badwillgoodwill

Trying to minimize regrets

[badwillgoodwill @badwillgoodwill]

@weary_centurion Literally the Leandro effect

[badwillgoodwill @badwillgoodwill]

@mattbarrynews @gerstenzang Literally everywhere you see in Germany

[badwillgoodwill @badwillgoodwill]

@Investor_NICK_ It's the Leandro effect

[badwillgoodwill @badwillgoodwill]

Another fintwit darling tanks. It never fails 👌

[Yellow Cat @YellowCatCap]

Fintwit thinking $TVK.TO is a long because "executive chairman going to jail doesnt matter" is bizarre.

The guy was key man on the story for a decade and is heavily involved. The stock is a rollup trading at huge premium to assets because we bet on management finding deals

GL

[Ronit Pereira @CAronitpereira]

Charlie Munger on the origins of Chinese-Americans 🇺🇸 🇨🇳

“The Chinese first came in USA trying to build the Sierra, trans-continental railroad in the winter.”

“Our people were dying and it was just impossible, so they brought in 50,000 Chinese coolies, who were in those days practically slaves.”

“They took the coolies in the mountains and said - you build the railroads and they did it! The Americans couldn’t do it by themselves.”

“Fade in fade out 150 years later, due to immigration, these asians have rapidly become Doctors, Lawyers, Businessmen and succeeded mightily.”

“Every instrument that’s hard to play in symphony orchestra, is played by a Chinese face.”

- Charlie Munger. 2019

[Kevin Carpenter @kejca]

Charlie Munger: "Nowadays, every director at a big company gets $300,000 a year — and everybody thinks we've arranged all this wonderful independence. A man who needs $300,000 extra a year as a director is not independent. The one thing you can guarantee is he'll try and stay a director. I don't think that's an ideal system."

[StockMarket.News @_Investinq]

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.

[Julian Klymochko @JulianKlymochko]

Classic Buffett

Negotiated the $TMHC deal in 4 days (CA signed May 27th)

Paid 10x EBIT (10% pre-tax yield -> his favorite multiple)

Taylor Morrison Home didn't run a sale process

No buy-side advisor used and no reverse break fee payable

[Alex Macdonald @alexfmac]

As a student in Paris I lived briefly in the infamous ‘93’ banlieue.

Burning cars, riots and violence a regular occurrence.

While social integration is bad in London, it’s significantly worse in Paris.

In London you’ll find social housing in every borough. From Hackney to Kensington & Chelsea.

There are council flats next to £20m homes.

In Paris they created a physical ring around the city where they built social housing and pushed immigrants to live.

The result of this is a 2 tier society unlike anywhere else.

And when there is a big occasion justifying a trip into town - like tonight - the chaos begins.

Integration requires a real investment from both the host and the guest in order to succeed.

[badwillgoodwill @badwillgoodwill]

@TihoBrkan @KoyfinCharts @theTIKR falling behind hard

[badwillgoodwill @badwillgoodwill]

Classic

[Yellowbrick Investing @joinyellowbrick]

This account had a couple thousand followers and sent a $100M Japanese stock up 20% on an fake thesis centered around a division the company previously divested.

The company literally does not do the thing this pitch says it does and a tiny account sent it limit up

[Goshawk Trades @GoshawkTrades]

Sir Chris Hohn bought Moody's at $50. sold at $100. thought he was clever.

bought it back at $150. it's now $400-500.

"the intrinsic value compounding matters more than the stock price."

"the multiple matters less than the growth when you look at it over a longer period."

"most investors are unwilling or unable to invest on a long-term time horizon because they think it's risky. which goes back to what Warren Buffett said when asked for the definition of risk: not knowing what you're doing."

applies to trading strategies the same way. the edge that compounds for 5 years will always beat the flashy backtest that blows up after 6 months. patience is the moat most people can't build.

[badwillgoodwill @badwillgoodwill]

@Biohazard3737 Maybe Fish Head of micro

[Rene Sellmann @ReneSellmann]

The ultimate lesson in industry economics from Charlie Munger 👇🏻

You can find a business with a great brand, but if you have just one competitor who behaves like a "demented Kellogg" and fights for market share at all costs, the economics of the entire industry get ruined.

So when analyzing an investment, look at competitor behavior just as much as the company itself.

[Steve Burns @SJosephBurns]

Jeff Bezos: “I once asked Warren Buffett, why don’t more people copy your investment strategy? It’s not that difficult to understand in principle. And he said, ‘Oh, Jeff, that’s easy. My approach is a get-rich-slowly scheme.’ And people don’t like those.”

“If you can think in terms of seven years instead of three years, and you can defer gratification and think long term, that will give you a head start against all of your competitors, because most people can’t do that.”

[Investment Wisdom @InvestingCanons]

Investor Chris Hohn's (2,924% returns since 2004) definition of a good investment:

"This is something a lot of people get wrong. They think it's [only] about growth, often, or something new. Neither of those things to us matter…by themselves."

Hohn shares 2 types of investments (and his preference):

"The types of investing that we do is high barriers to entry, the moats that Warren Buffett has talked about…Now, before we dig into that, can a distressed asset of a piece of real estate that's selling at half price because of a liquidation also be a good investment? Yes.

So can there be a role for cheap average assets? Let's call them low-quality assets, which are trading at big discounts to replacement cost. Yes, that's the type of investing that can work, that I've done in my time."

This echoes Howard Marks' principle: "It's not what you buy, it's what you pay."

But Hohn prefers the high-quality businesses with sustainable competitive advantages over underpriced, average-quality businesses:

"…I don't feel I can have confidence in that kind of investing because the earnings power of those average businesses is unpredictable."

[waterboy @waterboystocks]

"Buffett, whom Hohn speaks to often, tells the FT his record is 'exceptional'."

"Chris has an incredible ability to think in black and white. He focuses on one, maybe two things that drive the investment thesis and then has the confidence to scale up the bet so it’s a big part of the fund."

"For Hohn, the key metric for any company is pricing power, highly valuing the ability to push through inflation-busting price increases. He is not dazzled by stratospheric revenue growth like other investors."

"He does not accrue yachts and holiday homes like other billionaires; he drives a Toyota Prius and wears a cheap plastic watch."

[Rene Sellmann @ReneSellmann]

The arguably strongest economic moat in the world, visualized 👇🏻

When looking for compounders, look for network effects. Every new node creates an exponential web of connections that makes the ecosystem impossible for users to leave and impossible for competitors to replicate.

This is why winner-take-all dynamics happen.

[badwillgoodwill @badwillgoodwill]

@zeroxpectation That's a stretch

[badwillgoodwill @badwillgoodwill]

@lingams @AltayCapital @DaBao_ @MikeFritzell Textbook micro kap dump. Pitch on X a 30% float 100m mcap beautiful