Ideas were at the at the heart of why I started this blog. One of the first posts that I wrote there being a sweet spot in the complexity of products based on the ideas of Dan Greer. I wrote about the first online election fought by Howard Dean, which now looks like a precursor to the Obama and Trump presidential bids.
I articulated a belief I still have in the benefits of USB thumb drives as the Thumb Drive Gospel. The odd rant about IT, a reflection on the power of loose social networks, thoughts on internet freedom – an idea that that I have come back to touch on numerous times over the years as the online environment has changed.
Many of the ideas that I discussed came from books like Kim and Mauborgne’s Blue Ocean Strategy.
I was able to provide an insider perspective on Brad Garlinghouse’s infamous Peanut Butter-gate debacle. It says a lot about the lack of leadership that Garlinghouse didn’t get fired for what was a power play. Garlinghouse has gone on to become CEO of Ripple.
I built on initial thoughts by Stephen Davies on the intersection between online and public relations with a particular focus on definition to try and come up with unifying ideas.
Or why thought leadership is a less useful idea than demonstrating authority of a particular subject.
I touched on various retailing ideas including the massive expansion in private label products with grades of ‘premiumness’.
I’ve also spent a good deal of time thinking about the role of technology to separate us from the hoi polloi. But this was about active choice rather than an algorithmic filter bubble.
The nine people you meet in a pitch came out of talking with a couple of former colleagues about recent pitches that they’d been involved in. I was thinking about how I had experienced what it was like to pitch and to be pitched to as a client.
Based on all that, I thought I would share some experience and expertise that might be of help.
The nine people you are likely pitching to.
You can think of the panelists receiving your pitch as fitting into nine behaviour archetypes.
The advocate
The complainer
The detail lover
The late comer
The multi-tasker
The narcissist
The skeptic
The spectator
The surpriser
Right let’s get into this and meet the nine people.
The advocate
The advocate may be apparent before you are in the room for the pitch. They may have worked with the agency before and have likely advocated for the agency to be on the list. A good pitch lead with enough time will have primed the advocate with the thinking and themes that they would be sharing in the pitch.
Or not, some people are just very agreeable in nature.
How you’ll recognise them
They will seem receptive and positive to everything. Pitch teams and process tends to overlook the advocate in the pitch. But they can be used to the pitch team’s advantage.
The complainer
The complainer may be an advocate of the existing agency, may be having their budget cut to pay for the activity that the new agency will do, or may have been left out of the early process that started the agency search.
How you’ll recognise them
Negative towards everything, can be mistaken for the skeptic or the surpriser.
The detail lover
These people usually fit into one of three categories:
They are currently really in the trenches and want to ensure that you can really make their lives easier
They are product people who are domain experts on their area: use cases, technical details and probably less likely users
In a highly regulated sector they could have a legal or regulatory responsibility, in pharma companies they may be called MLR (medical, legal and regulatory)
How you’ll recognise them
Prior the pitch these people are most likely to push an agency to put much more detail in their decks so they become lengthy and take a lot of time to create. All the while the storytelling red thread goes missing.
In the pitch, given the volume of questioning you may mistake them for the surpriser or the narcissist. The key differences being that the narcissist won’t usually give you an opportunity to answer and the surpriser will bring completely new areas of questioning in.
The late comer
They turn up the meetings late. This could be personal factors such as workload and time management, or it could be an attention diverting tactic.
How you’ll recognise them
They turn up late, its more important to identify why they turn up late as you could actually be dealing with the narcissist, the skeptic or the surpriser.
The multi-tasker
This usually comes down to company culture usually rather than a character trait.
How to recognise them
If it’s company culture you will be likely dealing with a sea of laptop lids, or smartphone being used on the desk. Assume that they are listening, although they might be commenting and norming in real time on your presentation in a conversation thread on Google Workspace, Teams, WhatsApp etc.
If it’s one person then it might be the sign of distraction (child minder gets in touch saying their child is running a temperature or similar). Or it could be a sign of dissonance, keep an eye out for the complainer or the skeptic
The skeptic
They may have similar world view to the complainer in that they would prefer the status quo. Though they may view the status quo as the least worst option rather than be an advocate for it.
They may be:
Risk averse by nature
This may be completely new to them
They may have been part of a project that has gone wrong in the past
How you’ll recognise them
This could be tricky as they may look similar to the spectator or the surpriser. Generally statements and related questions made will seek proofs as part of the response.
The surpriser
The surpriser usually is a symptom of a client that doesn’t have internal alignment on their brief.
How you’ll recognise them
They will come in with new information, ideas and questions that may disrupt the meeting agenda and possibly the whole pitch process.
How to deal with them as you encounter the nine people?
General rules to work with
Which ever of the nine people you are engaging with it’s good to remember for your own sanity that it’s generally not personal so don’t take it that way. All of the nine people archetypes are under some sort of pressure / stress. At most, you’re a non-player character in the video game that they call life.
Given what I said about their likely personal stressors, try and empathise with what might be the root causes of their behaviour. You’ve experienced agency life and the way it can clobber you – you get similarly interesting times in most corporate environments.
Hanlon’s razor says something to the effect of “Never attribute to malice that which can be adequately explained by stupidity.” You can swap out stupidity for ignorance, thoughtlessness etc. but the message remains equally valid. I know it’s tough to be empathetic in the stressful environment of a pitch but try. Engage in a positive way.
If you are pitching an international team or a large company there is likely to be cultural differences. In my experiences large companies like Alphabet and Microsoft have their own language and world view just in the same way as the agency world has its own jargon.
If you are pitching in another country there is another layer of cultural differences. Erin Meyer’s The Culture Map is a great primer for different country cultures. Be mindful of cultural differences and sensitivities.
When you are making claims, assumptions or answering a question provide relevant proof where appropriate.
However tempting it is, never get into confrontation with any of the nine people archetypes. You won’t win and you may cause friction with your colleagues that would outlive the pitch. There’s a fine line between being clever and a ̶d̶i̶c̶k̶h̶e̶a̶d̶ misanthrope.
Specific tactics for each of the nine people portrayed.
The advocate
Get them to share their feedback.
If you can arrange it prior to the pitch, give them a role in the meeting.
As a watch out they are easy to ignore because you often focused on solving for other behaviours.
The complainer
Make them feel heard, for instance ask them about what is important in an agency partner.
Be prepared to move on, don’t get hung up on their questions.
Share evidence that would reassure the complainer such as case studies demonstrating competence, experience and expertise.
The detail lover
Emphasise the limited time to present and ask how the additional information will aid the decision-making process.
Co-opt other attendees in the room by asking them who would also find the additional information valuable to included.
The late comer
Prior to going into the pitch, have a plan on how you will handle a delay on the pitch. Having this pre-planned will make you feel far more settled if you need to use it.
In the pitch:
For the beginning of the presentation, see if you can cover the less important details first, so that late comers don’t miss out on the important items.
Offer to bring the late comer up to speed.
The multi-tasker
Dealing with the multi-tasker is down to going into the pitch with a high degree of engagement designed in that makes multi-tasking behaviour difficult to do. You need to outcompete other calls on their attention.
The narcissist
Acknowledge their input, but ask for input from others. This shifts the focus away from them and puts their input in a broader context.
Once you know who they are, reduce their impact on the meeting by looking for other people’s input first.
Make them feel that their input is valued by ensuring they know that you have captured their input.
The skeptic
Enquire about what causes them the greatest challenge.
Ask them about what good looks like from their perspective. What would help address their greatest challenge?
Reassure by sharing case studies, expertise and experience where similar challenges have been successfully addressed.
The spectator
You need to strike a balance between engaging them to ensure that they are heard, but not putting them on the spot. Everyone’s input is crucial. Acknowledge the value of their contribution.
The surpriser
Acknowledge their input, not doing so would quickly turn them to a complainer.
You need to make a judgement based on the situation in the room, if it makes sense to include their new input based on agreed goals. This will likely require one-or-more follow-up meeting.
February 2026 introduction – (31) get up & run edition
I am now at issue 31, or as a bingo caller would put it ‘get up & run’. In Cantonese 31 isn’t a famous lucky number, it could considered to mean ‘life first’ implying an importance of vitality. On the plus side, it doesn’t have negative connotations of say 14 – which sounds similar to definitely die.
I was sent a mix by an old friend of mine done by Frankie Bones at Amnesia House in August 1990 – as aural history its a fascinating treasure trove and occurred a pivotal time with several genres about to fragment from the original UK scene. Now we have our soundtrack let’s get into it.
New reader?
If this is the first newsletter, welcome! You can find my regular writings here and more about me here.
Things I’ve written.
I appeared in the What’s In My Now newsletter talking small wallets, cheaper alternatives to Apple Studio monitors and making better use of LLMs. More here.
I gave a presentation for Outside Perspective on my Dot LLM era paper. Here is my speaking notes that I prepared as I got the presentation ready, complete with the slides at the relevant points.
I spoke to the WSJ about my dot LLM era thinking and was name-checked on their Take On The Week podcast. And I compared my research with Marc Andreessen’s of A16z 2026 AI outlook here.
I wrote a letter to the FT about Sony surrendering its home entertainment business (TVs, home audio) to Chinese TV maker TCL. While Sony’s current involvement in sectors such as elder care and insurance are worthy endeavours – what does it mean when they are more core to Sony’s identity than the home entertainment equipment that the brand built its empire on?
As well as being a concerned Sony customer, I was also thinking about what it means to a brand when it gets rid of its core raison d’être? You can read my letter here.
I was talking to a friend about classic films and suddenly Matthew Frank’s newsletter dropped in my inbox and started me down a rabbit hole exploring the idea of forgettable cinema as part of the modern public zeitgeist.
I pulled together a collection of adverts and campaigns celebrating lunar new year from across Asia and a couple aimed at the wider diaspora. As brands look to benefit from the year of the fire horse.
ICYMI – Top five shares on LinkedIn
Publicis widening the business gap versus its rivals. A decade spent preparing their data and foundational technology for machine learning.
WPP’s big pivot to adapt to market conditions for the large holding companies.
Dentsu’s change of leadership to better control strategy and manage global capabilities.
Michael Farmer on why reorganisation isn’t strategy, instead strategy should drive any reorganisation to meet the strategic objectives. This one proved a bit controversial, I’m not sure why.
Books that I have read.
While I have been looking forward for David McCloskey’s latest book The Persian to come out, I managed to finish The Seventh Floor. On one level The Seventh Floor is about espionage and feels very now given the new cold war. But it’s also about friendship, loyalty and personal betrayal. McCloskey doesn’t only bring expertise from a past career at the CIA, but also a deep love of the espionage novel as an art form and this novel gives a nod and a wink to the works of John Le Carré.
While the agency world is focused on the rise of AI, I decided to revisit Michael Farmer’s Madison Avenue Manslaughter: An Inside View of Fee-Cutting Clients, Profit-Hungry Owners and Declining Ad Agencies. Ten years after it has been published, the diagnosis and the lessons from Farmer’s research seem to have been ignored by clients and the c-suites of holding groups. One thing I picked up on my revisiting the book was the challenge in defining strategic contribution and effort to campaigns. With creative output, Farmer managed to break down creative tasks into fixed ScopeMetric® Units (SMUs). But Farmer admitted that he couldn’t define strategy outputs in the same way because the context changed account-by-account. This makes sense given the difficulties I have had in the past when strategists were way oversold by the project management function within agencies.
Things I have been inspired by.
Insularity was the watch word of this year’s Edelman’s Trust Barometer. It was a pretty dark vision of the future. There is a huge delta between top income quartile of the population and their trust of authority and the bottom income quartile. In the lower quartile group there is little to no trust in authority figures (business, journalists, government). They only trust people like them.
Andrew Tindall published a new book for System1 based on their research and Effie data which reinforces previous publications by Orlando Wood, Les Binet, Peter Field and Byron Sharp at the Ehrenberg-Bass Institute. It also reinforces the importance of context as part of creativity when media and creative functions are co-joined at the hip. It’s very readable and available for free here.
Chart of the month.
The surge of US measles infections turned into a politicised debate about vaccinations, competence, why Canada’s rates were even higher and whether things were as bad as experts would have you believe?
The chart only tells part of the story.
The US CDC cites a general hospitalisation rate of about 20% (1 in 5 cases), recent years have seen significant fluctuations depending on the specific age groups and regions affected by measles outbreaks.
The “Age Factor”: The high rates in 2022 and 2024 were largely due to the virus hitting children under five—the age group most likely to develop severe complications like pneumonia.
2022 – driven by an outbreak in Ohio, which had a high paediatric hospitalisation rate.
2024 – remained high throughout the year with nearly half of cases affecting children under 5.
Outbreak Size vs. Severity: In 2025, even though the total case count surged, the percentage of people requiring hospital care fell. This often happens when an outbreak moves beyond high-risk “pockets” into a broader, sometimes older, population.
2023 – outbreaks in unvaccinated high-risk clusters.
2025 – hospitalisation rates dropped because the virus spread to older demographics and larger, but less severe clusters
2026 – infections in January had few children under 5 affected. Cases were able to be managed at home.
Vaccination Impact: Across all these years, the vast majority (over 90%) of hospitalised patients were either unvaccinated or had an unknown vaccination status.
Canada’s rates are high because the population has a significant amount of unvaccinated immigrants and refugees from conflict zones and the developing world.
Things I have watched.
Thomas Harris’ Silence of The Lambs still has legs in culture. Which is why Amazon Prime Video has gone back to the universe with Clarice. The story takes place in the aftermath of the buffalo Bill killings which drove the plot of Silence of the Lambs. The storytelling is top notch with a fantastic plot twist in episode 1. It is well worth your time to at least give the first few episodes a chance.
It started off in an unpromising way, several years ago a friend left a DVD with me. They said something along the lines of they liked a number of Werner Herzog films, but that this was too weird for them. I finally got to sit down and watch Fata Morgana.
It doesn’t have a story, but is beautifully shot footage of the Sahara and Sahel in 1969 with a focus on near horizon mirages (from which the film gets its name) and features the human effect on it from vistas of oil processing equipment to barbed wire and crashed planes.
There is a poetic narration in German over the top with a range of music to flt the landscapes. It feels like a forerunner of Godfrey Reggio’s Koyaanisqatsimade a decade later. It’s easy to watch.
I spent a weekend with my Dad going through old VHS cassettes and on one of them we found Four Fast Guns. It is a surprisingly good Hollywood western. While not a John Ford film, it has a grittiness due to superior character development and tight storytelling reminiscent of the very best spaghetti westerns. The film was produced by an independent studio and featured three well recognised character actors as its star performers.
Edgar Buchanan acted alongside the likes of Clint Eastwood, James Garner, John Wayne, Cary Grant and Randolph Scott he went on to appear in several TV series that I remember watching on repeat as a child in Ireland including The Beverley Hillbillies and The Twilight Zone.
Martha Vickers had appeared in The Big Sleep alongside Lauren Bacall.
James Craig had acted alongside everyone from John Wayne to Boris Karloff.
This gave the director much more creative freedom to make the performances pop on-screen. The climatic plot twist is very good.
I was inspired by watching Reflection in a Dead Diamond last month to watch Danger: Diabolik. The psychadelic motifs of and dream sequences of Reflection in a Dead Diamond seemed to draw from European cinema’s brief flirtation with super spy and super villain films during the 1960s. Danger: Diabolik was Mario Bava’s and Dino DeLaurentis’ take on the French Fantômas film series.
Bava’s expertise in genre films and special effects gives Danger: Diabolik a more sophisticated look than you would give it credit. Add in the film’s 1960s modernist aesthetic, James Bond type action sequences and you have a winning film. The humour-heist plot is very of its time but still entertaining and cried out for a remake. Terry-Thomas’ character performance as a government minister in the film is one of brilliance.
Useful tools.
I was saddened to read of the demise of The World Fact Book published by the CIA. I found it invaluable as a starting point when getting up to speed on international campaigns on parts of the world that I hadn’t visited. It even helped me win some work with Telenor Myanmar back before the current military regime got back into power. According to this post on the CIA website the World Fact Book is going away.
This personal productivity playbook by CJ Casseili was interesting to read and some of you may find tips and tricks that you can apply in your own work and personal life.
Ilina Scott’s quick guide to AI tools for strategists is worth a read if you are just dipping your toe in the field.
Occasionally software comes along what doesn’t become a mainstream success, but is well loved and much missed when it disappeared. Apple’s HyperCard was one, another was Yahoo! Pipes. The idea behind Pipes has been resurrected and in its latest iteration is very useful, even in a time of AI-with-everything.
The sales pitch.
i am a strategist who thrives on the “meaty brief”—the kind where deep-tech or complexity, business goals, and human culture collide.
With over a decade of experience across the UK, EMEA, and JAPAC, I specialise in bridging the gap between high-level strategy and creative execution. I was embedded within Google Cloud’s brand creative team, where I helped navigate the “messy steps” of global pivots and the rapid rise of Gen AI. And have recently been helping out agencies and startups in various sectors.
My approach is simple: I use insight and analytics to find the “surprise” in the strategy. Whether it’s architecting an experiential event or defining a social narrative for a SaaS powerhouse, I focus on making complex brands feel human and high-velocity businesses feel accessible.
The Strategic Toolkit:
Brand & Creative Strategy: From B2B infrastructure to luxury travel.
AI-Enhanced Planning: Deeply literate in Google Gemini and prompt engineering to accelerate insights and creative output.
Multi-Sector Versatility: A proven track record across Tech & SaaS (Google Cloud, Semiconductors), Consumer Goods (FMCG, Beauty, Health), and High-Interest Categories (Luxury, Sports Apparel, Pharma).
I am officially open for new adventures with immediate effect. If you have a challenge that needs a all-in, hit-the-ground-running strategic lead, let’s talk.
Ok this is the end of my February 2026 newsletter, I hope to see you all back here again in a month. Be excellent to each other and good luck with your new year’s resolutions. As an additional treat here is a link to a presentation I gave to the Outside Perspective crew, in Adobe Acrobat format.
Don’t forget to share if you found it useful, interesting or insightful as this helps other people and the algorithmic gods of Google Search and the various LLMs that are blurring what web search means nowadays.
Brand building for B2B PRs is a write up of an interview that I did with Miles Clayton of Agility PR. We talked about the importance of brand building, client challenges and techniques.
Participants:
Miles: Host (Agility PR)
Ged Carroll
Miles: I’d like to welcome Ged Carroll, a guru on brand building and advertising working with major tech and consumer brands. He offers insight into the world of proper advertising: campaigns we know and love, and, where the industry is leading today.
Welcome, Ged. Could you talk through what you’re doing at the moment and your current challenges?
Ged Carroll: Thank you, Miles. I am currently wrapping up an engagement with Google Cloud, working with their internal creative agency as a temporary vendor contractor.
My work focuses on brand building: out-of-home advertising, video advertising, and events. We look at how those creative experiences come to life through major trade shows and Google-hosted events. There is also sports sponsorship; for instance, the Formula E activation. Even though it’s a B2B brand, many tactics are exposed to a broader audience than just direct customers.
Miles: That’s fascinating. Regarding brand building, something many brands under-invest in, could you explain why it is important and how it differs from brand activation or performance marketing? I’d argue performance marketing is the obsession in B2B, but why should brand building weigh higher?
Ged Carroll: I’ll first address why brands focus on performance marketing, then explain brand building’s importance. Brands focus on performance marketing because they are measured on 90-day periods. They can simply say, “Here’s the money spent, here’s the result.” Measures include customer acquisition cost or engagement metrics along a marketing funnel. These seem like concrete measures.
Why do brand building? Smaller B2B brands often hesitate because of what Professor Byron Sharp calls “Double Jeopardy”: smaller brands have less market penetration and less loyal customers. Consequently, small enterprise software companies have a harder time moving the needle than larger ones. The bigger you are, the better you do; it has a flywheel effect.
What helps sell product is “mental availability.” If I think B2B PR, you want me to think “Miles.” For chocolate, you think Cadbury. For B2B software, most developers now think AWS. Fifteen years ago, that would have been Microsoft.
Miles: I sympathise. I’ve worked with brands famous in particular markets that struggled to break into adjacent markets because they hadn’t built the brand there.
Ged Carroll: That creates a ‘chickenand-egg’ situation: do you invest, or, try a “cargo cult” approach replicating past success? Past success was likely a confluence of luck, timing, and good practice. Many overnight successes are decades in the making.
Huawei seemed to spring from nowhere but is four decades old. Breaking one customer, BT, made them famous. That fame cracked the market.
Miles: Brand building is critical. You mentioned that in a typical SaaS subscription business, you should invest about 70% in brand building?
Ged Carroll: Heuristically, for a subscription business, about 70% should go into brand building and 30% into brand activation.
Brand building includes PR. I ask: how can we make this idea work for earned media as well? Does the campaign scale to generate “talkability”? People discussing it at the water cooler, in trade magazines, or on social media? Paid media works harder if you have talkability around it.
Miles: Is that what is now called integrated campaigns?
Ged Carroll: Integrated campaigns have been around for over 30 years. People used to discuss “media neutral” strategies. The core idea is that your paid media works significantly harder if the campaign generates conversation.
Miles: That starts with great advertising principles. The book Look Out focuses on “right brain” thinking. Can we discuss the right versus left brain tussle in advertising and how to address it?
Ged Carroll: Marketing has changed, but our thinking is hardwired by evolution. Analytical procrastination creates cognitive load. If our ancestors sat thinking, “Do I want this or this?”, a predator would have eaten them before they decided.
Miles: By the time you selected the next iPhone, you’re dead.
Ged Carroll: Exactly. Logical “System 2” thinking is a difficult construct, yet B2B marketers often communicate rational benefits this way. However, we evolved instantaneous “System 1” thinking, which emotions tap into. If I feel something sharp, I instantly move. That is why we don’t remember a commute unless something significant happens.
Current advertising often treats us as rational decision-makers, but feelings have a longer-term impact. If I feel sharp stones, I build longer-term thinking to wear sandals next time. Traditionally, advertising tapped into this. Brands like Accenture or Google Cloud attach themselves to emotional events like sports, or consumer ads use storytelling to build memory structures and automatic association.
Miles: Absolutely.
Ged Carroll: Procurement processes try to force a rational view, but organisational load often short-circuits this. Do you care where you buy paper clips? No, you go to the fastest place. Brand building gets you onto that procurement shortlist. Furthermore, people aren’t in the mood to buy 95% of the time. Unless you build memory structures while they are inactive, you won’t be considered when they are in the market.
Miles: Smaller companies can’t afford TV or billboards. What do you advise? I offer thought leadership and education. Tech businesses often say, “You aren’t buying now, but do you want to learn about prompts?” Is that brand building?
Ged Carroll: It could be. But whose brand is it building? It might just build the LLM model’s brand. My mum asks me to “Ask Google” about crochet patterns. She blames the specific websites for bad patterns, not Google. She associates Google with getting what she wants.
With thought leadership, are you building the person’s personal brand, or the company brand?
Miles: That’s an interesting question. I often do personal brand building for the CEO or CTO to express the business vision. But below the C-suite, say a VP of Sales, is it their brand you’re building rather than the company’s? Especially given high turnover.
Ged Carroll: Exactly. Founder-managers are different; they stay longer. Professional CEOs shipped in by VCs might only stay a few years. B2B marketers face dilemmas, not just choices. It’s about making the best choice within those dilemmas.
Miles: There are parallels between advertising and B2B marketing, but also budget challenges. Media has changed; 15 years ago, clients bought display ads to build brand. Now, the digital tendency is toward content and performance marketing. Is business stuck in short-term goal-orientated thinking?
Ged Carroll: It’s not strictly a B2B or B2C problem. We measure what can be coded. Ad-tech stacks are based on interactivity, not marketing science. We assume if someone does X, Y will happen—the sales funnel concept. The sales funnel is an interesting mental model, but it comes from century-old door-to-door sales and assumes rational decision-making and perfect memory through the process.
Miles: You’re saying consistent brand building short-circuits the funnel, leading straight to the sale.
Ged Carroll: Yes. When you want a beer, you choose Heineken because it’s in your mind. The consideration process shrinks. Brand building gets you into that consideration process much faster. Regularity is vital to reach people the 95% of the time they aren’t ready to buy.
Miles:Look Out discusses the narrowing and fragmentation of attention. Are there ways through that?
Ged Carroll: We have more media opportunities now, but fragmentation occurs because we have smaller gaps of consumption time to fill—like checking a smartphone on the tube. Unless you have repetition within those small gaps, you won’t build memory structures. It’s hard to make a six-second spot emotional.
You need an integrated approach: emotion and storytelling in long-form content (like a documentary), supported by short content that directs people to it. In B2C, this is easier using brand cues: music, mascots, fonts, colors. Build those cues and stick with them. Marketers often get bored of a campaign and change it, but the audience hasn’t seen it enough. Stick with it.
Miles: Stick with it.
Ged Carroll: Many consumer adverts run for years. My dad’s favorite Twix advert is from 2022. Flash has used the same dog and music for five years. Great brand-building campaigns “burn in” rather than “burn out.” Performance marketing might focus on a new feature, but it relies on the brand association already built.
Miles: It’s been a fascinating discussion crossing advertising, brand building, and B2B marketing. My big takeaway is to encourage more right-brain thinking. Thank you for your time, Ged.
Ged Carroll: Thank you, Miles. I look forward to chatting again.
I gave a talk to strategists and planners from the Outside Perspective group on my recent paper The Dot LLM era? The talk looked to summarise some of the key takeaways that I had written and also reflects a slight refinement on my thinking given current events since I had drafted the paper over the Christmas holidays.
About Outside Perspective
Outside Perspective is a community of brand planners and strategists. All of the members of Outside Perspective are freelance or self-employed. The members clients are drawn from all around the world and all sectors.
My presentation was the first Outside Perspective huddle of the year, where strategists share expertise and areas of interest with their peers.
I have put in the slides at the appropriate places alongside my notes.
Dot LLM era for Outside Perspective
Good afternoon everyone. I hope I’m not depriving you too much from lunch. If I am, just tuck in, just go on mute if you are tucking in because otherwise it’ll make me hungry.
So The Dot LL era came from a question that I posed to myself. I was working at the time for a client who is a major AI company. I was looking at all of the stuff happening around me and thought that the company that I’m working for it’s probably going to be all right. But we do feel like as if we’re in a bubble. So I then started to think about the bubble and eventually pulled it into a paper.
We (the Outside Perspective) will share the PDF of this presentation and you can get the paper from the QR code later on. My thinking has been refined slightly, as I’ve thought about this presentation, just nuances here and there based on what’s been happening since I originally published the paper.
Key points in the presentation
One of the first things I was taught when I present was tell them what you will be presenting, present it, and then tell them what you told them. So this is me telling me what I’m going to tell you.
So as a technology, LLMs (large language models), what people call AI at the moment, are making lasting changes from business to culture. It’s changing aesthetics, even though might have a negative impact like AI slop. The cultural effects are going to stay with us and evolve, just like previous technologies have done from the printing press on.
Now looking at the economics, the question is what’s really going to happen? Because the AI sector has a valuation in trillions which is an insane amount of money to think about. There are two main challenges from an economic perspective which is where I actually really looked at this from:
The amortisation risk so the speed at which the hardware becomes obsolete or literally burnt out is three to five years versus the likely time to pay off because of the trillions of dollars involved.
The self-defeating economics of AI as I’ll go through in a bit more detail. Economics are a limitation as to how fast AI can actually be adopted without actually destroying the AI providers themselves.
Both factors give a very narrow margin of success for Dot LLM era players from a business perspective, they need to thread themselves through to land at just the in order to succeed.
The Long Boom
When I came up with the term dot LLM era I was thinking about parallels to the dot com era. I’ll talk a little bit about the dot com era as well because I realise some people might not be terribly au fait with it. By comparison, I lived it and have the scars of my professional involvement with it.
The dot com era happened at a time that Wired magazine termed the long boom. During this time you had US preeminence as the Warsaw Pact had collapsed and China wasn’t yet a member of the WTO. During the Clinton presidency there was a US government budget surplus, so the US had headroom for monetary policy interventions if needed.
So if something like COVID epidemic had happened back then, they would have had much more economic flexibility to actually deal with it than we had coming into 2020.
Today in the US at least, much more like the Reagan era that preceded the long boom.
The West is on the back foot, there’s a resurgent Russia waging an invasion in Ukraine and ‘active measures‘ in the rest of Europe. China which is resurgent economically and militarily and from an innovation perspective which I’ll touch on a little bit later. There is high government debt particularly in the US, but also in Europe and much of the developed world as well.
There is sticky inflation and the overall inflation figures that are quoted in the business press are actually lower than what people are actually seeing in the shops. Consumer sentiment about the economy is much worse than the headline inflation number would suggest.
Finally, there’s a slackening labour market. That isn’t about AI at the moment. Companies say, oh, well, due to AI, we’re making layoffs. Usually they’re making layoffs through cost cutting, outsourcing and offshoring roles, they might be doing a little bit of AI in the background because we’ve given employees access to Microsoft Copilot or similar. That doesn’t mean to say that AI won’t have an impact in the near future.
The Dot Com Bubble
When we talk about the dot-com boom, we tend to think about is one thing but it was actually three interrelated bubbles that were going on.
There was an online business bubble which was relatively low capital but had a high burn rate through that capital in an attempt to build a moat. This is what most people think of when they think about the dot com era.
There was a smaller, less visible bubble related to open source software. With the internet, it suddenly became much more important because you had a way of contributing to open source projects and collaborating in a way that wasn’t available between different individuals or organisations previously. While open source made software development collaboration easier, and provided good quality software to download for free, businesses struggled to build a profitable open source business model.
Finally, there was a telecoms bubble which was capital intensive. There was a huge amount of infrastructure built out. There was vendor financing by manufacturers of networking equipment. There was industry incumbents, so companies like the BT in the UK or the Bells in the US. And then there was also new telecoms companies like Enron Broadband Services, MCI WorldCom and Qwest.
More on them in a bit later on. But with the graph on the right, what in fact you see is the peak that was reached on the NASDAQ in March 2020 was in It took the NASDAQ 15 years to hit that peak again after the dot-com bust later that year. This is considered to be not as bad as what happened during the 2008 financial crisis. But it gives you an idea of the way things can go.
Hyman Minsky financial instability hypothesis
I want to introduce an idea of Minsky moments.
Hyman Minsky, economist, he came up with his financial instability thesis. He considered this to be bound to three different steps that needed to occur.
First a self-reinforcing boom driven by easy credit. Our interest rates are higher than they’ve been, but they’re still relatively low from a historical point of view.
If you actually look at the amount of money and the valuations that are going into the likes of OpenAI and CoreWeave in January alone, you can clearly see that the self-reinforcing boom is under way.
The second step Minsky mentioned is a shock where investors re-examine cash flows and this is what’s often termed as a ‘emperor’s new clothes‘-type moment. They suddenly start asking questions like when are we actually going to get our money repaid let alone are we going to make an obscene amount of profit on that money. We’re not quite there yet, but there has been some signs of concern from investors, (for instance when Microsoft announced its recent quarterly results). There were always those dissenting voices, but they’re actually proved prescient only in hindsight.
Lastly, there’s a de-risking stage through rapid acid sales. So investors and management realise they’ve got a flaming bag of crap and want to hand it off to someone else. They want rid of it.
So let’s next think about those earlier three bubbles and think about how good analogy are they for our present era of technology.
Online commerce
So like the early web, pure-play LLMs like Anthropic and Open AI’s GPT are currently providing tokens at below their marginal cost. The cost you’re paying for to do AI actions is actually less than the cost those AI actions actually take to create. And that’s not thinking about the research and cost of capital invested in the company.
They’re losing money to build an AI moat just in the same way as e-tailers and service providers back in the dot-com era lost money in order to build a moat in a particular sector. For instance like Amazon did in books. Move forward 25 years and AI companies are so they’re trying to do the same for various different service models. The burn rates of dot-com failures mirror loss making AI businesses. But only at a surface level, dot-coms were capital light in comparison to their modern Dot LLM era counterparts.
Look at the dog sock puppet on the right, he was the mascot and brand spokesanimal from Pets.com. Pets.com had a horrendous burn rate for the time and went bankrupt. The cause of their bankruptcy was down to two reasons:
The logistics of actually sending out bags of dog meal and rabbit bedding were expensive compared to the amount that was being charged. It took Amazon the best part of a decade to radically reduce the cost of logistics for its own business. Even now, Amazon benefits from Chinese government overseas postal subsidies given to China-based businesses on Amazon.
The large amount of money they put into advertising and brand building. Around a dog sock pocket with attitude. Great marketing, but if the consumer proposition isn’t right the marketing can’t save your business.
Open source sofware
The open source bubble saw the rise of what’s known as LAMP. That stands for:
The Linux operating system
Apache HTTP web server
MySQL as a database management system
P was for the Perl, PHP and Python programming languages
If you’ve ever run a WordPress blog, all of that language probably sounds vaguely familiar to you because it is. Because that supports a lot of the web. Linux extended into laptops, tablets and cellphones including smartphones. (Apple products are based on a similar UNIX style software based on the Mach micro-kernel used in various BSD distributions).
During the dot com era there were numerous companies in this space. Red Hat was the outlier success with their enterprise grade support offering. Red Hat managed to sell themselves for $34 billion to IBM in 2019. Red Hat was the most successful exit and profitable business out of its peers, becoming the first of its kind to generate $1 billion in revenue.
Now you can see Chinese companies are competing against US rivals and winning a lot of users in the global south by providing open source and open weight models like Alibaba’s Qwen and Kimi K2.
These Chinese models provide perfectly usable models at lower costs. You can run the models on your own machines. They use a lot less processing power than US AI models, and are challenging closed AI models. Huawei have built a lot of infrastructure in the developing world, so you’ve got a lot of opportunity there that’s now closed off from American AI companies.
US organisations like Airbnb and Silicon Valley based VC companies are running these Chinese models for their own uses.
AirBnB is an interesting case; CEO Brian Chesky is a really good friend of Sam Altman, yet he’s still using to use open source software rather than use OpenAI because it makes commercial sense.
The telecoms bubble
The telecoms boom. There’s been a similar kind of optimism build out of massive infrastructure as happened during the telecoms boom. Back then, they invested about half a trillion in fibre-optic networks based on misreading of traffic growth data. In the dot-LLM era, we’re seeing orders of magnitude more investment across computing power, networking within the data centre and even data centre power generation.
The graph on the right just gives you an idea of how much AI capital expenditure has taken off.
Amortisation risk
I want to introduce you to some of the concepts. One I’ve alluded to already is this idea of hardware amortisation.
During the telecoms bust, there was dark fibre, so optical fibre networks that weren’t lit. Dark fibre that was laid in the 1990s had a useful life of at least a decade.
In the current dot LLM era the equivalent surplus would be GPUs and TPUs – the processors and the network internet connect hardware within the data centre that’s particularly used for training models has a useful life that becomes technically obsolete between three to five years. It’s usually more towards three years because they are used so intensively that a lot of the processors get damaged by the amount of heat generated from the extreme amount of processing they do.
With your laptop, even though things might run slow sometimes, 95% of the time your laptop processor is running idle in terms of what does unless you’re doing some like really hardcore 3d rendering, video editing or complex work in Photoshop.
Your computer’s processor aren’t running at full at full performance all day, all night. By comparison AI training processors wear out within three to five years depending whose numbers you believe.
The chart on the right gives you an idea of how over time a lot of the major hyperscalers have actually been increasing the amount of years that they actually write down their processor’s depreciation. While the the processors have stayed pretty constant in terms of that three-to-five year window that they have a life of before they need depreciation to zero.
A second aspect of this deprecation is that the amount of energy per token is dropping substantially with each new generation of chip. So a five year old chip, if it’s working is the cloud computing equivalent of an old decrepit gas-guzzler of a car.
Financial picture
From a financial perspective, the change in hardware amortisation has caught the attention of short sellers. The reason why, is that the AI hardware is collateral against loans for some AI companies. They have a mortgage out on their chips. So the length of time that those things have a useful life is really important. If you had a house that lasted three years and you’ve got a mortgage for five years, it’s not a great position to be in.
(The most high profile short seller is Michael Burry who runs a Substack newsletter. He’s the chap portrayed by Christian Bale in the film adaption of The Big Short. Extremely smart guy, not as arrogant as he appears in the Christian Bale portrayal. Really great Substack, recommend that you read it.)
There’s also been a number of financing accounting changes going on. So we’ve talked about the lengthening lives of the AI hardware. You’re also seeing off balance sheet deals being done to help finance data centre development. A number of the hyperscalers like Meta, Google, Amazon and Microsoft have been very cash generative businesses. This has been because software and online advertising are high margin businesses that generate a lot of cash.
Meta and Microsoft have teamed up with private equity companies to co-finance their data centre build out and their acquisition of processors. These loans those are in special financial vehicles that keeps them off Microsoft and Meta’s balance sheet. Short sellers are alarmed by this as it is similar to what we saw that in the telecoms business from the likes of Enron and MCI WorldCom during the dot com bubble.
There are also accusations of circular financing as well. So the chart on the right hand side came from Bloomberg in September of last year. This started to get people worried about the idea of an AI bubble because a lot of it is financed by loans to and from the major technology vendors to the AI players.
Short sellers allege that the values and the profits are being artificially over stated by the hardware depreciation costs and this circular financing. They wonder where are the real transactions? If you look at the circular financing there isn’t meaningful revenue at the moment.
Looking at recent market valuations when I wrote this paper at the end of the year the magnificent 10 (a lot of the hyperscalers, including Meta, the Amazon, Alphabet and Nvidia), had a price to earnings (P/E) ratio of 35x.
So that would mean that it would take 35 years of earnings to actually pay off the share. The S&P the 500 dot-com peak had a P/E ratio of 33 times earnings.
Those values then assume that LLMs would drive one to four trillion in revenue growth or cost savings for dot LLM companies in the next two years, which is a huge amount of money.
$1 trillion revenue target
So how are businesses going to get there? So I started to do a thought experiment:
Advertising will only contribute a relatively small amount. It’ll be big numbers for the rest of us here, but if we think about that target that needs to be hit, it’ll only contribute a small amount of the revenue needed.
Advertising is an industry. It’s about 1% of GDP globally. Also while AI can increase efficiency of advertising, which might be a reason to go there, it may even decrease effectiveness of advertising further.
If you look particularly for large brands, they’re not getting the returns out of digital advertising already that they should be. If you look for growth and increased earnings over the past 15 years or so.
So what about business efficiencies? Yes, it can automate tasks, might be able to reduce jobs. The way it’s optimally pitched is what Microsoft Research described as a wingman approach.
Then the third option which is a lower probability because it rules on a certain amount of serendipity and AI companies have a lot less control.
What does $1 trillion in job cuts look like?
Which raise the question what does a notional $1 trillion, in savings due to job cuts mean?
As a thought experiment it scared the living getting lights out of me. It equates to about 10.5 million jobs in the US. I used two economic models.
The Phillip curve, which models inflation.
Okun’s law, which looks at the impact of job losses on GDP.
A trillion dollars in job cuts, wipes $four trillion GDP and 3% deflation to start with. There would likely be additional secondary effects that I didn’t even attempt to calculate
It creates an efficiency paradox, that would destroy the dot LLM ecosystem financially. They rely on being able to get money to invest and that would drop through the floor. You would have less businesses and fund managers investing and less retail investors.
Rather than being able to gradually increase prices over time with a moat, AI companies would be having to continually decrease in prices due to deflation.
The efficiency paradox means there’s a sweet spot between the degree of productivity benefits that they actually provide within a market without destroying AI as a business.
This all assumes that we’re actually operating within the closed system of the American AI market. But it’s worse because it isn’t closed.
The China factor
The US doesn’t have global AI dominance. Some experts think that China may be ahead. I don’t necessarily think that that’s the right framing to use because I think that China’s running a slightly different race. It’s taking a very different approach to the US about these things.
Competition is not only economic, it’s geostrategic and that actually might change and impact the economics of what we talk about.
The Chinese models are about 10 times more power and processor efficient than their American counterparts. They’re already being used with million+ downloads. They obviously do a good enough job that some Silicon Valley companies trust them.
Seven Scenarios
I thought about scenarios, about how the dot LLM era is going to happen, and I represented everything on a continuum of economic transformation to total collapse. The more I thought about it, I saw that we’re going to have overlapping scenarios rather than a single outcome.
On the left hand side was what the AI product was trying to do. New value creation or drive efficiency, along the top what the net impact was likely to be in terms of either negative or zero value creation or a positive to transformation value creation effect.
A probability based approach
The moral hazard happens because AI no longer just economic in nature, but also becomes geopolitical and a national security issue. I rated it at 95% because China is already there in terms of treating vast amounts of its economy from food to technology as a national security issue. AI is no exception. I read a paper by a Canadian think tank equated every US AI company data centre as equivalent to having a US military base on your territory.
The AI players are too big to fail. There’s a national security imperative, and a government’s backstop for them. Taxpayers will have to foot the bill. The AI companies don’t necessarily have to innovate as hard. They can take financial risks, similar to banks pre-2008.
The wingman economy, we’re already seeing this in the way Google, Microsoft and Adobe position their AI offerings.
The idea is that you actually get avoid catastrophic job losses by striking a balance between growth and efficiency to land in just the right place.
The Red Hat model. The idea of that pure play AI businesses struggle to find probability. You get LLM model proliferation, like what I talked about with open source models. The open source models, like Qwen, already have people using it around the world. Then value starts to shift to enterprise support or integration. Like Airbnb who are integrating these models already into their services.
Telecoms bust. You only have to look at things like private equity Blue Owl pulling out of a funding round for an Oracle data centre. It could be basically how they feel about Oracle in particular’s AI offering, which a lot of money has been going into, but not a lot of results have been coming out of, or it could also be sentiment around the dot LLM eco-system in general.
The last three options are much lower probability events.
The new economy model. The idea that AI will make transactions frictionless, with agentic automation , a lot of things will happen. There’s an uncertainty around the economics of this at the moment and there’s numerous concerns like the AI might actually drift away from the original human intent over time. There are also bottlenecks with legal and regulatory issues.
The breakthrough is a black swan event by its very nature. So this would be like a major scientific breakthrough, but then it would likely take 10 years to commercialise. Think about new drugs like Wegovy or Ozempic. They were an innovation that launched during the COVID period. The actual discovery was done back in 1979.
It took decades to get them to be commercial as a weight management product.
With other technologies, that period might be down a bit. So a new oil field might be only a 10 year project from discovery to commercialisation. Either way, it won’t pay off in a two year period.
Where we’re at?
I do believe that the dot LLM era is a financial bubble and a technological shift. The shift will continue to happen and evolve. It will continue to influence culture and business.
The financial bubble may destroy economies. It will keep driving national rivalries.
There is likely to be, and at least in the major players like Google and Amazon, a wariness of self-defeating economics where efficiency seeking destroys consumer base. Even if there’s not worries within AI companies, governments will hit them pretty hard because if you actually see a four trillion drop in GDP in the US and a 10 million strong decline in employment rates, even the current Trump administration would have to step in and regulate.
they’ would regulate is they’d probably overcompensate on economic impact.
So a lot of the major companies, possibly with the exception of Elon Musk, will be thinking about these factors to a certain extent. I think we’re in phase one to the boom and go to the next stage at any time. We’ve got the seeds of a lot scenarios including moral hazards.
It’s the geopolitical things that are really complicating things at the moment.
Eventually we’ll get to a new normal. How long it will take depends on the amount of government intervention that actually happens from an economic point of view. It will also depend on geopolitical factors.
You get a Taiwan invasion, that will impact manufacture of GPUs and TPUs because they’re all made on the island of by TSMC.
Large hyperscalers like Alphabet , Amazon and Microsoft are the most likely to survive the bust as they have multiple revenue streams and can integrate their AI capabilities into these products.
A special thank you to Matthew Knight of Outside Perspective for organising and facilitating the session.
January 2026 introduction – (30) the dirty Gertie edition
I am now at issue 30, or as a bingo caller would put it ‘dirty gertie’. This phrase was the nickname given in the 1920s to a statue called by La Délivrance by French sculptor Émile Guillaume.
The statue was created to celebrate the German army having being stopped before Paris in World War 1. It was originally called La Victoire – there is a matching statue in Nantes, France.
1960s student activists claimed that you shouldn’t trust anyone over the age of 30, making a virtue of ageism. While activists were deeply suspicious, 30 in Cantonese is considered to be lucky as the number three sounds like alive or life.
It might be winter outside, but it doesn’t need to be winter in your head thanks to Graeme Park’s Best of 2025 part 1 which is two and a half hours of goodness. Now we have a sound track, let’s get into it.
New reader?
If this is the first newsletter, welcome! You can find my regular writings here and more about me here.
Things I’ve written.
Each year, I try and write an account of year as it happens. It provides a perspective on what appeared important at the time rather than in retrospect. Here’s the one I did for 2025.
The Dot LLM Era came out of my thinking about the massive expenditure in building infrastructure and the computing power needed by AI services like OpenAI and Anthropic, asking how it will be paid for and what it means for for business, consumers, investors and technologies.
There was so much happening from childhood beauty product usage alarming dermatologists to corporate and national moves in AI sovereignty. So I captured some of the most interesting of them here.
Books that I have read.
Mariana Mazzucato’s The Value of Everything. Mazzucato’s work was reflected in the Labour Party’s economic manifesto during the 2024 general election. The book does a good job of diagnosing the current challenges that the UK economy faces at the present time. More on the book here.
How to Write a Good Advertisement: a short course in copywritingby Victor O. Schwab. During the CoVID lockdown, I picked up several books on my craft. This was one of them. Schwab wrote this book in 1962, when his audience would have been predominantly writing advertising copy for campaigns run predominantly in newspapers – but all of the principles in the book remain solid. More on the book here.
Things I have been inspired by.
Every time I get a brief that defines an audience as a generation my heart sinks a bit for several reasons. Which is why I was glad to read this Ipsos View Point and share it as widely as possible. Generational Marketing: Breaking free from stereotypes provides research on the nuances missed by a generational approach, how we differ by age cohort and life stage, alongside what brings us together as common challenges.
While it won’t get as much ink as Christmas or Super Bowl adverts the CIA kicked off January with another video aimed at recruiting Chinese agents. They advised them to use a VPN and Tor browser to get in touch with them online.
Chart of the month.
After I came back to London after working on various brands including Colgate in Asia, I noticed that all the Colgate adverts followed a standard formula. It puzzled me: the ads were distinctive by their ‘undistinctiveness’. They had no emotion and a limited number of brand cues beyond name checks and a pack shot or two.
If like me, you’ve ever wondered why Colgate toothpaste adverts (in Europe at least) always seem to be based around a dentist or dental nurse (who may, or may not be a generative AI) character, then Ipsos Veracity Index 2025, may have the answer.
The Ipsos Veracity Index, is a great piece of longitudinal research launched in 1983. It does an annual poll studying change in public trust towards leading professions in Britain. Much of the headlines for this year was the low trust position scored by influencers, with just 6% of people generally trusting them to tell the truth.
I think that number has a number of problems with it, to do with the phrase general which would invite them to think about creators they don’t follow at least as much as those that they do follow. Secondly, not all influencer types are supposed to be trusted be it being videos on e-gaming play, humour and general ‘banter’ or shock jock-type content.
As Ipsos themselves noted, there was a tension between the declared trust level with the amount of news consumption that now happens on social channels from influencers.
Getting back to the Colgate question, the answer is at the top of the table. Healthcare professionals and technical experts are at the most trusted professions in the UK.
Things I have watched.
The TV schedule was terrible over the Christmas period and there were only so many reruns of Jessie Stone that even my Dad can sit through. So I entertained him with a mix of streamed films, old VHS tapes, DVDs and Blu-Rays.
Reflection in a Dead Diamond directed by Hélène Cattet and impressed the hell out of me. At its heart it’s a mystery full of illusion, delusion and deception. It oscillates between two timelines one from the late sixties on and the second as an elderly version of the protagonist in the present day. In his day, the protagonist had been a Francophone James Bond-type figure, but darker like Fleming’s novels rather than the version that we see on screen. There are also hints of modern French historical figures like Alfred Sirven and Jean-Claude Veillard. The film has a lot of French new wave motifs particularly at its beginning. I was reminded of Alain Delon’sTraitement de choc , Diabolik and the André Hunebelle directed OSS 117 series of films in the mid-1960s.
Bubblegum Crash– no that isn’t a typo. Bubblegum Crash was a follow on from the Bubblegum Crisis manga and OVA (original video animation – made for direct to video distribution without being broadcast or shown in a cinema first) anime series. I had these on VHS tape at my parent’s house and it was fantastic revisiting them decades later. Bubblegum Crash is less serious and the artwork isn’t as good as the original series, but it’s still great cyberpunk fiction.
It felt surprisingly fresh, wealth inequality, get rich schemes, large corporations behaving badly, an openly gay police officer, autonomous machines from robots to cars and normalised smartphone usage.
All this from an animated series that was produced in 1991, at this time robots were stuck in car plants, AI was image stabilisation in the latest high-end camcorders and handheld mobile phones were over 20cm long in use. Cellphones were only starting to become less than a kilogram in weight with the launch of Motorola’s MicroTAC in 1989.
Detective vs Sleuths – a Johnnie To-adjacent film that a friend in Hong Kong gifted to me. The film was directed by Wai Ka-fai who collaborated with To and co-founded production company Milkyway Image together. Detective vs Sleuths feels thematically and stylistically similar to Mad Detective which Wai co-directed with To in 2007. That similarity brought me back to happier days flying on Cathay Pacific, sipping Hong Kong-style milk tea and watching Mad Detective soon after it had came out for the first time on the airplane entertainment system.
Without spoiling the plot, old cold cases are having new light shone on them by a series of deaths. Sean Lau plays a Nietzsche-quoting former detective with his own sanity in question.
Production-wise, the film was shot in 2018, was in post-production until 2019 and finally released after the worst of CoVID was over in 2022. If you are a passionate Hong Kong film watcher, then you will notice the similarities with Mad Detective; but Detective vs Sleuths still holds up as a really enjoyable inventive film with a number of surprises for the audience.
Useful tools.
Kinopio – quick lightweight service similar to Miro and MilanNote.
Clean Links – for iPhone, iPad and Mac cleans out tracking codes from URLs when you share them, for instance in a Slack conversation.
Not a tool per se, but a technique that started on Chromium browsers and is now more widely supported, scroll to text fragments. Appending to the end of a URL:
#:~:text=startWord,endWord
When someone clicks on the link they are guided directly to a highlighted section on the page, rather than having to search or guess at what you meant. It isn’t perfect, but it’s rather good.
i am a strategist who thrives on the “meaty brief”—the kind where deep-tech or complexity, business goals, and human culture collide.
With over a decade of experience across the UK, EMEA, and JAPAC, I specialise in bridging the gap between high-level strategy and creative execution. Most recently, I was embedded within Google Cloud’s brand creative team, where I helped navigate the “messy steps” of global pivots and the rapid rise of Gen AI.
My approach is simple: I use insight and analytics to find the “surprise” in the strategy. Whether it’s architecting an experiential event or defining a social narrative for a SaaS powerhouse, I focus on making complex brands feel human and high-velocity businesses feel accessible.
The Strategic Toolkit:
Brand & Creative Strategy: From B2B infrastructure to luxury travel.
AI-Enhanced Planning: Deeply literate in Google Gemini and prompt engineering to accelerate insights and creative output.
Multi-Sector Versatility: A proven track record across Tech & SaaS (Google Cloud, Semiconductors), Consumer Goods (FMCG, Beauty, Health), and High-Interest Categories (Luxury, Sports Apparel, Pharma).
I am officially open for new adventures with immediate effect. If you have a challenge that needs a “wholehearted” strategic lead, let’s talk.
Ok this is the end of my January 2026 newsletter, I hope to see you all back here again in a month. Be excellent to each other and good luck with your new year’s resolutions. As an additional treat here is a link to my charts of the month for 2025, in PowerPoint format that you can freely use in your own presentations.
Don’t forget to share if you found it useful, interesting or insightful as this helps other people and the algorithmic gods of Google Search and the various LLMs that are blurring what web search means nowadays.
