AI reckoning as a term is a rather stark warning. Even more so when it comes from Aswath Damodaran who is a professor of finance at the Stern School of Business at NYU (New York University).
The reckoning that Damodaran is concerned about is a ‘Minsky moment‘ and represents a stark contrast to the boundless techno-optimism of Marc Andreessen.
The AI reckoning wasn’t on the mind of investors who bought into Cerebras Systems.
Cerebras Systems makes wafer sized chips with memory and processing on the same die. This reduces latency and increases speed. As cool as Cerebras Systems technology is, the company currently has sales of $510 million and was valued on its opening day of trading at $71 billion.
AI-Powered Cyberattacks | Robert Scoble and A glimpse into cyber-security’s AI-driven future | The Economist – A few years ago a participant used the conference network to hack a water-treatment facility in America (Messrs Wyler and Stump are cagey about the details). Another hid behind the din of legitimate hacker traffic to attack government websites and payment systems. The noc team traced him, sent him a message reminding him that doing illegal things from Black Hat was still illegal, then watched him close his laptop and walk away. Hackers on the other side of the world try their luck too. When the registration server was switched on, attacks began at once, including traffic that appeared to originate in Romania….Mr Stump says the noc has seen a pattern across multiple Black Hat conferences in which Taiwanese participants show up with hacked devices. “Most of [the traffic] goes back to China,” he says. ai-powered attacks by nation-states or cybercriminals are likely to intensify.. The team thinks the ai race is only beginning. For Mr Wyler, the vulnerabilities discovered by Mythos, including some that have gone undetected for decades, are to be welcomed rather than feared. “We now know they’re there.” All the same, cautions Mr Stump, the next two years will be turbulent, as more flaws will be uncovered; more breaches will occur as firms feed sensitive data into ai systems; and more insecure code will be written.
I am not a football fan, but I recognise the power that the 2026 World Cup has to move hearts and minds. This year it’s being hosted by Canada, Mexico and the United States. The 2026 World Cup fan experience is shrouded in uncertainty.
I found it interesting that both adidas with the launch of its match ball and Panini with the launch of its sticker book both marked the start of the countdown to the 2026 World Cup season.
Chinese countryside’s quieter strains – by Yuxuan JIA – Decades of son preference have left villages full of unmarried men, driving bride prices higher and sustaining a shadow market for “Vietnamese brides” that can slide into fraud, coercion, and trafficking. Young people, especially young women, are drifting away from rural patriarchy and the obligation-heavy world of kinship and “face”, while the influencer economy and short-video apps offer fantasies of easy money to teenagers with weak school prospects.
Why Catholicism is drawing in Gen Z men | Washington Post – “I don’t want to be too disparaging about them because they’re our Christian brothers and sisters, but worshiping in a big former supermarket with dry ice machines and a pop band, it’s not really traditional Christianity,” Father Longenecker said. His new parishioners are attracted to “very traditional worship with lots of incense and altar boys and sacred music in the traditional style.”
More NatSec | Big Lychee, Various Sectors – From the government’s press release… Safeguarding national security is a continuous endeavour with no end point. At its core this sounds like the Stalin derived Maoist principle of struggle. National security ‘enemies’ like Jimmy Lai, Chow Hang-tung, Lee Cheuk-yan and Albert Ho serve the same purpose as George Orwell’s character Emmanuel Goldstein in his novel Nineteen Eighty-Four. The ever-tightening system is allegedly concerned with leaderless lone wolves, small cells and external actors – but the reality is just control in Orwell’s novel.
Revising Hong Kong’s Past – Lingua Sinica – Among the changes noted was a complete erasure of references to the Tiananmen Massacre, which was recast as “political turmoil in the late spring and early summer of 1989.” Gone from the exhibit entirely, the Ming Pao reported, is a previous image that showed one million Hong Kongers taking to the streets in 1989 in support of the demonstrators in China.
I am skeptical about ‘gurus’. However, I found this Tony Robbins video good for getting out of a period of ‘stuckness’ in my thinking. Robbin’s ideas about priming, in particular they way he links physical activity to mental exercises works and has a good deal of neuroscience behind it.
Japan loses its thirst for vending machines | FT – Tens of thousands of vending machines are vanishing from Japan, as machines that once symbolised the nation’s love of innovation are shunned in a climate of rising inflation and deepening labour shortages.
The nation’s stock of 2.2mn drinks vending machines is down 23 per cent from its bubble-era peak in 1985, according to the Japan Vending System Manufacturers Association.
The faltering economics of running a national vending machine empire were exposed when DyDo, Japan’s third biggest operator, this month said it would scrap almost 7.5 per cent of its network of 270,000 units after posting its largest ever annual loss. – what surprised me was that the vending machines weren’t digitised.
Why do men love Stone Island | FT – “Football in the late 1980s and 1990s was a heavily policed environment under surveillance, where visibility carried risk,” says Andrew Groves, professor of fashion design at the University of Westminster. “On the terraces, clothing wasn’t decoration, it was risk management. Stone Island mattered because its garments were already structured around concealment, modulation and elective visibility. The detachable badge, reversible constructions, modular hoods and certain fabric treatments enabled wearers to calibrate how legible they were, depending on context. Football casuals were not simply performing taste; they were managing recognition.”
Apple rolls out UK age checks for iPhone users | FT – interesting move, I did notice that it assumed my account was adult due to the length it had been held. It reminded me of friends who registered email addresses, domain names and even social media handles for their newly born children – and did just enough to keep the accounts alive.
The 49MB Web Page | thatshubham – Beyond the sheer weight of the programmatic auction, the frequency of behavioral surveillance was surprising. There is user monitoring running in parallel with a relentless barrage of POST beacons firing to first-party tracking endpoints (a.et.nytimes.com/track). The background invisible pixel drops and redirects to doubleclick.net and casalemedia help stitch the user’s cross-site identity together across different ad networks.
When you open a website on your phone, it’s like participating in a high-frequency financial trading market. That heat you feel on the back of your phone? The sudden whirring of fans on your laptop? Contributing to that plus battery usage are a combination of these tiny scripts.
Ironically, this surveillance apparatus initializes alongside requests fetching purr.nytimes.com/tcf which I can only assume is Europe’s IAB transparency and consent framework. They named the consent framework endpoint purr. A cat purring while it rifles through your pockets.
So therein lies the paradox of modern news UX. The mandatory cookie banners you are forced to click are merely legal shields deployed to protect the publisher while they happily mine your data in the background
The online retail giant said there had been a “trend of incidents” in recent months, characterised by a “high blast radius” and “Gen-AI assisted changes” among other factors, according to a briefing note for the meeting seen by the FT.
Under “contributing factors” the note included “novel GenAI usage for which best practices and safeguards are not yet fully established”.
A glimpse into cyber-security’s AI-driven future | The Economist – A few years ago a participant used the conference network to hack a water-treatment facility in America (Messrs Wyler and Stump are cagey about the details). Another hid behind the din of legitimate hacker traffic to attack government websites and payment systems. The noc team traced him, sent him a message reminding him that doing illegal things from Black Hat was still illegal, then watched him close his laptop and walk away. Hackers on the other side of the world try their luck too. When the registration server was switched on, attacks began at once, including traffic that appeared to originate in Romania….
Mr Stump says the noc has seen a pattern across multiple Black Hat conferences in which Taiwanese participants show up with hacked devices. “Most of [the traffic] goes back to China,” he says. ai-powered attacks by nation-states or cybercriminals are likely to intensify… The team thinks the ai race is only beginning. For Mr Wyler, the vulnerabilities discovered by Mythos, including some that have gone undetected for decades, are to be welcomed rather than feared. “We now know they’re there.”
All the same, cautions Mr Stump, the next two years will be turbulent, as more flaws will be uncovered; more breaches will occur as firms feed sensitive data into ai systems; and more insecure code will be written.
OpenAI acquires popular tech talk show for ‘low hundreds of millions’ | FT – ChatGPT-maker moves into broadcasting with deal for TBPN after it had pledged to abandon ‘side-quests’ – I think that this is trying to balance the narrative with Anthropic which is ripping ahead. In past decades you would have dumped a lot of money into a campaign run by a PR agency, but time moves on
New Internet of Things Plan Targets Global Infrastructure – Jamestown – A new action plan for the Internet of Things (IoT) increases the possibility that Chinese-built connected infrastructure in the United States could become a platform for data access, cyber pre-positioning, and attacks on U.S. cyber-physical systems in a prolonged crisis or confrontation. The plan, launched jointly by nine ministries, defines IoT as a total cyber-physical environment that links “people, machines, and things” across sensing, networks, platforms, applications, and security, and sets targets for 10 billion terminal connections, more than 50 standards, and deployment across production, consumption, and governance. The plan indicates Beijing is moving from connected devices to connected backbone systems. It reinforces the new Five-Year Plan, suggesting that the People’s Republic of China (PRC) wants to supply not only endpoints like sensors, appliances, and vehicles but also the next generation of AI, computing, and space-ground communications infrastructure that will underpin them.
February 2026 introduction – (31) get up & run edition
I am now at issue 31, or as a bingo caller would put it ‘get up & run’. In Cantonese 31 isn’t a famous lucky number, it could considered to mean ‘life first’ implying an importance of vitality. On the plus side, it doesn’t have negative connotations of say 14 – which sounds similar to definitely die.
I was sent a mix by an old friend of mine done by Frankie Bones at Amnesia House in August 1990 – as aural history its a fascinating treasure trove and occurred a pivotal time with several genres about to fragment from the original UK scene. Now we have our soundtrack let’s get into it.
New reader?
If this is the first newsletter, welcome! You can find my regular writings here and more about me here.
Things I’ve written.
I appeared in the What’s In My Now newsletter talking small wallets, cheaper alternatives to Apple Studio monitors and making better use of LLMs. More here.
I gave a presentation for Outside Perspective on my Dot LLM era paper. Here is my speaking notes that I prepared as I got the presentation ready, complete with the slides at the relevant points.
I spoke to the WSJ about my dot LLM era thinking and was name-checked on their Take On The Week podcast. And I compared my research with Marc Andreessen’s of A16z 2026 AI outlook here.
I wrote a letter to the FT about Sony surrendering its home entertainment business (TVs, home audio) to Chinese TV maker TCL. While Sony’s current involvement in sectors such as elder care and insurance are worthy endeavours – what does it mean when they are more core to Sony’s identity than the home entertainment equipment that the brand built its empire on?
As well as being a concerned Sony customer, I was also thinking about what it means to a brand when it gets rid of its core raison d’être? You can read my letter here.
I was talking to a friend about classic films and suddenly Matthew Frank’s newsletter dropped in my inbox and started me down a rabbit hole exploring the idea of forgettable cinema as part of the modern public zeitgeist.
I pulled together a collection of adverts and campaigns celebrating lunar new year from across Asia and a couple aimed at the wider diaspora. As brands look to benefit from the year of the fire horse.
ICYMI – Top five shares on LinkedIn
Publicis widening the business gap versus its rivals. A decade spent preparing their data and foundational technology for machine learning.
WPP’s big pivot to adapt to market conditions for the large holding companies.
Dentsu’s change of leadership to better control strategy and manage global capabilities.
Michael Farmer on why reorganisation isn’t strategy, instead strategy should drive any reorganisation to meet the strategic objectives. This one proved a bit controversial, I’m not sure why.
Books that I have read.
While I have been looking forward for David McCloskey’s latest book The Persian to come out, I managed to finish The Seventh Floor. On one level The Seventh Floor is about espionage and feels very now given the new cold war. But it’s also about friendship, loyalty and personal betrayal. McCloskey doesn’t only bring expertise from a past career at the CIA, but also a deep love of the espionage novel as an art form and this novel gives a nod and a wink to the works of John Le Carré.
While the agency world is focused on the rise of AI, I decided to revisit Michael Farmer’s Madison Avenue Manslaughter: An Inside View of Fee-Cutting Clients, Profit-Hungry Owners and Declining Ad Agencies. Ten years after it has been published, the diagnosis and the lessons from Farmer’s research seem to have been ignored by clients and the c-suites of holding groups. One thing I picked up on my revisiting the book was the challenge in defining strategic contribution and effort to campaigns. With creative output, Farmer managed to break down creative tasks into fixed ScopeMetric® Units (SMUs). But Farmer admitted that he couldn’t define strategy outputs in the same way because the context changed account-by-account. This makes sense given the difficulties I have had in the past when strategists were way oversold by the project management function within agencies.
Things I have been inspired by.
Insularity was the watch word of this year’s Edelman’s Trust Barometer. It was a pretty dark vision of the future. There is a huge delta between top income quartile of the population and their trust of authority and the bottom income quartile. In the lower quartile group there is little to no trust in authority figures (business, journalists, government). They only trust people like them.
Andrew Tindall published a new book for System1 based on their research and Effie data which reinforces previous publications by Orlando Wood, Les Binet, Peter Field and Byron Sharp at the Ehrenberg-Bass Institute. It also reinforces the importance of context as part of creativity when media and creative functions are co-joined at the hip. It’s very readable and available for free here.
Chart of the month.
The surge of US measles infections turned into a politicised debate about vaccinations, competence, why Canada’s rates were even higher and whether things were as bad as experts would have you believe?
The chart only tells part of the story.
The US CDC cites a general hospitalisation rate of about 20% (1 in 5 cases), recent years have seen significant fluctuations depending on the specific age groups and regions affected by measles outbreaks.
The “Age Factor”: The high rates in 2022 and 2024 were largely due to the virus hitting children under five—the age group most likely to develop severe complications like pneumonia.
2022 – driven by an outbreak in Ohio, which had a high paediatric hospitalisation rate.
2024 – remained high throughout the year with nearly half of cases affecting children under 5.
Outbreak Size vs. Severity: In 2025, even though the total case count surged, the percentage of people requiring hospital care fell. This often happens when an outbreak moves beyond high-risk “pockets” into a broader, sometimes older, population.
2023 – outbreaks in unvaccinated high-risk clusters.
2025 – hospitalisation rates dropped because the virus spread to older demographics and larger, but less severe clusters
2026 – infections in January had few children under 5 affected. Cases were able to be managed at home.
Vaccination Impact: Across all these years, the vast majority (over 90%) of hospitalised patients were either unvaccinated or had an unknown vaccination status.
Canada’s rates are high because the population has a significant amount of unvaccinated immigrants and refugees from conflict zones and the developing world.
Things I have watched.
Thomas Harris’ Silence of The Lambs still has legs in culture. Which is why Amazon Prime Video has gone back to the universe with Clarice. The story takes place in the aftermath of the buffalo Bill killings which drove the plot of Silence of the Lambs. The storytelling is top notch with a fantastic plot twist in episode 1. It is well worth your time to at least give the first few episodes a chance.
It started off in an unpromising way, several years ago a friend left a DVD with me. They said something along the lines of they liked a number of Werner Herzog films, but that this was too weird for them. I finally got to sit down and watch Fata Morgana.
It doesn’t have a story, but is beautifully shot footage of the Sahara and Sahel in 1969 with a focus on near horizon mirages (from which the film gets its name) and features the human effect on it from vistas of oil processing equipment to barbed wire and crashed planes.
There is a poetic narration in German over the top with a range of music to flt the landscapes. It feels like a forerunner of Godfrey Reggio’s Koyaanisqatsimade a decade later. It’s easy to watch.
I spent a weekend with my Dad going through old VHS cassettes and on one of them we found Four Fast Guns. It is a surprisingly good Hollywood western. While not a John Ford film, it has a grittiness due to superior character development and tight storytelling reminiscent of the very best spaghetti westerns. The film was produced by an independent studio and featured three well recognised character actors as its star performers.
Edgar Buchanan acted alongside the likes of Clint Eastwood, James Garner, John Wayne, Cary Grant and Randolph Scott he went on to appear in several TV series that I remember watching on repeat as a child in Ireland including The Beverley Hillbillies and The Twilight Zone.
Martha Vickers had appeared in The Big Sleep alongside Lauren Bacall.
James Craig had acted alongside everyone from John Wayne to Boris Karloff.
This gave the director much more creative freedom to make the performances pop on-screen. The climatic plot twist is very good.
I was inspired by watching Reflection in a Dead Diamond last month to watch Danger: Diabolik. The psychadelic motifs of and dream sequences of Reflection in a Dead Diamond seemed to draw from European cinema’s brief flirtation with super spy and super villain films during the 1960s. Danger: Diabolik was Mario Bava’s and Dino DeLaurentis’ take on the French Fantômas film series.
Bava’s expertise in genre films and special effects gives Danger: Diabolik a more sophisticated look than you would give it credit. Add in the film’s 1960s modernist aesthetic, James Bond type action sequences and you have a winning film. The humour-heist plot is very of its time but still entertaining and cried out for a remake. Terry-Thomas’ character performance as a government minister in the film is one of brilliance.
Useful tools.
I was saddened to read of the demise of The World Fact Book published by the CIA. I found it invaluable as a starting point when getting up to speed on international campaigns on parts of the world that I hadn’t visited. It even helped me win some work with Telenor Myanmar back before the current military regime got back into power. According to this post on the CIA website the World Fact Book is going away.
This personal productivity playbook by CJ Casseili was interesting to read and some of you may find tips and tricks that you can apply in your own work and personal life.
Ilina Scott’s quick guide to AI tools for strategists is worth a read if you are just dipping your toe in the field.
Occasionally software comes along what doesn’t become a mainstream success, but is well loved and much missed when it disappeared. Apple’s HyperCard was one, another was Yahoo! Pipes. The idea behind Pipes has been resurrected and in its latest iteration is very useful, even in a time of AI-with-everything.
The sales pitch.
i am a strategist who thrives on the “meaty brief”—the kind where deep-tech or complexity, business goals, and human culture collide.
With over a decade of experience across the UK, EMEA, and JAPAC, I specialise in bridging the gap between high-level strategy and creative execution. I was embedded within Google Cloud’s brand creative team, where I helped navigate the “messy steps” of global pivots and the rapid rise of Gen AI. And have recently been helping out agencies and startups in various sectors.
My approach is simple: I use insight and analytics to find the “surprise” in the strategy. Whether it’s architecting an experiential event or defining a social narrative for a SaaS powerhouse, I focus on making complex brands feel human and high-velocity businesses feel accessible.
The Strategic Toolkit:
Brand & Creative Strategy: From B2B infrastructure to luxury travel.
AI-Enhanced Planning: Deeply literate in Google Gemini and prompt engineering to accelerate insights and creative output.
Multi-Sector Versatility: A proven track record across Tech & SaaS (Google Cloud, Semiconductors), Consumer Goods (FMCG, Beauty, Health), and High-Interest Categories (Luxury, Sports Apparel, Pharma).
I am officially open for new adventures with immediate effect. If you have a challenge that needs a all-in, hit-the-ground-running strategic lead, let’s talk.
Ok this is the end of my February 2026 newsletter, I hope to see you all back here again in a month. Be excellent to each other and good luck with your new year’s resolutions. As an additional treat here is a link to a presentation I gave to the Outside Perspective crew, in Adobe Acrobat format.
Don’t forget to share if you found it useful, interesting or insightful as this helps other people and the algorithmic gods of Google Search and the various LLMs that are blurring what web search means nowadays.
Marc Andreessen’s 2026 AI outlook was published by A16z. As one of the leading funder of Silicon Valley startups, his world view matters.
I’ve gone through and contrasted his 2026 AI outlook through the lens of the viewpoint I researched and wrote up. The core point over where we differ: Andreessen see’s the dawn of a new unbound industrial revolution. Whereas I think that the upside he sees is hard to navigate to; and also risk mirroring the echoes of financial bubbles past in many of the high profile pure play AI companies like OpenAI or C3.ai.
Andreessen has been the quintessential techno-optimist for at least the past two decades with his emblematic essay Why Software Is Eating the World. His worldview is of an industry where demand is insatiable and revenue is undeniably real.
Like Andreessen in his 2026 AI outlook, I would agree that players like Alphabet, Amazon and Microsoft are making money to variable degrees from their AI offerings as part of cloud computing and productivity software bundles.
But in my view there are two aspects of concern:
Pure plays with the notable exception of Anthropic don’t seem to have a clear path to profitability in a time period that would match their implied future revenues based on loans and valuations.
Hyperscalers like Meta and Microsoft are using unusual partnerships to fund their infrastructure and have been inconsistent over how fast they would depreciate their AI computing hardware.
Both Mr Andreessen and myself agree that we are witnessing a technological shift of seismic proportions, arguably, “bigger than the internet” as he put it.
I think that change will happen slower in the short term due to an economic sand box acting as a rate limiter on infrastructure and derived labour efficiencies. In particular, the economic viability of the infrastructure being built to support it.
The Nature of the Boom: Real Revenue or Irrational Exuberance?
A key question is the the solidity of the current market.
For Andreessen, the AI boom is not a speculation fuelled exercise but a demand-driven reality. He argues that unlike the early internet, which required years of physical infrastructure build-out before finding business models, AI is generating cash immediately.
“This new wave of AI companies is growing revenue like… actual customer revenue, actual demand translated through to dollars showing up in bank accounts at like an absolutely unprecedented takeoff rate.”
Andreessen points to “revealed preferences”, observed insight from what people do is more insightful than what they say when asked by market researchers. While the US and European publics express fear of job losses, they are simultaneously adopting AI tools at a fast pace.
My “Dot LLM Era” report, however, suggests this revenue may be dwarfed by the capital expenditure required to generate it.
It posits that the sector faces a “self-defeating economic” cycle. The report highlights that the current valuations of the “Magnificent 10” tech giants (including Nvidia, Microsoft, and Alphabet) imply a forward price-to-earnings (P/E) ratio of 35 times. This is alarmingly close to the S&P 500’s P/E ratio at the peak of the dot-com boom, which approached 33.
In the report I warn that current valuations assume LLMs will drive revenue growth by $1–4 trillion in the next two years.
If this growth is achieved through massive job automation, the resulting unemployment could depress the very economy needed to sustain these companies. Like the 2008 financial crisis, this would invite various forms of regulatory intervention.
This implies a sweet spot between speed of productivity gains versus efficiencies in terms of job losses realised by clients – and acts as a break on the velocity of adoption.
The Infrastructure Trap: The Amortisation Crisis
Perhaps the most critical technical divergence between our viewpoints concerns the hardware powering this revolution.
The “Dot LLM Era” report introduces a chilling concept: Amortisation Risk. It draws a comparison to the “telecoms bubble” of the late 90s, where companies laid massive amounts of fibre optic cable.
The Telecoms Analogy: Fibre optic cable had a useful life of over a decade, meaning even after the companies went bust (like WorldCom), the infrastructure remained useful for Web 2.0.
The AI Reality: Modern AI infrastructure relies on GPUs and TPUs. These processors have a useful life of only 3 to 5 years before becoming technically obsolete.
The report argues that if an AI bust occurs, the hardware will not be waiting for a resurgence; it will be electronic waste.
Hyperscalers are currently lengthening the assumed useful lives of this hardware in their financial filings—Google to 6 years, Meta to 5 years—which the report suggests may artificially overstate profits and adversely affect competitors with debt securitised against AI data centre hardware.
The rapid obsolescence of chips represents a “financial and technological amortisation risk” that could lead to trillions in write-offs, similar to the $180 billion loss left by WorldCom.
Andreessen views this hardware cycle through a different lens: Elasticity.
He acknowledges that massive investment leads to gluts, but argues that “the number one cause of a glut is a shortage”. He believes the price of AI compute is falling “much faster than Moore’s Law”.
As prices collapse, demand will expand exponentially. If chips become cheap and plentiful, AI can be embedded into everything, moving from massive “God models” in data centres down to small models running on local devices.
Geopolitics: The US-China Race
Both Andreessen and I agree that the AI landscape is a bipolar contest between the United States and China, but their assessments of the leaderboard differ.
Andreessen frames this as a “new Cold War” where the US must maintain dominance. He is encouraged by the “DeepSeek moment”, referring to a powerful open-source model released by a Chinese hedge fund. To him, this proves that catching up is possible and that the US cannot rest on its laurels. He advocates for open source as a way to proliferate American standards globally.
I offer a more sobering assessment of American pre-eminence. I argue that unlike the 1990s “Long Boom,” where the US was the undisputed hegemon; the current era is defined by high debt and strong competition. The US maybe on the Soviet side of a Reagan era ‘Space Defence Initative (aka Star Wars)’ AI race from an economic perspective.
Alibaba’s Qwen model claims to deliver comparable performance to American models while requiring 82% fewer Nvidia processors to run.
China doesn’t have a electrical power crunch in the same way that western data centres have due to its sustained investment in coal, nuclear, gas and renewable power sources.
Even Silicon Valley investors and major companies like Airbnb are opting for Chinese open-source models because they are “way more performant and much cheaper”.
Andreessen worries about regulation stifling US innovation, I think that the real geopolitical threats are:
China’s ability to operate largely immune to US sanctions, smuggling chips and utilising data centres in neutral geographies like Malaysia.
China’s speed at propagating the use of AI within its own populace, driving utility at a lower cost per token than their US competitors with state regulation defining trial ‘sand pits’. This will drive new uses faster in China and in China’s client states across the global south.
The Outcome: Transformation or “Minsky Moment”?
Where is this all heading? Andreessen is betting on a future where AI becomes as ubiquitous and essential as electricity. He envisions a “pyramid” structure: a few massive “God models” at the top, cascading down to billions of specialised, cheap models running on edge devices. He admits these are “trillion-dollar questions,” but his firm is aggressively investing in every viable strategy.
I think that LLMs will make a sustained technological impact, but it will be limited in velocity by economic boundaries. In the “Dot LLM Era” report, I outlined seven potential scenarios, ranging from total transformation to total collapse. Currently, it assigns a ~95% likelihood to the “Moral Hazard” scenario.
The Moral Hazard: This scenario posits that major AI players will be considered “too big to fail” due to national security imperatives. Governments will step in with loan guarantees and subsidies to backstop the massive infrastructure debts, effectively nationalising the risk . Rather like the banks during the 2008 financial crisis.
The Telecoms Bust: With a ~75% likelihood, the report fears a “Minsky Moment”, a sudden market collapse driven by the realisation that cash flows cannot cover the massive debts incurred to build short-lived data centres.
Comparative Summary of Perspectives
Feature
Marc Andreessen (The techno-optimist)
My own view (The economic limiting skeptic)
Current Phase
Inning 1. “Biggest technological revolution of my life.”
Phase 1: The boom / The Inflation of a bubble.
Revenue
Real, unprecedented, showing up in bank accounts.
Potentially illusory for at least some players; reliant on untenable cost savings.
Infrastructure
Shortages lead to gluts; cheap chips drive adoption.
Amortisation risk: hardware obsolescence in 3-5 years.
Pricing
Usage-based is great for startups; prices falling fast.
US dominance challenged; Chinese models are more efficient and effective enough for organisations from Singapore to Silicon Valley to adopt them.
Outcome
Long-term ubiquity; widespread prosperity.
Technological change bounded by economic limitations. Current high risk of “Minsky Moment” or government bailouts.
Conclusion: The Trillion-Dollar Questions
Marc Andreessen candidly admits that “companies… need to answer these questions and if they get the answers wrong, they’re really in trouble”. His firm’s strategy is to bet on everything: large models, small models, apps, and infrastructure. He is doing this on the assumption that the aggregate wave will lift all boats.
My ‘Dot LLM Era’ report offers a counterweight to this enthusiasm. A bubble decouples technological and financial progress. Technological utility does not always equal investor profit. As I note in ‘Dot LLM Era’, “Bubbles don’t kill technology from moving forwards”. The internet did change everything, but it also wiped out trillions in shareholder value along the way.
The defining question for the next five years is whether the demand for AI can grow fast enough to pay for the hardware before that hardware becomes obsolete.
If Andreessen is right, this elasticity of demand would save the day. If I am right, we may be heading for the most expensive recycling project in human history.
The Productivity Paradox and Society
A fascinating tension exists between Andreessen’s view of societal adoption and my own macroeconomic warnings regarding productivity and labour.
The “Wingman” Economy vs. The Phillips Curve
Andreessen describes a “symbiotic relationship” where AI acts as a productivity multiplier: a “wingman” for doctors, coders, and writers. He argues that higher pricing in SaaS can actually benefit the customer by funding better R&D, suggesting a cycle of value creation.
I argue that the wingman sweetspot is optimal but tricky to land, in the report I showed the risk through a darker macroeconomic “thought experiment.” It used the Phillips Curve and Okun’s Law to model what happens if Andreessen AI scenario succeeds too well.
The Thought Experiment: If AI automation generates $1 trillion in cost savings through job cuts, it implies approximately 10.5 million unemployed US workers.
The Consequence: Such a spike in unemployment could trigger deflation and a massive drop in GDP. This is “self-defeating economics”: the hyperscalers need a healthy economy to consume their services, yet their success might undermine the investor, enterprise customer and consumer base.
Andreessen counters this fear by citing historical context. He notes the “Committee for the Triple Revolution” in 1964, warned Lyndon B. Johnson that automation would ruin the economy—a prediction that proved false. He believes AI will follow the path of electricity or the internet: initially terrifying, eventually indispensable.
Automation did displace a massive amount of developed world jobs moving at a much slower pace than Andreessen predicted for AI. Electricity moved at an equally slow pace compared to the pace envisioned by AI’s champions.
The Open Source Debate
Both of us agree that the role of open source is pivotal in both narratives.
For Andreessen, open source is the great accelerator. He marvels at how knowledge is proliferating: “Some of the best AI people in the world are like 22, 23, 24”. He views the leak of knowledge as inevitable and beneficial for US competitiveness, provided the US stays ahead.
I analysed the “Red Hat Analogue.” It suggests that in the dot-com era, open-source (Linux) won, but the companies that built the models (or distributions) mostly failed, with Red Hat being the notable exception.
I assigned a ~70-80% likelihood to the “Red Hat Model,” where pure-play LLM creators (like OpenAI or Anthropic) might struggle to justify their capital burn as open-source models like Meta’s Llama or Alibaba’s Qwen commoditise the intelligence.
We have already seen Singapore’s national AI programme drop Llama for Alibaba’s Qwen, reinforcing the idea that the value might accrue to those who service the models, not those who create them.
Final Thoughts
The divergence between Marc Andreessen and my own analysis is not about whether AI works both of us would agree that the technology can be magical and transformative. The disagreement is about who pays for it, how that affects the velocity of AI andwho profits.
Andreessen sees a future of abundance where falling prices drive infinite demand.
My own view sees a future of financial reckoning shaping the 2026 AI outlook where shorter hardware lifespans and brutal competition erode margins, setting a slower pace at which we reach Andreessen’s abundance.
Andreessen’s viewpoint reminded me a lot of mid-20th century aspirations for nuclear power. Nuclear power offered a similar vision in the mid-20th century of electricity too cheap to meter. That was never close to being achieved in the likes of France – arguably the most passionate adopter.
As with the railway mania of the 1840s or the optical fibre boom of the 1990s, society may inherit a significant infrastructure, with a shorter lifespan built on the ashes of investor capital.
Our differing views boil down to a question for the 2026 AI outlook: are we in the “boom” phase, or are we staring down the barrel of the “amortisation crisis”?
As Andreessen himself concluded, “These are trillion-dollar questions, not answers”.
I gave a talk to strategists and planners from the Outside Perspective group on my recent paper The Dot LLM era? The talk looked to summarise some of the key takeaways that I had written and also reflects a slight refinement on my thinking given current events since I had drafted the paper over the Christmas holidays.
About Outside Perspective
Outside Perspective is a community of brand planners and strategists. All of the members of Outside Perspective are freelance or self-employed. The members clients are drawn from all around the world and all sectors.
My presentation was the first Outside Perspective huddle of the year, where strategists share expertise and areas of interest with their peers.
I have put in the slides at the appropriate places alongside my notes.
Dot LLM era for Outside Perspective
Good afternoon everyone. I hope I’m not depriving you too much from lunch. If I am, just tuck in, just go on mute if you are tucking in because otherwise it’ll make me hungry.
So The Dot LL era came from a question that I posed to myself. I was working at the time for a client who is a major AI company. I was looking at all of the stuff happening around me and thought that the company that I’m working for it’s probably going to be all right. But we do feel like as if we’re in a bubble. So I then started to think about the bubble and eventually pulled it into a paper.
We (the Outside Perspective) will share the PDF of this presentation and you can get the paper from the QR code later on. My thinking has been refined slightly, as I’ve thought about this presentation, just nuances here and there based on what’s been happening since I originally published the paper.
Key points in the presentation
One of the first things I was taught when I present was tell them what you will be presenting, present it, and then tell them what you told them. So this is me telling me what I’m going to tell you.
So as a technology, LLMs (large language models), what people call AI at the moment, are making lasting changes from business to culture. It’s changing aesthetics, even though might have a negative impact like AI slop. The cultural effects are going to stay with us and evolve, just like previous technologies have done from the printing press on.
Now looking at the economics, the question is what’s really going to happen? Because the AI sector has a valuation in trillions which is an insane amount of money to think about. There are two main challenges from an economic perspective which is where I actually really looked at this from:
The amortisation risk so the speed at which the hardware becomes obsolete or literally burnt out is three to five years versus the likely time to pay off because of the trillions of dollars involved.
The self-defeating economics of AI as I’ll go through in a bit more detail. Economics are a limitation as to how fast AI can actually be adopted without actually destroying the AI providers themselves.
Both factors give a very narrow margin of success for Dot LLM era players from a business perspective, they need to thread themselves through to land at just the in order to succeed.
The Long Boom
When I came up with the term dot LLM era I was thinking about parallels to the dot com era. I’ll talk a little bit about the dot com era as well because I realise some people might not be terribly au fait with it. By comparison, I lived it and have the scars of my professional involvement with it.
The dot com era happened at a time that Wired magazine termed the long boom. During this time you had US preeminence as the Warsaw Pact had collapsed and China wasn’t yet a member of the WTO. During the Clinton presidency there was a US government budget surplus, so the US had headroom for monetary policy interventions if needed.
So if something like COVID epidemic had happened back then, they would have had much more economic flexibility to actually deal with it than we had coming into 2020.
Today in the US at least, much more like the Reagan era that preceded the long boom.
The West is on the back foot, there’s a resurgent Russia waging an invasion in Ukraine and ‘active measures‘ in the rest of Europe. China which is resurgent economically and militarily and from an innovation perspective which I’ll touch on a little bit later. There is high government debt particularly in the US, but also in Europe and much of the developed world as well.
There is sticky inflation and the overall inflation figures that are quoted in the business press are actually lower than what people are actually seeing in the shops. Consumer sentiment about the economy is much worse than the headline inflation number would suggest.
Finally, there’s a slackening labour market. That isn’t about AI at the moment. Companies say, oh, well, due to AI, we’re making layoffs. Usually they’re making layoffs through cost cutting, outsourcing and offshoring roles, they might be doing a little bit of AI in the background because we’ve given employees access to Microsoft Copilot or similar. That doesn’t mean to say that AI won’t have an impact in the near future.
The Dot Com Bubble
When we talk about the dot-com boom, we tend to think about is one thing but it was actually three interrelated bubbles that were going on.
There was an online business bubble which was relatively low capital but had a high burn rate through that capital in an attempt to build a moat. This is what most people think of when they think about the dot com era.
There was a smaller, less visible bubble related to open source software. With the internet, it suddenly became much more important because you had a way of contributing to open source projects and collaborating in a way that wasn’t available between different individuals or organisations previously. While open source made software development collaboration easier, and provided good quality software to download for free, businesses struggled to build a profitable open source business model.
Finally, there was a telecoms bubble which was capital intensive. There was a huge amount of infrastructure built out. There was vendor financing by manufacturers of networking equipment. There was industry incumbents, so companies like the BT in the UK or the Bells in the US. And then there was also new telecoms companies like Enron Broadband Services, MCI WorldCom and Qwest.
More on them in a bit later on. But with the graph on the right, what in fact you see is the peak that was reached on the NASDAQ in March 2020 was in It took the NASDAQ 15 years to hit that peak again after the dot-com bust later that year. This is considered to be not as bad as what happened during the 2008 financial crisis. But it gives you an idea of the way things can go.
Hyman Minsky financial instability hypothesis
I want to introduce an idea of Minsky moments.
Hyman Minsky, economist, he came up with his financial instability thesis. He considered this to be bound to three different steps that needed to occur.
First a self-reinforcing boom driven by easy credit. Our interest rates are higher than they’ve been, but they’re still relatively low from a historical point of view.
If you actually look at the amount of money and the valuations that are going into the likes of OpenAI and CoreWeave in January alone, you can clearly see that the self-reinforcing boom is under way.
The second step Minsky mentioned is a shock where investors re-examine cash flows and this is what’s often termed as a ‘emperor’s new clothes‘-type moment. They suddenly start asking questions like when are we actually going to get our money repaid let alone are we going to make an obscene amount of profit on that money. We’re not quite there yet, but there has been some signs of concern from investors, (for instance when Microsoft announced its recent quarterly results). There were always those dissenting voices, but they’re actually proved prescient only in hindsight.
Lastly, there’s a de-risking stage through rapid acid sales. So investors and management realise they’ve got a flaming bag of crap and want to hand it off to someone else. They want rid of it.
So let’s next think about those earlier three bubbles and think about how good analogy are they for our present era of technology.
Online commerce
So like the early web, pure-play LLMs like Anthropic and Open AI’s GPT are currently providing tokens at below their marginal cost. The cost you’re paying for to do AI actions is actually less than the cost those AI actions actually take to create. And that’s not thinking about the research and cost of capital invested in the company.
They’re losing money to build an AI moat just in the same way as e-tailers and service providers back in the dot-com era lost money in order to build a moat in a particular sector. For instance like Amazon did in books. Move forward 25 years and AI companies are so they’re trying to do the same for various different service models. The burn rates of dot-com failures mirror loss making AI businesses. But only at a surface level, dot-coms were capital light in comparison to their modern Dot LLM era counterparts.
Look at the dog sock puppet on the right, he was the mascot and brand spokesanimal from Pets.com. Pets.com had a horrendous burn rate for the time and went bankrupt. The cause of their bankruptcy was down to two reasons:
The logistics of actually sending out bags of dog meal and rabbit bedding were expensive compared to the amount that was being charged. It took Amazon the best part of a decade to radically reduce the cost of logistics for its own business. Even now, Amazon benefits from Chinese government overseas postal subsidies given to China-based businesses on Amazon.
The large amount of money they put into advertising and brand building. Around a dog sock pocket with attitude. Great marketing, but if the consumer proposition isn’t right the marketing can’t save your business.
Open source sofware
The open source bubble saw the rise of what’s known as LAMP. That stands for:
The Linux operating system
Apache HTTP web server
MySQL as a database management system
P was for the Perl, PHP and Python programming languages
If you’ve ever run a WordPress blog, all of that language probably sounds vaguely familiar to you because it is. Because that supports a lot of the web. Linux extended into laptops, tablets and cellphones including smartphones. (Apple products are based on a similar UNIX style software based on the Mach micro-kernel used in various BSD distributions).
During the dot com era there were numerous companies in this space. Red Hat was the outlier success with their enterprise grade support offering. Red Hat managed to sell themselves for $34 billion to IBM in 2019. Red Hat was the most successful exit and profitable business out of its peers, becoming the first of its kind to generate $1 billion in revenue.
Now you can see Chinese companies are competing against US rivals and winning a lot of users in the global south by providing open source and open weight models like Alibaba’s Qwen and Kimi K2.
These Chinese models provide perfectly usable models at lower costs. You can run the models on your own machines. They use a lot less processing power than US AI models, and are challenging closed AI models. Huawei have built a lot of infrastructure in the developing world, so you’ve got a lot of opportunity there that’s now closed off from American AI companies.
US organisations like Airbnb and Silicon Valley based VC companies are running these Chinese models for their own uses.
AirBnB is an interesting case; CEO Brian Chesky is a really good friend of Sam Altman, yet he’s still using to use open source software rather than use OpenAI because it makes commercial sense.
The telecoms bubble
The telecoms boom. There’s been a similar kind of optimism build out of massive infrastructure as happened during the telecoms boom. Back then, they invested about half a trillion in fibre-optic networks based on misreading of traffic growth data. In the dot-LLM era, we’re seeing orders of magnitude more investment across computing power, networking within the data centre and even data centre power generation.
The graph on the right just gives you an idea of how much AI capital expenditure has taken off.
Amortisation risk
I want to introduce you to some of the concepts. One I’ve alluded to already is this idea of hardware amortisation.
During the telecoms bust, there was dark fibre, so optical fibre networks that weren’t lit. Dark fibre that was laid in the 1990s had a useful life of at least a decade.
In the current dot LLM era the equivalent surplus would be GPUs and TPUs – the processors and the network internet connect hardware within the data centre that’s particularly used for training models has a useful life that becomes technically obsolete between three to five years. It’s usually more towards three years because they are used so intensively that a lot of the processors get damaged by the amount of heat generated from the extreme amount of processing they do.
With your laptop, even though things might run slow sometimes, 95% of the time your laptop processor is running idle in terms of what does unless you’re doing some like really hardcore 3d rendering, video editing or complex work in Photoshop.
Your computer’s processor aren’t running at full at full performance all day, all night. By comparison AI training processors wear out within three to five years depending whose numbers you believe.
The chart on the right gives you an idea of how over time a lot of the major hyperscalers have actually been increasing the amount of years that they actually write down their processor’s depreciation. While the the processors have stayed pretty constant in terms of that three-to-five year window that they have a life of before they need depreciation to zero.
A second aspect of this deprecation is that the amount of energy per token is dropping substantially with each new generation of chip. So a five year old chip, if it’s working is the cloud computing equivalent of an old decrepit gas-guzzler of a car.
Financial picture
From a financial perspective, the change in hardware amortisation has caught the attention of short sellers. The reason why, is that the AI hardware is collateral against loans for some AI companies. They have a mortgage out on their chips. So the length of time that those things have a useful life is really important. If you had a house that lasted three years and you’ve got a mortgage for five years, it’s not a great position to be in.
(The most high profile short seller is Michael Burry who runs a Substack newsletter. He’s the chap portrayed by Christian Bale in the film adaption of The Big Short. Extremely smart guy, not as arrogant as he appears in the Christian Bale portrayal. Really great Substack, recommend that you read it.)
There’s also been a number of financing accounting changes going on. So we’ve talked about the lengthening lives of the AI hardware. You’re also seeing off balance sheet deals being done to help finance data centre development. A number of the hyperscalers like Meta, Google, Amazon and Microsoft have been very cash generative businesses. This has been because software and online advertising are high margin businesses that generate a lot of cash.
Meta and Microsoft have teamed up with private equity companies to co-finance their data centre build out and their acquisition of processors. These loans those are in special financial vehicles that keeps them off Microsoft and Meta’s balance sheet. Short sellers are alarmed by this as it is similar to what we saw that in the telecoms business from the likes of Enron and MCI WorldCom during the dot com bubble.
There are also accusations of circular financing as well. So the chart on the right hand side came from Bloomberg in September of last year. This started to get people worried about the idea of an AI bubble because a lot of it is financed by loans to and from the major technology vendors to the AI players.
Short sellers allege that the values and the profits are being artificially over stated by the hardware depreciation costs and this circular financing. They wonder where are the real transactions? If you look at the circular financing there isn’t meaningful revenue at the moment.
Looking at recent market valuations when I wrote this paper at the end of the year the magnificent 10 (a lot of the hyperscalers, including Meta, the Amazon, Alphabet and Nvidia), had a price to earnings (P/E) ratio of 35x.
So that would mean that it would take 35 years of earnings to actually pay off the share. The S&P the 500 dot-com peak had a P/E ratio of 33 times earnings.
Those values then assume that LLMs would drive one to four trillion in revenue growth or cost savings for dot LLM companies in the next two years, which is a huge amount of money.
$1 trillion revenue target
So how are businesses going to get there? So I started to do a thought experiment:
Advertising will only contribute a relatively small amount. It’ll be big numbers for the rest of us here, but if we think about that target that needs to be hit, it’ll only contribute a small amount of the revenue needed.
Advertising is an industry. It’s about 1% of GDP globally. Also while AI can increase efficiency of advertising, which might be a reason to go there, it may even decrease effectiveness of advertising further.
If you look particularly for large brands, they’re not getting the returns out of digital advertising already that they should be. If you look for growth and increased earnings over the past 15 years or so.
So what about business efficiencies? Yes, it can automate tasks, might be able to reduce jobs. The way it’s optimally pitched is what Microsoft Research described as a wingman approach.
Then the third option which is a lower probability because it rules on a certain amount of serendipity and AI companies have a lot less control.
What does $1 trillion in job cuts look like?
Which raise the question what does a notional $1 trillion, in savings due to job cuts mean?
As a thought experiment it scared the living getting lights out of me. It equates to about 10.5 million jobs in the US. I used two economic models.
The Phillip curve, which models inflation.
Okun’s law, which looks at the impact of job losses on GDP.
A trillion dollars in job cuts, wipes $four trillion GDP and 3% deflation to start with. There would likely be additional secondary effects that I didn’t even attempt to calculate
It creates an efficiency paradox, that would destroy the dot LLM ecosystem financially. They rely on being able to get money to invest and that would drop through the floor. You would have less businesses and fund managers investing and less retail investors.
Rather than being able to gradually increase prices over time with a moat, AI companies would be having to continually decrease in prices due to deflation.
The efficiency paradox means there’s a sweet spot between the degree of productivity benefits that they actually provide within a market without destroying AI as a business.
This all assumes that we’re actually operating within the closed system of the American AI market. But it’s worse because it isn’t closed.
The China factor
The US doesn’t have global AI dominance. Some experts think that China may be ahead. I don’t necessarily think that that’s the right framing to use because I think that China’s running a slightly different race. It’s taking a very different approach to the US about these things.
Competition is not only economic, it’s geostrategic and that actually might change and impact the economics of what we talk about.
The Chinese models are about 10 times more power and processor efficient than their American counterparts. They’re already being used with million+ downloads. They obviously do a good enough job that some Silicon Valley companies trust them.
Seven Scenarios
I thought about scenarios, about how the dot LLM era is going to happen, and I represented everything on a continuum of economic transformation to total collapse. The more I thought about it, I saw that we’re going to have overlapping scenarios rather than a single outcome.
On the left hand side was what the AI product was trying to do. New value creation or drive efficiency, along the top what the net impact was likely to be in terms of either negative or zero value creation or a positive to transformation value creation effect.
A probability based approach
The moral hazard happens because AI no longer just economic in nature, but also becomes geopolitical and a national security issue. I rated it at 95% because China is already there in terms of treating vast amounts of its economy from food to technology as a national security issue. AI is no exception. I read a paper by a Canadian think tank equated every US AI company data centre as equivalent to having a US military base on your territory.
The AI players are too big to fail. There’s a national security imperative, and a government’s backstop for them. Taxpayers will have to foot the bill. The AI companies don’t necessarily have to innovate as hard. They can take financial risks, similar to banks pre-2008.
The wingman economy, we’re already seeing this in the way Google, Microsoft and Adobe position their AI offerings.
The idea is that you actually get avoid catastrophic job losses by striking a balance between growth and efficiency to land in just the right place.
The Red Hat model. The idea of that pure play AI businesses struggle to find probability. You get LLM model proliferation, like what I talked about with open source models. The open source models, like Qwen, already have people using it around the world. Then value starts to shift to enterprise support or integration. Like Airbnb who are integrating these models already into their services.
Telecoms bust. You only have to look at things like private equity Blue Owl pulling out of a funding round for an Oracle data centre. It could be basically how they feel about Oracle in particular’s AI offering, which a lot of money has been going into, but not a lot of results have been coming out of, or it could also be sentiment around the dot LLM eco-system in general.
The last three options are much lower probability events.
The new economy model. The idea that AI will make transactions frictionless, with agentic automation , a lot of things will happen. There’s an uncertainty around the economics of this at the moment and there’s numerous concerns like the AI might actually drift away from the original human intent over time. There are also bottlenecks with legal and regulatory issues.
The breakthrough is a black swan event by its very nature. So this would be like a major scientific breakthrough, but then it would likely take 10 years to commercialise. Think about new drugs like Wegovy or Ozempic. They were an innovation that launched during the COVID period. The actual discovery was done back in 1979.
It took decades to get them to be commercial as a weight management product.
With other technologies, that period might be down a bit. So a new oil field might be only a 10 year project from discovery to commercialisation. Either way, it won’t pay off in a two year period.
Where we’re at?
I do believe that the dot LLM era is a financial bubble and a technological shift. The shift will continue to happen and evolve. It will continue to influence culture and business.
The financial bubble may destroy economies. It will keep driving national rivalries.
There is likely to be, and at least in the major players like Google and Amazon, a wariness of self-defeating economics where efficiency seeking destroys consumer base. Even if there’s not worries within AI companies, governments will hit them pretty hard because if you actually see a four trillion drop in GDP in the US and a 10 million strong decline in employment rates, even the current Trump administration would have to step in and regulate.
they’ would regulate is they’d probably overcompensate on economic impact.
So a lot of the major companies, possibly with the exception of Elon Musk, will be thinking about these factors to a certain extent. I think we’re in phase one to the boom and go to the next stage at any time. We’ve got the seeds of a lot scenarios including moral hazards.
It’s the geopolitical things that are really complicating things at the moment.
Eventually we’ll get to a new normal. How long it will take depends on the amount of government intervention that actually happens from an economic point of view. It will also depend on geopolitical factors.
You get a Taiwan invasion, that will impact manufacture of GPUs and TPUs because they’re all made on the island of by TSMC.
Large hyperscalers like Alphabet , Amazon and Microsoft are the most likely to survive the bust as they have multiple revenue streams and can integrate their AI capabilities into these products.
A special thank you to Matthew Knight of Outside Perspective for organising and facilitating the session.
