Everywhere you look, the story is the same.

It’s exhausting.

And if you’re a seasoned professional—if you’ve managed multimillion-dollar clinical trials, orchestrated global supply chains, or built B2B digital strategies over decades—you don’t feel envy.

You feel something quieter.

Something older.

A practiced skepticism, worn smooth by years of watching hype cycles rise like tides… and recede like ghosts. — Read More

Polymarket is moving deeper into private markets — and this time, the contracts are tied to companies most investors can talk about, but still cannot actually buy.

The company is launching prediction markets tied to private company milestones, including valuations, IPO timing and secondary-market activity for names like OpenAI and Anthropic.

Nasdaq Private Market will serve as the exclusive resolution data provider, supplying the information that determines whether these contracts pay out. — Read More

In this guide, you will learn how to add Anthropic’s financial-services plugin marketplace to Claude Code, install market research skills, and use them to create market research reports, equity analysis, earnings reviews, and Excel spreadsheets from your own prompts.

This is not financial advice. The useful version of this workflow is a research assistant that organizes public market information, creates structured reports, and helps you decide what to inspect next. — Read More

Meta Platforms reported $201 billion in revenue in 2025. Net income was a record. Free cash flow was a record. The stock closed the year at an all-time high.

On Thursday, the company announced it is laying off 8,000 people.

Those two things are not a contradiction anymore. They are the business model. — Read More

I am excited to update my original analysis from 2024: The Economics of Generative AI. This is the analysis I come back to more than anything else I’ve written because it’s a reminder of the “physics” of the AI industry.

Two years ago, I found that the Gen AI value chain was inverted: the compute layer captured ~83% of all revenue and ~87% of all gross profit. The application layer, despite being closest to end customers, earned almost nothing. I predicted this would flip over time, following the pattern of every prior platform shift.

Two years since, the AI ecosystem has grown roughly 5x, from ~$90B to ~$435B in annualized revenue. But what’s remarkable is how little the shape of economics has changed.

The bottom line upfront: Semi is a one-player game. Apps is a two-player game. Infra is the only competitive layer. The most profitable strategy in AI is still selling the shovels. 🙂 — Read More

hen it comes to OpenAI, smart money is starting to do the math out loud. And something doesn’t add up. On surface, today’s news that OpenAI is offering 17.5% guaranteed returns to private equity firms looks like a shot at the Anthropic threat. Scratch the surface, and you start to see the story behind the story.

The PE deal is the kind of deal you do when you’ve borrowed against the future and the future is taking longer than expected. — Read More

The United States is undertaking a historically unprecedented investment boom to build the computers, data centers, and other physical infrastructure needed to train and deploy Artificial Intelligence. Hundreds of billions of dollars have already been spent by hyperscalers racing to build smarter AI systems, and investment from major tech companies is set to shatter all previous records again this year. Amidst this boom, spending on data center construction has hit a new record high, now exceeding a $42B annualized pace, a more than 300% increase since the launch of ChatGPT in late 2022. While growth has slowed over the last six months, investment is still up more than 18% over the last year alone.

Yet that figure only reflects the costs to build data center facilities themselves, not the much larger costs of the expensive GPUs, TPUs, and other electronics housed within. Real US fixed investment in those computers and related peripheral equipment has surged to a record high of more than $270B annualized, up nearly 50% over the last year and up 77% since ChatGPT’s launch.

… America is making a globally and historically unprecedented bet on the success of Artificial Intelligence. As a share of the economy, that AI boom is already one of the largest investments in American history—dwarfing the peak of the broadband, electricity, or interstate highway buildouts and vastly exceeding the Manhattan or Apollo projects. And yet, US tech companies are doubling down, raising the stakes on their $1T gamble that AI models will continue their exponential capabilities growth and eventually become valuable enough to repay such a colossal investment. — Read More

The week AI killed software

Last Monday, $285 billion of market cap evaporated from software, financial services, and asset management stocks. Thomson Reuters lost $8.2 billion. In a single day. LegalZoom dropped 20%. India’s Nifty IT index posted its worst month since October 2008 — worse than the financial crisis.

he week AI killed softwarexxxxLast Monday, $285 billion of market cap evaporated from software, financial services, and asset management stocks. Thomson Reuters lost $8.2 billion. In a single day. LegalZoom dropped 20%. India’s Nifty IT index posted its worst month since October 2008 — worse than the financial crisis. — Read More

Years ago, some leading researchers told us that their objective was AGI. Eager to hear a coherent definition, we naively asked “how do you define AGI?”. They paused, looked at each other tentatively, and then offered up what’s since become something of a mantra in the field of AI: “well, we each kind of have our own definitions, but we’ll know it when we see it.”

This vignette typifies our quest for a concrete definition of AGI. It has proven elusive.

While the definition is elusive, the reality is not. AGI is here, now.

Coding agents are the first example. There are more on the way.

Long-horizon agents are functionally AGI, and 2026 will be their year. — Read More

#investing

The AI industry is in correction mode. Last week Nvidia reported its earnings and the world was holding its breath. If they miss, it is so over. If they crush it, we are so back. In the end, earnings beat expectations, but the stock slid anyway after an initial bump. Many things in the AI boom smell bad. The way money fuels the investment spree is quite questionable and it has become a meme, with the same $1T investment check moving hands among a small set of participants. We can call this “vendor financing”, but it is not a great look.

In my opinion the players more at risk here are the hyperscalers like Microsoft, Amazon and Oracle, and the neocloud players like Nebius and CoreWeave. They are in between the providers of chips like Nvidia and the buyers of compute like OpenAI. They really have no choice other than buying real chips from Nvidia, and hoping that there will be sustainable demand (read: revenue > COGS) from buyers of compute so that they can honor those commitments. If not, the buyers of compute will walk away, resizing their commitments (or going bankrupt), Nvidia already sold those GPUs, and the hyperscalers are left with billions and gigawatts of unused capacity that depreciate very quickly due to the short GPU lifespan. — Read More