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.

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.

• Why Funk thinks the AI boom is the “biggest bubble ever”
• OpenAI’s revenues, mounting losses, and opaque accounting vs. Microsoft’s audited numbers
• Nvidia, cloud providers, and “circular finance” in AI infrastructure
• Sora, video generation, and the economics of ultra-expensive AI features
• Comparisons with the 1929 crash, the dot-com bubble, and the 2008 housing crisis
• How much of AI is real utility vs. hype, scams, and accounting tricks
• Hallucinations as an inherent limitation of large language models
• World-model approaches, quantum computing, and why breakthroughs are harder than advertised
• Energy use, exploding electricity demand, and Bill Gates’ shifting climate rhetoric
• Possible winners after the bubble: why it’s still “wide open”
• Labor markets, layoffs, and why “AI took their jobs” is mostly a PR story
• College and career advice for young people in an AI-saturated economy
• China, regulation, and small language models
• What the pop might look like: shuttered data centers, broken pensions, and a long VC winter
• Final advice: how to think more clearly about tech futures and bubbles

• 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.

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
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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
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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
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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
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How human babies, big brains, and social life likely forced Homo sapiens to invent precise speech ~150–200k years ago—and what that means for learning, tech, and today’s kids.

Guest Bio:
Madeleine Beekman is a professor emerita of evolutionary biology and behavioral ecology at the University of Sydney and author of Origin of Language: How We Learned to Speak and Why. She studies social insects, collective decisions, and the evolution of communication.

Topics Discussed:

• Why soft tissues don’t fossilize; language origins rely on circumstantial evidence
• Three clocks for timing (~150–200k years): anatomy; trade/complex tech/art; phoneme “bottleneck”
• Why Homo sapiens (not Neanderthals) likely had full speech
• Language as a “virus” tuned to children; pidgin → creole via kids
• Second-language learning: immersion over translation
• Bees/ants show precision scales with ecological stakes
• Evolutionary chain: bipedalism → narrow pelvis + big brains → helpless infants → precise speech
• Ongoing human evolution (archaic DNA, altitude, Inuit lipid adaptations)
• Flynn effect reversal, screens, AI reliance, anthropomorphism risks
• Reading, early interaction, and the Regent honeyeater “lost song” lesson
• Universities, online classes, and “degree over learning”

Main Points:

• Multiple evidence lines converge on speech emerging with anatomically modern humans ~150–200k years ago.
• Anatomical and epigenetic clues suggest only Homo sapiens achieved full vocal speech.
• Extremely dependent infants created strong selection for precise, teachable communication.
• Children’s brains shape languages; kids regularize grammar.
• Communication precision rises when mistakes are costly (bee-dance analogy).
• Humans continue to evolve; genomes show selected archaic introgression and local adaptations.
• Tech-driven habits may erode cognition and language skill; reading matters.
• AI is a tool that imitates human output; humanizing it can mislead and harm, especially for teens.
• Start early: talk, read, and interact face-to-face from birth.

Top Quotes:

• “Only Homo sapiens was ever able to speak.”
• “Language will go extinct if it can’t be transmitted from brain to brain—the best host is a child.”
• “The precision of communication is shaped by how important it is to be precise.”

🎙 The Pod is hosted by Jesse Wright
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A candid conversation with psychologist Gerd Gigerenzer on why human judgment outperforms AI, the “stable world” limits of machine intelligence, and how surveillance capitalism reshapes society.

Guest bio: Dr. Gerd Gigerenzer is a German psychologist, director emeritus at the Max Planck Institute for Human Development, a leading scholar on decision-making and heuristics, and an intellectual interlocutor of B. F. Skinner and Herbert Simon.

Topics discussed:

• Why large language models rely on correlations, not understanding
• The “stable world principle” and where AI actually works (chess, translation)
• Uncertainty, human behavior, and why prediction doesn’t improve much
• Surveillance capitalism, privacy erosion, and “tech paternalism”
• Level-4 vs. level-5 autonomy and city redesign for robo-taxis
• Education, attention, and social media’s effects on cognition and mental health
• Dynamic pricing, right-to-repair, and value extraction vs. true innovation
• Simple heuristics beating big data (elections, flu prediction)
• Optimism vs. pessimism about democratic pushback
• Books to read: How to Stay Smart in a Smart World, The Intelligence of Intuition; “AI Snake Oil”

Main points:

• Human intelligence is categorically different from machine pattern-matching; LLMs don’t “understand.”
• AI excels in stable, rule-bound domains; it struggles under real-world uncertainty and shifting conditions.
• Claims of imminent AGI and fully general self-driving are marketing hype; progress is gated by world instability, not just compute.
• The business model of personalized advertising drives surveillance, addiction loops, and attention erosion.
• Complex models can underperform simple, well-chosen rules in uncertain domains.
• Europe is pushing regulation; tech lobbying and consumer convenience still tilt the field toward surveillance.
• The deeper risk isn’t “AI takeover” but the dumbing-down of people and loss of autonomy.
• Careers: follow what you love—humans remain essential for oversight, judgment, and creativity.
• Likely mobility future is constrained autonomy (level-4) plus infrastructure changes, not human-free level-5 everywhere.
• To “stay smart,” individuals must reclaim attention, understand how systems work, and demand alternatives (including paid, non-ad models).

Top quotes:

• “Large language models work by correlations between words; that’s not understanding.”
• “AI works well where tomorrow is like yesterday; under uncertainty, it falters.”
• “The problem isn’t AI—it’s the dumbing-down of people.”
• “We should become customers again, not the product.”

🎙 The Pod is hosted by Jesse Wright
💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/
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How a tech insider who helped build billion-view machines explains the attention economy’s playbook—and how to guard your mind (and data) against it.

Guest bio:
Richard Ryan is a software developer, media executive, and tech entrepreneur with 20+ years in digital. He co-founded Black Rifle Coffee Company and helped take it public (~$1.7B valuation; $396M revenue in 2023). He’s built multiple apps (including a video app released four years before YouTube) with millions of downloads, launched Rated Red to 1M organic subscribers in its first year, and runs a YouTube network—led by FullMag (2.7M subs)—that has surpassed 20B views.

Topics discussed:
The attention economy and 2012 as the mobile/monetization inflection point; algorithm design, engagement incentives, and polarization; personal costs (anxiety, comparison traps, body dysmorphia, addiction mechanics); privacy and data brokers, smart devices, cars, geofencing; policy ideas (digital rights, accountability, incentive realignment); practical defenses (digital detox, friction, community, gratitude, boundaries); careers, college, and meaning in an AI-accelerating world.

Main points:

• Social platforms optimize time-on-device; “For You” feeds exploit threat/dopamine loops that keep users anxious and engaged.
• 2012 marked a shift from tool to extraction: mobile apps plus partner programs turned attention into a tradable commodity.
• Outrage and filter bubbles are amplified because drama wins in the algorithmic reward system.
• Privacy risk is systemic: data brokers, vehicle SIMs, and IoT terms build behavioral profiles beyond traditional warrants.
• Individual resilience beats moral panic: measure use, do a 30-day reset, add friction, and invest in offline community and gratitude.
• Don’t mortgage your life to debt or trends; pursue adaptable, meaningful work—every field is vulnerable to automation.
• Societal fixes require incentive changes (digital rights, simple single-issue bills, real accountability), not just complaints.

Top 3 quotes:

• “In 2012, you went from using your iPhone to the iPhone using you.”
• “If you can’t establish boundaries and adhere to them, you have a problem.”
• “The spirit of humanity shines in the face of adversity—we love an underdog story, and this is the underdog story.”

🎙 The Pod is hosted by Jesse Wright
💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/
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Dr. Luke Kemp, an Existential Risk Researcher at the University of Cambridge shows how today’s plutocracy and tech-fueled surveillance imperil society—and what we can do to build resilience.

Guest bio:
Dr. Luke Kemp is an Existential Risk Researcher at the Centre for the Study of Existential Risk (CSER) at the University of Cambridge and author of Goliath’s Curse: The History and Future of Societal Collapse. His work examines how wealth concentration, surveillance, and arms races erode democracy and heighten global catastrophic risk.

Topics discussed:

• The “Goliath” concept: dominance hierarchies vs. vague “civilization”
• Are we collapsing now? Signals vs. sudden shocks
• Inequality as the engine of fragility; lootable resources & data
• Tech’s role: AI as accelerant, surveillance capitalism, autonomous weapons
• Nuclear risk, climate links, and system-level causes of catastrophe
• Democracy’s erosion and alternatives (sortition, deliberation)
• Elite overproduction, factionalism, and arms/resource/status “races”
• Collapse as leveler: winners, losers, and myths about mass die-off
• Practical pathways: leveling power, wealth taxes, open democracy

Main points:

• “Civilization” consistently manifests as stacked dominance hierarchies—what Kemp calls the Goliath—which naturally concentrate wealth and power over time.
• Rising inequality spills into political, informational, and coercive power, making societies brittle and less able to correct course.
• Existential threats are interconnected; AI, nukes, climate, and bio risks share causes and amplify each other.
• AI need not be Skynet to be dangerous; it speeds arms races, surveillance, and catastrophic decision cycles.
• Collapse isn’t always apocalypse; often it fragments power and improves life for many outside the elite core.
• Durable safety requires leveling power: progressive/wealth taxation, stronger democracy (especially sortition-based, deliberative bodies), and curbing surveillance and arms races.

Top 3 quotes:

• “Most collapse theories trace back to one driver: the steady concentration of wealth and power that makes societies top-heavy and blind.”
• “AI is an accelerant—pouring fuel on the fires of arms races, surveillance, and extractive economics.”
• “If we want a long future, we don’t just need tech fixes—we need to level power and make democracy real.”

🎙 The Pod is hosted by Jesse Wright
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A deep dive with historian Dr. Fyodor Tertitskiy on how North Korea’s dynasty survives—through isolation, terror, and nukes—and why collapse or unification is far from inevitable.

Guest bio:
Fyodor Tertitskiy, PhD, is a Russian-born historian of North Korea and a senior research fellow at Kookmin University (Seoul). A naturalized South Korean based in Seoul, he is the author of Accidental Tyrant: The Life of Kim Il-sung. He speaks Russian, Korean, and English, has visited North Korea (2014, 2017), and researches using Soviet, North Korean, and Korean-language sources.

Topics discussed:

• Daily life under extreme authoritarianism (no open internet, monitored communications, mandatory leader portraits)
• Kim Il-sung’s rise via Soviet backing; historical fabrications in official narratives
• 1990s famine, loss of sponsors, rise of black markets and bribery
• Nukes/missiles as regime-survival tools; dynasty continuity vs. unification
• Why German-style unification is unlikely (costs, politics, identity; waning support in the South)
• Regime control stack: isolation, propaganda “white list,” terror, collective punishment
• Reliability of defectors’ accounts; sensationalism vs. fabrication
• Research methods: multilingual archives, leaks, captured docs, propaganda close-reading
• Elite wealth vs. citizen poverty; renewed patronage via Russia
• Coups/assassination plots, succession uncertainty
• North Korean cyber ops and crypto theft
• “Authoritarian drift” debates vs. media hyperbole in democracies
• Life in Seoul: safety, civility, culture

Main points:

• North Korea bans information by default and enforces obedience through fear.
• Elites have everything to lose from change; nukes deter regime-ending threats.
• Unification would be socially and fiscally seismic; absent a Northern revolution, it’s improbable.
• Markets and graft sustain daily life while strategic sectors get resources.
• Collapse predictions are guesses; stable yet brittle systems can still break from shocks.
• Defector claims need case-by-case verification; mass CIA scripting is unlikely.
• Archival evidence shows key “facts” were retrofitted to build the Kim myth.
• Democracy’s victory isn’t automatic—citizens and institutions must defend it.

Top 3 quotes:

• “There is no internet unless the Supreme Leader permits it—and even then, someone from the secret police may sit next to you taking notes.”
• “They will never surrender nuclear weapons—nukes are the guarantee of the regime’s survival.”
• “The triumph of democracy is not automatic; there is no fate—evil can prevail.”

🎙 The Pod is hosted by Jesse Wright
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Dr. Devon Price unpacks “the laziness lie,” how AI and “bullshit jobs” distort work and higher ed, and why centering human needs—not output—leads to saner lives.

Guest bio: Devon Price, PhD, is a Clinical Associate Professor of Psychology at Loyola University Chicago, a social psychologist, & writer. Prof Price is the author of Laziness Does Not Exist, Unmasking Autism, and Unlearning Shame, focusing on burnout, neurodiversity, and work culture.

Topics discussed:

• The laziness lie: origins and three core tenets
• AI’s effects on output pressure, layoffs, and disposability
• Overlap with David Graeber’s Bullshit Jobs and status hierarchies
• Adjunctification and incentives in academia
• Demographic cliff and the sales-ification of universities
• Career choices in an AI era: minimize debt and stay flexible
• Remote work’s productivity spike and boundary erosion
• Burnout as a signal to rebuild values around care and community
• Gap years, social welfare, and redefining “good jobs”
• Practicing compassion toward marginalized people labeled “lazy”

Main points:

• The laziness lie equates worth with productivity, distrusts needs/limits, and insists there’s always more to do, fueling self-neglect and stigma.
• Efficiency gains from tech and AI are converted into higher expectations rather than rest or shorter hours.
• Many high-status roles maintain hierarchy more than they create real value; resentment often targets meaningful, low-paid work.
• U.S. higher ed relies on precarious adjunct labor while admin layers swell, shifting from education to a jobs-sales funnel.
• In a volatile market, avoid debt, build broad human skills, and choose adaptable paths over brittle credentials.
• Remote work raised output but erased boundaries; creativity requires rest and unstructured time.
• Burnout is the body’s refusal of exploitation; recovery means reprioritizing relationships, art, community, and self-care.
• A humane society would channel tech gains into shorter hours and better care work and infrastructure.
• Revalue baristas, caregivers, teachers, and artists as vital contributors.
• Everyday practice: show compassion—especially to those our culture labels “lazy.”

Top three quotes:

• “What burnout really is, is the body refusing to be exploited anymore.” — Devon Price
• “Efficiency never gets rewarded; it just ratchets up the expectations.” — Devon Price
• “What is the point of AI streamlining work if we punish humans for not being needed?” — Devon Price

🎙 The Pod is hosted by Jesse Wright
💬 For guest suggestions, questions, or media inquiries, reach out at https://elpodcast.media/
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