Given that this year was the year of our big AI pivot, I thought I’d share some of the reflections that I shared with the team today.

This remains the most liberating and exciting period of my career

Hands down this has been the most revolutionary year of my career – and of anyone’s I’ve spoken to. The breadth of what’s possible now is so exciting.

I always wanted to be a developer. I was a early 2000s computer nerd who built his own computers. But for whatever reason, coding just didn’t ever stick.

I’ve made over 6,000 contributions to github over the last 12 months, and for four of those months was inactive.

I’m building, prototyping, and shipping on a weekly and often daily basis. I’ve built stuff I literally dreamt of just two years ago.

Today, if you have a problem involving a computer, you can spin up an agent and build software to solve it. Most problems aren’t companies, but they are still problems.

Over the last month, two friends have told me about startup ideas. Being the entrepreneur and nerd in my friendship group, I’m often the one friends come to to discuss these sorts of things.

Both of these require some type of tech build. Two years ago, they would have both had to either (A) find an engineer to build it for them, or (B) create some lightweight non-real version of (MVP) to validate, then go raise money. If those ideas weren’t billion-dollar ideas, they wouldn’t get funded.

I told both of them the same thing: go spend 20 hours watching YouTube tutorials on how to build with Lovable. Then spend another 20 hours building it – you’ll have a working version by the end of it.

For two weeks of evenings hacking away, or a couple of weekends, anyone can now bring an idea to life. Maybe those ideas don’t go anywhere. Maybe they do but they’re just small ideas loved by a few hundreds or a few thousand people. Maybe they go on to be billion dollar businesses.

Before, unless you could write code, your idea had to potentially be worth a billion dollars to get funding for it.

The scope of what can be built now is many millions of times broader.

And remember, right now 99% of people aren’t doing this stuff. You can get so far ahead by experimenting now and the access to this is all free.

At least in the short term, the AI jobs apocalypse isn’t a thing

Yes, there’s a world post-AGI, post-abundance where we don’t need jobs anymore. But as that’s in the low probabilities of being a thing, I think its better to consider the extrapolations are where we’re at.

Last month, the Economist reported that AI has so far created one million jobs for 200,000 laid off as a result of it. Software engineering – the area which AI by far most affects – is seeing an increase in job posts. Other areas for employment most impacted by AI, are also seeing increases. And in the US at least, graduate unemployment has also been unaffected.

There are obviously counters to these stats too, job postings for junior roles are down – and the UK is disproportionately worse off than the US for areas impacted by AI. But the UK picture is less clear-cut than that survey suggests. Bloomberg Economics found that vacancies in AI-exposed UK roles were already falling before ChatGPT launched, and have risen since summer 2024.

Aaron Levie, CEO of Box, nailed this those.

Our hiring is up and will grow faster because of AI

Here’s what’s happened this year.

• AI has allowed us to significantly broaden our service offering, improve quality in it, and afford better return for price

• New business is 100-150% higher than last year, and same with MRR growth

• Headcount has grown at a faster rate throughout this year than the year before – and is forecast to continue next year.

We have become exceptionally more efficient.

And we are also busier than ever.

This take on jevons paradox is amongst those I talk to – other founders, leaders – the AI paradox writ large.

The jobs are 100% changing

A copywriter no longer simply sits at a computer and writes out lines of copy.

A video editor no longer simply sits in Premiere Pro and strings together video sources.

A paid social person no longer sits in Ads Manager and studies ad accounts.

A growth person no longer opens up Google Sheets and starts adjusting a model.

But those roles are all still present, its just their toolkits are radically altered.

And while not everyone dreamt of being a software developer growing up, often I find creative people have narrower jobs than they dreamt of. A video editor takes on the role of a creative director. A copywriter that of a cultural strategist and behavioural economist. The paid social analyst that of a CMO.

Our entire team now lives and breathes in Claude Code or Codex. But they are still their jobs. Their expertise and taste and judgement that got them to their roles is still the thing they need today.

I know that what I can build with AI will be a fraction of that which an actual engineer could achieve – at least in terms of quality and time-to-execute.

Similarly, I can be a video editor today using Opus 5.5. But give an actual video editor those tools, and the output is incredible to see.

It is still day zero

We may all be hitting out five hour token windows every few hours, but we are still at day zero. It is the yin and the yang, the anxiety and the excitement, which comes from the frenetic pace of development.

But every week, there is something new to learn, some new system to integrate, some new model to experiment with, some new process that could be improved.

I am absolutely blown away by how much our team has changed this year.

In our shared github, across all of our company repoes:

• 1.4m lines of typescript

• 988k lines of python

• 540k lines of html

• 374k lines of sql

• 133k lines of javascript

And outside of even out main databases (across BigQuery & Supabase), we also have:

• 68m lines of json and 4m lines of markdown

We are essentially now a tech company.

And yet, we are still at the very start of what the future looks like. The things we’ll achieve tomorrow will make what we managed yesterday feel small and prosaic.

Let’s be more ambitious

Andrej Karparthy wrote a couple of weeks ago about the widening gap of AI usage worldwide.

“So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.”

For those of us outside of the frontier, all we can aim for is to be in the top 0.2% of the population using AI agenticaly and radically.

Last week, Felix Rieseberg, an EM at Anthropic, wrote an article called How to be more ambitious: a tutorial for humans with models.

In it he reminds us of the story of George Dantzig, a statistic student at Berkeley in the late 30s. Late for a class one day, he copied some notes down from the blackboard, then handed it in the following week. He believed it to be the homework, had he have been in the class, he would have know they were two unsolved problems in statistics.

He had solved them. Because he hadn’t done the default human behaviour of believing we cannot achieve something.

We can do so much more than we believe we can.

Felix writes a great and practical framework for how to be more ambitious with AI that centres on four areas:

1. Speed: The same work in less time, the bread and butter for AI’s economic value: If you need some code written, I’ll do it faster with Claude. Well established and requires little creativity in how you approach AI, so I’ll skip this one.

2. Altitude: Going up in abstraction and considering the outcome rather than the individual task.

3. Depth: Considering a task the way a field-leading expert would. Rather than operating at the task’s layer, we go one level deeper and touch the underlying data, tools, and problems.

4. Range: Drawing on multiple fields rather than just the one the task might need at the surface, using knowledge, tools, and techniques you normally wouldn’t.

My rallying cry to the whole team was let’s be more ambitious.

Let’s pause work and reflect, and consider what we can do better: with more speed, altitude, depth or range.

It’s day zero, and I can’t wait to see what we can achieve today, tomorrow, and the next day.

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I’m the founder of Ballpoint. We’re a growth and technology company that helps consumers brands scale to kill their competitions. If you’re stuck at a growth ceiling and want to triple your profitable spend over the next 12 months, then get in touch.

We know first hand how powerful landing pages can be. Our most recent landing page we built outperformed the clients’ PDP by 35%. And historically we see 10-50% improvements against PDPs, homepages, or collections pages, to be the norm.

But we also wanted to study how the biggest brands are doing it.

A few questions I always wanted to answer:

• How much should you be testing?

• How many brands have singular hero LPs compared to multiple?

• Is there a ‘best’ structure?

• Does that best structure change over time?

Since April, we’ve analysed 1,956 landing pages across 70 brands. That’s 27 landing pages per brand. Now our list is a list of who’s who in Meta advertising. We specifically analyse those we know to be spending high six and seven figures per month on Meta. Across the 1,956 landing pages, we identified 11,058 content sections.

One of my core hypotheses on landing pages was:

“if this LP is used repeatedly by the brand, we should consider it shit hot”.

So we created a ranking based on the number of instances a landing page is used. If a brand launches 100 ads per week, how do they consolidate around certain pages. And how many of those pages still maintain the most prominent position a month later or maybe three or four?

Every day, when we scraped the LPs, we took screenshots in mobile view of the whole page, and then using Claude have got it to break out every single section into section types, tagged with everything from the type of messaging through to how ‘on brand’ that section is.

Key learnings from our analysis

First, we wanted to learn if brands had singular landing pages that they kept returning to.

We set the threshold so that at least 10% of the brands ads for an initial 30 day period, and then continued to be used until the end of our analysis period. We called these Hot Repeats.

• 68% of our analysed brands had Hot Repeats

• There were a total of 94 pages that were counted

• 4.8% of landing pages were hot repeats

If a brand had a page that lasted for at least two months, we called it an Established Page and 160 of the pages fell into this category.

In the last month, 525 pages had been tested for the very first time.

What we wanted to know was of the Hot Repeats and Established Pages, what were the key insights we could learn, relative to the broader groups.

From here on we will refer to the “Best LPs” as those in either Hot Repeats or Established Pages categories.

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1. Landing pages & landing page heroes beat product pages

When we look at the Best LPs we have out there, 82% begin with a hero section. And 60% of the Best LPs are landing pages, as opposed to product pages.

Both follow similar patterns.

The Best LPs usually open with a promise of some kind rather than the product itself. PDPs start with the product.

Despite this, non-hero based landing pages account for almost half of all current tests (49% over the last 30 days), and 55% are product pages. While the PDP explains some of that gap, that still shows 10% of all traditional Landing Pages not starting with a Hero

2. Explain the product

It’s time to explain the product, here’s a few stats to support how and why:

• Comparison sections are 2.2x more likely to feature in a Best LP than not.

• How it works sections are 1.5x more likely to appear

• “Why” it works appear 9.3 percentage points more often

It is not enough to just give a benefit. The best LPs repeatedly explain how it works, why it works, and compare it to alternatives

Here are some How it Works sections

How you might want to run Comparison tables.

3. Go long

The Best LPs feature 7 sections, compared to 6 amongst the rest of the LPs.

This goes against a classic trope of short attention spans. While on social, our job is to capture attention quickly and move it to click, once you’re on the LP, you’ve earnt permission to sell.

If you’re ever in doubt about length. Consider Amazon, the world’s most successful e-commerce business, and what they consider an appropriate length for a landing page.

Length shouldn’t be length for lengths sakes. You can’t just repeat information. But use each section to tell a different part of the story. And remember, write and design for skimming rather than full reading. Some people will want to know the why – others won’t care and just want to see how. You want to convert both.

4. Reviews are frequent – though overserved in testing

While the inclusion of product explainers feature more frequently, one area that bucks the trend is reviews.

• 63% of the Best LPs feature them, but that’s lower than the three-quarters of pages they feature in

They still feature the majority of the time, and so you’re likely to be safer by including them – but it begs the question as to why they are less likely to appear in the most frequent tests.

5. The sections you’ll want to include

With all this in mind then, here are the sections and their frequency of inclusion amongst the Best LPs.

• Hero – 86%

• Benefits – 54%

• Buy box – 34%

• Benefit detail – 31%

• How it works – 29%

• Comparison – 29%

• Offer – 45%

• Reviews – 63%

• FAQ – 51%

A few of our favourite landing pages

Having looked at hundreds of these in recent months, here are a few of our favourite landing pages. Check out our five favourites here:

These previews are only available to subscribers.

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Start with a best practice LP today

There is no one-size fits all approach, and even starting with those most common sections of the Best LPs, there’s still a huge amount of variety to be found within them.

But, we find it useful – especially for brands who have yet to testing landing pages – to begin with some of these best practices.

What we’ve not spoken about today is the messaging that feeds into your landing pages. For that, we recommend mirroring the same approach you use with ad creative:

1. Interview fresh customers to understand their problems and psychology

2. Write a landing page for that specific person and their problems

3. Build ads around it

4. Test the entire user journey

If you need support on landing pages and want to see what an insights & research-led approach to CRO and UX can do to your conversion rate, then drop me a line.

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I’m the founder of Ballpoint. We’re a technology and AI-driven growth agency that scales brands through creative and paid social. If you are spending £30k per month and want to scale to 10x that over the next two years – without losing any margin – then get in touch.

During covid, I was running my Wine List a DTC startup focused on wine education. And for a period of time it was easy.

• We managed to secure a Soho House partnership with minimal effort

• We got a £2 CPA deal with popular phone networks

• We ran terrible Facebook Ads and had a £20 CPA

  • We ran live tastealongs on YouTube and over 50% of the customers showed up

• We announced remote classes and people flocked

• 60% of people who signed up, purchased again after 30 days (and 75% of those bought again a month later)

This was despite the fact the product was full of issues, fulfilment was frequently a disaster, and none of us knew what we were doing.

We were doing well despite our own efforts.

We had product market fit.

When you have it, it’s unmistakeable.

But gradually, month by month that ease disappeared. Partnerships became harder to negotiate and response weakened. People made excuses for not coming to events then stopped replying altogether. Retention slowly bled away. And acquisition climbed up another rung of the ladder every month.

When I tell people the PMF story at Wine List, I usually say “12 months later, we suddenly had 10x higher CPA and 3x worse retention.” Except, it wasn’t sudden. It happened gradually.

We had lost product market fit.

Product market fit is transient

The thing no-one tells you about product market fit is that it’s transient.

Just because you’ve been lucky enough to find it, doesn’t mean you’ll have it forever.

Worse still is how hard it is to recognise it slowly slipping away. Anyone who’s read Thinking Fast and Slow will know that we don’t walk around processing every single piece of information we come across. Any number of pscyhological biases stop us from noticing small changes from inattentional blindness to change blindness to habituation.

So when something changes gradually, we don’t tend to notice it.

It’s highly unlikely product market fit will disappear overnight.

Your brand and positioning will stay in people’s minds for a long time. The existing customers don’t pay too much attention day-to-day. And so product market fit disappears not with a bang but a whimper.

At Wine List, it disappeared gradually. And every month when numbers slightly worsened, I was able to find excuses as to why. With hindsight it should have been easy to spot.

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The problem for established businesses

Imagine this scenario play out.

You work in a 15-to-20-year-old business. You’re doing £15-30m of turnover per year. You’re surrounded by some really smart and lovely people.

While maybe five years ago you may have seen yourself as a startup, today you don’t. You let go of your agile work practices. You now have a few people each in your marketing, operations, customer support, engineering, and design teams. People don’t imagine their job is at risk like it would be in a startup.

When you sit down with your board and think about OKRs you’re thinking in year-long horizons. You’re forecasting like you’re a Unilever brand.

You now get mentioned all the time by the press. You’re the go-to one mentioned. Your family and friends finally get what you do. You no longer work every hour under the sun.

Things feel peaceful. Indeed, you are operating as if you are in peacetime.

Humans create levels of structural incentives around them to protect their status quo. Leadership, management, heirarchy, planning, forecasting, accountability, meetings, meetings, meetings. All of this stuff creates safeguards against disruption.

At a certain point, maybe 10 years in, you were probably growing amazingly well. Everything you did just worked. You had the equivalent of the Soho House partnership that just happened. The CPA deals that felt like you were printing money. The FB ads that just worked with little effort. Customers loved you, and stayed around.

But somewhere over the last five-to-10 years that all changed.

And you’re looking at these tell-tale moments:

• CPA is 20-40% higher than it was a decade ago

• Retention is 10-20 percentage points lower per cohort

• Competitors seem to be springing up every day

• Lots of those competitors are fighting you on price and you’re wondering if you should respond

• Repeat is flat but for now it’s covering most of your opex

• You’ve pivoted marketing strategy once or twice trying to find a new answer

• You’ve launched new products only to see them fall a bit flat

• Everyone’s calendars are filled with meetings and less than half the week is for work

• You’ve launched brand campaigns on the tube and OOH but none seem to work

• Your huge influencer budget did something but you can’t tell what

• Another quarter passes by and you’re behind on target

I’ve met maybe a dozen founders over the last year who have described to me these sorts of problems.

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You no longer have product market fit

“But we’re not in those scrappy startup days anymore,” you might think reading this. “We definitely still have it, it’s just –––––”

Product market fit is a lot of things.

But at its crux, I think of it as:

You have a product that is desperately needed and adopted by a large-enough market to support your business goals

1. Your product needs to be exceptionally good
   Now in a market with no competition, you can afford to be less good than others.

   
   Likewise, if your goal is to build a great lifestyle business – or you do not need a big financial windfall – then it can probably be less good than others too.
   
   Less good doesn’t mean bad. But you can create a decent services business that gets you to £1m without needing to be 10x better than everyone else in your category.
   

2. Your market needs to be adopted by your market
   It’s not good enough to have a great product. If you can’t market it, you don’t have product-market fit.
   
   Products don’t live in vacuums (or at least they shouldn’t). Your product should be a living, reactive thing based on who your customer actually is. And you can’t do that without having your customers use it.
   
   This is why it’s the idea of “we’re going to find PMF then work out our marketing” doesn’t make sense.
   

3. The market needs to be large enough to support your business
   Maybe your goal now is to be profitable. Maybe it’s chasing the VC £100m exit. Maybe you never wanted that and you remain happy growing from £1m to £1.3m to £1.6m.
   
   All of these avenues are fine. But you need enough customers in it who can support it.
   
   And that means enough customers for your current product as it is, with the current marketing as it is. These three things all link together.

Take stock of your business and think deeply whether your business still satisfies those three demands.

Is your product still that much better than your competitors considering your marketing, market size, and aspirations?

Has competition now meant that the marketing is less effective per pound spent and so your marketing which once worked no longer does?

Is the market you’re acquiring from now big enough? Have you extinguished that cohort of users for whom the original version of the product and propositioning made sense?

The hard part of course is that all of these things interlink with one another. All tie together, and any change you make in any direction pushes on those numbers in odd and fascinating ways.

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Rediscovering late-stage product market fit

It turns out the process isn’t too different from what we’d recommend for early-stage brands:

1. Get enough customers to learn from
   Acquire some customers. Don’t worry too much about CPA, just get people through the door
   

2. Learn everything you can about those customers
   Speak to them. Meet them. Learn who they are and what problems they’re trying to solve. What psychological biases drive them.
   

   1. Evolve your product based on what you’re learning
      Now might be the time to realise there’s a need state that can be better satisfied if you adjust your product, now is the time to iterate.
      

3. Put that new messaging into your ads (this now trumps your product positioning)
   Test it. Probably entirely with statics, test different approaches to explaining what your customers have told you.
   

4. Measure
   Stay on top of this and work out what you need to see for the business to work.
   

5. Repeat until you get to good unit economics
   Until that point forget campaign structures, don’t worry about long-term. You are just repeating in weekly sprints trying to articulate why your customers are desparate for your product.
   

6. Scale to prove the market
   Then you hit scale and need to push to start to demonstrate the market size needs stack up. Then you move into a different mode of thought.

This might sounds like pulling teeth for lots of people. Especially if you feel like such days are behind you.

And while the above is definitely a ‘purist’ approach to finding product market fit, there’s going to be a million reasons why you can’t / won’t / don’t want to approach it in this way later on.

Feelings of job security might go down if you rip up the playbook. Managers who have been used to delegating work might find themselves having to do more IC work than before. There’s going to be someone in product or brand worried about what will happen to the existing customers as you think through these challenges. It’s going to feel tiring and stressful all over again.

But the bigger question should be the existential one: what happens if you don’t do this stuff?

Depending on what type of org you are part of will dictate how you can operate.

If you’re a physical product business with broad distribution and obligations to maintain, then the purist approach isn’t going to cut it. On the flipside, if you’re a consumer technology product, then you’ve got every opportunity to lean in.

I’d spend a quarter or two in PMF mode. Assume nothing. Turn off your acquisition. Tell your board you’re trying something radical to solve your core challenges and that you’re not going to be looking at or reporting on revenue for the next six months. You expect it to drop, and you expect runway to tighten.

Then get to work:

• Research your customers intensively

• Think through if product changes are needed

• Try entirely new approaches to advertising and marketing

  • try new ad accounts, new pixels,

  • maybe even a new website – you and fable or codex should be able to do that in a few hours)

  • ignore every “best practice” you’ve learnt so far – they have acquired the wrong people

  • try different creators who look totally different

  • make ads that look nothing like your brand

• Focus on learnings and hitting your unit economics all over again

You might be surprised at how radically different your CPA could be.

I speak to brands who are 15 years old who are amazed that their three-year old competitors have CPAs at one-tenth of their cost.

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This is a good thing

All is not lost. This might sound like a nightmare scenario. But it’s not.

To begin with, you’ve hired an amazing group of people – the best ones will respond well to these challenges.

Secondly, you’ve got the business maturity, and hopefully cash in the bank and cash flow to support some time in PMF mode. You should be less driven by short term today than you were historically or your upstart competitors still are.

Third, the competitors are doing this stuff now. And having read Innovators’ Dilemma you’ll know they have many advantages to you already. (If you haven’t read it buy yourself a copy today).

Finally, learning about customers is never a bad thing. Getting rid of all of your assumptions is no bad thing. People are small-c-conservative by nature, they hate change. Too quickly people do stuff ‘because that’s the way we do’. Ignore that urge, rip up the rulebook.

Worst case scenario, you hugely refine your knowledge of customer and realise you’re doing mostly the right things. Best case, you get to radically restart your business and the 10% annual growth you had last year is replaced by 50% growth this year.

If you want to chat this stuff through, I’d love to share what I’ve learnt from talking to other founds on this matter. And if you needed an agile growth agency who really gets this sort of challenge who can feel like an extension of the team, then we’d love to help.

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Josh is the founder of the technology-driven creative growth agency Ballpoint. He spent most of his career working on consumer startups, then he founded one himself (Wine List) which failed after two years and £1m in ARR. Today he loves writing about profitability, incrementality, advertising, and AI.

What our data says about who buys, what they spend afterwards, and the sale you can afford.

We matched one brand’s sale buyers against its email list.

• 1/3 of the people we considered “new” actually existed on their email list already

• The average amount of time people spent on the list was 191 days

• >50% of the remainder, showed up as “engaged” on Meta

This is a useful starting point for thinking about your Black Friday – because it’s only 67 days until the weekend begins.

And that means to a certain extent, some of your Black Friday performance will already have been determined. (Though thankfully just a small percentage at this stage).

When people talk about Black Friday planning, it’s usually focused on creative. That’s important of course, but so to is everything else around that. The media buying in advance, the offer testing, and perhaps most importantly really understanding your unit economics so you can be media buying based on live profit throughout the weekend.

We’ve put together a Black Friday advanced kit for Early Stage Growth subscribers. It covers the numbers to check before committing to a sale, with interactive examples you can work through against your own business.

Sign up to Early Stage Growth to view the report.

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Do not wait until November to ramp up your budgets

In our Black Friday analysis, existing customers accounted for 63% of sales revenue for the typical brand.

Even if they’re not becoming customers yet, they sign up to newsletters, they enter into Meta’s ads targeting. Then they convert during the weekend.

Growing your customer base now gives you more people who might return in November. Building the list gives interested people time to become buyers. Leaving all that work until the sale puts a lot of pressure on one weekend.

It also changes the budget. Be clear in advance about how much you want to spend on prospecting versus repeat customers. Some of both those customers, you’ll have already paid for.

Give each its own acquisition cost target, so a strong repeat weekend doesn’t hide expensive new customers.

And then you’re best off viewing H2 or Q4 as a whole and considering that total marketing period as one.

Making sure Black Friday is profitable

We compared first-time Black Friday buyers with first-time buyers from a matched October weekend, then followed both groups for the same amount of time. At the typical brand, Black Friday buyers spent 11% less on repeat orders in their first six months.

By nine months, that gap had grown to 22%.

Your brand may be different. And while these figures are averages, there’s definite examples to the contrary.

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A lower CPA can look good during the sale, but your COGS stay the same, returns are often higher, and CPA improvements rarely measure up.

Knowing your unit economics upfront is essential in planning. And even more important is having a live BFCM dashboard that updates hourly, you can see what’s in ours in the report.

Check out the report today

And answer these questions:

• Should I even be doing Black Friday?

• How much creative do I need?

• What unit economics do I need to understand?

• How should I forecast?

• What is the right level of spend for the weekend?

• How active should media buyers be?

• How do I make a profit?

Sign up to Early Stage Growth to view the report.

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I’m Josh, the founder of Ballpoint. We’re an AI & technology-led growth agency that’s deeply focus on customer psychology, performance creative, and marketing measurement. If you are in need of a killer Black Friday and don’t trust your current setup, then get in touch.

Today I want to talk about the dangers of chasing new shiny objects. Or what some call “Shiny Object Syndrome.”

Before I warn you off with the reasons why, I want to kick off by saying: I get it.

New stuff is fun. I should know, I work in advertising. And as any Mad Men fan will tell you, new is what works in advertising.

But “new” is not just fun, psychologically we’re predisposed to the “new” thanks to a cognitive bias called the ‘novelty effect.’ Our brains are hardwired to appreciate it.

Instead, the most important thing you can learn: is focus.

Today, I want to share some experience, data and and guidance that will hopefully help you replace shiny objects with focused mindfulness.

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The desire for new markets, new products, new channels

In growth and startup land, shiny object syndrome presents itself in a few ways. One is often in creative, but while creating a yapper because you’ve seen it on LinkedIn might be a distraction, it’s ultimately a temporary, quick, and cheap one.

The biggest causes for concerns are:

• New channels

• New products

• New markets

Purdy & Figg’s Jack Rubin posted this great quote a few months ago.

And if you wonder how well Purdy & Figg are doing, then know that their last filed Accounts, for the year March 2025 reported £39m of revenue up from £18m the year previously. And for most of that time they were: single product, single channel, single market.

Where does this pressure come from?

1. VCs

I was asked this by a VC when I was running my last DTC startup.

With hindsight, what I should have said was: “we’re not – if we can’t get Meta to scale to £25m turnover in the UK, we probably don’t have a business that is going to make the return you need from it.”

But at the time, I was less experienced and I remember thinking ‘hmm maybe we should?’

Diversification doesn’t only come from VC pressure, but I do hear it perioidcally from that community.

2. Ego

Before I joined Thriva as first employee in 2017, I applied for lots of roles where I had to put together case studies/marketing plans.

There were two I remember getting really excited about.

One I would have been overseeing their new TV campaign and helping think about measurement for it. The other, we were going to invest over £100k in content marketing.

Both would have had really cool deliverables. Things I could have stuck on my CV or LinkedIn and would have got lots of likes.

But, again with hindsight, the probability that those things would have turned those businesses around is like close to 0.

The truth was a lot of why I wanted to do those things was ego. If it goes well, you’re a hero. If it goes badly, hey you tried. And if nothing else you’ve got great stuff you can share and show people.

3. You’re at a growth dreaded ceiling

Now perhaps the most legitimate reason you have is that you’re at a growth ceiling.

I.e., you’re trying to push growth in your current conditions and for whatever reason you just can’t scale.

I used to include this slide in our pitch decks

The point was to show that growth is not a hockeystick curve but it’s actually a case of:

1. You potter along not doing much

2. You land on something that works and it scales hard and fast

3. You hit another ceiling and you keep pottering along

4. Repeat

And at each of those stages, you try to identify bottlenecks. And it’s true that product, market, and channel are all bottlenecks. But the one that most people ignore is your core growth engine.

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Experiment results from introducing complexity

Growth is probabilistic.

And so while the ‘focus’ argument is our default one, it’s also important we test ourselves perioidcally. And so, we’ve got a handful of experiments over the last 3.5 years from clients where complexity is added in different ways.

Given this, I wanted to share a few core hypotheses and the number of data points for and against we have. Here are the hypotheses/truths we now hold::

1. “More products or choices on site lowers the conversion rate”
   We’ve tested this systematically across eight clients. Three of those clients we’ve attempted this multiple times.
   
   The range of things here include: different flavours, product changes that suit entirely different customers, bundled extras, different payment tiers, and brand new adjacent products in a category.
   

   For: six clients supporting the hypothesis across 10 experiments.
   Against: two clients against the hypothesis across two experiments.
   
   In the ‘against’, we have one new flavour, and one bundled product extra that delivered significantly more value for the end-user. Every other instance resulted in lower conversion rate, higher CPA, worse CM3.
   

2. “Diversifying product mix on paid social increases CPA”
   This can happen as above when a new product launches, but often it comes from when a client asks ‘can we advertise these extra products we have.’ It feels like an easy win.
   
   Except, as with the above, more often than not it results in overall higher account CPA. But worst of all it diminishes the ability for us to learn and produce rocketship ads.
   
   For: five clients supporting the hypothesis across nine experiments.
   Against: two clients against the hypothesis across two experiments.
   
   The two ‘against’ clients were both brands with very large product catalogues (500+ SKUs). All of the ‘for’ were those with product catalogues under 30-50.
   

3. “Forcing a bigger first basket reduces conversion”
   This is often a result of either enforced bundling or adding extras that push people to higher purchase value.
   
   For: three clients supporting the hypothesis across three experiments.
   Against: one clients against the hypothesis across one experiments.
   

4. “Introducing a new channel will increase CPA and decrease CM3”
   We’ve tested TikTok nine or 10 times over the last 12 months. We’ve tested Pinterest twice. We’ve tested Reddit three times.
   
   One TikTok tests improve overall performance and proved to bring great CPA. But every other channel test over the last year has been a fail.
   

5. “Introducing a new market can work if the timing is right”
   The last one is one where the idea is sound, but timing is essential. We’ve run six US launches over the last 3 years. Three worked, three didn’t.
   
   Here the ones that worked were all brands spending over £100k per month consistently and had been doing so for a long time. They were also all brands that were profitable in the UK.
   
   The fails were all spending well under £100k, and were not yet at break-even in their core market.
   
   Of the ones that worked, one was almost instantaneous, the other two took six-to-twelve months. The three ‘fails’ we’re still trying to work on one, while the other two we’ve refocused efforts back to the UK.
   
   You don’t need ebit profitability to make the US work. But you should go into it when the timing is right. Having a solid engine that is scaling healthily in the UK is important.

   
   Being profitable is even better. Why? The US is expensive. Expect CPAs to be 10-50% higher. AOVs will be too, but at what cost to cash flow?

   
   Not only that but there are big behavioural differences between markets. The US likes to be sold to more aggressively than the UK does. Your premium brand-focused lifestyle imagery is not going to cut it in the US. You need creator content being super explicit about what problem you’re solving for them – and everything adjacent to it.

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Keep focus, stay calm and improve your core offering

Complexity can kill businesses. In the examples cited above, we had strong and clear experiment conditions where we’d pivot away.

Improving the growth engine is tough. It’s unknown. You have no idea whether you’re truly at a ceiling or you just haven’t found the latest thing that breaks through yet.

But it’s also where most of the time the magic happens.

Your core is also where the majority of all future revenue comes from. Secondary channels and products rarely make as much as the core one. And while a US launch does have the opportunity to help become the number one, it’s by no means gauranteed – and it’s bloody expensive to get right.

If you’re VC-backed, the chances are they’re looking for you to do £100m in turnover. If you’re not able to spend £150k per month or do £10m of turnover based on a single product, single channel, and single market, then you’re probably not going to make it to £100m either.

If you’re not, then this stuff is less relevant. If you’ve hit a ceiling at £2m of turnover, then maybe that’s fine. Only you will know if you can build the outcome you want at that level of turnover.

But know that the journey beyond your core focus is going to grow you slower. Diversification should feel like the beginning of a whole late-stage era of your business, not an early one.

Don’t get yourself into situations that are irreversible. Protect your runway. Protect optionality.

Eyes on the prize. Stay focused, and you might just find a second lease of life (and third, and fourth, and fifth).

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Josh Lachkovic is the founder of Ballpoint, a creative growth agency helping you scale ad spend from £50k to £500k per month. If you’re looking for support, then get in touch.

I remember writing it on board a flight to Los Angeles, feeling appropriately detached from the real world as I did. It felt so abstract, so exciting, and with that pinch of anxiety that underpins all AI pondering.

When I re-read it recently, I was amazed at how prosaic so much felt.

At the end of March, I took the team to France for an AI offsite. We did two days of training, futuregazing, and began a true pivot for us as an agency. My instruction to the team was to treat it like zuck’s mobile moment.

The goals I set out in that first article, it turns out, were narrow and unambitious. All of the stuff I was pondering then that felt months away we achieved pretty much within thirty days.

And so today, I want to revisit with battle-hardened eyes and express what I think it now means to be AI native.

I would love to hear how your organisation is approaching this as well.

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Defining “AI-native” or “AI first”

Here’s how I now think of AI native:

AI-Native. adjective.
Definition: 'A person or organisation who uses AI as the operating system for their work.”

The actual definition here isn’t too far away semantically from the original (getting AI to do work for you). But in practice the notion of an operating system changes things fundamentally.

Here’s what this looks like in practice.

Stages of AI adoption

1. Using AI as a chatbot

2. Using AI as a coding or writing or design assistant, but still within the core AI tool (ChatGPT for example)

3. Getting AI to do work for you (usually by integrating external systems)

4. Partial-agentic builds: you use AI to automate tasks you do, but still act as a human in the loop to validate and improve.

5. YOLO agentic: the AI is as good as humans at the task and does not need human supervision

6. Building self-improving systems that get better over time

Now the reason the idea of AI as an operating system is important is because to achieve stages five and six, you need to rethink how your work takes place.

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AI as an operating system

First of all, when we think of an operating system what do we really mean?

Windows or MacOS or iOS are the major interfaces we use when we ‘use a computer.’

For those of us who got into computers in the 90s and early 00s, we all had to understand what an operating system was.

But as technology has stripped away friction and moved towards convenience, most people won’t really consider what happens when you open up your laptop.

The way I think about it is this:

• The operating system is the mega app that you use to do all the other things you want to do

• The operating system allows you to carry out lots of different functions and tasks

• It has system files and data and infrastructure that mean all of your stuff just works together

For a long time, in work, we’ve used the operating system to open up all the separate applications that allow us to complete work (Excel, Sheets, Slack, MySQL, Photoshop, whatever it is).

Now with AI, work lives in the operating system layer itself. When my colleague Mark said “the way I use a computer has fundamentally changed,” this is what he meant – we no longer open up separate tools to get work done.

Back when I thought AI native was about delegating tasks, I didn’t quite see the level of other stuff that had to take place for that to work effectively.

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The work before the work (infrastructure and good data)

I spend twenty hours a week building with AI, and the majority of that time has been spent on infrastructure work.

AI is very good at interpretting random documents. You no longer need perfect data for a computer to interpret it.

But actually making sense of it reliably is a different issue.

There’s two major reasons why:

1. Context rot – what happens when you have too much context in one session

2. LLMs are probabilistic and so will give different answers

Now maybe those two things are fine for a one off piece work. But if you want to regularly have AI perform cross-app analysis and automation for you, you need it to be cost-effective and reliable.

Cost effective means having the data in an easy-to-read system for the AI (i.e. not Notion). And reliable means having results that have ‘truths’ within them and look the same no matter how many times you run them.

Data infrastructure is how you solve this.

For us, that has meant creating a Second Brain for our entire agency. But it has also meant migrating systems into bespoke databases and setups, which are built exactly for what we need them for.

I was always quite good at SQL and so the database side of things comes a bit more easily to me than most. But the Second Brain stuff relies on concepts that are more ephemeral. And it has required a lot of experimentaiton.

I’ve probably spent over £3k on building the Second Brain, and I’d still not consider it in a v1 state yet, (we’re v0.6).

In short, to do the job really well, there is a lot of work that takes place before the work begins.

You need your data layer as a foundation for the operating system to work.

This is the big thing: without the data layer properly in place, the rest of your operating system falls apart.

It’s not just individual apps within your OS that suffer – it’s how they interact with each other.

Consider this:

In our first setup, we realised that each team’s repos started having overlap. Maybe it was clients folders, or maybe it was team structure info, or maybe it was rules on how we accessed bigquery.

This is a reflection of pre-AI workflows. Every team even if well connected will end up duplicating sets of variables and templatable information.

Now we have a separate repo that looks after all uniform client data, and each individual repo accesses those client work directly. No overlap of work, no stray versions.

Your operating system is only as good as the infrastructure within it.

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The blurry lines of using your AI vs working on the AI

In the early parts of this year, I did a lot of building tools (mostly skills) for the team.

It didn’t take long for people to stop using them and instead start interacting with Claude itself. In some cases, we spent a long time building automated tools in Slack only for the team to prefer working in VS Code.

We went back and forth on whether this was the right workflow or not.

What I’ve realised now is that there’s four core modes of working with our AI:

1. Automations

2. Context-rich AI environment

3. Build & development

4. Acting as a backend for another tool

At the start these blurry boundaries felt wrong, but once you get into the operating system mode of thinking it all makes more sense.

Importantly all four of these can be inside one folder/project/repo/agent.

An example of how we might structure is this:

/cc/ballpoint/design/
/cc/ballpoint/growth/ 

And so within the ‘design’ folder we have the above four modes. Here they are in more detail.

Mode 1: Automations

After the initial hurdle of getting AI into a good enough place that it completes tasks for us, the next phase was removing ourselves from that process.

First you build the workflow. For us, this is usually a heavy mixture of scripts that the AI has vibe coded for us, with a small smattering of LLM skills thrown in.

Second, you automate it. There’s loads of ways of doing this from Claude’s Managed Agents, to cron jobs on your machine.

All of these live inside the core agent, maybe in a set of subfolders like:

/cc/ballpoint/insights/scripts/reddit_scrape.py
/cc/ballpoint/insights/skills/fresh-research.md

This is where I imagined most AI work would stop.

Mode 2: Context-rich AI environment

Once the team were comfortable working with Claude Code, the next thing was the realisation that everyone prefers being in the command line inside this agent.

Take the /insights/ agent. That likely exports information to a certain location. Maybe it also has a database that’s questionnable.

But what’s far more natural is to fire open a terminal window, point it at /cc/ballpoint/insights/ and ask Claude “what’s the oddest insight we have about Tesla?”

This is one of the most exciting day-to-day use cases for us because of how rich the context is. Not only that it still has the skills and scripts on hand to do further analysis. Throw in some memory/dreaming aspects and this gets richer by the day.

This is the mode that extends beyond the operating system idea. It’s as if an operating system had perfect search and context.

Mode 3: Build

In March, I view was that AI skills would often be one-and-done.

Instead, we now see AI as product development.

Every team is a product team.

There is a PM on every team who owns the roadmap, which filters up into the company roadmap. And building is everyone’s responsibility, where we log commit and push count.

Build mode is when you might be thinking about improving your software. Maybe it’s adding a new feature, or bug fixing, or maybe working towards much bigger releases. All of this work still exists inside the usual agent directory.

Mode 4: Acting as a back-end for another tool

We’ve been late to vibe coding inter faces, mostly because the team prefers the command line. But the last month or two we’ve been increasing the amount we’re doing here.

As such we hit the issue of realising that one agent or project or software may want to borrow data from another. While we aim to create good databases where we can, often data does live in markdown or as outputs from one agent’s workflows.

At this point we realised that having agents be able to talk easily to one another was important. Usually this is the case of adding an extra script or skill just to signpost what to share when called by another directory.

But this interoperability between agents we’ve made, I realised is one of the final strings to the ‘AI as an OS’ bow in my mind.

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What comes next?

Self-improving systems

The above is mostly where we’ve gotten to so far. A bunch of interconnected systems all with strong data infra beneath it. Product teams build agents. Agents talk to each other. Work is more comprehensive, based on more learning and more insight.

But the next stage is the most exciting.

It’s how we build self-improving systems.

Tom Blomfield talked about this a few months ago in a YC lecture.

I went away and built what he talked about.

We called it Lenny (named after the intern’s favourite PM Lenny Rachitsky). Lenny would listen to Slack every day, identify workflows that might be automated, then build those skills and set up those automations.

For us, it wasn’t the right order of magnitude to have an impact.

I think for products over services, maybe it makes more sense. You can analyse user behaviour or feedback en masse, and then build updates and get feedback loops.

We’re still a service business. And so for us many ‘servicing’ jobs are often one-offs or infrequent or bespoke, and so having a skill run them is a waste of time.

For us, self-improving we’re now thinking bigger. If we have a skill that gathers data for us, instead of us deterministically giving inputs, how can it analyse new requests, maybe even build fresh data structures and analyse them, for example.

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Closing thoughts

Underpinning all of these ideas is the fundamental rethink that AI is a new operating system.

It’s not a place you go to get it to do a piece of work for you.

Nor is it a place to go to build an automation.

It’s many things all at once, which all interact with each other.

When I first wrote about AI native in March, the idea was that we could one day get AI to do a task for you. After a month of pivot, over 80% of tasks at Ballpoint were done for us by AI.

If you’re not there, then getting there is a revolution. And I highly recommend that as your first goal. But the impact of AI seems to be exponential. What feels like climbing Everest one month will feel like a pebble in the road the next.

This remains the most fun I’ve ever had at work.

My only question now is: in five months time how prosaic will these ideas feel to me then? I can’t wait to find out.

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Josh Lachkovic is founder of Ballpoint, the AI-native growth agency for brands looking to grow ad spend from £100k per month to £1m profitably. If you want to see how we can help grow your brand through creative-driven performance marketing, then get in touch.

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