Bare Knuckles and Brass Tacks

Clinical psychologist, Dr. Sarah Adler, joins the show this week to talk about why “AI Therapy” doesn’t exist, but is bullish on what AI can help therapists achieve.

Dr. Adler is a clinical psychologist and CEO of Wave. She's building AI tools for mental healthcare, which makes her position clear—what's being sold as "AI therapy" right now is dangerous.

Chatbots are optimized to keep conversations going. Therapy is designed to build skills within bounded timeframes. Engagement is not therapy. Instead, Dr. Adler sees AI as a powerful recommendation engine and measurement tool, not as a therapist.

George K and George A talk to Dr. Adler about what Ethical AI looks like, the model architecture for personalized care, who bears responsibility and liability, and more.

The goal isn't replacing human therapists. It's precision routing—matching people to the right care pathway at the right time. But proving this works requires years of rigorous study. Controlled trials, multiple populations, long-term tracking. That research hasn't been done.

Dr. Adler also provides considerations and litmus tests you can use to discern snake oil from real care.

Mental healthcare needs innovation. But you cannot move fast and break things when it comes to human lives.

Mentioned:

A Theory of Zoom Fatigue

Kashmir Hill’s detailed reporting on Adam Raine’s death and the part played by ChatGPT 

(Warning: detailed discussion of suicide)

Colorado parents sue Character AI over daughter's suicide

Sewell Setzer's parents sue Character AI

Deloitte to pay money back after caught using AI in $440,000 report

Original Air Date: October 13, 2025

Graeme Rudd spent years taking emerging technology into austere environments and watching it fail.

He assessed over 350 technologies for a Department of Defense lab. The pattern was consistent: engineers solved technical problems brilliantly. Lawyers checked compliance boxes. Leadership approved budgets. And the technology failed because no one was looking at how humans would actually use it.

So he went to law school—not to practice law, but because solving technology problems requires understanding legal systems, human behavior, operational constraints, and business incentives simultaneously.

Now he works on AI governance, and the stakes are higher. "Ship it and patch later" becomes catastrophic when AI sits on top of your data and can manipulate it. You need engineers, lawyers, operators, and the people who'll actually use the system—especially junior employees who spot failure points leadership never sees—in the room together before you deploy.

This conversation is about why single-discipline thinking fails when technology intersects with humans under stress.

Why pre-mortems with your most junior people matter more than post-mortems with experts.

Why the multidisciplinary approach isn't just nice to have—it's the only way to answer the question that matters:

Does it work when a human being needs to operate it under conditions you didn't anticipate?

Original air date: Nov 24, 2025

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 journals

Original air date: Feb 16, 2026

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

Original air date: Feb 2, 2026

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. It started when 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 APIs learned on someone else's Amex, webhook integrations figured out under pressure, 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 take life at face value.

Originally aired: Feb 23, 2026

Hunter Grad, CEO and founder of Ameliogenix, joins the show to talk about developing mRNA immunotherapies for cardiovascular disease. George K and George A sit down with Hunter to discuss:

• How a procrastinated university project turned into a biotech startup tackling the leading cause of death worldwide
• The novel application of mRNA technology to permanently reduce cholesterol levels through targeting proteins within the body rather than viral diseases
• What it takes to bootstrap a biotech company in Ottawa, not Silicon Valley
• The brutal realities of fundraising in biotech versus software startups, and why pivoting isn't always an option when lives are on the line
• Clearing up the myths and misinformation around mRNA technology, from how it actually works to addressing fertility concerns
• The role of machine learning in accelerating biotech research and drug discovery, and why quality data matters more than flashy AI hype

Hunter breaks down complex immunology concepts into digestible explanations while sharing the raw challenges of being a young founder in a traditionally academic-led industry. This episode explores innovation at the intersection of technology and medicine, the importance of rigorous science over buzzwords, and what it means to swing for the fences on a problem that affects 2 billion people worldwide.

Originally aired: November 3, 2025

That’s a wrap on season 4! What's coming next isn't just another season.

Fifty-two episodes took the show past its cybersecurity origins into conversations with a corpus linguist, a nuclear security expert, an Amnesty International policy lead, a world champion Muay Thai fighter, and journalists from Fortune and 404 Media, just to name a few of this year’s guests.

That range is the reason for what comes next. We’re thinking through changes to the show’s cadence, format, and programming

The show isn't ending. But we’re dialing back while we build what’s next. We hope you stay along for the ride!

Cloud computing solved for scale by centralizing everything into a handful of massive buildings. Edge computing is the argument that scale was never the only thing worth optimizing for.

On this episode, Jeff MacMillan of Green Edge Computing Corp makes the case that sovereignty was never really about geography. It was about how many entities you'd have to strong-arm before someone gave in.

The conversation moves from mining sites with unreliable wireless to a Cloud Act clause most people have never read. It lands on an uncomfortable question: when decentralizing compute means decentralizing oversight too, who ends up accountable for what happens in the box nobody's watching.

The same hardware standard the U.S. military spent fifteen years refining now sits in clinics and 7-Elevens, and has implication and applications for bringing cutting edge software to the most remote areas. What does it mean when you can bring your own compute literally anywhere?

Mentioned:

• Jevon’s paradox

No one gets fired for doing what McKinsey says. That's not a joke, it's the actual product being sold in half of enterprise consulting: credibility a board can point to when a decision goes wrong. This week, Tim Lidman, founder of Clyde and formerly of a collaboration platform absorbed into Accenture in 2021, argues that AI won't replace that function — and says so without flinching, even though it means some prospective customers will balk.

Tim also shares why he thinks “human in the loop” is such a dehumanizing turn of phrase.

The conversation moves through the politics that quietly derail group decisions, the trap of building for experts at the expense of everyone else, and a real case where a district court judge canceled a trial because both sides had submitted AI-generated arguments neither side had bothered to check.

And this episode ends somewhere unexpected: a founder building frontier AI for a living who gives his own children zero screen time, and why he thinks boredom is the skill nobody is protecting anymore.

Mentioned:

• Deloitte Canada’s GenAI snafu citing made up research

•  • Judge cancels trial after finding out both sides use AI

This past week George K and A were off for the holidays. We revisit an episode from earlier this season. 

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