Inside AsembleAI: DeepTech, AI & Science

by Mac & Sam

Season 5

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 · E69

Sep 10, 2026

12:28

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 · E68

Sep 10, 2026

13:11

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 · E67

Sep 8, 2026

15:11

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 · E66

Sep 8, 2026

13:14

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

Sep 4, 2026

26:01

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/

S5 · E62

Aug 31, 2026

17:40

EP 60: Feeding the World with AI: Inside Syngenta's Data Playbook

Agriculture is the oldest industry in the world — and one of the least represented at AI conferences. In this episode, Sam talks with Jeremy Groeteke, CIO of Syngenta's vegetable division, about how a company operating in 90+ countries with 50,000 employees is building AI infrastructure to help feed the world. Topics covered: Syngenta's approach to merging AI, data science, and physical science in agriculture Using computer vision for early disease and pest detection in crops Why general-purpose LLMs like ChatGPT and Claude give farmers overly generic recommendations — and why domain fine-tuning matters The shift from data lakes to data mesh at Syngenta Single-agent vs. multi-agent AI architectures — and why accuracy often favors single agents right now The "metadata problem": why having data isn't enough without context about how it was collected Cropwise AI — Syngenta's tool for turning insight into actionable recommendations Why agriculture's long biological cycles make it a poor fit for typical VC/tech timelines Advice for technologists looking to break into agtech Guest Bio: Jeremy Groeteke is CIO for Syngenta's vegetable division, overseeing digital strategy for an organization that provides crop protection, biologicals, digital tools, and genetics to growers in over 90 countries. Connect: Find Jeremy on LinkedIn to learn more about Syngenta's work in AI and agriculture.

S5 · E59

Aug 30, 2026

18:05

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

S5 · E58

Aug 23, 2026

15:30

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

S5 · E57

Aug 23, 2026

22:48

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

S5 · E56

Aug 22, 2026

25:06

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