Artificial Intelligence Podcast: ChatGPT, Claude, Midjourney and all other AI Tools

Everyone now has the same AI. That should make markets smarter — Gary Brode says it does the opposite. Jonathan Green talks with Gary Brode (Managing Partner at Deep Knowledge Investing) about why AI is a terrible stock picker: it summarizes the herd, it cannot say "I don't know," it cannot disagree with the dominant narrative, and the places you actually make money are the moments when everybody else is wrong.

Key Takeaways:

• Access to information is not an edge — AI lets everyone read every 10-K in seconds, so that advantage is already priced in. The scarce skill is a different interpretation of the same facts.

• AI cannot say "I don't know." It answers with total confidence, including when it is guessing. Gary hires interns for the opposite: the candidate who hits the edge of their knowledge and says they will come back with an answer.

• AI is not objective. It is weighted toward mainstream media, Reddit, Substack, and a fairly monolithic view of politics and economics. Knowing those biases is the advantage.

• The hyperscaler treadmill is the risk story: trillions in capex, token prices down 75–90%, models obsolete in months, and no clean way to stop spending without writing the last few years to zero.

• Where you make real money is when the dominant narrative is incorrect. AI is built to repeat that narrative, not to challenge it.

Notable Quotes:

"Having better information is typically not a good way to make money in the stock market. I rarely have an information advantage, but I analyze it differently." — Gary Brode

"I don't know is a really good answer. AI doesn't do that. AI will give you the wrong answer or a made-up answer confidently." — Gary Brode

Connect with Gary Brode:

LinkedIn: https://www.linkedin.com/in/gary-brode-deep-knowledge-investing

Company: Deep Knowledge Investing — https://deepknowledgeinvesting.com

X: https://x.com/Gary_Brode

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
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• Podcast Website: https://artificialintelligencepod.com/  
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The cloud isn't actually in the sky — it runs on electricity, and AI is straining the grid like nothing before it. Jonathan Green talks with Stephanie Cox (CEO of GridEQ) about the energy reality behind the AI boom: why hyperscale data centers are a brand-new kind of load, why the real bottleneck is planning and permitting rather than power, how energy storage bridges fast-moving AI and slow infrastructure, why communities are pushing back, and where a smarter, more distributed grid goes from here.

Key Takeaways:

• AI data centers are a new kind of load — continuous, massive draw the grid was never designed for (it was built for peak and weather events, not 24/7 hyperscale demand).

• The real bottleneck isn't power, it's planning — transmission lines, interconnection queues, permitting, water, land, and community support. Securing power now takes far more than a purchase agreement.

• Energy storage is the bridge — it fills the gap while slow infrastructure (substations take years) catches up to fast-moving AI and software.

• Communities are pushing back — a stat she cites: ~90% of Americans don't want hyperscalers nearby (higher costs, water and power strain, few local jobs). The tension is power to the data center vs. power to the community.

• Bet on efficiency and a diversified grid — smart investors already model the decline (chips obsolete in 12–18 months, software getting leaner). The future is distributed centers, mixed generation + storage, and secondary markets for stranded equipment (GridEQ's new line).

Notable Quotes:

"90% of folks in the US don't necessarily want the data centers coming into play." — Stephanie Cox

"My hope is for a more diversified and resilient ecosystem — relying on more than just one technology." — Stephanie Cox

Connect with Stephanie Cox:

LinkedIn: https://www.linkedin.com/in/stephcox-co

Company: GridEQ

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
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Joshua Wilson has done roughly 2,500 interviews and runs Oneiron Network, a podcast network built for dealmakers, capital markets and investment banking. He remembers when an hour of recording meant ten hours of work — and when that work quietly killed the only part that mattered, which was building relationships with people. So he handed the tedious half to AI early, and then drew a hard line around the rest. Jonathan Green talks with him about why he now deliberately wants the chickens in the background, how a library of transcripts becomes a deal-signal machine that quietly brokers relationships between guests who have never met, how to break a media-trained guest out of their script, and the part of this no machine gets to touch.

Key Takeaways:

• Over-production now costs you trust. Joshua actively wants the ums, the kids in the background, the moment a guest says "that was probably dumb." When a show looks like a movie, the audience starts wondering whether any of it is real — so he tells his own network to edit less, not more.

• Your transcript archive is the asset, not the SEO. Where else do you get an hour of a billionaire's undivided attention on the record? Do a hundred of those a year, make them searchable, and then mine them for deal signals — who wants to buy, who wants to sell, who is raising — and broker introductions between two guests who have never met.

• Split the calibration by format rather than by taste. Audio can run long and unedited; the video cut gets trimmed for platform rules that can cost you monetization. That way the raw human moments survive where they are allowed to, instead of being sanded off everywhere.

• Break the frame on a media-trained guest. When someone keeps slipping their company name into every answer, a curveball question scratches the record they are playing — and once they suspect another one is coming, they stop reciting and start talking.

• Everything technical gets outsourced eventually; the human condition does not. AI will not tell you it knew an idea was wrong and did it anyway out of love for something. So use AI for all the work before and after the conversation — and never for choosing the guest, planning the questions, or the interview itself.

Notable Quotes:

"It lowers the trust factor when your show looks like a movie and people are like — are you even real?" — Joshua Wilson

"The challenging question we must ask ourselves is: who do I serve?" — Joshua Wilson

Connect with Joshua Wilson:

LinkedIn: https://www.linkedin.com/in/joshuabrucewilson

Network: Oneiron Network — https://oneironnetwork.com

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
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• Podcast Website: https://artificialintelligencepod.com/  
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T.J. Marlin spent most of his career investigating corporate crises — the kind that end up on the front page of the Wall Street Journal — and the pattern he found was not the one people expect. Across those global investigations, the overwhelming majority of the harm was not caused by external attackers. It was caused by helpful employees who did not know better, making assumptions nobody could see. Now CEO of Guardrail Technologies, he argues an AI agent is exactly that employee: a junior hire with no training, making real decisions on loans, power and infrastructure, who nobody would dream of onboarding with "never show me your work and do whatever you want." Jonathan Green talks with him about why mobile is the wild west, why MCP servers deserve the same scrutiny as any supplier, and the uncomfortable fact that you cannot train people to care about security — but you can constrain an agent.

Key Takeaways:

• An agent is a digital employee making decisions, not a process running a script. Your physical staff have an org chart, a job description and someone accountable for them. By default, agents have none of that — so nobody can say who let this one in the front door, or what came in behind it.

• The threat is mostly not the villain. Looking back across a career of corporate crisis investigations, the harm overwhelmingly came from well-meaning employees whose choices were invisible to management — which is exactly the shape of the shadow AI problem now.

• You cannot train people to care about security, and the industry keeps trying. Billions go into quarterly phishing simulations where most people click, take the training, and change nothing. Agents are different: you do not have to make them care, you constrain them with controls around and inside the thing.

• MCP servers are suppliers, so vet them like suppliers. Procurement exists to check who you are doing business with. Is this running in somebody's garage? Is the data landing in a geography you never approved? A client once sent a basic security questionnaire to a platform they wanted to connect and got complete silence back.

• Treat output like a magic eight ball and build the security cost in up front. Nobody is doing quality control — major consultancies have published cybersecurity reports containing falsehoods and handed them to governments. Secure by design costs more, and it costs far less than the liability.

Notable Quotes:

"It wasn't the bad actors externally that caused the harm. It was helpful employees that didn't know better." — T.J. Marlin

"AI is not secure by default." — T.J. Marlin

Connect with T.J. Marlin:

LinkedIn: https://www.linkedin.com/in/toddmarlin

Company: Guardrail Technologies — https://www.guardrail.tech

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
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Most teams call an AI project done the moment it works. Qing Zhang, Managing Director at LDV Partners, sees the deals before the rest of us do — and she thinks the finish line is somewhere else entirely. Jonathan Green talks with her about why AI is quietly redefining security, identity and the perimeter all at once, what happens when one person is running thousands of agents that talk to each other without anybody watching, why the honest answer to "is our AI secure?" is uncomfortable at board level, and which human skills survive when the tools keep changing names.

Key Takeaways:

• AI redefines security, identity and the perimeter simultaneously. Agents are digital companions that can do anything a person can, and they talk to each other without you knowing — so one person running thousands of agents is effectively running a small army you have to track.

• Chips are the headline bottleneck but only one link in a long value chain that starts at power and ends at deployment. The measure that matters is intelligence per watt.

• Price per token is commoditizing, so stop pricing on tokens and start benchmarking to value. Enterprises are paying for consumption; they need it tied to their own return.

• Security at board level is a balance, not a guarantee. Total security means doing nothing. The real shift is AI converging into cyber, which needs AI-native tooling rather than traditional firewalls and cloud security.

• When something breaks it is never just one thing — data loss, disrupted workflow, or an architecture you have to shut down and patch. The industrial parallel is thousands of robots on a factory floor: you need containment before you need scale.

Notable Quotes:

"It's redefining security, redefining perimeters, redefining identity in every single way." — Qing Zhang

"If you want to be a hundred percent secure, then you do nothing." — Qing Zhang

Connect with Qing Zhang:

LinkedIn: https://www.linkedin.com/in/qing-zhang

Company: LDV Partners — https://ldvp.com

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
• Free Gift: The Master Prompt for ChatGPT
• Free Book on Amazon: Fire Your Boss
• Podcast Website: https://artificialintelligencepod.com/  
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The reproducibility crisis in industrial science is estimated to waste thirty to fifty billion dollars a year, and John F. Conway says almost none of it is a technology problem. He is Chief Visioneer Officer at 20/15 Visioneers and has spent twenty-five years on scientific data — first building the models, then realising the world had a data problem so severe that the models could not save it. Jonathan Green talks with him about why smaller foundational models beat giant ones in the lab, why half of academic data quality leaks into commercial pharma, why he refuses to start any project without mapping the process first, and why "screen everything" turned out to be the wrong answer after fifteen years of trying.

Key Takeaways:

• The bottleneck is model-quality data, not model size. In science, smaller foundational models outperform the giants because you are not overfitting on a problem where barely any data exists in the world. Save the large language models for writing the paper about it.

• Capture the failures, not just the wins. An experiment that failed is data — it stops the next person repeating it, and analysing why it failed is often where the value sits.

• Unusable scientific data is a cultural problem wearing a technology costume. Roughly half of academic papers are considered not up to snuff, and that mindset leaks into industrial life science, where highly educated people will still say "I don't need to do that." Safety in the lab is a condition of employment — guarding data should be too.

• Never build software from what someone says their process is. Map it first. Scientists describe the process they think they follow, you build to that, and six months later the project is imploding. Go one level up and the managers often cannot say what their bench scientists actually do.

• If the tool's success means someone's job, you have incentivised sabotage. Companies that laid people off on the magic-bullet theory are now admitting they jumped the gun. Automate the mundane and put humans on the hard problems.

Notable Quotes:

"Safety in the lab is a condition of employment. So should be the guarding of scientific data." — John F. Conway

"Put all the forethought and the scientific rigor up front, screen less, and get better outcomes. It took fifteen years for people to figure that out." — John F. Conway

Connect with John F. Conway:

LinkedIn: https://www.linkedin.com/in/john-f-conway-678b2a3

Company: 20/15 Visioneers — https://20visioneers15.com

Conference: Paperless Lab Academy

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
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• Podcast Website: https://artificialintelligencepod.com/  
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Is AI about to replace your sales team? Jonathan Green sits down with Ellza Malok (Chief Psychologist & Researcher, Blue Monarch Group) on why AI is a great cost-cutting tool but a terrible trust-building one — and what that means for anyone who sells. They get into why trust and human relationships are about to become MORE valuable, how AI quietly removes accountability, why the buyer's experience beats your funnel, and the IKEA story that shows the real play isn't slashing headcount — it's redeploying people into revenue.

Key Takeaways:

• AI cuts cost but doesn't build trust. You'll coast on brand trust for a while, then watch your sales cycle and conversion rates plummet — often within six months.

• Sit in the buyer's shoes. A little empathy reveals how hard you are to buy from; a buyer-first process beats the funnel you'd design. (85% of buyers already have a preferred partner before they reach out.)

• AI removes accountability — which is exactly what makes it dangerous and easy to buy. "If the AI deletes your CRM, whose fault is it?" Keep a human-in-the-middle step.

• Cost-cutting is the antiquated play. IKEA automated low-value work, redeployed 8,500 support staff as advisors, and turned it into $1.4B in strategic value — reduce low-payoff work, don't just cut people.

• Top sales performance is deeply human — emotional tempo, team rhythm, recovering from a loss, the "human why" behind an RFP. AI can assist the what; it can't replicate the how.

Notable Quotes:

"AI is a great cost-reduction mechanism. But AI, so far, is not a great trust-building mechanism." — Ellza Malok

"Almost every study out there shows AI has produced a whopping total of zero percent ROI." — Ellza Malok

Connect with Ellza Malok:

LinkedIn: https://www.linkedin.com/in/ellzamalok

Company: Blue Monarch Group

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
• Free Gift: The Master Prompt for ChatGPT
• Free Book on Amazon: Fire Your Boss
• Podcast Website: https://artificialintelligencepod.com/  
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Can AI and humanoid robots actually transform manufacturing? Jonathan Green sits down with Jens Mobius (Managing Partner, Infinity Partners) on why Gen AI has gone from a nice-to-have to a defensive imperative for mid-market manufacturers, the "spaceship dilemma" of when to adopt, the staggering economics of humanoid robots ($10/hour today toward $1/hour by 2035 vs. $18/hour manual labor), the real bots already on factory floors (Figure 02 at BMW, Apptronik's Apollo at Mercedes, Tesla's Optimus), and the change-management discipline that makes adoption actually stick.

Key Takeaways:

• Gen AI is now a defensive imperative — 82% of knowledge workers already use it, so if your company hasn't set it up, it's running as shadow IT. You can still leapfrog with agents and RPA.

• The "spaceship dilemma": if you wait for perfect tech you never leave the dock — you have to commit to a tool and start experimenting now, or you miss the humanoid-robot wave.

• Humanoid economics are staggering — ~$10/hour today, dropping toward $1/hour by 2035, against $18/hour manual labor. General-purpose bots are versatile, so they hold value even when your strategy changes.

• With Gen AI the sequence flips: tool-first, experiment, THEN build the process — the opposite of traditional IT. Just keep the experiments aligned to company priorities, not wild.

• Adoption is change management: 6–8 leader-owned initiatives, biweekly "shared consciousness" meetings (McChrystal's Team of Teams), and 1:1 coaching spread best practice and keep costly experimenting to a minimum.

Notable Quotes:

"Gen AI is an absolute imperative to not miss the boat." — Jens Mobius

"If you decide to catch the wave, you better paddle out there." — Jens Mobius

Connect with Jens Mobius:

LinkedIn: https://www.linkedin.com/in/jmobius/

Website: https://infinity-partners.co

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
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• Podcast Website: https://artificialintelligencepod.com/  
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Are data centers the future of AI — or just the next bubble? Jonathan Green talks with startup advisor and VC Jason Finkelstein about the infrastructure boom behind AI: why the data center buildout mirrors the dot-com era, where the real money in xAI is hiding, and why today's billion-dollar server farms may be obsolete before we finish building them.

Key Takeaways:

• The data center boom is a bubble — miniaturization will make massive AI infrastructure unnecessary within a decade

• Fan base beats technology: companies with loyal ecosystems (Meta, Google) will outlast pure-play AI providers with no secondary revenue

• Cybersecurity is the next wave — deepfakes and AI-powered fraud are already outpacing most people's defenses

• xAI's IPO reveals where the real AI value lies: 85% of SpaceX's prospectus valuation rests on AI, not rockets

• The subscription model is coming for personal computing — the same shift Spotify pulled off with music is heading to AI hardware

Notable Quotes:

"I'm working right now in a $300 million data center in North Carolina. That's a new wave. But as you see with the iPhone and the technology and the computer, you're spot on. It's going to miniaturize. There's going to be no need for it. There is a bubble with the data centers." — Jason Finkelstein

"I think fan base wins out, to be honest with you. I think you get by as long as you have a strong fan base." — Jason Finkelstein

Connect with Jason Finkelstein:

LinkedIn: https://www.linkedin.com/in/jasonjfinkelstein

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
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• Podcast Website: https://artificialintelligencepod.com/  
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We think of AI as living inside computers — but it's reshaping the very physical world of fashion and manufacturing. Jonathan Green talks with operations and supply-chain advisor Omnya Elshafey about how AI is changing fashion: why the industry still isn't fully automated, how to keep warehouses lean, and how small companies can run an entire supply chain through disruption they can't control.

Key Takeaways:

• AI is reshaping fashion and manufacturing in the physical world, not just on screens

• Fashion still isn't fully automated — there's huge room (and friction) for AI

• Keep as little inventory in your warehouses as possible — lean beats bloated

• Global shocks (like the Middle East conflict) hit supply chains you can't control — build resilience

• Even a small company can manage its entire supply chain with the right systems

Notable Quotes:

"We want to keep as little stuff in our warehouses as possible." — Omnya Elshafey

"There's a lot of things out of our control that no one can predict." — Omnya Elshafey

Connect with Omnya Elshafey:

LinkedIn: https://www.linkedin.com/in/omnya-elshafey

Website: https://www.omneaadvisory.com

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Connect with Jonathan Green

• The Bestseller: ChatGPT Profits
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• Podcast Website: https://artificialintelligencepod.com/  
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