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?

Since I started running Facebook Ads in 2015, on an almost hourly basis from the start to end of day, I’d fire open Ads Manager to see how things were doing.

That means opening an interface, waiting for something to load, clicking around, maybe taking some screenshots to share in another channel or message, and then repeating with other clients.

Today, media buying is not an hourly task. But I find senior growth strategists still have that reaction built into them.

But, getting that data is pretty broken. Ads Manager is not by any stretch a perfect product, and in fact it’s something of an in-joke with most ads managers out there.

With the rise of AI, various unofficial MCPs made lots of promises this year. Then came the fear of the bans.

Lots pushed back and pointed out if you were going to break Meta TOS with rate limits, that’s going to be why. But still we quietly beavered away trying to make MCPs work for us.

And then, glory behold, last week 29 April 2026, Meta launches Ads CLI.

It’s a command line tool for the Marketing API. That sounds more technical than it is. In practice, it means you can ask Meta for spend, impressions, CPA, video metrics, campaign status and breakdowns from the terminal.

No Python scripts.

No Postman.

No clicking through Ads Manager just to answer one question.

We’ve been playing with this kind of setup for a while. Being in the Terminal is fun, for anyone who grew up on the Matrix or watching the Social Network, operating in the command line feels particularly l33t. But it’s actually not that it’s cooler, but it’s a far better environment for repeating questions.

Across an agency, those questions come up constantly:

• What did this account spend yesterday?

• What was CPA yesterday and did it deviate from the norm?

• Is any account overspending today?

• How is CPA progressing this week?

• Are there any rocketship winners in the new test?

• If the CPA is high, are breakdowns shifting at all?

• And so on

You can answer those inside Ads Manager. Of course you can. But it takes just enough time that it becomes a frustrating piece of manual work.

Not only that but setting up command line software has always been painful.

If you were like me, you spent a long time as a kid tinkering around with commands that looked like this:

tar -xzvf archive.tar.gz

Only for something to inevitably break as whatever it was you just tried to install didn’t open correctly. You then spend hours on StackOverflow trying to find the answer. At which point, you either felt like Boris in Goldeneye, or that code life wasn’t for you.

Claude changes that.

While AI hallucinates a lot, in code it hallucinates less.

And in management of command line interface tools, my experience is it operates even better.

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What you need before starting

You need:

• A Mac (Linux or Windows likely will work similarly, but this is untested there)

• Claude Code installed in the command line

• A Meta Business Manager with access to the ad accounts you want to query.

Step 1: get Claude Code to install Meta Ads CLI for you

Open up your terminal and let’s create a directory for Meta Ads to be installed from.

Prompt something like this:

josh@Joshs-MacBook-Pro-4 ~ % mkdir cc
josh@Joshs-MacBook-Pro-4 ~ % cd cc
josh@Joshs-MacBook-Pro-4 cc % mkdir meta-ads
josh@Joshs-MacBook-Pro-4 cc % cd meta-ads
josh@Joshs-MacBook-Pro-4 meta-ads % claude

‘mkdir’ means make directory, it creates a ‘cc’ (claudecode) folder, ‘cd’ means change directory, it changes to that directory. And then claude initiates Claude Code in that directory.

Claude will open and then prompt it something like this:

Please can you fully install for me - so that it works across my entire computer in all folders the following:  
https://pypi.org/project/meta-ads/   

Depending on what existing packages you have installed, Claude will guide you through the setup.

What you’ll likely see is a mixture if: checking and install python (and various versions), uv, homebrew and more.

As a sidebar, pre-Claude code, this was always one of those frustrating steps. So many apps had issues with python versions that meant lots of back and forth with StackOverflow, copying and pasting until something just worked. Claude solve all of that.

Eventually you’ll get something that looks like this:

At which point, you can herald happy days that you’re all set up.

To confirm, open a new Terminal (CMD+N), confirm you’re in the same directory as you were before (this time just using cd not mkdir to get there) and enter:

meta

And you should get an output that looks like this:

It’s not time to configure.

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Step 2: setting up the Meta admin side

Now you need to connect Meta to this setup. There’s a few things you need, first is an ‘app’ which while that sounds like you’re about to build an iPhone app, it really just reflects that you’re building an application to connect to Meta.

Create an App with Meta

1. Head to here https://developers.facebook.com/apps/ and click ‘Create App’

2. Give the app a name

   

3. Choose “Create & manage ads with Marketing API”

   

4. Next up choose your business or agency account, ‘Requirements’ should be empty, then click through to Overview and click Create. Authenticate if asked. Congratulations, you’ve just registered an app with Meta.

Set basic settings for your app

1. In the new app directory, click App settings and then Basic.

2. You’ll need to upload an app icon, a terms of service URL and choose a category.

3. Go through the remainder of the settings in here, and eventually click ‘Publish’

4. Keep this tab open and open a new tab.

Create a System User

1. Head over to here https://business.facebook.com/latest/settings/system_users

2. Click “Add” and create a system user.

3. Call it something like “Automation” or “Claude” and then give it the admin role.

Assign your account(s) and connect the app

1. Click the … and then “Assign Assets”

2. Choose your Instagram profile, Facebook page, and Ad Account here. If you manage multiple clients, this is where you add those too.

3. Check the accounts are all connected.

4. Under the app system user name you’ll see an “ID” click it to copy it

Assign your app to your user

1. Switch back to the app tab, click “Roles” under “App roles”

2. Click “Add People”, choose “Administrator”

3. Paste the user id you just copied, and complete the add.

4. Switch back to Business Manager, refresh and check the app is connected.

Generate an access token

Back in in “System Users” in Business Manager, you want to click “Generate Token”

1. Choose your new app

2. Keep ‘60 days’ selected for token expiry

3. Under “permissions” ensure the following are enabled:

   1. business_management

   2. ads_management

   3. pages_show_list

   4. pages_read_engagement

   5. pages_manage_ads

   6. catalog_management

   7. read_insights

4. Click “Generate Token”

At this point, you may need to wait for a fellow admin to authorise this request.

Once done, you’ll be granted a token which you need to make a note of.

Setting up your Meta Ads CLI locally

Now you need to join these things together back in your Terminal.

At this point, it’s where using an IDE like VS Code or Cursor is useful, because file editing is much simpler and native to the environment you’re working in.

Note at this stage:

Instead, we’re going to use a native simple text editor.

So in your same directory you’ve made in the terminal, open a new tab or window and then type:

Enter nano .env

This opens a plain text editor called nano and creates a file called .env which is an environment file.

Inside this doc enter the following:

ACCESS_TOKEN={INSERT_THE_ACCESS_TOKEN_HERE}
AD_ACCOUNT_ID={ENTER_ACCOUNT_ID}

Switch back to Business Manager to get your ad account id if you don’t have it to hand

To save and exit in nano, you need to:

CTRL+O to bring up save
Hit ENTER to save .env as the file name
CTRL+X to exit

To check those files are displayed in the Terminal (and at this stage, double check you are not in Claude Code but the Terminal etc, enter:

cat .env

Hit enter and this will show you those file contents.

Finally test the auth status:

meta ads adaccount list

At this stage it should show you available accounts you can access along with the following subheaders.

Using Claude Code to control Meta Ads CLI

At this stage, if you like, you can start experimenting in the command line. Read through the entire official documentation.

Some example commands you could use:

meta ads insights get --fields spend,impressions,ctr
meta ads creative create
meta ads adset list

The documentation is something that you do need to read. Claude Code is a great assistant here, but remember that you’ve now granted full control to your account to a third party tool. Consider how much any AI hallucinates and now consider how you use it.

And so as a starting point, I’d recommend fully reading that documentation so you know how each comamnd works.

Once you’re comfortable that you understand the commands, it’s time to open up Claude again.

At this point, I’d prompt Claude like this:

I want you to create for me a master reference doc for some simple commands to use the meta-ads cli tool.                                                    
                                                                                                                                                               
  Please begin by reading the full documentation here: https://developers.facebook.com/documentation/ads-commerce/ads-ai-connectors/ads-cli/command-reference  
  You may need to use firecrawl API to access it.                                                                                                              
                                                                                                                                                               
  The core tasks I will need solving are:                                                                                                                      
  1. How is spend looking today / yesterday / this week?                                                                                                       
  2. How does spend compare to X                                                                                                                               
  3. How is CPA doing today (cost / conversions) and how does this compare?                                                                                    
  4. Rank all of our ads from the last {time period} by spend                                                                                                  
                                                                                                                                                               
  Can you please create a master cheatsheet based on this for me and save it as a meta-cheat-sheet.md file       

Claude will go away and put together this cheat sheet.

Now I like to give my Claude a few further parameters for controlling the Ads CLI. And so I added the following to my cheat sheet:

Use as few API calls as you can each time, there is a strict rate limit for the Meta Marketing API.
Only use the Meta Ads CLI to connect to Meta. DO NOT use the Meta MARKETING API directly.

Meta has 200 rate calls within one hour. That means that every time you call the Meta API with any command, it counts towards your rate limit number. Hitting that number causes the service to pause. And if you abuse it, you can have your app banned. This is essential to consider.

The second is to give Claude some guidance. Because it has an access token, it could in theory connect directly to the API. However, this is bad. The API is more complex and Claude will test and learn its way to an answer, meaning you very quickly hit those rate limits and risk a ban.

As the old adage goes, the secret to getting better answers, is to ask better questions.

When you’re dealing with rate limits (and context windows too), writing better questions is essential.

How to use this

Even a cursory glance at the Meta Ads docs will make you realise that you can create ads with this. And then anyone who is building Claude Skills will know that means you can automate it.

Not only that but there’s now an easy believable route from:

• Create ad in Nano Banana

• Automatically upload the ad to the account

• Go live

• Measure success

• Turn off ad

All within the terminal/Claude Code.

Just because you can do this, doesn’t mean you should. Creating the ads is easy, choosing what ads you want to make is hard.

Similarly just because you can access the full ads manager from your terminal, doesn’t mean you necessarily should. Rate limits are there for a reason, and just because in theory can you fully check everything, for deep explorations it’s likely not the best environemnt to do it in.

As a closing note I’m reminded of this post from just before I launched Ballpoint. At the time the FB Marketing API was a complex web to be untangled.

Today with Ads CLI + Claude Code, that all just got a little bit easier.

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I’m building Ballpoint, the AI-native growth marketing agency I always wanted to hire when I was a DTC founder and before that a head of growth. We’ve scaled brands from £1m to £50m through digital advertising. If you’re looking for support, you can email me.

“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 job is the actual doing work of making great creative that converts, it is equally our job to prove its value as well.

Meta can be tricky. Sometimes it over-attributes, other times it under-attributes.

Today, I want to share a few scenarios you might be in, to help identify you true, or “incremental”, CPA.

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What you can expect today

1. Why Meta CPA is rarely what Meta says it is

2. How to measure true Meta CPA when:

   1. Meta is your first channel and so far your only channel

   2. You have grown your business through various other means, and you are now testing Meta

   3. You have scaled your business effectively with Meta but now know customers come from multiple sources

   4. You are running retention/remarketing activity

3. Our average adjustments to CPAs

Why Meta CPA is rarely what Meta says it is

Lazy marketers believe that Meta is out to get you.

I don’t buy that. They are of course a business that needs to make money. But there’s an equilibrium where return to advertisers needs to be substantial enough to continue to invest. My experience to date is always that the outcomes and incentives feel aligned for advertisers.

So if it’s not just Meta lying, then what it is it?

Problem #1: Data shifts post iOS14.5

Considering this change happened half a decade ago, it’s odd that this is still a discussion. But for anyone with a memory of advertising in the 2010s, they’ll remember how much more data flowed to Meta. Privacy laws, and proactive changes from Apple mean that’s no longer the case.

The result is less data in platform, showing lower numbers of attributions.

Problem #2: Attribution doesn’t reflect how people buy

The bigger problem is attribution. During the 2010s, we all bought into the lie of attribution. Attribution is a form of measurement that prioritises assigning a value to a channel. Google Analytics does this. Google Ads does it, so does Meta.

Attribution is a flawed measurement protocol.

It doesn’t reflect how people buy. More often than not many purchases these days go:

• See an ad → discuss with my wife → she or I purchase later on on a different device

Google likely steals that attributed sale, Meta often doesn’t get credit.

Or even put another way. Let’s say its a long consideration product, I see an ad, I sign up to the newsletter to stay on top of things. Then a few weeks later I decide to buy. Email captures the sale, but Meta caused it.

Attribution is a broken part of the picture full-stop.

For more on this, I recommend reading my startup guide to measurement.

Problem #3: Advertising to existing customers usually spells attribution issues

Problems 1 and 2 affect all marketing, but predominantly acquisition.

Retention marketing – ads spent on your existing customers – face a different problem.

Meta is only modelling behaviour. And with existing customers where there are more drivers of that purchase behaviour, its often less accurate - especially if you’re running viewthrough conversions with an existing audience.

Think of this scenario:

• I am a customer and in the morning on the way to work I see a Facebook ad in my feed

• Then I get your newsletter which I read and I go and purchase off the back of

• Meta quite rightly sees that a conversion happened after a view event and logs it

• But even without the viewthrough issue, similar scenarios happen with click-attribution too

Problems summarised

At a high-level we can often assume the following:

So what can we do about it

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How to measure true Meta CPA

Measuring Meta CPA depends on where you are in your journey and there’s a few key specifics.

For the majority of the time, there can be some form of lightweight modelled behaviour and so most of these won’t cost you anything. That is until your business gets more complex and you need to use more advanced statistical methods.

Here’s how you can measure the true incremental CPA for Meta based on a few scenarios.

Meta is your first channel and so far your only channel

For starters, this means that your buyers don’t have any issue deciding where to buy.

Every decision a customer has to make reduces their chance of purchase. Decision fatigue is real.

So too are distractions. If someone googles you and sees you’re also on Amazon, and heads there, it’s easy to get distracted with other things and forget all about your brand.

And another problem is price. If you’re on sale via third party retailers who cut costs, that eats into your sales. I’ve seen this first hand with a few brands and the brands ultimately closing off a lot of their distribution strategy to prevent the Google Shopping competition.

Simplicity is good. Single sales channels are good. Not only do they convert better, but you also retain all that data. DTC is not just a great way of acquiring customers – you acquire customers who can have way higher LTV because you can get to know them.

But single marketing channels are particularly good.

Why?

Measurement is simple with single channel

If you’re spending £10k per month on Meta, and you have no other marketing activity, then you know that your blend CPA is effectively your Meta CPA.

We call something like this the “halo multiplier” which is the halo impact of Meta impact / attribution issues. Maybe some of these show up as ‘word of mouth’ or ‘organic’ or ‘direct’ – but at the end of the day, they’re all being driven by your Meta activity.

Your CPA here is £22.22. Keep it simple.

Recommended Meta attribution window: 7day click, 1 day view

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You have grown your business through various other means, and you are now testing Meta

Now let’s assume you’ve come in from a different route. You’ve built a marvellous retail business and are stocked in Whole Foods or Ocado.

Things are great. Retail’s likely your background, or maybe you’ve built a great organic brand via SEO in a high intent area. Either way something’s going well, but you now need to think about DTC.

In this example, Meta is likely to go the way of a heavy ‘retention’ campaign and accidentally capture orders that don’t belong to it.

The reason being is that sales are happening truly organically at this point. Whether it’s SEO sales or those created by customers who have seen you in their local Boots or Walgreens, Shopify is going to be registering sales throughout the day.

Now Meta is very good at finding people to serve ads to.

And if you have a Meta pixel on your site, and a user visits it, Meta will almost certainly retarget them. Or in an equally believable world, Meta has good data on who your customers are and is already showing ads to the people who are about to become customers.

Customer purchases – through no influence of the Meta ad – but Meta claims the credit. Or, as with the retention example, maybe the person was browsing around with high intent. Didn’t make it all the way and then the ad nudged them.

Now in these examples, that’s not Meta driving that sale, though it might legitimately, based on your setup, lay claim to it.

Here you need to try to calculate the true CPA by looking at the incremental uplift.

Imagine this scenario:

Now in this case, looking at your blend CPA doesn’t help you.

You may find that a £24 CPA is great and so you start scaling, only to see it shoot up the month after, and again the month after.

Here we might try to calculate the incremental CPA using a ‘before and after’ test.

Why before and after tests are highly flawed

In the intro I said that turning your ads off entirely was a hackneyed solution. It is. And for the same reason as measuring incremental CPA as I’m about to describe.

Before and after tests assume everything else stays the same.

We manage 20 account across £20m in ad spend, and I can tell you that there is so much variance day-to-day, and week-to-week, that before and after tests are at best a single datapoint. Any change to product or website has a dramatic effect. The news has a dramatic effect. In short, it’s impossible to use a before and after test to truly predict what would have happened anyway.

But, it is a useful as a single datapoint in a period of discovery.

Let’s go back to our example. And now use our no spend months as a proxy for what would have happened anyway.

We measure the ‘incremental uplift’ as the difference between what might have happened and what did happen.

This gives you the incremental CPA.

As you can see this changes the picture dramatically. In fact the FB campaigns got better every month here, even though blend and FB CPAs got worse.

Now this rarely works cleanly, because more often than not you have months that look like this:

And as a result, it’s very hard to determine what a good ‘baseline’ is.

But as I say it’s a useful data point.

In times like this, your best bet is to switch up attribution.

Recommended Meta attribution window: Incremental attribution

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You have scaled your business effectively with Meta but now know customers come from multiple sources

Let’s say you went DTC first, or you’ve grown DTC substantially.

You’re now spending £100k/month+ across Meta and Google, and your physical retail popups are driving lots of sales in-store as well as online based on your post-purchase checkout survey.

Measurement now steps up a gear in complexity.

At this point, there’s two new tools that come your way for measurement.

Measurement tool #1: the marketing mix model (MMM)

Modern marketing mix models use machine learning, though the earliest ones predate ML practices.

In their simplest form they evaluate input metrics like channel spend and impressions, and output ones such as sales and revenue.

The model then attempts to determine the impact of a channel.

The cost of an MMM is usually high as you’ll need to find a data scientist to build one, or buy from one of the existing providers – all of whom are relatively expensive.

The downside to MMM is its a historic view, and usually requires weeks or months of data to examine at once. It’s not a daily, reactive view in most forms of the measurement.

Sidebar: we’re looking to launch a self-serve MMM for ~£399/month soon, register interest in comments or email if you want to be first to join

MMMs produce lots of output but at their simplest, they too can produce a simple CPA chart like we’re used to seeing. This comes from our internal tooling, data anonymised.

Measurement tool #2: a ‘lift’ or ‘holdout’ or ‘incrementality’ test.

MMMs are correlative, rather than causal. The only true causal measurement technique is the lift test.

Whatever you call this technique, it’s where you run a hold out.

It’s similar to what we did with our before and after test in example 2 above, but statistically rigorous.

You split your audience in half:

• Half get served ads

• The other half don’t

You look at the difference between the two.

These tests are expensive to run. They require running hold out audiences which minimises your advertising’s impact. They require making very few changes to setup, so if you’re having a bad month, you need to stick it out.

And they’re costly in other ways.

But they will give you the best outcome of answering:

“what is the true meta CPA?”

Recommended Meta attribution window: no set path, test multiple with your chosen measurement tools of choice.

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You are running retention/remarketing activity

Finally, the question of retention activity.

Retention marketing can both be highly cost effective, and a total waste of money. We have clients at both ends of the spectrum, and it really needs to be tested with your own data.

We highlighted above why this problem can happen.

Think of this scenario:

• I am a customer and in the morning on the way to work I see a Facebook ad in my feed

• Then I get your newsletter which I read and I go and purchase off the back of

• Meta quite rightly sees that a conversion happened after a view event and logs it

• But even without the viewthrough issue, similar scenarios happen with click-attribution too

How to solve for it

Once you get to a reasonably high number of customers, this actually becomes one of the easiest places to get a real statistical read.

You test this with a holdout / lift / incrementality test, as you would on prospecting activity in example 3 above.

However, you don’t need to invest thousands in this.

Our approach is:

1. Split your list in half and assign everyone an even split of cohorts

2. Upload and sync one of those lists to Meta

3. Expose ads to that audience for 4-8 weeks depending on list size and conversion rate

4. Measure the incremental uplift of the two.

I wrote about this in depth a while ago: you’re measuring retention all wrong – how to measure retention marketing.

We’ve run six of these tests over the last half year.

• 2 clients found it to be highly incremental and cost effective

• 1 client fund it to be incremental but not at a cost effective CPA

• The other 3 saw no impact of the activity

Recommended Meta attribution window: again it’s something to test, but in general, we’re more likely to recommend incremental attribution → click-only → click + viewthrough

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Our average adjustments to CPAs

Measurement is in some ways a luxury. In the early days its often left behind, and then investor pressure to hit this quarters’ goals usually stops it from becoming a regular occurence.

And so we have our own proxies that we often use as a standard baseline. These are the universal numbers which we recommend when fresh into an account.

• Acquisition we always expect Meta to deliver about 10-25% more value than it says it does.

• Retention we expect it to deliver 50% less value than it says it does (esp on 7dc1dv)

It’s really important to find out these baselines and proxies for yourself. Some of the swings are fairly substantial and so in a world where a 5% difference can be make or break, knowing for sure is important.

I hope you’ve enjoyed this. Measurement’s one of those subjects that can often get really complicated, but it needn’t be.

We’re experimenting heavily with ways of helping clients with measurement at the moment. If you’ve got questions and you don’t know your true Meta CPA, then get in touch. If not, please leave a comment below. Thanks.

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

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

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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 – based on our recent seasonality analysis we did – I imagine is a good sign. We’re seeing more and more brands move up the funnel in their marketing efforts in times like this, and of course a shift towards partnership ads.

I don’t know about you, but Claude’s felt disappointing this week, and yesterday’s news that they’ve pushed some bigger edits is maybe a welcome sign. That said, yesterday I kicked off experiments with Codex – and that was before last night’s drop of gpt-5.5. I’m dual testing workflows today, will see how I get on.

I hope you have a great weekend
Enjoy
Josh

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Signals

Key signals for builders, growers, and operators.

• Meta’s AI creative tools are creating trust issues [Meta Ads] - Those of us in the weeks of ad accounts know that Meta’s AI auto-optimisations are a bane, but this has now reached bigger marketing news. Marketing Brew reported that some advertisers have struggled to switch off Meta’s AI creative features, with ads altered in ways that made products look wrong or visibly AI-generated. Snag Tights said Meta’s tools modified creative without the brand’s knowledge.

• AI referrals are becoming a commerce channel [DTC / Ecommerce] - Adobe says traffic from AI sources to US retail sites grew 393% year-on-year in Q1. In March, AI traffic also converted better than non-AI traffic, after being meaningfully worse a year earlier. The caveat is that the absolute base may still be small, but the direction is obvious: product pages now need to be legible to humans, Google and LLMs.

• Thorne shows the return of brand-led DTC growth [DTC] - Modern Retail wrote about Thorne driving 63% DTC sales growth after a brand awareness push. The interesting move is that a supplements business is not just trying to win on information, despite being in a research-heavy category. It is moving toward aspiration, community and authority. In other words: performance is being rebuilt on brand.

• AI in fashion points to the human layer becoming the luxury layer [AI / Consumer] - Vogue’s piece on AI in fashion argues that luxury is likely to use AI behind the scenes, while keeping the human as the interface. This feels relevant beyond luxury. In a world of agentic commerce, the brands that feel human will stand out precisely because everything else is becoming easier to automate.

  ---

  The Stack

  A shortlist of things to read, watch and visit this weekend.

  🎧 Is AI going to turn us all into middle managers?. The Atlantic
  📝 142 things you need to know in consumer growth in 2026. Early Stage Growth
  📝When your digital life vanishes. New Yorker
  🎧 The science of why you hate small talk. NPR
  📝 What I learnt about Jeff Bezos’ billionaire retreat. The Atlantic

  📝 Where should your beauty brand be after 10 years?. Vogue Business

  📝 Out of office: what the Wallpaper* editors are looking forward to in April. Wallpaper

  📝 Five tables to book this spring. Monocle
  📺 You are the bottleneck in AI. Andrej Karparthy on No Priors
  📝 Sam Altman may control our future, can he be trusted. New Yorker

  ---

  What do you think?

  Please leave feedback and a comment on the format. This is a newly launched feature this year and we’re always looking to improve. If you like it, hit reply, leave a comment, like us on Substack, forward to a friend and all other needy call to action requests you can usually expect at the end of these posts.

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 Native, this was a blueprint for what I thought it meant for businesses to be AI native. My thinking has evolved since that article.

In the article my starting block was: ‘we get AI to do a task instead of us doing it ourselves.’

Two weeks after I published it, I led an internal all-hands stating we’d be pivoting the business to be AI-native. We’re now a month in.

Today, my view of being AI native is broader. Yes, it’s getting AI to do work for you. But it’s also being a systems thinker, it’s being a software developer, it’s a total revolution in how your organisation collaborates, and it’s a total pivot (ultimately) in how you make money.

It’s working out where humans add extreme value and where machines can operate at infinitely more capacity than you. It’s actively developing critical thinking, taste (sorry Kyle Chaka), and interdisciplinary thinking.

Since my original posts, I have had a lot of people ask me about how we’re using AI – and for training and workshops. This is something we’re exploring, so do comment / DM if this is for you.

And so this is an update on where we are, focused on stuff that is high leverage and really helping us already.

I find this is also important to state. I feel like we’re 0.01% of the way to where we’re getting. This post is not about “we’ve replaced every human we ever need and can service £100m of business”, it’s an attempt at an honest reflection of where we are today. Warts and all.

What I’ll be covering today

• Key Claude foundations you need to build

• 15 Skills, processes, and software we’ve built

  • Setting up proper Claude md files and folder structures

  • Moving the agency to GitHub

  • Building daily podcast for the SLT with most important 5 mins from last 24hrs

  • Ad account discrepencies flagger

  • Conversational joining of Notion to Meta ads data

  • X Influencers weekly roundup

  • Chief of staff that is my new 'notes to self' organiser, prioritiser, delivery

  • Automated ads inspo database that collects from 4-5 different sources

  • CHJ: my management agent for staying on top of 121s, L&D, progress

  • Creator discovery and outreach agent

  • Self-serve Google Meridian

  • Finance project that connects Xero, creator costs, Toggl

  • Marketing Science education site for myself (Duolingo for marketing sci)

  • Pitch decks we edit in review meetings via voice

  • Cosgrove: our new account manager

• What we’ve got planned

I hope you enjoy

Josh

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Key Claude foundations you need to build

Before we get into what we’re building, a quick primer on the things you need to understand to make Claude Code actually work for your business. If any of these aren’t familiar, then use these as starting blocks.

1. CLAUDE.md

Every project in Claude Code can have a CLAUDE.md file. Claude reads it automatically every time it opens that project. This is how you give Claude persistent memory about who you are, what you do, and how to operate.

I’m going to go into a bit more depth on this below, but if you’re unfamiliar and not yet used them. Start with Anthropic’s guide on making useful ones.

2. Context engineering

Prompt engineering was the key thing of 2025, context engineering is the key thing this year. This is more important than writing a better prompt.

This is about how you structure information around your prompt: examples, constraints, formats, objectives…. context is the important thing here. Again Anthropic’s own guide is great.

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3. Skills

Skills are actually very simple: they’re just sets of instructions to be carried out in order.

In other words, they’re recipes. Reusable recipes or workflows that you might call upon again and again. This might sound simple enough, but there’s a real skill (no pub) in creating good Skills at the right level.

About 15 years ago I did Harvard’s CS50 and to this day remember the ‘making a peanut butter and jelly sandwich’ segment. If you’re stuck with Skills, watch this to remind yourself of how many steps you need to think stuff through.

4. MCP

MCP (Model Context Protocol) is how you connect Claude Code to your actual tools. Slack. Notion. Google Drive. Your CRM. Instead of copying and pasting context between systems, Claude can read from and write to the tools your business already runs on.

5. Claude Code

Whereas once I maybe had 20 or 30 chats started with ChatGPT everyday, now I barely ask one thing to Claude desktop’s native chat interface per day.

I’ve 99% migrated to Claude Code and in the command line. This isn’t true for the entire team yet, but it does feel like it’s everyone’s natural nth degree rather than just the choice for some people.

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15 Skills, processes, and software we’ve built that have revolutionised what we do

1. Claude.mds and folders to set up for success

This is one of those ones I skipped over for so long and is probably one of the single big unlocks.

Claude Code reads CLAUDE.md files from every parent folder it sits inside. So if you structure your folders properly, context travels between your folders by inherting info from the folder above.

At the top, my personal CLAUDE.md. I gave it my background, people in my life, my key projects, how I want Claude to work with me, and my skill level.

Below that, a Ballpoint folder with its own CLAUDE.md covering the agency itself: our positioning, tone of voice, how we work, the team, our clients.

We share the maintenance of this as a team via GitHub (more on that below).

Inside Ballpoint, a Clients folder with a CLAUDE.md that contains key client info: systems for how we structure accounts, Notion organisations and so on.

Then each individual client folder has its own CLAUDE.md with their context: brand, competitors, constraints, current projects.

What this means is when I open a project inside a client’s folder it’s pulling on:

• the client’s claude.md

• our clients’ claude.md

• the Ballpoint claude.md

• and my own claude.md

When I first read about people doing this stuff, I delayed it for ages becuase it never felt high-leverage. There’s a reason I’m putting it number one today.

2. Moving to GitHub

I’ve never liked Notion’s version control, or Google Docs’ version control. After three months on GitHub, I finally understand why engineers love it.

The first unlock was shared maintenance. We have long had spreadsheets that contain things like Meta account ids, and another spreadsheet for Notion client links, and another for copywriting best practices. This is now a universally shared set of documents – all via GitHub. It means if something changes it changes for everyone.

Not only that but our bigger projects now get worked on like software ones. One person builds, ships, another tests and feeds back, branches get merged via PR.

For agency work this feels like a revolution.

3. Daily podcast for the senior leadership team

Every morning at 5am, our senior leadership team gets an email with a 5-minute MP3 attached. It’s a podcast summarising everything that happened the day before — key client meetings, key team meetings. It pulls from Slack, Notion, Fathom (where our calls happen), and Granola, synthesises it, and sends us a daily update.

I listen to it on the way into work. As the company has scaled, there’s a lot I’d miss otherwise. This bite-sized brief is how I stay close to the detail without being in every room.

Stack:

• Claude

• Python

• Elevenlabs

• GitHub Actions

4. Automated ad accounts checks (Skill)

Checking ad accounts is an ongoing part of the job, and I don’t think handing it entirely to an agent is right. But headline checks now get done automatically as a starting point.

The Skill checks each account against reference CPAs from `clients.json` and builds a rolling memory of how things have changed day-to-day, flagging outliers (was this spend increase actually big for Tuesday?).

Perhaps most importantly, it doesn’t send anything if there’s nothing to report. Instantly, it’s infinitely more useful than a daily report that you instinctively ignore.

• Claude Skills

• BigQuery & Gcloud’s CLI tools

5. Joining Meta data with Notion links via the CLI

We’ve long used Weld, which has a great internal SQL AI tool. In the later days, I used Claude to help write the SQL for me.

Now we’re connecting to BigQuery directly from the command line, which lets us join data sources on the fly. Because Bigquery sits alongside the Notion MCP, it means joining data together is done in human-speak rather than infra + data eng speak. It means that with some simple setup anyone can chat about any client’s data and it provide proper understanding, without the need for a line of code.

• BigQuery & Gcloud

• Claude Skills

6. Staying on top of X without getting sucked in

I used to waste a lot of time on social media following people sharing hacks and tidbits. It’s very easy to get sucked into the DTC timeline.

Now I have a handful of key people I care about. Every Monday morning, a Skill summarises their posts from the week, highlights anything new, and shares the original post alongside. If there’s a spike in conversation volume around a specific topic, that gets flagged too. Apify does the scraping as part of the Skill.

• Claude Skills

• Apify

7. A Chief of Staff that is my memory & coordinator

The problem I kept hitting: I always have ideas. At any given moment I’m jotting something down, forwarding an email to myself, or voice-noting a thought. I never build the time back in to go through it.

So I built a chief of staff. It ingests from a few places — Telegram API, an email subfolder (`josh+x@weareballpoint.com` goes straight in), voice notes. Whatever I share, it reads: if it’s a YouTube link, it pulls the transcript; if it’s an article, it reads the article. It’s logged into The Economist, FT, and The New Yorker, so paywalled pieces work too.

Then it decides what needs my full attention versus what can just be summarised and filed as a data point. It also acts on things. “Get a meeting with so-and-so” → it books the meeting. This is one of the areas I’m developing most — it’s starting to connect to my other Skills so it can trigger them automatically.

• Claude Skills

• Telegram API

• Apify

• GitHub Actions

• Claude Schedule

• Firecrawl for YT transcriptions

8. Auto-tagged ads inspiration database

Tools like Foreplay exist for this. But the reality for us is that ad ideas get shared in fifteen different places: Instagram DMs, WhatsApp groups, Slack channels, email etc etc.

What we now have is a centralised inspiration database that scrapes all of these sources automatically. The actual ad creative (video or image) is pulled via Apify and stored. Everything is auto-tagged by creative type, so we can pull references back whenever we need them.

• Claude Skills

• Apify

• Notion

9. CHJ: my agent for managing

Named after Claire Hughes Johnson, who wrote Scaling People (one of my favourite books on management).

This is a management agent that holds my team’s progress, notes from 121s, the key projects different team members are working on, and non-work projects they’re collaborating on. It has our growth and progression frameworks built in, so we can stay on top of how people are progressing. It also tracks things like team energy which is one of our internal benchmarks.

• Claude Skills

• Fathom API

• Granola API

10. Creator discovery & outreach agent

Finding creators is human-intensive and time-intensive — a natural place for AI to help.

The Skill is connected to Notion, where our briefs live. It pulls client characteristics and key customer notes from the brief itself, then searches Instagram or TikTok (depending on the client) against a tuned set of criteria. It builds a database of candidates, and via the Gmail API, it can start the outreach process too.

• Apify

• Gmail

11. Internal self-serve Meridian MMM

We’ve worked with data scientists for a long time to build MMMs with Meridian. For clients with a reasonable degree of similarity, we can now self-serve the Meridian rollout via this Skill.

It connects to our existing data warehouse, which makes it simple to run. We’ve also rebuilt the frontend in our own branding — a mixture of Astro and TypeScript — so the output looks like us, not like a research paper.

• Claude Skills

• Google Meridian

• Astro

• Typescript

12. Billing & Finance agent

We track time internally with Toggl, invoice via Xero, and have various ongoing discussions in Slack and Notion. This agent sees all of that.

I can ask how long a project took, whether we’re over-serviced on a client, how a retainer is trending. The answers are much richer because it’s looking across all the facets — not just the numbers in Xero.

• Toggl

• Xero

• Claude Skills

• Slack

• Notion

• Gmail

13. Marketing Science Education Site

I built this for marketing science specifically, but it’s the kind of thing you could do for anything.

I find reading isn’t actually how I learn best. So I built a simple spaced-repetition learning app to teach myself marketing science properly. It has an education layer and a revision layer. Essentially Duolingo for marketing science.

• Javascript

• HTML & CSS

14. Pitch decks we update via voice in the review meetings

Nobody likes building pitch decks.

Ours are now built in HTML, CSS, and a little JavaScript. There’s a uniform design system across every deck. Core page formats are specified. Videos and images slot in as examples. It means zero misalignment in design principles, and everything follows a proper hierarchy.

The payoff: we edit decks conversationally. Wispr Flow open, walk through the deck almost live, record notes, hit enter. By the time I’ve finished talking about the current slide, the previous slides have already updated. Deck creation has gone from hours to minutes.

• HTML, CSS, and JS

• Claude Skills

• Wispr Flow

• Gmail for context on clients

• Analysis subagents

15. Cosgrove: our meeting note account manager

Transcription notes suck. And the reason is not because of AI but because of context.

It doesn’t matter whether you’re using Fathom (like we do) or Granola (ditto) or any of the others, they are still universal tools. Now, what they’re great for is transcribing and managing that process – that said we may well one day replace that too.

But the important thing is adding in context.

One of our most recent internal tool releases is Cosgrove (named after Ken Cosgrove, Mad Men’s affable account man). This is trained on a series of best practice human meeting actions Slack messages, these were perfected over three years to get the right degree of actions, insights and notes – linked to all the right places.

This was a fun one to build, and while we’re not 100% at output yet – I’d imagine by the time this goes to print, we will be.

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That’s what we’ve built, here’s what we’ve got coming.

What we’ve got planned

As well as all of the above, we’re also currently building four separate bits of software that are combinations of code + skills + research + self-improving loops.

For the time being, we’re keeping these under wraps. Though as I joked last week, the possibilities are starting to feel majorly exciting.

Here’s a few areas where we still believe humans do the best work:

• Starring in UGC ads. While simple statics will arguably all be replaced by AI creative in the near-term, UGC is still something that works because of its authenticity.

• Customer interviewing and analysis. The nuance of how someone answers a question, the tone, the subconscious meaning because of how they smiled, and the ability to push harder in the interview itself, all mean that this is a skill that will forever remain human-focused. Maybe we’ll have AI moderators for our focus groups, but it won’t be any time soon.

• Creative strategy. By this, we should be mindful of the differences between (a) the deep thinking work of creative strategy, and (b) the executional work of writing a brief. Joining the dots together requires significant outlier thinking: not just following a set of prescriptive best practices.

• Strategy in general. With AI we could make 10,000 ads for you tomorrow. It doesn’t mean that would be useful. There still needs to be cognitive decision-making in the process, and humans are the best at working out what to say No to.

• Client servicing and deep communication. Not all client servicing requires humans. There is lots of factual information that doesn’t need context, and soon I imagine our Ballpoint AI talking directly to the client AI to provide updates for immediate status reports. But the human time as we go through decisions, thinking, context, as we try to get people aligned and incentivised right – I can’t see this going away any time soon. Not just this, but the human-to-human sense that we’re in these trenches together as we are with our clients is still the most valuable work we can do.

For me a lot of this is resetting my first principles.

I’m asking questions based on:

• If we ignored everything we knew about growth up until now and started with a blank piece of paper and Ballpoint, how would we design that system?

• What is software, and what is a skill?

• How can we build genuine self-improvement loops into our AI work so we can start seeing exponential improvements in output.

If I had to make a bet, then I imagine the way this will manifest is this:

This is the core bet I’m making at the moment and I’m deeply excited as we embark on this journey to get there.

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

This is the age old question. And usually for growth stage businesses, the answer lies somewhere in either product or marketing.

But perhaps the biggest factor is the one that we can’t control: the macro environment we all exist in.

We get glimpses of the macro as an agency when we analyse internal trends across £1.5-2m of ad spend each month. But sampling data like this is only part of a picture and is often more swayed by a particular strategy being deployed at any given time.

A lot has changed since the start of the year

This year has seen some pretty momentous change.

AI is now ubiquitous and we’re starting to see the real life impact of it, rather than the hypothetical (and this is before we get into future models).

But perhaps more immediately and tangibly impactful is the war in the Middle East.

Today’s post analyses a handful of sources including:

• Think tank releases

• Articles I’ve been reading the last few months from the Economist, FT etc

• Google Search Trends Reports

• Reddit

For most brands we work with, Q2 is a strong quarter that precedes the year’s weakest quarter, Q3. If I could predict the future, I’d be a rich man, but there’s still quite a few things I’ve seen so far that seem worth talking about.

Because I imagine this stuff will be having an impact for all of us in the coming months.

Josh

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29% of UK workers admit they’ve sabotaged AI tools at work

29% of UK workers admit they’ve sabotaged AI tools at work, growing to 44% for Gen Z.

The TUC finds 51% of the UK public concerned1, rising to 62% among 25 to 34 year olds.

There are hundreds of threads discussing AI anxiety in the last 30 days alone.

You won’t find this in Google Trends: searches related to “AI replacing jobs” have faded since April 2025. But ChatGPT usage in the UK tripled last year2.

Mortgage renewal, tax drag, and AI fear are hitting millennials at once

1.8 million mortgages expire this year into higher rates.

Frozen tax bands cost a £55k earner over £1,000 a year. And 52% of workers believe AI threatens their job.

On r/UKJobs, a 43-year-old on £50k described weighing a £27k offer.

On r/HousingUK, a first-time buyer watched their monthly payment jump £450 mid-conveyancing. No single dataset proves all three pressures act together. But the Reddit threads tell the same story from different angles: 30-to-45 year olds are absorbing rising costs, tax drag, and career uncertainty at the same time.

That’s the age group many premium DTC brands say drives their core revenue.

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Last time oil crossed $100, consumer mood fell 21 points in a month

In March 2022, Brent crude crossed $100 after Russia invaded Ukraine. Consumer mood (measured by the GfK survey) dropped from -17 to -38 in April.

By September it hit -49, the lowest in the survey’s 50-year history. In 2026, Brent spiked to between $109 and $1133 in early April on Strait of Hormuz tensions, and consumer mood is already at -21.

The pattern is the same: oil shock, energy prices rise, mood collapses. What’s different is the starting point, and what’s layered on top.

In 2022 the extremes won and the middle lost. It’s starting again.

After Brent crossed $100 in 2022, Aldi gained 1.4 percentage points of grocery share in 12 months.

Combined with Lidl, discounters grew 25% year on year against a market growing 7.6%.

Premium brands held.

In 2026, the same pattern is forming: Lidl is up 9.6%, Waitrose up 5.8%, Asda down 0.9%4.

If the 2022 pattern repeats, the middle loses the most share over the coming quarters. But there’s a difference this time: people are also dealing with AI job anxiety and a wall of mortgage renewals that didn’t exist in 2022.

People feel fine personally but are terrified about the economy

The UK’s main consumer-mood survey, run by GfK, asks two different questions.

How do you feel about your own finances?

And how do you feel about the economy?

The answers are diverging sharply.

The personal finances score sits at -7. Not great, but stable.

The economic outlook score collapsed to -37 in March, down 6 points in a single month and 8 points worse than March 2025.

That 30-point gap is the story. People aren’t struggling. They’re bracing. And bracing shoppers don’t stop spending entirely, they just get harder to convert.

People are saving more and spending less on big purchases

The GfK Savings Index rose 6 points in March to 27, the highest reading since January’s peak of 28. Context matters: it was 28 in January, fell sharply to 21 in February, then rebounded. So this is a recovery, not a breakout.

But GfK’s own consumer insights director put it plainly:

When people start saving instead of spending, brands need to earn the conversion harder.--

Health and beauty spending is up 6-8% while everything else flatlines

Note that this is February data and doesn’t capture most recent changes.

Barclaycard’s February data shows health and beauty up 6.4%, following 8.0% in January5.

Digital subscriptions grew 12.2%.

Entertainment grew 9.9%.

On the other side: household goods fell 2.9%, electronics fell 5.8%, airlines dropped 6.9%.

Overall spending grew just 1.0%, well below inflation of 3.2%.

People are choosing where to spend, and health is winning. Worth noting: this is February data, before the Strait of Hormuz oil shock.

Health and beauty was the most resilient category going into that shock, which is exactly the category you want to be in coming out of one. That resilience held through the oil spike. UK searches for strength training and gym memberships kept rising through March and early April, even as Brent crossed $100. Wellness isn’t a line item people cut first when the news gets worse.

“Strength training women” searches are up 261% in two years, protein powder peaked

Google Trends data for “strength training women” shows a 62% increase year on year and a 261% increase over two years. “Protein powder women” is up 80% over that same two-year period. Gym membership searches are up 88% year on year. This isn’t a January resolution spike. The trend has been compounding for two years and it’s still accelerating. The UK women’s health supplements market is worth an estimated £1.18bn, growing at 5.7% a year.

Vinted UK is up 80% and near all-time highs

“Vinted UK” sits at 93 out of 100 on Google Trends.

That’s up 80% year on year and 87% versus two years ago. It’s near the ceiling of search interest.

Meanwhile, ASOS reported sales down 9% globally and 5% in the UK in the first half of their financial year.

The secondhand market isn’t a niche. Every fashion DTC brand’s customer is now comparing prices against what they could get the same item for on Vinted.

The question isn’t whether resale competes with new. It’s whether “buy new” creative can make a convincing case.

Perimenopause is pulling health spending younger

Women as young as 33 are now spending on perimenopause solutions.

On r/AskWomenOver30, symptom management dominates the conversation: hot flushes during client meetings, brain fog at work, products that actually help.

The UK women’s health and beauty supplements market is valued at £1.18bn, growing at 5.7% a year.

Collagen supplements, a closely related category, are growing at 6.27%.

On Reddit, women describe buying fewer products but spending more per item, trading up to professional-grade solutions rather than adding more to the basket. That’s the pattern as people on these forums describe it, not confirmed market data, but it matches a market that’s growing in value faster than in volume.

One in six UK 16-24 year olds is unemployed

The UK 16-24 unemployment rate hit 16.0% in the three months to January 2026.

That’s 731,700 people and the highest rate since 2014.

There are now 3 unemployed young people for every vacancy, up from 1.9 a year ago. The King’s Trust reports6 that seven in ten teenagers wish they weren’t starting their careers in this economic climate. For brands targeting Gen Z, the spending power simply isn’t there for a growing share of this age group.

Closing thoughts

None of this is a prediction. If I could call the next six months with confidence, I’d be running a hedge fund, not an agency.

But there does seem to be a vibe shift going on – even before the war began. There already seems to be anxiety about work, the future, and cost of living, and while these things have for the last few generations impacted those already worse off, today they’re hitting the higher earners.

AI might be intangible, but petrol hitting £2 will not be, or flight routes will not be.

People are still choosing to invest in themselves for now. Health continues to be an area of prioritisation, but done in the right way. It’s strength training and gym memberships over the continual escalation of supplements.

We know inherently how impactful these things are, but we’re not yet seeing it on the spending power. But short of a big shift in direction, I imagine we’re about to start seeing one in the months to come.

This has been fun to put together, and I look forward to reflecting at the start of July to see how right or wrong these things ended up being.

Have a great week

Josh

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