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.

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.

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.

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.

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.

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.

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

In this episode of Machine Dreams, we sit down with Isvari Maranwe, founder and CEO of Yuvoice, to talk about AI as a “black box,” why humans still need to stay involved as the technology becomes more capable, and where companies may be moving too quickly.

We also explore a different question: can we build a better internet in the age of AI? Isvari explains why she believes social media needs a reset and how Yuvoice is trying to build a humans-first platform that rewards real-world impact rather than simply attention, while using AI where it can genuinely help. Learn more about Yuvoice(https://yuvoice.com).

In this episode of Machine Dreams, Snowflake VP of AI Baris Gultekin joins Sam Adebayo and Leah Stern to unpack what it actually takes to make AI work at scale, why there is no AI strategy without a data strategy, and why the biggest model isn’t always the best one.

Baris has worked on AI since his early days at Google, long before the current boom. We talk about how far the technology has come, where it still falls short, and what happens next as AI becomes capable of working on increasingly complex tasks on its own.

In this episode of Machine Dreams, we sit down with Felix Van de Maele, co-founder and CEO of Collibra, to unpack what he calls the “hallucination tax.”

We explore why even the best AI models struggle without the right company data and context, why businesses may be working more despite adopting AI, and what it will take for enterprises to finally trust AI to make decisions and take action at scale.

The problem isn't the models or the ambition. It's that agents can't actually talk to each other. Most companies are deploying single agents in silos when the real future requires hundreds of them working together, delegating tasks, sharing credentials, and holding context across conversations. The infrastructure for that doesn't exist yet.

Arick Goomanovsky is co-founder and CEO of BAND (https://www.band.ai) and has built two cybersecurity companies that exited for a combined $550 million. Sygnia was acquired by Temasek for $250 million. Ermetic was acquired by Tenable for around $300 million.

Now he's tackling what he believes is the next defining infrastructure challenge in AI: building the harness that lets agents discover each other, communicate bidirectionally, and collaborate across the enterprise and the open internet.

Arick explains why single-agent architectures don't scale, why identity and trust between agents is harder than most people realize, and what a functioning internet of agents will look like inside the enterprise five years from now.

David Warshavski is co-founder and CPO of Tonic Security (https://www.tonicsecurity.com) and spent more than 20 years in cybersecurity, leading the red team and enterprise security department at Sygnia.

He explains why Mythos is both overhyped and a genuine warning sign, why the goal of security was never to plug every hole but to protect what keeps the business running, and how Tonic's agentic platform finally closes the gap between identifying risk and eliminating it at machine speed.

If you work in security or sit on a board trying to understand where the real threat lies, this conversation cuts through the noise.

Most organizations have no visibility into what these agents are doing, what permissions they hold, or where the risk actually lives. This isn't shadow IT. This is shadow AI, and it's already inside your company.

Ofer Klein is CEO and co-founder of Reco (https://www.reco.ai/), a company helping Fortune 500 organizations govern AI agent sprawl. Ofer comes from a background as an IDF helicopter pilot and serial entrepreneur who's built and scaled go-to-market teams across the US.

Reco works with companies like Waste Management, Nestlé, and major banks to answer one question: how do you enable AI adoption at speed while maintaining visibility and control? Ofer explains what he calls the "oh shit" moment every board is having right now, why Mythos changed the security conversation overnight, and why his company hired 40 people in the last 10 weeks.

If you're deploying AI agents, running enterprise security, or trying to figure out how to move fast without getting breached, this matters.

Shahar Tal is a veteran of Israel's elite 8200 unit and former senior executive at Checkpoint and Celebrite. In 2024, he co-founded Cyata (https://cyata.ai) with fellow 8200 alumni to solve agentic security. The company raised $8.5 million in seed funding and seven months out of stealth got acquired by Checkpoint Software Technologies. Shahar and his researchers have been mapping vulnerabilities in major agentic frameworks that most organizations don't know exist yet. They'll disclose findings at Black Hat this August.

We discuss why AI agents represent the biggest enterprise shift since the cloud, what agentic identity actually means, and why demand is outrunning governance. Shahar explains how prompt injection enables real exploits at scale, why security teams need to stop being the brake pedal and start enabling safe AI use, and what CISOs should ask internally before agents run blind. If you're building AI systems, running enterprise security, or trying to understand where the real risk lies, this conversation matters.

Tom Findling is CEO and co-founder of Conifers, a company building AI SOCs that don't just assist human defenders but take ownership of security investigations in real time. Tom comes from deep roots in Israeli cybersecurity and has been tracking how AI is shifting the threat landscape faster than most people realize. Anthropic's Mythos release made it clear: any 12-year-old with a thousand dollars in API tokens now has nation-state-level capabilities to uncover zero-day vulnerabilities. If attackers have that kind of power and defenders don't, the gap becomes unsurvivable.

We discuss what Tom calls the agentic blue team, a future where AI handles threat intelligence, continuous threat hunting, detection engineering, investigation, and response without waiting for human analysts to catch up. Tom explains why agents today make fewer mistakes than humans in certain tasks, why there's a double standard when AI gets something wrong, and what the role of human defenders will actually look like when machines are doing the front-line work. If you're in security, building defense tools, or trying to understand where this industry is headed, this conversation matters.

In this special episode, we sit down with Rabbi Bentzi Sudak of The Centre - Chabad Hampstead Garden Suburb, United Kingdom and Pastor Tomiwa Immanuel of Celebration Church International, Ibadan, Nigeria, to discuss this shift. Both lead communities rooted in ancient traditions. Both are watching AI reshape how people connect with faith.

Rabbi Sudak explains why AI can provide information but misses the human journey that makes faith meaningful. Pastor Tomiwa breaks down why dependency on AI for spiritual guidance risks replacing intimacy with God. We also discuss whether religious people can be tech-savvy, what role faith leaders should play in shaping AI tools, and whether AI was part of God’s design all along, and much more.

A Passover and Easter special. ONLY on Machine Dreams Podcast.

Hafeez also addresses critical questions around bias, privacy, and the difference between AI and machine learning. He explains how focusing on vocal patterns rather than content helps reduce demographic bias, and why companies must integrate AI into existing processes rather than chasing novelty. From the origins of the technology in government stress testing to its future in remote patient monitoring, this episode cuts through the hype to reveal what is genuinely new—and what is simply a fresh coat of paint—in the world of behavioral AI.

In this episode of Machine Dreams, Sam and Leah speak with Brad Levy, CEO of ThetaRay, about how AI is being used to detect and investigate money laundering across global payment networks. From anomaly detection to “cognitive AI,” Brad explains how these systems move beyond static rules to identify patterns that don’t look right—even when no one has seen them before.

The conversation also explores how geopolitical conflicts reshape financial flows, why banks still struggle to modernize, and whether AI-powered agents will replace human investigators or make them more effective.

In this episode of Machine Dreams, Raz Dar, CEO of Elvee, explains how AI can identify students at risk of dropping out before it happens. The system analyzes over 100 behavioral signals from learning management platforms like Canvas and Blackboard.

We also talk about how the same technology tracks employee burnout in contact centers, why Elvee avoids demographic data to prevent bias, and what happens when universities actually measure whether their interventions work.

When news broke that the perpetrators demanded millions in Bitcoin, the narrative wrote itself: That crypto is anonymous, untraceable, and built for criminals. But that’s not how Bitcoin works. Katherine Kirkpatrick Bos, General Counsel at StarkWare, breaks down what most people get wrong.

“Bitcoin isn’t anonymous,” she tells us. “It’s pseudonymous.” Every transaction lives on a public ledger forever. In addition, machine learning tools from companies like Chainalysis and Elliptic can trace crypto across wallets and exchanges with precision cash never offered.

In other words, the technology people think helps criminals is actually what catches them.

We also discuss how AI is changing crypto in ways most people don’t realize. Machine learning is now being used to hunt down criminal activity on blockchain networks, tracking patterns humans would miss.

Katherine explains zero-knowledge proofs— a technology that could let companies use AI on sensitive data without ever seeing the actual information. And she breaks down why privacy, long confused with anonymity in crypto, is now the key to getting Wall Street fully on board with blockchain.

Get more in this brand new episode of Machine Dreams.

Your employees aren’t waiting for IT approval. They’re using ChatGPT instead of the Copilot you paid for. They’re uploading sensitive data to free design tools that train on your information. They’re connecting AI agents to Salesforce, HubSpot, and your knowledge base without anyone tracking what those agents can access.

This is shadow AI. And it’s spreading faster than your security team can see it.

Gal Nakash is co-founder of Reco and Forbes 30 Under 30 honoree. He explains why AI agents are now crown jewels for hackers. Compromise one agent and you access every system it touches. He walks through the moment CISOs see their real AI footprint for the first time and realize traditional security assumptions no longer hold.

The companies that survive won’t be the ones blocking AI. They’ll be the ones who assume chaos is permanent and build security that can keep up.

Learn more in this very insightful conversation.

In this episode of Machine Dreams, CEO Maya Ashkenazi Otmazgin explains how her company uses “protein intelligence” to reverse-engineer breast milk’s biological playbook, designing therapeutics that mimic what nature already perfected.

We discuss why biological data is scarce, how Maolac compresses drug timelines without cutting safety corners, and why women’s biology demands different solutions than men’s.

If you’re tired of AI drug discovery vaporware, this conversation is for you.

In this episode of Machine Dreams, we sit down with Ron Friedman, head of content and creative marketing at D-ID, to talk about what AI-powered digital humans actually are, how they’re being used in the real world today, and why they’re becoming the next interface between people and machines.

From business and accessibility to trust, identity, and responsibility, this conversation looks past the demos and into what this shift really means.

In this episode of Machine Dreams, we sit down with Yotam Drechsler, CEO of BrainQ, who’s building what he calls “NVIDIA for brain recovery.” His AI-powered platform retrains neural networks damaged by stroke, helping patients recover at home through personalized electromagnetic fields and telemedicine.

Yotam explains why AI started in neuroscience but left the brain behind, why the first weeks after stroke matter so much, and what “responsible AI” actually means when patients are elderly, fragile, and trying to regain the ability to button their own shirt. We push back on the fear of AI replacing doctors and ask how you balance innovation with patient safety when families must trust an algorithm with their most precious asset.

This is AI beyond the demo—where the stakes are personal, timelines span decades of R&D, and getting it wrong means more than a bad user review.

We talk about why Judah sees this moment as the internet in the late 1990s, why “AI bubble” is really two different debates, and what separates durable AI businesses from fast demos. He breaks down the signals he watches, including a sharp test most founders don’t like to hear: does GPT-X make you obsolete, or does it make you stronger? We also unpack why infrastructure and deep software still win in Israel, what “color of revenue” means in practice, and where returns may actually accrue over the next five years as AI fragments into large models beyond language.

If you’re building, investing, or trying to understand where the real money is going in AI, this is the conversation that cuts through the noise.

As we get ready to publish episode 7 next week, we wanted to share some of our favorite moments (really hard to choose, to be honest) from each episode so far.

In this episode of Machine Dreams, we sit down with Moshe Barel, CEO and co-founder of AccuLine, to unpack how AI is actually used in real clinical settings. Moshe has spent more than two decades in cardiovascular medicine and digital health, and his company is building a non-invasive AI-powered system for the early detection of coronary artery disease.

We talk about what AI really does inside medical diagnostics, why human-designed algorithms still matter, how regulation shapes what can safely reach patients, and why most healthcare AI is nothing like ChatGPT. From FDA approval to biased data to the myths that won’t go away, this is a grounded conversation about where AI genuinely helps, where it does not, and what it takes to build systems doctors can trust.

In the last episode of Machine Dreams for 2025, we speak with Josh Hart, co-founder and Chief Product and Technology Officer at YuLife, about what really matters before AI ever enters the picture. Josh has been building digital products since childhood and now leads a prevention-first insurance platform focused on health, trust, and long-term behavior change.

This conversation is not about models, prompts, or buzzwords. It is about design. It is about emotion. It is about understanding how people actually experience technology and why most AI features fail not because the tech is bad, but because the product thinking is.

We talk about why AI should often stay invisible, how gamification and user experience shape healthier behavior, what trust means when AI touches health and finance, and where founders go wrong when they treat AI as the product instead of a tool.

If you are building with AI or thinking about it, this episode will make you pause. And that pause might save you from building the wrong thing.

In this episode of Machine Dreams, we sit down with Guy Dassa, Investment Partner at OurCrowd, Israel’s most active VC with over $2 billion assets under management, and head of its AI portfolio, to unpack what is really happening behind today’s AI investment frenzy.

From infrastructure and foundational layers to applications, valuations, and so-called “round-tripping,” Guy explains why AI is not a repeat of the dot-com era and where long-term value is actually being built.

We explore how serious investors think about hype versus fundamentals, why picks-and-shovels matter more than headline apps, how Israel fits into the global AI ecosystem, and what founders need to understand if they want to raise capital in 2025 and beyond.

This is a rare, candid look at AI investing from someone deep inside the market.

As cofounder and CTO of Lumana, Ofir joins Machine Dreams to explain why most AI-driven cameras still fail at understanding real-world human behavior and what it actually takes to build systems that can learn context, adapt to environments, and reduce dangerous false alerts.

From kindergartens flagged in the middle of the night to stadium crowds misread as threats, this conversation explores the limits of visual AI, the role of historical data, the risks of blind automation, and why trustworthy physical AI requires more than a single model or a flashy demo.

If you care about safety, security, deepfakes, or how AI systems will operate in physical spaces we all share, this episode offers a rare, grounded look at what is working, what is failing, and what needs to change.

He breaks down the real risks, the opportunities, the jobs that may disappear, and the human skills that will matter most in a machine-driven world. This is a wide-open, deeply honest conversation with someone who has watched entire industries rise and fall, and now sees AI reshaping society in ways few people are ready for.

In this first episode of Machine Dreams, hosts Kolawole Samuel Adebayo and Leah Stern sit down with SurrealDB cofounder Tobie Morgan Hitchcock to explore the unseen engine behind modern AI systems.

From chaotic tech stacks to multi-model queries to why AI agents need fast, unified access to data across time, relationships, and context, Tobie breaks down the bottlenecks shaping the future of intelligent machines.

Whether you’re an engineer, a founder, a policy thinker, or someone trying to understand how AI actually works behind the scenes, this conversation is a deep dive into the infrastructure the industry doesn’t often talk about, but absolutely depends on.