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.

Best Of: What are we building? And the future of human flourishing...

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 [https://bookshop.org/p/books/this-is-how-they-tell-me-the-world-ends-the-cyberweapons-arms-race-nicole-perlroth/62372aa66ee6e45e], by Nicole Perlroth

CISO Mike Melo’s post on security theater [https://www.linkedin.com/posts/cisomike_staytuned-cybersecurity-ciso-activity-7434637121044402176-xSLc]

Why cybersecurity is broken and time is the enemy

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 [https://www.theverge.com/tech/885710/jack-dorsey-block-layoffs-job-cuts-ai]

What leaders get wrong about AI rollouts and employee adoption

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 [https://www.youtube.com/@BADHOMBRESTV] on YouTube.

The art and Science of grit, ambition, & betting on yourself

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 [https://bookshop.org/p/books/lost-connections-johann-hari/33248fe6585d4c88?ean=9781632868312&next=t&next=t&affiliate=12476]
 * On prescribing community work to treat depression [https://www.lucksyardclinic.com/lost-connections-and-social-prescribing-in-nature/]
 * More on Adam Grant and Jane Dutton’s study of contribution journals [https://www.inc.com/jessica-stillman/star-psychologist-adam-grant-for-motivation-and-happiness-a-contribution-journal-beats-a-gratitude-journal/91193347]

More about Tycoon Carter

https://www.tychoncarter.com/

Finding meaning and community in tech-fueled hustle culture

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 [https://www.youtube.com/@BetterOfflinePod]
 * Derek Thompson’s Plain English podcast interview with Paul Kedrosky on market conditions and signs of a bubble [https://www.theringer.com/podcasts/plain-english-with-derek-thompson/2025/09/23/this-is-how-the-ai-bubble-could-burst]
 * Stephen Colbert on “truthiness” [https://www.c-span.org/clip/white-house-event/user-clip-stephen-colbert-on-truthiness/4293026]
 * Enshittification, coined by Cory Doctorow [https://en.wikipedia.org/wiki/Enshittification]
 * MIT on the philosophical puzzle of AI [https://news.mit.edu/2026/philosophical-puzzle-rational-artificial-intelligence-0130]
 * Netflix’s main competition is sleep [https://www.fastcompany.com/40491939/netflix-ceo-reed-hastings-sleep-is-our-competition]
 * Point of view: Gen Z will remember more of other people’s memories [https://www.instagram.com/reel/DPIG_7sDfsp/] than their own
 * Blaise Pascal writing about attention in 1670 [https://en.wikipedia.org/wiki/Pens%C3%A9es]

AI market jitters, post-truth reality, data, and safeguarding what makes us human

What happens when AI-generated text masquerades as human research?

Kimberly Becker, PhD, [https://www.linkedin.com/in/kimberlypacebecker/] 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 [https://jmarriott.substack.com/p/the-dawn-of-the-post-literate-society-aa1]
 * Neil Postman’s seminal work, Amusing Ourselves to Death [https://bookshop.org/p/books/amusing-ourselves-to-death-public-discourse-in-the-age-of-show-business-neil-postman/ebe4569d9072fac7]
 * Derek Thompson, The End of Thinking [https://www.derekthompson.org/p/the-end-of-thinking]

•  • Linguistics Relevance Theory [https://en.wikipedia.org/wiki/Relevance_theory]

AI vs Human writing and what it means for our thinking

We need to stop treating our data like something to be stored and more like a mission critical supply lines.

Andrew Schoka [https://www.linkedin.com/in/andrew-schoka/] spent his military career in offensive cyber, including stints in the Joint Operations Command and Cyber Command. Now he's building Hardshell to solve a problem most organizations don't even realize they have yet.

Here's the thing: AI is phenomenal at solving problems in places where data is incredibly sensitive. Healthcare, financial services, defense—these are exactly where AI could make the biggest impact. But there's a problem.

Your ML models have a funny habit of remembering training data exactly how it went in. Then regurgitating it. Which is great until it's someone's medical records or financial information or classified intelligence.

Andrew makes a crucial point: organizations still think of data as a byproduct of operations—something that goes into folders and filing cabinets. But with machine learning, data isn't a byproduct anymore. It's a critical supply line operating at speed and scale.

The question isn't whether your models will be targeted. It's whether you're protecting the data they train and interpret like the supply lines they actually are.

Mentioned:

 * Destruction of classified tech in downed helicopter during Osama bin Laden raid [https://www.britannica.com/event/Killing-of-Osama-bin-Laden]

Protecting data as the critical supply line for AI Applications

Are we sleepwalking into a security crisis that makes ransomware look quaint?

Nuclear security expert Audrey Crowe joins the show to talk about the convergence of grey zone warfare, critical infrastructure, and nuclear security. This isn't your parents’ Cold War nuclear threat, this is about adversaries who've figured out they don't need missiles when they can manipulate our infrastructure through cyber operations, disinformation, and coercion that lives in the murky space below armed conflict.

While our adversaries operate in the grey zone with zero institutional friction, democratic nations tie themselves in bureaucratic knots. We demand attribution, legal frameworks, and perfect evidence before we can even acknowledge a threat. It's like showing up to a knife fight with a permission slip.

Audrey walks us through how Stuxnet changed everything, why the nuclear sector spans energy, transportation, healthcare, and government regulation, and why she's on a mission to get nuclear industry stakeholders share more information with one another.

We also get into the elephant in the room: Big Tech's sudden hunger for nuclear power to feed AI data centers. When profit-driven actors start controlling nuclear infrastructure, will safety remain sacred? Or will we sacrifice long-term security for short-term computational power?

Securing nuclear energy systems on all fronts with Audrey Crowe

What if the real AI revolution isn't about better models—but about unlocking the data we've been sitting on?

Mike McLaughlin [https://www.linkedin.com/in/michael-g-mclaughlin/]—cybersecurity and data privacy attorney, former US Cyber Command—joins us to discuss something most people miss in the AI conversation: we're building the infrastructure for a completely new asset class.

The conversation moves past today's headlines and LLM limitations into what becomes possible when we solve the data access problem:

Research acceleration at unprecedented scale. Imagine biotech startups accessing decades of pharmaceutical failure data, every null result, every experiment that didn't work. That's years cut from development cycles. That's drugs to market faster. That's lives saved.

Universities as innovation accelerators. Right now, research institutions pay to store petabytes of data collecting dust on servers. Mike argues they're sitting on billions in untapped assets to fuel innovation.

Beyond synthetic training. The next generation of AI won't be trained on Reddit threads and scraped websites. It'll be trained on high-quality, provenance-verified research data from institutions that have incentive to participate in the ecosystem.

Mike's vision isn't just about compliance or risk mitigation. It's about creating the conditions for AI to actually deliver on the promise everyone keeps talking about. The compute exists. The capital exists. The models are improving. What we need now is the mechanism to turn decades of institutional research into fuel for the next wave of moonshot innovation.

Mentioned

Google licensing deal with Reddit [https://www.reuters.com/technology/reddit-ai-content-licensing-deal-with-google-sources-say-2024-02-22/]

Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples [https://arxiv.org/abs/2510.07192]

MIT researchers discover new class of antibiotics using machine learning [https://news.mit.edu/2023/using-ai-mit-researchers-identify-antibiotic-candidates-1220]

Reducing bacterial infections from hospital catheters using machine learning [https://www.caltech.edu/about/news/aided-by-ai-new-catheter-design-prevents-bacterial-infections]

Why future applications of AI will need higher quality data

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.

George Kamide

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