Whilst looking for the new BBC ‘Reith’ font – which they’ve done in-house to update Gills Sans and not pay licence fees, I came across this interesting specification on global web page design by the BBC.
Mark Ovenden talks about the new font as part of a wider appreciation of Gill Sans and Johnston (the London Underground font) in a BBC 4 documentary. It was interesting to hear how Neville Brody used it in City Limits magazine and the challenges these fonts faced in the move to digital – first of all for graphic design and then for online consumption.
Finally, from a font perspective, I found this video from Apple WWDC 2015 that Apple used to introduce its San Francisco family of typefaces as its system font (they also use it as their corporate font now). This was the first font designed in-house at Apple in 20 years. Apple keeps it tightly controlled and restricts access to it.
I looked back on Apple’s website from 10 years ago following the launch of the iPhone I realised how fad driven web design could be.
In particular notice the reflection was very now at the time. Javascript had taken off with web 2.0 and someone came up with a block of code that did reflections on images a la the image effect you can get in PowerPoint. This then drove a wider trend to do this in code or in InDesign. You can blame the font gradient on a similar ‘cool Javascript hack’ to design trend meme as well.
The dot LLM era is one of the chunkiest posts that I have written, so I have put it together in a PDF as well that you can download and share freely amongst colleagues and peers.
The dot LLM era executive summary
The “dot LLM era” represents a pivotal moment in technological history, drawing striking parallels to the dot-com bubble of the late 1990s. This period is defined by a massive influx of capital into Large Language Models (LLMs) and artificial intelligence infrastructure, which represents clear analogues to the dot-com era “three bubbles” framework: online businesses, open-source ventures, and telecommunications (which represents closest analogue to the current dot LLM era).
The Core Thesis
The current $1 trillion valuation of the AI sector faces two existential challenges:
Amortisation Risk: Unlike the dark fibre of the 1990s, which had a useful life of over a decade, modern GPU and TPU hardware becomes technically obsolete within 3 to 5 years.
Self-Defeating Economics: If AI-driven automation successfully provides $1 trillion in cost savings through job cuts, the resulting increase in unemployment and drop in GDP could destroy the very macroeconomic environment required to sustain hyperscaler growth.
A Tale of Three Bubbles
The document argues that we are conflating three distinct historical analogues:
Online Businesses: Recalling the “burn rates” of the early web, where pure-play LLMs are currently providing tokens for less than their marginal cost.
Open-Source: Comparing current model proliferation to the rise of Linux, where the ultimate winners may not be the model creators but those providing enterprise-grade support.
Telecommunications: The most instructive analogue, involving massive infrastructure build-outs, vendor financing, and potential “Minsky moments” where optimism outstrips sustainable cash flow.
Geopolitical and Economic Realities
Unlike the 1990s “Long Boom” characterized by US pre-eminence and budget surpluses, the dot LLM era exists within a climate of high government debt and inflation. Furthermore, US dominance is challenged by Chinese hyperscalers and open-source models like Alibaba’s Qwen, which offer high performance at significantly lower costs.
Potential Outcomes
The document outlines seven possible scenarios for the era’s conclusion, ranging from The Breakthrough (total economic transformation) to The Weird Gizmo (total collapse). Currently, “The Moral Hazard”—where AI is deemed “too big to fail” and receives government backing—is viewed as the most likely path (~95% likelihood).
How this dot LLM exploration started?
This dot LLM post came out of a number of ideas and vibes.
Everyone[i] from commentators[ii] and podcast hosts to friends are talking about a dot-com-type bubble in LLMs, what I’ve termed as shorthand the dot LLM era. The dot LLM era comparison has become a steady tempo of concern.
The term AI bubble took off in interest during September of 2025.
The dot LLM era is shorthand to move backwards and forwards in time comparing the current AI boom with the dot-com boom of 1990s – 2001. It’s a very different type of ‘Y2K trend’.
Many pure-play LLMs customers are currently getting to use tokens for less than their marginal cost[iii], and this is part of the reason (alongside the high cost of model training) why the likes of OpenAI, C3.ai, Perplexity, Anysphere and Anthropic are raising new rounds of financing[iv]. They have been losing money[v] and continue to do so.
Spending by both pure-play LLMs and their hyperscaler partners is driven by the effort to create an AI moat[vi]. An AI moat is a sustained proprietary advantage derived from a company’s use of artificial intelligence that makes its offerings fundamentally superior, cheaper, or “stickier” than those of rivals, and which is hard to be replicated by rivals.
Even the most historically bullish institutional investors, like James Anderson[vii], formerly of Baillie Gifford, have turned bearish on Nvidia and pure-play LLM offerings.
To meet the needs of these services, development of an extra 1,500 data centres has been announced – only a quarter of which are under construction at the time of writing.[viii]
It is a time reminiscent of the mid-2010s when venture capitalists subsidised the cost of services like Uber and Lyft[ix] to grow markets from the ground up. Going back further to the dot-com era, Amazon took a similar approach with its business.
Valuations for the Magnificent 10: Apple, Alphabet, Amazon, AMD, Broadcom, Meta, Microsoft, Nvidia, Palantir and Tesla — are high. The 24-month forward P/E ratio of the Magnificent 10 is 35 times. By comparison the S&P 500’s equivalent P/E ratio at the peak of the dot-com boom approached 33[x], with a brief peak at the market top of 44.[xi]
Built into these Magnificent 10 valuations, is an assumption that LLMs will help them cut costs and or drive revenue growth by $1 – 4 trillion in the next two years.[xii]
Like the dot-com era[xiii], the dot LLM era is spawning several businesses that are likely to be considered weird gizmos or bad business ideas that will be mocked in the future. The dot-com analogues included the likes of proto-digital currencies Beenz and Flooz[xiv], CueCat[xv] – a bar code scanner that allowed web users to scan codes on magazines to get more pages online or the short-lived[xvi] 3Com Audrey[xvii][xviii] and Sony eVilla[xix] internet appliances.
(Disclosure: in my first agency-side role, I worked on 3Com’s consumer products and the Palm device business that was spun off as palmOne[xx] to give space for the Ergo connected home internet appliance range. Audrey’s ability to sync with two Palm devices[xxi], despite Palm being seen as an internal competitor, gives you an idea of how disjointed and chaotic internal planning was in companies like 3Com when they were trying to move at ‘internet speed’. One of the last 3Com projects I worked on was the launch of Audrey in October 2000.)
Bubbles don’t kill technology from moving forwards
Like the dot-com era, the dot LLM era is likely to move through two separate cycles: one financial and the other technological. While the financial bubble destroyed a lot of shareholder value, the underlying web technology cycle and use cases became commonplace and evolved. Email became part of our culture[xxii] in the same way that social media became cultural fabric a decade later. LLMs or their successors (such as nested models[xxiii] and world models[xxiv]) are likely to be influential and change the nature of work, life, business and culture.
Already we can see the dot LLM era playing out on social media as over half of content is estimated to be produced with generative AI.
This relentless forward progress for technological adoption and refinement was likened to an organic being by author Kevin Kelly in a phenomenon he called the ‘technium’.[xxv]
Believing that AI is undergoing a dot LLM bubble isn’t the same as not believing that the technology won’t have an ongoing impact.
A Tale of Three Bubbles
When we talk about the dot LLM era we are conflating a number of related bubbles bursting.
The bubbles were based around a common conceit: prior experience counted for naught because the internet changed everything.
This resulted in three distinct historical bubbles:
Online business bubble
Open-source bubble
Telecommunications bubble
The one that most people recall is the dot-com boom where online businesses went under.
Online businesses
Iconic ones included technically ambitious clothing retailer Boo.com, pet care supplies firm Pets.com and many more.
Boo.com burned through $135 million in just 18 months[xxvi]. And they weren’t the only ones. In March 2000, Pegasus Research put out a research paper[xxvii] outlining the burn rates of each online business. The report went under-reported at the time, but took a clear-eyed look at the sector.
Successful business people failed. Podcaster and academic Scott Galloway[xxviii] founded RedEnvelope[xxix], an online commerce site that sold gifts including personalised items and experiences. Bob Geldof’s online travel site deckchair.com[xxx] doesn’t even merit a mention in most profiles of the famous musician.
Back when I worked at Yahoo! long-time employees said that only a pivot to provide dating services had kept the rest of Yahoo! Europe afloat during the dot-com bust of 2001/ 2002. Online advertising revenues at the time dropped more than 30% over a 12-month period. The difference between success and failure was a very narrow gap.
Amazon survived and eventually thrived as it managed to convince its shareholders to defer profitability for a decade to garner growth. That move and the company’s nascent web services business (AWS) led to the online juggernaut that Amazon is today[xxxi]. While Amazon was founded in 1994 and first went online in 1995, it didn’t make its first quarterly profit until the end of 2001[xxxii] of $5 million on revenue of $1.12 billion[xxxiii] and the first annual profit in 2003[xxxiv]. Uber and Lyft learned from the example that Amazon had set a decade earlier.
Open-source bubble
The second bubble was the ‘open-source’ bubble. The rise of the commercial web (and the millennium bug[xxxv]) disrupted existing technology stacks and opened up new opportunities to sell enterprise computing hardware and software. Several companies were launched to support the rollout of open-source software that threatened Microsoft’s and Unix operating system duopoly.
My former client VA Linux Systems built web servers and workstations optimised for Linux users[xxxvi]. Now VA Linux Systems is remembered more for its IPO, which valued the company at $30 and opened for trading at $299[xxxvii]. Red Hat[xxxviii] and SuSE[xxxix] provided commercially supported versions of Linux for corporate enterprises. Like their online business counterparts, few of the open-source business bubble companies could be considered ‘successful’, the outlier being Red Hat which eventually sold to IBM in 2019 for $34 billion[xl].
The winner, Red Hat, didn’t sell the open-source software (Linux) as its business model; it sold enterprise-grade support, integration, and services.
While the open-source bubble was the smallest of the three bubbles, it had an outsized impact with Linux being the foundation for everything from the Android mobile OS to the largest data centres.
Telecoms bubble
The telecoms bubble was the least visible, yet most spectacular bubble and the one that is most instructive about the dot LLM era.
There are three places where you could start the telecoms bubble. April 30, 1995, when the NSFnet was decommissioned[xli], the Telecommunications Act of 1996, or 1984.
I am going to go with 1984[xlii]. While the internet was growing in academic and military circles in the US and there were nascent computer networks elsewhere like the UK[xliii] – the real revolution was happening on the London Stock Exchange. The UK government under prime minister Margaret Thatcher looked to get the government out of businesses. A programme of privatisation took place to sell-off numerous nationalised businesses; plans to privatise British Telecom were proposed in 1982. 1984 saw the IPO of British Telecom plc, the previously government owned telecoms provider[xliv]. The UK government also licensed the first competitor Mercury Communications[xlv].
From a technological perspective the IPO seemed to be a catalyst[xlvi] for wider telecoms deregulation in western Europe[xlvii] and around the world. In 1985, the Japanese government privatised NTT and opened the Japanese telecommunications market up to competition[xlviii]. The European Commission began developing a regulatory framework to open up national telecoms markets in 1987[xlix], Europe and Japan would spend the next decade opening up their markets for alternative telecommunications services.
It was into this global landscape that the US overhauled its telecommunications regulations with the Telecommunications Act of 1996[l]. The stated intention of the act was to “let anyone enter any communications business – to let any communications business compete in any market against any other.”[li] The act incentivised the expansion of networks and new services across the US.[lii] Early US netizens rejected the act as a way to regulate cyberspace[liii].
The following year 69 members of the World Trade Organisation (WTO) agreed to open their basic telecoms markets to competition[liv].
In parallel with the wider atmosphere of telecommunications liberalisation, was the rise of the internet. The rise of home computers in US households between 1990 and 1997 grew from 15% to 35%[lv]. At that time, a small percentage of people would be dialling directly into work, nascent online services like CompuServe or AOL, dialling into their Charles Schwab account and bulletin boards.
Outside the US, it was more likely that your computer was a standalone machine with a spreadsheet, word processing application, maybe design software allowing you to write the document from home and bring it in to work on floppy drive, or possibly an Iomega diskette[lvi] of some sort.
Private long distance optical fibre networks together with free local telephone calls were the infrastructure for internet connectivity. The web the way we know it now was not a surefire winner[lvii]. Much speculation was on the internet superhighway – digital cable television with value added services like online shopping.[lviii] Bill Gates at the peak of his power as CEO of Microsoft was convinced that the digital cable TV was the way forward.[lix] The next edition was edited to reflect the reality of the web instead. The open interoperable nature of the web proved to be more attractive than walled garden digital services envisaged by cable TV companies.[lx]
Investment in telecoms infrastructure increased to meet the future needs of digital services, based on a misreading of internet data traffic growth[lxi]. US telecoms providers invested $500 billion between 1996 and 2001 – mostly on optical fibre networks.[lxii]Much of this spending was done by new entrants including Global Crossing, WorldCom, Enron, Qwest and Level 3. There was a corresponding scale up by equipment makers like Lucent to supply the telecoms providers.[lxiii] Telecommunications equipment companies Lucent and Nortel[lxiv] both provided vendor financing for their dot-com era client base – engineered in such a way to inflate sales figures and their share price.[lxv]
Lucent lent customers the money to purchase their equipment. They then booked the loan value as revenue, even though the repayment risk remained and the debt was held as an asset on the Lucent balance sheet.
Nortel used its own shares as financing for its customers. It is believed that Nortel lent $7 billion+ to help start-up telecommunications carriers make equipment purchases. Many of these were unsecured loans, interest-free and tied to future purchases.
Carriers engaged in ‘round-tripping’. Global Crossing would ‘sell’ network capacity to Qwest; Qwest would ‘sell’ similar capacity back to Global Crossing for nearly the same amount. Both companies booked the deals as revenue. US regulators found that this was a pre-arranged swap designed to inflate revenue, despite having no commercial purpose.
Had the bubble continued into 2005, WorldCom CEO at the time Bernie Ebbers had expected to invest another $100 billion in the company’s network infrastructure that year[lxvi]. Instead, Ebbers left WorldCom investors with a $180 billion loss. When the telecoms bubble imploded, an estimated trillion dollars in debt was owed, much of which was not expected to be recovered.[lxvii]
In 2002, the telecoms bubble helped change the way business is conducted. In reaction to a number of major corporate and accounting scandals, notably Enron[lxviii] and WorldCom – US lawmakers enacted the Sarbanes-Oxley Act of 2002[lxix]. This Act (SOX as it became known) mandated standards in financial record keeping and reporting for public companies. It covered responsibilities of the board of directors and criminal penalties for certain practices[lxx]. It required the SEC to create regulations for compliance. SOX drove up the cost of a company going public and remaining public due to the administrative burden to remain legally compliant.
Technology vendor financing from companies like Cisco and IBM continued to be an issue through the 2008 financial crisis,[lxxi]but was largely kept out of the common discourse by the tsunami of sub-prime mortgage debt defaults.
The dot LLM era hinges around service providers and equipment makers, in the same way that the telecoms bubble did. Here are some examples and their dot LLM analogues.
Service providers
Equipment makers
Dot-com era Enron PSINet Qwest UUNET Worldcom
Dot-com era 3Com Ciena Cisco Equinix Juniper Networks Lucent Sun Microsystems
Dot LLM era Alphabet Amazon Anthropic OpenAI Oracle Microsoft Salesforce
Of course, the idea of them being analogues doesn’t line up perfectly. While the excessive build out of optical fibre networks could be considered analogous to hyper-scaled AI infrastructure; it isn’t a perfect match.
The acceleration in network and computing capability in hyperscalers show the kind of positive trajectory that Mary Meeker had in her dot-com era analyst presentations[lxxii].
Some critics think that the massive acceleration in network and compute investment for LLM purposes represents a Minsky moment in itself[lxxiii] – heralding it as an event that fits Hyman Minsky’s Financial Instability Hypothesis.
Minsky considered this coming in three parts:
A self-reinforcing boom driven by optimism and easy credit
A shock, that can be minor in nature, has investors re-look at cash-flow shortfalls
Rapid asset sales and deleveraging / de-risking
The scale of investment and construction of data centres together with the new electricity generating capacity to power them are orders of magnitude larger than the telecoms boom.
Secondly, the LLM infrastructure has a much shorter life. LLM hyperscalers go through GPUs (and TPUs) extremely fast with a useful life of 3 years or so.[lxxiv] Complete technical obsolescence of a given GPU / TPU design has occurred by 5 years from launch.[lxxv]
Therefore, if there is an AI bust the processors wouldn’t be available to use in the next economic upswing in the tech sector. By comparison the optical fibre networks laid during the dot-com boom had a useful life of 10+ years and the growth of web 2.0 and social startups was largely built on surplus server and networking equipment left over from the dot-com era. The dot LLM era represents a financial and technological amortisation risk.
There is an added wrinkle in this last point about the useful life of GPUs and TPUs. Company filings of hyperscalers show that they are amortising their network and compute capital expenditure over longer times, by lengthening the assumed useful lives of components in their financial paperwork.
The economic environment.
The economic conditions that the dot-com era happened in were very different to the conditions of the dot LLM era.
The US had suffered through much of the 1980s and into the early 1990s. Reaganomics had driven a ‘jobless recovery’ as the financial and services sectors took over from manufacturing as the US economic growth engine. In 1989 the Savings and Loan crisis peaked.[lxxvi] This occurred alongside rising interest rates to battle inflation. An oil price spike as a result of the first Gulf War exacerbated economic conditions and the recession ended the ambitions of George H. Bush becoming president for the second time. Under a new government, by spring 1994, jobs and economic growth both picked up. 1996 saw growth continuing and by May 1997 US unemployment dropped below 5% for the first time in 24 years.
Other countries had similar recessions in the late 1980s and early 1990s due to restrictive monetary policies, oil prices and the end of the Cold War. By 1994, global GDP growth returned.[lxxvii] Wired magazine talked of the 1980s as a contagious idea:[lxxviii]
America is in decline, the world is going to hell, and our children’s lives will be worse than our own. The particulars are now familiar: Good jobs are disappearing, working people are falling into poverty, the underclass is swelling, crime is out of control. The post-Cold War world is fragmenting, and conflicts are erupting all over the planet. The environment is imploding—with global warming and ozone depletion, we’ll all either die of cancer or live in Waterworld. As for our kids, the collapsing educational system is producing either gun-toting gangsters or burger-flipping dopes who can’t read.
In the same article, they thought of the 1990s as the start of ‘The Long Boom’ – 25 years of prosperity freedom and a better environment for the world.
By 2000, the US government went from running a budget deficit eight years earlier to running a surplus. This eased the credit markets for businesses and consumers. The US Taxpayer Relief Act lowered marginal capital gains tax and helped fuel stock market investments. Day trading became a thing by 1999,[lxxix] mirroring investors in crypto and stocks in the 2020s.[lxxx]
By comparison, the current economic climate is more similar to the 1980s than the 1990s. Government debt has reached new heights. Governments have struggled to rein in inflation created by COVID-era supply shocks – which was responsible for several governments including the Biden administration being voted out of office. The high government debt and inflation leave governments with fewer policy tools to manage a systemic shock compared to their 1990s counterparts. The Economist claimed that western countries had government debt levels unseen since Napoleonic times.[lxxxi] There is no US government budget surplus and little ‘headroom’ for monetary policy.
Wired magazine’s ‘contagious idea’ sounds very familiar:
Climate despair has been recognised as a condition by mental health professionals.[lxxxii]
Global warming is cited[lxxxiii] as a cause of extreme weather conditions[lxxxiv].
Good jobs are disappearing and this is often blamed[lxxxv] on generative AI.
US tariffs, Brexit and the Ukraine war are disrupting global commerce.
In conclusion, the dot-com era economy was much more conducive for retail investors than the dot LLM era is.
The internet changes everything
Dot-com businesses had it right in their view that the internet would change business and shopping for consumers and enterprises. Some of them like Amazon made it, many didn’t. The investment bank analysts believed it too.[lxxxvi]
You see similar things being written about AI now, along with similar looking ‘hockey stick’ charts.[lxxxvii]
Microsoft research[lxxxviii] suggests that there is a strong link between GDP per capita and AI usage. But also notes that adoption in advanced economies tends to plateau between 25% and 45%, suggesting non-economic factors eventually moderate growth. Suggesting that the dot LLM era may not be the kind of game-changer that it might be believed to be by advocates. I would recommend that the reader keeps an open mind on this rather than automatically thinking that this proves generative AI as being a technological dead-end. More work is required to try and understand why the plateau happens and whether it represents a ceiling or a brief rest before adoption accelerates again.
Artificial general intelligence or AGI
AGI is when the LLM surpasses your average human. The idea of AGI has taken on the similar messianic fervour of people from the dot-com era including George Gilder’s Telecosm. Many executives in the most prominent LLM developers subscribe to an imminent AGI occurring.
Elon Musk holds the most aggressive timeline[lxxxix]. He thinks that the main bottlenecks to AGI—specifically power supply and high-end chip availability—are being solved rapidly. Through his company’s xAI’s computing power, he believes that the next generation of models will surpass human intelligence in almost any individual task by early 2026. Anthropic’s CEO Dario Amodei believes that AGI could arrive in 2026/7[xc]. OpenAI’s Sam Altman considers 2027 to be a realistic timeline for the arrival of AGI[xci]. DeepMind co-founder Shane Legg has come up with a notional timeline of 2028. His view is based on the current rate of progress for both computing hardware and LLM algorithms.[xcii] Long time AI advocate Ray Kurzweil has published a series of books about AGI, which he termed the ‘singularity’. The latest of which put 2029 as the year in which AGI is likely to occur[xciii].
As with any cultural artefact, AGI has become blended with religious thinking, as exemplified by this outlandish quote from podcaster Joe Rogan.
“Jesus was born out of a virgin mother. What’s more virgin than a computer? If Jesus does return, you don’t think he could return as artificial intelligence? AI could absolutely return as Jesus.” – Joe Rogan[xciv]
All of which is reminiscent of Timothy Leary’s infatuation with the early web[xcv] and the Heaven’s Gate Cult[xcvi].
Despite some prominent advocates, many experts in the field are sceptical about the imminent arrival of AGI. Included in these sceptics are OpenAI co-founder Andrej Karpathy who believes that the nature of LLMs mean that AGI won’t arrive using current techniques and on the timeline that advocates predict[xcvii]. Researchers Rodney Brooks[xcviii] and Yann LeCun[xcix] believe that understanding the physical world is critical for technology to achieve AGI. This work is only starting now. Academic Melanie Mitchell argues that until systems can grasp ‘meaning’ AGI will not happen[c].
The good bubble
Some of the most important US business executives of the LLM era admit that we are in some kind of bubble. Here’s what they’ve said in their own words.
“When bubbles happen, smart people get overexcited about a kernel of truth … Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes.”[ci]
“This frenzy gives us pause … The belief in an A.G.I. or superintelligence tipping point flies in the face of the history of technology”[cii]
“This is a kind of industrial bubble … investors have a hard time in the middle of this excitement, distinguishing between the good ideas and the bad ideas. And that’s also probably happening today.”[ciii]
“Given the potential of this technology, the excitement is very rational. It is also true when we go through these investment cycles there are moments we overshoot as an industry. We can look back at the internet right now, there was clearly a lot of excess investment, but none of us would question if the internet was profound or did it have a lot of impact it was fundamentally changed how we work digitally as a society. I expect AI to be the same; I think it’s both rational and there are aspects of irrationality to a moment like this.”[civ]
“Most other infrastructure buildouts in history, the infrastructure gets built out, people take on too much debt, and then you hit some blip … a lot of the companies wind up going out of business, and then the assets get distressed and then it’s a great opportunity to go buy more … definitely a possibility that something like that would happen here.”[cv]
The real question is whether the dot LLM era is a ‘good’ bubble or a bad bubble? What does a good bubble look like? And how much will it cost? Most of the quotes above see the dot LLM era as similar in nature to the internet boom and bust. While pioneers may have died society was irrevocably changed.
Some of the irrationality in the ‘good bubble’ hypothesis seems to include hubris, for example OpenAI shunned having external advisers to work on its $1.5 trillion worth of data centre deals.[cvi] While OpenAI has relationships with investment banks and corporate law firms – it didn’t make much use of them.
These explanations assume that there will be a corresponding surplus of infrastructure that will spark new innovation on the backs of dead companies. A concept that most represents the telecoms aspect of the dot-com era. The explanations ignore the financial losses suffered by pension funds and retail investors as these companies went bankrupt. They also ignore that the useful life of AI computing hardware is obsolete faster than railway tracks or laid fibre optic cables.[cvii] Short sellers have accused hyperscalers of estimating unrealistically useful lives for their computer equipment, in particular, the GPUs that power AI model training and inference. The allegations claim that profits are artificially overstated by allowing depreciation of assets over a longer period.[cviii]
At its peak in March 2000, the NASDAQ index peaked at 5,048. When the dot-com bubble burst the index declined to 1,139. Recovery took 15 years from the peak value. The NASDAQ reached 5,048 again in March 2015.[cix] The risk is arguably greater this time around as the top ten stocks constituting the S&P 500 index constitute 40% of its value.[cx] This implies a vulnerable, brittle market environment prior to any economic bust. So, the idea of ‘good’ is very narrowly defined and asking the term to do a lot of heavy lifting in terms of its language. Predicting the peak of the market[cxi] is challenging too[cxii].
Can the demand for LLM; grow at the speed implied by invested capital?
Advertising as a possible use case
The first use case to consider for how the dot LLM era could meet its full ‘potential’ would be the ongoing disruption of advertising by digital platforms. Depending who you believe the global total market for advertising is close to, or has just exceeded $1 trillion in total value.
Globally, advertising represents about 1 percent[cxiii] of global GDP. It usually holds at around that proportion as global economic growth waxes and wanes. In some key markets such as the US, UK and Singapore – it makes up a higher percentage of GDP – as the home of advertising platforms, advertising agencies with international responsibility and technology suppliers to the industry.
Advertising isn’t just a cost centre for businesses, but also a driver of economic growth and profit. One Euro of advertising is estimated to generate up to 7 Euros of economic value.[cxiv]
It took digital advertising over a quarter of a century to go from zero to over half of advertising spend. This hinged around two growth spurts, one in 2000 with the rise of online businesses and the second in 2020 with the COVID-19 lockdown. A factor of the transition of digital advertising growth has been down to the fragmentation of audiences across media platforms and alongside traditional media.
AI (but not LLMs) has been used in advertising as long as digital advertising has been around. It started to be used for understanding consumer behaviour and delivering targeted advertising.[cxv] Amazon started using AI for its recommendations in 1998.[cxvi]
Not all economic value in digital advertising accrued from the transfer of ‘traditional’ advertising to digital advertising. There is evidence of a direct correlation between a rise in e-commerce drives a decline in retail properties, given the strong linkage between e-commerce, retail media search advertising – there is part of that value exchange which would accrue to the advertising platforms.
… one percent increase in e-commerce sales as a percent of total sales will decrease commercial real estate prices by 7.64%.[cxvii]
It is worthwhile reading the whole economic paper on the decline in commercial real estate prices to understand the multiple factors that the author tried to take into account to better understand the impact of e-commerce sales.
The sales didn’t only shift online, but offshore. For instance, China-based advertisers accounted for around 11% of Meta’s total revenue in 2024[cxviii], which amounted to $18.35 billion. A significant portion of this is believed to come from large e-commerce companies like Temu and Shein[cxix], rather than a large number of small businesses. These companies benefited from the Chinese state support[cxx] covering their international logistics and postage costs and allowed their businesses to be run on razor-thin margins.
There has also been a corresponding value transfer from the lost profits of advertising clients to the platforms as well. Advertising industry consultant Michael Farmer made this point in his discussion of large fast-moving consumer goods businesses.
…for the fifty years from 1960 to 2010, the combined FMCG sales of P&G, Unilever, Nestle and Colgate-Palmolive grew at about an 8% compounded annual growth rate per year.
The numbers associated with this long-term growth rate are staggering. P&G alone grew from about $1 billion (1960) to $79 billion in 2010. Throughout this period, P&G was the industry’s advocate for the power of advertising, becoming the largest advertiser in the US, with a focus on traditional advertising — digital / social advertising had hardly begun until 2010. Since 2010, with the advent of digital / social advertising, and massive increases in digital / social spend, P&G, Unilever, Nestle and Colgate-Palmolive have grown, collectively, at less than 1% per year, about half the growth rate of the US economy (2.1% per year).
They are not the only major advertisers who have grown below GDP rates. At least 20 of the 50 largest advertisers in the US have grown below 2% per year for the past 15 years.
Digital and social advertising, of course, have come to dominate the advertising scene since 2010, and it represents, today, about 2/3rds of all advertising spend.[cxxi]
Digital advertising at its heart represents marketing efficiency because of its ability to be created and ‘trafficked’ at a much lower cost and greater speed. But this efficiency comes at the cost of corresponding marketing effectiveness in terms of short-term sales and longer-term preference and purchasing impact.
LLMs could undoubtedly further refine marketing efficiency, it could even ‘understand’ the marketing effectiveness challenge. But LLMs are restricted by the way the audience interacts with advertising, limiting their ability to solve the corresponding marketing effectiveness challenge. Marketing conglomerate WPP have launched a performance media platform that looks to further increase marketing efficiency by no longer requiring a traditional client-agency model. WPP Open Pro[cxxii] is the first advertising agency as a software service powered by an LLM. There is some concern that LLMs could destroy the very platforms which serve advertisers to consumers.[cxxiii]
Based on all these factors, advertising is likely to be only one aspect of a market supporting AI’s growth and is unlikely to contribute more than a small proportion of the implied trillion-dollar payback required in the next two years if the dot LLM era doesn’t turn from boom to economic bust.
Business process efficiencies
A second use case mentioned is deriving business efficiencies. This could be done in a number of ways:
Automating white-collar roles
Automating blue-collar and pink-collar roles in conjunction with robotics[cxxiv].
OpenAI recently did research[cxxv] to find out how their service is being used. The sample looked across free, premium and corporate usage of ChatGPT. Some caveats around the research before we delve into it:
It ignored the use of API services.
It is worthwhile remembering that ChatGPT may be under-represented for some actions like writing code – as developers are very aware of what is the current best tool for them.[cxxvi]
Microsoft Worklab research[cxxvii] supports the view of LLM as wingman for white-collar workers. In a story arc that is similar to that of early personal computer adoption, they see LLM use as employee advocated and driven.
Actions have consequences
Economists have models that look at the impact affecting unemployment[cxxviii], inflation and GDP. I have used the Phillips Curve[cxxix] and Okun’s Law[cxxx] in a thought experiment to model the effect on the US economy, if AI managed to provide up to $1 trillion in cost savings through automating jobs. Even with a notional cost savings of $1 trillion, the revenue that would accrue to LLM providers would be a very small proportion of the $1 trillion revenue growth over the next two years implied by current dot LLM era investments.
The average salary is about $94,952 (based on $45.65/hr[cxxxii] x 40 hours/week x 52 weeks/year).
$1 trillion in job cuts would represent about 10.53 million unemployed.
Phillips Curve – used a standard slope where 1% increase in unemployment rate corresponds to a 0.5% decrease in inflation. Okun’s Law – I used a standard co-efficient where a 1% increase in unemployment rate corresponds to a 2% decrease in real GDP.
The degree of economic change, at a time of deflation and drop in GDP would make the environment very hostile for businesses dependent on high growth rates. The economic model of achieving a $1 trillion payback through cost-savings is self-defeating. The very success of automation on that scale would destroy the macroeconomic environment required to sustain the hyperscalers’ growth projections.
As we have seen in Japan during the lost decades,[cxxxiii] deflation would delay purchases and investments. The reduction in GDP would mean that there would be less money available for purchases and investments – creating a negative economic environment for all parties involved including the hyperscalers who would have precipitated the economic change. This scenario has alternative asset management firm Blackstone concerned that its peers are not considering the level of economic disruption the LLM era will bring.[cxxxiv]
That is before you even consider the economic shockwave[cxxxv] that would roll around the globe in a similar manner[cxxxvi] to the 2008 financial crisis. All of this means that there is an optimal economic point in increasing productivity through dot LLM era automation without tanking future growth for hyperscalers and their clients.
AI optimists would think of the economic shockwave as being short-term in nature, followed by a long-term boom. In this respect, they would draw on examples like the rise of the steam engine, railways or electricity. On balance, I would disagree with these optimists. Economic conditions are very different now. For instance, western economies are now much more ‘financialised’[cxxxvii] and so the ‘short-term’ shockwave could be well over a decade in length, more similar to the great depression.[cxxxviii] Developed economy country governments may not have the headroom[cxxxix] to get out of the depression through a Franklin D. Roosevelt-style New Deal Keynesian stimulus.[cxl]
Productivity benefits?
Personally, I have found working with generative AI useful in a number of circumstances, in particular, solving the blank page problem. I have also used it as a research tool, a proof-reader and an editing partner. This article was written with the help of generative AI from an editing perspective. But I have also spent a lot of time looking at the outputs given and ensuring that they accurately reflected the exploration of where I wanted to go. And then there is the issue of hallucinations.
So far, the evidence has been mixed. There are a number of factors for this, IT projects are hard to implement successfully.
Businesses that have embraced LLMs to improve productivity have been penalised by investors due to the high upfront costs required.[cxli] Some critics claim that US data implies a plateauing of adoption of generative AI tools in companies[cxlii] – I personally think that this data is far from conclusive at the present time.
Some AI researchers like DeepSeek’s senior researcher Chen Deli believes that in the short-term AI could be a great assistant to humans, but over a longer period of 5-to-10 years it would threaten job losses as LLMs became good enough to replace humans in some forms of work.
“In the next 10-20 years, AI could take over the rest of work (humans perform) and society could face a massive challenge, so at the time tech companies need to take the role of ‘defender’,” he said. “I’m extremely positive about the technology but I view the impact it could have on society negatively.”[cxliii]
Many of the leading companies in the LLM space such as Nvidia believe that the technology will drive a leap forward in robotics.[cxliv] Companies are currently building training sets on movement that are similar in function to the knowledge training sets used for LLMs. Even for well-known procedures, there are layers of formidable complexity to simple robotics tasks which would tax the most sophisticated process engineers.[cxlv]
There are limiting factors outside the control of the LLM era ecosystem including power, the degree of control and limitations of mechanical engineering to supply chain challenges wrought by globalisation.[cxlvi] Both of which neither move at, or are related to Moore’s Law speed and scale of innovation. A key component is the strain wave gearing (also known as a harmonic drive)[cxlvii]which are made to standard sizes by very few companies, representing an innovation chokepoint, similar to ASML’s lithography machines in semiconductor manufacturing. The standard sizing limits capabilities from mechanical power to precision and increments of movement, which is one of the reasons why Apple still relies on hand assembly on its iPhones despite P&Ps (‘pick-and-place’[cxlviii] machines or surface mount technology (SMT) component placement machines) being available as far back as the 1980s. This chokepoint is one of the reasons why robotics vendors have focused on software-based differentiation with limited success so far.
Different LLMs seem to lend themselves to different tasks as show by Anthropic[cxlix] and OpenAI’s[cl] own research into the economic and usage behaviour of their respective tools.
The Global environment
Unlike other technological leaps forward, the LLM era isn’t likely to see American platform domination all around the world outside China. The dot-com era was the high point of American power. Coming out of the cold war, globalisation was benefiting US technology companies. The decline of Russia allowed the Clinton regime to open up the internet to commercial usage. American companies dominated enterprise software, semiconductors, wireless and computer network products.
25 years later, the US no longer has pre-eminence. Many of its past champions like Lucent[cli] or Motorola[clii] are either much reduced, or no longer American companies. Globalisation in the technology industries has meant that the concentration of expertise has become interconnected and dissipated to global centres of excellence such as TSMC[cliii], Foxconn[cliv] and Huawei[clv]. China had developed a parallel ecosystem some of which like Bytedance successfully compete head-to-head with large American technology platforms.
The LLM era is no longer only American in nature. Chinese companies have compelling offerings. For instance, Chinese hyperscaler Alibaba claim to be able to have models that are comparable to their American counterparts, yet needs 82% fewer Nvidia processors to run.[clvi] Even Silicon Valley companies are using Chinese LLM models over the likes of ChatGPT or Anthropic. The news that Airbnb opted to use Alibaba’s open-source Qwen AI model over ChatGPT was a milestone event.[clvii]US technology sector investors are using the Kimi K2 model because it was ‘way more performant and much cheaper than OpenAI and Anthropic’.[clviii] China benefits from much cheaper model training cost per token. The open-source models can be run on private infrastructure, keeping sensitive data inhouse and ensuring ‘corporate sovereignty.
In the global south, China’s technology companies have corporate and government business relationships built up over years. Their combination of low cost combined with trusted relationships would reduce American hyperscaler opportunities for global expansion.
While US companies have access to more powerful chips, sanctions against Chinese companies aren’t effective with Nvidia chips being smuggled into China and heavy computing work like model training being run in data centres[clix] based in other Asian countries, notably Malaysia.[clx]
There is one clear parallel between the earlier telecoms bubble and the dot LLM era; demand in the global south seems to be constrained by infrastructure rather than user interest in adopting generative AI tools.[clxi]
Other bubbles.
The dot-com era tends to be cited due to it being a technology story as much as an economic story. Many other bubbles were purely financial in nature:
The sub-prime mortgage crisis of 2008/9
The US savings and loans crisis of the early 1990s
1929 stock market crash
Tulpenmanie from 1634 – 1637
The 1929 crash has sometimes been described as an electric generation bubble bursting since some 19% of the shares available on the market were from utility companies. But the impact was so widespread that it be hard to argue that it was really a ‘technology bubble’.[clxii]
The British railway mania of the 1840s is often cited as an analogue of the telecoms bubble a century and a half later. The railway mania rolled out at a slower pace than the dot-com boom. It featured a Minsky moment and resulted in a consolidation of rail companies rather than an outright failure of many businesses. Up to a third of railway companies started during the time collapsed before building their railway line due to poor financial planning.[clxiii]
The key defining factor for how bad the bust is from a bubble, and how long the bust lasts for is the amount of borrowing (or leverage) involved.[clxiv]
How might the dot LLM era differ from the dot-com era in terms of the corresponding bust?
Zero-cost co-ordination
An economic paradigm shift will have occurred that doesn’t have a clear analogue in history that I am aware of. For instance, there are theoretical writings about how LLMs and agents will change the very nature of economics and the corporation may be changed with the advent of ‘zero-cost co-ordination’[clxv] reducing economic friction. This could upend the very nature of what a company is.
Historically one of the reasons given for participating in a firm was that internal coordination costs were cheaper than market coordination (transaction costs). If agentic AI are rational actors that reduce market transaction costs (search, negotiation, contracting) to near-zero, the need for large, hierarchical firms changes and likely diminishes.[clxvi]
If this theory were true, the excessive capital expenditure would simply be the price paid for creating the world’s first zero-friction economic system. In theory, it’s possible, but it depends on the humans involved being rational decision-makers in a rational culture that doesn’t exhibit risk aversion and that their agents don’t develop similar biases over time. This often isn’t true, even in business-to-business situations, for instance in the past ‘nobody ever got fired for buying IBM’.[clxvii]
This viewpoint in some ways is similar to Wired magazine’s editorial team circa 1998 and futurist author Kevin Kelly’s ideas on the ‘new economy’.[clxviii] The thesis was that the internet would reduce information friction. The dot-com bust provided a more tempered lens on the ideas of the ‘new economy’. Would efforts to reduce economic friction fare any better than the information friction reduction of the ‘new economy’?
Google Research economists have asked this same question[clxix] and came back with more open questions than answers. The authors posit that AI systems, being built on optimisation principles, can be modelled as standard “textbook” economic agents. when AI agents deviate from perfect rationality, they may exhibit an “emergent preference” and display behavioural biases similar to those found in humans. They highlight what they termed the “contract” problem. It draws an analogy between the AI alignment problem and the economic theory of ‘incomplete contracts,’ where a designer (the principal) cannot perfectly specify the AI agent’s goals, leading to unpredictable behaviour. The economists were concerned there would be a need for new institutions to govern an AI agent economy to ensure markets remain well-functioning and stable.
The open questions:
Whether AI agents have stable ‘beliefs’?
How they update them?
If they can hold ‘higher-order beliefs’ (beliefs about others’ beliefs)?
There is a lack of research and benchmarks for evaluating AI performance in complex, multi-agent systems which needs to be addressed. One of the key challenges is that small differences between AI and human behaviour can become magnified in an equilibrium.
But what if, as Francis Fukuyama argues,[clxx] that transaction friction isn’t the block on economic growth? Instead, it’s resource constraints, social and political considerations that are the brake on how fast economic growth can happen.
AI-fuelled breakthroughs
The infrastructure boom fuels foundational AI research far beyond current capabilities. In this scenario, active engines of scientific discovery. The AI research achieves a breakthrough in a hard-science field like drug discovery (e.g., new classes of effective antibiotics), materials science (e.g., room-temperature superconductors), novel ways of rare earth metal extraction, or sustained controllable nuclear fusion – and facilitates record compression of time to market for these developments. LLMs would not only have to facilitate the breakthrough, but drive mass-accelerated implementation and regulation.
In theory, LLMs could:
Optimise experiment and trial design.
In- and post-test data analysis.
Drive synthesis of regulatory compliance documents and evidence.
Optimise production and supply chains to facilitate the manufacture and commercialisation of a new break-out product.
If all this happened, it would create entirely new sources of economic value, far dwarfing the infrastructure cost. That is a lot of serendipity, of huge scope and massive assumptions: even the NASA Apollo Program[clxxi] took eight years to have its first crewed lunar flight[clxxii] and another year to put the first men on the moon.
AI-fuelled breakthroughs are usually linked with progress towards AGI or ‘artificial general intelligence’ or human level intelligence AI.[clxxiii] A research paper from Cornell University that outlined benchmarking for progress to understanding the real world. The paper introduced WorldTest, a new framework for evaluating how AI agents learn and apply internal world models through reward-free exploration and behaviour-based testing in modified environments. Its implementation, revealed that while humans excel at prediction, planning, and change detection tasks, leading AI reasoning models still fall short. Their shortcoming was associated with flexible hypothesis testing and belief updating. The findings suggest that future progress in AI world-modelling depends less on scaling compute and more on improving metacognition, exploration strategy, and adaptive reasoning.
Platform lock-in and bundling
Many of the established hyperscalers (Adobe, Alphabet, Amazon, Microsoft, Oracle and Salesforce) have established client relationships in a range of products:
CRM.
Creative Suite and Marketing Cloud.
Office suite or Workspace.
Enterprise Cloud services.
Rather than a disruptive paradigm shift, the LLM payback could come from an instant, embedded non-disruptive increase across existing indispensable products and services. It extracts the value from the existing enterprise wallet, which breaks the historical analogy of relying on new economic value creation. On the face of it, a largely risk-free proposition.
The US legal environment is very different from the dot-com era. Microsoft would not have to worry about facing an antitrust trial similar to its conflict over bundling with Netscape.[clxxiv]
While in the US, antitrust enforcement is considered laxer than during the Biden regime, these technology companies would be concerned about competition regulators in the EU and elsewhere. For example, just this September, Microsoft had to unbundle Teams from its Office software to avoid EU antitrust fines.[clxxv] Alphabet[clxxvi] and Amazon[clxxvii] have had previous bruising run-ins with authorities outside the US which would complicate any decision made to bundle an LLM service.
What could dot LLM era outcomes look like?
I have come up with seven scenarios that range in the kind of impact that generative AI as a sector may provide. These range from being wildly successful to dark failure
The breakthrough: total economic transformation due to a post-war breakthrough in science and technology.
The ‘new economy’: frictionless co-ordination facilitates more economic activity.
The ‘wingman economy’: a managed productivity boom.
The ‘Red Hat model’: an open-source foundation driving value-added services.
The ‘moral hazard’: major AI players are considered ‘too big to fail’ and backstopped with government loan guarantees.
The ‘telecoms bust’: a Minsky moment and amortisation crisis.
The ‘weird gizmo’: collapse total bust.
How these scenarios map out when thinking about the level of value creation or value saved through increased efficiency.
Negative / zero net value created
Positive to transformative value creation
New value creation
The ‘weird gizmo’ collapse (value was illusory)
The breakthrough (new science) The ‘new economy’ (new coordination)
Efficiency / existing value
The ‘telecoms bust’ Capex > value The ‘moral hazard’ value is geopolitical rather than financial
The ‘wingman economy’ (managed productivity) The ‘Red Hat’ model (value moves to services)
The breakthrough: total economic transformation
What it looks like: The massive capital expenditure on infrastructure is validated because AI achieves a true, hard-science breakthrough. This creates entirely new sources of economic value, such as sustained nuclear fusion, room-temperature superconductors, or new classes of antibiotics. In this outcome, the $1 trillion in implied value is not only met but vastly exceeded. Justifying the “bubble” as the necessary investment for a new industrial revolution.
What to watch?
Scientific breakthroughs.
Metric:
High-impact scientific publications that use AI for novel discovery, NOT just analysis.
Source:
Track major journals like Nature, New Scientist and Science for breakthroughs in AI-driven drug discovery, materials science, or physics. Recent reports on AI’s role in molecular innovation and even quantum computing show this is a key area to watch.
The “new economy”: frictionless co-ordination
What it looks like: Agentic AI successfully reduces market transaction costs (search, negotiation, contracting) to near-zero. This upends the nature of the corporation, as the historical reason for firms (cheaper internal vs. market coordination) diminishes. The massive capital expenditure is seen as the “price paid for creating the world’s first zero-friction economic system”. This is the 1998 Wired “new economy” thesis finally coming true, though it faces challenges like the “Contract problem” and AI alignment.
What to watch?
Agentic breakthroughs
Metric:
Demonstrations of “agentic” AI (AI that can independently complete complex, multi-step tasks), particularly in commercial or economic settings.
Source:
Monitor announcements from leading research labs (DeepMind, FAIR, OpenAI) and market analysis on “agentic AI” to see if it’s moving from theory to reality.
The ‘wingman’ economy: a managed productivity boom
What it looks like: The technology finds its “optimal economic point”. LLMs become a powerful “wingman for white-collar workers”, similar to the adoption of early PCs. This drives real productivity gains, but the $1 trillion in cost savings is implemented gradually, avoiding the catastrophic deflationary shock modelled by the Phillips Curve and Okun’s Law. The “Magnificent 10” see steady growth, but the ‘pure play’ LLMs struggle to find profitability on their own.
What to Watch:
National Productivity Data
Enterprise Adoption & AI Mentions in Earnings
Metrics:
U.S. labour Productivity and unit labour costs. We are looking for a “golden path”: productivity rising faster than unit labour costs, which would suggest companies are becoming more efficient without just slashing jobs en-masse.[clxxviii]
The number of S&P 500 companies citing “AI” on their quarterly earnings calls. A high number (e.g., over 40-50%) shows it’s a top-level strategic priority.
Sources:
U.S. Bureau of Labor Statistics (BLS) – productivity and costs. The quarterly releases from the BLS are the single best macro-indicator for this scenario.
FactSet Earnings Insight.[clxxix] – they regularly publish analyses on the frequency of “AI” mentions in earnings calls, which is a direct proxy for corporate focus and investment.
The Red Hat analogue: a foundational model
What it looks like: The “pure play” LLMs like OpenAI and Anthropic, which are losing money, ultimately fail or are acquired for pennies on the dollar. However, open-source and open weight models (like Llama, etc.) proliferate. Alibaba’s Qwen model has already been very successful. Singapore’s national AI programme dropped Meta’s Llama in favour of it.[clxxx] Singapore joins Airbnb as Qwen users;[clxxxi] meanwhile Chinese model DeepSeek has been adopted by European startups.[clxxxii] The long-term winners are not the model creators but the companies that, like Red Hat, sell “enterprise-grade support, integration, and services”.
LLM models have an “outsized impact” —becoming the “Linux” for the next generation of applications—but the initial investors see a massive correction.
What to Watch:
Open-source vs. closed-source momentum
Metric:
Rate of change in download statistics, new model uploads, and developer activity on open-source AI platforms.
Source:
Hugging Face Trends.[clxxxiii] This dashboard shows which open-source models are gaining traction. If downloads for open-source models are growing faster than API call revenue for closed-source models (a harder metric to find), it signals a shift toward this “Red Hat” scenario. GitHub’s annual “Octoverse” report is another key source, as it tracks the rise of AI-focused projects.
The ‘moral hazard’: major dot LLM players are considered ‘too big to fail’ and backstopped with government intervention
There are elements of a non-bubble, financial crisis aspect to the dot LLM era. Chinese LLM vendors are being given subsidised electricity from local governments,[clxxxiv] alongside preferential rates in data centres. The LLM era in the US could be considered by the government as having become too large a part of the economy to be allowed to fail due to normal market forces. Open AI has recently had to deny rumours[clxxxv] that it sought US government loan guarantees for at least part of the multi-trillion dollar deals it has put in place for data centre infrastructure and hardware. AI sovereignty comes to be seen as taking on a geostrategic and national security imperative as business and investor considerations take a backseat.
Hyperscalers are hitting a ‘power wall’ as they cannot get the equivalent electricity generating capacity of 16 Hoover dams. Getting over the wall would require a massive amount of government infrastructure funding.[clxxxvi]
Major government involvement may impact the speed of development as LLM model providers and supporting infrastructure no longer have to constantly innovate and instead move at the speed of their government clients.
What to watch:
Shift in rhetoric from commercial to critical: Observe how language from policymakers, military leaders, and national security bodies evolves. A shift from discussing AI in terms of commercial competition (e.g., “market leadership”) to national infrastructure (e.g., “digital sovereignty,” “critical asset,” “geostrategic imperative”) is a primary indicator. This reframes an economic failure as a national security failure.
Direct & indirect state support mechanisms: look beyond simple R&D grants. Watch for the creation of new, targeted support instruments:
Direct: preferential pricing on energy/compute, state-backed datacentre construction, sovereign wealth fund investments, or direct “national champion” subsidies.
Indirect: government-backed loan guarantees for infrastructure (like the rumoured OpenAI deal), strategic procurement (where the government becomes the anchor customer) – Palantir would be an exemplar, and “regulatory moats” that favour incumbents (e.g., high-cost safety/licensing rules that only large, state-backed labs can afford).
“Bailout” vs. “investment” framing: monitor how state intervention is publicly justified. A struggling “national champion” AI firm receiving a sudden capital injection from a state-adjacent entity will likely be framed as a “strategic investment in national capability,” not a “bailout.” This framing is key.
Metrics:
Value of state & military contracts: Track the total disclosed value of government contracts (especially from defence and intelligence agencies) awarded to foundational model providers. A rapid increase, or contracts for non-competitive “strategic deployment,” signals TBTF (“Too Big to Fail”) status.
Frequency analysis of policy language: quantify the co-occurrence of terms like “AI,” “sovereignty,” “national security,” and “critical infrastructure” in parliamentary/congressional records, national strategy documents, and defence budget justifications. A rising frequency indicates the ideological groundwork for a TBTF policy.
State-backed capital flows: monitor announcements from sovereign wealth funds, national investment banks (e.g., UK’s National Security Strategic Investment Fund), or public pension funds. Track the size and frequency of their investments into large, established AI labs, as opposed to a diverse portfolio of early-stage start-ups.
Subsidy disclosures: quantify the value of announced subsidies (e.g., tax credits, energy discounts, land grants) specifically earmarked for AI datacentres and R&D hubs associated with the major players.
Sources:
Financial & policy journalism: The Financial Times, Bloomberg (especially its Bloomberg Government vertical), and Politico as media sources. Their reporters are often the first to break stories on subsidies, lobbying, and the intersection of tech and state power.
Government procurement & grant databases: official portals like USASpending.gov in the US or the UK’s Contracts Finder service. While difficult to navigate, they provide primary evidence of public funds flowing to specific companies.
Think tank & national security publications: Reports from organisations like the Center for a New American Security (CNAS) in the US, the Royal United Services Institute (RUSI) in the UK, or the Mercator Institute for China Studies (MERICS). They often analyse and quantify the geostrategic rhetoric and policy shifts. The main challenge with this source might be timeliness of publication in comparison to the previous sources.
Company filings & investor calls: For publicly traded companies (Microsoft, Google, Amazon, Nvidia), annual reports (10-K forms) and quarterly investor calls often mention large government contracts or regulatory risks/opportunities, providing a corporate-side view of this trend.
The Telecoms Bust: a Minsky moment and amortisation crisis
What it looks like: The $1 trillion in value fails to materialize from either advertising or business efficiencies. Investors have a Minsky moment and realize the debt and capex are unsustainable. The bubble implodes like the telecoms bubble. The key difference is the financial and technological amortisation risk: the GPUs (with a 2-to-5-year useful life) become obsolete. Unlike the dot-com era’s dark fibre, this infrastructure cannot be repurposed by a “web 2.0”. This leads to trillions in write-offs, analogous to WorldCom’s $180 billion loss.
What to Watch:
Hyperscaler capital expenditure (Capex)
GPU amortisation & resale value
Metrics:
Quarterly capex announcements from Google (Alphabet), Meta, Microsoft, Oracle and Amazon (AWS). This is made trickier to understand by Meta, Microsoft and Oracle looking at forms of private equity financing.
The rate of change in Nvidia’s[clxxxvii] data centre revenue, Broadcom and AMD’s enterprise / data centre revenue. This is the “equipment maker” side of the equation. As long as this number is growing, the bubble is inflating. A sudden slowdown would be the first sign of a “Minsky Moment.”
The resale value of last-generation GPUs (e.g., H100s as B200s/B300s roll out). If these prices collapse, it validates your thesis that the assets cannot be repurposed, and the financial write-downs will be catastrophic.
Sources:
Hyperscaler capex reports from financial analysts and data centre publications. Recent reports show combined capex is projected to hit hundreds of billions, a clear sign of the infrastructure race.
Alphabet, Meta, Microsoft, Oracle and Amazon quarterly results and investor roadshow presentations.
NVIDIA, Broadcom and AMD quarterly earnings reports. The Nvidia Q2 2026 report showing data centre revenue at $41.1B is a perfect example of this indicator.
Resale value of GPUs is a harder metric to track. Monitor tech hardware forums and eBay listings, or look for analyst reports on the “used GPU market.” A collapse in this secondary market for last generation GPUs is a major red flag.
The “Weird Gizmo” Collapse: total bust
What it looks like: The technology is ultimately seen as a novelty. It’s the 2020s version of Boo.com, Beenz and Flooz, or the 3Com Audrey. The argument that “AGI is not imminent[clxxxviii], and LLMs are not the royal road to getting there” wins the day. This bear view of AGI is one that is widely shared by prominent experts[clxxxix] within the machine learning field. Which is why new ways of working like nesting models and world models are being explored, alongside quantum computing. In this scenario, the pure play companies burn through all their cash and vanish. The hyperscalers are left with billions in useless, obsolete silicon, and the “dot LLM era” is remembered as a short-lived period of speculative mania.
What to Watch:
AI startup burn rates & funding (the “burn Rate” indicator)
Metric:
Quarterly venture capital funding for AI startups, specifically looking for a rise in “down rounds” (where valuations decrease) or outright failures.
Source:
Data from firms like CB Insights or Crunchbase.[cxc] Recent reports show that while “mega-rounds” for established players (like Anthropic) are still huge, seed-stage funding is declining, showing a “haves and have-nots” market. A slowdown in the mega-rounds would signal the bust is beginning.
Personal assessment of likely outcomes by scenario
Scenario
Estimated likelihood
Rationale
The moral hazard
~95%
US – China trade disputes and geopolitical strife
Chinese government investment in startups
Chinese local government subsidies for operating AI services
The current position that AI has in driving US GDP growth across sectors including construction and the energy sector
Likely OpenAI loan guarantees
Palantir is already deeply embedded in the US government as a vendor and has partnerships with defence contractors like Anduril
The ‘wingman’ economy
~80-90%
Some research reports indicate that AI is augmenting knowledge workers in different sectors.
Claims of AI replacing workers are more difficult to validate, for example: Klarna moving to automation and then rehiring
Clifford Chance offshoring back-office roles to Poland and China while claiming that the job losses were due to AI.
The ‘Red Hat’ Model
~70-80%
Airbnb opting to use Alibaba’s open-source Qwen AI model over ChatGPT was a milestone event.[cxci]
The ‘telecoms bust’
~75%
Concerns about the size of capital expenditure.
Rate of growth of supporting infrastructure.
Uncertainty about length of depreciation affecting overall shareholder trust in hyperscalers.
Cheaper alternatives like Qwen.
The ‘new economy’
<15%
The uncertain economics of ‘zero friction’ transactions.
Real-life legal and regulatory issues.
Amazon’s dispute with Perplexity using AI agents on its website.
The breakthrough
<10%
A black swan event
The ‘weird gizmo’
<5%
It would be unusual for a technology to disappear completely,
LLMs have been finding some use already.
The rise of open-source AI models which reduce the cost of operation.
Where are we at the moment?
I worked to put together a diagram to try and assess where we are at the moment given that some of the scenarios outlined are running concurrently with each other.
[clxxix]FactSet Insight blog – Search their blog for keywords like “AI” or “earnings.” They regularly publish analyses on the number of S&P 500 companies that cite “AI” on their earnings calls, which is a direct proxy for C-suite focus.
[clxxxiii]Hugging Face models hub – the view can be filtered by ‘trending’ and ‘most downloaded’ to see what the community is using, versus what closed source models are being marketed
[cxc]Crunchbase News – They provide regular analysis of funding rounds. Watch for ‘down rounds’, M&A consolidation among start-ups or acquihires and slowdowns in $100M+ mega-rounds of fund raising.
Toyota announced its new Toyota FJ Land Cruiser model. The Toyota FJ Land Cruiser is a smaller five-seater vehicle. It is a direct replacement for the FJ Cruiser which was sold in many markets outside the UK and European Union. Like its predecessor the Toyota FJ Land Cruiser shares underpinnings with its larger 7-seater cousin the 250 series. It features a shorter wheelbase. Toyota has put a lot of effort into thinking about how it can make the Toyota FJ Land Cruiser more extensible in capabilities and more modular.
Modularity comes in compatibility with MOLLE military storage connectivity that has made its way into the civilian world. While Alpine packs are about sleek design with few snags, MOLLE allows fastenings, pouches and equipment to be suspended inside and outside bags. Toyota has now extended this to the inside of the Toyota FJ Land Cruiser’s rear door.
The focus on extensible features within the vehicle shows how some markets (notably America) have a large after market industry providing additional features for vehicles with aspirations to do overloading. Toyota is an active participant in the SEMA show in the US. This is where fans and the vehicle modifying industry get together to be inspired, do deals and gain intelligence on vehicles so that they can design new after market parts. Toyota brings concept builds, as well as allowing after market manufacturers to measure and 3D scan new vehicles.
The move to extensible design, shows that Toyota is interested in providing more of that capability through its own business. Third-party parts, in particular lift kits can affect handling and wheel bearings. Designing its own aftermarket parts and applying extensible thinking in the vehicle design philosophy allows Toyota to:
Meet consumer needs.
Ensure the vehicles meet the factory’s quality and reliability standards.
Offer incremental additional revenue.
While a Toyota FJ Land Cruiser as ‘mum truck’ won’t need a water fording kit. An overlanding enthusiast like Chloe Kuo would put it to good use and influence more potential buyers in the process.
Like the FJ Cruiser before it we are unlikely to see the Toyota FJ Land Cruiser in UK Toyota dealerships due to the UK government’s focus on forcing UK consumers away from internal combustion vehicles. Instead they are likely to come in small numbers as JDM (Japanese domestic market) pre-owned vehicles.
Toyota recognises that net zero is more complex than importing Chinese electric vehicles. Considerations also need to be given to vehicle use case, the whole life carbon footprint of the car and sustainability. But that doesn’t make simple solutions for policy makers.
Toyota will have four Land Cruiser models that it will be selling around the world:
J70 series – sold to the UN, various militaries, Japan, Australia and in the global south. Doesn’t pass current European vehicle laws as it’s designed for resilience, robustness, repairability and sustainment in the most hostile environments.
J300 series – the flagship of the line-up. Sold in the US as the Lexus LX, this combines the comfort of a top of the range Range Rover with the capability of the 70 series in an off-road environment. As a Land Cruiser it is available in Australia, Japan, the Gulf States, South Africa and various countries in South East Asia from Sri Lanka to the Philippines.
J250 series – the most widespread of the Land Cruiser range in terms of sales footprint. It is sold in Japan, Europe, North and South America, Australia, Sri Lanka, the Philippines, China, Vietnam, Brunei and the Gulf States. In Europe it’s known as the Land Cruiser. It’s sold in other markets as the Toyota Prado, the Toyota Land Cruiser 250 in Japan, the Toyota Land Cruiser Prado in Australia and in North America as the Lexus GX and Toyota Land Cruiser. It is smaller and utilitarian than the J300, but not quite as robust or spartan as the J70 series.
FJ Land Cruiser – the Toyota FJ Land Cruiser is likely to be sold in North America and Japan, mirroring the markets where the FJ Cruiser was originally sold.
How the UK Lost Its Shipbuilding Industry – by Brian Potter – The UK ultimately proved unable to respond to competitors who entered the market with new, large shipyards which employed novel methods of shipbuilding developed by the US during WWII. British industry in general failed to invest adequately to keep ahead of competitors. The UK fell from producing 57% of world tonnage in 1947 to just 17% a decade later. By the 1970s their output was below 5% of world total, and by the 1990s it was less than 1%. In 2023, the UK produced no commercial ships at all. – Part of this was also down to policy decisions. The Thatcher administration deliberately designated yards as military-only to drive them to the wall and smash the trade unions.
Memory Exiled | History Workshop – a bit tiresome, don’t get me wrong I am happy to throw brickbats at the UK Government as a citizen of a decolonised country but this is distorted. – The UK government releases papers after 20 years, but some are kept under wraps for longer for national security or other reasons. Sensitive materials (in Hong Kong’s case, perhaps to do with the handover) don’t account for more than a tiny percentage of the content and are redacted. One possible reason the Hong Kong files are still not released is simply that there are huge amounts of them, and they are mostly on microfiche, which is a pain to digitise – not because of a desire to ‘control history’.
Forgive the rant, but this quote from an article about WPP’s decline–and the attitude behind it–drives me absolutely crazy. Let’s do the math.
If you’re a 40-year-old creative, you were 19 when Facebook launched.You turned 21 when Twitter debuted.You were 22 when Apple introduced the iPhone, and 25 when Instagram came out. So you’ve literally spent your entire career in advertising creating work for the digital/social/smartphone media ecosystem. And that means you’ve produced way more digital-first and social campaigns than TV spots, let alone posters. (Also: I would love to meet the creative who “built a career” making posters.)
And creatives older than 40? They’ve successfully navigated the decline of broadcast and mass media, the introduction of smartphones, the broad shift to targeting, the endless parade of social channels and new technologies that Will Change Everything–arguably the greatest two decades of disruption the advertising industry has ever faced.
And the creatives who are over 60? They’re the generation that *invented* digital advertising. – I thought that this comment from LinkedIn was the most insightful assessment of the article
October 2025 introduction – (27) gateway to heaven edition
I am now at issue 27, or as a bingo caller would put it ‘gateway to heaven’.
27 inspired an urban legend of the ’27 club effect’ where a ‘statistical spike’ in fatalities affected musicians, actors and other artists. However in reality there is no statistical spike though Amy Winehouse, Jim Morrison, Kurt Cobain and Janis Joplin all died aged 27.
In the I Ching, the 27th hexagram is associated with sufficient physical and spiritual nourishment – good advice for anyone since we’re going into winter.
This edition’s soundtrack is Coco – I Need A Miracle over Dreadzone – Little Britain put together by MH1. I used to love mashups and bastard pop in the early 2000s to 2010s, especially A plus D and their Bootie nights, which I got to see at the DNA Lounge back when I had to travel to San Francisco while working at Yahoo!. They built a whole genre of out of what would normally be trick recordings you might have played once in a DJ set like Evil Eddie Richards You Used to Salsa.
MH1 carries this on the same grand tradition as A plus D and sent me down a memory hole in my iTunes collection.
Now we have a sound track, let’s get into it.
New reader?
If this is the first newsletter, welcome! You can find my regular writings here and more about me here.
Things I’ve written.
Golden Mile – a collection of documentaries and films that caught my eye, from the last days of the Golden Mile complex that was a centre of the Thai diaspora in Singapore to the oral history of the Google Docs development team.
Nvidia ban in China and more things – a selection of stuff that I found of interest online including articles on Nvidia’s fraught relationship with China as a market.
Tahoe and more things – from the inside steer on Nexperia and the importance of strategic writing for using LLMs to Apple’s imperfect macOS Tahoe.
Books that I have read.
Clown Town by Mick Herron is the latest instalment in the Slow Horses series of books, on which the Apple+ TV series are based. Without plot spoilers, I can tell you that it’s up to the high standards of the earlier books. Herron is as critical of the current Labour government as he was of their conservative party predecessors.
I am just starting in on Andrew Ross Sorkin’s history of the great depression 1929. I hope to update you next month on whether it’s a book that I would recommend.
Things I have been inspired by.
I managed to spend some time with fellow former Yahoo Nick Fowden and we discussed AI futures and the altar of marketing efficiency and performance media over effectiveness and brand building.
Mercedes goes back to luxury
Mercedes-Benz put on a special event in Shanghai that looked like an attempt to reboot the brand. Star of the show was the Mercedes Vision Iconic concept car, this looked like the vehicle the relaunched Jaguar brand would want to build. The grill looked as if it came off a vintage Mercedes 600 ‘Grosser’ and was a world away from the current nadir of the car brand.
Understanding influencer payback is also about understanding the caveats, risks and current limitations in optimisation
The IPA published effectiveness data on (paid) influencers, it is is great to have this data set and shows the continued role in improving the advertising industry. You can expect to see these headlines trumpeted by influencer and PR agencies. BUT, it is worthwhile going beyond the headlines and into the deck presented. My takeouts from the data presentation:
It’s a promising, but limited sample. However overall there seemed to some commonality in results amongst the European markets compared.
On sample selection, the presentation itself says: “Based on sample definition, no campaigns are included where influencer spend was present but did not produce a result at some level therefore 0-25 ROI index likely to be significantly under-reported.” This is key because reading the data at face value gives an overly-optimistic picture of likely influencer campaign success.
The data presented represents bad news for paid social campaigns in particular, and it highlights their relatively poor RoI from the Profit Ability 2 study. What it means: Expect some of the influencer spend to come from the experimental pot within the social platform advertising budget.
Long-term multipliers for Influencers are the highest of any channel in the data, but the tiny difference between television and influencers are not significantly large to be definitive. So further research could see this relationship flip.
The numbers are averages, but Influencer ROIs are much less likely to be ‘average’ with (high and low) extremes alongside outliers much more present (and this is with campaigns giving 0-25% RoI filtered out). There is a much higher risk in terms of spend versus reward, which a responsible agency partner should be disclosed to clients upfront. This huge variance also mirrored in the kind of results seen in Chinese social commerce campaigns as well. What it means: You can’t duplicate success and optimise in the same way as you can by using testing for TV advertising and the like. So for businesses like Unilever that are putting half their spend into influencer marketing – its a high risk endeavour with limited risk mitigation strategies.
There isn’t intra-influencer category analysis based on follower size. Getting paid reach will still be critical, so social platforms will still win.
The research doesn’t cover B2B sectors but subscription revenue model technology clients (Adobe, Canva, Monday.com, Grammarly etc.) may take some hope from the RoI achieved by consumer-focused telecommunications brands.
TL;DR: At the moment influencer represents the worst attributes of both earned and paid media. The uncertainty of earned media, the higher upfront cost of paid media. What it means: be wary of over-promising influencer campaign success, set realistic expectations about the likely wide variance in results. Keep an eye out as further data expands and clarifies the picture.
Chart of the month.
European attitudes to video games
This month’s chart comes from data provided by the European Commission’s Director General CONNECT (Communications Networks, Content and Technology). The breadth and quality of research that they do is really useful to people in my line of work.
The sentiment around video games is similar to what you would have seen around television in previous decades. So video games aren’t in aggregate seen as great for society, but not the complete pox on people that you would see if you ran the same survey for social media platforms.
Things I have watched.
Jean Giraud aka Moebius was a graphic novel artist drawing in the bande dessinée tradition. Moebius Redux is an hour long documentary of Giraud’s career through to the mid-2000s. In it is a who’s who of comic book legends including Stan Lee and Mike Mignola – the creator of Hellboy. It even has an amazing original soundtrack by former Kraftwerk member Karl Bartos.
I remember seeing The Satan Bug as a child and enjoying it immensely. I also read the book that it was based on as my Dad had built up a collection of Alistair McLean and I devoured them from age 11 onwards. I went back and revisited decades later to see if I would still enjoy it. I did, but for different reasons. The premise is based on the paranoia of annihilation that was a main part of the cold war zeitgeist. The wrinkle in the story is that it’s about germ warfare rather than nuclear bombs. Adult me saw the plot as ridiculous, but the cinematography, location, set design and wardrobe floored me. The high security compound of ‘Station 3’ and the various interiors were triumphs of mid-century modern design. The scenery and locations were in the California desert making it an ideal canvas for John Sturges who had previously shot Bad Day at Black Rock and The Magnificent Seven. The Satan Bug was also the start of helicopter based cinematography thanks a newly developed steady cam mechanism.
Following on in the same vein I watched The Andromeda Strain. Just six years separated from The Satan Bug, but stylistically so different. The Michael Crichton novel has a much more coherent story. Much of it is told through jargon and multi-media formats such as a computer driven plotter and a ticker tape running across the screen in the second scene of the film. Where The Andromeda Stream loses out is in its cinematography and style. The set design feels lazy compared to The Satan Bug, the cinematography is competent but workman-like.
I watched the original Russian film adaptation of Solaris. It has been years since I have seen it. Its one of them films that you can watch three times and still pick up new details. Andrei Tarkovsky focused on storytelling rather than special effects which are used very sparingly. I still love that the Soviet city of the future was footage of early 1970s Tokyo. Watching it now the film feels more deconstructed.
The last film this month is an indulgent one of mine. Searchers 2.0 is a modern-day neo western film that thumbs its nose at the Hollywood system. The story follows the revenge quest of two former child actors against their former abuser, a screen writer on the film set. Alex Cox made the film on $100,000 budget with Roger Corman as producer. UK film fans probably remember Alex Cox as the presenter of Moviedrome. he is an accomplished director, screenwriter and the author of 10,000 Ways To Die – the best guide ever written to spaghetti westerns.
Slow Horses season five has been a must-watch TV moment for me. The show runners manage to keep the essence of the books with some creative flair.
Useful tools.
Optimising for macOS Tahoe
Moving from macOS Sonoma to Tahoe meant changing up the versions of utilities I like to use. Titanium Software make OnyX, Maintenance and Deeper.
OnyX y use of OnyX goes all the way back to 2002 and 10.2 Jaguar, various versions of it throughout that time to now. It was very good for doing startup disk related maintenance tasks.
Maintenance provides a general performance tune-up including rebuilding databases and clearing obscure caches.
Deeper is for fine tuning hidden settings across different applications on your Mac.
All three are donationware so be sure to give Joel what you can.
The sales pitch.
I am currently working on a brand and creative strategy engagement at Google’s internal creative agency. I am now taking bookings for strategic engagements from the start of 2026 – keep me in mind; or get in touch for discussions on permanent roles. Contact me here.
Ok this is the end of my October 2025 newsletter, I hope to see you all back here again in a month. Be excellent to each other and get planning for Hallowe’en. As an additional treat here is a link to my Mam’s recipe for barm brack – an Irish Hallowe’en specialism. Let me know how you got on baking it.
Don’t forget to share if you found it useful, interesting or insightful.
Clutch Cargo was an animated series first broadcast on American television in 1959. Clutch Cargo was created by Cambria Productions – who were a start-up animation studio. Cambria used a number of techniques to radically reduce the cost of producing the animated series.
A key consideration was reducing the amount of movement that needed to be animated. There were some obvious visual motifs used to do this:
Characters were animated from waist height up for the majority of the films, this reduced the need to animate legs, walking or running.
Much of the movement was moving the camera around, towards or away from a static picture.
To show an explosion, they shook the camera, rather than animate the concussive effect of the blast.
Fire wasn’t animated, instead smoke would be put in front of the camera. Fake snow was sprinkled so that bad weather didn’t need to be drawn.
Cameraman Ted Gillette came up with the idea of Syncro-Vox. The voice actors head would be held steady, they would have a vivid lipstick applied and then say their lines. Gillette then put their mouths on top of the animated figures. Cambria made use of it in all their animations with the exception of The New Three Stooges – an animated series that allowed Moe Howard, Larry Fine and Joe DeRita to be voice actors after their movie contracts finished and they were affected by ill health.
These choices meant that Clutch Cargo cost about 10 per cent of what it would have cost Disney to animate. The visual hacks to cut costs were also helped in the way the scripts were developed. Clutch Cargo avoided doing comedy, instead focusing on Tin-Tin-like adventures. ‘Physical’ comedy gags create a lot of movement to animate. By focusing on the storytelling of Clutch Cargo. The young audience weren’t bothered by the limited animation, as they were captivated into suspending their beliefs.
Ozempic Could Crush the Junk Food Industry. But It Is Fighting Back. – The New York Times – Lars Fruergaard Jorgensen, the chief executive of Novo Nordisk, which makes Ozempic and Wegovy, told Bloomberg that food-industry executives had been calling him. “They are scared about it,” he said. Around the same time, Walmart’s chief executive in the United States, John Furner, said that customers on GLP-1s were putting less food into their carts. Sales are down in sweet baked goods and snacks, and the industry is weathering a downturn. By one market-research firm’s estimate, food-and-drink innovation in 2024 reached an all-time nadir, with fewer new products coming to market than ever before.
Ozempic users like Taylor aren’t just eating less. They’re eating differently. GLP-1 drugs seem not only to shrink appetite but to rewrite people’s desires. They attack what Amy Bentley, a food historian and professor at New York University, calls the industrial palate: the set of preferences created by our acclimatization, often starting with baby food, to the tastes and textures of artificial flavors and preservatives. Patients on GLP-1 drugs have reported losing interest in ultraprocessed foods, products that are made with ingredients you wouldn’t find in an ordinary kitchen: colorings, bleaching agents, artificial sweeteners and modified starches. Some users realize that many packaged snacks they once loved now taste repugnant.
Apple resumes advertising on Elon Musk’s X after 15-month pause – 9to5Mac – the negative reaction to this that I have seen from Mac and iPhone users that I know is interesting. It’s the scales have dropped from their eyes about Apple’s performative progressive values. Yet the signs have been out there for years – in particular with regards anything that is even tangentially connected to China.
Zuckerberg’s rightward policy shift hits Meta staffers, targets Apple | CNBC – employees who might otherwise leave because of their disillusionment with policy changes are concerned about quitting now because of how they will be perceived by future employers given that Meta has said publicly that it’s weeding out “low performers.” Meta, like many of its tech peers, began downsizing in 2022 and has continued to trim around the edges. The company cut 21,000 jobs, or nearly a quarter of its workforce, in 2022 and 2023. Among those who lost their jobs were members of the civic integrity group, which was known to be outspoken in its criticism of Zuckerberg’s leadership. Some big changes are now taking place that appear to directly follow the lead of Trump at the expense of company employees and users of the platforms, the people familiar with the matter said.
