philosophical view that all events are determined completely by previously existing causes
POPULARITY
26 episodes with deterministic
23 episodes with deterministic
2 episodes with deterministic
3 episodes with deterministic
2 episodes with deterministic
2 episodes with deterministic
2 episodes with deterministic
2 episodes with deterministic
2 episodes with deterministic
Andrew Bowell, CEO of Iconic, who spent 15 years at Havok and a decade at Unity, discusses the future of game development, AI integration, and the challenges of building new game engines. He shares insights on technological shifts, AI's role in creating immersive worlds, and why his company is building an engine to “craft intelligent, living worlds”.https://iconicgames.io/02:10— The shift toward dynamic, emergent, personalized gameplay04:39— Why Iconic won't end up in the “engine graveyard”10:13— “Intelligent living worlds” explained16:12— Dogfooding and building the engine through its own game19:35— Deterministic vs open-ended gameplay23:11— What Unity got right about AI26:52— The real paradigm shift in gaming37:59— Player-first, not technology-first46:26— Where AI adoption in games stops today51:56— Remote vs hybrid culture at Iconic
Recorded live at New York Tech Week, Karl and Erum sit down with Brenton Alexander (CTO at Roebling) to unpack one of the biggest bottlenecks in scaling “biology as technology”: figuring out what it really takes to design and finance physical infrastructure. Brenton walks through how Roebling uses AI alongside deterministic engineering models (physics/thermodynamics) to accelerate early facility design, generate capex/opex estimates with uncertainty ranges (not false precision), and help teams run scenarios fast—so founders, investors, and operators can make better go/no-go decisions earlier, reduce wasteful iteration across siloed teams, and focus human expertise where it matters most.Grow Everything brings the bioeconomy to life. Hosts Karl Schmieder and Erum Azeez Khan share stories and interview the leaders and influencers changing the world by growing everything. Biology is the oldest technology. And it can be engineered. What are we growing?Learn more at www.messaginglab.com/groweverythingChapters:(00:00:00) Welcome to Grow Everything Live at NY Tech Week(00:02:10) The “infrastructure gap”: why feasibility work is slow and expensive(00:03:05) What Roebling does: accelerating the path from R&D to final investment decision(00:05:05) Live demo setup: building a yeast-based fermentation facility for a red bio-dye(00:07:15) What the platform decides (and why inputs matter): equipment, DSP, and cost drivers(00:10:00) “Why not just use Claude?” Deterministic models + AI tooling for defensible results(00:14:30) Handling uncertainty: ranges, distributions, and Monte Carlo-style scenario runs(00:18:40) What changes for engineers/consultants: shifting effort from manual work to judgment(00:23:10) Reading the outputs: capex/opex, IRR, and the “tornado chart” of uncertainty drivers(00:28:10) Audience Q&A: logistics/customer delivery, AI's impact on costs, review fatigue, and assumptions(00:29:30) Long-term direction: more fidelity, narrower bounds, EPC-ready handoff(00:30:05) Audience Q&A begins(00:30:30) Q1: logistics + customer delivery costs (not just “at the gate”)(00:32:55) Q2: how AI changes operating cost assumptions over time(00:34:15) Q3: review fatigue—how to structure checks and triage what matters(00:36:10) Q4: what did the model assume for “colorant”? (and why specificity matters)(00:38:15) Wrap-up + thank-yousLinks and Resources:RoeblingRoebling Early Access ProgramBrentan AlexandarEdward Shenderovich65. Scaling Cells, Dreaming Big: The Biomanufacturing Cloud with Synonym's Edward Shenderovich166. The Great Reformulation: Joshua Lachter Rethinks How We Make Everything at Scale172. Generating Needles in Haystacks: Elise de Reus Designs Proteins with CradleBioInnovations Events - For 25% off use code: Grow EverythingTopics Covered:Roebling, bioprocess modeling, techno-economic analysis, fermentation economics, food dyes, bio-based ingredients, process engineering, AI for biomanufacturing, scale-up planning, regulatory considerations, industrial engineering AI.Have a question or comment? Message us here:Text or Call (804) 505-5553Instagram / Twitter / LinkedIn / Youtube / Grow EverythingMusic by: Nihilore Production by: Amplafy Media
Fred Laluyaux has spent 25 years on the same problem: enterprises are drowning in decisions no human should be making. With 50 million digitized decisions across companies like Unilever, Exxon, and Hershey, he now has the data to prove it. When operators override the machine, performance goes down. Not sometimes — in aggregate, every time. In this episode, Fred breaks down the agentic vs. deterministic tradeoff most CIOs are getting wrong, why the software stack most companies rely on today is heading for collapse, and what a company whose entire stack is just SAP and Aera tells you about where enterprise software is going. Hit play. 3 Takeaways: After 50 million digitized decisions, the data is clear: when operators override the machine, performance drops. One Aera customer runs their entire operation on SAP and Aera. Nothing in between. That's where the stack is going. Fred calls them "born in digital" decisions — they can't be made by humans because the value is gone before the meeting starts. Chapters: [03:08] Fred's Career Journey and Lessons Learned [05:17] Why Aera Was Created [05:45] The Vision for a Self-Driving Enterprise [08:28] The Decision Memory Problem in AI [10:28] The Reality of AI ROI [11:58] From Analytics to Decision Intelligence [12:56] Humans vs Fully Autonomous Systems [15:28] What It Means to Digitize Decisions [18:42] How Aera Actually Works [22:42] Trust, Governance, and the Waymo Analogy [27:51] Deterministic vs Agentic AI [29:13] The Cloud Capacity Wake-Up Call [30:15] Where Aera Fits in the Enterprise Stack [31:54] Fast ROI and the “4-4-4” Framework [32:55] Why the Software Stack Is Collapsing [36:21] Delayering Organizations and New AI Roles [39:02] Born-Digital Companies and Micro-Decisions [43:57] Explainability, Governance, and Feedback Loops About Fred: Fred Laluyaux is Co-Founder, President, and CEO of Aera Technology, the leader in decision intelligence and creator of Aera, the first decision intelligence agent. An entrepreneur and Silicon Valley veteran, Fred brings an impressive track record building successful startups and driving technology innovation. Prior to launching Aera, Fred was the CEO of Anaplan, which he grew to a $1 billion valuation. He has held several executive positions at SAP, Business Objects, and ALG Software. As a thought leader on the future of work and host of the Decision Intelligence podcast, Fred frequently shares his vision with influencers through media interviews and speaking engagements at industry conferences. His views have been published in business and trade publications. A technology and startup advisor, Fred is an investor and active board member of several startups in the U.S. and Europe. Guest Highlights: "We're in 2026, and the reality is that our models have not changed for 100 years. We're still relying on people to decide how to forecast, how to allocate inventory, how to change a plan." "We've got enough data, I mentioned the 50 million decisions, to demonstrate that whenever the humans are touching the system and are messing with the recommendation, they actually degrade the performance." "The autonomy is not another version or better version of my planning tool or my replenishment tool. It replaces the need to have a human touch with that software, and therefore I don't need that software anymore." Get Connected: Ian Faison: https://www.linkedin.com/in/ianfaison Fred Laluyaux: https://www.linkedin.com/in/flaluyaux/ Our Sponsor: This episode is brought to you by Aera Technology. Enterprise AI has hit its stride. Across industries, companies are moving beyond pilots and proofs of concept, and into real, enterprise-wide results: better decisions, faster execution, and meaningful bottom-line impact. Aera's agentic decision intelligence is built to help you seize the opportunity. Aera dynamically composes decision flows using unified decision data and multi-engine orchestration to drive action at scale. It continuously senses what's happening across your enterprise, recommends and executes the best course of action within your transaction systems, and learns from every outcome to keep improving. Leading global companies are already using Aera across supply chain, inventory, logistics, and finance, delivering rapid ROI through reduced costs, lower working capital, and better customer outcomes. This is the self-driving enterprise. And it's here now. Visit AeraTechnology.com to book a demo Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Dr. Mark Grether, SVP and General Manager of PayPal Ads, joins Phillip from PayPal's Manhattan offices to argue that the merchant storefront is migrating off owned websites and into LLMs. This may make the mechanics of customer experience and loyalty a bit murky, but Mark explains how PayPal's "transaction graph,” built on real purchases across 30 million merchants and 400 million consumers, acts as the deterministic identity layer that the post-cookie ad world has been missing. We also cover the evolving world of commerce media, from zero-click commerce and CTV attribution to PayPal Ads' newest product, Storefront Ads, which transforms the creative into the checkout. The Cart Cartographer Key takeaways: Consumers now start product discovery on LLMs, not search engines or merchant sites. PayPal's transaction graph spans 30M merchants and 400M consumers, representing real purchases, not just clicks. Deterministic payment identity beats cookies and probabilistic IDs for cross-channel attribution. Storefront Ads turn any ad into a one-click, pre-populated checkout. Creators run two businesses: generating consumer data, then monetizing it. [00:04:03] "We're not just seeing behavior, we're actually seeing the real transactions. We know what people are purchasing — not whether they search for something or browse for something. We actually see what they are buying." – Mark Grether [00:11:00] "The trick about our identity is it was built from a finance perspective, meaning I need to understand that you are you and not your twin brother. Our identity has to clear a much higher bar compared to probabilistic IDs or cookies." – Mark Grether [00:13:40] "The idea of Storefront Ads is that the creative itself becomes the shop. You're getting exposed to the sneakers, and with one click, you can actually make the purchase. We already know who you are, we know your bank account, we know your address — everything is pre-populated. From a consumer perspective, it becomes super easy to finish a transaction." In-Show Mentions: PayPal's Storefront Ads Learn more about PayPal Ads Associated Links: Check out Future Commerce on YouTube Check out Future Commerce Plus for exclusive content and save on merch and print Subscribe to Insiders and The Senses to read more about what we are witnessing in the commerce world Listen to our other episodes of Future Commerce Have any questions or comments about the show? Let us know on futurecommerce.com, or reach out to us on Twitter, Facebook, Instagram, or LinkedIn. We love hearing from our listeners! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Independent hoteliers are under pressure from every direction, and now there's an AI mandate on top of it all. Wil sits down with Adam Harris, Co-Founder and CEO of Cloudbeds, to cut through the noise. Adam's argument: most operators aren't failing because they lack effort or the right tools. They're failing because they haven't defined the problem, and they're sitting on fragmented data that makes even the best AI useless. The takeaway: AI isn't the strategy. Better questions, better data, and better decisions are. This episode is presented by Cloudbeds. Connect with Cloudbeds at https://www.cloudbeds.com/gmh, and you can subscribe to Adam's newsletter here: https://www.cloudbeds.com/newsletter/ 00:00 Meet Adam Harris 01:37 The Operator Squeeze 04:04 When Tech Becomes a Burden 07:25 AI Starts With Questions 08:37 Deterministic vs Probabilistic 12:24 Cleaning Up Hotel Data 13:49 Cloudbeds Signals In Action 16:35 Autonomous Coding And Caution 18:21 Future Of Hospitality Systems 21:55 Closing Takeaway
Independent hoteliers are under pressure from every direction, and now there's an AI mandate on top of it all. Wil sits down with Adam Harris, Co-Founder and CEO of Cloudbeds, to cut through the noise. Adam's argument: most operators aren't failing because they lack effort or the right tools. They're failing because they haven't defined the problem, and they're sitting on fragmented data that makes even the best AI useless. The takeaway: AI isn't the strategy. Better questions, better data, and better decisions are. This episode is presented by Cloudbeds. Connect with Cloudbeds at https://www.cloudbeds.com/gmh, and you can subscribe to Adam's newsletter here: https://www.cloudbeds.com/newsletter/ 00:00 Meet Adam Harris 01:37 The Operator Squeeze 04:04 When Tech Becomes a Burden 07:25 AI Starts With Questions 08:37 Deterministic vs Probabilistic 12:24 Cleaning Up Hotel Data 13:49 Cloudbeds Signals In Action 16:35 Autonomous Coding And Caution 18:21 Future Of Hospitality Systems 21:55 Closing Takeaway
Winston Leung explains that QNX is enhancing safety for physical AI-based robots through its innovative microkernel architecture, which is designed for safety-critical applications. QNX architecture provides a reliable and deterministic platform, crucial for real-time control and decision-making in robotics. By partnering with industry leaders such as NVIDIA and Intel, QNX ensures its operating system is optimized for high-performance computing platforms, thereby enabling robust safety and security measures. Additionally, QNX's focus on integrating cybersecurity with functional safety standards underscores its commitment to protecting robots operating in human environments. Winston Leung is Senior Strategic Alliances Manager at QNX, where he manages key strategic partner relationships and programs to expand the company's product portfolio and ecosystem. He delivers strategies and thought leadership in functional safety, real-time performance, and reliability for embedded systems across robotics, medical, and transportation sectors. Download QNX's Inside the Robot: https://qnx.software/en/reports/inside-the-robot?utm_medium=podcast&utm_source=the-robot-report&utm_campaign=fy27-q2-inside-the-robot ### – SPONSORS – This episode is brought to you by GreyOrange If you're running a warehouse, your robots, people, and systems are only as powerful as their ability to work together. GreyMatter by GreyOrange is the AI-powered warehouse orchestration platform that coordinates every agent on your floor in real time, with over a million optimizations per minute, and delivering up to 4x productivity gains. GreyMatter works with the robots you already have, or with the ones you want. Ready to go beyond your WMS? LEARN MORE AT: https://www.greyorange.com/TheRobotReport/
After briefly de-emphasizing targeted TV ads during the Discovery merger, Warner Bros. Discovery has rapidly rebuilt its infrastructure to offer clients unprecedented transparency and accountability. In this live recording from the GoAddressable upfront breakfast, learn how premium IP content is joining forces with sophisticated data waterfalls to challenge the dominance of walled gardens. Key Highlights
In this Marketecture Live session, Keith Petri, SVP of Data, Identity, and Supply at Viant, and Sam Khoury of Marketecture Media discuss deterministic identity, supply path optimization (SPO), contextual targeting, attribution, and the challenges of measuring true advertising effectiveness in CTV. Learn why advertisers need proof, not promises, to maximize performance and incrementality. Takeaways - Deterministic Identity Requires Proof - Publisher Login Data Isn't Fully Available to Buyers - IP Addresses Are an Imperfect Identity Signal - Too Many Supply Chain Intermediaries Create Problems - Supply Path Optimization Is About Quality, Not Just Cost Savings - Identity and Context Must Work Together - Content-Level Context Remains Limited in CTV - Incrementality Is the Ultimate Goal - Identity Resolution Requires a Holistic View - Collaboration Across the Ecosystem Is Critical Chapters 00:00 Introduction and Session Overview 00:27 Why CTV Identity Is More Complicated Than Expected 01:43 The MacKenzie-Childs Case Study: The Ideal CTV Attribution Story 03:03 Why Publisher Data Doesn't Reach Buyers 04:25 What "Deterministic, Prove It" Really Means 05:35 Where Identity Breaks Down in Programmatic Advertising 07:23 The Real Purpose of Supply Path Optimization 09:05 Identity vs. Context: Why Both Matter 10:37 The Contextual Targeting Gap in CTV 11:58 The Measurement and Attribution Unlock 14:15 Advice for Advertisers and Buyers 16:00 Closing Remarks Learn more about your ad choices. Visit megaphone.fm/adchoices
Deterministic AI Sets the Roadmap for Safer Communications, ICA AI Podcast. Rather than sending every word of every conversation into a large language model, Christensen describes a model where much of the decision-making is based on known patterns, trusted relationships, keywords, context, policy, and call behavior. In sensitive verticals such as financial services, healthcare, legal services, and government, that can be especially important because communications may involve private data, personally identifiable information, account details, medical information, or other sensitive content By Doug Green “As AI gets more powerful, the question is not simply whether it can answer a prompt. The question is whether it can be trusted in the communications path,” says Gerry Christensen, associate founder of ICA AI. “For high-security communications, deterministic AI is not just different. In many cases, it is necessary.” In this Technology Reseller News podcast, Gerry Christensen of ICA AI joins Doug Green to define an important distinction that is becoming central to the future of AI-powered communications: probabilistic AI versus deterministic AI. The conversation is less about a single product announcement and more about setting out a roadmap. Christensen explains why most people experience AI through probabilistic systems, including large language models that generate answers based on patterns, probabilities and prompts. Those tools can be powerful, but they can also hallucinate, miss context, or create outputs that sound confident while being wrong. For communications providers, MSPs, UCaaS providers, MVNOs and telecom resellers, Christensen argues that this distinction matters because voice networks are entering an era where AI will be used on both sides of the call. Legitimate businesses will use AI in contact centers. Bad actors will use AI to scale fraud, spoofing, robocalls and deepfake-style attacks. Consumers and enterprises will increasingly need AI to help determine which calls should get through, which calls should be challenged, and which calls should be blocked. ICA AI, short for Intelligent Communications Assistant, is built around that problem. Christensen describes the platform as an AI-based assistant that can support outbound calling and, perhaps more importantly, inbound call handling. The goal is to allow trusted calls from colleagues, friends, family and legitimate businesses to pass through, while filtering unwanted or suspicious calls. The core idea is determinism. Rather than sending every word of every conversation into a large language model, Christensen describes a model where much of the decision-making is based on known patterns, trusted relationships, keywords, context, policy and call behavior. In sensitive verticals such as financial services, healthcare, legal services and government, that can be especially important because communications may involve private data, personally identifiable information, account details, medical information or other sensitive content. Christensen gives the example of a financial services call. A probabilistic AI system might need to listen broadly and process the conversation through an LLM to determine intent. A deterministic system, by contrast, can look for specific markers of trust or risk: whether the caller is known, whether the call matches expected behavior, whether suspicious phrases appear, or whether the interaction moves toward unusual requests such as gift cards, new account instructions or other red flags. That approach, Christensen says, also has implications for cost, latency and scale. If most decisions can be made deterministically, the system does not need to rely on a distant AI data center for every interaction. That can reduce exposure of sensitive data, lower dependency on token-heavy AI processing, and support faster call-handling decisions. Christensen says ICA AI's approach relies on deterministic AI for roughly 85% to 95% of transactions. He connects that idea to Zipf's Law, the linguistic principle that a relatively small portion of language often carries much of the meaning. In communications, that means many call-handling decisions may not require open-ended AI interpretation. They may require the right data, the right rules, and the right deterministic understanding of what matters in the moment. The roadmap Christensen lays out is not anti-LLM and not anti-probabilistic AI. Instead, it is a layered model. Probabilistic AI can still be used when needed, especially when a conversation falls outside known patterns or requires deeper interpretation. But for high-security, high-volume communications, Christensen argues that deterministic AI should carry more of the load. For MSPs, channel partners and telecom providers, the message is direct: AI call management may become a new category of value-added service. As agentic AI increases the volume and sophistication of automated calls, enterprises and consumers will need tools that can help them determine whether a call is authentic, legitimate and safe. Christensen compares the coming environment to an arms race. AI will make fraud more scalable, but AI can also make communications more defensible. The providers that begin testing, integrating and understanding these capabilities early may be better positioned to offer customers a practical answer to a growing trust problem in voice communications. “Everybody is going to need to have an AI-based solution for consumers to handle inbound calls,” Christensen says. “In the world of agentic AI, it is conceivable that networks could be plastered with AI-generated calls.” Learn more: ICA AI: https://icai.ai/
Graphiant Founder and President Khalid Raza explains why the AI era demands a new approach to connectivity, one built on deterministic infrastructure, observability, sovereignty, and automation rather than overlays. As AI traffic shifts east-west and agents operate everywhere, can existing IP VPN infrastructure evolve into the programmable AI fabric enterprises need? In this Executives at the... Read More The post Deterministic Networks: Rebuilding the AI Backbone appeared first on Mplify Alliance.
Tokenization. Context windows. Lost in the middle. Silent failures. RLHF. Anthropomorphism. Quantization. Top-P and Top-K. RAG. Deterministic checks.If you haven't heard of some of these topics, this podcast episode is for you.
Network automation has been "coming soon" for over a decade. So what's actually different this time? John Capobianco, Head of AI & Developer Relations at Itential, built NetClaw — a CCIE-level AI agent that manages network infrastructure through Slack and WhatsApp. It hit 300 GitHub stars in two weeks. It can analyze packet captures, configure routers, run compliance tests, and generate documentation — all through natural language. John spent 15 years as a network engineer before becoming one of the leading voices in network automation. He's published multiple books, created dozens of open-source projects, and just launched the VibeOps community where 600+ network engineers share AI code without judgment. Key takeaways: • Why natural language is the breakthrough that makes network automation finally work (hint: nobody has to learn Python anymore) • The 5 use cases beyond config management that deliver value on day one — all read-only, all low-risk • How to go from human-in-the-loop to fully agentic network operations without triggering panic • Why "shadow AI" is the new shadow IT — and what leadership needs to do about it • The contrarian case that writing configs by hand is now a solved problem Guest: John Capobianco — Head of AI & Developer Relations, Itential LinkedIn: linkedin.com/in/john-capobianco-644a1515 X/Twitter: @John_Capobianco NetClaw: github.com/automateyournetwork/netclaw VibeOps Forum: Reach John on LinkedIn or X for invite Chapters 0:00 Why AI Is Different for Network Automation 2:32 Natural Language: The Interface That Changes Everything 3:51 "The Network Should Be Like a Telephone" — Why Engineers Resist Change 6:08 The No-Win Life of a Network Engineer 8:08 OpenClaw: More GitHub Stars Than Linux 10:15 What NetClaw Actually Does (90 Skills, 43 MCPs) 11:37 The RFC Documentation Problem AI Can Solve 13:03 Day One Agent Rules: Start Read-Only 13:58 When Was the Last Time We Hired a Junior? 15:54 How NetClaw Hit 300 Stars in Two Weeks 19:54 Deterministic vs Non-Deterministic: Getting Engineers Over the Hump 23:36 War Stories: Fat Fingers, MTU Issues, and the DNS Nightmare 28:32 Documentation: The AI Use Case Nobody Can Argue With 32:34 Beyond Config Management: 5 AI Use Cases That Matter Now 36:00 The IDS/IPS Analogy: Why AI Agents Succeed Where Signatures Failed 40:02 AI Hallucination Is Overstated — Misalignment Is the Real Problem 41:53 Model Convergence: Why the Stuff Around the Model Matters More 46:00 Shadow AI Is the New Shadow IT 47:59 What Happens When AI Understands Your Business Context 53:59 The Optimistic Case for AI and Humanity 56:05 VibeOps: Building a Safe Space for AI-Curious Engineers 1:00:36 Is Vibe Coding Just Coding Now? 1:01:54 "Don't Write the Configs Anymore" 1:02:43 Closing & Where to Find John -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
At Google Cloud Next 2026, Finout co-founder and CEO Roi Ravhon and Google Cloud FinOps lead Pathik Sharma discussed how FinOps is rapidly evolving for the AI era. Ravhon argued that while cloud FinOps had a decade to mature, AI economics are forcing the industry to adapt within a year. Unlike traditional cloud workloads, AI costs are unpredictable because token usage varies even for identical prompts, while advanced reasoning models consume significantly more tokens despite falling prices. Both emphasized that effective AI FinOps requires intelligent orchestration, routing workloads to the cheapest capable models instead of defaulting to expensive frontier models. Sharma noted that AI costs extend beyond APIs to GPUs, storage, training, and organizational adoption. They also cautioned against relying solely on LLMs for operational automation. Deterministic systems, observability metrics, and human approvals remain essential guardrails. Ultimately, both stressed that FinOps is primarily an organizational and cultural discipline, recommending newcomers start with the FinOps Foundation before investing in tools. Learn more from The New Stack around the latest in FinOps: Why FinOps Isn't About Saving Money FinOps Foundation's FOCUS 1.2 Expands to SaaS, PaaS Join our community of newsletter subscribers to stay on top of the news and at the top of your game.
Business analysis is having a moment—and if AI feels overwhelming, this conversation might just change how you see your role.In this episode, I'm joined by speaker, author, and LinkedIn Learning instructor, Angela Wick, to unpack what may be the most underestimated role in AI-driven transformation: business analysis. Building on her recent article about the evolving role of business analysis in agentic AI, we explore how our work is shifting—from requirements and handoffs to decision-making, orchestration, and true partnership across business and technology.We talk candidly about what's actually changing (and what isn't), why AI demands our involvement earlier than ever, and how long-standing analysis skills suddenly matter more—not less—in a probabilistic, agent-driven world. If you've ever felt boxed into “requirements gatherer” territory or unsure where you fit as AI accelerates, this conversation is meant to spark confidence, clarity, and maybe even a little excitement about what's next.
Today, we have a special guest on the Code Story podcast - Patrick Vuong, Director of Product at Moderne. Moderne is the agent tools company, building the. Knowledge, discovery and execution tools that AI agents rely on - so they can operator faster, more accurately, and at far lower cost.In today's episode, Patrick is going to tell us about the company, and how Moderne is enabling developers to build software faster, and with the best context - using agents and agent tools. Their approach to semantic models produce deterministic over probabilistic, or inference driven, tools, which for this engineer/host, has been a point of skepticism for AI since the beginning.QuestionsTell me and my audience a little bit about you.What is Moderne?Moderne is enabling developers to operate software systems at the speed of agents. Tell me about this product suite.Why do Agents need tooling? Where do we see AI in ROISomething jumped out at me... you mentioned you are not only building tooling for agents that are deterministic.As we peer into tech stacks across the industry, where does Moderne fit?OK so this is clearly a pivot for Moderne. With this, who are your customers now?What does the future like for your product - what you offer - and your team?For you personally, you are entering into a new chapter with Moderne. What makes you most excited, going from Microsoft to entering the startup world with the company?In your journey, who has influenced the way you work? Tell me about a person, or many persons, or something you look up to and why.So you worked at Microsoft for 8 years, and are now transitioning to Moderne. Say you were getting on a plan and sitting next to someone about to make this same transition - what advice would you give them?SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://www.moderne.ai/https://www.linkedin.com/in/vuongpatrick/Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* 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
By Doug Green “Deterministic is more black and white, and we feel that it's a much better approach, not only from a scalability and cost perspective, but also it has a lot greater efficacy.” In this CCA podcast, I spoke with Gerry Christensen, associate founder of ICA AI, about the company's approach to AI-driven communications and the growing interest it is seeing following the MVNO show in Miami. The conversation offered a useful look at how ICA AI is positioning itself in a crowded AI market by focusing on a more structured and predictable model for communications technology. Christensen began by explaining that ICA stands for Intelligent Communications Assistant. At its core, ICA AI is a technology and infrastructure company applying AI to communications in a way that is designed to be practical, scalable, and dependable. Rather than leaning on the probabilistic models that dominate much of today's AI conversation, Christensen said the company is focused on deterministic AI. That distinction is central to ICA AI's message. Christensen described deterministic AI as more “black and white,” arguing that it provides clearer and more reliable outcomes than systems based primarily on probabilities. In his view, that creates important advantages not only in cost and scalability, but also in overall effectiveness. For communications environments, where trust and accuracy matter, that difference can be significant. The point becomes even more relevant in industry verticals where privacy and security are essential. Christensen cited areas such as financial services and healthcare, where organizations need communications technologies that can operate with a higher degree of certainty and control. In those settings, AI is not simply about automation or novelty. It must support real business processes while meeting serious operational and compliance expectations. The discussion also reflected growing market interest in ICA AI's approach. Coming out of the MVNO show in Miami, Christensen suggested that the company is seeing momentum as service providers and industry participants look for practical AI solutions that fit within real telecom infrastructure. That is an important signal in a market that is still working to separate useful, deployable AI from broader hype. What makes ICA AI's story worth watching is that it points to a different framing for AI in telecom. The opportunity is not just to make systems more automated. It is to make communications systems more trusted, more predictable, and better aligned with the requirements of industries where errors and ambiguity carry real consequences. This podcast continues an important conversation about where AI is headed in telecom and why the next phase may be defined less by flashy claims and more by dependable outcomes.
Autonomous software development creates a dilemma for leaders in regulated industries: adopt AI coding at scale or fall behind on product velocity without compromising auditability and code quality. In CXOTalk episode 917, Kris Tokarzewski, Group Chief Technology Information Officer at Vitality, describes how a 14,000-employee multinational insurer is rebuilding its software development life cycle around AI. This episode examines the impact of agentic AI on software development in the enterprise.Recorded at Blitzy's headquarters, the conversation examines deterministic code generation, Blitzy's infinite code context, context engineering, test-driven development, and the shifting bottlenecks that surface as throughput accelerates.YOU'LL DISCOVER✅ Why regulated industries require deterministic, auditable code rather than the probabilistic output most AI coding systems generate✅ How Blitzy's infinite code context (ingestion of codebases, engineering standards, and business rules) creates high-quality software aligned with compliance requirements✅ How Vitality reverse-engineers legacy systems with autonomous AI, achieving a measured 5x acceleration over manual methods✅ Why optimizing end-to-end SDLC throughput matters more than local efficiency at any single stage✅ How code review of 50,000 to 100,000-line pull requests becomes the next limiting factor, and how AI reviewers close the gap✅ How test-driven development pairs with autonomous code generation to raise quality and compliance pass rates✅ How the roles of requirements engineers, software engineers, and product teams converge inside an AI-native SDLC✅ How to instrument AI spend against velocity, quality, end-to-end throughput, and customer value rather than isolated gainsTIMESTAMPS0:00 Deterministic code vs. probabilistic AI output0:14 Meet Kris Tokarzewski, Group CTIO of Vitality0:32 Why Vitality is modernizing legacy insurance systems1:30 Event-driven architecture as agentic AI's natural partner3:00 Building an AI-native software development life cycle with Blitzy4:28 Throughput optimization versus local efficiency6:02 Reverse engineering legacy systems and deterministic code generation9:05 Infinite code context: ingesting codebases, standards, and rules10:00 Test-driven development with autonomous code generation10:49 Results: 5x faster legacy reverse engineering13:17 Product, engineering, and DevOps convergence15:04 Roles level up: requirements engineers and software engineers16:18 Reviewing 50,000 to 100,000-line pull requests17:56 Instrumenting AI spend against business outcomes19:16 Executive sponsorship for autonomous development20:16 Advice for CIOs and CTOs adopting AI-driven development
Healthcare AI has a trust problem—but not the one most people are talking about. Many AI-enabled clinical products lack a structured, validated clinical knowledge layer, leading to outputs that drift and can't be reliably trusted or acted on. In this episode, David Lareau, President and CEO of Medicomp Systems, explores the rise of deterministic AI and why it's becoming essential infrastructure for healthcare innovation. The conversation covers what's missing in today's AI stack, how a clinical knowledge layer enables more consistent and explainable results, and what leaders should look for when evaluating AI solutions.
Nerd alert. The main topic is a discussion on deterministic v. probabilistic models in the current betting environment. News includes the solider who bet on himself to capture a foreign leader and made $440k before getting arrested and a rant on the people complaining about FandDuels injury insurance.0:00 Deterministic v. Stochastic Sports Models26:00 News1:05:15 Q&A Welcome to The Risk Takers Podcast, hosted by professional sports bettor John Shilling (GoldenPants13) and SportsProjections. This podcast is the best betting education available - PERIOD. And it's free - please share and subscribe if you like it.Follow SportsProjections on Twitter: https://x.com/Sports__ProjFollow GP on Twitter: https://x.com/goldenpants013
Host: Andrew Birmingham, Editor - CX | Martech | Ecom Banks, telcos, and insurers are rethinking how they engage customers, shifting away from mass marketing campaigns toward real-time decisioning systems designed to respond to individual behaviour, according to Jonathan Tanner, a senior executive at Pegasystems. Tanner said many organisations still struggle with fragmented customer experiences, where interactions across channels are disconnected and force users to repeat themselves. “They get a very jarring experience,” he said, pointing to structural issues such as product silos and outdated segmentation models that fail to reflect how customers’ needs change over time. The emerging alternative is a decisioning approach that continuously evaluates customer context, including behaviour, signals and lifetime value, to determine the next best action. Unlike traditional campaigns, which Tanner described as a “blast approach” delivering only marginal returns, these systems aim to personalise interactions in the moment, sometimes choosing not to sell at all. “What we’re talking about here is a very different approach,” Tanner said. “It may not even be a selling decision at that point in time… but over time what that does is it builds that NPS, it builds that customer connection.” The shift requires a willingness to invest and the change. Firms are committing to significant investments annually over several years to build the underlying infrastructure. While returns can reach “multiple hundred percent,” Tanner said the gains depend on sustained investment and organisational change, not just technology deployment. “You’re not going to just wake up, implement this technology, and then suddenly discover that everything’s great,” he said, noting that many firms underestimate the effort required to align people, processes and systems. Artificial intelligence is central to the transformation, but Tanner warned against treating it as a single solution. Instead, organisations need to combine multiple approaches, including rules-based systems, statistical models and generative AI, each suited to different tasks. “If I’m making a decision that’s backed up by a set of very well-defined rules, why would I be hammering away at an LLM spending tokens… and getting a probabilistic decision?” he said. Deterministic systems, he added, remain critical for real-time execution, compliance and auditability. The stakes extend beyond marketing. Financial institutions are also using decisioning platforms to combat fraud, which is rising alongside real-time payments. Faster transactions benefit customers but also give fraudsters less time to be detected. “One of the best ways of preventing it is to add just a little bit of friction into the process,” Tanner said, citing examples such as delaying payments to new accounts. More broadly, Tanner said the most effective use cases focus on building trust rather than driving immediate sales. Examples include helping customers access government benefits or providing proactive support during financial hardship or natural disasters. “The obvious immediate reaction is, well, how can that possibly be a benefit to the bank?” he said. “But of course… it’s building customer loyalty… it’s building connection.” Looking ahead, Tanner expects the industry to move beyond the current hype cycle around AI and focus instead on practical outcomes. “I’d like to see us moving to it being more of a system-based conversation,” he said, where value is measured not by the technology itself but by the decisions it enables in real time.See omnystudio.com/listener for privacy information.
Most enterprises are excited about agentic AI. But very few are actually deploying it in production. In this episode of Eye on AI, Craig Smith sits down with Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere, to break down why agentic AI is so hard to get right in the enterprise and what it actually takes to move from a promising pilot to a mission-critical deployment. Adi explains why the future of enterprise automation is not agentic AI alone, but the combination of deterministic and agentic systems working together, and why companies that treat AI as a technology problem instead of a business outcomes problem are setting themselves up to fail. They dig into how Automation Anywhere is orchestrating agents across legacy systems, healthcare platforms, and financial services workflows, why governance and compliance are the first questions every enterprise asks, and how their Process Reasoning Engine is continuously improving agent performance using metadata from over 400 million running processes. The conversation also covers the real timeline to a fully autonomous enterprise, why the POC to production gap is the biggest failure point in enterprise AI today, and what companies that wait too long risk losing to competitors who started the journey earlier. If you want to understand where enterprise AI actually stands today and what it takes to deploy it responsibly at scale, this episode gives you a clear and grounded perspective. Subscribe for more conversations with the people building the future of AI and emerging technology. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Why Enterprises Are Struggling With Agentic AI (02:39) What Automation Anywhere Does and the APA Category Explained (08:01) Deterministic vs Agentic AI: Why You Need Both (10:59) How Human in the Loop Works in Enterprise AI (17:16) The Mozart Orchestrator and Process Reasoning Engine (23:50) How AI Is Upgrading and Replacing Classic RPA (27:31) How Automation Anywhere Works With Enterprise Customers (31:53) The Biggest Challenges of Scaling Agentic AI (41:10) The OpenAI Partnership and What It Means (47:06) Training Staff and Building AI Literacy at Scale (51:39) Staying Close to Customers as the Technology Shifts (53:17) Is the Autonomous Enterprise Actually Coming
Get ready for a big day of racing at Keeneland!
Get ready for a big day of racing at Keeneland!
Explore how the "cookie apocalypse" evolved into a hyper-fragmented identity landscape where iPhone users, cookieless browsers, and diverse CTV signals have created massive monetization gaps for the unprepared. I sit down with Intent IQ's Fabrice Beer-Gabel to reveal why the future of programmatic advertising isn't a choice between deterministic or probabilistic data, but a high-stakes race to balance scale with the 99% accuracy required to prevent AI from amplifying inaccuracies at scale. Episode Takeaways:
The role of the software engineer is shifting from execution to orchestration, and it's happening faster than most of us realize. Dennis Vink, Principal Consultant at Xebia, breaks down how he approaches code modernization with AI, why fundamentals and system design matter more now than ever, and what the engineering role is actually becoming.In this episode, we cover:Why you need to mature your old codebase before you can migrate away from itHow to prove feature parity between legacy and modern systemsWhy vibe coding without architecture knowledge gives you zero controlThe shift from execution-focused engineering to orchestrationWhy Dennis worries about the next generation of engineersWhether you're sitting on legacy code at work or wondering how your role as an engineer is evolving, this conversation will make you think about where you need to invest your time next.Timestamps:00:00:00 - Intro00:00:51 - Dennis's Early AI Engineering Assignments00:02:23 - Side Projects: Reviving a 20-Year-Old Game in Rust00:04:36 - Why Vibe Coding Without Fundamentals Fails00:05:15 - The Fundamentals You Need for Code Migration00:06:45 - Proving Feature Parity with Automated Testing00:08:12 - Writing Tests First as Risk Mitigation00:10:13 - How Much Should You Care About Code Structure?00:11:18 - Migrating in Small Pieces of Value00:12:26 - Will Engineers Still Find Fulfillment in Building?00:14:01 - How to Actually Start Side Projects (ADHD Brain)00:15:34 - Why Pivoting Is No Longer Painful00:16:12 - Prompting as the New Bottleneck00:17:23 - Parallelizing Work Across Projects00:19:08 - Why System Design Is the #1 Audience Demand00:20:19 - AI as a Differentiator for Strong Architects00:21:11 - Why the New Generation Should Worry00:23:01 - Are Bootcamps Still Worth It?00:25:15 - The Shift from Collaboration to Business Understanding00:27:56 - Infrastructure as a Core Competency Bet00:30:15 - Deterministic vs Non-Deterministic Code Generation00:32:16 - Can This Approach Scale to Million-Line Codebases?00:34:20 - Why a Finger-Snap Migration Would Scare You00:37:01 - Where to Start with Your Own Legacy Codebase00:38:43 - Which Languages Do AI Models Struggle With?00:40:24 - Building Around Hallucination with Scaffolding00:42:30 - Spec-Driven Development as the Future Way of Working00:43:30 - Turning a Non-Technical Colleague into a "Developer" in an Hour00:46:21 - When the House Is on Fire, That's When You Need Real EngineersProjects we discussed:Agent designer - hurozo.com Game project - Zorlore.com (https://github.com/zorlore/)Vibe coded solar system simulation - spacehaste.com #SoftwareEngineering #SystemDesign #AIEngineering
Epicenter - Learn about Blockchain, Ethereum, Bitcoin and Distributed Technologies
In this episode, host Friederike Ernst is joined by Alex Svanevik, CEO of Nansen, to explore the platform's radical pivot from passive on-chain analytics to active, AI-driven agentic trading. Alex unpacks the technical hurdles of labeling over 500 million addresses, the transition from raw data into harmonized insights, and why true alpha now lies in attribution rather than raw data . He explains how Nansen uses ClickHouse databases and a mix of algorithmic heuristics, agentic teams, and human specialists to maintain the highest industry precision. The conversation dives deep into the intersection of LLMs and blockchain, exploring how standard AI models lack domain-specific common sense and why Nansen augments them with real-time data and visual "artifacts". Alex introduces "Nansen Gym," a simulated historical replay environment for training trading agents and teases the upcoming release of "Smart Money 2.0", which aims to predict future profitable addresses with 2-3x uplift on precision. Finally, they discuss the existential risks of AI, the striking parallels between open-source AI and early DeFi, and why Alex believes agentic trading will be the absolute default by 2028. Chapters00:00 Intro & Context04:15 Nansen's Evolution & Agentic Trading09:30 Harmonizing Data & The Attribution Layer15:00 Deterministic vs. Inferred Labeling (Uniswap vs. Binance)21:45 Evaluating AI Agents: LLMs as Judges27:10 User Privacy & Public Blockchain Realities35:20 Building a Unified Trading OS42:15 Smart Money 2.0: Predicting Which Wallets Win49:00 The Limitations of Vanilla LLMs in Crypto55:30 Nansen Gym & Time-Traveling AI Agents59:45 The Open Source AI vs. DeFi Parallel LinksAlex Svanevik on X: https://x.com/ASvanevikNansen: https://www.nansen.ai/NEAR: https://near.ai/Sponsors:NEAR AI Cloud now lets developers deploy OpenClaw—the rapidly growing open-source AI agent platform—inside Trusted Execution Environments, providing hardware-level encryption with cryptographic attestations. With OpenClaw on NEAR AI Cloud, you can run agents with cloud convenience, but without traditional cloud data exposure. No hardware to manage. No trust assumptions required. Learn more at near.ai.
We each spent the week on our own projects, breaking then fixing things. Now we're back to compare progress, and a few lessons learned.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:
Mastering Ecosystem Growth and AI Transformation Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this episode, Vince Menzione sits down with Rebecca Jones, Chief Growth Officer of Bridge Partners, to deconstruct the “Power of Three” co-selling model and the shift from AI experimentation to scalable business outcomes. They explore the critical importance of customer-centricity, the role of agentic workflows in solving complex B2B problems, and why the most successful leaders prioritize progress over perfection to show momentum within weeks rather than years. From her background in the financial sector to her experience scaling with industry titans like Microsoft, Rebecca provides a masterclass on navigating the current “tectonic shifts” in technology through strategic alignment and executive commitment. Key Takeaways Bridge Partners focuses on connecting strategy to execution, boasting a 90% referral rate driven by deep expertise in product marketing and partner ecosystems. The market is shifting from mere AI “dabbling” to purposeful applications in MVP and scale, specifically through agentic AI that tackles real business problems. Success in today's landscape requires knowing your underlying value and maintaining an unwavering focus on customer-centricity. The “Power of Three” (Hyperscaler, GSI, and ISV) remains the ultimate design for go-to-market scaling, provided there is a clear joint value proposition. To show immediate momentum, new executives should focus on “quick wins” achievable within six to eight weeks rather than long-term three-year plans. Effective co-selling requires removing blockers like compensation misalignment and securing top-down executive sponsorship across all leadership silos. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. https://youtu.be/nClWjCm6S6A At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Rebecca Jones, Bridge Partners, Chief Growth Officer, co-selling, Power of Three, Hyperscaler, GSI, ISV, SAP, Microsoft, agentic AI, AI experimentation, pipeline velocity, pre-sales workshops, account-based marketing, ABM on steroids, GTM strategy, executive sponsorship, partnership ecosystems, B2B growth, tech industry trends 2026, Ultimate Partner, Vince Menzione, orchestration, value proposition. Transcript Rebecca Jones Audio Episode [00:00:00] Rebecca Jones: Because most of the agents I’ve seen drop into um, a lot of the areas where you and I can download are features. [00:00:07] Vince Menzione: Yes, [00:00:08] Rebecca Jones: they’re really feature agents. I love where we are ’cause we’re starting to tackle real business problems. [00:00:17] Vince Menzione: We just finished Ultimate Partners Winter Retreat here in beautiful Boca to a sold out crowd. Today I’m joined by Rebecca Jones, the Chief Growth Officer of Bridge Partners for this compelling discussion. Rebecca, welcome to the podcast. [00:00:33] Rebecca Jones: Thank you, Vince. [00:00:34] Vince Menzione: I am so thrilled to have you in Boca in the studio. [00:00:37] Vince Menzione: We’ve been working together now for a couple of years. We [00:00:39] Rebecca Jones: have, [00:00:40] Vince Menzione: and yesterday we were at the Ultimate Partner live executive winter retreat here in Boca. Uh, we’re recording in late February, early March timeframe. And, uh, just it was so thrilling to have everyone in the room yesterday. [00:00:55] Rebecca Jones: Was it? I mean, the energy. [00:00:56] Rebecca Jones: It was amazing. [00:00:57] Vince Menzione: Yeah, [00:00:58] Rebecca Jones: it was amazing. And thank you so much for having me. I mean, Florida’s gorgeous this time of year. It’s nice to get outta Seattle. [00:01:04] Vince Menzione: Well, it’s, it’s always, I, I, we, we love Seattle. Yes, we love, we do love to be in Seattle and especially in the spring, which we’ll be there together. We’ll talk about that in a little bit, but, um. [00:01:14] Vince Menzione: This is our first time actually having an interview. I mean, we’ve had you on stage. Yes. We’ve had Bridge as a part. Bridge Partners has been a partner. It’s ultimate partner. How’s that? And, uh, you’ve led some workshops. You help organizations to be successful and I thought just like to start out like, tell us more about you. [00:01:32] Vince Menzione: Yeah, bridge Partner and your role at Bridge Partners. And, uh, just to frame, to frame the conversation today. [00:01:40] Rebecca Jones: Okay. Of course. So let me tell you a little bit about my background. Um, I’ve been in the technology industry for a few decades now, and I started within the product and go to market, side of the house. [00:01:54] Nice. [00:01:54] Rebecca Jones: And I’ve navigated across a number of functional areas. From product to partner and sales. [00:02:02] Vince Menzione: So product development, [00:02:04] Rebecca Jones: engineering, [00:02:04] Vince Menzione: product marketing. Product marketing. [00:02:05] Rebecca Jones: Product marketing. [00:02:06] Vince Menzione: Yeah. [00:02:07] Rebecca Jones: Yes. And so when you look back on the areas of where I focus my time, it’s really how do you help customers grow and how do you help companies grow? [00:02:17] Rebecca Jones: Um, and a lot of my background is in B2B. [00:02:20] Vince Menzione: Very cool. [00:02:21] Rebecca Jones: Yeah. [00:02:21] Vince Menzione: And where’d you get your start? [00:02:23] Rebecca Jones: I started actually in the financial sector. [00:02:26] Vince Menzione: Very cool. [00:02:27] Rebecca Jones: Yeah, [00:02:27] Vince Menzione: very cool. That’s, well, that’s a good grounding and [00:02:30] Rebecca Jones: it’s an excellent grounding. And when you look back, and when I look back at what that provided as a foundation, it’s really the economics of a business and how do you help a business and what are the trend lines behind that by industry and and whatnot. [00:02:45] Rebecca Jones: And so I moved from that over to. More agency view, and so the real market facing view and then back inside to really look at how companies develop their products and bring ’em to market. [00:02:56] Vince Menzione: That’s an exciting, well, I think it’s exciting. I hope our listeners and viewers think it’s exciting and I know Bridge Partners because when I was at Microsoft, we worked with Bridge Partners. [00:03:06] Vince Menzione: But for the listeners and viewers that are with us today, maybe a little bit of background about the company and its, and its structure and go to market. [00:03:13] Rebecca Jones: Yeah, of course. So Bridge Partners is almost 20 years old. [00:03:18] Vince Menzione: Wow. [00:03:19] Rebecca Jones: Wow. [00:03:19] Vince Menzione: Yeah. [00:03:19] Rebecca Jones: Can you believe it? [00:03:20] Vince Menzione: We were newbies when I was working with you. [00:03:22] Rebecca Jones: We, we were newbies and uh, the company was really founded on the principle of how do you connect strategy to execution. [00:03:32] Rebecca Jones: And within that, our first customer was Microsoft. [00:03:36] Vince Menzione: Interesting. [00:03:37] Rebecca Jones: Yeah, yeah, yeah. Uh, and that was an incredible spot to be and an incredible time to be in a company that started to evolve and grow with one of the titans in the industry. And obviously a incredible market leader in the tech industry. [00:03:56] Vince Menzione: Well, and that time 20 years ago, ’cause I was, I was along for that journey. [00:03:59] Rebecca Jones: Yeah. [00:04:00] Vince Menzione: Uh, it was a time of tumultuous change at Microsoft. [00:04:03] Rebecca Jones: Yes. [00:04:04] Vince Menzione: Uh, in fact, we were talking about the, uh, entrepreneur’s dilemma earlier, uh, today, and Microsoft was going through that period where, you know, we, everyone loves Steve Bomber, but there was a time within the organization that it was stuck. [00:04:18] Rebecca Jones: Mm-hmm. [00:04:19] Vince Menzione: And it had to transform as an organization. [00:04:22] Rebecca Jones: A hundred percent. And so when you think about companies like Microsoft, it’s not only what they do, but how they bring that to market. Yep. And uh, so when you think about where Bridge Partners started and having the privilege to be in Microsoft of all places to, um, cut your teeth on you look at where we started and where we’ve grown from there. [00:04:44] Rebecca Jones: Uh, within the tech industry, we’ve worked across, um, multiple hyperscalers. We’ve worked across, uh. Really the top tier tech and telco, those top 100. Yep. And all the household names. And then throughout that, across the partner ecosystem, because you and I both know these companies grow and scale their businesses through the partner ecosystem, and so we’ve been privileged to work across. [00:05:08] Rebecca Jones: Multiple depth and breadth partners in that play. [00:05:12] Vince Menzione: And as an agency, are you more known for project management go to market? Uh, what, what are the areas and focus where the outcomes that you achieve? [00:05:21] Rebecca Jones: Yeah, so we’re known for. Being on the growth side of the house. And how I define that is you find us in marketing, but that center of gravity is in product marketing. [00:05:32] Vince Menzione: Yes. [00:05:32] Rebecca Jones: And then how you scale that through partner ecosystems and then supporting that field or that sales organization. So when you think about those three pillars within the organization, that’s where you’ll find us. [00:05:43] Vince Menzione: And why would I choose Bridge Partners? [00:05:46] Rebecca Jones: Oh, well, um, based on experience. Um, and then when you think about Bridge Partners, it’s not, um, just what we do, but when you take a look at our engagements and background, we’re over 90% referral. [00:06:01] Vince Menzione: Wow. [00:06:02] Rebecca Jones: And so people take us with them and um, what I look at is have we actually moved the needle or driven the customer outcomes? And when you think about the customers that we’ve worked with and the companies in this industry. It’s quite a roster and I don’t take that lightly because if you’re going to help support these companies and help them grow, it’s a testament to how we were able to accomplish that. [00:06:27] Rebecca Jones: Because all these companies have complex enterprise organizations. Their go to market is nuanced and how they want to, and then, um, get and grow. And so these are just a couple of the different ways that we’ve been able to be successful. [00:06:42] Vince Menzione: Fantastic. You know, you’ve done workshops at our events and talked to our community about how to help them achieve their greatest results. [00:06:50] Vince Menzione: What would you say to them? Now we’re living in this time? I, I I, I said this earlier, I don’t want to use the term tectonic shifts, but I’m running out of words to describe how tumultuous this time feels right now to me. [00:07:03] Rebecca Jones: It’s interesting you say that. I was thinking about that. ’cause both you and I have been in the industry for a bit. [00:07:08] Rebecca Jones: Yeah. And, um, there’s some pattern recognition happening right now for me and how I look at the go to market and these, these points in time and the evolution and. This point in time, it is a tectonic shift. But a lot of companies have other, have had to go through these challenges before. If you think about, um, the migration to the cloud and [00:07:33] Vince Menzione: yes, [00:07:33] Rebecca Jones: all of the unlocks that it has, and at the end of the day it’s, it’s shifting and thinking about new business models and it’s shifting and thinking about go to market, but there is. [00:07:43] Rebecca Jones: There are things that ring true no matter where you are. And one of the things I’ve always taken a look at is, do you know your underlying value and relevance in market? And are you being customer centric? That never goes outta style, right? Do [00:07:58] Vince Menzione: you know your value and are you customer centric? That makes a lot of sense, right? [00:08:02] Vince Menzione: Yeah. And do they, what do you do? And, and do they, how do what, how do they answer to that question? [00:08:07] Rebecca Jones: Well, that’s a, that’s a thinking question. Yes. Right? Yes. It takes a minute to think about that. Um, where is your moment of relevance with a customer? [00:08:16] Vince Menzione: Yeah. [00:08:17] Rebecca Jones: Where is your moment of relevance with a customer? [00:08:19] Rebecca Jones: And when you think about your reason to exist as a business, you have a really defined ICP, an ideal customer profile, and where’s your moment of relevance and. Yes. There’s a lot happening right now, and I think also because of where we sit in the industry and being in the midst of all of these giants with incredible technology to bring to market. [00:08:44] Rebecca Jones: Yeah. We’re, we’re in the front end of this wave or the, the, the tectonic shift that you’re talking about. It’s just, you know, it’s unsettling to a certain degree, but it’s really energetic and it’s. Dynamic and, and there’s so much opportunity out there. So [00:08:59] Vince Menzione: much so, you know, you had me thinking about the $600 billion that’ll be invested this year and just in cloud infrastructure and chips, right? [00:09:08] Vince Menzione: Yeah. So data centers and chips, and talk about that being like kind of creating this wave, this huge tsunami that’s coming for the beaches and, and everything seems to be. Every week there’s a new announcement, and recently it’s been philanthropic and clawed. And yes, uh, the markets are reacting. They’re, um. [00:09:30] Vince Menzione: They’re almost, uh, imploding in some ca in some cases because they’re trying to react the financial analysts, they’re trying to react to what’s happening right now. [00:09:38] Rebecca Jones: It, the investment is massive and it’s, it’s incredible and it’s massive. And over the last year, you saw a lot of experimentation. Yeah. And you saw a lot of dabbling, a lot of, you know, quite. [00:09:52] Rebecca Jones: Frankly, a little bit of concern about is this gonna pay off? [00:09:56] Vince Menzione: Yes. [00:09:57] Rebecca Jones: And when you look at where we are in this chain cycle and this adoption cycle, we’re right at the front end, the early adopters. And so a lot of the work that we’re doing, and where I’m focused on is how do you move from experimentation? To truly having some movement over into MVP and scale. [00:10:18] Rebecca Jones: And so I’ll just harken back to Yeah, [00:10:19] Vince Menzione: please. [00:10:20] Rebecca Jones: That product mindset of when you’re looking at opportunity within the business, there was a lot of, um, there was a lot of pockets of experimentation just for fun. Just for fun. And so when you look across the business, um, and what, what we observed was, um, businesses of all different sizes, experimenting and, and some were just, they’re fun, they’re dabbling, right? [00:10:45] Rebecca Jones: But it, it changed in the second half of last year, people became much more thoughtful, much more purposeful, um, thinking forward about how would this be applied to my business? Yeah, because the question now isn’t. Could we do this? It’s really, should we do this [00:11:03] Vince Menzione: right? And and there was a period of time, I don’t mean to interrupt you, but there was a period of time when we were talking about earlier in in last year, we were talking about halluc hallucinations still. [00:11:13] Vince Menzione: Yes. So there was a lack of confidence on the platform side. Yes. Microsoft had brought out. Uh, it’s copilot solutions early to market. And there was some, uh, pushback from the community saying, we’re not seeing the results of that. Yeah. From the financial community specifically. And then I think what you said is then the second half of the year things started to change. [00:11:35] Vince Menzione: There was greater confidence. The [00:11:36] Rebecca Jones: Yeah, [00:11:37] Vince Menzione: I’d say the models got better. [00:11:38] Rebecca Jones: The models got better. But when you think about innovation, that’s inherent risk, [00:11:43] Vince Menzione: right? [00:11:43] Rebecca Jones: Right. Yes. When, when you’re on an innovation curve, yes, that’s risk. And so you have to look at as any great CFO will tell you diversification innovation. [00:11:56] Rebecca Jones: When you start to look at that market landscape, you’re creating risks. Yes. So they’re investing a lot and they wanna know when the payoff is coming back into the business. Right? Or back into the market. [00:12:08] Vince Menzione: So Rebecca, where is the AI market right now? [00:12:13] Rebecca Jones: Oh, that is a tough and great question, Vince. [00:12:18] Vince Menzione: I mean, we’ve gone through it and I’ll, I’ll kind of frame this for, yes, for, for everyone, at least from my perspective of what’s happened, right? [00:12:24] Vince Menzione: So, uh, September, 2022. Chat, GBT. Yeah. So we get into chat bots or chat bot, chat bot, chat bot, chat bot the first year or so, beginning of last year, 2025. A agentic AI really starts to take hold. It’s, it becomes a new term. In fact, I don’t think we were even using the term agentic AI before the end of 24, beginning of 25. [00:12:47] Vince Menzione: And then agents have really proliferated, um, all of the marketplaces now have agents and people are developing their own agents and so on. And all the tools, like all, all the cloud tools have agent capabilities. And now, um. We’re in 2026 and we’re still in the first quarter. It feels like the agents are starting to rule the world and maybe taking over the world [00:13:10] Rebecca Jones: they might be. [00:13:11] Vince Menzione: Yeah, [00:13:11] Rebecca Jones: right. There is definitely a proliferation of agents and I’m anticipating a lot of consolidation of that. ’cause most of the agents I’ve seen drop into, um. A lot of the areas where you and I can download are features. [00:13:26] Vince Menzione: Yes. [00:13:26] Rebecca Jones: They’re really feature agents and those will get consolidated ’cause the where we are and you ask where we are in the market. [00:13:33] Rebecca Jones: What I love. I love where we are ’cause we’re starting to tackle real business problems. And what I’m observing and what we’re working on is really helping connect back into the business to really start that transformational work. [00:13:48] Vince Menzione: So take us through that. I’d love that. I’d love, give us a scenario or [00:13:51] Rebecca Jones: give us a use case. [00:13:52] Rebecca Jones: Do this. Yeah. I think’s really great scenarios here that I can walk you through. And first and foremost it is, and I’m gonna go back and I talked about specialization in specialty areas. Yes. That’s really important. Um, we talked yesterday during the conference around, um, industry. What industry are you in? [00:14:11] Rebecca Jones: You know, I’m in tech and that’s, that’s, we know that industry, we know those business models really well. That’s extremely important. And then you move within that. And what functions do you know and functions in this, you know, order are the product marketing function, how does that work? [00:14:30] Vince Menzione: Yeah. [00:14:30] Rebecca Jones: How does that work in an enterprise organization or a sales function or a. [00:14:36] Rebecca Jones: Partner function. And within that, what are all the workflows? How do these teams operate together? And so that’s where that curiosity comes in of not just how you did the work. How is the work orchestrated? [00:14:49] Vince Menzione: Inter orchestration is a huge topic area. [00:14:51] Rebecca Jones: Orchestration is a huge topic. Let’s, let’s go [00:14:53] Vince Menzione: there. [00:14:54] Rebecca Jones: E Exactly. [00:14:55] Rebecca Jones: And that’s where that curiosity, you know, I was talking about pattern recognition comes in how is the work designed? And that becomes. The blueprint for how you start to think about agentic workflows. And if you don’t have a great workflow, you don’t wanna replicate that in an agent, but Exactly. You definitely need to understand that. [00:15:18] Rebecca Jones: And so why don’t I take something that, um, I think will resonate for anyone listening to this podcast, because everyone is probably looking for growth this year and wanting to accelerate [00:15:28] Vince Menzione: Yes. [00:15:29] Rebecca Jones: Sales. Their pre-sales funnel. So if we just take that pre-sales motion and specifically now with where partners might play in that or where, um, technology companies might want to enable their partners better. [00:15:47] Rebecca Jones: When I start to break down a pre-sales function, you have areas within that. Whole workflow that your marketing department might be driving. They might be driving top of the funnel or or demand programs. And then as you move down the funnel, let’s call it mid funnel, that really has opportunities for partner and field sellers to come in and. [00:16:07] Rebecca Jones: You might be seen or observing that your, um, pipeline velocity is not where you want that, right? Mm-hmm. You might be, you know, as they say, stuck. Stuck. [00:16:18] Vince Menzione: Yep. [00:16:19] Rebecca Jones: And so when you start to look at what agents could do within that, I’ll use a real use case, um, around pre-sales workshops. You and I are both familiar with that. [00:16:28] Vince Menzione: We, we are, we were just talking about this last night, in fact, at dinner, about pre pre-sales workshops and how this is still such a vital component, how organizations work together. [00:16:37] Rebecca Jones: Such a vital component, um, for multiple reasons, right? You get to engage directly with the customer. You get to spend time with that customer. [00:16:46] Rebecca Jones: You get to ensure you understand what are their most pressing use cases and really help them design and buy into a solution far before you get to a proposal. And quite frankly, if you do this right. You also have an adoption plan, and then think about it from other functional areas in the organization. [00:17:02] Rebecca Jones: You start to pattern match across those presale workshops. You can start to see the use cases that are most valuable in market and start to put that into your messaging. So you think about presale workshop, it’s just not the activity of having a workshop, but if you could build an agent. To really help design around partners, enabling partners to deliver better presale workshops. [00:17:27] Rebecca Jones: Interesting. And how are you ingesting information that goes into the workshop? How are you helping, um, develop materials and first drafts faster for proposals post? How are you. Data is informing this. What are you collecting and what are you providing, and then what are you delivering? If you take that one simple component in a pre-sales process, you can see where I’m going. [00:17:53] Rebecca Jones: Yeah. All of a sudden, an ecosystem starts to show up around how could you connect better back with product marketing? What are they doing? What could you inform them with, with the data that you’re bringing in? [00:18:03] Vince Menzione: Interesting. [00:18:03] Rebecca Jones: And then what are the. Deterministic pathways outside of that, that you could be informing downstream down to first, first stress faster on proposals. [00:18:13] Rebecca Jones: Are you helping those partners with an adoption plan? The service partners in there. And so that is the designer and the architect of understanding how that workflow comes to life. And then you can really start to think about the outcomes that you wanna drive. And that’s where I love to start the conversations. [00:18:31] Rebecca Jones: That shouldn’t be an afterthought. That should be where you start. [00:18:35] Vince Menzione: So how do you, how do you, how do you start with this? You gave me a great example, but how do you apply this in the business? Like what do you take when you meet with a client to talk about pre-sales workshops as an example? [00:18:47] Rebecca Jones: Yeah. [00:18:47] Vince Menzione: You take a proforma of what a pre-sales workshop would look like. [00:18:51] Vince Menzione: I’m, I’m, I. I might be wrong on this, but you have, like, you, you now have, uh, AI or AI that they go out and pull the data that you would normally ask maybe in some, some, uh, process, uh, information flow process that we grab and, and pull this into the, to the, to the form. The [00:19:10] Rebecca Jones: first question I always ask is, why. [00:19:12] Rebecca Jones: Why is this so important and valuable? I might have an assumption why, based on my experience, but I want the facts, right? I wanna know how they’re measuring it today, so we have a baseline and I wanna understand what their goals are. [00:19:28] Vince Menzione: Okay? [00:19:29] Rebecca Jones: Are they looking to increase revenue? X percentage. Uh, how many deals are they anticipating? [00:19:38] Rebecca Jones: How many presale workshops do they typically deliver through partner a year? Are they looking to scale that? Probably, yes. Are they looking to increase the value that they’re getting into contract post presale workshop? Probably yes. But I want that empirical data. And then I also wanna know where are they storing that? [00:19:57] Rebecca Jones: Where are they sourcing that? And so it, it really. The question and the question set really is understanding the business outcomes and the why. I, I ask a lot of why, and it really helps you frame in what would be the best outcome or the best solution, and then where do you start? Because there’s a lot of appetite for a. [00:20:21] Rebecca Jones: A transformational workflow from A to Z. And that’s a hard place to, [00:20:26] Vince Menzione: it’s hard show momentum. It’s hard. It’s hard, [00:20:27] Rebecca Jones: right? [00:20:27] Vince Menzione: It’s, it’s hard to document your current workflow flows. [00:20:30] Rebecca Jones: Yeah. [00:20:30] Vince Menzione: Let alone come back and do this ally. [00:20:33] Rebecca Jones: Yes. [00:20:34] Vince Menzione: And create the best outcomes. [00:20:36] Rebecca Jones: Yes. [00:20:36] Vince Menzione: So I go back to this and I go, well, what, what creates the best outcomes? [00:20:39] Vince Menzione: Where the customer signs at the dotted line, and then how do you work back from that to the pre-sales workshop? Is that how [00:20:46] Rebecca Jones: you do it? A hundred percent. It’s a hundred percent. And then where do you start? How do you show, um, progress, not perfection. And so in this world, there’s a lot of, um, pressure. To show progress, outcomes, momentum. [00:21:00] Rebecca Jones: Yeah. And these very significant investments that are being made. And so how do you get them to quick wins? And so you know this, for any new executive coming into role, what are your quick wins? Yes. Right? Yes. You need to transform an organization, you need to transform a function. How do you set them up for success? [00:21:19] Rebecca Jones: And that’s always in my mind, that’s always in the mind of. The bridge partners, leaders of how do you set this leader up for success? And it’s that point between strategy and execution. How do you help them show quick wins? And so I broke you down that process. Yep. Of how would you think about in that use case, how to bring that back and help them show quick wins? [00:21:42] Rebecca Jones: Not in six months or a year, but in six weeks to eight weeks. How do you, how do you get them on that journey and then help them build to that next slide. And [00:21:51] Vince Menzione: in fact, that’s how you, you, you’ve made your, your name or your fame in the industry is really coming in and helping some of these executives, especially when they’re newer in role. [00:22:00] Rebecca Jones: Yes. [00:22:00] Vince Menzione: And those of us who’ve been around the Microsoft ecosystem know this well. Like you get asked day one, what’s your plan? The, while the fire, while the fire hose is blowing in your face at a hundred, a hundred miles an hour? Uh, what’s your plan? [00:22:14] Rebecca Jones: What’s your plan? What’s your [00:22:14] Vince Menzione: plan? [00:22:15] Rebecca Jones: What is your plan? [00:22:16] Vince Menzione: Yeah, yeah. [00:22:16] Vince Menzione: And then you have to show some measurable results fairly quickly. [00:22:19] Rebecca Jones: You have to [00:22:20] Vince Menzione: because you’re asked to get up in front of everyone. Yeah. Very soon. [00:22:23] Rebecca Jones: And that’s a blueprint that we have. We have, it’s a quick win. And when you think about all of these organizations that we’ve worked with, um, speed to market is a value signal. [00:22:36] Vince Menzione: Yep. [00:22:36] Rebecca Jones: Right? And that speed and quality. Where are you willing to take the risk? Where are you willing to fail fast? And what outcomes are non-negotiable and what are, and so when you look at that, there’s, there’s conversations that need to be had on. And being able to filter out the noise to get down to what’s really gonna move the needle, um, for our clients and for the executives that we work with. [00:23:06] Rebecca Jones: So they can show momentum and progress quickly. And then we talked a lot about it. We don’t do three year plans, right? We’re gonna help you show progress in months, [00:23:16] Vince Menzione: nice. [00:23:17] Rebecca Jones: And in quarters, right? It’s not, um, 10 years. [00:23:19] Vince Menzione: Can anybody even have a three year plan anymore? [00:23:22] Rebecca Jones: Who’s got one? [00:23:23] Vince Menzione: I’d love to spend some time on co-selling with you. [00:23:25] Vince Menzione: Yeah. Just because I know this was a topic that came up one of our workshops in the Yeah. We hosted, yes. Last year we hosted a session. With another partner. Bridge Partners. [00:23:34] Rebecca Jones: Yes. [00:23:35] Vince Menzione: And you talked about the power of three and I know you’ve published some information about the power of three. I thought maybe we’d talk about that. [00:23:41] Vince Menzione: ’cause I think that is fascinating and it seems very relevant even in yesterday’s conversation. Uh, there was a conversation about another partner, uh, that is looking to build an ecosystem that hasn’t really thought about building out an ecosystem before, as an example. And this, this, I think is some of the work that you do really applies against this. [00:24:01] Rebecca Jones: Yeah. This, I mean, it, it’s a hot topic, right? Yeah. Power of three, which fits under the umbrella of co-sell Yes. And co-selling. And everyone has a slightly different definition, so I’ll define where we play. Good in there. Um, and then I’ll talk to you about the power of three, um, because that’s one of. Um, I’ll call it the scenarios under co-selling. [00:24:23] Rebecca Jones: Yes. And it’s a very popular one. It [00:24:24] Vince Menzione: is pop Well, it is for v various reasons too because, and I’ll just set the context for this. We were used to co-selling being a technology organization and a and a hyperscaler, like a Microsoft. [00:24:37] Rebecca Jones: Yes. [00:24:37] Vince Menzione: Going to do something together and driving direct output or sales. Now we have finally seen where marketplaces, which has become the co-sell engine, have now enabled the channel. [00:24:49] Vince Menzione: Um, the reseller enabled, uh, offers now to now, uh, operate on behalf of, and so at least in that case, that’s three right there. Now, there might be more than just three. We talk about the seven seats of the table, but the power of three is palpable right now. [00:25:04] Rebecca Jones: Yeah. Let me tell you about that concept of the power of three. [00:25:07] Rebecca Jones: ’cause when you think about the classic one [00:25:10] Vince Menzione: yeah, [00:25:10] Rebecca Jones: it’s a hyperscaler. [00:25:11] Vince Menzione: Yep. [00:25:12] Rebecca Jones: A GSI. And then an ISB. [00:25:15] Vince Menzione: Yes. [00:25:15] Rebecca Jones: Right? [00:25:16] Vince Menzione: Yes. [00:25:16] Rebecca Jones: I mean that’s the, that’s the power, the powerful power, the three three, [00:25:19] Vince Menzione: the three giants in the [00:25:20] Rebecca Jones: room. The three giants. Yeah. And that’s rarefied air. [00:25:24] Vince Menzione: It is [00:25:25] Rebecca Jones: very [00:25:26] Vince Menzione: verified air. It’s, [00:25:26] Rebecca Jones: yeah. Right. And, uh, we do, we have a published article on that, um, and running a power three with SAP, uh, and it is, um, it changes the dynamics. [00:25:41] Rebecca Jones: Of how companies are gonna scale and grow in this market, right? [00:25:46] Vince Menzione: Yes. [00:25:46] Rebecca Jones: Because we know, um, that what got you to this point? Is likely not gonna get you to that next stage of growth. And all the conversations around the platform play is the partner ecosystem, right? And I look at the opportunity, not just with the power through, I’m gonna talk to you a little bit more about that story and what we’re doing there and how we’re looking at that. [00:26:12] Rebecca Jones: Um, but it is the ultimate. Design for your go to market. Yeah. When you think about how partners and the various types of partners can help you scale, but you need to know what you need. You absolutely need to know, [00:26:29] Vince Menzione: yeah. [00:26:30] Rebecca Jones: What are you trying to achieve in your go to market and what’s missing? [00:26:34] Vince Menzione: What are the gaps? [00:26:34] Vince Menzione: Gaps? [00:26:35] Rebecca Jones: What are the gaps? Are the gaps before you apply? Yes. The power of three, or I’ll talk to you about a couple other use cases within that. So the power of three. Has long been on everybody’s, you know, can, can we get this done right? Can you pattern match the customer set? I’ll often refer to it as a BM on steroids, account-based marketing and on steroids. [00:26:59] Rebecca Jones: Can you pattern match, um, the, the hyperscaler, let’s just use Microsoft in this scenario, the, the. High potential customers of Microsoft Joint with SAP joint, with A GSI. And the more specialized and specific you get in there, it’s not just any, because think about the size of these, you know, companies. Yeah, right. [00:27:24] Rebecca Jones: Then you start to look at, well, let’s get a little bit more specific on these product sets, these industries, these use cases. And then you start to refine that where you can start to identify your greatest opportunity for growth. So that’s the first stage of that. And it is, you know, we, we think about where is that overlap and where is that opportunity, but how do you activate that? [00:27:51] Vince Menzione: And it’s complex because, uh, as you, as you mentioned those three. Organizations, each of them have different go to markets. [00:27:59] Rebecca Jones: They do, [00:27:59] Vince Menzione: they have different, a different mapping of their geographies and their ideal customer profiles. [00:28:05] Rebecca Jones: Mm-hmm. [00:28:06] Vince Menzione: Um, and they, yeah, and they apply different tactics and selling tactics and channel tactics and so on that you have to layer in or you have to take into account when you build this. [00:28:15] Vince Menzione: And SAP’s a very different go-to market motion than a Microsoft, than a, than a, an EY or any name the GSI percent. Yeah. [00:28:23] Rebecca Jones: And so that is why not only is it, um, complex from a. Sharing and figuring out what data you’re going to share. Yeah. But how do you activate it? How [00:28:35] Vince Menzione: do you activate it? [00:28:36] Rebecca Jones: And uh, and that is what all companies are striving to do. [00:28:41] Rebecca Jones: Who are you gonna go to market with? Yeah. What is your best play in the industry? And so I, you know, while this one. There’s very few companies that are gonna be able to activate directly with the hyperscaler, right? Yes. Uh, Microsoft AWS or Google. Um, but there are ways in which you can apply this strategy no matter the size of your organization. [00:29:05] Rebecca Jones: And so when you think about. The power of three. It could be any combination. You are the designer, you are the decider of who is in your power of three. And when you start to kind of unpack that a little bit, it could be Microsoft, SAPN one ISV, or it could be a combination of complementary I ISVs that unlock a play. [00:29:28] Vince Menzione: Mm-hmm. [00:29:29] Rebecca Jones: Like migration to the cloud. [00:29:31] Vince Menzione: Right. [00:29:31] Rebecca Jones: Like it, it could be [00:29:33] Vince Menzione: backup and recovery. I could rattle off the different types of solutions. Yeah. [00:29:37] Rebecca Jones: What is, where are you seeing the greatest opportunity to scale and what ISVs could come in to help you do that? So when you extract that from the power of three, the classic power of three of Costone, you brought that down to, you know, how do you think about that in the masses of marketplace? [00:29:56] Rebecca Jones: Yeah. Or partners of any size. I like to bring this back to. Where do you believe your greatest opportunity is? Do you have, um, opportunity or weakness in your portfolio, your product set? Could a partner come in and help augment that? Do you have a tech platform and you need a services arm to help extend that? [00:30:19] Rebecca Jones: I I mean the, it it, the world’s your oyster. Yeah. You get to kit this together any way you need and then. The power of bringing these companies together. And you and I both know, and that was much of the conversation yesterday, is, um, the greater goodness of companies coming together Yes. To compliment one another to solve a customer problem. [00:30:39] Vince Menzione: How do you take it from concept to execution? Because to me, that’s. Especially when you’re talking about not just one organization like a micro, you’re working with a Microsoft or an SAP, but you’re layering in three types of organizations and you’re going across different sales motions. How do you get them all? [00:30:58] Vince Menzione: How do you get them all aligned in working together the right way? [00:31:02] Rebecca Jones: Magic. Magic. [00:31:03] Vince Menzione: Okay. [00:31:04] Rebecca Jones: I’m kidding. [00:31:04] Vince Menzione: Call bridge, call Rebecca [00:31:07] Rebecca Jones: Magic. [00:31:07] Vince Menzione: Nine nine nine five five five five. [00:31:09] Rebecca Jones: Let, let, let me, uh, let me talk about that because [00:31:13] Vince Menzione: Yeah, [00:31:13] Rebecca Jones: it’s one, there’s the good work, there’s the good thought work and the strategy of how to ensure you’re, you’re pointing and you’ve got the team lined up, right? [00:31:22] Rebecca Jones: Right. And the players lined up. But activation of that. Oh, [00:31:28] Vince Menzione: massive work. [00:31:29] Rebecca Jones: It’s massive work. Yeah. And it’s not a set it and forget it. [00:31:33] Vince Menzione: Right, [00:31:34] Rebecca Jones: right, [00:31:34] Vince Menzione: right. [00:31:35] Rebecca Jones: And when you think about the alignment, and you talked about we, we’ve got different fiscal year ends and we’ve got different sales and center plans. I will talk about a few things. [00:31:45] Rebecca Jones: One, executive sponsorship, top down. [00:31:48] Vince Menzione: Yep. [00:31:48] Rebecca Jones: Right. Um, ensuring, you know, compensation. You gotta get rid of the blockers and the barriers. [00:31:55] Vince Menzione: Yep. [00:31:56] Rebecca Jones: And you have to make it easy and you have to create that space because it’s really, and I’ll talk to you about some of the platforms and technology behind it, but it’s humans working together. [00:32:07] Rebecca Jones: There’s a lot of power in what we’re able to do now with, um, part tech platforms and with agentic solutions. And how do you automate this and how do you bring more power and visibility? Better than ever and, and more than ever. But at the end of the day, we’re activating teams. Across companies. Yep. To work together to bring this together. [00:32:34] Rebecca Jones: And there are playbooks, um, and any, there’s great playbooks out there, but you need to activate that. [00:32:41] Vince Menzione: You need to activate it. And you, you said you gotta get the executive commitment at the top? [00:32:45] Rebecca Jones: Yeah. [00:32:46] Vince Menzione: Not just at the CEO level, but across the leadership team. That’s right. In every silo. Uh, you’ve gotta get, uh, the organization, you have to get compensation taken care of because those, those can be blockers, those could be real blockers from getting the results you want to get. [00:33:00] Vince Menzione: And then you gotta get activation. [00:33:03] Rebecca Jones: Yeah. [00:33:03] Vince Menzione: Right? [00:33:04] Rebecca Jones: You gotta get activation and you have to be really clear on how you’re gonna activate what’s gonna move the needle. And you have to be ready to test, learn, optimize, and you need to put those into sprints. So I’ll give some examples around that. [00:33:20] Vince Menzione: Please do take us through the sprints. [00:33:21] Vince Menzione: ’cause this is, this is getting beyond the theory now. This is what I really wanted to capture with you. Take us through it. [00:33:28] Rebecca Jones: Yeah. [00:33:28] Vince Menzione: Yeah. [00:33:29] Rebecca Jones: So let’s just say we’ve got, we’ve got a power of three. [00:33:32] Vince Menzione: Yeah. [00:33:32] Rebecca Jones: You know, um, ready to roll and, and we’ve picked our industry and we have our use case. Um, between the three of us, the three players, you’re gonna start by allowing someone, and in this case it’s been Bridge Partners to really ensure we have a joint value prop, um, proposition for that end customer. [00:33:54] Rebecca Jones: Mm-hmm. And, you know, you gotta take a little ego out of the room. Typically on the power of three, you’ve got the leading companies coming in. But at the end of the day, if you’ve done this right, it’s, it’s customer first. It’s what’s gonna help solve this customer pain point in that language. And then when you think about activation, it’s who’s, who’s in role first? [00:34:20] Rebecca Jones: Right. And who’s taking point in these customer conversations. Right. Okay. And that is really, really, that’s important. Important. That is important. Who has the relationship? Yeah. Who is going to take lead and who’s gonna follow? And it gets all the way down to whose paper. Is this on? And that’s, that’s sometimes hard. [00:34:41] Rebecca Jones: You’ve got three players in the room, but it’s incredibly important to have those conversations and ensure that this is really end state for the customer. Yeah. So really going through roles and responsibilities and how are we gonna architect this for the customer’s success. Yeah. So that is a critical component of the playbook and then understanding. [00:35:02] Rebecca Jones: Where and what programs are we gonna drive, and then who’s taking what actions. And so I, I mentioned a BM on steroids a little before. Yes. There’s amazing things that you can be doing in market, [00:35:14] Vince Menzione: account-based marketing, [00:35:15] Rebecca Jones: m account-based based marketing, you dunno. Um, account-based marketing and there are some amazing things. [00:35:20] Rebecca Jones: Really truly connected sales and marketing, in this case. Connected sales, marketing and partner. Yeah. And how do you activate these partners together? [00:35:27] Vince Menzione: You used the term part tech, which. Not everyone understands partner technologies. Yes. Organizations like Partner Tap, work Span. Yeah. Tackle. [00:35:37] Rebecca Jones: Structured. Yeah. [00:35:38] Vince Menzione: Structured. If you, these are companies that help with co-selling methodologies, marketplace methodologies. [00:35:44] Rebecca Jones: Yes. [00:35:45] Vince Menzione: Or combining all of those, [00:35:46] Rebecca Jones: if you know, uh, J McBain, uh. Beautiful visual flat map of, um, it looks a little, the 28 moments. Yes. I was just, well, the 28 moments and he’s got the part tech landscape. [00:35:59] Vince Menzione: Oh, [00:35:59] Rebecca Jones: the islands. The islands. [00:36:00] Vince Menzione: Yes. The islands. [00:36:00] Rebecca Jones: Yes, we got it. But there are part tech solutions that support [00:36:03] Vince Menzione: Yeah. [00:36:03] Rebecca Jones: Partner programs, co-sell programs, partner marketing, you know. Yes. And really help to automate a lot of those processes. [00:36:11] Vince Menzione: Yes. [00:36:12] Rebecca Jones: Um, and a lot of those programs. [00:36:13] Vince Menzione: So Rebecca is such a great conversation today. [00:36:16] Vince Menzione: I mean, we can go. Thank you so deep on this. [00:36:18] Rebecca Jones: I know. [00:36:18] Vince Menzione: Which means that we’re all gonna have to be back together in Redmond. You live in the Seattle area? I do. And you’ll be with us. Um, we’ll be hosting the Ultimate Partner, live in, uh, may, May 11th to the 13th. If you’re marking your calendar as listeners and friends, uh, and you’ll be there and. [00:36:36] Vince Menzione: Probably driving some more of this conversation in a workshop format, I hope. [00:36:41] Rebecca Jones: I hope so too. Yeah, it was really rewarding last year. I mean, there’s nothing more powerful to be in the room with partners because the partners are frontline to customers. [00:36:51] Vince Menzione: Yes. [00:36:51] Rebecca Jones: And understanding what they’re seeing and hearing. [00:36:53] Rebecca Jones: And I always think voice of the customer is your ultimate signal. Yeah. So I can’t wait to be there. [00:36:58] Vince Menzione: Very cool. And I have a favorite question I ask all of my guests now. Uh, it is a favorite of mine. You are hosting a dinner party and you can choose where in the world you wanna host this dinner party, and you can invite only three guests, though from the present or the past to this amazing dinner party. [00:37:18] Vince Menzione: Whom would you invite Rebecca and why? And why? [00:37:22] Rebecca Jones: Yeah. Yeah. I’d, um, this is such a great question. I think on every single day I’d have a different collection of folks that I’d want at my home. Uh, I’ve had dinner at some amazing places for me. I would love to host this at my home. [00:37:38] Vince Menzione: Very cool, very [00:37:39] Rebecca Jones: cool. Uh, and the people that I would want there for this particular dinner party, I’m gonna pick, um, three iconic women. [00:37:51] Rebecca Jones: Coco Chanel, [00:37:52] Vince Menzione: Coco Chanel very cool [00:37:54] Rebecca Jones: designer. [00:37:55] Vince Menzione: Yeah. [00:37:56] Rebecca Jones: Um, really changed how women thought about an identity and wardrobe. Um, I would invite Georgia O’Keefe. Wow. She’s my favorite artist. [00:38:07] Vince Menzione: Yeah. [00:38:08] Rebecca Jones: Um, she is one of my favorite artists. Uh, I’m, uh, art and history background. And, uh, [00:38:16] Vince Menzione: that explains, [00:38:17] Rebecca Jones: that, explains that, um, a really interesting perspective. [00:38:22] Rebecca Jones: I love her view on landscapes and. She, [00:38:26] Vince Menzione: that’s why I know her as, you know, landscapes [00:38:28] Rebecca Jones: a landscape artist, um, and much more behind that. And then I would bring one of my favorite authors in, who’s Tony Morrison? [00:38:36] Vince Menzione: Tony [00:38:37] Rebecca Jones: Morrison. [00:38:38] Vince Menzione: I don’t know Tony Morrison. [00:38:39] Rebecca Jones: Oh, um, I would, beloved is her book and Oh, yes. When you think about. [00:38:45] Rebecca Jones: Um, and this is really my passion, my background in art and literature and design, and to have three, three women there, that voice of Tony Morrison, you’ve put that book on your list. Okay. It, it, it changed my life. Uh, and, um, Coco Chanel and, um, Giorgio O’Keefe, I think it would be a really interesting conversation. [00:39:07] Rebecca Jones: I love very cool trailblazers, women who really helped. I don’t know how much they recognize how much they really changed the narrative for other women, um, in their fields and together. But I think it’d be a really fun evening. [00:39:23] Vince Menzione: Very different. Very different. Uh, I was, I know a little bit about Cocoa Chanel ’cause my mom was always in the beauty and fashion industry. [00:39:31] Vince Menzione: So as a kid growing up, I mean her shoe was iconic. [00:39:34] Rebecca Jones: Yeah. [00:39:34] Vince Menzione: Iconic. Chanels an iconic brand was iconic. And, and she was a, wasn’t she a survivor of the. Of, uh, Nazi Germany maybe or something. There’s some, there’s some background or there’s [00:39:44] Rebecca Jones: some background. Flee. Flee [00:39:45] Vince Menzione: Nazi Germany [00:39:46] Rebecca Jones: or something. And what she’s really known for is, um, well many things, but yes, as a designer, really changing the tone and temperature Yes. [00:39:56] Rebecca Jones: Of um. How, you know, fashion and female identity. I think she, um, created the, what everybody knows is the little black dress and really got all that more structured and more modern look and feel of how to, how to wear and just really created a powerful path. [00:40:14] Vince Menzione: Very cool. Yeah. Very cool. [00:40:15] Rebecca Jones: So that’s who I’d have it, this one. [00:40:16] Vince Menzione: That will be a funer. [00:40:17] Rebecca Jones: Next time I’m on your podcast, I’d have a whole new crew. [00:40:21] Vince Menzione: Okay. Well I might. Bring dessert. If you don’t mind, I might bring a little, maybe a little chocolates I think maybe might be very appropriate would for this group and just maybe pop in for a few minutes. [00:40:29] Rebecca Jones: That would be great. [00:40:30] Vince Menzione: Because I don’t wanna inter interrupt the flow my, because this is be a great conversation. Oh my, [00:40:33] no, [00:40:33] Rebecca Jones: you would, I think you’d have a ball. [00:40:34] Vince Menzione: Okay. I, [00:40:35] Rebecca Jones: I mean, I know how close you were to your mother. [00:40:37] Vince Menzione: I am. [00:40:37] Rebecca Jones: And so, yeah. [00:40:39] Vince Menzione: So, um, this isn’t, again, I use this tumultuous term, but we are living in interesting times right now. [00:40:47] Rebecca Jones: We are. [00:40:47] Vince Menzione: And for all of our viewers and listeners. What is your advice to them? What is the one thing you would say? We’re in the first quarter of 2026. Yeah. This ball is moving fast or this puck is moving fast. Yeah. If you were a hockey player, um, what would you say to us now? What, what, what is the one thing you would go do if you’re not doing it now that you should be doing? [00:41:11] Rebecca Jones: Take a moment. Take a moment. As leaders. Your company and your organizations are looking for clarity. They’re looking for a path forward, and there’s a lot of energy out there, which is very exciting, but it can be also very distracting. [00:41:30] Vince Menzione: Yes. [00:41:31] Rebecca Jones: So hold some confidence and clarity for your organization and figure out where you need to be and where you’re going. [00:41:39] Rebecca Jones: That’ll help set your strategy, and this will all come into view. And so what I look to is how do we help enable the organization to grow? And by doing that, you ha you have to put the oxygen mask on yourself. Yeah. Take a moment. [00:41:53] Vince Menzione: Pause. [00:41:55] Rebecca Jones: Pause. Reflect, reflect. I told you I walked down to the beach this morning. [00:41:59] Rebecca Jones: It’s a great moment. Take a moment for yourself. It’s not passing you by. We’re just getting started. [00:42:06] Vince Menzione: Did you hear that? My friends and listeners? Take a moment. And so great to have you here in the room. Yeah. [00:42:13] Rebecca Jones: Thank you so [00:42:14] Vince Menzione: much. Thank you. And I want to thank our listeners, our viewers, for following along, ultimate Guide to Partnering and our YouTube channel Ultimate Partner. [00:42:23] Vince Menzione: And please, please, please come join us. We have an incredible year ahead. This was our event, number one of five. And Ultimate partner Live will be in Bellevue on the 11th through the 13th of May. [00:42:36] Rebecca Jones: Yeah, I’ll [00:42:36] Vince Menzione: see. You’ll see you there. Rebecca will be there. It’s [00:42:38] Rebecca Jones: in my backyard. [00:42:39] Vince Menzione: It’s in your backyard. And we are gonna have incredible leaders in the room. [00:42:42] Vince Menzione: So thank you for watching. Thank you for listening to The Ultimate Guide to Partnering. [00:42:47] Rebecca Jones: Don’t forget, ultimate Partner Live is coming [00:42:50] Vince Menzione: soon, May 11th through the 13th in beautiful Bellevue, Washington. I hope to see you there.s I, as I wrap up here, I just wanna make sure that what, where
Send a textAdrian McDermott is Chief Technology Officer at Zendesk, where he leads the company's product management and engineering teams and helps shape the technology behind one of the world's most widely used customer service platforms. He joined Zendesk in 2010 and has played a key role in guiding the company's product and platform strategy as customer experience continues to evolve in the age of AI. Drawing on years of experience building enterprise software used by service teams around the world, Adrian brings a thoughtful perspective on how AI can help organizations deliver better customer service while allowing people to focus on the work humans do best.In this conversation, we discuss:How customer service evolved from a cost center with rigid scripts and binders into a strategic function where technology helps teams deliver better experiences.Why customer service leaders shouldn't fear automation — and why everyone has a "service debt" that AI can finally help pay down.The shift from traditional contact centers to AI-enabled service platforms that help companies respond faster while improving both employee and customer experience.Lessons Adrian learned scaling Zendesk from a small product team to a global platform serving 100,000 customers and how product-led growth shaped that journey.The critical challenge of moving from non-deterministic, creative AI models to deterministic, reliable solutions necessary for enterprise trust and safetyThe future of context engineering and why the next major leap in AI won't be about superintelligence, but about building systems that capture and act on the knowledge created in every customer interaction.Resources:Subscribe to the AI & The Future of Work NewsletterConnect with Adrian on LinkedInAI fun fact articleOn How the impact of the pandemic on leaders, culture, and the evolving nature of work
Many people think physics / reality is either guided by a probabilistic distribution or is “determined.” Actually, there's a third, far‐more unsettling option. Curt Jaimungal explains why Einstein's general relativity isn't actually deterministic. He discusses how Cauchy horizons and closed time-like curves break predictability, showing that math and physics don't always guarantee a set future for our universe. This is a solo deep‑dive. One that he's been meaning to make for a while. As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe FOLLOW: - Substack: https://curtjaimungal.substack.com/subscribe - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 LINKS MENTIONED: - This Cosmologist Discovered Something Strange: https://youtu.be/73IdQGgfxas - The Most Abused Theorem in Math (Gödel's Incompleteness): https://youtu.be/OH-ybecvuEo - Harvard Scientist: "There Is No Quantum Multiverse" | Jacob Barandes [Part 3]: https://youtu.be/wrUvtqr4wOs - The Quantum Mechanics of Time Travel: https://youtu.be/yCQ_3qE6SmQ - The Dangerous Lie About Understanding: https://youtu.be/eASBzSNB8ts - Discovery That Changed Physics! Gravity Is Not a Force!: https://youtu.be/3pZNzF6LBII - Einstein's Amazing Theory of Gravity: Black Holes and Novel Ideas in Cosmology, Roger Penrose | LMS: https://youtu.be/xAcvNnSrkcM - The Geodesic Equation: Introduction and Derivation: https://youtu.be/5_79m-kHxts - Interpretation of the Wavefunction: https://youtu.be/R-5hjmV-bdY - Is the Future Already Set in Stone?: https://youtu.be/JBkB2D-_ZH0 - What Is Astrophysics Actually Explained: https://youtu.be/TCrRs_OBN0E - What Triggered the Big Bang? | How the Universe Works: https://youtu.be/gup4Cc0Ube0 - Visualization of the Gödel Universe: https://youtu.be/078jOiaevAQ - Iceberg of String Theory: https://youtu.be/X4PdPnQuwjY - The 300-Year-Old Physics Mistake No One Noticed: https://youtu.be/Tghl6aS5A3M - JB Manchak: Spacetime Asymmetry: https://youtu.be/lFbfhISreFY - Carlo Rovelli [TOE]: https://youtu.be/hF4SAketEHY - General Relativity Is Not (Technically) Deterministic: https://curtjaimungal.substack.com/p/general-relativity-is-not-deterministic - The Strong Cosmic Censorship Conjecture by Maxime Van de Moortel [Paper]: https://arxiv.org/pdf/2501.13180 - Some Black Holes Erase Your Past: https://www.sciencedaily.com/releases/2018/02/180221091334.htm - Determinism and General Relativity [Paper]: https://arxiv.org/pdf/2009.07555 - A Family of Local Deterministic Models for Singlet Quantum State Correlations [Paper]: https://arxiv.org/html/2408.09579v1 - Examples of Cosmological Spacetimes Without CMC Cauchy Surfaces: https://link.springer.com/article/10.1007/s11005-024-01843-7 - Asymptotic Dynamics on the Worldlines for Spinning Particles [Paper]: https://arxiv.org/abs/2009.07863 - World Line: https://en.wikipedia.org/wiki/World_line - Counterexamples in Topology [Book]: https://link.springer.com/book/10.1007/978-1-4612-6290-9 - Quantum Charged Black Holes [Paper]: https://arxiv.org/pdf/2404.07192 - Charged Hayward Black Hole with a Cosmological Constant and Surrounded by Quintessence and a Cloud of Strings [Paper]: https://arxiv.org/pdf/2511.02191 - Strong Cosmic Censorship in Charged Black-Hole Spacetimes: Still Subtle [Paper]: https://arxiv.org/pdf/1808.03631 - Chaos and Deterministic Versus Stochastic Non-Linear Modelling: https://academic.oup.com/jrsssb/article/54/2/303/7035838 - Reopening the Hole Argument by Klaas Landsman [Paper]: https://arxiv.org/pdf/2206.04943 - Is Time Travel Too Strange to Be Possible? [Paper]: https://arxiv.org/pdf/1704.02295 - Counterexamples in Topology [Book]: https://link.springer.com/book/10.1007/978-1-4612-6290-9 Learn more about your ad choices. Visit megaphone.fm/adchoices
Roy is a three-time founder who has cracked the code on enterprise AI. After selling his first company and realizing his second idea was too slow, he pivoted to solving a massive problem: customer service automation.In this episode, Roy breaks down how GetVocal went from zero to $1M ARR in just five months. He reveals the "Context Graph" technology that allows them to beat LLM wrappers, why he believes purely generative AI is useless for business, and how he turned a single deployment into an enterprise-wide contagion.Why You Should ListenHow to hit $1M ARR in 5 months with a single salesperson.Why "Context Graphs" are the secret to building AI that doesn't hallucinate.How to expand from a single agent to 80 agents across the enterprise.The critical difference between Deterministic and Probabilistic AI Why starting with a personal passion project failed, but pivoting to enterprise worked.Keywordsstartup podcast, startup podcast for founders, product market fit, enterprise AI, customer service automation, finding pmf, context graphs, AI agents, B2B sales, Roy Moussa00:00:00 Intro00:02:29 From Engineer to 3-Time Founder00:08:11 The Failed Pivot00:12:49 Solving Sales Efficiency First00:16:06 The Pivot to Customer Service00:18:57 Why Chatbots Failed & The Hybrid AI Solution00:25:43 What is a Context Graph?00:34:46 The "Contagion" Effect: 80 Agents in 8 Weeks00:39:34 Competing with Decagon & The Human-Centric Approach00:41:58 Hitting $1M ARR in 5 MonthsSend me a message to let me know what you think!
Tonight's WeatherBrains is all about the NWS Storm Prediction Center (SPC)'s convective outlooks. Guest WeatherBrain and SPC forecaster Bill Bunting joins us tonight. He grew up in Virginia Beach, VA where we experienced a series of hurricanes and Nor'easters at a very young age. He attended Old Dominion University as well as OU where he earned his Bachelor's Degree. He's been with the National Weather Service since 1985, where he's worked primarily at local offices in Norman (OK), Kansas City (MO) and Fort Worth (TX). He was Chief of Forecasting Operations at SPC in 2012, and is now the Deputy Director at SPC since 2024. Bill, welcome to WeatherBrains! Second and last Guest WeatherBrain (but certainly not the least!) is the brand new Warning Coordination Meteorologist (MIC) for the SPC. He grew up in Ohio and attended Valparaiso University, and has since worked for the NWS for over 15 years. He loves historical weather statistics and visualization. Evan Bentley, welcome to WeatherBrains! Our email officer Jen is continuing to handle the incoming messages from our listeners. Reach us here: email@weatherbrains.com. Organization/structure of NWS Storm Prediction Center (14:00) SPCS's Fire weather forecasting (16:00) Science behind SPC's new conditional intensity forecasts (20:30) Deterministic factors for a PDS (Particularly Dangerous Situation) watch (26:00) Improving communication of impactful weather events to family and co workers (40:00) SPC's philosophy on dealing with QLCS events (51:00) National team approach for warnings? (01:03:00) The Astronomy Outlook with Tony Rice (01:29:45) This Week in Tornado History With Jen (01:32:15) E-Mail Segment (01:24:00) and more! Web Sites from Episode 1051: Alabama Weather Network Picks of the Week: Bill Bunting - SPC Hazard Climatology James Aydelott - Okie James on Facebook: Two supercell rotation paths Jen Narramore - Southeast Severe Storms Symposium XXIV Rick Smith - NOAA Damage Assessment Toolkit Troy Kimmel - Foghorn Kim Klockow-McClain - Edwardsburg Schools offer support as community mourns loss of student John Gordon - Congressman Eric Sorensen introduces new legislation to investigate major weather disasters Bill Murray - Out James Spann - Out The WeatherBrains crew includes your host, James Spann, plus other notable geeks like Troy Kimmel, Bill Murray, Rick Smith, James Aydelott, Jen Narramore, John Gordon, and Dr. Kim Klockow-McClain. They bring together a wealth of weather knowledge and experience for another fascinating podcast about weather.
TestTalks | Automation Awesomeness | Helping YOU Succeed with Test Automation
How do you ensure software quality when the system you're testing doesn't give the same output twice? Go to https://links.testguild.com/inflectra and start your free 30-day trial, no credit card, no contract required. That's the core challenge facing every QA team building or testing AI-powered applications today and it's breaking all the rules we've relied on for decades. In this episode of the TestGuild Automation Podcast, I sit down with Adam Sandman, co-founder of Inflectra, to get into what non-deterministic AI testing actually means in practice, why traditional pass/fail testing no longer cuts it, and what quality professionals need to do differently right now. We cover: Why AI-generated code is raising the stakes for QA teams while budgets stay flat The fundamental difference between deterministic and non-deterministic systems — and why it changes everything about how you test How to set acceptable risk thresholds for AI systems (hint: it depends on whether you're building an e-commerce chatbot or an air traffic control system) Why testers who embrace AI as a tool — not a threat — will be the ones leading their organizations forward How a live demo failure at a conference inspired Inflectra's new non-deterministic testing tool, SureWire If you're a tester, QA manager, or automation engineer trying to figure out how to keep up with AI-driven development without losing your mind — or your job — this one's for you.
BONUS: When AI Decisions Go Wrong at Scale—And How to Prevent It We've spent years asking what AI can do. But the next frontier isn't more capability—it's something far less glamorous and far more dangerous if we get it wrong. In this episode, Ran Aroussi shares why observability, transparency, and governance may be the difference between AI that empowers humans and AI that quietly drifts out of alignment. The Gap Between Demos and Deployable Systems "I've noticed that I watched well-designed agents make perfectly reasonable decisions based on their training, but in a context where the decision was catastrophically wrong. And there was really no way of knowing what had happened until the damage was already there." Ran's journey from building algorithmic trading systems to creating MUXI, an open framework for production-ready AI agents, revealed a fundamental truth: the skills needed to build impressive AI demos are completely different from those needed to deploy reliable systems at scale. Coming from the EdTech space where he handled billions of ad impressions daily and over a million concurrent users, Ran brings a perspective shaped by real-world production demands. The moment of realization came when he saw that the non-deterministic nature of AI meant that traditional software engineering approaches simply don't apply. While traditional bugs are reproducible, AI systems can produce different results from identical inputs—and that changes everything about how we need to approach deployment. Why Leaders Misunderstand Production AI "When you chat with ChatGPT, you go there and it pretty much works all the time for you. But when you deploy a system in production, you have users with unimaginable different use cases, different problems, and different ways of phrasing themselves." The biggest misconception leaders have is assuming that because AI works well in their personal testing, it will work equally well at scale. When you test AI with your own biases and limited imagination for scenarios, you're essentially seeing a curated experience. Real users bring infinite variation: non-native English speakers constructing sentences differently, unexpected use cases, and edge cases no one anticipated. The input space for AI systems is practically infinite because it's language-based, making comprehensive testing impossible. Multi-Layered Protection for Production AI "You have to put in deterministic filters between the AI and what you get back to the user." Ran outlines a comprehensive approach to protecting AI systems in production: Model version locking: Just as you wouldn't randomly upgrade Python versions without testing, lock your AI model versions to ensure consistent behavior Guardrails in prompts: Set clear boundaries about what the AI should never do or share Deterministic filters: Language firewalls that catch personal information, harmful content, or unexpected outputs before they reach users Comprehensive logging: Detailed traces of every decision, tool call, and data flow for debugging and pattern detection The key insight is that these layers must work together—no single approach provides sufficient protection for production systems. Observability in Agentic Workflows "With agentic AI, you have decision-making, task decomposition, tools that it decided to call, and what data to pass to them. So there's a lot of things that you should at least be able to trace back." Observability for agentic systems is fundamentally different from traditional LLM observability. When a user asks "What do I have to do today?", the system must determine who is asking, which tools are relevant to their role, what their preferences are, and how to format the response. Each user triggers a completely different dynamic workflow. Ran emphasizes the need for multi-layered access to observability data: engineers need full debugging access with appropriate security clearances, while managers need topic-level views without personal information. The goal is building a knowledge graph of interactions that allows pattern detection and continuous improvement. Governance as Human-AI Partnership "Governance isn't about control—it's about keeping people in the loop so AI amplifies, not replaces, human judgment." The most powerful reframing in this conversation is viewing governance not as red tape but as a partnership model. Some actions—like answering support tickets—can be fully automated with occasional human review. Others—like approving million-dollar financial transfers—require human confirmation before execution. The key is designing systems where AI can do the preparation work while humans retain decision authority at critical checkpoints. This mirrors how we build trust with human colleagues: through repeated successful interactions over time, gradually expanding autonomy as confidence grows. Building Trust Through Incremental Autonomy "Working with AI is like working with a new colleague that will back you up during your vacation. You probably don't know this person for a month. You probably know them for years. The first time you went on vacation, they had 10 calls with you, and then slowly it got to 'I'm only gonna call you if it's really urgent.'" The path to trusting AI systems mirrors how we build trust with human colleagues. You don't immediately hand over complete control—you start with frequent check-ins, observe performance, and gradually expand autonomy as confidence builds. This means starting with heavy human-in-the-loop interaction and systematically reducing oversight as the system proves reliable. The goal is reaching a state where you can confidently say "you don't have to ask permission before you do X, but I still want to approve every Y." In this episode, we refer to Thinking in Systems by Donella Meadows, Designing Machine Learning Systems by Chip Huyen, and Build a Large Language Model (From Scratch) by Sebastian Raschka. About Ran Aroussi Ran Aroussi is the founder of MUXI, an open framework for production-ready AI agents. He is also the co-creator of yfinance (with 10 million downloads monthly) and founder of Tradologics and Automaze. Ran is the author of the forthcoming book Production-Grade Agentic AI: From Brittle Workflows to Deployable Autonomous Systems, also available at productionaibook.com. You can connect with Ran Aroussi on LinkedIn.
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...
In this episode of Run the Numbers, CJ Gustafson sits down with Dan Miller, CFO at RightRev. They unpack why leasing is underused in software, how RevTech emerged, and why revenue recognition may be the next AI battleground. Dan also shares how he evaluates durable growth vs. hypergrowth.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.com—LINKS: Dan on LinkedIn: https://www.linkedin.com/in/danmillercpa/RightRev: https://www.rightrev.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:00:00:00 Preview and Intro00:02:41 Why Operating Experience Matters for CFOs00:04:08 Defining Durable Growth00:06:06 Snowflake and Consumption Revenue Complexity00:10:17 Forecasting in Consumption Models00:11:29 AI's Role in Revenue Forecasting00:12:14 Sponsors — Rillet | Tabs | Abacus AI00:15:39 Comping Sales in Usage-Based Models00:18:15 Leasing as a Software Monetization Tool00:20:47 The CFO's Role in Sales and GTM00:22:29 How CFOs Help Close Deals00:24:14 Rev Tech vs RevOps00:26:20 Sponsors — Brex | Metronome | RightRev00:29:40 Where AI Actually Helps Rev Rec00:31:55 Deterministic vs Probabilistic AI00:33:05 Why Enterprises Hesitate on AI Agents00:34:18 Startups vs Incumbents in the AI Race00:35:13 FOMO, Overfunding, and Market Distortions00:38:13 CFO Playbooks Without Hypergrowth00:39:38 Finding PMF as a CFO00:41:15 Career Advice: Growth vs Shiny Objects00:42:00 Building the CEO–CFO Relationship00:42:49 Learning Beyond the Back Office00:43:22 Lightning Round00:44:28 Advice to My Younger Self00:45:09 Finance Tech Stack00:46:36 Credits
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop explores the complex world of context and knowledge graphs with guest Youssef Tharwat, the founder of NoodlBox who is building dot get for context. Their conversation spans from the philosophical nature of context and its crucial role in AI development, to the technical challenges of creating deterministic tools for software development. Tharwat explains how his product creates portable, versionable knowledge graphs from code repositories, leveraging the semantic relationships already present in programming languages to provide agents with better contextual understanding. They discuss the limitations of large context windows, the advantages of Rust for AI-assisted development, the recent Claude/Bun acquisition, and the broader geopolitical implications of the AI race between big tech companies and open-source alternatives. The conversation also touches on the sustainability of current AI business models and the potential for more efficient, locally-run solutions to challenge the dominance of compute-heavy approaches.For more information about NoodlBox and to join the beta, visit NoodlBox.io.Timestamps00:00 Stewart introduces Youssef Tharwat, founder of NoodlBox, building context management tools for programming05:00 Context as relevant information for reasoning; importance when hitting coding barriers10:00 Knowledge graphs enable semantic traversal through meaning vs keywords/files15:00 Deterministic vs probabilistic systems; why critical applications need 100% reliability20:00 CLI tool makes knowledge graphs portable, versionable artifacts with code repos25:00 Compiler front-ends, syntax trees, and Rust's superior feedback for AI-assisted coding30:00 Claude's Bun acquisition signals potential shift toward runtime compilation and graph-based context35:00 Open source vs proprietary models; user frustration with rate limits and subscription tactics40:00 Singularity path vs distributed sovereignty of developers building alternative architectures45:00 Global economics and why brute force compute isn't sustainable worldwide50:00 Corporate inefficiencies vs independent engineering; changing workplace dynamics55:00 February open beta for NoodlBox.io; vision for new development tool standardsKey Insights1. Context is semantic information that enables proper reasoning, and traditional LLM approaches miss the mark. Youssef defines context as the information you need to reason correctly about something. He argues that larger context windows don't scale because quality degrades with more input, similar to human cognitive limitations. This insight challenges the Silicon Valley approach of throwing more compute at the problem and suggests that semantic separation of information is more optimal than brute force methods.2. Code naturally contains semantic boundaries that can be modeled into knowledge graphs without LLM intervention. Unlike other domains where knowledge graphs require complex labeling, code already has inherent relationships like function calls, imports, and dependencies. Youssef leverages these existing semantic structures to automatically build knowledge graphs, making his approach deterministic rather than probabilistic. This provides the reliability that software development has historically required.3. Knowledge graphs can be made portable, versionable, and shareable as artifacts alongside code repositories. Youssef's vision treats context as a first-class citizen in version control, similar to how Git manages code. Each commit gets a knowledge graph snapshot, allowing developers to see conceptual changes over time and share semantic understanding with collaborators. This transforms context from an ephemeral concept into a concrete, manageable asset.4. The dependency problem in modern development can be solved through pre-indexed knowledge graphs of popular packages. Rather than agents struggling with outdated API documentation, Youssef pre-indexes popular npm packages into knowledge graphs that automatically integrate with developers' projects. This federated approach ensures agents understand exact APIs and current versions, eliminating common frustrations with deprecated methods and unclear documentation.5. Rust provides superior feedback loops for AI-assisted programming due to its explicit compiler constraints. Youssef rebuilt his tool multiple times in different languages, ultimately settling on Rust because its picky compiler provides constant feedback to LLMs about subtle issues. This creates a natural quality control mechanism that helps AI generate more reliable code, making Rust an ideal candidate for AI-assisted development workflows.6. The current AI landscape faces a fundamental tension between expensive centralized models and the need for global accessibility. The conversation reveals growing frustration with rate limiting and subscription costs from major providers like Claude and Google. Youssef believes something must fundamentally change because $200-300 monthly plans only serve a fraction of the world's developers, creating pressure for more efficient architectures and open alternatives.7. Deterministic tooling built on semantic understanding may provide a competitive advantage against probabilistic AI monopolies. While big tech companies pursue brute force scaling with massive data centers, Youssef's approach suggests that clever architecture using existing semantic structures could level the playing field. This represents a broader philosophical divide between the "singularity" path of infinite compute and the "disagreeably autistic engineer" path of elegant solutions that work locally and affordably.
In this episode, Jeff Mains sits down with KG Charles-Harris, a serial entrepreneur who has founded six companies across industries ranging from genomics to AI. KG is the founder and CEO of Quarrio, a deterministic AI platform that solves a critical problem: getting accurate, consistent answers from corporate data in seconds instead of weeks.KG shares his unconventional path to entrepreneurship, explaining how his companies emerge from late-night conversations with brilliant people who share a common problem. He breaks down the crucial difference between deterministic and probabilistic AI systems, making the case that when decisions involve real money, real lives, or real consequences, accuracy isn't optional—it's essential.Key Takeaways[0:00] Introduction to KG Charles-Harris and his multi-industry entrepreneurial journey[1:18] How companies are born from conversations: The pattern behind KG's six startups[2:30] The genomics company origin story: From 4:30 AM conversation to Norwegian startup[3:28] Why Quarrio exists: Even data company CEOs can't get the data they need[4:31] The Quarrio platform: 100% accuracy, plain language queries, auto-visualization[5:27] Real-world impact: The $60M margin leak that took two quarters to find (would take 5 seconds with Quarrio)[7:00] Deterministic vs. probabilistic AI explained: Why autopilots don't hallucinate[11:30] The cycle time framework: Information → Decision → Action → Results[13:00] Why ChatGPT's inconsistency is a dealbreaker for enterprise decisions[18:30] Organizations as "decision-making machines" and democratizing decisions to every level[20:30] The data explosion: Managing 300+ structured data sources in mid-sized enterprises[23:00] Why Quarrio focuses on structured enterprise data (SAP, Salesforce, Oracle) instead of PDFs[30:00] Go-to-market strategy: Why they started with Salesforce and sales teams[32:30] The Salesforce incubation story: Free office space and immediate investment[33:30] Team building philosophy: Surrounding yourself with people smarter than you[37:00] Stewardship as core ethos: Taking care of family, team, customers, and partners[38:30] The founder's dilemma: Resilience vs. delusion—knowing when to persist[43:00] Where to connect with KG and learn more about QuarrioTweetable Quotes"An organization is essentially a machine for making decisions and taking actions that have certain types of results." — KG Charles-Harris"Cycle time to information shortens cycle time to decision, which shortens cycle time to action, which shortens cycle time to results." — KG Charles-Harris"Agentic AI without context is useless. You need determinism to trust what is enacted within your system." — KG Charles-Harris"Effectiveness requires redundancy. Efficiency optimizes for the shortest time or best expense, but effectiveness accomplishes the goal." — KG Charles-Harris"I'm not very smart, and because I realize that, I ensure I work with people who are very smart. Then they make me look smart." — KG Charles-Harris"Most of us give up before we should have. The break would have come had we stuck it out one more month." — KG Charles-Harris"If you don't have their back, you cannot expect them to have yours. It's a
Tyson Singer (Head of Tech & Platforms @ Spotify) joins us to unpack how Spotify is transforming its product development lifecycle across creation, experimentation and maintenance to shift from "localized speed" to "systematic speed." We explore why the industry's current obsession with the "Build It" phase of development is shortsighted, and how Spotify is aggressively deploying AI in the "Think It" (prototyping/strategy) and "Maintain It" (fleet management) phases. Tyson also details the internal tools driving this shift, including AiKA and Honk, and shares why the future of engineering relies on moving from I-shaped specialists to T-shaped generalists. ABOUT TYSON SINGERTyson Singer is the SVP of Technology & Platforms at Spotify, where he leads technology infrastructure, developer experience, cybersecurity, and finance IT. Tyson is the executive behind Spotify's internal developer portal, Backstage, and Spotify's experimentation system, Confidence, which are now both commercially available. He has a background as an engineer, architect, and product lead, and he holds a Master's in Computer Science from Stanford University. Tyson is also an avid outdoor adventurer. This episode is brought to you by Retool!What happens when your team can't keep up with internal tool requests? Teams start building their own, Shadow IT spreads across the org, and six months later you're untangling the mess…Retool gives teams a better way: governed, secure, and no cleanup required.Retool is the leading enterprise AppGen platform, powering how the world's most innovative companies build the tools that run their business. Over 10,000 organizations including Amazon, Stripe, Adobe, Brex, and Orangetheory Fitness use the platform to safely harness AI and their enterprise data to create governed, production-ready apps.Learn more at Retool.com/elc SHOW NOTES:Tyson's 9-year journey @ Spotify: From the "crucible" of hyper-growth to leading Tech & Platforms (3:46)The pivot from "localized speed" to "systematic speed" (7:27)Core principles of Spotify's Platform org: Partnering with customers & "Taking the pain away" (10:37)The "Think it, Build it, Ship it, Tweak it" lifecycle framework & why the industry obsession with "Build It" (coding agents) is missing the bigger picture (14:57)How Spotify is investing in the "Think It" phase: AI prototyping with deep business context (16:49)AiKA (AI Knowledge Assistant): Context engineering for humans and bots (18:47)"Honk": Spotify's internal framework for large-scale automated code changes (22:17)Addressing the decline of code quality and the bottleneck of human PR reviews (25:50)Probabilistic vs. Deterministic code reviews: A new approach to quality checks (29:43)Identifying bottlenecks to company value outside of R&D (Legal, Licensing, etc.) (32:12)Why systems change is fundamentally about people and identity shifts (35:57)Rapid fire questions (38:49) This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Today's guest is Emma Vitalini, Head of Global Digital Health Technology Innovation at Amgen, where she leads initiatives at the intersection of digital health, data strategy, and clinical innovation. Emma joins Emerj Editorial Senior Editor Marilie Fouche to explore how data and AI are reshaping patient recruitment, consent, and execution in clinical trials, with a focus on decentralized models, scalable compliance, and explainable AI in regulated environments. Emma also shares practical guidance for enterprise leaders on where AI is delivering near-term ROI today, including accelerating patient screening by surfacing unstructured data, reducing enrollment delays through digital and remote monitoring tools, and designing modular, plug-and-play AI platforms that balance speed, flexibility, and regulatory trust. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!
In this episode of Run the Numbers, CJ sits down with Bruno Annicq, CFO of Wellhub (formerly Gympass), to unpack a practical finance playbook built around cash discipline, sustainable growth, and simplicity. Bruno explains how he rebuilt forecasting using an AI-driven, probabilistic ensemble model, moving teams beyond single-scenario planning. They also dig into his EMPOWER planning framework, usable OKRs, and why tighter alignment between finance, HR, and wellbeing is becoming a durable lever for long-term performance.—SPONSORS:RightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.com—LINKS:Bruno on LinkedIn: https://www.linkedin.com/in/bannicq/Wellhub: https://wellhub.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:“Run Toward a Tough Market” — Developing the Hard and Soft Skills To Be a Great Finance Leaderhttps://youtu.be/iNHbkcG7YEo—TIMESTAMPS:00:00:00 Preview and Intro00:02:19 Sponsors — RightRev, Rillet, Tabs00:06:43 Accidental CFO Origin Story00:07:34 Consulting to Operations Pivot00:08:12 Why Finance Clicked for Bruno00:09:28 McKinsey Prioritization in Real World00:10:02 Eisenhower Matrix and Prioritization00:11:08 Investing in Non-Urgent Work00:13:30 Lessons From AOL Reinvention00:16:10 Sponsors — Abacum, Brex, Metronome00:20:01 Career Growth Through Hard Problems00:20:52 Broadening Skills Through Change00:23:12 Five Core Finance Principles00:24:02 Cash Is King00:25:14 Driving Sustainable Growth00:26:01 No Surprises and Forecasting00:26:07 Finance as Business Enabler00:27:22 Less Is More Philosophy00:28:47 Hardest Principle: Less Is More00:29:46 Deterministic vs Probabilistic Forecasting00:31:11 Marketplace Volatility and Forecast Error00:32:10 Ensemble Models Explained00:33:37 Forecast Accuracy Gains00:34:53 Building Models In-House00:36:46 Why Explainability Matters00:37:48 Empower Framework Introduction00:47:47 Urgency, Compounding, Long-Term Thinking00:48:10 Advice to Younger Self00:50:06 Finance Stack and Expense Stories00:52:51 Credits#RunTheNumbersPodcast #CFO #FinanceLeadership #Forecasting #AIinFinance This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com
Get My NEW Book: Focus Like a Nobel Prize Winner: https://www.amazon.com/dp/B0FN8DH6SX Andrew Jaffe Book: The Random Universe: https://www.amazon.com/Random-Universe-Models-Probability-Cosmos/dp/0300250509 Is the universe intrinsically random? In this conversation, we dive deep into why the universe may be fundamentally, intrinsically random. Whether inflation on life support, the truth behind the Hubble tension, and whether cosmology is approaching the event horizon, limits beyond which humans can never know. Today we're joined by one of the architects of modern cosmological inference, Professor Andrew Jaffee, author of a new book called The Random Universe that argues that every observation in science is shaped by the models we bring to it, biases and all. KEY TAKEAWAYS 00:00–01:13 — Science and life rely on building models. 01:13–03:35 — Models of people and reality are often wrong and revised. 04:04–06:01 — Observation depends on prior theories. 06:01–07:32 — Models can't be escaped, only improved. 07:32–08:57 — No single scientific method exists. 08:57–11:25 — Science uses induction, not pure proof. 11:25–13:22 — Induction isn't certain, only probabilistic. 13:22–15:36 — Induction works because nature is regular. 17:44–19:08 — Big Bang emerges from well-tested models. 19:08–21:15 — Current cosmology is stressed, not broken. 29:19–30:36 — Probability gives meaning to models. 39:45–41:11 — Randomness often reflects limited knowledge. 43:46–45:00 — Quantum physics is fundamentally probabilistic. 49:09–50:04 — Inflation awaits decisive observational tests. - Additional resources: Get My NEW Book: Focus Like a Nobel Prize Winner: https://www.amazon.com/dp/B0FN8DH6SX?ref_=pe_93986420_775043100 Please join my mailing list here
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:DADVI is a new approach to variational inference that aims to improve speed and accuracy.DADVI allows for faster Bayesian inference without sacrificing model flexibility.Linear response can help recover covariance estimates from mean estimates.DADVI performs well in mixed models and hierarchical structures.Normalizing flows present an interesting avenue for enhancing variational inference.DADVI can handle large datasets effectively, improving predictive performance.Future enhancements for DADVI may include GPU support and linear response integration.Chapters:13:17 Understanding DADVI: A New Approach21:54 Mean Field Variational Inference Explained26:38 Linear Response and Covariance Estimation31:21 Deterministic vs Stochastic Optimization in DADVI35:00 Understanding DADVI and Its Optimization Landscape37:59 Theoretical Insights and Practical Applications of DADVI42:12 Comparative Performance of DADVI in Real Applications45:03 Challenges and Effectiveness of DADVI in Various Models48:51 Exploring Future Directions for Variational Inference53:04 Final Thoughts and Advice for PractitionersThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël...
Algorithms and automations have been buds for a decade plus.
Jamie and Don, freshly back from PAX Unplugged 2025 in Philadelphia, have been getting into some board, card, and dice rumbles lately, and they wanted to dig into the many styles of combat systems in board games. Deterministic versus random, complex versus streamlined, dice versus cards - there's a lot of ground to cover. Also, our yearly fundraiser is currently running on Kickstarter! Support us now at https://www.thesecretcabal.com/kickstarter
Send us a textHit replay on one of the most thought-provoking Agentic AI conversations on Making Data Simple. GTM Account Director Megan Gallagher makes the case for Agentic AI from the Maven AGI front lines, where AI agents stop following rigid decision trees and start acting with real autonomy over enterprise workflows.“We're still living like everything is deterministic,” Megan argues, “but this new generation of agents is inherently generative and predictive.” In this replay, she unpacks what that shift means for smaller specialized models, using real enterprise data, rethinking “assistant vs person,” and how to get started without boiling the ocean.If you want to understand how Agentic AI moves from slideware to shipped value, this is the episode to queue up again.01:30 All Great Podcasts start with Drinks05:27 Maven AGI 09:13 Smaller Models! 10:50 Why Maven AGI12:04 The Secret Sauce or Use Case15:13 Typical Client Persona 20:31 Using Enterprise Data 26:19 But AGI, Really?30:12 Assistant or Person?39:06 What's Next?40:28 My Thoughts on Getting Started?46:30 The AI Example49:30 The Maven AGI Pitch53:23 LearningMaven AGI: https://www.mavenagi.com/ Megan's LinkedIn: https://www.linkedin.com/in/megfgallagher/Al's LinkedIn: https://www.linkedin.com/in/al-martin-ku/#AgenticAI #FutureOfAI #MakingDataSimple #MavenAGI #AIAgents #EnterpriseAI #CustomerExperience #AIInProduction #PodcastReplayWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
Send us a textHit replay on one of the most thought-provoking Agentic AI conversations on Making Data Simple. GTM Account Director Megan Gallagher makes the case for Agentic AI from the Maven AGI front lines, where AI agents stop following rigid decision trees and start acting with real autonomy over enterprise workflows.“We're still living like everything is deterministic,” Megan argues, “but this new generation of agents is inherently generative and predictive.” In this replay, she unpacks what that shift means for smaller specialized models, using real enterprise data, rethinking “assistant vs person,” and how to get started without boiling the ocean.If you want to understand how Agentic AI moves from slideware to shipped value, this is the episode to queue up again.01:30 All Great Podcasts start with Drinks05:27 Maven AGI 09:13 Smaller Models! 10:50 Why Maven AGI12:04 The Secret Sauce or Use Case15:13 Typical Client Persona 20:31 Using Enterprise Data 26:19 But AGI, Really?30:12 Assistant or Person?39:06 What's Next?40:28 My Thoughts on Getting Started?46:30 The AI Example49:30 The Maven AGI Pitch53:23 LearningMaven AGI: https://www.mavenagi.com/ Megan's LinkedIn: https://www.linkedin.com/in/megfgallagher/Al's LinkedIn: https://www.linkedin.com/in/al-martin-ku/#AgenticAI #FutureOfAI #MakingDataSimple #MavenAGI #AIAgents #EnterpriseAI #CustomerExperience #AIInProduction #PodcastReplayWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
AI Assisted Coding: From Deterministic to AI-Driven—The New Paradigm of Software Development, With Markus Hjort In this BONUS episode, we dive deep into the emerging world of AI-assisted coding with Markus Hjort, CTO of Bitmagic. Markus shares his hands-on experience with what's being called "vibe coding" - a paradigm shift where developers work more like technical product owners, guiding AI agents to produce code while focusing on architecture, design patterns, and overall system quality. This conversation explores not just the tools, but the fundamental changes in how we approach software engineering as a team sport. Defining Vibecoding: More Than Just Autocomplete "I'm specifying the features by prompting, using different kinds of agentic tools. And the agent is producing the code. I will check how it works and glance at the code, but I'm a really technical product owner." Vibecoding represents a spectrum of AI-assisted development approaches. Markus positions himself between pure "vibecoding" (where developers don't look at code at all) and traditional coding. He produces about 90% of his code using AI tools, but maintains technical oversight by reviewing architectural patterns and design decisions. The key difference from traditional autocomplete tools is the shift from deterministic programming languages to non-deterministic natural language prompting, which requires an entirely different way of thinking about software development. The Paradigm Shift: When AI Changed Everything "It's a different paradigm! Looking back, it started with autocomplete where Copilot could implement simple functions. But the real change came with agentic coding tools like Cursor and Claude Code." Markus traces his journey through three distinct phases. First came GitHub Copilot's autocomplete features for simple functions - helpful but limited. Next, ChatGPT enabled discussing architectural problems and getting code suggestions for unfamiliar technologies. The breakthrough arrived with agentic tools like Cursor and Claude Code that can autonomously implement entire features. This progression mirrors the historical shift from assembly to high-level languages, but with a crucial difference: the move from deterministic to non-deterministic communication with machines. Where Vibecoding Works Best: Knowing Your Risks "I move between different levels as I go through different tasks. In areas like CSS styling where I'm not very professional, I trust the AI more. But in core architecture where quality matters most, I look more thoroughly." Vibecoding effectiveness varies dramatically by context. Markus applies different levels of scrutiny based on his expertise and the criticality of the code. For frontend work and styling where he has less expertise, he relies more heavily on AI output and visual verification. For backend architecture and core system components, he maintains closer oversight. This risk-aware approach is essential for startup environments where developers must wear multiple hats. The beauty of this flexibility is that AI enables developers to contribute meaningfully across domains while maintaining appropriate caution in critical areas. Teaching Your Tools: Making AI-Assisted Coding Work "You first teach your tool to do the things you value. Setting system prompts with information about patterns you want, testing approaches you prefer, and integration methods you use." Success with AI-assisted coding requires intentional configuration and practice. Key strategies include: System prompts: Configure tools with your preferred patterns, testing approaches, and architectural decisions Context management: Watch context length carefully; when the AI starts making mistakes, reset the conversation Checkpoint discipline: Commit working code frequently to Git - at least every 30 minutes, ideally after every small working feature Dual AI strategy: Use ChatGPT or Claude for architectural discussions, then bring those ideas to coding tools for implementation Iteration limits: Stop and reassess after roughly 5 failed iterations rather than letting AI continue indefinitely Small steps: Split features into minimal increments and commit each piece separately In this segment we refer to the episode with Alan Cyment on AI Assisted Coding, and the Pachinko coding anti-pattern. Team Dynamics: Bigger Chunks and Faster Coordination "The speed changes a lot of things. If everything goes well, you can produce so much more stuff. So you have to have bigger tasks. Coordination changes - we need bigger chunks because of how much faster coding is." AI-assisted coding fundamentally reshapes team workflows. The dramatic increase in coding speed means developers need larger, more substantial tasks to maintain flow and maximize productivity. Traditional approaches of splitting stories into tiny tasks become counterproductive when implementation speed increases 5-10x. This shift impacts planning, requiring teams to think in terms of complete features rather than granular technical tasks. The coordination challenge becomes managing handoffs and integration points when individuals can ship significant functionality in hours rather than days. The Non-Deterministic Challenge: A New Grammar "When you're moving from low-level language to higher-level language, they are still deterministic. But now with LLMs, it's not deterministic. This changes how we have to think about coding completely." The shift to natural language prompting introduces fundamental uncertainty absent from traditional programming. Unlike the progression from assembly to C to Python - all deterministic - working with LLMs means accepting probabilistic outputs. This requires developers to adopt new mental models: thinking in terms of guidance rather than precise instructions, maintaining checkpoints for rollback, and developing intuition for when AI is "hallucinating" versus producing valid solutions. Some developers struggle with this loss of control, while others find liberation in focusing on what to build rather than how to build it. Code Reviews and Testing: What Changes? "With AI, I spend more time on the actual product doing exploratory testing. The AI is doing the coding, so I can focus on whether it works as intended rather than syntax and patterns." Traditional code review loses relevance when AI generates syntactically correct, pattern-compliant code. The focus shifts to testing actual functionality and user experience. Markus emphasizes: Manual exploratory testing becomes more important as developers can't rely on having written and understood every line Test discipline is critical - AI can write tests that always pass (assert true), so verification is essential Test-first approach helps ensure tests actually verify behavior rather than just existing Periodic test validation: Randomly modify test outputs to verify they fail when they should Loosening review processes to avoid bottlenecks when code generation accelerates dramatically Anti-Patterns and Pitfalls to Avoid Several common mistakes emerge when developers start with AI-assisted coding: Continuing too long: When AI makes 5+ iterations without progress, stop and reset rather than letting it spiral Skipping commits: Without frequent Git checkpoints, recovery from AI mistakes becomes extremely difficult Over-reliance without verification: Trusting AI-generated tests without confirming they actually test something meaningful Ignoring context limits: Continuing to add context until the AI becomes confused and produces poor results Maintaining traditional task sizes: Splitting work too granularly when AI enables completing larger chunks Forgetting exploration: Reading about tools rather than experimenting hands-on with your own projects The Future: Autonomous Agents and Automatic Testing "I hope that these LLMs will become larger context windows and smarter. Tools like Replit are pushing boundaries - they can potentially do automatic testing and verification for you." Markus sees rapid evolution toward more autonomous development agents. Current trends include: Expanded context windows enabling AI to understand entire codebases without manual context curation Automatic testing generation where AI not only writes code but also creates and runs comprehensive test suites Self-verification loops where agents test their own work and iterate without human intervention Design-to-implementation pipelines where UI mockups directly generate working code Agentic tools that can break down complex features autonomously and implement them incrementally The key insight: we're moving from "AI helps me code" to "AI codes while I guide and verify" - a fundamental shift in the developer's role from implementer to architect and quality assurance. Getting Started: Experiment and Learn by Doing "I haven't found a single resource that covers everything. My recommendation is to try Claude Code or Cursor yourself with your own small projects. You don't know the experience until you try it." Rather than pointing to comprehensive guides (which don't yet exist for this rapidly evolving field), Markus advocates hands-on experimentation. Start with personal projects where stakes are low. Try multiple tools to understand their strengths. Build intuition through practice rather than theory. The field changes so rapidly that reading about tools quickly becomes outdated - but developing the mindset and practices for working with AI assistance provides durable value regardless of which specific tools dominate in the future. About Markus Hjort Markus is Co-founder and CTO of Bitmagic, and has over 20 years of software development expertise. Starting with Commodore 64 game programming, his career spans gaming, fintech, and more. As a programmer, consultant, agile coach, and leader, Markus has successfully guided numerous tech startups from concept to launch. You can connect with Markus Hjort on LinkedIn.
© 2020-2026 Ivy Podcast Discovery LLC
