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.

This is an internal document we’ve put together which we wanted to share with you. It’s inspired by Ben Horowitz’ 2012 essay Good Product Manager, Bad Product Manager.

A good growth strategist thinks of themselves as the head of growth for their client’s brand. They take responsibility for all of growth, not just the ad account. Ads don’t stop at the click, and neither do they. A bad growth strategist thinks of themselves as a media buyer.

A bad growth strategist poses questions and considerations. They say things like “it’s up to you.” or “we could potentially explore...” and leave the client to decide. Clients don’t hire you to present options. They hire you to know what to do. A good growth strategist has an opinion. When performance dips or a new channel is on the table, they come with a recommendation of what to do next.

A good growth strategist pushes back. When a client asks them to do something they disagree with, they say so, clearly and with reasoning. Then they know when to concede. A bad growth strategist either does whatever the client says, or fights every battle with equal intensity and loses trust. This is about focus. A good strategist helps the client focus on what matters most right now: what decision is going to make the biggest impact on their north star, and what can wait.

A good growth strategist cares about contribution margin, not platform CPA. They know that a £20 CPA on Meta means nothing if the blended CAC is £45 and the unit economics don’t work. The metric that matters is CM3: what’s left after ad spend, COGS, and fulfilment. A bad growth strategist lives inside the ad platform and never zooms out to what actually hits the P&L.

A good growth strategist owns the data. They know their client’s dashboard inside out because they shaped it. They know where the numbers come from and what’s missing. They may not be a data engineer, but they know what questions the data should answer. A bad growth strategist opens the dashboard on the morning of the client call and hopes the numbers make sense.

A bad growth strategist doesn’t have a structured approach to testing. They launch ads without clear hypotheses, which means when results come back, they can’t tell you why something worked or didn’t. The data is harder to interpret and the results are unclear, so nothing compounds. A good growth strategist runs structured experiments. They have a hypothesis, a test, a clear success metric, and a timeline. They know how many creative concepts to test each week and why. Every result, win or loss, teaches them something they can use next.

A good growth strategist is the first person to flag a bad day. They message the client before the client checks the dashboard. They’ve already looked at why, and they have context: “Spend was up but CPAs spiked because of X. Here’s what we’re doing.” A bad growth strategist waits for the client to ask “what happened yesterday?” and then scrambles.

A good growth strategist thinks about incrementality. They know that platform CPAs flatter themselves. They ask “would this customer have bought anyway?” and adjust their view accordingly. They know Google brand campaigns look cheap because they’re catching people who were already coming. A bad growth strategist takes the platform number at face value and never questions it.

A bad growth strategist is reactive. They pause campaigns on day two, change budgets daily, and confuse activity with progress. A good growth strategist evaluates on weekly cycles. They know that a single bad day means almost nothing, and that blowing up an account structure because of a Tuesday dip is how you destroy compounding. They have faith in their strategy and stick with it.

A good growth strategist knows the customer and product inside out, not just the ad account. They know what the product actually does for people, what the repeat purchase rate looks like, and why someone buys for the first time. They’re just as bought into the product and brand as the client themselves. A bad growth strategist knows CPMs and CTRs but couldn’t tell you what the brand’s best-selling product is or why people love it.

If you think you’re a good growth strategist, then you should know we’re hiring at the moment. Either drop me an email or check out the job post here.

It’s only in the last few years, as auto-save has become dominant, that we no longer ‘click save’.

But for a good 20-year run, people clicked on a little icon of a floppy disk not knowing why that button represented the action of ‘saving.’

We became an AI-native agency a few months ago. I’d also define that a few of our clients are AI-native versions of whatever their company is as well.

What’s interesting is seeing how two AI-native companies interact with each other. Or more specifically how the humans within those businesses react.

What’s immediately becoming clear is that so much of the way that organisations, teams, clients, partners operate, is because of the natural limitations of prior working conditions. And actually in this new world, just like the floppy disk, likely will soon become redundant.

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One Claude negotiates with another Claude

For the last 18 months, AI-written email has become the commonplace.

But like all AI-native evolutions, in the early days it was “I want to say something like this, re-write it so that XYZ happens.”

Today, those who are expert do this differently.

I’ve had a few email negotiations this year where the AI usage was far more evolved. You can tell through writing style and verbosity that it’s AI immediately, but actually beneath the tells, there was significant substance to what was being asked.

On our side, we have operations and business leadership AI that understands different service offerings, understands the systems, what clients actually use, and our ‘rules’ which we may have. So a response is not just me saying “I want xyz, go write me an email to get it,” the response is based on understanding the full context of our business.

What feels odd is that at the bottleneck of this was two or three humans acting as negotiatiors-cum-conduits.

In corporate life, you would have an army of lawyers and dozens of stakeholders on either side fighting these corners, but ultimately all comms leading through a single point of contact.

That is where this new negotiation world feels, except instead of the lawyers and stakeholders, you’ve got agents representing those views.

But I wonder if there’s a future where the human isn’t needed at all.

What if each side set their desired outcomes, their guardrails, their limits, and let the agents discuss freely?

Without human emotion wrapped up in it, it’s likely that more equitable outcomes for all parties might come out on top.

It feels to me we’re in the early floppy disk era with negotations, where we’re still mimicking the styles of the past, but soon, we might evolve into a new future.

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Catching out each others’ hallucinations

The other trend I’ve noticed is with hallucinations and in particular how they get caught out.

The old way

Go back two years, and an agency like ours when performance wasn’t good may have done a deep dive of data analysis. One or two people would have taken a few days to go deep on some data and write up a report. The report would be shared, and the client likely would have questioned on the data if they knew something to be off. But otherwise, the assumption was on the fact the data was right.

What this looks like in the new world

Here’s a new world example.

• Performance is bad

• Both client and agency go and get a bunch of analysis done by asking Claude to go deep

• Long form reports get written up and shared with each other

• Each party does some form of checking the other’s workings

• Desired outcome is discussed

Now one point of tension I notice during these times is what I think of as ‘trying to catch out hallucinations.’ Very often one party will see something, and question it. Maybe they know it’s not right, maybe it doesn’t feel right. Maybe they’ve asked their AI to cross-check and validate. Whatever it is, something has changed.

What this could look like going forward?

At present we’re in a murky area where the level of AI use varies wildly from company to company.

For those at the beginning of their AI expertise, you ask the question “Why is performance bad?” and the answer you’ll get is highly plausible, but ultimately worthless.

For us, approx half our time is spent on either data engineering or context engineering. This can mean all sorts of things from:

• Using a ‘deterministic’ script or programme over an AI one wherever we can

• Using evals in the system both at building and execution level

• Introducing adversarial agents to challenge data and scenario plan

Now when all companies are at this level, it means that the above example I gave is a great way for companies to collaborate. Company has all of its own context and data and customer understanding, third party provider has domain expertise and market knowledge. Both parties with good AI systems suddenly produce exceptional pieces of analysis collaborating together.

For working relationships, this default requires a connection of deep trust. But also a shift in what data means.

My guess is its one where that will take the form of: ‘we’re both incentivised towards the right goals, we’re both working towards things together, we’re both using AI to the best of our ability, we’re both using the best data we have at any time.’

Should we feel each other’s analysis into our own systems to validate it? Yes, absolutely. It’s almost a requirement to have additional adversarial agents investigate. Especially because what may be true with one set of parameters, won’t be true under another.

I think psychologically, this will be a big shift.

It puts the onus less away from ‘getting something wrong is bad and a sign of a bad job’, and more ‘so long as the system is structured properly, we recognise this is a part of it, let’s work together to solve the problem and move forward.’

This sounds easy in practice, but I think difficult in reality.

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The end of dashboards and reporting as we know them

In a year’s time, I imagine we’ll barely use dashboards and reports with our clients.

Here’s why.

Dashboards and reports are the way we showed metrics in a world where that was the best way to do it.

But dashboards and reports are rigid at best, and potentially dangerous at worse if they teach you to have narrow focus.

We are already at the stage now where we can spin up a dashboard of any kind in seconds.

Real world example from this week:

Growth strategist meeting
GS1: ‘we’ve just had a rocketship with client X, what do you think we can expect for budget?’

GS2: ‘it was pretty powerful for client Y, let me show you’

GS2 prompts our growth strategist agent to pull the data for said client. It spins up a chart that shows spend growth annotated with the launch of the ad in question.

A hypothetical future scenario:

Maybe we’re in a client meeting, we show data on a new LP we’ve launched. We’re focused on CM3, but client wonders about LTV. Somebody quickly prompts our agent for cohort retention for that product mix, spins up a quick dashboard and we can all look at that data together.

Or a third scenario:

A client hears from a customer they liked purchasing two products together, and ask in Slack for us to dig into that data.

Old world, we then go and do it and add it to the dashboard.

New world, we just get the one-off report easily and paste right in.

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We’re currently replicating our existing modes of business – but soon we will jump them entirely

We don’t use rigid reports because they’re the best way of doing a thing.

We use them because it’s at the tradeoff of: worth the time invested vs outcome of what they can display.

But today AI changes that value exchange because on both sides there is now little limit.

So much of working culture is based on the limitations of where tech was.

That’s about to change.

And I for one am very excited to see where it’s going.

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I’m the founder of Ballpoint. We’re a growth agency that help you scale spend from £50k per month to £500k per month through creative-driven performance marketing. If you want to see what working with an AI-native agency might mean for you, then drop me a line.

Last Friday, Jones Road Beauty CEO Cody Plofker tweeted about Meta performance this year.

Jones Road Beauty spend an estimated eight-figures a year on Meta, and anyone who follows Cody will know they are on it when it comes to marketing evaluation.

Cody references Il Makiage, the brand who skyrocketed to $800m of revenue with a heavy weighting thanks to Meta.

Il Makiage have seen an 83% increase in year over year H1 CAC compared to a typical rise of 15%.

In Il Makiage’s June earnings report, they say:

“We believe this data supports the view that the CPA dislocation is technical in nature, rather than driven by brand health or market saturation.”

Il Makiage state that this is happening evenly across their key markets of US, Canada, UK, Australia, and Israel.

They pre-warned this in the February earnings report, stating:

“We experienced a dislocation in our account with our largest advertising partner that we believe was driven by algorithm changes, which diverted us to lower quality auctions at abnormally high costs.”

Il Makiage spent an estimated $145m on Meta advertising in 2025.

These are two dominant, performance-focused, Meta-heavy brands who are deeply expert at marketing these channels. And importantly, they are not the people who jump to blame Meta at the first sign of poor performance.

This conversation has been growing in recent weeks. Northbeam, who provide marketing measurement services to high spending brands, have been reporting similarly over the last few weeks.

Northbeam’s data is across “billions” of ad spend and they have “950+” advertisers. Let’s say it’s $3bn of ad spend, it means the average advertiser is spending $3.2m/year as a Northbeam customer. The media is likely higher, but whatever the story – we can assume that these are some of the world’s biggest advertisers on Meta.

CAC hit a low in the first week of Feb of $190 and has grown month over month since then to $250 today. Meanwhile, ROAS is trending downwards.

Open up X and these anecdotal reports following this pattern are everywhere:

In WhatsApp groups everywhere I’ve heard the same questions asked since the beginning of the year: “is meta performance bad for you too?”

When we wrote about the shift in consumer behaviour in January, we shared two sides of the coin. Yes we’d seen some categories down, but others were doing well. And while January in the UK felt damp, the US was still firing.

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What we’re seeing in our data

The Northbeam report inspired me to go do the same and look back at our client data over last 18 months. Now, our dataset is considerably smaller than Northbeam’s, but I wanted to dig in to see what patterns were that we could see.

Headline stat:

That means there’s been 44% higher CPA growth this year than the year prior. But, for all clients we now have access to the 2026 data is much more extreme – and there is a bigger negative trend.

Now why the gap? There’s a few variables, caveats and notes we should add for context behind the numbers.

We typically work with brands attempting aggressive spend growth

A typical client trajectory is:

• Year 1: £30k/month spend

• Year 2: £80-120k/month spend

• Year 3: £200-300k/month spend

Every marketing channel suffers diminishing returns, and so it’s expected for their to be some CPA increase factored into spend increases of that nature. As a result, all things held equal, you’d expect CPA increases each year. In practice, this happens less than you expect given we only scale when the opportunity is there it means the aggressive scale clients typically have more stability than not, but it is still a factor.

We have a huge CPA range with clients

We have a broad range of CPAs across clients from £10-£250, and so using averaged numbers didn’t feel overly useful here. Instead, I prefer the index version which we indexed to Jan 2025 performance.

Most clients come to us because their CPAs are too high and need them fixing

Result here is we might have had a client join us in February of this year, who had a hugely volatile 2025, and now we’re into May-June, that’s starting to balance out as we learn and scale. But it means that the dataset has a huge external factor that isn’t spend, seasonality, macroeconomics – which is us.

Fixing a growth ceiling takes time and usually leads to volatility at the start

As with above, new client relationships usually come with higher CPA in the short term while we increase testing velocity and that creates instability.

Our dataset is naturally skewed by those who we’re more likely to have won recently, which means some of those increases this year are owing to this.

Often there are attribution issues, and we ultimately measure off-platform

Two clients in our dataset we switched from 7 day click, 1 day view, to incremental attribution when we started working together. This had a 40% and 82% increase in platform CPA, though an improvement to off-platform incremental CPA.

And so ultimately we’ve reset the the targets on Meta, but that isn’t accounted for in this data.

All of these things together indicate to take this data, as you do Northbeam’s with a pinch of salt.

Thoughts on our data

In short I summarise this as:

• For clients we’ve managed the whole time, we saw a:

  • 9% increase in CPA from Jan 2025 → May 2026

  • 13% increase in CPA from Jan 2026 → May 2026.

All clients that figure goes to 38% for total base.

For the ones we’ve worked with the entire data range, it certainly feels like there’s faster CPA growth than in previous years.

But it’s not the 83% that Il Makiage have seen – but a 44% one instead. That’s still a pretty substantial figure even when the absolute percentage points figure is less.

We’ve been fortunate that for our longest standing clients, we’ve been able to counteract and ‘beat the market’, which we put down to:

1. better retention data and measurement, and therefore a stronger ability to balance spend and incrementality,

2. 1000s+ of insights for each of those clients meaning we can produce creative win rates 50-100% higher than typical,

3. more data in Meta and more spend, meaning more stability.

For most clients we work with there is still gains to be made beating the market and so we remain optimistic – that said, the conditions do seem to be worse.

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More advertising dollars spent on Meta

This was an edit after the original post went out.

As points out in the comments, at least some of this story will be the fact there is more competition in platform.

In Q1 this year Meta saw:

• Ad revenue grew 33%

• Daily active users grew 4%1

By that token, the CPA changes we’re seeing are pretty minor. In our data we measured Jan → May year over year rather than Q1 to Q1, but a 13% increase is significantly below that 33% increase otherwise seen.

What’s driving the performance change?

This is where we get into unknowns. Sceptics point at Meta, and indeed Il Makiage and Cody Pfloker have both publicly raised those concerns themselves.

But we are also in a macro environment that has significant issues:

• AI is causing middle class job uncertainty and/or loss

• War in the Middle East is pushing up prices for many goods which we have yet to feel the full downstream impact of

• Tariffs are impacting import/export globally

• UK mortgage costs ballooning

The old age question is always: is it my marketing or is it the market? I think what’s clear is that there is some market condition driving things at the moment. To what extent that is platform & technical vs macro and economic, I don’t have enough visibility or data to answer.

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What do we do with this information?

Hopefully it provides some solace. If you’re seeing more CPA change than expected, understand that some of the smartest large advertisers in the world are seeing the same thing.

But more importantly, it means our job is harder. For most brands I know, we’re already at the efficiency threshold when things make sense. Marginal CM is difficult to find, and a 15% increase to CPA year over year compared to a 9% one is substantial.

The work gets harder, you need more insights, you need more experiments, and you need faster feedback loops.

Whether this is the worst Meta performance year on record is still to be seen. But right now it looks like it’s in a tough phase. Hang in there, get experimenting, and as ever if you’re not happy with your current level of experimentation – then get in touch.

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I’m the founder of Ballpoint. We’re a growth agency specialising in creative-driven performance marketing. In March 2026, we made the pivot to become AI native. For the clients we work with for more than a year, we typically triple their spend while maintaining CPA. If that’s something you want for your brand, then get in touch. Just be warned, we’re going to tell you how to sell.

In 1991, David Ogilvy stood up at the Association of National Advertisers (ANA) Convention and said:

I believe this war cry needs re-enacting today.

And the primary culprit of this as I see it is a desire to force paid social to be something it’s not. That desired ‘something’ is a platform to share high production creative that demonstrates vibes and values, over ads that actually sell.

The answers to how to make paid social work are not hidden in some LinkedIn-comment-SCALE-to-get-the-secrets, but in historic principles of advertising that need reawakening.

On holiday last month, I read Kenneth Roman’s excellent biography The King of Madison Avenue about David Ogilvy. It’s an instant 5-star for me on Goodreads. Maybe it was the Spanish sun, but my blood pressure was rising in shared frustration, and I felt like banging the drum to join the march of his ‘sell’ mantra.

Today, I want to explore the problems as I see them today, how we ended up here, why this is so important to address, and how we can all take this on board going forward.

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“Our creative has to be more premium”

If there is one thing we hear from almost every brand out there, it’s this.

And look, we get it.

When you look at a lot of the ads out there, it’s easy to grimace a bit.

For a lot of founders, you’ve invested all your savings into producing something you really care about. You spent time with product designers and brand designers, and ended up with a physical product you were proud of. You did your first photoshoots and the images at the end were stunning.

And then you see ads like this recommended to you.

The ‘mock-Pixar-animation style'. Here the ideal is getting an animated character based on your product, or product problem, and the animating a story using AI.

If AI slop is something that shows obviously signs of being AI, then this has to hit the nail on the head.

It’s not just AI slop, however. Post-it note ads were one of the big pushbacks a couple of years ago. And interspersed with all of this is various memes.

There’s something about ads like this, that when you show them to most people, you hear that phase ‘we need our ads to be a lot more premium than this.’

So what’s happening?

When did sales stop being the primary objective?

Forgive me a little wander through history here because I love this stuff. If you don’t care for the history skip on ahead.

When I entered the workforce in the early 2010s, it was into a digital and PR agency. I didn’t realise it at that the time, but that agency was one of the most forward-thinking there was when it came to measurement.

To my surprise, the current way that PR was measured was in a method called advertising value equivalent (AVE) where it judged column inches as ad spend and that was the ‘return.’ Most PR was so detached from achieving a sale, that it was judging itself wrongly against the rate card value of space in print.

But it wasn’t just PR that was at fault then. During the social media explosion, success was measured in likes and comments. And as it turns out the ad industry had shifted too.

The change happened slowly.

Perhaps the starting point was Cannes Lions beginning in 1954, initially as a festival for the ads that play in cinemas. It evolved from there. In 1959, the Clio Awards followed with categories for creativity outlined.

The 60s saw the ‘creative revolution’ - we got the Lemon ads – and then by the end of the decade ‘account planning’ was born. Stephen King of JWT said that advertising’s primary impact was to “intensify or lessen people’s existing predisposition,” and by the time the Account Planning Group (APG) was formed, the shift had been cemented towards long term brand building.

By 1980, the IPA had launched the IPA Effectiveness Award: already a signal that the industry was straying from actual impact.

Despite Ogilvy’s rallying cries throughout the 90s, by the time I entered the broader marketing profession in 2010, it seemed like selling was out of fashion.

Facebook launched the first growth team in the late 2000s. A team focused exclusively on improving important user metrics. As a tech company, it’s monetisation was a step removed at the time from the controllable asset. For them, to become monetisable, they needed one thing: daily active users, and that was what the growth team was responsible for.

While this was happening, traditional companies and big corporates continued down the route that advertising’s intent was not to sell but to think about values, storytelling, messaging, or vague brand building. Selling a product was the default norm for most big consumer businesses throughout the 2010s.

Growth meanwhile, born from Facebook, began to spread quickly through tech.

While Growth in b2b and tech became obsessed with funnel optimisation and less on the core way of getting traffic through the doors, the way it evolved in consumer and DTC brought advertising into the mix.

I think with hindsight two errors happened in the 2010s that contributed to today’s ‘selling issue’.

1. The data illusion that we could optimise towards last-click sales and have a true value of everything

2. The lack of need for payback due to influx of VC cash

I think these two things together brought a poor precedent to the industry. There was a belief that Growth was a bad force and “Growth at all costs” became a synonym for irresponsibility.

As we’ve entered the 2020s, another thing happened.

Platforms matured.

Today, it is less fluke or black box how you can grow a brand real big thanks to digital marketing.

There’s a system to approach it with and thousands of examples of businesses who have created their entire company value through paid social.

What that means as well is that more and more people pay attention to these numbers, these platforms, these creatives.

If in the 2010s most CMOs or heads of marketing in traditional businesses saw paid social as a misnomer and a black box; and by the early 2020s they saw it as a practice past its peak; then by 2026 it has now re-cemented its position in every marketing toolkit. And the outcome is more attention and eyeballs focused on this function.

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Playing the right game: native creative

It’s no longer a surprise when you hear of a brand go from £0 to £20m from paid social alone. Or in the US, these numbers are even bigger. Gruns rocketed to $100m of annual revenue from a DTC + paid social strategy.

Everywhere you look, there are brands rocketing to seven, eight, and nine figures of revenue purely from paid social. And when that happens it makes all founders and marketers take note.

Naturally, lots of people want a piece of the pie.

But this is where things often become tricky.

This is where you get people want to hit paid social hard, without playing by its rules. And you really need to play the right game to win.

Paid social creative isn’t about producing ugly ads for ugly ads’ sake. It’s not about creating things that are disgusting to stand out. On the contrary, it’s about producing things that are relevant to the environment you’re playing in.

For us we broadly see a few camps of creative these days:

• Native

• Creator

• Off-brand

• Branded

Creator is your UGC and influencer, stuff created by a third-party either scripted or not.

Native is advertising imitating the environment (so types of images or videos that would get shared by people in Stories, Reels or DMs).

Off-brand is advertising that is still clearly an advert, but it’s not referencing the brand’s design, look and feel. We used to think of this as brand-native.

Branded is advertising that is in the brand design, look and feel.

While we can see ads in all brackets work, the data is overwhelmingly in one direction.

This is from our broader dataset, where native and creator are merged into one category but the trend is clear:

Add normalised CPAs into this chart and the CPAs reflect a similar picture: native is cheapest, then off-brand, then brand./

A few things to draw out from this data:

1. Spend per ad is over 2x higher for native vs brand.

2. Native and brand have almost even creation distribution across our agency

3. Off-brand has 1/5th of creation volume of either native/brand

Meta views spend as ultimate success metric and so here native is outperforming brand 2:1. And while we put down Off-Brand’s low performance to a very low experimentation volume, the native dataset is almost even with brand’s.

The message is clear, if you want to sell on paid social and want to put your best foot forward, create native & creator ads over branded ones.

If you want to sell via paid social, create as many native ads as possible.

What is native – and why does it work

There’s an important mental position I think we all need to take with paid social.

That is: it’s not about making ugly ads, it’s about playing into the environment that we know works best.

The native ads of the 50s

To understand some of Ogilvy’s success is to first understand Holiday magazine.

Holiday was an upmarket 50s magazine. And the key things to take away from this are the large colour photographs, plenty of white space, and well-set, long form serif typography.

From the Kenneth Roman book:

Impatient with trying to convey to writers and art directors what he wanted, more and more Ogilvy was showing them how to do it, writing the advertising and directing its look—big beautiful photographs (never artwork), usually a one-line headline beneath the photo serving as a caption, plus three blocks of readable text in a simple classic typeface. Ogilvy moved the names of products, usually relegated to logos at the bottom of ads, to the headline at the top.

I went and bought a few old issues of the Saturday Evening Post to see what this looked like in practice. While the typefaces and photography quality in the articles is an immediate tell, you otherwise wouldn’t know this was a magazine from 70 years ago compared to one published today.

While the left-article still contains much illustration, this 1955 article on the ‘Uninhibited restaurant’ hits on a lot of the Ogilvy design tropes: colour photography, headlines, long text.

Often when browsing these magazines, it’s not clear where an advert begins and an article ends. The advertisers matched the space they were living in and played the game of the platform.

These were the native ads of the golden age of print advertising. And this was the era when print advertising’s primary motivation was to sell.

When we think about most big budget TV advertising, we have to consider again the environment it’s playing in.

If you’re the ad at the beginning of a film in the cinema, you need to mirror the expectation of the environment you’re playing in. Same with TV: if you’re interrupting a feature length film or high budget TV series, then anything less than stunning and you’ll switch off.

The environment is the important aspect. The Guinness Horses ad is a native ad.

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The world we’re interrupting on social

Let’s remember why we open social media.

Once upon a time, we opened social media to see what our friends were up to. And the primary way we did that was by looking at what people were sharing.

Photos of dogs, children, nights out, holidays. All shot on your phone with the Paris or Valencia filter over them. An evolution of the early, Polaroid-aping days.

We weren’t on the platforms to discover brands and a polished ad would have stuck out like a sore thumb.

Today, we use these platforms differently. While we ostensibly still call them social media platforms, their new single-player mode is one for entertainment.

That entertainment means videos and images which are funny, enlightening, enraging, heartwarming, loving, educational, and everything in between. One minute you could be watching a YouTube rip of an 80s TV show pixellated to high heavens, the next it could be someone street interviewing someone on your favourite food.

The platforms have social sides too. Instagram’s head Adam Mosseri posted this back in January on Threads:

Instagram does have a social side today, but it’s more lo-fi than it’s ever been: blurred photos, shaky videos. Raw. Authentic. It is not polished.

Let’s take a look at a typical five-minute scroll on Instagram today, split evenly between Reels and Stories.

Once you get past the ads, and the small % of friends sharing their dogs, here’s what’s left.

A start on Stories and we see:

• A street selfie with a joke from a comedian I follow

• A clipping from a 90s TV show showing off 90s NYC fashion

• An overhead phone shot from the Arsenal parade

• A YouTube rip of a music video captioned

Open up Reels and what have we got?

• Maybe a fake podcast, maybe a real one? I don’t care it’s just content

• Nostalgic journey down 2000s indie lyrics with a comparatively ‘high production’ edit

• Lo-fi video of pianist playing Born Slippy as a wedding song (ultra millennial coded)

• Comedy sketch of someone dressed as John Dutton

• Menswear content creator recreating the looks of Hugh Grant

• Woman not saying anything with a POV caption selling the dream

The messaging in all of these differs.

We’ve got:

• Nostalgia

• Community

• Social relevance

• Escapism

• Aspiration

• Comedy

But the visual style is almost uniform in its distribution: everyone is shot with a phone. One video is reasonably edited but it’s still a pretty social-first edit style. Very scrappy and felt like it was edited on the phone.

Everything else is low number of edits, maybe some images popping up onto screen, but it’s most just someone with a phone recording something.

This is the world we’re interrupting.

And if we want to sell, we need to understand this world intimately.

For Ogilvy, that meant laying out your ads to look like a double page spread from Holiday or The Saturday Evening Post. For Guinness that meant creating a mini film to sit in between prime time film and TV.

But if your channel is social, you have to respect the channel. Create ads that look and feel authentic to the medium.

And if you’re worried about rich people having unfavourable views of your brand, just remember they’re also seeing this content too. Lo-fi, phone shot, authentic, natural shooting styles. That is after all why they opened Instagram this morning.

Start selling – and start doing it natively.

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A few parting words on the ‘impact on brand’

We had a client last year who asked us about the brand impact of lo-fi, native advertising.

We used Meta’s Brand Lift study to examine some classic brand metrics.

The above chart shows the brand favourability scores. Blue was those unexposed to ads, purple those who had been.

The native, scrappy, lo-fi, often ugly ads had a 11.3 point positive impact on favourability scores. It reduced neutral views by 9.1 points, and reduced very unfavourables as well.

Overall the swing was hugely positive. The people exposed to the ads liked the brand more than those who weren’t.

We’ve also seen the direct impact of Meta-only advertising on retail sales with a number of clients. Retail is the archetype of needing ‘brand recognition’ to make a sale. But in the multiple clients where we’ve run structured studies, we’ve seen the huge impact that Meta has on those offline sales channels.

Meta creates brand – and it creates it favourably and effectively.

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We sell, or else

From day one, we have been about helping you sell. The point of advertising is not to create things that internal teams like the look of. It is not to preach to the converted. It’s not to find something. Although it can be those things.

The point of advertising is to sell.

Paid social is experiencing an all-time high because of the impact it can have when done right.

You can start a brand today in the US and get to $100m of revenue with Meta and a Shopify store alone. But you have to play the right game.

And if nothing else, then by creating content that looks like the Stories or Reels in your feed, we’re respecting our customer in their place of refuge. And even if we don’t manage to sell that time, at least we might leave an impression.

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I’m the founder of Ballpoint. We’re a growth agency specialising in creative-driven performance marketing. In March 2026, we made the pivot to become AI native. For the clients we work with for more than a year, we typically triple their spend while maintaining CPA. If that’s something you want for your brand, then get in touch. Just be warned, we’re going to tell you how to sell.

When I was a teenager I wanted to be a video games programmer. I bought C++ for Dummies and tried to learn how to code. I couldn’t, for some reason my mind just wasn’t set up for it.

Over the years I’ve tried python and javascript, and each time I get bogged down in trying to memorise syntax, and frustrated. The thing I never wanted to do necessarily was to code, but it was build the thing that came from the code.

AI has taken that wall down. And I’m having the most fun I’ve ever had in my career.

But getting here has meant mourning something first.

I’m 37. Like a lot of millennials who entered professional services in our early 20s, I saw a career path in front of me. My first CEO was in his early 30s. I grew up with nothing and that felt like proof of what was possible if you worked hard and played the game. The path was clear at every rank. Execs became senior execs, then managers, then senior managers, associate directors, directors. Then you either left to start something or you went into leadership.

62% of millennials say work is central to our identity, behind only family and friends. But when I sit down with friends, work is very often one of the primary sources of conversations. We have wittingly or not built a sense of self around that career ladder.

We became defined by our work, and in particular, the workplace has had a cult of management for a long time.

Everyone who goes through the path recognises how odd it is. We spend years learning to execute well – often with very specific training at the start. Only then to be thrust into a management role which none of us are prepared for.

Tech was at the forefront of pushing back on that, inventing the role of the ‘individual contributor’ or IC. After all, not every great software engineer had the want or ability to manage people, and so the IC path allowed them an alternative. You still gained the status of evolving with your job, but it was now linked to building.

At Ballpoint, we’ve always been very top-heavy. Growth strategists run strategy on the accounts, the client relationship and are still in the weeds. But for us, this was about the belief it produced better results for clients – rather than a prediction on an AI future.

But, I think that’s about to be the reality for most professional services businesses.

Think about what a growth strategist actually does. At the core, strategy is deciding what to say no to.

The more experience you have:

• the faster you pattern match,

• the better your decisions,

• the quicker you change direction when something isn’t working.

Two years ago, when a strategist pointed in a direction, a more junior person ran the analysis and built scenarios. Now a good AI does that analysis, and a great AI comes back with experiment ideas the strategist with 10 years of experience hadn’t thought of.

The middle layer of execution and organisation: making sure shit gets done, and carrying out the core analysis tasks, can now be done almost infinitely better with AI.

The result is senior people are doing more hands on work than ever, not less. But it’s not just about the fact these people are doing more hands-on work, they’re also expected – at least at Ballpoint – to be building the systems that improve over time as well.

Two months ago we set a new bar at Ballpoint. Stop using AI to assist your work, and start getting AI to do the work for you. Over 80% of tasks at the agency now begin that way. And where we don’t do it, it’s deliberate. For me, longform writing is the primary one. Writing remains the best way I have ever found to think about a subject.

Getting people to get AI to work for them was phase one. Phase two is getting the AI to do better work than humans. And it’s in these systems where we now expect everyone to plan, research, build, test and iterate.

As of today, I’m most weighted in that direction. A glance at my timesheets show that 20 hours a week go into building. That’s a third of my time spent on executional work. My 22 year old self never would have expected that.

Our head of creative strategy is responsible for building the systems that power his creative strategists. Our head of creative studio for implementing systems that produce results faster. Every single person has an OKR this quarter focused on building.

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What happens to juniors in this world?

So how do you train someone into this world?

I watched a documentary years ago about a restaurant chasing its third Michelin star. Junior chefs weren’t allowed to do anything for days except chop onions. They might have been sous chefs elsewhere but in this kitchen you couldn’t touch a stockpot until you’d chopped onions for days and days and days.

I think one version of the future of training looks like that.

Last year, I touched on this in my article on the rise of growth artisans. The idea that individuals will be able to own the entire growth field – and that training has to be done almost as an apprenticeship along the way.

You learn performance marketing by hand. You listen to customer interviews and write the JTBD specs yourself. You write copy yourself. You export the data and analyse it in a spreadsheet. Only once you’ve done it by hand and you’re expert at doing it that way, do you give it to AI.

The cost implications of this are obviously huge. (And for any government worker out there with a grant, we will happily explore this as a training scheme with you). But you’re essentially doing apprenticeships accepting that grads won’t create comparative value for a long time.

There’s a second version that mirrors the multi-disciplinary nature of our new roles.

There’s a future where actually doing that stuff manually is like knowing long division. Maybe it’s worth learning at school to exercise the brain, but in day to day life it’s just never going to come up.

Maybe it doesn’t matter if you’ve never built an ad campaign or written a line of copy or spoken to a customer in real life. The journey from 0 to good enough will be fast, and then better development of AI systems will get you further.

Here the training is broader but maybe shallower. We might learn product management, customer research, AI engineering, copywriting, a bit of Python, graphic design, ethics, taste. They’d be multidisciplinary by default, because the senior role they’re being trained for is multidisciplinary by default. And this idea, especially that of developing taste, is something I’m focused on at a senior level too.

Mourning our old identity

The old world of work we know is about to change, and I think the unspoken thing at the moment is the knowledge we’re going to need to mourn our old expectations and identity.

Last week, Tom Blomfield of YC said that middle management will die. I reckon it will too, though probably slower than some might predict. I think corporates and the humans inside them have too many incentives to protect the status quo.

But for most, I think it’s important to come to terms with the fact that our old career trajectory is about to change. And I think the idea that there are people who just manage other people and don’t do more hands on work themselves or build themselves, will soon feel out of date.

Not every CEO will be a builder. But I think the best ones will be – or perhaps as Jones Road Beauty’s Cody Pfloker just experienced, it’s the AI-enabled CEO who does the job of exploration, only to rebuild the function internally.

But while not every CEO might end up a builder, I do think everyone else will. And I’m fortunately here to say, having come out the other side of that, it’s a pretty fun world.

Ideas were always cheap, execution now is as well. The question is what will you do with it?

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“Have you ever considered turning Meta off?”

I had a client ask me this recently. They’re a newish client, and we’re pre-product channel fit. I’ve been asked variants of this question for a long time.

While it’s a slightly hackneyed solution (which we’ll get into in this post today), the cause for it as an idea is a good one.

As marketers, only some of our…

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Hello to our many new subscribers. This week’s post on 15 ways Claude has changed our business went a little bit viral. 👋

As a heads up for you. Our usual cadence is a weekly Tuesday essay and a weekend lighter snack. I like to think of this as FT Weekend for startup, DTC, and consumer growth people.

April is coming to an end, which for most of you – bas…

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It’s everywhere. X, LinkedIn, Substack, even the NYT got in on it.

The truth is nobody really knows what’s coming. But the possibilities are starting to feel genuinely endless.

At the beginning of March, I published Becoming AI Nati…

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Today, I’m back with a reflection on where we are today.

This is the age old question. And usually for growth stage businesses, t…

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

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Last week, we were away on our company offsite as we embarked on the journey to become AI-native. The current goal state is:

For any task, can it first of all be given to AI to complete for you

I take the David William Silva view that LLMs likely won’t be the singularity answer. There is just too much devolving to the baseline and average inv…

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

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I’m testing times we deliver this weekly aggregate roundup. Let me know what you think of the Saturday delivery.

Context and experience are becoming the most valuable skills in AI.

But something else we all need to learn is perseverance.

If you tell Claude to connect to an Ads MCP and ask it for recommendations, it’s going to tell you to increa…

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It usually happens like this: performance has gradually been heading in the wrong direction. First, you rightly ignore it as these things usually work themselves out. Then it starts to become a trend but you still allow…

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In SF, AI billboards were everywhere. As well as making it seem like the entire economy there was AI-focused, it also acted as a good reminder of forgotten launches.

Even brands we’ve already signed up for, we forget about quickly. If your product use isn’t daily or weekly at worst, then you risk being forgotten. If your pro…

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