It’s hard to explain to someone who didn’t live through it how transformation technology has been. When I was a child a computer was something mysterious. My Dad has managed to work his way up from the shop floor of the shipyard where he worked and into the planning office.
One evening he broad home some computer paper. I was fascinated by the the way the paper hinged on perforations and had tear off side edges that allowed it to be pulled through the printer with plastic sprockets connecting through holes in the paper.
My Dad used to compile and print off work orders using an ICL mainframe computer that was timeshared by all the shipyards that were part of British Shipbuilders.
I used the paper for years for notes and my childhood drawings. It didn’t make me a computer whiz. I never had a computer when I was at school. My school didn’t have a computer lab. I got to use Windows machines a few times in a regional computer labs. I still use what I learned in Excel spreadsheets now.
My experience with computers started with work and eventually bought my own secondhand Mac. Cut and paste completely changed the way I wrote. I got to use internal email working for Corning and internet connectivity when I went to university. One of my friends had a CompuServe account and I was there when he first met his Mexican wife on an online chatroom, years before Tinder.
Leaving college I set up a Yahoo! email address. I only needed to check my email address once a week, which was fortunate as internet access was expensive. I used to go to Liverpool’s cyber cafe with a friend every Saturday and showed him how to use the internet. I would bring any messages that I needed to send pre-written on a floppy disk that also held my CV.
That is a world away from the technology we enjoy now, where we are enveloped by smartphones and constant connectivity. In some ways the rate of change feels as if it has slowed down compared to the last few decades.
Anthropic and the US Department of Defense defined the debate about AI for the start of March. Trying to understand the truth is murky.
The media pitches a clash of personalities between Pete Hegseth and Anthropic CEO Dario Amodei.
Anthropic’s Claude LLMs have a number of points of expertise from helping programmers develop software code more quickly to assisted decision making and automation.
Anthropic had concerns about weapons with no humans in the loop, but you could consider ‘fire-and-forget’ weapons are already the same thing. This would include the FGM-148 ‘St’ Javelin anti-tank missile successfully used by the Ukrainians or the British Brimstone air-to-ground missile.
Fire-and-forget saves lives, autonomous vehicles in areas like casualty evacuation and supply runs could save more lives. The Anthropic breakdown seems to be down to trust. Anthropic felt that its models weren’t ready for full autonomy of operation and there were also concerns about facilitating mass surveillance of Americans.
There seems to be undertones of taking action against a ‘woke’ company. Why Anthropic seemed to have been able to double down is the limited impact they claim it will have on their business.
And yes the term ‘seem’ is doing a lot of heavy lifting due to difficulty in discerning what is going on.
China
China: Quieter, more fretful than I remember – by Whipling – it’s immediately obvious there is a current vibe in China. It isn’t frantic. It isn’t charged. It appears to be a collective sigh. Pride at what’s been achieved; acknowledgement that things are going to stop improving at the speed they forever have; resignation that life will be a little bit harder hereon in; and gratitude that there are messier places around the world to live. Many terms have been thrown at interpreting elements of this current behaviour in China. “Involution”. “Lie Flat”. I’ll add another: “Eh, fine.”
Why Everyone Is Suddenly in a ‘Very Chinese Time’ in Their Lives | WIRED – As is often the case with Western narratives about China, these memes are not really meant to paint an accurate picture of life in the country. Instead, they function as a projection of “all of the undesirable aspects of American life—or the decay of the American dream,” says Tianyu Fang, a PhD researcher at Harvard who studies science and technology in China.
At a moment when America’s infrastructure is crumbling and once-unthinkable forms of state violence are being normalized, China is starting to look pretty good in contrast. “When people say it’s the Chinese century, part of that is this ironic defeat,” says Fang.
As the Trump administration remade the US government in its own image and smashed long-standing democratic norms, people started yearning for an alternative role model, and they found a pretty good one in China. With its awe-inspiring skylines and abundant high-speed trains, the country serves as a symbol of the earnest and urgent desire among many Americans for something completely different from their own realities.
Alibaba’s Qwen App Commits ￥30B to Chinese New Year AI Giveaway Campaign | Pandaily – China’s tech giants are using the Lunar New Year — the world’s largest annual migration — to turn niche AI assistants into household names. They are betting billions that “Red Packet” marketing can do for AI what it did for mobile payments a decade ago.
Former Alibaba Executives Join Robot Leasing Platform BotShare as President and CSO – Pandaily – Li Liheng, former head instructor of Alibaba’s renowned B2B sales force known as the “China Supplier Iron Army,” has joined robot leasing platform BotShare as President. He will be joined by Wang Mingfeng (Tianxiang)—another Alibaba veteran previously responsible for management training under Alibaba’s “Three Axes” leadership framework—who will serve as Chief Strategy Officer.
BotShare officially launched in December 2025 and disclosed its seed funding round on January 15, 2026. The round was led by Hillhouse Ventures, with participation from Fosun Capital and other investors. According to Qichacha data, Agibot (Zhiyuan Robotics) holds a 55% stake in BotShare, while Feikuo Technology owns 15%. Founded in 2024, Feikuo focuses on deploying and operating robots in real-world scenarios such as cultural tourism, commercial performances, and guided exhibitions.
As a robot leasing platform, BotShare aggregates robots from multiple brands and models, offering rentals for scenarios including corporate annual meetings, livestreaming, store openings, and promotional events.
Available brands currently include Accelerated Evolution, Unitree, Zhiyuan, Zhongqing, Lingchu Intelligence, and Zhujie Dynamics, among others. Robot delivery, retrieval, and maintenance are handled by local leasing partners across different regions.
Platform data shows that within three weeks of launch, BotShare surpassed 200,000 registered users, with daily rental orders stabilizing at over 200.
Hong Kong’s Sogo mall operator seeks $1 billion loan refinancing | Jing Daily – Sogo malls, especially the flagship Causeway Bay one, have long been among Hong Kong’s prime retail destinations. However, traditional retailers like department stores have been facing even more pressure from the mainland’s growing e-commerce penetration, the rise of low-end stores and weak domestic consumer sentiment.
Lifestyle International was taken private by its chairman, Hong Kong billionaire businessman Thomas Lau Luen-Hung, in a HK$1.9 billion deal after the company warned of an at least 80% plunge in profit in the first half of 2022.
Still, Hong Kong’s retail landscape has shown signs of stabilizing. Government data indicates that retail sales rose 6.5% year-on-year in November 2025, citing improving local consumption amid sustained economic growth and increasing visitor numbers.
“Hong Kong continues to drive the strongest demand in the region,” Perazzi says. As a global gateway, the city draws international bidders competing for trophy pieces — particularly Rolex and Patek Philippe — and increasingly, independents.
Taiwan, meanwhile, reflects consistency rather than spikes. “Taiwanese collectors are renowned for their long-term approach. Compared to Hong Kong’s appetite for headline-grabbing lots, Taiwan is characterized by quieter but reliable demand,” Perazzi adds.
A surprise force is Southeast Asia. Vietnam and the Philippines are now producing first-generation collectors with expanding wealth pools and few legacy constraints. “Southeast Asia has emerged as a dynamic growth region,” Perazzi says, citing a younger collector profile and faster adoption of new independents.
Indonesian woman collapses after 140 lashes for sex and alcohol | South China Morning Post – A woman in Indonesia’s Aceh province collapsed after being caned 140 times last week for extramarital sex and drinking alcohol in one of the harshest sharia punishments on record. The woman and her partner were struck with a rattan cane in a public park in Aceh province on Thursday as dozens watched, Agence France-Presse reported. Each received 100 lashes for extramarital sex and another 40 for consuming alcohol, according to Banda Aceh sharia police chief Muhammad Rizal. – the move to more Gulf-orientated interpretation of Islamic rule is likely to cramp globalisation in Indonesia by western firms, despite it being the most populated Muslim country and will affect service industries such as tourism
When Real Beauty Met Reddit | LBBOnline – Reddit is very underestimated, interesting to see Dove using it in this way. Also worthwhile noting that Reddit is a key training source for LLMs.
America must follow China in treating data as an asset – In 2024, China became the first country to allow enterprises to classify data as intangible assets on their balance sheets. Beijing had already declared data a “factor of production” alongside land, labour, capital and technology. The National Data Administration now oversees dozens of data exchanges. China Unicom, one of the world’s largest mobile operators, reported Rmb204mn ($29mn) in assets in its first filing under the new rules.
Most of the major AI players went to Davos, though they weren’t the main focus due to the Trump administration. Google Deepmind founder Demis Hassabis admitted that the current AI market is ‘bubble-like’.
Marc Andreessen’s 2026 AI outlook was published by A16z. As one of the leading funder of Silicon Valley startups, his world view matters.
I’ve gone through and contrasted his 2026 AI outlook through the lens of the viewpoint I researched and wrote up. The core point over where we differ: Andreessen see’s the dawn of a new unbound industrial revolution. Whereas I think that the upside he sees is hard to navigate to; and also risk mirroring the echoes of financial bubbles past in many of the high profile pure play AI companies like OpenAI or C3.ai.
Andreessen has been the quintessential techno-optimist for at least the past two decades with his emblematic essay Why Software Is Eating the World. His worldview is of an industry where demand is insatiable and revenue is undeniably real.
Like Andreessen in his 2026 AI outlook, I would agree that players like Alphabet, Amazon and Microsoft are making money to variable degrees from their AI offerings as part of cloud computing and productivity software bundles.
But in my view there are two aspects of concern:
Pure plays with the notable exception of Anthropic don’t seem to have a clear path to profitability in a time period that would match their implied future revenues based on loans and valuations.
Hyperscalers like Meta and Microsoft are using unusual partnerships to fund their infrastructure and have been inconsistent over how fast they would depreciate their AI computing hardware.
Both Mr Andreessen and myself agree that we are witnessing a technological shift of seismic proportions, arguably, “bigger than the internet” as he put it.
I think that change will happen slower in the short term due to an economic sand box acting as a rate limiter on infrastructure and derived labour efficiencies. In particular, the economic viability of the infrastructure being built to support it.
The Nature of the Boom: Real Revenue or Irrational Exuberance?
A key question is the the solidity of the current market.
For Andreessen, the AI boom is not a speculation fuelled exercise but a demand-driven reality. He argues that unlike the early internet, which required years of physical infrastructure build-out before finding business models, AI is generating cash immediately.
“This new wave of AI companies is growing revenue like… actual customer revenue, actual demand translated through to dollars showing up in bank accounts at like an absolutely unprecedented takeoff rate.”
Andreessen points to “revealed preferences”, observed insight from what people do is more insightful than what they say when asked by market researchers. While the US and European publics express fear of job losses, they are simultaneously adopting AI tools at a fast pace.
My “Dot LLM Era” report, however, suggests this revenue may be dwarfed by the capital expenditure required to generate it.
It posits that the sector faces a “self-defeating economic” cycle. The report highlights that the current valuations of the “Magnificent 10” tech giants (including Nvidia, Microsoft, and Alphabet) imply a forward price-to-earnings (P/E) ratio of 35 times. This is alarmingly close to the S&P 500’s P/E ratio at the peak of the dot-com boom, which approached 33.
In the report I warn that current valuations assume LLMs will drive revenue growth by $1–4 trillion in the next two years.
If this growth is achieved through massive job automation, the resulting unemployment could depress the very economy needed to sustain these companies. Like the 2008 financial crisis, this would invite various forms of regulatory intervention.
This implies a sweet spot between speed of productivity gains versus efficiencies in terms of job losses realised by clients – and acts as a break on the velocity of adoption.
The Infrastructure Trap: The Amortisation Crisis
Perhaps the most critical technical divergence between our viewpoints concerns the hardware powering this revolution.
The “Dot LLM Era” report introduces a chilling concept: Amortisation Risk. It draws a comparison to the “telecoms bubble” of the late 90s, where companies laid massive amounts of fibre optic cable.
The Telecoms Analogy: Fibre optic cable had a useful life of over a decade, meaning even after the companies went bust (like WorldCom), the infrastructure remained useful for Web 2.0.
The AI Reality: Modern AI infrastructure relies on GPUs and TPUs. These processors have a useful life of only 3 to 5 years before becoming technically obsolete.
The report argues that if an AI bust occurs, the hardware will not be waiting for a resurgence; it will be electronic waste.
Hyperscalers are currently lengthening the assumed useful lives of this hardware in their financial filings—Google to 6 years, Meta to 5 years—which the report suggests may artificially overstate profits and adversely affect competitors with debt securitised against AI data centre hardware.
The rapid obsolescence of chips represents a “financial and technological amortisation risk” that could lead to trillions in write-offs, similar to the $180 billion loss left by WorldCom.
Andreessen views this hardware cycle through a different lens: Elasticity.
He acknowledges that massive investment leads to gluts, but argues that “the number one cause of a glut is a shortage”. He believes the price of AI compute is falling “much faster than Moore’s Law”.
As prices collapse, demand will expand exponentially. If chips become cheap and plentiful, AI can be embedded into everything, moving from massive “God models” in data centres down to small models running on local devices.
Geopolitics: The US-China Race
Both Andreessen and I agree that the AI landscape is a bipolar contest between the United States and China, but their assessments of the leaderboard differ.
Andreessen frames this as a “new Cold War” where the US must maintain dominance. He is encouraged by the “DeepSeek moment”, referring to a powerful open-source model released by a Chinese hedge fund. To him, this proves that catching up is possible and that the US cannot rest on its laurels. He advocates for open source as a way to proliferate American standards globally.
I offer a more sobering assessment of American pre-eminence. I argue that unlike the 1990s “Long Boom,” where the US was the undisputed hegemon; the current era is defined by high debt and strong competition. The US maybe on the Soviet side of a Reagan era ‘Space Defence Initative (aka Star Wars)’ AI race from an economic perspective.
Alibaba’s Qwen model claims to deliver comparable performance to American models while requiring 82% fewer Nvidia processors to run.
China doesn’t have a electrical power crunch in the same way that western data centres have due to its sustained investment in coal, nuclear, gas and renewable power sources.
Even Silicon Valley investors and major companies like Airbnb are opting for Chinese open-source models because they are “way more performant and much cheaper”.
Andreessen worries about regulation stifling US innovation, I think that the real geopolitical threats are:
China’s ability to operate largely immune to US sanctions, smuggling chips and utilising data centres in neutral geographies like Malaysia.
China’s speed at propagating the use of AI within its own populace, driving utility at a lower cost per token than their US competitors with state regulation defining trial ‘sand pits’. This will drive new uses faster in China and in China’s client states across the global south.
The Outcome: Transformation or “Minsky Moment”?
Where is this all heading? Andreessen is betting on a future where AI becomes as ubiquitous and essential as electricity. He envisions a “pyramid” structure: a few massive “God models” at the top, cascading down to billions of specialised, cheap models running on edge devices. He admits these are “trillion-dollar questions,” but his firm is aggressively investing in every viable strategy.
I think that LLMs will make a sustained technological impact, but it will be limited in velocity by economic boundaries. In the “Dot LLM Era” report, I outlined seven potential scenarios, ranging from total transformation to total collapse. Currently, it assigns a ~95% likelihood to the “Moral Hazard” scenario.
The Moral Hazard: This scenario posits that major AI players will be considered “too big to fail” due to national security imperatives. Governments will step in with loan guarantees and subsidies to backstop the massive infrastructure debts, effectively nationalising the risk . Rather like the banks during the 2008 financial crisis.
The Telecoms Bust: With a ~75% likelihood, the report fears a “Minsky Moment”, a sudden market collapse driven by the realisation that cash flows cannot cover the massive debts incurred to build short-lived data centres.
Comparative Summary of Perspectives
Feature
Marc Andreessen (The techno-optimist)
My own view (The economic limiting skeptic)
Current Phase
Inning 1. “Biggest technological revolution of my life.”
Phase 1: The boom / The Inflation of a bubble.
Revenue
Real, unprecedented, showing up in bank accounts.
Potentially illusory for at least some players; reliant on untenable cost savings.
Infrastructure
Shortages lead to gluts; cheap chips drive adoption.
Amortisation risk: hardware obsolescence in 3-5 years.
Pricing
Usage-based is great for startups; prices falling fast.
US dominance challenged; Chinese models are more efficient and effective enough for organisations from Singapore to Silicon Valley to adopt them.
Outcome
Long-term ubiquity; widespread prosperity.
Technological change bounded by economic limitations. Current high risk of “Minsky Moment” or government bailouts.
Conclusion: The Trillion-Dollar Questions
Marc Andreessen candidly admits that “companies… need to answer these questions and if they get the answers wrong, they’re really in trouble”. His firm’s strategy is to bet on everything: large models, small models, apps, and infrastructure. He is doing this on the assumption that the aggregate wave will lift all boats.
My ‘Dot LLM Era’ report offers a counterweight to this enthusiasm. A bubble decouples technological and financial progress. Technological utility does not always equal investor profit. As I note in ‘Dot LLM Era’, “Bubbles don’t kill technology from moving forwards”. The internet did change everything, but it also wiped out trillions in shareholder value along the way.
The defining question for the next five years is whether the demand for AI can grow fast enough to pay for the hardware before that hardware becomes obsolete.
If Andreessen is right, this elasticity of demand would save the day. If I am right, we may be heading for the most expensive recycling project in human history.
The Productivity Paradox and Society
A fascinating tension exists between Andreessen’s view of societal adoption and my own macroeconomic warnings regarding productivity and labour.
The “Wingman” Economy vs. The Phillips Curve
Andreessen describes a “symbiotic relationship” where AI acts as a productivity multiplier: a “wingman” for doctors, coders, and writers. He argues that higher pricing in SaaS can actually benefit the customer by funding better R&D, suggesting a cycle of value creation.
I argue that the wingman sweetspot is optimal but tricky to land, in the report I showed the risk through a darker macroeconomic “thought experiment.” It used the Phillips Curve and Okun’s Law to model what happens if Andreessen AI scenario succeeds too well.
The Thought Experiment: If AI automation generates $1 trillion in cost savings through job cuts, it implies approximately 10.5 million unemployed US workers.
The Consequence: Such a spike in unemployment could trigger deflation and a massive drop in GDP. This is “self-defeating economics”: the hyperscalers need a healthy economy to consume their services, yet their success might undermine the investor, enterprise customer and consumer base.
Andreessen counters this fear by citing historical context. He notes the “Committee for the Triple Revolution” in 1964, warned Lyndon B. Johnson that automation would ruin the economy—a prediction that proved false. He believes AI will follow the path of electricity or the internet: initially terrifying, eventually indispensable.
Automation did displace a massive amount of developed world jobs moving at a much slower pace than Andreessen predicted for AI. Electricity moved at an equally slow pace compared to the pace envisioned by AI’s champions.
The Open Source Debate
Both of us agree that the role of open source is pivotal in both narratives.
For Andreessen, open source is the great accelerator. He marvels at how knowledge is proliferating: “Some of the best AI people in the world are like 22, 23, 24”. He views the leak of knowledge as inevitable and beneficial for US competitiveness, provided the US stays ahead.
I analysed the “Red Hat Analogue.” It suggests that in the dot-com era, open-source (Linux) won, but the companies that built the models (or distributions) mostly failed, with Red Hat being the notable exception.
I assigned a ~70-80% likelihood to the “Red Hat Model,” where pure-play LLM creators (like OpenAI or Anthropic) might struggle to justify their capital burn as open-source models like Meta’s Llama or Alibaba’s Qwen commoditise the intelligence.
We have already seen Singapore’s national AI programme drop Llama for Alibaba’s Qwen, reinforcing the idea that the value might accrue to those who service the models, not those who create them.
Final Thoughts
The divergence between Marc Andreessen and my own analysis is not about whether AI works both of us would agree that the technology can be magical and transformative. The disagreement is about who pays for it, how that affects the velocity of AI andwho profits.
Andreessen sees a future of abundance where falling prices drive infinite demand.
My own view sees a future of financial reckoning shaping the 2026 AI outlook where shorter hardware lifespans and brutal competition erode margins, setting a slower pace at which we reach Andreessen’s abundance.
Andreessen’s viewpoint reminded me a lot of mid-20th century aspirations for nuclear power. Nuclear power offered a similar vision in the mid-20th century of electricity too cheap to meter. That was never close to being achieved in the likes of France – arguably the most passionate adopter.
As with the railway mania of the 1840s or the optical fibre boom of the 1990s, society may inherit a significant infrastructure, with a shorter lifespan built on the ashes of investor capital.
Our differing views boil down to a question for the 2026 AI outlook: are we in the “boom” phase, or are we staring down the barrel of the “amortisation crisis”?
As Andreessen himself concluded, “These are trillion-dollar questions, not answers”.
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.
