Inspired by New York Mag’s recent 247 things you should know as a New Yorker, we thought we’d compile our own version for you.

On measurement

1. ROAS is a vanity metric in a profitability costume.

2. Contribution margin per session tells you more about a DTC brand’s health than ROAS ever will. CM3 = revenue minus cost of goods, minus fulfilment, minus paid media spend.

3. Blended ROAS conceals whether you’re winning or subsidising losses with organic.

4. If you’re optimising for ROAS, you are not optimising for incrementality.

5. MER (marketing efficiency ratio) is blended ROAS with a different name and the same limitations.

On Meta

6. Meta is a scale channel, not an efficiency channel.

7. CTR does not predict CPA.

8. Thumbstop rate does not predict CPA.

9. If you’re not running awareness spend, your conversion campaigns are doing the work of warming cold audiences and paying conversion CPMs to do it.

10. You should evaluate awareness investment after a minimum of three months. Judging it on last-click in week two is a category error.

11. The 45+ demographic is cheaper to reach and often has higher AOV and LTV. Most brands under £5m ignore it because their founders are 28.

12. Logos and CTAs in ads actually perform better on average, which is annoying if you built your whole brand around looking “authentic.”

13. Static ads frequently beat video because the message lands before the thumb moves.

14. The biggest mistake in DPA setup is combining incompatible product sets under one campaign.

15. I’ve seen accounts with 1x campaign, 1x ad set scale to £350k/month.

On creative

16. Jobs-to-be-done research starts not with what people buy, but with what they were doing before they searched.

17. The four forces of progress are: push, pull, anxiety, and habit. The most important two for paid social are anxiety and habit.

18. 1 star reviews are more valuable than 5 star reviews.

19. “I’ve tried everything” is not a customer complaint. It’s a hook.

20. UGC can be scripted. It only fails with bad copywriting or acting.

21. Yappers, talking-head videos of real people just talking, are quietly the top-performing format in several DTC categories.

22. When talking to customers always preface anxieties with “apart from price, what....”

23. Your brand tagline is almost certainly your worst-performing ad headline. Taglines are written for boardrooms, not for the scroll.

24. Review mining at scale is most efficiently done with AI, if you give it the right taxonomy first.

25. Creative fatigue isn’t as simple as many let on. Turn old ads back on. Go on.

26. Testimonial ads work because they remove anxiety rather than add aspiration.

27. “You put olive oil on that?” is a great hook because it assumes knowledge the viewer is compelled to validate. Use your customers’ language.

On strategy

28. Full-funnel advertising lowers CPMs on conversion campaigns. It’s not just brand investment, it’s efficiency investment.

29. At every growth stage, ask: what’s the single constraint that, if removed, unlocks the next order of magnitude?

30. “Diversify away from Meta” is advice given by those who don’t understand most consumer ecom/DTC.

31. Be aware of LTV debt: don’t get to £10m/year with the same LTV you had at £1m.

32. At a certain stage, you have to accept your natural CPA. If it doesn’t work, you don’t have product-market fit.

33. Incrementality testing is the closest thing we have to answering “would these customers have bought anyway?”

34. If your gross margin is under 50%, paid acquisition is extremely difficult to make profitable after fulfilment and media costs.

35. Track profit per employee. It forces you to confront whether each hire is actually driving margin or just making the org chart look right.

On experiments

36. Most creative testing on Meta is flawed because the algorithm redistributes spend within ad sets based on early signals. You’re not testing creative in isolation, you’re testing what Meta chooses to spend on.

37. A creative test needs roughly 20 purchases before you can call a result with any confidence.

38. 80% net new creative, 20% iteration.

39. Media buying experiments need a month, not 5 days.

40. Experiment velocity matters more than confidence per experiment, at early stage especially.

41. Statistical significance is misused in most DTC contexts because sample sizes are too small and observation windows too short.
    

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On AI and what it actually changes

42. AI lets a growth team produce the creative volume of a team three times its size. The advantage isn’t cost reduction, it’s test velocity.

43. AI video works best when it’s either immediately obvious, or not noticeable at all.

44. The gap between “someone who uses AI” and “someone who’s AI-native” is whether AI is the workflow or adjacent to it.

45. Learn to build with AI coding tools now. The growth people who can spin up custom dashboards, scrapers, and analysis tools without waiting for engineering will have a compounding advantage for the next two to three years.

46. AI fluency is a temporary advantage. As tools become easier and more embedded, the differentiator will shift back to judgement, taste, and domain knowledge.

47. “AI-native” will be a redundant phrase by 2028, for the same reason “internet-native business” became redundant. It’ll just be what you are.

On data

48. A blended CPA of £24 means nothing without knowing what the customer bought and whether they came back.

49. Power analysis before an incrementality test tells you whether your test cells are large enough to detect the effect you actually care about.

50. Most “data-driven” decisions in marketing are intuition-driven with data bolted on afterwards.

51. Invest in your data ETL and warehouse.

On category dynamics

52. Health and wellness DTC brands have a structural advantage in creative because pain is a universal hook.

53. Visual DTC (fashion etc) leans into huge volumes of different creator/people so someone can recognise themself somewhere.

54. There are no hard categories, just people who ignore the rules of Meta.

55. If you can get your F&B work DTC alone, then your retail will fly.

56. The 45+ female demographic is the most underserved audience on Meta and often has the highest LTV.

57. Whether you’re dtc or retail-first, don’t add a second sales channel until £5m of revenue.

On the industry

59. The claim that “native beats brand” in creative is true on average, but not exclusively. You need a mix.

60. Video receives roughly 2x the budget of statics across most accounts. Statics often convert better.

61. AI-generated ads are a volume lever, not a creative strategy.

62. The reason most performance agencies don’t evolve into strategy firms is that strategy is harder to sell and slower to prove.

63. The biggest structural shift in DTC since 2020 has been the collapse of reliable last-click attribution as iOS changed everything.
    

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On growth stage

64. From £0 to £1m, the constraint is almost always creative and targeting, finding what works.

65. From £1m to £5m, the constraint shifts to operational capacity and repeatable testing processes.

66. From £5m to £10m, the bottleneck is usually unit economics and whether the model holds at scale.

67. The moment to introduce a full-funnel strategy is when you can see CPMs rising on conversion campaigns without a corresponding CPA improvement.

68. A brand that can’t survive a three-month Meta creative drought doesn’t have a sustainable acquisition model. It has a hot streak.

69. Email and SMS matter most in the £1m to £5m revenue band, where repeat purchase rate determines whether the model is profitable.

On psychology and persuasion

70. Social proof works differently at the top and bottom of funnel. At the top you’re reducing scepticism. At the bottom you’re removing the last reason not to buy.

71. Fear-based creative underperforms in beauty and lifestyle categories where aspiration is the primary purchase driver.

72. Anxiety (one of the four forces of progress) is reduced most effectively in creative by social proof and specificity, not reassurance statements.

73. The “exhaustion arc” customer story works because it validates every previous failed attempt before offering a solution.

74. Reviews from the 45+ demographic convert better because they tend to be specific, context-rich, and written by people who’ve already tried everything else.

On what most people get wrong

75. Most DTC founders underinvest in creative volume and overinvest in creative polish.

76. Most performance agencies optimise for reported ROAS because it’s easier to present than contribution margin.

77. Most creative testing is actually budget testing masquerading as creative testing.

78. Most retention strategies are just email sequences with a better subject line. Not a repeat purchase model.

79. Most people who claim to understand LTV don’t understand the margin at which that LTV was generated.

80. Most agency pitches fail because they answer “why us” instead of “why now, and why does this matter to you specifically.”

On what almost nobody knows

81. Cost per reach going up while CVR also goes up is not a contradiction. It means Meta is spending into higher-value audiences.

82. A brand with a 3.5x ROAS might be losing money on every customer if COGS, fulfilment, and variable costs are high enough.

83. The incrementality of branded search is lower than most brands assume. Many of those clicks would have happened anyway. Assume it’s 0.1x.

84. “Meta’s algorithm has gotten smarter” usually means “we got better at brief writing and structure.” The credit rarely belongs entirely to the platform.

85. The measurement problem that MMM still can’t solve well is real-time responsiveness. It’s a retrospective tool used as a planning tool.

On writing and content

86. The most shared pieces of content make the reader feel smart, not informed, smart.

87. Contrarian takes work best when the conventional wisdom you’re attacking is genuinely prevalent, not a straw man.

88. The difference between Grade 7 writing and Grade 12 writing is that Grade 7 says what it means on the first read.

89. The best hook for a founder-vulnerability post opens in the middle of the failure, not with context.

90. The best Substack growth lever is not frequency. It’s having a clear reason to exist that nobody else fills.

On how growth teams work in 2026

91. Every growth team is now somewhere on a spectrum from “we use AI occasionally” to “we wouldn’t function without it.” The gap is widening fast.

92. The growth team that treats creative as a hypothesis rather than a deliverable will outpace the one that treats it as a production output.

93. “Move fast and break things” was a software company mantra applied to marketing by people who didn’t understand the difference. You can’t A/B test a brand perception.

94. The thing most growth teams under-budget for is time to think. The calendar fills with execution and the strategy atrophies.

On how consumer brands work in 2026

95. The brands that tried to be “authentic” by mimicking UGC without the actual user part have mostly been found out.

96. Founder-led content performs better than brand content for most sub-£20m DTC companies. Not because it’s personal, but because it’s legible, you know who’s speaking.

97. Most brands building “communities” are actually building email lists with extra steps.

98. The Dyson problem: great product, brutal creative. The reverse exists too. Creative that oversells a product is worse than creative that undersells it, because the reviews will eventually kill you.

99. The brands winning on TikTok in 2026 are mostly winning because someone in the building is genuinely interested in making content, not because they cracked an algorithm.

On what it actually feels like to work in growth right now

100. The feeling of having a great creative test result land on a Friday is still one of the best things about this job. It hasn’t been automated yet.

101. The worst thing about performance marketing culture is the tendency to confuse busyness with rigour.

102. Lean is the new default, and that’s going to require a mindset shift for psychological comfort.

103. The most underrated skill in growth is writing. Not copywriting. Writing, the ability to explain what you’re doing and why to someone who isn’t in the room.

104. The growth meeting that never ends is the one where nobody has established what decision needs to be made by the end of it.

105. Context switching is the new norm, and AI only accelerates that.
     

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On the agency relationship

106. The best agencies act like lawyers, not yes people

107. At the moment AI hasn’t helped with cost.

108. Your agency should challenge your thinking all the time.

109. Agencies get you access to usually over £1m of head count per account. That’s irreplaceable in-house.

On the craft

109. Writing a brief forces you to know whether you actually understand the customer. If you can’t write it, you don’t.

110. Best practice” in performance marketing has a shorter half-life than most people admit. What worked in 2025 is often actively wrong in 2026.

111. Spending two hours doing customer interviews is usually worth more than any AI assisted research.

On things that are genuinely changing in 2026

113. Creative production that used to take a week now takes a day. The time saved is not being reinvested in thinking, in most teams.

114. Incrementality is finally getting the attention it always deserved.

115. The post-cookie world turned out to be fine for everyone who wasn’t entirely dependent on third-party data. That group was larger than expected.

116. Growth people are becoming engineers.

117. The brands winning on YouTube in 2026 figured out that long attention and short attention require fundamentally different creative approaches, and stopped applying the same creative to both.

118. Subscription models in DTC turned out to be harder than they looked when every brand launched one in 2020-2022. The ones that work have genuine habitual use cases.

119. Some brands are realising they have audiences, not communities, and that’s fine.

On things that haven’t changed

120. Meta advertising is advertising.

121. Advertising has to sell.

122. Simplicity beats complexity.

123. Operating at 100% capacity creates a fragile business, build in space to crack.
     

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On the wider world that growth operates in

124. The cost of living crisis changed consumer behaviour more than most teams have updated their models to reflect.

125. The brands winning in grocery and everyday essentials are the ones that made the implicit trade-off explicit: this is cheaper, or this is better, pick one.

126. The environmental claims conversation has become complicated enough that most brands have quietly retreated from it. The ones with genuine credentials now have more white space.

127. Gen Z’s relationship with brands is not what most brand strategists predicted. They’re not inherently anti-brand. They’re just better at detecting nonsense.

128. The resurgence of in-person retail is real and most DTC brands still treat it as secondary. The ones blending both well are building the most defensible positions.

129. Social commerce is still not quite the thing it kept being predicted to be in the UK. The infrastructure isn’t there yet.

130. The creator economy has matured. The hobbyists have mostly dropped out. What’s left is more professional, more expensive, and more effective when briefed well.

131. Economic uncertainty has lengthened consumer consideration cycles in discretionary categories. Most brands haven’t updated their channel mix to reflect this.

On the culture around the job

132. Most growth Twitter/X is people restating what worked for them as universal law. Read it, but hold it loosely.

133. “Founder mode” became a phrase that was used to justify almost anything. The useful version of it is simple: stay close to the work.

134. The tendency of growth people to dress up industry gossip as market insight is a known failure mode. Name the pattern so you can catch yourself doing it.

And finally

135. The best DTC brand you know is probably one you heard about from a friend, not an ad. That’s still the industry’s most important unsolved problem.

136. The teams that ran the best experiments in the last year are the ones that wrote down what they expected to happen before they ran them.

137. The best briefs are written in the customer’s language, not the strategist’s.

138. Every successful DTC brand has a moment when a single creative unlocked scale. The lesson is not to try to recreate that creative. It’s to build the system that finds it again.

139. The teams most resistant to AI adoption are often the most skilled at their current way of working. Capability and change readiness are different things. This is the individual’s version of the Innovator’s Dilemma.

140. The growth industry in 2026 is better at measurement, better at structure, and still not great at the thing that actually drives results: making things people find interesting.

141. The question a founder should ask before hiring any performance agency: “Can you show me a client where you improved margin, not just ROAS?”

142. The question that reveals more about a brand’s health than any dashboard: “What’s your repeat purchase rate at 90 days?”

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Meta is shipping fast at the moment. When I initially wrote this, the latest ability to push spend to specific ads hadn’t been shipped. As I rewrite this, yet another new feature is launching. This is some of the most useful and fast-paced product development in Ads Manager we’ve seen yet.

Late last year, Meta started testing a ‘creative testing tool’ where you can launch five ads inside an ad set without disrupting learning. Five ads for most accounts is limited, but when in conjunction with the creative testing tool, it’s potentially a way to run accounts.

We’ve been testing these for the last few months across a series of our accounts. We’ve done a mixture of isolated AB tests as well as before and after experiments.

Here’s what we’ve learnt so far this year, and what we’re going to be doing with it.

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Flexible ad units: the promise vs the reality

The beginning of last year, we won an early-stage client who had one singular flexible ad that was working. It contained 10 separate, distinctly different ads within it.

One of the first moves we made was to move away from the flexible ad unit.

Our methodology is strongly rooted in creative testing, learning and iteration. We are intentional about creative tests, approach them strategically, and use every week’s experiment to inform follow-ons.

So for us, flexible ad units remove information that allows us to learn.

With that client, we were never able to find product-channel fit for the client. We eventually re-tested the flexible ad unit in case there was some magic in the delivery of it itself, but it never rebounded. For a long time we were confident in our decision.

Then late last year (2025), I saw rumblings on X about the unit. Some media buyers were claiming strong performance from these units over and above typical setups. The 99th percentile Consolidationists would argue that the more diversity you can fit into individual units the better. And lots of belated-Andromeda hype all pointed to the valuelessness of variants within individual units.

Throw into the mix the new creative testing tool, and it felt like it was time to revisit.

We started running a series of tests. Some before/after, and then a couple of stricter tests.

Flexible ad units: A/B test results

We ran two isolated AB tests using Meta’s testing tool. In each example, we ran the same setup.

• BAU contained 3-5 variants of an ad

• Flex ads contained a single ad unit with each of those variants within it

We ran this for a week.

The outcome in both tests was stark: the flexible ad unit variant ran at double the CPA of our BAU.

This was an annoying one for us. Had the flex unit won, this would have hugely increased our capacity for ad volume in a consolidated way.

As it stands, we have two very similar test results. With anecdotal before and after results showing similar things.

Summary: we’re derprioritising flexible ad units

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Meta’s creative testing tool: good for the wrong reasons

The other tool, which we first ran tests on in October 2025, is different.

The concept here is that you can run creative testing inside your main hero campaign and ad set, rather than inside a separate setup.

Most accounts these days are running some form of setup that encompasses: (1) dedicated testing, (2) scale/bau/hero setup. We are the same.

This new tool means being able to test inside those heroes without disrupting performance.

You force spend into them, and at the end of the tests, they can then scale.

The logic here is good: you get benefit of not having to disrupt something that’s working, and they build data in the place where they will live.

These have limits. You’re capped at five launches per week, which given we like to variant test still, means that we’re limited by what we can launch.

We’ve been testing this now in a series of accounts. Here’s what we’ve found.

Test-week performance is misleading

During the test week, almost every concept looks like a loser. CPAs during the learning phase were consistently 20-60% higher than the business-as-usual benchmark.

Depending on client stage, we expect to see a 20-40% win rate defined as concepts producing CPAs that are the same or better than the account average.

In these tests, no launches saw winning ads. Now an individual week doesn’t prove anything, nor does a single client experience. But we have now looked at this across a series of accounts, as well as across multiple weeks. Repeatedly we see the same things, win rate is about 6% rather than the usual 20-40%.

Note: we’ve not run explicit AB tests here where we ran usual creative testing structure alongside this setup. Given complexity and cost of isolating that test with audiences, we opted against it in favour of faster learning.

If you treated these on the test-week alone, we’d have binned all the ads.

But we decided to keep ads live to see what would happen afterwards, and this is where it got really interesting.

Post-test-week performance improved almost across the board.

One ad that had 60% higher CPA during test week graduated and had a CPA 40% below the account average.

Not only was the test week not a good indicator for CPA, nor did it indicate what would happen with scale.

Here was activity from one campaign we saw.

Note: numbers have been anonymised but % differences remain the same.

During test week, Concept A was one of the rare concepts that beat the test setup.

B,C, and D were all higher and what we would have deemed fails.

And yet in the post-test period, we saw:

• Concept A scale with better CPA than the account, as we would hope for such a definitive winner.

• Concept D, previously a marginal loser, scaled to about half of Concept A’s growth, but with an improving CPA.

• Concepts B and C both remained fails, and were ads we ended up turning off.

In other account tests, we saw more results like concept D: those with CPAs that were higher than account CPAs, which in usual structures we’d deem losers, becoming winners afterwards.

Creative testing tool is great for forcing spend

Our takeaway when we’ve round up internally is that the creative testing tool isn’t an effective way to test creative, but it is an effective way to force spend and give ads a head start in a hero campaign.

And so there are instances now where we’re looking to use this setup with this in mind.

A few examples:

1. Where overall account spend is low and running isolated tests is expensive

2. Accounts with high CPA where we use less correlative higher funnel events for testing, as a means of getting spend into a conversion-asset

3. Instances where certain ad sets have high spending, low performing ads that just can’t be shaken

The testing tool itself still helps identify rocketships – they seem to win regardless – but the lions share of winners don’t get picked up.

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Meta update: March 2026 – push delivery to an ad

As I write this (Friday 27th), Meta has this week rolled out a new feature allowing you to force spend to individual ads inside the hero.

Our current state with creative testing tool is essentially a way to force spend into a hero campaign to new assets before we see how they truly behave.

With this new launch in mind, it’s likely we’ll use the creative testing tool less and instead move to this new feature.

We are rolling out tests across the agency over the next two weeks and will be testing a variety of setups. Key things we want to understand:

• How much spend to force in

• Whether ads should be tested first

• Does this differ depending on creative testing method

If you’ve been playing with the new tool, I’d love to understand your setups.

Leave a comment as I’d love to hear how you’ve been finding these features.

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Josh Lachkovic is the founder of Ballpoint, a creative growth agency for DTC and ecommerce brands. Subscribe to Early Stage Growth for weekly insights on growth, creative strategy, and experimentation.

As with my last visit, I was blown away by the volume of new, challenger brands in US retail. In the UK, we see the ‘posher’ supermarkets take bets on small indies. And there are maybe half a dozen excellent but tiny shops in London that stock almost exclusively challenger brands. But our shelves might still be 80% big Consumer, and 20% shared between half a dozen entrants in each category.

The US is a different beast altogether. Go into Erewhon, and it’s the size of our supermarkets with the selection of our tiniest independents. Whole Foods shares shelf space with Big Consumer and small indies. But even your typical neighbourhood grocery store stocks a strong selection of up and coming challenger brands.

I’ve visited the US four times in the last four years, and this only seems to be accelerating. It was while I was in the US this time, however, that this all started to signal something else: slowly, somewhere, we’ve seen the death of the hipster.

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What is a hipster?

In 2007, Christian Lorentzen wrote in Time Out New York that

”hipsterism fetishizes the authentic”

Hipster has had many definitions over the years. In the UK in the mid-2000s, it was used interchangeably to describe a cross section of music scene kids (indie, rock, punk). Later it was heavily associated with handlebar moustaches, coffee-drinkers and cyclists. And by proxy of where they associate (Shoreditch, Williamsburg), for a while it was a proxy for the pejorative ‘gentrifier.’

Hipster didn’t begin with Lorentzen. Its earliest etymology goes back to the 1940s jazz scene, through Norman Mailer’s 1957 essay “The White Negro” and into the counterculture that followed. But it’s the ‘pursuit of the authentic’ that is the definition I’ve always returned to.

I think about a couple of relative shopping experiences between London and Los Angeles.

In Los Feliz, LA, I visited a shop called ‘General Store’. We have a ‘General Store’ in Peckham, LDN too and so we went to see how they compared.

Our Peckham General Store sells great coffee, locally made sourdough and fresh pastries, wild garlic foraged in the rural home counties, fruit and veg via Natoora but with the implication of being direct-from-farmer. It stocks a tiny selection of natural wines, and a very curated selection of brands.

The brands it stocks are all intentional. The merchandising policy seems to be:

• never stock more than one or two products per category,

• never stock brands that are available on the mass market,

• only stock brands that come from specific locations where that product is seen as the best

You might for example have a type of nougat from a very specific village in Tuscany.

In many respects, this is our hipster pursuit of the authentic.

Los Feliz General Store shared some initial similarities. Head in and the smell of great coffee, and fresh, locally sourced pastries fills the air as well. But this is where the similarities end.

Los Feliz General Store now stocks dozens if not hundreds of new, independent challenger brands. Some, like DTC darling Grüns, I recognise, but the vast, vast majority are all new to me.

I’m like a kid in a candy store and want to try so many of these products. Gummies for every ailment, candles which promise to bring the scent of California home with me, aftershave brands that ooze cool, protein in all manner of forms.

And the selection in this General Store, like the London equivalent, is at the narrower end of the spectrum. 10 minutes down the road in Erewhon, and there’s half a dozen choices of creatine to choose from, another dozen multi-vitamins, crackers and chocolates that depending on your need come packed full of protein, fibre, or carbohydrates, and 30+ different waters.

I’m reminded of a conversation I had with an angel investor when I was running my last business Wine List. We were an education wine play where the typical desired outcome was our consumer would understand wine a bit better and begin their own ‘wine journey.’ It was Lorentzen’s Hipster by definition. What better way to pursue the authentic than find individual winemakers who have made something that tastes uniquely of their own land?

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Said angel investor recommended I build a wine DTC: try and build the definable wine brand for the future generations, like how Jacob’s Creek or 19 Crimes had beforehand. If you get that right, that’s one of the few ways to make a lot of money in wine, but it was a completely different game to the one we were playing.

Some of the UK-US differences are innate cultural ones. The UK remains a country default-cynical towards consumerism.

But in many ways, lots of US culture eventually finds its way to the UK market. And so when I look at the breadth of brands that the US has on offer, I imagine we will one day share at least some of that breadth as well.

Part of why this shift has happened is practical. DTC and social media have made it radically cheaper to launch a brand. You no longer need a decade of trade distribution to earn shelf space. But I think the deeper reason is that the consumer has changed. The generation buying these products doesn’t find identity in provenance the way an earlier cohort did. They find it in optimisation, in function, in lifestyle as a project. We now find brand identity in creatine, rather than PDO tomatoes.

And maybe that’s the real death knell for the hipster. From San Francisco to Los Angeles, the bougie independent shopper is no longer pursuing the authentic. They’re pursuing what works, what’s new, what fits the life they’re building. Functional lifestyle is the new hipster, and the shelf space proves it.

For challenger brands, particularly UK ones looking at the US market, this matters. Origin stories and craft credentials are at best the minimum entry requirement, and by no means a USP.

Function and lifestyle are key. Strong visual identity that stands out and resonates is vital.

These are observations, not conclusions. The US is further along this curve than we are, and the UK’s natural scepticism towards consumerism means it won’t land here in the same way. But the direction of travel seems clear. If you’re building a brand in the UK and planning to scale, it’s worth asking: are you still selling the authentic, or are you selling the functional? Because the American consumer already knows which one they’re reaching for.

I’m building Ballpoint: the growth agency I always wanted to hire when I was a DTC founder and before that a head of growth. We have scale brands from £1m → £50m through digital advertising. If you’re looking for support, then you can email me on josh@weareballpoint.com.

In the meantime, if you enjoyed this please consider giving it a like, a comment or forwarding it to a friend.

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“The way I interact with a computer has completely changed forever.”
– Mark Holland, our Head of Creative Strategy

Something changed over the last month. I and another colleague have become AI-native.

My colleague Mark, our head of creative strategy, told the team at Friday Beers & Cheers “the way I interact with a computer has completely changed forever.”

As soon as he said it I realised it was the framing I’d been mulling on for a week or two.

It’s statements like this that start to turn heads away. You risk sounding like a hype merchant or a peddler of NFTs. But this time it does feel true.

By this stage I’ve heard a lot of people reference Matt Shumer’s essay Something Big Is Happening1. The intro lands the plane:

Think back to February 2020.

If you were paying close attention, you might have noticed a few people talking about a virus spreading overseas. But most of us weren’t paying close attention. The stock market was doing great, your kids were in school, you were going to restaurants and shaking hands and planning trips. If someone told you they were stockpiling toilet paper you would have thought they’d been spending too much time on a weird corner of the internet. Then, over the course of about three weeks, the entire world changed. Your office closed, your kids came home, and life rearranged itself into something you wouldn’t have believed if you’d described it to yourself a month earlier.

I think we’re in the “this seems overblown” phase of something much, much bigger than Covid.

This is a bit of a longer read (12 minutes or so) than usual and so am giving you a bit of a breakdown of what to expect here:

• The job market today

  • Defining AI native

• I think we’re months away from AI being able to do most of our work

  • Creating a handover podcast for a client

  • Identifying product focus based on retention, LTV and cross-sells

• The new skills we need to learn

  • Taste, multi-disciplinary learning, and critical thinking

  • Product & engineering

• What I think this means

  • What this means for agencies and growth (and by that token Ballpoint)

  • What this means for employees

• Closing thoughts

Anything shared at this stage is really a reflection of current understanding. And so more so than anything else I encourage all of you to research this yourself and form your own viewpoint.

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The job market today

A lot of this conversation can often be too abstract. And so today I want to really go through specifics for our industry.

First let’s consider the current state of the employment market.

The IPA release the annual Agency Census2 data in February. Key insights from it:

1. Employment in creative and non-media agencies is down 14.3%

2. Overall agency headcount is down 6.7%

3. Part time headcount is down 9%

4. Under 25s is down 19.2%

5. Open job roles decreased 41% down to 680

6. Graduate recruitment is 56% down

7. 24% of agencies expect to reduce workforce this year due to AI

Jack Dorsey just reduced headcount at Block by 4,000 down to 6,000. Block haven’t replaced those jobs yet he just knows its coming and is pre-empting.

This pattern is being seen across the wider economy. Job vacancies are down and layoffs are happening, in large part, due to the expected impact of AI rather than its current state. And as the Economist reported last week, we have yet to see the AI impact in macroeconomic data on productivity3.

The current wave of job cuts, when AI is referenced, for now seem to be excuses.

And you can tell this because when you talk to people in different industries, when you hear how the economy is still working, you know that the world is not yet AI-native.

Defining “AI-native” or “AI first”

I’ve been mulling on the idea of what being AI-first means or being AI-native. I see this as the following:

AI-Native. adjective.
Definition: 'Someone whose starting point for problem solving is giving the job to AI, rather than asking AI to help them with their own solving of that task.’

For the last month, I’ve been spending at least two-to-three hours a day, seven days a week experimenting with AI. I’m already blown away.

AI has been able to either (A) dramatically reduce time of a task, or (B) do something to a level we never would have been able to do without.

But importantly my default is not ‘ask how AI can help’ but ‘instruct AI to do the job.’

So what does this look like today?

We’re less than a year away from AI being able to do up to 90% of the current jobs

When I think about the strategic and executional work we to do in growth, performance marketing, and as an agency today, I think we’re months away from AI being able to do all of it.

The more I’ve used Claude Code, the more I’ve seen how much it can do. And I don’t think it’s there yet, but the leap from today until that point now feels measurable. It also may be something we can already achieve, if we put enough work into it.

I imagine within the next few months, if not by the end of the year, we’ll be in a place where almost all the current types of work that performance marketing entails will be significantly improved by AI.

I imagine the time required will go down by maybe 25-90% depending on the task itself, and capacity for what’s possible will increase infinitely.

But where are we today?

Here are a couple of specific use cases I’ve played around with recently.

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Creating a handover podcast for a client

Background
We were migrating one client from one growth strategist to another. This is a process that historically requires the departing growth strategist complete a handover.

The old way

• Sitting down and from memory writing out what the last 3, 6, 12 months looked like

• Rewatching recordings of the quarterly/half-yearly reviews or the ‘Bigger’ meetings like strategy resets, and adding more specific notes from those to the handover

• Doing a load of fresh long term data analysis, getting charts showing monthly performance for the last year

• Taking screenshots from reports and dashboards

• Doing a summary of key players and providing verbal background to it

• Summarising creative wins

• Sitting down and drafting a long memo or deck

• Sharing the deck. Getting the person to read it

• Meeting to discuss

Done well, it’s probably 8-16 hours of work for the person creating the document.

Anyone looking at this methodology knows it’s flawed.

Humans are choc-a-block full of biases. Our brains prioritise and store information relative to those biases. And so we will inherently leave so much context out during this process.

The AI-native way

Flipped around, I wondered if there was a better way to solve this.

I’m a huge fan of the Acquired podcast series: an entire business story including an investment thesis over a four-hour period. There’s a strong narrative interspersed with business analysis. It’s captivating, you actually listen to it as entertainment, and retain more because humans like stories.

We’ve got 3 years of:

• Growth meeting recordings

• Experiment writeups (~1,000)

• Meta, Google, Shopify, COGS data in BigQuery

• Internal and client-facing Slack conversations

• Growth models and forecasts

So I fire open Claude Code, stick it in GSD planning mode and begin discussing the problem at hand.

My initial prompt was something like this.

“I need you to help prepare for me a 45-60m handover podcast. One of my colleagues is departing and another taking over their account. I think the Acquired podcast series is exceptional at podcast storytelling for business stories and I want us to take inspiration from that. You can connect to the Fathom MCP to access all of the call archives (there’s about 150 of them, so you’ll need a good systematic way of analysing them all), there’s Notion where there’s over 1,000 experiments (again with lots of depth, video, statics included, so please take that into consideration), Slack conversations – both with the client and our own internal, recognising the nuances between those two, my Google Drive contains lots of individual reports often needed for annual reviews and one-off reports, and their BigQuery database contains data from Meta, Shopify, with key reports on acquisition, growth, and LTV. The podcast should take the form of a two way conversation with one person the knowledgable expert, and the other an inquisitive newbie. I imagine you’ll use Elevenlabs for the generation itself.”

GSD now went back and forth on clarifying questions. We went quite specific in certain areas as to what we should include in the final output.

I should point out, I’ve got a claude.md file set that explains how Ballpoint work as an agency, types of work, and importantly a writing style guide that was generated on over 100,000 words of copy I’ve written by hand.

About 20 minutes in, I realised this should be a repeatable process and so I interrupted it to tell it to turn this into a skill.

The output was great. I was going to skim listen to it to QA it but ended up listening to all 45 minutes because it was genuinely captivating. As CEO, I couldn’t possibly have this level of context of a client anymore, but every single sentence spoken was so rich in information I felt like I lived through the choices, the turns, the ups, the downs of that client’s journey (and our relationship with them).

The person who received the podcast said it was the most comprehensive podcast she’d had so far. As a bonus we had a knowledge file which she could now quiz and ask questions to: a knowledgebase informed by 100 hours of interviews, 1,000 experiments, and thousands of messages.

It took me four hours to make this. But this was probably my fifth proper AI project. I’m confident I could get to it again now in less than an hour. It also is now reusable.

This was 2-4x faster than the old way, but infinitely better in terms of output.

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Identifying product focus based on retention, LTV and cross-sells

Another brand we work with has hundreds of SKUs across dozens of categories. We’ve long tracked LTV and cohort retention. It is after all that LTV we use to inform what the CM3 target should be.

But we’ve never sat and really analysed those customer journeys.

Why?

There’s also no real plug and play SaaS that does this. No company’s product catalogue is uniform enough for this. Sure if you’re a single-product LTV, out-of-the-box Shopify plugins will give you an answer, but beyond that you’re out of luck.

And for us to do it would cost a lot and take up a lot of time. It’s what I always think of as ‘exploration mode’ in data analysis. You don’t quite know what you’re looking for but you’re giving room to be inquisitive.

But in a client-agency relationship when its effectively paid on time, it’s harder to warrant a cost for something that might yield no results.

The AI-Native way

AI doesn’t have these time limitations.

So once again, I open up Claude Code, I get into gsd:plan mode and start explaining what I want to do.

“Client X has hundreds of SKUs across dozens of categories. They anecdotally find a lot of their customers migrate between product categories as their knowledge and needs expand. But it’s very difficult to analyse this. We want to know if there are clear user paths through products, if there are patterns we can analyse, and therefore see if there’s different ways we can market or focus with retention. Additionally, we want to find out payback and LTV per product. I imagine you’ll want to connect via the BigQuery MCP to access the tables we have we have these tables xxxx, yyyy, zzzz that contain CM2 per order info, spend levels, and product categories. We’re connected to the Shopify API so if we need extra data not in those tables already we can grab that too, just let me know.”

Again this took me maybe 3 or 4 hours to get a decent output for it. Still a large amount of time, but this is because I’m doing this while learning. Now I’ve gone through this process I can prompt better and faster.

The result in this case was identifying some products which seem to have better payback periods and LTV profiles that *aren’t* the key area of focus, so we’re now validating this and seeing if the pattern remains with new focus. Are those entry products correlative or causal?

Let’s find out.

I write twice weekly here every week. This is the weekly Tuesday essay: a key learning from my 16 years experience growing consumer businesses. Every Friday is a weekend debrief: key stories, stats, and reads for the weekend.

Posts are (currently) free, but the archive is paid.

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The new skills we need to learn

Taste, multi-disciplinary learning, and critical thinking

Scott Barker wrote in his Substack last week on How to prepare for the next decade4.

There’s lots in there to dissect from a mental health, identity, and career mindset. But pulling out some practical elements to do with skills for work, I thought this section was great:

“What happens when the skill you spent your life learning can be done at 80% proficiency by a novice in a matter of minutes. We’re already there across most disciplines. AI and robotics will eventually flatten all surface-level skills.

The skills that will matter in the future will be things like judgement, taste, the ability to make connections across disciplines, story telling and moral/ethical reasoning. Going back to the car example, the skills you will need will more closely mirror a navigator than that of the actual driver.

Taste, judgement, cross-disciplinary connections, story-telling and moral reasoning are much harder to foster. These require life experience, failures, interests in many different areas and a lot of time spent in contemplation. It will require you to understand how you actually view the world, what you stand for and who you are.”

Reading this I remarked on how well place people in growth will be.

Growth to many is just rebranded marketing. But I’ve always seen it much deeper.

• Growth mindset comes from Carol Dweck and is the idea that you can constantly self-improve and learn, and abilities are not fixed

• Growth in tech brings together multiple functions of marketing, product, data, and engerineering

• We’re storytellers by nature in our advertising and copy

• We’re visual by nature

There is still lots to learn. Taste is something that needs to be learnt through experience. Reasoning requires active work. And judgement comes by default from experience.

How do we build a world where we foster this stuff together?

So much of this also fits into the adaptation to harder skills as well.

Last year, when I called for the death of the t-shaped marketer, I did so because I thought that old version was too narrow. Instead, I called for the creation of the ‘growth artisan’.

This is the idea that a single person could grow something from start to finish. In current terms that means Shopify, conversion, customer, copy, ads, measurement.

Already with AI, knowledge is at our fingertips, but soon the ability to create within those disciplines will be as well.

As we all play with AI within our current areas of expertise, we know that out of the box, the context is to hallucinatory and broad to be useful. But with the right degree of context, memory-setting, and relevance, it becomes powerful.

Product, design & engineering

I asked Claude Code if I should learn to code the other day, and it told me I didn’t. I did smile at that.

Whether or not I need to, only time will tell.

But I know that being a nerd and someone who broadly understands concepts of TCP/IP, networks, the internet, the web, databases, and high-level computational science has been useful for me as I go.

Having grown up messing around with the command line helps too.

So too does the fact that I used to sit next to a graphic designer, am good friends with a product designer, and I flirted with the idea of being a product manager.

Having some grounding in those functions has been useful. And I believe it’s going to be beneficial for anyone using AI to understand them to some degree.

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What I think this means

What this means for employees

If we look 10 years out, I don’t think there’ll be an AI advantage. It’s available to everyone. And currently there’s no switching costs between the core platforms.

As it currently stands, I fundamentally believe that learning to use AI properly is going to be the same as how everyone had to learn to use a computer. There will be people better at it than others, but if you want a job you just need to know how to use it.

We’re dedicating a lot of time internally to get everyone up to speed on Ai.

We’re not hiring a chief AI evangelist. We’re not outsourcing this. We’re not going to create a function responsible for it.

It’s in everyone’s OKRs. It’s on everyone’s roadmap. It’s in everyone’s progression plan.

We’re betting that everyone has to become AI Native, not just a handful of people in each company.

If we hit OKRs, then by about mid-summer, we should be a fully AI-Native agency.

I think businesses that don’t do this eventually will go extinct. Lots will go out of business, and they’ll get replaced by native/first businesses.

When we look at those job stats, when we think about the future Matt Shumer writes about, and the preparedness that Scott Barker encourages upon is, a few things become clear:

1. The nature of ‘work’ will change forever

2. We are all going to have to reskill

3. Junior team members are going to be hit the hardest and so will need the most support

4. We need to change the internal view of what success looks like

5. Lots of jobs will go

Like many others before, I’d encourage anyone reading this to go deep soon.

What this means for growth (and agencies)

So much AI narrative at the moment is about cost reduction. But to me that doesn’t play out too much.

a16z are looking for AI native agencies, and the reason being is that for the first time agencies could have software like margins at scale.

It feels a bit like that’s a view of agencies from those who haven’t dealt with demanding clients. If they had, they’d know that no client is ever going to let their agency get away with 90% margins. Long term, it just doesn’t feel like there’s a business case for cost-efficient focused agencies.

For me, I see more excitement as we did in my LTV example above. We are at day zero with our AI work, but already it’s enabling us to do things that we just couldn’t have done before.

There are services I’d love for us to offer because I know they’re in the interests of the client, but the client maybe can’t afford them, or we can’t hire the person for a short budget or one-off project to do it. And so the work doesn’t get done. AI solves this.

And then we have the longer term view. In a few years time, when everyone has access to this. Once the great resettling has separated the wheat from the chaff, and the AI-refuseniks go out of business, what happens then?

If advertising is about how we sell products to consumers, then creativity is how we do that and stand out against our competitors. That need won’t ever change. Humans will always desire more, and when they do they’ll always buy more. Companies will always want to sell more and beat more competition. And so the need to stand out remains. Creativity is the answer, we will all just be more excitingly armed.

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Closing thoughts

This feels like the most exciting period of my career to date. It also feels like one of the most anxiety-inducing ones. What I’ve become deeply aware of this last month is that we can no longer keep our head in the sand.

At Ballpoint, we have already begun the work of trying to internally revolutionise. This to me feels like the ‘go mobile’ moment at Facebook all those years ago.

Personally, I’m experimenting with almost every spare minute I have. And I encourage everyone close to me, on my team, and more broadly to do the same.

I am a deeply optimistic person, and I remain so. I think there is huge upside to be had. But that is not inevitable, and it won’t just happen to us, we have to take that opportunity ourselves.

I’m going to be writing more this year on how this journey goes.

In the mean time, I’d love to hear what you all think.

Josh

If this sounds exciting to you, then maybe you want to work here? We’re currently hiring for a head of growth level paid social expert, a data engineer and analyst, and a PPC manager to join our London office.

And if you are the CMO at a consumer company that feels stuck and fed up that the current way isn’t working, then maybe you’d like to work with us. We don’t have all the answers yet, but we’re sure as hell going to try find them as fast as possible. If so, drop me an email josh@weareballpoint.com

I’m building Ballpoint: the growth agency I always wanted to hire when I was a DTC founder and before that a head of growth. We have scale brands from £1m → £50m through digital advertising. If you’re looking for support, then you can email me on josh@weareballpoint.com.

In the meantime, if you enjoyed this please consider giving it a like, a comment or forwarding it to a friend.

4

The Wake Up Call

How to prepare for the next decade

🌒 🌓 🌔…

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2 months ago · 2693 likes · 195 comments · Scott Barker

With our first full week of Jan behind us, we’ve examined multiple categories and trading is on average slower than expected. As ever in times like this, I speak to friends who work in marketing and are founders.

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41 newsletters (42 including this one). Roughly 40,000 words. One year of thinking out loud about growth marketing, Meta ads, and building Ballpoint.

At the bottom of this newsletter, you’ll see a roundup of my top posts of the year. But first a reflection.

I’ve been blogging on some subject or other for over two decades now. No matter what it is I’m writ…

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Here’s my reflection on where we are as we go into 2026.

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1. Alcohol and the era of moderation

The IWSR recently published a report on drinking that did the rounds. There were two charts that showed the story.

First, is the absolute chart that reveals that Brits are now on a par with Americans for amount of alcohol drunk per capita each week.

There is a clear decline within the UK down to just over 10 drinks per week on average.

Gen Z’s role is shifting

The common narrative for a long time was it was Gen Z leading this charge, but data from summer showed that trend is bucking.

In fact, as that article suggests, a lot of Gen Z’s prior teetotalism can be accounted for being underage, unemployed or in less stable working environments generally. The generation is older, working and more affluent than they were in previous years.

And so overall declines in drinking in the UK must be broader.

Let’s look at that IWSR data again, but indexed rebased to 1990.

The shape of this chart got me digging into a few other thoughts of what correlates at this time.

A few key thoughts here:

• There’s a clear financial crash change around 2007-2010 that correlated with the downward trend

• Drinking rose throughout the 90s and peaked in early 2010s (just as I and other millennials starting drinking)

I was interested in how this correlated to public sentiment towards politics – whether the New Labour optimism was fuelling it.

What this means for brands

There’s a decline in drinking for sure, but it’s not as clear cut generationally as once thought. For anyone selling in the low or no alcohol space, this won’t be a surprise.

The bigger questions might be why this is happening. Understanding the motivations between reduced alcohol will help shape why and how to sell to someone.

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2. GLP-1 users are the new happy, premium consumers

GLP-1s are tricky beasts.

When I think of GLP-1s, my default is to think more of toxic culture of skinniness than it is to think of overweight people creating better relationships with their bodies.

I know statistically that isn’t correct. In the UK, the distribution of of weight leans far more to the overweight and obese end.

And when we look at global trends the picture is clear too:

Obesity is on the rise, and most sharply in the faster-growing middle income economies.

So this set of stats is interesting from the New Consumer report.

Life satisfaction is 14% higher for GLP-1 users than the average. And in fact is one of the best predictors for life satisfaction – second only to those who earn $150-200k/year.

Most interestingly for brands is what that increased life satisfaction correlates to in spending power.

Unlike the general population, where there is a mixture of trading up and down depending on category, GLP-1 users are almost trading up in every category from the Coefficient Capital survey.

Perhaps the surprising ones are in restaurant meals (17% up), and snack foods (24% up). But as their report also states a lot of this interest will come from new brands or products specifically catering for them.

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3. Drug use on the rise

While alcohol make be down, and low and no down with it, lots of drug use is on the rise.

18.1% of British 16-24 year olds have taken an illegal drug in the last year, which when you consider the 39% who never drink, might offer part of the picture.

In the US, while daily alcohol drinkers are flat or showing slow growth, daily cannabis users overtook drinkers a few years ago.

Meanwhile, lifetime use of cocaine has doubled since 2001 at a time when regular drinking has declined significantly. The overall numbers are still far apart, but the relative trend is clear.

It is too simple to look at alcohol moderation and say that society is moderating. For some, it is definitely about blanket moderation, but for others one drug is being swapped for another. And in many cases, some of those swaps will be worse rather than better.

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4. Longevity is on the rise

2025 started with the Don’t Die doc landing on Netflix. At the time, everyone laughed.

Today, the laughter seems to have quieted down.

Lots of biohacking is expected to rise.

Oura Ring sales have close to tripled in five years.

£200m is being spent on anti-ageing in the UK.

I had an IV in early December, and part of that was a NAD shot. NAD is still expensive to get as an injection or home shot. Most packages you’re looking at £300+ per month to properly get the benefits.

The number of people who will spend £3,600 per year on this is low. But this is the tip of the iceberg for the market.

5. Comms & storytelling is more important than ever

My old boss, colleague, board member Peter Sigrist used to consistently extol the value of storytelling. Whether it was in corporate comms, education, politics, or now quantum computing, I’ve never heard someone so stalwartly defend the value of being able to communicate a narrative.

Turns out the world is waking up to Peter’s worldview.

This year, we saw all the big AI companies invest heavily in storytelling, comms-focused content positions. It caused a few eyerolls given what these companies are doing for broader content around the world.

But as this WSJ article reinforced, it’s never been as important to find storytellers in the world. Source.

• The number of roles including the word storyteller have doubled year over year.

• This isn’t just marketing, companies want specific storytelling skillsets

• Continuing trend of owning your own content distribution

• Huge salaries to go with it

In advertising, this is important too, but while adverts try to capture attention in nano-seconds, this feels like the longer sell once you get someone into your world.

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6. Confidence in consumer investing is back

Sugar Capital, the venture arm from the POPSUGAR founders, posted a call to arms in early December with their post “The Golden Age of Consumer Investing.”

I read Brian Sugar’s post enthusiastically.

“A gummy vitamin company reaching $300 million in revenue on $20 million of primary capital. Contribution margins above 40 percent. Meaningful EBITDA before the Series B.”

“Product-market fit” is bandied around a lot in startup folk lore. I know it because I had my own startup fail without it. But so much discourse around PMF is focused on enterprise, b2b, and proper tech companies. You have comments referring to how sticky user engagement is or what your DAUMAU ratio needs to be. And how to solve it is focused on switching product categories or strategies with winners the likes of Twitch, Instagram, Notion, or in the UK more recently Fyxer.

But a lot of PMF discourse doesn’t talk to consumer non-tech problems.

At Ballpoint this year, we’ve worked with over 50 brands, and I’ve audited maybe another 150. We see up close and personal the companies that:

1. Don’t have PMF and is unachievable unless something massive happens

2. Do have signs of PMF, but haven’t worked out how to scale a growth engine yet

3. They have it, they’re scaling, but want to push harder because they’re in a growing category and need to be dominant

When Brian talks about contribution margins and ebitda, this is not an outcome of modern consumer PMF – it’s a part of it. PMF is about sustainable business models and huge accelerated growth opportunities. You need to a path to $200m and you need 30%+ CM.

Health is the focus

It’s unsurprising perhaps that this post has focused so much on health. Brian puts down this new wave of optimism to health specifically.

“Something shifted in American consumption over the past five years. People read labels. They actively avoid the processed food that built the twentieth century’s largest companies. Ozempic and its cousins rewired how America thinks about what it puts in its body.”

We share Sugar Capital’s optimism and focus.

7. There’s still scepticism in the M&A markets

Before you start pulling money from your index funds to invest in early stage consumer, there’s still some scepticism in the market.

The excellent Drew Fallon in his Substack Making Cents, posted this week the “2026 M&A Outlook.”

In it he highlighted just a few of the big issues we’re facing at the moment. Namely:

• Coca-Cola are trying to get rid of Costa for $2bn having bought it for over $5bn

• Waldencast are maybe selling Obagi

• Estee Lauder are likely to sell off some brands

• LVMH is trying to shift its 50% stake in Fenty

• Nestle wants to further investment in health and is reviewing elsewhere

And the list goes on.

It wasn’t all doom & gloom, as Drew pointed out interest rates are likely to continue dropping in the US and that will be good news for all. This will particularly be true for the portfolios with lots of DTC weighting which are now approaching the end of the 8-10 year fund cycles. Expect lots of pressured dealmaking for brands in this part of the cycle.

And the areas Drew has the highest confidence in:

“Protein and fiber [sic] companies (and hydration to a lesser extent) that are gobbling up market share right now are critical assets that must be owned”

This said, however, 2025 ends with broader M&A at an all-time high with $4tn in dealmaking done this year.

8. The two horsemen of the 8-figure world: LTV debt and Brand debt

I was reminded of Sean Frank (the Ride CEO)’s comment this month about Stanley:

Go back two years and Stanley were unavoidable. They were on every Meta ads inspo board out there. They were being case studied. And now they’re in a clearout section.

If you were YETI, who of course are broader in catalogue than Stanley were, you could have easily fallen for switching strategy. Fortunately their Google Trends data shows a slow but steady move up and to the right. They’ve remained stable and growing, while Stanley hit a hype curve they couldn’t follow back from.

We don’t know Stanley’s unit economics, but it’s a pattern we see often:

1. Hype doesn’t create a brand

2. Beware of LTV debt.

9. Beware of category cycles

This was a great chart shared by Ben recently about category/brand growth.

One key thing jumped out for me here about how to think through strategy.

Lots of businesses I know and meet aim for contribution break-even or contribution-profitability and their goal is to scale up to that point. In early days it’s a good strategy because it helps you build up to a level of net margin profitability by building enough revenue to cover your opex.

But once you’re there, you should think about category.

Now every founder is biased towards the strength of their own category. You are after all a founder and have long term belief in it.

But trends are cyclical. Just because GLP-1s and healthy snacks are big this year, it does not mean that’s a permanent state. We’ve seen in the space of a decade the explosion and death of artificial meats. The things that were rampant for millennials are unimportant for gen z and so on.

The move towards green looked like an inevitable change for the 2010s, but there’s backlash against it now. Governments, populism, consumers are shifting and moderating.

We are in a health cycle at the moment, and so lean in to your strengths. Lean into the growth.

But also make sure that you are not creating fragility. Running at £0 net profit is better than negative. But if you’re in the position to have customer growth for the benefit of cash flow growth – and you’re in a cycle that could change – then build in some optionality.

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10. The constant battle with affordability

The West seems to be battling away a recession every month. Only 42% of Brits believe they will ever be able to afford to buy a house. 56% of Brits say they’re impacted by the cost of living crisis this year.

Amongst young people, it’s the single biggest issue affecting the country. More than seven million people in the UK are going each month without essentials.

And amongst the highest earners, tax rates plus loss of child benefit mean that if you’re earning £125k per year, you’re actually worse off than earning £99k per year.

There is a higher number than ever who forgo basic necessities. There is a growing middle who who without support from Bank of Mum and Dad can’t afford housing. And the country’s best earners are disincentivised to earn more.

The ongoing joke about millennials is if they ditched flat whites, they’d be able to afford housing. Except for many, all that saving in the world won’t get them on the ladder. For them, buying premium makes sense.

There is of course lots to be optimistic about in the economy. But it’s also important that brands really think seriously about what type of company they are. Who they appeal to.

Are you really a mass market product? Or are you super premium? Does your growth and ambition support your customer?

And equally important are you actually talking to your customer in the right way? How you speak to middle England Facebook 50+ people is not how you speak to Real Housewives of Clapton London.

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Closing thoughts

The world feels more mixed than ever. Some macro signals are noisy and potentially worrying: ongoing affordability crisis, M&A scepticism, category cycles that could turn at any moment. On the flipsde, certain classes of consumer are spending more than ever in newfound categories, giving an opening to new types of brands.

Health, longevity, and alcohol moderation are certainly all singing in unison. But it’s not universal – THC products in the US continue to be prominent, and illict drug use is on the up in some categories as well. Not everyone is following the Bryan Johnston playbook quite yet.

Whatever type of consumer business you’re building, profitability remains king. Strong and early contribution margin is vital, but building net profit growth into your business is what is going to make M&A accessible.

The best founders and teams I’ve spoken to this year are all building optionality. They’re not content with cheap acquisition costs or break-even CM, they’re focusing on LTV debt and building cash into the business. Profitable enough to survive a downturn. Ambitious enough to lean into growth when the window opens. And perhaps most importantly, more realistic about who their actual customer is than ever.

If I was the founder of a growth business at the moment, I’d be using 2026 to think about ebitda profitability rather than fragile 100% growth maximisation.

We’re not in a universally good time, but for certain sectors it certainly feels like it.

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AI became a real possibility for ads creation this year.

At the centre of much of the AI backlash is the fear for …

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September was brutal, and by many degrees worse than last September.

I published mid September this chart on LinkedIn and it got a lot of conversation going.

Off the b…

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Since we’ve published this post, we’ve significantly updated our AI thinking, which you can read here.

AI is now the most frequent conversation topic of anyone in business I know now. One of our copywriters Mark said to me recently that “every conversation eventually turns to AI”. It’s become the modern Godwin’s Law.

There is a mixture of excitement and anxiety.

The AI influencer community is already in full swing accelerating that anxiety. If you believe every AI influencer then you might think there are entire companies now fully operated with just 25 AI agents and one person. You’re probably worried you’ll be left behind.

Anxiety in times like this is good. One of the best business books you’ll ever read is Only the Paranoid Survive by Andy Grove. The period we’re going through now is one of existential change, and if you bury your head in the sand, you probably will be left behind. Realising that this is an issue is important.

But at the same time, once you dig in to most of what the AI world is currently selling in today-terms, I’m often left wondering how many people actually use all of this day-to-day?

And what is there today that can be useful, recognising that ultimately every part of a system and workflow today will likely feel old in six months time.

I want to share very openly how we’re experimenting at the moment and our approach to it. I hope this is less a ‘here’s stuff you can borrow to be better at growth’ and more ‘here’s how we could experiment in our organisation' specifically.

Things to cover

1. How we ran our recent AI hackathon/weekender

2. Key lessons learnt

3. What we built

4. What comes next

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How we ran the AI Weekender

Most teams I know now are highly lean. Revenue per employee as a metric is back. And with AI companies now reaching tens and hundreds of millions with only a handful of employees, that will only continue.

But one of the issues with operational efficiency is that it doesn’t allow room for experimentation and play.

New systems and technology require experimentation and play. And there’s only so much snippets of time in evenings and weekends you can do to properly explore.

This was the backdrop to why Fiona, our head of ops and creative studio lead, and I went and did an our own Weekender. Except it wasn’t over the weekend, and we didn’t go clubbing. We spent two days of blocked out, deep work time, off site working only on AI.

Here’s how we did it

• Booked a workspace and hotel away from London to create different environment setting

• Blocked out calendars entirely, did handovers as if we were on holiday

• Came up with a long list of ideas ahead of the two days of what we wanted to build

For us this meant two days in Guildford where we worked solidly in deep work mode on AI ideas for two days.

What do we really mean by AI?

In this context important to point out the parameters.

1. The use of AI within automation frameworks to help us automate things we couldn’t have done previously

2. Exploration specifically of AI tools and how we could bring those into our workflows

How we worked throughout the day

We had a Notion board with our long list of ideas, and we then gave each an expected value.

We measured: (1) potential forecast contribution margin over a year the initiative could bring to the business and (2) probability we could finish it in two days

The CM forecasts looked at:

1. Can this reduce existing human time to the business?
   For example, can we replace 90 minutes of human time per week with 5-10 minutes of human time by automating a repetitive process? And therefore we can calculate 75 minutes of salary x 52 weeks of the year to give us a CM figure.
   

2. Can this accelerate our ability to do our jobs better and therefore increase client spend, which would then increase our revenue as well?
   Harder to forecast, but to be simplistic, let’s assume 1 ad in 20 we create allows us to push client spend by 15% per month, does this activity increase that ratio to be 1 in 18 and therefore offer a 10% increase in potential revenue to the business? Apply our gross margin to that revenue increase multiply it by number of clients it would impact, and hey bingo we’ve got a CM figure.
   

3. Does this activity reduce the likelihood of churn through better servicing of the client?
   Another hard to forecast one, but we have a set of principles we believe are important to client retention. Can we improve our ability to do that through an activity? And to do this we looked at our churn rate across the year, forecast a cost of churn in revenue terms, and again applied the CM calculation to it.
   

4. Does this improve our marketing of our agency?
   Very hard to quantify as measuring individual pieces of activity within the agency’s marketing is difficult. Yes we can look at substack reads/engagements, or LinkedIn likes, or follower increases, but ultimately, we don’t have a good answer to specifically how much that ties back yet.

And then we did a probability forecast of likelihood to complete within two days.

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Key lessons learnt from our AI experimentation

1. Computational thinking

First and foremost, this felt closest mentally to coding work that I’ve done in a long time.

This is process of approaching a problem in a way a computer scientist would do. It requires thinking through the ideas of like data structures, inputs and outputs, loops, and abstraction.

I’ve done CS50 in the past, and am fairly good at SQL, but I never really got into a proper language. I found it incredibly useful however, to understand things like defining variables, or iterating across loops.

For anyone who’s never done any coding or computer science stuff, in plain English this means:

• Breaking apart your day-to-day actions into the tiniest and most specific steps. For example, you might say:

  • press the button on the top-left of the coffee machine that has a has a circle with a dashed line through the top of it.

  • Wait for two minutes until the machine stops making any noises

  • Put your hand on the handle of the wooden block above the coffee machine, grab it, and move it to the right.

  • Pick up the bag that sits on the lowest shelf (position 0), on the far left of the shelf. Check that the bag says “coffee” on it.

  • and so on

• When presented with an either or action, using IF statements to set conditions and directions. For example:

  • IF the coffee machine has no coffee in it THEN add coffee to the back ELSE continue to next step

• Thinking about everything as data. I.e. every action or object or anything needs to be a data structure. And to use them properly, you’ll need to define them a lot. For example:

  • Every object in the cupboard is an object in a data array with different parameters

  • Every mug on the shelf is a different object

• When presented with a list of tasks you want to repeat for a list of things, ‘iterating’ with a ‘for loop’, i.e.:

  • For every object in the cupboard:

    • Check that it says coffee on it

I always find the first few easy to comprehend, but for the last one, it’s amazing how frequently you have to think like that when it comes so naturally as humans.

🌟 Key lessons: be prepared to think computationally and it would probably help to go and do lesson one of Harvard’s CS50.

2. What to automate

One of the first things we tested was seeing how we ChatGPT could automate analysis of different adverts.

It was one of the four hour deep pieces of work that worked based on a series of triggers or prompts. Ask a question about an ad in Slack? Great it gets an AI response reviewing the ad. Have an ad automatically hit a certain spend threshold, great get it to analyse.

It was technically beautiful.

Except the actual raw prompt at the end wasn’t something we ever do manually. We use AI a lot but we don’t use it that much in analysis and find humans far better analysts of visuals at the moment. So why did I think automating a route would be better.

🌟 Key lessons: only automate a process you have already tested

3. You do need the time

I context switch 9 or 10 times per day. Most work I do fits into 15, 30, or 60 minute chunks of time. If it doesn’t fit into that I have to make time for it.

With AI, it takes 60 minutes just to get your head back into a problem.

Inevitably, it could take another 60 minutes until you figure out how to solve it.

Then you need another hour to test it, let it break, and go and rebuild it.

And then you’re at a point that it’s done.

And so, you really do need to set aside big chunks of time. And given how many meetings and usual day-to-day work people have, I imagine that’s impossible to properly do without building that time in.

🌟 Key lessons: you can’t do AI properly in 60 minutes. It needs days.

4. It doesn’t naturally suit my mindset

I am very much a “done is better than perfect” person who is “vision, not detail.” I’ve hired exceptional people who are the opposite to that, but this level of work does require going very deep and stuff works or it doesn’t.

I still think AI sits with leadership to properly learn and implement, at least for us, but I’d murder a technical co-founder right now.

🌟 Key lessons: it suits perfectionist, detailed-orientated people

5. I now know n8n & how APIs work better

I always knew sort of how APIs worked before but now I get them a lot better. I’d still like to do a bit of a crash course on them rather than learn by doing. And want to explore Postman a bit so I get really comfortable – maybe even via Terminal. But overall my knowledge is 0 to 1.

🌟 Key lessons: n8n & APIs are great

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What we built

“This is all well and good Josh, but what did you build?” I hear you cry.

Ok. Here’s what we achieved.

Auto-generate simple statics using created backgrounds on nano-banana

GenAI comes down to great prompting. Great prompting is a skill that a handful of people have on the team but not every one else.

Workflow

1. Notion card where you drop a product link and a description of a scene

2. n8n takes that and sends it to ChatGPT

3. ChatGPT takes your input prompt and generates 10 variants of that prompt.

4. Each of those prompt get sent to nanobanana

5. Results come back to a thread on Slack and go to a Google Drive

Analyse recent creative winners

This was the ‘fail’ because we don’t use AI to do our visual analysis usually, but we got it working with a few inputs.

Workflow

1. Either (A) field updated on Notion, or (B) specific adname requested in an internal-client channel

2. Data is joined with other data from Notion related client insights

3. Claude prompt combines visual + insights and asks questions to analyse it

4. Result gets returned to Slack

Call actions shared to internal client Slacks

This never seems to work 100% but it does cover 90%.

Workflow

1. New Fathom call transcript

2. Transcript gets uploaded to Claude with a prompt about being marketer/exec assistant

3. Asks it to pull out all of the actions discussed on either side

4. Share those actions to client slack afterwards

High/Low Spend Alerts

We did get the non-AI version of this working. Still to build the next layer.

Workflow

1. Check Meta every hour across all clients to pull spend + conversions, then calculate CPA

2. Share the CPA update at a few key points to internal client Slacks

3. Check against a “danger” and “great” CPA range and if it’s good then either WhatsApp account owner / DM the account owner.

The bit here that’s not AI-ed is step 3, I’d love this to be relative to overall performance “recently” rather than set CPAs like this. If you’ve got ideas on how to build, let me know.

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What comes next

We’ve got 20 unfinished AI initiatives and dreamt up plenty more. I’ve also been tinkering away more at weekends and evening. It’s addictive.

We will also likely bring on someone to support with AI as a focus at least on a freelance basis while we build in additional workflows. But ultimately it’s still something I’m investing a lot of time in.

How we implement this amongst the team, tbc. Lunch & Learns are definitely one thing, but ultimately it’ll probably be something we do at the next offsite and dedicate a four hour chunk to so people can understand the true power of it.

I still haven’t done any vibe coding, but that is coming next.

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🔗 When you’re ready, here’s how Ballpoint can help you

→ Profitably grow paid social spend from £30k/m → £300k/m
→ Create full funnel, jobs to be done-focused creative: Meta, TikTok, YouTube
→ Improve your conversion rate with landing pages and fully managed CRO
→ Maximise LTV through strategic retention and CRM - not just sending out your emails

Email me – or visit Ballpoint to find out more.

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