Machine Dreams

Hackers do not need your password. That is the warning at the center of this episode, and the story that proves it came from one of the world's leading AI companies. Last week, OpenAI found a recording device hidden behind a TV in one of its executive rooms, reportedly concealed in toilet paper and plugged into the television. An employee was arrested and charged. No network was breached. No credential was stolen. Someone simply walked in and left something behind.

This week on Machine Dreams, Sam Adebayo and Leah Stern sit down with Shmulik Yehezkel, Col. (Res.), founder and CEO of Cygon (https://www.cygon.io/). Shmulik spent more than 25 years securing defense, intelligence, and government environments in Israel. He headed the Mossad School for Cyber and Operational Technology and served as national CISO for Israel's defense industrial base before founding Cygon in 2024.

His argument is that this was not a failure of cybersecurity spending. It was a structural blind spot. We secure the network, the cloud, and the endpoint, but the attacker who comes close to the facility almost always succeeds, because that proximity attack surface sits outside the network where traditional defenses cannot see it. Inside any building there is unmanaged IoT, unmanaged IT, and people, which makes it, in his words, like a kid in a candy shop. He notes the device reportedly cost about $14, and points to commercial off-the-shelf tools available online for around $50. Then there is Flipper One, an AI autonomous hacking tool that sells for roughly $350 and can open doors, jam networks, and launch cellular attacks. His line: you do not need to be a cyber hacker anymore, you just need $300 and an internet connection. He also shares that more than 55 percent of the incidents Cygon identifies start with an insider, including a SOC team member who used a Flipper Zero to open the CIO's office door at night.

He closes by arguing that AI infrastructure has become critical infrastructure and should be treated like a new embassy, secured not only remotely but against boots on the ground. The attacker lives in the seam between the physical and digital domains, so the answer is holistic security rather than more of the same spending. Along the way he shares incidents that go well beyond data, including a fired employee who shut down a five-star hotel's electricity for 48 hours from his own home, and a Bluetooth spam attack that sent bomb threats to passengers and forced the Israeli Air Force to scramble two F-15s.

If you are responsible for securing an organization, this conversation is a grounded place to start.

AI agents can answer questions, handle repetitive work, and complete tasks across applications. But the real question is not what they can do in a demo. It is what happens when one makes a mistake, gets access to information it should not have, or simply cannot do the task.

This week on Machine Dreams, Sam Adebayo and Leah Stern sit down with Nate Herk, founder and CEO of Uppit AI (https://www.uppitai.com/), the education platform behind the AI Automation Society. Nate left Goldman Sachs as a business intelligence analyst to build an AI consultancy, scaled it to $100,000 a month, and exited in December 2025. He now runs a YouTube channel with more than a million subscribers, and he is the Amazon best-selling author of Becoming AI Native.

So what does it really take to succeed? Nate's answer starts before any tool is chosen. A large share of the companies that call him are not buying AI, they are buying relief. They need to tell their board, their employees, or their stakeholders that they have an AI strategy. His advice is to find the actual problem first. He also draws a clear line on automation. Not everything that can be automated should be, and he uses his own community manager as the example, a role that could be 95 percent automated but is one of the reasons members stay. His framing for agents is simple and practical. You onboard an agent the way you onboard a person, you give it context, you keep the guardrails on, and you let it earn more autonomy over time. You would not hand an intern a credit card to a real bank account and tell them not to use it.

That is where the real advantage sits, and it is not in the tools. Anyone can spend a weekend and emerge with a demo that looks like two years of experience, so what separates people is what cannot be faked. Governance, meaning who owns the system and what happens when it fails. Evals, proving it works over a thousand runs and that its failures are safe. And stakeholder communication, because the operator whose workflow is changing is often the last person told. He also walks through the small stack of tools he actually uses to run his business.

When you ask an AI a question about your company's data, it looks something up before it answers. Most of the time that works. But often it joins the wrong tables and returns an answer that looks completely reasonable and is completely false, and nothing catches it. There is no error message.

This week on Machine Dreams, Sam Adebayo and Leah Stern sit down with Weimo Liu, CEO and co-founder of PuppyGraph https://puppygraph.com​ Weimo previously worked on query systems at Google and spent time at TigerGraph, and he now helps write the international standard for how these systems get queried.

PuppyGraph's approach is to check the question before the AI runs it. The product was designed for agents as the user, not humans, and a semantic layer sits over the enterprise data so the AI only needs to know that a teacher teaches a student, not which table holds which or how to join them. The engine handles the deterministic mapping and guarantees accuracy, so the AI reasons and something outside the model verifies. Weimo also covers the Sonnet 4.6 inflection, where early work with customers including AMD became reliable after proper context engineering, how Coinbase runs long-running compliance agents in natural language without writing a single query, and why chaotic enterprise data needs an interactive human-in-the-loop agent rather than automation.

He closes with the warning that stayed with us. One AI agent writes a financial report, another AI agent reviews it, and no human looks at how it was generated, so nobody can know whether the answer is right. He contrasts this with code, where test cases can cover the gaps, and names the human factor plainly: with a tool this powerful, nobody wants to check every detail.

Is AI really conscious? This week on Machine Dreams, Sam Adebayo and Leah Stern sit down with Andy Thurai, founder and principal analyst at The Field CTO https://thefieldcto.com/ an independent technology advisory firm helping executives turn complex technology into clear business strategy. Andy brings more than 25 years of enterprise technology experience, with senior roles at IBM, Intel, and Oracle, and now advises business and technology leaders on AI, observability, and modernization.

Andy's answer to the consciousness question is careful and grounded. Machines today have simulated awareness, not self-awareness. His sharpest illustration: throw hot water on a dog and it reacts immediately because it feels pain. Throw hot water on a robot and it only reacts if someone trained it to. That gap is the difference between real consciousness and simulation, and it is also where AI fails, when unknown unknowns arrive. He then offers his most provocative claim: the rush to prove AI is conscious may not be about philosophy at all. If a model can be shown to act on its own, its creators can step back from liability when it causes harm. He also draws a firm line on ethics. When an AI refuses an unethical prompt, it is not consulting a conscience. It is calculating which answer yields a higher reward. It is, in his words, an advanced calculator doing statistical ethics.

He closes with practical advice for enterprise leaders. Work backwards from the problem instead of choosing a model and hunting for uses. Treat the LLM as a reasoning engine, and put safety, compliance, and guardrails outside the model in a deterministic control plane. Do not ask a probabilistic system to police itself, because it is architecturally impossible. His summary line: don't trust, always verify.

If you are trying to separate the real AI risks from the noise, this conversation is a grounded place to start.

Everyone is worried AI will destroy us. This week on Machine Dreams, Sam Adebayo and Leah Stern sit down with Rob T. Lee, Chief AI Officer and Chief of Research at the SANS Institute: https://www.sans.org/. Rob is known as the godfather of digital forensics. He was a founding member of the US military's first information warfare unit, served with the NSA and the CIA, led threat intelligence at Mandiant, created the original SIFT workstation, and has trained more than 100,000 security professionals.

Rob places the current panic in historical context. His parents practiced duck-and-cover drills for a nuclear war that never came. But his real argument is more practical: cybersecurity has been containing the worst malware ever written for 25 years, and none of it has escaped. The last real containment failure was the Morris Worm. His claim is that frontier AI labs are missing that know-how at the table, and that if they built containment the way security teams do, we would all be in better shape. He also addresses the learning gap. Setup is getting easier, but the awareness curve is the new problem. People are handing over bank accounts and private data to agents without asking what happens when the agent gets it wrong.

He closes with a counterintuitive finding. He has been tracking entry-level AI jobs for six months, and the data shows flat or slightly increased numbers, not decline. The jobs will change, and the new role might be a junior analyst managing five SOC agents, but they are not disappearing. His hope is that AI, built with proper security involvement, makes the digital world safer. His worry is not apocalypse but culture: likeness rights, deepfakes, and schools that ban AI instead of teaching kids to use it well.

If you are trying to figure out where the real AI threat actually lives, this conversation is a grounded place to start.

Salespeople spend most of their week on everything except selling. Research, CRM updates, meeting prep, follow-up emails. Only about a fifth of their time goes to actually talking to customers. This week on Machine Dreams, Sam Adebayo sits down with Johan Torssell, co-founder and CEO of Spiich: https://spiich.ai/ a Stockholm-based company building AI agents for B2B sales teams. Johan founded Spiich in June 2025 with CTO Dennis Hadzialic, and the company is backed by operators and founders from across the AI and enterprise software world, including investors behind Lovable and Neo4j. Before Spiich, he turned down offers from companies like Palantir and Meta because he wanted to build this now.

Johan's position is simple and increasingly contrarian. Sales is intrinsically human. People buy from people. An agent can qualify a lead, research a company, draft a follow-up, and keep the CRM current, but it should not press send or close the deal, because that is where trust lives. He points to companies that tried to replace salespeople with AI and had to pull back and hire reps again. He also draws a clear line on autonomy: agents can act freely on anything reversible, but the moment a decision touches a relationship, the human should be in the loop.

Looking ahead, he expects two kinds of salespeople to survive: those who build systems and those who build relationships. He also thinks the buyer side is changing, and that in five to ten years, agent-to-agent purchasing will shift sales from relationship and trust toward data.

If you are a sales leader trying to figure out where AI actually belongs in your pipeline, or a founder wondering what to automate and what to protect, this conversation is a grounded place to start.

Cameras used to have one job: record what happened. Now they are starting to make sense of what they see while it happens, and that raises a bigger question. What does it mean for a camera to understand?

This week on Machine Dreams, Sam Adebayo sits down with Prof. Ron Kimmel, Chief Scientific Officer at Lumana: https://www.lumana.ai/ and professor of computer science at the Technion. Ron has spent decades in computer vision, image processing, and 3D analysis, and he co-founded InVision, the company Intel acquired to form the foundation of its RealSense line. He is also the founder of the Technion's Geometric Image Processing Lab.

Ron makes the case that AI has largely closed the classical open problems in computer vision, and that the remaining bottlenecks are often our own hardware choices. He points out that most cars today, including his Tesla, rely on a single forward-facing camera, when biological evolution settled on two eyes hundreds of millions of years ago. He also holds a firm line on what these systems can and cannot do. Cameras can flag patterns and correlations. They cannot find causality or predict the future, and he thinks we should stop asking them to. He also shares work from computational pathology, where AI reading tumor images has matched what was previously only possible through DNA analysis. The research was published in The Lancet Oncology, and it points to patients potentially avoiding radiation or chemical treatment.

If you are a business leader wondering what your existing cameras could already tell you, or a builder trying to understand where machine vision actually stands, this conversation is a grounded place to start.

Why do so many enterprise AI projects get built and then quietly abandoned? That's the question at the heart of this conversation with Yevgeniy Vahlis, co-founder and CEO of Shakudo: https://www.shakudo.io/.

Before founding the company in 2021, Yevgeniy built and led central AI teams at BMO and Borealis AI, and advised later-stage startups at Georgian. Everywhere he went, he saw the same pattern. Innovation labs produce impressive work that never reaches production. The experiments get shelved and the effort disappears. Shakudo exists to fix that, giving enterprises an end-to-end environment where a project goes from first idea to full deployment without ever leaving their own infrastructure. Their customers include Loblaw, Canada's largest retailer, plus organizations in finance, healthcare, defense, and energy.

In this episode, Yevgeniy offers a working definition of sovereign AI: the people making the decisions should have full control over the AI their organization uses. He explains why that matters more than it first appears. Data used to be backward-looking. Now it sits underneath the actual decision-making of the business, and employees at every level are sending their hardest strategic questions to third-party models. Internal snapshots of a company's risks, opportunities, and competitive position are leaving the building through ordinary daily use. Yevgeniy covers what bringing AI in-house actually requires, the guardrails that still have to be in place, and the geopolitical angle most teams haven't thought about yet.

He closes with a look five years ahead: AI running a large share of internal business processes, not replacing people but changing their jobs and raising the scale of what a team can do.

Bridges, roads and tunnels can last for generations, and engineers have spent decades photographing, inspecting and documenting how they change.

In this episode of Machine Dreams, we sit down with Saar Dickman, CEO of Dynamic Infrastructure, to explore how AI can bring that history together, measure damage from photographs, and help engineers understand how critical infrastructure is deteriorating over time.

Saar explains what AI can see in years of infrastructure data, how it could help engineers decide where repairs and funding are needed most, and why the technology could allow one person to oversee thousands of structures in the future.

We also get into the bigger question of what happens when an experienced engineer and an AI disagree, and why Saar believes humans should ultimately remain behind the wheel.

Learn more about Dynamic Infrastructure⁠ here: https://diglobal.tech/

Chris Voss spent 24 years with the FBI and became its lead international kidnapping negotiator. Today, he is the bestselling author of Never Split the Difference and founder and CEO of The Black Swan Group.

In this episode of Machine Dreams, we sit down with Chris to explore what happens when AI comes to the negotiating table. Can machines learn tactical empathy? Could AI eventually negotiate better than humans? And what advantages do people still have that machines may never be able to replicate?

Chris explains why AI could become a powerful negotiation tool, why machines have an advantage when humans are tired or emotional, and why he still believes a human being at their best is unbeatable.

We also discuss AI negotiating against AI, what he would teach a machine if he were training it to become a world-class negotiator, and how anyone can start using AI to prepare for real negotiations today.  

Here is why you should listen to Chris;

Chris Voss is a former FBI lead kidnapping negotiator, bestselling author of Never Split The Difference, and founder and CEO of The Black Swan Group; a negotiation, training, coaching, and consulting organization that teaches people how to achieve better outcomes in high-stakes conversations.

Recognized globally as a leading authority on negotiation, Chris developed his approach through decades of experience in crisis negotiation. Today, he and The Black Swan Group apply those principles to business, sales, leadership, difficult conversations, and high-stakes communication through The Black Swan Method® and Tactical Empathy®.

Never Split The Difference has introduced millions of readers around the world to Chris's negotiation methodology. Through The Black Swan Group, Chris and his team help people go beyond understanding the concepts in the book and develop negotiation as a practical, repeatable performance skill through training, coaching, practice, and live experiences.

The Black Swan Group offers in-person negotiation training, live and self-paced online negotiation training, coaching, keynote speaking, and advanced negotiation experiences for business owners, leaders, sales professionals, and other professionals who want to become more effective dealmakers.

Learn from Chris Voss and The Black Swan Group:
Negotiation training and resources: https://www.blackswanltd.com/?utm_source=youtube&utm_medium=social&utm_campaign=machinedreamspodcast
In-person and live training: https://www.blackswanltd.com/live-events?utm_source=youtube&utm_medium=social&utm_campaign=machinedreamspodcast
Online negotiation training: https://www.blackswanltd.com/online-negotiation-classes?utm_source=youtube&utm=social&utm_campaign=machinedreamspodcast