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2026 AI outlook of Marc Andreessen

2026 AI outlook introduction

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.

Marc Andreessen has 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.	Pure-play LLMs selling tokens below marginal cost (burning cash).
Geopolitics	A race the US must win; open source is key.	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 and who 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”.

February 22, 2026

Brand building for B2B PRs
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.

You can watch the interview on video here.

I gave Miles a reading list in advance of us chatting. Here it is:
- How Brands Grow Parts one and two by Byron Sharp & Building Distinctive Brand Assets by Jenni Romaniuk – these three are based on the research by the Ehrensberg Bass Institute for Marketing Science
- Pre-suasion by Robert Caldini & Influence
- Lemon by Orlando Wood
- Look Out by Orlando Wood
- IPA guides with the FT
- The Long and The short of it by Binet and Fields
- Making Effectiveness work (IPA)
- Marketing is an Investment (IPA)
- Beyond Engagement – understanding influencer payback (IPA)
February 17, 2026
CNY 2026
Chinese new year CNY 2026 also known as lunar new year, spring festival or Tết festival. 2026 marks the year of the fire horse. In the same way that the Super Bowl and Christmas are the stand out times of the year for advertising in the US and Europe, CNY 2026 will be the same for much of east Asia and Southeast Asia.

There has a large amount of tradition and rituals around celebrating the festival, which are rich seams of inspiration for strategists and marketing moments.

I featured an advert from Brunei for the first time.

As with previous years, Malaysia had a lot of campaigns running, many of which were partnerships with local musicians to collaborate on a seasonal song. One of the advantages of partnering with local musicians is their ability to cross post on their own channels broadening the videos reach.

In the Malaysian adverts that were storyteller driven, coping with aging relatives suffering with dementia came through as a common social theme.

Social video has been a great leveller. I have a featured a few videos from small businesses this year which were nicely executed despite operating with minimal budgets.

Coca-Cola in China was notable in that it showed strategic thinking closer to what we now see in the west with social-first ‘Instagrammable’ tactics.

Australia

Godiva

Anywhere up to 8 percent of Australia’s population have some connection to China, which explains why Godiva have done a Chinese new year themed range of chocolates.

Brunei

Flower Journal

Flower Journal is a florist shop based in Brunei, yet they have created a cinematic advert with great storytelling. The craft is arguably better than a number of the big brands featured this year. The work by local agency Cinekota really impressed me.

China

Adidas

Adidas made a film about a school football team and focuses on how the team is a ‘football family’. Reuniting with family is an important part of lunar new year. It’s also about looking forward to the future, hence the children’s wishes.

Apple

TBWA\ Media Arts, Shanghai teamed up with film director Bai Xue for Apple’s CNY 2026 advertisement. The film joins Apple’s series of ‘shot on an iPhone‘ mini movies.

Coca-Cola

Coca-Cola China took a social and experiential approach focused around togetherness. A drone show in Chongqing paired with fireworks that are considered part of China’s intangible cultural heritage was supported by social video clips of a famous father and daughter.

All of this was to address young adults dual sense of togetherness during spring festival as mainland Chinese call CNY 2026. Being together with friends a la Friends and This Life, as well as more traditional family connections.

Valentino

Valentino put relatively subtle lunar new year symbols into a Chinese take on an American diner. The galloping horse zoetrope and red accents throughout the restaurant from neon signs to red floor tiles. As for the film itself, it’s basically a video lookbook.

Hong Kong

Hang Seng Bank

Hang Seng Bank ties into the the importance of welcoming good fortune into your life at Chinese New Year. Celebrities dress as the god of good fortune giving wishes for flourishing prosperity to different neighbourhoods across Hong Kong.

https://www.youtube.com/watch?v=mqYWpeDrtZ8

Malaysia

AEON

Japanese supermarket chain AEON did a Malaysian market specific film featuring a mix of well known entertainers. The giddy up line telegraphing its horse related theme and the cultural impact of K-pop is evident in the whole video.

Affin Bank

Affin Bank is consistent in their lunar new year campaigns. Each year they tell of how a famous business customer battled adversity to succeed. This time it was Malaysian book retailer BookXcess.

Affinity

Affinity is a Malaysian estate agent. The video creative is a pretty run of the mill reenactment of Chinese new year with the horse head mask hinting at the CNY 2026 theme. The song itself is a bit an ear worm.

Air Selangor

Air Selangor hits you with a gut punch of an emotional Chinese New Year story that felt like it came straight of the Thai advertising agencies rather than Malaysia. (Thai agencies are famous for wringing you through an emotional shredder leaving you drained after an insurance ad).

Alpro

Malaysia’s largest prescription pharmacy chain put together a humorous new year film based around the mechanic of three wishes.

AmBank

The film melds together traditions around fabric sharing and lion dance to tell a Chinese new year story of a community coming together.

Astro

Astro is a Malaysian holding company that has a mix of linear TV, connected TV and radio assets. Think the reach of the BBC, but a private enterprise.

Bamboo Green Florist

Bamboo Green Florist is a single shop business based in Penang. For a small business their Chinese new year advert punches above its weight.

Coca-Cola

The first of two appearances in this list by Malaysian group 3P.

GVRide

GVRide is a Malaysian ride hailing app, they sponsored a new year song music video by Namewee alongside other brands.

IJM Land

IJM Land is a Malaysian property developer (part of a larger conglomerate). They position themselves as “one of Malaysia’s property development”. The film sits at the tension between the love of heritage, accumulating wealth and the non-monetary aspects of CNY 2026 – coming together, family, building memories and legacy.

JinYeYe

JinYeYe sell seasonal hampers, so lunar new year is their peak sales time. Their advert is targeted at the global Chinese diaspora and they partnered with Tourism Malaysia alongside local musicians. A bee is considered to a symbol of blessings and represents sweetness, hope and companionship.

https://youtu.be/0YvLVF4TJAE?si=sn4nMWPwykr7WzjM

Lee Kum Kee

Hong Kong’s Lee Kum Lee were the inventors of oyster sauce and have a place in every Asian kitchen cupboard. But their advert is weak sauce (pun intended) that could have been knocked out on PowerPoint.

Listerine

Listerine just straight up sponsored the video of Malaysian producers 1119 for this new year themed music video.

Loong Kee

Loong Kee is a Malaysian food company who makes everything from processed meats to baked goods. This is at least the third year that they have partnered with local musicians who are internet-famous to collaborate on a new year themed song.

Lotus

Lotus supermarket was formerly part of Tesco’s international footprint before the UK brand divested itself of its international stores to Thai conglomerate Charoen Pokphand (CP) Group. This advert taps into family friction and a couple of nice wushu cinema referencing touches. It reminded me a lot of SingTel’s films from previous years.

It handles the diversity of Malaysia well, without the awkward approach that Malaysian Airlines went for.

Malaysian Airlines

Malaysian Airlines focuses on Malaysians coming home. Given that the airline is a government company. While ethically Chinese, and speaking Chinese at home – the woman is a devote muslim.

In reality that’s about 1-2% of the ethnic Chinese population – for ethno-political, social and cultural reasons that I don’t want to get into on this post. The video is as much about a government approved theme as it is about the airline.

Marrybrown

Marrybrown is a Malaysian quick service restaurant. It is really nice how the story moves through time with relatively small but important cues on screen.

Maxis

Malaysian broadband provider took an unusual angle bringing together two erstwhile business rivals in a spirit of shared community.

McDonalds Malaysia

Great storytelling but with a serious topic as middle-aged siblings deal with an aging parent with signs of dementia.

Nescafé Gold

Instant coffee brand Nescafé Gold goes down the sponsored music video route. But with a few noticeable differences:
- Better product placement that articulates the customer moment.
- A more diverse cast than most of the other adverts.
- The video title Gongxi Kemeriahan – is a mix of mandarin and malay – gongxi meaning best wishes or congratulations and kemeriahan means excitement.
All of which are likely to because of Nestlé being a western multinational and the marketers are looking to target all Malaysians rather than just ethnic Chinese.

PMG Healthcare

PMG Healthcare is a regional provider of pharmacies, medical and dental clinics to private health insurance customers.

Mr Potato

Mr Potato is a local potato chip brand in Malaysia. Their CNY 2026 advert is a spoof of the Jackie Chan kung fu film Drunken Master.

Public Bank

Public Bank is a Malaysian headquartered bank. This year they have done an AR-based activation. Each Chinese new year you can go into your bank and get a pack of red envelopes and crisp new bills to give out to family, friends and junior colleagues. So this execution makes sense.

RHB

Malaysian bank RHB continued its theme of inspiring stories told in previous Chinese New Year campaigns through to its CNY 2026 campaign. This year tells the story of Komuniti Tukang Jahit, a small tailors shop that empowers women through sewing skills and fair income opportunities.

Setia

Malaysian house builder Setia takes a lighter comedic approach telling the story of a family’s new year celebration through the eyes of its youngest member. Its lightness of tone is in contrast to other adverts this year which are more of an emotional rollercoaster.

Shopee

Singaporean e-commerce platform Shopee partnered with local act 3P to a Chinese New Year song for its Malaysian ad campaign. Thoughout Asia lunar new year songs and playlists are all over TV, films, Spotify and YouTube playlists. This leans right into that trend.

SPD Racing

SPD Racing is a small workshop that service motorcycles and sell after market parts. This short video is really nicely executed, replacing parts on the motorcycle with red fittings in the same way that people would wear new red outfits on Chinese new year for good luck.

https://www.youtube.com/shorts/1x7RpOLHcTA

Tenaga

Tenaga is a Malaysian electrical utility. There is a nice bit of storytelling about a lion dance troupe. This could be rerun in future years given its lack of specificity to CNY 2026.

U Mobile

U Mobile is a Malaysian wireless operator. Their advert focuses on on the travel use case over lunar new year as more people travel rather than staying at home.

UCSI University

USCI is part of Malaysia’s private education system that sprang out of the positive discrimination of successive Malaysian governments towards Malays in comparison to Chinese and South Asian Malaysians. This was enshrined in article 153 of the Malaysian constitution, New Economic Policy, National Development Policy, National Vision Policy and the concept of Ketuanan Melayu which continues to be a pillar of government decision-making.

https://www.youtube.com/watch?v=ILuFokNxHck

In common with several other films here this year it focuses on the treasure of memories built over the festival and also has a dementia plot line.

Vida C

Vida C is kind of like an energy drink, in a number of Asian countries high vitamin C content is used in the same way that taurine and caffeine are in western energy drinks. They did a relatively subtle product placement in this comedic music video. It’s much less PC than western multinationals would allow.

Watsons

Watson’s is the Boots of Asia. Like previous years it tells a story of family coming together with the joy and chaos that usually ensues. It features Maria Cordero – a Macau born entertainer, radio and TV personality with a famous cooking show based in Hong Kong – but known throughout the region.

Singapore

Carlsberg

Carlsberg launched a pan-Asian campaign with a mix of horse themed packaging design and having it promoted by SKAI ISYOURGOD – a popular Malaysian rapper with appeal across Asia.

FairPrice

Singapore supermarket chain FairPrice focused on the small family moments of the new year celebrations and their ability to build lasting memories. The advert was created by TBWA\ Singapore.

Grab

At first I thought that this ad was aimed at the Malaysian market, but I think it’s aimed at both Singapore and Malaysia. It would work in either, even though some of the brands are Malaysia only like JayaGrocer. It’s unusual because of the amount of brand collabs in it, count them:
- Vinda tissues
- 7Up
- GXBank
- Jasmine SunWhite Rice
- JayaGrocer
- Kyochon Chicken
- Oriental Kopi
- Subway
Secondly, there was the filming of an ad within the ad concept that Orson Welles would have enjoyed.

LVMH

LVMH’s drinks portfolio has been suffering from declining sales. Family get togethers are an ideal consumption moment, so it makes sense that Hennessy leant in with special packaging and a Singapore family reunion ‘kit’.

SIMBA

Australian owned mobile network SIMBA did a very simple sales promotion which is very much in keeping with its value proposition , but the horses are nicely done.

Singapore government

A comedic short film with relatively light social engineering aiming at harmonious relationships and community during CNY 2026. The family were framed as being salt-of-the-earth Singaporean Chinese living in old HDB flat. The universal food photography was very on point.

Taiwan

Coca-Cola

Coke did a really simple sales promotion with a giveaway competition attached to each purchase.

United States

Panda Express

Panda Express is an American fast food chain that specialises in American Chinese food. It kind of sits outside usual lunar new year traditions becoming a Roald Dahl style fantasy.

Vietnam

Coca-Cola

Really simple creative by Coca-Cola. They missed a trick by not creating something as iconic as the US Coca-Cola truck adverts. Instead they phoned in the creative with this spot.

Ensure Gold

Abbott Health’s Ensure Gold is a Complan-type drink designed to fortify health and restore strength. The film uses family union traditions to focus on the past, recover during the Tết festival and look to the future with a shared sense of resilience. The theme is even reflected when the family does traditional ancestor worship and we hear the wishes of their departed family.

https://www.youtube.com/watch?v=bdSib8exz6I

Home Credit

Home Credit are an online financial services company. They provide credit cards, vehicle loans, pre-payment accounts and instalment payments for consumer products. The advert focuses on everyday people and how they prepare for Tết, including decorating the home, getting new clothes and a new karaoke machine for the family gathering.

Mirinda

Mirinda is a Vietnamese soft drinks brand similar to Tango. Their adverts were noticeable for their shortness. They were running 3 five-second spots and two 15-second spots. No real story, but there is energy, brand colours feature heavily and it gives off a joyous vibe.

MyKingdom

MyKingdom is a Vietnamese toy retailer similar to Toys R Us. Their mobile first content focuses on the challenges of parents looking to buy toys that will last longer than the spring festival.

https://youtube.com/shorts/EjSu9ybiwwo?si=NWMbimjWHizp9faY

Sunhouse

Sunhouse is a home electronics brand. Everything from kitchen appliances to to cookware.

In the advert, they focus on starting the new year healthy, there is a belief in starting the new year as you would like it to go on.

Viettel

Wireless carrier Viettel subverts the idea of a family reunion storyline during Tết. Instead when the family can’t come home, an uncle visits his family members around the country.

As I find more CNY 2026 campaigns I will add them here.

Past years

CNY 2025

CNY 2024

CNY 2023

CNY 2021

CNY 2019

CNY 2018
February 12, 2026
Forgettable cinema
The idea of forgettable cinema came to me while reading Matthew Frank’s newsletter for The Ankler where he repeated a thought exercise that one of his colleagues posed.

Name five films of this decade that will go down as classics.

Okay, I’m waiting.

…still waiting.

I consume cinema the way members of Soho House were famed for consuming gak. Also given my movie tastes, you may disagree with what I think of as classics.

My answer would be:
- Sinners – vampires in 1920s America amidst a slice of pre-civil rights life in the deep South
- The Boy and the Heron aka (How do you live?) – A Studio Ghibli film, like everything from Studio Ghibli it’s a masterpiece. Just watch it in Japanese with English subtitles as the English dub is awful.
- The Order – Jude Law as an FBI field agent in 1970s American Mid-West hunting white supremacists.
- The Goldfinger – A retelling of a financial scandal in Hong Kong’s go-go era of the 1970s and 1980s. It draws on the story of the Carrian Group which went belly up in the midst of a corruption and fraud scandal. It saw a bank auditor killed and buried in a banana tree grove. Lawyer John Wimbush was found dead in his home swimming pool. A nylon rope around his neck tethered to a concrete manhole cover at the bottom of the pool. The names had to be changed for legal reasons as the main protagonist George Tan was still alive when the film went into production.
- The Old Woman with the Knife – A film adaptation of a Korean novel about a skilled female assassin coming up to pensionable age. It is a thriller that also addresses an aging Korean society and the invisibility of older people.
Bonus
- Oppenheimer – Robert Oppenheimer biopic by Christopher Nolan that is visually amazing and does some interesting things with the storytelling.
But the point of the thought experiment was the most films now are forgettable cinema and most normal people would have struggled to name five future classic films.

They are watched by millions – yet never become part of culture. This idea of forgettable cinema used to be a novel idea with only the occasional blockbuster; notably the Avatar series, falling into this category.

Now Matthew Frank argues that forgettable cinema applies across all film making output. He described the phenomenon as ‘Cinemanesia’.

Why do we have forgotten cinema?

The problem might not be the films or the film making, but the change to discover and rediscover films. Frank posits that this is down to the way we now consume media.

Pre-Netflix, we had:
- Repeat showings at the cinema, the most prominent example of this for Londoners would be the ‘sing-a-long’ screenings of The Rocky Horror Picture Show.
- Movie marathons and midnight screenings.
- TV re-runs.
- TV broadcast movie ‘festivals like Moviedrome.
- Seasonal standards shown on television, such as Holiday Inn at Christmas time.
- Movie rental shops like Blockbuster.
There is comfort and familiarity in their repetition. Just in the same way that adverts build mental models, fame and salience in our heads through repeated exposure – classic films do too.

Yet we have got to a point where storied film actor / director Robert Redford was better known amongst young adults as the person in the ‘nodding with approval’ man GIF as internet meme, than his film career. The GIF came from Redford’s performance in Jeremiah Johnson.

By comparison Netflix provides us with a conveyor belt of entertaining enough content. We don’t get to build that depth of relationship through repetition. The business model for TV was different. The right films on the right channel had people tuning in because they wanted the familiar.

Instead what we have now is the video algorithmic equivalent of the Spotify playlist or the shuffle play of an Apple iTunes library. Like the music our relationship is largely broken – we can choose something that suits our mood or a broad range of interests.

For most of the time the movies and shows aren’t culture shifting.

Hellhound as a case in point.

Others like Korean drama Hellbound (지옥) have cultural relevancy for a brief while before disappearing again.

I have used Google Trends as a quick and dirty way of showing this phenomenon.

Google Trends isn’t search volume, but the rate in change of search volume and web search volume is an indicative rather than absolute measure of consumer interest.

Channeling my inner Marshall MacLuhan: we have forgettable cinema because the medium is disappearing the message.

All is not completely lost yet.

All is not completely lost. I am a member of Letterboxd, which acts as a sort of ‘movies watched’ diary for me, (you can find my profile here). The Letterboxd community hosts challenges for its members like the Criterion challenge that encourages members to watch films from the Criterion collection in each of several different categories. It’s dynamic is reminiscent of the photowalks and meet-ups that built a real world community around Flickr the photo-sharing site and helped many develop an interest in photography.

More posts similar to this one.
February 8, 2026
Outside Perspective talk on The Dot LLM era
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.
February 4, 2026