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

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

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

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

The cash is hiding the cracks

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

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

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

Money changes how you behave

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

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

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

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

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

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

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

The runway hides more than it fixes

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

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

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

Keep the discipline that got you funded

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

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

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

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

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

The round only buys you time

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

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

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

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

ScaleSignals

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

By Ben Botes | GP & 4x Founder

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

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

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

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

Hiring pressure comes first

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

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

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

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

Skill does the heavy lifting

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

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

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

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

The work that needs you

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

The fantasy version still breaks

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

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

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

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

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

Building is cheaper, demand is not

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

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

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

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

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

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

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

Taste is the limit

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

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

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

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

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

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

The viral myth

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

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

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

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

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

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

“Which one is working so far?”

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

He sounded exactly like the old me.

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

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

Demand comes from going narrow

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

Code can be a distribution channel

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

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

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

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

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

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

Build something useful first

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

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

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

Go where trust already exists

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

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

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

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

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

That strategy brought in 25,000 users.

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

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

Automate reach carefully

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

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

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

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

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

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

What didn’t work

We also tried plenty of things that went nowhere.

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

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

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

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

The developer’s advantage

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

But getting off the ground is a different phase.

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

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

Technical skill can become distribution skill.

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

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

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

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

Behind the Scenes

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

By Jason Tan

She doesn’t sell on Amazon, and she hasn’t relied on Meta or TikTok ads. Instead, she built the business through independent refill stores, salons, and surf shops, one relationship at a time.

On paper, some of those decisions look expensive. Kate believes the opposite. The shortcuts were the expensive option.

Beneath the sustainable beauty story is a bigger question about how businesses grow and what happens when convenience starts pulling you away from the thing that made people trust you in the first place.

Whether you’re building a SaaS company, a consultancy, a media business or a consumer brand, the tension is the same. Every growth decision changes the business underneath it.

🔗 Find Kate on LinkedIn

Key takeaways

1️⃣ The shortcut usually costs more

A lot of founder shortcuts look attractive because they promise speed. Kate’s experience was the opposite. The co-founder she didn’t need, the agencies that overpromised, the marketplaces that could have accelerated growth. Again and again, the expensive mistakes came from trying to find an easier path. The businesses that last are usually built on stronger foundations than people realise.

2️⃣ Selling once isn’t the win

Kate spent years watching beauty brands sell products through clever marketing. What changed her thinking was a simple question: does the customer come back? A first purchase proves your marketing worked. A second purchase proves the product worked. That distinction shaped everything about how she built Dip and it’s relevant to almost any business.

3️⃣ Trust is becoming a competitive advantage

Fake reviews, AI-generated content, SEO games, affiliate incentives, misleading sustainability claims. Consumers are surrounded by noise. Kate’s response wasn’t to play harder in those channels. It was to get closer to customers and retailers. As trust becomes harder to earn, businesses with genuine relationships have an advantage that can’t be copied overnight.

4️⃣ Growth changes the business underneath

Every new channel brings trade-offs. Amazon could probably make Dip much bigger. Kate knows that it’s what happens to the business when you become dependent on it. Founders often focus on the growth opportunity in front of them and spend less time thinking about the long-term consequences attached to it.

5️⃣ Generosity compounds

One of the more unusual parts of Kate’s story is how much she invests in people. Retailer trips, events, partnerships, personal relationships, support for independent stores. Most founders default to frugality when resources are tight. Kate’s view is that generosity creates trust, loyalty and opportunities that don’t show up immediately on a spreadsheet but compound over time.

In this episode

00:00 Introduction to Kate Assaraf

02:09 Starting again after a co-founder split

04:22 The beauty marketing tricks Kate rejected

06:11 Why refill stores changed the business

07:39 Building through independent retailers

09:18 Going analog when everyone went digital

10:09 Why small stores became the real influencers

11:44 The expensive lesson of taking on a partner

15:11 Competing with beauty giants, not other bar brands

17:37 Selling sustainability without guilt

18:57 Why Dip refuses to sell on Amazon

23:13 The real cost of marketplace convenience

26:40 Why paid ads don’t fit this brand

29:09 The trust recession in beauty and ecommerce

36:43 The biggest lie in beauty marketing

39:09 Why Kate started her own factory

44:17 Why generosity beats frugality

45:13 Why shortcuts always cost more

48:52 Working with your husband without chaos

50:17 Rethinking growth and success

51:29 The real sacrifices behind building Dip

53:00 Costly founder mistakes and bad vendors

57:06 How to avoid getting sold the shortcut

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

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

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

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

AI judges your business before people do

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

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

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

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

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

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

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

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

The deal died before the demo

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

Why weak proposals get filtered

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

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

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

1️⃣ Vague benefit language

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

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

“We help sales teams work smarter” gets filtered.

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

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

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

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

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

2️⃣ Feature-first structure

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

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

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

3️⃣ Trust signals without proof

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

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

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

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

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

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

Assertion without evidence scores at the bottom.

4️⃣ Nothing to compare

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

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

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

What stronger proposals prove faster

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

1️⃣ Lead with the outcome

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

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

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

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

2️⃣ Attach proof to every claim

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

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

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

3️⃣ Use the buyer’s own words

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

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

“Our pipeline is clogged with unqualified leads.”

“We can’t tell which deals are real.”

Then I use those exact phrases in the proposal.

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

4️⃣ Make the next step clear

I close each proposal with a clear next step.

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

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

The 10-minute AI audit

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

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

Paste your executive summary into Claude or ChatGPT and ask:

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

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

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

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

ENG Sales

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

By David Roy

That experience now shapes his work at Future Ready, where he helps founders understand what’s really happening inside the business before growth makes the problems harder to see.

Revenue can make things look healthier than they are. You can have sales coming in and still be dealing with weak margins, loose systems, bad hiring, and decisions made from guesswork.

In this episode, we get into the financial and operational habits that help founders build a business that can actually handle growth.

🔗 Find Nate on LinkedIn and YouTube

Key takeaways

1️⃣ Revenue can hide bad decisions for a long time

A business can look healthy from the outside while the foundations underneath are getting weaker. Nate kept coming back to the same point: growth covers up a lot. Weak margins, messy operations, poor hiring, unclear reporting. If the numbers look good enough, founders stop asking harder questions until the pressure eventually catches up.

2️⃣ Founders lose clarity as the company grows

The early stage is simple because everything runs through you. Then the team expands and decisions start getting made further away from the founder. Nate’s point is that growth creates distance. If you don’t build good reporting, clear ownership, and visibility into the business, you slowly stop understanding what is actually happening day to day.

3️⃣ Bad hiring gets expensive very quickly

One wrong senior hire can create months of confusion, wasted money, and extra management weight. Nate talked a lot about founders hiring too quickly because growth creates pressure to “level up” the team. The problem is that complexity rises faster than most companies are ready for. More people only help when the structure around them is strong enough to support them.

4️⃣ Financial discipline gives you better decisions

A lot of founders treat finance like reporting history instead of understanding reality. Nate’s view is much more operational than that. Good financial visibility helps you spot pressure earlier and avoid drifting into problems you only notice once cash gets tight or growth slows down.

5️⃣ Growth should make the business clearer, not heavier

The goal is not to keep adding layers until the company becomes difficult to run. Nate’s thinking is much more about simplification. Better systems, cleaner communication, stronger accountability, clearer priorities. When growth only creates more chaos, the business usually needs better structure, not just more effort.

In this episode

00:00 Introduction to Nate Littlewood

02:32 From Wall Street to entrepreneurship

05:10 Lessons from building Urban Leaf

08:00 What financial health actually looks like

10:46 Focus, delegation, and founder visibility

13:34 Spotting profitability problems early

16:04 Why revenue and profit tell different stories

18:55 Common management mistakes during growth

21:43 Customer retention and product quality

31:43 Understanding founder archetypes

36:59 The time advantage in bootstrapping

41:33 Working through founder comfort zones

51:04 Finding the work that matters most

55:06 How your understanding of the business evolves

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

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

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

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

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

Good products still need demand

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

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

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

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

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

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

AI changed what’s scarce

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

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

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

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

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

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

Building feels safer than selling

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

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

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

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

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

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

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

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

The pain has to be clear first

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

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

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

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

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

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

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

Polish won’t save a weak offer

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

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

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

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

The product isn’t the whole job

You don’t need to become an influencer.

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

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

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

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

Can the business actually handle more speed?

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

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

AI will find the cracks

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

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

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

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

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

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

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

What a fragile business looks like

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

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

Why AI makes the cracks visible

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

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

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

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

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

The work gets worse before it gets better

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

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

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

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

Ask what would break first

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

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

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

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

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

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

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

The Adaptive Organization

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

By Sam Rees, Reg.Psych

He’s spent the last decade moving through startups, scale-ups, international expansion, leadership, and now a portfolio career, while watching technology accelerate faster than most companies or careers can comfortably adapt to.

That perspective makes this conversation especially useful right now. Hugo is not talking about AI from the outside. He’s actively building with it, testing workflows, rethinking how teams operate, and trying to understand what still matters when software gets cheaper, faster, and easier to produce.

At the same time, he’s unusually honest about the human side of all this: the pressure of trying to stay adaptable without losing yourself in constant change.

In this episode, we get into scaling across markets, what bad management looks like before teams break, why AI gives speed for free, and why curiosity and builder mindset matter more than chasing every new tool.

🔗 Find Hugo on LinkedIn and Substack

Key takeaways

1️⃣ AI gives speed for free, not judgment

Hugo’s point is that AI now removes a lot of the friction around execution. You can translate faster, prototype faster, and build faster. That does not mean the thinking gets better automatically. You still need judgment around positioning, priorities and markets. Fast wrong decisions are still wrong decisions.

2️⃣ Expansion fails when you skip the local reality

Germany completely changed how EVBox approached expansion. The assumptions that worked elsewhere fell apart once regulation, buying behaviour, trust signals, and technical expectations changed. Hugo’s warning is that AI translation and automation now make it easier to enter markets quickly while still misunderstanding them completely.

3️⃣ Don’t mistake high performers for good leaders

One of the clearest leadership points in the episode was around promotion. Companies often take the best salesperson, operator, or contributor and turn them into a manager by default. Hugo’s experience showed that those are different skill sets. Some people are brilliant at doing the work and completely drained by leading people.

4️⃣ Relevance now comes from staying present

Hugo’s definition of relevance was one of the strongest parts of the conversation. He is less focused on chasing status or looking impressive from the outside and more focused on staying engaged with what is actually changing. That means protecting time to learn, experiment, build, think, write, and stay close to the real world instead of drifting into autopilot.

5️⃣ The builder mindset matters more than the tool

Hugo kept coming back to the same behaviour: build things, test things, try things. Not because every experiment becomes a business, but because building teaches you how the technology actually works. The people who stay curious and hands-on are going to adapt much faster than the ones waiting for perfect clarity before they start.

In this episode

00:00 Introduction to Hugo Pereira

02:07 Why career plans break faster now

05:01 What failed startups actually teach you

08:14 How EVBox scaled across Europe

11:06 Why Germany breaks expansion plans

14:09 Build an industry, not just a company

17:12 Why most scale-ups ruin their positioning

20:03 Stop asking marketing for more leads

23:18 What bad management looks like early

26:41 Why clarity matters more than trust

30:02 Stop promoting your best performer

33:14 Protect deep work before AI kills it

36:08 AI gives speed for free

39:27 The builder mindset is becoming essential

43:02 Why more people will build for themselves

47:18 AI is making companies leaner

52:11 Relevance means presence

Also mentioned in this episode:

• Hugo’s book, Teams In Hell: How To End Bad Management

• Hugo’s newsletter, The Fractional Dad

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

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

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

The old company breaks first

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

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

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

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

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

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

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

Headcount can lie

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

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

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

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

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

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

The job changes on you

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

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

Early heroes can turn blockers

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

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

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

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

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

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

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

That’s how loyalty becomes expensive.

Clarity becomes part of the work

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

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

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

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

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

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

The numbers get heavier

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

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

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

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

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

The job you wanted disappears

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

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

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

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

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

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

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

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

Scale is a different company

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

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

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

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

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

Millennial Masters

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

By Daniel Ionescu

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

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

AI search is reading your LinkedIn

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

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

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

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

The numbers have changed

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

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

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

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

AI is building a picture of you

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

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

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

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

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

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

LinkedIn’s own AI matters too

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

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

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

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

5 things to fix now

1️⃣ Treat your headline as a keyword field

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

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

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

3️⃣ Post original long-form content on your main topics

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

4️⃣ Post consistently on the same two or three subjects

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

5️⃣ Think beyond the profile page

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

The question has changed

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

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

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

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

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

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

The Link Tank™️

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

By Melanie Goodman

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

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

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

Urgency takes the week before strategy touches it

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

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

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

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

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

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

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

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

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

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

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

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

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

Here’s what that looks like in practice.

Protect the time first

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

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

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

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

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

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

See where your focus is

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

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

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

Track your attention

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

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

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

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

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

All The Margins

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

By Daphne Mavroudi-Chocholi

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

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

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

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

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

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

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

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

Understanding the difference matters before a crisis forces the issue.

The cut that looked rational

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

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

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

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

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

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

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

The damage shows up later

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

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

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

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

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

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

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

Not every cost is overhead

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

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

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

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

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

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

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

Where cuts bleed

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

Sales support and business development activity

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

Retention roles and people with institutional knowledge

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

Training and development spend

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

Delivery quality investments

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

Brand and trust-building activity

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

Ask these four questions first

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

Does this cost help us win work?

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

Does this cost help us keep work?

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

Does this cost protect trust, speed, or quality?

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

Would losing this cost show up later rather than now?

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

Start with the bank statement

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

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

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

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

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

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

The spreadsheet won’t warn you

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

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

Overhead can usually be reduced without damaging the engine.

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

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

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

The Profit Lens

By Nadia Codreanu

He’s also the author of Product Driven, a book about turning product thinking into real business growth.

That matters because Matt has lived close to the gap between making software and building a company people actually want.

His warning is especially useful now that AI has made product building look easier than ever. Shipping faster doesn’t solve the harder parts of entrepreneurship.

You still need to understand the customer, the market, the problem, the positioning, and why anyone should care enough to buy.

That’s where a lot of technical builders get stuck. They can make the thing, improve it, and keep adding to it, while the business problem sits untouched.

In this episode with , we get into why builders often struggle to become entrepreneurs, why product vision can’t be outsourced, and what still matters when AI makes the first version easier to create.

🔗 Find Matt on LinkedIn, Substack & YouTube

Key takeaways

1️⃣ Builders still have to sell

Matt’s warning is blunt. Being able to build the product doesn’t mean you know how to build the business. A lot of technical founders stay close to the work they enjoy and avoid the part that creates revenue. You can keep improving the product forever, but none of it matters if customers don’t understand it, want it, and pay for it.

2️⃣ AI makes hiding easier

AI has made building faster, cheaper, and more addictive. That creates a new trap for people who already like making things more than selling them. You can spend months prompting, shipping, tweaking, and still have no revenue. The danger is mistaking output for progress. At some point, the work has to leave the build stage and meet the market.

3️⃣ Product vision stays with you

If the idea lives in your head, the team will always be guessing. Matt’s point is that you can’t hand off the product vision and hope someone else turns it into the right thing. You have to keep explaining what and why it matters, and how each decision connects back to the customer. A quick video, a weekly note, or five minutes of clear feedback can save weeks of wasted work.

4️⃣ Your code will never stay perfect

Software doesn’t stay pristine for long. Teams change, tools change, standards move, and the next engineer will usually hate what came before. Matt’s point cuts through the builder’s obsession with perfect craft. The code matters, but the business can’t be built around the fantasy that everything you make will stay clean, loved, and untouched.

5️⃣ Entrepreneurship (and success) changes your circle

Matt described founder life as lonely because the problems get harder to explain to people outside the journey. You can still care about old friends, but they might not understand what your head is full of anymore. That’s why finding people who get the pressure matters. You need conversations where you don’t have to explain the whole context before getting to the real problem.

In this episode

00:00 Introduction to Matt Watson

02:57 The birth of VinSolutions

05:43 Growth, pressure, and early challenges

08:11 Why he decided to sell

10:19 The founder and CTO trap

12:49 Scaling and delegation problems

16:03 What AI changes in software development

18:01 From engineers to developers

19:42 Product Driven as a way of thinking

22:04 The changing role of product management

29:50 What technical debt actually does

36:50 Leadership inside development teams

44:52 From AI prototypes to scalable products

46:32 AI in prototyping and development

48:14 The code review problem

51:43 Building trust in business relationships

56:07 How exits affect personal relationships

01:00:37 What entrepreneurship takes out of you

The best AI business may already exist

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

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

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

Margin comes first

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

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

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

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

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

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

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

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

Hiring gets pushed back

Once margin improves, the next shift is headcount.

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

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

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

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

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

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

Judgement still decides

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

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

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

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

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

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

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

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

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

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

Product is easier, winning isn’t

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

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

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

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

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

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

The graveyard has money in it

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

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

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

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

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

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

The premium moves elsewhere

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

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

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

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

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

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

Related conversations:

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

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

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

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

Your behaviour trains the team

Low-trust leadership usually shows up fast.

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

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

Trust is set from the top

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

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

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

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

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

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

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

Where trust is built

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

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

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

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

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

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

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

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

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

But empathy on its own is still not enough.

Behaviour teaches the team

That’s what emotional intelligence looks like in practice.

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

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

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

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

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

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

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

From the start, he wanted the business to become sellable. That forced a different kind of thinking around hiring, delivery, and how much still depended on him.

One of the clearest lessons from his exit is that founders often hire help when they really need ownership. A helper takes tasks off your plate. An owner takes responsibility for an outcome.

This difference changes how the business grows, how much pressure stays with the founder, and whether the company can ever run properly without you in the middle.

In this episode, we get into building a business that someone would actually want to buy, why your first hires shape the standard, and how to stop slowing the company down without realising it.

🔗 Find Gavin on LinkedIn and his website

Key takeaways

1️⃣ Hire owners, not helpers

A helper takes tasks off your plate, but you can still end up managing every detail. Gavin’s point is that the better hire owns a whole part of the business. That could be client delivery, content, sales, or any other area that keeps pulling you back in. If you want the company to grow without you sitting in the middle, you need people who can carry outcomes, not wait for instructions.

2️⃣ Systems make the business sellable

Yatter became easier to sell because it didn’t depend on Gavin doing everything himself. He knew when each person was close to capacity, when to start hiring, and how the work moved through the agency. That matters because a buyer doesn’t just want revenue. They want proof the business can keep working when the founder leaves.

3️⃣ Doing the job first teaches you the standard

Gavin knew how many clients an account manager could handle because he’d done the work himself. That gave him a clearer sense of capacity, quality, and when the business needed another hire. Founders can delegate better when they understand what good looks like. Without that, it’s much harder to know whether someone is owning the work properly or just keeping busy.

4️⃣ Stepping back too late creates dependency

Gavin admitted his first version of delegation involved checking too much. It made sense at the time, but it also taught people to keep coming back for approval. That’s the risk when the founder stays too close for too long. You think you’re protecting quality, but the team learns that nothing is really finished until you’ve looked at it.

5️⃣ Build around the life you actually want

After selling Yatter, Gavin realised he didn’t want to build another client-facing agency. He wanted a business that matched the kind of work he actually enjoys. That’s a useful filter for any founder starting again. The business model is not just about market size or revenue potential. It also decides what your days feel like once the company starts working.

In this episode

00:00 Intro to Gavin Bell

01:46 From fitness to Facebook ads

03:59 The scaling problems that showed up early

06:57 Why he rebranded and built Yatter

09:55 What the early Yatter years taught him

12:34 Delegation, trust, and building a team

15:20 Systemising delivery inside the agency

17:48 How the acquisition process unfolded

20:27 What changed in advertising over time

22:39 AI, personalisation, and the future of ads

26:14 The downside of hyper-personalised advertising

33:41 Where social media and AI go next

37:17 What he learned from building and exiting

41:08 Starting a new venture in healthcare

46:52 Personal brand and why it matters

52:13 Building a business that works without you

57:30 How AI fits into business operations

📚 Gavin’s book recommendations

Built to Sell by John Warrillow — Gavin’s pick for understanding how to turn a service business into something someone would actually want to buy. It follows an agency owner who pulls himself out of delivery and makes the client process repeatable. Gavin says it basically became the blueprint for Yatter.

The E-Myth Revisited by Michael E. Gerber — The book that got Gavin thinking properly about systems and processes. It shaped how he built Yatter from day one, and those systems became part of why the business sold. Useful for any founder who’s still too central to how the company runs.

Related conversations:

Don’t miss these insights:

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

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

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

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

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

Gary Das realised he was the problem.

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

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

You stay too central

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

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

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

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

Standards are just a cover story

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

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

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

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

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

You teach the wrong habit

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

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

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

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

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

The role changed, you didn’t

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

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

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

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

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

Then you resent the business

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

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

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

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

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

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

When pushing harder stops working

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

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

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

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

What changes when you step back properly

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

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

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

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

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

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

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

Related conversations:

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

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

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

The real skill is choosing what to build

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

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

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

The filter is gone

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

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

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

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

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

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

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

Exploring is not building

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

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

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

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

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

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

A simple way to do it

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

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

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

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

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

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

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

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

The trap you need to see coming

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

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

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

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

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

The real advantage now

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

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

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

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

Alpha Bytes

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

By Barry Winata

He works closely with companies going through the process, and the pattern is consistent. Founders focus on the pitch, the deck, the story they want to tell. Investors are reading something else entirely.

The decision starts forming long before the meeting. Your traction, your positioning, how clearly you explain the problem, how you show up in the market. Those signals carry more weight than any polished slide.

That’s where a lot of founders get caught out. They treat fundraising like a moment instead of a process, and by the time they’re pitching, most of the work that actually matters has already happened or hasn’t.

In this episode, we get into how investors really make decisions, why fundraising is closer to sales than storytelling, when not to raise, and how to build the kind of trust that makes people want to back you before you even ask.

🔗 Find James on LinkedIn

📙 James is offering Millennial Masters readers his best selling book, The Investable Entrepreneur, free of charge via his website.

Key takeaways

1️⃣ Fundraising starts before you ask for money

By the time you’re in the room, most of the decision is already forming. Investors are looking at how you show up, what you’ve built, and whether the story holds together outside the pitch. If your positioning is unclear or the signals are weak, no deck fixes that. The real work happens earlier, in how the business presents itself over time.

2️⃣ A pitch doesn’t carry as much weight as you think

Founders spend weeks polishing slides and rehearsing answers. It helps, but it’s not what tips the decision. Investors are checking whether the business makes sense on its own terms. If the numbers, traction, and narrative don’t line up, a clean pitch just makes the gap easier to spot.

3️⃣ Fundraising is closer to sales than storytelling

You’re not just explaining an idea, you’re helping someone get comfortable making a decision. That means understanding what they care about, what risk looks like from their side, and how your business fits into that. Founders who treat fundraising like a performance often miss the commercial side of what’s actually happening.

4️⃣ Trust builds long before the deal

People back founders they believe can figure things out. That comes from how you think, how you communicate, and how consistently you show up over time. It’s built in conversations, updates, and the way you handle pressure, not just in one meeting. When that trust is there, the process moves differently.

5️⃣ Not every business should raise

James is clear that funding only makes sense if you’re building for a much bigger outcome. If the business is really a lifestyle business, or you only need money because cashflow is tight, investment can be the wrong answer. A smaller, profitable company may give you more control and a better life than forcing yourself into a venture-scale path.

In this episode

00:00 Introduction to James Church

02:32 From graphic design to investment consulting

05:25 Understanding the high-performance founder

12:41 The art of investor engagement

17:12 The journey of fundraising

23:38 Timing your fundraising efforts

35:51 Overcoming shyness and building confidence

44:06 Networking and using existing connections

49:33 Understanding angel investors and their expectations

52:49 Navigating dilution and equity distribution

01:02:41 When not to raise funds

⚡️ More startups & fundraising: