Podcasts about deterministic

How much of construction's cost, delay, and waste begins with information that fails to survive the journey from design to delivery? That question runs through my conversation with Julian Geiger, Chief AI Officer at Nemetschek Group, as we look at the practical role of AI across architecture, engineering, construction, and operations. Julian describes what he calls the industry's 90, 40, 20 problem. According to the figures he shares, 90 percent of projects are over budget or over time, the built world accounts for roughly 40 percent of global carbon emissions, and around 20 percent of material is wasted. His argument is that many poor outcomes start as information and decision problems. Each project phase may work reasonably well on its own, but handovers can strip away context. A building information model becomes a PDF, a PDF becomes an email, and a decision may never be recorded against the object it changed. We discuss how AI, building information modeling, and digital twins can identify missing information, scope gaps, clashes, and design choices before they become expensive construction site problems. Julian shares Nemetschek's work bringing Firmus AI into Bluebeam to review two dimensional drawings, then explains the broader goal of feeding lessons from construction back into design and engineering tools. The commercial promise is easy to understand. Finding a mistake while a wall exists only in software costs far less than finding it after workers and materials are waiting on site. The conversation also moves beyond the assumption that every task needs the largest available model. Julian sets out a four tier approach. Deterministic calculations such as structural math should remain deterministic. Stable, high volume checks may be handled by conventional rules. Smaller domain models can classify objects, retrieve data, and interpret geometry close to the source. Frontier models earn their place when the work involves ambiguity, reasoning across documents, or several dependent steps. His test is refreshingly practical: use the least expensive method that is reliably right and fast enough for the person waiting on the answer. That discipline matters when finance teams ask for proof. Time saved on drawing reviews or tender preparation can be measured quickly, while reductions in rework or missed issues require a longer data series. Julian also notes a familiar problem for enterprise AI programs. If a firm never established a baseline, it becomes difficult to show what improved. Usage can indicate that people find a tool useful, but adoption alone does not settle the return on investment question. Data sovereignty adds another layer. Construction files can include valuable designs, commercial information, and details tied to national infrastructure. We discuss where the data is stored, who processes it, which jurisdiction applies, and whether customer material is used for model training. Julian argues for separating genuine intellectual property from routine usage data, then matching controls to the sensitivity of each project rather than treating every data set as identical. Finally, we consider people. In an industry facing a skills shortage, removing junior roles creates a future shortage of experienced professionals. Julian sees AI as a way to shorten the apprenticeship period and reduce repetitive documentation, while preserving a clear line of accountability: AI proposes and a qualified human decides. Could that model help construction professionals spend more of their time on judgment, design, and better buildings, and where should the industry draw the line? Listen to the episode and share your thoughts with me

Introduction What does an insurer actually own after a few years of buying AI one use case at a time? Feathery co-founder Zack Khan argues that the answer decides whether AI ever produces more than incremental gains, because a workflow you cannot change without filing a vendor ticket is a workflow you are renting. Fresh off a $30 million round led by Portage, with Allstate and Erie both in as strategic investors, Khan walks host Joshua R. Hollander through what it takes to put workflow control in the hands of the operating teams themselves. Guest Bio Zack Khan is Co-Founder of Feathery, the AI operating and decisioning system for financial services. He started as a software engineer building complex forms at Robinhood and Nextdoor alongside co-founder Peter Dun, then became the fifth employee at Hightouch, where he led marketing as the company grew toward a $3 billion valuation. Khan and Dun founded Feathery in 2021 as a developer-focused form builder and grew it into a platform that now serves more than 300 firms, including Tokio Marine, Hiscox, Baldwin Group, and Hylant, orchestrating submission intake, virtual inspections, quoting, and benefits workflows across carriers, MGAs, and brokers. Feathery ran profitably before raising its $30 million round in July 2026. Key Topics -Vendor-led vs. team-owned workflows - Why the last generation of insurance software put every change behind a vendor ticket, and what changes when underwriting and operations teams define workflows in natural language instead. -Copilot licenses vs. step changes - Giving everyone a copilot produces small gains; redefining one workflow end to end took a carrier from an eight-hour time to quote down to fifteen minutes. -Orchestrate rather than replace - Feathery's agent, Robin, works across existing rating engines, AMSs, and even legacy desktop applications, automating the data entry instead of ripping out the system. -Rules where you want them, judgment where you need it - Deterministic guardrails handle rating inputs, while objective-driven agents handle tasks like checking a virtual inspection for a pool or a tree touching the roof. -The Baldwin Group benefits example - Custom employee benefit guides that took 20 to 30 or more hours to build now generate in minutes, across 10 to 15 assets and multiple languages, and the team's hours moved to consultative work. -What a platform actually is - Khan's test is control: if you cannot apply your own business logic to your own workflow without a vendor ticket, you bought a point solution. -Start in specialty lines - Fast-growing lines with less tech debt and less red tape are where AI transformation finds its first champions inside a large carrier. Notable Quotes "An actual platform gives you control. If you don't have fundamental control over the actual experience, and you're applying your own firm-specific business logic to your workflow, that is a point solution." "If your rating engine is working great, it's just the annoying part is typing data into it. You don't rip out the whole rating engine; you just automate the data entry part of it." "Some of our customers have gone from an eight-hour time to quote to less than fifteen minutes responding to producers. That's the stuff that actually will impact your bind ratio." "For AI to truly have the impact you want, you need to redefine the workflow, think about the end-to-end ideal process that you want your best underwriter, your best claims adjuster, your best producer to be following." Resources Guest: Feathery: https://www.feathery.io/ Zack Khan on LinkedIn: https://www.linkedin.com/in/zackkhan101/ Host & Organization: Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ Horton International (USA): https://www.horton-usa.com/ Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

We built a working AI agent in 5 minutes. Not a demo. Not a prototype. A live agent managing my inbox. I sat down with Akshhat at the Kore.ai office in Hyderabad, and one thing became very clear. The prototyping era is over. Organizations in healthcare and banking, some of the most regulated industries on the planet, are now deploying 50 to 100+ agents to run complex, real-world workflows. This is production, not experimentation.But here is what surprised me most. You do not need to be a massive enterprise to do this.As a content creator, my biggest bottleneck is a flooded inbox. So Akshhat challenged me to build an Inbox Assistant Agent on the new Kore.ai Agent Platform, the Artemis edition. Here is how we did it in 5 minutes with zero code:- We started with Arch, the AI agent architect. Plain natural language commands. No coding.- We uploaded my existing SOP document directly into the chat. The platform ingested it, broke down the requirements, and structured the architecture on its own.- It designed a multi-agent topology. An Inbox Agent to read and draft responses. A Reviewer Agent to enforce quality control before anything goes out.- Governance was built in from the start. Deterministic guidelines and custom guardrails keep the agents from hallucinating or going off-script.- Before deployment, the platform automatically ran 100 test conversations to benchmark safety, accuracy, and responsiveness. Evaluation first, deployment second.- We connected my Gmail securely in seconds. The agent went live in the background.This is why analysts are paying attention. Kore.ai was just named a Leader in the 2026 Gartner Magic Quadrant for Conversational AI Platforms and a Leader in The Forrester Wave for Conversational AI. Very few vendors hold both.The paradigm has shifted. We are moving from test-driven development to autonomous execution with human escalation built in.If you can write out your business process, you can build an agent to run it. That is the takeaway.Thank you Akshhat and the Kore.ai team for the walkthrough.Are you integrating agentic workflows into your daily operations yet? Let's discuss in the comments.#aiagents #agenticai #koreai #enterpriseai #conversationalai #generativeai #dataandai #theravitshow

In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Dr. Jacek Marczyk, co-founder and CEO of BioDynLab, about a contrarian view of computational drug discovery: that the next leap may come not from more data and bigger models, but from physics.Dr. Marczyk brings a background in aerospace engineering, automotive, Silicon Graphics and complexity science. His work led to quantitative complexity theory, which he now applies to molecules through BioDynLab's deterministic, training-free approach.The conversation explores why high precision and high complexity cannot coexist, and why throwing more compute at biological problems does not automatically produce useful knowledge. Dr. Marczyk argues that machine learning can produce impressive outputs, but without explainability, teams may get a result without understanding the physics behind it.He explains how BioDynLab uses molecular dynamics and complexity theory to study how atoms and amino acids move, how information flows through molecules, and which residues act as key “hotspots” in that dynamic system. Instead of treating molecules as static structures, this approach looks at the motion and information patterns that help determine biological function.The key message is that AI and physics should not be seen as enemies. In data-sparse areas such as rare diseases, novel targets and first-in-class chemistry, physics-led methods may offer a complementary route to insight, especially where machine learning has little or no training data to rely on.Topics CoveredWhy pharma's AI gold rush may miss key biologyThe principle of incompatibilityPhysics-first drug discoveryQuantitative complexity theoryWhy explainability mattersMolecular dynamics and information flowAtomic and amino acid participation factorsComplexity hotspots in moleculesStatic structures versus molecular motionRare disease and data-sparse discoveryAbout EularisEularis builds AI capability inside pharma and biotech — over 20 years applying AI to real pharmaceutical problems, inside real pharmaceutical and biotech companies.Keynotes and live sessions — Working sessions for pharma teams where nobody leaves with notes. They leave with working prompts and real capability they've already run on their own work.The AI Enablement Institute — Strategy and a workshop get you started; neither stays current. Most pharma companies already have a generic AI course library. None of it is written for a regulatory writer, an MSL or a market access lead trying to get today's work done. Training is an event; enablement is capability that stays current. The Institute runs shared foundations for the regulated constraints, then tracks by business unit function, with new content monthly, live office hours with Dr Andree Bates, and per-person records a sponsor can show an auditor. One price per business unit, no per-seat charges.AI Strategic Blueprint and Governance — Board-ready strategy that links initiatives to commercial outcomes, with the sequencing, governance, capability and financial logic to survive scrutiny.AI Custom Builds for BioPharma — Design and build of the AI solutions that make strategic sense in your operating reality, tied back to the Blueprint.Start with the Institute → https://eularis.com/institute/Everything else → https://eularis.comDr. Andree Bates⁠ LinkedIn⁠ |⁠ Facebook⁠ |⁠ X

Sponsored by Guardsquare (guardsquare.com), Episode 331 focuses heavily on the growing role of AI agents in application security and how organizations should build and defend against agentic systems. Ken and Seth argue that effective AI security systems should combine deterministic tooling with the probabilistic reasoning of LLMs rather than handing an entire security workflow to a model. Deterministic steps can map repositories, identify dependencies, reconstruct code relationships, and narrow the areas requiring investigation, while LLMs provide reasoning and creativity where those capabilities add value. Preparing for AI-assisted attackers, emphasizing secure development practices, guardrails, sandboxing, pre-production testing, and faster detection and response. The episode also examines HTTP request smuggling and CRLF-based attacks, including how differences in request parsing between proxies and backend services can create authorization bypasses and other exploit chains. Seth and Ken emphasize identifying the critical vulnerability within an exploit chain and discuss how service-oriented architectures can increase risk when components interpret the same request differently. Finally, they question whether bug bounty programs adequately reward researchers for discovering complex, high-impact vulnerabilities, especially as AI agents increasingly automate vulnerability discovery.

The storefront stopped being the differentiator. Kibo CEO Ram Venkataraman argues the money and the difficulty both moved to order management, and that most B2B distributors have an OMS problem they have no name for.Rick opens on accountability. An autonomous agent takes an order, routes it to the wrong warehouse, approves a return it shouldn't have. Who owns that outcome? Ram says shoppers will blame the retailer every time, and the burden falls on vendors to build systems that earn the retailer's trust. He also draws a line most vendors blur. Kibo's routing runs on machine learning models, not LLMs, because LLMs stay too probabilistic for that job today. The LLM work sits in configuration and explainability, and every write operation keeps a human in the loop.Also in this episode: why Ram calls OMS the margin layer and a conversion rate optimizer; how account hierarchies, quoting and scarce supply make B2B order matching harder than first come first served; Ace Hardware as roughly 5,000 separately owned businesses running their own pricing on one platform; Vulcan Materials selling construction aggregates by the truckload to contractors and by the bag to homeowners; Kibo's path from Vista's 2016 roll-up through the Mozu rebuild and the Certona and Monetate divestiture; and Ram's answer on what the Forrester Wave placement should mean to a buyer. The commissioned Forrester Total Economic Index study can be found here: https://kibocommerce.com/resource-center/forrester-total-economic-impact-oms/Plus the one tell that exposes a distributor with an order management problem. Out of stock on the website while the product sits in the warehouse.The Watson Weekly interview is sponsored by Avalara.. See what they built for growing brands at avalara.watsonweekly.comChapters 00:00 Who owns the outcome when an agent gets the order wrong 03:04 What Kibo is and the four complexity vectors 07:00 Why the energy moved to the back office 09:20 What B2B calls order management instead 13:01 Ace Hardware and Vulcan Materials 16:28 Sponsor: Avalara 19:48 Engage, configure, explain, analyze, optimize 23:20 Deterministic vs non-deterministic order workflows 25:55 Where Kibo's growth is coming from 29:09 Vista, Mozu, and the Forrester Wave 33:50 The one sign you have an OMS problem#watsonweekly #KIBOcommerce #ordermanagement #b2bcommerce #acehardware

Stewart Alsop sits down with Juan Verhook, founder of Tender Market, for a second conversation that ranges from the mechanics of European public tenders to the future of how we organize digital information. They cover how Tender Market helps smaller companies work around barriers like SOC 2 and ISO certification requirements, the surprising scale of public procurement (roughly 20% of GDP), and how AI and machine learning are reshaping the bidding process. From there the conversation opens up into bigger territory: the changing tolerance for being wrong in an AI-saturated information landscape, how language and culture shape perception, the reverse Turing test and the challenge of verifying human versus AI identity online, and Juan's daily workflow running eight or nine MCP servers through Claude Code. They close out talking about whether the folder and file system will survive the shift to AI-native interfaces, tying back to Stewart's own Stewart Squared episodes on the history of the PC. You can visit Tender Market at tendermarket.eu.Timestamps05:00 — Tender Market's origin story and how they help smaller companies work around SOC 2 and ISO certificate barriers.10:00 — Public procurement and its scale, roughly 20% of GDP, plus a look at public-private partnerships.15:00 — Local LLMs on a plane with no Wi-Fi, and comparing local model performance to frontier models.20:00 — Supply versus demand in AI infrastructure and whether hyperscaler token efficiency is quietly improving.25:00 — Whether AI will replace knowledge work tasks, and the shifting reality of what lawyers and other professionals actually do.30:00 — Reverse Turing test, digital identity verification, and the idea of a "pre-AI internet."35:00 — Model poisoning, RLHF, and the difference between pretraining and post-training.40:00 — Interleaved tool calling and how Tender Market ties pricing to task deliverables instead of billable hours.45:00 — RAG versus fine-tuning, prompt engineering, and when context windows actually matter.50:00 — Deterministic programming versus probabilistic agents, and when to build custom tools versus buy existing ones.55:00 — Juan's daily MCP stack (Supabase, GitHub, Calendly, CRM), and whether the folder-and-file system will survive the shift to AI-native interfaces.Key InsightsCertification requirements aren't dead ends—they're routing problems. When smaller companies got rejected from tenders for lacking SOC 2 or ISO certificates, Juan didn't turn them away. He found that EU procurement rules allow bidding as a consortium or subcontracting to a certified partner, turning a disqualifier into a workaround that builds trust with clients.Public procurement is a massive, underexamined market. Roughly 20% of GDP flows through public purchasing of private-sector goods and services, yet most people have no visibility into how tenders work or how governments post and award these contracts.Being wrong has become more socially acceptable. Juan traced this shift to the falling cost of information: in the Stack Overflow era, giving a wrong answer was costly, but now that answers are instant and abundant, both mistakes and corrections happen faster, changing how people learn and communicate.Task-based pricing beats hourly billing for AI-era services. Rather than charging per hour, Tender Market prices around the deliverable, winning a tender, which avoids the perverse incentive of hourly billing to be inefficient and instead rewards actually solving the client's problem.RAG and fine-tuning solve different problems. RAG helps a model reference large documents without hitting context limits, while fine-tuning changes a model's internal weights so it learns new behavior or style. Juan noted that true RAG use cases needing thousands of pages of context are rarer than the hype suggests.Deterministic code should replace repeated LLM calls once a pattern is found. Stewart described his own workflow: solve a task with an LLM a handful of times, then convert the repeated pattern into deterministic software so tokens are no longer spent on it, freeing the model for genuinely new problems.AI agents are never truly autonomous. Both hosts agreed that no matter how many steps an agent chains together, a human operator always initiates the first prompt, meaning accountability and intent trace back to a person even in multi-agent systems.

In this episode 600 of the TestGuild Automation Podcast, Joe Colantonio talks with Jason Arbon, founder of Testers.ai, Jank.AI and IcebergQA and author of the new book Testing AI: Engineering Confidence in Non-Deterministic Systems. Take Our 2027 Survey Now: https://testgld.link/27data Jason makes a case most testers have not heard yet. Coding is being absorbed by AI. Specification work is thinning out. Product, development, and test roles are converging into one. And when the music stops, the only seat left belongs to the person who can look at what the machine produced and make an evidence backed call on whether it ships. He calls that confidence engineering, and he argues it is not a rebrand of QA. It is what QA was always supposed to be. Along the way, Joe and Jason get into the containment problem and why alignment, not lockdown, is now the real safety goal. They dig into why testing cost scales quadratically, meaning ten times more generated code creates roughly a hundred times more testing demand. Jason also pushes back hard on skeptics of agentic testing, pointing out that almost nobody has run the obvious experiment of testing a site themselves for a week and comparing their results against what AI finds. You will also hear Jason's most practical piece of advice in the whole conversation. If you are not running the same suite five times against the same build and looking at the actual results, not just flake, you are not testing seriously in an AI world. Plus a detour into grokking, the Chinese Room, and Geoffrey Hinton, because it would not be a Jason Arbon episode without one. Listen up!

Trainer Miguel Clement joined this week's TDN Writers' Room to discuss a memorable weekend, from his late father's Hall of Fame induction to Deterministic's GI Fourstardave victory. The team also recapped the four Grade I races on Whitney weekend at Saratoga and debriefed the Fair Hill shippers situation.

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/Checkout our episode stacks on Stacklist! https://stacks.codestory.co/ Hosted by Noah Labhart | Technical Founder & Startup Mentor.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Today's guest is Charlie Guo, developer experience engineer at OpenAI. With Charlie, we explore the evolution of AI coding tools, how this is affecting team organization, and the future of AI integration in software development. Charlie shared strategies and tricks to leverage AI effectively, and how this is all transforming the workflows that we all know and love.(00:00) Episode start(01:20) Introducing Charlie Guo(02:31) Inside the Codex team(03:49) Sponsor break(05:35) The philosophy behind Codex(09:19) From CLI to desktop app(12:29) The m.google.com of AI(17:28) Are MCPs here to stay?(21:58) Sharing AI workflows across teams(24:56) Generalists vs specialists: converging roles(33:22) Deterministic guardrails for coding agents(39:02) Using Codex as a personal knowledge base(46:01) Design with the image model first-Today's episode is brought to you by Notion.Learn more about Notion's Developer Platform today at https://ntn.so/refactoring-You can also find this at:•

We have a special return episode, by our good friend Rickard Hansson. Rickard joined us previously on the podcast in Season 8 to tell the creation story of Weavy - collaboration infrastructure for serious builds. Today, he makes a follow up visit to tell us all about Gainable, his new project - which removes data and engineering from being the middle man, and enables your team to build the apps they need now.Questions;Last time we talked in Season 8, you were building Weavy. Whats happened since we last talked with that company?Tell me about Gainable - give me the pitch there, and tell me why this is the right approach to using AI.Most AI builders wire straight to a frontier model and wait for the next release to fix the gaps. I didn't. Where does the model actually sit in Gainable product, and why only there?Why is an app factory that is deterministic important? Dig into that.You use the term "free-range coding".. what does this mean? Unpack the phrase for us.You point out that tokens still appear to be heavily subsidized to me. What do you mean by that, and what happens to all these AI products when that ends?We've all read the headlines - Fable 5 got switched off by the government for 18 days. Why do you see this as a turning point, not a footnote?You suspect flat subscriptions for the top models are done, and it all drifts to credit-based. What signal are you seeing that tell s you this?If the model is a commodity everyone rents, where's the moat?What is next for Gainable, and how can someone get started using the platform?SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://www.gainable.dev/https://www.weavy.com/https://www.linkedin.com/in/rickardh/https://codestory.co/podcast/bonus-rickard-hansson-weavy/Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Join the Millionaire University AI Mastermind at ⁠⁠⁠MillionaireUniversity.com/AI⁠⁠⁠ #1025 What if the fastest way to get more out of AI isn't building complex automations — but simply asking better questions? In part 2 of this 2-part episode, host Brogan Williams continues his conversation with entrepreneur David Mitchell to explore practical, everyday ways business owners can put AI to work. David shares why AI should be treated as a thought partner instead of a magic solution, how to identify the highest-impact tasks to automate first, and why research, meeting summaries, and email management are some of AI's most underrated use cases. They also discuss building a personal AI "second brain," choosing between tools like ChatGPT, Claude, Gemini, and Grok, and simple strategies to help beginners start using AI with confidence today! What we discuss with David: + AI is a tool, not magic + Find your biggest pain point first + Deterministic vs. agentic AI + Build an AI "second brain" + Choosing ChatGPT, Claude, Gemini, or Grok + Use AI for research and summaries + Turn meetings into actionable notes + Train AI to match your style + Start with simple everyday tasks + Let multiple AI models challenge each other Thank you, David! Check out Part 1 of this episode. Check out Dialed in Trades at ⁠DialedinTrades.com⁠. Follow David on ⁠Instagram⁠, ⁠Twitter⁠, and ⁠YouTube⁠. Watch the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠video podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ of this episode! Get your FREE 5 Minute Business Plan at ⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/Plan⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/Sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

0:00 Welcome and intro1:00 Origin story: a 2002 AI research spinoff that became Markup AI3:27 Why Matt says "content is the next code"4:16 Real client results: productivity, quality, and the previously unimaginable6:13 Cloud infrastructure and how Markup AI works across multiple LLMs7:29 How Markup AI proves impact with standards and risk scoring9:32 The agent to agent economy and machine readable brand11:12 A quick detour on the metaverse and multiverse content11:48 Matt's life outside the CEO seat12:32 Teaching media literacy in the age of AI13:57 Gartner's guardian agents and why AI has to check AI15:02 Deterministic trust scores explained15:51 Who checks the checker: verifying Markup AI's own agents16:17 Selling safety, confidence, and revenue through search visibility17:10 The shift from traditional search to AI powered discovery18:24 Where paid search fits in an AI first world19:55 Predicting search one to two years out21:37 Tackling the AI slop problem22:55 AI influencers worth following24:10 What a great 2026 looks like for Markup AI24:48 Where to find Matt and Markup AI

AI agents are good at producing outcomes. The harder question is whether they can produce the same outcome twice, through a process you can inspect, test, and trust. In this episode, Keith Townsend talks with Adam Jacobs, CEO of Swamp Club and founder of Chef, about deterministic automation in the age of AI agents. The [...]

Topics covered in this episode: Some more things about Django I've been enjoying Who cleans up after the vibe-coding party? Where Did All Your AI Tokens Go? AgentsView to the rescue! Careful with phishing all Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Some more things about Django I've been enjoying Julia Evans is learning "2010-style" web dev (Django + SQL + server-rendered HTML) after years of Go backends and JS-heavy frontends Query builders: likes defining custom QuerySet classes with chainable filter methods (.approved().future().with_tags()) — more readable than raw SQL Template filters: highlights urlize, linebreaksbr, json_script, and especially querystring for building/modifying query-string links in templates Migrations: still loves Django's auto-generated migrations — 19 and counting on her project Skips inheritance for class-based views; prefers function-based views for sharing code, though fine using Django's own mixins/interfaces Performance surprise: CPU profiling (via py-spy) — not slow DB queries — revealed the culprit; she'd accidentally disabled the cached template loader, and re-enabling it took throughput from ~2-3 req/s to ~12 req/s on a $10/mo VM Michael #2: Who cleans up after the vibe-coding party? FT Magazine piece by Sam Learner (July 11) on AI coding tools overwhelming open source maintainers - sent in by listener Dylan McConnell, whose main point was that this ran in the Financial Times, not a dev blog. cURL as the case study - Daniel Stenberg has been the only full-time person on it for years; libcurl has been installed an estimated 20+ billion times with 3,000+ listed contributors. Bug bounty killed - cURL ended its paid security bounty program in January, citing an "explosion of AI slop reports" that take real time to debunk and drain morale. Extractive contributions - authoring a PR is now nearly free, reviewing one still costs a human; tldraw's Steve Ruiz closed outside contributions entirely, asking why he'd want someone else writing the easy part. Guido weighs in - van Rossum says projects are holding emergency meetings over the slop flow, and notes LLM patches tend to touch unrelated parts of a file, making review more tedious. "Vibe Coding Kills Open Source" - paper from Miklós Koren's group: packages frequently recommended by coding models saw big download jumps with no matching engagement, breaking the reputation loop that sustains maintainers. Stack Overflow flatlined - over 100,000 questions a month before ChatGPT, under 1,500 last month, with the response rate cut roughly in half; the public archive is now stale training data. The course-creator angle - Josh Comeau's newest web dev course launched at about a third of prior enrollment, and he worries about devs who never learn which questions to ask. But the most interesting portion is what was omitted. Focused on: The end of the curl bug-bounty Omitted: High-Quality Chaos Why the omission is interesting It fits a narrative. The FT piece is a maintenance-and-decline story, and January-Stenberg is a perfect witness for it. April-Stenberg complicates it - same person, same project, better data, opposite direction on the specific claim being used. The tell is already in the article. Learner quotes Stenberg saying AI tools are much better at finding problems than fixing them. That's the April thesis in one line, and it goes undeveloped. Reason for the shift is process, not vibes. Killing the bounty removed the cash incentive and the venue change filtered the rest. Worth saying out loud, because "AI reports got better" isn't quite it - "no bounty plus a real triage platform" is closer. Joke too: Sarah O'Connor wrote a related piece (is this just before skynet launches?) Calvin #3: Where Did All Your AI Tokens Go? AgentsView to the rescue! Local-first desktop/web app for browsing, searching, and analyzing your past AI coding agent sessions (Claude Code, Codex, Copilot, Cursor, Gemini, Aider, and dozens more) Auto-discovers session files on your machine — no config needed; everything stored locally in SQLite, no cloud/accounts agentsview usage is a drop-in ccusage alternative — reads from pre-indexed SQLite, reports run 80–220× faster on large histories New Activity dashboard shows peak concurrency, active vs. idle time, agent-minutes, and cost — filterable by project/agent/machine, with a -json CLI report too Full-text + optional semantic search across every session; also imports Claude.ai/ChatGPT chat exports Install via pip install agentsview, uvx agentsview, brew install --cask agentsview, or download desktop binaries from GitHub Releases Michael #4: Careful with phishing all The situation I pass this along because it was a pretty sneaky bit of targeted phishing, and happened to play off an old interaction in bandit's repo. As usual with phishing scams there are a bunch of tells that this isn't legitimate, but just enough plausibility that I could see falling for it in a weak moment. Relative nobodies like me haven't historically been worth the effort to hit with scams this specific. Agents change the game though :-/. Be careful out there folks! Original message From: "Patrick (Blacktrace)" [HTML_REMOVED] To: LISTENER EMAIL Subject: Your Bandit #1350 (B105 NextToken false positive) -- just fixed that exact case Date: Wednesday, July 15, 2026 12:02 AM Hi AJ, Saw your Bandit issue #1350 -- the B105 hardcoded-password false positive on the string NextToken. I build a deterministic gate that filters that class of Bandit noise, and #1350 was literally the case I just fixed: NextToken / next_token / page_token / nextPageToken now stay quiet, while a genuine hardcoded token like api_token="sk-live-..." still fires. Verified against your exact case. 30-second paste: https://blacktrace.co/noise-eraser Where it still trips, published: https://blacktrace.co/kruc Curious whether it clears what you hit -- and if it trips on something of yours, that's the more useful reply. Patrick, Blacktrace I asked Claude for some analysis too. It was pretty good at finding them. The message name-drops enough real detail to feel legit, but the structure is pure phishing - everything in it exists to get AJ onto blacktrace.co. The strongest ones: Freemail sender, corporate signoff. Signs as "Patrick, Blacktrace" but sends from emailpjv@gmail.com. Real company outreach comes from the company domain, not a personal Gmail - and there's no last name. Over-specific targeting. It mirrors AJ's exact public activity - issue #1350, the B105 rule, the NextToken false positive, even the token variants. That's the "just enough plausibility" AJ flagged, and it's exactly what agents make cheap: scrape a GitHub issue, auto-generate tailored bait. Legit cold outreach rarely reads your history back to you this precisely. The entire payload is two links. Strip the technical flattery and the message is just "paste here" plus "see results here." When the whole point of an email is the click, that's the tell. "30-second paste." Low-friction urgency, and "paste" most likely means paste your source into their tool - handing your code to a stranger's site. Exfiltration dressed as convenience. Brand-new, no-reputation domain. blacktrace.co has no track record, and the name is doing some ominous work. The /kruc slug is random noise, not how real product pages get named. Precise-sounding jargon that's actually vague. "Deterministic gate," "noise-eraser" - impressive, empty. Bolted onto correct real details (B105 is the Bandit hardcoded-password test, sk-live- is a Stripe live-key prefix) to borrow credibility. The disarming close. "if it trips on something of yours, that's the more useful reply" - engineered humility that flatters your expertise and baits a response. Makes engaging feel like you're doing them a favor, which drops your guard. Extras Calvin: DjangoCon US 2026 is rapidly approaching, August 24-28, Chicago Ruff v0.16.0 massively expands its default rule set Ruff now enables 413 rules by default, up from 59 https://astral.sh/blog/ruff-v0.16.0 Michael: Completely redesigned the home page. Try /insights in Claude Code (terminal) Joke: We're Safe

Why AI Mental Health Chatbots Fail When It Matters Most: The Hidden Vulnerabilities Stress-Testing Reveals - An Interview with Shirali and Arul Nigam of Circuit Breaker Labs Shirali and Arul Nigam of Circuit Breaker Labs on why AI mental health chatbots fail, how stress-testing exposes their hidden vulnerabilities, and what therapists need to know. Curt and Katie talk with Shirali and Arul Nigam, the sibling co-founders of Circuit Breaker Labs, about what therapists tend to get wrong about AI, why the safety infrastructure behind many mental health chatbots is weaker than it looks, and how their team stress-tests these tools to find dangerous failures before real users ever encounter them. Generative AI is probabilistic, so the same prompt can return a safe answer one moment and a harmful one the next. Shirali and Arul explain how guardrails get bypassed by a misspelled word, a teenager's slang, or the hundredth message in a long conversation, why mental health chatbots tend to fail in the moments that matter most, and what stress-testing hundreds of thousands of simulated conversations actually reveals about model safety. The conversation closes on what clinicians can do now, why clinical insight is the missing ingredient in AI safety, and why third-party validation is becoming the standard regulators and developers expect. Used well, AI can be a supplement to care or a gateway to a human therapist, but it is not a replacement, and getting there safely starts with building clinical insight in from the foundation. In this episode, we discuss: - Why people usually turn to AI in place of no care, not in place of a therapist - Why generative AI's unpredictability, not a single bad answer, is the real safety problem - How a misspelling, slang, or a long conversation can slip past chatbot guardrails - Why AI mental health chatbots tend to fail in the highest-risk moments - What stress-testing hundreds of thousands of conversations reveals about model safety - Why clinical insight is the missing ingredient, and what clinicians can do now Timestamps: 0:00 - Introduction 1:38 - Meet Shirali and Arul Nigam and Circuit Breaker Labs 3:37 - What therapists get wrong about AI 5:28 - Deterministic versus generative AI, and why the risk scales 8:26 - The safety problem in AI mental health: trust, training data, and agreeableness 11:29 - Guardrails, lifeguard models, and the 988 problem 17:30 - Deploying clinical insight at scale and building safety in from the start 21:09 - How stress-testing works: context pollution and adversarial simulation 27:11 - What the stress tests reveal: variance, typos, and bypassed guardrails 30:59 - Regulation, credential hallucination, and third-party validation 36:58 - What clinicians can do, and the missing clinical insight 40:20 - Where to find Circuit Breaker Labs Guest Bios: Shirali and Arul Nigam are siblings and the co-founders of Circuit Breaker Labs, which autonomously pressure-tests the AI systems that interact with people to find hidden mental health vulnerabilities before they reach real users. Shirali brings expertise in neuroscience, translational research, and clinical work, with experience at the Howard Hughes Medical Institute's Janelia Research Campus, NIH NINDS, Harvard's Wyss Institute, Johns Hopkins, and Children's National. She holds a BS in Biomedical Engineering from The George Washington University and an MBA from The Wharton School, University of Pennsylvania. Arul has conducted technical and policy research on ethical AI, with a focus on bias and fairness, at Georgetown University and Thomas Jefferson High School for Science and Technology, and holds a BSBA in Operations and Analytics from Georgetown University. Learn more at circuitbreakerlabs.ai. Full show notes and transcript: mtsgpodcast.com Join the Modern Therapist Community Patreon: https://www.patreon.com/c/mtsgpodcast Facebook Group: https://www.facebook.com/groups/therapyreimagined Modern Therapist's Survival Guide Creative Credits Voice Over by DW McCann: https://www.facebook.com/McCannDW/ Music by Crystal Grooms Mangano: https://groomsymusic.com/

The traditional retail media playbook is undergoing a massive transformation as platforms shift away from transactional clicks toward holistic, real-world shopping experiences. This week, Sam's Club VP and GM Harvey Ma, breaks down how their membership data and cross-enterprise synergy with Walmart are setting a new standard for full-funnel brand measurement. Key Highlights

In this episode of Shift AI, Patrick Hillmann, Chief Strategy Officer at Logical Intelligence, joins host Boaz Ashkenazy for a conversation about why the next era of AI cannot be built on probability alone.Patrick shares his unconventional path into AI, from crisis communications and cybersecurity work at Edelman and General Electric, to steering Binance through its most turbulent years and a major DOJ settlement. At Logical Intelligence, Patrick now works alongside Yann LeCun, a Fields Medalist, and engineers from Meta, Google, and Cruise to build deterministic, energy-based reasoning models.Patrick explains why LLMs behave like a confident intern, fast and articulate, but wrong in ways you only catch if you already know the answer, and why critical systems like power grids, hospitals, and self-driving cars need a layer of certainty that probabilistic systems cannot provide. He and Boaz dig into Logical Intelligence's benchmark results, including a 98% score on the notoriously difficult Putnam math competition, and a public Sudoku test where their energy-based model, Kona, beat every major LLM combined while running on a fraction of the compute cost. This episode is essential listening for CTOs, technical leaders, and anyone trying to understand what comes after the current generation of large language models.Chapters[00:00] Patrick's Improbable Path: From Grad School to the Front Lines of a Geopolitical Crisis[02:43] From Binance to Chief Strategy Officer at Logical Intelligence[02:54] The First Paid Job: Unloading UPS Trucks in 100 Degree Heat[04:31] The UPS Lesson That Still Shapes How He Thinks About Work[04:47] Why LLMs Are Confident Guessing Machines, Not Truth Machines[07:14] The Team Behind Logical Intelligence: A Fields Medalist, Yann LeCun, and Math Olympiad Engineers[08:55] Is Logical Intelligence Betting Against LLMs?[10:32] The AI Sandwich: Where LLMs, Reasoning Layers, and World Models Fit[12:36] The Putnam Benchmark and Why Formal Proofs Don't Get Partial Credit[15:44] What Is an Energy-Based Model, Really?[19:54] Eve Badia's 15-Year Path to the Energy-Based Reasoning Model[22:04] Formal Verification and the Future of Secure Code Generation[23:33] When Unverified Code Fails: The Molson Coors Ransomware Story[25:39] Why AI Coding Tools Create Rat's Nests Engineers Can't Debug[28:53] The Sudoku Test: 98% Accuracy for $4 vs $14,000 for the Leading LLMs[31:13] ByteDance, China, and the Race for Formal Methods[33:42] Two Words for the Future of AI: Chaotic Determinism[37:12] Where to Follow Logical Intelligence and Founder Eve BadiaConnect with Patrick HillmannLinkedIn: https://www.linkedin.com/in/crisiscommunicationsConnect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Nick Jain and Daniel Settel, co-founders of Eagle Rock CFO, to discuss how AI is reshaping FP&A and fractional CFO services. Nick and Dan explain how their AI-powered system combines structured data processing, automation, and financial expertise to help companies analyze complex financial data faster, reduce manual workload, and uncover hidden value in their operations.Dan and Nick are co-founders of Eagle Rock CFO, a financial advisory firm helping mid-size businesses grow faster and improve profitability. They combine AI and technology to deliver operational finance insights at a fraction of traditional consulting costs. Both are Harvard Business School graduates, with undergraduate degrees from Stanford and Dartmouth. Dan previously co-founded FinTech company Zanbato and worked as a professional investor at PrimeCap, while Nick has experience in private equity investing and scaling companies across SaaS, footwear, and trucking.In this episode, you will discover:AI works best when paired with structured financial data, not raw inputsWhy deterministic systems still matter alongside AI in finance workflowsHow Eagle Rock's 5-step system improves financial analysis accuracyWhy human oversight is still needed in AI-powered FP&A systemsHow fractional CFOs can save 20–50 hours per month using AI toolsNick and Daniel demonstrate how AI is transforming finance by automating analysis while still relying on structured systems and human judgment. Their approach shows that the future of FP&A is not fully autonomous AI, but a hybrid model where AI enhances decision-making, improves efficiency, and strengthens financial visibility.Follow Nick:Website: https://www.eaglerockcfo.com/LinkedIn: https://www.linkedin.com/in/nickmjain/Follow Dan:Website: https://www.eaglerockcfo.com/LinkedIn: https://www.linkedin.com/in/dsettel/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Trailer[04:10] – Founding Eagle Rock CFO[06:21] – Deterministic vs probabilistic systems[07:22] – Why agents are not enough[09:13] – Data compression and structuring[10:13] – Human oversight in AI workflows[14:51] – Data mess in SMB finance[18:38] – White-label and consulting model[22:18] – How to start using AI safely[29:36] – CFO vs CEO vs CIO experience[33:45] – Closing thoughts

Kingsley Madikaegbu is the founder of HealID, a startup building agentic AI on top of the Model Context Protocol (MCP) for one of the most heavily regulated environments there is: healthcare.Recorded at MCP Dev Summit North America in New York, Kingsley sits down with Alex Salkever of the Agentic AI Foundation to break down how you give patients, doctors, caregivers, and family members each their own agent over the same medical record — without breaching HIPAA, leaking PHI, or letting an agent quietly go off the rails. In this conversation:

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

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.   

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

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:

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

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

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.

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.