Podcast
Bare Knuckles and Brass Tacks
Hosted by BKBT Productions · EN
Bare Knuckles and Brass Tacks is the tech podcast about humans. Hosted by George K and George A, this podcast examines AI, infrastructure, technology adoption, and the broader implications of tech developments through both guest interviews and news commentary.
Our guests bring honest perspectives on what's working, what's broken, and new ways to examine the roles and impacts of technology in our lives.
We challenge conventional tech industry narratives and dig into real-world consequences over hype. Whether you're deeply technical or just trying to understand how technology shapes society, this show will make you think critically about where we're headed and who's getting left behind.
24episodes
Episodes
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Leveling Up is about fixing your own broken code (REPLAY)
4d ago00:43:03Tap to summarizeThe 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.Mentionedhttps://www.kornferry.com/about-us/press/71percent-of-us-ceos-experience-imposter-syndrome-new-korn-ferry-research-findshttps://www.mogawdat.com/solve-for-happyhttps://jamesclear.com/atomic-habits
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The token math ain't mathin', so time to get back to what makes us human
2w ago00:40:56Tap to summarizeThe 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
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Why your network is shallower than you think, and how to change it
3w ago00:41:18Tap to summarizeWhat 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
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What the AI datacenter build out looks like from the ground up
4w ago00:42:19Tap to summarizeWhat 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
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Best of: What leaders get wrong about AI rollouts and employee adoption
May 1100:40:33Tap to summarizeIn 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
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The Everything Machine and the Trillion-Dollar Bet [Replay]
May 401:02:02Tap to summarizeWhat 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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AI is doing real good and real harm, but the hype is hiding both
Apr 2700:41:16Tap to summarizeThe 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
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Distinguishing between movement and progress, in AI, security, and more
Apr 2000:44:39Tap to summarizeAre 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
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Using focused AI to help small farmers and reduce food insecurity
Apr 1300:35:46Tap to summarizeWhat 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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AI Security Is Just as vague as "Cloud Security", but With Sparkle Emojis
Apr 600:40:41Tap to summarizeAmber 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"
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