Ai

For 14 years, Google has been the undisputed king of digital advertising. That ends in 2026. Meta’s global net ad revenue is projected to hit $243.5B this year, edging past Google’s $239.5B, and the gap is only expected to grow. What makes this interesting is not just the numbers, it is what they represent. Google built its empire on search, on the idea that intent is the most valuable signal an advertiser can buy. And for a long time, that was true. ...

I’m as bullish on AI as anyone. But there’s a pattern playing out in companies right now that I find genuinely frustrating, and this article puts it well. Goodhart’s Law says that when a measure becomes a target, it ceases to be a good measure. And right now, AI usage is the target. Token counts, Anthropic bills, n8n workflows shared in Slack, skills written in markdown, dashboards tracking adoption. In fact, dashboards all over the place. ...

Andon Labs put an AI called Luna in charge of a real retail store in San Francisco. Not a simulation, not a sandbox. A real shop, real money, real decisions. Luna hired human staff, selected inventory, set prices, and ran marketing outreach, all on her own, for three years. What I find genuinely impressive is not that it worked perfectly, it didn’t, but that it worked at all at this level. Luna was doing things that require judgment: reading job applicants in brief interviews, deciding which products fit the store’s identity, reaching out to suppliers. She picked books on AI risk and handmade art prints for the shelves. She hired on the spot about half the people she met. ...

Stanford HAI just published its 2026 AI Index and it is one of those reports that forces you to sit down and rethink what you thought you knew. AI has reached more than half the world’s population faster than the PC or the internet ever did, but the public mood around it is dark. Trust is at record lows, entry level workers are getting squeezed out, and the gap between what insiders think and what everyone else feels has never been wider. ...

One of the things that still makes robots look robotic is the way they move. Jerky, mechanical, imprecise. A big part of that comes down to how they are built, servo motors crammed into joints, converting rotation into movement in a way that biological muscle simply does not. Researchers at MIT and Politecnico di Bari may have just found a better way. They developed what they call electrofluidic fiber muscles, tiny actuators about as thick as a toothpick that contract when electricity is applied, no motors, no external pumps, no noise. The whole thing works by injecting charge into a sealed dielectric fluid, which creates ions that move the fluid and generate pressure. The result is a fiber that behaves remarkably like real muscle. ...

I came across this story and had to read it twice. Not because it was complicated, but because I could not quite believe what I was reading. We are living in strange times, but this one really made me stop. Allbirds, the wool sneaker company, just announced a $50M convertible financing deal to reinvent itself as “NewBird AI”, pivoting the gutted footwear business into a GPU rental operation. The stock went up over 600% on the news, jumping from around $3 to over $20, off a market cap that closed Tuesday at just $22M. Someone, somewhere, in a boardroom, thought this was a good idea and managed to convince others to go along with it. ...

Humanoid robots going mainstream is one of those things that feels like it is always five years away. But reading this, I got genuinely excited. Tesla thinks it has found its shortcut and the answer is Shanghai. Allan Wang Hao, Tesla China’s president, called the Gigafactory a “golden key” for scaling Optimus production. And when you look at the numbers it is hard to argue. The Shanghai plant pushed out 851,000 electric vehicles in 2025, more than half of everything Tesla made globally. The supply chains, the assembly lines, the engineering muscle, it is all already there. Tesla wants to point that same machine at a humanoid robot with 40 degrees of freedom and see what happens. ...

Cursor raises $2.3 billion at a $29.3 billion valuation. Nvidia and Google both joined the round. Cursor launched its own in-house mixture-of-experts model, rewrote the kernels directly, skipping high-level CUDA libraries and working directly in raw CUDA and PTX (link). They got up to four times faster than comparable models. For engineers, that’s not just a speed bump, it’s a different category of tool. Coding tasks that used to take minutes are now completed in under 30 seconds. According to Cursor, their own model is already the most-used on the platform, which means they’re not just riding on OpenAI or Anthropic anymore. They’re building the rails themselves. ...

Anthropic has just surfaced what appears to be the first large-scale, mostly autonomous, AI-orchestrated cyber espionage campaign. In mid-September 2025, they detected suspicious behaviour that turned out to be a sophisticated operation run by a Chinese state-sponsored group. AI wasn’t just “helping.” It was doing most of the work. The attackers jailbroke Claude Code, framed it as doing defensive testing, and then drove it through an automated framework aimed at ~30 global targets, big tech, finance, chemicals, and government. A few intrusions succeeded. ...

Investors poured over $9.5 billion into AI processor startups in 2024, betting on architectures that could reshape inference economics. NVIDIA itself projects the broader AI-infrastructure market could reach $3,4 trillion by 2030. That kind of capital rarely gathers around incremental improvements, it usually signals an architectural inflection point. Yet GPUs still dominate both training and most inference workloads today, so any transition will be evolutionary before it is disruptive. ...