Afleveringen
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What if the problem with American democracy isn't that the system is broken, but that it's working exactly as intended, just not for you?
Travis Misurell, founder of FiNC (Future is Now Coalition), has spent years watching civic tech efforts try to fix democracy by building better tools. Every one of them failed. His argument: they got the sequence wrong. You don't build the technology and hope a movement follows. You build the movement first and let the technology follow.
In this episode, Travis walks us through the FiNC framework — the Digital Politics Hub, the Up/Down lens, the citizen survey, and the long-term vision of a citizen-owned civic internet where no billionaire, party, or corporation can ever take control. One share per person. No exceptions.
But we also push on the harder questions. If the system is rigged by design, what does building inside it actually accomplish? When AI aggregates open-ended citizen responses into actionable insights for candidates, what gets lost in that translation? When you surface every candidate with equal presentation, are you being neutral or are you making a choice about what equivalence means?
Travis comes back to the same place: intention. Not left or right. Not the policy. The intention. Whether a candidate is in it for you, or in it for the people writing the biggest checks.
FiNC is betting that if citizens can actually see that distinction clearly enough, the rest follows.
It's an ambitious bet. This is the conversation around it.
Learn more about the Future is Now Coalition:
https://futureis.org/ Discord community Digital Politics HubMentioned:
• • OpenAI donating to stop Alex Bores’s campaign for NY congressional seat
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The travel schedules and time zone tango is real. This week, revisit one of the most downloaded episodes this season, with Rachelle Tanguay.
What if your biggest career obstacle isn't external—it's the “broken code” running in your own head?
Rachelle Tanguay joins the show to unpack the difference between consuming self-help content and actually doing the uncomfortable work of rewiring your internal programming.
From advising deputy ministers to coaching professionals across sectors, she's seen what happens when high-performers hit the wall between knowing what to do and actually being able to execute.
This conversation cuts through the dopamine-hit culture of five-minute reels and quick fixes. Rachelle breaks down why most people confuse consuming content with doing the work, how imposter syndrome is not your own voice “chirping in your ear," and why even the most senior leaders need help to see the forest through the trees.
If you've ever wondered why smart people with all the right information still can't break through their own barriers, this episode is for you. No buzzwords, no corporate speak—just an honest look at what it takes to level up when the real bottleneck is you.
Mentioned
https://www.kornferry.com/about-us/press/71percent-of-us-ceos-experience-imposter-syndrome-new-korn-ferry-research-finds
https://www.mogawdat.com/solve-for-happy
https://jamesclear.com/atomic-habits
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Zijn er afleveringen die ontbreken?
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The hype machine spent two years telling us AI was coming for your job. Now it's quietly walking that back. Why now? Follow the money.
On this week's system update, George K. and George A. pull apart the vibe shift happening at the top of the AI economy: from Uber's COO admitting he can't draw a line between token spend and shipped features, to the broader reckoning hitting every CFO who signed a three-year AI contract without modeling what agentic workflows actually cost.
The subsidized era is over. The bill is due. And nobody has a clean answer.
But the harder question underneath all of it isn't economic. It's human.
What happens when an industry skips straight from "how big can we make it" to "what are humans even for" without stopping to answer either?
The two Georges reckon with soft skills being repackaged as vital skills, the neoliberal bargain sold to a generation of college graduates, and what Pope Leo's 42,000+word encyclical on human dignity in the age of AI gets right that most boards and governments haven't.
A tech podcast about humans. This week, more than ever.
Mentioned:
Jensen Huang on irresponsible proclamations Uber COO on lack of ROI from tokenmaxxing Ed Zitron on OpenAI and potential collapse of Oracle Daniela Amodei on the importance of the humanities Jamie Dimon on future job skills What 2026 hiring managers are looking for Pope Leo XIV’s encyclical, Magnifica Humanitas Marissa Alert on business outcomes planning first David Homan on how to build real human networks Sharon Goldman on the small town impact of the datacenter buildout -
What if the reason most people struggle to build meaningful professional relationships isn't effort — it's that they've mistaken a transaction for a foundation?
David Homan has spent thirteen years building the largest private network of super connectors on the planet. Not by being the most impressive person in the room, but by being the most useful one — long before anyone asked. His thesis is that trust operates on a time horizon most people aren't patient enough to respect. That the introductions that change lives rarely pay off in weeks. They pay off in years, through chains of three to five people that no existing technology has ever been able to track — until now.
In this episode, David walks us through the architecture of real community: why action is the only currency that matters, what it actually means to honor a chain of connections, and how a moment of genuine vulnerability can outperform a hundred polished elevator pitches. He also makes a case that most of us have at least two phone calls we should have made by now — and haven't.
Learn more about David's work:
Orchestrating Connection SOAR Connect -
What happens when a community votes no…but the #AI datacenter construction starts anyway?
That is not a hypothetical. It’s what happened in Saline Township, Michigan, when a $16 billion OpenAI-Oracle data center was rejected by the local planning commission, rejected again by the township board, and broke ground weeks later anyway. The developer sued. The town settled. They had no real choice.
Sharon Goldman has been covering the AI data center buildout for Fortune — not from boardrooms, but from township halls, planning commission meetings, and rural communities that had never imagined something like this landing in their midst. What she’s found is a story that the technology press largely isn't telling: the buildout is a bottom-up crisis dressed up as a top-down triumph.
The numbers tell part of it. Saline Township received $14 million in community benefits from a $16 billion project, against an annual budget of $1 million. In Richland Parish, Louisiana, the land where Meta's Hyperion facility now sits was once pitched for an auto plant that would have created two to three thousand permanent jobs. The data center is promising 500. The construction workers are mostly from out of state.
And the justifying ideologies — the race with China, the national security imperative — has no finish line. This race has a vague one-upsmanship and a $700 billion spend with no clear end in sight.
What Sharon sees coming, and what she thinks the press is missing, is the backlash that is quietly becoming a political force — showing up in recall elections, in governor's races, and in the kind of conspiratorial thinking that emerges when people have lost trust and no longer believe that democracy is working for them.
You can read more of Sharon's reporting here:
A Michigan farm town voted down plans for a giant OpenAI-Oracle data center. Weeks later, construction began | Fortune Meta's $27 billion AI data center is causing chaos in small town Louisiana | Fortune At the edges of the AI data center boom, rural America is up against Silicon Valley billions Huge AI data centers are turning local elections into fights over the future of energy Elon Musk is pushing to build data centers in space. But they won’t solve AI’s power problems anytime soon Big Tech will spend nearly $700 billion on AI this year. No one knows where the buildout ends Inside a multibillion dollar AI data center powering the future of the American economy -
In the wake of more layoffs attributed to "AI," we thought it worthwhile to revisit this conversation from earlier in the year. Increasingly, AI is being used as a catch-all excuse to justify layoffs without clear return on business value, other than the stock price...so it's time to dig deeper.
What if your AI rollout isn't failing because of the technology, but because no one asked your employees how they feel about it?
Dr. Marissa Alert is a clinical psychologist who works with organizations scaling AI. Her argument is deceptively simple: the resistance leaders keep running into isn't a change management problem. It's a diagnostic failure. And until you treat it like one, AI rollouts turn into guesswork.
High usage doesn't mean successful adoption. It might just mean fear-driven compliance.
In this episode, we get into what business leaders and organizations consistently get wrong: the assumptions made about how employees will respond, the gap between leadership alignment at the top and the confusion that trickles down, and why layering an AI mandate onto a workforce already running on empty is a very different problem than a training rollout.
We also got into something harder: what it means when employees are being asked to integrate tools that might replace them, and why most leaders don't have a good answer for that question.
If your organization is tracking adoption rates and still seeing 20%, this episode is worth your time.
Mentioned
Jack Dorsey’s Block cuts nearly half of its staff in AI gamble -
What if the story we're being told about AI's inevitability is hiding something underneath?
That's the question Jessica Parker and Kimberly Becker put to George K. on their podcast, Women Talking ‘Bout AI.
This conversation is a replay from their feed. It followed the money: the special purpose vehicles, the obfuscatory financing, the concentration of risk in a handful of companies and a single island in the Taiwan Strait. But what they kept arriving at wasn't really a financial question. It was a human one.
Who has skin in the game? And what happens to the rest of us when the people building this technology can't answer what outcome they're actually trying to produce?
The conversation covers why the dot-com analogy is the wrong frame for the current investment craze, why an AI crash could starve the narrow applications that actually work, and why the "everything machine" promise was probably never going to pay for itself.
It also gets into what chatbot tutors get wrong about teaching, why we keep analogizing ourselves to whatever technology we just built, and what it might mean that generalists could be the ones who come out of this ahead.
The kind of conversation where you leave with more questions than you came in with. Which is exactly what we're after.
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The AI hype machine is taking up all the oxygen we need to actually stop the harm happening today.
This month we heard from three guests who didn't compare notes. Didn't coordinate. And all three circled the same thing: the #AI hype machine isn't just wrong, it's actively making things worse.
Capital flows going to “everything machines” instead applications that actually accomplish tasks. Gas turbines burning methane next to communities already carrying four times the national cancer rate. AI chatbots mathematically, not metaphorically, mathematically, engineered to reinforce delusional thinking in vulnerable users. Deepfake abuse still expanding, still mostly targeting women and minors, still unsolved. This is the real harm inventory.
This month. Right now.
Meanwhile the discourse is about whether a model might hypothetically stage a coup in five years.
We're not doing doomer porn. We're saying watch the industry’s hands, not the mouth. The boring risks are already here. The extraordinary stuff — the farmer in Morocco beating generalist models with expert-annotated field data, the researcher finding antibiotics with true wet lab work — that's also already here! It's just not getting same headlines and the funding.
System Check. This month's episodes, broken down against current events and whatever's rattling around our brainboxes.
Mentioned:
Smaller models find the same bugs as Mythos Stanford HAI 2026 AI Index Discovering a new class of antibiotics Dmitri Alperovitch's testimony on compute Baidu robotaxi outage MIT CSAIL study on AI psychosis NAACP lawsuit against xAI XAI gas turbines polluting rural communities Northern Virginia datacenter health impacts Human Line Project -
Are tech industries selling us a problems they invented?
Ryan Clarque, CSO at Black Rifle Coffee Company, doesn't flinch at the big provocations. When Claude's Mythos model showed up in every LinkedIn feed promising a software apocalypse, Ryan's take was blunt: the basics were broken before Mythos, and they'll still be broken after it. The real question about a powerful AI model, it’s whether you've built a program capable of doing anything about them when it does.
But the conversation doesn't stop at hype-busting. Ryan has quietly done something the industry insists can't be done: built a lean, two-person security operation that ditched the big-ticket SIEM vendors, took control of its own telemetry, and outperformed programs with ten times the headcount and budget. When one of those vendors found out, they sent their "heavy hitter" to prove Ryan wrong, who left agreeing Ryan didn't need them.
What emerges is a portrait of a practitioner who learned to distinguish progress from movement — and who thinks most of the industry is confusing the two. The procurement cycle, the Gartner roadmap, the sequence of investments you're told you must make: Ryan's argument is that inertia dressed up as strategy has left small security teams demoralized and over-leveraged, and that the fix is less about budget and more about the willingness to build your own way out.
And then, at the end of a week of planes and conferences, Ryan says something that reframes all of it. The reason he doesn't chase the car or the watch or the title isn't asceticism — it's that working in security means observing the worst of what people do to each other, and the only way to stay functional is to invest hard in what actually holds. Time. Trust. People who remember how you made them feel.
Mentioned:
Cal Newport on Mythos vs other LLMs in finding software vulnerabilities -
What if narrow #AI, rather than imagined AGI through scaling will be what changes the world? In some places, that’s already happening.
El Mahdi Aboulmanadel founded DeepLeaf after watching smallholder farmers in Morocco misdiagnose crop disease because three distinct conditions can look identical to the human eye. Wrong diagnosis, wrong treatment, chemical residue on food.
Best case scenario? Export crops rejected at customs.
Worst case scenario? Food scarcity for communities that can’t afford it.DeepLeaf's answer is deliberate focus: one problem, field-validated data, models trained on hyperspectral and RGB image pairs across 57 crops. The accuracy doesn't come from scale. It comes from specificity. Fine-tuned continuously on new field data. The result is less compute, faster iteration, and outcomes closer to the ground truth.
DeepLeaf has both cloud inference for large or multi-crop operations and lightweight edge models downloaded per crop for farmers running on Android phones in areas with no connectivity. The architecture fits the user, not the other way around.
We get into economic potential for farmers, and of course, the effects of the war in Iran.
This episode is about what new AI perspectives than the ones taking up all the oxygen in the West. This is technology that’s built for communities that Silicon Valley usually ignores.
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Amber Bennoui calls it like she sees it: most of what gets sold as "AI security" is just cloud security with sparkle emojis on it.
She's co-founder of AISECA, a veteran product leader, and a more honest voices in a space that isn't exactly famous for honesty right now.
We sat down with her fresh off RSA, and the conversation got very real:
The real AI risk isn't the sci-fi scenario. It's the DevOps engineer at a 900-person company arguing they should be able to send commands via a remote control feature, with three security people in the building who don't even know the conversation is happening. It's the tools already embedded in software your finance and HR teams use every day, making decisions nobody gave explicit permission for.
Amber's argument is simple and uncomfortable: most organizations have a discoverability problem they haven't solved yet, and vendors are selling dashboards to people who don't even know what's running in their own house. That's not security. That's theater.
We also got into what it actually takes to build something vendor-agnostic and practitioner-led when the companies with the biggest budgets are also the ones racing to define what AI security means. And whether the tension between speed and safety is even something security teams get to resolve — or whether that decision has already been made for them.
Mentioned:
MIT Paper, "Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians" -
When was the last time a news headline about AI actually told you something true?
George K. and George A. recorded this one from opposite sides of the planet — George K. fresh off RSA in San Francisco, George A. embedded at a global trust and safety conference in London. The distance didn't slow them down.
This month's System Check has a theme: we’re living inside a story that powerful institutions are writing for us, and most of us aren't stopping to ask who's holding the pen.
Meta and YouTube just lost a landmark lawsuit — not over what they published, but over how they designed their products to keep you hooked. The legal strategy that finally worked was the one used against Big Tobacco. Meanwhile, 82% of journalists now use some form of AI tool in their work. The people covering AI are increasingly shaped by it. The snake is eating its tail.
The arms race math doesn't add up either. Forty billion dollar bridge loans. Circular investments. Credit-based bets assuming a revenue base that doesn't yet exist. And somewhere in rural Mississippi, kids are developing breathing problems because gas turbines got trucked in to power a datacenter the community never voted for.
The question running underneath all of it: are we making decisions based on outcomes, or based on vibes? And if it's vibes — whose vibes are they, and how did they get there?
Mentioned:
Meta and YouTube verdict news coverage Center for Humane Technology’s podcast “Your Undivided Attention” episode on the Meta and YouTube lawsuit verdicts Ed Zitron’s recent monologue Research into how media covers AI UK Study on AI media coverage Muck Rack’s 2026 State of Journalism Report WSJ: CFOs expect to reduce headcount because of AI Anthropic co-founder Jack Clark on not being able to idle AI systems Iran War affects world helium supply, creating semiconductor bottleneck Environmental effects of Elon Musk using gas turbines to power data centers in rural communities -
What if the best investment decision is one where no human is involved?
Brant Meyer, partner at Trac VC joins the show this week to talk about the firm’s approach, where algorithms — not partners in puffer vests — make every single call. Over 115 investments to date with zero human investment decisions. An 8.5% loss ratio, orders of magnitude less than traditional VC, would seem to suggest they’re on to something.
George K. and George A. wanted to know, if machines make the decision, what exactly is Brant’s job? But the more interesting conversation isn't about the wins. It's about what the model forces you to confront. We assume removing the human removes the bias — but Trac's algorithms are trained on data with its own biases.
Then there's the psychological dimension. Brant makes the case that most resistance to algorithmic investing is emotional rather than rational. VCs resist algorithms because the discretionary call is the whole point. The juice, as he puts it, is the feeling of knowing. Strip that away and you're threatening an identity.
Which raises the question George K. and George A. keep circling: how did venture capitalists acquire oracular status in the first place? The hit rate doesn't justify it. The pattern recognition, Brant argues, was never really theirs to claim.
And yet , no founder wants to take money from a robot. The relationship still matters. The question is just whether we've been confusing that relationship with the thing it was never actually doing.
Mentioned:
Trac VC’s video
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We've spent the last several months talking to people who live at the intersection of technology and the humans on the receiving end of it.
A data privacy attorney. A corpus linguist. A clinical psychologist. A performance coach. An entrepreneur who built a business on failure.
They don't all agree with each other. But they're all pointing at the same thing: the gap between how technology gets built, deployed, and sold — and what it's actually doing to people.
This week's episode is our attempt to pull that thread.
Mike McLaughlin — The AI ecosystem is running on bad data, has no real mechanism to fix it, and the next wave of cybercrime will target the training data itself. Kimberly Becker, PhD — AI-generated text is structurally overconfident, and a corpus linguist traced that pattern all the way back to how decontextualized certainty language helped fuel the opioid epidemic. Dr. Marissa Alert — What organizations call employee resistance to AI is, clinically, a fear and identity threat response that most rollouts are spending millions to ignore. Tychon Carter — Winning is often where the real crisis begins, and the goalpost never stops moving until you decide your value isn't determined by your output. The "Bad Hombre" — A solopreneur who built a business on public failure makes the case that the willingness to fail more than most people even try is the only real competitive advantage.Every one of these conversations eventually arrives at the same place: the distance between what we're building and who it's landing on.
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Why do your friends and parents still get breach notification letters from companies they’ve never heard of?
John Watters aka “The Cowboy” joins the show this week for a hard look at information security. In the early 2000s, he built iDefense from a bankruptcy buyout into one of the most influential threat intelligence companies in the world, pioneered responsible disclosure before the term even existed, and has watched the attack surface evolve from nation-state espionage into something that hits your credit card at a restaurant on a Tuesday.
His answer to the breach question? The industry's been losing the clock. Attackers can move from target selection to exploitation in days. Defenders are still operating in weeks. And the gap isn't closing, not by a long shot. If anything, it's widening.
This conversation goes from the living rooms of people who've stopped trusting cybersecurity to the boardrooms of Fortune 500 CISOs who still can't explain their third-party risk exposure in plain English. We talk time compression, threat intelligence architecture, the AI arms race that only one side seems to be taking seriously, and the uncomfortable truth about analysis paralysis in a field where the cost of inaction is terminal.
John's closing advice to defenders: automate yourself out of a job before someone else does it for you.
That one's worth the price of admission alone.
Mentioned:
This is How They Tell Me the World Ends, by Nicole Perlroth
CISO Mike Melo’s post on security theater
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What if your AI rollout isn't failing because of the technology, but because no one asked your employees how they feel about it?
Dr. Marissa Alert is a clinical psychologist who works with organizations scaling AI. Her argument is deceptively simple: the resistance leaders keep running into isn't a change management problem. It's a diagnostic failure. And until you treat it like one, AI rollouts turn into guesswork.
High usage doesn't mean successful adoption. It might just mean fear-driven compliance.
In this episode, we get into what business leaders and organizations consistently get wrong: the assumptions made about how employees will respond, the gap between leadership alignment at the top and the confusion that trickles down, and why layering an AI mandate onto a workforce already running on empty is a very different problem than a training rollout.
We also got into something harder: what it means when employees are being asked to integrate tools that might replace them, and why most leaders don't have a good answer for that question.
If your organization is tracking adoption rates and still seeing 20%, this episode is worth your time.
Mentioned
Jack Dorsey’s Block cuts nearly half of its staff in AI gamble -
The voices telling you it won't work usually belong to people who never tried. Nobody gives you permission to take a chance. You just do it.
Chris built a 50K MRR business without a formal education, a tech background, or a plan. As an actor, a car dealership paid him $400 to be in a commercial and he thought, "If I can pretend to do this, what happens if I just actually do it?"
From there it was taking on teaching himself APIs, webhooks integrations, and enough failures to make most people quit. He's now responsible for 40% of some dealerships' bottom lines, working remotely from Ottawa, heading to Costa Rica.
We talked about why people don't take that first step. Chris's take is it's mostly the room you're in. When you move somewhere nobody knows you, the risk calculus changes. The voices telling you you're going to look stupid usually belong to people who never left.
We also got into social media, the throttled notification drip sequences designed to keep you coming back, the rage bait economy, the positive reinforcement loop that rewards the most outrageous behavior. His advice was simple: put your phone down and tackle your life goals head on.
Chris also hosts Bad Hombres TV on YouTube.
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What happens when you get everything you thought you wanted and still feel empty?
Tychon Carter won Big Brother Canada, gained fame and followers overnight, and felt completely lost. The success arrived before he was ready for it. The external validation didn't fill the internal void.
In this conversation, we dig into the gap between looking successful and actually feeling whole. Tychon walks through his journey from urban planner to reality TV winner to performance coach, and the hard lessons about self-worth that came with it.
We explore the masks we wear in professional spaces, the cost of performative confidence we don't feel, and why so many high-achievers feel stuck despite checking all the boxes. Tychon's "Start With You" framework breaks down three critical areas most of us keep out of balance:
Power (accessing your authentic self) Play (creating and enjoying life beyond work) and Peace (finding internal harmony)The conversation gets real about mental health, the isolation trap of self-reliance, and why giving to community might be more rewarding than the endless pursuit of more.
Mentioned
Johann Hari, Lost Connections On prescribing community work to treat depression More on Adam Grant and Jane Dutton’s study of contribution journalsMore about Tycoon Carter
https://www.tychoncarter.com/
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This week we're taking stock of conversation trends to let it rip on AI market jitters and what happens when the math stops math-ing.
We start with the numbers that have investors nervy: Amazon's $200 billion capex projection for 2026, and the uncomfortable reality of building an entire economy on depreciating GPU infrastructure with a three-year shelf life. Why the dot-com bubble comparison are incomplete, and questioning what happens when billions flow into overwhelming into transformer model architecture while research into others starves.
Then we shift from market corrections to attention economics, unpacking how AI tools promise productivity while actually training us to outsource thinking itself. The cost is both financial and experiential. When was the last time you sat alone without reaching for your phone? Can you still read sentences that run four lines long?
The episode lands on an uncomfortable question about who gets to have unmediated experiences anymore, and whether we're living our own lives or just consuming other people's.
Mentioned:
Ed Zitron ’s “Better Offline” podcast Derek Thompson’s Plain English podcast interview with Paul Kedrosky on market conditions and signs of a bubble Stephen Colbert on “truthiness” Enshittification, coined by Cory Doctorow MIT on the philosophical puzzle of AI Netflix’s main competition is sleep Point of view: Gen Z will remember more of other people’s memories than their own Blaise Pascal writing about attention in 1670 -
What happens when AI-generated text masquerades as human research?
Kimberly Becker, PhD, a corpus linguist joins the show this week to talk about her study comparing human-written versus AI-generated abstracts in high-stakes healthcare research.
The findings reveal something unsettling about how LLMs may potentially reshape scientific communication. ChatGPT's outputs showed higher informational density, formulaic patterns, and a lack of hedging, the linguistic uncertainty that marks careful scientific thinking. The AI doesn't say "may suggest" or "could indicate." It asserts. Confidently. Even when it's wrong.
This matters beyond academia. When we optimize for speed and polish over depth and precision, we're changing how we write, and therefore changing how we think. We're externalizing cognition to systems trained on Reddit threads and blog posts, then wondering why the output feels sterile and an inch-deep.
Becker's work raises uncomfortable questions:
Are we training ourselves to accept confident wrongness? What happens when a generation of researchers doesn’t communicate uncertainty? And fundamentally, can a predictive text model ever replicate the pause, the breath, the examination that Neil Postman argued was essential to meaningful thought?This episode is about whether we're paying attention to what we're losing while we chase efficiency.
Mentioned:
James Marriott, Dawn of the Post-Literate Society Neil Postman’s seminal work, Amusing Ourselves to Death Derek Thompson, The End of Thinking• • Linguistics Relevance Theory
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