Economics or the dismal science was something I felt that I needed to include as it provides the context for business and consumption.
Prior to the 20th century, economics was the pursuit of gentleman scholars. The foundation of it is considered to be Adam Smith when he published is work An Inquiry into the Nature and Causes of the Wealth of Nations. Smith outlined one of the core tenets of classical economics: each individual is driven by self-interest and can exert only a negligible influence on prices. And it was the start of assumptions that economists model around that don’t mirror real life all the time.
What really is a rational decision maker? Do consumers always make rational decisions? Do they make decisions that maximise their economic benefit?
The problem is that they might do actions that are rational to them:
Reducing choice when they are overwhelmed
Looking for a little luxury to comfort them over time. Which was the sales of Cadbury chocolate and Revlon lipstick were known to rise in a recession
Luxury goods in general make little sense from a ration decision point of view until you realise the value of what they signal
Having a smartphone yet buying watches. Japanese consumers were known to still buy watches to show that they care about the time to employers when they could easily check their smartphone screen
All of which makes the subject area of high interest to me as a marketer. It also explains the amount of focus now being done by economists on the behavioural aspect of things.
AI reckoning as a term is a rather stark warning. Even more so when it comes from Aswath Damodaran who is a professor of finance at the Stern School of Business at NYU (New York University).
The reckoning that Damodaran is concerned about is a ‘Minsky moment‘ and represents a stark contrast to the boundless techno-optimism of Marc Andreessen.
The AI reckoning wasn’t on the mind of investors who bought into Cerebras Systems.
Cerebras Systems makes wafer sized chips with memory and processing on the same die. This reduces latency and increases speed. As cool as Cerebras Systems technology is, the company currently has sales of $510 million and was valued on its opening day of trading at $71 billion.
AI-Powered Cyberattacks | Robert Scoble and A glimpse into cyber-security’s AI-driven future | The Economist – A few years ago a participant used the conference network to hack a water-treatment facility in America (Messrs Wyler and Stump are cagey about the details). Another hid behind the din of legitimate hacker traffic to attack government websites and payment systems. The noc team traced him, sent him a message reminding him that doing illegal things from Black Hat was still illegal, then watched him close his laptop and walk away. Hackers on the other side of the world try their luck too. When the registration server was switched on, attacks began at once, including traffic that appeared to originate in Romania….Mr Stump says the noc has seen a pattern across multiple Black Hat conferences in which Taiwanese participants show up with hacked devices. “Most of [the traffic] goes back to China,” he says. ai-powered attacks by nation-states or cybercriminals are likely to intensify.. The team thinks the ai race is only beginning. For Mr Wyler, the vulnerabilities discovered by Mythos, including some that have gone undetected for decades, are to be welcomed rather than feared. “We now know they’re there.” All the same, cautions Mr Stump, the next two years will be turbulent, as more flaws will be uncovered; more breaches will occur as firms feed sensitive data into ai systems; and more insecure code will be written.
I am not a football fan, but I recognise the power that the 2026 World Cup has to move hearts and minds. This year it’s being hosted by Canada, Mexico and the United States. The 2026 World Cup fan experience is shrouded in uncertainty.
I found it interesting that both adidas with the launch of its match ball and Panini with the launch of its sticker book both marked the start of the countdown to the 2026 World Cup season.
Chinese countryside’s quieter strains – by Yuxuan JIA – Decades of son preference have left villages full of unmarried men, driving bride prices higher and sustaining a shadow market for “Vietnamese brides” that can slide into fraud, coercion, and trafficking. Young people, especially young women, are drifting away from rural patriarchy and the obligation-heavy world of kinship and “face”, while the influencer economy and short-video apps offer fantasies of easy money to teenagers with weak school prospects.
Why Catholicism is drawing in Gen Z men | Washington Post – “I don’t want to be too disparaging about them because they’re our Christian brothers and sisters, but worshiping in a big former supermarket with dry ice machines and a pop band, it’s not really traditional Christianity,” Father Longenecker said. His new parishioners are attracted to “very traditional worship with lots of incense and altar boys and sacred music in the traditional style.”
More NatSec | Big Lychee, Various Sectors – From the government’s press release… Safeguarding national security is a continuous endeavour with no end point. At its core this sounds like the Stalin derived Maoist principle of struggle. National security ‘enemies’ like Jimmy Lai, Chow Hang-tung, Lee Cheuk-yan and Albert Ho serve the same purpose as George Orwell’s character Emmanuel Goldstein in his novel Nineteen Eighty-Four. The ever-tightening system is allegedly concerned with leaderless lone wolves, small cells and external actors – but the reality is just control in Orwell’s novel.
Revising Hong Kong’s Past – Lingua Sinica – Among the changes noted was a complete erasure of references to the Tiananmen Massacre, which was recast as “political turmoil in the late spring and early summer of 1989.” Gone from the exhibit entirely, the Ming Pao reported, is a previous image that showed one million Hong Kongers taking to the streets in 1989 in support of the demonstrators in China.
I am skeptical about ‘gurus’. However, I found this Tony Robbins video good for getting out of a period of ‘stuckness’ in my thinking. Robbin’s ideas about priming, in particular they way he links physical activity to mental exercises works and has a good deal of neuroscience behind it.
Japan loses its thirst for vending machines | FT – Tens of thousands of vending machines are vanishing from Japan, as machines that once symbolised the nation’s love of innovation are shunned in a climate of rising inflation and deepening labour shortages.
The nation’s stock of 2.2mn drinks vending machines is down 23 per cent from its bubble-era peak in 1985, according to the Japan Vending System Manufacturers Association.
The faltering economics of running a national vending machine empire were exposed when DyDo, Japan’s third biggest operator, this month said it would scrap almost 7.5 per cent of its network of 270,000 units after posting its largest ever annual loss. – what surprised me was that the vending machines weren’t digitised.
Why do men love Stone Island | FT – “Football in the late 1980s and 1990s was a heavily policed environment under surveillance, where visibility carried risk,” says Andrew Groves, professor of fashion design at the University of Westminster. “On the terraces, clothing wasn’t decoration, it was risk management. Stone Island mattered because its garments were already structured around concealment, modulation and elective visibility. The detachable badge, reversible constructions, modular hoods and certain fabric treatments enabled wearers to calibrate how legible they were, depending on context. Football casuals were not simply performing taste; they were managing recognition.”
Apple rolls out UK age checks for iPhone users | FT – interesting move, I did notice that it assumed my account was adult due to the length it had been held. It reminded me of friends who registered email addresses, domain names and even social media handles for their newly born children – and did just enough to keep the accounts alive.
The 49MB Web Page | thatshubham – Beyond the sheer weight of the programmatic auction, the frequency of behavioral surveillance was surprising. There is user monitoring running in parallel with a relentless barrage of POST beacons firing to first-party tracking endpoints (a.et.nytimes.com/track). The background invisible pixel drops and redirects to doubleclick.net and casalemedia help stitch the user’s cross-site identity together across different ad networks.
When you open a website on your phone, it’s like participating in a high-frequency financial trading market. That heat you feel on the back of your phone? The sudden whirring of fans on your laptop? Contributing to that plus battery usage are a combination of these tiny scripts.
Ironically, this surveillance apparatus initializes alongside requests fetching purr.nytimes.com/tcf which I can only assume is Europe’s IAB transparency and consent framework. They named the consent framework endpoint purr. A cat purring while it rifles through your pockets.
So therein lies the paradox of modern news UX. The mandatory cookie banners you are forced to click are merely legal shields deployed to protect the publisher while they happily mine your data in the background
The online retail giant said there had been a “trend of incidents” in recent months, characterised by a “high blast radius” and “Gen-AI assisted changes” among other factors, according to a briefing note for the meeting seen by the FT.
Under “contributing factors” the note included “novel GenAI usage for which best practices and safeguards are not yet fully established”.
A glimpse into cyber-security’s AI-driven future | The Economist – A few years ago a participant used the conference network to hack a water-treatment facility in America (Messrs Wyler and Stump are cagey about the details). Another hid behind the din of legitimate hacker traffic to attack government websites and payment systems. The noc team traced him, sent him a message reminding him that doing illegal things from Black Hat was still illegal, then watched him close his laptop and walk away. Hackers on the other side of the world try their luck too. When the registration server was switched on, attacks began at once, including traffic that appeared to originate in Romania….
Mr Stump says the noc has seen a pattern across multiple Black Hat conferences in which Taiwanese participants show up with hacked devices. “Most of [the traffic] goes back to China,” he says. ai-powered attacks by nation-states or cybercriminals are likely to intensify… The team thinks the ai race is only beginning. For Mr Wyler, the vulnerabilities discovered by Mythos, including some that have gone undetected for decades, are to be welcomed rather than feared. “We now know they’re there.”
All the same, cautions Mr Stump, the next two years will be turbulent, as more flaws will be uncovered; more breaches will occur as firms feed sensitive data into ai systems; and more insecure code will be written.
OpenAI acquires popular tech talk show for ‘low hundreds of millions’ | FT – ChatGPT-maker moves into broadcasting with deal for TBPN after it had pledged to abandon ‘side-quests’ – I think that this is trying to balance the narrative with Anthropic which is ripping ahead. In past decades you would have dumped a lot of money into a campaign run by a PR agency, but time moves on
New Internet of Things Plan Targets Global Infrastructure – Jamestown – A new action plan for the Internet of Things (IoT) increases the possibility that Chinese-built connected infrastructure in the United States could become a platform for data access, cyber pre-positioning, and attacks on U.S. cyber-physical systems in a prolonged crisis or confrontation. The plan, launched jointly by nine ministries, defines IoT as a total cyber-physical environment that links “people, machines, and things” across sensing, networks, platforms, applications, and security, and sets targets for 10 billion terminal connections, more than 50 standards, and deployment across production, consumption, and governance. The plan indicates Beijing is moving from connected devices to connected backbone systems. It reinforces the new Five-Year Plan, suggesting that the People’s Republic of China (PRC) wants to supply not only endpoints like sensors, appliances, and vehicles but also the next generation of AI, computing, and space-ground communications infrastructure that will underpin them.
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”.
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.
A post on AI sovereignty came out of one of those times when a casual conversation suddenly has you seeing the theme in your news feeds. I was having one of them conversations with a friend over a paper cup of coffee, mentioned I’d been embedded at Google and they said ‘we can’t trust the Americans with AI, the way we did with social’.
That opens opportunities. Chinese open source models are working in Singapore government data centres, Korean cloud computing company Naver is looking beyond its own country for clients who want an alternative to US big technology. France has gone it alone with its own defence AI – as the ultimate expression of AI sovereignty.
The All-Star Chinese AI Conversation of 2026 | ChinaTalk – Interesting discussions on China based AI platforms on their successes and challenges. By their nature, the give China defacto AI sovereignty. Risk taking and GPUs or TPUs performance seem to be the main sources of concern. A good deal of focus on squeezing out the maximum intelligence per watt rather than scaling to infinity and beyond. Tonality wise it’s refreshing down to earth in comparison to Altman et al.
‘South Korea’s Google’ pitches AI alternative to US and China | FT – Korea has built up positive relations in the Middle East since the 1970s when they helped on major construction and engineering projects. They would be viewed positively and as a good hedge to both the US and China from a technology dependency point-of-view. Their offer is greater AI sovereignty for Middle Eastern countries in particular, you might also winning business in Central Asia as well.
It comes as a growing number of brands are moving into the children’s, teenage and young adult skincare market. In October, the first skincare brand developed for under-14s, Ever-eden, launched in the US. Superdrug has just created a range for those aged between 13 and 28.
A number of brands have surged in popularity among very young social-media users, creating a phenomenon known as “Sephora kids”. These children share videos showcasing beauty products from Drunk Elephant, Bubble, Sol de Janeiro and similar brands.
A Theory of Dumb: Why Are IQ Scores Suddenly Falling? | Intelligencer – a century ago, if you asked someone what dogs and rabbits have in common, they might answer “Dogs hunt rabbits,” not “They’re both mammals.”Maybe, then, all the noise and novelty wasn’t rotting our minds but upgrading them. (Studies suggest that better nutrition and reduced exposure to lead may have also helped.) In any case, the Flynn effect held steady for so long and through so many apparent threats that there was no reason to believe it wouldn’t last forever, even if, someday, somebody invented a chatbot that could do homework or Theo Von started podcasting.
Or so thought Elizabeth Dworak, now an assistant professor at Northwestern University’s medical school, when she chose the topic of her 2023 master’s thesis. She decided to analyze the results of 394,378 IQ tests taken in the U.S. between 2006 and 2018 to see if they exhibited the same climb. “I had all this cognitive data and thought, Hey, there’s probably a Flynn effect in there,” she says. But when she ran the numbers, “I felt like I was in Don’t Look Up,” the movie in which an astronomy grad student played by Jennifer Lawrence discovers a comet speeding toward Earth. “I spent weeks going back through all the code. I thought I’d messed something up and would have to delay submitting. But then I showed my adviser, and he said, ‘Nope, your math is right.’” The math showed declines in three important testing categories, including matrix reasoning (abstract visual puzzles), letter and number series (pattern recognition), and verbal reasoning (language-based problem-solving). The first two, in which losses were deepest, measure what psychologists call fluid intelligence, or the power to adapt to new situations and think on the fly. The drops showed up across age, gender, and education level but were most dramatic among 18-to-22-year-olds and those with the least amount of schooling.
How Hustle Culture Got America Addicted to Work – Business Insider – in America, the long, steady march toward a more leisurely future came to an abrupt halt. Today, according to the international economic database Penn World Table, the German work year is an astonishing 380 hours shorter than ours — which means that Germans work almost 10 weeks less than we do every year.
Even stranger, Americans began to glamorize their lack of free time. As the boomer generation reshaped society in its own image, it brought its ’60s, countercultural ethos to the workplace — transforming the staid, conformist office into a vessel of self-expression. Work became the central means by which you undertook to live your best life, follow your passion, and change the world. As Goldman bankers and Google idealists alike began to toil through the nights and weekends that previous generations had fought so hard to secure for them, mental-health professionals bemoaned the rise of what became known as “hustle culture.” Working long hours was suddenly the ultimate status symbol, a peculiarly American form of humblebrag. In 2017, a clever marketing study found that if you told an American you worked long hours, they assumed you were rich. If you told an Italian the same thing, they assumed you were poor.
Waymo Has Come for the Kids in Los Angeles – The New York Times – “Here, it is not unusual for families to have multiple children attending different schools far from home. School buses, if you are deemed eligible, are limited to dropping off and picking up children at locations and times that are often unhelpful. The city bus, if there is somehow a direct route to school, comes with its own set of risks that can make parents uneasy.
Ms. Rivera, a psychiatric social worker, is stuck at work until 6 p.m. most days, while her husband, who installs and repairs glass, comes home even later.
The couple struggles to coordinate their jobs and their three children. They tried Uber, and Lyft, but found that those drivers tended to cancel after discovering their riders were minors. They turned to HopSkipDrive, a service geared toward students, but the drivers had to be scheduled in advance, and would leave if children were late.
Then, a few months ago, Ms. Rivera and Alexis did a test run with Waymo.
“It was the only option where I was like, ‘Oh my God, she can order a car, nobody’s in there, she can unlock it with her phone,’” Ms. Rivera, 42, said. “I know she’s going to be safe and she’s going to get home.” – interesting use case
Chinese luxury goes local | WARC – High-end Chinese brands are stealing a march on their Western rivals with homegrown labels that appeal to more discerning local consumers who are looking for luxury items that feel tailored to them. China’s $49bn luxury market is “changing fast”: ecommerce sales at jeweller Lapou Gold, for instance, have surged more than 1000% in the first three quarters of this year compared with two years ago. Songmont, a Chinese brand that claims to have ‘experiential’ designer bags, has grown its online sales 90% while Gucci online bag sales in China have fallen 50%, according to the Business Times. – This was inevitable when you had so many talented (and a number of mediocre) Chinese people being brought through the likes of Central St Martins.
Coca-Cola CMO Manolo Arroyo on WPP, AI and a new era for media | The Drum – Coca-Cola’s marketing ecosystem was sprawling and complex. The business was working with approximately 6,000 agency partners globally, while the majority of its multi-billion-dollar media budget was allocated to traditional channels. Arroyo wanted fewer partners, deeper integration and a shift towards digital-first execution at scale.
That ambition led to the consolidation of Coca-Cola’s global advertising account into WPP and the creation of Open X, a bespoke unit designed to manage the brand across markets and disciplines. Nine studios were established in key regions, housing a mix of Coca-Cola employees, WPP staff and specialist partners.
“It’s a marketing factory,” says Arroyo. “There are more than 2,000 employees of Coca-Cola and more than 2,000 employees of WPP […] and ultimately it’s enabled us to move from a company that in 2019 was investing close to 75% of our paid media on traditional TV, to a company that’s going to end up this year putting 70% of all our paid media on digital, particularly social and influencer led, marketing. For us, it’s our new TV.”
Outcry after French army chief’s ‘prepared to lose children’ warning | Le Monde – “We have all the knowledge, all the economic and demographic strength to deter the Moscow regime from trying its luck by going further,” said Mandon. “What we lack, and this is where you have a major role to play, is the strength of spirit to accept suffering in order to protect who we are.”
Paying tribute to French forces deployed worldwide, he added: “If our country falters because it is not prepared to accept – let’s be honest – to lose its children, to suffer economically because defense production will take precedence, then we are at risk.” – I don’t think that the west is ready or able to face Russia or China because of this. The war is lost before its fought
SOF, AI, and Changing Western Conceptions of War | Small Wars Journal by Arizona State University – Each generational shift in technology impacts military operations. Consequently, a shift in military training, command, and promotion structure should follow. Much of the conversation surrounding AI makes it seem like an unprecedented esoteric concept. While this is partly true, the same was said about steam engines during the Industrial Revolution. Simply put, AI is the next technological breakthrough and there will be more after it. As Clausewitz stated, the character of war changes, not the nature of war. A willingness to adapt while following strategic tenets will enable us to weather the storm and thrive in AI generation warfare. Failure to do so will only bring obsolescence while America’s adversaries gain global hegemonic status. Proper implementation of AI will result in faster decision making, more accurate intelligence, improved resource allocation, better spatial awareness, more effective messaging, and more impactful strategies. The key to reaching this level of success is SOF. SOF is uniquely equipped and trained to implement AI quickly and effectively, delivering results that can be scaled to the rest of the military.
A New Anonymous Phone Carrier Lets You Sign Up With Nothing but a Zip Code | WIRED – Phreeli, the phone carrier startup is designed to be the most privacy-focused cellular provider available to Americans. Phreeli, as in, “speak freely,” aims to give its user a different sort of privacy from the kind that can be had with end-to-end encrypted texting and calling tools like Signal or WhatsApp. Those apps hide the content of conversations, or even, in Signal’s case, metadata like the identities of who is talking to whom. Phreeli instead wants to offer actual anonymity. It can’t help government agencies or data brokers obtain users’ identifying information because it has almost none to share. The only piece of information the company records about its users when they sign up for a Phreeli phone number is, in fact, a mere ZIP code. That’s the minimum personal data Merrill has determined his company is legally required to keep about its customers for tax purposes.
Waking the Sleeping European Giant – by Matthew C. Klein | The Overshoot – “Europe” as a geopolitical entity does not exist. Instead of a strong and independent continent capable of securing the lives and freedoms of its citizens, Europe is divided into dozens of countries, all of which are too small individually to stand up to external threats. The problem is compounded by the mismatch between where the military resources can be found and where they are most needed. There is relatively little overlap between the places with the balance sheet capacity (mostly in the north), the places with the productive capacity (mostly in the center), the places with the largest populations of otherwise unoccupied fighting-age men (more in the south), and Europe’s front lines (largely, although not exclusively, in the east).
Bending Spoons raids the digital graveyard for paranormal returns | FT – businesses in the Bending Spoons stable: AOL, the dial-up internet service that had been most recently attached to Yahoo, and Evernote, the virtual scratch pad. – alongside Vimeo and Brightcove with Eventbrite due to join them
