Ep. 267: Social media = cigarettes? In March, juries in California and New Mexico delivered seminal verdicts holding Meta and YouTube liable for failing to protect young users from harm. Both verdicts found that the companies were negligent in the design or operation of their platforms and that each company knew their platforms could be dangerous when used by a minor. The courts found that the design elements of the platforms could be separated from the content hosted on the platforms, thus removing the need to consider the First Amendment or Section 230 of the Communications Decency Act. CEO & founder of @techdirt & @CopiaInstitute @mmasnick joins @TheFIREorg's @NicoPerrino to break down the rulings and their possible free speech implications. Timestamps: 00:00 Intro 02:29 Why these verdicts scare the hell out of Mike 10:34 Are social media algorithms “addictive”? 21:45 Did Meta fail to protect kids? 30:37 The First Amendment and Section 230 43:13 Is social media the new Big Tobacco? 55:15 The role of parents in social media use 59:04: Outro Enjoy listening to the podcast? Donate to FIRE today and get exclusive content like member webinars, special episodes, and more. If you became a FIRE Member through a donation to FIRE at and would like access to Substack’s paid subscriber podcast feed, please email sotospeak@thefire.org.

Would you date someone without knowing their age? And deeper than that: does age really matter in love? I recently came across Netflix’s dating show "Age of Attraction", where people date each other without knowing one of the most basic “filters” we usually apply almost instantly: age. It sounds like a reality TV gimmick, but the question behind it is actually fascinating. Because age is never just a number - Age carries assumptions. We hear someone is 25, 35, 45, or 55, and immediately we start filling in the blanks: life stage, fertility, maturity, lifestyle, financial stability, emotional baggage, future plans, social status, even desirability. Sometimes those assumptions are useful. A 25-year-old and a 45-year-old may genuinely be in very different places when it comes to marriage, children, career, energy, and worldview. But sometimes those assumptions become a cage. We may dismiss someone before discovering their emotional intelligence, curiosity, depth, humor, tenderness, or capacity to love. What I find interesting is how differently cultures treat age in dating. In many Asian cultures, age can still be tied closely to marriage timing, family expectations, fertility pressure, and “appropriate” life stages. A woman’s age is often judged more harshly than a man’s. A man who is older may be seen as stable; a woman who is older may be unfairly treated as “past her prime.” In Western dating culture, there is often more language around individual choice, chemistry, and personal freedom. But even there, age gaps are not free from judgment. Older men dating younger women are normalized in some circles, criticized in others. Older women dating younger men are increasingly visible, but still often treated as a statement rather than simply a relationship. So the real question is not only: “Does age matter?” The better question may be: What exactly are we using age to measure? Are we measuring maturity? Life goals? Power dynamics? Fertility? Social approval? Shared cultural references? Emotional readiness? Or are we simply using age as a shortcut because true compatibility is harder to evaluate? In dating, age can matter. But it should not matter alone. A healthy relationship still needs aligned values, emotional maturity, mutual respect, attraction, timing, and the ability to build a life together. Maybe age is just a clue. And like all clues, it needs context. Not caring about age at all, and letting age replace discernment - both are dangerous! So I’ll ask again: Would you date someone without knowing their age? And if the connection felt real, when would you want to know? 😉 Do you think people should only date people their age?

Why Generic Humanoid Robots Will Fail — And What's Next Imagine an alternate world where we never invented the car. In that world, a robotics engineer might reasonably conclude that robotic horses are the future — replace the living ones, keep the stables and saddles, ride them to work. Convenient, modern, and the roads stay free of manure. It sounds absurd only because you already know about cars. We keep making the same mistake with humanoid robots. Consider transportation. To finally make driving safe, we had two options: put a humanoid in the driver's seat, or embed sensing and compute directly into the vehicle. Waymo chose the latter. It has no steering wheel. It exists purely to move people efficiently from A to B. The humanoid was not needed. Consider a sock factory. Yes, you could replace workers with humanoid robots one-for-one on the assembly line — and gain maybe 2-3x efficiency. Or you could completely redesign the workflow around a purpose-built autonomous sewing system and eliminate most of the factory, the chairs, the cafeteria, the manual sewing machines, the HVAC, the doors, and the restrooms. The actual optimization is to side-step the previous human-imposed physical constraint. Look at Ukraine. The front lines aren't filling up with Terminator-style humanoids carrying rifles. Human soldiers are being replaced by heterogeneous swarms of purpose-specific drones: some for reconnaissance, some for logistics, some for delivering munitions. War is being restructured around the desired outcome (survival), not the soldier's shape. Consider a 1970's office. Want to move information through teams of people? We once used typists, paper, trucks to supply the paper, typewriters, and repair technicians. A linear improvement would have been to replace the human typist with a 10-fingered humanoid. What actually happened? The entire workflow — paper, printers, typewriter factories, delivery trucks, the desks, the offices — was obliterated. Email deleted the human clerk's entire universe. Consider cancer early detection by mammography. Today, getting a mammogram requires expensive hardware, logistics infrastructure, human nurses and doctors, a biopsy workflow, a human pathologist with a microscope (imported from Germany or Japan), a written finding, multiple physician reviews. Sure, you could replace the pathologist with a humanoid (the microscope focus knob requires finger dexterity) and get a modest efficiency gain (and faster responses at 2 am). Or — the far more likely future — we all swallow a cancer detection pill every few months, and 24 hours later a color-changing sticker on our arm turns red or green. No hardware. No hospital. No logistics. No pathologist. No office. No desk. No humanoid. The workflow isn't optimized by a literal drop-in swap of a human pathologist for a humanoid. The entire workflow simply ceases to exist. Consider life sciences research and drug development. We're seeing excitement about robot arms and humanoids pipetting water in research labs. Robot horses, episode 7. We don't design aircraft by crashing test planes — we simulate them entirely in software first. Biology will go the same way. The path to scalable drug discovery isn't robot arms in conventional wet labs demonstrating 10 fingered prowess in manipulating Eppendorf tubes filled with purple food coloring. Rather, we need in-silico biological models that evaluate billions of hypotheses computationally, with physical manipulation of atoms only at the very end. The clear pattern. Efficient automation doesn't try to replicate a 10-fingered human in a static context. Automation eliminates physical rate-limiting steps in their entirety. That's why "classical" humanoid robots, as a generic category, will largely fail. They're robotic horses. They assume the infrastructure and workflows stay fixed and only the 10-fingered human is swapped out. That's not how economic and technological pressure works. What actually matters? If humans continue to inhabit the physical world, then moving atoms will remain important, and that requires five things: atoms, energy, force generation and actuation, sensing, and compute. Everything else — form factor, number of limbs, type of end effector — is a variable to be optimized for the task. So if you are a pathologist, a robotics engineer, a teacher, a parent, a politician, or a sewing factory owner - please think different. Most obviously, we should all anticipate, and build for, a future in which robots exhibit extreme physical fluidity: Two arms or four. Wheels or legs. Tentacles or flippers. Three fingers or twelve, or none at all. Eyes at the front, side, or tip of a tentacle. At OpenMind, we don't care what you look like right now - we got you, in all your physical form factors. OM2 ships in July, for all machines. Let's build.

Jensen Huang says we've achieved AGI when an AI can build a billion-dollar app then immediately die. The AGI debate has one definition problem. Jensen Huang gave Lex Fridman a one-word answer. "I think it's now," Huang says. "I think we've achieved AGI." The reason is the small "forever" clause Lex didn't include. "You said a billion, and you didn't say forever." "It is not out of the question that a Claw was able to create a web service, some interesting little app that all of a sudden, a few billion people used for 50 cents, and then it went out of business again shortly after." "We saw a whole bunch of those type of companies during the internet era, and most of those websites were not anything more sophisticated than what OpenClaw could generate today." The bar isn't an AI that runs Apple. The bar is an AI that builds a dotcom-era app that goes viral and dies. "It's happening right now," Huang says. "When you go to China you're gonna see a whole bunch of people teaching their Claws to go out and look for jobs and do work, make money." Jensen Huang isn't asking when AGI arrives. He's pointing at the AI agents already on the way to a job interview. They're already filling out the application. P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers. 5,000+ downloads. 113 five-star reviews. Grab a free copy here: If you're new here, follow @GeniusGTX for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( @nvidia ), NVIDIA CEO, on Lex Fridman's ( @lexfridman ) podcast

The anti-AI coalition continues to maneuver to find arguments to slow down AI progress. If someone has a sincere concern about a specific effect of AI, for instance that it may lead to human extinction, I respect their intellectual honesty, even if I deeply disagree with their position. However, I am concerned about organizations that are surveying the public to find whatever messages will turn people against AI, and how the public reacts as these messages are spread by lobbyists or by politicians seeking to alarm constituents, companies pursuing regulatory capture or seeking to promote the power of their technology, and individuals seeking to gain attention or to profit by being provocative. A large study (link in original article below; h/t to the AI Panic blog) by a UK group tested different messages that are designed to raise alarm about AI. Their study found that saying AI will cause human extinction has largely failed. Doomsayers were pushing this argument a couple of years ago, and fortunately our community beat it back. But AI-enabled warfare and environmental concerns resonate better. We should be prepared for a flood of messages (which is already underway) arguing against AI on these grounds. Further, job loss and harm to children are messages that motivate people to act. To be clear, I find AI-enabled warfare alarming; we need to continue serious efforts to monitor and mitigate the environmental impact of AI; any job losses are tragic and hurt individuals and families; and as a father, I hold dearly the importance of every child’s welfare. Each of these topics deserves serious attention and treatment with the greatest of care. But when anti-AI propagandists take a one-sided view of complex issues to benefit their own organizations at the expense of the public at large — for instance, when big AI companies argue that AI is dangerous to block the free distribution of open source projects that compete with their offerings — then we all lose. For example, public perception of data centers’ environmental impact is already far worse than the reality — data centers are incredibly efficient for the work they do, and hampering their buildout will hurt rather than help the environment. While job loss is a real problem, the “AI washing” of layoffs — in which businesses that had over-hired during the pandemic blame AI for recent layoffs, although AI hasn’t yet affected their operations — has led to overblown fears about the impact of AI on employment. Unfortunately, this sort of propaganda easily leads to regulations that create worse outcomes for everyone. For example, oil companies worked for years to create fear of nuclear energy. The result is that overblown concerns about the safety of nuclear power plants has stifled nuclear power development, leading to millions of premature deaths from air pollution that was caused by other energy sources and a massive increase in CO2 emissions. Let’s make sure overblown concerns about AI do not lead to a similar fate for the many people that would benefit from faster AI development. Last week, the White House proposed a national legislative framework for AI. A key component is a federal preemption framework to prevent a patchwork of state regulations that hamper AI development. I support this. After failing to gain traction at the federal level, a lot of anti-AI propaganda has shifted to the state level. If just one of the 50 states passes a law that limits AI in an unproductive way, it could lead to stifling AI development across all the states and potentially across the globe. The White House proposal rightfully respects each state’s rights to control its own zoning, how it enforces general laws to protect consumers, and how it uses AI. But if a state were to pass laws that limit AI development, federal rules would preempt the state law. The White House proposal remains a proposal for now. However, if the U.S. Congress enacts it, it will clear the way for ongoing efforts to develop AI in beneficial ways. Where do we go from here? Let’s support limiting applications — those that use AI, and those that don’t — that harm people. When the anti-AI coalition argues against AI, in addition to considering the merits of the argument, I consider whether their position is consistent and persuasive, or if they are just promoting whatever concerns they think will sway the public at a given moment. And, let’s also keep using a scientific approach to weighing AI’s benefits against likely harms, so we don’t end up with overblown concerns that limit the benefits that AI can bring everyone. [Original text with links: ]