Inside AsembleAI: DeepTech, AI & Science

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.

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

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

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.

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

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.

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

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.

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

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.

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.

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.

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

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.

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/

This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Rafi Khardalian — CEO & co-founder of Actualyze AI, and a former technology leader at Cisco who helped build its container and Kubernetes platforms — about the governance, security, and orchestration layer most enterprises are missing. What's Covered: The Foundation for Enterprise AI — Rafi lays out why a patchwork of point solutions doesn't cut it, and how Actualyze unifies governance and security across every model and provider — at the inference level. The "Shadow AI" Problem — Employees signing up for AI tools on their own, with no governance, no visibility, no controls. Why fragmentation is a company-wide risk, not a per-team one. One Endpoint to Govern Them All — By becoming the sole endpoint for all inference, Actualyze can mask PII, catch secrets and passwords, apply guardrails, and allow or block MCP tool calls — enforcing policy in one place instead of trusting each provider. Why AI ROI Keeps Failing — Rafi's take: the hardest transformation isn't technical, it's cultural. Over-ambitious projects, unbudgeted bills, weak controls — and the trap of building a proprietary LLM you can't scale. Reliable vs. Impressive — What decades of Kubernetes, OpenStack, and large-scale distributed systems taught him about production reliability over demo magic. The Next Five Years — Why the over-rotation on frontier models will give way to small, open-weight, and purpose-built models — and why the real bottleneck is organizational readiness, not technology. Key Quote: "It really comes down to organizational readiness rather than technology readiness. A lot is ready today." Connect with Rafi: LinkedIn: https://www.linkedin.com/in/rkhardalian/ Actualyze AI: https://actualyze.ai/ Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack

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.

This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Fred Rica — cybersecurity leader at BDO USA, former Big Four partner, and a regular voice at RSA and Black Hat — about why AI is cybersecurity's "SATAN moment" and what defenders have to change to survive it. What's Covered: "The Attackers Have the Advantage" — Fred's blunt read on where defenders stand today: back on their heels, facing external attacks and blind spots in their own internal agents at the same time. AI's SATAN Moment — The 1995 parallel. When SATAN — the first real automated scanner — arrived, defenders thought it was the end of the internet. It wasn't, but it changed everything. Fred explains why AI (and Mythos) feels the same, and why the era of large patch windows is over. The Patch Window Is Zero — Why vulnerability management is breaking down and giving way to exposure management and attack-path thinking. Fred's "why is the number going up?" story — printers, missing context, and the CFO on the nightly call — is a masterclass in what's broken. If You're a CISO Today — Fred's three priorities: patch at machine speed ("automated but not automatic," with a human in the loop on critical systems), rewrite the zero-day playbook, and train teams under real pressure. AI Governance Done Right — Anchor to a framework (NIST, ISO, EU AI Act), keep a "big red button," and demand AI cards from every vendor using AI in your ecosystem — the new version of the SOC report. AI Slop & the Agent Problem — On the "10 billion agents" hype, why visibility and a control plane are job one, and where agentic AI genuinely earns its place: continuous monitoring, testing, and SOC tier 1/2 that "don't stand a chance unless they're AI-enabled." Key Quote: "I truly believe that AI is gonna fix AI. I'm a strong believer in the power of AI to transform humanity. However, we have a little bit of work to do at the moment." Connect with Fred: LinkedIn: https://www.linkedin.com/in/fredrica/ BDO: https://www.bdo.com/ Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack

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

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