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.

Last year, Meta released incremental attribution. When we reviewed it last year, we were skeptical.

On paper, it was a great product. Meta has been way ahead of other platforms by offering incrementality testing products via Conversion Lift for many years and incremental Attribution was built on that.

But the first release was a damp squib. In one of our tests we saw our off-platform incremental CPA increase 5x when using it. It just wasn’t effective at finding the people on platform.

Fast-forward a year, and boy has it had it a glow-up.

How incremental attribution works

All attribution these days is modelled in some way. Ever since iOS14.5, platforms have had less “true” data than they had before. Thanks to cookies and ad blockers and privacy-modes, it’s harder to track a user journey deterministically.

And so even the “7 day click, 1 day view” attribution model we’ve all defaulted to over the last few years is a predictive model.

For businesses with no brand or organic or other presence, we find on average that model under-attributes the true performance. And for those with high volumes of external stuff happening it does the opposite.

So the old attribution model was always broken.

The new one is based on good stuff: all of the conversion lifts that Meta have run.

For those unaware, these are holdout tests that expose your ads to 80-90% of an audience and not the last 10-20%. You count the number of purchases in your unexposed audience and treat it as a baseline, then count the volume in the exposed audience. The difference is your incremental CPA.

Using that as a model made great sense. But last year, it just didn’t work out.

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California Wine Club: on-platform and off-platform improvements

We’ve been working with California Wine Club for over a year now.

Summer in DTC is never a peak period, and so we wanted to run a backlog of media buying tests. One of which was incremental attribution.

Now, given our history with IA, we ranked it with low confidence – however, we always like to revisit experiments every 6-12 months. Why? Meta’s algorithm is constantly changing. As well as the major updates like andromeda and gem and lattice, there’s also the smaller day to day updates they push all the time.

Not only that, but people change. Browsing habits, culture, usage of the platforms. With every change comes an opportunity to re-test something.

The results

We decided that the test needed to be a total account switchover and we’d measure before/after. In a dream world we’d run a conversion lift, but time and cost got in the way.

And so we ran a full switchover of our hero campaign setup.

Now, ignoring the fact that new campaigns usually take time to get up to speed, this straight away got off to a great strat.

The key findings were this:

• Platform CPA itself barely changed (3% up in one like for like period)

• Platform Incremental CPA went down a big chunk (29%)

• And off-platform new customer CPA went down even more (33%)

This was an account where spend was – owing to off-peak – static recently. But as a result of this we’re scaling spend and those ncCPA figures remain stronger than before.

Interestingly, before IA, most of the acquisitions attributed were viewthrough, whereas now they’re predomiantly click.

Testing on other clients

As you can imagine, a test like this created quite the excitement in our #growthstrategists chanel.

Since this July test, we’ve now begun rolling out incremental attribution tests with a series of other clients.

Other early results are positive:

• One client saw platform CPA come down 20% overnight

• Another we saw a 7% decrease in blend, while platform remained flat

• We’ve begun testing it with earlier stage clients where data is more volatile to good effect to.

But it’s not all singing and dancing.

We have also seen one instance of almost no impact, and at the same time an increase in ncCPA.

Outgoing thoughts

From automatically turning on AI optimisations to the buggy Ads Manager experience, it’s easy to get frustrated at Meta. But this is one of those great examples of algorithm improvements taking place in the background helping things along.

In the space of six weeks, IA has gone from a ‘steer clear of this’ to a position of almost being the new default.

It’s not 100% success rate (nor is anything). And so testing is vital. And we would highly recommend running our approach to testing which is:

Wholesale account switchover: before and after time window tests

Without this, our hypothesis is that Meta – given the choice of two ads to serve in the auction, will opt to serve the easier to attribute one (non-IA).

We have seen stronger impacts where accounts were viewthrough-driven, but it is impactful in most accounts even when that’s not the case.

How are you finding Incremental Attribution?

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Josh Lachkovic is the founder of Ballpoint. We’re the growth agency you hire when you want to dominate your competition. We find profitable growth through psychology-first creative. We’re AI-native, meaning you get the technology power of a tech startup powering a boutique agency. Get in touch if you want to scale spend from £100k to £500k.

Teams often lionise every pound spent on Meta ensuring its efficient and effective.

But there’s a huge area that is practically ignored by almost everyone I know – that is, what is the ideal cost per ad creation?

If you’re an in-house team, you may not even be calculating this. Time often gets ignored inside startups (I know in all five startups I’ve worked inside, pre-Ballpoint, I never once saw time tracking in play).

If you’re paying an agency or freelancer, no doubt you’ll have a sense of it. But I imagine most of the time decisions come down to gut (“That creator is charging how much?” or “we want to make this look really special, we’re happy to invest in it”).

It feels like a nice-to-have. Just the cost of doing business on Meta. But in fact it’s one of the most important figures you can have as a chief growth officer or founder. If your agency isn’t talking about this with you, share this post with them and go and ask why.

This might sound like a nice-to-have question, but actually it’s the difference between your Meta ad account working or not working.

Why is this?

To understand the answer to this we’re going to learn today a little bit about probability, ‘expected value’, and why poker players make excellent performance marketers.

Topics like this can easily stray into deep theory.

So fear not, I’m going to keep this concise.

And there is a huge practical output.

Background context

A small number of ads have hugely outsized returns

Open up Ads Manager, organise all ads by spend, and it’ll be dominated by just a few ads.

Meta's auction pushes spend towards whichever ad it thinks is most likely to convert

The result is a distribution curve that looks like those above from a couple of our clients. A huge longtail of ads that get effectively no spend, and a small selection that get a lot of spend.

Ads diminish in impact over time

Anyone who has managed ad accounts for a long time also knows that ad impact diminishes over time. But if Meta’s signals indicate it’s still the most probable convertor, it’ll continue to spend into it.

Some ads last longer than others, but at their core every ad has a lifespan and it diminishes beyond that.

And when you work back from there it explains why you need to create high volumes of ads.

This is why a high volume of creative is important

Looking at those two client charts above and the top 10 ads are spending 2,000x and 200x the median. One client has 1,500 ads in there, the other over 2,000.

Distribution is heavily, heavily skewed towards a select number of ads.

If the strength of an account is pinned on a small selection of domiannt, hero ads, but the number of those that reach that status is tiny, then that demonstrates quite how hard it is to predict success.

Our entire process is about improving the probability of that successful outcome, but at the very best it is still a coin toss whether an ad will go on to beat the median or not.

What this means for thinking through testing

As a result, we have to think of ads as a version of placing bets.

We don’t know what the result will be. No amount of data or insight can gaurantee that outcome.

And so one of the biggest levers we have is the volume of bets we make.

After all if one in 140 ads goes on to be a ‘top 10 ad’, then not only is volume vital, but so too is understanding the cost per creative.

Cost of ad creation becomes the straw that can break the camel’s back

Creating 140 ads is one thing.

But the cost of creating those is one of the big unspoken of things in performance marketing.

And this is where understanding a little about probability and expected value (EV) comes into the fore.

At a simple level think of it like this:

1. You should see the overall cost per test as the media spend + the creative cost

2. Say you do £300 on media, but £500 on creative (time, shoots, editing), then your cost per creative is £800

3. Identify the “expected value” of each ad

4. Identify the maximum you’re willing to pay for each creative.

What is expected value?

Expected value is calculated by multiplying all possible outcomes by their probability of occurring.

A simple EV calculation is this:

• Let’s imagine we’re playing a betting game on a coin flip.

• A coin flip pays £100 on heads, nothing on tails.

• Expected value is each outcome multiplied by its probability, added together.

• (50% × £100) + (50% × £0) = £50. The EV of this flip is £50.

• That £50 is the most you should ever pay to play. It’s your break-even.

• Pay £40 per flip and you make £10 per flip on average, even though you lose half of them.

• Pay £60 per flip and you lose £10 per flip on average, even when heads comes up.

• The skill isn’t predicting the flip. It’s knowing the maximum price worth paying to be in the game.

With ads, the price of the bet is media plus creative. The payout is what a winner contributes over its lifetime

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Use our Maximum Creative Cost Calculator

We’ve built a calculator so that you can forecast these figures by yourself.

Here are the figures you need:

• The share of your ads that become winners, workhorses, and duds

• Expected lifetime spend for an ad in each bucket

• Revenue per £1 of spend (aMER) for each bucket

• Your gross margin before marketing costs

• What you currently spend creating each ad

Calculate my target creative cost

Josh Lachkovic is the founder of Ballpoint. We are a creative-first growth agency built for the brands looking to become the dominant players in their categories. If your goal is to triple your ad spend this year, while maintaining efficiency goals, then get in touch.

RIP creative strategists! Claude is here and now you are all dead!

If you spend any time in the corners of the internet where performance marketing people rage bait each other, you’ll have seen two types of post about AI. The first is some variation of of the above: “It’s over for [insert job title], time to get into pest control.” The second comes from copywriters and designers arguing that AI is fundamentally rubbish and always will be. Dread or wild exaggeration on one side, dismissal of its potential on the other.

Obviously, both of these extremes are way off. I’m a creative strategist, I’m alive, and our agency is looking to hire more of us. Also, I use AI all the time and it makes my output much better than it was six months ago. The question isn’t really whether AI is good or bad at creative strategy; it’s where the line is between what it’s good at and what it isn’t, and how you work with it once you’ve figured that out.

What AI is very good at

Performance creative strategy has always involved dealing with a lot of information. Customer insight reports, creative sprint writeups from your growth strategist colleagues, Notion databases full of past results. Even when all of that is well maintained, it’s a big ask for any individual (especially one who says they’re “more of a creative than a systems person”) to spot every pattern across hundreds of tests and thousands of data points. There’s too much.

Not a problem for LLMs. Given the right context, working with Claude is like working alongside a strategist who remembers every test you’ve ever run, sees every possible correlation across visuals and copy, and works at ridiculous speed. The combinations of format, visual style, and customer insight are essentially endless, and AI can map that stuff faster and more comprehensively than any human team.

What AI is less good at

The data that AI works from is either (a) insights from ads you’ve already tested or (b) insights that it knows we’re yet to test in a certain format. It can tell you what won, what patterns your best ads share, what combinations you still need to try.

But a strategy built only on what has already won will always have limitations. The tests AI suggests will be very sensible and certainly worth briefing, but always fundamentally unoriginal. AI won’t intentionally break a rule and it won’t ever decide that a strange, untested idea is actually a good one. Rely on AI alone and things will stagnate quickly.

Why new ideas need people

The further you push into new creative territory, the more you need someone who’s engaged with the subject matter. Somebody who has spoken to customers and who has spent time thinking about what changes for them when they buy the product you’re selling.

If no emotional being has really thought about whether an idea or a message is in some way moving or persuasive, it probably isn’t going to move or persuade anyone. It might tick every best practice and creative diversity box, but it probably won’t make you feel anything. And ads that don’t make you feel something do not work.

Division of labour

Where does this leave the creative strategist? I think the job divides fairly neatly. AI handles the heavy lifting: pattern recognition, diversity mapping, the structured side of testing at volume. The creative strategist handles new creative directions, judgment calls, and the parts of the role that come from engaging with people and products.

The judgment aspect here is important, because when the possible combinations of format, visual style, and customer insight are endless, someone has to decide what’s actually worth testing. A lot of people are calling this “taste”, but I think it’s closer to instinct. Taste implies a kind of refined sense of what’s relevant or cool, but really we’re talking about a feeling for what people actually connect with.

TL;DR

AI isn’t killing creative strategists and it isn’t rubbish either. I’ve come to think of it as an incredibly capable coworker that may ultimately create the space to do the work that we’re especially good at, and dare I say, enjoy. Something like that please, make it sound like me and don’t use any em dashes.

If this sounds like the kind of work you want to do, we’re hiring creative strategists at Ballpoint. Drop me an email at mark@weareballpoint.com.

We’re hiring for two roles at Ballpoint currently and you might like to apply.

A quick reminder of what we’re doing.

Today, we’re a creative growth agency working with 20 of the most exciting growth stage brands in the UK and US.

For brands we work with for more than a year, we typically triple spend year over year. And that holds into years two and three.

Our 12 month client retention is way way way above industry average, and we have over 88% 12→24 month client retention.

Why?

The team of people we have are the very best, most creative, brightest minds I’ve ever worked with in growth. The calibre of conversation we have is second to none. That turns our process into an engine, and our learnings into performance.

We’re office-based too, which means that we collaborate and communicate quickly, creatively, and unencumbered by needing screens.

But what about tomorrow?

All readers will know we have been AI native since February.

For us we come back to a few simple principles:

1. If we were to launch a business today with the vision of helping brands grow aggressively, what would it look like?

2. How can we create output that far outperforms anything we ever could have done just as humans?

Those are the two guiding principles to our future, and the way that’s manifesting is through a series of AI agents, software, and systems we’re building now.

As such, these roles have two gears.

Gear one is: how do we help grow our exceptional clients of today.

Gear two is: how do we all build the future of growth for tomorrow.

And both of these roles have both of these as job spec requirements.

Who will do well here: head of growth

Our job offer rates are lower than Goldman Sachs or Harvard. That is to say, we are only hiring the very, very best.

Here are a few prerequisites we have:

• In-house consumer experience at a brand or startup

• Deep paid social experience

• Insanely strong communicator

• Highly organised, we are super lean and agile

A few weeks ago, Sophie, our head of growth strategy wrote Good growth strategist, bad growth strategist – which should give you a further steer.

If this is you, apply via here – or drop me an email with your LinkedIn.

Who will do well here: creative strategist

Creative strategy is this new field for what we do. We’ve found there’s a few characteristics that really stand out.

Here are a few prerequisites we have:

• You care about performance. Brand strategists need not apply

• You like copy that sells. This is vital. If it doesn’t sell, it’s not an ad

• Highly organised and fast worker, we are super lean and agile

If this is you, apply via here – or drop me an email with your LinkedIn.

Join us

I’m biased, naturally. But I think what we’re building has the potential to be the future of what growth looks like. Not just in agencies but across the industry.

We’d love you to be a part of it.

Here’s one absolutely massive AI fail I had this weekend.

I thought I’d share it because AI has become a world of look at these amazing things I achieved.

But (like everything) getting good at AI involves a lot of failing before you eventually succeed. And you see far less of that side of it.

I’d been seeing the “I no longer prompt, I create loops” stuff everywhere.

I’ve been desperate to learn loops since Ralph Wiggum came out last year, and I’ve had some really great successes with them too.

But I wasn’t doing the whole:

Here’s my prompt, Fable. You go away for three days, and when we next speak my world will have changed.

And, well, I wanted my world to change.

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What I was trying to do

Over the last few weeks, I’d built a particular data science model quite slowly with AI in the “old” way.

I went through long brainstorming and planning sessions. I interrogated those documents through a mixture of my own reading, AI evals and our data team.

I built a frontend for it using Claude Design, and suddenly had a beautiful model and report to share with a client.

It took a lot of hands-on time, but the output was there.

Over the weekend, I thought I’d use loops to extend it across the whole company roster.

I was using Claude Code, set to Fable 5. I told it that it could use a mixture of Fable 5, smaller Claude models, and all the new GPT-5.6 models it could access through Codex.

I set the goal that it needed to reach parity with the model we’d already built:

• same safety precautions

• same checks

• same planning decisions

• the output should contain all the same aspects as the original

I’d pre-empted a few of the conditions it was likely to hit because the data was slightly different from client to client.

I gave it permission to make judgements where one client’s data wasn’t sufficient.

I gave it evals.

And then I set it on its way.

So, what went wrong?

First, it overengineered almost everything.

The judgement around client data became a whole rabbit hole. It started creating its own models to judge whether each dataset was right or not.

It spent more than half a day on this, going through two five-hour context windows.

But perhaps the bigger failure was that my definition of “parity” around the output wasn’t clear enough.

There were around 100 variables that the original model produced. Part of my original work had been turning a dozen of those into a usable client report with actual insights.

The loop took the 100 variables as the goal.

It deleted my original frontend and got every client through to completed data. But the variables that were actually important were invalid for most of them.

It had focused on output rather than the right eval.

The other big problem was how it broke down the work.

It created six phases. But instead of doing one client all the way through to completion, it took all 21 clients through phase one, then all 21 through phase two, and so on.

The result was that when those six phases were wrong, they were wrong for everyone.

I’d been nudging it with feedback all weekend, but I didn’t notice the full extent of the failure until Monday morning. I eventually interrupted it and asked it to show me where it had got to.

That was when I realised:

1. The original client output had been deleted. Fine, it was recoverable from the commit history, but the whole new workload had been based on false data.

2. I’d used 80% of my weekly Claude tokens (mine reset on Saturday mornings), plus two of my OpenAI reset windows. I’d also spent quite a few API tokens after deciding to force some production work through in an effort to get it live.

3. Almost all of that work was wasted.

4. I’d given up six hours of my weekend for something that was a waste of time.

It didn’t create the best motivation for a Monday morning.

What I learned

There are learnings in all failures.

I’ve had success with loops like this before, but on quite different projects. It’s clear I need to refine my process if I attempt something like this again.

The obvious change is to take one client all the way through to completion before allowing a loop to repeat the process across 21 of them.

But one of the bigger lessons is the same as ever: don’t just follow the trend.

There’s new AI stuff every week. You don’t need to try it all, and you don’t need to replace something that already works just because there’s a new way of doing it.

Your objective is the most important thing, not the tool.

Hello, Josh is away this week so I’m filling in. I’m Sophie, Head of Growth Strategy here at Ballpoint. With three years experience of growing brands like Thriva, Mother Root, Tilly Sveaas, and Origin Coffee under our belts, we’ve reflected on what it takes to do this job exceptionally well.

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