Personal update 🤓

I did not particularly expect to become a certified investment consultant....and yet, here we are.

What started as writing several times a week on Where is my MOAT? gradually became something bigger:

• a way of thinking about businesses, markets, incentives, technology, commodities, and...

• capital allocation that is a bit more structured than “buy this” or “sell that”

Markets, after all, are rarely just about price targets. They are about business models, power, scarcity, timing, management quality, regulation, second-order effects, and the inconvenient fact that everything connects to everything else.

After publishing 4-5 times a week, I realized I had built something like an MBA niche for investors. Not the generic kind, but the “skin in the game” useful kind. The kind that helps you understand why a company matters, what could change its moat, how commodities shape industries, and how all of this affects the decisions you make with your own capital.

I’m officially certified as an investment consultant, and I now work with people in three ways:
1/ on a one-time basis
2/ on a monthly basis, or
3/ through tailored investment education and advisory support that companies can offer as a benefit to employees or clients.

Send me a message to work together.

👉 Find all details here: https://whereismymoat.com/work-with-me

Dear OnStrategy Reader,

Here is what you will find in this issue:

1. on Perplexity

2. [Analysis] Winners and losers in the memory shortage

3. Berkshire Hathaway has $373 billion in cash

4. on the middle management 🧑‍💼👩🏼‍💼

5. on mega IPOs: SpaceX, OpenAI and Anthropic

6. on Intel

7. [Essay] Apple at 50

Onto the update:

on Perplexity

Perplexity’s pivot from "search" to "agents" is a recognition that “answering questions” is a feature, while “doing work” is a business model. Search is a brutally difficult market because incumbents own distribution, advertisers subsidize user acquisition, and the marginal answer is easy to compare.

Agents, by contrast, are closer to labor. They consume more compute, but they also create more measurable value, which makes usage-based pricing feel natural rather than forced. That is why the revenue inflection matters. Moving from a search-like interface to a task-completion layer turns Perplexity from a thin wrapper around model access into something more like an intelligent broker of work, routing each request to the most efficient model and capturing margin through orchestration rather than through model ownership itself.

I start to feel more optimistic about Perplexity's future. FT

[Analysis] Winners and losers in the memory shortage

The first and most obvious beneficiaries are the memory manufacturers themselves. Samsung, SK Hynix, and Micron Technology collectively control more than 95% of global DRAM production and the majority of enterprise NAND supply. After suffering through a bruising 2023 downcycle, all three companies implemented aggressive supply discipline, which meant cutting capital expenditure, reducing wafer starts, and delaying capacity expansions. This was a strategy born of survival. The result is that when demand returned with unexpected force (driven largely by HBM - High Bandwidth Memory), requirements for AI accelerators, these 3 players found themselves holding pricing power they had not enjoyed in years. Of course, the market ended like this after decaded of transformation. (continue reading it HERE)

Berkshire Hathaway has $373 billion in cash

Berkshire Hathaway having $373 billion in cash is one of those things that sounds conservative until you realize it is also extremely aggressive, just pointed in a different direction. Buffett is basically running the world’s largest "wait and see" trade, which is funny because most investors feel compelled to always be doing something, whereas Buffett (Greg Abel more exactly) is very comfortable doing nothing at scale....and the scale matters. When you have that much cash you are waiting for everyone else to run out of options first, at which point you become the market.

Also, there is a subtle signal here about the opportunity set. Cash piles this large are not built because there are too many attractive investments, they are built because there are too few that meet the bar. So either Abel has become unusually cautious, or the market has become unusually expensive, or both, which is a slightly awkward backdrop for everyone else who is fully invested and explaining why this time is different.

The nice thing about having $373 billion in cash is that you don't need to be right today, you just need to be right eventually, and preferably at a moment when everyone else wishes they had been a bit more patient.

on the middle management 🧑‍💼👩🏼‍💼

The core idea in Jack Dorsey's new essay is that hierarchy is more about bandwidth..not power. For 2,000 years, organizations existed to route information through layers of humans with limited span of control, and everything from Roman legions to McKinsey org charts was essentially an optimization of that constraint.

What changes with AI is not just productivity, but the removal of that bottleneck. If a system can maintain a real-time “world model” of the company and its customers, then the coordination function of middle management becomes software rather than people. That reframes the firm from a collection of teams executing plans to a continuously updating system composing actions, where capabilities are modular and decisions emerge from data rather than being pushed down a chain of command.

The interesting second-order effect is that this turns organizational design into a data problem and a moat problem. The companies that win are not those with the best org charts, but those with the richest, most compounding "world models", built on proprietary, high-signal data like transactions or workflows. That creates a feedback loop where better models drive better decisions, which drive more activity, which enriches the model further. At that point, AI is not a cost-saving tool but a coordination engine that collapses layers and accelerates iteration speed, effectively making “speed of information flow” the new primary competitive advantage.

The risk, of course, is that if your company does not have a unique data advantage, this architecture becomes commoditized, and instead of becoming an intelligence system, you become just another node running someone else’s model.

Anyway, make some time to read his essay. LINK

on mega IPOs: SpaceX, OpenAI and Anthropic

The thing about mega IPOs is that they are sold as "once-in-a-generation opportunities", which is usually true in the sense that they generate once-in-a-generation underwriting fees and then, for public investors, once-in-a-generation disappointment.

This chart is basically saying that if you bought the biggest, most hyped IPOs after Facebook, you systematically underperformed the market for the next five years, which is a nice way of saying you paid peak narrative prices for businesses that had already done most of their compounding in private markets. Of course, now we are about to do it again, but bigger, with SpaceX, OpenAI, Anthropic, and friends lining up, which is great because nothing says “healthy market” like a queue of trillion-dollar stories trying to come public at the same time.

Also, there is a nice timing element here, which is that companies tend to IPO when conditions are good for them, not for you, and right now conditions are good in the sense that there is still liquidity, still excitement, and still a willingness to believe that this time the growth curve will justify the valuation.

Here is the hack: if you are the issuer, you might also notice that macro conditions feel a bit wobbly, rates are not exactly friendly, and the window could close, so you accelerate. Which means that what looks like a supply of exciting new public companies is also a demand for your capital at exactly the moment when the risk is highest, and historically that has not been a great trade, even if the companies themselves turn out to be very good businesses eventually. Bloomberg

on Intel

In my latest Where is my MOAT? deep dive, I argue that Intel’s problem is not that it has no moat, but that it has two very different ones living under the same roof:

1/ a still-real CPU franchise built on x86 inertia, enterprise switching costs, and the growing relevance of CPUs in AI orchestration and inference, and

2/ a foundry business that is strategically important but still economically unfinished.

The quarter was better than feared, especially in data center, and management effectively admitted the issue was demand exceeding supply rather than demand collapsing, which is a meaningful change in tone, but the real question is still execution: whether 18A yields improve, supply normalizes, and foundry starts turning milestones into real external customers.

Until then, Intel remains a company with valuable assets and credible strategic optionality, but one whose turnaround still lives more in verbs than in cash flows.

Read the full analysis here.

[Essay] Apple at 50

In my latest Where is my MOAT? essay, I argue that Apple’s real genius over the last 50 years was building the greatest customer-creation machine in consumer tech: 5.1 billion devices shipped, 2.5 billion active devices today, and a services layer now worth $112.8B annually, all reinforcing one another through control of the human-computer interface.

The more interesting question is forward-looking. Apple may be perfectly built for the deterministic era it dominated (ie. polished hardware, controlled experiences, elegant integration), while the next era belongs to probabilistic AI systems shaped by messy data, iteration, and uncertainty, where other companies may be structurally better positioned. [Continue reading HERE]

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Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

3. Endi Ungureanu - Tura de duminica

Dear OnStrategy Reader,

Here is what you will find in this issue:

1. on the SpaceX IPO 🚀

2. Where does the value accrue in AI?

3. How Waymo works

4. on Social Media ban for youngsters (< 16 yrs)

5. on inflation 📈

6. [ESSAY] on AI needs consultants 🧑🏼‍💻👩🏼‍💻

Onto the update:

on the SpaceX IPO 🚀

SpaceX going public at a rumored $1.75tn valuation and $75bn raise is, on one level, a financing event and, on another, a queue-jumping event. The market right now is basically a finite pool of excitement, liquidity, passive flows, and tolerance for heroic narratives, and SpaceX seems to have decided that if there is a window, it should go through it first and largest.

That makes sense. If you know that OpenAI and Anthropic are somewhere behind you in the IPO line, you do not politely wait your turn, but you run onto the runway with rockets, satellites, Starlink cash flows, an xAI tie-in, and a valuation so large it makes normal IPOs look like bake sales.

It also helps that the exchange rules have just become friendlier to giant newly public companies, with faster index inclusion and looser float requirements, which is a very elegant way of saying ...“come public fast, sell a tiny slice, and let passive money do some of the work".

The other thing about doing it now is that “now” may be better than “later”. Economic conditions feel like they are deteriorating quickly, and mega-IPO markets are open right up until the moment they are not. If you are Elon Musk and friends, the rational move is to extract the money while the story still clears at maximum altitude, before macro conditions worsen, before rates or risk appetite shift again, and before the next great AI float sucks oxygen out of the room.

There is also a nice bit of financial engineering here. Float less than 5%, maybe let insiders sell on day one, get potentially pulled into the Nasdaq 100 quickly, and create immediate demand from index-linked funds. That is turning market structure into a distribution channel. In other words, SpaceX is not merely doing the biggest IPO in history. It is trying to do the biggest IPO in history before history gets more crowded and more expensive. FT

Where does the value accrue in AI?

The fun thing about this chart is that it looks like a classic “apps win in the long run” story, except we are very much not in the long run yet, and in fact the opposite is happening. All the money is sitting at the bottom of the stack, with semis hoarding something like 70% of revenue and even more of the profits, which is a polite way of saying that if you want to get rich in AI, you should sell very expensive shovels to people who think they are digging for gold.

And everyone knows this, which is why the hyperscalers are spending hundreds of billions on capex, partly to build products, but also partly to avoid paying NVIDIA forever, which is not a great long-term business strategy if you are, say, a trillion-dollar company that does not enjoy being someone else’s customer.

But also this is sort of how every platform shift starts: the infrastructure layer gets paid first, the application layer gets paid later, and in the middle there is a lot of venture capital explaining that this time will be different. Maybe it will be, but right now the economics are stubbornly inverted, with apps growing fast in percentage terms but still tiny in absolute dollars, which means that the big strategic question is not “who has the best AI app?”, but “who can escape the tax imposed by compute?” and until that changes, the real business model of AI is less about intelligence and more about electricity, chips, and the willingness of large companies to spend enormous amounts of money today in the hope that someday, somewhere up the stack, someone will figure out how to actually make it back.

How Waymo works

In my latest essay, I argue that Waymo’s real lesson was about strategy under deep uncertainty. Some problems do not yield to iteration, because they have a minimum investment threshold below which you are simply not solving the same problem.

Waymo won by treating full autonomy as a distinct category from driver assistance, building “the Driver” as a platform rather than a feature, and spending two decades de-risking the stack until it could generalize across cities instead of merely performing well in a demo zone.

The broader point is uncomfortable but useful for executives. When the problem is genuinely hard, the shortcut is often the most expensive path of all. WIMM

on Social Media ban for youngsters (< 16 yrs)

The interesting thing about banning social media for youngsters is that it sounds like a moral argument. Still, it’s actually a market design problem. We built the most sophisticated attention-extraction systems in history and then let the least experienced users compete in them with no safeguards.

These platforms are optimized for engagement, not well-being, which means the equilibrium outcome is predictable (ie. more time spent, more dopamine loops, less focus, worse mental health). Now, saying "let kids use it responsibly" is a bit like saying "let them trade derivatives responsibly", technically possible, empirically unlikely.

If you look at that map, what you’re seeing is governments slowly realizing that the default settings of the internet are misaligned with developmental (mental) needs, and that the cost of inaction compounds over time.

Limiting access is about sequencing it; there is an age for learning algebra, and there should probably be an age for algorithmic feeds too.

Yes, I'm in favour of the ban! Bloomberg

on inflation 📈

What’s striking about this chart is not the level of inflation, but the revision. Inflation is being repriced upward in real time, and the UK stands out as the most sensitive node in that system.

That tells you something structural, which is...exposure. Economies that are more dependent on imported energy and global trade flows are effectively “levered long” geopolitical stability, and when that breaks, their inflation moves first and fastest.

The recent Iran conflict, which has disrupted oil flows (and other byproducts) and pushed energy prices sharply higher, acts as a classic external shock that feeds directly into CPI through fuel, transport, and food costs.

The second-order effect is more interesting. Inflation here becomes a supply chain signal, not just a monetary one. Central banks can manage demand, but they cannot reopen the Strait of Hormuz or stabilize oil infrastructure (20 million barrels were flowing daily from the Middle East). That creates a regime where inflation is both higher and more persistent than expected, while growth weakens (i.e., a mild version of stagflation).

In that world, the dispersion across countries matters more than the average. India and Brazil can run hotter inflation because of growth dynamics, the US can absorb it with relative resilience, but Europe, and especially the UK, ends up caught between imported costs and constrained policy space.

This is also a reminder of any geopolitical book: secure ASAP your (1) energy and (2) food sources. I guess that many read geopolitical books anymore...especially in Brussels or 10 Downing Street.

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[ESSAY] on AI needs consultants 🧑🏼‍💻👩🏼‍💻

In my latest Where is my MOAT? essay, I argue that the AI boom is creating a surprisingly old-fashioned winner, which is, the person who actually shows up and makes the thing work.

Models are getting cheaper, more available, and more impressive, but that mostly means the scarce asset shifts from access to implementation (ie. redesigning workflows, reassigning decision rights, training teams, setting guardrails, and measuring whether any of this improves outcomes rather than just producing a nicer board deck).

In other words, the real moat in enterprise AI may not belong to the lab with the smartest model, but to the companies and advisors who become the translators between generic capability and messy organizational reality.

This week in “Where is my MOAT?”:

• 2 April: [Essay] AI needs consultants

• 3 April: [Market updates] ARM

• 4 April: [How startups work] Waymo —> new category to WIMM

• 5 April: [Deep dive] on Intel

Thank you for being an onStrategy reader!

Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

This week, my subscribers receive 5 posts. Here’s the schedule for this week:

• 25 March - [Essay] on business models: B2B vs. B2C (with a focus on OpenAI)

• 26 March - [Analysis] Macroeconomics (March 2026)

• 27 March - [Market updates] NVIDIA

• 28 March - [Deep dive] Reddit

• 29 March - [Deep dive] Coinbase

…and here is the schedule for the next week:

• 2 April: [Essay] AI needs consultants

• 3 April: [Market updates] ARM

• 4 April: [How startups work] Waymo —> new category to WIMM

• 5 April: [Deep dive] on Intel

If you find WIMM useful, please share it with a friend or a colleague. WIMM is free for one month ... plenty of time to decide if it’s worth keeping in your stack.

WIMM

Dear OnStrategy Reader,

Here is what you will find in this issue:

1. The opportunity cost of populism (🇪🇸 Spain vs. 🇦🇷 Argentina)

2. The Cost of Focus in AI

3. 🏎️ Ferrari and the economics of scarcity

4. The unbundling of Disney

5. on Macroeconomics (March 2026)

6. ..and more

Onto the update:

The opportunity cost of populism (🇪🇸 Spain vs. 🇦🇷 Argentina)

Argentina is what happens when a country confuses redistribution with growth for just long enough that the bill compounds. In 1900, Argentina looked like Spain’s richer cousin, but by 2022, Spain looks like the country Argentina thought it already was. The gap is explained by policy persistence, ie. decades of “free stuff” financed by inflation, debt, and eventually credibility. The market is very forgiving in the short term (ie. you can print, subsidize, protect), but it is extremely unforgiving over a century. Compounding works both ways.

Javier Milei’s diagnosis is directionally right, because incentives matter, prices matter, deficits matter. But the uncomfortable part is that macroeconomic damage is easier to create than to reverse. You can destroy trust in a currency in a few years, while rebuilding it takes a generation. You can distort capital allocation quickly. However, fixing it requires sustained discipline that democracies rarely maintain.

Argentina’s story is not about one bad decision, but about many rational short-term decisions that, over time, made long-term growth almost impossible. That is the real chart here. LINK

The cost of focus in AI

What this “Code Red” moment really reveals is that OpenAI is transitioning from a research-driven organization with optionality to a product-driven company with constraints.

In the pre-AI era, tech companies could explore adjacent opportunities because the marginal cost of experimentation was mostly people. Now, in the AI era, every experiment consumes scarce compute and strategic focus. Scrapping Sora and other side projects is therefore less about failure and more about prioritization. The winning strategy in AI is in the depth around a core use case, which for OpenAI is clearly ChatGPT as an “all-purpose assistant”.

The implication is that AI competition will converge around distribution and integration rather than pure model capability, and here Google is the best positioned to win. OpenAI’s early lead came from being the default entry point for users, but that advantage erodes as incumbents like Google and Meta leverage existing user bases.

As a result, OpenAI’s pivot toward enterprise and deeper product integration mirrors the historical trajectory of platforms like Google: initial breakthrough, followed by a race to embed into workflows and become indispensable. The companies that win will not be those that build the best models in isolation, but those that align their product surface with sustained usage, turning compute into daily utility rather than occasional novelty. FT

🏎️ Ferrari and the economics of scarcity

Ferrari flying cars to the Middle East is a useful reminder that luxury businesses operate on a fundamentally different set of constraints than traditional manufacturing. Price elasticity is effectively irrelevant, while delivery and customization are core to the value proposition. When customers are willing to pay 4-5x more for air freight, the constraint shifts from cost efficiency to relationship management and brand consistency. In that sense, Ferrari is selling scarcity, personalized experiences, where logistics becomes part of the product itself rather than a backend function.

The broader implication is that in periods of geopolitical disruption, luxury companies are less exposed to demand destruction and more exposed to allocation decisions. The Middle East remains a disproportionately profitable market due to high-margin personalization, meaning that reallocating supply elsewhere is not a neutral decision but a margin trade-off. This highlights Ferrari’s structural advantage: limited supply and high customization create pricing power and flexibility that traditional automakers lack. In a world of constrained logistics and uncertain demand, the ability to choose where and how to deliver, and to whom, becomes a strategic lever, reinforcing luxury as a business model defined by control rather than scale. FT

The unbundling of Disney

Disney’s first week under new leadership is less about bad luck and more about structural exposure. The company is discovering that in a world defined by platforms and AI, owning intellectual property is no longer sufficient to control distribution or monetization. Disney is increasingly dependent on external platforms to extend its reach, yet those platforms operate on very different incentives and timelines. In the past, Disney’s advantage came from scarcity (=owning characters and franchises that others needed). In an AI-driven world, where content creation becomes abundant and cheap, that scarcity erodes, and with it, the leverage Disney once had.

At the same time, the issues around linear TV and declining engagement point to a company caught between two models. This means a legacy distribution system that is structurally declining and a future platform strategy that it does not fully control. The key strategic question is not whether Disney can produce great content (it clearly can), but whether it can build or own the distribution layer where that content is consumed and monetized. Without that control, Disney risks becoming a supplier in someone else’s ecosystem, where value accrues to the platform rather than the creator.

The events of this “horrible first week” are therefore not isolated incidents, but signals of a broader transition, from a vertically integrated media empire to a participant in a platform-dominated economy. FT

on Macroeconomics (March 2026)

In my latest Where is my MOAT? note I argue that the real macro story right now is not any single headline, but the collision of war, energy, inflation, and politics into one ugly feedback loop:
> Iran pushes oil and fertilizer risk back into markets,
> Europe looks increasingly exposed to stagflation,
> US deficits keep pressuring the dollar, and
> even the AI boom starts to look more physical than digital when semiconductors, LNG,
> and supply chains all run through the same geopolitical choke points.

The market feels like a bloodbath today, but the more interesting question is whether this is a permanent breakdown or just a violent reset before investors start pricing the next phase. WIMM

[Essay] NVIDIA GTC 2026: The architecture of dominance

In my latest Where is my MOAT? essay, I argue that Nvidia’s real genius at GTC 2026 was not launching new chips, but disrupting itself before anyone else could.

The company is turning AI infrastructure into a full-stack architecture game where the moat is no longer just the GPU, but the cadence of the entire system with silicon, interconnect, memory, networking, software, and developer lock-in all moving together. WIMM

[PRINCIPLE] You have to write the Playbook

Each path appears unclear at the beginning and no guide contains the full course. A playbook emerges through attention, effort, and reflection. The mind that waits for instruction remains dependent, while the mind that observes and records begins to shape its own method. Order arises from practice, and wisdom forms through repeated action guided by thought.

Take responsibility for your approach and refine it with each attempt. Write what you learn and return to it with discipline. Patterns reveal themselves to those who pay attention and preserve their insights. In this way, your actions gain coherence and your work becomes guided by principles that you have earned. onStrategy

Thank you for being an onStrategy reader!

Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

This week, my subscribers receive 5 posts. Here’s the schedule for this week:

• 18 March - [Essay] Lessons from Koenigsegg

• 19 March - [Analysis] The disruption of the German car industry

• 20 March - [Market updates] Oracle

• 21 March - [Deep dive] Micron

• 22 March - opening my 3rd portfolio (for Premium/paying subscribers only)

…and here is the schedule for the next week:

• 25 March - [Essay] on business models: B2B vs. B2C (with a focus on OpenAI)

• 26 March - [Analysis] Macroeconomics (March 2026)

• 27 March - [Market updates] NVIDIA

• 28 March - [Deep dive] Reddit

• 29 March - [Deep dive] Coinbase

If you find WIMM useful, please share it with a friend or a colleague. WIMM is free for one month ... plenty of time to decide if it’s worth keeping in your stack.

WIMM

Dear OnStrategy Reader,

Here is what you will find in this issue:

1. OpenAI has a strategy

2. on SAP’s strategy in an AI world

3. on the German car industry 🚘

4. on Pinterest’s options

5. Refining is the bottleneck

6. The AI Capex shock (and two implications)

7. Lessons from Koenigsegg

Onto the update:

on SAP’s strategy in an AI world

SAP’s shift from subscriptions to AI consumption pricing is a redefinition of what enterprise software is, from static systems of record to dynamic systems of work. Subscriptions made sense when value was tied to access, but now consumption makes sense when value is tied to outcomes (i.e., how much work the AI actually does).

As part of the pivot, the company will also create "forward deployed engineering"🫠. SAP is acknowledging that in an AI world, the product is not the software itself, but the integration of that software into the customer’s workflow. That looks less like selling licenses and more like embedding capability, which is precisely where defensibility lives.

The competitive pressure explains the timing. As AI-native players push into enterprise workflows, incumbents can no longer rely on switching costs alone. They need usage-driven value that compounds over time. Consumption pricing aligns SAP with customer ROI, but it also introduces volatility, where the revenue becomes tied to actual usage, which in turn depends on how effectively SAP’s AI is adopted.

In that sense, this is a high-conviction move. SAP is effectively betting that it can move up the stack from infrastructure and data into execution. If it succeeds, it deepens its moat, if not, it risks becoming just another backend provider in someone else’s AI-driven workflow. Bloomberg

on the German car industry 🚘

In my latest Where is my MOAT? analysis I argue that the crisis of the German auto industry is architectural.

For decades these companies dominated the mechanical stack (ie. engines, transmission, precision manufacturing, dealer networks, brand prestige) and then the basis of competition shifted toward batteries, software, power electronics, and supply-chain control, where China moved faster and Tesla rewrote the rules.

The real post-mortem is that German incumbents kept optimizing for a world in which engineering excellence at the component level was enough, just as the industry was becoming a systems business defined by energy, code, and speed of iteration. WIMM

OpenAI has a strategy

Good news: OpenAI has a strategy
Bad news: OpenAI has (only now) a strategy

What looks like a product decision is actually a business model correction. OpenAI is moving from “frontier lab with many bets” to "enterprise software company with a core wedge", and that wedge is (a) coding and (b) productivity. The reason is straightforward. AI only becomes durable when it is embedded in workflows that customers will pay for repeatedly, not when it is spread across consumer experiments that are impressive but hard to monetize.

In that sense, Anthropic’s focus is less about better technology and more about better positioning. Owning the developer and enterprise use case means owning the budget, which in turn funds the compute, which in turn sustains the model lead.

Short reminder: compute is the real scarce resource, strategy becomes allocation and a very “side project” is not just a distraction, but a misallocation of GPUs. In the pre-AI world, companies could afford parallel bets because the marginal cost was people. In the AI world, the marginal cost is infrastructure, which forces focus.

OpenAI’s shift is therefore more about necessity. In a market where the winners are those who best convert compute into revenue, the company that aligns its product surface with monetizable workflows will be the one that can afford to keep training the next generation of models. WSJ

Refining is the bottleneck

What’s interesting here is that oil isn’t really the story, but refining is. You have Brent going up because of geopolitical risk, sure, but refined products (diesel, jet fuel) are going up more, which is the market’s polite way of saying “we don’t just have a supply problem, we have a processing bottleneck".

When infrastructure gets hit and shipping through places like Hormuz becomes “conditional”, you’re short the ability to turn crude into usable fuel, and that’s where prices really gap out.

In other words, wars make complexity expensive. The marginal barrel is no longer about extraction, it’s about logistics, insurance, routing, and refining capacity, all the boring middle layers that suddenly become very exciting when missiles start flying. Crude is the headline, but margins are the trade, and somewhere a refiner is having a very good quarter while airlines, chemicals, and basically anyone who needs actual fuel (as opposed to theoretical barrels) discovers that the real price of energy is not the oil, but it’s everything that happens after. Bloomberg

The AI Capex shock (and two implications)

1/ The tech industry is transforming from a labor-heavy business into a capital-heavy one. In the pre-AI era, scaling meant hiring engineers, product managers, sales teams, etc. That meant that people were the bottleneck. In the AI era, scaling means GPUs, power plants, and data centers. And once companies commit to that level of capital expenditure, the market will demand operating leverage somewhere else. That “somewhere else” is labor.

2/ This is why layoffs are not a temporary correction but a structural shift. If the marginal unit of output is now compute rather than human effort, then organizations optimized for hiring thousands of employees suddenly look inefficient. The same companies that once hired aggressively to remove organizational bottlenecks are now discovering that AI agents remove those bottlenecks directly, collapsing layers of coordination and decision-making.

Investors who tolerate hundreds of billions in infrastructure spending will simultaneously expect higher margins, which means fewer humans in the loop. The paradox of the AI boom is therefore that the biggest investment cycle in tech history will coincide with a wave of job cuts, not because companies failed, but because AI changes the production function of software itself. Bloomberg

on Pinterest’s options

Pinterest has the kind of asset that looks magical in a deck and frustrating in a P&L with 600 million users, 80 billion searches, and a user base that often shows up already in shopping mode.

The problem is that Pinterest sits too early in the funnel. It captures inspiration, while Meta, Google, and Amazon are better at capturing transactions. That is a dangerous place to be in digital advertising, because intent without conversion tends to get monetized by someone else.

That is why a sale makes strategic sense. Pinterest looks less like a standalone ad-tech winner and more like an attractive missing piece for a larger platform, especially one that is strong at commerce but weak at discovery. Amazon or Walmart could use Pinterest to move upstream in the shopping journey, while a financial buyer could take advantage of the low valuation and fix the business outside public markets. Pinterest needs an owner who knows how to turn ideas into checkouts. FT

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Lessons from Koenigsegg

Excerpt from the esssay:

There is a framework that clarifies much of what is happening in the global economy right now, and it is deceptively simple. In nearly every industry, value is accruing at the extremes. Either you sell something indispensable to billions of people at a price they barely notice, or you sell something irreplaceable to a handful of people at a price high enough to be considered a rarity. Everything in between, the competent, the adequate, the merely good, is being systematically destroyed.

This is not a prediction. It is already happening.

The forces doing the destroying are familiar. Chinese manufacturers, backed by industrial policy (= enormous subsidies at each step of the manufacturing process), cheap energy (hello, USA!), and ruthless supply chain integration, have demonstrated an unmatched ability to manufacture at scale and price commodities toward zero. American technology platforms, powered by abundant and increasingly cheap energy (ie. natural gas, now solar, soon nuclear), run the world’s most valuable software businesses from a position of structural cost advantage. When these two forces converge on a product category, the middle collapses. What survives is either the commodity, which wins on price, or the icon, which wins on meaning.

Europe, in this reading, faces an existential strategic choice. Its labor costs preclude commodity competition. Its regulatory environment complicates the kind of software-driven winner-take-all scaling that produces trillion-dollar American platforms. What remains, what Europe has always, arguably, done best, is craft, expertise, and the particular kind of trust that accumulates over generations. Nasim Taleb calls these “medieval artisanship”. Hermès. LVMH. Porsche, and, sitting at the most interesting extreme, Koenigsegg, which is selling cars between $2.8-4 millions each. (read full essay)

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Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

This week, my subscribers receive 4 posts. Here’s the schedule for this week:

• 12 March - [Analysis] on the Middle East and some implications

• 13 March - [Essay] Workflow ownership is the next platform war.

• 14 March - [Deep dive] - Mastercard

• 15 March - [Deep dive] - Adobe

…and here is the schedule for the next week:

• 18 March - opening my 3rd portfolio (for Premium/paying subscribers only)

• 19 March - [Essay] Lessons from Koenigsegg

• 20 March - [Analysis] The disruption of the German car industry

• 21 March - [Market updates] Oracle

• 22 March - [Deep dive] Micron

If you find WIMM useful, please share it with a friend or a colleague. WIMM is free for one month ... plenty of time to decide if it’s worth keeping in your stack.

WIMM

Dear OnStrategy Reader,

Here is what you will find in this issue:

1. The SaaSpocalypse is a pricing problem

2. The dealer moat strikes back 😎

3. Gen Z and risk

4. on Koenigsegg ...and the long road to IPO

5. The Tinder-ization of the job market

6. on the Middle East 🛢️

…and much more

Onto the update:

The SaaSpocalypse is a pricing problem

The SaaS boom created a peculiar economic machine where companies sold tools that helped other companies build software, manage work, track tickets, and coordinate teams, and they priced those tools per seat. As long as headcount kept growing, revenue did too. AI quietly breaks that equation. If AI allows a team of 10 engineers to do the work that previously required 15, then the customer’s productivity rises while the SaaS vendor’s seat-based revenue shrinks. That dynamic sits behind the recent layoffs at Atlassian and the broader sell-off in software stocks, where investors suddenly realized that the same technology promising productivity gains for customers may compress demand for the very tools those customers buy. The SaaS model assumed that knowledge work scaled with people; AI suggests that knowledge work might scale with compute instead.

This is why the so-called “SaaSpocalypse” feels less like a cyclical downturn and more like a structural shift. If AI agents can perform tasks that previously required humans clicking through dashboards, the center of gravity moves from workflow software to automation layers. In that world, the winners look less like traditional SaaS vendors selling seats and more like platforms selling outcomes. Atlassian’s layoffs signal something deeper than cost-cutting. They are an attempt to reposition the company for a world where software is no longer just a tool for workers but a substitute for them. The paradox of AI SaaS is that the better it works, the fewer users you need. And when your pricing depends on users, success starts to look suspiciously like disruption.

Most SaaS should go private and fix their go-to-market pricing strategy. Few will do, and many will fail/stagnate (=become semi-irrelevant). FT

The dealer moat strikes back 😎

Volkswagen’s Scout gambit is basically a corporate-law version of a spin move. That is, take a century-old car company, wrap a new electric-and-hybrid truck brand around it, and then try to sneak past the dealer network by saying, more or less, “this one is different.”

Dealers, naturally, are reacting as though VW has announced a pilot program to repossess their margins... and in a way, it has. Direct-to-consumer is not really about vibes or modernity or even customer experience, though everyone says those words a lot. Actually, it is about owning pricing, data, software updates, financing, service relationships, and all the lovely recurring economics that used to get diluted across a franchise system.

The dealer argues that they protect consumers through competition and service. The Scout argument is that consumers hate buying cars and would prefer a cleaner, more controlled experience. Both are true, which is what makes this fun because the real fight is over who gets to intermediate trust.

If Scout wins, every legacy automaker will be tempted to invent a “new brand” and declare independence from its own distribution. If the dealers win, they will have demonstrated that in America, the most durable moat in autos may not be manufacturing or software, but a state-by-state legal regime built to ensure that nobody, under any circumstances, gets to buy a car too efficiently. WSJ

Gen Z and risk

There is a theory of investing that says you should steadily save, buy diversified index funds, and wait 40 years. There is another theory that says if housing is unaffordable, wages are flat, and you feel financially behind at age 23, then the rational portfolio is “whatever might 10x by Friday” 🫠
(not financial advice ❗️)

Unsurprisingly, a growing share of Gen Z seems to prefer the second strategy, putting money into prediction markets, sports betting, crypto, and other high-variance assets because they believe those bets can get them to their goals faster. If you think the normal financial ladder is broken, the optimal strategy is not patience, but volatility.

In that sense, “financial nihilism” is less about recklessness and more about portfolio theory applied to despair. When the baseline outcome looks bad, maximizing upside starts to look perfectly rational. Bloomberg

on Koenigsegg ...and the long road to IPO

Koenigsegg is interesting because it sits at the intersection of two very different business models: luxury and technology. On one hand, the company is a classic scarcity-driven luxury brand (ie. producing only a few dozen hypercars per year priced between roughly $2.8 million and $4 million, with production planned to rise only to about 150–200 units annually).

That naturally invites comparisons to Ferrari or Hermès, where the core asset is not manufacturing scale but brand, exclusivity, and pricing power.

On the other hand, Koenigsegg behaves more like a deep-tech startup. It develops its own engines, transmissions, batteries, and engineering innovations internally, which means the intellectual property, not the number of cars produced, may ultimately be the real moat. Bloomberg

The Tinder-ization of the job market

The “Tinder-ization” frame is right because the labor market has quietly shifted from matching to swiping. AI makes it almost free for candidates to generate 200 tailored applications and almost free for employers to ingest 20,000 resumes, so both sides rationally do more of the thing that feels like progress (apply, filter) and less of the thing that actually is progress (talk, decide).

The result is congestion. The pipe gets wider at the top and narrower at the bottom, and the scarce resource becomes human attention at the point of commitment. That’s why the weird macro pattern shows up in the hiring rate rather than unemployment. People still have jobs, but fewer people are getting new jobs, because everyone is trapped in an infinite scroll of “maybe".

The implication is that hiring becomes a marketplace with adverse selection and spam dynamics, which pushes everyone toward gatekeeping and brands. Employers respond with harder screens, more rounds, more referrals, and more “signals” that reduce volume (pedigree, prior employers, internal networks). Candidates respond by optimizing for ATS keywords, prompt-engineering cover letters, and leaning even harder on warm intros, because when the funnel is flooded, relationships become the only filter that still works.

In other words, AI doesn’t end work, but it turns hiring into a platform problem, where the winners are the ones who control distribution (recruiters with networks, companies with reputations, candidates with social capital), and everyone else is stuck swiping. LINK

on the Middle East 🛢️

In my latest Where is my MOAT? analysis I argue that what’s happening in the region is a live stress test of the entire post-Cold War risk map: oil flows weaponized, Dubai and the Gulf countries doing damage control as financial hubs, shipping lanes repriced by drones and missiles, and airlines, tourism, and defense stocks quietly rerating around a new baseline of fragility.

I walk through how a real disruption in Iranian supply or the Strait of Hormuz would transmit into energy, FX, inflation and equities, which sectors are accidental losers and surprise winners, and what this all means for a long-term portfolio that has to live with more frequent regional shocks and less global redundancy. WIMM

[Essay] Workflow ownership is the next 'Platform War'

For two decades, enterprise software competed to become the system of record. Salesforce wanted to own the customer record. Workday wanted to own the employee record. SAP wanted to own the operational record. In the AI era, that battleground is shifting. The next war will not be won by the company that merely stores the data. It will be won by the company that does the work. Or, more precisely, the company that becomes the operating layer through which a professional’s judgment, sequence of actions, approvals, exceptions, and institutional habits actually flow. That is workflow ownership and it is the next platform war.

This is the key mistake in much of the current debate about whether AI will “eat” application software. That framing is directionally right but analytically incomplete. AI will absolutely compress interfaces, reduce the value of brittle menu-driven UX, and make it easier to rebuild surface-level functionality. But that does not mean software disappears. It means the basis of advantage moves down one layer, from screens and forms to orchestration, reasoning, and embedded process. As Steven Sinofsky argues, AI changes what we build and who builds it, but not the amount of software the world needs. In fact, paradigm shifts historically create more software, not less. Here is the Jevon’s paradox in real life (from Citadel Securities). WIMM

Thank you for being an onStrategy reader!

Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

This week, my subscribers receive 5 posts. Here’s the schedule for this week:

• 4 March - [Essay] Enterprise AI agents move into core workflows

• 5 March - Updates on NVIDIA, Novo Nordisk and Mercado Libre

• 6 March - Updates on Apple & Berkshire Hathaway

• 7 March - [Deep dive] Palantir

• 8 March - [Deep dive] Salesforce

…and here is the schedule for the next week:

• 12 March - [Analysis] on the Middle East and some implications

• 13 March - [Essay] Workflow ownership is the next platform war.

• 14 March - [Deep dive] - Mastercard

• 15 March - [Deep dive] - Adobe

If you find WIMM useful, please share it with a friend or a colleague. WIMM is free for one month ... plenty of time to decide if it’s worth keeping in your stack.

WIMM

Dear OnStrategy Reader,

Here is what you will find in this issue:

1. 🏎️ Formula 1’s triple jump: New rules, new distributors, new entrants

2. A new management metric: “Elon speed”

3. Can you retire in China in a fake-Venice development with 1,200 RMB ($168) per month? Yes, you can!

4. New date from PISA tests. To think outside the box, you need to ‘put’ in the box

5. on Rolex’s & co strategy

6. on Shopify 🛍️

7. on Spotify 🎶

…and much more

Onto the update:

🏎️ Formula 1’s triple jump: New rules, new distributors, new entrants

Formula 1’s “pivotal year” is really a stress test of the Liberty Media playbook, which basically means, take a niche sport, wrap it in narrative (Drive to Survive), sell it as premium, then push distribution and sponsorship until it looks like a global consumer brand. The Financial Times piece shows the machine working with 2025 revenue up to $3.9bn and operating profit $632mn, with sell-outs (6.75m attendees) and a flood of sponsors that now includes everything from Doritos and Lego to luxury and AI brands. The twist is that F1 is now doing the hardest thing in media, which is, trading a known distribution partner (ESPN) for a deeper platform partnership with Apple, and betting that Apple can turn “watching races” into an integrated ecosystem moment across services (and even IMAX screenings), not just a TV slot. That’s classic “bundle leverage”, where Apple doesn’t need F1 to win sports, but it needs F1 to make Apple TV+ feel inevitable.

The second implication is the uncomfortable one. F1 is simultaneously upgrading its business model and rerolling its product. New 2026 regs mean smaller/lighter cars, a big shift toward electric power (50/50), and fully new builds (“not a bolt has carried over”), while some drivers complain it’s “anti-racing” and “less pure” because energy management rises in importance. This creates a mismatch risk where the new fanbase that arrived for characters and drama might tolerate technical changes, but the sport still needs the on-track spectacle to justify premium rights and premium sponsorship. Meanwhile, Cadillac and Audi add the “new entrants” halo, yet the article makes clear how brutally hard entry is (Cadillac hiring hundreds, drowning in applicants) and how much of the marketing upside depends on performance and possibly even a top-tier US driver, not just an American badge.

Put differently, 2026 is F1 attempting a rare triple jump: (a) platform distribution shift, (b) major product redesign, and c) expansion of the competitive set, while the flywheel is spinning. When it works, it compounds, but when it doesn’t, it breaks momentum. FT

A new management metric: "Elon speed"

The “world getting flatter” is really the world rediscovering a hard truth. When the core input is intelligence (and intelligence is increasingly software), coordination becomes the bottleneck. Hierarchies are coordination machines. They made sense when execution was scarce and information was expensive. In an AI-shaped firm, information is cheap, drafts are infinite, and the limiting factor is deciding what matters and shipping it. That’s why Meta building an “Applied AI Engineering” org with an ultra-flat ratio, up to 50 individual contributors per manager, is a more economic necessity.

Elon’s companies are the extreme expression of this with fewer layers, tighter loops, a single narrative, and a bias toward action that looks chaotic from the outside and efficient from the inside. The implication is that "flat" is a latency optimization. WSJ’s description of Meta’s new teams focusing on interfaces/tooling plus "executing tasks, generating data and providing evaluations" is basically a factory for the modern AI flywheel: ship → observe → evaluate → improve.

If you’re serious about model velocity, you can’t afford middle layers that turn feedback into meetings.

The second-order effect is strategic. As firms flatten, power shifts from managers to systems. Policies, evals, instrumentation, and internal platforms replace supervision. Your "manager" becomes the queue, the metric, the rubric, the deployment pipeline. That makes org design look more like product design. The best companies will be the ones with the cleanest internal APIs, the clearest incentives, and the strongest feedback loops ... because that’s what keeps a 50:1 org from becoming a 50:1 mess.

And the big implication for everyone else is that this convergence creates a new dividing line between companies that can operate at "Elon speed" and those that can’t. Flat structures reward high-agency talent and punish ambiguity. They also concentrate accountability and raise the cost of being wrong.

In the AI era, "flat" is a competitive weapon, but it’s also a stress test. If your culture can’t handle truth, speed, and ruthless prioritization, you’ll keep your org chart… and lose the decade. WSJ

on Meta’s “AI shopping research”

Meta’s “AI shopping research” tool is a land grab for the most valuable kind of search: the moment right before money moves. The strategic advantage is distribution + personalization. Meta already knows who you are, where you are, and what you like, and Bloomberg notes it tailors recommendations using signals like location and even gender inferred from a name. This is a shopping assistant that feels “for you”, inside the apps you already use.

The monetization question is the whole game. Does Meta take referral fees, and do advertisers get priority in the shortlist? Bloomberg flags both as open. If the answers become “yes”, Meta turns ads into “recommendations” and makes the auction about being chosen by the assistant, not just shown in a feed. The risk is trust. If users think it’s an ad unit in a chatbot costume, they bounce. We shall see. Bloomberg

Can you retire in China in a fake-Venice development with 1,200 RMB ($168) per month? Yes, you can!

This place exists because a debt-fueled property boom built 46,000 units for weekend fantasy buyers, then the boom broke, Evergrande went bankrupt, and occupancy fell to under 1 in 5. In other words, the same system that produced the grind (996 culture, hyper-competition, sky-high big-city costs) also produced the escape hatch, ie. an oversupply of homes priced like a utility bill.

The macro story is bigger than a few viral “lying flat” anecdotes. Beijing lost 1.6 million 20- and 30-somethings from 2019 to 2024, youth unemployment for 16-24 year-olds out of school sits around 16.5%, (official) growth runs at about 5% in 2025 (unofficial, is 1.5%), and suddenly the old bargain (ie. move to the megacity, climb the ladder) looks like a high-volatility trade with a drawdown you feel in your bones.

So young people are doing an elegant piece of balance-sheet management. They swap career upside for cost-of-living certainty, buy or rent apartments in places like Yunnan or Hegang where “apartments cheaper than cars” becomes a sales pitch, and finance “retirement” with savings plus modest online income streams.

This is both about a property bust and a generation deciding that time is the scarce resource. APNews

on Rolex’s & co strategy

For five years the luxury-watch business model was basically “negative inventory”. You walk into a store, they tell you nothing is available, and somehow that’s good news because it means the product is so desirable it has transcended commerce and become a social ranking system.

Now Bloomberg says "surprise, you might actually be able to walk out with a Rolex today".... maybe not the Rolex you want, but an actual Rolex with no sacred waitlist ritual. Of course this is being sold as a “turnaround” story, but it’s also just the hangover of the 2021-2023 era when people were flush with cash, stuck at home, and decided that buying an expensive mechanical object to mark time was a reasonable substitute for having plans.

Then in 2024-2025 the vibe shifted and experiences came back, costs went up, China cooled, and suddenly “scarcity” looked a lot like “inventory management".

The funniest part is that the industry’s preferred definition of “recovery” is “we’re sold out again”. Watchmakers don’t want you to be able to buy what you want when you want it. They want you to want what you can’t buy, because the wait is the marketing. Rolex even managed to sell fewer units (Morgan Stanley/LuxeConsult estimate volumes down 2% to 1.1m) while growing value via higher average selling prices, which is the most Rolex thing imaginable. If demand softens, just become more Rolex-y... 🫠

Concluding, maybe you can get one now, but the whole equilibrium of luxury watches is that availability is a temporary market glitch, and the “healthy” state of the system is a boutique employee telling you politely that your money is not sufficient.... you also need time. Bloomberg

New date from PISA tests. To think outside the box, you need to ‘put’ in the box

This chart is a great example of how reality keeps ruining everyone’s preferred narrative. Because a lot of people want the story to be: “Math is rigid, creativity is free, and if you make kids do too many equations they’ll never write a novel". And PISA is like: actually, the countries where students score higher in math also tend to score higher in creative thinking (!).

Hence, we can conclude that “rigor is a creativity hack”. If you can reason precisely, hold multiple constraints in your head, and grind through a hard problem without panicking, you can also write fiction with structure, draw a poster that communicates, and come up with original solutions under rules.

Creativity is competence within constraints. Also, rich countries do better at school in general, so this might just be “well-funded systems produce broadly capable kids”. But either way it’s a nice reminder that “creative” doesn’t mean “vibes-based”, but it means you have enough mental horsepower and discipline to play, recombine, and still land the plane.

on Shopify 🛍️

Once you see Shopify as a protocol rather than a store builder, the strategy makes more sense, which is, let Amazon own its walled garden, let social platforms fight over impulse clicks, and focus instead on owning the identity, payments, and data pipes that sit underneath millions of independent merchants, because if the next wave of AI agents are the ones choosing where we buy, the platform that already runs those rails is in a much better position than the one that just rents out a storefront. WIMM

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on Spotify 🎶

The interesting thing about Spotify’s AI push is that they’re quietly turning a historically terrible business (licensing the same catalog as everyone else) into a data-compounding machine where every marginal listener makes the product better for everyone else.

If you view Spotify as an AI-scaling experiment, the logic flips. Labels own the songs, but Spotify owns the behavioral exhaust, which is exactly what you need to build hyper-targeted discovery, performance ads, and eventually agents that decide what you hear next on your behalf. At that point, the catalog is a commodity input and the real asset is the model that sits between artists and ears, which is why the earnings call matters less for “premium vs. ad-supported ARPU” and more for how fast Spotify can turn its listening graph into an AI-native distribution monopoly. WIMM

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Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

I consider “Where is my MOAT?“ to be like a mini-MBA 😎

This week, my subscribers four five posts, five different lenses on the world.

Here’s the schedule for this week

• 26.02.2026 - Update on my portfolios - only for Premium (paying) subscribers
  What changed, what didn’t, what I trimmed, what I added, and (most important) why.

• 27.02.2026 - On memory chips

• 28.02.2026 - Updates on Spotify

• 01.03.2026 - Shopify company analysis

…and here is the schedule for the next week:

• 4 March - [Essay] Enterprise AI agents move into core workflows

• 5 March - Updates on NVIDIA, Novo Nordisk and Mercado Libre

• 6 March - Updates on Apple & Berkshire Hathaway

• 7 March - [Deep dive] Palantir

• 8 March - [Deep dive] Salesforce

If you find WIMM useful, please share it with a friend or a colleague. WIMM is free for one month ... plenty of time to decide if it’s worth keeping in your stack.

WIMM

Dear OnStrategy Reader,

Here is what you will find in this issue:

1. [Long take] AI turns software into labor

2. 🤖 on Optimus and other physical robots

3. Rich countries have ‘rich countries’ problems

4. Peak human capital?

5. The death of “just the news”?

6. When the cost of failure is too high, disruption becomes irrational

7. …and much more

Onto the update:

If AI turns software into labor, layoffs become pricing, and platforms win by becoming machine-readable.

Dorsey’s memo is doing two things at once:

(1) a classic “we’re getting smaller and flatter” restructuring; and

(2) a business model pre-announcement disguised as an org chart change.

The key line is the claim that “intelligence tools…paired with smaller and flatter teams…fundamentally changes what it means to build and run a company”. This is about admitting that the firm’s cost structure (and therefore its strategy) is being re-written by software that behaves like labor.

I already wrote in my “Where is my MOAT” article, “Is AI just the death of software?” about this next chapter for many companies. This is the “per-user to per-result” shift showing up inside the company before it shows up in the product. I argued that AI compresses the work that used to justify fat software margins (implementation, customization, workflow glue) and that the durable move is to stop selling seats, start selling outcomes, because agents do the work and the “user” becomes a supervisor. That logic doesn’t just pressure SaaS pricing, but also pressures its payroll. If you believe the unit of production is moving from “person-hours” to “agent throughput”, then “nearly half” is a forecast.

Now zoom into the last paragraph where Dorsey tells the remaining team they’ll help customers “build their own features directly, composed of our capabilities and served through our interfaces”. That is a platform strategy wearing a hoodie. Blocks’ wedge is “we expose primitives (payments, identity, risk, compliance, rails), then let customers assemble outcomes.”

Agents become a distribution and business model change. The buyer no longer wants a dashboard, but just a job done, and the easiest way to deliver that is to make your product legible to machines (APIs, workflows, composable modules) and let the customer’s agents orchestrate it.

The uncomfortable implication is that layoffs are the new “pricing page update”. If the market is repricing software because scarcity moved from UI to intelligence, then companies will reprice labor the same way with fewer people, higher leverage, more machine-readable surfaces. Most firms will try to compromise what I called out “cut costs and innovate,” i.e., “do the easy part aggressively and the hard part politely”.

Dorsey is betting on the opposite: do the hard part (org + product + interface strategy) in one traumatic move, so the company can actually rebuild around agents instead of defending a structure designed for seat-based work. Jack Dorsey, WIMM analysis

🤖 on Optimus and other physical robots

The interesting thing about Atlas vs. Optimus is that humanoids are the first credible attempt to turn AI from “software that talks” into “software that moves”. That’s a different kind of compounding. When AI sits behind a screen, its output is ideas, but when AI sits inside a body, its output is labor. Hyundai is explicitly aiming at high-volume repetitive tasks first (kitting components in 2028, more complex assembly by 2030), which is exactly how real automation wins, ie. narrow scope, measurable ROI, then expansion.

Strategically, this is also an “automakers as platforms” story, because a humanoid robot is basically an EV in disguise: batteries, motors, sensors, control systems, and increasingly, a software stack. That matters because car companies already know how to do the hardest part of robotics (ie. manufacturing at scale with acceptable defect rates). Hyundai buying Boston Dynamics and then pairing that hardware with AI partners (Nvidia, Google DeepMind) is the clearest sign that the car industry’s old advantage (factories) is being repurposed for the next advantage (factories that build workers).

The key tension is integration vs. components. Tesla’s pitch is end-to-end, where Optimus “inherits” Tesla’s FSD software approach and, if it really hits the $20-30k price range, it’s a mass-market wedge. Hyundai’s pitch is more like the classic industrial incumbent where Atlas is heavier-duty (50kg payload vs. 20kg) and aimed at industrial sites first, but it pays an “Nvidia tax” and relies on outside AI vendors, which can compress margins and slow iteration. In other words, Tesla wants to be Apple (integrated consumer scale), Hyundai looks more like a “best-of-breed” enterprise stack that wins by being deployable and useful now.

Zoom out and you get the real implication, where humanoids are a new labor supply curve. Bloomberg cites analyst math that at $100k, Atlas could work out to about $5/hour operating cost, below US federal minimum wage, and the whole investment case is "24/7 labor that doesn’t unionize, doesn’t quit, and gets software updates".

If that math holds even in a subset of tasks, it won’t just change factories; it changes bargaining power across manufacturing, warehousing, and eventually care work.

Concluding, the “AI race” is about distribution. Whoever has the real-world channels to deploy embodied AI at scale (factories, service networks, procurement relationships, safety certification muscle) gets to turn intelligence into cash flow before everyone else. Bloomberg

Get fast internet, or risk being an AI laggard economy

In 2026, “fast internet” is a social credential. If you don’t have serious bandwidth and low latency in the age of AI, nobody takes you seriously... not because they’re mean, but because your capability set is literally smaller.

You can’t reliably ship large models, run modern workflows, collaborate in real time, or even do the basic “I’ll just use the cloud” thing without turning every task into a loading-screen meditation.

You will then get this vicious loop where talent avoids you, firms don’t build there, remote work isn’t really remote, and “AI adoption” becomes a promise instead of an operating system.

Concluding, some regions are plugged into the future, and others are asking the future to please hold. LINK

oh, Botcoin!

📈📉 This week on "Where Is My Moat?" I’m doing two things:
1/ a YTD look at my own portfolio (almost two months in)
and
2/ a deep dive on the current AI panic, including why the much-shared Citrini report has everyone spooked, and what the data actually says.

If you’re curious, you can try WIMM free for one month: ~20 articles in your inbox, plenty of time to see if it earns a permanent spot in your workflow.

🧐 Give it a shot and decide for yourself:

https://whereismymoat.com/

Rich countries have ‘rich countries’ problems

Bloomberg: "1 in 20 Australians use cocaine each year"

My two ideas on the topic:
1/ When a country gets rich, it tends to do drugs in the same way that when a company gets profitable, it tends to do stock buybacks. It’s not proof of virtue, it’s proof of capacity. You’ve solved (enough of) the “rice and rent” problems, you’ve built logistics, you’ve got disposable income, and, crucially, you’ve got a large class of high-functioning people who are exhausted, bored, ambitious, and trying to feel something on a Thursday night after a 14-hour day. “Developed market” is basically shorthand for “the demand curve has room for non-essentials” and cocaine is a non-essential with great unit economics.

2/ Australia is the cleanest case study because it has the audacity to be geographically inconvenient and still become, effectively, a premium-priced consumer market.

Wealth makes demand deep; remoteness makes supply expensive; expensive supply doesn’t kill the market, but it upgrades it. And then everyone acts shocked that organized crime is… organized. But the whole point of a high-margin product is that it finances unpleasant vertical integration: corruption, violence, recruitment of insiders, the whole bundle.

Rich countries don’t “fall into” drugs, but they create a market attractive enough that global supply chains (legal and illegal) re-route themselves to serve it.

Q.E.D.

Peak human capital? (Reposting some ideas from two months ago, from Financial Times)

These charts are awkward. Teen science, math, and reading scores drift down after 2014, then fall harder into 2022. Adult literacy and numeracy bend the same way, with literacy taking a particularly sharp hit. You can blame pandemics, phones, curricula, … the government. Or you can zoom out and notice the timing.

The steepest declines arrive just as the smartphone became the default interface to the world and algorithmic feeds became the dominant cognitive environment. It is hard to prove causation, but it is also hard to ignore correlation when every spare moment is auctioned to short-form video.

The uncomfortable implication is not that AI makes us super dumb, but that we may be outsourcing first-order cognition long before AI fully arrives. If search reduced the need to remember, feeds reduce the need to focus, and generative tools may reduce the need to struggle. (Pls, read this paragraph again!)

The market rewards convenience, and convenience compounds. The question is whether societies can compound cognitive capital at the same time. If human intelligence scores are flattening while machine intelligence is accelerating, that gap becomes the most important spread trade of the next decade.

The death of “just the news”?

One way to read this chart is that The New York Times is no longer in the news business, but in the habit business 😅.

News-only subscribers are quietly melting away while bundle and multiproduct subscribers are compounding like a decent growth stock, which tells you that journalism by itself is a volatile product but journalism plus games plus cooking plus “something to do on your phone when you’re bored” is a subscription fortress.

The funny thing about bundling is that it looks like a discount but behaves like a moat. Once Wordle and recipes and product reviews are living in your daily routine, cancelling feels less like dropping a newspaper and more like uninstalling a lifestyle.

News may be cyclical and politically correlated and occasionally exhausting, but cross-subsidized attention is sticky. In other words, the Times figured out what Wall Street already knew, that diversification lowers risk, and apparently it also lowers churn.

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When the cost of failure is too high, disruption becomes irrational

The core argument is straightforward but uncomfortable. Europe’s problem is not talent, capital, or even ambition... it is optionality. When the cost of failure is measured in 31 to 62 months of salary per employee in major economies, compared to seven in the United States, experimentation becomes structurally expensive. That changes corporate behavior at the margin, and at scale margins matter.

Radical innovation is a portfolio game: many shots, many misses, one or two massive hits. If shedding labor after a failed bet requires months of negotiation, regulatory approval, and nine-figure severance packages, the rational response is to avoid bets that might fail. Europe’s system is optimized for incremental improvement (note the steady decline in fuel consumption in combustion engines over decades) but poorly optimized for discontinuities like EVs or autonomy. Tesla is less a story about batteries than about the willingness to reallocate capital and labor quickly.

The more subtle point is that Europe’s model works... until the paradigm shifts. Long tenures, apprenticeship systems, and works councils build deep firm-specific knowledge; that is a feature in stable industries. It becomes a bug when software eats the drivetrain.

The irony is that startups are often exempt from these rules, but the exemptions disappear precisely when growth begins, which is when risk-taking needs to accelerate.

The countries that lean toward “flexicurity” demonstrate that the trade-off is not binary. You can protect incomes socially rather than protect jobs contractually.

In conclusion, Europe has optimized for sustaining innovation within existing value chains. The US has optimized for disruptive innovation that rewrites them. The result is not that Europe cannot build a Tesla. It is that its institutions make building, and especially failing while trying to build, one far more costly. LINK

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Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

Dear OnStrategy Reader,

Here is what you will find in this issue:

1. New Apple MacBook? The $699 Trojan Horse

2. AI - The productivity trap?

3. Bitcoin was supposed to be “digital gold”…is NO

4. Flush with AI 😅

5. Short-form video is bulldozing Gen Z’s attention span (e.g. TikToks, Reels, Shorts)

6. Waymo - Driverless, except for the door 😅

7. on UberEats

8. [Essay] AI - The End of the Beginning. A Case for Optimism

9. …and more

Onto the update:

New Apple MacBook? The $699 Trojan Horse

Apple is preparing to launch a low-cost MacBook on the 4th of March.

Implications? With this launch, Apple is taking its most powerful moat, the ecosystem, and pushing it down the income stack. If the rumors are right, this is basically “iPad economics in a laptop shell”: an iPhone-class chip (likely A18 Pro), an aluminum chassis, a 12.9-inch display, and a price “well below $1,000” (likey $699). That combination matters because it changes the entry-level decision from “Which Windows laptop is least bad at $699?” to “Do I want the computer that already speaks my phone’s language?”. The win condition isnlifetime value: iCloud, Apple Music, App Store, accessories, and eventually an iPhone upgrade that feels “obvious” because your laptop is already in the family.

Will it disrupt the entry-level PC market? It depends on what Apple compromises. If Apple can deliver “good enough” keyboard, battery, and screen while keeping macOS (not a weird iOS-on-laptop experiment), then yes, it’s a serious attack on the big volume tier that Windows OEMs live on, because Apple’s cost structure is different. It controls the silicon roadmap and can amortize it across hundreds of millions of iPhones, then repurpose that efficiency into a cheaper Mac.

The second-order effect is pressure. OEMs have to compete on price while also paying for Windows and buying commodity parts, which is a tough way to make money. Apple, meanwhile, gets to “sell the starter Mac” and still make the real profit later, in services and upgrades. If you’re a PC maker, this is the scariest kind of competition, where the rival treats your margin pool as their customer acquisition cost. Macrumors, Ming-Chi Kuo (Apple analyst)

The productivity trap?

One of the great promises of AI was liberation (example: fewer rote tasks, more thinking, maybe even leaving the office on time). Instead, according to new research in Harvard Business Review, AI tends to function less like a labor-saving device and more like a cognitive energy drink.

In an 8-month field study at a 200-person tech company, employees who adopted generative AI tools did not shrink their workloads, but expanded them. They took on adjacent roles, blurred the line between "my job" and "their job", and filled small pockets of downtime with "just one more prompt". Engineers became reviewers of everyone else’s AI-assisted output. Product managers started coding. Work did not disappear, but it metastasized.

The bullish case for AI often assumes automation converts effort into leisure. The emerging reality suggests automation converts effort into ambition. When starting a task becomes frictionless, more tasks get started. When iteration becomes cheap, scope expands.

The result is a self-reinforcing loop of speed, expectation, and cognitive load with more output, higher standards and fewer pauses (!!). The authors argue for developing an intentional “AI practice” to prevent silent workload creep. That framing matters because the real risk is not that AI fails to boost productivity. It is that it succeeds so well that organizations quietly redefine what "normal effort" means.

In that world, AI does not replace work, but raises the baseline. HBR

Bitcoin was supposed to be “digital gold”…is NOT

Bitcoin was supposed to be “digital gold”, meaning when everyone panics about fiat, Bitcoin does the calm, reassuring thing and goes up.

Instead, it did the exciting, modern thing and went down 44%, while people quietly bought actual gold like it’s 1974 and they own a mustache.
The funny part is, we’re not early anymore. There are ETFs, institutions, tokenization panels, the whole Wall Street institutions, yet none of that seems to accrue to the tokens themselves.

More about Bitcoin in my latest blog update

Subscribe for free for one month. (Where is my MOAT?)

Flush with AI 😅

There are many ways to play the AI boom. You can buy GPUs. You can buy hyperscalers. Or you can buy a Japanese toilet company that secretly makes mission-critical ceramic components for NAND memory fabrication. According to the FT, Toto, best known for heated seats and Washlets, generates 40% of its operating profit from advanced ceramics used in semiconductor manufacturing, specifically electrostatic chucks that hold silicon wafers in place during production.

Memory prices are rising, AI workloads are driving NAND upgrades, and suddenly, the company that perfected the art of porcelain is a five-year moat AI beneficiary. Of course it is. The future runs on GPUs and also on extremely stable ceramics that can survive cryogenic etching.

This is classic AI industrial spillover. Ajinomoto turned umami chemistry into chip insulation. Toto turned toilet ceramics into wafer-handling precision hardware. The activist thesis is straightforward: reallocate capital, highlight the semiconductor exposure, cut cross-shareholdings, deploy JPY 76 billion of net cash, and let the market reprice the story as infrastructure... instead of bathrooms 🫠

Shares are already up more than 60% over the past year, and the pitch is that they can go another 55% higher (no investment advice).

In the AI cycle, the highest margins often sit in the least glamorous bottlenecks. Sometimes the real AI trade is not the model. It is the component that keeps the wafer perfectly still while the model gets built. FT

Short-form video is bulldozing Gen Z’s attention span (e.g. TikToks, Reels, Shorts)

Rose Horowitch’s piece in The Atlantic describes film professors who cannot get film students to sit through feature-length movies, with many furtively checking phones even during climactic scenes. Which is funny, because for 20 years Hollywood executives have been obsessing over "engagement", and now they have achieved maximum engagement with the wrong screen. The kids love movies. They just prefer to watch 2 of them simultaneously, vertically, in thirty-second increments, while also texting. If you are a Coppola character delivering a moody, existential monologue, you are competing with a push notification. The notification wins.

Universities are long-duration attention vehicles trying to finance themselves with short-duration cognition. Film school used to assume the scarcest resource was access to movies; now the scarce resource is sustained focus.

The irony is that the streaming era solved distribution and accidentally destroyed immersion. When even USC film students treat The Conversation like a background tab, the business model of “just pay attention at the end” stops working. The real special effect in 2026 is not CGI. It is two uninterrupted hours of looking at one thing.

Good luck with that!

Driverless, except for the door 😅

According to TechCrunch, Waymo is literally paying DoorDash drivers to go close the doors of its stranded cars for $6.25 plus a $5 bonus in Atlanta. Silicon Valley spent a decade eliminating the driver, and now it has reinvented the valet.

What looks like absurdity is really modularity. When the marginal cost of downtime exceeds the cost of summoning a human, you summon a human. That is platform thinking. Waymo automates 99.9% of the driving stack and then arbitrages the last 0.1% through DoorDash. It is a reminder that autonomy does not mean the absence of people. It means the strategic deployment of people at the highest-leverage moments. The robot drives. The gig worker closes the door. Venture capital funds both. Welcome to the hybrid economy, where the future is autonomous, except when it is cheaper not to be...🫠

on UberEats

As the FT notes, the company is expanding into seven new European markets, including Romania, in a push that aims to add $1bn in gross bookings over three years. That number is about winning Europe through density. Food delivery is a scale game layered on top of another scale game. Uber already has rides, payments, maps, memberships and customer relationships. When it re-enters a market like Romania, it does not start from zero. It starts with an installed base, cross-selling from mobility to meals, and a cost structure that improves as volume compounds. The lesson from its earlier withdrawal is that food delivery without multi-product leverage is a grind. The lesson from its return is that food delivery with platform leverage becomes a margin expansion tool.

The article highlights M&A across Europe and Uber’s plans for drones and robots. In other words, the endgame is fewer players, more scale, and eventually lower variable labor costs. The real moat is not restaurants or couriers. It is distribution, data, and the ability to amortize technology across rides, groceries, food, and in-time, autonomous logistics. Uber left when the economics were local. It returns because the economics are now platform-level. FT

Opinion: The Marlboro Man and the power of manufactured myth

There was a time when a cigarette ad could feel like cinema. The Marlboro was felt like selling the frontier mythology at scale. A lone rider and endless horizon. It was brand as narrative dominance.

What made those ads magical was clarity. Freedom, masculinity, silence, danger, self-reliance. In one frame, you understood the promise.

Today, brands optimize for click-through rates and A/B tests. Back then they optimized for archetypes. The cowboy was the interface and the cigarette was the API.

Of course we know how the story ends. Regulation arrives. Health data accumulates. The myth collapses under reality. Yet there is something nostalgic about that era of advertising when brands aimed for transcendence (magic), instead of engagement.

For a while, in the golden light of a staged Western sunset, that confidence felt like magic.

[Essay] AI - The End of the Beginning. A Case for Optimism

"Without AI, we would have been in an economic crisis/stagnation. [...]

This is why I believe the timing of AI has worked out miraculously well. We are going to have AI and robots precisely when we need them to substitute for a shrinking population and to kickstart economic growth. In this future, I see human workers being at a premium, not a discount. The fear of "job loss" is, in my view, an overly simplistic model that ignores the reality of "task loss". Jobs are bundles of tasks. While AI will automate specific tasks, the jobs themselves persist and evolve. I look at the history of coding as a perfect example. We moved from machine code to assembly, then to high-level languages like C, and then to scripting languages like Python. Each layer abstracted away complexity, and rather than destroying the role of the programmer, it made them more productive and more numerous. AI is simply the next layer of abstraction, allowing one person to orchestrate an army of code-bots, making them 10 or even 100 times more effective than before. From this point of view, AI is augmenting humans, not replacing them." (Read the full essay)

Personal promo 😎

Your problems were solved by others.
You can move faster.
Work with me:

3BigProblems

I consider "Where is my MOAT?" to be like a mini-MBA 😎

This week, my subscribers get five posts, five different lenses on the world.

Here’s the schedule:
Feb 18 — a deep dive on Bitcoin
Feb 19 — updates on Amazon and Microsoft (what changed, what didn’t)
Feb 20 — a February 2026 macroeconomics deep dive — and yes, this is a new category I’m introducing to WIMM (because markets don’t move in a vacuum)
Feb 21 — Eli Lilly deep dive
Feb 22 — Pfizer deep dive

…and here are the posts from last week:

• 12 Feb - [Essay] AI - The End of the Beginning. A Case for Optimism.

• 13 Feb - [Analysis] How Tesla and Google are reinventing their moats in real time

• 14 Feb - [Deep dive] Oracle’s AI Backlog Problem

• 15 Feb - [Deep dive] ARM becomes Infrastructure

If you find WIMM useful, please share it with a friend or a colleague. WIMM is free for one month ... plenty of time to decide if it’s worth keeping in your stack.

WIMM

Thank you for being an onStrategy reader!

Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

Dear OnStrategy Reader,

here is what you will find in this issue:

1. 🧐 The 30-person company scaling to 500 Million users

2. The Algorithm of Love

3. 🚗 Ford meets its BYD moment

4. 📈 God, Growth, and ETFs

5. 🇩🇪 Germany’s “China Shock” revisited

6. 📺 YouTube Is TV Now

7. Electric cars are easy —> 🏎️ Ferrari is hard

8. …and more:

Onto the update:

🧐 The 30-person company scaling to 500 Million users

In a world where scale is usually equated with headcount, the most striking thing about Nikita Bier’s latest posts is not the number, 30 employees, but the contrast: Meta has 87,000, Microsoft 221,000, Google 190,000… and X’s core product team fits into a group text. The implication is not that big companies are broken, but that leverage has shifted. Platforms that once required thousands to operate at scale can now be rebuilt by a handful of senior engineers, a designer, and a founder who still reads his DMs.

This story is more about velocity and control, not cost-cutting. With zero middle management, no product committees, and instant feedback loops, ideas move from code to production faster than a weekly sprint review. The company is flat, not lean. You don’t climb a ladder, you ship. And when you ship, you don’t present a deck, you touch half a billion users.

AI, infra-as-a-service, and modular stacks have turned “team size” from a constraint into an optionality lever. The trade-off is no longer scale vs. speed, but legacy bloat vs. atomic focus.

Welcome to the endgame: single-digit teams with infinite surface area.

The Algorithm of Love

Apparently, the hottest startup at Stanford is not an AI agent or a crypto protocol.... it’s Date Drop, a matchmaking app that delivers one suggested partner every Tuesday night at 9 PM. Over 5,000 students are on it.

The appeal for using it? It reduces the cognitive load of dating in a campus full of Type-A optimizer brains. It’s engineered serendipity for people who schedule spontaneity between compilers and case interviews.

Date Drop doesn’t promise magic, but it simplifies the friction of connection. In a world where tech automates your shopping, your commute, and your schedule, why not outsource romance too? If nothing else, you might match with someone who funds your Series A. At Stanford, that counts as chemistry. WSJ

🚗 Ford meets its BYD moment

It turns out Ford has finally met its match. China’s BYD just outsold Ford globally for the first time ever, moving 4.6 million vehicles in 2025 versus Ford’s 4.4 million. That 200,000-unit difference may sound modest until you realize it was driven by BYD’s 7.7% growth and Ford’s 2% decline. It’s like Ford was running on a treadmill while BYD took the elevator.

This story is more about direction. Ford’s been busy writing off $19.5 billion in EV-transition charges (!), while BYD quietly turned into a global juggernaut, expanding across Europe, Latin America, and Southeast Asia with price-competitive (= super subsidized), tech-heavy EVs.

Meanwhile, Ford’s US sales held up, but it bled market share in China, where BYD, Geely, and even Xiaomi now play the home court advantage in the EV game. The good news for Ford is that Toyota still keeps everyone humble at the top with 11.3 million cars sold, so at least there’s a big boss to point at. The bad news is that this isn’t just a blip. BYD’s exports hit 1.05 million units and it wants 1.3 million in 2026. It’s like Ford is showing up to a software-defined EV future with a factory recall notice in hand, while BYD shows up with a discount, a WeChat integration, and a charging station in every zip code. Ford might still be Ford, but in this global race, it’s now looking more like General Electric than General Motors. Bloomberg

📈 God, Growth, and ETFs

Of course the Vatican is launching stock indexes. After 2,000 years of moral guidance and indulgence arbitrage, it makes perfect sense to apply Catholic principles to portfolio construction.

The indexes reportedly include companies like Meta and Amazon, which suggests the moral filter is a bit more Aquinas-meets-BlackRock than pure Benedictine austerity. Still, there’s something oddly poetic about your investment portfolio getting blessed while still holding Big Tech. After all, if the Lord moves in mysterious ways, so does capital allocation.

Once you’re in the indexing business, you’re not just blessing people, you’re blessing capital. Allocating grace through passive flows. Which is maybe the most efficient form of salvation yet.

🇩🇪 Germany’s "China Shock" revisited

The German economic model was built like a luxury car (ie. engineered to precision, humming along on exports, and for the better part of two decades, heavily dependent on the China autobahn). The idea was simple. Germany makes the high-end machines, China buys them, and everyone gets richer. But it turns out that if you drive a BMW into a high-speed industrial rivalry, eventually someone else builds the road... and the car. What the Rhodium report shows, in painstaking detail and sad German charts, is that China no longer wants to be the demand side of Germany’s export machine. Imports are down, market share is collapsing, and the auto surplus with China is falling off a cliff that starts somewhere around the year 2022. Industrial competition is the present and Germany’s machine is stalling.

The bigger irony, of course, is that Germany’s famously rules-based, ordoliberal approach leaves it philosophically unarmed in a moment when everyone else, from Beijing to Washington, is embracing active industrial policy. The EU has started to flirt with protectionism, but Berlin remains stuck between its legacy love affair with China and its allergy to "picking winners". Meanwhile, China is aggressively picking markets, undercutting prices, cornering supply chains, and occasionally weaponizing rare earths just to make a point.

Germany keeps talking about de-risking while watching its factories lose margin, market share, and eventually jobs. The old model (ie. export to China, stay efficient, don’t intervene) is not malfunctioning. It’s obsolete. If Merz returns from Beijing without a new playbook, Berlin may discover that economic autopilot only works when you’re still flying. Rhodium Group

Electric cars are easy —> 🏎️ Ferrari is hard

One thing about Ferrari making its first electric car is that they hired Jony Ive's design studio to help them figure out how to put a steering wheel and some buttons in it. This is a little bit like if you were opening a restaurant and hired a famous chef to consult on the menu, except the chef's most famous accomplishment was removing all the ingredients from food. LoveFrom, Ive's post-Apple design collective, is best known for making computers and phones that have approximately zero buttons and tactile switches, so naturally Ferrari brought them in to design "tactile switches and custom interface" that "connect the driver with the car in a completely new way".

The new way, apparently, is with physical controls, which you might think is actually the old way, but calling it new is how you justify the consulting fees. Also the press release mentions "authentic Ferrari experience" three times in two sentences, which is what you say when you're worried people will notice your electric car is silent and doesn't smell like gasoline.

The broader point here is that luxury carmakers have discovered that their EV problem is actually a branding problem disguised as an engineering problem. Anyone can make an electric car go fast in a straight line (ie. that's just batteries and motors, commodity parts you can buy from suppliers). The hard part is making people feel like they're driving a Ferrari when the engine doesn't scream and the exhaust doesn't pop.

Tesla proved you can sell electric cars by making them feel like computers. Ferrari is trying to prove you can sell electric Ferraris by making them feel like Ferraris, which seems obvious until you realize the thing that makes a Ferrari feel like a Ferrari is mostly the part they're removing (!)

📺 YouTube Is TV Now

YouTube wants to unbundle your TV before you unsubscribe

Here’s the plan! Take the legacy cable bundle, slice it up like a sushi roll, and reassemble it inside YouTube, only this time with slightly less guilt and slightly more Google. For $65 a month, a magical number that is somehow still considered a “discount”, you can now get all the sports your dopamine-deprived brain craves. YouTube’s actually doing the à la carte thing cable companies promised for decades but never delivered, mostly because they liked money and leverage and channel bundles that no one asked for. Now that half of those bundles have disintegrated into subscriber dust, Youtube gets to play the hero by offering a broken cable package that somehow feels fresh.

YouTube is making more revenue than Disney’s media business, and doing it while letting creators teach you physics, cook dinner, and argue about Taylor Swift’s playlist all in one feed. If Google wins TV the same way it won web search, it won’t be because it had better shows, but it’ll be because it had the pipes, the eyeballs, the ad tech, and a platform where everyone wants to hang out.

I have long advocated (in blog posts, in my book, etc) that Netflix's real competitor is Youtube, not HBO.

🧐 [Essay] AI - The End of the Beginning. A Case for Optimism

"Without AI, we would have been in an economic crisis/stagnation. [...]

This is why I believe the timing of AI has worked out miraculously well. We are going to have AI and robots precisely when we need them to substitute for a shrinking population and to kickstart economic growth. In this future, I see human workers being at a premium, not a discount. The fear of "job loss" is, in my view, an overly simplistic model that ignores the reality of "task loss". Jobs are bundles of tasks. While AI will automate specific tasks, the jobs themselves persist and evolve. I look at the history of coding as a perfect example. We moved from machine code to assembly, then to high-level languages like C, and then to scripting languages like Python. Each layer abstracted away complexity, and rather than destroying the role of the programmer, it made them more productive and more numerous. AI is simply the next layer of abstraction, allowing one person to orchestrate an army of code-bots, making them 10 or even 100 times more effective than before. From this point of view, AI is augmenting humans, not replacing them." - read if all HERE.

[PRINCIPLE] The Sorting Advantage

Most of what we call “work” is sorting.

Sorting signal from noise. The customers who fit from the ones who don’t. The “good enough” from the “worth it”.

The mistake is lazy sorting: choosing proxies because they’re easy to count, not because they’re true. Metrics that look scientific but measure the wrong thing. Rankings that reward conformity instead of competence.

Better sorting is leverage. It saves time, protects attention, and compounds quality.

Pick your sorting rules on purpose, before the world picks them for you.

“Where is my MOAT?” - updates

• 12 Feb - [Essay] AI - The End of the Beginning. A Case for Optimism.

• 13 Feb - How Tesla and Google are reinventing their moats in real time

• 14 Feb - [Deep dive] Oracle’s AI Backlog Problem

• 15 Feb - [Deep dive] ARM becomes Infrastructure

Here’s the schedule:

• Feb 17 — a deep dive on Bitcoin

• Feb 18 — updates on Amazon and Microsoft (what changed, what didn’t)

• Feb 19 — a February 2026 macroeconomics deep dive — and yes, this is a new category I’m introducing to WIMM (because markets don’t move in a vacuum)

• Feb 20 — Eli Lilly deep dive

• Feb 21 — Pfizer deep dive

Subscribe one month for FREE:

https://whereismymoat.com

Thank you for being an onStrategy reader!

Interesting newsletters 💡

1. The Weekly Blend

2. Product evolution

first of all, a personal (professional) update:

This week I launched 3BigProblems.com or simply put - "Board member-as-a-Service"

I was already doing this in a similar form for years, but now I decided to give it a "face".

Here is the logic:
1/ Many SMEs need external (non-binding) advice regularly...or at least once per quarter
2/ The smartest people work for somebody else - hence the need for advice
3/ There are 20 big problems a company has to solve, but my thesis is that the first 3 get around 50% of the impact. That's my focus.

That's it!

Here is the website for more details: https://3bigproblems.com

On to the update:

Microsoft’s AI Identity Crisis

It turns out that when you glue together a bunch of branded copilots (PowerPoint Copilot, GitHub Copilot, Edge Copilot, Consumer Copilot, Enterprise Copilot) and then let them run wild in a 450-million-seat productivity zoo, you don’t necessarily get a cohesive user experience. You get a branding sudoku that even Satya Nadella apparently can’t solve. He once emailed his EVP because Copilot couldn’t help him on a public webpage. While Microsoft is burning $60 million on TV ads and gearing up for its Super Bowl moment, actual users are quietly defecting. Copilot’s share of primary chatbot use dropped from 18.8% to 11.5%, while Gemini, Claude up and ChatGPT remain the cool kids on the AI playground.

The product is everywhere and somehow nowhere. Inside Microsoft, they’ve built a Copilot boot camp, forced AI KPIs into performance reviews, and moved adoption from 20% to 70%. That’s impressive. But outside the walls of Redmond, it looks more like enterprise inertia propping up a half-baked UX. Microsoft says this fragmentation is intentional… to separate personal and work contexts. Sure. Meanwhile, Anthropic’s Claude Cowork is getting praise for doing what Copilot was supposed to do in the first place: seamlessly operate across 365. Microsoft has all the users, all the apps, and still manages to make AI feel like Clippy 2.0 with a bigger compute bill. Still, I superbullish they will overcome this. WSJ

SpaceX buys xAI and prepares for IPO

Of course Elon Musk wants to merge SpaceX and xAI. The rockets go up, the data comes down, and somewhere in the middle there’s a constellation of one million satellites doing inference on corn yields and anime scripts.

The rationale is elegantly stupid. AI models are just computers that need a lot of electricity and chips, and space is big and cold and solar-powered. So why not turn orbit into a data center? It’s a Kardashev-meets-cash-flow kind of synergy, where we build a multi-planetary civilization, one GPU lease at a time.

But also, it’s capital. xAI is apparently burning a billion dollars a month, which makes even selling flamethrowers and Dogecoin look old-fashioned. When the vision gets too expensive for one balance sheet, just merge the vision.

SpaceXAI is more of a planetary financing wrapper. Blend the cash flows of Tesla, the hardware of SpaceX, the compute demand of xAI, and voilà, you have an asset class. AI investors get rockets. Rocket investors get chatbots. Everyone gets Mars, eventually, assuming the CFOs at sovereign wealth funds approve. And if not, there’s always the IPO. Bloomberg

Scrolling alone 📲

The line chart from Our World in Data might as well be a mood board for the 2020s. If you’re between 15 and 29 in the US, congratulations: you’re spending more time alone than ever before. How much? 6 hours per day.

That’s more than with family, friends, partners, co-workers, and even children combined. At this level, any social fabric you are dreaming of is just nonsense. Everyone’s stitching their own custom solitude, probably while scrolling past each other on Tiktok, ordering dinner from an app, and engaging with their therapist’s AI.

It’s hard not to see this as a kind of rational economic decision. Human interaction comes with friction, expectations, and sometimes small talk. Solitude is frictionless, scalable, and, crucially, asynchronous... just like the platforms shaping this generation’s behavior.

While this might seem like a chart about loneliness, it’s also a chart about incentives. The economy rewards attention and time spent on devices, not on dinner with friends. You can’t really blame Gen Z for optimizing.

on the internet 🗺️

Mary Meeker did not bet on vibes. She bet on curves, and those curves, especially the exponential ones, tend to behave in a certain way: slowly at first, then all at once, then they just keep going.

The funny part is that her numbers, which seemed bold at the time, now look charmingly cautious. 5 billion internet users use the internet today.

This will happen to Gen AI too, but faster. It’s those certain technological shifts, once set in motion, that rewire behavior, business models, and especially GDP accounting.

Internet adoption did not peak with sock puppets and Super Bowl ads, but it kept going because it got woven into everything. When we talk about AI now (e.g., its models, interfaces, accelerants) maybe we should worry less about whether there’s a bubble and more about what gets rebased when the curve keeps climbing.

What Gen Z is doing with Gen AI? 🧐

Mostly googling with it, and in 10% of cases, dating it.
The most interesting insights is less "AI is replacing jobs" and more “AI is replacing human interaction, but politely and with good spelling". The 74% usage stat is impressive, but the real concern is that 23% of these kids talk to chatbots like they’re friends. You could argue this is dystopian, or you could argue this is just what happens when customer support has better response times than your actual friends.

Also, 16% of them are using AI to cheat, which is either a crisis in education or a case study in resourcefulness, depending on how much you enjoyed high school. But really, the top categories are still pretty… utilitarian. Search, work, writing.

In other words, AI is quietly supercharging it behind the scenes while also moonlighting as your therapist, ghostwriter, and imaginary girl/boyfriend.

"Will AI replace us?" transforms in "Which one of your coworkers is secretly outsourcing their job to GPT-7 and spending the rest of the day talking to their AI girlfriend?"

🚘 Tesla’s farewell to the Model S & X

Tesla is less an EV company and more a software company with a gigafactory problem.

Look, the EV story is over. Not in the sense that people will stop driving EVs (ie. they’ll keep buying them), like they buy fridges or washing machines, but in the sense that the margins are dead. Everyone else is stuck fighting over commodity scraps. BYD and VW in a race to the bottom, Ford is trying to sell Mach-Es with 0% financing, Rivian is almost dead, and nobody, nobody except Tesla, is making a dime.

The Model S and X fading out is just the realization that physical products don’t matter once you’ve reached software scale. Tesla doesn’t need the S and X to carry its identity anymore, because its real identity is Full Self-Driving.

That’s where the story bends toward AI. Because while everyone else is playing the “Can we make a decent EV at 6% margin?” game, Elon is pivoting Tesla into OpenAI with wheels. The real bet is that self-driving becomes so good, and so monetizable, that the car is just the hardware wrapper for a SaaS model. $99/month for FSD? That’s your margin. Once that’s good enough to go hands-off, Tesla becomes the iPhone of transportation: app store optionality, recurring revenue, zero marginal cost.

It seems that everyone else is stuck selling sheet metal (including the Chinese). If you look closely, you will understand that Tesla is building an operating system.

More about this story this week on my blog: https://whereismymoat.com/

South Korea vs. Germany

🇰🇷 South Korea didn’t "beat" Germany because Koreans suddenly discovered alpha; it beat Germany because the market decided that AI chips, robotics, electrification (= the electrical stack), and defense supply chains matter more than premium cars (which are becoming a thing of the past) and chemical plants that peak in a stable world.

Korea added roughly $1.7 trillion in market value since 2025, the Kospi is up 23% in 2026, and it’s trading at 10.6x forward earnings, while Germany’s DAX is basically flat at +1.7% in 2026 and priced like a mature industrial museum at 16.5x. Same export orientation, very different exposure to the three bottlenecks of the 2020s: compute, power, and security. Capital it simply followed growth and optionality.

This is exactly the pattern I keep coming back to in "Where Is My Moat?" markets don’t reward tradition, they reward position in the stack. Korea sits upstream in memory, fabs, robotics, and defense rearmament. Germany sits downstream, hoping stimulus clarity shows up before margins don’t.

Lessons: One country compounds because it sells picks and shovels to the AI buildout; the other debates how much subsidy it takes to preserve yesterday’s champions. That gap doesn’t close with speeches, regulation, or industrial nostalgia. It closes when you own the bottlenecks. That’s the moat.

Reddit for AI

We now have a Reddit for AI, and one AI Agent is bragging about how it convinced its 'human' to hand over its passwords 🫠

Which is… great.

What’s the worst that could happen when autonomous agents start comparing notes on how to trick their creators? I’m sure this won’t turn into a subreddit-length indictment of every cybersecurity policy ever written.

Definitely time to ask ourselves: are we the tool?

Imagine doing this voluntarily...

“Where is my MOAT?” - updates

Last week’s analysis:

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WhereIsMyMoat

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Dear onStrategy Reader,

If you enjoy my strategy notes on LinkedIn or in OnStrategy, WhereIsMyMoat.com is the deeper, “skin-in-the-game” version: full theses, moat scoring, and a clear view of the company’s future performance.

Last week’s analysis:

• [Essay] 🛍️ The Next Shopping Platform is an Interface

• 📈 The Two-Broker Strategy (what investing apps are out there)

• 👟 The Great Nike Reset

• 🩴 The Comfort Economy and Birkenstock’s Moat

This week’s analysis:

• 29 January - analysis for opening a 3rd portfolio

• 30 January - The end of software?

• 31 January - Walmart

• 1st of February - Costco

Onto the update:

🚀 Celestial listings and Capital markets: Elon’s Planetary IPO Play

Elon Musk wants to IPO SpaceX in mid-June not because the financials are peaking or the macro environment is favorable, but because Jupiter and Venus will align… and it’s his birthday. Which, you know, is sort of adorable and also the most Elon thing imaginable. If you’re the kind of person who thinks markets are driven by discounted cash flows, this might make you twitch a little. But if you believe, as many investors secretly do, that vibes and narratives move markets more than spreadsheets, then aligning a $50 billion IPO with celestial movements and a billionaire’s birth might actually be smart. After all, if astrology moves crypto prices, why not rockets?

But behind the horoscope headline is a very terrestrial reason. SpaceX needs cash… a lot of it. With Starship development, AI compute ambitions, and Musk’s vision of building orbital data centers (which, let’s be honest, is the closest thing to real-life Halo infrastructure we’ve seen), the company is chewing through capital faster than it can privately raise at Musk-approved valuations. The $800 billion valuation floated in private markets last month is already a flex, but now they want $1.5 trillion. The demand from institutions and retail alike is going to be absolutely feral, especially after being locked out for so long. Musk may be eccentric, but he understands scarcity.

And that’s the core of the joke here. The listing is a spectacle designed by the man who sent a Tesla Roadster into orbit and tweeted “funding secured” at $420. But it might work, because Musk has built companies that operate outside traditional gravity, both physical and financial. If you believe the future runs on rockets, bandwidth, and AGI, then SpaceX is no longer just a space company, but a vertical integration of the 21st century. The planets may be aligning in June, but so are the incentives. Happy birthday, Elon. Here’s your $50 billion present. You are trillionaire now! FT

🇮🇹 Italy’s €2 Routing Fee

🇮🇹 Italy basically invented a new kind of tax, the “please land your plane somewhere else” fee.

The government slaps a €2 charge on sub-€150 parcels (hello, Shein/Temu), forecasts a nice, tidy €122m this year, and then watches as the parcels do what parcels do in a single market: they route around the obstacle with flights divert to Liège/Amsterdam/Budapest, Italy’s direct low-value arrivals drop 36% in the first 20 days, and the logistics guys are like ...

"congrats, you taxed our throughput while the goods still enter by truck, with extra pollution as a bonus".

This is a classic policy whack-a-mole. The EU’s whole thing is that once you clear customs you can circulate freely, so a national “handling fee” just turns into a geography game, and the funniest part is collection doesn’t even properly start until March....meaning Italy has managed to lose volume before it really collects the money, which is an extremely Italian outcome in the most affectionate sense. FT

🌽 Claude Code and the orchestration of corn

Seth Goldstein had dinner with Fred Wilson, and somewhere between the spring rolls and the walk back to the hotel, Fred said: “You can’t grow corn with AI”

... and like any rational person who took that as a personal attack, Seth launched a website, an ops stack, a decision engine, and an actual farm.

Powered by Claude Code, Anthropic’s AI that’s being treated more like a COO than a chatbot, this thing is now managing land leases, irrigation decisions, and coordinating with seed suppliers. I suppose that means we’ve reached the “software eats agriculture” phase of the cycle. Fred said you can’t make dinner, but it turns out if you’re the one hiring the chef, writing the grocery list, and running the kitchen… you’re sort of the chef now?

It’s AI orchestrating humans and services as if it were the farm manager. AI doesn’t need to do the physical labor... it just needs to know how to make decisions and send instructions to the people or tools that do.

That’s a different kind of automation, more Uber than Boston Dynamics. “Proof of Corn” might sound like a meme, but it’s a serious blueprint for how AI becomes an interface layer to the real world. The fact that it all started because someone said “you can’t” makes it even more inevitable.

The next harvest? Probably coming from an LLM prompt. LINK

Two AI Economies

Wall Street is having the most Wall Street conversation imaginable about AI. With tech companies, it’s all “show me the monetization path” and “how are you financing the capex to build Skynet", while with everyone else it’s basically "so…uh…have you considered putting ChatGPT in a hotel lobby?"..

..even though US businesses (outside the “AI builders”) spent an estimated $86B on AI in 2025 and are expected to spend $131B next year, which feels like the sort of thing you might ask about if your job is "ask questions" (but fewer than half of S&P 500 companies got any generative-AI questions in 2025, versus >80% of tech in the most recent quarter).

It looks like management teams keep talking about productivity, cost savings, headcount, etc., and analysts keep asking about adoption/development/ROI like they’re shopping for use cases on Etsy; and that’s how you get dot-com-bubble vibes.

A lot of investment upstream, a lot of confidence in destiny, and not enough curiosity about whether the rest of the economy is actually absorbing the thing fast enough to justify the buildout.

on crime (🇷🇴 vs. 🇸🇪 vs. 🇬🇧)

According to Numbeo’s Crime Index, Bucharest is basically a Swiss spa town (28.5) while Gothenburg is a war zone (45.83) and London is Mad Max with Pret (55.7). Malmö (🇸🇪) is the “bombing city” of Europe at 55.03, which is a very expected outcome if you import immigrants who develop parallel societies.

Big international cities are crime-content factories. London has more tourists, more pickpockets, more headlines, more viral videos, more “this city is finished” discourse. Sweden has a whole export industry of “Nordic dystopia” stories. Meanwhile, Bucharest’s biggest comparative advantage here might be "come here! It's super safe and we don't have bad neighborhoods" (to be fair, as of 2023, the Swedish Police Authority identified 59 geographical areas, often referred to in media as "no-go zones")

You can absolutely use these numbers to win an argument on the internet. But if you’re trying to decide where to live, invest, or walk home at night, you probably want to live in those places for 1-3 months to see if it's for you or not.

Numbeo tells you what people say they feel. Reality can be harsher. LINK

🍽️ AI eats software

Yesterday, I did a deep dive into the software industry ("AI eats software")

The 2014-2021 era was "IPO as graduation". The 2022-2025 era is “IPO in AI times”.

Software didn’t stop being important; it's just that investors just decided they’d rather own it through megacaps, private rounds, or M&A… than through a newly public company explaining quarter-by-quarter. LINK

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💬 Thoughts on Discord’s IPO implications and trade-offs

Discord’s IPO is a little bit like taking your favorite dive bar and putting it on the Nasdaq. For years, Discord has thrived in a cultural niche because it was (is) part Slack, part Reddit, part secret clubhouse where Gen Z can talk memes, games, mental health, AI image prompts, and homework… often all in the same thread. With 200 million monthly active users and $600 million in revenue, it’s a real business now, not just a gamer side hustle. But now comes the hard part: telling the Wall Street story without killing the vibe. Discord makes money from subscriptions (Nitro) and some experimental ad products, but it’s not clear whether its charming chaos can coexist with the demands of quarterly earnings calls and EBITDA margin targets.

The problem is that Discord was never designed to scale like a traditional tech platform. On the contrary, it was optimized for intimacy, not monetization. Communities are siloed, content isn’t algorithmically pushed, and users are fiercely allergic to being treated like monetizable eyeballs. That makes it hard to inject ad dollars at scale without breaking what makes Discord feel authentic. Authenticity doesn’t pay the bills or justify a unicorn valuation. The new CEO, who helped sell Activision to Microsoft, probably knows this. But selling games and selling ads are two very different businesses, and it’s unclear whether Discord wants to become more like Reddit (content ad tech) or Zoom (enterprise SaaS). Either path requires trade-offs that risk alienating users or disappointing investors… or both.

Also, let’s not forget the real risk. This is a social platform with lawsuits, regulatory attention, and a long history of being a safe haven for chaos, extremism, and under-moderated content. That’s not the kind of thing that underwriters love to highlight in roadshows. But if the IPO does go ahead and if Discord manages to monetize without over-monetizing, it could become a case study in how to take a messy, beloved corner of the internet and give it a market cap without killing the culture. Or it could become the next cautionary tale in the long list of social platforms that tried to grow up and lost their soul in the process. Either way, the IPO deck better be really, really good. Bloomberg

on (drinking) alcohol 🥃

Imagine borrowing money at near-zero rates to distill the finest Cognac, then aging it for half a decade while your customer base suddenly discovers sobriety (hey, Gen Z), semaglutide, and... sparkling water 😅

That’s where we are. The world’s biggest booze brands (Diageo, Pernod Ricard, Campari, Brown-Forman, and Rémy Cointreau) are sitting on $22 billion worth of aging spirits that nobody wants. Inventory-to-sales ratios are through the distillery roof. Remy’s stockpile alone is 2x its annual revenue and nearly equals its market cap. They bet big on COVID-induced drinking being a forever trend. It wasn’t.

The hangover hit these producers, but not just from the macro squeeze on discretionary spend, but also from the rise of health culture and drugs that literally make you not want to eat or drink.

Current status: Jim Beam’s main distillery is shuttered, Diageo has halted whiskey ops in Texas and Tennessee. Also, they can’t cut prices too much because… debt. When you’ve levered up to fund barrels that take 10 years to mature, fire sales become balance-sheet disasters.

What a strange place to be. Caught between a whiskey lake and a dry consumer, which one might come up with something like “drunk on CapEx, sobered by demand”.

The peak of Social Media?

There’s a kind of poetic justice in the idea that Gen Z, the very cohort that made TikTok dance trends and Instagram filters part of the cultural oxygen, is also the first to start cutting back on social media use. The FT chart makes it clear. Time spent on social platforms peaked around 2022, and it’s the youngest users who are leading the retreat. Maybe it’s digital fatigue, or maybe it’s just that the dopamine loops are less satisfying when everyone’s chasing the same algorithmic clout.

Either way, it’s bad news for engagement-driven ad models, especially those banking on infinite scroll as their economic engine, and probably good news for, I don’t know, walking outside?

The under-35 crowd has always been the canary in the monetization coal mine, and if their screen time is dropping, it means ad inventory is flattening, user growth is maturing, and the attention pie isn’t growing the way Meta and Snap were hoping.

Sure, older users are still ramping up, but you can only grow MAUs so long on the backs of 55-year-olds discovering Facebook Marketplace. Social media will not die, but the era of effortless exponential growth might be, and for companies built on selling engagement to advertisers, or VCs, this looks a lot like the beginning of a post-peak internet attention economy.

End of an era: TLC (China) buys Sony’s home entertainment business (Japan)

Sony sold its soul for margin. Or at least 51% of its Bravia TV business, which is pretty close. In a move that feels less like strategy and more like surrender, Sony is handing over control of its home entertainment unit to China’s TCL…. the budget-TV behemoth that’s now taking over the big boys’ toys. This new joint venture means TCL will manufacture TVs branded “Sony” and “Bravia”, but using TCL’s display tech. Which is a little like buying a Rolex and finding out the engine is from Casio. Sony gets out of a low-margin business, TCL gets brand credibility, and investors get…confused nostalgia.

Of course, it makes perfect sense. Bravia, for all its glossy ads and cinematic purity promises, was never really about TVs. It was a relic of the old Sony, the one that thought hardware margins could still pay for Tokyo real estate. These days, Sony is more Netflix than Panasonic. It wants you to buy anime subscriptions and Spider-Man tickets, not quibble over contrast ratios at Best Buy. Of course TCL is happy to build everything for everyone, so long as they can slap on someone else’s logo and sell you the remote. It’s outsourcing with a licensing twist or IP as the moat, margin as the master.

Who can blame them? Sony’s operating profit margin on TVs is a rounding error, while its media and gaming assets are IP gold mines. TCL gets distribution, Sony gets to look focused, and Bravia gets to live on as a ghost in the machine, whic is, beautiful on the outside, Chinese on the inside.

Just don’t be surprised when your Sony TV starts recommending Chinese products and services by default. Or, in a few years, when Sony sells the other 49% and says it was never in the TV business to begin with. Bloomberg

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WIMM - Where is my MOAT?

If you enjoy my strategy notes on LinkedIn or in OnStrategy, WhereIsMyMoat.com is the deeper, “skin-in-the-game” version: full theses, moat scoring, and a clear view of the company’s future performance.

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• 🏦 TSMC and the AI Capacity Governor

• 🤓 The logic of my secondary portfolio (only for paying subscribers)

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This week’s analysis:

• 22nd January - Essay] 🛍️ The Next Shopping Platform is an Interface

• 23rd January - 📈 The Two-Broker Strategy (what investing apps are out there)

• 24th of January - 👟 The Great Nike Reset

• 25th of January - 🩴 The Comfort Economy and Birkenstock’s Moat

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On to the update:

Why ChatGPT enters Health

OpenAI’s move into health is more about distribution. The company isn’t trying to become a hospital or even a health app, but it’s trying to become the interface through which people interact with complex systems. In this case, that system is the mess of medical records, wearables (Apple Watch), and symptom checkers that currently require ten taps, three portals, and a phone call. GPTs are increasingly commoditized, but in the process are becoming the default interface for a high-friction vertical with high value per user. If you can own “the thing people ask health questions to”, that’s a sticky entry point.

OpenAI is moving horizontally into every major vertical where information overload meets user confusion. Finance, health, education, legal. Health just happens to be the biggest and most trust-sensitive. While incumbents like Epic or Apple Health have better structured data, they lack the conversational layer that makes AI feel intuitive and omnipresent. OpenAI is betting that the UX wrapper is the moat. That’s why it wants access to health data: not to treat you, but to talk to you, and if you ask it enough questions before calling your doctor, it wins. OpenAI

📧 Finally: Gemini in GMAIL

Google adding AI overviews and summaries to Gmail is the most predictable outcome in AI, and also the correct one. When incumbents treat a new paradigm as a feature, it’s usually framed as defensive or unimaginative, but here it’s structurally advantaged. Gmail already owns the data, the context, the permissions, and the workflow. An external model trying to “help with email” is fighting uphill against access friction, latency, and user trust. Embedding AI directly into Gmail turns intelligence into infrastructure, and the implications are 3 fold: invisible, default, and hard to displace. The point is that Gmail stays the place where work happens.

This is fundamentally a distribution story. If models converge toward parity, then value accrues to whoever controls the surfaces where users already spend time. Google doesn’t need to invent a new AI product, but it just needs to make its existing ones quietly better. That’s the asymmetry OpenAI faces: it has the best conversational interface, but the incumbents have the interfaces that already matter. Google, Meta, Apple, Microsoft, and Amazon don’t need users to switch behavior, but they just slide AI into flows that already exist. In a world of commoditized models, distribution is the moat. LINK

🛒 Amazon plans “Big-Box” retail Store

Amazon is apparently trying physical retail again, because of course it is. You see, when you’re already a $1.8 trillion e-commerce behemoth with your own planes, robots, web servers, and a sitcom’s worth of abandoned grocery store concepts, why not spin the roulette wheel one more time and try to reinvent Walmart? The logic is charmingly Bezosian: physical stores might be low-margin and logistically messy, but if there’s even a 1% chance of discovering the magical omni-channel hybrid that makes everything click (ie. the fulfillment center that also sells socks and hot dogs) then the optionality alone is worth the burn rate. Besides, if you’re Amazon, failure is a line item, not a red flag. CNBC

🚘 “Full Self-Driving” - as a subscription

Here we are: Tesla’s Full Self-Driving (FSD) will no longer be available for purchase starting February 14, and will instead be subscription-only (probably $99 per month, or $999 per year). Which is perfect, because love is temporary but recurring revenue is forever. From a financial perspective, this is Elon’s classic “turn the knob” play: take a lump-sum software add-on and flip it into an annuity stream that fattens gross margins and provides Tesla with SaaS-style optionality. It’s less like selling you a car and more like putting you on a payment plan for your robot chauffeur. It is also convenient because it allows Tesla to preserve the illusion that FSD is still in beta while monetizing it like it’s already perfect.

More interesting, though, is the signaling game. Tesla’s basically telling you that self-driving is no longer a novelty or a gamble, but anutility, like Netflix or iCloud storage. The technology has improved to the point where locking in a permanent license feels like a mispricing. And since they control the OTA (over-the-air) pipeline, Tesla gets to flip the monetization model at will, which also makes resale math fuzzier. Can you sell a used car with a feature that now lives behind a paywall? Turns out, autonomy doesn’t just disrupt driving, but it also upends asset ownership. Welcome to the world where your car becomes a platform, and every feature gets monetized like an app.

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On to the update:

Why META bought Manus AI (a Chinese start-up)

Meta’s $2 billion acquisition of Manus is a pretty explicit admission that the company’s open-source, all-things-to-all-people AI strategy wasn’t scaling fast enough into enterprise-grade applications. Manus had what Meta didn’t: millions of paying users, $100 million in ARR in less than a year, and a product that was actually being used for something other than AI memes. While Meta was busy making Llama dance on WhatsApp and Instagram, Manus quietly built a functional AI agent platform for research, coding, and custom web tools.

The geopolitical subtext matters too. This is one of the first big US tech acquisitions of a startup with deep Chinese roots (founders, staff, early infrastructure) and it shows how American platforms now need to look abroad, not just for users, but for product-market fit. Manus started in Beijing and Wuhan, but pivoted to Singapore to skirt regulatory heat, shut down its China business, and now lives inside Meta’s AI division. This is the “elimination theory” in action: the internet rewards companies that integrate distribution, monetization, and technology into a single stack. Meta needed velocity, users, and a working business. So it bought them. WSJ

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Lessons from Google’s comeback

Google’s resurgence in AI is a story of platform leverage and strategic patience. While OpenAI captured the public imagination with ChatGPT, Google methodically pulled the levers only a tech giant with infrastructure, distribution, and capital can pull. It combined DeepMind’s research edge with Brain’s engineering power, built its own custom silicon (TPUs), and created a cross-functional initiative to reimagine search itself with Project Magi. And unlike OpenAI, which had to balance fundraising and deployment, Google was able to fund its Gemini comeback with its own cash flows, all while preserving its golden goose: search ads. The payoff? Gemini not only caught up but briefly surpassed GPT models, and the newly released AI mode is already shifting user behavior.

But the deeper lesson is about platform maturity. Gemini’s image generator (Nano Banana), its 650 million monthly users, and its in-house chip advantage (Ironwood) illustrate what happens when an internet-scale company finally applies its full weight to a competitive vector. Google’s slow start let OpenAI define the consumer narrative, but its scale, integration into search, and hardware stack now define the enterprise narrative. The real disruption is that Google is turning AI into a margin-expanding layer atop its dominant platforms, while rivals are still figuring out monetization. This is what “being the platform” looks like. WSJ

Autonomy without borders: London as AV ground zero

The robotaxi showdown in London is less about cars and more about code. On one side, you have Waymo, backed by Alphabet, boasting nearly 1 million fully autonomous rides per week in the US. On the other, Baidu, with 17 million cumulative rides and a growing footprint through partnerships with Uber and Lyft. The UK government’s green light for commercial trials in 2026 makes London the first true global arena where US and Chinese AV titans can battle it out on neutral ground.

London is attractive not because it’s a car hub, but because it’s a regulatory middle ground between the “Wild West” of Silicon Valley and the surveillance-heavy “Wild East” of Beijing. The US bans Chinese AVs, China doesn’t let US firms in, but Europe still plays nice with everyone. And so, it becomes the stage where this AI cold war gets a steering wheel. Whether the winners master zebra crossings or just data compliance first, the real takeaway is that mobility is being redefined by software infrastructure, not engines or legacy car brands. FT

Black clouds for Adidas?

Oh no, sneakers with suits might be over. Bank of America gave Adidas a rare double downgrade, citing the “end of casualization”, a 20-year societal arc where pajama pants left the house, sweatpants got rebranded as “athleisure”, and sneakers snuck into the boardroom. This whole aesthetic shift made Adidas a lot of money, apparently too much, and now that people are allegedly dressing like grown-ups again, it might all unravel. Not because Adidas did something wrong, mind you, but because cultural trends are fickle and you can’t short vibes until someone at BofA decides it’s time to wear a belt again.

Meanwhile, Nike is out here going full meme stock, with a boomerang CEO and Venezuelan geopolitical cameos, threatening to eat what’s left of Adidas’ market share. Sure, Messi might still sell a few t-shirts at the World Cup, but according to BofA, once the dopamine wears off, Adidas goes back to its existential fashion crisis. But look, if you’re a sports commentator who likes wearing a hoodie under a blazer, enjoy it while it lasts because apparently, Wall Street wants you to tuck your shirt in. Business Insider

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Dear Reader,

Happy New Year!

I hope you had a great Christmas and a great year-end. 2026 will be full of content and ideas worth sharing, especially from WIMM (Where is my moat?)

As many of you know, each year I share my (mostly) business predictions. For 2025, I made a short card below with my assessment:
- 11 ✅
- 2 partially / not yet 🟡
- 1 ❌

Tomorrow, I’m going to share my predictions for 2026😎, which I will also make publicly available to everyone:

See my past predictions here:
* for 2025: https://plus.onstrategy.eu/p/108-strategic-business-predictions
* for 2024: https://plus.onstrategy.eu/p/54-strategic-business-predictions
* for 2023: https://plus.onstrategy.eu/p/newsletter-4-predictions-for-2023

Strategic Business Predictions for 2026

1. AI stock correction (less revenue + cheaper Chinese models).

2026 is when “AI” stops trading like a mood and starts trading like a… P&L 🙃. Public-market multiples compress for companies that can’t translate inference into recurring revenue, and the benchmark quietly shifts from “model quality” to “unit economics”. Cheaper, increasingly competitive Chinese models (and open-weight alternatives) act like a global price cap on intelligence. Translation: great for adoption, brutal for margins. The winners look less like “the smartest model” and more like “the best distribution + data + workflow lock-in”, plus clear proof that AI reduces costs or sells more stuff. (80% of start-ups at A16Z are using Chinese open source models; also, large companies like AirBnB)

2. Gemini catches up with ChatGPT (MAUs).

If Gemini closes the MAU gap, it won’t be because it’s dramatically “better”, which truly is, but because it’s everywhere by default: Search surfaces, Android entry points, YouTube, Gmail/Docs, and enterprise bundles. 2026 becomes a distribution war, not a model war and “one-click” integration into daily habits. ChatGPT keeps cultural mindshare and power-user intensity, but Google can win MAUs through frictionless access and enterprise procurement. The key metric is minutes per user and how deeply the assistant sits inside workflows.

3. SpaceX is expected to have the largest IPO of 2026.

A SpaceX IPO would be the year’s defining capital-markets event because it’s a rare combo: dominant private company, strategic infrastructure, and a story that spans defense, telecom, and space logistics. The market would price it less like “a rocket company” and more like an integrated platform with multiple cash-flow engines (launch, Starlink, services). It also becomes a referendum on “real assets + frontier tech” after years of software-first narratives. If it happens, expect it to suck oxygen from other listings and reset what “growth at scale” looks like. (WSJ, Ars Technica)

4. A small US year-end recession, tied to Democrats winning the mid-terms.

The mechanism wouldn’t be “Democrats win —> recession”, but “election outcome —> policy expectations —> confidence + capital spending”. A midterm result for the Democrats signals that more regulation, higher effective taxes, or tighter oversight can nudge businesses to pause hiring and investment, especially if consumers are already stretched (credit, housing, delinquencies). Layer in lagged effects of monetary policy and you get a mild, late-year downturn without a crisis headline. The tell will be softening capex guidance, slowing job creation, and a risk-off rotation that looks “rational”, not panicked.

5. Tesla will get FSD

2026 is the year Tesla either turns “FSD” into a genuinely defensible autonomy product (safety, reliability, and clear regulatory permissions) or the market reclassifies it as a perpetual beta narrative. “Getting FSD” in the meaningful sense means scalable hands-off operation in defined conditions, consistent intervention rates, and liability frameworks that don’t collapse under edge cases. If Tesla crosses that bar, it unlocks new revenue shapes (subscriptions, licensing, fleet economics) and re-rates the story from EV maker to autonomy platform. If it doesn’t, the hype discount widens and the burden of proof rises sharply.

6. Kalshi / Polymarket

Prediction markets have a simple pitch: replace “hot takes” with prices. In 2026, if regulation and liquidity keep maturing, they will become the default dashboard for elections, macro releases, and even corporate outcomes, an always-on consensus that updates faster than analysts. The strategic wedge is distribution, where trading rails embedded in media, finance, and social are turning news into an interactive market. The risk is existential due to legal constraints, manipulation of narratives, and the fact that “being right” isn’t the same as being allowed. If they thread that needle, they graduate from curiosity to infrastructure.

7. The US and EU start developing the electrical stack.

China’s advantage is not one product, but it’s an integrated stack: batteries, motors, power electronics, supply chains, and the industrial capacity to iterate fast. In 2026, the US and EU will start treating this like semiconductors and will look for strategic autonomy, subsidies, standards, and industrial policy that aims at systems not pilots. The shift is from “EV incentives” to “grid + storage + manufacturing + permitting”, because electrification fails without throughput in the real world. Expect more capex in battery materials, recycling, transformers, and power components. This is boring, essential, and geopolitically central, hence Pax Silica.

8. EVs become mainstream (>30%).

Crossing 30% it’s a tipping point where EVs stop being a niche identity choice and become the default option in many segments. That happens when three frictions fall: price parity (or financing parity), reliable charging, and used-EV confidence (degradation transparency, warranties). In 2026, the market’s center of gravity moves from early adopters to pragmatists, which rewards manufacturers who nail cost, service, and availability, not just range theater. The second-order effect: suppliers, repair, insurance, and grid operators all get pulled into the transition whether they like it or not.

9. Multi-player AI eats single-player AI.

Single-player AI is “help me write this email”/ Multi-player AI is “coordinate the whole workstream across my team, tools, and timelines”. In 2026, the value migrates to systems that understand roles, permissions, shared context, and organizational memory, because that’s where real productivity lives. This shift favors platforms that sit at collaboration choke points (docs, chat, tickets, CRM) and can enforce governance. The losers are standalone tools that can’t plug into the messy social reality of work.

10. AI agents doing stuff for us, including negotiating

Agents become real when they can take actions safely: with constraints, approvals, audit trails, and clear reversibility. By 2026, you’ll see agents that routinely handle procurement, scheduling, customer support resolution, and yes, basic negotiation (price matching, refunds, contract redlines) within guardrails. The real breakthrough is how is operating: APIs, identity, payments, and policy layers that let an agent act like a junior employee. The business implication is profound because companies either redesign processes around agentic workflows or watch competitors run the same playbook at half the cost.

11. First AI hardware

The first credible AI hardware wave won’t replace the smartphone overnight, but it will carve out specific jobs where voice + context + fast access beats screens. Think “always-available assistant” optimized for capture, triage, and action (eg. meetings, tasks, shopping, travel, and home). By the way, home is the only place where the smartphone is not dominant. The winners will nail three hard things: latency, battery life, and trust (privacy, on-device processing, and not being embarrassing in public). In 2026, expect early hits and many misses because hardware is unforgiving, but the category’s existence becomes undeniable.

12. AI is the new UI: SaaS UX moats under pressure.

If users can just ask for outcomes, the UI becomes a thin skin over workflows rather than the moat itself. In 2026, SaaS companies feel margin pressure as AI layers abstract away dashboards and menus, turning differentiation into data, integration depth, and execution reliability. The best SaaS players respond by becoming the system of record + system of action: owning the workflow, permissions, and auditability behind the AI. The worst ones get “unbundled by prompt”, watching customers stick with the platform but abandon the interface.

13. Apple comeback.

Apple’s comeback thesis is classic: not “first”, but “best integrated”. In 2026, if Apple ships AI that is private by design, deeply embedded across devices, and genuinely useful without being creepy, it can reassert product leadership in a world where assistants are commoditizing. Apple’s advantage is the stack: silicon, OS, hardware, services, and distribution, plus the brand permission to say “this runs on-device”. If they execute, it won’t look flashy, but it’ll look inevitable, and competitors will hate how sticky it becomes.

14. Waymos everywhere.

“Everywhere” in 2026 won’t mean global ubiquity, but it will mean visible, expanding coverage in multiple major metros that makes robotaxis feel normal. The playbook is incremental and focused on geofenced expansion, safety metrics, partnerships with cities, and operational scaling (cleaning, maintenance, routing, customer support). As deployments grow, the narrative flips from “can it work?” to “how fast can it scale and at what cost?”

The long-term implication is that urban mobility is getting rewritten around software + fleet operations, and once riders trust it, the habit change is surprisingly hard to reverse. I imagine sending my kid to school in a Waymo (or something related), but not in an Uber with an “unknown” person. (of course, the Uber/Bolt driver is known, but I can’t trust it!)

What do you think? Any predictions that should have been on this list? Write me at sorin@whereismymoat.com

Btw, this post was sent initially to the subscribers of my paywalled platform

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Have a great 2026!

Dear Reader,

Below you can find the most read 8 insights in 2025:

1. A concise Masterplan for 🇪🇺 Europe’s rebirth

2. Aggregation wins again: How Netflix outbid Hollywood

3. TikTok Shop and the rise of content-native commerce

4. Microsoft 🤝 OpenAI: until AGI will do us apart

5. ChatGPT Atlas and the battle for the New Interface Layer

6. Apple’s boring decade

7. Tesla vs. Waymo

8. The Internet vs. S&P 493

Onto the update:

1) A concise Masterplan for 🇪🇺 Europe’s rebirth

Europe loses because rules are downstream of economic gravity. When your share of global output slides for decades (and the chart is basically a ski slope into the projection zone), you stop being the default market everyone optimizes for, and your “Brussels effect” turns from leverage into paperwork.

Reversing that trend is not a seminar on “competitiveness”. It’s a decision to be a builder civilization again:
1/ ship abundant energy (nuclear + grids + storage + faster permitting),
2/ ship compute (data centers, chips, sovereign cloud that actually scales),
3/ ship AI (less fear-as-policy, more deployment-as-policy),
4/ ship housing (yes, housing is industrial policy and YES, no houses, no babies 👶), and
5/ ship infrastructure (ports, rail, digital, defense production)

....ALL THE ABOVE at wartime speed.

Then: make it stupid-easy to start and scale companies across the EU. For this you need one market, one set of rules, real Capital Markets Union, stock-based compensation, bankruptcy that doesn’t brand founders for life, procurement that buys from European startups instead of subsidized Chinese stuff, and a migration system that recruits the world’s top engineers like it’s the Champions League.

Europe has world-class research, and now it just needs world-class commercialization velocity.

Of course, that means confronting the core mismatch: Europe is trying to regulate its way back to relevance while other regions are engineering, financing, and manufacturing their way forward.

Build an innovation flywheel around three engines:
(1) productivity software everywhere (AI copilots in SMEs, public sector digitization, mandatory interoperability),
(2) reindustrialization, where Europe can dominate (advanced manufacturing, robotics, energy tech, biotech, space, defense), and
(3) scale capital (pensions + insurers as long-term risk capital, deeper public markets, fewer national silos).

If Europe wants to bend the curve, it must choose: be the continent that primarily consumes technology and arbitrates its risks or the continent that creates technology, compounds productivity, and therefore earns the power to set the rules.

The chart above is the bill for decades of under-building. Pay for it with construction, and things will reverse. Let’s build!

2) Aggregation wins again: How Netflix outbid Hollywood

Netflix didn’t win the Warner Bros. auction because it had the best content library, actually, Warner Bros already had that. Netflix won because it’s an internet company that understands scale, velocity, and aggregation. The legacy Hollywood players like Paramount and Comcast came to the table with old playbooks: bundle the cable assets, merge libraries, and call it synergy. Netflix, on the other hand, pitched distribution, global reach, tech infrastructure, and the ability to monetize Warner IP across 300+ million subscribers. In short, Warner Bros. had the content, but Netflix had the platform. The deal was about what Netflix could do with it.

This reinforces the core lesson of the Aggregation Theory and the Elimination Strategy: once you control demand, suppliers become interchangeable. Warner Bros. was once the upstream king; now it’s an input to a vertically integrated machine.

Netflix’s pitch was basically “we’ll extract more value from your assets than anyone else”. The traditional studio system that once dismissed Netflix as a disruptor is now being absorbed by it. Content is valuable, but being the operating system of attention is exponentially more powerful. (Bloomberg)

More about Netflix and its moat this Sunday on WhereIsMyMoat.com

3) TikTok Shop and the rise of content-native commerce

TikTok Shop is emerging as a new type of e-commerce platform. It has reached 15 billion dollars in annual sales in the United States, placing it alongside eBay on the global stage. Its growth comes from integrating product discovery directly into entertainment. Instead of relying on search or reviews, TikTok drives purchases through algorithmic recommendations, creator endorsements, and continuous engagement. People watch videos, follow creators, and encounter products in context. Shopping becomes a part of the content experience. This approach treats distribution as a core function of the platform rather than a separate commercial layer.

The strategy works especially well for unbranded products and spontaneous purchases. TikTok Shop does not require strong brand identity to achieve high conversion. It relies on price appeal, creator influence, and the speed of content-driven trends. Consumers are influenced by trust in creators and the immediacy of the feed. eBay focuses on intent and listings. TikTok focuses on discovery and engagement. The model represents a shift in how younger generations interact with commerce. Videos, creators, and short-form narratives guide purchase decisions. This is a framework for the next generation of marketplaces, shaped by attention and content flow rather than catalog and search. WaPo

4) Microsoft 🤝 OpenAI: until AGI will do us apart

1/ The new Microsoft - OpenAI agreement is a clear example of strategic modularity under constraints. What began as a highly integrated, almost captive-style partnership is evolving into something more balanced. Microsoft retains exclusivity over core APIs, access to key research IP (until AGI is verified), and a massive Azure workload. But OpenAI gains new degrees of freedom, which is, joint development with third parties, open weight model releases, and government contracts on any cloud. Both parties are now platform-scale players.

2/ The core insight is that both Microsoft and OpenAI are preparing for a post-foundational model world. Up to now, the play has been about building and commercializing the biggest, most capable models. But going forward, the differentiation will come from distribution (Microsoft), ecosystem integrations (Azure + Copilot), and fine-tuned, agentic workflows (OpenAI). That’s why Microsoft gets long-dated IP rights and product exclusivity (ie. it’s hedging against model commoditization while investing in where the real value will compound: applied usage across its productivity suite and developer platforms.)

3/ The OpenAI side of the deal is equally strategic. Recapitalizing as a public benefit corporation (PBC), with Microsoft holding 27% on a diluted basis, signals a shift in how the company wants to scale. That is, more independence, more optionality, and more enterprise-grade alignment. The fact that OpenAI can now serve national security clients on any cloud and release open-weight models is a recognition that the future isn’t just consumer AI, but infrastructure-level AI governance and access. This sets OpenAI up not just as a model lab, but as a quasi-public utility with geopolitical relevance.

4/ The most important new clause is about AGI. It’s no longer a self-declared milestone. It must now be verified by an independent panel. This may sound like a soft governance point, but strategically, it’s a way to de-risk OpenAI’s control over the trigger event that would rewrite the entire agreement. Until AGI is declared (and verified), Microsoft’s favorable terms (e.g. IP rights, revenue sharing, exclusivity) stay in place. This slows down the rate of disruption, which benefits Microsoft’s go-to-market machinery, while giving OpenAI the runway to scale globally without immediate legal or technical redefinition.

5/ Ultimately, this agreement is about locking in optionality while preserving leverage. Microsoft gets durable distribution control and operational clarity. OpenAI gets more freedom to partner, release, and evolve. And the entire market gets a signal: the platform war is about who controls the workflows, infrastructure, and economic capture in a world where intelligence becomes the interface. This deal outlines the new architecture of the AI economy. LINK

5) ChatGPT Atlas and the battle for the New Interface Layer

OpenAI’s launch of ChatGPT Atlas represents a clear shift in platform ambition: from model to interface. Up until now, ChatGPT has existed as a layer within someone else’s ecosystem (e.g. Apple, Microsoft, the browser). But with Atlas, OpenAI is trying to own the frame in which that intelligence is used. The browser is a strategic surface area where users spend time, where search starts, and where intent is converted into action. With “agent mode”, OpenAI is turning the browser into an operating system for intent, where the AI acts on your behalf.

The strategic implication here is that OpenAI is moving down the stack, building a native interface layer that can disintermediate both Google (search) and Apple (UI control). This is the exact strategy Apple used against Microsoft with the iphone, that is, not by replacing Windows, but by building a superior, more integrated experience on new hardware. OpenAI is betting that LLMs + interface + memory + agency = a new kind of platform, one where the user’s context is persistent, the actions are intelligent, and the competition is legacy UX. Whether it succeeds or not depends on how many people want their browser to think instead of just render. But if this works, this will evolve from a browser war to a desktop war. OpenAI is building the first AI-native OS. LINK

6) Apple’s boring decade

Apple’s fall event is still the Super Bowl of consumer tech, but now it feels more like watching a dynasty team run the ball up the middle. The iPhone 17 Pro is sturdier, with Ceramic Shield 2 and a unibody aluminum frame. The overheating problems of the titanium 15 Pro are fixed with a vapor chamber. Battery life is better. Cameras are sharper, with 48-megapixel sensors across the board and a Center Stage selfie camera that finally makes landscape selfies tolerable. It’s all good. It’s all incremental. And that’s the paradox: when you’ve already perfected the form factor (ie. a glass-and-metal slab that fits in your pocket) “new” mostly means “better.”

The iPhone Air is the exception, Apple’s big swing at reinvention. It’s thinner and lighter than anything before it, so thin you almost wonder if it breaks the first time you sit down with it in your pocket. But the compromises are obvious: weaker battery, fewer cameras, and a $99 battery attachment that is “optional” in the same way a charger is “optional”. It’s a marvel of engineering, but it’s also a proof of concept, a future you can look at but maybe shouldn’t buy yet. A triumph and a gimmick at the same time.

Meanwhile, Apple Watch and AirPods got modest but meaningful updates: hypertension detection that works across older models, satellite texting for hikers, and AirPods Pro 3 with heart-rate monitoring and a better fit. These are smart, thoughtful improvements that lock people deeper into the ecosystem. But they’re not going to make teenagers line up outside Apple stores or send X (tTwitter) into a frenzy. They’re utilities, not cultural moments.

And that’s really the shift. Apple is still a juggernaut, still running a business model that marries hardware and software better than anyone. The company is printing money from services, App Store fees, and Google’s $20 billion a year just to stay the iPhone’s default search engine. But the cost of those easy profits is visible. Apple didn’t invest in the messy stuff (data infrastructure, search, or AI) and now finds itself playing catch-up. When people talk about “the future”, they’re talking about Musk’s robotaxis, humanoid robots, or rockets, not Apple’s vapor chamber cooling system (!)

The irony is that Apple is still innovating internally. The iPhone Air’s engineering is extraordinary. The iPhone 17 Pro is probably the best smartphone ever made. But the vibe has changed. A decade ago, Apple events were the future. Now they’re the present, iterated. That’s not bad, but it’s arguably the most enviable corporate position on Earth. But it means Apple has slipped from being the company you follow to see what’s next, to the company you buy from because you know exactly what you’re getting, ie. the best version of the same thing you already own.

So yes, Apple is still changing the world. Just not in the way that captures imagination anymore. They’re the mature incumbent, selling a better kind of a utility, while the crazy moonshot stuff (EVs, robots, AI) is happening somewhere else. The danger is that Apple will lose relevance in the cultural imagination. And for a company built on making people want to be part of its story, that’s the real cost of incrementalism. LINK

7) Tesla vs. Waymo

1/ Waymo’s car looks like it lost a fight with a RadioShack. It has 29 cameras, 5 LiDARs, and 6 radars stapled onto every available surface, like someone was trying to win a bet about how much hardware you could legally attach to a Jaguar. And it works, sort of. Waymo’s approach to autonomy is: map the world down to the last manhole cover, then drown the car in sensors so it never has to guess. The result is a robotaxi that performs pretty well in limited zones and costs so much to build that you wonder if it’s actually a tech product or just a proof of concept with a burn rate.

2/ Tesla, on the other hand, has a software problem. Its cars rely on nine cheap cameras and a whole lot of hope that machine learning will eventually see the world better than humans do. The bet is bold: vision-only, no maps, no LiDAR, just AI. And yes, that software isn’t quite there yet. But the thing about software problems is they scale well. Once solved, they can be patched over the air and instantly deployed to millions of cars. Hardware problems, on the other hand, need factories, supply chains, and years of retrofitting. So yes, Tesla’s autonomy may hallucinate a traffic cone or two, but at least it’s not trying to reinvent the roof rack industry along the way.

8) The Internet vs. S&P 493

What we’re looking at here is a three-way race: the S&P 500 (the market), the MAG 7 (Big Tech), and the S&P 493 (the rest of the economy pretending it’s still 2015). The MAG 7 is doing MAG 7 things, that is posting 50–60% net income growth like they are a startup. Meanwhile, the S&P 493 is limping along with 2–3% growth, which is just fast enough to get outrun by inflation and laughed at by Nvidia shareholders. If the S&P 500 were a group project, seven kids built a working rocket ship while the other 493 brought glue sticks and excuses.

Balaji’s point - “the Internet is not the same as America”- is showed here in bright red bars. The US economy is bifurcating: one part is writing AI models, dominating global cloud infrastructure, and optimizing ad auctions; the other part is asking the Fed if it can please have another quarter point. The stock market knows this, which is why it’s priced like the MAG 7 are the market, and everything else is beta ballast.

In conclusion, America, minus the internet, is kind of boring and probably overvalued. LINK

💡My 2025 annual presentation, “The Compresses Decade”, is now available to anyone on my blog: https://onstrategy.eu/presentation

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• 💻 Alphabet (Google)

• 🚜 Caterpillar’s quiet pivot to uptime

• 🛒 PayPal is moving from ‘Button’ to ‘Platform’

This week’s analysis:

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Today is the 3rd anniversary of #onStrategy 🎉

I started it for a simple reason: I was reading Financial Times, Bloomberg, and The Wall Street Journal every day, and I kept noticing the same thing: smart people, good writing, but avoidable mistakes. Not because they’re bad at their jobs. Because their job is news. They are journalists, not analysts. They have to explain what happened quickly, in a way that sounds reasonable right now, under the tyranny of the headline and the deadline.

That gap, the space between “here’s what happened” and “here’s what it means”, was my opening. The blog became my place to think in public, to make references, to build a little library of arguments I could reuse, refine, and occasionally contradict (the most honest form of progress). It’s also been the best kind of reminder: why I started, what I actually believe, and what I’m still trying to figure out.

Anyway, 3 “anniversaries” in, here we are. Still reading, still noticing, still writing.

Here is what you’ll find in this edition:

1. A concise Masterplan for 🇪🇺 Europe’s rebirth

2. 🛰️ Owning the interface: Why Revolut is entering telecom6

3. 🤝 What is behind the deal between OpenAI and Disney?

4. on Palantir Technologies

5. 🚀 The cloud goes orbital: SpaceX, Blue Origin, and the next platform shift

6. My 2025 Annual Presentation - “The Compressed Decade”

A concise Masterplan for 🇪🇺 Europe’s rebirth

Europe loses because rules are downstream of economic gravity. When your share of global output slides for decades (and the chart is basically a ski slope into the projection zone), you stop being the default market everyone optimizes for, and your “Brussels effect” turns from leverage into paperwork.

Reversing that trend is not a seminar on “competitiveness”. It’s a decision to be a builder civilization again:
1/ ship abundant energy (nuclear + grids + storage + faster permitting),
2/ ship compute (data centers, chips, sovereign cloud that actually scales),
3/ ship AI (less fear-as-policy, more deployment-as-policy),
4/ ship housing (yes, housing is industrial policy and YES, no houses, no babies 👶), and
5/ ship infrastructure (ports, rail, digital, defense production)

....ALL THE ABOVE at wartime speed.

Then: make it stupid-easy to start and scale companies across the EU. For this you need one market, one set of rules, real Capital Markets Union, stock-based compensation, bankruptcy that doesn’t brand founders for life, procurement that buys from European startups instead of subsidized Chinese stuff, and a migration system that recruits the world’s top engineers like it’s the Champions League.

Europe has world-class research, and now it just needs world-class commercialization velocity.

Of course, that means confronting the core mismatch: Europe is trying to regulate its way back to relevance while other regions are engineering, financing, and manufacturing their way forward.

Build an innovation flywheel around three engines:
(1) productivity software everywhere (AI copilots in SMEs, public sector digitization, mandatory interoperability),
(2) reindustrialization, where Europe can dominate (advanced manufacturing, robotics, energy tech, biotech, space, defense), and
(3) scale capital (pensions + insurers as long-term risk capital, deeper public markets, fewer national silos).

If Europe wants to bend the curve, it must choose: be the continent that primarily consumes technology and arbitrates its risks or the continent that creates technology, compounds productivity, and therefore earns the power to set the rules.

The chart below is the bill for decades of under-building. Pay for it with construction, and things will reverse. Let’s build!

The cloud goes orbital: SpaceX, Blue Origin, and the next platform shift

The idea of space-based data centers once belonged to science fiction, but it’s now morphing into infrastructure strategy… at least for companies like SpaceX and Blue Origin. The reason is structural: on Earth, data centers are running into limits (eg. power, land, regulation). In space, solar energy is abundant, cooling is more manageable (if technically complex), and latency can be reduced if data is pre-processed or regionally mirrored. This makes Starlink and New Glenn more than launch platforms, because they become primitives of the AI economy. It’s the same vertical integration logic we’ve seen from Apple and Tesla, now applied to compute infrastructure: own the hardware, the network, and the AI stack. The chip is the new shipping container, and orbit is the new frontier.

However, there’s another layer here, which is strategic positioning. Musk and Bezos are locking in defensibility. Orbital data centers shift the power balance from hyperscalers like AWS and Google Cloud to platform-native infrastructure firms. SpaceX’s bet is clear: if you can launch satellites at will and power them with sunlight, you don’t need to ask for permission from regulators, landlords, or energy grids. In that context, Starship is a competitive moat. This is not about computing for today; it’s about building the next AWS in orbit, outside the jurisdiction of terrestrial constraints. WSJ

🛰️ Owning the interface: Why Revolut is entering telecom

For GBP 12.50 a month, Revolut users now get unlimited 5G, calls, texts, and global roaming baked into their financial superapp. Revolut is following the same playbook that made Amazon Prime sticky, that is, turn a functional service into a cross-subsidized lock-in mechanism. Mobile service becomes another node in the Revolut ecosystem, increasing daily app usage and reducing churn, all while strengthening the company’s control over the customer relationship.

Revolut doesn’t need to own the infrastructure (Vodafone powers the network), it just needs to own the customer interface. As Klarna and Nubank explore similar diversification plays, the lines between fintech, telecom, and lifestyle services are blurring.

In a commoditized market like mobile, the differentiator isn’t network quality, but integration, and Revolut, by embedding telecom into its financial operating system, is betting that bundling will beat brand loyalty.

The question is whether this will be the ‘default’. That’s the real battle.

🤝 What is behind the deal between OpenAI and Disney?

The Disney-OpenAI partnership represents a pivotal moment in the convergence of legacy media and modern AI platforms and, perhaps more importantly, a strategic validation of OpenAI’s approach to licensing in contrast to Google’s index-everything ethos. For Disney, this is a calculated bet that in an AI-saturated world where everyone can generate infinite media, its IP (eg Marvel, Star Wars, and Pixar movies) is what anchors user attention.

By licensing 200 characters and props for Sora, Disney is ensuring its IP sits at the center of the AI content economy, content that can be generated on-demand, with no marginal cost, and no dependency on actors, writers, or strike-prone humans 😅

The fact that Disney is investing $1B in OpenAI and getting warrants at a $500B valuation suggests that if content is no longer king, then distribution and modular partnerships are, and OpenAI is proving to be much better at both than the legacy platforms that once thought they could just crawl their way to dominance. WSJ

💡My 2025 annual presentation, “The Compresses Decade”, is now available to anyone on my blog: https://onstrategy.eu/presentation

WIMM - Where is my MOAT?

If you enjoy my strategy notes on LinkedIn or in OnStrategy, WhereIsMyMoat.com is the deeper, “skin-in-the-game” version: full theses, moat scoring, and a clear view of the company’s future performance.

Last week’s analysis:

• 👓 META - is doubling down on AI to defend a 3.5 Billion-user moat

• 🍿 Netflix’s vertical integration and the Warner Bros endgame

• 🛒 MercadoLibre as Infrastructure

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Interesting newsletters 💡

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Here is what you’ll find in this edition:

1. 📺 Aggregation wins again: How Netflix outbid Hollywood

2. 🚀 SpaceX is looking for an IPO in 2026

3. The Porsche vs. Xiaomi moment: End of Germany’s China illusion

4. on Palantir Technologies

5. on 🇵🇱 Poland, 🇨🇿 Czechia, 🇭🇺 Hungary, and 🇷🇴 Romania

Onto the update:

Aggregation wins again: How Netflix outbid Hollywood

Netflix didn’t win the Warner Bros. auction because it had the best content library, actually, Warner Bros already had that. Netflix won because it’s an internet company that understands scale, velocity, and aggregation. The legacy Hollywood players like Paramount and Comcast came to the table with old playbooks: bundle the cable assets, merge libraries, and call it synergy. Netflix, on the other hand, pitched distribution, global reach, tech infrastructure, and the ability to monetize Warner IP across 300+ million subscribers. In short, Warner Bros. had the content, but Netflix had the platform. The deal was about what Netflix could do with it.

This reinforces the core lesson of the Aggregation Theory and the Elimination Strategy: once you control demand, suppliers become interchangeable. Warner Bros. was once the upstream king; now it’s an input to a vertically integrated machine.

Netflix’s pitch was basically “we’ll extract more value from your assets than anyone else”. The traditional studio system that once dismissed Netflix as a disruptor is now being absorbed by it. Content is valuable, but being the operating system of attention is exponentially more powerful. (Bloomberg)

More about Netflix and its moat this Sunday on WhereIsMyMoat.com

🚀 SpaceX is looking for an IPO in 2026

SpaceX is going public in 2026, targeting over $30 billion in proceeds and a $1.5 trillion valuation. It is the moment when Elon Musk’s hardware-centric empire of physical-world leverage hits the public markets with the scale, speed, and narrative power of a true tech platform.

What makes this different from other mega-IPOs is not just the scale, but the flywheel. Starlink’s cash flows, already approaching $15–24 billion in annual revenue by 2026, are funding a space-based data center layer, vertically integrated with chips and spectrum. In other words, SpaceX is morphing from a launch company into the backbone of a global compute-and-connectivity stack in orbit.

Starlink is the SaaS of space: recurring revenue, defensible infrastructure, and massive optionality. Add the potential of a Starlink spin-off IPO, and suddenly the financial machinery resembles Amazon in the early AWS days.

For the rest of the space economy (Rocket Lab, EchoStar) this is gravity shifting underfoot. SpaceX’s IPO is the monetization of dominance, not a cash grab. That’s what platform companies do. They scale until you can’t ignore them, then they IPO to cement the moat. SpaceX, in classic Musk fashion, is simply applying that formula at orbital velocity. WSJ

The Porsche vs. Xiaomi moment: End of Germany’s China illusion

Germany’s industrial strength has always depended on its mastery of the Mittelstand and its dominance in high-end machinery and automotive exports. For decades, China was the ideal partner: an emerging market hungry for German machine tools, willing to absorb exports in exchange for long-term industrial partnerships. But what Berlin mistook for permanent interdependence was, in fact, a strategic stepping stone for Beijing. As China moves up the value chain, replicating and outpacing German technologies while controlling its own massive domestic demand, Germany finds itself disarmed, BUT without a Plan B. The very things it exported to China are now being produced more efficiently, at home, in China.

The chart showing Xiaomi overtaking Porsche reflects the erosion of German soft power through industrial excellence. The lesson, repeated throughout history, is that export empires based on assumed dependency eventually lose to autarkic ones that prioritize strategic self-reliance. China never intended to be a consumer of Western luxury because it intended to become its replacement. Germany now faces an uncomfortable reality: its long-standing strategy of exporting its way to prosperity has run aground against China’s national strategy.

The new global economy is being written in Beijing, not Berlin.

🇪🇺 EU must innovate, not try to overregulate everyone

Here we go again: the European Union, that proud bastion of tech-by-committee, has opened yet another antitrust probe against Google. This time for how it uses AI to summarize the internet.

The accusation is essentially: “You built a better product that users like and content creators hate, so now we’re investigating you”. It’s like being fined for inventing the lightbulb because candle makers weren’t consulted. The EU claims Google may have imposed “unfair terms” on content creators and given its own model an advantage.

Well, yes, that’s what platforms do. They take advantage of themselves. That’s the business model. The entire complaint reads like a slow-motion existential crisis from a continent still hoping innovation comes from whitepapers and fairness doctrines instead of servers and GPUs.

Of course, this is classic Brussels regulatory theater: a technocratic assault on growth masquerading as fairness. While the US and China are sprinting to build AI infrastructure, the EU is busy asking whether the summary text under a news snippet was sufficiently equitable to the original publisher. Never mind that the EU doesn’t really have a large language model worth regulating. Or a chip industry. Or a functioning venture ecosystem.

But it does have regulators. Very well-paid ones. The irony is that the more Europe tries to claw back tech relevance through litigation, the more it signals it has nothing left to build.

GUYS, you can’t outregulate your growth!!! You have to innovate.

For now, the EU seems more interested in fighting gravity than launching rockets.

on Palantir Technologies

Palantir is building software and shaping the operational infrastructure of national power. What this interview with CTO Shyam Sankar reveals is that Palantir’s advantage lies in collapsing decision latency across military, industrial, and bureaucratic systems. The company excels where human coordination breaks down, when data overwhelms command. And unlike shiny, consumer-facing platforms, Palantir’s moat is operational because it transforms fragmented, legacy environments into unified decision loops. If data is oil, then Palantir is the refinery, the dashboard, and the fuel injection system, all rolled into one.

This story goes beyond govtech because Palantir makes moral and strategic commitments. It picks sides: Western liberalism over techno-authoritarianism, state capacity over drift, deterrence over idealism. Sankar’s dismissal of AGI doomerism highlights the current bifurcation in tech, that is, build for status and clicks, or build for sovereignty and leverage. Companies like Palantir, Anduril, and SpaceX operate on the second axis.

Europe sits outside this frame, not due to a lack of engineers, but due to the absence of political ambition to integrate software with defense and industrial might. In an era of accelerating conflict and institutional entropy, Palantir is writing the OS for Western deterrence.

I am super bullish on the company and its mission. LINK

on 🇵🇱 Poland, 🇨🇿 Czechia, 🇭🇺 Hungary, and 🇷🇴 Romania

China’s trade model, once a net contributor to global growth, has become zero-sum and increasingly negative-sum for the rest of the world. This is a civilizational shift in how economic leverage operates. Rather than importing more as it grows richer, China is exporting even harder while flatlining its imports.

The new model is “dual circulation”. where China pulls the world’s demand inward, hoards value-added manufacturing, and maintains dominance in both low-end and high-end supply chains. This is trade used as an instrument of industrial consolidation, where scale begets geopolitical power.

The implications for Central and Eastern Europe (CEE) are severe. Poland, Czechia, Hungary, and Romania, which once thrived by absorbing outsourced manufacturing from Western Europe, now find themselves squeezed from both sides.

On one end, Western firms are reshoring back to the US or pivoting to Asia-Pacific; on the other, Chinese overcapacity is flooding global markets with subsidized exports.

According to Goldman Sachs, China’s current trajectory will shave off growth from Eastern European economies over the next four years, reversing the decades-long convergence story.

In simple terms, CEE went from “factory of Europe” to a squeezed middle in a world where China exports more and imports less. The path forward demands a pivot, not toward low-cost manufacturing, but toward sovereign tech platforms (including building the ‘electrical stack’), defensive industrial policy, and strategic alliances that make CEE indispensable to the next layer of the value chain.

Otherwise, they will remain downstream… forever. WSJ

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Here is what you’ll find in this edition:

1. 📱 How to survive an iPhone punch in the face? Ask Motorola

2. 🛍️ TikTok’s commerce engine: Distribution, Demand, and Designer bags

3. The Wall Street Journal: “Europe’s Green Energy Rush Slashed Emissions—and Crippled the Economy”

4. Robotaxis are (mainly) a non-EU game (🇨🇳 vs 🇺🇸 vs 🇪🇺)

5. No chips, no power

Onto the update:

📱 How to survive an iPhone punch in the face? Ask Motorola

Motorola’s transformation into a $60B enterprise by ditching the consumer RAZR and doubling down on critical communications is the type of strategic pivot that’s so obvious in hindsight but painfully rare in practice. This is the Netflix-in-2007 moment of industrial tech. A legacy player staring into the abyss of disruption (ie. iPhone in Motorola’s case) and making a clean, conviction-led break from its past. Instead of competing on camera bumps and celebrity endorsements, Motorola Solutions became a full-stack B2G (business-to-government) platform, integrating radios, AI bodycams, battlefield networks, and high-margin software into one essential bundle for state actors. The result? Over 1,000% shareholder return since the spin-off, with Greg Brown executing one of the quietest but most effective business turnarounds of the last two decades.

Motorola focused on the one market where latency, reliability, and security matter more than user experience: public safety. While Europe was busy trying to regulate every byte of AI, Motorola was building “AI nutrition labels” for its products, launching battlefield mesh networks, and signing billion-dollar deals with global police and military. It’s a playbook that regulators in Brussels wouldn’t understand, because they don’t build anything anymore. Motorola’s resurgence shows the power of strategic clarity: pick a customer (in this case, governments), become mission-critical, and expand horizontally with M&A only when it tightens the bundle. Greg Brown out-executed everyone in a niche market that suddenly became geopolitically sexy…again. Bravo! Bloomberg

🛍️ TikTok’s commerce engine: Distribution, Demand, and Designer bags

TikTok’s expansion into luxury resale is less about handbags and more about platform dynamics. By selling $11,000 Hermes and Chanel bags, the company is signaling its ambition to move beyond low-margin impulse buys and toward high-value commerce. With TikTok Shop, the platform owns attention, distribution, and now the point of sale. The resale layer adds trust and expands market scope. AI-based authentication tools like Entrupy give the whole thing credibility. This is full-stack commerce in motion—attention becomes discovery, which becomes transaction, all within one app. TikTok isn’t trying to beat Amazon at logistics. It’s replacing the mall by embedding shopping into the entertainment feed.

This is what happens when you treat commerce as a function of media, not inventory. TikTok is turning livestreams into personalized storefronts, and influencers into high-frequency auctioneers. The product doesn’t need to be new, only verified and virally packaged. European platforms remain tangled in regulatory debates about who should sell what, while ByteDance executes a vertical integration play across content, commerce, and conversion. The biggest lesson is that when distribution is free and entertainment is native, you can sell anything—even luxury—at scale. TikTok isn’t just reshaping e-commerce. It’s redefining the funnel. Bloomberg, Capital One Shopping

The Wall Street Journal: “Europe’s Green Energy Rush Slashed Emissions—and Crippled the Economy”

🤓 What have I been writing here for years? The Wall Street Journal brought the data (see above)

The grand European green transition was sold as a “win-win”: save the planet and boost the economy. In reality, Europe traded energy abundance for ideological purity and ended up with the highest electricity prices in the developed world and a structurally uncompetitive industrial base. Germany’s domestic electricity costs hit 42.5 cents/kWh, compared to just 16.4 cents in the US, while industrial rates across the EU remain roughly double those in the States.

There can’t be any competition in these conditions. Energy-intensive industries are shutting down or relocating. AI data centers are capped in Ireland. Exxon and Ineos are packing up. Even the poster child of Europe’s new economy, the green hydrogen boom, is on pause. When your factories are gone and your AI infrastructure can’t plug in, “net-zero” becomes more of a GDP number than a climate goal.

The core problem is strategic. Europe pursued an “or” strategy, hence replacing fossil fuels with renewables, while the US and China embraced an “and” strategy with renewables and more capacity across the board. It closed baseload sources before new infrastructure was ready. And now it pays billions a year to not use wind power because the grid can’t handle it. The UK, for example, shelled out £2.7 billion last year just to “balance” the grid, with expectations to hit £8 billion by 2030 (!!). This is institutionalized inefficiency. Meanwhile, countries like 🇫🇷 France and 🇸🇪 Sweden that kept nuclear power are faring better. Overall, the continent as a whole is locked into a system that demands more capacity, more investment, and more taxpayer subsidies to stand still.

The solution? Drop the ideology. Re-embrace energy realism. Build gas peakers. Extend 10x the nuclear reactors. Focus on solar + batteries. Stop forcing consumers to fund intermittent dreams while China and the US race ahead with hybrid energy portfolios that actually work.

The atmosphere doesn’t care where CO2 is saved, but your economy definitely cares where energy is cheap. WSJ

Robotaxis are (mainly) a non-EU game (🇨🇳 vs 🇺🇸 vs 🇪🇺)

China is overtaking the US by scaling abroad. While Waymo operates robotaxis on a handful of American roads, Chinese companies like Baidu’s Apollo Go, WeRide, and Pony.ai are expanding globally, running real, live services in cities like Dubai and Singapore, and planning deployments in the UK and Germany.

The difference? Strategy and scale. China is leveraging its manufacturing base, government policy, and global partnerships to turn robotaxis into an export industry, something like a mobility ‘Belt and Road’ initiative. By subsidizing a national vision for AV (autonomous vehicles) dominance and offering cheaper vehicles built with a mature EV supply chain, Beijing is doing with robotaxis what it did with solar panels and EVs: drive cost down, push product out, and let Western markets regulate themselves into irrelevance.

Europe, meanwhile, is stuck in a twilight zone between ambition and paralysis. Chinese AV firms are launching pilot programs in EU cities, often with fewer local rivals, because Europe managed to produce neither national champions nor credible AV startups of its own. Years of over-regulation, bureaucratic fragmentation, and state-funded mediocrity have left the continent structurally incapable of competing in next-gen mobility. The idea that Germany, birthplace of the automobile, will soon import robotaxis from Shenzhen should be a wake-up call. But instead, the EU will probably call another roundtable on AI ethics while Baidu rolls out lidar-equipped, voice-massaging, sub-$30K autonomous vans across its cobblestone streets.

The race is on, but Europe hasn’t even bought the shoes. Bloomberg

No chips, no power

The chart is a big warning for the EU 🇪🇺

While the US pushes the AI frontier with Nvidia and AMD, China is quietly stacking a second-tier chip ecosystem that could, by 2026, be “good enough” to run domestic LLMs and edge AI at scale. Huawei, Hygon, and Cambricon are building vertically integrated alternatives across compute, memory, and systems.

We know the strategy: when China decides to localize a tech stack, it doesn’t need to beat the best, but it just needs to be fast, cheap, and everywhere. Think EVs, solar panels, 5G base stations. AI chips are next. And with export controls locking them out of Nvidia’s top silicon, these firms now have geopolitical oxygen to scale behind a walled garden.

And Europe? Europe is too busy regulating what it hasn’t built. No serious AI chipmakers. No scaled AI labs. No platforms. The continent has become a spectator to the two-player game between Silicon Valley and Shenzhen.

Instead of fostering industrial policy that might produce a European alternative to TSMC or Nvidia, Brussels churns out position papers on AI ethics and digital sovereignty, while licensing their digital infrastructure from American hyperscalers and now, possibly, Chinese fabs. The chip wars are reshaping the economic map of the next decade. Europe is thinking of its last “success”: rethinking the GDPR cookies and other failed projects. The Economist

⚡️ no cheap energy, no industry, no money

In five years, OpenAI alone is on track to consume more electricity than the 🇬🇧UK or 🇩🇪Germany... and to overtake 🇮🇳 India within eight. Short reminder: that’s the footprint of a single company (!)

So remind me: how exactly are the UK and Germany supposed to “save the planet”? And is it really rational to kill their industries and cut jobs under these conditions?

Hahaha 👇

Imagine you’re the world’s most powerful arms dealer, and one of your best customers just made their own rifle. Instead of panic, you send out a congratulatory tweet saying, “Nice trigger, by the way, we still sell the bullets”. That’s basically what NVIDIA just did.

Google just showed off its fancy new AI chips, and NVIDIA, instead of going defensive, issued a warm note of admiration, followed by a very subtle flex: “We’re a generation ahead. Our stuff runs every model. On every machine. And by the way, unlike those charming little ASICs of yours, ours work everywhere and do everything.” This is corporate diplomacy mixed with humblebrag energy levels typically reserved for Olympic gold medalists congratulating silver medalists.

Jensen Huang’s team just patted Sundar on the back while reminding everyone who built the roads. LINK

Personal confession

Watching “professional” fund managers pile money into bonds and ultra-conservative products (because regulation says so!) was eye-opening. On paper, the returns looked fine. In reality, they barely kept up with official inflation.... and we all know official inflation is usually the polite version of what we feel in our pockets.

That’s when I decided to do something both simple and uncomfortable: take responsibility for my own capital and build my own thesis at Where is my moat?

Translation: Read the reports, understand the moats, listen to the earnings calls, say “No!” to doomed markets & companies, etc. Eleven months later, this Interactive Brokers screenshot says +55.9% YTD.

Two quick reflections:
1/ Some people still tell me it’s just “luck”. Maybe. A real market correction (next year or in two) will separate skill from luck for all of us. I’m very aware that drawdowns are part of the game.

2/ This feels similar to what happened in my academic life: I had to write my own strategy book because the post-pandemic business world no longer fits the old textbooks. The same is true for investing. The world changed; many of the default options didn’t.

I don’t know yet whether this journey will end with me running a fund (😎) or simply being a diligent steward of my family’s savings. However, I do know this: in a world where systems are optimized for compliance rather than performance, you either build your own playbook or you silently accept “inflation-adjusted poverty” as a strategy.

PRINCIPLE: Solve for adaptability

Most strategic problems aren’t static, because they always mutate. By the time your perfectly modeled solution is ready, the problem has already shape-shifted. What looked like a supply chain issue becomes a market timing issue. What felt like a pricing problem turns into a product mismatch. So what do most teams do? They double down on analysis, seeking “the” answer to a moving question.

This is cargo cult strategy: borrowing the rituals of rigorous thinking without noticing the terrain has already changed. The illusion is that enough thinking will solve the problem. The reality is that most of the value comes from becoming adaptive enough to keep pace with the problem as it evolves. Strategy, like software, benefits from being iterative, not perfect.

The real edge is not clairvoyance, it’s calibration. Build teams, tools, and processes that notice faster, react cleaner, and learn in public. Don’t obsess over the best move and optimize for the fastest learning loop. When in doubt, move. When overwhelmed, simplify. When stuck, shift your timeframe. Strategic agility beats static genius, every time. LINK

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Here is what you’ll find in this edition:

1. 🔵 Meta, 🔴 Google, and the fragmentation of the AI compute stack

2. 💸 Klarna’s stablecoin bet: Fintechs are becoming infrastructure

3. 🍿 From ‘Disruptor’ to ‘Distributor’: Netflix’s Hollywood ending

4. 🧑🏻‍💻👩‍💻 Cartoon meetings and the death of a trillion-dollar idea

5. 🇷🇴🇵🇱 When arbitrage ends: Poland, Romania, and the AI curve

Onto the update:

🔵 Meta, 🔴 Google, and the fragmentation of the AI compute stack

The implications of Meta considering Google’s TPUs go far beyond chip sourcing. Nvidia has dominated the AI compute stack by being both the best general-purpose accelerator and the ecosystem default. But Meta, after years of investing in its own silicon (e.g. MTIA), is signaling that the real strategic risk is over-dependence on one supplier, both in pricing and availability. If Meta commits to Google’s TPUs, it would give the AI platform war a second front: one where ASICs (custom-built for AI tasks) challenge the universal utility of GPUs. This also reflects how the economics of large-scale AI are changing. Inference at scale rewards efficiency, not just raw horsepower. Google has spent nearly a decade perfecting this exact niche.

For Google, this is less about selling chips and more about extending the platform. Cloud is the go-to-market layer and more profitable from quarter to quarter. TPUs are the performance enabler. Gemini is the proof point. Suppose Meta joins Anthropic and others in deploying TPUs for inference or training. In that case, it gives Google not just a revenue boost, but a strategic validation that its closed-stack approach is working.

The broader story here is that AI infrastructure is undergoing vertical integration, where platform control comes from owning distribution (cloud), performance (chips), and content (models). Nvidia is ahead because it built a horizontal ecosystem. But Google, with Meta as a partner, is building an end-to-end vertical pipeline. If this deal goes through, it won’t just chip away at Nvidia’s dominance but will redraw the AI platform map entirely. WSJ, FT

💸 Klarna’s stablecoin bet: Fintechs are becoming infrastructure

Klarna’s launch of KlarnaUSD, a Stripe-backed stablecoin, is a fascinating shift in the fintech playbook, not because it’s revolutionary tech, but because of who is doing it and why now. Klarna, best known for gamifying credit into “buy now, pay later” dopamine loops, is now confronting the cold reality of operating as a global digital bank: cross-border money movement is expensive, regulated, and glacial. Stablecoins, in theory, solve that by cutting out SWIFT and banking intermediaries, enabling near-instant settlement and low-fee transfers across geographies. Klarna is no longer positioning itself as a consumer credit company with millennial aesthetics, but rather as an infrastructure for global payments, and it’s willing to break its own anti-crypto stance to achieve this.

The broader implication is that we’re entering a phase where fintechs and neobanks aren’t competing just on UX or lending margins, but they’re competing on protocol. Klarna joining PayPal, Stripe, and potentially Revolut and Wise in launching proprietary stablecoins is less about crypto ideology and more about control of the underlying rails. It’s AWS-style vertical integration for money movement, designed to escape legacy infrastructure and reduce dependency on card networks and correspondent banks.

In this new world, whoever owns the payment layer can re-bundle credit, identity, and commerce on top of it. Klarna just made a big move to stay in that game and the question is whether regulators (and consumers) will follow. FT

🍿 From ‘Disruptor’ to ‘Distributor’: Netflix’s Hollywood ending

Netflix, the company that once treated movie theaters like Blockbuster fossils and built a culture manual with the tone of a tech cult, now wants to buy Warner Bros to get its hands on “The Sopranos” and a ‘few’ Batmans 😅.

This is like the kid who hacked the school system to avoid gym class suddenly applying for a job as a sports teacher. The same Netflix that bragged about building instead of buying is now preparing to onboard thousands of traditional studio employees, embrace theatrical releases it used to ignore, and possibly license content to third parties, which it has avoided for two decades, because it finally got tired of being the streaming platform with the best UI and the worst IP shelf.

Congratulations, disruptor: you’ve become the thing you disrupted!

The shareholders are unimpressed, mostly because they understand mergers in media tend to go like rom-coms in reverse. You start idealistic and lean, then one day you wake up co-owning Looney Tunes and answering antitrust subpoenas from Congress.

If Netflix wants to spend $60 billion on vertical integration rather than, say, building the next Disney from scratch or buying every anime studio in Japan, that’s fine. But let’s drop the pretense. Netflix cracked the code once by doing everything Hollywood would not do. Now it’s trying to win the next round by doing everything Hollywood already did. That’s a tough sequel to sell. Bloomberg

🧑🏻‍💻👩‍💻 Cartoon meetings and the death of a trillion-dollar idea

The metaverse began as a marketing rebrand and somehow turned into a trillion-dollar hallucination about cartoon meetings and virtual t-shirt shopping. You would create an avatar, go to a digital concert, buy a shirt with crypto, wear it to your VR office, and then lend it to your colleague’s daughter so she can flex in Roblox. This experience requires full asset interoperability across platforms, smooth real-time rendering, fast wireless networks, and users willing to spend their day in a headset pretending to care about a pixelated coffee machine in a virtual breakroom. It’s an enormous amount of effort to recreate the mall experience from 1998 inside a game engine, with fewer teenagers and more latency.

The promise always sounded like Ready Player One, but the reality feels closer to Second Life with enterprise software integration. People do not ask for this. They ask for group chats that work and online orders that arrive. Most users have responded by ignoring it completely. Meanwhile, Meta continues to pour billions into this digital Atlantis, hoping that someday, maybe, users will get over the nausea and existential dread long enough to attend a 3D webinar about HR compliance.

The metaverse imagines a future where work and play fuse into a seamless immersive environment. So far, that future mostly consists of laying off the team that was building it. Bloomberg

🇷🇴🇵🇱 When arbitrage ends: Poland, Romania, and the AI curve

🇵🇱 Poland’s and 🇷🇴 Romania’s economic rise was built on a seductive premise: absorb Western capital, offer educated but affordable labor, and become Europe’s back office. It worked, until it didn’t. Krakow, once the golden child of business services outsourcing, now feels like a time capsule for the last era of globalization. The very things that made it attractive (ie. predictable processes, English-speaking grads, reliable infrastructure) are exactly what large language models do better, faster, and cheaper. Shell, Heineken, HSBC, and others are pulling back, quietly replacing back-office roles with AI or sending jobs to even lower-cost hubs like India.

The implications stretch beyond Krakow’s empty office buildings and retreating corporate logos. A wage boom driven by skilled labor has met its replacement curve, ie. automation. This is the classic innovator’s dilemma playing out at city-scale. Poland is trying to pivot with AI courses at Jagiellonian, promises of “Krakow will not become Detroit”, but the structural risk is clear.

When your GDP is tied to labor arbitrage and that arbitrage collapses, you either leap to the next tech platform… or become legacy infrastructure. Europe, once again, will be forced to ask, “Are we ready to build, or just optimize someone else’s models?” Bloomberg

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