Ben Guerin's Blog

The surprising truth about AI and jobs

Ben Guerin — Sun, 05 Apr 2026 00:00:00 GMT

A collaborative op-ed based on research and analysis with my friend Matthew Kilcoyne, originally published in The Spectator.

Lord Stockwood, the minister for investment, recently floated the idea of universal basic income to cushion AI-driven job losses. Last month, the European Central Bank published a study of 5,000 eurozone firms showing that companies which adopt AI are 4 per cent more likely to hire. Something doesn't add up. So what's going wrong?

In 2013, a widely cited Oxford study told us 47 per cent of American jobs were at high risk of automation. Since then, every serious forecast has done the same thing: decompose a job into tasks, score which tasks machines can do, announce a crisis. The renowned VC investor Marc Andreessen refined the point this January: it's tasks, not jobs, that get replaced. He's right. But he doesn't explain why the job survives once the tasks are gone.

For an answer to that question, one should look to Adam Smith. It should come as no surprise that just when we think the rules of economics are about to be utterly changed, that the core insight comes from the father of the subject.

The pin factory is the most famous anecdote in economics. Ten men can create 48,000 pins a day performing eighteen operations between them. Every textbook reads it as a story about task decomposition. But Smith was describing something else. The wire drawer's output has to fit the straightener's technique. The pointer's work must be compatible with the header's. Each worker depends not just on their own skill but on the relationships between them: timing, trust, the tacit knowledge that builds over months and years of working side by side.

Smith said the division of labour is limited by the extent of the market. That's the line everyone quotes. But the extent of the market isn't a task list. It's a web of human relationships. And it's the nature of those relationships that determines what a job looks like in the age of AI.

The best granular data comes from across the Atlantic. America's Bureau of Labor Statistics projects every occupation out to 2033. They say software developer jobs will increase by 18%, financial advisors by 17% and lawyers by 5%.

Personal financial advisors are heavily exposed to AI. Robo-advisors already handle portfolio rebalancing and tax-loss harvesting. On paper, doomed. In reality, this profession is booming. And that’s because the financial advisor's real job isn't rebalancing a portfolio but rather being the person a client trusts enough to ring.

Software engineer job postings hit multi-year highs in early 2026, even as AI writes more of the code. When something gets cheaper to produce, you produce more of it. And the software developer isn't writing code in isolation. Instead she is coordinating with product managers, designers, and other developers all day, every day. AI can take the tasks. It can't take her position in the network.

One of us runs a creative and digital agency that has worked on more than 25 election campaigns across 40 countries. The other has spent a decade in technology policy. Between us, we've seen the AI-replaces-everything thesis tested in both directions: in client relationships and campaign war rooms, and in the policy frameworks supposed to prepare workers for what comes next.

The honest answer, at least in the knowledge economy, is that about 40 per cent of what an organisation does is automatable right now. Research synthesis, first-draft copy, scheduling, data processing are just some of the tasks that AI is brilliant at. But the other 60 per cent is relationships, judgment and taste: knowing when the machine's output is brilliant and when it's rubbish. That scales at the speed of human trust.

The anthropologist Robin Dunbar showed that humans can maintain roughly 150 stable relationships. The limit is neurological, and shows up across cultures throughout history. Hutterite farming colonies have split at that number for centuries. Gore-Tex caps its factory units at 150 employees. The relational capital in any job builds slowly, can't be parallelised, and is destroyed in an instant when someone is displaced.

That's the real problem with retraining. Every programme focuses on skills. But a displaced worker's actual deficit isn't skills. It's that she doesn't know who to ring or whose emails matter. Anyone who has started a new job knows the first six months aren't about learning the tasks. They're about learning the network. Policy that ignores this will keep failing.

If the evidence points to augmentation, why does the public assume displacement? Look at the name.

We call it Artificial Intelligence. Artificial: the GMO of cognition. Almost fraud-coded before the conversation begins. And then Intelligence is coming for your livelihood. No wonder people are frightened!

What if we'd called it what it actually does? Not Artificial Intelligence. Augmenting Intelligence. One name says the machine is coming for you. The other says it's working alongside you. The evidence supports the second. But we've spent three years trapped by the first.

The question was never whether the machine is coming for us. It's what we become alongside it.

Ben Guerin is co-founder of Topham Guerin, a global creative and digital agency. Matthew Kilcoyne is a policy analyst at the Centre for Data Innovation.

TikTok didn't make them climb the mountain

Ben Guerin — Thu, 02 Apr 2026 00:00:00 GMT

The BBC is running a piece this week about mountaineers getting into trouble after being "inspired by TikTok." Mountain Rescue England and Wales says it's become a familiar story: people see a video, head for the hills, and end up stranded.

It's a tidy narrative. Social media bad. Reckless youth. Platform to blame.

But people have been making stupid decisions in the mountains since long before anyone had a smartphone. Between 2002 and 2006, UK mountain rescue teams responded to 6,814 incidents involving nearly 8,000 people. TikTok didn't exist before 2017. So what were we blaming back then?

The answer, of course, is whatever was nearest.

Before TikTok it was Instagram hotspots. Before Instagram it was guidebooks that oversold easy routes. Before guidebooks it was word of mouth down the pub. Stefan Winter, head of sports development at the German Alpine Club, put it well recently: in the past, individuals plunged down Niagara Falls in wooden barrels. Today, influencers have to settle for the Matterhorn.

The impulse hasn't changed. People have always wanted to test themselves, show off, and occasionally do something profoundly idiotic in the process. Eve nicked the apple off the tree. Your grandad probably did something on a dare he still won't tell your nan about. Fear Factor had millions of viewers watching people eat insects for money, and nobody wrote a BBC Indepth feature asking where the responsibility lay.

But when TikTok is involved, the framing shifts. Suddenly the individual disappears and the platform becomes the actor. People aren't making bad choices. They're being "inspired" or "lured" into danger, as if they had no say in the matter.

This is where the media deserves a proper swing.

There's a reason "social media is dangerous" is such a reliable news story. It doesn't require the journalist to say anything uncomfortable about personal responsibility. It doesn't ask the reader to consider that adults are capable of making their own decisions and living with the consequences. It just points at the screen and says: that thing did it.

And it lets everyone off the hook. The person who didn't check the weather gets to be a victim. The parent who didn't teach basic risk assessment gets to be outraged. The outlet gets a clean villain for the headline.

Yes, callouts are up 24% since 2019. And yes, 18 to 24-year-olds are now the most rescued age group, overtaking the over-50s for the first time. But the post-pandemic outdoor boom, cheaper travel, and better mobile coverage all play a role in those numbers. Blaming TikTok is the easy answer. It's rarely the complete one.

Of course social media creates trends. Of course some people attempt things they're not ready for because they saw a 30-second clip. But that's not a technology problem. That's a judgment problem. And judgment has always been unevenly distributed.

The uncomfortable truth is that most of us don't want to hear "you made a bad call." We'd rather hear "you were misled." It's more comfortable. More sympathetic. And it means we don't have to change anything about ourselves.

Making content at scale with AI requires a whole new set of skills. I call it "AI ProductionOps"

Ben Guerin — Mon, 30 Mar 2026 00:00:00 GMT

With Photoshop, Illustrator, Premiere Pro and After Effects, the tool just worked. You opened it, it was there, it did the thing. Every time. Reliably. For years.

Nobody had a fallback workflow for Photoshop crashing. Because it didn't.

AI creative tools don't work like that. They're more powerful than anything the creative industry has ever had access to. They're also less reliable than anything the creative industry has ever depended on.

That gap between power and reliability is creating something that, at the risk of sounding like a LinkedIn thought leader, I've been calling AI ProductionOps. It's not a phrase I'm particularly impressed by. But there genuinely isn't a word for this yet, and the lack of one is part of the problem.

The routing problem

At Topham Guerin, our creative team works across multiple AI tools every day because each tool does something different, and none of them do everything to the standard we need the fina outputs to be at.

Kling is our go-to for video, especially when we need frame-to-frame consistency between a start image and an end image. Best in class for that. But it's often overloaded, and when it's down, there's no obvious replacement. You just wait. Try explaining that to a client on a deadline.

Gemini handles detail brilliantly. If you need precision in an image, accurate text rendering, or complex compositions, it's the strongest option right now. Midjourney is better for atmosphere and scenes, the kind of output where mood matters more than accuracy.

Different tools, different strengths, same brief. The team needs to know which model handles which style, and when to switch.

When it comes to agentic software development, Opus 4.6 in Claude Code is exceptional at handling complex web builds, and working with it feels like chatting to a peer on slack. But it often misses small details and can get tangled up when debugging, which is where we turn to Codex and the GPT 4.5 family of models for precision and hyperfocus.

Claude Code is much better at seeing the big picture, but Codex is more reliable at handling complex details.

The volatility problem

Every week there's something new. A model update changes the output quality of a tool you'd finally figured out. A new feature appears that solves a problem you'd been working around for months. A pricing change makes your primary tool uneconomical overnight.

The old creative tools were less powerful but reliable. The new ones are more powerful but volatile. That's just the reality of working with technology that's evolving this fast.

And it creates an operational challenge that most agencies haven't thought about yet. When your primary video tool goes offline during a client delivery, what's the fallback? When a model update changes the visual style of outputs you've been producing for weeks, how do you maintain consistency? When a new tool launches that's genuinely better than what you're using, how quickly can the team evaluate and switch?

These aren't creative questions. They're operational ones. And they need someone thinking about them before the deadline arrives.

What the team actually needs

The creative skill hasn't changed. You still need people with taste, with judgment, with the ability to look at AI output and know immediately whether it's good enough or whether it's generic nonsense.

But on top of that, the team now needs:

1. Knowledge of which model handles which style, and when to switch between them.
2. A way to maintain visual consistency across outputs from different tools.
3. Fallback workflows for when the primary tool is throttled or offline.
4. Time carved out just to stay current, because the toolkit shifts constantly.

That last one is easy to underestimate. Keeping up with the tools isn't a nice-to-have. It's operational overhead that has to be budgeted for, just like training on any other production platform. Except this one changes every month instead of every year.

The bigger point

There's a reason I think this matters beyond our own workflow at TG.

The conversation about AI in creative agencies is almost entirely about output. Can AI make images? Can it generate video? Can it write copy? The answer to all of those is yes, often impressively well.

But almost nobody is talking about the operational layer underneath. The routing decisions, the fallback plans, the consistency management, the tool evaluation cadence. The infrastructure of actually shipping creative work with these tools, reliably, on deadline, at a standard clients will accept.

That's AI ProductionOps. And the agencies that build this discipline first will have a significant advantage over those still treating AI tools like a single-player creative shortcut.

Because the tools are going to keep getting better. They're also going to keep changing. And the gap between "we use AI" and "we have a system for using AI" is where the real competitive advantage lives.

The most important decision in AI right now isn't what to build

Ben Guerin — Wed, 25 Mar 2026 00:00:00 GMT

February 2024. Sam Altman calls Sora "a remarkable moment." The internet loses its mind. The future of video has arrived.

September 2025. The Sora app launches publicly. One million downloads in five days. The GPT 3.5 moment for video, if you believed the hype.

December 2025. Disney signs a billion-dollar deal to put 200 of its characters inside Sora. Mickey Mouse, Marvel, Star Wars, all of it.

March 2026. OpenAI announces Sora is shutting down. Disney found out less than an hour before the public did.

Twenty-four months from revolution to shutdown. There has been lots of criticism of OpenAI for this "failure". But if you ask me (and given you're reading my blog, I'm sure you are eager to hear what I think) they should be commended just as much for their ambition to experiment as for their discipline for stopping.

The maths that stopped working

According to the Wall Street Journal, Sora was burning roughly a million dollars a day. User numbers peaked at around a million and then collapsed to fewer than 500,000. Every GPU generating a ten-second video of a dancing Pikachu was a GPU not running the text and code tools that actually pay OpenAI's bills.

With an IPO reportedly coming as early as late 2026, the maths stopped working. Wall Street wants recurring revenue, not just experiments.

Meanwhile, Anthropic doesn't generate images. Doesn't do video. Just text and code. Their run-rate revenue hit $19 billion in March 2026, according to Bloomberg. Up from $9 billion three months earlier. The growth is driven almost entirely by Claude Code, their coding tool.

OpenAI tried to do everything. Text, code, images, video, hardware, search, social media. Anthropic do less, and have a more expensive product targeted to the enterprise. And the focused company is winning on the metric that actually matters: money.

I wrote recently that strategy is what you choose not to do. Sora is the case study. OpenAI killing it wasn't a failure. It was, belatedly, a strategic act. However, Anthropic never needing to kill their equivalent is an even better one.

The pattern is everywhere

This isn't just an AI story. The same logic keeps showing up across industries, and the companies that get it right tend to win.

Apple spent a decade and reportedly more than $10 billion on Project Titan, its electric car. In February 2024 they killed it. Two thousand employees were redirected to the AI and machine learning division. Investors shrugged. Some applauded. The market understood: the opportunity cost of continuing was higher than the cost of stopping.

In January 2026, Mercedes pulled its Level 3 self-driving system from the facelifted S-Class entirely. BMW followed a month later. Both companies went backwards, from fully autonomous highway driving to a system where the human stays actively engaged. And as I wrote in a blog on AI autonomy, Mercedes' CEO described the downgraded experience as better.

Going backwards was the right move. Because Level 3 self-driving had the same problem Sora had: technically impressive, commercially unworkable, and consuming resources that could be better spent elsewhere.

Steve Jobs understood this instinctively. When he returned to Apple in 1997, he killed 70% of the product line. Drew his famous two-by-two grid. Four products. Everything else was gone. The strategy wasn't what Apple decided to build. It was the dozens of things they decided to stop.

What Google gets wrong

Of course, not all stopping is strategic.

Google kills products constantly. Reader, Stadia, Inbox, the list runs into the hundreds. But Google's product shutdowns aren't exercises in strategic focus. They're a side effect of internal incentives that reward launching over maintaining. Engineers get promoted for shipping new things, not for keeping old things alive. That's not discipline. It's dysfunction wearing discipline's clothes.

The difference matters. Stopping because the economics don't work, like Sora. Stopping because you're focusing on what matters, like Anthropic and Apple. That's strategy. Stopping because you got bored, like Google with Reader, is something else entirely.

The incentive problem

Here's the thing that makes strategic stopping so rare. The entire incentive structure of the tech industry, and honestly most industries, rewards building.

VC rewards growth. Media covers launches. LinkedIn celebrates announcements. Founders write threads about what they're building next. Nobody writes a press release about the thing they decided not to do.

At Topham Guerin we run into a version of this constantly when advising clients - whether on AI transformation of sharpening their marketing operation. The temptation is always to adopt the next thing. The harder, more valuable conversation is about which tools to drop. Which experiments didn't work. Which capabilities looked promising six months ago but aren't earning their keep.

That conversation never gets a standing ovation. But it's where the actual strategy lives.

The AI industry right now is obsessed with what to build next. The winners over the next twelve months will increasingly be defined by something less glamorous: what they choose to stop.

The cloud is a building, and it's vulnerable

Ben Guerin — Mon, 23 Mar 2026 00:00:00 GMT

The cloud is a building. It has a street address and a power bill. And on March 1st, it got hit by a drone.

Iranian Shahed-136 drones struck two Amazon Web Services data centres in the UAE and a third in Bahrain. Two of three availability zones in the UAE region went offline. Structural damage, fires, water damage from the suppression systems. Banking apps across the Gulf stopped working. Payments platforms went down. Careem, the region's biggest ride-hailing app, couldn't process rides. Emirates NBD and First Abu Dhabi Bank customers couldn't access their accounts.

Three weeks later, Bahrain was hit again.

The tech industry chose the word "cloud" for a reason. It makes infrastructure feel weightless. Everywhere at once. Invulnerable. Your data isn't sitting in a warehouse with diesel generators and water cooling systems in a country with its own politics and geography. It's in the cloud. Floating safely above all of that.

This is the most successful piece of marketing language in the history of technology. And it's a pattern.

"Serverless" computing still runs on servers. Someone else's servers, which you don't manage but absolutely depend on. "The edge" sounds like proximity, closeness, speed. It's still a building. A smaller building, closer to you, but a building with power and plumbing and a government that controls the land beneath it. And "AI" implies intelligence floating in the ether. The reality is data centres consuming as much water as small cities, powered by energy grids that are already under strain, dependent on chip supply chains that run through Taiwan.

Every one of these terms was deliberately chosen to make something physical feel immaterial.

And it works brilliantly, right up until something physical happens.

What changed on March 1st

Previous disruptions to cloud infrastructure were weather events, power failures, human error. Operational risks. Annoying, but containable within the usual frameworks.

A drone strike is different. Iran's Revolutionary Guard explicitly claimed the attacks, citing the data centres' role in supporting US military and intelligence operations. This wasn't a storm that happened to hit a server farm. It was a deliberate military decision to target commercial cloud infrastructure.

That's a new category of risk. And it's one that the word "cloud" makes it very easy to forget about.

The boundary between commercial cloud computing and military operations has largely vanished. The same infrastructure that processes your bank transfers also runs Pentagon workloads. When everything shares the same pipes, an attack on one is an attack on all.

Two weeks after the strikes, Rachel Reeves announced a £2.5 billion investment in AI and quantum, including a £500 million Sovereign AI Fund. The language was all about "sovereign capability" and "reducing reliance on overseas infrastructure."

It's not hard to connect the dots. If your AI systems depend on data centres in another country, you're dependent on that country's stability. March 1st made that dependency visible in a way that PowerPoint decks about geopolitical risk never could.

What this actually means

Nobody is going to stop using the cloud. The abstraction is genuinely useful. You don't need to think about where every server is to send an email.

But abstraction layers are also what make you vulnerable to surprises. The drone strikes in Bahrain weren't surprising to anyone who'd thought about where the building was. They were only surprising to people who'd internalised the metaphor.

If you're a business running on cloud infrastructure, do you know which region your data is in? Do you know what happens if that region goes offline for 48 hours? Have you ever asked your cloud provider where the building actually is?

Most people haven't. That's the point.

The word "cloud" is a masterpiece of marketing. It's so good that even the people running their businesses on it forget they're renting

A folder full of skill files isn't going to replace an agency any time soon

Ben Guerin — Wed, 04 Mar 2026 00:00:00 GMT

Someone posted a screenshot of a .claude directory this week. Eight departments. Thirty-eight agents. Eight people. The caption: "This is what an agency looks like in 2026. Not a team. A folder."

I'm calling bullshit.

Not because I'm threatened. Because I've actually done the work of building AI into an agency, and the argument doesn't survive contact with reality. If it were true, McKinsey would be four people and a Claude subscription. WPP would be a GitHub repo.

But rather than just say that on LinkedIn and move on, I want to stress-test it properly. Walk through a real agency workflow, mark what's automatable and what isn't, and see what's actually left.

What agents can genuinely handle

I'll start with what AI is already good at, because being honest about this is what makes the rest credible.

Research synthesis. We used to spend days pulling together landscape reviews, competitor audits, media analyses. AI does this in hours. At TG we've built internal tools that do exactly this. They're brilliant.
First-draft copywriting. Need twenty headline options or a rough press release to react to? AI gets you there fast. The output always needs editing, but the blank page problem is mostly solved.
Scheduling, project management, reporting. Automatable. Largely already automated.

I'd estimate around 40% of what a modern agency does could be handled by agents today. That's a real number. Anyone pretending it's zero isn't paying attention.

Where the thesis falls apart

Last year we were on a client call where the CMO said "yeah, that's great" about a creative direction. Flat tone. No follow-up questions. Slight pause before the next agenda item. Every person on our side of the call knew instantly that she hated it. We pivoted before the next meeting. The project was saved.

No agent reads that room. No skill file picks up on a two-second pause.

Or take the campaign war rooms I've sat in at 2am, where strategists disagree about the next move. The data points in three directions. The political dynamics are shifting hourly. Someone has to make a call, informed by pattern recognition across dozens of campaigns, a gut sense for what the media will do with it, and relationships built over years.

I wrote recently about how different tasks sit at different points on a consequence-reversibility matrix. Research synthesis? Low consequence, easily reversible. Automate away. A crisis communications response at 2am? High consequence, hard to reverse. That's where you want a human who's been here before.

The pattern: execution is increasingly automatable. Judgment is not.

We've seen this film before

McKinsey was supposed to be killed by data analytics. Instead they acquired analytics firms and charged more. LegalZoom was supposed to destroy law firms. It commoditised the bottom of the market and left the high-value advisory work untouched. Tax software killed the basic return business. Accounting firms moved upmarket.

Every time: tools commoditise the execution layer, which makes the judgment layer more valuable.

Agencies that were basically execution shops, churning out templated deliverables and billing for hours, are genuinely threatened. If the value you provide can be captured in a set of prompt templates, your clients will work that out soon enough.

But agencies built on judgment, relationships, and the ability to know what good looks like across a dozen domains? AI makes those agencies more dangerous, not less.

The tell

Here's what I've noticed about the "agencies are dead" crowd. They are almost never people who've actually commissioned serious agency work.

If you've got the budget and the business need for a high-stakes campaign, a brand repositioning, a crisis strategy that has to survive contact with a hostile media cycle, you go to experts. You know exactly what an agency is good at and where it falls short. You've sat in the room. You've seen the difference between a technically correct strategy deck and one that actually lands.

The people loudest about agencies being replaced by folders tend to be people who've never been in the market for the work in the first place. They see the deliverables, the deck, the copy, the media plan, and assume that's what you're paying for. The actual value is invisible to them. The moment someone spotted that the brief was solving the wrong problem. The relationship that meant a difficult conversation happened early instead of too late.

If you've never bought it, you don't know what it looks like. And if you don't know what it looks like, you'll assume a markdown file can do it.

That's not an argument about the future of agencies. It's a tell.

For the rest of us, the tools just got better. The ceiling just got higher. And the work that actually matters just got more valuable.

Everyone's treating AI autonomy as a ladder. What if it's a dial?

Ben Guerin — Thu, 26 Feb 2026 00:00:00 GMT

There's an assumption buried in almost every AI product pitch right now: more autonomy is better. That the goal is to move from "AI assists you" to "AI handles it", and that human involvement is friction to be eliminated.

Stripe's annual letter this month crystallised that assumption perfectly. John Collison described five levels of "agentic commerce", a framework presenting AI autonomy as a ladder from basic automation to full anticipation. As he noted, most of the hype focuses on the later levels, but real activity is already happening at levels 1 and 2. It's a brilliant framework. And almost everyone will read it as a staircase to climb.

https://x.com/collision/status/2026332177118732364

The same month, Mercedes-Benz and BMW both abandoned Level 3 self-driving, going backwards to a system that keeps the human more involved, not less.

One industry is building the ladder. Another just discovered the middle rungs don't hold.

The question is whether autonomy is a ladder at all, or whether it's something you should be adjusting based on context.

The Stripe framework (and why it matters beyond shopping)

In their 2025 annual letter, Stripe co-founders Patrick and John Collison laid out five levels of what they're calling "agentic commerce." It describes how purchasing decisions progressively shift from human to AI:

Eliminating web forms. You decide what to buy. The AI handles checkout.
Descriptive search. You describe what you want in plain language instead of using keyword search.
Persistence. The system remembers your preferences across interactions.
Delegation. You set objectives and budget. The system handles everything else.
Anticipation. There is no prompt. The system acts proactively based on what it thinks you need.

Stripe built this for e-commerce ($159 billion valuation, $1.9 trillion in payment volume, roughly 1.6% of global GDP). They launched an Agentic Commerce Protocol with OpenAI and a suite letting brands like Anthropologie, Etsy, and Coach sell through AI interfaces.

But strip away the commerce layer and what you're left with is a clean description of how agency transfers from human to machine in any domain. Research. Writing. Strategy. Operations. Hiring. Campaign management.

It's a genuinely useful framework. But it has a hidden assumption baked in: that Level 5 is the destination. That progress means climbing. That more autonomy is always the goal.

Every AI company pitches it this way. We're at Level 2 now, but we're building towards Level 4. Our roadmap takes you from co-pilot to autonomous agent. The direction is always the same: more autonomy, less human involvement, onwards and upwards.

The self-driving car industry believed the same thing for years. And they had to learn, at considerable cost, that it's wrong.

What the car industry learned the hard way

The Society of Automotive Engineers published a framework for autonomous driving that looks a lot like the Collison model. Six levels, from zero (no automation) to five (full automation). And for years, every car manufacturer treated it as a ladder.

The plan was to climb from Level 2 (cruise control and lane keeping, human actively supervising) through Level 3 (car drives itself in defined conditions, human ready to intervene) up to Level 4 and 5 (full autonomy, no human needed).

Then something became clear. Level 3 was where the problems lived.

Not Level 1, which is too basic to cause harm. Not Level 5, which is genuinely capable. Level 3, the middle of the ladder, where the system is competent enough that you stop paying attention but not competent enough to not need you.

Researchers had flagged this decades earlier. Parasuraman and Riley published a foundational paper in 1997 showing that the more reliable automation becomes, the more likely humans are to become complacent monitors. The system handles 95% of situations perfectly, so your brain checks out. And then the 5% arrives and nobody's at the wheel.

A 2023 study in Safety Science put it bluntly: the entire concept of a "fallback-ready user" is essentially a contradiction. You can't ask someone to monitor a system they've been told they don't need to operate. The brain doesn't work that way.

Level 3 didn't fail because the technology wasn't ready. It failed because the monitoring problem has no clean solution. The human is supposed to be "ready to intervene" while the system handles everything. But the more competent the system appears, the more supervision decays. That's not a design flaw you can fix. It's how human attention works.

Mercedes-Benz was the first manufacturer to ship a commercially available Level 3 system. They called it Drive Pilot. It was restricted to highway driving under 40 mph, essentially traffic jams only, and it required a $2,500 annual subscription on top of an already expensive car. That's how narrow the use case had to be to manage the risk.

Then, in January 2026, Mercedes removed Drive Pilot from the facelifted S-Class entirely. Their spokesperson was candid: they didn't want to offer a system with limited customer benefit when a better approach was coming within two to three years. In February, BMW followed, discontinuing its own Level 3 "Personal Pilot" system from the 7 Series facelift.

Both companies made the same move. They went backwards, from Level 3 to what they're calling Level 2++. More capable driver assistance that works in cities and highways, but with the human actively engaged: hands on wheel, eyes on road.

And here's the detail that matters most: Mercedes' CEO described the Level 2++ experience as better. Ola Källenius tested it in San Francisco and said it felt like "the car is on rails." He drove for over an hour through heavy city traffic, onto the freeway, and back into the city. The system handled everything, with him actively engaged throughout.

Going back to Level 2 wasn't a retreat. It was a discovery: keeping the human engaged produced a better outcome than asking them to passively monitor.

Meanwhile, the companies that skipped Level 3 entirely went the other direction. Waymo built fully autonomous Level 4 systems, but only in extremely narrow, pre-mapped domains. Robotaxis in specific cities. Not full autonomy everywhere. Full autonomy in a tiny, well-defined box.

Nobody stayed at Level 3. The industry split in two: back to smarter L2 for open-ended driving, forward to L4 in tightly bounded domains. The middle of the ladder emptied out.

The AI industry is in the dead zone and hasn't noticed

Most AI systems are technically Level 2. They propose, you approve. The copilot suggests code, you accept or reject. The chatbot drafts a paragraph, you edit or rewrite. The human is supposed to be actively engaged.

But behaviourally, that's not what's happening.

Anthropic published research recently showing that new users let Claude work unsupervised about 20% of the time. By 750 sessions, that figure rises to over 40%. The more people use AI, the less they check its work. Not because they've decided the AI is infallible. Because it's good enough, often enough, that their attention drifts.

That's Level 3 by behaviour, regardless of what the product was designed to be. And the more competent the system appears, the faster supervision decays.

I wrote recently about how AI's intelligence is upside down: brilliant at the hard stuff, unreliable at the easy stuff. It can synthesise 30,000 news articles into a coherent narrative but lose the thread of a three-message conversation. It can produce strategic analysis that would impress a CEO and then misspell their name.

That's the perfect recipe for the monitoring problem. The impressive outputs build your trust. The mundane failures happen in exactly the places you've stopped checking.

Now, the stakes are obviously different. Nobody dies because an AI hallucinated a source in a research report. But the failure pattern is actually harder to deal with, not easier.

When a self-driving car crashes, everyone knows immediately. It triggers investigations, press coverage, recalls. The feedback loop is fast and loud. That's why the automotive industry confronted Level 3's problems relatively quickly.

AI errors at Level 3 are the opposite. They're frequent, subtle, and often invisible. A hallucinated statistic in a strategy document gets passed up the chain. A slightly wrong financial assumption compounds across a model. A legal brief with a fabricated citation gets filed. A campaign message goes out with a factual error nobody caught because the tone and strategy were brilliant.

The cost of each individual error might be small. But they accumulate quietly, in exactly the places where humans have disengaged. And because nobody crashes, nobody investigates. The pattern embeds itself before anyone realises it's there.

The automotive industry learned about Level 3's problems the hard way. The AI industry might not even realise it's learning them.

The dial, not the ladder

Here's the reframe.

The five levels aren't a staircase you climb. They're a dial you set differently for every single task. And sometimes the right move is to turn the dial down, not up.

The car industry proved this. Mercedes went from Level 3 back to Level 2++ and the experience got better. Waymo went to Level 4 but only in tightly bounded domains. The lesson wasn't "build more autonomy." It was "match the level to the context."

The same logic applies to AI.

Your email drafts? Let the system run. Crank the dial up. The cost of error is a slightly awkward email that you can correct with a follow-up.

Your legal filings? Keep it at Level 1. The AI handles the formatting and mechanical work. A human makes every substantive decision.

Your content calendar? Let the AI handle the first draft and scheduling, but a human reviews before anything goes live. That's not a compromise. That's the dial set correctly for the risk.

Your financial models? Level 2 at most. The AI analyses and recommends. A human checks the numbers and executes.

The right level isn't determined by what the AI is capable of. It's determined by two things: how much it matters if the AI gets it wrong, and how easy it is to undo the damage.

Jeff Bezos has talked about this in a different context. He calls them one-way doors and two-way doors. Most decisions are two-way doors, reversible, so delegate them freely. A few are one-way doors, irreversible, so think carefully. The same logic applies to AI autonomy.

Put those two dimensions together and you get a simple matrix:

Low consequence, easily reversible (email drafts, calendar scheduling, data formatting): automate aggressively. Mistakes are cheap and fixable.
High consequence, easily reversible (financial transactions, content publishing): automate with active monitoring. You can fix it, but you want to catch it fast.
Low consequence, hard to reverse (deleting data, sending messages, committing code): automate cautiously. Each individual mistake is small, but you can't take it back.
High consequence, hard to reverse (legal filings, medical decisions, crisis communications): keep the human in the loop. The cost of error is too high and the undo button doesn't exist.

Commerce, where Stripe's framework was designed to live, naturally suits higher autonomy because most purchases can be reversed. The problems start when people take the same ladder and apply it to domains where the undo button doesn't exist.

That's four quadrants, not five levels. And a product should operate in all four simultaneously.

Human in the loop is a feature, not a bug

The deeper insight here is one that cuts against the grain of almost everything the AI industry is saying right now.

The assumption is that human involvement is friction. That every level of autonomy you climb removes a burden. That the destination is a world where humans set the objectives and AI handles everything else.

But the car industry discovered something that should give the AI industry pause. For many tasks, Level 2 isn't just safer than Level 3. It's better. The user experience of working actively with a capable system, staying engaged, maintaining judgment, is often a better outcome than passively monitoring one.

This matches everything I've seen using AI tools daily over the past two years. The best work doesn't come from letting the AI run and scanning the output. It comes from working with it. Pushing back when it's wrong. Giving it context it's missing. Recognising when the output is brilliant and when it's generic nonsense. That's what I've previously called taste: the human judgment that makes AI output trustworthy.

That judgment isn't friction to be eliminated. It's the thing that makes the output good.

At Topham Guerin we think about this constantly. The question is never "how much can we automate?" It's "where does this specific task sit on the matrix?" Research synthesis for a blog post is a very different risk profile to a client-facing strategy document. A draft social media calendar is a very different risk profile to a crisis communications response. And for the high-stakes work, keeping humans actively engaged isn't a compromise. It's the whole point.

The competitive advantage isn't capability. It's judgment.

The Collison brothers built a genuinely brilliant framework. Five clean levels. A clear progression. A useful way to think about where your product sits and where it's heading.

But the smartest thing you can do with that framework is resist the temptation to read it as a one-way journey from 1 to 5.

The automotive industry spent billions learning that more autonomy isn't always better. Mercedes and BMW shipped Level 3, tried it in the real world, and went back to Level 2. Waymo went to Level 4 but only inside tightly bounded domains. Nobody stayed in the middle. The dead zone emptied out.

The AI industry has the chance to learn that lesson without paying the same price. Whether it will is another question. Right now the race is to remove humans from the loop as fast as possible. But the evidence from the one industry that's been through this before suggests the opposite: for most tasks, the human in the loop is what makes the system work.

The winners won't be the companies with the smartest model. They'll be the ones that know when not to use it.

The most transformative AI applications will come from people who can't code

Ben Guerin — Tue, 24 Feb 2026 00:00:00 GMT

Last week, a cardiologist who'd never written a line of code placed third in a coding competition. Out of 13,000 applicants. He built it in seven days. While working his hospital shifts.

The Claude Code hackathon gave 500 builders a week to create something with Anthropic's Claude Code and Opus 4.6. The five winners were a personal injury attorney, a software engineer, an interventional cardiologist, an electronic musician, and an infrastructure and roads systems worker.

One software engineer out of five.

https://x.com/0xkyle__/status/2025398536712716332

The cardiologist, Michal Nedoszytko, works in a cath lab in Brussels. He's done thousands of procedures. But he says the real struggle begins the moment the patient leaves the room. They don't understand their diagnosis. They don't remember what the doctor said. They go home confused.

So he built postvisit.ai, a tool that turns visit transcripts and medical records into personalised, ongoing health guidance.

First place went to Mike Brown, a personal injury attorney. He built CrossBeam, a tool to speed up California's building permit process. Not because he knew how to code, but because he'd spent years drowning in the problem.

There's a pattern here. The winning projects weren't technically impressive for the sake of it. They were built by people who understood a problem so deeply that the moment the technical barrier dropped, they knew exactly what to make.

Boris Cherny, the creator of Claude Code, said on Lenny's Podcast last Thursday: "Coding is largely solved. At least for the kinds of programming that I do, it's just a solved problem."

If that's true, and 277 hackathon projects in six days suggests it might be, the bottleneck has shifted. The scarce resource isn't technical skill anymore. It's domain knowledge. Knowing what to build. Knowing when the machine got it wrong.

A cardiologist knows that patients leave confused. A lawyer knows that permit corrections cost builders months. A roads inspector knows where infrastructure data falls through the cracks. No amount of engineering talent gives you that insight. You have to have lived it.

This is where it gets interesting.

Because if domain expertise is the new bottleneck, the question becomes: how many domain experts are actually using these tools?

The answer is almost none.

A chart by Damian Player this week puts it in perspective. Each dot represents 3.2 million people. 2,500 dots for 8.1 billion humans. The colour tells you their most advanced interaction with AI.

https://x.com/damianplayer/status/2025234388137468387

84% of the world's population has never used a generative AI tool. 16% have used a free chatbot. 0.3% pay for one. 0.04% use AI to write code.

If you've ever typed a prompt into ChatGPT, you're ahead of 6.8 billion people by AI adoption. If you pay $20 a month, you're in a club smaller than the population of Australia.

The AI conversation on LinkedIn and X is dominated by a tiny, unrepresentative sliver of the population talking to itself about model benchmarks and agent frameworks. It feels like everyone's already in the pool. They're not. Almost nobody is.

And the hackathon just showed us what happens when they jump in.

Every cardiologist who hasn't started yet is a potential Michal. Every lawyer wrestling with broken processes is a potential Mike Brown. Every professional sitting on decades of domain expertise, who currently thinks AI is "that thing my kid uses for homework," is sitting on a problem worth solving that no engineer would think to build for.

The AI revolution, as it's currently discussed, is a conversation among the 0.3% about what the 0.3% are building for the 0.3%. That's not the revolution. That's the warm-up.

The real revolution will start when the grey dots turn green.

Strategy is a choice, not a plan

Ben Guerin — Sat, 21 Feb 2026 00:00:00 GMT

A Twitter thread got me thinking about one of the most common mistakes I see almost every day in my work.

Pedro Domingos, a well-known AI researcher, claimed that Anthropic has no strategy. His evidence? Claude Code started as someone's side project. So did Cowork. So did MCP. No grand plan, no top-down roadmap. Ergo, no strategy.

He's wrong. But he's wrong in a way that's incredibly common, and incredibly costly.

He's confusing a plan with a strategy.

The mistake almost everyone makes

Most organisations think they have a strategy. What they actually have is a plan. Sometimes it's a 50-page McKinsey deck. Sometimes it's a set of OKRs. Sometimes it's a CEO's vision statement laminated on the office wall. These are all fine things to have. None of them are a strategy.

A plan is a sequence of actions. A strategy is a choice.

Specifically, a strategy is the choice you make about how to get from where you are to where you want to be. It results in a plan, but the plan is the output, not the thing itself.

The test is simple: is there a credible alternative? If there isn't, you don't have a strategy. You have an aspiration.

"We want to grow revenue by 20%" IS NOT a strategy. Nobody is choosing not to grow revenue.

"We will grow revenue by focusing exclusively on enterprise clients and walking away from small business" IS a strategy, because the opposite choice (doubling down on small business) is a perfectly viable path someone else might take.

Why this matters

The plan-strategy confusion isn't just semantic. It has real consequences.

When leaders mistake plans for strategy, they optimise for detail instead of clarity. They spend months refining implementation timelines when they haven't actually made the hard choices about direction. They end up with beautifully formatted documents that describe what they'll do without explaining why this path and not another.

The result is organisations that are busy but not directed. Every team has a roadmap. Nobody can explain the underlying logic that connects them.

Worse, it creates a false sense of security. The plan exists, therefore we have a strategy. The deck has been approved, therefore we've made the hard decisions. But the hard decisions never actually got made.

Strategy is a list of things you're not doing

A real strategy is not a list of things you will do. It's a list of things you have chosen not to do.

When Steve Jobs returned to Apple in 1997, the company was producing dozens of products. Jobs killed 70% of the product line. He drew a simple two-by-two grid: consumer and professional, desktop and portable. Four products. Everything else was gone.

The strategy wasn't what Apple decided to build. It was the dozens of things they decided to stop building.

Southwest Airlines built one of the most successful airlines in history the same way. No assigned seating. No hub airports. No meals. No interline baggage transfers. No first class. Every major competitor tried to offer everything to everyone. Southwest's strategy was a list of things they would not do, and every one of those refusals reinforced the others.

IKEA is perhaps the most studied example in business school for exactly this reason. No sales assistance. No delivery. No assembled products. No premium materials. Michael Porter, the Harvard professor who essentially wrote the book on competitive strategy, uses IKEA to teach strategy because its defining choices are all negatives. The things IKEA refuses to do are what make the things it does do possible.

In each case, the strategy isn't the plan. It's the set of trade-offs that make the plan coherent.

Back to Anthropic

This is why Domingos got it wrong about Anthropic. He looked for a plan, a pre-authored product roadmap, and couldn't find one. But the strategy was obvious.

Anthropic made a choice: build an organisation where exceptionally talented engineers have extreme autonomy, can pursue side projects, and can ship things without layers of approval. That is a strategy. It's a clear choice with a clear alternative. Most technology companies choose centralised planning, stage-gate processes, and top-down prioritisation instead.

Claude Code, MCP, and Cowork aren't evidence of a missing strategy. They're what the strategy was designed to produce. The whole point was to create conditions where things like this could happen, even if you couldn't predict what specifically would emerge.

You could argue it's a risky strategy. You could argue it only works at certain scales or with certain talent densities. But you can't argue it isn't a strategy just because it didn't come with a Gantt chart.

Strategy is hard because choosing is hard

None of this is a failure of intelligence. It's a failure of nerve.

Making a real strategic choice means closing doors. It means saying "we will not pursue this market" or "we are walking away from this capability" and accepting that you might be wrong.

A plan can't be proven wrong until it's been executed. A strategy can feel wrong the moment you say it out loud, because someone in the room will immediately point to the opportunity you're leaving on the table.

That fear is why most organisations default to plans. Planning is comfortable. You can spend six months building a detailed implementation roadmap and feel productive without ever having committed to a bet.

Strategy requires you to commit to a bet, and most corporate cultures punish being wrong more than they reward being bold.

This is also why the word "strategy" gets attached to everything. You end up with a social media strategy, a data strategy, a content strategy, a hiring strategy, a partnerships strategy. But if none of those documents contain a trade-off, if none of them articulate what you've chosen not to do, they aren't strategies. They're activity lists with an ambitious title. The word gets used so freely it loses its meaning.

And that's part of the problem. It's easier to label something strategic than to make a strategic choice.

Everyone faces this. If you've ever sat in a room where the "strategy" discussion was really just a planning discussion, where nobody was willing to name the thing you should stop doing, you know exactly what I'm talking about.

Making better strategies

If you're responsible for strategy in your organisation, here's what I'd suggest.

Start with what you're choosing not to do. Before you write a single slide, try to express your strategy as a list of refusals. What markets are you not entering? What customers are you not serving? What capabilities are you not building? If you can't name what you're giving up, you haven't found your strategy yet.
Stress-test it by looking for the opposite. A good strategy should have an intelligent counterargument. If nobody reasonable would choose the opposite path, your strategy is probably just a goal dressed up in strategic language.
Keep it short. If your strategy can't be explained in a few sentences, it's probably a plan masquerading as a strategy. The choice itself should be crisp. The plan that follows can be as detailed as you need.
Accept that it will be uncomfortable. Real strategic choices involve trade-offs. They mean saying no to good things to focus on better things. If your strategy doesn't make anyone in the room nervous, it probably isn't one.

The takeaway

A plan tells you what to do. A strategy tells you what you've decided not to do, and why.

Most organisations have plenty of plans. Far fewer have actually made a strategy.

Try this: take your current strategy document and rewrite it as a list of things you've decided not to do. If you can't, you know what you actually have.

It's worth checking which one yours is.

This chart is a roadmap of which industries get transformed next

Ben Guerin — Fri, 20 Feb 2026 00:00:00 GMT

Anthropic published something this week that most people scrolled past. A chart showing which domains their AI agents are being deployed in, based on millions of tool calls through their API.

Software engineering sits at the top with 49.7%. No surprise there. Developers were the earliest adopters of AI tools, they're the most technically literate users, and the feedback loops are tight: the code either works or it doesn't. They're furthest along the adoption curve.

But the interesting part isn't the top of the chart. It's everything underneath it.

Back-office automation at 9.1%. Marketing and copywriting at 4.4%. Sales and CRM at 4.3%. Finance and accounting at 4.0%. Then data analysis, academic research, cybersecurity, customer service, gaming, education, healthcare, legal, travel, all trailing behind between 3.5% and 0.8%.

Now here's what I think is worth paying attention to. I don't think this is just a snapshot of where things stand today. I think it's roughly the order in which these industries will be transformed.

Each category represents a different point on the same adoption curve. Software engineering is out in front because the early adopters there had the skills and the incentive to move first. Back-office automation is next because the tasks are structured and repetitive, with clear rules. And those categories sitting between 1% and 5%? They're where the early adopters within each industry are just getting started.

The pattern makes intuitive sense once you think about what makes a domain easy or hard for agents to operate in. Software has clear inputs, clear outputs, and low-risk failure modes. You can revert the commit. Back-office automation is similar: invoices, data entry, scheduling. The kind of work that was already being semi-automated before LLMs arrived.

But then you hit the middle of the chart, and the stakes start to change. Marketing requires judgment about tone and audience. Sales involves relationship dynamics that are harder to codify. Finance demands accuracy with real consequences for getting it wrong.

These aren't impossible problems for AI agents. They're just harder ones. And the data suggests they're being tackled right now, just at lower volumes while people figure out the right level of human oversight.

The Anthropic research tells us something else worth knowing. Among the longest-running Claude Code sessions, the time the AI works autonomously before pausing has nearly doubled in three months, from under 25 minutes to over 45. New users let the AI run unsupervised about 20% of the time. By 750 sessions, that's over 40%.

And here's the detail I found most interesting: on complex tasks, the AI stops itself to ask for clarification more often than humans interrupt it. It's not just getting more capable. It's developing something that looks a lot like knowing what it doesn't know.

The agents are getting more capable. The humans are getting more comfortable letting them run. That combination is what turns a 4% category into a 40% one.

At Topham Guerin, we sit right in the middle of this chart. Marketing, copywriting, strategy, research. All on there. And we've been building AI into our workflows for the past couple of years, not to replace people, but to make good people faster. To turn a week of research into a day. To test twenty creative approaches instead of three.

The work still needs taste. It still needs judgment. Someone who knows when the AI output is brilliant and when it's generic nonsense. But the ceiling of what a small team can achieve has shifted dramatically.

If your industry is sitting at 1-5% on that chart, you're in a window right now. Not a crisis. A window. The companies in your space who are experimenting with custom agents today are building an advantage that compounds quietly, until it doesn't.

The question is whether you're the early adopter in your sector, or whether you're waiting to see what happens.

Because the queue is already forming. And it's moving faster than most people think.

Davos 2026 quietly revealed who will shape the next 12 months of AI

Ben Guerin — Thu, 12 Feb 2026 00:00:00 GMT

Every January, around 3,000 of the world's most powerful people descend on a Swiss ski town for a week of panels, dinners, and what I'm told is very good fondue. The World Economic Forum at Davos is easy to dismiss as an elite echo chamber, but the media coverage it generates, who gets quoted, how often, and in what tone, is one of the most honest readouts available of where power is actually moving.

This year, my agency Topham Guerin tracked that coverage with our data partner Omniscan. We analysed 30,020 articles from 1,346 publications, extracting 319,000 attributed quotes across the week. The aim was to map the people and companies that commanded elite attention and how that attention was framed.

The results look nothing like the AI leaderboard most people carry in their heads.

AI and its enablers dominated the top ten companies by coverage volume. But the composition was surprising. The Musk empire (Tesla, SpaceX, X and xAI, treated as one entity for reporting purposes) came first with 443 articles quoting Elon Musk. Then JPMorgan Chase (238) and BlackRock (224). Then NVIDIA (224). Then Anthropic (125), Microsoft (115), Google DeepMind (99), Amazon (91).

OpenAI came ninth. 74 articles. Behind Palantir.

If you'd asked most people before Davos to rank AI companies by importance, OpenAI would top the list. ChatGPT has hundreds of millions of users. They've raised more capital than almost anyone else here. By most consumer measures, they are the defining AI company of this era. And at the most important annual gathering of political and business leaders, they were an afterthought.

I don't think the Davos rankings tell you who's building the best AI. But I think they tell you something more important: who's building the political capital to shape what happens next.

Because the next year of AI won't be defined primarily by model benchmarks or product launches. It will be defined by regulation, government procurement, international deployment deals, and decisions about who gets access to compute, data, and strategic partnerships. Those decisions get made by exactly the kind of people who attend Davos: heads of state, finance ministers, sovereign wealth fund managers, and the executives who already have their trust.

Start with Musk. 443 articles, 4,940 personal quotes, only 53% positive sentiment. Polarising, obviously. But his proximity to Trump means proximity to US AI policy, executive orders, and federal contracts. He's not winning a popularity contest at Davos. He's winning something more valuable: direct access to the decisions that will determine how AI gets deployed by the world's largest economy. You don't need to be liked. You need to be in the room.

Jensen Huang is the opposite play. NVIDIA's CEO had 1,799 quotes with 97% positive sentiment. Nearly perfect. He achieved this by telling a story that had nothing to do with semiconductors and everything to do with national necessity. Every country at Davos wants AI capability. Huang positioned NVIDIA as the company without which that capability doesn't exist. When those countries start signing AI infrastructure deals this year, and they will, Huang won't need to pitch. He's already been pre-sold. A geopolitical argument dressed in a leather jacket.

JPMorgan and BlackRock are the less obvious winners but possibly the most consequential. They don't build AI models. They fund them, value them, and decide which ones get institutional credibility. They took second and third at an AI-dominated conference because every AI company on this list eventually needs their capital or their client relationships. Their presence at Davos wasn't about understanding AI. It was about making sure AI understands who writes the cheques.

Anthropic is the quiet overperformer. 125 articles, 41% positive sentiment, just 4% negative. They outperformed OpenAI on both volume and tone despite having a fraction of the brand recognition. That pattern, elite credibility running ahead of consumer awareness, is exactly right if you believe regulation is coming soon. You want the people drafting AI safety frameworks to know your name and trust your intentions before the public debate even starts.

Which brings us back to OpenAI. 74 articles. 30% positive. 17% negative. The worst sentiment of any major AI player. It's obvious that OpenAI has a consumer product story. A brilliant one. But Davos doesn't reward product stories, it rewards power stories. "We built something hundreds of millions of people love" is a great pitch to users. It doesn't land with the people deciding how AI gets regulated, funded, and deployed at national scale. Those people want to know what you control, who needs you, and what breaks if you're not at the table.

None of this is destiny. OpenAI could build political capital fast. Anthropic's quiet advantage could evaporate. Regulation might stall entirely and consumer adoption could remain the only race that matters.

But here's what isn't uncertain: the people at Davos, the regulators, the capital allocators, the heads of state, will have an outsized role in shaping AI's trajectory. And the coverage tells you clearly which companies already have their attention and which are still outside the room.

Davos 2026 was billed as AI's coronation. It was, in a way. But the companies that won weren't necessarily the ones building the best models. They were the ones that had already figured out that the next 12 months of AI will be shaped less by technology and more by the people who decide where it gets to go.

The scoreboard most people are watching is downloads and benchmarks. The scoreboard that might matter more was at Davos. And most of the AI industry wasn't paying attention.

The AGI debate has it all wrong: AI is Intelligence Upside Down

Ben Guerin — Fri, 06 Feb 2026 00:00:00 GMT

There's a lot of talk of insanely-powerful AI models at the moment, but there isn't enough talk about just how weird artificial intelligence is:

It can win a gold medal at the International Mathematical Olympiad but can't count the letters in a word.
It can synthesise 30,000 news articles into a coherent narrative but lose the thread of a three-message conversation.
It can produce strategic analysis that would impress a CEO and then misspell their name.

There are hundreds more examples like this - and all of them illustrate the problem with the AGI as a benchmark.

On Monday a paper in Nature argued AGI has already arrived. Within hours, the man who actually coined the term pushed back. They'll go back and forth for years.

But both sides are asking looking at AI in the wrong way.

The interesting thing isn't whether AI has reached human-level intelligence. It's that AI's intelligence is upside down. The hard stuff is easy. The easy stuff is hard.

And as soon as you realise that, it unlocks a cheat code for how you should actually work with it.

Intelligence built in the wrong order

Here's what's happening. Four researchers at UC San Diego just published in Nature arguing that we already have AGI.

Their evidence? GPT-4.5 passes the Turing test 73% of the time. LLMs are winning olympiad gold medals, proving theorems and diagnosing diseases. By any reasonable standard, they wrote, this is artificial general intelligence.

Shane Legg, co-founder of Google DeepMind, the man who actually coined "AGI" over twenty years ago, disagrees.

His argument? If a system still fails at tasks any human can do trivially, it hasn't crossed the line. "Minimal AGI" requires a machine that can do all the cognitive things people can typically do. Not some of them brilliantly and others not at all. All of them.

They're both right about the evidence. They're just looking at different floors of a building that was constructed upside down.

Every intelligence we've ever observed builds from the bottom up:

Babies learn to count before they learn calculus.
Students learn grammar before they write novels.
Junior analysts learn to check their numbers before they produce strategy memos.

The assumption, so deep it's almost invisible, is that simple cognitive tasks are foundational. You master them first, then build upward.

Then LLMs arrived and did it backwards.

They didn't start with routine cognitive tasks and work up.

They started at the top.

Synthesis, analysis, creative problem-solving, cross-domain reasoning, all turn out to be easier for LLMs than counting, basic consistency, sequential logic, or simple arithmetic.

There's an old observation in AI called Moravec's Paradox: what's hard for humans is easy for computers, and what's easy for humans is hard for computers.

Chess, calculus, data analysis? Easy.

Recognising a cat in a photograph, walking across a room, catching a ball? Incredibly hard.

A famous xkcd comic from 2014 that captures this perfectly:

That comic made sense for years, and Moravec's Paradox drew the line between cognitive tasks and physical/sensory tasks.

But now with LLMs, that line has moved inside cognition itself.

That's what makes this moment genuinely new.

Not that AI is getting smarter, that's to be expected. What's weird is that it got smart in the wrong order.

Not just autocomplete

So how did this happen? The most common explanation for how LLMs work is that they "predict the next word."

It's the line everyone reaches for when they want to sound informed. And it's technically true. That is what LLMs do. They take a sequence of text and predict what comes next.

For a couple of years, that felt like the best explanation I had. Not a dismissal or trivialisation of these tools, just a genuine attempt to understand the mechanism.

Then I started noticing how badly it failed as a mental model for actually working with these tools.

The thing that really shifted my thinking was a piece of research Anthropic published last October. One of their experiments forced Claude to output a word it hadn't planned, like "bread." The model treated it as a glitch. Something had gone wrong.

But when the researchers injected internal evidence that "bread" had been intended earlier in the process, it accepted the word and built a plausible reason around it.

It was checking some kind of internal record of its own prior processing to work out whether it had actually meant to say something. That's not autocomplete. That's something closer to a colleague catching themselves mid-sentence.

That sent me down a rabbit hole. And the thing I found that made it all click was a line from Geoffrey Hinton: if you want to predict the next word well, you have to understand the sentences.

His argument is simple. Language is compressed knowledge about the world. To predict it accurately, you need to decompress it back into the world that generated it.

And the world's highest-level patterns, the conceptual relationships, the causal reasoning, the strategic logic, are exactly what's richest in training data. That's what humans write about.

Nobody writes down how to count letters in a word. Billions of people write down how to think about strategy, how to diagnose problems, how to weigh competing arguments.

So the model learned the top of the cognitive hierarchy first, because that's where the signal was strongest.

That's why "just autocomplete" is technically correct but practically useless. It's like saying human thinking is just neurons firing: while true at the implementation level, it doesn't help you understand what humans actually do.

And if you're using that as your mental model, you'll treat AI like a fancy search engine.

You'll miss what it can actually do. And you'll be blindsided by failure modes you never saw coming.

The search for a better analogy

So if "just autocomplete" doesn't cut it as a mental model, what does?

People have tried.

Ethan Mollick called AI a "jagged frontier," a map of uneven capabilities across different tasks, peaks here, valleys there.

Andrej Karpathy called it "alien intelligence," shaped by completely different pressures to our own.

Both useful, but both trying to categorise AI as a type of thing. And you can't build a working relationship with a thing. You can't learn when to trust it and when to check behind it.

So people have tried describing AI as various types of person.

"Brilliant intern." "Savant."

And I get why people reach for savant in particular - it seems like the best description we have for why AI is freakishly good at some things, hopeless at others.

But it's not quite right either.

A savant has specific areas of brilliance, and lacks general intelligence. What makes AI strange is that the pattern isn't domain-limited. It's inverted.

The stuff that should be hardest is easiest. The stuff that should be trivial is where it falls apart. That's not a savant. That's something more familiar.

That's someone who operates at the top of the cognitive hierarchy and falls apart at the bottom.

And actually, we've all worked with someone like this.

We already know this person

We have all worked with them:

The visionary creative director who produces the most brilliant campaign you've ever seen but can't book a meeting room.
The born networker who knows everyone, remembers every detail about everyone, yet their email inbox is a disaster zone.
The strategist who writes a memo so sharp it changes the direction of the entire company, but it's got basic factual errors and major typos.

We don't call these people "not intelligent."

We don't refuse to work with them until they can manage their calendar.

We learn their shape.

We figure out what to hand them and what to keep away from them.

We develop a feel for when they're in the zone and when they need someone to check behind them.

And the people who learn to work with that kind of person, rather than trying to force them into a standard performance framework, get disproportionate results.

That's the relationship most of us need to build with AI.

Not categorising it. Not endlessly debating whether it's "really" intelligent. Learning its shape.

What that looks like

I'm no expert on how these models work under the hood. But I've been spending a lot more time working with AI lately than the average person, and a few things have stood out to me.

The turning point was when Anthropic released Claude Opus 4.5.

Before that, I was using AI to make little pieces of the puzzle. A draft here, an analysis there, help fixing a specific coding bug .

With Opus 4.5, I could suddenly build the whole thing at once. I could give it the full picture of what I wanted, and it could visualise the end result and take me there:

Data analysis that would have taken a team days
Complex web builds that would have taken weeks
Research synthesis across thousands of sources.

All done in hours.

And then it would hallucinate a source that doesn't exist. Or slap !important on a CSS rule instead of taking the time to find the underlying formatting conflict. Lazy fixes. The kind of thing you'd pull a junior developer aside for.

Once you stop being frustrated by this and start treating it as useful information, everything changes.

Because the pattern is consistent: trust it more on the hard stuff, check it more on the easy stuff. Which is the exact opposite of what your instincts tell you to do.

You develop a feel for that over time, the same way a creative director develops an eye for good work.

And the gap is widening fast between people who use AI once a week and people who use it all day, every day. They're developing completely different levels of calibration.

Build the right team

Some of the best advice I ever got came from an executive coach.

He'd done a deep psychological profile with me, the kind that maps out your strengths and your blind spots in uncomfortable detail. And his advice wasn't what I expected.

The most common mistake, he said, is that people see their blind spots and immediately try to plug the gaps.

They pour energy into fixing weaknesses, and in the process they sacrifice the strengths that made them effective in the first place.

The better move is to lean into what you're genuinely good at, and then find people whose strengths are your blind spots. Build a team of complementary capabilities rather than trying to become well-rounded on your own.

I think about that advice every time I talk to someone who's tried AI a couple of times, found it unreliable, and quietly moved on.

They're waiting for the perfect colleague.

They're going to be waiting a long time.

The answer isn't to wait for a single AI to be perfect at everything. It's to build a team. In practice, that means a few things.

Make upside-down intelligence work for you

Flip your instincts. Your natural impulse is to trust AI on simple tasks and double-check the complex ones. Do the opposite. Trust it more on the hard stuff. Check it more on the easy stuff. This single mental shift will save you more time than any prompt trick.
Use different models for different jobs. Different AIs have different strength profiles. The best practitioners I know use Codex for one part of the engineering workflow, Claude Code for another. One model for generation, a different one for review. You wouldn't hire one person to do every role. Same logic.
Set up adversarial checks. Sometimes the most effective setup is deliberately pitting one AI against another. A second model whose whole job is to catch the blind spots of the first. It sounds redundant. It's not.
Invest in the harness. The raw model is like the raw person. Talented but inconsistent. The harness is everything you put around them: custom instructions, reference files, structured workflows, guardrails. A few hours of setup upfront will completely transform the quality of what you get back. Most people skip this step and then blame the model.
Give it real feedback. Most people ask a question, take the answer, move on. That's using AI like a search engine. The practitioners who get the best results push back. "That's not quite right, here's why." They give the model context it's missing. They iterate. They treat it like briefing a colleague, not querying a database.
Give it real context. The quality of your output is directly proportional to the quality of your input. If you give the model a vague brief, you'll get a vague answer. Give it the full picture of what you're trying to achieve, who it's for, what good looks like, and the output transforms. The people who complain AI produces generic work are usually giving it generic briefs.
Develop your taste. This is the human part that doesn't get automated. The ability to look at AI output and feel whether it's good or not. Whether it's understood the brief or just produced something plausible. That's a judgment you build over time, the same way a creative director develops an eye. There is no shortcut. You have to put in the reps.

The bottom line

The AGI debate will keep going. People will keep arguing about whether AI has crossed some threshold of general intelligence.

But for anyone actually using these tools every day, the more useful insight is simpler.

AI didn't learn intelligence the way we did. It learned the top of the cognitive hierarchy first and the bottom last.

It's brilliant where you'd expect it to struggle and unreliable where you'd expect it to cruise.

Once you internalise that, you stop asking "is it intelligent?" and start asking the same question you'd ask about any brilliant, frustrating colleague:

What are they actually good at?
How do I get the best out of them?
What do I need to catch before it goes out the door?

You wouldn't ask the visionary creative director to proofread their own deck.

You wouldn't expect the born networker to keep their own CRM up to date.

You'd build a team around them.

Someone to catch the typos, manage the details, handle the stuff that's beneath their operating level but still needs to get done.

That's the move.

Not waiting for a perfect AI. Building a system of complementary strengths, human and machine, that covers the blind spots without sacrificing what makes each part brilliant.

The people who figure that out first will have an extraordinary advantage.

Not because they understand the theory better. Because they've learned the shape of the people they're working with, and built a team accordingly.

ismypubfucked.com: how a side project helped make the pub crisis impossible to ignore

Ben Guerin — Thu, 29 Jan 2026 00:00:00 GMT

On Thursday 9 January, I launched a website called ismypubfucked.com. By Friday morning, 100,000 people had visited. By Monday, I was on Sky News. And on 27 January, the government announced a support package for pubs worth £100 million.

I'm not claiming credit for the policy. The pub industry had been lobbying for relief for months. But there was a story buried in a government spreadsheet that wasn't getting the attention it could, and this website played a role in forcing politicians to recognise the scale of the issue.

This is how it happened.

The story hiding in the spreadsheet

In late 2025, the Valuation Office Agency published draft rateable values for 2026. These are the numbers that determine how much business rates a property pays. For pubs, the picture was grim. Rateable values were jumping 50%, 100%, sometimes 500% or more. On top of the National Insurance increases, minimum wage rises, and energy costs were already squeezing margins.

The data was all public. The VOA publishes these spreadsheets every revaluation cycle. But it was scattered across massive CSV files, with codes and reference numbers that meant nothing to anyone outside the property valuation industry. There was no way for someone to just look up their local pub and see what was coming.

So I built a site that did exactly that.

Building it

The whole thing came together in a day, working with Claude.

I run an agency that has built countless client websites and dashboards, but I'm certainly not a full-stack developer by training. I can read code, debug simple problems, and I've shipped enough side projects to know roughly what's possible. But I couldn't have built this five years ago. Probably not two years ago. The gap between having an idea and being able to execute on it has collapsed in ways that still surprise me.

The stack ended up being Next.js and React on the frontend, FastAPI on the backend, PostgreSQL with PostGIS for the database. Hosted on Vercel and Railway. I'd never used any of these tools before this project.

The real work was the data pipeline. Eight Python scripts that downloaded the VOA rating lists, parsed them, joined the 2023 and 2026 data together, fetched pub locations from OpenStreetMap, matched VOA records to actual pubs, geocoded everything, calculated what I called the "Fucked Pub Index", and loaded it all into the database.

46,000 pubs. Each one with a rateable value change, a severity score, and a location on a map.

The Fucked Pub Index (FPI)

A note on the way I decided to calculate the severity score. The FPI is deliberately unscientific. It's a 0-100 score based on how much the rateable value is increasing.

Under 10% increase = 0-10 (Somehow Fine)
10-50% = 10-30 (Feeling It)
50-100% = 30-60 (Struggling)
100-200% = 60-90 (Fucked)
Over 200% = 90-100 (Absolutely Fucked)

Is this the complete picture of how business rates actually work? No. Transitional relief and small business rate relief complicate the picture considerably.

But the FPI captures the underlying pressure in a way that anyone can understand immediately. And "Fucked Pub Index" is a lot more memorable than "Rateable Value Percentage Change Indicator".

The launch

Thursday night. Tweeted the link. Went to bed. Woke up to a lot of notifications.

The thing about building a site called ismypubfucked.com is that people want to share it. The domain does half the marketing work. Someone looks up their local, sees it's "Absolutely Fucked", screenshots the result, and sends it to the group chat. The name makes it inherently shareable in a way that "UK Pub Business Rates Impact Calculator" never would be.

Within 24 hours, the site had over 100,000 page views. It was trending on Reddit, both r/unitedkingdom and r/ukpolitics. Journalists from City AM, Foodism, and local papers were reaching out.

Squashing bugs

There were naturally plenty of bugs, as you would expect in a project that took barely 6 hours from idea to a live website. Each one taught me something different.

My original code matched pubs by name across the entire country. Which meant "The Ship Inn" in Dorset was getting coordinates from "The Ship" in Shoreditch. Not very helpful if you're trying to find your local and end up 150 miles away.

The fix was obvious once I spotted it: only match OSM pubs within the same postcode. Fall back to postcode centroid geocoding if there's no match. But it took a user on Twitter pointing out that their pub was showing up in completely the wrong place before I thought of a better way to do the geolocation.

There were several others. Null handling on severity scores that crashed the page. Mobile layout overflow on the pub crawl planner. In-app browsers from Facebook and Instagram breaking the geolocation API, which required building a custom error page that told people to open it in a real browser. Each one was a small fire to put out, usually within hours of someone reporting it.

Shipping fast to leverage the attention

Once something gets attention, you have to move quickly to make the most of the opportunity.

Sunday (12 January): TV prep. I added rate limiting, caching, SEO improvements. The site needed to survive broadcast traffic without falling over. Nothing kills momentum faster than a 502 error during a live segment.
Monday (13 January): Built a pub crawl planner. Enter a postcode, get the most fucked pubs nearby, optimised into a walking route using a Travelling Salesman approximation. This drove a second wave of sharing because now people weren't just looking up their local, they were planning nights out.
Tuesday (14 January): Mobile improvements. Added warnings for pubs that might have closed. The VOA data includes properties that are no longer trading, and there's nothing more frustrating than trekking to a pub that shut six months ago.
Saturday (18 January): Self-service closure reporting. Instead of relying on Twitter DMs to flag closed pubs, I built a system where users could report closures directly, one report per IP per pub, with the ability to dispute reports. Privacy-preserving and crowdsourced.

Each feature took a few hours. The hard part was already done. Once you've got the data infrastructure in place, adding new views on that data is relatively straightforward.

The coverage

The media coverage came in waves, and I didn't pitch any of it.

City AM ran a feature and video. Foodism did a full Q&A interview. Square Mile and Yahoo News syndicated coverage. Local papers ran stories about their area's most affected pubs. GB News and Sky News both ran segments.

The Bloomberg and Telegraph pieces were tangential, about AI and productivity more broadly, but both referenced the project as an example of what's now possible for individuals to build.

The pattern was clear: the site spread because it was useful and shareable. The media coverage followed the conversation, not the other way around. Nobody needed to be convinced that pubs were under pressure. They just needed a tool that let them see it for themselves.

The policy response

On 27 January, the government announced a support package:

15% business rates cut from April 2026
Two-year real-terms freeze until 2029
Average pub saves £1,650 in 2026/27
75% of pubs see bills fall or stay flat
£10 million Hospitality Support Fund

The British Beer and Pub Association, local MPs, and trade press had been making the case for months. ismypubfucked.com didn't create the pressure. But I think it helped shift the narrative by making an abstract policy problem concrete. And the owned and earned coverage it generated boosted the issue's salience.

When a Treasury official is weighing up whether to act, there's a difference between reading an industry lobby document and seeing that scores of people are looking up their local pub on a website called ismypubfucked.com and sharing the results. One is expected. The other suggests something is cutting through.

What this taught me

Here's what I keep thinking about.

The data was always public. The VOA publishes these spreadsheets every revaluation cycle. Anyone could have built this. A wiser developer with more experience could have built it faster, cleaner, with fewer bugs.

But the gap between "the data exists" and "people understand the story" is enormous. Most of the time, that gap doesn't get bridged. The spreadsheet sits on a government website, a few specialists read it, and the public conversation never happens.

What's changed is the cost of bridging that gap.

Two years ago, building ismypubfucked.com would have required either hiring a developer or spending months learning to code properly. Now it takes a day and a willingness to iterate in public.

I don't want to overstate this. Taste still matters. Knowing what's worth building, how to frame it, what will actually resonate, these are human judgments that the tools can't make for you. The implementation is the easy part now. The hard part is having something worth implementing.

At Topham Guerin, this is exactly what we think about: the intersection of storytelling, data, and the ability to build things quickly. Most of our work is for clients. But sometimes the best way to prove you can do something is to just do it yourself.

The harder question for the pub sector

The immediate policy pressure has eased. The relief package addressed the worst of it, at least until 2029.

But the underlying dynamic hasn't changed. There's public data everywhere, sitting in government spreadsheets, FOI responses, open data portals, that tells stories nobody is hearing. Most of it will stay that way because the cost of turning data into narrative has historically been too high.

That cost is collapsing. The tools to build, to visualise, to make complex things accessible, they're getting better every month. Which means more stories will get told. More pressure will be applied. More things that were easy to ignore will become impossible to ignore.

The question is who's going to tell them.

For anyone in politics, policy, or communications, this should be both exciting and uncomfortable. Exciting because the barriers to surfacing a story are lower than ever. Uncomfortable because those same tools are available to everyone else too.

ismypubfucked.com took a day to build and helped put the pub crisis on the front page. The next project like it might not be built by someone sympathetic to your cause.

The gap between data and narrative is closing. What matters now is who gets there first.

The thing you call social media hasn't been social for years

Ben Guerin — Mon, 26 Jan 2026 00:00:00 GMT

At the Political Tech Summit in Berlin last week, I was asked to debate whether social media is dying.

My answer: it's the wrong question. Social media, in the sense that your friends recommend stuff to you, died in 2022. What replaced it is something different. More powerful. And most campaigns haven't caught up.

2022: the year your friends stopped mattering

Four things happened that year:

Instagram surrendered. Went full TikTok. Your feed filled with Reels from strangers. The Kardashians complained. Meta didn't care. They'd seen the data: your friends are boring. Dopamine-optimised strangers are not.
Meta posted its first revenue decline. First drop since the 2012 IPO. TikTok had proved you could dominate attention without a social graph. Friends became a liability, not an asset.
Twitter became X. Musk bought it. The "town square" became a construction site. The conversation didn't stop. It scattered across Threads, Bluesky, Mastodon, Telegram. One public square broke into a thousand private rooms.
TikTok overtook Google. Most visited domain on earth. Gen Z started using it as a search engine. Discovery stopped being something you do. It became something that happens to you.

By the end of 2022, the social graph was dead. Your friends no longer shaped your feed. The algorithm did.

What replaced it

What we call "social media" now is algorithmic media with a comments section.

It's defined by three characteristics:

Algorithmic, not social. Your feed is shaped by what keeps you scrolling, not who you know. The platform doesn't care about your friendships. It cares about your attention.
Global feedback loop. A video can reach 50 million people in 48 hours. Not because of who shared it, but because the algorithm decided it was engaging. No gatekeepers. No friction. Instant global distribution.
Content consumption with comments. It's television you can reply to. The "social" part is residue from an earlier era. The product is content. Your role is to consume it.

This isn't weaker than the old model. It's stronger. The reach is bigger. The feedback loops are faster. The potential for something to explode is higher than ever.

But it requires a completely different playbook.

Three hard truths for anyone doing campaigns or comms

1. The EU is regulating yesterday's war

European regulatory frameworks assume platform boundaries that increasingly don't exist.

I've worked on campaigns in more than 40 countries. The real action has moved to places most western observers aren't watching: WhatsApp broadcast groups in Latin America and South Asia. Telegram channels in Eastern Europe. Discord servers everywhere. Gaming lobbies where political arguments happen between matches.

The EU is writing rules for Facebook and TikTok. Meanwhile, organising has migrated to platforms those rules can't touch.

How do you regulate political advertising on Discord? On Telegram? On a football forum?

You don't. And by the time the frameworks catch up, the battlefield will have moved again.

This doesn't mean regulation is pointless. But anyone who thinks ad bans on Meta and Google will clean up political discourse is missing where the discourse actually happens.

2. When you can't buy reach, you have to earn it

European campaigns can't throw money at Meta and Google the way US campaigns can. Ad bans and spending limits mean paid distribution is capped or blocked entirely.

So what do you do instead?

First: invest in what you're saying, not just how you distribute it.

When you can't outspend, you have to out-create.

Zohran Mamdani beat the Cuomo machine in New York City on a fraction of the budget. How? Content that was genuinely worth sharing. His team posted moments from that morning, not last week. It felt like momentum because it was momentum. Voters saw authenticity because it was authentic.

The lesson isn't "be more authentic." Everyone says that. The lesson is: speed and specificity create authenticity. Generic content scheduled three weeks in advance will never compete with something that happened this morning.

Second: build distribution infrastructure that algorithms can't throttle.

WhatsApp groups. Telegram channels. Discord servers. Email lists. Volunteer networks with phones.

These aren't backup channels. In a post-ad-ban world, they're primary.

The algorithm can't throttle a WhatsApp forward. A Telegram broadcast reaches everyone who subscribed. This is owned distribution versus rented distribution.

Campaigns that build community infrastructure now will have an asset that compounds. Campaigns relying on algorithmic reach are renting attention they don't control, on platforms that can change the rules overnight.

3. AI isn't the problem. Publishing without judgement is.

My co-panellist Clare O'Donoghue Velikić was sceptical about AI in political comms. Understandably. 44% of LinkedIn content is now AI-generated. Most of it is slop.

But the AI debate is missing the point.

The question isn't whether to use AI. Every campaign will. It's too useful not to. Research at 2am. Rapid response drafts in minutes. Briefing docs synthesised from dozens of sources.

The question is whether you have people with the judgement to know what's worth publishing.

The 44% of LinkedIn that's AI slop exists because people publish the raw output. No filter. No taste. No sense of whether it's good or garbage.

The campaigns winning right now use AI for preparation, not publication. They let it do the heavy lifting on research and first drafts. Then they add the human layer: real names, real places, things that happened that morning. The specific details that couldn't have been generated by a machine.

The skill isn't prompting. It's editorial judgement. It's taste.

And taste is the one thing you can't automate.

What this means

Social media isn't dying. But the thing we called social media, the thing where your friends mattered and the feed was shaped by your connections, that's been dead for four years.

What replaced it is a global algorithmic content engine. It's faster, more powerful, and completely indifferent to your social graph.

The campaigns that win from here will be the ones that understand three things:

Regulations are chasing a ghost. Build for where the conversation actually is, not where policymakers think it should be.
When you can't buy attention, you have to earn it. That means better content and owned distribution channels.
AI changes the speed of the game, not the nature of it. The differentiator is still human judgement. Taste still wins.

The Political Tech Summit asked if social media is dying. The more honest answer: it died in 2022. The interesting question is what you're going to do about it.

I spoke alongside Clare O'Donoghue Velikić at the Political Tech Summit in Berlin, January 2026. The panel was moderated by Benjamin Läpple.

Your audience will simplify your message if you don't. It's better to beat them to it

Ben Guerin — Thu, 22 Jan 2026 00:00:00 GMT

I was recently interviewed by Tom Hashemi for Policy Unstuck, a newsletter that's become essential reading for anyone working at the intersection of policy, advocacy and campaigns.

The core idea: your audience is never going to remember the footnotes, no matter how good they are. And they're going to have something to say about you whether you like it or not.

So the question becomes: do you want the one thing they say and remember to be what you want it to be, or what they want it to be?

It's a lesson we learned over and over during the 2019 UK election, and again during the COVID pandemic. And it applies far beyond politics.

Here's the full interview, with Tom's take at the top in italics:

Ben Guerin, Co-Founder of Topham Guerin, and one of the architects of the Conservatives’ Get Brexit Done campaign, speaks to Tom Hashemi.

Who remembers the Conservatives’ use of Comic Sans? Aaron Bastani responded to that tweet by saying:

“This is either genius or you’ve been absolutely conned by a leftist design studio. I suspect it’s the latter.”

Aaron was wrong.

The content went viral and prompted a series of commentary pieces. Even Creative Review got involved: Is Comic Sans going to get Brexit done? with many commentators realising they had just been introduced to shitposting.

Ben was one of the brains behind the campaign. I’ve admired his work from afar for some time, and on seeing him subscribe to Policy Unstuck, couldn’t help but take the chance to speak to him for the series.

Takeaway: if you can’t be bothered to distil your ideas down to something simple, your audiences will do it for you. Do you want your distillation, or theirs, to be the one that is used?

The reality of public engagement in the attention economy

Boris Johnson understands that politics isn’t just about a battle of ideas. It’s also a battle of wits. And he knows that politics competes for people’s attention. You’re competing with the Premier League. You’re competing with whatever’s on Netflix… you’ve got to be at least as entertaining.

Everyone knows that zip wire thing or that rugby tackle. He was willing to look silly to compete for that attention, in complete contrast to most politicians who are terrified of looking undignified.

Authenticity means refusing to mould to crowds

Everybody knows that a large part of success in communications comes down to being authentic, but the problem is everyone says you’ve got to be authentic and it ends up meaning nothing.

Being authentic means having the kahunas to not do what everyone else is doing. Most people don’t act authentically in their normal behaviours. They mould to adjust and adapt to the crowd.

The question for any aspiring politician is ‘why did you bother getting into this in the first place?’

It’s a crap job. Everyone hates you—especially if you win. The pay is garbage unless you really make it to the top and then go work for OpenAI and pull an Osborne. So you’re not really in this for the money. The UK is not like one of these tinpot dictatorships where politicians live like kings, at least for now anyway.

So there’s genuinely some reason why you’ve gone into politics. Don’t be ashamed to tell people what that is.

If you can’t distill your argument down to a slogan…

Your audience is never going to remember the footnotes no matter how good they are. And the audience is going to have something to say about you, whether you like it or not. The question is do you want the one thing that they say and remember to be what you want it to be, or what they want it to be?

If you can condense your message down into something really simple and clear that makes sense to people, they’ll use it. So, you may as well work it out and give it to them.

Also, it’s a slogan. It’s not a manifesto. It’s a heuristic, right? It’s a thing that people use to label the information they store about you in their brain. You’ve got to lose stuff to get down to that lowest common denominator.

Memes are information units, not just images

Forget the idea of a meme as an internet picture with words on it; a meme is a unit of information that can spread across a network.

Memes are powerful because they compress all these heuristics about how people think and views they hold into a single word or group of words. It’s why Boris is so interesting because when you say ‘Boris,’ your brain immediately conjures up this disheveled, charismatic politician who’s not like all the others, and he’s posh but he doesn't act like it.

That word evokes so much emotion and so much interesting context, and that’s what gives it meme potential because you've got all this information encoded in a single word.

When you’re thinking about meme potential, you should be thinking about how much implicit knowledge is attached to whatever phrase or word you are using.

Hyper-targeting general public content is over-hyped

So much of the value of content that you produce is the social potential of it.

If you and I live in completely different social feed universes and see completely different things because of that, we aren’t going to have much to talk about. But the chances are that we probably see a lot of the same stuff from a lot of the same people and that gives us things to geek out about together.

So from a social content perspective, you don’t actually want everything to be hyper-targeted. You want people to be able to share that experience and talk about it.

The other thing is that you’re paying a premium for the sniper rifle compared to the shotgun, right? The way that data targeting works is the more layers you add on, the more expensive it’s going to be. So the question is: is that premium justified, especially given the social component to behaviour change that we’ve just talked about?

Delegate campaign decision-making to win

It doesn’t matter how good you are as a leader, if you’re spending your time signing off tweets, then you’re creating a bottleneck that’s going to slow a campaign down.

Boris trusted the campaign machine, and the campaign machine trusted us. That meant we could operate at crazy speed–I think on some days we were doing 20 Facebook posts a day.

When you can work quickly, you can do a lot. We often describe our political campaign approach as water dripping on a stone. You've got to say the same thing again and again and again until you wear down your audience. Only then are you finally starting to cut through, because quite frankly no one gives a damn–you have to make them.

The real AI skill isn't prompting. It's taste

Ben Guerin — Mon, 01 Dec 2025 00:00:00 GMT

Three years ago today, Sam Altman tweeted "try talking to ChatGPT."

You could argue it's since been overtaken. Grok for real-time analysis. Claude for coding. Gemini for complex visuals. But none of that matters. This was the moment AI became mainstream.

Three years on, the dramatic predictions haven't landed. No mass unemployment. No industries wiped out overnight. The apocalypse keeps getting postponed.

The real shift has been subtler. A widening gap between people using these tools well and those who aren't. Work that took days now takes hours, if you know what you're doing.

Prompt engineering is overrated

There's been enormous noise about "prompt engineering" as a killer skill. Courses, certifications, job titles. A whole cottage industry built around crafting the perfect instruction.

I think it's overrated.

The models are getting better at understanding intent. A year ago, you needed elaborate prompts with specific formatting instructions and role-play setups. Now you can often just ask for what you want. The gap between a mediocre prompt and a good one is shrinking with every model update.

What isn't shrinking is the gap between people who can recognise good output and those who can't.

Taste is the real skill

Here's what I mean by taste: the ability to look at what the AI produces and know, immediately, whether it's good enough or whether it's trash.

This sounds simple. It isn't.

I've watched people accept AI outputs that are obviously wrong, obviously generic, obviously not what they asked for. They can't tell. They don't have the reference points to recognise quality in that domain.

And I've watched people with deep expertise in a field reject AI outputs that are 90% there, because they can see the 10% that's off. They know what good looks like, so they know when they're not looking at it.

The difference isn't in how they prompt. It's in how they evaluate.

What taste looks like in practice

In writing, taste means recognising when AI prose is bloated, generic, or structurally predictable. When it's using filler phrases. When it's hedging everything into meaninglessness. When it sounds like every other AI output.

In code, taste means knowing when the solution is elegant versus when it's a brittle hack that will break in edge cases. When the architecture makes sense versus when it's overengineered nonsense the model hallucinated from Stack Overflow fragments.

In strategy, taste means spotting when the AI has given you a plausible-sounding framework that doesn't actually fit your situation. When it's pattern-matched to something superficially similar but fundamentally different.

In all cases, the skill is the same: knowing what good looks like in that domain, and therefore knowing when you're not looking at it.

You can't outsource judgment

This connects to something I've been thinking about with AI and junior hiring.

Research from Harvard shows junior hiring at AI-adopting firms is down 22% since early 2023. The logic is obvious. If AI can do the entry-level knowledge work, why hire entry-level people to do it?

But here's the problem. Taste comes from exposure. You develop judgment by seeing thousands of examples of good and bad work, by making mistakes, by having someone more experienced tell you why your output isn't good enough.

If we stop training juniors, we stop developing the taste that makes AI useful. We end up with a generation that can prompt but can't evaluate. That can generate but can't judge.

The models will keep getting better at producing output. The bottleneck will be humans who can tell whether the output is worth anything.

Three years in

So where does that leave us, three years after ChatGPT launched?

The tools are better than most people expected. The disruption is slower than the headlines predicted. The gap between skilled users and everyone else is wider than either camp anticipated.

The people getting the most value aren't necessarily the best prompters. They're the ones who already knew what good looked like in their domain, and can now produce it faster.

As for which models we'll be using in twelve months? Who knows. But the genie isn't going back in the bottle. And the skill that will matter most isn't learning the perfect prompt syntax. It's developing the judgment to know when the output is good enough to ship.

That takes years of practice. No shortcut. No course. Just reps.

"Social media" is dead. Long live social media

Ben Guerin — Mon, 27 Oct 2025 00:00:00 GMT

Every time someone says "young people aren't engaged with politics," what they really mean is "young people aren't engaged with MY politics."

I was at the Asian Conference on Political Communications in Singapore last week, on a panel about winning the next generation. The conversation kept circling back to the same question: why do some political movements ride waves of youth support while others drown?

The answer isn't complicated. It's just uncomfortable for most political operators to hear.

You're not competing with other politicians

Politicians think their competition is the other party. It isn't. They're competing for space in the feed against MrBeast, Taylor Swift, Manchester United highlights, and whatever brain-rot video game clips are trending this week.

This isn't a metaphor. It's literally how the algorithm works. A 22-year-old scrolling TikTok will see your policy announcement sandwiched between a makeup tutorial and a clip of someone falling off a skateboard. You have the same two seconds to earn their attention as everyone else.

To succeed, campaigners need to be at least as interesting as the entertainment they're competing against. Most aren't even close.

"Responsible social media" cuts both ways

Commentators spend enormous energy discussing responsible social media tactics. Misinformation. Foreign interference. Platform manipulation. All legitimate concerns.

But almost nobody talks about the democratic responsibility that politicians have to present their pitch to voters in a way people can actually understand and care about, on the platforms they use every day.

MPs being on TikTok shouldn't be a "clever campaigning tactic." It should be the bare minimum. If you're not where the voters are, you're not really trying to reach them. You're just performing politics for people who already agree with you.

Reform UK understood this. Nigel Farage's TikTok presence dwarfed every other party leader's in the last UK election. You can disagree with everything he stands for and still recognise that he's playing a different game than his competitors.

The term "social media" is basically dead

Here's what happened in Nepal last month.

The government tried to ban TikTok, Facebook, YouTube, and two dozen other platforms. Young people didn't log off. They moved to Discord.

What started as chat rooms turned into debates, then votes, and ultimately the rise of Sushila Karki, a 73-year-old former Chief Justice, now sworn in as interim PM. A gaming app became a parliament.

This is where the conventional understanding of "social media" breaks down completely.

In 2025, saying social media means X, Facebook, Instagram and TikTok is deeply inaccurate. Policy discussions happen on WhatsApp. Political organising happens in gaming chatrooms. Discord servers have more civic engagement than most town halls.

WhatsApp is now the UK's fourth largest news source. Facebook has overtaken the BBC. Half of the top ten most-consumed news sources are now social platforms.

Either we use "social media" as an umbrella term for anywhere people can share content and message each other, or we stop using the term entirely. The current definition is useless.

Platform bans are whack-a-mole

The Nepal case makes something obvious that governments keep failing to understand.

Take away TikTok, they move to Instagram. Block Instagram, they switch to Discord. Shut down Discord, they'll use a football forum or the chat function in Call of Duty.

Once a society has learned how to coordinate online, you can't put that knowledge back. The habit of organising, mobilising, and making decisions together digitally is now muscle memory. Banning apps is like trying to ban conversations in a town square. You're not stopping the conversation. You're just moving it somewhere you can't see.

That's why platform bans are a pointless game. They don't restore order. They fuel unrest by forcing people onto platforms with even less moderation and oversight.

What this actually means

For political campaigns, the implications are straightforward but painful.

You need to be everywhere your voters are, which increasingly means places that don't look like traditional political battlegrounds. Gaming platforms. Messaging apps. Whatever comes next.

You need content that competes for attention against professional entertainers, not just other politicians.

And you need to stop thinking about "social media strategy" as a separate workstream. Every digital space is now social. The distinction has collapsed.

The genie is out of the bottle. The question isn't whether to engage. It's whether you're engaging on terms that might actually work.

The EU's political ad ban is a gift to populists

Ben Guerin — Fri, 12 Sep 2025 00:00:00 GMT

The EU thinks it just reined in Big Tech. In reality, it just handed a massive advantage to the AfD, Rassemblement National, and every other populist insurgent in Europe.

Starting in October, Meta will stop running political and social issue ads across the EU. Not because they want to, but because Brussels made the compliance burden so onerous that pulling out entirely was easier than playing along.

The stated goal was to protect democracy from misinformation and foreign interference. The actual result will be the opposite.

The asymmetry nobody wants to talk about

Paid advertising was one of the few tools moderate parties had to compete online.

Think about what centrist political content looks like. Policy explanations. Nuanced positions. Detailed plans. It's worthy, sensible, and utterly hopeless at generating organic reach.

Populist content is different. It's engineered to travel for free because it hits exactly the emotions that social media algorithms reward: outrage, fear, anger, hope. Data from the Financial Times shows this gap is particularly stark on social platforms compared to television.

On TV, moderate and populist content compete on roughly equal footing. Online, the playing field tilts hard toward whoever can generate the strongest emotional response.

The CDU puts out a video explaining their fiscal policy. It gets 50,000 views. The AfD posts something inflammatory about immigration. It gets 5 million.

That's just the reality of how these platforms work.

What paid ads actually did

For mainstream parties, paid advertising was the equaliser. You couldn't out-viral the populists, but you could buy your way into people's feeds. You could target persuadable voters with messages they'd never see organically. You could compete.

That option is now gone in Europe.

The irony is brutal. Brussels set out to limit the influence of money in politics. Instead, they've made politics entirely dependent on what travels for free. And what travels for free is whatever triggers the strongest emotional response.

Who benefits

The question answers itself.

When you remove one of the few tools moderates had to cut through, the loudest voices get louder. The parties that were already winning the organic game, the ones skilled at generating outrage and fear, now face even less competition.

This isn't speculation. We've seen it play out already. In the German federal election, the AfD's TikTok and X presence dwarfed the SPD and CDU combined. The mainstream parties stuck to YouTube, Facebook and Instagram, platforms where you still need either paid support or genuinely viral content to get reach. The AfD had the latter. The SPD didn't.

Now imagine that dynamic with paid advertising removed entirely.

What comes next

Some will argue that mainstream parties should simply get better at emotional content. Learn to play the game. Out-populist the populists.

That's easier said than done - after all, we've been able to build a business at Topham Guerin teaching politicians around the world (mostly on the centre-right) how to do exactly that.

Centrist parties have a particularly tough challenge because their actual positions don't lend themselves to viral outrage. "We will incrementally improve the tax system" doesn't hit the same as "they're coming for your jobs."

You can't fake authenticity. And you can't manufacture outrage about moderation.

The EU wanted to protect European democracy from manipulation. They may have just made it harder for anyone except the manipulators to be heard.

What's really behind Gen Z's personality shift?

Ben Guerin — Mon, 11 Aug 2025 00:00:00 GMT

This is one of those charts that stops you in your tracks.

The Financial Times analysed the University of Southern California's "Understanding America Survey" and the results confirm what everyone suspects: young people today really are wired differently.

Since 2014, the personality profiles of 16-39 year olds have diverged sharply from everyone else. The over-60s have barely moved. But young adults? The lines are falling off a cliff.

Conscientiousness has dropped from around 45 to below 30. More procrastination, more missed commitments, less self-discipline.

Neuroticism has spiked from the low 50s to nearly 70. More anxiety, more stress, more emotional volatility.

Agreeableness is down. Less trust, less deference to authority, less willingness to compromise.

Extroversion is declining across all ages, but steepest among the young. Smaller circles, fewer big social settings.

The obvious explanation

Social media. Smartphones. The dopamine treadmill of infinite scrolling. The comparison trap of curated feeds. Jonathan Haidt has written extensively on this and he's probably right.

But I think this explanation, while probably true, is incomplete.

The less comfortable explanation

Consider what it means to be 22 in 2025:

You took on student debt because you were told education was the path to success.
You graduated into a job market where "entry-level" means three years' experience.
House prices are ten times the average salary.
The triple lock guarantees your grandparents' pension rises faster than your wages.
By the time you retire, the state pension will likely have collapsed or been reformed beyond recognition.

Many Gen Zers I speak to don't see the point of playing by the rules when the game is rigged.

And when you frame it that way, the data looks different.

Why invest in long-term planning when the long term feels uncertain? That's conscientiousness declining.

Why wouldn't you be anxious when housing is unaffordable and the climate science is terrifying? That's neuroticism rising.

Why defer to institutions that keep failing to deliver? That's agreeableness dropping.

Why engage with a public sphere where every interaction is monetised and recorded? That's extroversion retreating.

The pattern

Every generation gets told it's uniquely broken. Boomers were self-indulgent hippies. Gen X were cynical slackers. Millennials were entitled snowflakes. Now Gen Z are anxious introverts who can't commit.

The consistency should make us suspicious.

What actually matters

Looking at this chart, it's easy to ask "what's wrong with young people?"

But surely it's better to ask: why shouldn't they feel this way?

The mental health crisis is real. The skills gap is genuine. But if we treat these shifts as pathology, we design interventions to fix broken individuals. If we treat them as adaptation, we ask harder questions about what they're adapting to.

My guess is we'll get the former: resilience training, mental health apps, productivity hacks. All designed to help people cope with systems that stay unchanged.

The data suggests something harder to accept. The problem might not be the kids. The problem might be what we've built for them to inherit.

To understand the shock upset in NYC's Democratic primary, just look at the Instagram feeds

Ben Guerin — Fri, 27 Jun 2025 00:00:00 GMT

In March, if you'd told anyone that Zohran Mamdani would beat Andrew Cuomo in the race for New York City mayor, they'd have laughed you out of the room.

Cuomo was leading by 20-30 points in the Democratic primary. He had name recognition, establishment backing, and the kind of political machinery that's supposed to guarantee victory in a city like New York. Mamdani was a relatively unknown state assemblyman with a fraction of the resources.

Yet on Tuesday, Mamdani picked up the Democratic nomination.

What happened? A masterclass in digital-first campaigning that should be required viewing for every aspiring politician, particularly those trying to unseat establishment candidates.

Just look at the Instagram grids

Sometimes the story is right there in the visuals. Look at the Instagram feeds of both candidates during the final weeks of the campaign, and you'll see two completely different approaches to politics – and two very different understandings of how voters make decisions in 2025.

While Cuomo was shouting at his audience to "VOTE, VOTE, VOTE," Mamdani was smiling and chatting with a diverse mix of New Yorkers. Where most of Cuomo's feed was filled with clips from TV appearances or screenshots of media articles, Mamdani's feed was entirely content produced specifically for that platform.

If you look closely at Mamdani's photos and videos, you'll notice something else: he's using a consistent filter that makes him look warm and positive, almost Wes Anderson-esque. It's a small detail that creates a massive cumulative effect.

Tactics that made a difference

Local political analysts with far more context and knowledge than I have already written plenty about the political machinations and demographic trends at play, so I'm not going to attempt to get into that here.

That said - at Topham Guerin, we've worked on more than 25 election campaigns around the world in the last decade, and having faced similar challenges as Mamdani and his team plenty of times before, there were four things that really stood out to me as key to his success.

A unifying and timely message

While Cuomo concentrated on crime – always a safe bet for establishment Democrats – Mamdani focused on something more fundamental: affordability. He called for rent freezes, free buses, and universal childcare.

In a city where housing costs dominate every conversation, this wasn't just good politics. It was smart positioning. Crime matters, but rent is what keeps people awake at night. Mamdani understood which issue would actually drive turnout.

A distinctive brand unlike any other

Inspired by the color of the iconic New York taxi, Mamdani's branding was immediately recognizable and distinctly New York. It stood out sharply against Cuomo's classic American red, white, and blue logo.

This matters more than most campaigns realize. In a world of infinite scroll, you have maybe half a second to register with someone before they move on. Distinctive branding isn't vanity – it's basic information processing.

Carefully curated visual moments

Mamdani shunned the traditional campaign event where politicians speak to voters from a podium. Instead, he chose to pound the pavement, talking to voters one-on-one and using that footage for his social content.

In a deeply Democratic city with diverse demographics, this positioned Mamdani as the "people's champion" and provided a stark contrast to an establishment politician with a controversial record.

Speed of digital execution

Here's the detail that impressed me most: Mamdani's digital team was turning content around so quickly that there would be a video posted within 60 minutes of each visit or event.

For anyone who has worked on a political campaign, you'll know just how insanely impressive that is. Most campaigns take days to get content approved, edited, and published. Mamdani's team was operating in real-time, capturing moments and getting them online while they were still fresh.

This speed created a sense of momentum and authenticity that you simply cannot manufacture in a traditional media strategy. When voters saw Mamdani's content, they were seeing something that had happened that morning, not last week.

Implications for campaigners

This will probably encourage a lot of copycat campaigns – especially on the left – around the world in the coming months. The aesthetics, policies, and specific tactics will have varying degrees of relevance outside of NYC, but the underlying principles are universal.

The traditional advantages of establishment candidates – name recognition, endorsements, media relationships – matter less when voters are consuming content on their phones rather than reading newspapers or watching television news.

What matters more is having a clear strategy, a deep understanding of digital platforms, and knowing how to tell a story in a creative and memorable way.

Mamdani's victory is part of a broader shift in how political campaigns work. We have moved away from broadcast-era tactics – the big speech, the TV ad, the newspaper endorsement – toward something more conversational and immediate.

This doesn't mean traditional media is dead. But it does mean that campaigns that understand how to combine both approaches – digital-native content with broader media strategy – will have a significant advantage over those that don't.

If you have a clear strategy, deep understanding of digital platforms, and know how to tell a story in a creative and memorable way, you can do almost anything. Even speed-run becoming mayor of one of the most important cities in the world.

The establishment is learning this lesson the hard way, one election at a time.

30 things at 30: reflections from Tuscany

Ben Guerin — Wed, 11 Jun 2025 00:00:00 GMT

As of today, any hopes of ever making Forbes 30 under 30 have been dashed.

But I did make it to 30, and I’m celebrating in Tuscany, so I feel like I'm still a winner.

Ten years ago I dropped out of uni to build what would become Topham Guerin. I never would have thought at the time that Sean and I would end up advising world leaders and leading a global creative agency.

So to mark the big 3-0, here are thirty things I wish I’d known then:

Work should feel like play. If it doesn’t, you’re doing it wrong.
The biggest moments don’t always feel big at the time - when we did boomer memes and comic sans “Get Brexit Done”, we didn’t realise it would re-write the political playbook.
The hockey stick graph has lots of wobbles when you zoom in. It’s not always roses and buttercups.
It’s better for something to happen than for a perfect plan to never get off the page.
If you’re growing in your career, by definition you will often feel out of your depth. Lean into it!
When doing something ambitious, lots of people will have an opinion. Most of them can be ignored.
Similarly: if everyone agrees with what you’re doing, it’s probably not bold enough.
If I’d stayed at uni to finish my degree, TG wouldn’t exist. A certificate can be helpful, but isn’t always necessary to do meaningful work.
A random coffee or after-work pint can turn into a major client or other new opportunity. Always take the meeting.
Curiosity didn’t kill the cat. It built rocketships.
You’re capable of more than you think is possible - it might just take time.
There is basically nothing that a group of smart and committed people can’t achieve together. Just find the right people.
You can build a good business with a smart idea. But building a great one is only possible with a co-founder that shares your vision, values and work ethic. Cheers Sean!
What’s right isn’t always popular, and what’s popular isn’t always right.
Depending on what you do, AI will either take your job, give you superpowers or (in some rare cases) make little difference. Work out which one applies to you and act accordingly.
Some nights are for cheap Prosecco, others are for the 10yr old Bordeaux. Both are good!
Health and productivity can go hand in hand - it doesn’t have to be one or the other (I learned this one far too late).
A little is always better than too much or nothing at all. Try the cake!
The brain is a muscle - exercise it daily.
There is no such thing as “too many books”.
My 1,592 day Dutch language Duolingo streak is more useful than the 5 years of French I did in high school. A little each day is better than a lot a long time ago.
Running 500km sounds nuts. But 10km each week is doable, and even with two weeks off you’ll run 500km in a year. The same principle applies for any other big goal.
Don’t set goals that are just about wishing for an outcome. Goals are achieved by creating & sticking to habits.
Take time to reflect on each day, each month and each year. If you’re not where you want to be, that’s ok: just seek to understand why and what you can do about it.
It’s exceedingly rare for things to go accordingly to plan, and that’s ok. If you set a direction and act with intention, you’ll get there in the end.
Who you choose as a life partner is everything.
On that note: my fiancée Mirjam is always right.
When working to a 9am deadline, getting up at 5am to smash out the final stretch is always a better option than working past midnight.
If your calendar’s full of tasks, you’re leaving no room for the big-picture thinking that actually moves things forward. Both matter.
If in doubt, take the leap.

Grateful for everything I’ve learned so far, and excited for everything I don’t know yet.

Big thanks to all that have supported, tolerated, indulged and challenged me on this journey so far.

Here’s to the next 30!

Work is changing. Are you?

Ben Guerin — Fri, 30 May 2025 00:00:00 GMT

Will AI take your job? Maybe. Maybe not. But this shift didn’t begin with ChatGPT.

Not long ago, the average S&P 500 company needed eight employees to generate $1 million in revenue. Today it’s just two.

That’s not because everyone suddenly got four times more productive. It’s decades of pressure from software, outsourcing, automation.

And now AI is just the latest force continuing the trend.

And while nobody can predict what white-collar work will look like in 5, 10, or 20 years, one thing’s certain: it won’t look like how we work today.

Instead of just asking “will a robot take my job?”, it's worth taking a moment to dig deeper.

If you're an employee, it’s worth questioning how relevant the idea of a linear career path really is.

In most industries, the idea of working your way steadily up the ladder feels increasingly outdated. Some people are optimising for flexibility, others for fulfilment, others still for financial gain, and very few are treating those priorities as fixed.

You might have once been told to specialise early. Now you’re just as likely to be rewarded for range. And the lines between employee, freelancer and founder are already blurry. In the next few years, you might cycle through all three, or do them all at the same time!

If you're building a team, different questions emerge.

What do you actually need humans to do? Not what you’ve always hired for, but the actual value humans deliver that AI tools can’t.

That requires a different approach to headcount planning.

It means getting comfortable with having AI agents in the org chart, while also thinking about culture in a new way by asking new questions.

For example - how do you maintain morale, shared purpose, and creative energy with a hybrid team of humans and AI agents?

These are the questions we’re thinking about at Topham Guerin in our strategy sessions as we plan for our next phase of growth. And they are hugely exciting conversations to have!

Because this isn't about robots vs people.

It's about what does it actually mean to do good work, create value from your time, and enjoy what you do.

Google I/O just killed your startup

Ben Guerin — Tue, 20 May 2025 00:00:00 GMT

Let's take a moment to step inside the world of a startup founder in 2025.

You raise $100 million, hire a team, obsess over the product, and launch something great.

Then a giant tech company drops a new feature that does the same thing: except it’s integrated into a suite of products already used by billions, maybe even offered for free.

This week, that's exactly what just happened to entire categories of startups, all at once.

Don't believe me? Let's take a look at what was announced at the Google I/O event:

Real-time translation on video calls

Google Meet now does real-time voice translation, and it keeps your voice, tone, and timing intact.

Initially it will support English and Spanish, with more languages coming soon.

RIP to... KUDO

KUDO raised $28 million to bring live interpretation to enterprise video calls.

They built a strong product, but Google just turned the feature into a default setting.

AI-generated video with native audio

Google has introduced a new version of Veo, its video generation model. It now creates 1080p clips with ambient sound, dialogue, and audio effects baked in.

This is the first model of its kind that combines high-quality video and audio in a single output.

RIP to... Runway

Runway has raised over $300 million and has long led the charge on AI-generated video.

But its models still don’t handle audio. Google just leapfrogged them.

(I'm exaggerating to say that Runway is dead - their video model is still excellent and we used it a huge amount in our work at Topham Guerin. But the pressure is on for them to overtake Veo 3 before customers jump ship.)

AI-powered video editing

Flow is Google’s new creative tool built into YouTube Studio. It uses Gemini to help creators script, storyboard, and edit videos faster.

The goal is clear: turn your idea into a Short in minutes, without leaving the platform.

RIP to... Captions

Captions raised $60 million to bring AI editing tools to creators.

Now Google has built the same tools directly into the biggest video platform on Earth.

Smarter, safer password management

Chrome and Android are becoming full-service sign-in assistants that:

Generate strong passwords
Flag compromised ones
Fix issues automatically
Seamlessly log you in to web and apps

RIP to... 1Password and Dashlane

Between them, they raised over $800 million to manage passwords.

But now Google is giving that away for free, built into tools people already use every day with a product that is easily a generation ahead.

I use NordPass and after this demo am already considering cancelling my subscription.

Virtual try-ons with your own photo

Upload a photo and Google will show you how clothes will look on your body. The system accounts for shape, pose, fabric, and texture.

It's live now in Search Labs for US users.

RIP to... Every ecom startup pitching virtual try-ons, including Fashable who seem to have already deactivated their website

Fashable raised $6M to build virtual try-on tools, but Google just shipped a version that works at global scale, across billions of listings, directly inside Search.

It's a tough time to be building in this space.

So what now?

We have looked at five examples, and you could easily list a dozen more just from the Google I/O event.

Real-time translation. AI video generation with audio. AI video production studio. Agentic login. Smarter shopping.

All companies worth investing in, now just another Google feature.

And you could repeat the exercise with recent events from Microsoft and OpenAI and knock out another dozen startup categories.

Now, I exaggerate when I say these product categories are wiped out.

If some of these startups move fast to leapfrog Google or go deep on specific niches, they might hold their own.

Competing on price, UX, or meeting the needs of a unique customer segment are viable options.

The deeper problem

This isn’t just a platform advantage. It’s a fundamental structural challenge of building a product offering built around AI.

Owning a distribution platform now matters more than building a perfect product. When you’ve got the right model and data stack, launching something new is the easy part.

And that’s the real issue for venture-backed startups. VC relies on winner-take-all outcomes. You need a few portfolio companies to dominate their category.

But what happens when the “winner” is just a feature inside someone else’s ecosystem?

As far as VCs are concerned, it's not enough for their portfolio companies to survive or even thrive - they need to dominate the market entirely to justify the investment.

That is just going to be more difficult than ever in an age of AI -powered product development

Because if distribution is the moat, and the platforms keep pulling features closer to the metal, then the number of breakout challengers might be smaller than anyone wants to admit.

We were already living in a vibe-based civilisation long before "vibe coding" came along

Ben Guerin — Wed, 16 Apr 2025 00:00:00 GMT

Everyone’s worried about the rise of “vibe coding” — the idea that we’re starting to let LLMs generate content, code, or legal arguments that we don’t fully understand.

But that’s not new. We were already doing that before AI showed up.

This meme sums it up perfectly: engineers who can’t code, lawyers who can’t read legal docs, politicians who don’t know their own laws, and yet somehow the whole thing still runs.

Modern life is too complex for anyone to hold in their head.

Most software is built on codebases so layered and outsourced that even senior engineers only understand the top few layers.

Politicians regularly legislate on issues they have no direct expertise in, relying on advisors, briefings, and party lines.

Even something as essential as healthcare is delivered through institutions shaped by protocols and policy frameworks most of us couldn’t begin to explain.

We navigate those systems not by understanding everything, but by reading the signals, asking the right questions, and trusting the right sources.

Rick Rubin, one of the most successful music producers alive, even openly admits: “I have no technical ability and I know nothing about music.” He doesn’t know how to operate a soundboard. Does that make him less credible? Or more?

This is how high-trust systems work. We rely on tools, people and institutions to fill in the gaps for us. The key skill isn’t mastery. It’s navigation.

That’s why I think the anxiety around LLMs is often misplaced. They’re not eroding some golden age of human understanding. They’re compressing access to knowledge. They’re giving people a way into systems that would otherwise take years of training to approach. And in doing so, they’re levelling the playing field.

If that sounds like cheating, so is using a calculator. So is Googling an error message. So is asking someone smarter than you to show you how it works.

No system is perfect. But if it helps more people make smarter decisions, that’s a win.

The future isn’t going to be clean, linear or fully explained. But if you can read the room, work with the tools, and stay curious you’ll do just fine.

Even if you’re just going on vibes.

What does the future look like when AI is a "datacentre of geniuses"?

Ben Guerin — Mon, 10 Mar 2025 00:00:00 GMT

Most five-year plans feel like a guessing game. But this one is different.

We start our financial year each April here at Topham Guerin, so we're currently thinking hard about the future as we do our annual planning: what we’ll look like, how we’ll work, what we’ll actually do.

And the honest answer is, I don’t know. Not because we lack a strategy. But because the tools are changing so fast that planning beyond a couple of years starts to feel a bit pointless.

In 2030, we might not be using any of the platforms or workflows we rely on today. That’s not a warning. It’s an opportunity.

I don’t believe AI will replace the creative and strategic work that agencies like ours do. But I do believe that AI will enable small, lean teams to deliver the kind of impact that once required whole departments. The work doesn’t vanish, the shape of the team just changes.

That’s a big deal for an agency like TG. And an even bigger one for our clients.

Because it means the barriers between ambition and execution keep getting lower. Want to build a brand from scratch, launch a movement, test a dozen campaign routes in 48 hours? You can. And you won’t need a skyscraper full of staff to do it.

But that’s just the near-term shift.

Zoom out a bit further, and you start to see what Dario Amodei, CEO of Anthropic, calls the "country of geniuses in a datacenter". He predicts that powerful AI, smarter than Nobel Prize winners across most domains, could emerge by 2026 or 2027.

If he’s right, we’re not just getting better tools. We’re dealing with a new operating system for society. One where AI can tackle complex problems in biology, governance, and economics faster than most institutions.

In his essay Machines of Loving Grace, Amodei lays out a vision that’s both radical and grounded. Eradicating disease. Personalised education. Transparent governance. Accelerated economic development. Not in 100 years, but 10. Maybe even less!

And his company is putting that into practice. Anthropic’s recent submission to the U.S. government’s AI Action Plan outlines how to prepare: secure labs, expand energy infrastructure, stress-test national security systems, and modernise how we measure the economy.

We should take it seriously.

Because the next wave of AI will reshape how intelligence operates: who uses it, who benefits, and who gets left behind.

The question for creative and strategic agencies like TG isn’t whether AI will replace us. It’s how we stay relevant when the world gets smarter.

That’s what we’re thinking about. Not in 10-year plans or annual forecasts, but in experiments, hires, projects and habits that help us stay adaptive.

The future isn’t written yet. But it’s being coded, tested, and deployed right now.

And if we’re smart, we’ll do more than keep up. We’ll help shape it.

Talking to the Nation is harder than it used to be

Ben Guerin — Fri, 07 Mar 2025 00:00:00 GMT

The strategic communications consultancy Charlesbye has just released the Talking to the Nation 2025 report — the largest study ever conducted into how UK adults consume, trust and talk about news with a sample size of 8,000 UK adults.

At Topham Guerin we have worked closely with Charlesbye’s founder, Lee Cain, since the 2019 election campaign, where we helped deliver the Conservatives’ digital strategy. We also worked together through the COVID-19 pandemic, shaping the government’s comms response during one of the most challenging public information efforts in modern history.

Charlesbye understand not just what’s shifting in the media landscape, but how to turn that insight into real-world strategy when the stakes are high. This report is a great example of their expertise, with a clear-eyed look at how fast things are changing and why most comms playbooks are already behind.

I was on the panel for the launch event alongside voices from The Spectator and The Sun where we dug into the findings, and there were six key insights that are worth paying particular attention to.

1\. Social media has overtaken traditional news sources

Facebook is now the UK’s most popular news source. Not the BBC. Not ITV. Facebook.

Half of the top 10 most-consumed outlets are now social platforms. That alone should tell you how much the media landscape has been redrawn.

For years, the national conversation was shaped by a few trusted gatekeepers. That’s gone. People no longer tune in at the same time or start their day with the same front page. The algorithm decides what they see, not an editor. And that shift from editorial control to personal feed changes not just how news is delivered, but what people absorb and act on.

2\. People are their own fact-checkers

Under-45s are using 8 to 12 different news sources a day. Over-75s are averaging six. That’s not passive consumption. That’s active, deliberate comparison.

We’ve moved from a one-to-many broadcast model to a many-to-many ecosystem. The public isn’t waiting to be told what to think. They’re assembling their own view — sometimes well, sometimes badly, but always with a higher degree of autonomy. They’re skimming, scanning, cross-referencing and weighing up tone and context as much as content.

For anyone communicating at scale, this should be a wake-up call. If your strategy relies on the public accepting your version of events unchallenged, it’s going to fall flat.

3\. Podcasts are eating legacy media

Joe Rogan now reaches more Brits each day than the Today programme on Radio 4. That’s not a niche stat. It’s a generational handover.

What draws younger audiences in is the space to think out loud. Podcasts offer long-form discussion, disagreement, personality and friction. They feel less staged, more revealing. And in a world where trust is fragile, that sense of transparency matters. It’s not about whether you agree. It’s about whether the conversation feels real.

Legacy formats still rely on heavily edited segments, polished soundbites, and a top-down tone. That’s not how people under 45 are consuming content. If you’re not adapting to that shift, you’re missing your moment.

4\. WhatsApp is shaping the conversation

WhatsApp is now the UK’s fourth most popular news source. It’s not where stories start, but where they spread. Quietly, quickly, and with more built-in trust than any news brand can buy.

When a link comes from a friend, it hits differently. The sender brings credibility. The format encourages sharing. And once a story has landed in a group chat, it becomes part of the conversation whether it’s accurate or not.

For communicators, the challenge is clear. Messages need to be built for velocity. If they don’t survive the screenshot, they won’t spread. If they aren’t quotable in two lines, they won’t be repeated. And if they don’t feel personal, they’ll never leave the feed.

5\. Trust is moving from institutions to individuals

More and more people are turning to independent voices, creators and niche commentators instead of traditional media brands. Not because they always have better information, but because they feel more human.

In a high-noise environment, people don’t just trust facts. They trust people. They’re looking for consistency, personality and perspective. They want to hear what someone thinks, not just what happened. And they’re deciding who to believe based on tone, track record and audience interaction over job titles.

This isn’t just about media. It also affects politics, activism, brand loyalty and public policy. If your message isn’t delivered by someone the audience already trusts, it probably won’t land at all.

6\. The generation gap isn’t just about platforms. It’s about trust.

One of the most striking findings in the report is the gap in trust between younger and older audiences when it comes to social media.

Under-45s have a net trust of +23 percent in social media as a news source. Over-45s come in at minus 23 percent. That’s a 46-point swing. On the same platforms. Looking at the same content.

Older generations grew up with gatekeepers. Younger generations grew up with feeds. They’re not just consuming news differently. They’re thinking about it differently.

For communicators, this creates a serious problem. You can’t rely on a single channel to reach everyone. But more importantly, you can’t assume the same content will be trusted by different demographics in the same way. Relevance and trust are now inseparable from context.

So, it’s not just about where you place your message. It’s about which channels your audience trusts enough to even listen.

If you work in politics, communications, or media, the Talking to the Nation report is essential reading. You can find it here.

It’s the most detailed snapshot we have of how the UK public is thinking, reading, watching and deciding what to believe. And it should be required reading for anyone trying to shape the conversation in 2025.

Where are Europe’s tech giants?

Ben Guerin — Wed, 26 Feb 2025 00:00:00 GMT

“WTF? Surely that can’t be right.”

That was my reaction when I first saw this chart, and I bet you thought the same.

We all know the US is home to the big tech names: Apple, Amazon, Microsoft, Google, Meta. No surprises there.

And no one will be shocked to learn that the US economy is bigger than Europe’s. In 2023, the numbers looked like this:

United States: $25.44 trillion
European Union: $16.75 trillion

That’s a 52 percent gap in nominal GDP. Big, but not outrageous. The real shock comes when you look at what each economy has produced in terms of new companies.

Specifically, from-scratch public companies started in the last 50 years with a market cap over $10 billion.

But that doesn’t come close to explaining what the chart shows.

When it comes to that criteria, the US doesn’t just outperform Europe: it's no comparison at all.

Even if you ignore the tech giants, the US list is long and varied. Logistics, finance, pharma, consumer, media. Hundreds of serious businesses built within a generation.

And as for Europe? Fourteen (14).

That’s the total number of companies in the EU that meet the threshold of being founded from scratch, not by merger, takeover or spinout.

Here’s the list, with market caps from November 2024:

1. Spotify - $93.3 billion (Sweden, 2006)
2. DSV - $48.8 billion (Denmark, 1976)
3. Adyen - $45.7 billion (Netherlands, 2006)
4. Argenx - $36.5 billion (Belgium, 2008)
5. EQT AB - $33.6 billion (Sweden, 1994)
6. Amadeus - $30.2 billion (Spain, 1987)
7. BioNTech - $28.4 billion (Germany, 2008)
8. Hexagon AB - $26.5 billion (Sweden, 1975)
9. Ryanair - $21.0 billion (Ireland, 1984)
10. Evolution AB - $18.7 billion (Sweden, 2006)
11. ICON - $17.5 billion (Ireland, 1990)
12. Pandora - $11.8 billion (Denmark, 1982)
13. Delivery Hero - $11.5 billion (Germany, 2011)
14. AB Sagax - $10.0 billion (Sweden, 1995)

I’d heard of about five or six of them. The rest were new to me.

Nearly half the list is made up of companies from Sweden or Denmark. Ireland and Germany show up twice. Spain makes the cut once. Southern Europe is barely represented.

The stat that inspired this chart came from Mario Draghi’s report into European competitiveness. It’s a blunt one:

“There is no EU company with a market capitalisation over EUR 100 billion that has been set up from scratch in the last fifty years, while all six US companies with a valuation above EUR 1 trillion have been created in this period.”

That’s the sentence that Andrew McAfee picked up on. He and his team dug through the data to visualise what that gap really looks like, resulting in this chart and more, originally published here.

They used Draghi’s definition of a from-scratch company: not created through mergers, acquisitions, or corporate spin-offs. And they only included public companies, not private valuations. Which is why a company like Celonis, despite being valued at $13 billion, doesn’t appear.

The 14 companies listed above have a combined market cap of around $430 billion. On one hand, that’s a lot. On the other, it’s less than half the value of Tesla. It’s one-eighth of Apple.

The gap isn’t just in scale. It’s in surface area. The US has produced more large companies, in more industries, with more upside still ahead. Europe hasn’t.

So what’s going on?

You’ll hear plenty of theories. The US is a single market with one language and consistent regulation. Europe isn’t. There’s more risk capital in America. There’s more ambition. More tolerance of failure.

All probably true, to some extent.

But language isn’t the main problem. German, French, Spanish, Italian, and English are spoken by tens of millions of Europeans. Language doesn’t stop Netflix or TikTok. It shouldn’t stop new companies either.

The real problem is structural.

Draghi again:

“Innovative companies that want to scale up in Europe are hindered at every stage by inconsistent and restrictive regulations.”

Inconsistent is bad. Restrictive is worse. Europe isn’t just fragmented, it’s cautious. And while caution is fine in some areas, it kills startups.

The US has created not just giants, but ecosystems. Europe hasn’t.

Of course, this isn’t just a European problem. It’s a global one. The world needs more centres of innovation, not fewer.

If everything is built in California, the rest of us become dependent, both economically and strategically.

We need more successful companies being built all over the world. Not just in America.

The German election result barely counts as a win for the centre-right

Ben Guerin — Mon, 24 Feb 2025 00:00:00 GMT

There’s a lot going on in Germany’s election results. But the takeaway is simple: the government got punished, the far right surged, and nobody came out looking strong.

The CDU/CSU, led by Friedrich Merz, technically “won” with 28.5% of the vote. But that’s still one of their weakest results since the 1940s, and only a modest lift from 2021.

The AfD doubled their national vote share to 20.8% and came out on top in much of the former East, where they picked up an enormous 34% of the vote.

The SPD, led by Olaf Scholz, slumped to 16.4% - their lowest ever result, down more than nine points from the last election.

Digital tactics played a role. The AfD leaned heavily into TikTok and X. The SPD and CDU stuck with YouTube, Facebook and Instagram. That split isn’t just about platform choice. It reflects something deeper about risk appetite and audience instincts.

But the real story sits outside the tactics.

This was a referendum on failure. The electorate didn’t so much vote for something new as they voted out what they had. Scholz promised change in 2021. He didn’t deliver. And voters didn’t wait around.

That’s a warning to first-term governments everywhere. In Australia, New Zealand and the UK, leaders elected on a message of change should be paying close attention. Intentions aren’t enough. People want outcomes.

Turnout in Germany was the highest since reunification at an incredible 83%. People showed up. And while they didn’t agree on who should lead, they made it clear that what came before wasn’t working.

Now Merz faces a brutal coalition puzzle. A deal with the SPD or Greens might be the only path forward, but it won’t excite anyone and risks hollowing out the political centre even further.

The same forces that punished Scholz could be coming for Merz next. And if you’re in government and trailing in the polls, don’t assume you’ll get time to figure it out.

The electorate isn’t waiting.

Panic over "brand safety" is a waste of energy

Ben Guerin — Wed, 12 Feb 2025 00:00:00 GMT

Meta has announced a scaling back of its moderation policies: ditching third-party fact-checkers, reducing content demotions, and taking a more “hands-off” approach to political content. Cue the panic.

"Brand safety experts" are now clutching their pearls, warning that ad placements next to controversial content could destroy brand trust, spark boycotts, or worse.

But here’s the reality: consumers don’t judge brands based on where they show up. They judge based on what those brands say and do.

This isn’t a new dilemma: traditional media has had adjacency “issues” forever.

Billboards next to strip clubs. Luxury watch ads in papers running financial scandals. Your brand’s lovingly crafted TV spot sandwiched between war footage and political takedowns.

My personal favourite? A McDonalds billboard alongside a diabetes ad (below):

Somehow, none of that brought the advertising industry to a halt.

The difference now is that digital makes adjacency feel more granular, more visible, more “trackable.” But that doesn’t mean it’s more consequential.

Most people scrolling Instagram or Facebook aren’t drawing a line between the weird meme they just saw and the sponsored post beneath it. They understand how algorithms work. They know your ad didn’t personally endorse the post above it.

You don’t lose trust because your banner ad appeared next to a spicy meme. You lose trust because you treated your customers like idiots. Or ran a tone-deaf campaign. Or ducked a hard question. Or stood for nothing at all.

Everyone knows ads are served algorithmically.

The average person isn’t scrolling Instagram, pausing mid-story, and thinking, “Wait, my bank’s ad appeared under a conspiracy theory. Time to change accounts.” They’re moving on in half a second.

The real risk isn’t adjacency. It’s irrelevance.

Because while you’re busy reviewing screenshots and writing escalation memos, your competitors are saying something that actually matters. They’re picking a fight, offering a point of view, making people laugh, or just being useful. And no one cares what post they appeared under.

So by all means, keep an eye on placements. Avoid actual harm. But stop pretending that safety comes from silence or sanitisation. It comes from clarity, confidence, and having something worth saying.

In other words: if you’re worried about where your ads show up, you’ve already missed the point.

Too young to fix government, or too smart to ignore?

Ben Guerin — Wed, 05 Feb 2025 00:00:00 GMT

Some of the brightest minds of our generation are choosing to fix the US government.

So naturally, people are mad about it.

This week, WIRED ran a piece naming a group of young engineers working inside Elon Musk’s Department of Government Efficiency (DOGE). The angle? They’re too young. Too online. Too inexperienced.

What happened next? An incredibly patronising meme went viral.

“Who are these little boys? And why are they in charge of our money?”

So who are these guys?

Akash Bobba, 21, has already worked at hedge funds, Meta, and Palantir.
Edward Coristine, 19, has clearly already impressed his colleagues after interning at Neuralink.
Ethan Shaotran, 22, founded an AI startup backed by OpenAI.
At 22, Luke Farritor built an AI that deciphered a 2,000-year-old Roman scroll no one had been able to read.

Luke is the perfect example of what critics are missing. He didn’t just show up to work on a government project: he’s already made history.

Using machine learning, he helped unlock the words inside a scroll buried in volcanic ash for two millennia. Historians said it couldn’t be done. He proved otherwise.

When I first heard about that project three years ago, I was blown away. It’s one of the best examples I’ve seen of science, tech, and history coming together to do something nobody thought was possible. And it’s exactly the kind of thinking we should want in public service.

We need more people like Luke. Not fewer.

None of them has a background in federal bureaucracy. But they’re bringing something else: actual talent. And probably a few mattresses in the office too — they’re working round the clock.

It’s totally fair to scrutinise DOGE. Question the reforms, the process, the politics. But dismissing the team just because they weren’t in student government ten years ago is lazy thinking.

More importantly, young people have always been at the forefront of change:

Young people have always led revolutions

Mary Shelley wrote Frankenstein at 18.

Blaise Pascal invented the first mechanical calculator at 19.

Isaac Newton developed calculus & the laws of motion at 23.

Napoleon Bonaparte became a general at 24.

Pablo Picasso co-founded the Cubist movement before 25.

And given that we are talking about fixing the bureaucracy of the US Federal Government, let's take a look at the ages of America's own Founding Fathers on July 4, 1776:

James Monroe was 18
Aaron Burr, 20
Alexander Hamilton, 21
James Madison, 25
Thomas Jefferson, 33
John Adams, 40
Paul Revere, 41
Washington, the elder statesman, was all of 44

And on a personal note - I was just 21 when we founded Topham Guerin. If we’d listened to the people saying we were too young, we’d never have built what we have today.

I'm used to being the youngest person in the room, and having to deal with condescending comments from people that respect "experience" over ability. That's why people's attacks on the DOGE team for their age really winds me up.

The fact that some of the brightest minds of our generation are choosing to fix public service should be something to celebrate.

But instead of engaging with their ideas, critics are stuck on how old they are. Maybe that says more about them than it does about the DOGE team.

Meta's pivot to freedom of speech

Ben Guerin — Tue, 14 Jan 2025 00:00:00 GMT

Mark Zuckerberg sat down with Joe Rogan recently to talk through Meta’s latest changes to moderation, fact-checking and political content. It’s pretty clear the company is heading in a new direction.

I’d strongly recommend listening to the full podcast. Zuck’s more candid than usual, and the conversation covers a lot of ground: from content policy to jiu-jitsu to open-source AI.

But three things in particular stood out to me:

1. No more third-party fact-checkers

Meta is scrapping its US fact-checking program and shifting to a model inspired by X’s Community Notes. The idea is to add context rather than label posts as true or false — a more decentralised and (in theory) less biased approach.

In Zuck’s words: “Trust in centralised fact-checking has eroded. People want to see context from others, not be told what’s true or false by a single source.”

This is a significant shift, especially given how heavily Meta leaned into third-party fact-checking during COVID and the 2020 election cycle. He admits, in hindsight, the company may have deferred too much to media and political pressure.

2. A lighter touch on moderation

Meta is also scaling back how aggressively it moderates content — especially for topics like immigration or gender identity that sit within mainstream political discourse.

The goal is to focus enforcement on high-severity violations (like terrorism or fraud), while reducing overreach and false positives. Fewer takedowns, fewer demotions, fewer people wrongly locked out of their accounts.

Zuck: “Over-moderation has often silenced legitimate voices. Platforms like ours should support open dialogue, not stifle it.”

3. Political content is back in the feed

After years of downranking political content, Meta is changing course. Users who want to see political content will see more of it — and political posts won’t be penalised in the algorithm like they were in recent cycles.

This matters for campaigns. Facebook and Instagram have become less central to political strategy in recent years as more attention has shifted to TikTok, YouTube and X. That could now start to reverse.

Zuck: “We don’t want people to feel like their views are being suppressed — especially on issues that matter most to them.”

Why now?

It comes down to three things: Trust, relevance, competition.

Zuckerberg seems to be repositioning Meta as a platform for free speech, robust debate and fair representation. Musk’s changes at X clearly rattled the cage, and now Meta is playing catch-up.

The context is important too. Nick Clegg is out. Dana White is in. Pressure from the Biden administration during COVID and the backlash that followed seems to have left a lasting impression.

The podcast is worth a listen if only to see how publicly Zuck is now embracing ideas that would’ve been unthinkable for Meta five years ago.

Plenty of people will disagree with the direction of travel. But one thing’s clear: Meta wants to be back in the political game, and it’s betting big on backing free speech as the way to get there.

Five years on: How a parody ad became the defining moment of the 2019 election

Ben Guerin — Thu, 12 Dec 2024 00:00:00 GMT

Five years ago today, Boris Johnson secured an 80-seat majority in the 2019 UK general election -- and this ad by Topham Guerin earned a place in political advertising history.

Getting the Prime Minister to re-enact that iconic scene from Love Actually was a crazy idea - and one that successfully dominated the news agenda in the final days of the campaign.

What most people don’t know: it almost didn’t happen.

Despite coming up with the idea right at the beginning of the campaign, we shelved it after a Labour candidate released her own version.

It wasn’t until the 11th hour that we got the green light - at which point we only had 24 hours to deliver a fully packaged-up ad for the BBC to run on TV.

With the clock ticking, we raced to find a location, actors, props and rent the specialist camera equipment in record time.

Even though we were short for time, we didn't want to compromise on making the parody as accurate as possible, shot-for-shot.

I think we managed ok... Sean, my TG Co-Founder, made a surprisingly good fill-in for Andrew Lincoln when we were framing the shot!

The only available slot to squeeze in filming with Boris was late on an extremely cold December evening.

The PM came straight from a full-on NATO summit, and our wacky idea for a movie parody scene was the last thing standing between him and going home.

Incredibly, he managed to shoot it in just two takes.

The result?

A moment that went viral, dominated headlines, and cut through the noise.

And critically - every frame was relentlessly on message, delivering the campaign's closing arguments in a novel and attention-grabbing way.

Proof that the best ideas often come together when time is tight and stakes are high.

For me, this will always be a reminder of what’s possible under pressure when you have the right team around you.

After all, that's what we do at Topham Guerin.

The forecaster that correctly predicted Trump's win over Harris more than a year ago

Ben Guerin — Thu, 07 Nov 2024 00:00:00 GMT

Predicting election results is hard. Really hard.

Even the famous Nate Silver said it was a 50/50 toss-up between Trump & Harris - as illustrated in the below screenshot of his final pre-election update.

But some people called it correctly, and did so a long time ago...

Back in April 2023, Ben Warner modelled Trump's chances against Biden compared to Kamala Harris or AOC - and predicted that Trump would beat the VP by 311 electoral college votes to 227.

Dominic Cummings used Ben's analysis in this blog post, illustrated in the below screenshot:

Now, while the exact result will take a few more days to finalise, the NYT is forecasting that Trump will secure 312 electoral votes, with 226 going to Harris.

That's just one vote off from a prediction made 18 months ago when Biden was still President!

Dumb luck? It’s possible.

But this is the same data scientist who correctly predicted the narrow win for Vote Leave in 2016.

And who also predicted that the Conservatives would win 364 seats in the 2019 election (they won 365!).

Of course, there are lots of factors that went into the various models that successfully called these outcomes - e.g. one of the things he focused on for Trump vs Harris was the "nonresponse bias" problem with non-college whites, which pollsters have struggled with in the last 3x presidential races.

But the real issue is that there are a bunch of systematic problems with the market research tools available today:

\- People behave differently in focus groups compared to real life.

\- Voters lie about who they are going to vote for.

\- A poll is just a snapshot of a single moment of time, not a prediction of a future outcome.

\- The list goes on...

That's why Ben and his business partner Alex Cooper are building the next generation of tools to help understand and model human behaviour with his company Electric Twin.

Because when you can predict the future, you can shape it.

I've had the pleasure of working with Ben during the 2019 election and during the COVID-19 pandemic, and he's the smartest guy in this game.

I've also been lucky enough to have a peek behind the curtain at what they're building, and it's a game changer for anyone that is interested in understanding and influencing human behaviour.

Definitely worth keeping an eye on Electric Twin...

Labour ran a slick digital campaign – but no party dominated the air war

Ben Guerin — Wed, 17 Jul 2024 00:00:00 GMT

For a historic election that ended 14 years of Conservative government, the social media campaigns from the main parties were surprisingly tame.

While the new Labour Prime Minister and his team will be very happy with their 174-seat majority, the social media contest didn’t have a clear winner.

After all, Rishi Sunak may not have won the election, but he did win on Instagram with 4.2m engagements – more than any other leader. Nigel Farage and Reform dominated Facebook and Twitter (now X), with 2.1m engagements on Facebook and 39bn views on X.

However, Labour dominated on TikTok, racking up 7.5m engagements and 75m views, more than the other parties combined. They reached triple the views and six times the engagement that the Conservatives achieved on the platform. Labour’s digital success was helped by embracing memes and following popular trends. Their digital team clearly understand how TikTok works as a platform, so their content felt natural and authentic.

This is consistent with what we saw in New Zealand’s 2023 election, where the National Party – just like UK Labour – was able to crack the formula for TikTok success and deliver a change of government.

Labour ran a slick and sophisticated digital operation that did all the right things – but most voters would probably struggle to remember any particular stand-out social media content from them.

This didn’t really matter, though, because Labour spent far, far more on Facebook, Instagram and Google advertising than any other party, along with heavyweight ‘takeovers’ of the big online news sites including The Sun and the Daily Mail, that ensured their simple message of ‘Change’ cut through.

Labour didn’t just spend more overall, they spent money where it mattered – with a targeted approach they were the highest-spending party in 376 constituencies (according to Who Targets Me). The digital team also supported local candidates to get their message out online, and cleverly used lots of user-generated content with compelling video testimonials from people making the case for change.

While Ed Davey’s stunts made for great broadcast coverage, the Lib Dems weren’t able to translate that into engaging platform-native social media content. The hyper-local focus of the Liberal Democrat campaign machine probably works against them when it comes to trying to win the air war on social media, as potholes and planning reform don’t get the same kind of emotional response on the newsfeed as other topics.

In contrast, we all know Nigel Farage has never been camera-shy, and his willingness to front his own personal video content and fully ingratiate himself into meme culture on TikTok was key to Reform winning 14.3 per cent of the national vote share (more than the Liberal Democrats’ 12.1 per cent) – even if they only managed to win five seats compared to the Lib Dems’ 72.

While Labour is the biggest party on TikTok, Farage’s more than 800k followers on the platform make him by far the biggest personality, helping #ReformUK become the fastest growing political TikTok hashtag.

This election was always going to be an uphill battle for the Tories. Against a backdrop of a dissatisfied electorate that was ready for something different, the Conservatives’ digital strategy pushed the risks of a Labour government with visuals including a tube of superglue branded ‘Labour Supertax’.

Given the polls narrowed towards the end of the campaign this could indicate their messaging achieved a degree of cut-through, but it clearly wasn’t enough.

Column originally written for The House__.

Interview with The Drum on strategy, persona and trust in political marketing

Ben Guerin — Thu, 20 Jun 2024 00:00:00 GMT

I was invited onto The Drum's podcast Politics for Drummies with host Alastair Duncan against the backdrop of the ongoing UK election.

We had a great discussion covering a broad range of topics including:

Why a political campaign is the ultimate "marketing startup"
What brands can learn from politicians
The difference between strategy and tactics in a campaign
What makes governing more difficult than winning elections
My advice for the current UK party leaders

You can listen to the podcast at this link, with the full transcript below:

Alastair Duncan (AD): Welcome back to Politics For Drummies. I'd like to welcome to the podcast today, Ben Guerin, who is a Kiwi.

Ben Guerin (BG): G’day!

AD: A very proud one. And do you want to tell us a little bit about what you've been doing?

BG: Well, great question. What have we been doing? So I'm, you know, one of the co-founders of the creative advertising agency Topham Guerin. In a sign of pure creativity, we named it after ourselves.

AD: Yeah that's original.

BG: It's really original. No one's actually done that in the sector, so we got off to a flying start. The other thing that's been different is, neither my co-founder Sean or myself had ever worked at a creative agency or advertising agency before we started one. We think that gives us an advantage because we've got absolutely no idea what we’re supposed to be doing. So, you know, we just start things from scratch.

It's a really interesting time for us. We worked on a lot of political campaigns over the years. You know, we started in New Zealand, back in 2016 we started the company and worked on the campaign there the next year, 2017. We've done campaigns in Australia and the UK. And I just realised as I was on my way over here that I think you know in pretty much every country that we've worked in, when there's an election on: we've tended to be involved in some way. And that won’t always be the case! So it's just something to come to terms with.

But we're working, you know, alongside corporate clients now which will probably be of sort of interest to The Drum’s listeners to understand what are the lessons for brands out of the political space. We worked on the 2019 Get Brexit Done campaign with Boris Johnson. The Conservatives had a whopping majority on election night and we learned quite a lot about how you craft campaigns under pressure, how you change hearts and minds at a mass scale and in a limited amount of time. And that's really helped us build a portfolio of corporate clients around the world, but particularly here in the UK doing exactly that.

AD: Do you wanna talk about that a little bit? So you know, what got you into political campaigning in the first place?

BG: Well, look, in New Zealand, everybody knows everybody, and at uni, I was studying science. I thought I was gonna go down that route but ended up not finishing my degree. But I was sort of hacking together websites for money for my friends. They knew that I could use a computer.

I was also interested in politics and in New Zealand if you're interested in student politics, you'll end up, you know, having a pint with the Prime Minister within a couple of weeks. You know, it's a pretty small place. So you build great relationships. In fact, you know, a lot of my regular drinking buddies at Uni then went on to become MPs in New Zealand now on all sides of politics, which is great.

And so in New Zealand because I was someone that was interested in politics and knew how to use a laptop and could hold a camera, that was sufficient qualifications to take up a role working with former New Zealand Prime Minister John Key as a sort of digital person - which didn't really have a job description. It involved everything from coding a website through to writing tweets, writing some press releases, holding your camera up to do live streams and designing graphics and things using a bootleg copy of InDesign.

AD: Yeah, let’s keep the bootleg bit quiet! I think it's a classic journey of some digital campaigners: that they have multiple skills and just end up falling into things, and they get asked to do more and more and more.

BG: I think that's exactly right, and that's really the story of how TG started.

AD: You've been involved in quite a few political campaigns, I think, of interest. There are two I would like to talk about. One is the Boris Johnson campaign or several of them. But I think the Get Brexit Done campaign, which a lot of people are very familiar with. And he's ended up with a sort of tarnished person or with a tarnished reputation, but I think he did actually win a lot of campaigns for the Conservatives. What went wrong?

BG: Well, Boris is a brilliant campaigner. He always has been. 2019 was a campaign where, you know, we saw that. You know, he had a really clear message. He's a phenomenal communicator. He's charismatic. And what he had to say really resonated with a lot of people.

AD: Yeah.

BG: And, you know, we can talk in a moment about some of the things that we did during that campaign. I think it goes to show the difference between being a campaigner and being a reforming Prime Minister. I think changing the way that government works is really, really hard. It's almost easier to persuade millions of people to vote for you than it is to persuade a few thousand people to change the way that they work in the British state.

AD: Yeah.

BG: That's a really niggly question. You know, I think any incoming Prime Minister has to think about what got them there and then think about a completely different set of skills and strategies that are going to help them change the way that the government operates.

AD: Yeah. I think you're right because there was so much, I suppose that there was so much campaigning, the campaigning skills were very effective but the government skills were not so much. And I think that he found it - I don't want to talk about any of it that much, really, but - I think he found it quite difficult to get what he wanted to do done, because he didn't know how to pull or exercise the levers of power in a sort of concerted way. Maybe.

BG: I think that could well be the case. You know, I think you probably have to go back to Tony Blair to, you know, find a Prime Minister and his team that were really deeply thoughtful about how the government worked. I mean, people like Dominic Cummings have thought a lot about this. You know, he's had plenty of ideas about how to change the way the government works. Maybe if COVID had turned out differently and if he had stuck around we'd be in a very different situation now. I think the reality is it's not sexy or interesting to think about reforming state institutions.

AD: Yeah.

BG: That's ultimately what really matters. You know, we've got amazing work being done with GDS, the Government Digital Service, around gov.uk. It's an incredible platform and it could be doing so much more, you know, and so you mentioned that Boris was a great campaigner and in 2019 we all saw that. That carried through into 2020 and we got brought back in to help with some of the COVID advertising and you know there are also things that didn't go well in terms of COVID, but one thing that really did was the clear communications particularly at the beginning, with Stay Home, Protect the NHS, Save Lives, which is a slogan that we helped come up with right at the beginning days of COVID. You had a team with a lot of people that had been involved in 2019 who came back to help out, knowing how important it was to save lives during the pandemic.

AD: Could you just explore a bit the challenge of selling an idea to a politician and a client? What are the differences in that? What's the stakeholder group like, compared to, for example, a bank?

BG: Well, firstly, a bank probably has a few more layers of inward management before you get to your key decision maker, and in a bigger company, there are more people that can stop an idea, and that's, I think, a real struggle that any advertiser has dealt with in the past, right? When you've got to keep everyone happy so by the time the final idea gets to the CEO or the CMO - who might have actually been backing it and really wanting an innovative idea - it's often watered down. Politics is by nature flatter, so it is easier to get a bigger bolder idea to a key decision maker.

Politicians are also used to looking at a lot of market research, probably more than a lot of other business leaders, and as a result, they know how to read polling, and they also know what polling and focus groups can and can't do. I think in advertising, we're really addicted to testing ads and saying “well it performed well in groups, so we run it or it didn't so we won't”. In politics you know, because you're doing so many focus groups, that you understand the volatility of them, that you use focus groups as a tool to sort of check that you're covering all bases and then help expand your thinking rather than a simple AB test on an idea. So politicians are more acutely aware of that. Though, ideas will still be tested, the strategy that the idea is based on is probably being refined off all sorts of focus groups and quantitative research as well as polling, but the big thing is politics is about personalities and the best ideas are authentic because they resonate with that personality. I think there are very few politicians that could have run with “Get Brexit Done” as authentically and charismatically as Boris Johnson did.

AB: Where did that originate? The Get Brexit Done slogan? A lot of people say they were involved in the origination of it.

BG: Ironically, that came from a focus group, and this is a great use of focus groups, you know. So we worked with, obviously, a fantastic campaign team. Our job was on the digital and creative side, but Isaac Levido was the campaign manager and in charge of the research and inside of the campaign. And, when you're trying to understand the mood of the nation, you do a lot of listening, and people were using their own language to describe how they feel and people said in groups that we just need to “get it done”.

And that resonated with everybody. It was actually a really unifying message, which is ironic because people talk a lot about politics these days being divisive with micro-targeting etc - but Get Brexit Done worked because it appealed to people that wanted Brexit but it also appealed to people that may have voted to Remain in 2016 that were just fed up with everything that had happened since and said, look, I don't really mind what happens as long as it doesn't drag on for another decade and that unification is why they were able to get an 80-seat majority and win in places that the Conservatives would never have thought of winning in the past.

AB: And I think it's gone down in sort of campaigning history as an effective slogan.
Do you want to talk a bit about the work you've been doing in New Zealand as well? Because I think that's interesting, with Christopher Luxon.

BG: New Zealand's a really interesting case study, New Zealand's an island, we're a pretty small economy.

AB: We have that in common.

BG: Yeah. We do.

AB: Well a small economy now.

BG: I think New Zealand and the UK actually have a lot of things in common culturally. Obviously, very close cultural ties. Both big fans of rugby, both big fans of cricket. At any one time, one of us might be talking about it more than the other. For me, the best All Blacks game I ever saw was at Twickenham, not in New Zealand. See, there's great cultural ties between our countries.

AB: I am a Scotland rugby fan.

BG: Oh sorry to hear that! But when it comes to politics, New Zealand's a pretty small place, like I said, but they are a little bit more likely to take risks, and we had an opposition leader at the time, Christopher Luxon. He's got a fantastic business career. So he was a CEO of Air New Zealand before he came in, one of the biggest corporate jobs in New Zealand, getting people there. That's pretty big.

AB: And they've had quite a good history of doing quite attitudinal advertising.

BG: Great, interesting, novel, edgy advertising, which is important. You're basically telling everyone to shell out a couple of thousand quid to hop on a plane for 24 hours and go to a rock at the other end of the planet. Now there are nice things waiting for you on that rock, but it's a tough job to sell that. Right?

AB: Yeah.

BG: But before that, he was at Unilever in Canada and had a really serious corporate role there. So you had an incoming party leader, a party leader leading the opposition, who understood the value of marketing, and to his credit, he also understood what he didn't know. So when we worked with them, we said, hey, look you've got to be embracing vertical video formats, like Instagram Teels and TikTok, we think they're gonna be really important. New Zealand has a very small spend cap, So we said actually, you can't just rely on paid advertising. You've got to do marketing beyond advertising, and so one of the things that we did, it was a long term play. We had people working with their in-house team for a long time before the actual campaign itself to build the capability to get them in a position where they could be a real fighting force at the campaign. And one of the things that we did is, said you've got to use social media to show your true personality, your true side of yourself to the world.

And the thing with politics is a lot of people get to where they are in politics by micromanaging their appearance, making sure they're always in a crisp pressed suit, really nice ties, all the rest of it, and that's helpful to work your way up within the party machinery but when it comes to talking to voters you've got to speak their language and you've got to be authentic and relatable.

AB: That's really interesting actually.

BG: Well, we often see this with politicians that we work with where we actually have to get them to unlearn things that they've learned to become a good politician, in order to become a better communicator.

And in the case of Christopher Luxon, to his credit, he really jumped in and he went all in. So we showed him the importance of TikTok, dipped the waters in, took a bit of time, but eventually he was coming up with his own content ideas. He was really comfortable doing a selfie video in the back of the car explaining policy. And as the campaign came to its final final few months, we launched a TikTok game where we turned him into a filter. He's a bald guy, and he sort of made lots of jokes about how he didn’t have a head of hair. And you could use the National tax filter, and you'd see yourself as a Christopher Luxon look alike and then you could play the tax calculator game and try and catch coins with your wallet. And it was a TikTok trend that I think we had about 10% of the population equivalent in New Zealand that used it during the campaign. And it helped bring a relatively dry topic of tax cuts to life in the context of a political campaign.

AB: Well, I think it's putting our idea into the vernacular of the audience and I think that's one of the things that I know I think you do pretty well, actually.

BG: Yeah. Just don't take yourself too seriously is the crux of it.

AB: Well, there's that as well. I think that in many of the campaigns you've been involved in, you've been much more, put it attitudinal in the work rather than formal. Do you want to just talk about that a bit? Because you like to take a piss essentially.

BG: We like to have fun! It’s actually one of our values as an agency… Everyone's gonna have values. They’re very important and they've got to be unique. Right? So one of ours is “work is play”.

If you're in the creative industry, and if you're not enjoying what you're doing coming into work every day, then you're in the wrong role. And so we think you've got to have fun with what you do. And if you do that, you do better work for your clients. We think that's really important.

In a campaign, you know, you've got these 80-plus page manifestos. You've got all these, like, dry parliamentary debates. There's a lot of waffle in there. But actually, everyone's got feelings about politics. You know, your friends, your colleagues, your football team mates, they might not describe themselves as interested in politics, but they've got attitudes and perspectives and really interesting views on how they think the country should be run, and the best politicians can bridge that gap, right.

They can go from the machinery of government and parliamentary procedure and interesting policy positions to having conversations like this one where they really listen, they understand, and they empathise with people's concerns. And the best politicians are able to bridge that gap, and that's really important because you can't do the job of all the official stuff if you're not bringing the perspective of everyday people into it.

AB: We've got an election going on right now, obviously, in the UK. Do you think that we have personalities in leadership positions there that can do that effectively, bridge the gap between making it something that's sort of understandable versus the sort of technocratic perspective?

BG: That's a really interesting question. I think you've actually got two quite similar leaders at the moment in the UK in the sense that, you know, both Rishi and Keir, are immensely capable and intelligent politicians. You know, if they were accountants, I would trust either of them to do my taxes. They both really have a command on the details, and people that work with either of them would sort of say that about them. We've come a long way from sort of the era of charismatic politicians like Boris Johnson and Jeremy Corbyn, I think that's just the state that the world is in at the moment.

We’ll all take a bit of time to recover from the tumultuous years of the past decade. And so I think parties have put these leaders in charge that they think are gonna be better at managing the economy and fixing the country and taking us where they want to be. That does mean that, you know, no one would say that the current two party leaders are the most charismatic leaders of their parties. And I think it means, you're having a different type of campaign than we have in the past.

AB: Mhmm. Maybe there's more room for policies to be examined more closely, perhaps.

BG: Yeah. This is a really interesting question. You'd love to think so, none of this is a real battle of ideas because you know that’s what we needed. This is where the UK and New Zealand are in exactly the same space. You know, we've had productivity, basically nose diving or plateauing for the last 10 years or so. Houses are going up like crazy, young people are going into the workforce, not thinking they'll ever buy a house. The whole social contract of buying a house, watching it appreciate, and that covers your retirement plus you get a pension, is probably not gonna exist for my generation.

We desperately need politicians that are gonna make transformational changes to the economy, to fight climate change, to achieve better productivity for everyone. Unfortunately I don't think those ideas are gonna cascade their way down into the political argument that we're having. If you look at some of the content on either side of the political debate at the moment, they're saying basically the same things. It’s “we're gonna keep the economy under control, we're gonna fund the NHS”. I'm yet to see a politician that says we're gonna cut NHS funding! That's not a particularly different set of policies.

So we don't actually really have a battle of ideas. We have a battle of vibes. And these vibes are basically the energy that the politicians give out about themselves. It's the signals that they give to different audiences and their content to sort of show that they're aligned with different parts of the population that they share different values, but they're not really contesting any meaningful policy change.

AB: I think that this is a sort of disassociation from the actual doing of government with these guys who maybe, that they try to present themselves as boring and will get stuff done. But they have to persuade people that they can do that. I think they're definitely both outside of their comfort zone, charming a large audience on television. I mean, Sunak comes across as a sort of children's TV presenter, and I don't think people know quite how to place him. He's not of the mould of an old Etonian, a smooth operator like Cameron, or with a boisterous laugh like Boris Johnson, or just slightly, off the wall. I'm talking a lot about conservative Prime Ministers. So we have quite a few, but he does have a job to win the 5th election in a row for the same party, which has never been done before in the UK. I expect to see an unprecedented use of the word, unprecedented in terms of the things people have to do.

And Keir, I think, is like a lot of politicians, I'm sure you've met many of them. When you talk to them face to face, they're like this, they're really compelling, interesting, knowledgeable people. But sometimes when they're put in a position, like, on television, they're a bit stiff. They don't necessarily have the same energy. And I think Keir's doing work to try to present a better personality on television. But It would be interesting, how would you advise - I don't know whether you're involved and you don’t have to admit something like that. How would you advise either of them to win?

BG: Well, it's quite funny, actually, because a couple of years ago, I attended a Labour conference event on a panel, with exactly that question. It was ‘how can Labour win?’ And I'll tell you now what I sort of said then, which was you've got to have a clear strategy. And we talk a lot, on podcasts like this one and op-eds all over the place and Twitter's alight with it - tactics. AI this, micro-targeting that, Facebook spend this, and look tactics are great. But Michael Bloomberg spent a billion dollars and didn't become President. So clearly, it's not just about tactics. It's about strategy.

And in 2019, there was a really clear strategy. With Boris Johnson, he was going to get Brexit done. Whether you wanted Brexit or you just wanted it to be over, he was gonna get it done. And then he was gonna unleash Britain's potential and we're all ready for that whatever that meant. We just wanted Brexit to be done and to move into the sunny uplands and that was a strategy that had been crafted based on listening to what people were calling out for and it really resonated.

Because we had a really set strategy, we were able to experiment with tactics. And we sort of showed that there's nothing in politics that can't be done. We put Comic Sans on a billboard on the final days of the campaign. We had Boris Johnson re-enact that iconic scene from Love Actually, but saying Get Brexit Done instead of “I love you”. We did all these things that sounded quite wacky as tactics, but were all just different ways of executing a very simple clear strategy and that was where ultimately the Conservatives got their victory from and everything comes down to that.

Don't get distracted by the shiny tactics, despite whatever everybody's saying, “you've got to do this, you've got to do that”. We used TikTok in New Zealand, it was really powerful, but this election won't be won or lost on TikTok or any single platform. If the strategy of the winning party resonates with people, then that strategy will also cascade down to their campaign team and allow them to do a better job as well of the different tactics.

AB: I think the tactics do end up being talked about a lot more. I mean, in political advertising in this country, it's not regulated. You say you can sort of say anything. Do you think that, for example, truth matters in that context or do you shape truth?

BG: I think ultimately because of social media if you're calling something out that is obviously complete nonsense you're going to get caught out for it. I mean all the platforms now have something like Twitter's community notes and YouTube have a similar thing, I mean it's a really positive step for online discussion. If a politician says something that is just completely incorrect, people are gonna balance out that debate pretty easily. That's why social media is so powerful. People often fixate on claims that maybe a fact checker said actually those claims aren't 100% accurate but the reality is politics is really complicated. If you want to show all the information, you just slap a manifesto in front of somebody and say, right, compare notes in here. We have to simplify in order to connect with people. And that simplification of a policy message inevitably means someone might say, oh, but that doesn't apply to my constituency or that doesn't account for this accounting treatment of how these things are funded. You've got to lose a bit of that nuance in order to simplify your message to connect with people. Sometimes people will say, well, that's a falsehood because you're not sharing all the information, but that's just the reality of communication.

AB: Yeah. I think there’s the freedom of social media combined with the tyranny of feeding the feed, mistakes happen because there's so much done. But I think it's interesting how there are more controls in place now. I suppose there's some concern about the Wild West nature of things with AI and fake news and all of that, but I think we can very quickly see whether something is real or not. That's the risk of doing things that are tricky.

BG: Exactly. I mean, look, if you've got a fact that- people are talking a lot about the rise of “deep fakes” and everything. Well, first of all, AI technology doesn't really change what's possible. We've been able to do deep fakes for years. Nowadays, people may just make them faster and more at once, right? But, ultimately, in all the time that people have had Photoshop, we haven't had really big scandals with deep fakes. And the reason for that is pretty simple. We can verify. If the politician says, “Hey that's fak,. that's not me”. If the channels decide to take something down that they think is harmful, then that risk is isolated.

A lot of the discussion around misinformation, I think, is actually worse than the threat of misinformation itself because when we say there's so much misinformation online, you can't trust what you read, well, that's undermining trust in everybody and that means that people are more cynical, they're more likely to go for alternative media, maybe media that's influenced by state actors rather than, actually a really constructive debate.

AB: There's a worry because, the more we talk about misinformation I mean, politics is becoming a below interest category, if we go back to the sort of marketing strategy, Politicians are least trusted more than they ever have been, if you like. We're only a little bit above advertising people that are slightly more interested in politicians than journalists, maybe. Do you think the comparison between political advertising and commercial advertising, how do you think that sort of one can learn from the other?

BG: Look a huge amount, and that's really the crux of the business that I run. It's like, politics is really an incubation area for marketing in general. You’ve had plenty of experts on in the past. I think Sam Jeffers of Who Targets Me was on a while back, and he talked about, you know, how politics often has much bigger budgets than many marketing clients might have, but much less experienced marketers. And that really resonated with me, actually, because I don't come from a traditional marketing background.

And, we've done a lot of work in the political space, and it's because a political campaign is basically a marketing startup. You've got millions of people to convince, fixed budget, you've got a few things that you control, like, you know when the debate's going to be, you know when you're going to announce your manifesto. So you know the equivalent of your product launch, right? You've got a really basic outline of a go-to- market strategy. But you've got to fill in the blanks yourself.

You need to understand audience insight. You need a really strong brand platform. You need lots of different tactical activations off the back of that. And then you've got to be understanding what works with different segments of the population to reallocate budget and shift tactics, and in a political campaign, you have so much more data than you might often have in another marketing campaign. But ironically, you don't have the data that matters because there's only one time when people buy your product, they all buy it once and you don't even find out who's bought what until the election results are announced.

So you rely really heavily on proxies, you're relying on data points like engagement rate on social media, which content and messages are resonating with people, you rely on polling obviously, you rely on focus groups, so political marketing is a great incubation ground for marketers to be comfortable with uncertainty and to do things at pace and really innovate and that's really helpful for a lot of brands that also increasingly need to be comfortable with uncertainty.

We're not a performance marketing agency. You know, you have brands that have amazing performance marketing agencies that can work out down to a fraction of a penny where to spend your marketing budget on different channels. But then when they need to start educating a key group of stakeholders or a property developer wants to engage more closely with the local community, everything that they've learned about traditional marketing has to be thrown out the window.

Because you've got to be comfortable with uncertainty in much the same way as you do in a political campaign and that's particularly relevant these days because so much of how people choose products and the brands that they associate with is driven by values not product benefits.

AB: Yeah. There's a big debate about the marketing funnel, as it’s traditionally known, how that's been broken. And the way that people come to that decision making just appears so random. I think it's a time for really good research, actually, now to be more intelligent about how people make decisions and so on. What is effectiveness for you?

BG: Well, I mean, that depends on the client's objective. What is the effectiveness? I mean, it's getting the result that the client asked us for. You know, creative agencies can easily fall into the trap of thinking that the KPI is getting the work signed off.

You know, we've sometimes been guilty of this in the past. “How did the meeting go? Oh, the client loved it. No feedback.” Everyone's high fiving themselves and patting themselves in the back and so we say, okay, right. So that means that the ads are going through to delivery. We shouldn't be patting ourselves on the back until we get the sales data back or we get the research data back on how opinions are shifted because ultimately we are problem solvers for our clients. So effectiveness for us is: have we solved the client's problem, that was the reason they came to us and paid us money to address that problem. That's all it comes down to, and we've got to be comfortable with all sorts of proxy and actual data to help us understand that.

AB: Mhmm. Politicians you admire?

BG: Do you know what? I'm gonna give you a really wishy-washy answer, then I am going to give you a serious answer.

The wishy-washy answer, which I don't think gets talked about enough, is anybody who has the chutzpah to stand up and put their name on a ballot paper should be commended, and we need more people doing that in this country.

There'll be what, 650 seats, dozens of candidates, essentially. That's a few thousand people who’ll be running in their local elections this year. Every one of them with a local campaign, teams, volunteers, the thousands of counsellors around them, every single one of those people on all sides of politics should be commended and I don't think we do a good enough job of that. When we complain about politicians not being trustworthy and all the rest of it, you get the politicians that you ask for. And if we keep having such a negative view of politicians then why would anyone go into that career? So number one we should just recognize and value the democratic process.

AB: That's really well said actually.

BG: So I have tremendous respect for everyone that's out there campaigning and I'm, you know, for the election here and everywhere else where we work. I also think it's really interesting that people who follow politics are often really tribal. The closer you get to the heart of politics, the more you, I think, appreciate the human factors on both sides. And it probably surprises people to know that the politicians they admire are really good friends of the politicians that are on the other side of the aisle, and that they have drinks together. So I think that's probably a helpful thing for people to remember.

I've been really surprised over here by just how much more tribal the UK is compared to New Zealand. I feel like it's a bigger economy. You can have bigger bubbles where you don't really interact with as many people who share different views. I think a lot of people in this country use the word Tory like an insult. And to be honest, that's understandable, given what we've had over the last few years! And I can see that, but that's not healthy. You know, we've got to find better ways of bridging that divide. So there's my wishy-washy answer, but I think it's important to talk about that stuff.

As a serious answer, I started my political career working for a guy called John Key ,the New Zealand Prime Minister. Really, really impressive guy. When he was a young kid, growing up with a single mom in Christchurch, South Island of New Zealand, he was bullied at school and he told his mum, he said look I'm gonna be a millionaire I'm gonna be Prime Minister as I'm sure many little kids do and his mum said alright John, good on you, I believe you. But she did actually believe him.

He left school, got great grades, went off to uni and ended up with a very, very successful career in finance. He would have ended up being, before 2008, one of the CEOs of one of the investment banks if he had carried on the trajectory that he was on. But he left that job, came back to New Zealand, ran in a local selection, a local seat, took the local party on board, listened to their concerns, became their MP, and shortly afterwards became Prime Minister of New Zealand, a few months before the 2008 financial crisis.

And I always really respected the grit and the hustle that he had to sort of build a fantastic commercial career but then a really impressive political career where he still steered New Zealand through the financial crisis. He built a great approach called social investment of actually investing in government programs based on effectiveness and how they work rather than just throwing more money at things that sound nice and feel good.

AB: I think that's really necessary in this country actually because there's a lack of investment in social welfare.

BG: Or lack of the right investment. NHS money goes up every year, but no one feels like it's necessarily getting better. And you need a more data driven approach to investment. So I admired him as a politician when I was in uni. I then had the pleasure to work with him, and I think that we need more politicians like that who are willing to bring something from the outside world into politics.

AB: Yep. But he gave you a break, didn't he?

BG: He gave me a break, he looked at this kid - I had purple hair at the time. I'd left uni before I finished my degree, and he said, oh, you can work for me. That's pretty good.

AB: Yeah. Well, listen, Ben. It's been fantastic having you on the podcast. Thank you very much. And, good luck in your future endeavours. And, thank you..

BG: Thank you very much, I really enjoyed it.

AB: Thanks for listening to Politics for Drummies. We look forward to the next episode. We're going to be talking more about the campaign going on in the UK at this moment in time, but it's been a pleasure to speak to Ben Guerin.

The false promise of AI-powered search results

Ben Guerin — Fri, 31 May 2024 00:00:00 GMT

So it turns out that Google, the supremely powerful all-knowing search company with the admirable mission to organise the world's information, has some rather surprising advice to offer.

For example, did you know that running with scissors is good exercise?

Or that taking a bath with a toaster is a fun way to unwind?

Meanwhile many of you may be concerned about not eating the recommended intake of at least one small rock per day.

So what have these results got in common, aside from each being nonsense, and quite dangerous advice?

All of these snippets are powered by Google's new feature - the "AI Overview", which is an admirable attempt at solving a real problem.

After all, it takes time and effort when you have to click through multiple pages to scroll through information relating to your query, consider the context of the various search results, and make your own conclusion about the answer to your search result.

Wouldn't it be easier if when you searched something into Google, it just told you the answer right away, and you didn't even have to click anything?

AI Overview in itself isn't a completely new step for Google - they have been experimenting with a similar product "featured snippets" for years where an excerpt from a web page relevant to your search is highlighted at the top of your search results.

The wider problem with featured snippets

Of course, there are often many cases when there isn't a single true answer to a query.

Featured snippets make sense for simple questions that have clear answers like "What is the capital city of Canada?" - Google knows the only valid answer is Ottawa, so it’s obviously worth pulling that city name out nice and large at the top of the page.

But what about questions with more complex answers that can’t be easily summarised in a word or a short sentence? An attempt to oversimplify here can mask the contested answers to debated topics.

Other drawbacks of featured snippets have been extensively documented. Take when the question has a false premise - e.g. “who is the King of America”.

Rather than refuting the premise and saying “there is no King of America”, the featured snippet from this notable example in 2014 falsely claims that the answer is Barack Obama.

But setting aside the philosophical questions about the extent to which featured snippets should be applied, there are some very real concerns about how AI makes this problem worse even when there should be simple answers.

Where AI gets it wrong

What the AI Overview enables (and where it all starts to get messy) is a manufactured snippet based on a broad corpus of training data, where Google uses large language models (LLMs) to generate a new answer to your specific query rather than extracting a snippet from an existing webpage.

Interestingly enough, it seems like questions with a false premise are often what confuses an LLM - similar to the long-standing challenges with featured snippets.

Google’s AI agents are so eager to answer our questions that they aren’t able to tell us that actually running with scissors is dangerous, you shouldn’t take a bath with a toaster, and there is no recommended daily intake of rocks because you shouldn’t be eating them at all.

Part of the problem is also where the training data comes from. When a snippet is taken from a search result then it’s relatively easy to find out the context from where the snippet originated.

But an AI overview generated by an LLM might be fusing together snippets from multiple articles, and also giving weight to nonsensical or comical results without understanding the humorous context.

Take the example about eating at least one small rock per day, which seems to have been generated from this satirical Onion article.

It’s safe to assume that the widely-reported content licensing deal between Reddit and Google is also largely responsible for polluting a lot of the AI Overview’s training data with user comments from Reddit.

For example, another viral AI Overview result was the suggestion to add glue to pizza to stop the cheese falling off. This result seems to have been generated from a comment by Reddit user “/fucksmith” 11 years ago. Thank you fucksmith for your wisdom!

From AI nonsense to training data inbreeding

So AI results that have been trained on nonsense source material end up generating nonsense results. But that’s not the only problem with training data.

See, as more and more AI-generated content is set loose into the world, it creates a recursive loop where AI models are being trained on more and more AI content. This reduces the weighting of human content in AI models, and over time it will make AI content more and more, well, obviously generated by AI..

Ed Zitron puts it best in his recent piece Are We Watching The Internet Die? as per the below extract:

The Habsburgs, European monarchs who ruled Austria, Germany and eventually the Holy Roman Empire, were notoriously protective about their bloodline, which after two centuries resulted in all sorts of genetic abnormalities.

Is the future of search going to be defined by inbred abnormalities and fake results?

The only solution (for now) is to turn back time

Hopefully Google will learn from these mistakes and take steps to exclude satire and poor quality data from their training results, and also improve the ability of AI Overview and featured snippets to recognise false premises.

But this could take months or years. What can we do in the meantime if you don’t want health tips that could kill you in your search results?

The answer is &udm=14 which is a URL parameter you can add to your search queries to remove the AI results, as well as other recent features like knowledge panels and even ads.

It’s essentially a time machine to an early 2000s version of Google, accessible by using a magical code. After all, how else would you get a time machine to work?

Even better, udm14.com is a search engine that automatically appends this URL parameter to your searches, to make it even easier to get unadulterated results.

So that’s some positive news, but a tool like &udm14 really shouldn’t need to exist.

Let’s hope that Google learn from this and put quality control ahead of chasing the hype train with their next release.

What's going on with New Zealand's trade deficit?

Ben Guerin — Mon, 18 Sep 2023 00:00:00 GMT

"As a country, we won’t get rich selling things to ourselves"

These words from former NZ Prime Minister John Key have stuck with me since he said them in 2015 to mark the conclusion of negotiations of the Trans Pacific Partnership (TPP).

International trade was a top priority for the Key government, and his successor Prime Minister Bill English who contested the 2017 election campaigned on how important trade is to boosting livelihoods.

During this election the National Party was one of Topham Guerin's first clients, and as you can see we had some fun working on the creative content.

While National and Bill English lost that election, six years later voters in NZ look likely to elect another National-led government.

So after six years of a Labour-led government, how is NZ tracking these days when it comes to trade?

Let's take a look at the most recent international trade data from Statistics NZ. For the June 2023 quarter:

NZ had a trade deficit of $1.3b for the quarter - meaning we imported more than we exported.
Just three countries (China, Australia and the US) accounted for more than 51% of all NZ exports.
Telecommunications, computer, and information services were just 1.9% of exports, while "other business services" were 2.9%.
NZ's world-beating wine industry had the same share of exports (1.7%) as intellectual property charges.
Meat, dairy, wood, fish and fruit accounted for half of all NZ exports (with milk powder, butter, and cheese 21.9% of the total).

Now for sure, NZ has some of the world's best agricultural and primary products - but our economy is still hugely reliant on these sectors and a small number of export markets.

So when there there are downwards market conditions like the current drop in global dairy prices, we're on shaky ground.

NZ's economy is more volatile than it needs to be, and an overconcentration of a handful of export markets and an insufficiently diversified economy are partly to blame.

The best way to increase living standards in NZ is to bring more money into the country - and that's got to happen by diversifying our export sectors and export markets.

The Metaverse is over before it began

Ben Guerin — Tue, 25 Apr 2023 00:00:00 GMT

In October 2021 Mark Zuckerberg announced a bold new vision for the next wave of technology that would transform how people work, play and interact: the Metaverse.

He painted a novel picture of the Metaverse would look like in this open letter:

In this future, you will be able to teleport instantly as a hologram to be at the office without a commute, at a concert with friends, or in your parents’ living room to catch up. This will open up more opportunity no matter where you live. You’ll be able to spend more time on what matters to you, cut down time in traffic, and reduce your carbon footprint.

Think about how many physical things you have today that could just be holograms in the future. Your TV, your perfect work setup with multiple monitors, your board games and more — instead of physical things assembled in factories, they’ll be holograms designed by creators around the world.

You’ll move across these experiences on different devices — augmented reality glasses to stay present in the physical world, virtual reality to be fully immersed, and phones and computers to jump in from existing platforms. This isn’t about spending more time on screens; it’s about making the time we already spend better.

This vision is either exciting or dystopian, depending on your perspective.

But there’s no denying that it was an extremely ambitious move to commit to completely repositioning one of the world’s largest and most profitable businesses ever. It’s no small feat to lead the charge towards a brave new world where VR headsets are commonplace and real-world interactions are replaced with virtual ones.

A cynical take is that the Metaverse hype was a PR tactic to distract from the growing threat of TikTok especially among teens, the massive threat of Apple’s privacy changes on Facebook’s advertising business (the company now knows much less about its users on iPhones), and a steady drip of leaked documents that shone a light on the chaos and disarray inside the company - among countless other headwinds.

Of course, it was also a decision made to cash in on what Zuckerberg saw as a key driver of his company’s future revenue:

Our hope is that within the next decade, the Metaverse will reach a billion people, host hundreds of billions of dollars of digital commerce, and support jobs for millions of creators and developers.

And to his credit, he put his money where his mouth was, committing to spending at least US$10 billion towards building the Metaverse in 2021, and expecting to increase investments in the years to follow. Not to mention renaming his company accordingly.

But if you’ve followed Facebook Meta in the news in the last few months, you’ll have seen a rather different story playing out.

Search interest for Metaverse-related search terms have been consistently dropping since January 2022. As the chart below shows, this decline started just a few months after Mark Zuckerberg renamed Facebook to “Meta” and announced his vision for the Metaverse.

Search interest trends are far from perfect as a metric, but they are a great proxy to show how interest in particular topics has changed over time.

This chart is the opposite of what you would expect if the Metaverse really was the “Next Big Thing”, as the world was led to believe.

And if you look at Meta’s own numbers, they tell a similar story.

In 2022, Meta’s “Reality Labs” unit made a loss of more than $13.7 billion - in part because people just aren’t excited enough about the Metaverse to actually use it..

The jewel in the crown of Zuckerberg’s Metaverse empire is the Horizon Worlds platform, a free online game/community/experience accessed via the Meta Quest VR headset.

Meta set an initial target of 500,000 users by the end of 2022 for Horizon Worlds, but had less than 200,000 actual users by then, according to the WSJ. And of those, most visitors generally don’t return to the app after the first month, with the user base steadily declining for most of the year. As reported in the same article:

Horizon is designed to be a sprawling collection of interactive virtual spaces, or worlds, in which users appearing as avatars can shop, party and work. Yet there are rarely any girls in the Hot Girl Summer Rooftop Pool Party, and in Murder Village there is often no one to kill. Even the company’s showcase worlds, such as Questy’s, a virtual arcade featured in a Super Bowl commercial earlier this year, are mostly barren of users.

According to internal statistics, only 9% of worlds built by creators are ever visited by at least 50 people. Most are never visited at all.

Given that 2.87 billion people use at least one of Meta’s products daily, and they are one of the world’s most profitable and influential companies, you would think they would be able to nudge 0.01742% of their users onto their newest platform. Sadly no.

There are a myriad of potential reasons for this. The Meta Quest headset costs US$400. Graphics in Horizon Worlds are so basic and uninspiring to be meme-worthy. There have been widespread reports of harassment and “virtual groping”.

But more fundamentally, the Metaverse sounds like a solution to a problem that doesn’t quite exist. Given the hassle of buying and wearing a headset, you would expect the virtual experience it enables to make it worthwhile. In the early days, Facebook had games like Farmville to help keep users coming back - not to mention all the updates from friends. But the killer app for widespread adoption of Horizon Worlds doesn’t seem to have been invented yet.

In September 2022, less than a year after the rebranding of Facebook to Meta, the company announced that Horizon Worlds was going into a “quality lockdown” for the rest of the year and that performance issues would be improved before it was opened up to more users.

Meanwhile, as Meta has struggled to build a world that nobody asked for, there’s a new kid in town: generative AI, with the most well-known example being ChatGPT.

Already, search interest in ChatGPT has dwarfed the buzz around the Metaverse - and at a truly massive scale.

And after spending more than US$1 billion per month on the Metaverse, Mark Zuckerberg now has a new focus.

Publicly, he has quietly stopped talking about the Metaverse in the way he used to. While laying off tens of thousands of employees, he has committed to making AI the new focus for Meta:

Our single largest investment is in advancing AI and building it into every one of our products.

And privately, he and other senior executives including chief product officer Chris Cox and chief technology officer Andrew Bosworth are spending most of their time working on AI.

So far Meta has introduced a response to ChatGPT with their own large language model LLaMA and made it available to researchers. And we can expect more AI-enabled features to be made available to Facebook, Instagram and WhatsApp very soon.

In 2021, Meta repositioned based on what the company’s leaders thought was going to be the “next big thing” - immersive VR experiences. After backing the wrong horse, the company is now going through a similar exercise with AI. Only time will tell if this bet will be more successful.

What makes ChatGPT so helpful to Russian propagandists?

Ben Guerin — Thu, 23 Mar 2023 00:00:00 GMT

This thread from journalist Melissa Chan got me thinking about how generative AI tools like OpenAI's ChatGPT or Google's Bard can unwittingly be used to prop up the arguments of anti-democratic states like Russia and China.

The classic Russian disinformation playbook is to undermine confidence in proven facts to create a hazy cloud of confusion.

After all, it's in Russia's interests for us to understand the war in Ukraine as a "complex" situation that needs to be understood from "different perspectives" rather than recognising it for the war of aggression that it is.

This kind of response from ChatGPT is a classic example of "bothsidesism", where an issue is presented as more balanced than it actually is.

It states some undisputed facts but uses a passive voice - “the conflict has resulted in thousands of deaths” rather than attributing these consequences to Russia’s actions. And where Russia is criticised it is done by using language like “the Ukrainian government and Western countries have accused Russia…” or “the international community has condemned Russia’s actions” where criticism of Russia is seen as just one point of view.

Russia invaded Ukraine and has committed atrocious war crimes - including the abduction of children - which has led to the ICC issuing an arrest warrant for Vladimir Putin. But you wouldn't know that just from asking ChatGPT.

Why is that? We can't be sure, because despite the name, OpenAI isn't actually open at all. They have refused to disclose information about the training data used for the latest release of GPT-4, so we don’t know whether GPT-4’s training data included propaganda from Russia Today, Putin’s speeches or pro-Russia apologists in the Western media. Nor do we have any transparency on the rules that have been created to shape ChatGPT’s responses.

Generative AI is an incredibly powerful tool that has the potential to transform so many aspects of our lives - especially reporting and online media.

But before we hand over the keys to the newsroom to the robots, let's be clear about what's at stake.

Democracy relies on calling out authoritarianism and holding criminals to account. Let's not let AI whitewash Putin's crimes - and the same applies for the situation in Xinjiang and Tibet (as also included in Melissa Chan’s original twitter thread).

My top books of 2022

Ben Guerin — Wed, 04 Jan 2023 00:00:00 GMT

Each year I try to read at least 100 books, for no other real reason than because reading is broadly a useful and interesting thing to do and 100 is a nice round number.

I figured that if I consistently reached 100 books per year, then over a decade I would be able to read 1,000 books. And since 1,000 is an even nicer round number than 100, that seems like a rather worthwhile goal.

In practice, my reading target normally means I start by taking on ambitious biographies and long-form histories in January, but by October I'm powering through novellas, poetry compilations and anything else under about 250 pages.

One good thing about trying to read lots of books each year is that I get to read widely on all sorts of interesting topics.

I tend to read a lot more non-fiction than fiction, but have tried to start to balance that out recently.

It won't surprise anyone who knows me that I keep a database of everything I read, so drumroll please..

📊 Some key stats for the books I read in 2022:

81 non-fiction
19 fiction
Average publish date was 2010
Oldest book read was published in 1855 (Song of Myself by Walt Witman)
9 books were published in 2022

Top 5 books of 2022

Some of my favourite books I've read this past year include the following:

Quantum Economics: The New Science of Money (David Orrell)

David Orrell is a Canadian mathematician who has some strong opinions about the state of the Economics profession (reader, he's not a fan. See his other book Economyths: 11 Ways Economics Gets It Wrong). A decade after the financial crisis Orrell rightly argues that economics has lost its way and needs some fresh thinking.

Orrell's thesis is twofold, firstly, that Economics sees itself as the science of scarcity when it should be about the science of money, a concept which has been overlooked in much of mainstream economic theory. And secondly, when you take a closer look at money, it is a phenomenon that has a quantum nature of its own, and can be best understood by applying principles from quantum mechanics.

For example - the concept of wave-particle duality in quantum mechanics posits that a quantum entity may behave like a wave until it is observed, at which point it behaves like a particle. It's a complicated idea to get your head around, until you consider that there are some real-life scenarios that are very similar. Take real estate: a property doesn't have a price until it is sold, and yet may change in value dramatically between sales. When the value of a property is not being observed it may behave like a wave function, responding to factors acting upon it, but when it is "observed" (i.e. a transaction is made to put an offer on the property to purchase it) it then behaves like a particle, with a defined value.

What I loved about this book was the way Orrell draws parallels between quantum theory and how we think about money, which is a fascinating way both to learn more about quantum theory and think critically about economic dogma. Would highly recommend this book or any of David Orrell's other works.

Entangled Life (Merlin Sheldrake)

Merlin Sheldrake (who sounds like he should be the substitute Herbology teacher at Hogwarts) has written this hugely entertaining exposé about everything you didn't realise you needed to know about fungi.

Sheldrake takes the reader on a deeply-researched and somewhat poetic tour of lichen, yeast, psychedelics, and much much more. He charmingly describes mycelium, the network of threads that make up a fungi, as "ecological connective tissue, the living seam by which much of the world is stitched into relation."

By the end of this book there is a profound appreciation of just how much we don't know about one of the fundamental types of life on our planet, and it contributes to a real sense of wide-eyed wonder.

If, like me, you thought fungi had something to do with how things rotted down but were ignorant of the vital importance they can play to therapy, medicine and emerging technology, then you will very much enjoy this book.

The Dawn of Everything (David Graeber & David Wengrow)

David Graeber was an American anthropologist and anarchist who sadly passed away in 2020, widely known for his 2018 book Bullshit Jobs that looked at the existence of meaningless jobs and the harms they inflict on society. David Wengrow is a British archaeologist and Professor of Comparative Archaeology at UCL, and together David and David have written one of the meatiest (stacking up at over 700 pages) and most provocative challenges to established historical paradigms.

The book starts by arguing that the popular view on how Western civilization developed, as set out by authors like Jared Diamond, Steven Pinker, Francis Fukuyama and Yuval Noah Harari, is out of step with anthropological and archeological evidence. They then proceed to provide this evidence in spades, and show a host of various political models, including societies that switched between authoritarian and communal systems with the changing of the seasons.

The authors paint a picture of a much more diverse and nuanced picture of "development" than what is conventionally understood, and point to how our world has lost three social freedoms that were once common: the freedom to escape one's surroundings and move away, the freedom to disobey arbitrary authority, and the freedom to reimagine and reconstruct one's society in a different form. It's a well-argued and strongly-evidenced book, and suggests that much can be learned by looking into our history to find alternative models of organising society and managing political power.

Invention: A Life (James Dyson)

Everybody's favourite vacuum cleaner inventor shares his story in this enlightening autobiography. Famously, James Dyson made 5,127 prototypes of what would ultimately become his hugely popular cyclonic vacuum cleaner, and he shares how success was far from guaranteed in the early days of building his eponymous company.

Reading this book worked its magic on me: I'm now the proud owner of a V15 Detect that makes me genuinely look forward to cleaning. More importantly though, Dyson's anecdotes about what it was like to build such a successful consumer technology brand in the UK reveals much about what it is to be an entrepreneur, the state of the tech industry in the UK, and how the UK's inability to navigate the EU's regulatory regime meant that British firms were regularly outmanoeuvred by French and German rivals.

I remember one point in the book, where once Dyson has made his fortunes he is asked by a similarly wealthy acquaintance when he will stop working - which points to a fundamental difference between British and US cultures. In Dyson's experience, many wealthy people in the UK often don't actively work once they have built successful and established business. According to Dyson, many of his peers choose to live off their passive income where possible, compared to somewhere like the US where titans of industry like Jeff Bezos or Elon Musk are driven to continually reach new heights. Maybe there is something in that which can help us understand why the UK hasn't fostered as many hyper-successful tech giants as the US?

Everyone is Everyone Except You (Jordan Hamel)

This last one is brilliantly weird and wonderful. Jordan Hamel was the 2018 New Zealand Poetry Slam champion and represented NZ at the World Poetry Slam Champs in the USA in 2019.

His debut poetry collection is eclectic, modern, and hugely entertaining. I laughed out loud at least a dozen times reading this short collection, and would recommend it to be read widely.

One highlight was You’re not a has-been, you’re a never was! which is a great example of Hamel's biting wit, and begins thus:

I used to think I was meant for great things
until I nearly died choking on ‘Very Thin’ Vogels
watching The Mighty Ducks: D2 after
chairing a Flash Fiction Zoom conference.

Like God reaching into to my Scrabble-bag mouth
dropping mixed-grain marmite letters onto my
iPhone spelling out ‘stick to poetry’ immediately
ending the game and the indoor rhino stampede.

...

You get the gist. Witty, weird and wonderful.

Full list for 2022

Below is the full list of what I read in 2022, in the order I read them through the year:

1. Diamonds, Gold, and War - Martin Meredith (Non-Fiction, 2007)
2. The Lords of Strategy - Walter Kiechel III (Non-Fiction, 2010)
3. The Dragons and the Snakes: How the Rest Learned to Fight the West - David Kilcullen (Non-Fiction, 2020)
4. Masters of Scale: Surprising Truths from the World's Most Successful Entrepreneurs - Deron Triff, June Cohen, and Reid Hoffman (Non-Fiction, 2021)
5. Just Deserts: Debating Free Will - Daniel Dennett and Gregg D. Caruso (Non-Fiction, 2021)
6. Quantum Economics: The New Science of Money - David Orrell (Non-Fiction, 2018)
7. Einstein's Unfinished Revolution - Lee Smolin (Non-Fiction, 2019)
8. The Mythical Man-Month - Frederick P. Brooks, Jr. (Non-Fiction, 1995)
9. Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It - Azeem Azhar (Non-Fiction, 2021)
10. Entangled Life - Merlin Sheldrake (Non-Fiction, 2020)
11. The Platform Society: Public Values In a Connected World - José van Dijck, Thomas Poell, and Martijn de Waal (Non-Fiction, 2018)
12. The Antisocial Network - Ben Mezrich (Non-Fiction, 2021)
13. Hivemind: The New Science of Tribalism in Our Divided World - Sarah Rose Cavanagh (Non-Fiction, 2019)
14. The Rules of Contagion: Why Things Spread--And Why They Stop - Adam Kucharski (Non-Fiction, 2020)
15. The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health--and How We Must Adapt - Sinan Aral (Non-Fiction, 2020)
16. The Art of Statistics: Learning from Data - David Spiegelhalter (Non-Fiction, 2019)
17. How To Live Like Your Cat - Stéphane Garnier (Non-Fiction, 2017)
18. Invention: A Life - James Dyson (Non-Fiction, 2021)
19. The Future of British Politics - Frankie Boyle (Non-Fiction, 2020)
20. The Power of Geography: Ten Maps that Reveal the Future of Our World - Tim Marshall (Non-Fiction, 2021)
21. Seven Brief Lessons on Physics - Carlo Rochelle (Non-Fiction, 2014)
22. Why We Sleep: The New Science of Sleep and Dreams - Matthew Walker (Non-Fiction, 2017)
23. Do Humankind’s Best Days Lie Ahead? - Malcolm Gladwell, Steven Pinker, Matt Ridley, Alain de Botton (Non-Fiction, 2016)
24. Moneyland: Why Thieves And Crooks Now Rule The World And How To Take It Back - Oliver Bullough (Non-Fiction, 2018)
25. Butler to the World: How Britain Became the Servant of Oligarchs, Tax Dodgers, Kleptocrats and Criminals - Oliver Bullough (Non-Fiction, 2022)
26. Uncommon Wealth - Kojo Koram (Non-Fiction, 2022)
27. Matchmakers: The New Economics of Multisided Platforms - David S. Evans & Richard Schmalensee (Non-Fiction, 2016)
28. This Is Your Mind on Plants - Michael Pollan (Non-Fiction, 2021)
29. Ten Cities that Led the World: From Ancient Metropolis to Modern Megacity - Paul Strathern (Non-Fiction, 2022)
30. The Status Game: On Social Position and How We Use It - Will Storr (Non-Fiction, 2021)
31. Platform Scale - Sangeet Paul Choudary (Non-Fiction, 2015)
32. The Strategy of Conflict - Thomas C. Schelling (Non-Fiction, 1960)
33. Why Do Smart People Make Such Stupid Mistakes - Chris Merrington (Non-Fiction, 2011)
34. Fluent Forever: How to Learn Any Language Fast and Never Forget It - Gabriel Wyner (Non-Fiction, 2014)
35. The Circle - Dave Eggers (Fiction, 2013)
36. And Finally: Matters of life and death - Henry Marsh (Non-Fiction, 2022)
37. Four Thousand Weeks: Time Management for Mortals - Oliver Burkeman (Non-Fiction, 2021)
38. The Age of Unpeace: How Connectivity Causes Conflict - Mark Leonard (Non-Fiction, 2021)
39. Do Deal: Negotiate better. Find hidden value. Enrich relationships - Richard Hoare & Andrew Gummer (Non-Fiction, 2022)
40. Why the Dutch are Different: A Journey into the Hidden Heart of the Netherlands - Ben Coates (Non-Fiction, 2015)
41. Drown - Junot Díaz (Fiction, 1996)
42. Our Souls At Night - Kent Haruf (Fiction, 2015)
43. Real Estate - Deborah Levy (Non-Fiction, 2021)
44. Notes on Grief - Chimamanda Ngozi Adiche (Non-Fiction, 2021)
45. Books v. cigarettes - George Orwell (Non-Fiction, 1946)
46. Luminous Moments - Paul Callaghan (Non-Fiction, 2014)
47. Money Made Simple - Sam Stubbs (Non-Fiction, 2022)
48. Committed Writings - Albert Camus (Non-Fiction, 1960)
49. Thorndon: Wellington and Home: My Katherine Mansfield Project - Kirsty Gunn (Non-Fiction, 2014)
50. I know this to be true: Nelson Mandela - Sello Hatang & Verne Harris (Non-Fiction, 2020)
51. Longitude: The True Story of a Lone Genius Who Solved the Greatest Scientific Problem of His Time - Dava Sobel (Non-Fiction, 1995)
52. All Tito’s Children - Tim Grgec (Fiction, 2021)
53. Funkhaus - Hinemoa Baker (Fiction, 2020)
54. Everyone is Everyone Except You - Jordan Hamel (Fiction, 2022)
55. 100% Pure Future: New Zealand Tourism Renewed - Sarah Bennet (ed.) (Non-Fiction, 2020)
56. A Child's War: The German Occupation of Guernsey - Molly Bihet (Non-Fiction, 1999)
57. The Story of Peking Man - Penny Van Oosterzee (Non-Fiction, 2001)
58. William Massey - W. J. Gardner (Non-Fiction, 1969)
59. False Divides - Lana Lopesi (Non-Fiction, 2018)
60. Automation and the Future of Work - Aaron Benanav (Non-Fiction, 2020)
61. Ruth, Roger and Me: Debts and Legacies - Andrew Dean (Non-Fiction, 2015)
62. The Struggle for Sovereignty: New Zealand and Twenty-First Century Statehood - Margaret Wilson (Non-Fiction, 2015)
63. The First Migration: Māori Origins 3000BC - AD1450 - Atholl Anderson (Non-Fiction, 2016)
64. The Edge of Life: Controversies and Challenges in Human Health - Mike Berridge (Non-Fiction, 2015)
65. Amnesty - Aravind Adiga (Fiction, 2020)
66. On Coming Home - Paula Morris (Non-Fiction, 2015)
67. Liberty - Virginia Woolf (Non-Fiction, 2017)
68. Seven Pillars of Science: The Incredible Lightness of Ice, and Other Scientific Surprises - John Gribbin (Non-Fiction, 2020)
69. Haerenga: Early Māori Journeys Across the Globe - Vincent O’Malley (Non-Fiction, 2015)
70. Fragments from a Contested Past: Remembrance, Denial and New Zealand History - Joanna Kidman, Vincent O'Malley, Liana MacDonald, Tom Roa and Keziah Wallis(Non-Fiction, 2022)
71. The Fox - Frederick Forsyth (Fiction, 2018)
72. Heatwave - Victor Jestin (Fiction, 2021)
73. Alexa, what is there to know about love? - Brian Bilston (Fiction, 2021)
74. In the Garden: Essays on Nature and Growing - Various (Non-Fiction, 2021)
75. The Fell - Sarah Moss (Fiction, 2021)
76. The Dawn of Everything - David Graeber & David Wengrow (Non-Fiction, 2021)
77. Assembly - Natasha Brown (Fiction, 2021)
78. Wilfred Owen (Poets of the Great War) - Wilfred Owen (Fiction, 2014)
79. Conspicuous Consumption - Thorstein Veblen (Non-Fiction, 2011)
80. The Eye - Vladimir Nabokov (Fiction, 1930)
81. Small Things Like These - Claire Keegan (Fiction, 2020)
82. Invitation to a Beheading - Vladimir Nabokov (Fiction, 1935)
83. A Month in the Country - J. L. Carr (Fiction, 1980)
84. Be The Worst You Can Be - Charles Saatchi (Non-Fiction, 2012)
85. The End of Bias - Jessica Nordell (Non-Fiction, 2021)
86. The Order of Time - Carlo Rovelli (Non-Fiction, 2017)
87. 21 Letters on Life and Its Challenges - Charles Handy (Non-Fiction, 2019)
88. On Human Nature - Roger Scruton (Non-Fiction, 2017)
89. The Invisible Universe: Why There's More to Reality Than Meets the Eye - Matthew Bothwell (Non-Fiction, 2021)
90. Letters to a Young Poet - Rainer Maria Rilke & Charlie Louth (Non-Fiction, 2016)
91. Tongues of Fire - Seán Hewitt (Fiction, 2020)
92. The Psychedelic Experience - Timothy Leary, Ralph Metzner & Richard Alpert (Non-Fiction, 1992)
93. Emotional Branding - Marc Globe (Non-Fiction, 2010)
94. Social Chemistry: Decoding the Patterns of Human Connection - Marissa King (Non-Fiction, 2020)
95. Sold Out: How Broken Supply Chains, Surging Inflation, and Political Instability Will Sink the Global Economy - James Rickards (Non-Fiction, 2022)
96. Histories of Nations: How their identities were forged - Peter Furtado (ed.) (Non-Fiction, 2012)
97. Ten Discoveries That Rewrote History - Patrick Hunt (Non-Fiction, 2007)
98. Song of Myself - Walt Whitman (Fiction, 1855)
99. Platform Strategy - Laure Claire Reillier & Benoit Reillier (Non-Fiction, 2017)
100. The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power - Michael A. Cusumano, Annabelle Gawer, David B. Yoffie (Non-Fiction, 2019)

Why I give my photos away for free

Ben Guerin — Thu, 22 Sep 2022 00:00:00 GMT

My photos have had more than 10 million views on a stock image site - and I haven't earned a cent.

Believe it or not, that's the point.

I've been publishing my personal photos to the stock image platform Unsplash since 2018, where photographers upload their own images to be used for free, with no attribution required.

So far, I've only uploaded 35 images, which together have been downloaded 130,000+ times and used all kind of ways: from Sigma projects to Trello boards, and even BuzzFeed quizzes.

The vast majority of these downloads (105k) are from a single image - a waterfall in lake Taupo that you can only access by boat.

My all-time favourite use was when the UK Green Party held a local conference in Brighton, and used my photo as the promo image.It's been a great excuse to take my camera out - even if I do so infrequently!

More importantly, it's my small way of contributing to a platform that I've benefited hugely from for years.

Unsplash have an awesome mission, and it's by far the easiest stock photo site to find inspiration for a project.

My next big Unsplash milestone is 500k downloads... so I think it's time to take the camera out again soon!

You can check my photos out for yourself here.

The chart that keeps Mark Zuckerberg up at night

Ben Guerin — Wed, 24 Aug 2022 00:00:00 GMT

Mark Zuckerberg has plenty on his plate: from legal settlements for privacy violations, trying to avoid being seen to support interference in elections, and working out how to build and promote the “Metaverse” (whatever that means) while being mocked for how rubbish it looks.

But it’s the rising threat of TikTok that is the biggest threat to the social media juggernaut that is Meta.

Beyond the social graph

Facebook & Instagram make it easy for users to keep up with what their friends and favourite celebrities are up to. Both platforms are built around the “social graph”, which is the data relating to interconnections between people, their friends, and the accounts they follow and interact with.

This data is used by the newsfeed algorithm to show relevant content on each platform based on who we are connected to - whether it’s a page we follow, an update from a friend, or something that Facebook thinks we will find interesting.

By keeping users hooked on interesting content, Meta can then sell their captive attention to advertisers. And that’s why Mark Zuckerberg is a billionaire.

But TikTok uses big data & AI to recommend addictive videos from people we don't know, and never would have thought to follow. Instead of recommending content in the feed based on who we are connected to, TikTok curates a personalised feed based on the content we have watched, liked or shared on the platform in the past. Every piece of content a user watches on the platform is another datapoint to provide better recommendations.

Network effects: TikTok vs the rest

Network effects are when the value of a platform increases for each individual user as the overall number of users grows. Other social networks benefit from network effects when more users connect with each other - after all, Instagram is pretty pointless when you aren’t following anyone, but is much more fun when you have a few dozen friends whose content is appearing in your feed, and when everyone you want to hear from is using the platform.

But TikTok is less of a social network and more of a content engine that is insanely good at finding and recommending interesting videos. You don’t have to add any friends to get value from the platform, compared to other social networks like Facebook, Instagram, Twitter and LinkedIn.

TikTok also gets the benefit of network effects from every video people interact with: which is a much smaller action for a user to take than finding and connecting with friends.

While Meta needs your social graph to build you a newsfeed that will capture your attention, TIkTok can just keep recommending you videos until the platform has worked out how to get you hooked.

TikTok’s approach is working

Globally, average monthly engagement on TikTok is 25.7 hours per user, compared to just 16 hours for Facebook and 7.9 hours for Instagram.

In the UK, adults (18+) are spending more time on TikTok than on any other platform (closely followed by Instagram). That’s not bad for a platform that only launched properly in 2018 - compared to the 12 years Instagram has had to become the behemoth it is now.

Meta knows that the days of Facebook’s dominance of the social media landscape are over. That’s why Zuckerberg renamed the parent company of Facebook, Instagram, WhatsApp and Oculus to Meta instead of Facebook.

That’s also largely why we’ve been hearing so much about the “Metaverse” from Zuck over the past year - he needs investors to back his long-term vision, because the future sure isn’t Facebook.

The future of Facebook and Instagram

It will be a long time before we know whether or not Zuckerberg’s ambitions for the “Metaverse” will work out. In the meantime, Meta needs to work out what to do with Facebook and Instagram.

Essentially, Zuckerberg's challenge ahead is to transform Facebook & Instagram:

From: follower platforms that show content based on people's social graph (i.e. friends and people/brands we follow).

Into: discovery platforms that use data and algorithms to recommend content from creators that we wouldn't have otherwise known about.

Essentially: Meta is trying to play catchup by turning Instagram into a TikTok clone - but it's an uphill battle, and mega-influencers like Kylie Jenner and Kim Kardashian have already kicked up a fuss about how Instagram is changing, forcing Meta to backtrack.

For now, Meta has had to cancel the proposed changes to Instagram - but they will have to find a way to catch up in order to stay competitive in the battle for our attention.

It won't be easy.

If Zuck is successful, Meta will continue to be the most important company that shapes how people communicate and connect.

But if he fails, then Meta will continue on a downward spiral towards irrelevance.

And meanwhile, Meta's share price is already taking a hit, after posting its first YoY quarterly revenue decline since it went public in 2012 - in large part because of declining user engagement as shown in this chart.