Tidiani Jeff Tall

CEO #renewableenergy #solarpower

[Giorgio @perennialmystic]

“Everything we hear is an opinion, not a fact.
Everything we see is a perspective, not the truth.”

— Marcus Aurelius

[Veloria🐬 @veloriahq]

“𝘗𝘦𝘰𝘱𝘭𝘦 𝘷𝘪𝘴𝘪𝘵 𝘵𝘩𝘦 𝘳𝘪𝘤𝘩 𝘸𝘪𝘵𝘩 𝘨𝘪𝘧𝘵𝘴, 𝘣𝘶𝘵 𝘵𝘩𝘦𝘺 𝘷𝘪𝘴𝘪𝘵 𝘵𝘩𝘦 𝘱𝘰𝘰𝘳 𝘸𝘪𝘵𝘩 𝘢𝘥𝘷𝘪𝘤𝘦.”

— 𝘈𝘧𝘳𝘪𝘤𝘢𝘯 𝘗𝘳𝘰𝘷𝘦𝘳𝘣

[Friedrich Nietzsche Notebook @QuoteNietzsche]

"Madness is rare in individuals - but in groups, parties, nations, and ages it is the rule."

—Friedrich Nietzsche

[pingu @pingu4ll]

you can’t discuss the ocean with a frog
it only knows the pond

[Ronit Pereira @CAronitpereira]

“We are pragmatists. We don't stick to any ideology. Does it work? Let's try it, and if it does work, fine, let's continue it.”

“If it doesn't work, toss it out, try another one. We are not enamored with any ideology.”

- Lee Kuan Yew

[Swami Maharaji @SwamiMaharaji]

You never identify yourself with the shadows cast by your body, or with its reflection, or with the body you see in a dream or in your imagination. Therefore you should not identify yourself with this living body either.

Adi Shankara

[Voices of Liberty @VoLSubstack]

Hayek on what true scientists understand that most intellectuals don’t:

“The man who has learned a little science lacks the humility the real scientist gladly acquires. The typical intellectual believes everything must be explainable, but a scientist knows that a great many things are not. A good scientist is essentially a humble person.”

[Giorgio @perennialmystic]

“Start by doing what's necessary; then do what's possible; and suddenly you are doing the impossible.”

~ St. Francis of Assisi

[Beatriz @btrizmart]

“Observa lo difícil que es cambiarte a ti mismo y comprenderás lo ingenuo que es intentar cambiar a otra persona. — Voltaire”

[Words For You @_Wordsforyou11]

"You can return to a place, but you can never return to a moment."

— Fyodor Dostoevsky

[Today In History @historigins]

"Socialism never took root in America because the poor see themselves not as an exploited proletariat but as temporarily embarrassed millionaires."

- John Steinbeck

[Anthologie de la répartie @Antho_Repartie]

Accusé d'être un imposteur, un devin est condamné à mort. Avant l'exécution, Louis XI le met à l'épreuve :

- Toi qui prédis si bien l’avenir, sais-tu quand tu mourras ?

- Deux jours avant Votre Majesté.

Superstitieux, le roi décida de le gracier.

[Natural Philosophy @Naturalphilosy]

“You cannot give to people what they are incapable of receiving.”

— Agatha Christie

[Al Smizzle @AlZeidenfeld]

This is possibly the most accurate tweet of all time.

[Living Tricks @LivingTricks_]

📊 Extreme Poverty by Country (1992–2026)

[John Ziegler @Zigmanfreud]

I showed my 14 year old daughter this amazing chart illustrating the progression of best-selling musical artists during the 1970s and 80s, so that she would get some sense of how insane Michael Jackson’s run during the 1980s was.

And now in 2026 he’s back near the top again…😱

[Deep philosophy @DeepPhilo_HQ]

"Don't tell me what they said about me; tell me why they felt so comfortable saying it to you "

- Victor Hugo

[Philosway @philosway]

“The last fortress of a man is his mind. If that stands, nothing has truly fallen.”

— Epictetus

[The Figen @TheFigen_]

When the game is over, the king and the pawn end up in the same box.

- A powerful Italian Proverb

[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.