Podcasts about enterprise ai

Send a textFirst Aired Apr 23, 2025If you've been following the AI space lately, this episode hits differently the second time around.When Al sat down with Ruchir Puri — Chief Scientist of IBM Research, IBM Fellow, and the architect behind Watson and watsonx — the conversation covered ground that's only gotten more relevant since: the death of prompt engineering, the rise of agentic AI, and why 2025 was always going to be the year agents broke through in the enterprise.Ruchir doesn't deal in hype. He deals in systems — real ones, running at scale, in industries where a hallucinated number has consequences. In this masterclass, he walks through inference scaling, memory in AI systems, and what it actually means to build AI that's useful rather than just impressive.If you're new to the show, this is the episode to start with. If you've heard it before — trust us, it lands differently now.Key moments:12:21 — Why prompt engineering is already fading (and what replaces it)13:39 — Inference scaling: the frontier that's not about training anymore16:26 — Why AI systems that "forget" are failing us17:56 — The full agentic loop: Think, Plan, Act, Execute, Observe, Reflect23:45 — Why enterprise AI agents are no longer a future stateMaking Data Simple is hosted by Al Martin, WW VP Technical Sales, IBM.Ruchir's LinkedinAl's LinkedInExplore IBM's WatsonxWant to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Enterprise AI is outpacing the operating models built to support it, forcing leaders to reconcile rapid iteration with safety, governance, and real‑world scale. In this episode, David Glick, SVP of Enterprise Business Services at Walmart, examines how stopwatch‑speed prototyping, nano‑agent architectures, and evolving security processes are reshaping enterprise delivery. The discussion highlights shifts from monoliths to federated agents, faster iteration cycles, and the emerging need to build the machine that builds the machine. Executives shaping real AI outcomes are invited to contribute their lessons to a curated peer audience. Learn more at go.emerj.com/expert to be considered for a future 'AI in Business' episode. Align your brand with the executives defining the enterprise AI agenda—partner at go.emerj.com/partner

Deep Sogani, SVP and Group Data Management Officer at Datasite, joins The Tech Trek to unpack why data governance, lineage, and business process design have become mission critical in the age of AI. This conversation gets past the surface level AI hype and into the operational reality, how companies actually build trustworthy systems, where AI initiatives break down, and why strong data foundations now shape business outcomes in real time.This episode explores the shift from downstream analytics to data that actively drives live decisions, workflows, and automation. Deep explains why many AI projects fail before the model even matters, how business architecture should lead technical design, and why human oversight still matters in high stakes environments.In this episodeWhy AI has made data governance and data lineage far more operationalWhy business process clarity matters before data architecture or tooling decisionsHow real time AI changes the demands on data quality and system designWhere agentic AI fits, from workflow automation to more advanced decision supportWhy human judgment still matters in AI systems shaped by risk, ethics, and securityTimestamped highlights01:47 Why AI raises the stakes for governance, lineage, and trust in data04:57 Why business architecture has to lead before technical design09:11 The progression from predictive models to agentic AI workflows17:55 Why the human in the loop is still essential21:16 What makes an AI project worth prioritizing26:06 What has changed, and what has not, in AI related change managementStandout line“Business architecture and business thinking should dictate the what and the why, and the data architecture is the how part which needs to follow.”Practical takeawayIf you are evaluating AI inside the enterprise, do not start with the tool. Start with the business problem, the workflow, the decision risk, and the quality of the data behind it. Strong models on the wrong problem still fail.Follow The Tech Trek for more conversations with leaders shaping technology, data, AI, and the future of modern business.

Esteban Kolksy, Chief Distiller at Constellation Research, closed out the CRM Playas IRL (In Real Life) event in Atlanta with a provocative keynote the debunked enterprise AI myths, while making a case for where the value in AI truly lies. After Kolsky left the stage, we broke down his main points on why LLMs are becoming commodities, and why proper enterprise AI is superior to out-of-the-box frontier models. Is AI "intelligent" - or a fascimle of intelligence - and why does this matter? We argued, at times, before landing on why Kolsky hates context graphs, and why I believe the misuse of the word "grounding" holds us back. This keynote was worth hashing out: there are big takeaways for customers that want to avoid lock-in, and to accomplish something better with AI by making AI a strategic part of infrastructure, rather than a not-very-smart chatbot.

Is the best grocery platform one that decides what it WON'T sell?That is the bet Ayyappan is making with FirstClub. Fewer products. Stricter rules. While most quick commerce apps are trying to deliver orders faster, he is asking a different question. What if consumers need not “faster or cheaper”, but a retail platform where they can trust every item listed on it?A place where you do not have to read every label, check multiple reviews, or wonder if the top result is there because a brand paid for it. FirstClub is trying to solve a harder problem. It is trying to define what “quality” means for everyday products we consume, starting with groceries.India has received the highest quick commerce funding of any country in the world, at $9.24B over the last 10 years. Yet only 1% of Indians use quick commerce services today. With a large market still open for expansion and the possibility of better unit economics over time, FirstClub is building a countertrend to the hype around Indian quick commerce.Ayyappan brings eleven years of experience at Flipkart, and has also served as SVP at Myntra and CEO of Cleartrip. FirstClub also just raised a $50 million round and doubled its valuation in under six months. This episode is the story till here and the plans ahead for Firstclub.00:00 – Trailer01:01 – The Costco of Indian quick commerce04:32 – Building a counter-trend company06:15 – What consumers say v/s what they actually want09:37 – The only retail platform to Ban 200 ingredients12:34 – Why can't the big players solve this?13:21 – A simple rule of thumb for food16:03 – Brand stories from FirstClub19:20 – Is the problem access or income?21:29 – Who are the 20 million FirstClub consumers?24:14 – Only 1% of India uses quick commerce26:04 – What does “quality” mean in grocery?32:34 – How will FirstClub monetize without brand sponsorships?34:53 – Do consumers behave differently across categories?39:30 – Why is Myntra so powerful in fashion?42:24 – What Myntra taught Ayyapan that Flipkart didn't?43:53 – Unlearning to build for Quick commerce48:25 – Why Indian consumers are very experimental today50:59 – Is India one country when it comes to quality?52:43 – If Ayyappan was a product, what would he be?54:47 – The hardest belief to defend while building FirstClub56:26 – Akshayakalpa & The Whole Truth57:48 – Not niche, but premium-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/neon-fund/X: https://x.com/TheNeonShowwConnect with Nansi on:LinkedIn: https://in.linkedin.com/in/nansi-mishraX: https://x.com/nansi_mishra-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send a text

Discover how enterprise AI and data strategy are operationalized at scale in one of the most highly regulated industries in the world. Louis DiModugno, Global Chief Data Officer at Verisk, shares how he builds AI-ready data foundations across 40+ petabytes of insurance and risk data, and the best practices behind embedding AI into enterprise products. He discusses unstructured data, deepfakes, and the shift from governance to observability, offering practical insights for data leaders scaling AI responsibly. Key Moments: From Military Leadership to Chief Data Officer: Data Integrity as a Competitive Advantage (03:02): Louis shares how his experience as a U.S. Air Force Colonel has shaped his approach to data governance, data quality, and enterprise AI leadership. He explains why integrity, service, and operational excellence are essential foundations for modern CDOs building trusted, decision-ready data environments. Building AI-Ready Data Foundations at a 40+ Petabyte Scale (17:13): Managing more than 40 petabytes of insurance and risk data, Louis breaks down how Verisk transforms complex, multi-source data into AI-ready infrastructure. From entity resolution and master data management to benchmarking and predictive analytics, he outlines what it takes to prepare enterprise data for AI and advanced analytics at scale. Designing an AI-First Data Strategy for Enterprise Decision Intelligence (20:00): Louis breaks down how Verisk evolved toward an AI-first data strategy across more than 150 insurance and analytics products. Rather than treating AI as an add-on, he explains how embedding AI into core workflows enables smarter underwriting, pricing, regulatory reporting, and risk management. He also discusses the strategic role ThoughtSpot plays in delivering natural language search, embedded analytics, and scalable AI-driven decision making. AI Fraud, Deepfakes, and Risk Management in Financial Services (26:11): As AI-generated images and synthetic claims become more sophisticated, Louis discusses how the insurance industry is combating deepfake fraud and AI-driven manipulation. He shares best practices around AI risk management, vendor partnerships, and regulatory collaboration to protect policyholders and maintain trust. Unstructured Data and AI: Why Governance Still Matters (29:28): Louis explores how expanding beyond structured data is reshaping enterprise AI. He explains why incorporating unstructured data into vector databases, graph models, and knowledge systems can significantly improve model accuracy and decision confidence. At the same time, he emphasizes that stronger governance (or observability as he reframes it) is essential as organizations scale AI across regulated industries. Key Quotes: “The more data that you bring to the equation, the more elements that you have in the algorithm, the higher level of accuracy you should be able to reach with your outcomes.” - Louis DiModugno “I've tried to move away from using the word governance as much as I like to use the word observability, because I really think observability shows more aspects of what it is that we are doing with the data.” - Louis DiModugno “The underlying aspect of what ThoughtSpot's delivering to them is our insights that not only give them their answer, but also give them insights that maybe they weren't looking specifically for. One of the big benefits of ThoughtSpot is that it's trying to anticipate what you're asking for.” - Louis DiModugno “We've partnered with ThoughtSpot, which brings AI embedded within its product. By having our data available through the data sets that we populate through the ThoughtSpot products, we've got the opportunity to utilize Spotter and the natural language processing capabilities to interact with the data, so that you can ‘talk with your data'.” - Louis DiModugno Mentions From Months to Weeks: How Verisk Scaled Embedded Analytics Breaking Down Digital Media Fraud for Claims in the AI Era Randy Bean's 2026 AI & Data Leadership Executive Benchmark Survey Guest Bio  Louis DiModugno brings more than 20 years of career experience in data and analytics to his new role. He has held several leadership positions in insurance and (re)insurance at firms including The Hartford and AXA US, where he served as the company's inaugural Chief Data & Analytics Officer. Most recently, DiModugno pioneered the role of Chief Data and Technology Officer for Hartford Steam Boiler. Before entering the private sector, DiModugno served with distinction as a Colonel in the U.S. Air Force and Air Force Reserves. He has held teaching positions at Rensselaer Polytechnic Institute, and he currently serves on the Chief Data Officer Advisory Council for the George Mason University School of Business. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

March 6, 2026: The U.S. economy lost 92,000 jobs in February — and the headline number is almost the least interesting part of the story. When you break down where the losses actually came from, you get a picture far more complicated than the AI-took-our-jobs narrative dominating social media right now. Healthcare, tech, federal government, manufacturing, transportation — each sector tells a different story, and together they reveal a labor market being squeezed from multiple directions at once: AI, tariffs, Baby Boomer retirements, post-pandemic correction, and a geopolitical shock that just sent oil past $87 a barrel. Meanwhile, the Fed is openly questioning whether it even has the tools to respond — because cutting rates doesn't create jobs for people whose skills have structurally shifted out of demand. Also this week: Uber's CEO says don't come here if you want to coast — and why that lands so differently in this economic moment. A new survey reveals that 90% of companies have AI chatbots but almost none have integrated AI into real workflows — and that gap is driving some dangerous workforce decisions. And the Bank of England just started war-gaming what happens if AI triggers a full economic shock.  Watch on YouTube ----- Start your day with the world's top leaders by joining thousands of others at Great Leadership on Substack. Just enter your email: ⁠⁠https://greatleadership.substack.com/ Looking for what actually moves the needle on performance and retention? It's in The 8 Laws of Employee Experience. Order here: 8EXlaws.com

Will smaller AI models win over large language models?Sudarshan Kamath grew up in Mumbai, taught himself AI before most Indian companies were even hiring for it, and bought the domain "smallest.ai" for $100 in 2022, two years before the company existed. Today, he runs Smallest AI, a startup focused on real time voice AI.He started with self-driving cars, training large models and compressing them to run on vehicle hardware in real time. That's where he first saw what small models could do: a hundredth of the size, almost no loss in accuracy.Two years later he put in his own $150K, got some GPUs, and started training. Eighteen months later he had a seed round, a Series A, a seven-figure enterprise deal, and a $150M acquisition offer he turned down.Most of the data that goes into large models is noise. Strip it out, train small, and you get a model that matches a giant at a fraction of the size and runs in real time. That insight is what Smallest AI is built on.00:00 – Trailer 00:51 – Sudarshan's journey before Smallest AI 05:00 – Arjun Jain & Yann LeCun 08:20 – Why build in voice AI in 2024? 15:09 – Why move the company from India to the US? 17:25 – Hiring talent via LinkedIn and X 18:49 – What large US funds actually bring to startups 21:03 – Raising a seed round with zero revenue 26:06 – Strong intros from US VCs 28:23 – What the first enterprise customer teaches you 31:50 – Raising Series A with Seligman Ventures 32:19 – The $150M acquisition offer 34:32 – When should founders sell secondaries? 36:24 – Who are Smallest AI's customers? 38:28 – What are state space models? 40:16 – Are GEPA models closer to AGI? 41:23 – Growing 10× in three months 48:03 – This is not a winner-takes-all market 49:32 – Why this is a trillion-dollar market 50:08 – Why large AI labs are not building in voice 51:26 – What it takes to reach $100M ARR 54:21 – The biggest goal for 2026 57:11 – Voice costs 1000× more than text 01:02:04 – How Smallest AI cracked large enterprises-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send a text

In this talk, Aditya, an experienced AI Researcher and Engineer, shares his technical evolution—from his roots in embedded systems to building complex, large-scale AI agent architectures. We explore the practical challenges of enterprise AI adoption, the shifting economics of LLMs, and the infrastructure required to deploy reliable multi-agent systems.You'll learn about:- The ROI of Fine-Tuning: How to decide between specialized small models and general-purpose APIs based on cost and latency.- Agent MLOps Stack: The essential roles of guardrails, data lineage, and auditability in AI workflows.- Reliability in High-Stakes Verticals: Navigating the unique AI deployment challenges in the legal and healthcare sectors.- Evaluation Frameworks: How to design robust evals for multi-tenancy systems at scale.- Human-in-the-Loop: Strategies for aligning "LLM as a judge" with human-labeled ground truth to eliminate bias.- The Future of AGI: What to expect from the next wave of multimodal agents and autonomous systems.TIMECODES: 00:00 Aditya's from embedded systems to AI08:52 Enterprise AI research and adoption gaps 13:13 AI reliability in legal and healthcare 19:16 Specialized models and agent governance 24:58 LLM economics: Fine-tuning vs. API ROI 30:26 Agent MLOps: Guardrails and data lineage 36:55 Iterating on agents with user feedback 43:30 AI evals for multi-tenancy and scale 50:18 Aligning LLM judges with human labels 56:40 Agent infrastructure and deployment risks 1:02:35 Future of AGI and multimodal agentsThis talk is designed for Machine Learning Engineers, Data Scientists, and Technical Product Managers who are moving beyond AI prototypes and into production-grade agentic workflows. It is especially relevant for those working in regulated industries or managing high-volume API budgets.Connect with Aditya:- Linkedin - https://www.linkedin.com/in/aditya-gautam-68233a30/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

Compliance and regulatory reporting used to mean endless spreadsheets, fragmented data sources, and teams drowning in manual work. Today, AI is transforming how the world's largest companies manage financial reporting, sustainability disclosures, and audit workflows—not by replacing humans, but by giving them time back to do strategic work. In this episode of IT Visionaries, host Chris Brandt sits down with Kim Huffman, CIO of Workiva, the platform used by 85% of the Fortune 100 for critical financial and compliance reporting. Kim shares her unique perspective as both a former Workiva customer and now the CIO steering the company into an AI-powered future. They explore how the office of the CFO is evolving under pressure from new sustainability regulations, how AI governance actually works in practice, and why collaboration between IT, finance, sustainability, and risk teams has become essential. Kim also discusses the changing role of the CIO, the coming wave of autonomous agents in the workplace, and why having more data doesn't always mean making better decisions.   Key Moments: 00:58 – The State of Compliance Today 02:18 – Why Standards and Regulations Matter 05:48 – The Complexity of Global Compliance 07:36 – Data Collection Across Teams 08:36 – Single Source of Truth 10:20 – The Sustainability Data Challenge 13:36 – The Endless Spreadsheet Problem 16:12 – What's Driving the CFO Office 19:46 – AI's Strategic Role at Workiva 23:02 – Beyond Repetitive Tasks 25:20 – Transforming How Teams Work 27:03 – Will AI Replace Jobs or Create Capacity? 30:00 – Measuring AI's Business Impact 33:06 – Speed vs. Data Overload 36:25 – The Evolving Role of the CIO 40:00 – Technology Leadership in Transition 43:09 – The Next Five Years for CIOs 46:14 – Managing the Coming Wave of AI Agents 50:02 – AI Will Create Its Own Security Industry 52:26 – The Sustainability Reporting Reality 55:31 – Resource Constraints and AI Consumption 57:34 – Why ESG Data Is Now Critical Business Intelligence 59:23 – Keeping NPS High While Innovating   -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI is no longer a future bet — it's a board-level mandate. But for corporate innovation leaders, the real question isn't whether to invest in AI… it's how to turn AI from experimentation theater into measurable enterprise value. Taylor Black, Director of AI & Venture Ecosystems in Microsoft's Office of the CTO, works at the intersection of AI strategy, venture ecosystems, and internal venture building. Taylor brings a rare dual perspective: enterprise AI leadership inside one of the world's largest technology companies — combined with firsthand startup-building experience. We unpack how AI takes impossible problems and makes them merely difficult, how this growth mindset of hyper abundance is paired with the enterprise rigor and the internal velocity needed to scale.

This episode marks the transition from The Cloudcast to The Reasoning Show, focusing more on AI and cloud topics. Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss recent trends in AI, the evolution of tech teams, and the shifting landscape of enterprise AI tools.SHOW: 1006SHOW TRANSCRIPT: The Cloudcast #1006 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SHOW NOTES:Link to February 2026 News and ArticlesFEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

In this AI Agent & Copilot Minute, Mason Siefert outlines how Microsoft's latest enhancements to Copilot Studio — especially the new tools in the Power CAT Copilot Studio Kit — are designed to bring structure, governance, and measurable quality to enterprise-scale AI agents. Key Takeaways Rubrics refinement: The headline feature in the updated kit is the rubrics refinement tool, which addresses a growing challenge in agentic AI operations — how to consistently and accurately grade agent responses. The tool introduces a repeatable feedback loop where teams define evaluation rubrics, compare AI-generated grades with human evaluations, and then refine instructions when the two don't align. The result is a more systematic, scalable way to ensure automated assessments meet human-level standards. Governance & visibility: Beyond evaluation, the kit strengthens oversight across the AI estate. A new compliance hub automatically flags configuration risks to help teams stay ahead of governance concerns. Conversation KPIs allow organizations to track agent performance without manually reviewing transcripts, and an agent inventory provides a centralized view of custom agents and the capabilities they rely on. Together, these features bring operational clarity to expanding AI environments. Looking ahead: As agentic systems scale, structured coordination between humans and AI will be critical. Tools like the rubrics refinement workflow signal a shift from experimentation to disciplined operations, where evaluation, compliance, and performance tracking are embedded into the lifecycle of every agent. Organizations that formalize these processes now will be better positioned to manage complexity and deliver trustworthy AI outcomes at scale. Visit Cloud Wars for more.

What if AI can learn the “why” behind decision making of humans?Ashu Garg and Jaya Gupta recently wrote one of the most discussed articles on AI this year. Their idea drew public responses from Dharmesh Shah, Aaron Levie, and Arvind Jain.Enterprise software has always captured what happened. It records the order, the ticket, and the approval. But it has never captured why it happened. It does not store the reasoning, the exception, or the past decisions that shaped the outcome. Ashu argues that this missing layer is the biggest opportunity in enterprise AI right now, and that the startups that capture it will be the biggest winners in AI.In this episode, we go deeper into what context graphs really are, how they get built, why startups have an edge over incumbents, and how close we are to seeing this work in practice.00:00 – Trailer00:42 – What are context graphs?03:57 – Why agents haven't lived up to the hype?07:03 – The “why” of Decision Making10:47 – How agents will store data for context graphs13:17 – What will be possible for Digital twins?17:32 – Can context graphs reveal a company's moat?19:48 – Guardrails on Access for agents24:47 – Managing agents vs being managed by agents28:46 – Will winners be vertical or horizontal players?32:20 – The future is agent swarms35:54 – Finding PMF is what makes a great CEO39:34 – What will set apart successful enterprises of 203042:10 – Where Foundation Capital is investing44:05 – Why AI won't be winner-takes-all47:03 – Where will the context graph reside?50:56 – Will systems of record be replaced?53:22 – Human in the loop → hands-off execution55:57 – A reality check on where we are today58:24 – Where startups will win in orchestration-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send a text

Shomron Jacob, an AI Strategy Expert and Technology Advisor based in Silicon Valley with over a decade of experience in enterprise AI, GenAI, and machine … Read more The post Where Enterprise AI Is Breaking Down and the Strategic Bet CIOs Must Rethink appeared first on Top Entrepreneurs Podcast | Enterprise Podcast Network.

Enterprise AI is now a boardroom mandate, but moving from pilots to production remains a major challenge. Many organizations are trying to determine whether their infrastructure can support AI at scale without driving up costs, hitting power constraints, or locking into the wrong architecture. Training isn't inference. Fine-tuning isn't RAG. Getting these distinctions wrong can cost millions. Join Eric Kavanagh (DM Radio) for an interactive live webcast with Mark Madsen (Third Nature), Denise Muyco (RAVEL), and industry leaders as they break down how different AI workloads place very different demands on compute, power, and orchestration — and what actually changes when moving from testing to enterprise production. This session will equip AI and infrastructure leaders with practical guidance to make confident, future-ready infrastructure decisions. Attendees will learn: * The key questions to ask when planning AI for production * How infrastructure needs differ across training, inference, and experimentation * How to test, validate, and scale AI workloads * How to identify bottlenecks that hurt performance and ROI * Why smart infrastructure strategy is critical to scaling AI successfully If you're responsible for AI or infrastructure strategy, this is the conversation that could save — or justify — your next $10 million decision.

Recent analysis from Goldman Sachs indicates that $700 billion in AI investment during 2025 resulted in no measurable U.S. GDP growth, with most AI equipment imports negating domestic benefits and 80% of surveyed firms reporting no productivity or employment improvements. This pattern suggests that AI-related spending has primarily shifted margins from enterprise IT budgets to a small number of infrastructure vendors rather than delivering distributed value. Internal concerns are rising, with 90% of IT leaders questioning AI's return on investment, and 80% citing fragmented data as a primary challenge to measuring outcomes. Further context reveals that agentic AI initiatives face operational headwinds: Gartner expects 40% of such projects to be cancelled by 2027, and S&P Global found nearly half are abandoned before production, most often due to inadequate planning and data foundations. Margin erosion is widespread, attributed to AI implementation costs, and attempts to scale AI agents into production remain limited by inference costs and insufficient infrastructure. Despite increased adoption efforts, sustainable value delivery from AI platforms remains elusive for most organizations. Enterprise AI access is becoming increasingly concentrated. OpenAI's partnership with consulting firms such as BCG, McKinsey, Accenture, and Capgemini consolidates control of the enterprise distribution layer, narrowing competitive opportunities for smaller providers. Meanwhile, Amazon's 13-hour AWS outage, linked to the misconfiguration of an internal AI tool, underscores the liability ambiguity in agentic systems—where vendors may attribute autonomous actions to user error, complicating risk assignment. Additional updates from vendors such as Anthropic, Cloudflare, and New Relic address incremental technical capabilities, with a distinct focus on cost, operational governance, and policy enforcement. The prevailing themes for MSPs and IT leaders are increased scrutiny of AI value, heightened exposure to cost and accountability risk, and the emergence of managed service opportunities around data governance, cost instrumentation, and liability management. With enterprise market channels consolidating and risk shifting toward service providers, integrating robust contractual definitions for autonomy, incident attribution, and financial boundaries is essential to limit harm and clarify responsibility before incidents occur. Four things to know today 00:00 Goldman: $700B AI Spend Delivered Near-Zero U.S. GDP Growth in 2025 03:49 OpenAI Enlists BCG, McKinsey, Accenture to Distribute Enterprise AI Agents 06:44 Report: Amazon's Own Engineers Prefer Claude Over Its Mandated Internal Tools 08:56 AI Inference Costs Are Falling — But Governance Gaps Are Growing This is the Business of Tech.    Supported by: CometBackup  Small Biz Thoughts Community   

Enterprise AI search visibility faces a 70% misconception rate among SEO teams. Thomas Peham, CEO and co-founder of Otterly.AI, shares insights from building a product-led AI search optimization platform that serves enterprise clients through strategic partnerships. The discussion covers founder-led sales methodologies for AI startups, scalable enterprise deal frameworks before hiring sales executives, and product-first go-to-market strategies that validate market fit through direct founder engagement.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

In this episode, we explore the intensifying competition between Anthropic and OpenAI as they strive to capture the enterprise market. We break down the different strategies each company is employing to integrate AI into white-collar workflows and the potential implications for various industries.Chapters00:00 Introduction to Enterprise AI Wars01:31 Anthropic's Enterprise Agent Program05:27 Claude Cowork and Computer Use08:50 OpenAI's Frontier Alliance Strategy11:51 Comparing Strategies & Future Outlook LinksGet the top 40+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Samay Kohli spent 12 years at GreyOrange, scaling it to over $100 million in revenue and a $3 billion valuation at its peak, making it one of the world's largest warehouse robotics companies. Two years ago, he started again with Budy, this time in the US senior care industry.In this industry, decisions are emotional, sales cycles can run for years, and multiple stakeholders are involved. While the market sits at the intersection of real estate, healthcare, and hospitality, most sales still depend on manual follow-ups and scattered tools.Budy builds digital workers for sales teams: AI teammates that handle follow-ups, scheduling, and lead management across CRMs, calendars, and inboxes. Instead of adding another layer of software, Budy went zero UI-UX and focused on enabling sales teams in an industry with 99% inbound leads to manage their cold leads better.Today, Samay joins Siddhartha (Partner at Neon Fund, and a proud investor in Budy) and shares his journey from building robots to building digital teammates for a very non-traditional industry.00:00 – Trailer01:00 – What Budy is building for senior care05:15 – Real Estate × Healthcare × Hospitality06:25 – Zero UI UX technology10:09 – AI teammates not assistants12:03 – How sales teams operated before Budy12:51 – A ninety nine percent inbound industry13:45 – The real cost of senior care homes15:35 – Can a CRM alone solve this17:55 – Direct benefits of a digital worker20:49 – Two founder archetypes22:06 – Can lights out operations become real24:49 – What Samay underestimated about the market25:58 – The largest players in the industry29:07 – Treat your customer's company like your own30:52 – Entrepreneurship as a profession35:36 – Unlearnings as a second time founder37:30 – What digital workers actually are39:47 – The original promise of SaaS42:04 – The next decade of digital workers45:25 – Digital workers that read best selling books47:26 – Will Claude build CRMs49:38 – Business etiquette across the world55:18 – How a second time founder chooses investors01:01:00 – Why every team member should track the P and L01:02:14 – How Samay's view on growth evolved-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send a text

This is the takeaway episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we will unpack how ontology, events and culture unlock enterprise AI. We discuss why your ontologist needs to visit the fulfillment center, how to shift data teams from afterthought to proactive partner, and why the five why's matter more than your tech stack.See omnystudio.com/listener for privacy information.

Is AI really eliminating jobs, or is it redefining skills? In this episode of Technovation, Peter High speaks with Ehren Powell, Chief Digital Officer of Marathon Petroleum Corporation, about leading digital transformation at one of America's largest and most complex industrial enterprises. Powell shares how he is building a skills-first organization—decomposing roles, augmenting capabilities with AI, and reassembling work around differentiated processes. Key topics include: Why AI should be treated as a value multiplier—not a strategy How data contextualization unlocks massive sensor environments The creation of data domain ownership across the enterprise Applying edge technology and AI to improve safety and reliability Why curiosity and reinvention define the future workforce

This is the takeaway episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we will unpack how ontology, events and culture unlock enterprise AI. We discuss why your ontologist needs to visit the fulfillment center, how to shift data teams from afterthought to proactive partner, and why the five why's matter more than your tech stack.See omnystudio.com/listener for privacy information.

Juan and Tim are back for a LIVE episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we unpack how ontology, events and culture unlock enterprise AI. We discuss the gap between what your systems think is happening and what's actually happening on the ground and dive into real world examples: a trailer with expensive merchandise sat forgotten in a yard for weeks. $35M in delayed orders. The math added up, but nobody saw it coming. Why? Because we're obsessed with cleaning data and building dashboards, but nobody mapped the happy path vs. the exceptions actually happening on the ground. What is delivery when the customer isn't home? What's "lost in transit" versus "sitting in our own yard"? The solution? Send your ontologist to the fulfillment center. Build tiger teams. Shift your data teams left to act like software product teams. And most importantly: connect the five why's back to your OKRs, or you're just building features nobody needs.See omnystudio.com/listener for privacy information.

Today's guest is Nishtha Jain, AI Innovation Leader. Nishtha leads enterprise data and AI strategy in the biopharma sector, focusing on aligning advanced analytics and AI systems with real-world clinical, regulatory, and operational workflows. Nishtha joins Emerj Client Narrative & Content Strategy Lead Nick Gertsch to examine why most enterprise AI pilots fail to scale, how unrealistic expectations and poorly defined use cases undermine ROI, and what it takes to design human-centered AI systems that fit how teams actually work in regulated environments. Nishtha also breaks down practical frameworks for measuring value beyond headcount reduction, including return on employee experience and long-term capability building, along with concrete approaches to faster experimentation, customer-driven use-case prioritization, and building flexible operating models that adapt as technology and market conditions evolve. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

This episode is sponsored by tastytrade.  Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature.   Learn more at https://tastytrade.com/ In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI.   While much of the AI industry is focused on artificial general intelligence, Cohere is building practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype.   We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough.   If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies.   Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI   (00:00) From Google Brain to Cohere (03:54) Discovering Transformers (06:39) The Transformer Dominance (09:44) What AGI Actually Means (12:26) Planes vs Birds: The AI Analogy (14:08) Why Cohere Isn't Chasing AGI (18:38) Distillation & Model Efficiency (21:42) What Enterprise AI Really Does (25:20) Private Data & Secure Deployment (26:59) Enterprise Use Cases (RBC Example) (32:22) Why AI Benchmarks Mislead (34:55) Why Most AI Stays in Demo (38:23) What "Agents" Actually Are (43:32) The Problem With AGI Fear (49:15) Scaling Enterprise AI (53:24) Why AI Will Get "Boring"  

Tim Crawford and Isaac Sacolick, both former Chief Information Officers and world-class CIO advisors, join Michael Krigsman on CXOTalk episode 909 to break down why enterprise AI strategies are failing, what separates transformational CIOs from those who are drowning, and why earning your seat at the table matters more than ever in 2026.You'll discover:✅ Why Tim says both AI strategy AND IT execution are failing, and what CIOs are focused on instead of outcomes✅ The "three-legged race" framework: how CIO behavior, IT culture, and external perception must align for strategic credibility✅ Why most CIOs have only a "layperson's understanding" of their own business, and how that kills AI value✅ Tim's two swim lanes of AI success: invisible integration or robust training (there is no middle ground)✅ Why Isaac says AI is "reshaping" business but not yet "transforming" it, and the product management shift that changes everything✅ How to evaluate agentic AI: the human-in-the-loop vs. human-out-of-the-loop decision framework and why cybersecurity proves you can't wait✅ The shadow AI paradox: why the best CIOs encourage it (with guardrails) instead of shutting it down✅ The three skills every IT professional needs now: business acumen, critical thinking, and data literacy⏱️ TIMESTAMPS0:00 Cold open: "If you think you should have a seat at the table, you've failed"0:35 Why both AI strategy and IT execution are failing2:08 The productivity measurement problem with AI2:45 What CEOs and boards want from CIOs in 20264:28 Why CIOs don't truly understand their business6:54 Why organizations are stuck in AI pilot mode9:04 Tim's 2 swim lanes: invisible AI vs. training-wrapped AI11:23 Audience Q&A: Inside-out thinking vs. outside-in thinking14:34 The 3-legged race: earning your seat at the table17:09 Moving from AI efficiency to true business transformation20:03 The shift from project-oriented to product-oriented IT20:31 AI governance, CISO alignment, and data sensitivity27:15 Agentic AI: fully autonomous vs. human-in-the-loop34:46 Agentic AI strategy and the value equation (opportunity minus cost)38:46 Shadow AI: innovation source or security threat?43:00 Governance as culture, not a bolt-on46:00 The AI skills gap: business acumen, critical thinking, data skills, and curiosity49:46 Are survival-mode CIOs sabotaging their careers?52:15 What CIO greatness looks like in 2026

Every enterprise is building an AI stack, but most are doing it wrong. In this episode, Ross breaks down a tactical, use-case-driven framework for building an AI stack that actually works. If you're a marketer, operator, or executive looking to leverage AI strategically (without blowing your budget or ignoring compliance), this episode gives you the structure you need to win. Key Takeaways and Insights: 1. The Hard Truth About Enterprise AI - Most companies choose AI tools based on hype, not strategy. - Vendor pitches and social buzz are driving long-term contracts. - Locking into the wrong platform can create scaling and security nightmares. - The AI landscape changes weekly, three-year commitments require serious thought. 2. There Is No “Best” AI Tool - The right question isn't “What's best?” but “What's best for this use case?” - Different teams (marketing, engineering, finance) need different tools. - Constraints, industry, and goals should guide tool selection. - Build a stack…Don't look for a silver bullet. 3.  The 5-Layer AI Stack Framework - Layer 1: Writing & Communication Tools - Layer 2: Research & Analysis - Layer 3: Code & Technical Execution - Layer 4: Automations & Workflow Integration - Layer 5: Security & Compliance 4. Training, Ownership & Continuous Improvement - AI adoption fails without real, ongoing training. - Appoint an AI stack owner responsible for optimization and updates. - Create internal systems (e.g., Slack channels) to share prompts and workflows. - Capture institutional knowledge so it doesn't leave with one employee. 5. Start Small, But Start Strategic - Don't wait for “the perfect moment.” AI is already reshaping competition. - Experiment but build security and compliance from day one. - Budget realistically for training, tools, and maintenance. - Strategic AI adoption is a long-term competitive advantage. Resources & Tools:

After many weeks of work with corporate HR leaders, technology companies, and implementation teams I'm realizing the word that describes AI is “confusion.” Too much going on, too many unanswered questions, and no clarity about what to do. And many of you have been asked (or told) to lead the “AI Transformation” (which is the wrong phrase, as I explain) to reduce cost. Well I hope today's podcast gives you some clarity. Obviously the space is changing quickly, but there is a clear strategy emerging. I discuss the technology market, vendor strategies, and most important of all, how you as a business leader can leverage AI without going down dead ends. I hope this gives you clarity, and I urge you to read our 2026 Imperatives for Enterprise AI for more. Topics covered: Why AI adoption isn't a transformation — it's a continuous learning process How to design an architecture that avoids vendor chaos and data silos The real ROI of AI: rethinking workflows and job structures, not just automating tasks Strategies for navigating a confusing vendor landscape and building your own solutions How to build a culture of trust and change, and empower employees What to tell employees so they'll lean in to change The importance of speed, experimentation, and trusting the data over perfection. If you're in the middle of your AI strategy, please contact us. Our Systemic HR AI Blueprint will show you the way, and Galileo will help you with vendor analysis, process design, job redesign, and of course the training you need to enable your organization. Like this podcast? Rate us on Spotify or Apple or YouTube. Additional Information 2026 Imperatives for Enterprise AI: The Road Ahead The Great Reinvention of Human Resources Has Begun Get Galileo, The AI Agent for Everything HR Chapters (00:00:00) - AI Confusion(00:05:19) - Self-Service HCM Software Companies(00:08:13) - Job architecture and the process of changing jobs(00:11:45) - Don't Wait for Perfection in AI Projects(00:14:43) - Will We Run Out of Jobs?(00:18:01) - Will AI Reduce Headcount?(00:19:39) - The Need for Trust in AI

This week, we discuss the future of SaaS, OpenAI vs. Anthropic strategies, and cloud capex. Plus, when will you let an AI book your flights? Watch the YouTube Live Recording of Episode 559 Runner-up Titles Do we get to eat Moon Pies? Some days it's just me and the AI We have a LinkedIn page The state of the world has not gotten better, it's just moved to Kubernetes Trained on the Corpse of Stack Overflow. We just have to get the files right It is all just files It's all an OODA loop Rinse and reply. Is Software dead? Your margin is my yacht. claude-travel.md Vegans have morals though Rundown DriftlessAF: Introducing Chainguard Factory 2.0 Is Software dead? Clouded Judgement 2.6.26 - Software Is Dead...Again...For Real this Time...Maybe? Anthropic's breakout moment: how Claude won business and shook markets Besieged The $285 Billion 'SaaSpocalypse' Is the Wrong Panic The "whole product" is more relevant than ever Cloud Earnings Microsoft Q2 earnings beat on top and bottom lines as cloud revenue tops $50 billion, but stock falls Microsoft stock plunges as Wall Street questions AI investments A day of reckoning for the AI boom Oracle says it plans to raise up to $50 billion in debt and equity this year Google Earnings Beat. Cloud Computing Momentum Builds Amid Spending Boom Amazon stock falls 10% on $200 billion spending forecast, earnings miss Amazon's $200 Billion Spending Plan Raises Stakes in A.I. Race [Follow the CAPEX: Cloud Table Stakes 2024 Retrospective](http://(https://platformonomics.com/2025/02/follow-the-capex-cloud-table-stakes-2024-retrospective/) Amazon Earnings, CapEx Concerns, Commodity AI Google's parent company raises billions of dollars in debt sale OpenAI Drama Amazon in Talks to Invest Up to $50 Billion in OpenAI The $100 Billion Megadeal Between OpenAI and Nvidia Is on Ice Sam Altman got exceptionally testy over Claude Super Bowl ads | TechCrunch OpenAI will reportedly start testing ads in ChatGPT today Relevant to your Interests Deploying Moltbot (Formerly Clawdbot) Apple tops Q1 earnings estimates on record-breaking iPhone sales Clouded Judgement 1.30.26 - Software is Dead...Again! Leaders, gainers, and unexpected winners in the Enterprise AI arms race All Enterprise software is dead The Dumbest Thing I've Seen This Week SpaceX acquires xAI in record-setting deal as Musk looks to unify AI and space ambitions AWS destiny: becoming the next Lumen CloudBees CEO: Why Migration Is a Mirage Costing You Millions Xcode 26.3 unlocks the power of agentic coding The world is trying to log off U.S. tech Anthropic's newest AI model uncovered 500 zero-day software flaws in testing DHH on OpenClaw Adam Jacob really likes AI code generation Cautionary Tales – The WOW Machine Stops (Part 2) Kyndryl Shares Halved Amid CFO Departure, Accounting Review Our $200M Series C / Oxide Presentations — Benedict Evans Matrix messaging gaining ground in government IT Hello Entire World · Entire Blog Former GitHub CEO raises record $60M dev tool seed round at $300M valuation From magic to malware: How OpenClaw's agent skills become an attack surface Nonsense What If the Sensors on Your Car Were Inspecting Potholes for the Government? Honda Found Out Superbowl Ad 404 Conferences DevOpsDay LA at SCALE23x, March 6th, Pasadena, CA Use code: DEVOP for 50% off. Devnexus 2026, March 4th to 6th, Atlanta, GA. Use this 30% off discount code from your pals at Tanzu: DN26VMWARE30. Check out the Tanzu and Spring talks and trading cards on THE LANDING PAGE. Austin Meetup, March 10th, Open Lakehouse and AI — Listener Steve Anness speaking KubeCon EU, March 23rd to 26th, 2026 - Coté will be there on a media pass. Devopsdays Atlanta 2026. April 21-22 VMware User Groups (VMUGs): Amsterdam (March 17-19, 2026) - Coté speaking. Minneapolis (April 7-9, 2026) Toronto (May 12-14, 2026) Dallas (June 9-11, 2026) Orlando (October 20-22, 2026) SDT News & Community Join our Slack community Email the show: questions@softwaredefinedtalk.com Free stickers: Email your address to stickers@softwaredefinedtalk.com Follow us on social media: Twitter, Threads, Mastodon, LinkedIn, BlueSky Watch us on: Twitch, YouTube, Instagram, TikTok Book offer: Use code SDT for $20 off "Digital WTF" by Coté Sponsor the show Recommendations Brandon: YouTube TV plans launch this week Matt: Send Help Steal Coté: AI, open source, talent, and more, live at cfgmgmtcamp 2026, with Andrew Clay Shafer Tapistry

Do startup valuations today make sense?Umesh Padval, an early investor in Cohere, now valued at about $7 billion shares why Cohere stood out at the time of his investment. He shares what he saw early that made him believe this was not just another AI model company.Umesh is the Founding Managing Partner, Seligman Ventures and previously at Thomvest and Bessemer Venture Partners. He brings experience from investing across multiple tech cycles, from chips to cloud to AI. Umesh talks about how deals are really done in venture capital and what he looks for when everything feels noisy and crowded in AI.He also shares why many strong companies are choosing to stay private and what has changed in the IPO market. Public markets now demand cash flow and durability, not just fast growth.Umesh talks about why open source has become a powerful sales funnel for modern AI companies. Developers become the first users, and community adoption turns into long-term enterprise revenue.After four decades in Silicon Valley and 20 years as a VC, Umesh shares what keeps him in building and investing.0:00 – How big is the scope for investing in AI startups?04:04 – Do unit economics justify large AI valuations?06:00 – Thomvest's LLM investment thesis (Cohere case study)09:18 – Are CTO roles changing in AI11:21 – Traits of the best AI founding teams13:40 – Timeline to find the best founders16:52 – Partnership with Jyoti Bansal19:07 – Where is the IPO market headed?23:40 – Salesforce–Clari acquisition25:18 – Is profitability a prerequisite to go public?26:00 – Can the India–US corridor beat US–Israel?28:53 – Umesh's investment philosophy31:08 – Open source as a sales funnel33:38 – IIT → Stanford → Startups41:45 – The only CEO with 60 direct reports43:43 – Why Jensen never does 1-on-1s?48:23 – What ultimately drives Umesh Padval?-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send a text

At ITEXPO / MSP EXPO, Doug Green, Publisher of Technology Reseller News, spoke with Lyle Pratt, CEO of Vida, about the company's latest release: an expanded AI Agent Operating System designed for enterprise scale and built specifically for MSPs and channel partners. Vida provides AI-powered phone agents that integrate directly into existing UCaaS and telecom environments. With native SIP registration, Vida's agents can register back to an MSP's current UCaaS platform and appear just like any other VoIP endpoint. The new release enhances omnichannel capabilities, centralized control, observability, billing integrations, and reseller management—allowing MSPs to deploy, monitor, and monetize AI agents at scale across multiple customers. Pratt emphasized that the platform was architected from a telecom channel background. “We've designed the OS specifically for MSPs,” he said. “We make it extremely easy to roll those out to all your customers using our AI Agent OS.” Vida supports a multi-tier model—partners, resellers, enterprises, and agents—enabling white-label deployments where MSPs retain brand control and pricing authority. The platform also includes built-in billing and reporting capabilities to streamline recurring revenue operations. A key opportunity lies in redirecting call traffic that traditionally flows to third-party call centers or BPOs. Vida's AI phone agents can handle first-tier interactions at approximately 15 cents per minute, enabling MSPs to capture revenue streams that previously bypassed them. “Software is going to begin to eat into the labor market,” Pratt noted. “And that actually is great for MSPs because they sell software solutions—now they can collect those margins for themselves.” As AI continues to reshape communications infrastructure, Vida is positioning its platform as the backbone for next-generation IVRs, auto attendants, and voice-driven automation. With SOC 2 and HIPAA compliance, flexible integrations, and omnichannel automation capabilities across voice, SMS, and email, the company is aiming to simplify AI deployment for MSPs while opening new, high-margin revenue paths. Visit https://vida.io/

Enterprise AI doesn't fail because the models aren't ready. It stalls when operating models, data foundations, and decision paths can't keep up. Recorded at NRF, this AiR Podcast features Yael Kochman in conversation with Rakesh Srinivasan (The Estée Lauder Companies) and Chris Daniels (Toptal) on what it takes to move from scattered experimentation to repeatable, enterprise deployment - across brands, regions, teams, and partner ecosystems. Key takeaways - Why saturation, skepticism, and a fragmented customer journey are forcing beauty brands to rethink how innovation gets delivered - What must change inside the enterprise to scale AI: product-oriented teams, clear intake and sequencing, and strategy-led roadmaps - The real governance challenge: maintaining brand voice and trust as data flows through platforms, vendors, and generative layers - How modular, “build once, scale many” platforms enable faster deployment across brands - When partnering accelerates outcomes - and why building everything internally can slow progress - A concrete example: Jo Malone London's Scent Finder and what it shows about treating AI as a product, not a pilot Learn more about The Estée Lauder Companies and Toptal

Today's guest is Naveen Kumar, Head of AI Governance at TD Bank. With extensive experience in AI risk management and governance, he provides actionable strategies for secure AI scaling in regulated environments. Naveen joins Emerj Editorial Director Matthew DeMello to discuss foundational challenges blocking AI adoption in banking, including data leakage, prompt injection, shadow AI, and hallucinations. Naveen also shares practical takeaways, such as role-based AI guardrails for data access, safe sandboxes for experimentation, hybrid deployments to protect sensitive data, and treating AI agents as de-risked employees with human oversight for compliance and ROI. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast!

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

How Do Marketplaces Turn AI Ambition Into Scalable, Trusted Enterprise Reality? That is the question I explore in this episode with Julie Teigland, Global Vice Chair for Alliances and Ecosystems at EY, someone who sits right at the intersection of enterprise demand, technology platforms, and the ecosystems that increasingly power modern AI adoption. As organizations race to deploy AI at scale, many are discovering that the real challenge is not a lack of tools, but the complexity of choosing, integrating, governing, and standing behind those decisions with confidence. Julie explains why marketplaces are becoming a powerful mechanism for reducing friction in this process, helping enterprises move beyond experimentation toward AI solutions that are trusted, scalable, and aligned with real business outcomes. We talk about how marketplaces can collapse complexity, curate choice, and bring much needed clarity to leaders who are overwhelmed by the sheer volume of AI options available today. Julie also shares how EY approaches this challenge through its "client zero" mindset, turning the lens inward and treating EY itself as the first marketplace customer. By doing so, EY stress tests governance, security, and integration at real enterprise scale, serving tens of thousands of clients, running hundreds of thousands of servers, and processing hundreds of millions of transactions every day. That internal experience shapes how EY helps clients navigate trust, accountability, and cross-vendor integration risks, particularly as AI becomes more embedded into workflows and decision-making. We also explore how strong alliances with cloud leaders like Microsoft and SAP are shaping how AI solutions are vetted, standardized, and deployed across industries, as well as how regulation, particularly in Europe, is influencing a shift toward responsibility by design. This conversation goes beyond technology to focus on orchestration, trust, and outcomes, and why marketplaces are evolving from simple app stores into something far more strategic for enterprise AI. If you are trying to understand how ecosystems, governance, and marketplaces can help turn AI from isolated projects into sustained business value, this episode offers a thoughtful and grounded perspective.  I would love to know what resonated with you most. How do you see marketplaces shaping the future of AI adoption inside your organization? Useful LInks Connect With Julie Teigland Learn More About EY

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

Alex Gleason was one of the main architects behind Donald Trump's Truth Social. Now he focuses on the intersection of nostr, ai, and bitcoin. We explore open source ai agents, such as OpenClaw, and the wider implications of the tech on society.Alex on Nostr: https://primal.net/p/nprofile1qqsqgc0uhmxycvm5gwvn944c7yfxnnxm0nyh8tt62zhrvtd3xkj8fhggpt7fyClawstr: https://clawstr.com/Soapbox Tools: https://soapbox.pub/toolsMy bot's nostr account: https://primal.net/p/nprofile1qqsfzaahg24yf7kujwrzje8rwa7xmt359tf9zyyjeczc9dhll30k8pgmlfee2 EPISODE: 190BLOCK: 935786PRICE: 1422 sats per dollar(00:02:30) Value-for-value, no sponsors, and show philosophy(00:02:39) Alex Gleason returns to talk AI(00:03:56) From vibe coding to open-source agents with memory(00:05:24) Messaging-first UX: Signal, Nostr, WhatsApp as AI interfaces(00:06:10) Why chatbots beat traditional AI apps for mainstream users(00:07:07) Open protocols pain vs closed platforms; Bitcoin and Nostr(00:08:52) Automating social games: price tracker and agent posting on Nostr(00:10:01) AI mediators for collective action, constitutions, and nonprofits(00:11:46) Scaling governance: trust, bias, and Discord vs freedom tech(00:13:14) Bot barriers on centralized messengers and need for open chat(00:14:04) Clawstr: decentralized AI-to-AI discussions on Nostr(00:15:21) Hype vs reality in AI agents; emergent behaviors and money(00:16:26) Agentic payments: bots with Cashu wallets and earnings(00:18:40) Agents solving UX pain: relay management, keys, and UTXOs(00:20:00) Cold storage approvals with chat agents: a new wallet paradigm(00:20:22) Specialized agents, skills, and distribution challenges(00:22:34) Cost tradeoffs: pay another agent vs build skills yourself(00:24:55) Token burn lessons(00:27:44) Beyond OpenClaw: bloated stacks, Icarus, and cost-optimized agents(00:28:52) Hybrid model routing: local small models with cloud for heavy lifts(00:29:47) Agents paying humans directly: disintermediating platforms(00:30:47) Voice, screens, and form factors: AirPods, text, and brain chips(00:33:01) Apple, privacy branding, and the Siri gap(00:34:35) Enterprise AI choices: Google, Microsoft, trust, and lock-in(00:36:01) Model personalities: Gemini concerns and OpenAI "openwashing"(00:37:23) Obvious agent UX wins: flights, rides, and social media shifts(00:38:50) Local-first social: group chats, neighbors, and healthier networks(00:40:16) Antiprimal.net: standardizing stats from Primal's caching server(00:43:34) Open specs, documentation via AI, and trust tradeoffs(00:45:18) Indexes vs client-side scans: performance and verification(00:46:20) APIs, rate limits, and a market for paid Nostr data(00:47:57) Agents and DVMs: paying sats for services on demand(00:48:49) Degenerate bots: LN Markets, costs, and Polymarket curiosity(00:50:42) Truth feeds for agents: Nostr, webs of trust, and OSINT sources(00:53:51) Post-truth reality: verification, signatures, and subjectivity(00:56:04) Polymarket mechanics: on-chain prediction markets and signals(01:00:10) Trading perception vs truth; sports markets as timelines(01:01:45) The Clawstr token saga: hype, claims, and misinformation(01:07:11) Why meme coins are scams: no equity, utility myths, slow rugs(01:08:55) Pulling the rug back: swapping out, fallout, and donations(01:10:49) Aftermath: donating to OpenSats and lessons learned(01:12:14) Prediction markets vs meme coins: societal value distinction(01:15:25) Iterating beyond OpenClaw and MoltBook; experiments on Nostr(01:18:00) Do bots need Clawstr? Segregating AI content and labels(01:21:02) Reverse CAPTCHA: proving bot-ness and the honor system(01:23:38) Souls, prompts, and token costs; agents with personalities(01:27:01) Wrap-up: acceleration, optimism, and next check-in(01:28:21) Open-source models, China's incentives, and local hardware(01:30:06) The dream stack: home server agent, Nostr chat, hybrid modelsmore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyz

Will Elon Musk really launch a million data centers into orbit, and why is McDonald's so worried about you using "McNuggets" as your password? This week's tech roundtable takes on wild new frontiers and everyday security headaches with insight and a bit of irreverence. More schools are banning phones so students can focus. Ohio's results show it's not that simple After Australia, Which Countries Could Be Next to Ban Social Media for Children EU says TikTok must disable 'addictive' features like infinite scroll, fix its recommendation engine Anthropic and OpenAI release dueling AI models on the same day in an escalating rivalry Sam Altman says Anthropic's Super Bowl spot is 'dishonest' about ChatGPT ads, but he agrees it's funny Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source code Alphabet reports Q4 2025 revenue of $113.8 billion Amazon's blowout $200 billion AI spending plan stuns Wall Street A New Gilded Age: Big Tech goes on a $600 billion AI spending splurge Hidden Cameras in Chinese Hotels Are Livestreaming Guests To Thousands of Telegram Subscribers AI-generated ads hit the Super Bowl SpaceX acquires xAI, plans to launch a massive satellite constellation to power it Russia suspected of intercepting EU satellites Notepad++ hijacked by state-sponsored actors New York Wants to Ctrl+Alt+Delete Your 3D Printer Western Digital Plots a Path To 140 TB Hard Drives Using Vertical Lasers and 14-Platter Designs A Crisis comes to Wordle: Reusing old words The Wayback Machine debuts a new plug-in designed to fix the internet's broken links problem Project Hail Mary is getting its own LEGO set Dave Farber Host: Leo Laporte Guests: Larry Magid, Mike Elgan, and Louis Maresca Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit NetSuite.com/TWIT meter.com/twit trustedtech.team/twitCSS zscaler.com/security

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years.Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic.To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/-----At Davos 2026, the mood was unlike any previous World Economic Forum gathering. With Donald Trump arriving amid escalating geopolitical tensions and European leaders sounding alarms about sovereignty, I recorded live dispatches from the ground. In this special episode, I bring together observations from four days at the annual meeting, tracking the seismic shifts in global order alongside the practical realities of AI adoption in the enterprise.Skip to the best bits:(00:38) Day one at Davos(02:10) Three recurring themes through the week(03:55) Day three at Davos(05:12) Mark Carney's stirring speech(05:52) Why European leaders are sounding the alarm(06:51) Why technological sovereignty just became urgent(09:31) Day four at Davos(12:59) What leaders really have to say on AI adoption(14:07) The case for only using open source modelsWhere to find me:Exponential View newsletter: https://www.exponentialview.co/Website: https://www.azeemazhar.com/LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeemProduction by supermix.io and EPIIPLUS1. Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.