Rajesh Jain

FT: “[Jensen] Huang’s take on AI economics is based around the production, consumption and monetisation of tokens. These are the most basic units of output from large language models: it takes about 1,300 tokens to generate 1,000 words of text. The key metric, he argues, is the cost per token of output. And as the main input into AI-powered services, he adds, tokens translate directly into revenue.”

WSJ: “Claws are autonomous agents and can plan and execute tasks on their own, and, critically, spin up their own subagents to tackle specialized tasks, access files and themselves delegate tasks to other subagents. They represent a big leap beyond question-and-answer-style AI chatbots as well as recent iterations of AI agents, which typically have narrow use cases and run for a set amount of time—although claws also come with a new set of security concerns. For claws to work as a true personal assistant, they need access to all of a user’s data. So what are people using them for today?”

WSJ: “AI tools like Anthropic’s Claude Code, Cursor and OpenAI’s Codex can now write and debug software, unlocking huge new sources of revenue. That success is pushing their makers toward a bigger ambition: automating our entire lives. What began as a way to autocomplete code quickly evolved into semiautonomous AI bots, or “agents,” that can work for hours on end with little human oversight. We can tell a bot to create a presentation for work, coordinate the family’s schedules and pick a March Madness bracket, all while it learns our personal preferences, no coding needed…The shift has permanently changed the lives of coders and sparked a $1 trillion market selloff as investors and executives contemplate the technology’s potential to reshape industries, including finance, legal and healthcare. Tens of thousands of job cuts have already been attributed to AI.”

Pete Boettke: “In the late 19th century, Italian economist Vilfredo Pareto (1848-1923) expanded on this point, observing that co-ordinating even a modest economy and matching resources to uses and preferences would soon cause an explosion in the number of equations to be solved. But today’s computers can handle quintillions of computations per second, more than Pareto could possibly have imagined. Doesn’t that make a difference? This is where Nobel laureate economist Friedrich Hayek (1899-1992) comes in. Hayek explained that the problem is not merely that the relevant knowledge is decentralized — spread out across millions of individuals — but that it is often tacit. Local shopkeepers’ understanding of their customers’ buying habits cannot be translated into one data point to feed into an AI or any other kind of model. Nor can we predict the emergence of an entrepreneur dreaming up a product that did not exist before…Prices are not lying around in the wild, waiting to be harvested and fed into an algorithm. Rather, they are the result of constantly evolving discovery. Without this process of discovery, the knowledge embedded in a price simply doesn’t come into existence…As powerful and helpful a tool as AI can be to improve logistics, better manage inventories and analyze markets, it remains just that, a tool. It can help us gain a better understanding of markets but only markets themselves can predict and co-ordinate the results of the billions and billions of voluntary exchanges that take place every day.”

Forbes: “As CEO, you face challenges every day. Whether it’s a meeting that spirals, a missed deadline or rising tension, it’s easy to react negatively. I’ve found that instead, it helps to pause and ask a simple question: “Isn’t this interesting?” It’s one of the most effective tools I use to stay grounded, make better decisions in fast-moving environments and build a culture that does the same. When you’re observing, you’re not in fight-or-flight. You’re not reaching for control or trying to protect your ego. You’re just seeing what’s there, reframing your frustration into curiosity. That clarity leads to smarter decisions.”

WSJ: “Getting fooled into thinking that AI is thinking is what I call the Turing Trap. Alan Turing, godfather of modern computing and AI, proposed a simple test to determine whether a computer had attained human-level intelligence: If a person chatting with a bot couldn’t tell if it was human, it might as well be declared intelligent. What became known as the Turing Test doesn’t stipulate how a machine achieves this. At the time, language was thought to be closely associated with reasoning, but modern neuroscience shows us that it’s a separate process. Speaking isn’t the same as thinking, let alone being. Rather than demonstrating that machines have achieved intelligence, the Turing Test shows that linguistic fluency is possible even in its absence.”

Naomi Klein: “A “doppelgänger” is a German word that means, literally translated, a “doublegoer” or a “double walker.” It’s the idea that out there, somewhere, you could bump into somebody who looks just like you — but isn’t you. It’s that uncanny vertigo that addresses the strangeness of that which is most familiar — which is yourself. “Mirror world” is a term I use to describe the relationship between the liberal left world and the far right world, and the ways in which, when people are ejected from our world, they end up in a world that is the exact mirror of where we live — in replica social media platforms, the same but different doppelgänger publishing worlds, doppelgänger narratives of the narratives that we tell ourselves.”

WSJ: “The core tenets of somatics are a series of slow movements designed to release tension that leads to pain and hinders flexibility and mobility. The practice proposes something more rare than perfectly toned arms: un-jangled nervous systems.”

Noah Smith: “In the late 20th century, we2 invented three things that utterly changed the game. These three inventions were the lithium-ion battery, the rare-earth electric motor, and power electronics. A little over a year ago, I wrote a post about why these three inventions were such game-changers: Basically, these three things allow electric motors to replace combustion engines (and steam boilers) over a wide variety of applications. Batteries make it possible to store and transport electrical energy very compactly and extract that energy very quickly. Rare-earth motors make it possible to use electrical energy to create very strong torques — for example, the torque that turns the axles of a Tesla. And power electronics make it possible to exert fine control over large amounts of electric power — stopping and starting it, rerouting it, repurposing it for different uses, and so on. With these three technologies, combustion’s main advantages vanish in many domains. Whether it’s cars, drones, robots, or household appliances, electric technology now has both the power and the portability that only combustion technology used to enjoy.”

Siddharth Pai on OpenClaw: “The most useful term in this debate is the ‘lethal trifecta,’ popularized by Simon Willison. The three parts are precise. First, the agent has access to private or sensitive data. Second, it is exposed to untrusted content such as text, images or other material that an attacker can influence, whether through a webpage, email, document or bug report. Third, it can communicate externally; for example, by sending a message, calling an API or writing outside its trust boundary. The phrase ‘lethal trifecta’ doesn’t mean the software is evil, but that the architecture is dangerous. Private data supplies the prize, untrusted content supplies the attack path and external communication the escape route. If these features co-exist in one agent, prompt injection can turn a helpful assistant into an unwitting exfiltration channel.”

Ben Thompson: “Many of the biggest flaws from the original ChatGPT have been substantially mitigated, at least for verifiable use cases like coding: LLMs are much more likely to be right the first time, they reason over their results to increase their chances, and now agents actively verify the results without humans needing to be in the loop. That leaves one flaw: actually figuring out what to use these for.”

Asymco: “Apple turned 2 billion devices into the data center.​ Every iPhone, Mac, iPad gets distributed AI at a scale no server farm can match. While its rivals burn cash, Apple is doing the opposite. $90.7 billion in stock buybacks last fiscal year.​ Its competitors? Combined buybacks collapsed 74% from their peak.​ Apple didn’t miss the AI revolution. It just bet that the winners won’t be the ones who build the infrastructure. They’ll be the ones who own the customer and no one else on Earth owns the best customers.”

WSJ: “You can think of AI as a restaurant. The model is the chef. After it undergoes a period of intensive training, learning hundreds (or billions) of recipes and techniques, it is ready to begin taking orders. Inference is the day-to-day operation of the restaurant. Diners place their orders (often in the form of a query to a chatbot) and the chef prepares their meals (the chatbot’s response). Inference consists of two phases, known as prefill and decode. Prefill happens when a user enters a prompt, forcing the model to interpret the query by processing each word, symbol or image it contains. Decode is the process by which the model, using all it has learned in training, spits out a response to the query. The two phases of inference require different attributes from chips: Prefill demands more processing power, while decode requires more memory, in part because it has to draw on all the knowledge it has accumulated to serve up nice, piping-hot tokens to the user.”

Tyler Cowen: “If strong AI will lower the value of your human capital, your current wage is relatively high compared to your future wage.  That is an argument for working harder now, at least if your current and pending pay can rise with greater effort (not true for all jobs). If strong AI can at least potentially boost the value of your human capital, you should be investing in learning AI skills right now.  No need to fall behind on something so important.  You also might have the chance to use that money and buy into the proper capital and land assets. So…WORK HARDER!”

FT: “Human intellect rests on three pillars: seeing (observing the world), doing (intervening in it) and imagining (simulating what might happen under different choices). Right now, artificial intelligence inhabits only one of these pillars. Expanding existing frontier AI models will not address this problem. The breakthrough that set off today’s frenzy was the transformer architecture, developed at Google and scaled up into large language models trained on much of the public internet and used to write text and code. Then came agents that stitch these models together into automated workflows. Now the focus is on “world models”, which try to capture the physical environment from vast streams of video and other inputs. World models are an important evolution from LLMs. This so-called spatial intelligence is being used to develop technology that can enable driverless cars and robotic factory workers.”

WSJ: ““We are, hands down, the sweatiest animal on the planet,” writes Bill Gifford in “Hotwired.” His splendid book is devoted to a topic—the health benefits derived from exposure to high temperatures—that could be tedious in the wrong hands…He begins “Hotwired” with an accessible tour through the biology and evolutionary history of perspiration. There are two kinds of sweat glands in mammals: apocrine and eccrine. The vast majority of those in humans are the eccrine variety; adults have between two and four million in all. These glands are concentrated on the hands and feet and are missing from only a few places on the human body (our lips, for example). Eccrine sweat glands perform a critical function: cooling the body via evaporation. Mr. Gifford characterizes this adaptation, which dates back millions of years, as “evolution’s nuclear weapon.””

Dylan Patel: “The biggest bottleneck is compute. For that, the longest lead time supply chains are not power or data centers. They’re actually the semiconductor supply chains themselves. It switches back from power and data centers as a major bottleneck to chips. In the chip supply chain, there’s a number of different bottlenecks. There’s memory, logic wafers from TSMC, and the fabs themselves. Construction of the fabs takes two to three years, versus a data center which takes less than a year. We’ve seen Amazon build data centers in as fast as eight months. There’s a big difference in lead times because of the complexity of building the fab that actually makes the chips. The tools also have really long lead times. The bottlenecks, as we’ve scaled, have shifted based on what the supply chain is currently not able to do. It was CoWoS, power, and data centers, but those were all shorter lead time items. CoWoS is a much simpler process of packaging chips together. Power and data centers are ultimately way simpler than the actual manufacturing of the chips. There’s been some sliding of capacity across mobile or PC to data center chips, which has been somewhat fungible.”

Tim Wu: “Until now, companies like Meta and Google have relied on some powerful legal defenses. They do not deny that their products can be highly absorbing. But so, they contend, is a good novel, and no one suggests that a beach thriller is a public health hazard; a novel is speech protected by the First Amendment. Moreover, the companies note, unlike the publisher of a novel, which can still be responsible for defamation, social media companies cannot be held responsible for what appears on their platforms, thanks to Section 230 of the Communications Decency Act of 1996, which was intended to protect platforms from being destroyed by tort lawsuits. But changing circumstances have undercut these arguments. For one thing, if the platforms in the 1990s and 2000s were passive carriers of others’ content (albeit filtered by human moderators), they are now active purveyors. The platforms use aggressive tactics to keep users compulsively engaged — algorithmic recommendations, infinite scroll, auto video play and intermittent reinforcement (in which likes, comments and refreshed content are rewarded unpredictably rather than consistently). This goes far beyond merely hosting and moderating third-party content.”

Tom Rothman: “Windows, in entertainment lexicon, refers to a period during which a product — whether a film, TV show or sporting event — is available exclusively to the public in one place. For movies, these windows occur sequentially: first in movie theaters, then on home video, then on pay TV and streaming, then eventually on free TV. This system was meant to ensure that if you made a good film at an appropriate budget and the audience liked it, it would usually be profitable. Of course, if you made a bad film, all the windows in the world won’t save you.”

Jason Lemkin: “Let me ask you a simple question. When was the last time you were genuinely excited to buy a pre-AI SaaS tool? Not “fine with it.” Not “it gets the job done.” Genuinely excited. The way you were excited the first time you saw Slack replace email threads. Or the first time Figma made you forget Sketch existed. Or the first time Notion made you feel like your entire company’s knowledge actually lived somewhere…Now let me ask you a harder one: When was the last time you felt like you were overpaying on a renewal for a pre-AI SaaS tool — and seriously thought about cancelling?…Now ask yourself.  How do your customers feel about both these points?  Be brutally honest…Your product probably isn’t magical anymore. And your customers know it.”