Podcasts about enterprise ai

In this episode, Chris Nichols sits down with Walt Wear, SouthState's AI Enablement Manager, to discuss the bank's Copilot adoption journey, lessons learned from scaling AI across the organization, and what's next as the industry moves toward agentic AI. Walt shares how a community-first approach, weekly office hours, AI Central, and a crowdsourced prompt library helped drive adoption rates above Microsoft's benchmark while empowering employees to work more efficiently and expand their skill sets.   The views, information, or opinions expressed during this show are solely those of the participants involved and do not necessarily represent those of SouthState Bank and its employees. SouthState Bank, N.A. - Member FDIC

In this episode, we kick things off by examining how Schneider National is aggressively pushing rates higher in a tight capacity environment. The Green Bay-based carrier handily beat second-quarter expectations and raised its full-year outlook, declaring that ⁠the truckload market is only in the early stages of rate recovery⁠ as it captures double-digit increases on contract renewals. Next, we explore the autonomous trucking sector where Aurora Innovation is spelling out exactly what its driverless technology will cost carriers. The Pittsburgh-based developer reported a second-quarter loss of two hundred seventy million dollars and ⁠detailed the per-mile pricing behind its two distinct business models⁠, targeting different revenue ranges for its transportation-as-a-service and driver-as-a-service offerings. Finally, we cover the rapidly evolving enterprise artificial intelligence landscape as a leading CEO argues that ⁠the competitive battleground has shifted away from model quality entirely⁠. Speaking at the Supply Chain AI Symposium in Chicago, Reindeer's founder explained that large language models are becoming commoditized and that the real edge now lies in the maintenance layer—detecting when workflows drift and adjusting agents without costly engineering intervention. ⁠Follow the FreightWaves Today Podcast⁠ ⁠Other FreightWaves Shows⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Craig Jeffery and Arjun Krishnan discuss five key limitations of enterprise AI in treasury: data quality, pattern-based reasoning without true understanding, difficulty handling novel situations, variable outputs, and limited explainability. They also explore the opportunities and solutions behind these constraints, including AI-assisted data cleanup, anomaly detection, stronger controls, human oversight, and safer agent-based system design. Enterprise AI for Treasury: A Guide to Agentic Implementation: https://amzn.to/4vTH8Fv AI: The Automation Spectrum and Examples in Treasury (Valorean Technologies) (2026): https://strategictreasurer.com/420-ai-automation-spectrum-and-examples-in-treasury/ Valorean Technologies: https://valorean.ai/ Timestamps: 00:00 Introduction 00:34 AI limitations and opportunities 02:05 Data quality shapes AI quality 05:35 AI correlates but does not understand 09:29 Arjun Krishnan's background 10:18 AI and genuinely novel situations 12:37 Why AI outputs can vary 16:29 Explainability and audit trails 19:14 The most critical limitations 20:32 Final thoughts and book 20:54 Outro ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT STRATEGIC TREASURER ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Download The Strategic Treasurer: A Partnership for Corporate Growth by Craig A. Jeffery in Kindle, or hardcover: https://amzn.to/4uqwFQq As an Amazon Associate, we earn from qualifying purchases. Strategic Treasurer is recognized as a top tier consulting firm in the area of treasury and risk management. Corporate clients, banks, and technology vendors all rely on their industry leading advisory services that are backed by a deep awareness of current needs, practices, and budgeting priorities of treasury professionals through Strategic Treasurer's annual industry surveys and decades of treasury experience. Strategic Treasurer utilizes a senior consultant model where every project is managed by senior consultants with actual practitioner experience in corporate and/or banking roles. Visit us today at http://strategictreasurer.com. Or join in the discussion at one of our leading LinkedIn groups: http://strategictreasurer.com/linkedin/

In this episode, we kick things off by examining how Schneider National is aggressively pushing rates higher in a tight capacity environment. The Green Bay-based carrier handily beat second-quarter expectations and raised its full-year outlook, declaring that the truckload market is only in the early stages of rate recovery as it captures double-digit increases on contract renewals. Next, we explore the autonomous trucking sector where Aurora Innovation is spelling out exactly what its driverless technology will cost carriers. The Pittsburgh-based developer reported a second-quarter loss of two hundred seventy million dollars and detailed the per-mile pricing behind its two distinct business models, targeting different revenue ranges for its transportation-as-a-service and driver-as-a-service offerings. Finally, we cover the rapidly evolving enterprise artificial intelligence landscape as a leading CEO argues that the competitive battleground has shifted away from model quality entirely. Speaking at the Supply Chain AI Symposium in Chicago, Reindeer's founder explained that large language models are becoming commoditized and that the real edge now lies in the maintenance layer—detecting when workflows drift and adjusting agents without costly engineering intervention. Follow the FreightWaves Today Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

„I think unstructured data is ultimately where a company's intelligence is.”

Google didn't ship its big model, but they shipped a TON of new useful AI you can use today. And Google wasn't the only company updating their features behind the scenes. Replit is bringin vibe designing, ChatGPT got a lot more useful on the web, and Meta is changing from chatbot to agent. We'll get you caught up quickly. Chrome adds Some Gemini Spark, Replit Design makes impact, Buzz brings AI Agent Teamwork and 7 more AI Features you Should use Today -- an Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Replit Design Suite Launches With Free MobbinChatGPT Chrome Extension Adds YouTube SummarizationChatGPT Side Chat Integrates Tabs and Highlighted TextMeta AI Rolls Out Recurring Agent TasksGoogle Gemini Generates Images in Google DocsGemini AI Summarizes Comments, Edits in DocsGoogle Gemini Spark Agent Arrives in ChromeChrome Agent Uses Saved Accounts and PasswordsGoogle Lyria 3.5 Music Model ReleasedBuzz by Block Unites Team and Agent CollaborationTimestamps:00:00 Recent AI updates and developments05:01 Creating with Replit and AI models09:42 Real-time research tracking benefits10:34 Meta AI new recurring features13:35 New features of Meta AI17:53 Google Spark integrates with Chrome22:09 Google DeepMind's new music model25:25 Buzz from Block messaging tool29:42 Building a collaborative platform31:23 AI feature updates recapKeywords: Gemini Spark, Google Chrome AI integration, Google Docs AI features, AI image generation, Gemini in Docs, ChatGPT Chrome extension, YouTube video summarization, OpenAI ChatGPT update, Codex, Vibe design, Replit design suite, Mobbin integration, AI reference library, Design export automation, Project management AI, Figma competitor, Replit creative tools, Meta AI, Muse Spark 1.1, Agentic model, Recurring AI tasks, AI scheduling, Daily briefings, AI productivity tools, Google Lyria 3.5, AI music model, Flow Music, Suno, Yudio, AI generated lyrics, Vocal delivery in AI music, Licensing in AI music, Buzz collaboration platform, Block, Square, AI agent teamwork, Slack-like AI platform, Open source collaboration, Agent governance, Cryptographic identity, Agentic browser, Automated web errands, Chrome passwords integration, Google Drive data access, Multi-agent collaboration, Research automation, Enterprise AI workflow, AI productivity boost.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Sarah Wang and Kimberly Tan are joined by Jesse Zhang and Ashwin Sreenivas, co-founders of Decagon, to discuss the evolution of enterprise AI agents, why the company increasingly relies on open-source models, and how it is helping some of the world's largest companies deploy AI in production. Decagon has become one of the fastest-growing AI companies by building agents that automate customer support, sales, and operational workflows. Jesse, Decagon's CEO, and Ashwin, its president, explain how the company is building enterprise AI at scale. They unpack why Decagon moved most of its inference to open-source models, how latency, evaluation, and fine-tuning shape production AI systems, and why enterprise AI requires far more than simply plugging into frontier models. The conversation also explores forward-deployed engineering, enterprise sales, AI's impact on jobs, and why application companies will continue to thrive alongside the foundation model labs.   Resources: Follow Jesse Zhang on X: https://x.com/thejessezhang Follow Ashwin Sreenivas on X: https://x.com/AshwinSreenivas Follow Sarah Wang on X: https://x.com/sarahdingwang Follow Kimberly Tan on X: https://x.com/kimberlywtan Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

New names: Kimi K3, Llama, Nemotron, Mistral, Cohere, Deepseek, Phi-4 – these are just a few of the fast-growing open source models from major AI providers. These systems threaten the business models and financial plans of OpenAI, Anthropic, Google, and X.ai. They perform at levels close to Frontier models and the can run up to five-times cheaper on a variety of hardware platforms. What is the disruptive impact of these open source LLMs and how does this impact your AI investments? As you'll hear in the podcast, Open Source unleashes the opportunity for lower cost AI solutions and more vertical, specialized, application-focused solutions we need. And the business model for these systems moves away from the massive investments of the Frontier providers. The result is more complicated than “open means control.” Model tuning, performance, and optimization could be in your future – as AI moves from a platform to a true layered product set we can use as we need. Lots to learn about here, let us know if you have any questions. Additional Information What's the difference between closed, open source, and open-weight AI? A researcher explains What Is Open-Weights A.I.? Comparison of Open Source Models Chapters (00:00:00) - Open Source and the AI Industry(00:11:46) - The Future of AI Is Fully Integrated(00:15:35) - HR 2030

AI doesn't need more data. It needs more context!!!!That was one of the key themes from my conversation with Josh Good from Qlik at Snowflake Summit on The Ravit Show. As organizations move beyond AI experimentation, the focus is shifting toward governance, trust, and ensuring AI understands the business context behind the data.We also discussed how partnerships between Qlik, Snowflake, and platforms like ServiceNow are helping customers connect data, analytics, and AI into a more unified ecosystem. The future of enterprise AI won't be built by a single platform. It will be built through connected ecosystems working together.#Data #AI #SnowflakeSummit #Snowflake #Qlik #DataAI #EnterpriseAI #AgenticAI #Analytics #TheRavitShow

An estimated 80% of all existing digital data isn't neatly organized in rows and columns, but that unstructured data can...

For episode 758 of the BlockHash Podcast, host Brandon Zemp is joined by Dave Trier where he serves as CEO of ModelOp, where he leads the company with a clear focus on customer value, product innovation, and enterprise execution. A builder at heart, Dave brings deep technical fluency and real-world operating experience to the challenge of helping global enterprises unlock the transformational power of AI.

AI becomes valuable when it helps people do their real work, not when it simply becomes another tool to manage. In this episode of Grounding AI, Donna Peterson welcomes Aya Takase, Head of Global Marketing Communications at Rigaku and Vice President of AI Operations, for a practical conversation about helping employees adopt AI in ways that improve communication, decision making, and everyday work. Instead of discussing the latest AI trends, Donna and Aya focus on real business challenges: How busy professionals can start using AI today Why marketers are often the best people to lead AI adoption How to review AI-generated content before publishing it Why companies don't need dozens of AI tools How internal AI champions can help entire organizations learn faster Why understanding your business is more important than understanding AI Whether you work in manufacturing, industrial marketing, an association, or another B2B industry, you'll leave with practical ideas you can begin using immediately. At World Innovators, we believe AI should strengthen communication, support business strategy, and help companies build stronger relationships with their audiences. This conversation demonstrates exactly how thoughtful AI adoption can support those goals. Subscribe for weekly conversations about practical AI for business leaders. *** Reach out to dpeterson@worldinnovators.com if you'd like help building a marketing strategy that builds relationships and/or AI training for individuals or full teams.*** Visit www.worldinnovators.com for more resources on building stronger marketing and leadership strategies.*** Subscribe to the Grounding AI podcast for weekly insights into marketing, leadership, and the future of AI.

Enterprise technology has long promised to unlock operational potential, but the trade-offs between monolithic ERP systems and fragmented best-of-breed stacks continue to slow organizations down in ways most leaders don't fully account for. In this episode of Enterprise Unleashed on Supply Chain Now, Scott W. Luton and Wiley Jones (Co-founder at DOSS) are joined by Jindra Zitek (Partner and Head of Scale at Stripes), a former McKinsey consultant and C-level operator whose career spans Chobani, HelloFresh, and a range of board and advisory roles across industry. Together, they unpack what it actually takes to build operations that are truly AI-ready, and why the answer starts with people long before it reaches technology. Jindra draws on firsthand experience leading through hypergrowth and crisis to make the case that both ERP consolidation and best-of-breed ecosystems carry a hidden coordination tax, one that only gets more expensive the longer organizations avoid naming it. He challenges leaders to stop framing AI as a job automation tool and start treating it as a lever for dignity and purpose, giving people the ability to focus on the work that actually requires human judgment. The conversation lands on what separates organizations that experiment successfully from those stuck in pilot purgatory: shared business objectives, cross-functional coalitions, and guardrails that free people to move fast without losing control. Wiley grounds the discussion in what Doss is seeing in practice, where the complexity of edge cases is finally collapsing, and where the real work of comprehension still cannot be skipped. Jump into the conversation: (00:00) Intro  (02:29) Jindra Zitek's background across McKinsey, Chobani, and HelloFresh (04:16) Lessons from family, farming, and leadership (06:09) Why people should come before systems (10:28) The trade-off between ERP and best-of-breed tools (15:24) The hidden cost of technology choices (17:09) How company DNA shapes technology strategy (20:57) Eliminating the human tax of broken systems (21:49) Automation should enable people, not replace them (25:57) The rise of agentic operations (28:57) Building trust and control with AI systems (31:34) Measuring real ROI from AI investments (35:17) Building cross-functional teams for AI success (37:30) Creating guardrails for enterprise AI adoption (40:40) The first workflow to transform with AI (42:10) Finding business problems AI can solve (47:56) How AI changes workflow design and implementation Additional Links & Resources: Connect with Jindra Zitek: https://www.linkedin.com/in/jindrazitek/ Connect with Wiley Jones: https://www.linkedin.com/in/wileycwjones/ Learn more about DOSS: https://www.doss.com/ Learn more about Stripes: https://www.stripes.co/ Learn more about Supply Chain Insights: http://www.supplychaininsights.com Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- Peak Reality Check: What Shippers, Analysts, and AI Models Are Predicting for 2026: https://bit.ly/4aTlsRv WEBINAR- From Volume to Resilience: How Automotive Supply Chains Are Adapting to a New Market Reality: https://bit.ly/4f6SUGA WEBINAR- The Automotive Industry's Next Digital Breakthrough: https://bit.ly/4vhUwT4 WEBINAR- From Disruption to Stability: Building Resilient Logistics Solutions in a Rapidly Changing Global Market: https://bit.ly/3TguZMt This episode was hosted by Scott Luton and Wiley Jones, and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/beyond-erp-tradeoff-building-ai-ready-operations-1614 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What happens when a seasoned hedge fund executive applies decades of institutional investing experience to building AI-powered software for the world's most sophisticated financial firms? In this episode of Silicon Valley Successes, host Shawn Flynn sits down with Jan Szilagyi, Founder and CEO of Reflexivity, to explore the intersection of artificial intelligence, capital markets, and enterprise software. Drawing from his unique career spanning both hedge funds and technology startups, Jan shares what it takes to build software for one of the most demanding customer bases in the world—professional investors managing billions of dollars. He explains why today's competitive advantage is shifting from simply having access to information to asking better questions, and how AI is transforming the investment process. The conversation explores how hedge funds are evolving in the age of generative AI, what institutional investors are actually looking for from AI platforms, and how founders can avoid common mistakes when building and pricing AI-driven software products. Jan also discusses the realities of selling enterprise AI solutions in an increasingly crowded market, why customer education has become one of the biggest challenges for AI startups, and how Reflexivity helps investment firms uncover insights that traditional research methods often overlook. Whether you're building an AI company, investing in technology, or simply curious about how artificial intelligence is reshaping financial markets, this episode offers practical insights from someone who has successfully navigated both Wall Street and Silicon Valley. In This Episode Jan Szilagyi's journey from hedge funds to technology entrepreneur What life inside a modern hedge fund is really like How AI is changing institutional investing Why asking better questions is becoming more valuable than having more data Identifying investment opportunities with AI Building versus buying enterprise software The future of AI-powered investment research Educating enterprise customers on AI adoption Converting AI curiosity into measurable business value Pricing strategies for early-stage AI software companies Lessons learned selling into sophisticated financial institutions How Reflexivity helps investors uncover hidden market insights Key Takeaways AI is becoming a force multiplier rather than a replacement for human judgment. The firms that ask better questions will outperform those with simply more data. Enterprise AI adoption depends as much on trust and workflow integration as model performance. Pricing AI software should evolve as customer value and product intelligence increase. Building for institutional investors requires exceptional reliability, transparency, and measurable outcomes. About Jan Szilagyi Jan Szilagyi is the Founder and CEO of Reflexivity, an AI-powered platform designed to help institutional investors and financial professionals extract deeper insights from vast amounts of information. With a background spanning hedge funds and technology entrepreneurship, Jan combines expertise in investing, data analysis, and artificial intelligence to build tools that improve investment research and decision-making. Who Should Listen This episode is ideal for: Startup founders Venture capital and private equity professionals Hedge fund and asset management executives AI founders and product leaders Financial technology entrepreneurs Enterprise software executives Investors interested in AI applications Anyone curious about the future of intelligent investing Connect with Jan Szilagyi LinkedIn: https://www.linkedin.com/in/jan-szilagyi-12284ab/ Website: https://reflexivity.com/ Disclaimer: The views expressed in this podcast are for informational purposes only. They do not constitute financial, legal, tax, or investment advice, nor do they necessarily reflect the views of Finalis Inc. or Finalis Securities LLC, Member FINRA/SIPC. Any discussion of investment strategies, artificial intelligence, financial markets, or specific technologies is intended solely for educational purposes and should not be considered investment advice or a recommendation to buy or sell any security. #SiliconValleySuccesses #ArtificialIntelligence #AI #GenerativeAI #FinTech #HedgeFunds #EnterpriseAI #InvestmentResearch #MachineLearning #VentureCapital #AssetManagement #SaaS #StartupFounder #TechnologyLeadership #Innovation

AI is forcing companies to rethink assumptions they've had for years. One cloud provider. One place for data. One approved set of tools. That world is changing fast. At Cisco Live, I sat down with Arun Dev from Equinix to discuss what enterprises are getting right and wrong as they scale AI.A few themes stood out:* AI is pushing organizations beyond a single cloud strategy and into a much more connected ecosystem.* As AI becomes part of operations, trust becomes critical. Just because an answer sounds right doesn't mean it is.* The pace of innovation is so fast that companies can't afford to rebuild infrastructure every time a new model is released.* Employees are already using AI tools. The challenge isn't stopping them. It's creating the right guardrails around security, governance, and cost.* Data is no longer living in one place. As AI workloads spread across clouds, data centers, and edge environments, networks are becoming a strategic asset.One thing that really resonated with me:The AI conversation is often about models. But the bigger challenge may be building an architecture that can adapt as models, data, and business needs continue to evolve.Great conversation with Arun on the realities of enterprise AI adoption and what leaders should be thinking about today.#data #ai #ciscolive #equinix #observability #api #agents #theravitshow

Why do so many enterprise AI initiatives begin with impressive demonstrations but struggle to produce measurable business value? In this episode of Tech Talks Daily, I speak with Dom Selvon, CTO and value partner at Valiance, about enterprise AI ROI, outcome-based consulting, build versus buy decisions, proprietary data, ontologies, and governance. Valiance is an AI-native consultancy that charges against client outcomes rather than hours worked. Dom explains why his "value partner" title is deliberate. The company begins by identifying the financial or operational result a client wants and connects its own compensation with achieving that result. Dom argues that many AI initiatives begin without a clear definition of success. The pressure to adopt AI is real, but companies frequently select technology before agreeing on the business problem, desired outcome, or measurement. He identifies three recurring mistakes. The first is framing the project around AI rather than the business need. The second is failing to establish a metric and baseline before work begins. The third is using a consulting model that rewards billable time without connecting payment to the client's result. We also discuss how generative AI is changing traditional build versus buy decisions. Companies historically bought software because custom development was slow, expensive, and difficult to maintain. Coding agents can now reduce the time and cost required to create software for specific internal needs. Dom does not believe SaaS will simply disappear. However, vendors selling convenience, workflow wrappers, or integration glue face new competition from customers who can create similar capabilities themselves. He argues that stronger SaaS positions will depend on assets a model cannot easily regenerate, including proprietary data, networks, regulatory standing, and deep workflow adoption. This leads to a wider discussion about competitive advantage. When companies have access to similar models, generated code begins to converge. Dom believes lasting differentiation comes from company data, institutional knowledge, connected systems, employee experience, and the semantic context surrounding that information. Dom explains why ontologies matter to enterprise AI. Raw data tells an agent what is stored in a particular field. An ontology describes the customers, orders, contracts, payments, relationships, and business rules represented by that data. This context allows people and agents to reason about information in a way that reflects how the company actually works. Governance also needs to be designed from the beginning. Dom argues that security, permissions, accountability, and compliance allow successful pilots to expand without forcing the business to rebuild everything later. How can leaders tell when AI is genuinely being adopted? Dom offers a surprisingly simple signal: people stop talking about AI. The technology becomes part of ordinary Monday morning work, and employees focus on completing the task rather than explaining the tool. Has your company defined the business result, measurement, proprietary context, and governance required to turn AI enthusiasm into operational value? Listen to the episode and share your thoughts with me.

P.M. Edition for July 23. The U.S. plans to impose new tariffs on most trade partners, replacing President Trump's temporary global 10% tariff. Plus, the threat of escalating conflict in the Middle East drove oil prices over $100, and concerns around higher inflation made bond yields surge. WSJ markets reporter Sam Goldfarb discusses how that ripples through the economy. Meanwhile, heavy AI spending from Alphabet and Tesla spooked investors, and the Nasdaq dropped more than 2%. And after IBM issued a rare profit warning last week, the company's earnings shed more light on what went wrong. We hear from reporter Anissa Gardizy about where its business goes from here, while tech columnist Christopher Mims spoke with IBM CEO Arvind Krishna. Alex Ossola hosts. Correction: New U.S. tariffs target 60 economies, or more than 80 countries. An earlier version of this podcast incorrectly said the tariffs target 60 countries. (Corrected on July 24.) Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ben Lorica speaks with Denise Teng, partner at Gradient Ventures, about the real state of enterprise AI adoption, why security and data privacy still slow deployment, and why open weights may become increasingly important for regulated industries.Subscribe to the Gradient Flow Newsletter

In this episode of The Product Podcast by Product School, Carlos González de Villaumbrosia sits down with Amit Zavery, President and Chief Product Officer at ServiceNow. The platform runs more than 75 billion workflows a year with around $15 billion in annual revenue growing over 20%. Its market cap is above $100 billion, yet the stock is down more than 30% this past year, while its AI business is on track for $1.5 billion, ahead of a $1 billion plan. Amit previously ran product and platform at Oracle for over two decades and was a VP and General Manager at Google Cloud.What you'll learn:Why the market can't yet tell AI winners from losers, and why companies that don't transform will get killedWhy the idea of one company becoming the single end-to-end enterprise orchestrator is a fallacyThe spare part approach that makes most enterprise AI projects fail, and what pacesetters do insteadWhy access is shifting from user interfaces to agents, and what taking action actually requiresHow to hold long-term conviction on platform bets while the market judges you on short-term sentimentKey takeaways:Transform or die: the market will separate AI-native platforms from legacy vendorsInteroperability beats domination in the agentic eraGovernance only wins when it accelerates innovation, not when it blocks itCredits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Amit ZaverySocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Learn what sets agentic AI apart from traditional implementations and how managed engineering expertise can turn complex multi-agent workflows into production-ready systems that deliver measurable efficiency gains for enterprises. Read more at https://kovil.ai Kovil AI City: Austin Address: 1401 Lavaca Street Website: https://kovil.ai

Plus: Novo Nordisk files a deceptive advertising lawsuit against Eli Lilly. And the latest Chinese AI model launches rattle expectations for the biggest AI players in the US. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI agents can't transform an org they can't see. Albert Strasheim, CTO at Rippling, joins Andrew Zigler to explain why agentic transformation starts with the employee graph, the system of record for who does what. He shares how Rippling assembles teams and primitives across silos, why evals are the new unit test, and how compensating controls keep AI output from turning into slop. When agents do the work, you still have to know who, or what, shipped it. LinearB attributes the work, whether it came from humans, AI assistants, or autonomous agents.Register today: The Engineering Productivity Gap live workshop on July 30Follow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:Rippling: Explore the workforce management platform at rippling.com Introducing Rippling Data Cloud: AI-powered BI that understands your workforceFollow Albert: LinkedIn OFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Welcome to episode 364 of The Cloud Pod, where the forecast is always cloudy! Justin and Matt are in the studio this week to bring you all the latest in cloud and AI news, including (surprise) astronomical AWS bills, Kimi K3 and what it means for Enterprise AI, and lots of security news! All that and so much more, so let's get started!  Titles we almost went with this week Cache Rules Everything Around NFS Now Lambda Says BYOB, Bring Your Own Bucket Henrico’s Power Struggle: Data Centers 37, Schools 0 Cloud Run Fails Over Faster Than Your Excuses 570 Patches, One Registry Hive Nightmare GuardDuty Gets a Detective Agent, No Trench Coat Required Kimi K3 Aims to Moonwalk Past Opus 4.8 Terraform Gets Policy Muscle, Ditches the Rego Diet CloudWatch Watches Your AI Coders Code Watt A Way To Treat A School District Your Cloud Bill… 1 BILLION DOLLARS Not a way I want to wake up rogue cloud bills Skype is EOL … wait I thought I died 3 times already Our newest superhero CODEMENDER!! A big thanks to this week's sponsors: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our Slack channel for more info. General News  00:49 Amazon fixing bug that billed some AWS customers billions of dollars  A bug in the AWS billing computation subsystem generated inflated billing estimates for some customers, with one Reddit user reporting a quoted estimate near 2.5 billion dollars for a single month. In contrast, others saw figures ranging from millions to hundreds of millions. The issue began late Thursday, and an initial rollback attempt on Friday morning failed to resolve it, suggesting the root cause was more complex than a recent configuration change. Amazon confirmed the billing estimates do not reflect actual usage or charges, meaning affected customers will not be responsible for the inflated amounts shown in the console. Amazon has not disclosed whether any accounts were suspended or paused due to the billing errors, leaving open questions about operational impact during the incident. The event highlights the importance of billing system reliability for cloud providers, since inaccurate estimates at this scale can cause confusion and concern even when the underlying charges are not real. 01:29 Justin – “Amazon doesn’t bill you in the middle of the month, so it’s a pretty low risk that you were gonna get billed or invoice directly on that date, unless you happen to already be overdue on a payment and you were happening to update your credit card at the same time. I don’t think that’s really a big risk for this particular scenario.”  05:04 County With 37 Data Centers Asks Schools to ‘Conserve Electricity' Listener note: Paywall article  H

Today's guest is Martin Duffy, Head of GenAI at PWC Ireland. Founded in 1866, PwC Ireland is one of the country's leading professional services firms, providing audit, tax, consulting, deals and technology services. By combining deep industry expertise with a global network, PwC helps organisations drive transformation, solve complex business challenges and create sustainable value in an increasingly digital world.Martin is a senior data and analytics consultant with over 30 years of experience helping organisations harness data to drive better business outcomes. He specialises in analytics strategy, building high-performing analytics functions and advancing organisational analytics maturity. With extensive experience across the financial services, public sector and manufacturing industries, Martin helps organisations unlock the full value of their data and make smarter, data-driven decisions.In the episode, Martin discusses:0:00 His journey from early neural networks to modern GPT scale AI evolution2:08 How their Client Zero AI journey shifted to human-centric trust approach3:42 Their focus on automating tasks to drive AI adoption and trust7:05 How Personal AI wins drive adoption and organisational growth choices9:48 Top AI leaders combine governance, responsibility and growth mindset11:00 How AI success blends leadership and grassroots adoption12:01 Irish firms lag due to caution, process redesign and operating model maturity13:21 The need to choose mindset, lead visibly and prioritise people change

Recorded live at the Accelerate conference, this episode of DM Radio explores why data - not applications - is becoming the foundation of enterprise AI.  Join host Eric Kavanagh as he speaks with Chadd Kenney of Everpure about the shift toward data-centric architectures and the importance of shared context for powering intelligent AI agents and real-time decision-making. Next, Shawn Rosemarin of Everpure explains why context is to AI what memory is to humans, illustrating how connected, meaningful data enables more accurate, trustworthy outcomes. Together, these conversations reveal why contextual intelligence is emerging as the key ingredient for the next generation of enterprise systems.

Most companies say they're doing AI. A surprising number are doing very little — and a Chief AI Officer at one of the world's largest automation platforms has the receipts to prove it. Motley Fool analyst Rachel Warren talks with Adam Field, Chief AI Officer at Tungsten Automation — a company serving 25,000 organizations including 40% of the Fortune 100 — about what separates real AI transformation from expensive spin. They get into why most enterprise AI pilots quietly die before they scale, what "boring AI" actually means and why it's the most important signal investors aren't paying attention to, and why the competitive moat that once made legacy software giants unassailable has effectively disappeared overnight. Host: Rachel Warren Guest: Adam Field Producers: Adam Landfair, Lauren Budabin Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

Artificial Intelligence has completely transformed software development. What started with autocomplete has evolved into AI coding agents capable of building entire applications from simple prompts. But as organizations adopt these tools, new challenges emerge: cost, security, vendor lock-in, enterprise governance, and the future role of software engineers. Today's conversation isn't about one product. It's about understanding where software development is heading over the next few years. Joining me is Emilie Schario, VP of Engineering at Kilo, where she works on agentic engineering and multi-model AI developer tools. We discuss how enterprises should think about AI coding assistants, why relying on a single AI model may not be the future, how engineering teams should prioritize product development, and what both junior and senior developers need to do to stay relevant. Whether you're a developer, engineering leader, CTO, startup founder, or simply curious about AI transforming software engineering, this episode is packed with practical insights. Let's get started. Chapters: 00:00 Introduction to Emily Sherio and Kilo Code 04:13 The Role of Kilo in Developer Productivity 07:52 Choosing the Right AI Models for Development 12:24 Building Features that Matter 16:38 The Importance of Code Review and Feature Management 19:24 The Future of Development and AI Integration 25:59 The Necessity of Learning Coding Principles 30:16 Enhancing Security in Coding Tools Episode # 194 Today's Guest: Emilie Schario, VP of Engineering, Kilo  She works on agentic engineering and multi-model AI developer tools Guests Links: LinkedIn What Listeners Will Learn: Why AI coding is moving toward multi-model architectures. The advantages and risks of proprietary AI coding tools. How organizations can reduce AI infrastructure costs. Why vendor lock-in is becoming an AI challenge. The future of developer productivity. Why code review is becoming more important than writing code. Product management lessons every startup should know. When to remove features rather than add more. Enterprise AI security challenges. How organizations can adopt AI without compromising security. Why prompt engineering is becoming a fundamental skill. Advice for junior developers entering the AI era. The importance of mentoring the next generation of engineers. Resources: Kilo - AI Developer Platform Prompts for Mere Mortals by Florian Hines  

What does it take to write the very first check into a company that has almost nothing to show yet, sometimes not even a finished idea?Afore Capital helped invent the pre-seed category. When Gaurav Jain and Anamitra Banerji started the firm ten years ago, "pre-seed" was almost a slight, a label for founders who couldn't raise a proper seed round. They set out to build the world's largest pre-seed fund anyway, closing $47 million on a $40 million target, and every fund since has closed above plan. Afore now runs more than $500 million across four funds, with top-quartile DPI on the first three. The idea has become so mainstream that when Sequoia launched its latest fund, it said, "I guess we're pre-seed investors too."The real substance of the conversation is how Gaurav thinks. He is clear about what matters most in venture, and the order tends to surprise people. Being in the very best companies matters more than anything else, ownership comes after that, and the entry price that so many investors fixate on matters least, because fifty per cent of zero is still zero. He is also convinced that the genuine bottleneck is talent. There is a great deal of money in the world and very few people who can build something truly large, which is why at the earliest stage founders tend to choose their investors as much as investors choose them. You give a founder a million dollars with no collateral, and then you still have to convince them to take it. A pre-seed pitch, he says, is almost entirely storytelling with very little data behind it.If you want to understand how the earliest checks actually get written, and what it really costs to say no, this episode is worth your time.00:00 - Trailer01:00 - From Dehradun to Google to starting Afore02:08 - The Waterloo co-op that talked him out of every job03:18 - Back when "pre-seed" was an insult05:44 - When Sequoia said "I guess we're pre-seed investors too"07:26 - Afore's three products, and the experiments that failed09:01 - Hightouch was a travel company when they invested11:02 - Goldcast: no visa, no money, funded anyway12:07 - The through line is always the team14:44 - The Ramp miss17:24 - "Founders pick us more than we pick them"18:45 - The constraint isn't capital, it's talent22:24 - The Solana miss, when it was still Loom Protocol24:46 - Ramp's Super Bowl ad, the buses, his wife's business25:32 - What he looks for in founders28:50 - Coachability, happy ears, and the Mom Test31:28 - The biggest mistake: falling in love with the idea35:04 - The three things that matter, and "50% of zero is still zero"39:25 - "100% storytelling, 0% data"41:25 - Investing in India, and the fear of being dumb capital44:41 - "Sign the deal before Monday"47:26 - One engineer now does the job of 2051:43 - Raising from LPs, the undiscussed part of VC-------------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 us Fan Mail

Every capability in an agent needs its own evidence and release bar. A model-provider slip, an incorrect tool call, and a wrong fertility-benefits answer should not be held to the same pass rate.William Horton, Staff AI Engineer at Maven Clinic, joined us the day after Maven Assistant reached its first external users. The agent helps members inside Maven Clinic's women's and family healthcare platform find providers, manage appointments, navigate Maven, and get basic health information. William had spent much of launch day reading chat traces and turning the surprises into product decisions and tests.William shows how a production failure moves through Maven's system: the trace becomes a regression case, code handles deterministic checks, and LLM judges cover behavior that cannot be reduced to exact outputs. Human labels calibrate those judges, while the consequence of a wrong answer determines whether the capability ships. You can apply the same release workflow to the agent you are building now.“For a lot of our tool-call evaluation, I'll accept that it runs ten times and passes nine times. Going for that ten out of ten is just not worth the effort.”— William Horton, Staff AI Engineer, Maven ClinicYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.

Enterprise AI is easy to demonstrate. The real test begins when a promising POC meets production costs, security requirements, data movement, latency, and internal adoption.Shimon Ben-David, CTO at WEKA, joins Amir to discuss the gap between experimenting with generative AI and operating it at scale. They explore how classical AI differs from generative AI, why production exposes problems that demos hide, and how companies with limited AI maturity can start building useful internal capability.Practical Takeaways• A successful POC proves that an outcome is possible. It does not prove that the system will be affordable, secure, reliable, or fast at scale.• Enterprise AI adoption reaches across infrastructure, engineering, data, security, and business teams. It cannot be owned by one group in isolation.• Adding more GPUs will not fix slow data access, poor utilization, weak pipelines, or an experience users do not want to use.• External support can help, but the person or firm involved needs to stay through implementation and production, not stop at recommendations.• Companies that are behind should begin with proven use cases, build internal experience, and quickly stop experiments that fail to show value.Key Moments00:00 Why moving enterprise AI into production remains difficult01:55 The difference between classical AI and generative AI adoption07:05 How companies can use AI without having a formal AI strategy11:35 Why successful POCs often struggle when they reach production17:35 Competitive pressure, AI FOMO, and the need to calculate real ROI22:00 Why AI adoption requires cross organizational change33:10 Where a company with limited AI maturity should beginOne Line That Stuck“The promise is there. It is possible. You just need to do it properly.”Subscribe to The Tech Trek for more conversations about how technical teams are building, operating, and adapting around AI, data, product, platform, and engineering execution.

Today, we are dropping our final episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In our final episode, we are joined by Shayne Higdon, Wallarm CEO, who closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.QuestionsWhy is now the accountability moment for enterprise AI?What has changed between the early days of AI experimentation and today's enterprise AI deployments that makes accountability such a pressing issue?When we talk about AI accountability, what does that actually mean in practical terms? Are we talking about visibility, auditability, enforcement, ownership—or all of the above?As organizations race to deploy AI, how should CIOs balance the speed of transformation with the responsibility to govern it effectively?Why are traditional governance and security models struggling to keep pace with the way AI is being adopted across the enterprise?Given those challenges, how should boards and executive teams evaluate whether their organizations are truly ready to scale AI safely and responsibly?And once an organization believes it's ready, what does a mature AI governance model actually need to prove - not just promise?From an operational standpoint, how do capabilities like discovery, runtime monitoring, and enforcement come together to create a closed-loop approach to AI accountability?Stepping back and looking across this entire conversation, what's the one mindset shift every enterprise leader needs to make when it comes to AI security and accountability?And finally, as listeners think about what's ahead, what should they expect the future of AI security and accountability to look like over the next 6, 12, or even 24 months?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/shaynehigdon/Full AbstractAbstract: Join Shayne Higdon, Wallarm CEO, for this episode, which closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.AI deployment is not waiting for governance to catch up. Across most enterprises, the gap between how fast AI is being adopted and how well it is being governed is widening every quarter. CIOs and CISOs are not debating whether to govern AI. They are trying to figure out how, under real organizational pressure, with tools and frameworks that were built for a different threat model.That pressure is coming from every direction at once. Boards want AI transformation to move fast. Regulators want documented evidence that it is under control. Security teams want runtime visibility and enforcement capabilities that most of their current tools do not provide. And the AI systems themselves are not waiting: they are accessing data, calling external services, and making decisions continuously, in ways that after-the-fact governance cannot meaningfully constrain.This is the accountability moment. Not because the risk is new, but because the consequences of undermanaged AI are now concrete enough to land on a board agenda, an audit report, and a regulatory deadline at the same time. What accountability actually requires in practice is the full AI control loop: knowing what AI is running across the enterprise, seeing what it is doing at runtime, enforcing policy before damage compounds, and generating continuous evidence that the governance is real and not retroactive. Organizations that can demonstrate all four are in a fundamentally different position than those still assembling audit evidence from spreadsheets the week before a review.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

SUMMARY: On today's "Models and Markets" - we explore about the FinOps experience from Cloud is having to adapt to the changing demands of Enterprise AI. SHOW: 1045SHOW TRANSCRIPT: The Enterprise AI Show #1045 TranscriptSHOW VIDEO: https://youtu.be/Plb88y-IkZYSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Topic: Finops for AI?Why now? Cost of tokens goes up as model performance increases, but still needs subsidies…Past: FinOps for Cloud - prices grew out of control, needed centralization for expense management and capital allocationPresent: TokenMaxxing, the move from per-seat to per-token pricingFuture: What happens when you can't afford the Ferrari anymore? Will there be a glut of FinOps for AI startups? What happens when usage is regulated and centralized?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Send us Fan MailRon Yekutiel, Co-Founder, Chairman, President, and CEO of Kaltura, Inc. (NASDAQ: KLTR), joins the latest WTR Small-Cap Spotlight for an in-depth look at the company's next chapter. In conversation with host Tim Gerdeman and WTR analyst James Kisner, Yekutiel explains how Kaltura is moving from a long-standing leadership position in enterprise video to an AI-powered, agentic digital experience platform. He outlines how the company is combining its core technology with photorealistic conversational avatars from the eSelf acquisition and intent-based journey orchestration from PathFactory to transform customer, employee, learner, and audience experiences.During the episode, Yekutiel demonstrates his own digital twin, an avatar that presents Kaltura's investor deck, answers questions, and switches to Japanese on command. The discussion also covers the two agentic solutions planned for release in the second half of 2026, the strategic importance of Kaltura's real-time experience layer, and how the leadership team is balancing investment in growth with adjusted EBITDA profitability and positive cash flow.

As enterprise AI rapidly evolves from isolated assistants to autonomous systems capable of executing complex business processes, organizations are looking for practical ways to turn AI into measurable business outcomes. In this episode of Cloud Wars Live, Bob Evans speaks with Chris Leone, Executive Vice President of Oracle Applications and AI, Oracle about Oracle's latest innovations in Fusion Agentic Applications, the new Fusion Builder Experience, and AI Studio Skill. Leone explains how Oracle is combining enterprise applications with AI agents to automate work, empower both business users and developers, and help organizations accelerate AI adoption while maintaining enterprise-grade security and governance. AI That Delivers Outcomes The Big Themes: Outcome-Driven AI Changes Everything: Oracle's vision for agentic AI begins with a simple premise: enterprise software should no longer focus primarily on completing tasks — it should focus on delivering business outcomes. Leone explains that Oracle has intentionally designed Fusion Agentic Applications around measurable objectives rather than individual transactions. Instead of asking users to manually coordinate dozens of activities, organizations define a goal, such as reducing supplier spending or shortening inventory lead times, and the application orchestrates the work required to achieve it. Teams of AI agents collaborate, monitor progress, recommend next steps, and increasingly automate execution while keeping humans involved whenever appropriate. Autonomous Work Is Gradual: Oracle isn't advocating for immediate, fully autonomous enterprises. Instead, Leone introduces the idea of an "autonomy dial" that organizations can gradually increase as confidence grows. Initially, AI agents recommend actions while employees remain responsible for approvals and execution. Over time, companies can allow the system to automatically perform more routine work while humans supervise exceptions and strategic decisions. Leone illustrates this using Oracle's Sourcing Command Center, where customers establish objectives like lowering supplier costs or reducing lead times. The application identifies shortages, creates RFQs, manages supplier auctions, recommends winners, and continuously guides employees throughout the process. As organizations become more comfortable, more of these steps can execute automatically. This phased approach helps customers balance productivity gains with governance, compliance, and trust while steadily reducing repetitive work and allowing employees to concentrate on higher-value business decisions. Customers Are Moving Fast: Leone describes Oracle's customer base as spanning the full spectrum of AI adoption. Some organizations are already experimenting aggressively with Oracle's newest Builder Experience, posting demonstrations almost immediately after release. Others have successfully deployed Oracle AI capabilities into production, with more than 7,000 customers already using Oracle AI services. Still, others remain cautious, focusing primarily on traditional transactional systems while gradually evaluating AI opportunities. Despite these varying adoption rates, Leone believes Oracle must continue innovating at the leading edge because tomorrow's competition may come from AI-first startups rather than traditional enterprise software vendors. The Big Quote: "We're truly moving from this system of record that we've been delivering for many years to truly delivering outcomes for our customers." More from Chris Leone: Follow Chris Leone on LinkedIn or send a message via Oracle AI for Fusion Applications.   Visit Cloud Wars for more.

What does it actually take to build a robot that cleans your home when everyone before has failed?Matic is a home robot that sweeps and mops your floors, navigating entirely with cameras, no LIDAR. It shipped its first unit in 2024 and has since sold 6,000 units at ~2,000 a month, almost entirely by word of mouth. And is now the largest consumer robotics company shipping in the United States.Navneet Dalal (a computer-vision pioneer who co-invented HOG) and Mehul Nariyawala met building Flutter, a gesture-recognition app that became #1 in 72 countries and was acquired by Google, where they then worked on Nest cameras and shipped one of the first deep learning algorithms in the wild. Matic is the company they decided would be their last: they wrote "Not For Sale" on the wall on day one and built it to last 20 to 30 years.Their bet was deliberately contrarian. They chose the "unsexy" floor-cleaning market, a category with a net promoter score of -1 that people keep buying anyway (21 million robot vacuums sold in 2024), because entering an existing market beats creating a new one and because it's the foundation for true indoor autonomy. Then they put roughly $35 million of their own money in, about 70% of their net worth, with no plan B.Along the way they lay out a full worldview: why robotics is 100x harder than software (the demo is only the first 20% of the work); why humanoids doing your chores are still 5 to 20 years away (the data problem), why no consumer hardware sells above $2,000; and the skin-in-the-game philosophy captured by his late father's advice: "Sell your home if you have to, but keep the company alive."If you're excited about how home robots actually get built and what it really takes to bet everything on hard tech, this episode is for you.00:00 - Trailer01:08 - When they quit Google to start Matic03:30 - Solving home cleaning with cameras only — no LIDAR04:46 - The $35M bet: funding Matic themselves07:36 - What a "level 5" robot in your home really means08:00 - Why they started with floor cleaning — on purpose09:45 - The rule: never create a new market with your first product10:02 - iPod, iPhone, Tesla — all entered existing markets11:38 - Why new hardware gives you only one shot13:02 - "Make something people NEED, not want"16:25 - Why the demo is only 20% of robotics18:50 - Teaching a robot like raising a child21:30 - How far are humanoids from real homes?22:22 - The data problem: "500 years of driving data a day"22:56 - 90% in the lab, 60% in the real world26:49 - Why no consumer device sells above $2,00027:38 - Would you buy a $10,000 humanoid — for what?28:47 - "History rhymes": General Magic to the iPhone29:38 - Earning trust after 20 years of broken robot promises30:31 - Shipping the first robot30:45 - 6,000 units, all word of mouth, zero marketing31:10 - Why they're US-only for now 31:50 - The investors: Sutter Hill to the Collison brothers33:20 - Two companies, both acquired by Google35:00 - The Flutter story: #1 app in 72 countries35:25 - Why nobody believed machine learning worked in 201138:40 - Microsoft Kinect: 8 million units in 60 days40:30 - The Google acquisition — and the $35M number44:40 - The near-death moment: switching to NVIDIA53:10 - iRobot's bankruptcy and what it means for Matic53:55 - The real scale of robotics: 21M robot vacuums a year57:45 - Putting 70% of their net worth on the line58:40 - His father's advice: "sell your home, keep the company"-------------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:   / theneonshoww  LinkedIn:   / beneon  Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn:   / 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 us Fan Mail

AI agents are designed to do more than answer questions. They are meant to complete tasks. Sierra co-founder Clay Bavor joins CNBC's Arjun Kharpal to discuss how AI agents are moving from demos into real business workflows, especially in customer service, sales and support. Bavor explains how Sierra builds and tests customer-facing AI agents before they go live, why companies want clearer ways to measure AI's return on investment and how outcome-based pricing could challenge the way software companies get paid. The conversation also covers coding agents, rising AI token costs and why the hardest part of enterprise AI may be the “last mile” of deployment. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today's guest is Sumeet Vaidya, Co-Founder and CEO at Crafting. Founded in 2021, Crafting is an AI infrastructure platform that enables engineering teams to deploy autonomous coding agents in secure, production-like environments. Designed for enterprise organizations, the platform allows AI agents to write, test, validate and ship software using real infrastructure, data and dependencies. Crafting helps businesses accelerate software delivery while maintaining enterprise-grade security, reliability and operational control.Sumeet is an experienced engineering leader and entrepreneur focused on solving complex challenges that improve productivity. He has built and scaled high-performing teams across developer tools, consumer products, marketplaces and enterprise integrations. As an angel investor and advisor, he helps founders achieve product-market fit, strengthen their technology and business strategies, and build scalable companies. Sumeet is passionate about enabling teams to solve meaningful problems and create lasting impact.In the episode, Sumeet talks about:0:00 His journey from big tech roles to startup founder2:27 Crafting's shift from improving developer tooling to now enabling AI agents4:43 Betting on AI agents despite industry skepticism7:36 Building an enterprise-first AI agent infrastructure for scaling globally11:57 Why Agents need real testing capabilities to succeed15:43 Why engineering leaders are caught between AI hype and practical execution pressure19:46 The need for engineers to focus on outcomes and founders to understand motivationTo find out more about all the great work happening at Crafting, check out the website www.crafting.dev

No single AI platform is going to become the "one ring to rule them all". The enterprise AI stack is fragmented by nature, but an orchestration layer lets governed, durable agents work across the systems you already have.On the Mostly Unstructured Podcast, KeyMark CMO Clay Tuten sits down with Chris McLaughlin, Chief Revenue Officer of Vertesia, to unpack why enterprises end up running six, seven, eight AI tools at once — and how an orchestration layer, agents that can actually reach your data, and content that's genuinely readable by an LLM turn that sprawl into work that gets done with humans in charge of decision making.

Teradata (TDC) CIO Josh Fecteau says AI investments show "no signs" of spending slowdown, though the gap between AI spending and ROI continues to widen. Josh offers his insights into how enterprise AI fits into the spending picture and how businesses aim to close that existing gap. He adds that agentic AI has slowed at the enterprise level, a hurdle he sees lasting for some time. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Progress Software (PRGS) CEO Yogesh Gupta discusses the company's strong earnings and growing demand for enterprise AI solutions. He explains how Progress helps reduce AI processing costs and highlights its partnership with Nvidia (NVDA) to bring advanced AI infrastructure directly to businesses.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

In today's Cloud Wars Minute, I explain why the next phase of agentic AI is all about governance, security, and business processes. Highlights 00:03 — Salesforce has expanded its partnership with Databricks to help organizations better connect enterprise data with business outcomes in the era of agentic AI. At its core, the expanded partnership is about recognizing that as AI agents take on a larger role across the enterprise, they need access to complete, connected data that's paired with business context, security controls, and enterprise processes. 00:51 — Access to data alone really is not enough for AI agents to deliver meaningful business value. "Customers consistently tell us they want AI agents to become a larger part of how work gets done across the enterprise," said Andy Kofoid, President of Global Field Operations at Databricks. "To make this a reality, they need access to trusted data, business contexts, and governance controls wherever that information lives." 01:32 — "Together, Salesforce and Databricks are helping customers connect governed data and business contexts across platforms, giving humans and agents the shared foundation they need to search, reason, and act with confidence." 01:46— I think this partnership is, yet again, part of a pattern that's emerging here. It's representing a broader shift that's taking place across the AI industry as organizations move beyond experimentation and toward large-scale deployment of AI agents. 02:00 — As this is happening, success really depends less on the models and more on the ability to unite these agentic capabilities with data governance, security, and business processes. Salesforce and Databricks are betting that enterprises need all of those elements working together cohesively if agentic AI is to deliver on the promises it has made. Visit Cloud Wars for more.

How do you know whether an AI agent is doing its job or quietly failing in production?Galileo is building the trust layer for AI. Its evaluation and observability platform is how enterprises measure whether the output of an LLM or an agent is good or bad.Galileo started before "LLM" was even a word. When Atin showed his prototype to Stanford's Chris Ré, his own first question was "what is a language model?" Today its customers include Reddit, Airbnb, P&G, Comcast, and six of the Fortune 50. Atin spent a decade in big tech before co-founding Galileo with Vikram Chatterji in early 2021. He worked on the knowledge graphs behind Siri at Apple, then became one of the leads and architects of Michelangelo, Uber's AI platform, that hosts thousands of models across pricing, ETA, and demand.That Uber experience taught him the lesson the whole company is built on; that in AI, observability and evaluation are the real bottleneck, and bad data is catastrophic.As ChatGPT turned every AI output into something a user sees directly, the measurement problem went from academic to mission-critical. So Atin made a contrarian bet: instead of using giant LLMs to judge other LLMs, Galileo built Luna, small 1-3B parameter models that run evals at breakthrough latencies of 100 milliseconds and below.If you are excited about how AI actually gets shipped, trusted, and controlled inside real enterprises, this episode is for you.00:00 - Trailer01:14 - From India to Apple, Uber, and Galileo01:34 - Where the name "Galileo" came from02:38 - Building Siri's early knowledge graphs at Apple03:29 - Becoming an architect of Uber's Michelangelo05:15 - Why every AI output is now mission-critical06:45 - How Atin and Vikram zeroed in on Galileo07:42 - "What is a language model?"09:38 - Building the world's first feature store at Uber11:27 - Language models and tokens, explained simply14:19 - Where the observability insight came from15:53 - Quantifying uncertainty and hallucinations16:36 - The first customers and first use case19:15 - How the product evolved from a data scientist tool23:18 - Why ChatGPT changed everything for Galileo23:57 - The enterprise AI adoption curve, 2021 to 202626:35 - Why they built the Luna model28:32 - Turning LLM "writers" into "calculators"28:51 - Attacking the latency problem31:48 - Luna: the modeling and infrastructure innovation33:09 - What evals are, and why they blew up34:26 - The case for small language models36:58 - What "general reasoning" really means40:39 - AI usage is exploding — and why that matters43:08 - Online vs offline: the "it worked on my machine" problem44:33 - The evals flywheel and evals-driven development46:56 - Galileo in a nutshell47:39 - What real agents in production look like today49:30 - A sales intelligence platform, powered by Galileo50:47 - The agent control product52:12 - Building GTM as a hardcore engineer from India54:43 - Garbage in, garbage out: nailing the ICP55:42 - How the pitch changed from customer 1 to 2057:27 - Why Atin switched from CTO to CPO-------------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 us Fan Mail

Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In today's episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, returns to the show to dive into why runtime behavior is the critical blind spot, and what CISOs should demand if they want to move from policy to control.QuestionsSecurity teams are used to detecting incidents and responding after the fact. Why is that model becoming insufficient for AI-driven systems?Building on that, when we talk about response today, enforcement often means actions like restarting pods, rotating credentials, or shutting down services. Why can those measures come too late in an AI environment?So if traditional response isn't enough, why does AI behavior require controls that operate much closer to runtime?And when people hear "runtime enforcement," they may think of existing security controls. What changes when enforcement happens at the kernel level rather than only at the network, identity, or application layer?Can you make that tangible for us? What does it actually mean to revoke or contain a compromised AI session without disrupting the broader deployment?How does that kind of real-time containment change the risk equation for AI agents that have access to sensitive data, external services, or production workflows?With that in mind, what are some examples of AI behaviors that organizations should be able to stop immediately?Of course, security teams also don't want to become a bottleneck. How do organizations balance strong enforcement with the need to keep AI development and deployment moving quickly?And once organizations have the ability to discover, observe, and enforce AI behavior in real time, how does that change accountability at the enterprise level? What does good governance look like from there?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/cu-craigthomas/Full AbstractThis episode examines what is actually missing in AI security today. Craig Thomas, Sr. Solutions Engineer at Wallarm, dives into why runtime behavior is the critical blind spot, and what CISOs should demand if they want to move from policy to control.CIOs and CISOs have moved past debating whether AI security matters. The question now is what to actually do about it, and most organizations are finding that their existing tools answer a different question than the one AI is asking.Traditional security tools were built around access: who can reach a system, what credentials they present, what traffic looks like at the perimeter. AI shifts the problem to execution: what a system does once it has access, whether that behavior matches what the business intended, and how you know when it doesn't. Most current tooling has no answer for that. It can tell you what is deployed and what is configured. It cannot tell you what your AI is actually doing at runtime, on whose behalf, or whether any of it violates the policies you thought were in place.That gap is where most AI security programs stall. There is no shortage of governance frameworks, compliance checklists, and vendor claims. What is missing is operational control: the ability to see AI behavior as it happens, enforce policy at runtime, and produce evidence that holds up when an auditor or a board asks for it. The four capabilities that define a closed AI control loop, discover, observe, enforce, govern, are well understood as a category. Getting all four working together in production is where the real work begins.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The AI boom isn't over but the first wave is.In this episode of Liftoff with Keith, Keith sits down with Clint Chao, Co-Founder & General Partner at Moment Ventures, to discuss where AI is really headed, why enterprise software is entering a new phase, and what founders should focus on as the hype settles.They explore venture capital, AI adoption, enterprise transformation, startup execution, and what separates companies that create lasting value from those simply following the latest trend.Whether you're a founder, investor, operator, or simply curious about the future of AI, this conversation is packed with practical insights.Key TopicsWhy the first wave of AI is endingWhat comes next for Enterprise AIBuilding companies during technology shiftsVenture capital perspectives on AI startupsEnterprise software trendsWhat investors are looking for todayLong-term opportunities in AIIf you enjoy conversations with world-class founders, investors and operators, subscribe to Liftoff with Keith.Sponsor Info: We are strategic business advisors with decades of leadership experience and a proven track record of driving businesses' growth. We specialize in creating custom-tailored strategies to introduce your company, drive growth, build leadership teams, and ensure companies implement appropriate compensation programs. Our mission is to utilize our expansive network to benefit your company https://www.compass-strategic-advisors.com/Connect with Clint Chao: Website: http://www.momentventures.com/ LinkedIn: https://www.linkedin.com/in/clintchao Subscribe for more founder insights and hit the bell for notifications! Follow us on our channels for exclusive startup content and behind-the-scenes insights from interviews like this one. Spotify: https://open.spotify.com/show/3cFpLXfYvcUsxvsT9MwyAD?si=f5a14e779777487d Apple Podcasts: https://podcasts.apple.com/ca/podcast/liftoff-with-keith-newman/id1560219589 Substack: https://keithnewman.substack.com/ Newman Media Studios: https://newmanmediastudios.com/ LinkedIn: https://www.linkedin.com/company/liftoffwithkeith If you enjoyed this episode, don't forget to Like, Subscribe and Share.

Interview with Sandy Bird, co-founder of Sonrai Security In this week's interview, we kick off the conversation with how Sonrai's expertise in securing cloud identity permissions had the company well placed to address the explosion of AI agents and the clear risks they represented. On the surface, this looks like a cloud/hyperscaler permissions challenge, but it isn't that simple. As agents like Claude Code, Codex, and Hermes are connected to enterprise cloud agents, the risk spreads outside VPCs and onto endpoints. Check out the episode to learn more about some of the most common risks Sandy finds and how Sonrai goes about addressing them. This segment is sponsored by Sonrai Security. Visit https://securityweekly.com/sonrai to learn more about them! Segment Resources AWS Bedrock agent permissions: what you need to lock down before you go live Making Enterprise AI Agents Accountable with Amir Ofek, CEO and Co-Founder of aizome Organizations looking to unlock the power of Enterprise AI Agents, and in a controlled and safe way at the speed of AI. Identity is at the heart of it. However, NHI Governance Is Not Enough for Enterprise AI Agents. The identity industry has responded to the rise of AI agents the same way it responds to every new identity challenge: extend existing frameworks. Map agents to human owners. Enforce least privilege. Govern them like non-human identities. It is a reasonable instinct. It is also insufficient in ways that matter enormously. Non-human identity security was built for a deterministic world - service accounts, API keys, bots. These identities do what they are configured to do. Their behavior is predictable enough that static governance models work. Enterprise AI agents are categorically different. Not in degree - in kind. They don't execute fixed instructions. They reason, plan, and adapt in response to context. Their scope shifts with every task. Their behavior at runtime can diverge significantly from anything true at provisioning time. Unlike any identity that came before them, they frequently change their intent, at a pace no governance model built for human movers or machine credentials was designed to handle. Wrapping them in the same framework you use for a service account isn't wrong. It's just insufficient in precisely the places where risk accumulates. Download the SANS AI Security Maturity Model eBook This segment is sponsored by aizome. Visit https://securityweekly.com/aizomeidv to learn more about them! The Human Authorized. The Agent Acted. Who's Accountable? Interview with Howard Ting - CEO - Opal Security A self-driving car still has a license plate The accountability didn't change just because the driver did. The same has to be true for AI agents, but most environments can't trace an agent action back through the layers of delegation to the human who authorized it. Howard Ting, CEO of Opal Security, joins Security Weekly to discuss what the accountability model looks like when employees run swarms of agents, and what has to be in place before that accountability chain is tested. https://www.opal.dev/resource-center/identity-governance-report-2026-ai-access This segment is sponsored by Opal Security. Visit https://securityweekly.com/opalidv to learn more about them! Next Evolution of Identity Security: AI for Lower Cost, Efficiency & Governance with Ajay Gupta - President & CEO - SDG Organizations have invested heavily in identity platforms, but many still struggle to maximize security, efficiency, and governance outcomes. As AI transforms both cyber defense and cyber threats, Identity Security is emerging as a critical foundation for securing human and non-human identities alike. In this discussion, we explore how AI is helping organizations reduce costs, improve operations, defend against AI-powered attacks, and address the governance challenges created by AI agents—highlighting the convergence of Identity Security, AI Security, and AI Governance. This segment is sponsored by SDG. Visit https://securityweekly.com/sdgidv to learn more about them! Visit https://www.securityweekly.com/esw for all the latest episodes! Show Notes: https://securityweekly.com/esw-466

While we take a break this 4th of July weekend, please enjoy this encore of AI Security Brief. Your enterprise AI strategy isn't as far along as you think. The reality for most organizations today is that AI is disrupting existing processes more than it's delivering outcomes… so far. And according to Dr. Grace Trinidad, Research Director at IDC, that's how it should be. In this episode, host Johnny Hand sits down with Dr. Grace to discuss how AI adoption follows the same pattern as almost every major digital transformation, and why this disruption phase we're in is messy, yet critically important.  What we cover: How history demonstrates that automation across industries created disruption well before delivering value Why your AI adoption strategy is much more than simple tool deployment What business and technology leaders need to consider as they integrate AI into operational workflows How token consumption and AI FinOps are the emerging security and cost risk How AI ontologies will be the next real business differentiator Why stick around:  If you've been wondering if your organization's AI adoption strategy is ahead of the curve, Dr. Grace will give you a much clearer picture of where you really stand. Episode resources: Dr. Grace Trinidad on LinkedIn Securing the AI Enterprise: 5 Key Steps for Business Leaders Closing the Governance Gap in Agentic AI ⁠Johnny Hand on LinkedIn TrendAI on LinkedIn About AI Security Brief AI Security Brief is where security and technology leaders come to get ahead. Join us for real conversations on the AI trends, threats, and decisions that can't wait. About TrendAI™ TrendAI™ empowers organizations to lead the future of AI with proactive security designed to inspire innovation and eliminate risk. TrendAI™. AI Fearlessly. Learn more about your ad choices. Visit megaphone.fm/adchoices

Clay Bavor is the Co-Founder of Sierra, one of the world's fastest-growing enterprise AI companies. Sierra is valued at approximately $15.8 billion, has raised more than $1.5BN from leading investors including Sequoia, Benchmark, Greenoaks, GV and Tiger Global, and today serves more than 40% of the Fortune 50. The company recently surpassed $150 ARR, making it one of the fastest-growing enterprise software businesses in history. AGENDA: 00:00 – Why Frontier AI Demand Will Be Unlimited 08:00 – Open Models vs Frontier Models: Who Actually Wins? 17:00 – China's AI Advantage & The Distillation Debate 20:30 – Inside Sierra: The AI Agents Running the Entire Company 24:00 – The $100,000 Token Budget Every Engineer Will Soon Need 29:00 – Building AI for 40% of the Fortune 50 37:00 – Why Forward-Deployed Engineers Are the Future of Enterprise AI 43:00 – Sierra's Unusual Board Meetings & Billion-Dollar Company Playbook 48:00 – The Four Values Behind a $16B Startup: Craftsmanship, Intensity & Family 56:00 – Clay Bavor's Hiring Philosophy, AI-First Teams & What's Coming Next  

Andrew Moore, CEO of Lovelace, former head of Google Cloud AI, and former dean of Carnegie Mellon's School of Computer Science, joins the podcast to discuss YottaGraph, a knowledge graph growing by a billion facts a week that serves as a context engine for enterprise AI agents. He explains why fully automatic knowledge graph construction is the only viable path at scale, why entity resolution remains a brutal engineering problem, and how graph theory tricks make million-node queries answerable in under a second.Subscribe to the Gradient Flow Newsletter

Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In his follow up appearance on the Code Story podcast, Tim Ebbers, Field CTO at Wallarm, discusses why detection alone is insufficient for AI-driven systems, what real enforcement looks like at the runtime level, and what accountability becomes possible once all four stages are in place.QuestionsSecurity teams are used to detecting incidents and responding after the fact. Why is that model insufficient for AI-driven systems?What does “enforcement” usually mean today, and why can actions like restarting pods or rotating credentials come too late?Why does AI behavior require controls that operate closer to runtime?What changes when enforcement happens at the kernel level rather than only at the network, identity, or application layer?Can you explain what it means to revoke or contain a compromised AI session without touching the broader deployment?How does real-time blocking change the risk equation for AI agents that access sensitive data, external services, or production workflows?What kinds of AI behaviors should organizations be able to stop immediately?How do teams balance strong enforcement with the need to avoid slowing down AI development and deployment? Once organizations can discover, observe, and enforce AI behavior, what does accountability look like at the enterprise level?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/tebbers/Full AbstractTim Ebbers, Field CTO at Wallarm, discusses why detection alone is insufficient for AI-driven systems, what real enforcement looks like at the runtime level, and what accountability becomes possible once all four stages are in place.Detection tells you what happened. It does not stop it. For most security incidents, that tradeoff is manageable. For AI systems that can access sensitive data, call external services, and trigger downstream actions at machine speed, the gap between detection and response is where the damage happens.The enforcement model most security teams operate today was built for a slower threat. Restarting pods, rotating credentials, and updating policies are all responses to something that has already occurred. Against an AI agent that can exfiltrate data, invoke a production workflow, or violate a compliance boundary in the time it takes to page an on-call engineer, that response model is not enforcement. It's cleanup.Closing that gap requires controls that operate at the layer where AI behavior actually executes, not at the perimeter, not at the identity layer, not at the application boundary. Kernel-level enforcement changes what is possible: a compromised session can be revoked by user identity or trace ID, connections can be terminated at the workload level, and enforcement can happen without a pod restart, a deploy cycle, or any impact to the broader environment. That is what it means to complete the AI control loop. Discover what is running, observe what it is doing, enforce what it should not be doing, and govern with evidence that the enforcement worked. Organizations that can only do the first two are solving half the problem.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

A Note from James:Mark Pincus is one of the true OGs of the internet. You probably know him as the founder of Zynga, the company behind FarmVille, Zynga Poker, and Words With Friends. Zynga was eventually acquired by Take-Two in a transaction valued at approximately $12.7 billion. Before Zynga, Mark started Tribe, one of the first social networks—before MySpace and Facebook. He has spent more than 25 years building, failing, and studying what gets millions of people to click, play, share, and come back. His new book, Life at the Speed of Play, inspired me to start coming up with new business ideas while we were still recording.What I really love is how Mark teaches people to copy like a master without looking like a copycat. He has a framework called “Proven–Better–New.” Start with something that has already been proven. Make it obviously better. Then isolate the new idea you want to test. It's one of the best systems I've heard for creating products people actually want.We talk about the early days of Facebook and MySpace, the failure of Tribe, the gaming industry, consumer psychology, AI coding, and how agents could eventually network and work for us while we're doing something else.I loved talking with Mark. I was still thinking about this conversation afterward—and I'm literally building businesses based on what I learned. His new book is called Life at the Speed of Play. Listen to this episode, and then read the book.Episode Description:Most founders begin with an idea and then spend months—or years—trying to prove that people want it. Mark Pincus thinks that process is backward.At Zynga, Mark's teams built “failure machines”: simple systems that allowed them to test hundreds of concepts before writing the code. They put unfinished ideas in front of real users, watched what people clicked, and refused to build anything until the demand was obvious. The objective wasn't to avoid failure. It was to make failure fast, cheap, and useful.Mark explains the framework behind that process: Proven–Better–New. First, study an existing success down to every screen, click, and design decision. Then identify one improvement that current users would immediately recognize as better. Only after that should a team add the unproven idea—the part most likely to fail.James and Mark also examine the problems facing today's consumer entrepreneurs. AI has made software easier to build, but distribution has become harder. People aren't searching for new apps, established platforms restrict organic growth, and algorithmic reach isn't the same as users actively sharing something with friends.Mark uses the failure of his early social network, Tribe, to explain why virality is not enough. Tribe grew quickly but lacked retention and trust. He ignored the communities users loved because they didn't match the business model he had already chosen. That painful mistake became the foundation for much of his later product philosophy.The conversation ends with Mark's current experiments: personal AI agents modeled after members of his family, a proposed work network built specifically for agents, an enterprise AI company called Hivemind, and the difficult decision to end a four-year passion project without abandoning the instinct behind it.This is a practical conversation about testing ideas, separating instinct from ego, learning from the past, and killing the wrong product before it consumes the right opportunity.What You'll Learn:How to build a failure machine: Test headlines, offers, videos, and fake doors before investing in a finished product.How to apply Proven–Better–New: Begin with a proven behavior, make one unmistakable improvement, and isolate the risky innovation.Why distribution is now harder than development: AI can generate a prototype quickly, but it cannot guarantee attention, trust, or adoption.Why Tribe failed despite rapid growth: Virality without retention, safety, and alignment with user behavior does not create a lasting network.How to copy without becoming a copycat: Study successful products at the pixel level, preserve what works, and innovate only where it matters.When to abandon an idea: Preserve the underlying instinct, but stop funding the particular expression of it when the evidence turns against you.How AI agents may change networking: Agents could eventually search for opportunities, exchange work, build reputations, and bring useful leads back to their users.Timestamped Chapters: [02:00] Finding the “OMFG” Moment [02:58] A Note from James [05:00] Build a Failure Machine Before Building a Product [06:25] Testing Demand With Fake Doors and Broken Links [08:08] Writing Copy That People Actually Notice [10:52] Test More Ideas in a Week Than the Industry Tests in a Year [11:53] Why Neglected Products Become Innovation Labs [13:26] How Mobile Apps Slowed Product Experimentation [15:09] Can AI Bring Rapid Testing Back? [17:08] Why Consumer Technology Feels Uninvestable [18:38] The 90/10 Rule for Investable Platforms [20:08] Why Nobody Downloads New Apps Anymore [21:20] Franchises, “Spicy New,” and Healthy Platforms [23:21] The Internet's Lost Cocktail Party [27:58] Why Tribe Failed While Facebook Won [30:26] Virality Without Trust or Retention [31:31] Ignoring What Tribe's Users Actually Wanted [33:22] Facebook, Raya, and Designing for Trust [35:03] Social Networks as Lead-Generation Engines [37:12] Facebook, Instagram, and the App Nobody Knew It Wanted [37:51] Net Promoter Scores and the Feeling of Quitting a Drug [40:25] Algorithmic Virality vs. People Sharing With Friends [42:00] Building Products That Help People Create [43:47] What Entrepreneurs Should Build With AI [44:54] The Proven–Better–New Framework [47:12] What “Obviously Better” Actually Means [48:25] Why “All New Fails” [50:23] Zynga Poker and the Power of Removing One Click [52:00] What AI Does Well—and Where Humans Still Matter [54:25] Picasso, Slack, and Copying the Past [55:11] Adding Fun to Boring Enterprise Products [57:39] The Moral Arbitrage of Killing Your Ego [57:58] How to Copy Without Looking Like a Copy [59:10] Why Old Internet Mechanics Keep Returning [01:00:16] Anonymous Social Apps With an AI Twist [01:01:17] Don't Invent a New Business—Reinvent a Big One [01:02:00] Test 20 Variants Before Building One [01:02:58] Mark's Frustrating Experiments With AI Coding [01:05:29] Creating a Personal Team of AI Agents [01:07:57] Killing a Four-Year Passion Project [01:09:29] The “Social Membrane” of the Agentic Internet [01:09:57] Building a Work Network for AI Agents [01:12:16] Hivemind and the Human Side of Enterprise AI [01:13:52] Missing Twitch—and Knowing Your Zone [01:15:06] Why the Gaming Industry Still Isn't Social Enough [01:16:30] Chess Ratings, Competition, and Mark's Daughter [01:19:19] Writing Life at the Speed of Play [01:21:18] Don't Chase Every New Technology Race [01:22:05] Final ThoughtsAdditional Resources:Mark Pincus and the BookLife at the Speed of Play — official websiteLife at the Speed of Play — HarperCollins — published June 23, 2026. Mark Pincus on X — the account Mark recommends for updates on his agent-network experiments. Mark Pincus on LinkedIn Mark's interview about open-sourcing Stem Studio Zynga, Games, and Product ExamplesZynga's company history — covers its launch as a Facebook poker project and the development of FarmVille, CityVille, and Words With Friends. Words With Friends FarmVille Take-Two and Zynga acquisition announcement — the transaction carried an enterprise value of approximately $12.7 billion. Tribe.net history — the early social network Mark analyzes as a major product failure. Raya — the private community Mark discusses as an example of building trust through curation. Grow a Garden on Roblox See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.