消費者

@learning_yohei So does that mean Portuguese men no longer do any housework? lol

The world is slowly changing. 🌍✨ It’s not just Japanese fans anymore—Portuguese fans also started cleaning up after the match. 🇵🇹🧹 Much respect to Portugal. 👏❤️

@talktonenice @realmikolson sure but I’m actually American

@minato71216 @realmikolson Thank you Japanese soothsayer.

This may just be the single greatest tweet of all time #worldcup

@inwoodcapital Definitely the latter

Seth Klarman keeps saying the same stuff over and over yet has miserably underperformed an SP500 index. So is he still right? Or did he just catch a streak during the value days and now is just clueless investing in public stocks? …I think the latter

@Ross__Hendricks for a split second i thought this was a parody account

It’s hard to overstate just how horrific the long-term economics of Americas largest tech companies will become after sinking trillions into a commoditized industry that’s competing with China on price

@SemiAnalysis_ not their best work

After the disappointing WWDC 2026 conference, Apple should authorize more stock repurchases to return cash flow to investors. That way, investors can hopefully find real innovation somewhere other than Apple.

@TheStalwart one man made money selling cigarettes, brown fizzy water and junky sugary candy. the other man unleashed a global revolution in EVs that helped clear our air while launching a new orbital industry, while saving Ukraine to boot. Karma perhaps

This is insane. But it makes some sense. One of these men is a fantastic storyteller and wizard of financial structures, while the other is a pioneer in the world of electric vehicles and reusable rocketry.

@BoringBiz_ There is actually a kind of logic there

Investment committee: “so explain why we should pay 15x for this business?” Private equity associate: “one of the things that makes this business so valuable is how valuable it is”

@darioperkins token generation costs will over the longer and medium term keep coming down. they have to. right now we have acute compute shortages with only 10bps of the relevant population actually using agentic AI

But people said these things could only move higher (?)

@darioperkins Wanted to add: economists are very data driven, and you wouldn't be doing your job if you weren't. But note famous Andy Grove comment about how when there's a strategic inflection point, the data is no longer useful and you need anecdote/intuition. This applies to AI methinks

PODCAST TIME. This one should have been called A MESSAGE TO THE HATERS... because we read out (and respond) to that nice chap's email...

@johnarnold @freemktsfreemen Let’s face it valuation analyses don’t really work with musk and his companies. He’s a meme / cult leader and that’s better more useful lens. I’m a huge fan of Musk’s achievements and am not deriding his business leadership

@freemktsfreemen backwardation is when today's price>future prices. One reason oil companies trade at a low p/e now is that the curve is backwardated. Future discounted cash flow is based on forward prices, not oil prices today in a tight market. Compute is similarly tight in near term.

Most of the SpaceX neocloud analysis changes dramatically if you understand that there's a backwardated curve for compute today.

@darioperkins point taken re circularity of capex but look at the trajectory of the revenues earned by anthropic and openai and this is just the beginning

@BenBajarin so Google trained fresh models specifically for Apple?

Per Craig Federhigi on how much Google gemini stuff they use for Apple Intelligence: "we don't have the Gemini app as our app. In fact, none of that client code is part of how we run an iOS for these models. We use none of the models that Google deploys to their customers, nor do we use the infrastructure and means by which they deploy models to their customers. And then, when it comes to the knowledge base, we, of course, don't use Google search or anything like that as the foundation of our system, so hope that's clear. This is the amount of the Google Assistant we use, which is none."

@darioperkins There's indeed a distinct mindset shift as you go from London to NYC to Bay Area. Like reading comments in the FT vs WSJ vs The Information

I'll let you know if I get any more "correspondence" 😂

@TheStalwart As many other posters have noted, it's with an eye to the inference business and planning to be sure you're cost competitive in serving tokens in the medium to long term (cost of silicon, model alignment with chip architecture, etc)

This seems to be common answer. But I find it unsatisfying. The actual physical scarcity is obviously at the foundry level. Everyone cutting NVDA out doesn’t solve that. And if the answer is that NVDA chips are exceptionally well designed, well.., obviously many others can do it

@John_Hempton no wonder memory stocks have been on a roll

Just had a very long AI conversation where after a long and detailed discussion we got to the point where a nuanced answer came up. And I checked via other means and it was right. But it took a long time. And then 30 questions down the context window it forgot what we painfully worked out and gave the original stupid (and wrong) answers. The AI is very smart, but it isn't learning.

@John_Hempton @slackercapital nominal gdp growth is 6-7% a year so it’s not quite as bad as that, closer to 1% lol

@slackercapital Its only 14 years away. And global GDP is currently about 125 trillion rising a few percent per year. So we are predicting something like 2% of global GDP in 14 years. Okay. I'll run with that.

Cool we get to buy Space X at just 0.5x 2040 revenue. It's a steal!

@John_Hempton are you able to flex your portfolio and go balls long AI and keep investors on board?

My long book remains a shitshow. This is tiresome.

@darioperkins "non-believer" is not the right term. "late" would be better. I'd use "out of touch" but I don't want to be mean ;-)

we are at the stage of the cycle where investors dont want to hear scepticism about AI anymore. In fact, they get angry when you express reservations about what is going on. "You're just a non-believer".

@_Investinq 5 year depreciation? I don't think so.

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.