Bo Simango @BoSimango

This really is beautiful. The moose, polar bear, whale. Indigenous Nations of Canada welcoming the world to the World Cup in Toronto.

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Canadian fans show out for team Canada in Toronto! Thousands of Canadian supporters on the streets 🇨🇦

@Devin_Heroux Phenomenal!

A moving founder will meet their luck

THE CITY IS ALIVE.

@Montreal Schwartz’s!

@cowincrisis Numbers

@PsudoMike When write access opens in 2027, who’s making sure the sensitive financial data flowing between institutions is actually protected?

@intro Cybersecurity specifically data security and privacy

Marc Andreessen: We need to drastically increase the number of founders: "It's shocking to me how few people actually give entrepreneurship a shot. The fate of the world over the next 1,500 years is riding on the people who actually want to give it a shot." — @pmarca

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@tobi This is fantastic

@asim_lfc @DistanceCovered Great combo, objective and balanced takes

My conversation with @dkhos, CEO of Uber. Dara took over in 2017, when Uber was losing roughly $4.5B a year. Today the company generates $10B in free cash flow and is worth about $150B. We discuss: - How Daniel Ek convinced him to take the job - How Uber spent a full year of its AI budget in a single quarter - Uber's approach to autonomous vehicles - Drones, hotels, and building a superapp - Lessons from Allen & Co, Barry Diller, and Reed Hastings Enjoy! Timestamps 0:00 Intro 3:44 Bringing Order to Uber’s Chaos 7:22 Managing Stress and Going All In 14:28 Why Uber Is at the Center of AI and Physical 22:39 How to Win in Autonomous Vehicles 32:25 The Trillion-Dollar AV Opportunity 37:05 Drones, Robotaxis, and Global Adoption 38:20 Uber Eats, Uber One, and Aggregating Supply 47:00 The Future of the Uber App 55:55 Lessons from Barry Diller

@mynameisyahia Congrats Yahia, how did you approach customer acquisition and what was the most successful channel?

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.

This is brilliant from Jamie O’Hara 🤣🤣

We’re all feeling it 🤞🏼 🇫🇷

Yup.

Vivek Sen @Vivek4real_

2 weeks ago

LARRY ELLISON: AI IS RAPIDLY COMMODITIZING BECAUSE MOST MODELS ARE TRAINED ON THE SAME PUBLIC INTERNET DATA. THE REAL COMPETITIVE EDGE ISN’T THE MODEL ANYMORE — IT’S ACCESS TO EXCLUSIVE, PROPRIETARY DATASETS. THAT MAY BE THE ONLY MOAT LEFT.

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fantastic interview of Luca di Montezemolo, erstwhile chairman of Ferrari by Charlie Rose