Bare Knuckles and Brass Tacks

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 [https://futurism.com/artificial-intelligence/nvidia-ceo-begs-execs-stop-fired-ai] * Uber COO on lack of ROI from tokenmaxxing [https://www.businessinsider.com/uber-coo-andrew-macdonald-ai-token-spending-harder-justify-2026-5] * Ed Zitron on OpenAI and potential collapse of Oracle [https://www.youtube.com/watch?v=NHtGXCWXyZs] * Daniela Amodei on the importance of the humanities [https://fortune.com/2026/02/07/anthropic-cofounder-daniela-amodei-humanities-majors-soft-skills-hiring-ai-stem/] * Jamie Dimon on future job skills [https://www.cnbc.com/2025/12/11/jamie-dimon-ai-will-eliminate-jobs-but-these-skills-will-get-you-opportunities.html] * What 2026 hiring managers are looking for [https://www.msn.com/en-us/news/insight/experts-urge-shift-to-human-skills-as-job-market-tightens/gm-GMF1A41634] * Pope Leo XIV's encyclical, Magnifica Humanitas [https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html] * Marissa Alert on business outcomes planning first [https://open.spotify.com/episode/7ABtVkMeFEFVkJ0YVXjRXV?si=a10dec23e96f450a] * David Homan on how to build real human networks [https://open.spotify.com/episode/4iPxqxL69EKDCox6FuSK0X?si=b564db6ac39e4d35] * Sharon Goldman on the small town impact of the datacenter buildout [https://open.spotify.com/episode/3oiUrqViDUNpRtWTPgfLN3?si=w5XsbmbSQ--CbxhtdjLR-Q]

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 [https://fortune.com/author/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 [https://fortune.com/2026/05/06/ai-data-center-michigan-saline-politics-farmland/?sge456] * Meta's $27 billion AI data center is causing chaos in small town Louisiana | Fortune [https://fortune.com/2026/03/26/meta-ai-data-center-hyperion-louisiana/?sge456] * At the edges of the AI data center boom, rural America is up against Silicon Valley billions [https://fortune.com/2025/12/27/ai-data-centers-arizona-hassayampa-ranch/?sge456] * Huge AI data centers are turning local elections into fights over the future of energy [https://fortune.com/2025/10/22/ai-data-centers-politics-elections-energy/?sge456] * Elon Musk is pushing to build data centers in space. But they won't solve AI's power problems anytime soon [https://fortune.com/2026/02/19/ai-data-centers-in-space-elon-musk-power-problems/?sge456] * Big Tech will spend nearly $700 billion on AI this year. No one knows where the buildout ends [https://fortune.com/2026/04/30/big-tech-hyperscalers-will-spend-700-billion-on-ai-infrastructure-this-year-with-no-clear-end-in-sight-eye-on-ai/?sge456] * Inside a multibillion dollar AI data center powering the future of the American economy [https://fortune.com/2026/01/27/data-centers-ai-meta-microsoft-google-amazon-openai-gpu/?sge456]

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 [https://www.womentalkinboutai.com/]. 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.

Are tech industries selling us a problems they invented? Ryan Clarque, CSO at Black Rifle Coffee Company [https://www.blackriflecoffee.com/], 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 [https://www.youtube.com/watch?v=k-8stQCeQiE]

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" [https://arxiv.org/pdf/2602.19141]

What if the best investment decision is one where no human is involved? Brant Meyer, partner at Trac VC [https://www.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 [https://www.youtube.com/watch?v=OkDJnBrEX2k]

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]

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.

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]

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]

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]

Wishing you a very happy and prosperous New Year! We'll be back in 2026!

We're off this week, deep into planning and scheduling for next year. Please enjoy this Best Of episode, originally released in October. Hannah Storey, Advocacy and Policy Advisor at Amnesty International [https://www.amnesty.org/], joins the show to talk about her new brief that reframes Big Tech monopolies as a human rights crisis, not just a market competition problem. This isn't about consumer choice or antitrust law. It's about how concentrated market power violates fundamental rights—freedom of expression, privacy, and the right to hold views without interference or manipulation. Can you make a human rights case against Big Tech? Why civil society needed to stop asking these companies to fix themselves and start demanding structural change. What happens when regulation alone won't work because the companies have massive influence over the regulators? Is Big Tech actually innovating anymore? Or are they just buying up competition and locking down alternatives? Does scale drive progress, or does it strangle it? What would real accountability look like? Should companies be required to embed human rights due diligence into product development from the beginning? Are we making the same mistakes with AI? Why is generative AI rolling forward without anyone asking about water usage for data centers, labor exploitation of data labelers, or discriminatory outcomes? The goal isn't tweaking the current system—it's building a more diverse internet with actual options and less control by fewer companies. If you've been tracking Big Tech issues in silos—privacy here, misinformation there, market dominance over here—this episode is an attempt to bring those conversations together in one framework. Mentioned: Read more about the Amnesty International report and download the full report here: "Breaking Up with Big Tech: a Human Rights-Based Argument for Tackling Big Tech's Market Power" [https://www.amnesty.org/en/documents/pol30/0226/2025/en/] Speech AI model helps preserve indigenous languages [https://it-online.co.za/2024/01/22/speech-ai-model-helps-preserve-indigenous-languages] Empire of AI, [https://www.penguinrandomhouse.com/books/743569/empire-of-ai-by-karen-hao/] by Karen Hao Cory Doctorow's new book, "Enshittification: Why Everything Suddenly Got Worse and What To Do About It" [https://www.versobooks.com/products/3341-enshittification]