Podcasts about ctos

Kishore Ravilla: How CTOs Influence Business DecisionsKishore Ravilla is a CTO with 25 years of leadership across healthcare, insurance, and financial services. In this episode, we explore how technology leaders can better communicate with business stakeholders, why storytelling matters in digital transformation, and how strong execution and operational excellence create lasting business value. To learn more about Kishore, visit https://www.linkedin.com/in/kishoreravilla/__TEACH THE GEEK (http://teachthegeek.com) Prefer video? Visit http://youtube.teachthegeek.comGet Public Speaking Tips for STEM Professionals at http://teachthegeek.com/tips

Rory O'Neill, CMO of Checkout.com, doesn't just solve for payments- he's solving for brand preference in a crowded payments space. And he's doing it by competing on what's different, not what others do better. That insight changes everything, from how you position payments to how you build a team that can sustain growth as a challenger. In the latest episode of Scratch, Rory breaks down the playbook that lets Checkout compete with global giants. Brand preference wins 95% of B2B deals before salespeople ever show up- so your marketing owns the invisible 60% of the buyer's journey. Challenger brands win by picking one fight and building culture around it, not chasing everything competitors do. He reveals the three-part formula: focus your core business, build your culture, reinvest profit. Consumer marketing skills-data, insight, action-are B2B's secret superpower. And his rule: if you wouldn't say it at dinner, don't write it in marketing. The key takeaway: Brand preference wins deals - 95% of the time, the brands on the day-one top-five list are the ones that win. B2B buyers spend 60% of their journey before contacting a salesperson. Define your focus as a challenger - Compete on what's different, not on what competitors do better. Checkout only does digital payments to stay focused while competitors spread across multiple business lines. Three elements beat category norms - Focus on your core business, build the human operating system (culture, people, vision), then reinvest capital in new products. Consumer marketer skills are powerful in B2B - Data, insight, action, brand building, and performance marketing from the consumer world unlock B2B success. Understand stakeholder maps - B2B is complex: CTOs influence CFOs, recommenders influence buyers. Map those relationships to win. Simplify your language - Ditch jargon like "frictionless" and "seamless." Use words you'd use at dinner. Marketing becomes more interesting and understood. Marketing is logic and magic - Be both data-driven and creative. Avoid letting fiefdoms kill integrated work. Join everything together. Watch the video version of this podcast on Youtube ▶️: https://youtu.be/chR0mn9Pum0 Scratch is a production of Rival, a marketing innovation consultancy that develops strategies and capabilities that help businesses grow faster. Scratch is hosted by Eric Fulwiler, and he's joined by Rory O'Neill of Checkout.com in this episode. Find Rival online at www.wearerival.com, LinkedIn  Find Eric on LinkedIn Find Rory on LinkedIn  Say hi at media@wearerival.com, we'd love to hear from you. Rival is a marketing consultancy for brands that want to challenge convention in their category. We're on a mission to understand what challenger brands do differently to grow in categories that are being disrupted, and use a challenger playbook to deliver outsized impact through an integrated, tech-enabled approach. Past guests include CMOs from Mastercard, GE, Shell, Hyperloop, Adobe, PepsiCo, and Papa Johns.If you're interested in learning more about marketing from successful CMOs, we compiled a list of the top 5 CMO podcasts to listen to in 2024; check it out here

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Dave Trier, CEO of ModelOp, to discuss how enterprises can govern, manage, and operate AI at scale. Dave shares insights on implementing AI responsibly, tracking ROI, managing risks, and creating an enterprise-wide AI portfolio that drives value while ensuring compliance and governance.Dave Trier leads ModelOp with a focus on customer value, product innovation, and enterprise execution. With over 20 years in data science, AI, analytics, cloud, and enterprise software, he brings technical expertise and a pragmatic leadership style, helping CIOs, CTOs, and AI leaders deploy AI effectively across organizations .In this episode, you will discover:How enterprises can scale AI responsibly and reliablyThe CFO's role in AI oversight and portfolio managementMeasuring AI value through ROI, usage, and internal feedbackDistinctions between AI governance and traditional data governanceImportance of change management and structured AI adoptionDave provides a framework for enterprise AI adoption, emphasizing disciplined management, measurable impact, and alignment with regulatory and operational requirements. This episode is essential for finance and tech leaders looking to integrate AI at scale while ensuring oversight, efficiency, and business value . Follow Dave:Website: https://www.modelop.com/LinkedIn: https://www.linkedin.com/in/davidetrier/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[02:38] – AI Compliance & Governance Challenges[04:35] – Distinction Between AI & Data Governance[07:28] – Measuring AI Value & ROI[12:41] – Treating AI as a Portfolio of Investments[15:05] – Change Management & Enterprise Adoption[17:39] – Wild West of AI & Need for Rigorous Processes[18:54] – CFO Oversight in AI Implementation[21:00] – Closing Remarks

Most enterprises are renters, not owners, of their technology and AI. Raffi Krikorian, Chief Technology Officer of Mozilla, explains why dependence on a handful of closed model providers means losing control over model behavior, pricing, and your own data.In CXOTalk episode 920, Krikorian lays out where open-source AI actually wins in the enterprise, how lock-in happens quietly, and what CIOs and CTOs should do about it now. Krikorian draws on his experience building infrastructure at Twitter and running the self-driving division at Uber to ground the discussion in real engineering and economic tradeoffs, not hype.YOU'LL DISCOVER✅ Why 85% of enterprises believed they could switch AI vendors, but only about 30% actually could when they tried✅ The "renters vs. owners" framing and what it means to control your AI destiny✅ Why Krikorian wants data "protected by architecture, not legal handshakes"✅ How Pinterest reportedly saved on the order of $10 million in a single quarter by switching from closed to open models✅ Why IT is becoming "the HR team for agents," and the read/write "dangerous triangle" of agentic permissions✅ The case for recording your prompts and running your own evaluations instead of trusting public benchmarks✅ Why roughly 70% of enterprise GPUs sit idle, and the missing "LAMP stack for AI" that could put them to work✅ How closed "validation machines" can quietly steer answers toward sponsored outcomes⏱️ TIMESTAMPS (estimated, verify before publishing)0:00 Renters vs. owners: who controls enterprise AI2:26 The risks of depending on closed model makers6:23 How lock-in happens and where open source fits9:53 Regression testing and building your own evals13:24 Pricing instability and the post-IPO cost question23:31 Governance: IT as HR for AI agents32:38 Can a small organization own its AI stack end-to-end?38:47 Validation machines, trust, and sponsored answers43:39 Keeping humans at the center, not in the loop47:23 Can open source beat big tech in AI?51:39 Inside Mozilla.ai: Otari, CQ, Octanus, Thunderbolt55:21 The "rebel alliance" strategy

What does it take to go from helping millions of people breathe easier to helping patients keep their legs?   Join The Heart of Innovation as Kym McNicholas talks with Sarvajna Dwivedi, Ph.D., entrepreneur, inventor, and CEO of AngioSafe, whose career has spanned some of the most challenging problems in medicine.   Sarvajna co-founded Pearl Therapeutics, a company focused on breakthrough respiratory therapies that was ultimately acquired by AstraZeneca for $1.15 billion. Along the way, he helped develop inhaled therapies and drug-device combinations designed to improve the lives of patients with asthma and COPD. (AngioSafe United States)   Today, his focus has shifted from the lungs to the arteries.   As CEO and co-founder of AngioSafe, Sarvajna is leading the development of the Santreva-ATK Endovascular Revascularization Catheter, a novel device designed to restore blood flow through some of the most challenging chronic total occlusions (CTOs) physicians encounter in patients with peripheral artery disease (PAD). The technology is designed to cross completely blocked arteries, compress plaque, create a new channel, and restore blood flow without relying on a guidewire or external power source. (Medical Economics)   In this episode, we discuss:   • How a pharmaceutical scientist became a medical device innovator   • The story behind Pearl Therapeutics and its $1.15 billion acquisition   • Why chronic total occlusions remain one of the biggest challenges in PAD treatment   • How AngioSafe's Santreva-ATK technology works   • What it means to restore blood flow through arteries that are 100% blocked   • The future of cardiovascular and vascular innovation   If you or someone you love has peripheral artery disease, diabetes, leg pain while walking, non-healing wounds, or has been told an artery is completely blocked, this is a conversation you won't want to miss.  

In this episode of the Finovate podcast, host Greg speaks with Oren Buskila, CEO and co-founder of Cobalt, a FinovateSpring 2026 Best of Show winner. Cobalt has developed enterprise architectural intelligence specifically designed for financial institutions, addressing a critical challenge in modern banking: the lack of understanding of complex system dependencies.Banks operate on enormously intricate systems comprising their own code and dozens of third-party vendor applications, yet most institutions don't fully comprehend how these systems interconnect and depend on one another. This knowledge gap leads to significant problems, including development slowness—with banks spending 70% of their IT budgets on maintenance rather than innovation—and costly production failures that can result in millions of dollars in direct and indirect costs when changes are deployed without full visibility into system dependencies.Cobalt's solution automatically maps both IT systems and business processes in real-time by scanning existing data sources including event logs, API logs, code, and database tables. The platform creates a comprehensive topology map that aligns business processes with their underlying technical infrastructure, allowing banks to see exactly which systems support which business functions and understand the full impact of any proposed changes. This "bank on a page" architecture view enables technical teams to anticipate the consequences of modifications before implementation, preventing failures and ensuring safer deployments. The platform maintains a live, continuously updated view of the system architecture by taking frequent snapshots that can be compared to detect changes and investigate issues, a capability that was previously impossible with manual architecture mapping methods.The demo at FinovateSpring resonated strongly with attendees, particularly technical leaders like CIOs and CTOs from medium and large banks who recognized the transformative potential of having complete visibility into their systems. The presentation also attracted significant interest from venture capitalists and leaders from smaller banks and credit unions, the latter group seeing opportunities to introduce agentic AI into their operations by first mapping existing business processes. Looking ahead, Cobalt positions itself as the essential architectural layer for AI-driven development in banking, as the industry moves toward having AI agents generate, maintain, and modify code at unprecedented velocities—a shift that will require the contextual understanding and change management capabilities that Cobalt provides.More info:Cobalt AI: https://www.getcobalt.ai/; https://www.linkedin.com/company/getcobalt/Oren Buskila: https://www.linkedin.com/in/oren-buskila/Greg Palmer: https://www.linkedin.com/in/gregbpalmer/Finovate: https://www.finovate.com; https://www.linkedin.com/company/finovate-conference-series/FinovateSpring: https://informaconnect.com/finovatespring/#Finovate #FinovateSpring #Banking #banks #creditunions #personalization #data #communitybanks #AI #backoffice #corebanking #digitaladoption #podcast #fintechpodcast #financialservices #innovation #digitraltransformation #fintech #finserv #modernization

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Dave Trier, CEO of ModelOp, to explore the challenges and opportunities of implementing AI at scale in enterprises. Dave shares how organizations can manage AI responsibly, measure ROI, and move from scattered pilots to a disciplined, industrialized approach. He also discusses the critical role of CFOs in AI oversight, change management, and creating measurable business value from AI initiatives Dave Trier is CEO of ModelOp, leading the company with a focus on customer value, product innovation, and enterprise execution. With over 20 years of experience across AI, data science, analytics, cloud, and enterprise software, Dave is a patent-holder and trusted partner to CIOs, CTOs, and AI leaders. Prior to becoming CEO, he shaped ModelOp's product strategy and held senior roles at Think Big Analytics, Powered by Action, and Accenture Technology Labs. He holds a BS in Electrical Engineering from the University of Notre Dame. In this episode, you will discover:How to industrialize AI delivery across an enterpriseManaging risk, governance, and compliance for AI implementationsMeasuring AI ROI using financial, feedback, and usage metricsThe CFO's role in AI oversight and rationalizing AI investmentsKey lessons for change management and process discipline in AI adoptionDave Trier highlights how enterprises can move from scattered AI pilots to a disciplined, industrialized approach that delivers measurable business value. He emphasizes the importance of governance, change management, and cross-functional collaboration to ensure AI initiatives succeed. CFOs play a key role in oversight, setting financial parameters, and rationalizing AI investments. Follow Dave:Website: https://www.modelop.com/LinkedIn: https://www.linkedin.com/in/davidetrier/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[02:07] – Meet Dave Trier, CEO of ModelOp[04:57] – ModelOp & AI Governance Explained[06:21] – AI vs Data Governance[08:11] – Evaluating AI ROI for CFOs[13:24] – AI as a Managed Investment Portfolio[16:43] – Change Management & Process Discipline[20:48] – CFO's Role in AI Oversight[27:38] – Tips to Maximize AI ROI[30:16] – Enterprise AI Complexity & Coordination[32:13] – Dave's Journey: Electrical Engineer to AI CEO[35:12] – Closing Thoughts

In this episode of The New P&L TO THE POINT, Paul explores the growing conversation around AI that increasingly positions it as an ‘existential crisis' for businesses and society.Drawing on insights from dozens of executive roundtablesheld across the UK and Europe with CIOs, CTOs, CMOs, HR leaders and transformation executives, Paul reflects on how the AI conversation has evolved at remarkable speed. In just a couple of years, organisations have rapidly shifted from asking What is AI? to How do we deploy it? and now increasingly Why are we using it in the first place?At the heart of the discussion is a critical observation: many organisations approached AI implementation in reverse order. Businesses rushed into experimentation and deployment before establishing strategic clarity around purpose, culture and long-term impact. According to Paul, this is where the real challenge lies.Rather than focusing solely on future fears around AGI orsuperintelligence, this episode argues that today's AI crisis is more immediate and human: a leadership, capability and adaptability crisis. AI is not simply another technology tool; it is transformational and foundational, requiring organisations to rethink leadership, culture, communication and workforcedevelopment.Paul also examines how AI acts as a mirror for organisational health, exposing weak leadership, fragmented data, siloedcultures and poor communication. Without clear vision, employee trust and meaningful upskilling pathways, businesses risk creating fear, disengagement and resistance internally.Ultimately, this episode challenges leaders to rethink their relationship with AI: not as a transactional solution, but as a force that will fundamentally reshape the nature of work, organisations and leadership itself. Those who fail to adapt may face their own existential crisis far sooner than the technology does.To discuss the topics outlined in this episode on moredetail, email: hello (at) principlesandleadership.com To learn more about The New P&L and the work we do, goto: ⁠⁠⁠www.principlesandleadership.com

Most sales teams are reactive — waiting for buyers to fill out a form, book a demo, or respond to an email. Tal Peretz, co-founder and CEO of OnFire AI, is building the infrastructure to change that. OnFire monitors millions of public signals across Reddit, Stack Overflow, LinkedIn, Slack, and technical forums to identify high-intent buyers before they ever contact your sales team.In this episode, Tal breaks down how AI is transforming go-to-market for companies selling to technical buyers — CTOs, CISOs, and engineers — who notoriously resist generic outreach and respond only to context-rich, well-timed conversations. Tal shares his journey from engineer to CEO, how he and his co-founder interviewed 275 revenue leaders before writing a line of code, what it's really like to raise a $20M seed round, and the hard-won lessons of learning to sell as a first-time founder. From ICP discovery and outcome-based pricing to the future of AI in sales, this is a masterclass in signal-driven, intent-based revenue growth.Key Takeaways0:00 — Why most sales teams miss buyers who are already signaling intent publicly2:07 — Intro to Tal Peretz: Co-founder & CEO of OnFire AI3:56 — The origin story: 275 revenue leader interviews before building the product4:36 — How OnFire works: Capturing public web signals, de-anonymizing prospects, and delivering real-time context to sales teams6:25 — Why selling to CTOs, CISOs, and engineers is uniquely difficult — and uniquely valuable7:36 — The 50-million-engineer insight: Turning public technical conversations into revenue intelligence10:04 — What true AI ROI looks like: efficiency gains + directly attributed pipeline11:15 — The 4X pipeline result: What customers see in their first quarter with OnFire11:52 — Speed + personalization + human touch: Why all three are required for signal-based outreach13:03 — Raising a $20M seed round and what hypergrowth pressure really means13:47 — What makes a great investor: shared values, chemistry, and true partnership in hard moments15:59 — Managing pressure: Working backwards from a 24-month North Star to break goals into milestones17:07 — Building vs. selling: What was harder in the early days17:59 — An engineer who learned to love sales: How Tal found his passion for closing deals19:21 — The ICP trap: Why selling to everyone early is the most costly mistake a founder makes20:51 — The outbound playbook: Cold calling, LinkedIn, and the "stealth company" message that landed their biggest customers22:10 — The consulting approach: Why leading with curiosity instead of a pitch built their enterprise pipeline24:41 — The three-layer go-to-market machine: Brand, field/events, and outbound working together26:45 — Selling six-figure enterprise deals: Going on-site, acting as a partner, not a vendor28:51 — Staying focused in a crowded AI market: The "build on top of the platform" rule30:02 — Building go-to-market teams as a technical founder: The hardest challenge32:14 — The biggest AI pricing mistake: Why outcome-based pricing is the future35:03 — Sales-led vs. product-led growth: How Tal thinks about when and how to make the shift38:09 — The future of go-to-market: How AI eliminates the 80% of busy work reps do today40:53 — The one thing founders must nail to break through from product to real revenue41:38 — Where to find Tal and OnFire AITweetable Quotes"We monitor the public web for signals — competitors, pain points, product mentions — and surface them to your sales team in real time. Your buyers are already talking. You just have to listen." — Tal Peretz"It's not about quantity. It's about the quality of the data. Act fast, personalize based on the pain point, and always keep the human touch in the loop." — Tal Peretz"We take your existing team and infrastructure and make the pipeline 4X better — not by adding headcount, but by giving them the right signal at the right moment." — Tal Peretz"Every revenue is not good revenue. Nail your ICP first — where you see the biggest pain, the best retention, and the growth potential — then press the pedal." — Tal Peretz"The best investors aren't just writing checks. When something breaks — and something always breaks — that's where you find out if you have a true partner." — Tal Peretz"AI will eat the 80% of the sales rep's day that is busy work. The reps who win will be the ones who know how to leverage those tools and still build real relationships." — Tal Peretz"Outcome-based pricing is the future. Align what your customer pays with the value they actually receive — then you're never fighting about ROI again." — Tal Peretz"We started with outbound and a simple message: 'I'm a stealth founder. I want to learn from your experience.' No pitch. Just curiosity. Our biggest customers today came from that exact message." — Tal PeretzSaaS Leadership Lessons1. Validate the market before you build the product. Tal and his co-founders interviewed 275 revenue leaders before writing a single line of code. They didn't fall in love with a solution — they found the problem first. For early-stage founders, this discipline separates products that get traction from ones that get ignored.2. Your ICP is not a marketing decision — it's a survival decision. Selling to every prospect early feels like progress, but it's a trap. Tal's hard-won insight: identify the customers with the biggest pain, the highest retention potential, and the best growth trajectory early, then build everything around them. Chasing the wrong customers burns runway and muddies your product roadmap.3. Great investors are chosen for the downside, not the upside. When everything is working, any investor looks great. The real test comes when something breaks. Tal defines great investors by shared core values, authentic chemistry, and willingness to engage as a true partner — not just a capital source — when the hard moments arrive.4. Act like a consultant before you act like a vendor. OnFire's biggest enterprise wins came from going on-site, meeting the full revenue team, mapping the customer's strategic goals, and co-designing a plan — before ever talking contract. For founders selling complex, high-ACV solutions, acting as a partner rather than a vendor changes the entire sales dynamic.5. Outcome-based pricing aligns your success with your customer's success. Charging by seat or token puts you in constant translation mode — always proving value. Pricing tied to outcomes (pipeline generated, conversations resolved, deals influenced) makes the value self-evident and creates a partnership, not a vendor relationship. The companies doing this best in AI are winning stickier, larger contracts.6. The future sales rep is an AI orchestrator, not a data processor. Today's reps spend ~80% of their time on research, sourcing, and admin — not selling. AI will progressively eliminate that 80%. The reps who thrive won't be those who resist the change, but those who master AI tooling and redirect all of their energy to the irreplaceable human skill: building trust and closing deals.Guest Resourcestal@onfire.aihttps://onfire.aihttps://www.linkedin.com/in/tal-peretz/instagram.com/peretztalx.com/TalPeretz13Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Artificial intelligence can generate code faster than ever before. But are organisations actually delivering better products? In this Leadership Podcast episode, Brenn Hill joins Niels Brabandt to discuss one of the most overlooked business risks in the age of AI: the delivery gap between software output and productive quality. Why are organisations generating dramatically more code while struggling to achieve meaningful business outcomes? Why do pull requests increase while quality, reliability and ROI frequently decline? This episode explores: • The Delivery Gap in AI-enabled software development • Why speed without quality becomes a business risk • The hidden cost of AI-generated code • Pull requests, quality assurance and production readiness • AI coding, compliance and regulated industries • The Verification Triangle: inputs, outputs and cost • Why prototypes are not production systems • Measuring ROI in AI software delivery • Leadership lessons for technology transformation This episode is essential for CEOs, CTOs, engineering leaders, founders, technology executives, board members and business decision-makers seeking to implement AI responsibly and profitably. Host: Niels Brabandt / NB@NB-Networks.com Contact Niels Brabandt: https://www.linkedin.com/in/nielsbrabandt/ Niels Brabandt's Leadership Letter: https://expert.nb-networks.com/ Niels Brabandt's Website: https://www.nb-networks.biz/ 

For more thoughts, clips, and updates, follow Avetis Antaplyan on Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/avetisantaplyan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠In this solo episode of The Tech Leader's Playbook, Avetis Antaplyan breaks down what he calls “The Great White Collar Compression”, the growing disconnect between a strong-looking economy and the pressure many white-collar professionals are feeling in real time.Avetis explores why corporate profits, AI investment, and stock market strength are not translating into the hiring booms many workers expected. Instead, companies are flattening teams, raising performance expectations, slowing hiring, and demanding more output from fewer people. Drawing from his perspective inside the hiring market, Avetis explains how AI, remote work abuse, salary inflation, and shifting leadership priorities are reshaping the future of work.He shares candid stories from conversations with CTOs, candidates, and professionals who feel uncertain about their roles despite working at successful companies. The episode also digs into the decline of the “comfortable middle,” the rise of hybrid roles, the need for AI fluency, and why adaptability may now be the most valuable career currency.This episode is a direct, practical warning and roadmap for leaders and professionals who want to stay relevant, valuable, and hard to replace.TakeawaysThe economy can look strong while white-collar workers still feel pressure.AI investment is increasing productivity without creating proportional hiring.Companies are flattening teams and cutting unnecessary management layers.Average performance is becoming more vulnerable in the modern workplace.Remote work abuse and inflated salaries contributed to employer distrust.Hiring is slower because companies now expect rare hybrid skill sets.Professionals need to get closer to revenue, customer impact, and business outcomes.AI fluency is no longer optional for most white-collar roles.Adaptability and learning velocity are becoming premium career skills.Building a reputation matters more than relying on a resume alone.The future belongs to builders, operators, and people willing to evolve quickly.Chapters00:00 Introduction to the Great White Collar Compression02:36 Why Traditional Hiring Growth Is Changing05:00 Fewer Layers, Higher Expectations, and AI Pressure07:20 Why Workers Feel Weak Despite a Strong Economy09:43 Hiring Freezes, Salary Pressure, and Market Uncertainty11:46 Efficiency, Profitability, and Leaner Operations12:49 The Death of the Comfortable Middle14:55 Why Hiring Feels Broken Right Now17:19 The Rise of Team-Elevating Talent20:03 Adaptability as the New Career Currency22:28 Getting Closer to Revenue and Business Outcomes24:50 Building Hybrid Skills and Becoming Indispensable27:13 Reputation, Network, and Proof of Work28:40 Final Thoughts on the Future of White-Collar WorkResources and Links:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.hireclout.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.podcast.hireclout.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/hirefasthireright⁠

Background checks sound straightforward. Until you try to build the infrastructure behind them at scale. In this episode of CTO Confessions, TC Gill sits down with Luca Bonmassar, CTO of Checkr — a platform that helps millions of people find meaningful work and helps companies hire with confidence. Luca brings over 20 years of experience across Fintech, Crypto, Social Media, and AI/ML, and has co-founded and sold three startups. He's one of the rare CTOs who moves as comfortably in a business conversation as a technical one. In this episode: → Why understanding the business side isn't optional for a CTO → The surprising complexity of data that still exists only in physical form → Why hiring fast and firing faster may cost you more than you think → His philosophy on over-engineering — and why patience often wins "It's better to be late knowing that what you build will have greater impact than building things and hoping someone will use them one day."

In this episode of the AI at Health series on The Beat Podcast, host Sandy Vance sits down with Venky Ananth, Executive Vice President and Head of Healthcare at Infosys, for a wide-ranging and energizing conversation about what it actually means for AI to transform healthcare at scale. Venky brings a refreshingly honest and structured perspective to a conversation that is often dominated by hype, breaking down why AI is fundamentally different from every other technology wave healthcare has been through, laying out the five areas where Infosys is seeing real traction with payers, providers, and PBMs right now, and sharing the story behind two exciting developments: the acquisition of Optimum Health IT and the Pacesetters podcast and executive leadership community. If you are a healthcare leader trying to figure out where to start or how to think about AI as a whole-enterprise challenge rather than a point solution, this episode is essential listening.  In this episode, they talk about: AI is not a point solution; it is a new operating system that will touch every function in every organization Healthcare is broken, fragmented, and frustrating, and AI is the first technology with the potential to fix all three at once Legacy modernization must come before AI adoption because you cannot layer intelligence on top of broken processes AI can now reverse engineer legacy systems that used to depend entirely on tribal knowledge The five pillars of AI transformation are strategy and engineering, legacy modernization, data, process reengineering, and physical AI Training AI on your own private data is the competitive wedge that separates leaders from followers Agents are the new team members, and organizations need to rethink how humans and agents orchestrate workflows together Infosys acquired Optimum Health IT, the number one-ranked Epic implementation partner according to KLAS, to deepen its provider capabilities Epic now covers an estimated 220 to 230 million distinct patients in the US and is growing internationally The Pacesetters podcast and annual executive gathering bring together CIOs, CTOs, academics, and analysts for candid, off-the-record dialogue about the future of healthcare A Little About Venky Ananth: Venky is a technology and transformation executive with deep experience leading a global business unit, scaling high-performance organizations, and delivering large-scale change through AI, cloud, and modern growth operating models. His career has focused on helping enterprises modernize core systems, improve operational efficiency, and unlock new growth through platform innovation and disciplined execution. He founded and scaled Infosys Helix, a cloud-native platform business that continues to shape payer and health platform modernization. In addition, he has led global teams across engineering, delivery, consulting, and product, giving him a broad view of strategy, technology, operations, and organizational scale. He operates at the intersection of technology, business model transformation, and leadership development, with a track record of strengthening enterprise performance and building organizations capable of sustained growth. Beyond his operating role, I host PaceSetters, a CXO leadership platform featuring conversations with leaders from healthcare, academia, private equity, and technology. 

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

Shall AI kill marketing? Sounds like a hackneyed question, yet it’s on any marketer’s lips these days. Thomas Husson, Vice President and Principal Analyst at Forrester Research, covers the intersection of marketing, technology, and consumer behaviour from his base in Paris. In a wide-ranging conversation, he cuts through the European Gen AI paradox, the persistent CMO-CIO divide, the gap between POC enthusiasm and production reality, and the thorny question of what AI actually means for the next generation of marketing professionals and CMOs. His answers are measured, occasionally blunt, and consistently grounded in Forrester Research data. AI Will Not Threaten the Existence of Marketing But It Will Reshape It Beyond Recognition Thomas Husson believes that Marketing will be changed profoundly. But he doesn’t believe in the death of Marketing. Photo: Thomas Husson at Paris Retail Week, in late 2023 My first question was the obvious one: are CMOs going to be made redundant by artificial intelligence? Thomas Husson’s response is categorical, and worth stating plainly at the outset. It’s a blatant ‘No’. The role will change. The how will change. But the existence of marketing as a discipline is not, according to him, in question. “Marketing is still going to be about understanding your customer, defining a brand strategy, and delivering the brand promise through customer experience.” Thomas Husson, Forrester Research Unclear prospects, obvious pressures That said, Husson is not naive about the pressures building on marketing organisations. Some tasks will be automated; that much is not in dispute. The real questions are which tasks, how quickly, and whether automation of a task necessarily kills the job around it. His answer to that last question is no, at least not in any simple mechanical sense. “Jobs will evolve for sure. New jobs will be created. Most jobs will change. The way we work will change. The way we work with agencies, with external partners, the processes, the workflow. It is the shape of work that is being reshaped, not work itself,” he added. For those expecting a more dramatic verdict, Husson’s framing may feel anti-climactic. But it reflects what Forrester Research data actually shows, and it points to the most important practical challenge for AI and CMOs alike: managing a profound transformation without either catastrophising or sleepwalking through it. AI Will Not Kill Marketing according to Forrester’s Thomas Husson, there is light at the end of the tunnel. The European Paradox, Overhyped and Exciting at the Same Time Forrester Research produced a result that initially looks contradictory, Husson stressed in our interview. Fifty-five percent of European B2B marketers consider generative AI overhyped. Yet 81% of European frontline marketers describe themselves as enthusiastic about it. How can both be true simultaneously? Husson explains the split without difficulty. At the decision-maker level, scepticism is entirely rational. AI is inescapable at conferences, in vendor pitches, and in media coverage. “There is AI fatigue. And more importantly, some of the vendors are indeed over-pitching, and the productivity gains they promise are not happening,” he stated. The gap between the pitch and what we actually experience in the field is wide enough to breed genuine frustration. Saving Time and Working Differently But the people actually using these tools, often through shadow AI channels their organisations have not officially sanctioned, are discovering something different. They are saving time and are doing their jobs differently. They are finding capabilities they did not expect. “In the short term, everything is overhyped, including the number of job losses. In the longer term, things are underestimated, because AI will be linked to other technologies, and yes, it will reinvent many things.” Thomas Husson, Forrester Research This is a precise restatement of Amara’s Law. Roy Amara, former president of the Institute for the Future, observed that we tend to overestimate the short-term impact of new technology and underestimate its long-term impact. The quote is frequently misattributed to Bill Gates, but Husson is careful to restore proper credit. He applies it directly to the AI and CMOs conversation: the short-term noise is drowning out a more important long-term signal. When asked how long “long term” actually means in an era of accelerating AI development, Husson was specific: probably closer to five to seven years than to ten or fifteen, but still not tomorrow. From POC to Production, Europe’s Real AI Problem The Forrester Research State of AI Survey 2025 contains a figure that deserves more attention than it typically receives. European organisations lag behind their non-European peers in production use of generative AI: 62% versus 72%. The gap is not in experimentation. It is in execution. Regulation is the explanation most commonly offered, and Husson dismisses it with characteristic directness. The AI Act is a genuine consideration, but it is not the primary cause of Europe’s production deficit. It functions, he argues, as a double-edged excuse. Pioneers claim it prevents them from moving fast enough, while cautious organisations invoke it to justify not executing at all. Neither position holds up to scrutiny. A Deep Cultural and Organisational Divide The deeper issue is organisational and cultural. American and Chinese firms tend to think global from day one; European firms, particularly larger ones, still default to a market-by-market approach. France first, then the UK, then Germany. The ambition is calibrated differently. There is also a structural challenge around funding and the capacity to scale. That said, France, the UK, and Germany lead adoption among European countries in the Forrester Research data. The problem for these leading markets is not whether they are using generative AI. Twenty-eight percent of European B2B marketing decision makers cannot clearly identify where to apply it. They have the tool. They lack the strategy. “It’s not AI for the sake of AI. How do I use AI to serve my marketing objectives? That is the question. The only one.” Thomas Husson, Forrester Research Husson advocates for small, targeted AI projects with transparent return on investment as a way to build momentum and demonstrate results. When pushed on whether that risks staying permanently incremental, he conceded the point readily. “If you only do small targeted projects, it’s going to be incremental and it’s not going to be bold enough. You need to align it with a vision and a roadmap.” Thomas Husson, Forrester Research Measuring Productivity Honestly Productivity is the dominant driver of AI adoption in the Forrester Research State of AI Survey 2025. It is also, Husson suggests, the metric most subject to vendor inflation. In Forrester Research’s modelling, a 50% conversion factor is applied to vendor productivity claims. If a tool saves an hour, the realistic productivity benefit is approximately 30 minutes of additional output. This is not a marginal adjustment; it halves the headline figures that vendors routinely publish. “You need to apply a discount to the pitch of vendors when they say you’re going to get 40, 50, 80, 100% productivity gains. There are productivity gains, but they are not as high as one would expect.” Thomas Husson, Forrester Research There is also a motivational dimension that is rarely modelled. When work becomes easier to produce, it can also become less engaging to produce. The cognitive effort that used to drive focus and satisfaction is partly removed, with consequences for quality and commitment that no vendor presentation accounts for. AI and CMOs, Who Is Actually in Charge? The CMO-CIO divide is a perennial theme in marketing technology discussions. Forrester Research data suggests the gap at the strategic leadership level has narrowed, partly as a result of post-COVID collaboration. But at team level, the tensions persist, and the data on AI governance is striking. CMOs account for only 8 to 10% of AI strategy leadership in organisations. In the vast majority of cases, the deployment of AI is being driven by CIOs and CTOs. Husson understands the logic: data governance, security, scalability. These are real concerns. But he believes the outcome is a mistake. “It is the exact same mistake that happened with digital transformation. AI has to be at the service of, first, the client, and consequently the business functions that serve them. There is too big a disconnect between a secure, scalable AI platform and marketers’ needs.” Thomas Husson, Forrester Research The structural consequence of this dynamic is predictable. When CIOs control the tools and CMOs do not have what they need, shadow AI flourishes. The more tightly the CIO locks down the official platform, the more widely teams proliferate unofficial solutions. It is a cycle that widens governance risk while creating the illusion of control. The MarTech landscape compounds this problem. According to data Husson cites, 2,500 new AI solutions were added to the market in a single year while 1,211 pre-AI-era tools were removed. Evaluating this landscape requires cross-functional expertise that neither CMOs nor CIOs possess in isolation. The case for genuine collaboration, rather than the polite coexistence that currently passes for it in most organisations, has never been stronger. Jobs, Agencies, and the Students in the Room The survey data on jobs is sobering. Fifty-seven percent of European frontline marketing decision makers believe AI adoption will lead to job reductions in their teams. Sixty-eight percent say new roles will be created. The gap between those two numbers is the space where real anxiety lives. For a wider perspective on AI’s job impact, including Forrester Research’s US forecast, see our earlier piece: AI Job Impact in the US: the Apocalypse Can Wait. For a longer-range view of how generative AI is reshaping roles, see also: GenAI Impact on Jobs. Contact centres and basic marketing task execution are already seeing measurable impact. Agencies are under visible pressure. But Husson returns consistently to the distinction between task automation and job elimination. Most job losses are not yet directly attributable to AI; the picture requires nuance rather than alarm. On new roles, the honest answer is that specifics are difficult to name in advance. Twenty years ago, nobody was hiring community managers. The jobs that will emerge from the current transformation will be as hard to predict precisely as that one was. What Husson does say is that working with agents, managing their outputs, and understanding their limitations will become core competencies rather than specialist skills. “Teach them the basics of marketing, those won’t change. Infuse a lot more of traditional social sciences: ethics, emotion, anthropology. These dimensions will gain importance. Curiosity. And they have to use these tools, to learn how to use them so they can develop their own critical thinking.” Thomas Husson, Forrester Research There is irony embedded in this advice that Husson acknowledges implicitly. Digital roles are likely to bear the earliest impact of AI-driven automation precisely because they are already the most digitised. The analogue parts of marketing, which seemed most vulnerable to digital disruption, turn out to be more resistant than expected. AI is a continuation of digital transformation, not a departure from it. There is also a structural problem this conversation surfaced that neither party resolved entirely. If organisations are reducing entry-level hiring to cut costs, and those entry-level roles were the traditional training ground for the next generation, then the iterative learning process that produces senior expertise is being severed. AI can teach many things, but the social dimension of learning alongside a colleague over time is not easily replicated. B2B Marketing, Ahead of the Curve A widespread assumption holds that generative AI enthusiasm in marketing is largely a B2C phenomenon. Husson disputes this firmly. B2B marketers, in his assessment, are actually ahead of the curve in several areas, particularly content generation, personalisation, and sales support through complex multi-stakeholder buying processes. What B2B is also discovering is that the sharp distinction between rational B2B decision-making and emotional B2C engagement is less solid than commonly assumed. When a buying group is making a decision with significant professional consequences, emotion is not absent; it is differently structured and, in some ways, higher-stakes. “It’s not the ‘human plus AI blah blah blah’ we hear all the time. It needs a more nuanced approach. At the end of the day, AI is about replicating the human brain, but we don’t really know how the human brain works. We don’t know how consciousness works. So I would take a pinch of salt and take a step back before making any definitive judgment.” Thomas Husson, Forrester Research The Long View I ended by asking Husson how he uses AI in his own work. His answer was practical: summarising the relentless volume of content published daily on AI, filtering what is genuinely new from what merely repackages existing ideas. Behind him on the video call was a photograph taken in Thailand, of Buddhist monks. He smiled at the mention of it. “It’s a good reminder that not everything is digital and not everything is about technology. It’s about real life.“ For AI and CMOs, that is perhaps the most useful frame of all. The technology is real, the disruption is real, and the urgency is real. But so is the inertia of organisations, the pace of culture change, and the irreducible complexity of how human beings actually make decisions, form relationships, and build trust. Amara’s Law is not a reason to wait. It is a reason to plan carefully, act deliberately, and resist the temptation to mistake announcements for outcomes. Forrester Research reports cited in this article The AI CMO: Growth Accountability Gets Next-Level — Mike Proulx et al., April 2026 The State Of CMO/CIO Collaboration For 2026 — Thomas Husson et al., January 2026 Generative AI Adoption In European B2B Marketing Organizations — Christina Schmitt et al., December 2025 About Thomas Husson Thomas Husson is Vice President and Principal Analyst at Forrester Research, based in Paris. He covers marketing strategy, brand management, mobile marketing, and the intersection of technology and consumer behaviour across European markets. His research addresses how CMOs and marketing organisations navigate digital transformation, AI adoption, and the evolving relationship between brands and customers. Forrester Research analyst profile: forrester.com About Forrester Research Forrester Research is one of the most influential research and advisory firms in the world, founded in 1983 and headquartered in Cambridge, Massachusetts. It serves business and technology leaders across marketing, IT, and customer experience, providing data, analysis, and frameworks to guide strategic decision-making. The data referenced in this article draws on two primary Forrester Research publications: the Forrester Marketing Survey 2025 and the State of AI Survey 2025, both covering Gen AI adoption and its organisational implications across European and global B2B markets. Forrester Research website: forrester.com The post AI Will Not Kill Marketing appeared first on Marketing and Innovation.

In this episode of The Brand Called You, host Ashutosh Garg interviews Eddie Irvin, an accomplished AI Strategist and Fractional CTO at Nashville AI Advisory.Explore actionable leadership lessons on AI adoption, including:The biggest mistakes leaders makeHow CEOs and CTOs approach AI differentlyWhy hands-on experience is criticalGovernance frameworks and risk managementWhere AI delivers measurable efficiency gains

Should proptech be defined as a real estate specific problem to which a technology solution is applied to? Why can new real estate applications not ignore the workstream they are a part of and the ecosystem they must work nicely within? How is it possible for companies to balance encouraging innovation but not concurrently creating shadow IT departments? Why is the build vs buy decision front and center again for CTOs and other innovation leaders in proptech? Why was it a blessing in disguise for David to start off his professional career as a custody fund accountant? What led David to start a business operations group? Why is bottoms up technology adoption much easier than top down mandates? What led Dave to uncover the opportunity in real estate AP coding? What percent of general ledger transactions originate in AP? Who are the different purchasers of PredictAP? What new insights has the deployment of PredictAP within customers surfaced? In the fast changing world of AI, how do companies differentiate between solutions that demo well and give the appearance of solving a problem versus well architected and researched solutions that truly solve the fundamental business problem?David Stifter - Founder and CEO of PredictAP, joins Proptech Espresso to answer these questions and discuss how we worked at the first social network in college just prior to the early 2000s dot-bomb crash.

Welcome to another episode of Data Debrief, the companion show to Driven By Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom sit down to unpack Tuesday's conversation, share what's been on their minds, and explore what's really happening across the data and AI landscape.Fresh off Kyle's return from holiday, the pair dive into Tuesday's episode with Daragh Kelly, Chief Data Officer at The Economist, unpacking the ideas that stood out most, and a few that challenge the dominant narratives in the market right now.They cover:Why the concept of “Trad AI” (traditional machine learning and data science) is a useful lens, and how the market is blurring the lines between legacy AI and the new wave of generative and agentic capabilitiesThe ongoing hype cycle in AI, why it's nothing new, and how organisations risk getting distracted by buzzwords rather than focusing on real outcomesThe growing gap between building AI solutions and making them scalable, reusable, and commercially viableThe importance of defining what “AI” actually means inside your organisation, and why vague language is creating confusion at the board levelThe tension between speed and direction, and why moving fast means nothing if you're not solving problems that actually matterWhether operating models really need to change for AI, and why Dara's perspective challenges the prevailing narrativeThe shift from analysts as insight generators to “toolmakers”, and what that means for the future of data and analytics rolesThe rise of self-serve capability across organisations, and the risks of uncontrolled experimentation without governanceThe ongoing power struggle between CDOs, CIOs, and CTOs over AI ownership, and why the answer is far from settledThe role of optics, titles, and external brand in shaping career progression for data leaders in an AI-first marketPlus, in this week's Thoughts of the Week, Kyle challenges the long-standing narrative around “having a seat at the table,” arguing that it's often used as an excuse for not delivering value, and that true impact comes from driving outcomes, regardless of reporting lines. Catherine reflects on the role of diversity, equity, and inclusion in the data community, why the conversation is still far from where it should be, and the responsibility leaders have to actively shape a more inclusive industry.Like and subscribe wherever you listen, and if you've got a question or topic you'd like the team to cover, email community@orbitiongroup.com

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

EPISODE 703 - Michael Hunter - Resilience for leaders in tech, integrating heart, mind, body, and spirit to sustain leadersIn this episode of Living The Next Chapter, host Dave welcomes Michael, a seasoned guide for tech leaders who positions himself not as a fixer, but as a partner uncovering hidden dynamics that sap energy and stall progress in teams. From Columbia, Missouri—near the quirky geographic heart of the continental U.S.—Michael shares his winding path: childhood passions for drawing floor plans and self-taught coding on an Apple IIe, a pivot from architecture school to software engineering, and now, three decades later, authorship of The Resilient Tech Leader. Releasing in early 2026 alongside an online workbook and audiobook, the book distills his 16 practical tools, refined through personal reinvention and client work, into a roadmap for building resilience amid tech's chaos.​Michael emphasizes resilience as the foundation for leadership evolution, likening it to a personalized diet: universally applicable yet uniquely tailored. Tech pros excel at logic, he notes, but overlook heart, body, and spirit—leading to paradoxes where "every technical problem is a people problem" due to ambiguous human communication. His chapters blend TL;DR summaries, whimsical vignettes of CTOs and engineers, core problem-solution frameworks, personal examples, and team-application strategies, appealing to all learning styles with whimsy akin to Mary Poppins' spoonful of sugar.​Guests and clients rave about its impact, like one veteran who revisited basics and found fresh relevance in focusing amid distractions. Michael's agnostic illustrations and simple, safe, sustainable approach amplify the message: integrate your whole self to lead authentically, boosting personal gusto and team metrics like efficiency and engagement. More evolutions await in future books, with his newsletter at resilienttechleader.com offering updates, podcasts, and metaphors customized to real-world tech hurdles.​Key Takeaway: Cultivate resilience by tuning into your heart, mind, body, and spirit—your unique path to sustained leadership energy starts with one resilient step forward.https://uncommonteams.com/Send us Fan MailSupport the show___https://livingthenextchapter.com/podcast produced by: https://truemediasolutions.ca/Coffee Refills are always appreciated, refill Dave's cup here, and thanks!https://buymeacoffee.com/truemediaca

It can be hard to justify expenditures on security by citing their ROI or financial impact. Jeremy Molnar, Principal Cybersecurity Advisor, Healthcare Lead at CTG, prefers to collaborate with clients on the impacts of cyber attacks on patients. A denial-of-service or ransomware attack can prevent patients from getting crucial services, and breaches of sensitive data can cause extended problems later such as identify theft.CTG is an IT consulting solutions and services firm, supporting the healthcare space with industry-specific knowledge and cybersecurity solutions. Chad Alessi, Managing Director of Cybersecurity at CTG, claims that many CIOs and CTOs don't understand the impact and risk of installing new technologies, particularly AI.Learn more about CTG: https://www.ctg.com/Healthcare IT Community: https://www.healthcareittoday.com/

What does it take to modernize healthcare infrastructure when uptime is not just an SLA, but a patient outcome?In this episode, Amir talks with Jeff Sponaugle, CTO of Surescripts, about building and operating mission critical healthcare systems, navigating the move from on premises infrastructure to the cloud, and figuring out where AI can create real value without compromising reliability. It is a sharp conversation on engineering judgment, modernization, workforce evolution, and why technical leadership still needs real technical depth.What stood outCloud migration in healthcare is not just a cost or architecture decision. It is a reliability decision with real downstream impact on patients.The best reliability strategy is not pretending nothing will ever break. It is designing systems so the customer never feels the break.In regulated industries, structure can be an advantage. Standardized data and consistent formats make AI more useful, especially in healthcare.AI can already improve the patient and clinician experience in practical ways, from transcription to summarizing complex records and surfacing relevant context faster.Technical leaders cannot afford to drift too far from the work. Jeff makes the case that strong CTOs stay close enough to the technology to understand the tradeoffs, guide teams well, and spot what matters next.Timestamped Highlights00:00Jeff Sponaugle joins the show to unpack mission critical technology in healthcare, cloud migration, AI, and workforce upskilling.01:57Why Surescripts sits in a critical layer of healthcare, and why reliability matters when prescriptions need to move in real time.04:02A simple but powerful view of reliability: things will break, but the customer should not know they broke.06:47How to adopt new technology without risky hard cutovers, and why parallel systems matter in high stakes environments.08:53Upskilling legacy teams, preserving tribal knowledge, and why continuous learning matters more than any single technical skill.11:58How regulation can actually help AI in healthcare by creating more consistency in the data.17:33Where AI and agentic systems could create meaningful value in prescribing, diagnostics, and clinical workflows.20:29Why AI has changed executive and boardroom conversations in a way cloud migration never did.A line worth remembering“The customer should not know that something broke.” Pro TipsIf you are modernizing a high stakes platform, avoid the big overnight cutover. Run systems in parallel where possible and learn behind the scenes before customers ever feel the change.If you lead technical teams, do not treat upskilling as a one time event. Give people a path to split time between legacy work and emerging systems so the transition is real and sustainable.If you are evaluating AI in a regulated environment, start with narrow, useful workflows where context, speed, and summarization matter, then expand from there.Stay connectedIf you enjoyed this episode, follow the show, subscribe wherever you listen, and share it with someone building in healthcare, cloud infrastructure, or AI. You can also connect with Amir on LinkedIn for more conversations at the intersection of technology, leadership, and the future of work.

In this episode of the Shift AI Podcast, Derek Slager, CTO and co-founder of Amperity, joins host Boaz Ashkenazy for a conversation that spans 10 years of company building, the evolution of AI-assisted software development, and what it really means to lead a technical organization through genuine disruption.Derek shares the founding story of Amperity, how he and co-founder Kabir Shahani stumbled into the customer data problem while building marketing automation at their previous company, Aperture, and how that experience became the thesis for building an entire platform around getting data right. The conversation moves into the heart of how AI has transformed Derek's work as a CTO and as an engineer. He describes the moment the shift felt real, the team dynamics of moving from individual AI exploration to a true team sport, and how Amperity is compounding the institutional knowledge locked in a decade of after-action reviews into something agents can now actually learn from. Derek addresses the "SaaS is dead" narrative head-on arguing that Amperity's data foundation is precisely the asset that makes agents genuinely useful for their customers.Boaz and Derek close with a forward-looking exchange on agentic workflows in marketing, the importance of redesigning process and what a learning mindset means for individuals and organizations navigating what comes next.This episode is essential listening for CTOs, data leaders, and operators who want to understand how the companies with the best data foundations are positioned to thrive in the agentic era.Chapters[00:00] Introduction: Derek's Path to Building Amperity[02:13] What Amperity Is and Why It Took 10 Years to Build[04:41] First Job: Early IT Work at Dad's Small Business in Monroe, WA[06:14] The Founders Club: How Amperity Went to Market in 2016[08:20] Why They're Running a New Founders Club 10 Years Later[10:13] Both Sides of Claude Code: What Changed and When[13:30] Living Through Disruption as a CTO and Engineer[15:36] Making AI a Team Sport Instead of an Individual Pursuit[17:04] The Moment It Really Clicked: A Simple Tool That Took 5 Minutes to Build[19:09] Cultural Adoption: Skeptics to Believers Inside Amperity[21:50] Compounding Engineering: After-Action Reviews as AI Training Data[23:45] The Agent Wave Is Real: What It Means for a Customer Data Platform[25:09] Amperity's Data Foundation as the Perfect Agent Substrate[27:00] Redesigning Process, Not Just Adopting Tools[28:57] Systems Thinking and the Future of Work[30:04] Two Words: Learning Mindset[33:01] How to Connect with Derek and AmperityConnect with Derek SlagerLinkedIn: https://www.linkedin.com/in/derekslager/Website: amperity.comConnect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

Timothy Li, CEO and Co-Founder of LendAPI, has spent nearly a decade trying to solve the same problem: launching a lending product takes too long and costs too much. With LendAPI, he's built a no-code platform that lets banks, credit unions, fintechs, and retailers go from idea to live lending product in weeks, not months or years. Think of it as a GoDaddy-style experience for financial services. Timothy joined me again on the show (he was last on in 2017) to talk about what's under the hood, what the Sunglass Hut deal reveals about embedded finance, and where he thinks AI is actually useful in lending today.What We CoveredTimothy's path from the Fluid college credit app to building LendAPIHow the drag-and-drop product builder works for non-technical usersPython model deployment for credit risk officers inside the same platformWinning Best in Show at FinovateThe Sunglass Hut deal and how it came together in three monthsWhy retailers are moving away from pure-play BNPL providersIntegration options: bank cores, side cores, and direct e-commerce embedThe 300-plus partner marketplace and the SEO strategy behind itDoc AI and single-task AI agents for document processing and underwritingTimothy's experience in the CURQL accelerator and how credit unions differTeaching FinTech Fundamentals at USCThe five consumer verticals with the most opportunity in fintechKey TakeawaysThe build vs. buy debate is essentially over. When Timothy talks to bank CTOs today, the conversation is "can you launch this next week?" not "should we build this ourselves?" Speed to market has become the dominant concern.Pure-play BNPL approval rates are outside a retailer's control and can swing 10 points overnight. Private label embedded finance, built on infrastructure like LendAPI, lets retailers and banks own the underwriting criteria and the customer experience, which matters especially for high-ticket items where the financing decision happens in-store.Single-task AI agents are the near-term opportunity in lending, not fully automated credit decisions. Automating document verification, data extraction, and intake workflows saves minutes per application, and at scale, that compounds quickly.The five consumer fintech verticals worth building in: mortgages, auto, credit cards and personal loans, payments, and bank accounts. If it's in someone's wallet, there's still work to do.About Timothy LiTimothy Li is the CEO and co-founder of LendAPI, a no-code lending platform that launched in 2024 and won Best in Show at Finovate. He previously built Fluid, a credit-building app for college students, and has been building lending infrastructure across multiple ventures over the past decade. He also taught FinTech Fundamentals at the University of Southern California.Connect with Fintech One-on-One:Tweet me @PeterRentonConnect with me on LinkedInFind previous Fintech One-on-One episodes

Episode 360: The #1 AI Governance Mistake Schools Are Making ft. Betsy CooperWhat's the biggest mistake schools are making with AI right now? According to Betsy Cooper, it's not taking it seriously from day one. In this episode, Dr.Alfonso sits down with Betsy Cooper, Founder and Executive Director of the Aspen Policy Academy, for a powerful conversation on the AI governance decisions quietly reshaping schools — and what educators, leaders, and parents can do about it.Betsy brings a one-of-a-kind perspective shaped by her work as a former DHS attorney, her time leading the UC Berkeley Center for Long-Term Cybersecurity, and her doctorate from Oxford. Through Aspen Policy Academy, she's on a mission to democratize policymaking, helping teachers, parents, technologists, and community advocates learn how to identify problems, build solutions, and actually drive change.Together, Dr. Alfonso and Betsy unpack why "ooh, that looks pretty, let's try it" is the wrong way to evaluate new tools, how smooth vendors and shrinking budgets are pushing districts into risky decisions, and why K-12 students need adult stewards more than ever in this moment. Betsy also shares the castle and moat metaphor every school leader needs to hear, a three-step crisis plan for overwhelmed CTOs and superintendents, and her four-step policy impact framework for educators ready to advocate for change.Whether you're a teacher, CTO, superintendent, or parent, this episode will leave you with practical tools and a renewed sense of agency to push back, ask better questions, and advocate for the students who can't speak up for themselves.Chapters00:00 — Welcome & Sponsor Shoutouts01:30 — Meet Dr. Betsy Cooper & The Origin of Aspen Policy Academy07:00 — What Policy Literacy Means for Educators (and Why It's Free)14:00 — The 4-Step Policy Impact Framework19:30 — The #1 AI Governance Mistake Schools Are Making24:30 — How CTOs Should Evaluate AI Tools & Vendors31:00 — Who Should Be Writing AI Policy for Schools35:00 — Cybersecurity in K-12: The Castle, The Moat & The Breach Plan39:30 — Cyber Civic Engagement & Becoming a Local Advocate43:30 — Speed Round & Closing ThoughtsDon't forget to:✅ Subscribe to My EdTech Life on your favorite podcast platform✅ Leave a review and share this episode with a fellow educator✅ Visit www.myedtech.life for more amazing conversations

Piyush Jain, Founder and CEO of Simpalm and co-founder of Ducknowl, is on a mission to solve real-world challenges by combining technology and entrepreneurship. With over 15 years of experience building custom software solutions, Piyush helps businesses turn complex ideas into practical applications by blending technical depth, business acumen, and a strong problem-solving mindset. We explore Piyush's AI Ideation Framework—Validate idea, Proof of concept, Design, Competitor analysis, and Feature selection—a practical approach to building software in the post-AI era. Piyush explains how AI can help teams better understand user personas, validate product assumptions, and rapidly prototype ideas, while human expertise remains essential in design, architecture, and production-grade development. He also shares how prompt engineering, peer-reviewed prompting, and a right-shoring delivery model can help businesses build smarter, faster, and more cost-effectively. — 3D Print Your Software with Piyush Jain Good day, dear listeners. Steve Preda here with the Management Blueprint, and my guest today is Piyush Jain, the Founder and CEO of Simpalm, a custom software development company, and the co-founder of Ducknowl, a candidate screening and assessment application business for high-volume recruiting. Piyush, welcome to the show.  Thank you, Steve. Thanks for inviting me.  Well, I’m very curious about the stuff that you have to share with us, and I’d like to ask first about your personal purpose. What is your “why,” and how are you manifesting it in your business?  Yeah, so that’s a very interesting question. And I think for every entrepreneur or tech founder, really, that's the motivation—why you want to do certain things. So for me, if I look at it, my personal “why” is: why are we not solving challenges? Or why are we not solving them the right way? Why are we not transforming our lives? I grew up in India and then came to the US, so I've seen many different parts of the world—from Asia to North America. I see people face different challenges, but then we are not focusing on solving those problems. A lot of it I see is there’s a lot of challenges in the world because I believe there are not enough entrepreneurs. Because entrepreneurs are the ones who really take risks, combine everything, and create solutions. That was like me, right? That’s what I learned growing up, that I think I can do that, right? I can combine the technical knowledge and the business acumen and create solutions that people like, solve their challenges. Growing up, like I'm more on the technical side.Share on X I was inclined more toward science and technology, but then as I got into my undergrad and grad school, I realized that I have that entrepreneurship aspect, but it's still around science and technology. That’s when I realized that, you know what, I cannot be a pure scientist or maybe a pure entrepreneur, but I can be someone who can combine these two, because my main driving factor is problem-solving. I can combine these two and then live my life, be very happy with what I do. That has been my motivation. I like it. So solving challenges and being an entrepreneur, and kind of combining the two—being the technical expert and the entrepreneur in one. Now, one of the things that we always talk about on this podcast is frameworks. And you have developed a really good one for AI ideation, which I think is something that everyone needs to do these days or use these days, and it helps you create business apps and other business applications. Can you share with me how that framework works, and what are the steps in it?  Sure, yeah, definitely. So just to give you a brief background, we've been building software for the last 15 years. Some companies have used different frameworks, whether it's Agile or Waterfall in SDLC, in building the software, right? There are different methodology that companies have used, and they've been good, successful—they've played their role. But now, with the advent of AI, things have changed. We had to figure out, in our organization, how to use AI, and that's how this framework was built. My team helped me building this framework as well.Share on X But we realized that we were losing business—we were losing clients—since we didn't have an AI framework that would fit our clients. Again, for me, it's a challenge. So anytime I see a challenge, it create brain juice in me, right? So I said, okay, let's figure out how we create this framework. How did you do it?  So really, we built this framework—very interesting. A lot of the steps are similar, but then a lot of things are different.Share on X Whenever client comes to us and says, “Hey, we want to solve this challenge,” what we do is we do enough research. And now we use a lot of AI tools to really understand the problem better and understand the user persona. When you build any software application, there is a person who's going to use that. Sometimes we used to do user research or focus studies to understand that. Now, with the help of AI, we can get a lot of ideas about the user persona. For example, maybe we are building a healthcare application for an anesthesiologist. I don’t know much about that. I know, I mean, because I have been through some medical surgery and all that, but I can't fully understand their user persona or their requirements with respect to the application we're building. But now, with AI, I can actually ask different AI models, “Hey, we are building this app for anesthesiologists. What are their pain points? How would they see it?” So all that deeper mindset and psychology we can get using AI.  You are validating the idea by interrogating AI applications.  What users are going to like and all that. So I will always use this term earlier. In software engineering, now we have this pre-AI and post-AI, right? If you read history, we talk about before Christ and after Christ, right? Yeah. So it's a similar thing now. Yeah, exactly. Or before Covid, after Covid. Before AI, after we did all the user research and everything and created a requirements document, we would usually do design, create like a visual design of the software. But now, with the AI framework, we don't do that. That's not the next step. What we do instead is create a quick prototype using AI platforms.Share on X  So there are a lot of AI platforms—like Lovable, Claude. Now ChatGPT launched Codex for coding, and Replit. Depending on what kind of application you're building—for example, maybe if you're building a web-based application—then I recommend using Lovable or Replit. They're very good at creating that. Whatever software you want to build, whatever user personas that you’re addressing, you can feed into that and it’ll create like a prototype application. Okay.  So what that does is actually, then this prototype, clients can just take it to their customers or internal users and get feedback. A picture is better than a thousand words. Organizations discussing an idea is very different from when they actually see something. Then everybody starts chipping in—“Oh yeah, I see this in the prototype, but I don't want this,” or “I want to move things around,” or “This is what I want.” Basically, building a prototype on AI platforms is much faster than building wireframes and design prototypes like we used to do earlier. So that has changed. So you're 3D printing your software, right?  Yes, exactly. There you go. Well, that’s a very good way you put it together. Yeah. So, yeah, exactly. You’re just 3D printing the software, right? So you can see it, visualize it, and then once you go through that, it creates a lot of better ideas about the software in faster time. So once you have that, then you go into UI/UX design. So in that also, there are two steps. One is wireframing. Wireframing is like creating the flow in black and white. It's like creating a skeleton of your software. It does not have the color, the font, or the branding, but you just create all the different user journeys, the screens, the flow, and the fields that will be there on the screen. So we have integrated AI into that step as well. Earlier, it used to be created by a designer or a business analyst. Now we are using software like Uizard or UX Pilot, where we define what we want—what kind of user journey, flows, and screens—and it creates that. It spins out those wireframes in minutes. So really that has reduced now. The time it used to take to create wire frames is faster now.  So you're designing the wireframes with AI?  Yes, but it's just the wireframe part of it, and it's still guided by our expert VA or designer—someone who knows how to really visualize things and has done a lot of wireframes and sketches. So they know what to tell the AI. Prompting is very important. It's very important that you know how to prompt—what to ask for—so that you can get variations and differentiation in the wireframes. You don't want a standard AI-created wireframe. Everybody can recognize AI-generated images now, right? If I show you one, you'd say, “Oh yeah, it's AI-generated.” I know that, right? Yeah. So again, we keep the human intelligence. We're not asking AI to create the full software end-to-end. It never works—it'll never work. It just doesn't. I know that's a strong statement, but I'm saying that based on experience and an understanding of human behavior and psychology. So AI agents will not be able to code software, in your opinion?  No, they can do the coding, but they cannot build the whole software end-to-end—a production-deployed software. Because these software are being used by humans. You have to have human intelligence to understand and define what you need and how it works.Share on X You can maybe create some software, but it doesn't work very well. Even if you use all these platforms, you can cut down your production time and cost by 30%, 40%, 50%, right? That's the number we are seeing—30 to 50% reduction, depending on the software you're building and the objectives. So just to recap—you validate the idea by interrogating Claude and ChatGPT, asking about the needs of that customer, the psychology of the customer—that's step number one. Step number two is 3D printing the software with Lovable or Replit—so proof of concept. And then you design the wireframes. And then what's next after you design the wireframes? What's the next step?  So that’s a good thing. That’s it. Now I'm going to talk about the human element—some people listening to this podcast will be surprised. Now it comes to visual design, right? So you've created the skeleton, and now you have to add the skin, the tone, the color, the emotion to the design, to the workflow. Now, we have tried AI, but it doesn't work. It's very monotonous. So we use an experienced visual designer, a UX designer, for that step—to give it emotion. When you use AI—I wish I could show you some examples—it creates very similar kinds of designs for apps and software. So what we did is we gave it three different apps with very different objectives and everything, and the designs it came up with were very similar—blocks, buttons—very monotonous. So there's no differentiation. And design is the main thing that becomes the differentiator, right?  Yeah.  So that's what we learned from our experience. And I say that very categorically in all of my talks—that visual design, final UX, has to be human, not AI.Share on X Because you are communicating emotions, right? And AI is still not there to communicate emotions.  Yeah. It doesn’t have emotions.  Well, some people will argue with you and say, “No, it can understand if you're sad or unhappy.” But my response to that is—it's because we've programmed it that way. But things change based on situation, context, ethnicity, culture, fear—how people express nervousness, fear, and all that—it's very different. So there was this AI video interviewing company five or six years ago. They were sued by the Department of Justice because they were trying to detect emotions of people like anxious, nervous, when the interview was happening.  It turned out their model was trained only on one race—they didn't account for other races or ethnicities. So their model failed, and they were sued by Department of Justice for that. So yeah, emotions is something—maybe they have unlimited dimensions, we don't know. So it's hard to program that. So basically: ideation, prototype, wireframe, and then final visual design—that's the discovery and design framework. Now, when it comes to development framework, this is where AI has been a game changer—the coding part. But again, you have to be very careful about how you use AI in your coding pattern with your coding team. It depends on the application, it depends on the tech stack, right? Every platform has its own strengths and weaknesses. For example, if you want to build a web-based application in the React JS framework, then Lovable is great. That's very good—very efficient and cost-effective. Then Claude is there. Claude has been really good in software engineering. I would say it has been built and designed mostly for coding, right? Anthropic—their idea, their starting point—was coding, how to make coding and software engineering better.  So they've been a front runner in the race. ChatGPT is trying to catch up using Codex, and Copilot is great. Copilot is mostly used by enterprises who are on the Microsoft stack. They use Copilot a lot for coding in .NET and enterprise-level applications. They’re used to co-pilot. It’s because they feel comfortable with Microsoft security policies and all that. That’s fine. But in general, we see Claude to be at the top—from our perspective. We've also built a framework for software coding. In software development, there's a popular process called peer review. So when you create source code, you get it reviewed by your peer—your colleague.Share on X  Is this what happens on GitHub?  Yeah, yes. So basically anywhere—any source code repository—you can do that. So your team members can help you make your code better and more efficient.  Yeah, I understand. But now we have a step called prompt peer review. When you're using prompts to build software, those prompts get reviewed by team members. Because if your prompts are not very specific or good enough all the way through the SDLC, you can run into a lot of challenges trying to fix the code. Because now you have a situation where you have code that you have not written fully, and when you ask AI to change something in the code, sometimes it ends up changing a lot of things that you don't want it to change. Yeah.  That's what we've seen, and that's why we evolved. Before we build any software, we create maybe a 10-, 20-, 30-page prompt document, where we go through each screen and function and write it out. It's very sophisticated—it has evolved really well. But the thing is, it takes a few days to do that within the team, because we know if we do it right, the next step is faster and more accurate. So really, the prompt document—think of it more like an architecture document. Earlier, we used to create a solution architecture document, defining all the tools, the design, everything.  But now it's more like an AI-driven solution architecture document with prompts, which get reviewed by team members. So we do that, and then we run that, and we get the code and everything. So I have a CTO club—I run a CTO Club in Maryland—and I was talking to CTOs. They're all using this, but some of them are so advanced that they actually define the test cases in the beginning. They define, “Okay, this is what I want, this is the function I want, and these are the test cases I want it to pass.” That's even more advanced. If you can do that, you can have very efficient code.  Yeah, I love it.  So is that the end? You have your test cases, you design the prompt, you peer-review the prompt, and you already had the prototype, so now you're coding the software—what's the last step?  Yeah. Then there’s an integration as well. So AI doesn’t do the integration so well. You can do the front-end coding, you can do the back-end coding, you can probably create the APIs. APIs require a lot more human intervention. But once you have that, then you have to connect it, right? You have to connect the front end with the backend. A lot of that is still done by the programmer. It's hard to rely on AI for doing that. And again, it depends on the application. Maybe if it's a smaller application, maybe you can have AI do that. But if it's a bigger application—we mostly build bigger applications—then integration, then final QA and testing, and deployment.  So all that is there. But in each of these steps, you can use some sort of AI tool to speed up the process. But the key is you still have to have your architecture, the process. You have to know the steps more. You have to be a good, experienced developer to use AI efficiently if you want to build a production-ready application. You can build a prototype. Anybody can build a prototype on Replit or Lovable, but it's not going to be production-ready that you can give to your customer and charge them money. So that’s the differentiator.  Yeah, I understand. So Piyush, I’d like to switch gears here. I understand the AI ideation framework—that's great. We talked about the technical part of it, the curiosity, the technical challenges. Let’s talk about the entrepreneurship part, which is also part of your profile. So what drives the growth of your business? What would you say drives it?  For us, there are multiple factors that drive the growth of our business. The first is, again, our problem-solving attitude. Any client that comes to us we communicate in that modelShare on X The problem, the challenge, the solution, the business part, the value proposition we bring. And the second factor is our location. We are here in Maryland, and we have another office in Chicago. So being here, we have a global shoring model—that's a main driving factor of our business from the entrepreneurship perspective. So what the global shoring model is: our client-facing team, the senior team, is here—solution architects, sales engineers, designers, project managers, business analysts—they are here in the US, client-facing. And our dev team and testers are in our offshore locations.  Some people call it hybrid shoring. I call it right shoring. The reason I call it right shoring is because in this model, you have the right people at the right shore, so you get the most value. Here, you have people who understand the culture, the product, the context—because products are used by people in a certain culture. And if you are not in that culture, if you haven't experienced it, it's always harder to design the right software solution. I was one of the first people to start that model here in the DMV area for mid-size and smaller companies. This model existed before, but mostly for large enterprise companies. They have used that. But I started to offer that 16 years ago to smaller companies. Either companies were just going offshore, or they were doing onshore, right? I introduced this hybrid—or right-shoring—model, and it has been well received by our customers. So that’s it.  So what is one thing that you’re trying to figure out in your business right now?  Right now, what I'm trying to figure out in my business is scaling. I mean, we have built solutions for many different industries. We have built solutions for different clients in fintech, healthcare, education, nonprofit, startups, IoT, construction. But now what we are trying to figure out is how do we create some off-the-shelf solutions for different industries? Because one challenge we see is that, from the client's perspective, getting custom software built takes time and money. But in certain use cases, we can have off-the-shelf, industry-specific solutions, and then customize those based on the client's needs.  So that's what we are trying to figure out—across different industries, what those solutions can be—so we can scale and also make it easier. And these are more like AI-driven, off-the-shelf solutions that are customizable. So think of it like Salesforce—its core is off-the-shelf, but then you can customize the front end and a lot of other things. Not exactly like Salesforce, but more like industry-specific solutions for different use cases—nonprofit, construction, right? With those, overall, we can build solutions faster.  That’s fascinating. So how has the offshoring—or right shoring, as you call it—model evolved over the past 10 years? Is it different now than it was 10 or 20 years ago?  Yeah, I think that's a great question. It has evolved and changed. Earlier—maybe 10, 12 years ago—when we were talking about hybrid shoring, we were mostly talking about the US and Asia. But now we have different players. We have the nearshore model, which has become quite popular as well—like South America. We have team members in nearshore locations as well, in South America, because we want to leverage different time zones, resources, and culture. And we've seen very positive results. Then you have Eastern Europe. We have competition from countries like Ukraine, Belarus, Romania, Poland. I think it’s the part of the globalized world, right? It's like energy flowing in different spaces—it's not limited to one place, which is great. That's one way it has evolved.  I also know some companies working in Kenya—there are developers there. Some companies are setting up in East Africa, West Africa. So different places are playing roles now. That’s one thing I see. And now, with the help of AI, what's going to happen is it will play two roles. One— in many situations, with AI, you can do more things onshore. That’s one aspect of it. And second—with AI, someone sitting offshore who knows how to use AI can become very competitive as well. We don't have enough data yet to fully see how this will evolve, but maybe in a year or so, we'll see how it plays out.  But I also find that with these simultaneous translation tools—like Apple, I think an iPhone can now translate in all languages. Essentially, another barrier falls that if the language and knowledge of your offshore contractor is not perfect, they can understand things much more clearly because of simultaneous translation. Even on Zoom, you can now flip a switch and they can read what's being said in their own language during a conversation. So that's amazing, I think.  Yeah. That’s amazing. That’s amazing. They can understand more about the culture and mindset. So that's something have to see. Again, I think it depends on the use case, the application, the problem we're solving. But in some cases, it might be great news for onshore—we can keep more dollars here. But keeping dollars here with AI also means a lot of that spend is going to AI, right? So that's one thing—we have to be very careful. Yesterday, in our tech breakfast, our presentation was about how to optimize your AI tokens. There are some companies spending $150,000 per year per employee on tokens. Wow.  That's like the salary of one employee.  Yeah.  A mid-level developer—$150K—they're spending that much. And then they’re trying to figure out how to optimize it. And on top of that, they have cloud costs, right? AWS, Azure—those costs are still there—and then you add AI. So it's a lot of money. You really have to be very smart about understanding and optimizing it. That’s why the prompting is so important, right? It's not just about getting the right software—it's also about getting the cost down.  Yeah. Again, you need expert people who can prompt well, because it's about being able to communicate well. Prompting is about communication—it's about clarity, brevity, security, all that stuff. So, Piyush, we're coming close to the end of the recording. If someone would like to learn more about the applications you develop, how you're using AI, and how you can help their business develop technology, where can they find you? What's the best way to get in touch with you? Sure, there are many ways people can reach out to me. They can go to my website, www.simpalm.com—we have a contact form there. They can submit the form, or they can reach out to me via email directly at contact@simpalm.com. They can also connect with me on LinkedIn. I'm on LinkedIn—message me there if somebody needs anything. I always like discussing problems and what the solutions can be. If anybody reaches out to me, I'm always very quick to respond.  That's awesome. So Piyush Jain, the CEO of Simpalm—and we didn't even talk about your other business, Ducknowl—thank you for coming, and thank you for sharing your insights and your framework on how to build an ideation framework for AI. So thanks for sharing that. And if you're listening and you enjoyed this conversation, then stay tuned, because every week we have another entrepreneur sharing their insights and frameworks with you. So make sure you follow us on YouTube, subscribe, and give us a review on Apple Podcasts. So thanks for coming. Thank you, Steve. It was a pleasure talking to you. Important Links: Piyush's LinkedIn Piyush's website

Send us Fan MailYour AI looks 80% done. In reality, it's 20% done, and the last mile is a cliff. Greg Whalen, CTO of Prove AI, breaks down why CTOs are getting blindsided by AI, why vibe coding creates a false sense of progress, and what most teams are missing when it comes to AI telemetry and observability.Greg has led engineering at global scale as CTO of ZendIt, GM for Amazon WorkMail at AWS, and was an AI researcher at Columbia's NLP group. Now he's building Prove AI to help enterprises move from "mostly working" to production-grade AI systems.We dig into: why AI isn't just another tool in the toolbox, the morning coffee debugging nightmare, how agents exploit other agents for restricted data, why thumbs up/down tells you nothing about outcomes, the vibe coder vs. super principal divide, why CTOs need to "get back in the ring," and why being a first mover beats waiting for convergence that isn't coming.Prove AI → https://proveai.comClick Here to Subscribe: FUTR.tv focuses on startups, innovation, culture and the business of emerging tech with weekly podcasts talking with Industry leaders and deep thinkers.Occasionally we share links to products we use. As an Amazon Associate we earn from qualifying purchases on Amazon.

Suzanne Daniels is a Top Microsoft Advisor who works with CTOs and engineering leaders across EMEA on developer productivity, GitHub, and AI adoption. Her take: the industry is obsessing over coding speed, but that was only ever level one. The real shift is in who defines the solution, not who writes the code.In this episode, we cover:Why the "55x faster coding" marketing misses the point entirelyThe counterintuitive research showing junior engineers adopt AI faster than seniors"Coding is cheap, software is expensive" and what that means for your careerHow the boundary between product and engineering is disappearingWhy most AI coding tools are 80% the same and what to focus on insteadWhether you're early in career and struggling to land a role, or a senior engineer rethinking where your value lies, Suzanne breaks down what actually matters when the coding part becomes cheap.Timestamps:00:00:00 - Intro00:01:15 - Is AI Productivity the Whole Story?00:03:26 - Why Outcomes Matter More Than Code Output00:04:13 - The Real Value Was Never in the Coding00:06:06 - The Product-Engineering Boundary Is Disappearing00:07:37 - Why Junior Engineers Are Actually in High Demand00:09:41 - Research Says Juniors Adopt AI Faster Than Seniors00:11:31 - The Rise of Comb-Shaped Engineers00:12:32 - The Energy Juniors Bring That Teams Need00:14:06 - How Seniors Codify Knowledge for Agents and Humans00:16:35 - Advice for Early Career Engineers Right Now00:19:04 - Old Principles Getting a New Polish00:21:13 - Coding Is Cheap, Software Is Expensive00:22:52 - Will Agentic Development Change Your Programming Language?00:24:53 - What Even Is an Application in the Agent Era?00:28:34 - The Authenticity Paradox of AI-Written Content00:30:12 - Why Your AI Output Needs a Human Value Add00:32:12 - Is Open Source at Risk Because of AI?00:35:09 - When Your Favorite Tool Doesn't Follow You to the Next Job00:36:45 - Most AI Coding Tools Are 80% the Same00:38:15 - What Engineering Leaders Should Enable Beyond Licensing00:42:58 - Should You Leave If Your Company Won't Let You Experiment?00:45:16 - Platform Engineering as the Foundation for AI AdoptionGuest: Suzanne Danielshttps://www.linkedin.com/in/suzannedaniels#SoftwareEngineering #AICoding #BeyondCoding

THIS is how you know you're not really ready for primetime. Today, we're bringing you our most timeless advice from our last conversation with Alan Williamson, Author of Think Like a CTO. We discuss why most first-time CTOs struggle to communicate with non-technical executives, how to think about budgeting and engineering costs like a true technology leader, and why the ability to articulate a clear vision is what separates a real CTO from a CTO in name only. All of this right here, right now, on the Modern CTO Podcast!  To learn more about Alan Williamson, check out his website here.

En este nuevo episodio de Road to CTO, nos sumergimos en una de las trayectorias más inusuales y honestas del ecosistema tecnológico actual junto a David Vrensk, CTO de Ealyx, antes  co-founder y CTO en SeQura.Lejos de seguir una carrera lineal, David nos cuenta por qué decidió ejecutar un "downgrade" voluntario, dejando la dirección para volver a las trincheras como ingeniero de software, priorizando la inspiración del proyecto por encima de cualquier título jerárquico.En el plano técnico, analizamos el dilema de 2013 entre la madurez de Ruby y el potencial de Elixir, además de descubrir por qué los LLMs modernos tienen una "relación especial" con la estructura de Rails. David no se guarda nada y comparte su mayor cura de humildad: una migración de base de datos que terminó reenviando miles de SMS a usuarios de hace cuatro años, recordándonos que en sectores críticos como el FinTech, el respeto por el sistema y el "developer happiness" son más importantes que el ego del directivo.Support the show

Are leg arteries ever "too small to treat"?   Around the world, many patients with Peripheral Artery Disease (PAD), especially those with below-the-knee and small vessel disease, are told their arteries are "too small" or "too distal" for intervention.   In this episode of The Heart of Innovation, hosts Kym McNicholas and Dr. John Phillips interview Dr. Naoki Hayakawa, Chief and Director of Endovascular Therapy at Asahi General Hospital in Japan.Dr. Hayakawa is internationally recognized for tackling the most complex chronic total occlusions (CTOs), including small-caliber below-the-knee vessels that others may consider untreatable.  He has served as a live demonstration operator at major international meetings including JET, CCT Peripheral, Kokura Live, and Peripheral CTO Seminars, and has published extensively on:  • IVUS-guided wiring techniques  • Below-the-knee chronic total occlusions  • Drug-coated balloon therapy  • Transradial approaches for complex PAD  • Advanced re-entry and retrograde access techniques   His work challenges outdated assumptions about what is and isn't possible in limb salvage.In this conversation, Dr. Hayakawa sets the record straight on:  • What can truly be treated in small vessel PAD  • When vessels are actually too small  • The importance of imaging and IVUS guidance  • Why patients must seek experienced operators for complex disease  • What global standards of care should look like  If you or someone you love has been told "nothing more can be done," this episode is essential viewing.   - Concerned about leg circulation or told your vessels are too small?Call the Leg Saver Hotline: 1-833-PAD-LEGSBecause "too small to treat" should never be the final answer without expert evaluation.   Subscribe to The Heart of Innovation for global leaders in vascular innovation, limb salvage, and PAD care.  #PeripheralArteryDisease#PAD#LimbSalvage#BelowTheKnee#ChronicTotalOcclusion#EndovascularTherapy#IVUS#CriticalLimbIschemia

Most recruiters are chasing the wrong market, and it's quietly destroying their desk. Will Wegert watched his billings collapse after chasing coastal fees and shiny opportunities he had no business pursuing. So he did something most recruiters won't: he looked at the data, killed the distractions, and went all-in on owning one niche in one city. What happened next took 7 years to build and looked like an overnight success. In this episode, Will breaks down the exact attraction-based marketing system, mindset shifts, and daily disciplines that turned him into the most recognized dev recruiter in Colorado and the blueprint any recruiter can steal right now. What you'll learn: — The Inner Circle Model: the 3-spoke system (digital, face-to-face, direct outreach) that warms up clients before you ever pick up the phone — How to audit your placement data to find where your real money comes from and what to cut immediately — The ABC Marketing Method: the simplest relationship drip system in recruiting, 52 weeks a year — Why saying no to bigger fees is the fastest path to higher billings — The exact LinkedIn content formula Will uses to pull CTOs to him without a single cold pitch  — The accountability conversation that snapped his career back into focus after his worst year — Why "my business runs on referrals" is a trap and what to build instead Will Wegert is a Denver-based software engineering recruiter with 8 years in the industry. After nearly halving his billings by chasing the wrong market, he rebuilt around a ruthless niche focus — and never looked back. This episode will change how you think about your desk. Stop chasing. Start owning. TIMESTAMPS 00:03:01 — Why Will said no when Benjamin first invited him on 00:04:28 — The "7-year overnight success" explained 00:06:26 — His non-traditional path: resume writing → copywriting → recruiting 00:09:18 — Why referrals aren't a system — and what to build instead 00:15:47 — The data audit that killed his coastal ambitions 00:20:48 — The message to Danny Cahill that redirected his career 00:26:34 — The Inner Circle Model: 3 spokes every recruiter needs 00:37:01 — The CTO lunch strategy: show up, add value, never sell 00:39:28 — His LinkedIn formula: ⅓ human, ⅓ value, ⅓ algorithm 00:44:40 — ABC Marketing: the simplest desk-revival strategy in recruiting 00:52:12 — The Chrome tool and LinkedIn video trick cutting through AI noise 00:57:01 — The question Will wishes every recruiter would ask him SPONSORS Atlas — AI-First Recruitment Platform Every email. Every interview. Every conversation. Instantly searchable, always available. Atlas customers report up to 41% EBITDA growth and 85% increase in monthly billings. → https://recruitwithatlas.com SUMMIT & COMMUNITY This Is Your Year — Recruiter Summit → https://this-is-your-year-recruiter-summit.heysummit.com/ Elite Recruiter Community — Replays, Billers Club, Roundtables & Split Space. $49/month. → https://elite-recruiters.circle.so/checkout/elite-recruiter-community TOOLS PeopleGPT Free Trial → https://juicebox.ai/?via=b6912d Talin AI Free Trial → https://app.talin.ai/signup?via=recruiter Pin Free Trial → https://www.pin.com/ Email Newsletter → https://eliterecruiterpodcast.beehiiv.com/subscribe CONNECT YouTube → https://youtu.be/eveVeg5UV6I Will Wegert on LinkedIn → https://www.linkedin.com/in/willwegert/ Benjamin Mena → http://www.selectsourcesolutions.com/ Benjamin on LinkedIn → https://www.linkedin.com/in/benjaminmena/ Benjamin on Instagram → https://www.instagram.com/benlmena/

Unlock the future of cybersecurity where AI agents no longer just assist—they act autonomously, making decisions that could impact your entire organization. In this eye-opening episode, Vidit Arora, founder and CEO of Quillr AI, reveals how rapidly AI-powered agents are transforming the digital landscape—and why traditional security systems are already obsolete.As AI agents gain full control over data movement, system modifications, and even decision-making processes, security professionals face unprecedented challenges. Vidit uncovers why existing frameworks like DLP and CASB fall short in this new era, and how the lack of contextual understanding enables agents to bypass legacy controls. You'll discover how the speed at which AI agents evolve makes zero-day threats look slow—and the urgent need for inline reasoning and adaptive defenses to keep pace.We break down critical topics such as:The shift from AI assisting to AI acting with autonomy and intentWhy current security paradigms can't catch or control fully autonomous agentsHow understanding agent context, intent, and ecosystem visibility is now a security imperativeThe role of a new decision layer that inlines reasons over agent actions in real timePractical strategies for achieving comprehensive AI footprint discovery and controlFailing to adapt to this new AI-driven environment risks data breaches, operational chaos, and the loss of control over your digital assets. But by embracing a proactive, context-aware security approach, you open the door to innovation—without risking your organization's future.Perfect for security leaders, CTOs, and AI strategists, this episode will challenge everything you thought you knew about cyber defense. If you're serious about safeguarding your organization amid AI's explosive growth, you'll want to hear this now.Visit quiller.ai to explore cutting-edge AI visibility tools and learn how to future-proof your security stance. Don't let autonomous agents catch you off guard—stay ahead of the curve before the next disruptive move takes you by surprise.

Want to Be the Best Version of Yourself? Sign Up Here.https://app.beerbiceps.com/web/checkout/699d46a79b98fa69b168b402Check out BeerBiceps SkillHouse Courses Here - https://www.beerbicepsskillhouse.in/For all BeerBiceps vlog content Watch Life Of BeerBiceps - https://www.youtube.com/@LifeOfBeerBicepsCheck out my Mind Performance app: Level SuperMindLink:- https://level4665.u9ilnk.me/d/F1ZOZV4OnTShare your guest suggestions hereMail - connect@beerbiceps.comLink - https://forms.gle/aoMHY9EE3Cg3Tqdx9Join the Level Community Here:https://linktr.ee/levelsupermindcommunityFollow BeerBiceps SkillHouse's Social Media Handles:YouTube: https://www.youtube.com/@BeerBicepsSkillHouseInstagram: https://www.instagram.com/beerbiceps_skillhouseWebsite : https://beerbicepsskillhouse.inFor any other queries EMAIL: support@beerbicepsskillhouse.comIn case of any payment-related issues, kindly write to support@tagmango.comFollow Sauvik Banerjjee's Social Media Handles:-Instagram: https://www.instagram.com/sauvikbanerjjeeofficial/?hl=enLinkedIn: https://in.linkedin.com/in/sauvik-banerjjee-492b65aWebsite: https://www.sauvikbanerjjee.com/In this 472nd episode of The Ranveer Show, we are joined by Shauvik Banerjjee, one of the top CTOs to come out of India, who has worked with global giants like Meta. He shares mind-bending and often terrifying insights on the Future of Artificial Intelligence, Data Privacy, Cyber Security, AI Relationships, and Life in 2035. This episode takes you into the reality of the dark side of the internet, the evolution of technology, and how AI will fundamentally change human existence.In this conversation with Shauvik, we talk about AI Girlfriends and Boyfriends, the rise of Robotics in Intimacy, and why using ChatGPT as a Therapist can be dangerous. We also discuss the scary reality of Deepfakes, AI-generated P*rnography, and essential Cyber Security advice for women and men in the modern age. This episode also covers the Intersection of AI and Spirituality, explaining how Mantras and Sanskrit interact with Spectrograms, the future of Quantum Computing, the possibility of Teleportation, and the concept of Digital Immortality by uploading human consciousness to the cloud. This podcast is a valuable resource for anyone interested in Technology, Future Trends, AI Safety, Science Fiction becoming Reality, and the future of Humanity.(00:00) – Start of the episode(05:35) – Don't Use AI For Therapy(12:30) – The Dark Reality of AI Girlfriends(14:20) – Sex Robots & Haptic Suits(19:34) – Can AI Cure Human Loneliness?(27:15) – Smartphones Will Die By 2035(33:20) – How To Use AI Correctly(40:40) – AI P*rn & Deepfakes Exposed(53:40) – Warning For Girls: Stop Taking Photos(58:36) – Sam Altman's Data Warning(1:12:00) – Is Your Data Safe With China?(1:17:15) – 5 Things To Never Tell AI(1:18:30) – Smart Glasses: The New Reality(1:25:20) – The End of Mobile Apps(1:32:00) – Science of Mantras & Spirituality(1:43:30) – Teleportation Is Real : SHOCKING(1:49:15) – Quantum Computing & 10G Speed(1:56:30) – Humans Living 200 Years?(1:59:17) – Uploading Consciousness to Cloud(2:05:36) – End of the episode

Most organizations are drowning in data they can't process fast enough — leaving critical security gaps that adversaries exploit. Michael Cucchi, Chief Marketing Officer at Hydraulics, reveals how a groundbreaking new data architecture is transforming real-time security analytics, slashing processing costs by up to 40X while capturing every byte of telemetry across global networks.In this episode, you'll discover why traditional Security Information and Event Management (SIEM) systems are no longer sufficient for today's threat landscape. Michael breaks down the limitations of legacy data storage, ingestion bottlenecks, and costly rehydration issues that leave security teams blind during breaches. He shares how leading companies are adopting a new security data fabric designed for hyper-scalability, instant analysis, and unprecedented data retention — all at a fraction of the cost.We break down:The evolution and modern challenges of the SIM market, including why outdated architectures struggle with today's data volumes.How security analytics are rapidly moving toward real-time, agentic automation driven by AI and large-scale data fabrics.The critical importance of low-latency querying, cost-effective storage, and flexible architectures that enable security teams to operate at machine speed.Why the next wave of security operations will depend on maintaining and rehydrating vast, granular data stores without breaking the bank.How innovative companies like Hydraulics are building the emerging data fabric that will underpin zero-trust, AI-driven security in the years ahead.This episode is essential listening for security professionals, CTOs, and data architects eager to stay ahead of the exponential growth in security signals, threats, and complexity. Miss out on these insights, and your organization risks falling behind—armed only with legacy systems that can't keep up. A smarter, faster, cheaper future for security analytics is here.Plus, Michael shares exclusive research coming to RSA — including advances in AI-driven bots and zero trust frameworks. Whether you're defending enterprise assets or building next-generation SOCs, this conversation is your gateway to the future of security data management.Timestamps: 00:00 – Introduction and episode overview02:24 – Michael's background and experience in data science and security04:52 – How infrastructure and SIEM technologies have evolved over the past decade08:15 – Limitations of current SIEM architectures and data retention challenges12:10 – Hydraulics' approach to scalable, cost-effective security data platforms15:24 – The importance of real-time analytics in security operations17:00 – AI and automation in breach detection and incident response19:34 – Scaling security telemetry across global networks and CDN signals22:10 – The object-oriented storage analogy in security data management25:05 – Crossing the chasm: from traditional SIEM to real-time data fabric28:13 – Future of AI in security automation and the next decade in security tech31:01 – Final insights and how to connect with HydraulicsResources & Links:https://hydrolix.ioAWS Object StorageUnderstanding Data Fabrics in Security (hypothetical link)

OSFF Toronto 2026 Preview: FINOS Ecosystem, AI, HPC, Fluxnova, CALM, CDM & Open Data CommonsIn this episode of the Open Source in Finance Podcast, host Grizz Griswold delivers an essential preview of the upcoming inaugural OSFF Toronto. Grizz breaks down why Toronto's unique position as a top-tier global financial hub—home to Canada's "Big Five" banks and a world-class AI research community—makes it the perfect environment for the next evolution of open-source collaboration. The episode explores the shift from Canadian institutions being open-source consumers to becoming active leaders in projects like FDC3 and Common Cloud Controls, providing a roadmap for what to expect when the forum debuts in the "6ix."

Most organizations struggle to balance building their own AI infrastructure with leveraging reliable, scalable solutions. Oded Sagie and Perry Krug reveal how partnering with Pinecone transformed their approach—turning complex infrastructure challenges into seamless, "boringly reliable" systems. Discover how this shift unlocked faster innovation, lower operational overhead, and the peace of mind to focus on delivering real customer value.In this episode, you'll break down the core architectural innovations behind Pinecone's platform, including its adaptive indexing and serverless design, which support workloads from low-latency high-throughput applications to massive multi-tenant environments. Oded shares real-world lessons on choosing build vs. buy—highlighting the long-term costs of ownership versus operational simplicity and scalability. Perry dives into how Pinecone's managed vector database facilitates rapid deployment on cloud platforms like Azure, helping teams focus on their core product, not infrastructure.If you're navigating the complexity of deploying AI at scale—especially in industries demanding high reliability and performance—this episode is your game plan. Perfect for data engineers, AI leaders, and CTOs ready to ditch operational headaches and embrace "boringly reliable" technology that accelerates innovation while minimizing risk. Tune in to discover how to build smarter, scale faster, and focus on what truly matters—your customers.Apple @ https://podcasts.apple.com/us/podcast/generate-now/id1566458654Spotify @ https://open.spotify.com/show/43XcU8A1dsNfW3YGT8KXhp?si=62e09c6df65b4dc9&nd=1&dlsi=e9e6a138e7064929Youtube @ https://www.youtube.com/@generatenowpodcast/featuredConnect with Oded @ https://www.linkedin.com/in/odedkal/Perry @ https://www.linkedin.com/in/perrykrug/James @ https://www.linkedin.com/in/jmcaton/

Regaining clarity at work is one of the biggest challenges developers face as responsibilities grow, distractions multiply, and expectations rise. Burnout rarely appears overnight. More often, it creeps in quietly—through constant context switching, mental fatigue, and the feeling that you're busy all day but not making real progress. For developers and technical leaders, clarity isn't a "nice to have." It's what allows you to make good decisions, focus deeply, and enjoy the work you're doing. Without it, even small tasks feel heavier than they should. About Andrew Hinkelman Andrew Hinkelman is a certified executive coach and former Chief Technology Officer who works with tech founders, CTOs, and engineering leaders to strengthen their leadership and people skills. With over 25 years of corporate experience, including 8 years as a CTO, Andrew understands firsthand the pressures technical leaders face as they move from hands-on execution to leading teams and organizations. His coaching focuses on helping leaders build trust, develop others, and stay strategic as responsibilities grow. Andrew's philosophy is simple: all professional development is personal improvement. After experiencing burnout in his own leadership journey—constantly stepping in to fix problems and being needed by everyone—he learned the value of trusting his team instead of controlling outcomes. Today, Andrew helps leaders avoid that same trap by building resilient teams, focusing on relationships, and creating environments where others can succeed. Follow Andrew on Instagram and LinkedIn. Why Regaining Clarity at Work Matters for Developers When regaining clarity at work starts to slip, the symptoms are subtle at first. Decisions take longer. You second-guess yourself more often. Work that once felt engaging starts to feel draining. This isn't a motivation problem. It's a clarity problem. Developers often push through this phase by working longer hours, assuming effort will fix it. In reality, the lack of clarity compounds the problem—leading to frustration, reduced quality, and eventually burnout. How Distractions Undermine Regaining Clarity at Work Modern work environments make regaining clarity at work especially difficult. Messages, emails, meetings, and notifications constantly pull attention away from focused thinking. Even well-intentioned tools can fragment your day into shallow work. The issue isn't that developers aren't capable of focus—it's that focus is constantly interrupted. Over time, this makes it harder to think clearly, prioritize effectively, or feel confident in decisions. The result is mental overload, not progress. Regaining Clarity at Work Through Better Daily Habits One of the most practical ways to regain clarity at work is by examining daily habits. Not in a rigid or extreme way, but by noticing patterns. What creates a good day? What leaves you feeling depleted? Sleep, movement, downtime, and boundaries play a much larger role in clarity than most developers expect. Clarity isn't created in moments of intensity—it's supported by consistency. Self-Discipline as a Foundation for Regaining Clarity at Work Self-discipline is often misunderstood as pushing harder. In reality, it's about protecting the habits that keep your energy stable. Waiting for weekends or vacations to reset burnout doesn't work if every weekday drains you. Regaining clarity at work means building routines that prevent depletion before it happens. Regaining Clarity at Work by Trusting Yourself When developers feel stuck, the instinct is often to search for more input—another article, another video, another framework. But more information rarely creates clarity. In many situations, you already know how to handle the challenge in front of you. Learning to pause, quiet your mind, and trust your experience can be more effective than consuming more advice. Regaining clarity at work often comes from removing noise, not adding insight. Regaining Clarity at Work with Allies and Peer Support Clarity is much easier to regain when you're not working in isolation. Talking through challenges with trusted peers helps break mental loops and introduce new perspectives. These allies don't need to be your manager. In fact, regaining clarity at work often comes faster when support comes from peers across teams or outside your organization—people who understand the context but aren't tied to the outcome. Expanding Beyond Your Manager to Regain Clarity at Work Strong peer relationships act as soundboards. They help you reality-check assumptions, think through decisions, and feel less alone in complex situations. Over time, these relationships become one of the most reliable ways to avoid burnout. Regaining Clarity at Work with Coaching and AI Tools Coaching and AI tools can both support regaining clarity at work, but they serve different roles. Some developers find value in AI prompts or structured reflection. Others need human conversation, body language, and shared experience. For many, a hybrid approach works best—using tools when they're helpful, and people when nuance, accountability, or emotional context matters. The goal isn't to replace connection, but to support clarity when it's needed most. Signs You're Losing Clarity at Work Constant distraction, overthinking, and decision fatigue Relying on weekends or time off as the only recovery strategy Simple Habits That Restore Clarity Daily actions that protect energy and focus Consistency over intensity when rebuilding clarity When to Use Coaching, AI, or Allies Choosing the right support for the situation Combining human insight with practical tools Conclusion Regaining clarity at work isn't about doing more—it's about doing what matters consistently. By protecting your energy, trusting yourself, and leaning on the right support, developers can avoid burnout and move forward with confidence. Take one small step this week toward regaining clarity at work, and start building habits that support sustainable, focused growth. Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Detecting and Avoiding Burnout Three Ways To Avoid Burnout Avoid Burnout – Give Time To Yourself Building Better Foundations Podcast Videos – With Bonus Content

At Davos this year, some of the biggest names in tech sent a clear signal. AI is no longer a novelty. It is no longer a proof-of-concept exercise. As Demis Hassabis of Google DeepMind suggested, AI will shape more meaningful work. And Satya Nadella of Microsoft was even more direct. AI only matters if it improves real outcomes for people. So what does that look like inside the enterprise? In this episode of Tech Talks Daily, I'm joined by Andrew Boyagi, Customer CTO at Atlassian, to unpack how the conversation has shifted from experimentation to execution. Developers, in many ways, are the perfect lens for understanding this moment. Over the last two decades, their role has expanded far beyond writing code. They now own products, infrastructure, operations, and business outcomes. AI is simply the next chapter in that evolution. Andrew argues that AI will not replace engineers. It will raise expectations. As intelligent tools absorb repetitive work, the real value moves up the stack. System design. Architectural thinking. Reviewing and refining AI-generated output and orchestrating solutions that solve genuine business problems. And through it all, humans remain firmly in the loop. We also explore what this means for leadership, why mindset is starting to matter more than technical skill alone, how organizations can avoid layering AI on top of broken processes. And why the companies pulling ahead are treating AI as a strategic discipline, not a feature upgrade. This is a conversation grounded in reality. It speaks to product leaders, CTOs, CIOs, and anyone asking a simple but powerful question. If we are investing in AI, what are we actually getting back? And before we close, we look ahead to Team '26 and the themes Andrew and his team are already working on.  If this year has been about proving value, what will the next chapter demand from enterprise leaders? As always, I'd love to hear your thoughts. Are you seeing proof of value in your organization yet, or are you still working through the pilot phase?

For many developers and engineering leaders, executive coaching feels like something you turn to only when things go wrong. We're trained to solve problems, push through obstacles, and rely on our own expertise. So when progress slows, the default reaction is often to work harder—not to step back and reassess. That's exactly why executive coaching can be so valuable when used intentionally. At its best, coaching isn't about fixing weaknesses. It's about uncovering blind spots, challenging assumptions, and helping capable leaders see where their habits are limiting growth. When the fit is right, coaching brings clarity and momentum. When it's wrong, it simply adds noise. About Andrew Hinkelman Andrew Hinkelman is a certified executive coach and former Chief Technology Officer who works with tech founders, CTOs, and engineering leaders to strengthen their leadership and people skills. With over 25 years of corporate experience, including 8 years as a CTO, Andrew understands firsthand the pressures technical leaders face as they move from hands-on execution to leading teams and organizations. His coaching focuses on helping leaders build trust, develop others, and stay strategic as responsibilities grow. Andrew's philosophy is simple: all professional development is personal improvement. After experiencing burnout in his own leadership journey—constantly stepping in to fix problems and being needed by everyone—he learned the value of trusting his team instead of controlling outcomes. Today, Andrew helps leaders avoid that same trap by building resilient teams, focusing on relationships, and creating environments where others can succeed. Follow Andrew on Instagram and LinkedIn. What executive coaching actually does Leadership coaching is frequently misunderstood, especially in technical environments. It's not mentoring, consulting, or performance management. Rather than providing answers, a coach helps leaders examine how they think, make decisions, and show up—particularly under pressure. This kind of perspective is difficult to gain from inside your own day-to-day context. For technical leaders, this distinction matters. Many engineers advance by being exceptional problem solvers. Over time, that strength can become a constraint. Coaching helps leaders recognize when execution, control, or perfectionism starts to limit influence, trust, and scale. At its core, this work builds awareness—and awareness is what enables meaningful change. When executive coaching is the right move Coaching isn't necessary at every stage of a career. If progress feels steady and challenges are manageable, it may not add much value. However, it becomes especially useful during moments of transition or tension, such as: Stepping into a new leadership role Navigating organizational or team change Feeling stuck despite sustained effort Noticing that familiar approaches no longer work These moments often signal that your environment has changed—but your operating model hasn't. A strong coaching relationship helps leaders adapt intentionally instead of reacting out of habit. Executive coaching for leaders in new roles New leadership roles come with unspoken expectations. Success is no longer defined purely by output, and feedback becomes less direct or less frequent. Many leaders assume they need to "get everything under control" before working with a coach. In reality, coaching is most effective when things still feel unclear. That uncertainty highlights where growth is needed—whether in communication, prioritization, delegation, or decision-making at scale. You don't need to show up polished. You need to show up honestly. What a real coaching engagement looks like One common misconception is that leadership coaching is a one-time conversation or a motivational reset. In practice, effective coaching is an ongoing engagement built around clarity, feedback, and behavior change over time. It starts with defining what success actually looks like—not in abstract terms, but in concrete outcomes that matter to you and your organization. From there, the work focuses on identifying what's getting in the way. Often, these are habits that once helped you succeed but now create friction. If they were obvious, you would have addressed them already. Many engagements begin with structured feedback to ground the work in reality. This helps align self-perception with impact and reduces guesswork. It's not about judgment—it's about accuracy. How to evaluate coaching fit Coaching is a relationship, not a transaction. Talking to multiple coaches isn't optional—it's essential. A strong indicator of fit is experiencing a real working session rather than a polished sales call. Pay attention to how the coach listens, challenges assumptions, and guides reflection. Productive discomfort is often a good sign. If you leave a session seeing a situation differently or questioning a long-held belief, growth is likely. If you leave feeling simply validated, it probably isn't. Red flags that signal a poor coaching fit Coaching is not a rescue tool for poor performance. When someone is disengaged or unwilling to grow, it rarely works. Another red flag is a coach who consistently agrees with you. Comfort feels good in the moment, but it doesn't change behavior. Effective leadership development introduces intentional, constructive friction that leads to insight. Executive coaching during burnout and plateaus Burnout often comes from effort without impact. Leaders work longer hours, take on more responsibility, and still feel stuck. Coaching can help identify a keystone goal—the one focus area that makes everything else easier. It also helps leaders stop over-investing emotional energy in things outside their control, which is a common and costly source of exhaustion in senior roles. Executive Coaching Checklist Signs coaching may help you move forward Indicators that a coach will challenge rather than placate Coaching Fit Test: One Session What a meaningful trial session should reveal How to tell if the coach will stretch your thinking Stuck or Burned Out? Find the Keystone Goal How to identify the one change that unlocks momentum A reset approach for overwhelmed leaders Conclusion Executive coaching isn't about hiring someone to give advice—it's about choosing a partner who helps you see yourself and your situation more clearly. If you're navigating change, feeling stalled, or sensing that effort isn't translating into progress, this kind of support may be less about doing more and more about seeing differently. Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Embrace Coaching To Advance Your Career Giving Back As A Mentor, Coach, and Lead Detecting and Avoiding Burnout Building Better Foundations Podcast Videos – With Bonus Content

If your podcast has 10,000 downloads and only two sales meetings, Jason's take is blunt: you're doing everything wrong. In this solo episode of Pipe Dream, host Jason Bradwell breaks down why most B2B podcasts become expensive therapy sessions for executives who like hearing themselves talk, and more importantly, how to fix it. Jason's core point is clear: downloads don't pay salaries, pipeline does. Most B2B podcasts fail commercially for four reasons. They borrow strategy from B2C entertainment instead of building revenue assets. They optimise for vanity metrics because that's what vendors sell. They exist in a silo with no connection to sales motion or funnel stages. And the generic interview format doesn't map to the buyer journey. The problem isn't production quality or download numbers. The problem is that marketing makes the show, sales doesn't know it exists, and when sales don't use it, it's just an expensive content theatre. One 45-minute conversation with a random influencer doesn't help a prospect at the consideration stage trying to figure out if you can actually deliver results, or help a champion sell your solution internally to their CFO. Instead of downloads, impressions, and social shares, here's what actually matters. Leading indicators like enterprise guests booked from your ABM lists, meetings created attributed to podcast touch, and accounts touched. Commercial outcomes like deal stage acceleration, rep usage in sequences and discovery calls, and pipeline influenced. That's the difference between vanity metrics and revenue metrics. One makes marketing feel busy, the other moves the business forward. Jason shares a real example. A B2B tech company ran a podcast for 18 months with 40 episodes, a few thousand downloads, and zero pipeline influence. They interviewed random influencers because "that's what podcasts do." Their sales team had never heard of the show. B2B Better killed the influencer strategy and started interviewing their own clients, CTOs and engineering leaders who'd worked with them but would never sign traditional case studies due to compliance constraints. They packaged content as battle cards and sales enablement artifacts, not social clips. Within 90 days, sales used clips in 60% of discovery calls, influenced £3 million in pipeline, and improved outbound reply rates by 34% when reps included a 92-second client clip in sequences. Same production effort, completely different outcome. The only difference was strategy. Here's the process. Audit your funnel gaps to find where deals actually stall. Map content to that stage. Design multi-segment episodes that serve different funnel stages, not one 45-minute interview that does nothing particularly well. Package for sales with battle cards, objection handlers, and committee packs. Measure commercial impact through meetings created, accounts touched, pipeline influenced, and deal velocity, not downloads. If you can't answer "which specific deals will this help us close," you're not ready for a podcast. You don't have a content problem, you have a strategy problem. Stop trying to be Joe Rogan. You're building a revenue asset, not an entertainment show. Chapter Markers 00:00 - Why downloads don't pay salaries, pipeline does 01:00 - The word podcast has become a red herring 02:00 - Four reasons B2B podcasts fail commercially 03:00 - No connection to sales motion equals content theatre 04:00 - Revenue metrics that actually matter 05:00 - Real example: Zero to £3 million pipeline influenced 06:00 - The process: Audit, map, design, package, measure 07:00 - Multi-segment episodes serving different funnel stages 08:00 - Most teams shouldn't have a podcast yet 09:00 - The activation test: Ask sales if they've used it Useful Links Connect with Jason Bradwell on LinkedIn Listen to Pipe Dream Podcast on Podbean HubSpot ABM reporting guide for tracking accounts touched Explore B2B Better website and the Pipe Dream podcast 

Podcast: Industrial Cybersecurity InsiderEpisode: Former NSA now Founder & CTO Breaks Cybersecurity Down: Satellites to ManufacturingPub date: 2026-02-10Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationDino sits down with Dick Wilkinson, CTO and co-founder of Proof Labs, to explore the intersection of space technology and industrial cybersecurity.Dick shares his 20-year journey in the U.S. Army with the National Security Agency, transitioning from signals intelligence to becoming a CISO for critical infrastructure organizations, including New Mexico's Supreme Court and the Albuquerque water authority.The conversation dives deep into the challenges of securing satellite systems with onboard intrusion detection and the persistent gap between IT and OT security teams. We also explore why the "castle wall" perimeter security model is dangerously outdated.Dick reveals how AI is lowering the barrier to entry for both attackers and defenders, and discusses the real-world applications of satellite communications in oil and gas operations.He also introduces a revolutionary physical layer-one air gap device called Goldilock Secure, which could transform how we protect remote industrial assets.This episode is essential listening for CISOs, CTOs, and security leaders looking to understand emerging threats in space-based infrastructure and practical solutions for securing distributed industrial environments.Chapters:(00:00:00) - Dick's Journey: From NSA to Space Cybersecurity(00:04:32) - What is Proof Labs and Why Space Security Matters(00:08:15) - Satellites as OT Assets: Oil, Gas, and Critical Infrastructure(00:12:47) - How Onboard Intrusion Detection Works in Spacecraft(00:16:23) - The Castle Wall Problem: Moving Beyond Perimeter Security(00:19:41) - IT vs OT: Bridging the Gap in Manufacturing Cybersecurity(00:24:18) - AI's Impact: Lowering the Barrier for Attackers and Defenders(00:27:35) - The Visibility Challenge: Why Most Plants Don't Know Their Assets(00:30:12) - Goldilock Firebreak: A Physical Air Gap Device That Changes Everything(00:35:20) - Real-World Applications for Remote Industrial Asset ProtectionLinks And Resources:Want to Sponsor an episode or be a Guest? Reach out here.Dick Wilkinson on LinkedInProof Labs WebsiteIndustrial Cybersecurity Insider on LinkedInCybersecurity & Digital Safety on LinkedInBW Design Group CybersecurityDino Busalachi on LinkedInCraig Duckworth on LinkedInThanks so much for joining us this week. Want to subscribe to Industrial Cybersecurity Insider? Have some feedback you'd like to share? Connect with us on Spotify, Apple Podcasts, and YouTube to leave us a review!The podcast and artwork embedded on this page are from Industrial Cybersecurity Insider, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.

AI coding tools are writing more code than ever, but your software isn't shipping any faster. Welcome to the AI Paradox and the solution, intelligent orchestration.Bill Staples, CEO of GitLab, explains why AI-accelerated coding is actually creating massive downstream bottlenecks in code reviews, security checks, and deployment, and why adding more AI tools only makes the problem worse. GitLab's solution: intelligent orchestration across the entire software development lifecycle.You'll discover:✅ The "AI Paradox:" why faster coding isn't translating into faster software delivery✅ How tool fragmentation and context-switching are killing developer productivity✅ Why agents that thrive on context fail when your tools are siloed✅ The "inner loop architecture" that makes AI agents 40% more accurate and 25% faster✅ How GitLab's intelligent orchestration approach combines workflows, context, and guardrails✅ Why mid-level developers are about to become strategic orchestrators (not just coders)✅ The exact metrics CIOs should track, and why "lines of code" is the wrong one✅ First steps: audit, consolidate, and pilot before going all-in on AI⏱️ TIMESTAMPS0:00 The AI Paradox: Why faster coding doesn't mean faster delivery1:10 How tool fragmentation creates developer bottlenecks3:40 Why AI agents make complexity worse (not better)5:12 Solving the AI automation problem: people, process, and technology6:36 Inner loop architecture: co-locating agents and data9:14 Intelligent orchestration: workflows, context, and guardrails10:32 How GitLab's knowledge graph supercharges agent accuracy12:49 Universal guardrails for humans and AI agents13:39 Real-world results: 2-3x more merge requests, pipeline fixes in minutes15:00 Common threads driving customer success16:36 How AI transforms the mid-level developer's role19:06 Advice for CIOs and CTOs putting this into practice20:49 First steps: audit, measure, and pilot22:45 Core metrics to evaluate AI's real value25:02 Wrap-up

What happens when leaders are confident about AI, but the people expected to use it are not ready? In this episode of Tech Talks Daily, I sat down with Caroline Grant from Slalom Consulting to explore one of the most persistent tensions in enterprise AI adoption right now. Boards and executives are spending more, moving faster, and expecting returns sooner than ever, yet many organizations are struggling to translate that ambition into outcomes that scale.  Caroline brings fresh insight from Slalom's latest research into how leadership, culture, and workforce readiness are shaping what actually happens next. We unpack a clear shift in ownership for AI transformation, with CTOs and CDOs increasingly leading organizational redesign rather than HR. That change reflects how deeply AI now cuts across technology, operations, and business models, but it also introduces new risks.  Caroline explains why sidelining people teams can create blind spots around skills, incentives, and trust, especially as roles evolve and uncertainty grows inside the workforce. The result is what Slalom describes as a growing AI disconnect between executive optimism and day-to-day reality. Despite the noise around job losses, the data tells a more nuanced story. Many organizations are creating new AI-related roles at a pace, yet almost all are facing skills gaps that threaten progress. We talk about why reskilling at scale is now unavoidable, how unclear career paths fuel employee distrust, and why focusing only on technical capability misses the human side of adoption.  Caroline also challenges assumptions about skill priorities, warning that deprioritizing empathy, communication, and change leadership could undermine effective human-AI collaboration. We also dig into ROI expectations, with most UK executives now  expecting returns within two years. Caroline shares why that ambition is achievable, where it breaks down, and why so many organizations remain stuck in pilot mode. From governance and decision rights to culture and leadership behavior, this conversation goes beyond tools and platforms to examine what separates experimentation from fundamental transformation. As AI becomes a test of leadership as much as technology, how are you closing the gap between vision and execution within your organization, and are you building a workforce that can keep pace with change rather than resist it? Connect With Caroline Grant from Slalom Consulting The Great AI Disconnect: Slalom's Insights Survey Learn More About Slalom

Confirm uses organizational network analysis to surface hidden high performers and toxic actors that traditional performance reviews miss - identifying the quiet contributors everyone relies on and the problematic employees who manage up effectively. In this episode of BUILDERS, I sat down with David Murray, Cofounder & CEO of Confirm, to dissect their most painful go-to-market lessons. David shares why leading with methodology superiority torpedoed their early sales, the specific discovery framework that flipped their win rate, and how they segment the four distinct HR buying motions that require completely different sales approaches. Topics Discussed: Why traditional performance reviews are 60% manager bias according to research by Maynard Goff How organizational network analysis identifies introverted high performers and manages-up toxic actors The catastrophic early GTM mistake: positioning against existing processes Discovery frameworks for conservative buyers in compliance-heavy functions Talk ratio targets and silence techniques from clinical psychology applied to enterprise sales Channel testing methodology that identified LinkedIn ads as their primary acquisition driver The four-quadrant framework for HR sales: CHRO vs line manager, company-wide vs HR-only tools Messaging strategies that balance shock factor with substantive education GTM Lessons For B2B Founders: Discovery trumps differentiation in category creation: Confirm's design partner had promoted toxic employees and lost quiet high performers in the same cycle—a perfect case study for their ONA methodology. But when they pitched other HR leaders with "here's why your approach is broken," they hit walls. The shift: stop selling methodology, start diagnosing pain. Reference what you've observed at similar companies—"Some folks at your size tell us they struggle with X, is that true for you?"—then let prospects surface their version of the problem. Only after they've articulated their pain do you map your differentiated approach to their specific context. Target buyer timing, not just buyer titles: Confirm identified a specific trigger: HR leaders in their first 1-2 months at a new company. These leaders are hired to make change and need early wins. The outreach question: "How are you looking to make your mark?" This surfaces whether they're hungry for innovation or managing political capital. A newly hired CHRO has different motivations than a 5-year veteran protecting their process choices. Map your outreach to career timing, not just seniority. Enforce 50/30/20 talk ratios in discovery: David's target: prospects speak 60-80% of discovery calls, with 50% being acceptable. If you're talking more than half the time, you're pitching, not discovering. The clinical psychology technique: positive encouragers ("yeah," "huh") plus deliberate silence after open-ended questions. Prospects will fill silence with the real issues—budget constraints, political dynamics, past vendor failures. This intel is gold for multi-threading and objection handling later. Test channel-message fit with minimal spend: Confirm's approach: "do everything a little bit and see what sticks." They found LinkedIn ads with precise targeting (title, company size, recent job changes) delivered qualified pipeline cost-effectively, while other channels didn't. The framework: allocate 10-15% of budget across 5-6 channels for 60 days, measure cost-per-qualified-meeting, then concentrate spend. Plan for 3-6 month creative refresh cycles as audiences develop ad fatigue—this isn't set-and-forget. Map your product to the HR buying matrix: David identifies four distinct quadrants: (1) CHRO buyer, company-wide deployment = traditional enterprise sale, 6-18 month cycles, heavy multi-threading required; (2) CHRO buyer, HR-only tool = shorter cycles but still executive selling; (3) Line manager buyer, company-wide = requires bottom-up adoption mechanics; (4) Line manager buyer, HR-only = SMB-style transactional sale. Confirm operates in quadrant 1—the longest, most complex sale. Most founders don't explicitly map which quadrant they're in, leading to mismatched sales motions and blown forecasts. Use provocative messaging with technical substance: "One-click performance reviews" generated meetings because it triggered both excitement (managers hate writing reviews) and concern (is AI replacing human judgment?). The key: the shock factor gets the meeting, but you need depth on the call. Confirm's explanation: the AI aggregates data from Asana, Jira, OKRs, peer feedback, and self-reflections to reduce recency bias, then generates a draft managers edit. The dystopian concern becomes a feature when you explain the data anchoring. Surface-level shock without technical credibility burns trust. Adjust for organizational risk tolerance by function: HR and healthcare share conservative buying cultures due to compliance, documentation, and legal requirements. David contrasts this with selling to CTOs or engineers who "kick tires and want to break things." This affects everything: longer evaluation cycles, more stakeholders in legal/compliance, emphasis on security and data handling, reference checks weighted heavily. If you're selling to risk-averse functions, adjust your content (white papers, compliance documentation), your timeline expectations, and your change management positioning. Reframe education as extraction, not instruction: David's mental model shift: "I need to learn from them" replaced "I need to educate them." In practice: "I've heard from others that calibration meetings consume 10+ hours per cycle with unclear outcomes. They tried approaches like forced ranking or manager-only decisions. Have you experimented with either?" This positions you as a pattern-matcher across their peer group, not a lecturer. They become receptive to alternatives because you've demonstrated you understand their world through other customers' experiences. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Matt is joined by Karell Ste-Marie, founder of The Serious CTO YouTube channel. Together, they tackle one of the biggest hidden challenges in software companies: the language and cultural barrier between engineers and executives.Karell and Matt break down why innovation is so rare in large organizations, why engineers and business leaders often talk past each other, and how the CTO role often becomes the critical bridge between the two worlds.Key Discussion PointsThe cultural resistance to change inside enterprisesHow introversion and communication style shape engineering cultureWhy the best CTOs speak “both languages”Lessons from mistakes made on the path to leadershipResources & LinksThe Serious CTO on YouTube – Karell's channel where he shares insights on engineering leadershipProduct Driven - Get the BookSubscribe to the Product Driven NewsletterWhat Smart CTOs Are Doing Differently With Offshore Teams in 2025Subscribe to the Global Talent SprintFull Scale – Build your dev team quickly and affordably

THIS is how you keep your infrastructure costs from spiraling out of control. Today, we're talking to Albert Strasheim, CTO at Rippling. We discuss the cost crisis facing CTOs in the age of AI, how he reduced infrastructure costs by 30% while growing traffic by 25%, and why holding back feedback is actually the most selfish thing a leader can do. All of this right here, right now, on the Modern CTO Podcast!  To learn more about Rippling, check out their website here.

In our latest episode, co-hosts Robby and Tim talk with Julien Mangeard, Co-Founder of open source backup platform Plakar. Plakar's open source, also called plakar, has 1.5K stars on GitHub and provides a backup solution powered by open source, immutable data store Kloset.The podcast discusses why data backup remains a critical but unsolved problem, especially as the number of data sources has exploded across SaaS applications, cloud databases, and on-prem systems. For CISOs and CTOs, this complexity makes it increasingly difficult to ensure everything is done “the right way.” The core argument is that the only truly safe approach is maintaining an independent, secure copy of your data - without vendor lock-in and with guaranteed long-term access, sometimes for decades. End-to-end encryption, immutable storage, and compatibility with different storage backends are emphasized as essential foundations rather than optional features.The conversation contrasts hype-driven cloud-only backup companies like Eon with Plakar's back-to-basics approach: an open source, resilience-focused system designed to handle large and diverse datasets securely. Built around an immutable storage engine (Kloset), Plakar aims to let individuals or small teams manage their own backups while also supporting collaboration at scale. The founder's motivation is rooted in personal experience- having previously lost critical data as a CTO - which reinforced the need for security, openness, and community involvement to continuously add and validate new data sources in a rapidly evolving data landscape.

There's a lot of noise around AI in recruitment. Some people are selling it as a silver bullet. Others are predicting a job apocalypse for recruiters. Neither is true. In this episode of The Resilient Recruiter, Mark Whitby sits down with Rebecca Hastings to talk about what's actually happening inside businesses using AI right now and what recruiters are getting wrong. Rebecca advises CEOs, boards, and AI leaders on strategy, governance, and implementation. She's reviewed hundreds of real-world AI transformation case studies and brings a grounded perspective most recruiters never see. Alongside that, she's built a retained-only executive search firm focused on senior AI leadership and a sales system that consistently books high-quality meetings without volume-driven hustle. This conversation isn't about tools or tactics. It's about judgment, trust, and process. You'll hear why AI doesn't make work faster unless human capability is already in place, how weak sales systems are exposed when automation is added, and why recruiters who can explain how they use AI will earn more trust, not less. Rebecca also breaks down how she thinks about sales as a system, from market focus and listening time to multichannel outreach and AI-supported preparation. The result is fewer calls, better conversations, and more consistent meetings. If you want to understand where AI genuinely helps recruiters and where it quietly causes damage, this episode will change how you think about it. In this episode, you'll learn: Why there won't be a job apocalypse for recruiters How AI shifts bottlenecks instead of removing them Why trust and psychological safety matter in AI adoption How to build market expertise AI can amplify The sales system Rebecca uses to book more meetings with less effort Episode highlights: [3:42] How Rebecca billed £360,000 in her first year [14:08] Lessons from market downturns [32:17] Why listening time beats talk time [59:37] What actually happens when AI is introduced [1:15:26] The multichannel sales system behind consistent meetings Guest bio: Rebecca Hastings is the founder of Lucent Search, specialising in senior AI leadership appointments globally. She works with CEOs, CTOs, heads of AI, and boards on AI strategy, governance, and transformation, and is an AI and systems coach with Recruitment Coach.

If you're running a startup, chances are you're the bottleneck. Brittany Rastsmith joins Product Driven to talk through why founders constantly end up in this trap and how to escape it. She works with early-stage companies through her consulting firm, Bloom Remote, and she's seen it all. We get into how to create clarity, visibility, and accountability across your team so you're not stuck answering every question, solving every problem, or staying up all night wondering if anything is getting done. If you want your team to take ownership and drive outcomes—not just check boxes—this episode is for you.[01:00] - Why being the bottleneck it's a stage [02:30] - Choose your hard: micromanage or build trust [07:30] - How to measure what matters[10:30] - Delegating doesn't work if you dump chaos [14:30] - Explain your thinking if you want your team to carry it out [16:00] - The power of decision logs and written rationale [19:45] - Why psychological safety is key to team ownership [21:30] - Rubber-stamping is the death of progress [24:00] - Why most managers are untrained (and why that matters) [28:00] - Productivity vs. busyness: where your team might be stuck [29:15] - Inputs vs. outcomes: how to tell what's actually broken [31:05] - Where to find Brittany and learn more about Bloom RemoteLinks & Resources:Brittany Rastsmith on LinkedIn: Bloom RemoteGet the Book: https://mybook.to/productdrivenNewsletter: productdriven.comConnect with Matt: https://linkedin.com/in/mattwatsonGet the Offshore Hiring Guide: https://hirefullscale.com/offshore-hiring-guide