Inside AsembleAI: DeepTech, AI & Science

Mac & Sam
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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

  1. 2d ago

    EP 58: Every Millisecond Matters: Diffusion LLMs and the Future of Voice AI | Aditya Grover, Inception

    Recorded live at the Ai4 Podcast Pavilion, Sam wraps Day One with Aditya Grover, Co-Founder & CTO of Inception, on why the next generation of LLMs won't look anything like the ones we use today. What's Covered: "Every Millisecond Matters" — Why latency, not intelligence, is the real bottleneck holding back voice agents and multi-step AI agents alike. How Mercury Actually Generates Text — Instead of predicting one token at a time like every autoregressive model, Mercury generates a rough draft of the full response and refines it into coherence — diffusion, applied to language instead of images. Solving Voice AI's Impossible Tradeoff — Fast-but-lower-quality, or high-quality-but-too-slow: Aditya explains how Mercury 2 finally delivers both. A Term Coined Live at This Conference — From Aditya's own Ai4 keynote: "We're moving from token maxing to value maxing." Advice for the Next Generation — Ten-plus years into AI research, Aditya's honest take on why this is still the best time to pursue a PhD, join a startup, or do both. The Next 5-10 Years of Voice AI — A prediction for a future where voice becomes humans' predominant mode of interacting with AI, the same way it is with each other. Key Quote: "Sequential generation is not a law of nature... AI can have a different way of generation, one that's more parallelizable." Connect with Aditya: LinkedIn: https://www.linkedin.com/in/aditya-grover/ Inception: https://www.inceptionlabs.ai/ Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack #Ai4Conference #InceptionLabs #DiffusionLLM #VoiceAI #AsembleAI

    EP 58: Every Millisecond Matters: Diffusion LLMs and the Future of Voice AI | Aditya Grover, Inception

  2. 3d ago

    EP 57: Ai4 Podcast - Why "The Context Layer" Is What Enterprise AI Is Actually Missing | Andrei Manolache, Designverse

    Recorded live at Ai4, Mac sits down with Andrei Manolache, Founder & CEO of Designverse, an AI platform that builds and delivers complex enterprise software by ingesting a company's own documentation, codebase, and internal rules. What You'll Learn: 🔹 The Model Plateau Everyone's Noticing — Andrei's central argument: the industry banked on models just getting better, but the ROI curve has flattened. Companies like Uber, Microsoft, and Anthropic have already voiced concerns about frontier models being oversold out of the box. 🔹 You Don't Need a 3-Trillion-Parameter Model — With the right context layer, a 150-billion-parameter model can match frontier-model coding performance. Andrei explains why context — not raw model size — is becoming the real differentiator. 🔹 Same Model, Different Company, Wildly Different Output — If two companies use the identical LLM, what separates the results? According to Andrei, it's entirely how well each company's own architecture, testing standards, and business logic have been translated into a usable context layer. 🔹 Why Trust Is the Real Product — Enterprises are being asked to hand over 10-30 years of proprietary code and documentation. Andrei breaks down how Designverse earns that trust — full transparency on data usage, small proof-of-concepts before any real integration, and IP that's never exposed outside the client relationship. 🔹 Why Finance and Healthcare, Specifically — Regulated industries are forced to maintain rigorous documentation — which, counterintuitively, makes them better candidates for context-layer ingestion, not harder ones. 🔹 The Future Software Team: Humans + Agents, Together — Andrei's five-year prediction: small, highly specialized teams — a mix of human engineers and AI agents working side-by-side on individual features — replacing today's large, centralized engineering departments. 🔹 The Honest Answer on AI's Limits — With hundreds of billions of dollars invested industry-wide, Andrei doesn't dodge the hard question: AI still cannot autonomously build and ship a million-line-of-code enterprise application. It can help build a narrow internal tool for 15 people. It cannot yet replace a real engineering team at scale. 🔹 Demo vs. Reality — Why Designverse refuses to sell off a generic demo: "A high schooler can build a demo with Replit and a prompt." Real enterprise buyers, especially in the U.S. right now, are optimizing AI spend hard — and won't pay for anything that isn't solving a real, currently-unsolved pain point. Key Quote: "It's not really about the model — it's more about how you govern this data, and how you give it in the best way possible to a model, whether it's 150 billion or 1 trillion parameters, to write only the more consistent output." About Designverse:  Designverse is an AI-native software development platform that ingests an organization's existing codebase, documentation, and architecture to build a "context layer" — enabling any underlying model to generate code that's consistent with how that specific company actually operates. Backed by a $5.5M seed round from operators at Adobe, UiPath, and LSEG. Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack | Instagram #Ai4Conference #EnterpriseAI #AIcoding #ContextEngineering #Designverse #LegacyModernization #AgenticAI #AsembleAI

    EP 57: Ai4 Podcast - Why "The Context Layer" Is What Enterprise AI Is Actually Missing | Andrei Manolache, Designverse

  3. 3d ago

    EP 56: Ai4 Podcast - Why AI-Generated Code Needs Its Own Kind of Security | Anand Revashetti, Lineaje

    Recorded live from the Ai4 podcast pavilion, Sam talks with Anand Revashetti, Co-Founder & CEO of Lineaje, about a problem most companies don't realize they have: they're confident their AI-generated code is secure, but very few actually have visibility into it. What's Covered: Where the Trust Gap Comes From — Executives see AI adoption metrics and productivity gains. Security teams see code shipping thousands of times a day with no clear record of who — or what — generated it. Anand explains exactly where that disconnect forms inside real organizations. A New Class of Attack — Reasoning-based attacks that exploit a model's decision weights directly, with no traditional vulnerability involved. Anand walks through how a small test exploit can scale into a multi-million-dollar fraud incident. Not a Roadblock, a Provenance Layer — How Lineaje operates inside the developer's own environment, attaching a clear record — which developer, which AI model, which skills — to every piece of code, without slowing anyone down. The Bad Habit Nobody's Talking About — Unlike traditional software that stayed stable for years, AI models get effectively rewritten on every release. Anand explains why that breaks the old maintenance playbook, and what continuous assurance actually looks like instead. On the AI Job Apocalypse — A grounded, experience-based take on the doom rhetoric circulating the conference: from a security standpoint, AI has been a genuine boon, not a threat to the field. Key Quote: "The worst thing you can do to a person who is driving and enjoying on a speedway is implement some sort of roadblock. Lineaje doesn't try to be a roadblock — but we manage all your policies." Connect:  https://www.linkedin.com/in/arevashe/ https://www.lineaje.com/ Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube #Ai4Conference #Lineaje #AISecurity #SoftwareSupplyChain #AsembleAI

    EP 56: Ai4 Podcast - Why AI-Generated Code Needs Its Own Kind of Security | Anand Revashetti, Lineaje

  4. Aug 17

    Ep 55: Ai4 Podcast - Turning 35 Years of Paper Archives into AI Training Data | Dilo Wijesuriya, ARC

    Recorded live from the Ai4 conference floor in Las Vegas, Sam sits down with Dilo Wijesuriya, President & COO of ARC Document Solutions, to talk about the unglamorous but essential layer of enterprise AI: getting decades of paper archives into a format models can actually learn from. What's Covered: The Technology — ARC holds patents for OCR on wide-format documents (architectural drawings, engineering blueprints) that standard scanning tools can't accurately process, backed by a 200-person engineering team in India. Security & Compliance — SOC 2, SOC 3, ISO 27001, and HIPAA compliant, running on AWS — built for regulated industries like healthcare and financial services. Will Paper Disappear?  Dilo's view: not for a long time. Most organizations' most critical institutional knowledge still exists only on paper, meaning today's LLMs simply can't learn from it yet. The Book Destruction Debate — A direct response to recent controversy over companies destroying physical books after digitizing them, and why ARC's non-destructive robotic scanning preserves originals for high-value collections at universities, libraries, and museums. Looking Ahead — Why Dilo believes the next competitive advantage for most enterprises isn't a better model — it's finally accessing the data already sitting in their own archives. Key Quote: "The challenge isn't finding more data. It's making existing information accessible." Connect with Dilo and ARC: Dilo Wijesuriya: https://www.linkedin.com/in/dilo-wijesuriya/ ARC Document Solution: https://www.e-arc.com/ Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube #Ai4Conference #DocumentDigitization #AIReadyData #EnterpriseAI #OCR #AsembleAI

    Ep 55: Ai4 Podcast - Turning 35 Years of Paper Archives into AI Training Data | Dilo Wijesuriya, ARC

  5. Aug 17

    EP 54: Ai4 Podcast - Enterprise AI's "Pilot Purgatory" — and the Deepfake Threat | Kathryn Harrison, Concentrix

    This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Kathryn Harrison, Global VP of Strategy in AI Commercialization at Concentrix — and an exited founder who built and sold the B2B SaaS platform MakePay, founded Deep Trust Alliance, and previously helped lead IBM Blockchain — about what it actually takes to turn AI into measurable value across a global enterprise. What's Covered: Humans Plus AI, at Global Scale — Concentrix runs customer and technical support across 75 countries and 150 languages. Kathryn makes the case that the future workforce isn't AI replacing people — it's humans plus AI and automation — and what that looks like across 400,000 employees with segmented AI access. Three Rules for Commercializing AI — Kathryn's framework for doing it at scale: start with outcome-based use cases, redesign the work instead of bolting AI on, and build in guardrails, integration, compliance, observability, and humans-in-the-loop. Plus why she frames "tokenomics" as capital allocation. The Agentic Operating System — How Concentrix uses agentic workflows to recruit and onboard 50,000 hires a year, with a 21-day implementation goal — a real production system, not a demo. From Pilots to ROI — Why most AI stalls before it delivers, how to actually measure return, and where enterprise AI spend most often goes wrong. The Deepfake Threat — Drawing on her work founding Deep Trust Alliance, Kathryn on the rise of deepfake-driven fraud, how it differs from traditional cybersecurity, and the broader societal risks. The Coming Shakeout — Orchestration across messy client tech stacks, consolidation among AI startups, and where Kathryn sees AI and automation heading next. Connect with Kathryn: LinkedIn: https://www.linkedin.com/in/kathrynannharrison/ Concentrix: https://www.concentrix.com/ Deep Trust Alliance: https://www.deeptrustalliance.org/ Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack #Ai4Conference #AICommercialization #Deepfakes #AgenticAI #Concentrix #AsembleAI

    EP 54: Ai4 Podcast - Enterprise AI's "Pilot Purgatory" — and the Deepfake Threat | Kathryn Harrison, Concentrix

  6. Aug 17

    EP 53: Ai4 Podcast - Revolutionizing Healthcare: AI in Drug Discovery | Alex Zhavoronkov, Insilico Medicine

    This episode was recorded live from the Ai4 conference podcast pavilion, Sam sat down with Alex Zhavoronkov, Founder & CEO of Insilico Medicine, about what it actually takes to turn AI-generated molecules into approved drugs. What's Covered: From Laughed-Out-of-the-Room to 33 Candidates — Alex pitched generative AI for drug design in 2015 and got dismissed. Today: 33 developmental candidates in six years, zero failed toxicity studies, and deals with Eli Lilly, Takeda, Servier, and SK Bio at a pace of nearly one per month. The Real Bottleneck — "It's not about a story. Many people in our field love to tell a story, but they don't have a single drug in the clinic discovered by AI." Alex's direct take on separating hype from results in AI drug discovery. A Lucky Breakthrough — The story of how Insilico stumbled onto a novel, non-opioid pain mechanism that outperformed morphine in animal testing — now targeting a $70 billion market. Why Abu Dhabi — Not for speed, but for geopolitical neutrality. Alex explains why Insilico built a 60-person AI lab in the UAE, and how two drugs now trace their origin to the Middle East for the first time in modern history. Quantum-Generated Drugs — A December 2025 Nature Biotechnology cover story: a molecule generated on a real IBM quantum computer, validated experimentally, with the University of Toronto. Pharmaceutical Superintelligence vs. AGI — Where Alex thinks AI drug discovery already stands, and why he draws a hard line between a useful scientific partner and the "conscious AI God" version of AGI. Key Quote: "In terms of pharmaceutical superintelligence, we're very close to being there. In terms of AGI - the future AI God - we're still not there, and we might never get there." Connect with Alex: LinkedIn: https://www.linkedin.com/in/zhavoronkov/ Insilico Medicine Website: https://insilico.com/ Alex's published manuscript about longevity medicine in the Nature journal: https://www.nature.com/articles/s43587-020-00020-4 Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack #Ai4Conference #InsilicoMedicine #DrugDiscoveryAI #Longevity #AsembleAI

    EP 53: Ai4 Podcast - Revolutionizing Healthcare: AI in Drug Discovery | Alex Zhavoronkov, Insilico Medicine

  7. Jul 14

    EP 52: Shadow AI: Why $10.3M is Costing Your Organization More Than You Know

    Shadow AI is costing organizations $10.3 million a year—more than malicious insider threats combined. Employees are using AI tools nobody approved, on data nobody's tracking, and most leadership teams have no idea it's happening at this scale. Banning AI doesn't work. You can't solve this with another policy PDF nobody reads. You need real behavioral change. In this episode, hosts Sam Dey and Mac Goswami sit down with Kate Marshall-founder of TheGrai and author of AI at Work—to expose why most enterprise AI rollouts fail at the most critical layer: getting people to actually adopt and stick with new tools and processes. What You'll Learn: 🔹 The $10.3M Shadow AI Problem — What that number actually represents and why banning AI just drives it underground 🔹 The Maturity Model Trap — Why organizations get stuck between Level 1 (Awareness) and Level 2 (Shadow AI), with leadership presenting vendor demos while employees silently use unapproved tools 🔹 Why Generic Training Fails — The fatal flaw of all-hands lunch-and-learn sessions and what role-specific, sticky AI training actually looks like in practice 🔹 The Habit Layer™ Framework — Kate's proprietary methodology for turning one-time training into lasting behavior change 🔹 Data Hygiene as the Foundation — Why cleaning up your downloads folder, emails, and redundant files is where AI transformation actually begins 🔹 The Book: AI at Work — Why Kate wrote a 3-chapter workbook for non-technical professionals instead of another theory-heavy guide Kate's Closing Insight: "Adoption is not a training day. It's a habit. You have to give employees not just access to tools, but time, space, and role-specific guidance to actually learn how to use them." Key Takeaway: The gap between knowing about AI and actually using it effectively is the difference between organizations that transform and those that waste millions on failed pilots. Connect with Kate Marshall: Website: katemarshall.ai LinkedIn: https://www.linkedin.com/in/kate-b-marshall/ Book: AI at Work Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube #ShadowAI #AIAdoption #HabitLayer #AIatWork #ChangeManagement #EnterpriseAI #AsembleAI

    EP 52: Shadow AI: Why $10.3M is Costing Your Organization More Than You Know

  8. Jun 21

    EP 51: AI-Native Software Development: Building Production Systems with Multi-Agent AI

    "AI native software development" gets thrown around everywhere right now—and almost nobody can define it clearly. Not a chatbot bolted on. Not Copilot autocomplete. We mean production-grade systems where AI agents write, orchestrate, and ship the work end-to-end. In this episode, hosts Sam Dave and Mac Goswami sit down with Mohamed Faker, Engineering Leader, Financial Services AI at Vanguard Group and co-founder/CTO of Hirin, a fractional leadership hiring platform built almost entirely by orchestrating specialized AI agents. Key Insights: What AI-Native Actually Means — Every line of code in Hirin was AI-produced. Mohamed's role: architect, decision-maker, final say on direction—not the one typing code.From Solo Orchestrator to Manager of Agents — How he evolved from manually prompting individual AI chats (architect, UX expert, engineer) to building agent hierarchies with sub-agents and dedicated "audit" agents reporting directly to him.Where Agents Fail — Spotting when an agent burns tokens without progress, takes conversations sideways, or simply isn't suited to the task—and knowing when to stop.Validation at Scale — Building internal "audit department" agents that verify other agents did exactly what was asked, nothing more, nothing less.Product Management Is the New Core Skill — Knowing how to break down features, prioritize by dependency and complexity, matters more than knowing how to code.Biggest AI Adoption Mistakes — Rushing to adopt AI without defining real ROI, plus strategies that fail because the workforce isn't trained or willing to execute them.Human-AI Collaboration — Why the human must always stay in the loop as critical thinker and decision-maker, even as the agent-to-human ratio shifts dramatically.The Horse-and-Carriage Analogy — Entire industries can disappear in 15 years, but the people who adapted earned more by managing the new technology rather than resisting it. Mohamed's takeaway: "The future is you managing a subset of AI agents. Think about it-you're going to have multiple versions of yourself working together." Connect with Mohamed Faker: https://www.linkedin.com/in/mohamed-faker/ Check out Hyern: https://hyern.com/ Subscribe: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube #AINative #MultiAgentAI #SoftwareDevelopment #AIAdoption #ProductManagement #AsembleAI

    EP 51: AI-Native Software Development: Building Production Systems with Multi-Agent AI
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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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