Johannes von Cramon

Meta got a roughly 10% pop for planning to resell its excess AI compute, a business the market can finally put on a spreadsheet. In contrast to Zuck’s Superintelligence, which seems to lead nowhere. But reselling compute to monetize overbuilt infrastructure is a sign that a company has stopped making the case for its core business. Barely anyone at Meta speaks for the advertising engine that funds it all.

Eric Seufert traces this vacuum to a specific absence:

But I believe that Meta is currently gripped by a strategic morass that prevents it from making a convincing argument that these investments are justified and commercially prudent. I noted in a recent episode of the Stratechery podcast that Meta has a communication problem: Mark Zuckerberg simply refuses to engage with the value of digital advertising infrastructure through either a tactical or conceptual lens. Sheryl Sandberg used to fulfill that role: she would articulate the value that Meta provided to its 10MM advertisers on each earnings call, making the case that Meta’s advertising platform is especially powerful for small businesses. But beyond that, she would meet with customers, large and small; I know dozens of CMOs and marketing leaders who have taken pictures with her over the years. No Meta executive occupies that role now.

In Q1 2026, library titles drew roughly 25 billion streaming hours against 7.3 billion for originals. More than 75% of all streaming time was spent on catalog content such as Friends, Suits, and Modern Family.

Strip out Netflix and the catalog share climbs even higher to roughly 90%. Netflix is the only major streamer where originals nearly match catalog viewing, and the reason is simple: more than $135 billion in content spend over the past decade, with roughly $20 billion projected for 2026 while rivals cut.

A University of Chicago study surveyed 338 students. 60% admitted to using AI in their own coursework, while 90% reported that their peers did. People disclose what they judge acceptable to admit about themselves.

Classic social-desirability bias.

According to an internal OpenAI study, a quarter of Codex users now delegate 8+ hour tasks, up from 2% a year ago. But fewer than 1% of individuals use Codex at all. Inside OpenAI, 99.8% of work runs through it.

This is them measuring themselves, not the market.

4 in 5 under-16s in Australia are still on social media, three months after the ban.

A BMJ study found most were verified by selfie or a typed age. Almost none were asked for real ID.

What a farce. The UK will be next in 2027.

An Anthropic study on agentic coding sessions found that experienced users completed working, verified solutions at twice the rate of novices: 28-33% versus 15%.

The gap comes from how they prompt. Experts issued 12 actions per session and wrote around 3,200 words. Novices only issued 5 actions and wrote around 600. More prompts and longer ones mean more precise specifications and more course corrections along the way. Producing more output by itself is not the win. Directing the agent with better detail is.

The deciding factor is domain knowledge, not coding skill. You need to know what a good result looks like in order to steer toward it.

SemiAnalysis puts the compute each Claude Max subscriber actually burns at $8,000 of API list-rate tokens, and Codex users closer to $14,000. Although serving costs are roughly a quarter of the list price, the actual loss per plan amounts to a few thousand dollars. Still a serious burn rate.

But every Max session produces a detailed record of how people work through real problems, the kind you cannot get just by buying tokens. Anthropic recently extended 30-day data retention to accounts that previously kept nothing. This kind of data only gets more valuable and harder to get.

Four companies just wrote a $1BN check into AppsFlyer. Meta, Google, Unity, and Moloco do not commit that kind of capital to a neutral third-party MMP unless they have come to one conclusion: Apple’s own attribution framework is dead.

Five years ago, Apple forced the ecosystem onto SKAN under the threat of ATT. Companies spent millions rebuilding measurement around a framework they had no hand in designing. Then Apple suddenly stopped all communication around it. Apple quietly replaced SKAdNetwork with AdAttributionKit as the named successor in 2024, and this year’s WWDC passed without a single update to it.

No roadmap, no deprecation notice for what it replaced.

Drew Gooden’s The Music Industry is Broken video is the best breakdown of how Spotify and other music streaming services pay artists so poorly, and why AI makes an already bad situation even worse.

Spotify doesn’t pay a per-stream rate. It pays by streamshare: a fixed monthly pool of subscription revenue, divided according to each rightsholder’s proportion of total streams.

Every new track added is another claimant on the same money. Flood the catalog with cheap functional tracks or AI-generated filler, and individual artists' payouts shrink even if their own stream counts hold steady.

Well, unfortunately, that means that at the end of the month, about 99% of your subscription revenue has contributed to a bunch of anonymous, faceless stock music that you weren’t even awake to hear. And the remaining 12 cents goes to your favorite band.

The money flows toward whoever controls the volume: major labels, playlist curators, Spotify’s own commissioned content. Spotify’s margin improves because the filler costs almost nothing to license.

But that’s not it. Introducing: Discovery Mode. Artists can pay for visibility on the same platform that just diluted their payout. The fee is a 30% royalty cut. Spotify created the problem to sell the solution.

Alice Han and James Kynge’s China Decode newsletter makes a case that most coverage of the AI race gets wrong. The real contest is physical, and China is already ahead:

I’ve long believed the AI race extends well beyond generative AI and large language models. That’s certainly the way China sees it. While the private sector in the U.S. spends 12 times as much on computing power, China spends 42% more on robotics, a gap that’s only going to widen. Where AI and robotics intersect, China has an edge.

China now produces roughly 90% of the world’s humanoid robots. At the same time, China’s NDRC has flagged a domestic bubble. More than 150 robotics makers are active, most operating at Level 0 or 1, running scripted actions or teleoperated demos.

Both things are true. A structural lead built on factories that can repoint to robotics, and a manufacturing culture built for scale, does not require every startup to survive.