FT: “For now, most US tech groups treat AI like an exclusive resource, restricting access to their most powerful models behind paywalls…But Chinese tech groups are taking a very different approach. By open sourcing AI, they not only sidestep US sanctions but also decentralise development and tap into global talent to refine their models. Even restrictions on Nvidia’s high-end chips become less of an obstacle when the rest of the world can train and improve China’s models on alternative hardware. AI advances through iteration. Every new release builds upon the last, refining weaknesses, expanding capabilities and improving efficiency. By open sourcing AI models, Chinese tech groups create an ecosystem where global developers continuously improve their models — without shouldering all the development costs. The scale of this approach could fundamentally reshape AI’s economic structure. If open-source AI becomes just as powerful as proprietary US models, the ability to monetise AI as an exclusive product collapses. Why pay for closed models if a free, equally capable alternative exists?”
Shane Parrish: “The most powerful productivity tool ever invented is simply the word “no.”…The single most effective habit is the willingness to change your own mind…If you want to understand someone, figure out the narrative they tell themselves about themself. If you want to change your behavior, change your narrative. If you want to change someone else’s behavior, offer them a more compelling narrative they can tell themselves.”
Niranjan Rajadhyaksha: “The 1991 reforms did away with all sorts of controls on investment, trade and prices. However, even more than three decades later, there is an entire web of regulations that impose immense burdens on companies, especially the smaller ones. Not all these are under the Union government. Many of the most onerous regulations are within the ambit of various state governments. Current attempts at deregulation in India will thus have to target constraints on firms at all levels of government.”
VentureBeat: “Perhaps the most technically intriguing aspect of Zoom’s AI strategy is its focus on SLMs. Rather than following the industry trend of distilling smaller models from larger ones, Zoom built its 2-billion parameter model entirely from scratch. The technical advantage of this approach becomes apparent when customizing for specific domains. “When you customize, it takes more effort, it’s just hard to steer a bigger ship,” [Zoom CTO Xuedong] Huang explained. As it turns out, the ability to customize the small model is a critical component to the development of specific agentic AI workflows. Looking ahead, Zoom envisions its SLMs eventually running directly on user devices, enabling both better privacy and more personalized experiences. At the heart of Zoom’s updates is AI Companion 2.0, which transforms Zoom’s AI capabilities from meeting support to fully agentic functions. With 2.0, Zoom is evolving from assistant to agentic AI that is capable of reasoning, memory and task execution.”
Econlib: “Populism is one of the most important political phenomena of our time. Yet, it is still poorly understood. At its core, populism is built on the notion that the masses are engaged in a struggle against corrupt elites who have rigged the political and economic system to their advantage. Whether left-wing or right-wing, this is the essence of the populist narrative: an appeal to “the people” against “the elite” and the claim to restore power to ordinary citizens by breaking the grip of entrenched interests. But can populism effectively challenge crony capitalism—a system where the political and economic elite are entangled? Can it truly dismantle the grip of entrenched interests? In a recent working paper, we argue that populist movements are likely to fail to deliver on their promises. The reason is that populism does not resolve the dual epistemic and incentive challenges necessary for success.”