retail expands

20:13 How subscriptions changed the business

23:22 What selling on Amazon added

27:08 Why repeat customers started buying more

30:16 Protecting quality as competition grows

33:38 Learning to launch before everything is perfect

36:18 Building real relationships around the brand

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. 👇🏻

A clothing retailer asked us to build a promotion analyser.

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

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

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

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

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Helping founders make better business decisions.

By Daniel Ionescu

What the system couldn’t see

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

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

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

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

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

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

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

What left with the manager

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

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

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

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

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

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

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

Where the work went

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

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

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

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

What to capture before you automate

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

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

AI forces some of that knowledge into the open.

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

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

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

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

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

Who still knows when this recommendation is wrong?

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

Between Roles

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

By Om Prakash Pant

Barry Cryan sees the cost of those interruptions differently. Through his company, Do More Better, he works with business owners to reduce how much work keeps flowing back to them and build systems that give them more room to focus.

He calls the problem the invisible tax. The more decisions that depend on you, the harder it becomes to get proper time on the work that actually moves the business forward.

AI can help, but Barry makes an important distinction. Using it to answer an email faster still leaves you doing the email. The bigger opportunity is to build systems that remove repetitive work from your day altogether.

In this episode, we get into how founders become too central to the business, where that hidden drain usually starts and how better systems can give you time back without adding more hours.

🔗 Find Barry on LinkedIn

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

Helping founders make better business decisions.

By Daniel Ionescu

Key takeaways

1️⃣ Every tiny decision has a cost

The problem isn’t usually one huge interruption. It’s the constant stream of small decisions that keeps pulling you away from deeper work. When everything still needs your attention, the business quietly trains itself to depend on you.

2️⃣ AI should remove work from your day

Using AI to answer an email faster can help, but you’re still doing the email. The bigger gain comes when AI takes repetitive work out of your hands altogether and reduces how often you need to get involved.

3️⃣ Hiring more people won’t fix founder dependency

A bigger team can create even more questions if people still need you to approve every decision. Clear processes give them something to work from and make it easier for you to stop being the default answer.

4️⃣ Protect your attention before you optimise your time

Notifications and constant availability make it harder to stay with the work that matters. Creating fixed windows for communication gives you longer stretches where you can actually think and finish what you started.

5️⃣ Free time only matters if you use it well

Getting an hour back doesn’t automatically improve the business. That space can disappear into more small tasks unless you deliberately protect it for work that needs your judgement or for time you want back outside the business.

In this episode

01:42 What AI is changing inside businesses

04:12 AI operators and AI builders

06:20 The invisible tax of tiny decisions

08:35 Building systems that remove bottlenecks

11:00 What constant small decisions really cost

13:20 Reducing unnecessary decisions

16:12 Putting useful systems in place

18:03 Giving teams playbooks they can actually use

20:24 Protecting focus from constant interruptions

22:14 Giving the team room to own decisions

28:12 What happens when you keep doing everything

32:55 Delegating without staying in the middle

38:10 Using AI to remove repetitive work

39:42 Measuring capacity as the business changes

42:44 Filtering out the noise

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. 👇🏻

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

Helping founders make better business decisions.

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.

Vinnie Lauria has spent more than fifteen years on the other side of that decision. As a founding partner at Golden Gate Ventures, he has backed companies across Southeast Asia after starting his own career as an entrepreneur.

That gives him a useful view of what investors notice once a founder gets in the room. A polished pitch can open the conversation, but Vinnie is far more interested in the evidence behind it. He wants to see whether you’ve actually found something people want and whether you understand how to turn that into a business.

AI has pushed that bar higher. Products can be built faster, decks can look better and early versions can appear far more developed than they would have a few years ago. Investors know that too.

In this episode, we get into what makes a startup investable now, why founders waste time approaching the wrong investors and what Vinnie looks for before he decides a company is worth backing.

🔗 Find Vinnie on LinkedIn

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

Helping founders make better business decisions.

By Daniel Ionescu

Key takeaways

1️⃣ Building the product is only the beginning

AI has made it possible to get something into the world with far fewer people and much less money. That also means investors are less impressed by the fact that you managed to build it. They want evidence that people genuinely want what you’ve made and are willing to keep using it.

2️⃣ Retention tells you whether the demand is real

A spike in users can come from an ad, a post or a burst of publicity. Vinnie looks much harder at what happens afterwards. If people keep coming back without constantly being dragged back in, you’ve started proving there’s something worth building a business around.

3️⃣ Pitch investors who already understand your world

Fundraising gets much harder when you treat every lead as a possible investor. Look at what they’ve backed before, where they invest and the kinds of businesses they understand. A rejection from someone who was never a realistic fit tells you very little about the quality of your company.

4️⃣ Your deck can’t hide a weak business

AI can make the presentation neater and the language cleaner, but an investor can still get underneath it quickly. The customers you’ve won and what they actually pay you for reveal far more than a beautifully designed slide about how large the market could become.

5️⃣ Investors are still betting on the founder

AI can take work off the team, but it doesn’t remove the need for someone who can make decisions as the company changes. Investors are trying to work out whether you can lead people through problems you haven’t encountered yet. That judgement can matter as much as what the product looks like today.

In this episode

01:28 Introduction to Vinnie Lauria

03:57 Understanding fundraising stages

07:44 Lessons from startup failures and successes

10:17 Navigating the AI landscape and market strategies

12:26 The role of pitch decks in fundraising

14:18 Common mistakes founders make with investors

16:58 Understanding competition and market positioning

19:10 Crafting a compelling narrative for investors

23:00 Messaging for different stakeholders

24:15 The importance of team presentation in pitch decks

25:53 Understanding traction vs momentum in startups

27:26 The role of investor theses in startup funding

28:26 Asking the right questions as a founder

30:03 Identifying BS in startup pitches

32:23 Evaluating founders’ growth potential

35:48 Selling hard without sounding desperate

37:33 The impact of AI on pitch decks and presentations

39:48 Founders talking themselves out of deals

40:28 Effective follow-up strategies with VCs

41:27 Navigating a colder fundraising market

43:55 AI startups and investor expectations

45:41 The importance of team dynamics

46:51 Finding opportunities around big platforms

48:01 The right mindset for founders

50:36 Lessons learned from investing

53:54 Balancing risk and intuition

55:39 Giving teams room to take risks

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. 👇🏻

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Millennial Masters

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

By the time the offer arrived, the real work had already happened. He’d spent years building the team, tightening how the company ran and making sure it could keep moving without him at the centre of everything.

That is what a buyer is really looking for. They want to know the clients will stay, the team can make decisions and the business will not wobble the moment the founder steps away.

Luke is also honest about the parts of growth that look good from the outside while making the company weaker underneath.

More revenue can still mean thinner margins. A large client can still damage the team. Loyal people can still end up in jobs they are not ready for.

In this episode, we get into what makes a service business worth buying, how founder dependency affects the price and why the best time to prepare for a sale is long before you plan one.

🔗 Find Luke on LinkedIn & his website

Millennial Masters

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

By Daniel Ionescu

Key takeaways

1️⃣ Build like the buyer is already watching

A business becomes more valuable long before anyone makes an offer. Every system you document and every problem the team can solve without you makes the business stronger. Those are the same things that make your day-to-day easier, whether you ever plan to sell or not.

2️⃣ Revenue doesn’t tell you how healthy the business is

A growing business can still be under pressure. More clients and more people can create hidden strain if the numbers underneath aren’t improving as well. Looking beyond turnover helps you spot problems while you still have time to fix them, instead of finding them when growth starts slowing everything down.

3️⃣ Every client isn’t worth keeping

It’s easy to look at the monthly fee and ignore everything else. A difficult client can wear down the team and pull attention away from better work. Protecting the business sometimes means saying no to something that looks valuable on paper but keeps making everything harder behind the scenes.

4️⃣ Make your knowledge repeatable

In the early days, the founder often knows the answer because they’ve seen every problem before. That stops working as the business grows. Writing things down and giving people the confidence to make decisions means the business keeps moving even when the founder isn’t in the room.

5️⃣ AI gives you more time. Use it wisely.

AI can give you back hours that used to disappear into repetitive work. That time can go into better thinking, harder problems and work that still needs human judgement. The gain comes from how you use the space it creates.

In this episode

00:00 Intro to Luke Tobin

01:41 Growth can make the business weaker

04:53 Inside an eight-figure sale

07:29 What rapid scale exposes

10:36 The numbers revenue can hide

14:43 Overdelivery starts eating the margin

16:19 Some clients make the business worse

19:50 The client relationships that last

23:43 Taking the founder out of sales

28:32 Founder dependency kills value

34:00 What buyers see behind the curtain

37:52 The paid work trial that fixed hiring

42:05 Loyalty doesn’t make someone a leader

48:19 AI rewrites service business economics

54:35 What AI-native actually looks like

58:36 The reality of an eight-figure exit

01:02:51 Losing the business identity

01:07:23 Building again without the same mistakes

01:10:50 Build like the buyer is already watching

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

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

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

It didn’t start with the invoice

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

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

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

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

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

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

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

Every extra day adds up

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

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

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

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

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

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

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

Who makes the call?

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

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

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

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

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

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

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

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

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

Where the delay really starts

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

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

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

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

The Profit Lens

By Nadia Codreanu

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

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

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

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

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

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

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

More tools won’t fix it

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

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

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

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

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

The work between the tools is still yours

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

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

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

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

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

Three habits that survive the switch

This is where you stop starting from scratch every time.

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

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

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

These habits let you change tools without rebuilding the work.

Find out where you’re stuck

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

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

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

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

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

Fix the habits before adding tools

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

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

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

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

Cash & Cache

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

By Raghav Mehra

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

The candidate everyone liked

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

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

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

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

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

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

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

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

When smooth becomes the signal

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

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

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

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

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

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

What your process is actually testing

The process was thorough and still had a blind spot.

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

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

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

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

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

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

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

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

The cost arrived later

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

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

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

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

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

Add a step they can’t rehearse

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

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

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

More stages only help when each one tests something different.

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

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

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

The Leverage Point

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

By Daryna Karuna