エピソード

  • E172: MMT Is Going Mainstream - Right as the AI Bubble Is About to Pop: Explained by Dr. Maggiori

    E172: MMT Is Going Mainstream - Right as the AI Bubble Is About to Pop: Explained by Dr. Maggiori
    2025/12/09
    A wide-ranging conversation with economist and AI consultant Dr. Emmanuel Maggiori on why Modern Monetary Theory overpromises a “free lunch,” what really causes inflation, how Bitcoin and AI are misunderstood, and why seductive economic stories are so dangerous.GUEST BIO:Emmanuel Maggiori is an armchair economist, computer scientist, and AI consultant based in the UK. Originally from Argentina, he has a PhD (earned in France), works with companies to build AI systems, and writes widely about economics and artificial intelligence. He is the author of several books, including If You Can Just Print Money, Why Do I Pay Taxes? Modern Monetary Theory Distilled and Debunked in Plain English, Smart Until It’s Dumb, and The AI Pocket Guide, and has a large following on LinkedIn and X/Twitter.TOPICS DISCUSSED:What Modern Monetary Theory (MMT) actually claimsHow money is created in modern economies (broad money vs reserves)Why MMT’s “taxes don’t fund spending” story is misleadingStephanie Kelton’s accounting error and the “deficit myth”The Cantillon effect and who really pays for money printingArgentina, Venezuela, Zimbabwe, and real-world inflation episodesJavier Milei, austerity, and Argentina’s recent disinflationGovernment debt, “we owe it to ourselves,” and default via inflationBitcoin as a supposed solution to monetary problemsWho really created Bitcoin and what it’s actually good forThe current AI boom, why it’s a bubble on the business side, and unit economicsOpenAI, DeepSeek, Nvidia, and why foundational models lack a moatHow AI will change the labor market (coders, translators, blue-collar work)AI, Hollywood/TV writing, and the gap between “good enough” and truly excellent workFinal cautions about seductive economic theories and AI hypeMAIN POINTS:MMT in a nutshell: MMT says a government with its own currency can always create money to pay for spending and debt, and that taxes exist mainly to control inflation, create demand for the currency, and shape behavior—not to “fund” spending.Accounting problems in MMT: Emmanuel argues that key MMT figures (especially Stephanie Kelton) made basic accounting errors about government bank accounts and money aggregates like M1, then papered over them with exceptions (e.g., temporary overdrafts at central banks).Why taxes really matter: Even if a government could print money, in practice you need taxes before spending because the Treasury’s accounts can’t just go endlessly negative—and politically, raising taxes fast enough to control inflation is extremely unlikely.Cantillon effect & asset swaps: Paying off debt with newly created money is not a harmless “asset swap.” It channels new money first to financial institutions, inflates asset prices and credit, and ultimately erodes the real value of ordinary people’s cash savings.Real-world inflation is not an accident: In cases like Argentina, Venezuela, Zimbabwe, or Weimar Germany, there were real triggers (droughts, war reparations, commodity shocks), but the hyperinflation came from repeated resort to money printing as the default response.Argentina as a warning: Emmanuel’s personal experiences—suitcases of cash for a normal dinner, unusable mortgages, dollarized house purchases—illustrate how chronic money printing and price controls destroy trust, planning, and basic economic functioning.Javier Milei & austerity: Milei sharply cut deficits and money printing; inflation has fallen quickly. Critics say it’s just recession-driven demand collapse, but Emmanuel notes history shows disinflation often follows when governments stop printing and cut spending.Debt and “we owe it to ourselves”: Government debt is a real intertemporal deal: some people give up current consumption so the state can use resources now, in exchange for more consumption later. Unexpected inflation is an economic default on those savers.Bitcoin skepticism: Bitcoin solves a fascinating technical problem (a decentralized, hard-to-alter ledger), but Emmanuel questions its use as a stable store of value (because of huge volatility) and notes there are other ways to protect savings (equities, etc.).AI bubble dynamics: AI as a technology is here to stay and genuinely useful, but foundational model providers have thin or no moats—methods are public, competitors catch up, and models become commodities competing on price with brutal compute costs.Nvidia and the “shovel sellers”: Chip makers selling GPUs may fare better than model labs, but there are worrying signs (like unsold inventory) that they may be over-producing “shovels” for a gold rush that can’t all pay off.AI startups on top of models: Most AI-powered apps (wrappers for therapy, yoga, productivity, etc.) have almost no defensible edge. Anyone can build similar products, so profits will be squeezed and many will fail.Work & careers in the AI age: He wouldn’t steer a kid away from computer science—but urges them to be at ...
    続きを読む 一部表示
    1 時間 44 分
  • E171: How the Internet Got Tamed: James Corbett on Media & Power

    E171: How the Internet Got Tamed: James Corbett on Media & Power
    2025/12/06

    Independent journalist James Corbett joins Jesse to trace how media, tech, and elite power have reshaped the information landscape—from Time’s 2006 “You” to today’s post-truth, AI-saturated world.

    GUEST BIO:
    James Corbett is an independent journalist and documentary filmmaker based in Japan. Since 2007 he’s run The Corbett Report, an open-source intelligence project covering geopolitics, media, finance, and technology through long-form podcasts, videos, and essays.

    TOPICS DISCUSSED:

    • Time’s 2006 “Person of the Year” and the early optimism of user-generated media
    • Smartphones, YouTube, and the shift to always-on, short-form video
    • Legacy media vs podcasts, Rogan, and long-form conversation
    • Adpocalypse, subscriptions, foundations, and “post-journalism”
    • AI “slop,” dead internet theory, and human vs synthetic content
    • Left–right vs “up–down” (authoritarian vs anti-authoritarian) politics
    • Elite networks and foundations: Rockefeller, Gates, philanthropy as power
    • Climate narratives, health framing, and energy demands of AI
    • Future crises: hot war, financial bubbles, AI and labor, UBI and control

    MAIN POINTS:

    • The early internet briefly empowered ordinary people. Corbett’s own path—from teacher in Japan to reaching millions—shows how 2000s platforms genuinely opened space for bottom-up media.
    • The smartphone changed how we think, not just what we see. Moving from long-form text/audio to short, swipeable video has compressed attention and pushed politics toward slogans and clips.
    • The business model broke journalism before AI did. As ad money fled to platforms, outlets turned to paywalls, patrons, and foundations—pulling coverage toward causes and away from broad public-interest reporting.
    • The real divide is power, not party. Corbett argues we miss the “up–down” axis—authoritarian vs anti-authoritarian—so we keep swapping parties but getting similar outcomes on war, finance, and surveillance.
    • AI and automation are economic and political weapons. If AI displaces labor and the state replaces wages with universal income, whoever controls those payouts gains unprecedented leverage over everyday life.
    • Long-form human conversation is still a resistance strategy. Despite dark trends, he sees deep, sustained, human-made media as one of the few ways left to think clearly and build real communities.

    BEST QUOTES:

    • On the shift since 2006:
      “We went from ‘You are the Person of the Year’ to ‘You are the problem’—from celebrating amateur voices to treating them as a disinformation threat.”
    • On media form and attention:
      “I started in an era where you could play a ten-minute clip inside an hour-long podcast. Now if you go over two minutes, people think you’re crazy.”
    • On politics:
      “Left and right exist, but the missing axis is up and down—authoritarian versus anti-authoritarian. Once you see that, a lot of ‘flip-flops’ make sense.”
    • On AI and control:
      “If the state is the one feeding and clothing you after AI replaces your job, then the state effectively owns you.”

    🎙 The Pod is hosted by Jesse Wright
    💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/
    📬 Never miss an episode – subscribe and follow wherever you get your podcasts.
    ⭐️ If you enjoyed this episode, please rate and review the show. It helps others find us.

    Thanks for listening!
    続きを読む 一部表示
    59 分
  • E170: Boomers Didn’t Steal Your Future. This Did - Dr. Jennie Bristow

    E170: Boomers Didn’t Steal Your Future. This Did - Dr. Jennie Bristow
    2025/12/03
    Sociologist Dr. Jennie Bristow joins Jesse to dismantle “generation wars” rhetoric—especially Boomer-blaming—and re-center the real story: stalled economies, broken higher ed, housing dysfunction, and a culture that’s leaving young people anxious and unmoored.Guest bio:Dr. Jennie Bristow is a professor of sociology at Canterbury Christ Church University in the UK and a leading researcher on intergenerational conflict, social policy, and cultural change. She is the author of Stop Mugging Grandma: The Generation Wars and Why Boomer Blaming Won’t Solve Anything and the forthcoming Growing Up in the Culture Wars, which examines how Gen Z is coming of age amid identity politics, pandemic fallout, and collapsing institutional confidence.Topics discussed:How “intergenerational equity” became a fashionable idea among policymakers and millennial commentators after the 2008 financial crisisWhy blaming Baby Boomers for housing, student debt, and climate change hides deeper structural problemsThe role of journalism, English majors, and the broken media business model in manufacturing generational conflictHigher education as a quasi–Ponzi scheme: massification, student loans, and the weak graduate premiumHousing, delayed family formation, and why homeownership is a bad proxy for measuring generational “success”Millennials vs. Gen Z: growing up with 9/11 and the financial crisis vs. growing up with COVID-19 and AIAI, “zombie economies,” and why societies still need real work, real knowledge, and real skillsSocial Security, ageing, low fertility, and what’s actually at stake in pension debatesIdentity politics, culture wars, and how an obsession with personal identity fragments common lifeMedia polarization, rage clicks, and how subscription-driven, foundation-funded journalism blurs into activismMain points & takeaways:Generation wars are a distraction. The Boomer-vs-Millennial narrative was heavily driven by media and policy elites after the 2008 crisis. It channels anger away from structural issues—stagnant productivity, weak labor markets, housing policy failure, and a dysfunctional higher-ed and welfare state.Boomers didn’t “steal the future” — policy did. Baby Boomers are just a large cohort who happened to be born into a period of postwar economic expansion. Treating them as a moral category (“greedy,” “sociopaths”) obscures the role of monetary, housing, education, and labor-market policy choices.Class beats cohort. Within every “generation” there are huge differences: inheritance vs no inheritance, elite degrees vs low-quality credentials, secure jobs vs precarity. Talk of “Boomers” and “Millennials” flattens these class divides into fake demographic morality plays.Housing is a symbol, not the root cause. The rising age of first-time buyers and insane rents are real problems—but they’re manifestations of policy and market failures, not proof that Boomers hoarded all the houses. Using homeownership as the key generational metric gets the story backwards.Higher education is oversold. Mass university attendance, especially in non-vocational fields, has left many millennials and Zoomers with heavy student debt and weak job prospects. Degrees became a costly entry ticket to the labor market without guaranteeing meaningful work or higher wages.AI is a wake-up call, not pure doom. AI will automate a lot of white-collar tasks (journalism, marketing, some finance), but it also exposes how shallow “skills” education has become. Bristow argues students need real knowledge and disciplinary depth so humans can meaningfully supervise and direct AI systems.Ageing and pensions are solvable political questions, not excuses to scapegoat the old. Longer life expectancy and rising dependency ratios do require institutional redesign—but that should mean rethinking work, welfare, and economic dynamism, not treating older people as fiscal burdens to be phased out.Gen Z is growing up in a culture of fractured identity. Instead of being socialized into a shared civic culture, young people are pushed into micro-identities and online culture-war camps. That emphasis on personal identity over common purpose undermines their ability to form stable adult roles.Media business models amplify rage and generational framing. As ad revenue collapsed and subscriptions and philanthropy took over, many outlets shifted toward more partisan, activist-style content. Generational blame is a cheap, emotionally potent frame that fits this economic logic.Top 3 quotes:On the myth of Boomer villainy“Baby Boomers are not a generation of sociopaths who set out to rob the young of their future; they’re just people born at a particular time in history. Turning them into moral scapegoats lets us avoid talking about policy failures.”On universities and the millennial bait-and-switch“We raised millennials to believe they were special, told them to follow their dreams, pushed them into ...
    続きを読む 一部表示
    1 時間 11 分
  • E169: Why Diets Fail: The Hidden Forces Controlling What You Eat - Julia Belluz

    E169: Why Diets Fail: The Hidden Forces Controlling What You Eat - Julia Belluz
    2025/11/27
    Investigative health journalist Julia Belluz breaks down what really drives obesity and chronic disease—metabolism myths, ultra-processed food, bad incentives, and why our entire food environment is quietly rigged against us.Guest bio: Julia Belluz is a Paris-based health and science journalist and co-author of Food Intelligence: The Science of How Food Both Nourishes and Harms Us, written with NIH researcher Dr. Kevin Hall. Over more than a decade reporting for outlets like Vox and The New York Times, she’s become one of the sharpest explainers of nutrition science, chronic disease, and the politics of the global food system.Topics discussed:The Biggest Loser study: what Kevin Hall actually discovered about extreme weight loss and metabolic slowdownWhy “a slow metabolism” is not destiny—and why the biggest losers had the biggest metabolic dropsIs a calorie a calorie? Low-carb vs low-fat when calories are controlledProtein “maximization,” the protein appetite, and why excess protein isn’t magicVitamins, supplements, kidney stones, and the $2T wellness industryThe 10,000+ chemicals in the U.S. food supply and the GRAS loopholeUltra-processed foods, added salt/sugar/fat, and the simple math of calorie surplusFood environments vs willpower: why it’s so hard to “eat right” in the U.S.What France gets right on markets, school lunches, and prepared foodsIndustry funding, NIH underinvestment in nutrition, and government’s failure to regulatePractical strategies: reshaping your home food environment and demanding better policyMain points:Extreme weight loss = extreme metabolic slowdownBiggest Loser contestants showed huge willpower and lost enormous amounts of weight—but the biggest losers had the largest and most persistent drops in metabolic rate, even six years later.Metabolism followed weight loss; it didn’t cause it. “Slow metabolism” is not a life sentence, and it’s not the main driver of the obesity epidemic.For fat loss, calories still mostly ruleWhen Kevin Hall tightly controls calories in the lab, low-carb vs low-fat leads to almost identical fat loss, with only a trivial edge for low-fat.Macro wars are wildly overstated; total calories and food environment matter far more than whether you’re Team Carbs or Team Fat.Protein is essential, but not a cheat codeHumans (and many animals) seem to have a “protein appetite” that keeps intake in a fairly narrow range worldwide.Overshooting that range doesn’t give you free fat loss—you essentially excrete the extra nitrogen and keep the calories.Supplements are often useless—or harmfulRoutine multivitamins rarely help people who aren’t deficient and can sometimes increase risk.Under-regulated “metabolism boosters” and weight-loss pills are a real source of ER visits and kidney issues.The chemicals loophole is real—and alarmingSince 1958, and especially after 1997, U.S. companies have been allowed to classify new food chemicals as “generally recognized as safe” without real FDA oversight, independent review, or even notification.We don’t yet know how much these chemicals contribute to disease, but we already have more than enough evidence to indict excess calories and the salt–sugar–fat trifecta.It’s the food environment, not your moral characterObesity has risen across ages and countries as food environments have shifted—cheap, omnipresent, ultra-processed, aggressively marketed calories.France shows what policy can do: strong school-meal standards, protected fresh markets, and widely available healthy prepared foods all make “the default choice” less toxic.Policy and leadership, not just personal hacksLess than ~5% of NIH funding goes to nutrition research, while industry funding quietly shapes what gets studied.Individual strategies (cooking more, controlling home food, simplifying meals) matter—but large-scale change requires political pressure and better rules of the game.Top quotes:“The people who lost the most weight on The Biggest Loser ended up with the greatest metabolic slowdown—and that slowdown was still there six years later.”“We don’t need conspiratorial chemicals to explain the obesity epidemic—an endless supply of cheap, ultra-processed food high in salt, sugar, and fat is plenty.”“Obesity is not a mass failure of willpower. It’s what happens when entire populations are dropped into toxic food environments and then told the problem is their character.” 🎙 The Pod is hosted by Jesse Wright💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/📬 Never miss an episode – subscribe and follow wherever you get your podcasts.⭐️ If you enjoyed this episode, please rate and review the show. It helps others find us. Thanks for listening!
    続きを読む 一部表示
    45 分
  • E168: AI - Biggest Bubble in Human History? Tech Economist Says YES

    E168: AI - Biggest Bubble in Human History? Tech Economist Says YES
    2025/11/20
    Tech economist Dr. Jeffrey Funk argues that today’s AI boom is the biggest bubble in history—far larger than dot-com or housing—because colossal infrastructure spending is chasing tiny, unprofitable revenues.Guest bio:Jeffrey Funk is a technology economist and author of Unicorns, Hype and Bubbles: A Guide to Spotting, Avoiding and Exploiting Investment Bubbles in Tech. A longtime researcher and professor of innovation and high-tech industries, he now writes widely on startup hype, AI economics, and investment manias, including a popular newsletter and presence on LinkedIn.Topics discussed:Why Funk thinks the AI boom is the “biggest bubble ever”OpenAI’s revenues, mounting losses, and opaque accounting vs. Microsoft’s audited numbersNvidia, cloud providers, and “circular finance” in AI infrastructureSora, video generation, and the economics of ultra-expensive AI featuresComparisons with the 1929 crash, the dot-com bubble, and the 2008 housing crisisHow much of AI is real utility vs. hype, scams, and accounting tricksHallucinations as an inherent limitation of large language modelsWorld-model approaches, quantum computing, and why breakthroughs are harder than advertisedEnergy use, exploding electricity demand, and Bill Gates’ shifting climate rhetoricPossible winners after the bubble: why it’s still “wide open”Labor markets, layoffs, and why “AI took their jobs” is mostly a PR storyCollege and career advice for young people in an AI-saturated economyChina, regulation, and small language modelsWhat the pop might look like: shuttered data centers, broken pensions, and a long VC winterFinal advice: how to think more clearly about tech futures and bubblesMain points:Investment vs. returns: A bubble is simply when more money goes into companies than comes out; by that standard, AI is extreme—OpenAI’s losses and projected $115B cash burn dwarf its revenues.Subsidized demand: OpenAI’s ultra-low prices and free tiers artificially inflate usage and pump up Nvidia and cloud revenues; if prices reflected true cost, demand (and infra spending) would fall sharply.Accounting red flags: Discrepancies between OpenAI’s figures and Microsoft’s audited statements, plus aggressive depreciation assumptions for AI chips, echo Enron-style financial engineering.Bigger than past bubbles: Unlike dot-com, where consumers paid for internet access, PCs, and e-commerce (≈$1.5T in 2024 dollars), AI currently generates tiny, niche revenues relative to the trillions being poured into infrastructure.Tech limits: LLM hallucinations are a built-in feature of statistical generative models, not a temporary bug; GPT-5 and similar systems haven’t solved this, and world-model or quantum fixes would be extremely costly and distant.Real but narrow use-cases: AI can help with things like drafting emails, simple ads, and some coding assistance, but broad productivity gains across manufacturing, construction, healthcare, etc., remain largely unrealized.Jobs & layoffs: Headlines about AI-driven mass unemployment are mostly hype; unemployment overall is low, many “AI layoffs” are reversals of pandemic over-hiring, and outsourcing plus H-1B dynamics matter more than LLMs.Crash mechanics: When the narrative finally flips and big investors (like Michael Burry) exit or short AI, overbuilt data centers, utility expansions, and VC portfolios will be left stranded, hurting pensions and index investors.Careers & education: Young people should be skeptical of hype, but still learn math, coding, and predictive AI; trades and biotech remain attractive, and the key skill is learning to reason about trends instead of chasing bandwagons.Top 3 quotes:On what a bubble really is:“When people are putting more money into companies than they’re getting out, it becomes a bubble. It’s just exaggeration.”On Nvidia, cloud, and OpenAI’s losses:“Who cares if Nvidia and the cloud providers are making so much money if OpenAI is losing billions to subsidize them? The car might be selling, but if you’re selling it for half price, it’s not a good business.”On how young people should respond:“If you’re young, don’t worry too much about the bubble. Be open-minded, be curious, learn to think for yourself instead of believing what the tech bros say, and things will work out.” 🎙 The Pod is hosted by Jesse Wright💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/📬 Never miss an episode – subscribe and follow wherever you get your podcasts.⭐️ If you enjoyed this episode, please rate and review the show. It helps others find us. Thanks for listening!
    続きを読む 一部表示
    1 時間 39 分
  • E167: Nuclear Rockets, AI Agents & Science Hype | RealClear Science’s Ross Pomeroy

    E167: Nuclear Rockets, AI Agents & Science Hype | RealClear Science’s Ross Pomeroy
    2025/11/13

    Steven Ross Pomeroy, Chief Editor of RealClearScience, joins the podcast to discuss NASA’s abandoned nuclear propulsion programs, the future of AI and white-collar work, the rise of “scienceploitation,” and how information overload is reshaping human cognition.

    GUEST BIO:
    Steven Ross Pomeroy is a science writer and Chief Editor of RealClearScience. He writes frequently for Big Think, covering space exploration, neuroscience, AI, and science communication.

    TOPICS DISCUSSED:

    • NASA’s nuclear propulsion program (1960s–1970s)
    • Why nuclear rockets were abandoned
    • Differences between chemical, nuclear thermal, and nuclear electric propulsion
    • Using the Moon as a launch hub
    • Moon-landing skepticism & conspiracy thinking
    • The future of space mining
    • AI adoption trends & hidden usage
    • Agentic AI vs chatbots
    • Job displacement: white-collar vulnerability
    • Higher ed, skills, and career advice
    • “Scienceploitation” and how marketing hijacks scientific language
    • Immune-system myths & quantum woo
    • Information overload and Google/AI-driven forgetting
    • Critical thinking in the AI era
    • The myth of speed reading
    • How vocabulary and deep engagement improve comprehension

    MAIN POINTS:

    • NASA had functional nuclear-rocket tech in the 1960s, but political priorities, budget cuts, and waning public interest ended the program.
    • Nuclear thermal rockets are ~2x as efficient as chemical rockets; nuclear electric propulsion could unlock deep-space exploration and mining.
    • Space mining is technologically plausible, but its economic impact (like crashing gold prices) creates new problems.
    • AI adoption is much higher than official numbers—many workers use it quietly and off the books.
    • Companies see low ROI today because they’re using simple chatbots, not advanced “agentic” systems that can take multi-step actions.
    • White-collar jobs — not blue collar — are being automated first.
    • Scienceploitation hijacks scientific buzzwords (“quantum,” “immune-boosting,” “natural”) to sell products with no evidence.
    • We process 74 GB of information per day, roughly a lifetime’s worth for a well-educated person 500 years ago.
    • Speed reading works only by sacrificing retention; the real way to read faster is to build vocabulary and deep attention.
    • Skepticism, not cynicism, is the core skill we need in the AI-mediated media environment.

    TOP 3 QUOTES:

    • “It would’ve been harder to fake the moon landing than to actually land on the moon.”
    • “Companies aren’t getting ROI from AI because they’re only using chatbots. The real returns come from agentic AI — and that wave is just beginning.”
    • “We now process 74 gigabytes of information a day. Five hundred years ago, that was a lifetime’s worth for a highly educated person.”

    🎙 The Pod is hosted by Jesse Wright
    💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/
    📬 Never miss an episode – subscribe and follow wherever you get your podcasts.
    ⭐️ If you enjoyed this episode, please rate and review the show. It helps others find us.

    Thanks for listening!
    続きを読む 一部表示
    40 分
  • E166: Is the Internet Too Big to Moderate? — John Wihbey

    E166: Is the Internet Too Big to Moderate? — John Wihbey
    2025/11/06

    A wide-ranging conversation with Northeastern’s John Wihbey on how algorithms, laws, and business models shape speech online—and what smarter, lighter regulation could look like.

    Guest bio: John Wihbey is a professor of media & technology at Northeastern University and director of the AI Media Strategies Lab. Author of Governing Babel (MIT Press). He has advised foundations, governments, and tech firms (incl. pre-X Twitter) and consulted for the U.S. Navy.

    Topics discussed:

    • Section 230’s 1996 logic vs. the algorithmic era
    • EU DSA, Brazil/India, authoritarian models
    • AI vs. AI moderation (deepfakes, scams, NCII)
    • Hate/abuse, doxxing, and speech “crowd-out”
    • Platform opacity; case for transparency/data access
    • Creator-economy economics; downranking/shadow bans
    • Dead Internet Theory, bots, engagement gaming
    • Sports, betting, and integrity (NBA/NFL)
    • Gen Z jobs; becoming AI-literate change agents
    • Teaching with AI: simulations, human-in-loop assessment

    Main points & takeaways:

    • Keep Section 230 but add obligations (transparency, appeals, researcher access).
    • Europe’s DSA has exportable principles, adapted to U.S. free-speech norms.
    • States lead on deepfake/NCII and youth-harm laws.
    • AI offense currently ahead; detection/provenance + humans will narrow the gap.
    • Lawful hate/abuse can practically silence others’ participation.
    • CSAM detection is harder with synthetics; needs better tooling/cooperation.
    • News/creator models are fragile; ad dollars shifted to platforms.
    • Opaque ranking punishes small creators; clearer recourse is needed.
    • Engagement metrics are Goodharted; bots inflate signals.
    • Live sports thrive on synchronization; gambling risks long-term integrity.
    • Students should aim to be the person who uses AI well, not fear AI.

    Top 3 quotes:

    • “Keep 230, but add transparency and obligations—we don’t need censorship; we need visibility into how platforms actually govern speech.”
    • “AI versus AI is the new reality—offense is ahead today, but defense will catch up with detection, provenance, and human oversight.”
    • “The platform is king—monetization and discoverability are controlled by opaque algorithms, and that unpredictability crushes small creators.”

    🎙 The Pod is hosted by Jesse Wright
    💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/
    📬 Never miss an episode – subscribe and follow wherever you get your podcasts.
    ⭐️ If you enjoyed this episode, please rate and review the show. It helps others find us.

    Thanks for listening!
    続きを読む 一部表示
    1 時間 34 分
  • E165: STUDY Shows NFL Favors the Chiefs — Lead Researcher Explains

    E165: STUDY Shows NFL Favors the Chiefs — Lead Researcher Explains
    2025/11/01

    Finance professor Spencer Barnes explains research showing postseason officiating systematically favors the Mahomes-era Chiefs—consistent with subconscious, financially driven “regulatory capture,” not explicit rigging.

    Guest bio: Dr. Spencer Barnes is a finance professor at UTEP. He co-authored “Under Financial Pressure” with Brandon Mendez (South Carolina) and Ted Dischman, using sports as a transparent lab to study regulatory capture.

    Topics discussed (in order):

    • Why the NFL is a clean testbed for regulatory capture
    • Data/methods: 13,136 defensive penalties (2015–2023), panel dataset, fixed-effects
    • Postseason favoritism toward Mahomes-era Chiefs
    • Magnitude and game impact (first downs, yards, FG-margin games)
    • Subjective vs objective penalties (RTP, DPI vs offsides/false start)
    • Regular season vs postseason differences
    • Dynasty checks (Patriots/Brady; Eagles/Rams/49ers)
    • Rigging vs subconscious bias
    • Ratings, revenue (~$23B in 2024), media incentives
    • Gambling’s rise post-2018 and bettor implications
    • Taylor Swift factor (not tested due to data window)
    • Ref assignment opacity; repeat-crew effects
    • Tech/replay reform ideas
    • Broader finance lesson on incentives and regulation

    Main points & takeaways:

    • Core postseason result: Chiefs ~20 percentage points more likely than peers to gain a first down from a defensive penalty.
    • Subjective flags: ~30% more likely for KC in playoffs (RTP, DPI).
    • Size: ~4 extra yards per defensive penalty in playoffs—small per play, decisive at FG margins.
    • Regular season: No favorable treatment; slight tilt the other way.
    • Ref carryover: Crews with a prior KC postseason official show more KC-favorable outcomes the next year.
    • Not universal to dynasties: Patriots/Brady and other near-dynasties don’t show the same postseason effect.
    • Mechanism: No claim of rigging; consistent with implicit bias under financial incentives.
    • Policy: Use tech (skycam, auto-checks for false start/offsides), limited challenges for subjective calls, transparent ref advancement.
    • General lesson: When regulators depend financially on outcomes, redesign incentives to reduce capture and protect fairness.

    Top 3 quotes:

    • “We make no claim the NFL is rigging anything. What we see looks like implicit bias shaped by financial incentives.” — Spencer Barnes
    • “It only takes one call to swing a postseason game decided by a field goal.” — Spencer Barnes
    • “If there’s money on the line, you must design the regulators’ environment so incentives don’t quietly bend enforcement.” — Spencer Barnes

    Links/where to find the work: Spencer Barnes on LinkedIn (search: “Spencer Barnes UTEP”); paper Under Financial Pressure in the Financial Review (paywall) and as a free working paper on SSRN (search the title).

    🎙 The Pod is hosted by Jesse Wright
    💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/
    📬 Never miss an episode – subscribe and follow wherever you get your podcasts.
    ⭐️ If you enjoyed this episode, please rate and review the show. It helps others find us.

    Thanks for listening!
    続きを読む 一部表示
    1 時間 2 分