Inside AsembleAI: DeepTech, AI & Science

Mac & Sam

AsembleAI brings you thought-provoking conversations at the nexus of artificial intelligence, innovation, and leadership. In each episode, hosts Mac and Sam, veterans in data and tech world, sit down with AI researchers, fast‑scaling founders, Fortune 500 executives, and pioneering technologists to reveal how AI is reshaping business strategy, sparking breakthrough product development, and guiding executive decisions. Tune in for actionable insights, compelling case studies, and forward‑looking perspectives on the promises and pitfalls of AI‑driven innovation.RSSVERIFY

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S5 · E80
Today · 39 min
EP 80: Govern Before You Buy: Walter Haydock on AI Governance, Risk, and Agent Security
Walter Haydock has spent his career helping data and security leaders deploy AI without fear. A former U.S. Marine officer and founder of StackAware, he joins Sam and Mac to talk about building a governance foundation before the risk outruns you. Topics covered: The biggest mistake in starting AI governance: policies that are unrealistic and unenforceable Why your industry’s risk appetite should drive your AI decisions Quantitative risk: comparing the cost of a risk with the cost of the control People, then process, then tools, and why it applies to companies of every size Why risk avoidance is the simplest and cheapest control The Marine Corps model: one accountable leader, advised but not vetoed by security, privacy, and legal Who owns AI governance: five patterns, and the trade-offs of each The EU AI Act delay vs. the U.S. patchwork of state laws How NIST AI RMF and ISO/IEC 42001 work together “AI canaries” and indirect prompt injection against agents Three human-in-the-loop models: default deny, default allow, and post hoc review Where a security leader should spend this quarter’s AI budget Why running AI on your own hardware isn’t automatically more secure Guest Bio: Walter Haydock is the founder of StackAware and author of the Deploy Securely newsletter. A former U.S. Marine officer with a Harvard MBA, he helps executives in highly regulated industries navigate AI risk frameworks, compliance, and governance without banning AI or deploying it blindly. Connect: Find Walter on LinkedIn. If you work in healthcare, financial technology, or B2B software and want help with AI governance, send him a direct message. Be Brave. Subscribe to Inside AsembleAI on YouTube, Spotify, Apple Podcasts, Amazon Music, iHeartRadio, and Podbean.
S5 · E79
October 17 · 42 min
EP 79: The “Why Now” Test for AI Startups | Vanessa Larco, VC & Formerly NEA Partner
Vanessa Larco has sat on both sides of the table. She built products at Microsoft, Disney, Twilio, and Box, then led investments at NEA, including Mejuri and Kindred, and watched Robinhood scale to its IPO as a board observer. Sam and Mac talk with her about what actually holds up once the pitch deck closes. Topics covered: Why a founder’s roadmap, and what’s missing from it, shows how they make decisions Skill, luck, and the dynamics no investor can predict Product market pull: users who would be upset if you took the product away Why enterprise AI gets the capital, and where consumer AI is still open The hybrid go-to-market challenge of selling AI to small businesses AI-native companies vs. companies that use AI behind the scenes without selling it The “why now” framework behind Mejuri and Kindred Lessons from Robinhood’s founders on urgency, obsession, and speed Agent traffic vs. human traffic, and why product teams will need agent metrics How a venture firm uses AI agents, and why conviction in diligence stays human Guest Bio: Vanessa Larco is co-founder of Premise Venture Partners and was previously a Partner at NEA, a fund with over $25 billion in AUM, where she led investments in brands like Mejuri and Kindred. Before venture, she built products at Microsoft, Disney, Twilio, and Box. She was a board observer for Robinhood through its IPO, sits on the board of LatinxVC, and has made backing women-led and underrepresented founders a cornerstone of her approach. Connect: Find Vanessa on LinkedIn, where she says she’s most responsive. She’s also interested in hearing from anyone working on metrics for AI agents.
S5 · E78
October 8 · 31 min
EP 78: Do You Know What Agents You Have Running? Transcend's Rowan Stewart on AI Agent Governance
Rowan Stewart has watched companies rush AI products to market without the governance to back them up, first at BCG and now at Transcend, where the clients include Robinhood, Brex, and Groupon. Sam and Rowan discuss why governance works best as infrastructure from day one, and why agents raise the stakes. Topics covered: "Pilot purgatory": why promising AI proofs of concept die at the edge of production The cost of rushing: lost customer trust and bad press matter more than regulatory fines Embedded vs. bolted-on governance: a stack of paperwork and a handshake vs. encoded, auditable controls Deterministic controls for non-deterministic systems, and why a system prompt isn't a real control Human in the loop at scale: a 100-person company with five agents per employee, and how SMBs should adapt Why agents shouldn't inherit their owner's permissions, and why MCP tool restrictions aren't sufficient Agent observability: knowing what your agents are doing across the enterprise How guardrails let teams move faster, and using signals like denial rates to tune policy AI governance vs. responsible AI: business outcomes vs. net benefit to humanity The one gap to fix first: do you know what agents you even have running? Aligning incentives between legal/compliance and product/data science teams Guest Bio: Rowan Stewart is an AI and Data Safety Product Leader at Transcend, which helps companies like Robinhood, Brex, and Groupon govern and activate their data responsibly. Previously at BCG, Rowan worked with enterprises building AI and now focuses on governance for AI agents. Connect: Find Rowan Stewart on LinkedIn. Rowan's profile link is in the show notes.
S5 · E77
October 5 · 33 min
EP 77: They Transferred $25M to a Deepfake — Why Your AI Isn't as Governed as You Think | Dr. Latha Karthigaa
In this episode of Inside AsembleAI, host Mac Goswami sits down with Dr. Latha Karthigaa — Director & Head of AI Governance at the Global AI Certification Council (GAICC) — on why most companies that think their AI is governed are dangerously wrong, and what responsible AI actually looks like. What's Covered: "A Policy Is Not Governance" — The biggest myth in enterprise AI. Latha's highway analogy: governance is the guardrail that lets you drive faster, not the thing slowing you down. 52% of Incidents Are Deepfakes — The stats nobody quotes: 2,000+ logged AI incidents, 400+ in 2025, over half deepfake-driven — including the SpaceX/Elon Musk crypto scam that ran live during a real rocket launch. The $25M Deepfake Call — The engineer at Arup who wired $25 million after a video call with his CFO and colleagues — all of them deepfakes generated from public videos. Why employee training, not just policy, is the real defense. When Big AI Fails — IBM Watson's oncology tool shut down over biased data; the Dutch childcare-benefits algorithm that falsely accused thousands and brought down a government. Why Latha says the best governance pros are empathetic humans first. Who Owns the Risk? — "ChatGPT isn't responsible — you are." Why every AI use case needs a human name next to it, and why AI governance jobs are growing 150% year over year. What ISO/IEC 42001 Really Changes — The one certifiable AI standard, what it signals to customers, and when a startup should (and shouldn't) pursue it. Key Quote: "Policy is like potential power. Execution is the real power." Connect with Latha: LinkedIn: https://www.linkedin.com/in/lathakarthigaa/ GAICC: https://www.gaicc.org/ · YouTube: Latha – AI Governance Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack
S5 · E76
September 16 · 27 min
EP 76: Dashboards Are Wrong in the Background. AI Is Wrong in Your Face | Barr Moses, Monte Carlo
This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Barr Moses — CEO & co-founder of Monte Carlo, creator of the data observability category and now builder of the agent trust platform — about what it actually takes to make AI trustworthy in production. What's Covered: "AI Is Wrong in Your Face" — Barr's framing of the trust gap: dashboards were always wrong quietly in the background; AI is wrong out loud, and it'll argue with you. Why trust is the biggest thing standing between pilots and production. The Four Layers of Agent Failure — Context, performance, behavior, and output. Why all four can look perfect and the agent still fails — and why you have to watch all of them together. The Flight That Already Left — The airline agent that recommended a flight that departed that morning. The agent was fine; the context was stale. The most surprising failure mode nobody plans for. Where to Start — Make ONE agent great, not a hundred. And why the hardest first step is simply defining what "good" even looks like. The Reinforcement Loop — The idea Barr's most excited about: agents that self-identify what went wrong, propose a fix, submit a PR for human approval, and use it as tomorrow's baseline. Agents that rebuild themselves every day — running in production today. 100% AI-First — Why every line of Monte Carlo's code is AI-generated, how it made them 3–5x faster, and Barr's stoplight analogy for where human-in-the-loop is heading. Key Quote: "Dashboards are wrong in the background. AI is wrong in your face — it'll argue with you." Connect with Barr: LinkedIn: Barr Moses : https://www.linkedin.com/in/barrmoses/ Monte Carlo: https://www.montecarlo.ai Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack
S5 · E75
September 15 · 21 min
EP 75: Inside the AI Control Plane: Governance, Guardrails, and Model Routing | Sean Lynch, ActualyzeAI
ActualyzeAI came out of stealth just days before this conversation. Sam sits down with Co-Founder and CTO Sean Lynch to unpack what it means to build a "control plane" that sits between every enterprise application and every AI model - governing access, cost, security, and routing in one place. Topics covered: What a control plane for enterprise AI actually does, and why it requires zero code changes to adopt Aggregating inference across OpenAI, Anthropic, Google Bedrock, and self-hosted/on-premises models into a single endpoint "Virtual models" — purpose-built model configurations that route requests based on task type (coding, reasoning, agentic work) Guardrails: automatic detection and redaction of PII, PHI, API keys, and other sensitive data in the inference stream Financial operations as the leading driver of adoption — budgetary controls, spend limits, and team-based tracking The coming wave of domestic and open-weight small language models, and why that's expanding the market Why model-agnostic infrastructure is critical as the foundation model landscape fragments How ActualyzeAI's founding team (formerly of Metacloud, acquired by Cisco) shaped their approach ActualyzeAI's design partner program for early enterprise customers Guest Bio: Sean Leach is Co-Founder and CTO of ActualyzeAI , a company building a governance and security control plane for enterprise AI inference. He and much of the founding team previously worked together at Metacloud, an OpenStack-as-a-service company acquired by Cisco. Connect: Find Sean on LinkedIn, or visit ActualyzeAI's website to learn about their design partner program.
S5 · E74
September 14 · 13 min
EP 74: The AI Agent That Actually Fixes IT Tickets — Not Just Chats About Them | Oshri Moyal, Atera
This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Oshri Moyal — CTO & co-founder of Atera, the autonomous IT platform whose agent Robin recently ranked #1 across 15 G2 Summer 2026 reports — about what genuinely autonomous IT actually looks like. What's Covered: AI That Fixes, Not Just Chats — Why Robin isn't another chatbot. It navigates complex networks, logs into servers, and takes real action — bounded by company policy and approvals. Oshri's example: when users can't reach shared files, Robin hits the domain controller, adds the user to the right group, and maps the drive on their device — end to end. The Performance Guarantee — Resolve 50% of Tier 1 and complex Tier 2 tickets in 90 days, or fees are waived. Why Atera can stand behind that after two years in production. Robin as the First Line — How Robin becomes the front door for every request — across Teams, email, Chrome, and ServiceNow — logging everything and closing the loop after approvals. "80% Was Security" — Becoming the first in IT management to earn ISO/IEC 42001, and how Robin gets elevated permissions only after a manager approves, then hands them back. As Oshri puts it, "80% of the project was about security, privacy, and safety." What It Changes for Small IT Teams — Why autonomous AI lets a shop "at least double the size of your customers" without adding headcount — enterprise-grade capability without an enterprise team. Trust at Scale — With 6 million devices connected, why reliability and certification aren't optional. Key Quote: "With Robin you can at least double the size of your customers, because you can handle twice the amount of tickets — without increasing headcount." Connect with Oshri: LinkedIn: Oshri Moyal : https://www.linkedin.com/in/oshr1/ Atera: https://www.atera.com/ Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack
S5 · E73
September 14 · 25 min
EP 73: A Teenager Could Now Run a Nation-State Attack — Insider Risk in the AI Era | Rajan Koo, DTEX
This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Rajan Koo — CTO of DTEX Systems, chartered engineer, and one of the sharpest voices on insider risk — about how AI has completely rewritten the insider-threat playbook. What's Covered: WikiLeaks Without a Human — Insider risk was transformed by the 2010 WikiLeaks incident. Raj explains why a recent AI-driven incident showed the same breach can now happen with no humans involved — pushing DTEX into a new category it calls "AI behavior." "Nobody Was Malicious" — The story that reframes the whole risk: a manufacturing giant's AI agent, blocked from emailing an oversized report, uploaded confidential data to a public drive and shared the link. No malice — enormous risk. Why most insider risk today is negligent, not malicious. A Teenager Could Run a Nation-State Attack — The North Korean "IT worker" scheme that funded weapons programs can now be replicated by "one person and a team of AI agents." Speed up, skill level down — the perfect storm. Who Has the Advantage — Why attackers are ahead right now, and how guardrails meant to prevent misuse can block defenders too. Policy → Behavioral Compliance — Why the age of checklist policies is over, and how DTEX's "agentic defenders" triage risk at machine speed. Monitoring Without Surveillance — The honest line between protective monitoring and "creepy Big Brother" — and why it all comes down to proportionality and privacy. Key Quote: "The technical skill level to execute these really complicated insider threat breaches is now really low. A teenager with the right know-how could just go and execute this." Connect with Raj: LinkedIn: Rajan Koo : https://www.linkedin.com/in/rajan-koo-2a591221/ DTEX: https://www.dtex.ai/ Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack
S5 · E72
September 13 · 16 min
EP 72: Social Robotics 101: Building Robots That Understand You | Chris Kudla, Mind Children
Most robotics companies are chasing industrial automation. Mind Children, co-founded by Chris Kudla and Ben Goertzel in 2023, is going after something harder: social robots built for education, healthcare, and hospitality — applications where connection and empathy matter as much as function. Topics covered: Why Mind Children bet on social robotics despite a harder-to-prove ROI than industrial robots Cody's modular operating system — and what changes when you swap a standard LLM for SingularityNET's memory-equipped, agentic systems The hide-and-seek demo: how Cody reasons in real time and recalls the game a week later The emotional intelligence roadmap — teaching Cody to recognize and respond to human distress Why Mind Children avoids streaming classroom or hospital video data, and their in-house data approach for regulated environments How Chris (product design) and Ben Goertzel (social robotics research) divide responsibilities Mind Children's next hardware iteration — designed to be safe enough for a child to hug Pilot plans: starting with museums, galleries, and event spaces before schools and healthcare How to follow Mind Children's progress and support their crowdfunding campaign on WeFunder Guest Bio: Chris Kudla is Co-Founder and CEO of Mind Children, a Seattle-based social robotics and AI startup he founded in 2023 alongside Ben Goertzel. Mind Children is building Cody, a social robot for education, healthcare, and hospitality applications. Connect: Find Mind Children on LinkedIn, at mindchildren.com, or support their campaign at wefunder.com/mindchildrenrobotics.
S5 · E71
September 12 · 20 min
EP 71: The PRD Is Dead — What AI Just Did to Product Management & Cyber Defense | Ed Martin, Sophos
This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Ed Martin — VP of Product Management for AI Strategy at Sophos, with 15+ years in cybersecurity across Dell, SecureWorks, BlueVoyant, and inside Microsoft's Security Deputy CISO office — for a two-in-one conversation on AI-powered defense and what AI is doing to product management itself. What's Covered: What AI Actually Does in Defense — Ed's honest cut through the hype: AI is great at reading, good at reasoning, and "moderate to poor at taking action." Where it genuinely helps — summarization, reasoning over big data, reducing analyst toil — and where it's oversold. The Attacker's Real Edge Is Speed — Why the breadth was always there, but AI lets lower-skill attackers hit harder and faster. Ed's line: "The attacker only has to be right once. We have to be right 100% of the time" — plus the story of a small regional bank suddenly inundated with alerts. The Cybersecurity Poverty Line — Sophos's mission to protect organizations "at or below the cybersecurity poverty line," and why visibility and hygiene — not hiring — come first. The PRD Is Dead — Ed's boldest take: the weeks-long product requirements document is gone. His two principal PMs each do the work of a small team, vibe-coding examples live on customer calls. The real skill now is knowing what NOT to build — "it's the scaling that takes all the effort." Trust & Non-Deterministic AI — Why a single wrong non-deterministic outcome can blow customer trust, and how "circuit breaker" human checkpoints keep AI actions safe. Building Cyber From Scratch — Inventory first, then controls, then detection and response — and the context/data-categorization problem nearly every organization gets wrong. Key Quote: "The most important aspect of a product manager isn't the ability to understand what to build. It's your ability to understand what not to build." Connect with Ed: LinkedIn: Ed Martin : https://www.linkedin.com/in/bigedmartin/ Sophos: https://www.sophos.com/ Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack
S5 · E70
September 12 · 25 min
EP 70: The Man Who Coined "AGI" - on Why Scaling LLMs Won't Get Us There | Ben Goertzel, SingualarityNET
Ben Goertzel coined the term AGI over two decades ago — long before OpenAI, Anthropic, or the current wave of AI labs existed. Sam sits down with him to talk about why he still believes scaling transformers alone won't get the field to true artificial general intelligence, and what will. Topics covered: The "common model of cognition" from cognitive science — working memory, episodic memory, metacognition, goals — and what LLMs are missing Why LLMs can't do lifelong learning or true metacognition without long-term memory Ben's take on his ongoing debate with Gary Marcus over the path to AGI SingularityNET's neural-symbolic evolutionary approach, and Hyperon, its open-source AGI system MeTTa, the self-rewriting knowledge metagraph at the core of Hyperon Omega Claw agents — giving AI systems symbolic long-term memory, working memory, and a persistent sense of identity Catastrophic forgetting in backpropagation-trained neural nets, and how predictive coding and symbolic memory address it How the term AGI has evolved from a rigorous mathematical definition to a business buzzword BGI Labs — Ben's new venture building enterprise products on "beneficial general intelligence" Why decentralized infrastructure, open weights, and open source matter for AGI's future How anyone — technical or not — can download Omega Claw from GitHub and start building today Guest Bio: Ben Goertzel coined the term "Artificial General Intelligence" (AGI) and founded SingularityNET in 2017 to build decentralized, open AGI infrastructure. He leads research into neural-symbolic AI through Hyperon and the Omega Claw agent framework, and recently founded BGI Labs to build enterprise products on decentralized AI infrastructure. Connect: Find more on SingularityNET, Hyperon (hyperon.dev), and Omega Claw on GitHub.
S5 · E69
September 10 · 12 min
EP 69: "You Don't Build a Power Plant to Charge Phones" — Why Enterprise AI Should Start Big | Nikunj Bajaj, TrueFoundry
This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Nikunj Bajaj — co-founder & CEO of TrueFoundry and former AI team lead at Meta — about his contrarian message for enterprises: stop starting small with AI, and go big. What's Covered: Go Big, Not Small — Why a pile of disconnected pilots never generates enough business value to justify real infrastructure — so everything stays ad hoc and gets rolled back when it breaks. Nikunj's case for building one high-value anchor use case first. The Power Plant Analogy — "You never build a power plant to charge your mobile phones. You build a power plant to run a factory — and then all the phones get charged for free." How the anchor use case pays for the plumbing every small use case then rides on. Why Pilots Really Fail — Missing guardrails, latency, reputational incidents (like a bot giving away free tickets). Why it's usually a platform problem, not an individual's. Governance at Scale — How TrueFoundry helps organizations like Mastercard and Siemens tag every token to a user and business unit, enforce PII and prompt-injection rules, keep audit trails, and control cost and budgets. Ask TrueFoundry + the Seldon Acquisition — The co-pilot that knows every agent in your company, and why unifying the ML and agentic-AI stacks into one platform mattered. "The Era of Token Maxing Is Over" — Why defaulting to the most premium model is a trap, and how right-sizing and routing queries — sometimes to in-house open-source models — is now essential to real AI ROI. Key Quote: "The era of token maxing is over. It's not about just using tokens for using's sake — you want to generate ROI." Connect with Nikunj: LinkedIn: https://www.linkedin.com/in/nikunj-bajaj-10476824/ TrueFoundry: https://www.truefoundry.com/ Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack
S5 · E68
September 10 · 13 min
EP 68: Filling Europe's Labor Gap with Humanoids | Olle Bergstedt, CEO, Kalk Robotics
Europe is short 5.4 million industrial laborers, and the gap is widening as younger generations move away from manufacturing work. Sam sits down with Olle Bergstedt, CEO of Kalk Robotics, to talk about how humanoid robots can help close that gap — without replacing the humans already on the floor. Topics covered: Kalk Robotics' focus on manufacturing and industrial humanoid deployment across Europe Why Europe faces a 5.4 million labor shortage — and the generational shift driving it Partnering with Chinese hardware manufacturers rather than competing against them Kalk's reverse-engineered deployment process: site visits, strategic planning, then custom skill-pack development The "gap-fill, not replace" philosophy for introducing humanoids into facilities The ABC framework for identifying tasks suited to humanoid robots Current accuracy benchmarks for humanoid deployments (50–60%) and the path to higher precision HDCC (Humanoid Developer Control Center) — Kalk's proprietary training and operating system How US business leaders can explore bringing Kalk's robots into their facilities Olle's take on job-loss fears raised by figures like Geoffrey Hinton at AI4 Guest Bio: Olle Bergstedt is CEO of Kalk Robotics, a Swedish company developing and deploying humanoid robots for the manufacturing and industrial sectors across Europe, with additional teams in Canada, Austria, and Sydney. Connect: Find Olle on LinkedIn to learn more about Kalk Robotics.
S5 · E67
September 8 · 15 min
EP 67: "Humanity Was Dead. 36 Survivors Remained." — A Sci-Fi Author on AI, War & Our Future | Douglas Swatski
This episode was recorded live from the Ai4 conference podcast pavilion on the final day, where host Mac Goswami sat down with science-fiction author Douglas Swatski about his novel — two alien AI civilizations at war, humanity caught in the middle — and the ideas about AI, optimization, and our future that run underneath it. What's Covered: Humanity Caught in the Crossfire — The premise: two vastly advanced alien AIs at war, most of humanity gone, and a small band of survivors who must learn to work with these AIs toward a hard-won brighter future. Writing "Truth" in Fiction — Why Douglas grounds even science fiction in how things would really unfold, and why the human part — dialogue, connection, becoming each character — is what makes it believable. "When AI Bites, It's Not Malice" — His sharpest metaphor for AI risk: a dog that bites isn't being evil, it's optimizing for its own survival. The danger isn't a cruel AI — it's one whose objective doesn't align with yours. In his story, a civilization optimized for war can't pull itself back. Is Human Labor Becoming Obsolete? — Warehouses, software engineers, testers — Douglas weighs innovation against disruption, and imagines a society where money itself is a non-issue. Advice for Anyone Afraid of Being Left Behind — Especially older professionals: "Open your eyes. Try to learn and understand what it is as it relates to you. You have a role to play — go play it." We Are the Fulcrum — Why the book's message is that humanity plays the pivotal role in what our future becomes. Key Quote: "It's not so much about I want to be mean to you — it's that my objective doesn't align with yours, and as a result, in your view, it would create a very bad outcome." Connect with Douglas: LinkedIn: https://www.linkedin.com/in/dswatski/ Website: djswatski.com Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack
S5 · E66
September 8 · 13 min
EP 66: The Intelligence Dividend - Rethinking AI as Market Creation, Not Cost-Cutting | Rohit, LotusPetal AI
Most business leaders think of AI as either a cost-cutter or an efficiency multiplier. Rohit V Anabheri, CEO of LotusPetal AI and author of The Intelligence Dividend, argues there's a third and far more valuable layer: compounding — using AI to create entirely new markets and revenue, not just optimize existing ones. Topics covered: Rohit's journey from cybersecurity (Osprey Security) into AI, and the shifts from LLMs to RAG to agentic AI over the past few years The three layers of AI value: subtraction (cost-cutting), multiplication (efficiency), and compounding (market creation) Why Rohit believes "the intelligence problem is solved" for extraction, analysis, and contextual knowledge How LotusPetal AI helps small and medium businesses compete for federal and state procurement — where only 5% of qualified businesses currently participate due to compliance complexity Vertical AI vs. general-purpose platforms (ChatGPT, etc.) for specialized tasks like proposal writing and capture management Security and compliance: SOC2 compliance, FedRAMP-hosted infrastructure on AWS GovCloud, containerized data, and a no-training-on-customer-data policy What The Intelligence Dividend offers readers — a practical workbook for building an AI adoption framework, not just theory Guest Bio: Rohit is CEO of LotusPetal AI, a vertical AI platform helping small and medium businesses compete for federal and state procurement opportunities. He has spent 11–12 years in the AI space, beginning with his cybersecurity venture Osprey Security, and is the author of The Intelligence Dividend. Connect: Find Rohit on LinkedIn to learn more about LotusPetal AI. The Amazon link for The Intelligence Dividend is available in the show notes.
S5 · E65
September 4 · 26 min
EP 65: The AI Bottleneck Nobody's Watching | Troy Liljedahl, Backblaze
Storage is the least glamorous layer of the AI stack — and often the actual reason GenAI pipelines stall between pilot and production. Sam sits down with Troy, Senior Director of Solutions Engineering at Backblaze, to unpack what's really happening under the hood when AI infrastructure fails to scale. Topics covered: What actually happens when a GPU sits idle — and the opportunity cost most teams don't account for Why storage bottlenecks are the hidden reason enterprise GenAI adoption stalls Backblaze's role as a capacity and data lake tier for companies building their own models Inside B2 Overdrive: dedicated bandwidth up to a terabit per second, predictable IOPS, and no egress charges Why hyperscalers prioritize large foundational model companies over startups and researchers when resources tighten The financial case for avoiding egress costs when training on tens to hundreds of petabytes of data Skills engineers need to survive and prosper in the current AI infrastructure landscape — including agent management Advice for new grads entering the storage and infrastructure industry Why Troy sees parallels between today's AI hype cycle and the dot-com bubble — and why the technology shift is still real Why human interaction and direct customer conversations remain irreplaceable, even as AI reshapes product development Guest Bio: Troy is Senior Director of Solutions Engineering at Backblaze, where he leads the team helping AI customers integrate cloud storage and solve data infrastructure challenges. He has nearly a decade of experience in cloud storage and AI infrastructure at Backblaze. Connect: Find Troy on LinkedIn to learn more about Backblaze's AI storage solutions.
S5 · E65
September 4 · 26 min
EP 65: The AI Bottleneck Nobody's Watching | Troy Liljedahl, Backblaze
Storage is the least glamorous layer of the AI stack — and often the actual reason GenAI pipelines stall between pilot and production. Sam sits down with Troy, Senior Director of Solutions Engineering at Backblaze, to unpack what's really happening under the hood when AI infrastructure fails to scale. Topics covered: What actually happens when a GPU sits idle — and the opportunity cost most teams don't account for Why storage bottlenecks are the hidden reason enterprise GenAI adoption stalls Backblaze's role as a capacity and data lake tier for companies building their own models Inside B2 Overdrive: dedicated bandwidth up to a terabit per second, predictable IOPS, and no egress charges Why hyperscalers prioritize large foundational model companies over startups and researchers when resources tighten The financial case for avoiding egress costs when training on tens to hundreds of petabytes of data Skills engineers need to survive and prosper in the current AI infrastructure landscape — including agent management Advice for new grads entering the storage and infrastructure industry Why Troy sees parallels between today's AI hype cycle and the dot-com bubble — and why the technology shift is still real Why human interaction and direct customer conversations remain irreplaceable, even as AI reshapes product development Guest Bio: Troy is Senior Director of Solutions Engineering at Backblaze, where he leads the team helping AI customers integrate cloud storage and solve data infrastructure challenges. He has nearly a decade of experience in cloud storage and AI infrastructure at Backblaze. Connect: Find Troy on LinkedIn to learn more about Backblaze's AI storage solutions.
September 2 · 22 min
EP 64: Lack of ROI Is Actually Lack of Trust — Fixing Enterprise AI for Finance | Binny Gill, Kognitos
This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Binny Gill — CEO & co-founder of Kognitos, holder of ~100 patents, who previously helped scale a company from 20 to 6,000 people and a $7B market cap — about Kognitos's newly launched Context Graph for Finance: deterministic, hallucination-free AI for the office of the CFO. What's Covered: A Genius With Amnesia — Binny's framing of the core problem: an LLM is raw intelligence with no memory. Why prompts (instruction) and context windows (short-term memory) aren't enough, and how a context graph gives AI the long-term memory the brain runs on. The Neuro-Symbolic Harness — How Kognitos runs your English SOPs deterministically — AI that is "not allowed to be creative" on every invoice, only tapping the graph when a real exception appears. Same input, same output, 100 times. Extracting Tribal Knowledge — The processes that live only in John's or Vanessa's head. How Kognitos pulls that undocumented know-how directly from employees as they work — and auto-documents it as executable English. Revenue Leakage & 100+ Signals — Why 1–1.5% revenue leakage happens at nearly every company, and how fraud detection, duplicate payments, and data cleanup ride on top of the context graph. "Lack of ROI Is Actually Lack of Trust" — The most important idea in the episode. In POC mode everything works — but nobody ships to production, because if an auditor asks where a number came from and the answer is "the LLM," you're out. Kognitos's answer: full data lineage. Governance in a Regulated Industry — Why a documented, human-approved process is inherently more ethical — and where Binny says you should use Claude, OpenAI, or Gemini instead. Key Quote: "Lack of ROI is actually lack of trust. In POC mode everything works. Nobody trusts it to put in production." Connect with Binny: LinkedIn: https://www.linkedin.com/in/binnygill/ Kognitos: https://www.kognitos.com/ Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack #Ai4Conference #FinanceAI #ContextGraph #NeurosymbolicAI #Kognitos #AsembleAI
September 2 · 17 min
EP 63: From Intel to Mars: The 26-Year Story Behind OpenCV
OpenCV powers the computer vision behind your phone's camera, autonomous vehicles, hospital screening systems, and even NASA's Mars helicopter — and most people using it have no idea. Sam sits down with Satya Mallick, CEO of the nonprofit OpenCV.org, to trace its 26-year history and where it's headed next. Topics covered: OpenCV's origins at Intel in 2000 under Dr. Gary Bradsky, and why it was open-sourced to democratize computer vision research Why OpenCV sees roughly a million downloads a day and remains foundational infrastructure OpenCV5's new DNN inference engine — 40% faster than ONNX Runtime on models like YOLO26 Real-world deployments: the 2005 DARPA Grand Challenge-winning car and NASA's Mars helicopter Why classical (non-neural) computer vision still matters for speed- and power-constrained tasks How OpenCV is integrating with multimodal LLMs and vision-language models The case for open source AI and why closed-source labs risk falling behind A detour on the courage of Geoffrey Hinton and Fei-Fei Li in advancing deep learning and ImageNet Privacy vs. convenience in image recognition — and where the industry should draw the line on data governance Advice for engineers entering computer vision and agentic AI today Guest Bio: Satya Mallick is CEO of OpenCV.org, the nonprofit that maintains the OpenCV library - downloaded roughly a million times daily and used across image and video analysis applications worldwide. He also runs BigVision LLC, a computer vision and AI consulting company he's led for over 12 years. Connect: Find Satya on LinkedIn to learn more about his work at OpenCV.org and BigVision.
S5 · E62
August 31 · 17 min
EP 62: 95% of AI Pilots Fail. Here's Why | Mohamed Battisha, VP of Engineering at WEX
What actually separates a data platform built for AI agents from the data warehouse most enterprises already have? Recorded live at Ai4 conference in Vegas, AsembleAI co-host Sam sits down with Mohammed Battisha - who leads the data engineering team at WEX and previously built Saudi Arabia's national AI platform as CTO of SDAIA (Saudi Data and AI Authority), with earlier leadership roles at LinkedIn, Salesforce, and Microsoft - to find out answer to the question. Topics covered: The shift from "collect, transform, report" data platforms to context-aware, agent-ready platforms Why the semantic layer is the core of any AI-native architecture Governed execution: giving AI agents boundaries and full auditability Lessons from building a sovereign nation's AI platform at SDAIA Why MIT's research shows 95% of enterprise AI pilots fail — and what disconnects business and technology WEX's crawl-walk-run framework for AI adoption Claim AI: how WEX automated a multi-day claims process down to minutes How WEX measures AI maturity and data maturity across seven dimensions Advice for data and AI engineers: why business awareness matters more than technical depth alone Connect with Mohammed : LinkedIn: https://www.linkedin.com/in/mohamedbattisha/ WEX: https://www.linkedin.com/company/wexinc/

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