Podcasts about Databricks

Soham Mazumdar, CEO and Co-Founder of WisdomAI, discusses how organizations can break free from the "drowning in data but starving for insights" paradox that plagues modern enterprises. We explore his journey from Google's TeraGoogle project to co-founding and scaling Rubrik through its $5.6 billion IPO, and why he left that success to build an agentic AI approach to Business Intelligence (BI) that transforms how businesses extract value from their data investments.SHOW: 971SHOW TRANSCRIPT: The Cloudcast #963 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:WisdomAI websiteTopic 1 - Welcome to the show, Soham. We overlapped briefly at Rubrik. Give everyone a quick introduction and tell everyone a bit about your time at Google prior to RubrikTopic 2 - You helped scale Rubrik from inception to a $5.6 billion IPO in 2024. What was the "aha moment" that made you leave that success to tackle the enterprise data analytics problem with WisdomAI?Topic 3 - Let's define the core problem. Organizations invest heavily in modern data platforms - Snowflake, Databricks, etc. - but there is the term "drowning in data but starving for insights." What's broken in the traditional BI stack that prevents business users from getting answers?Topic 4 - How do agentic AI and BI fit together? WisdomAI introduces the concept of "Knowledge Fabric" and agentic data insights. Break this down for us - how does this fundamentally differ from traditional dashboards and BI tools?Topic 5 - One of the biggest challenges with GenAI in enterprise settings is hallucination. You've emphasized that WisdomAI separates GenAI from answer generation. How does your approach tackle this critical trust issue?Topic 6 - Let's talk about data integration complexity. Your platform works with both structured and unstructured data - Snowflake, BigQuery, Redshift, but also Excel, PDFs, PowerPoints. How do you handle this "dirty" data reality that most enterprises face?Topic 6a - With so much data, how do most organizations get started? What's a typical use case for adoption?Topic 7 - If anyone is interested, what's the best way to get started?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Dhanji R. Prasanna is the chief technology officer at Block (formerly Square), where he's managed more than 4,000 engineers over the past two years. Under his leadership, Block has become one of the most AI-native large companies in the world. Before becoming CTO, Dhanji wrote an “AI manifesto” to CEO Jack Dorsey that sparked a company-wide transformation (and his promotion to CTO).We discuss:1. How Block's internal open-source agent, called Goose, is saving employees 8 to 10 hours weekly2. How the company measures AI productivity gains across technical and non-technical teams3. Which teams are benefiting most from AI (it's not engineering)4. The boring organizational change that boosted productivity even more than AI tools5. Why code quality has almost nothing to do with product success6. How to drive AI adoption throughout an organization (hint: leadership needs to use the tools daily)7. Lessons from building Google Wave, Google+, and other failed products—Brought to you by:Sinch—Build messaging, email, and calling into your product: https://sinch.com/lennyFigma Make—A prompt-to-code tool for making ideas real: https://www.figma.com/lenny/Persona—A global leader in digital identity verification: https://withpersona.com/lenny—Where to find Dhanji R. Prasanna:• LinkedIn: https://www.linkedin.com/in/dhanji/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Dhanji(05:26) The AI manifesto: convincing Jack Dorsey(07:33) Transforming into a more AI-native company(12:05) How engineering teams work differently today(15:24) Goose: Block's open-source AI agent(20:18) Measuring AI productivity gains across teams(21:38) What Goose is and how it works(32:15) The future of AI in engineering and productivity(37:42) The importance of human taste(40:10) Building vs. buying software(44:08) How AI is changing hiring and team structure(53:45) The importance of using AI tools yourself before deploying them(55:13) How Goose helped solve a personal problem with receipts(58:01) What makes Goose unique(59:57) What Dhanji wishes he knew before becoming CTO(01:01:49) Counterintuitive lessons in product development(01:04:56) Why controlled chaos can be good for engineering teams(01:08:07) Core leadership lessons(01:13:36) Failure corner(01:15:50) Lightning round and final thoughts—Referenced:• Jack Dorsey on X: https://x.com/jack• Block: https://block.xyz/• Square: https://squareup.com/• Cash App: https://cash.app/• What is Conway's Law?: https://www.microsoft.com/en-us/microsoft-365-life-hacks/organization/what-is-conways-law#• Goose: https://github.com/block/goose• Gosling: https://github.com/block/goose-mobile• Salesforce: https://www.salesforce.com/• Snowflake: https://www.snowflake.com/• Claude: https://claude.ai/• Anthropic co-founder on quitting OpenAI, AGI predictions, $100M talent wars, 20% unemployment, and the nightmare scenarios keeping him up at night | Ben Mann: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann• OpenAI: https://openai.com/• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Llama: https://www.llama.com/• Cursor: https://cursor.com/• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Top Gun: https://www.imdb.com/title/tt0092099/• Lenny's vibe-coded Lovable app: https://gdoc-images-grab.lovable.app/• Afterpay: https://github.com/afterpay• Bitkey: https://bitkey.world/• Proto: https://github.com/proto-at-block• Brad Axen on LinkedIn: https://www.linkedin.com/in/bradleyaxen/• Databricks: https://www.databricks.com/• Carl Sagan's quote: https://www.goodreads.com/quotes/32952-if-you-wish-to-make-an-apple-pie-from-scratch• Google Wave: https://en.wikipedia.org/wiki/Google_Wave• Google Video: https://en.wikipedia.org/wiki/Google_Video• Secret: https://en.wikipedia.org/wiki/Secret_(app)• Alien Earth on FX: https://www.fxnetworks.com/shows/alien-earth• Slow Horses on AppleTV+: https://tv.apple.com/us/show/slow-horses/umc.cmc.2szz3fdt71tl1ulnbp8utgq5o• Fargo TV series on Prime Video: https://www.amazon.com/Fargo-Season-1/dp/B09QGRGH6M• Steam Deck OLED display: https://www.steamdeck.com/en/oled• Doc Brown: https://backtothefuture.fandom.com/wiki/Emmett_Brown—Recommended books:• The Master and Margarita: https://www.amazon.com/Master-Margarita-Mikhail-Bulgakov/dp/0802130119• Tennyson Poems: https://www.amazon.com/Tennyson-Poems-Everymans-Library-Pocket/dp/1400041872/Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed.My biggest takeaways from this conversation: To hear more, visit www.lennysnewsletter.com

In this episode of Real Talk with Anant Veeravalli, the discussion revolves around the evolving data landscape and the necessity for strategic partnerships to achieve holistic measurement. The team unpacks the importance of ethical data sourcing, privacy compliance, and the utilization of clean room environments like Snowflake and Databricks to bridge data gaps. Enabling secure and scalable data connectivity and facilitating real-time data sharing is key for brands to derive meaningful intelligence, including predictive modeling and AI-driven insights. This episode is essential listening for anyone focused on governance, security, and future-proofing data systems.Thanks for listening! Follow us on Twitter and Instagram or find us on Facebook.

Disciplined, purpose-driven innovation, anchored in governance, data, and the human experience, beats shiny-object hype.In this mega-episode, Lisa Fry, Chief Strategy & Innovation Officer at SCP Health, discusses “purposeful innovation” that reduces clinician burden and elevates patient experience: ED-volume prediction to align coverage, early pilots of ambient scribing, and patient-preferred models like hospital-at-home. She explains the guardrails, an enterprise architecture review board, commitments to core platforms, and stage-gated pilots with predefined success metrics, to avoid the “tyranny of the urgent” and scale only what works. Nancye Feistritzer, DNP, RN—VP, Center for Care Delivery & Innovation at Emory Healthcare, talks about how bold initiatives, including the Apple hospital work and implementing Epic on Apple devices, succeed only when they explicitly align with an organization's strategy, mission, and values. Nick Yaitsky, Board Member for TAG Digital Health, urges outcome-first AI roadmaps: accept that healthcare data is imperfect, mitigate bias by fine-tuning models to local populations and even individual patients, and build trust in the same way we came to trust GPS, through consistent, measurable results and governance. Olga Ryzhikova, Founding Partner at Kepler Team, tackles adoption by starting integration where clinicians work (SMART on FHIR/SSO), designing modern user experiences, and favoring ambient, low-click workflows so tools remain in use. Ron Strachan, Global Healthcare CIO Advisor, addresses rural access, noting that resilient, low-bandwidth virtual care and platform economies can “meet patients where they are.” His own brain-tumor journey underscores how imaging precision and reliable infrastructure can change outcomes. Finally, Wes Whitaker, AVP of Growth Strategy & Data Analytics, shows population health at scale: unifying EHR, eligibility, claims, and ADT into a modern cloud/Databricks stack, then applying predictive models to anticipate ER visits, target outreach, drive attribution, and prove ROI, while tightening security with role-based access. Together, their message is clear: govern hard, integrate early, pilot fast, measure relentlessly, and scale empathetically. Tune in and learn how to innovate with rigor, scale with empathy, and deliver measurable value!ResourcesConnect with Lisa Fry on LinkedIn here.Follow SCP Health on LinkedIn here and visit their website here.Follow and connect with Nancye Feistritzer on LinkedIn.Learn more about Emory Healthcare on LinkedIn and their website.Connect with and follow Nick Yaitsky on LinkedIn.Discover more about the TAG Digital Health Society on LinkedIn and explore their website.Follow and connect with Olga Ryzhikova on LinkedIn.Learn more about the Kepler Team on their LinkedIn and explore their website.Connect with Ron Strachan on LinkedIn here.Explore Zoom's website and learn more about them on their LinkedIn.Follow and connect with Wes Whitaker on LinkedIn.Discover more about Premise Health on their LinkedIn and visit their website.

Monzy Merza (@monzymerza, CEO/Founder @Crogl) talks about build a next-generation Enterprise SOC by leveraging AI to stay ahead of Cybersecurity threats.SHOW: 969SHOW TRANSCRIPT: The Cloudcast #969 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS" SPONSORS:[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.[TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcastSHOW NOTES:Crogl websiteTechCrunch articleForbes ArticleIntellyx ArticleLast WatchDog ArticleTopic 1 - Welcome to the show, Monzy. Give everyone a brief introduction and tell us about your unique journey from government research to Splunk to Databricks to founding Crogl.Topic 2 - Let's start with the current state of cybersecurity and AI. We're seeing headlines about AI being the top cybersecurity concern for 2025, even overtaking ransomware. From your perspective, what's driving this shift and why should organizations be paying attention to the intersection of cybersecurity and AI?Topic 3 - You've described Crogl as an "Iron Man suit" for security analysts. That's a compelling metaphor. Can you break down what you mean by that and how your approach differs from the traditional "reduce alerts" mentality that most vendors have been pushing?Topic 4 - Let's talk about your "knowledge engine" and what you call an “AI for the Enterprise SOC”. You're using compound AI systems with LLMs, smaller models, and knowledge graphs. This sounds quite different from vendors who are just "bolting on" LLMs to existing tools. Walk us through this architectural decision and why it matters.Topic 5 - The cybersecurity industry is experiencing massive alert fatigue - 4,500 alerts per day, with analysts only able to investigate 8-25 of them. Your philosophy is "every alert should be analyzed" rather than filtering them out. That seems counterintuitive to what the market has been doing. How does your autonomous investigation approach actually work in practice?Topic 6 - Where do you see this evolution heading, and what are the implications for SOC teams and security practitioners? Are we heading toward fully autonomous SOCs?FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodI

Gorkem Yurtseven is the co-founder and CEO of fal, the generative media platform powering the next wave of image, video, and audio applications. In less than two years, fal has scaled from $2M to over $100M in ARR, serving over 2 million developers and more than 300 enterprises, including Adobe, Canva, and Shopify. In this conversation, Gorkem shares the inside story of fal's pivot into explosive growth, the technical and cultural philosophies driving its success, and his predictions for the future of AI-generated media. In today's episode, we discuss: How fal pivoted from data infrastructure to generative inference fal's explosive year and how they scaled Why "generative media" is a greenfield new market fal's unique hiring philosophy and lean

In this episode we are discussing the groundbreaking partnership between SAP Business Data Cloud and SAP Databricks. We discuss how SAP BTP is driving AI-first enterprise transformation through seamless SAP Datasphere integration, zero-copy data sharing, and advanced AI/ML workflows. Also, how Business Data Fabric and SAP Business AI are enabling future-ready enterprise IT, making it easier than ever to turn enterprise data into real business value. From hands-on use cases like cash flow forecasting, margin analysis, and attrition analysis to practical tips for engineers and data scientists, you'll get a front-row seat to digital transformation outcomes that matter.

In this episode we are discussing the groundbreaking partnership between SAP Business Data Cloud and SAP Databricks. We discuss how SAP BTP is driving AI-first enterprise transformation through seamless SAP Datasphere integration, zero-copy data sharing, and advanced AI/ML workflows. Also, hear how Business Data Fabric and SAP Business AI are enabling future-ready enterprise IT, making it easier than ever to turn enterprise data into real business value. From hands-on use cases like cash flow forecasting, margin analysis, and attrition analysis to practical tips for engineers and data scientists, you'll get a front-row seat to digital transformation outcomes that matter.

In deze aflevering van Techzine Talks duiken Coen en Sander in de strategieën van grote enterprise AI-platformen. Salesforce lanceert ITSM-oplossingen, ServiceNow gaat de CRM-kant op, en Workday wil het AI-platform via HR worden. Maar wie wint uiteindelijk?De hosts bespreken hoe bedrijven zoals Salesforce, SAP, ServiceNow en Workday allemaal claimen 'het AI-platform' voor bedrijven te zijn, terwijl ze elkaar steeds meer beconcurreren. Van agent orchestration tot MCP-integratie en data lakes - iedereen belooft openheid, maar de praktijk blijkt weerbarstiger.Belangrijke inzichten uit deze aflevering:• Waarom Salesforce ineens ITSM lanceert (reactie op ServiceNow?)• De realiteit van 'open' platformen en zero-copy koppelingen• Hoe bedrijven 30-50% besparen met AI in klantenservice• Agent orchestration: het ontbrekende protocol-probleem• MuleSoft Fabric als oplossing voor agent-beheer• Workday's overname van Sana voor $1,1 miljard• Waarom één platform waarschijnlijk niet gaat winnenChapters:0:25 - Introductie AI-platformen2:22 - Platformstrategie en data-integratie9:14 - Salesforce AgentForce en service-automatisering10:22 - Workday's HR-strategie met AI13:09 - ServiceNow versus Salesforce28:43 - Agent orchestration en MCP30:41 - MuleSoft Fabric voor agent-beheer39:43 - Toekomst van AI-automatiseringKeywords: AI-platformen, Salesforce, ServiceNow, Workday, SAP, agent orchestration, MCP, enterprise AI, ITSM, CRM, ERP, AgentForce, MuleSoft Fabric, business automation

Christophe Blefari est le créateur de la newsletter data Blef.fr la plus connue en France. Il a été Head of Data, Head of Data Engineering et Staff Data Engineer dans des startups et des grands groupes et est selon moi l'un des plus grands experts data en France. Récemment, il a cofondé Nao Labs, un éditeur de code à destination des équipes Data qui utilisent l'IA.On décrypte les news data qu'il ne fallait pas rater en 2025.On aborde :

Reed Smith's Jason Garcia, Gerard Donovan, and Tyler Thompson are joined by Databricks' Suchismita Pahi and Christina Farhat for a spirited discussion exploring one of the most urgent debates of our era: Should AI be regulated now, or are we moving too fast? Settle in and listen to a dynamic conversation that delves into the complex relationship between innovation and regulation in the world of artificial intelligence.

What's up everyone, today we have the pleasure of sitting down with Aboli Gangreddiwar, Senior Director of Lifecycle and Product Marketing at Credible. (00:00) - Intro (01:10) - In This Episode (04:54) - Agentic Infrastructure Components in Marketing Operations (09:52) - Self Healing Data Quality Agents (16:36) - Data Activation Agents (26:56) - Campaign QA Agents (32:53) - Compliance Agents (39:59) - Hivemind Memory Curator (51:22) - AI Browsers Could Power Living Documentation (58:03) - How to Stay Balanced as a Marketing Leader Summary: Aboli and Phil explore AI agent use cases and the operational efficiency potential of AI for marketing Ops teams. Data quality agents promise self-healing pipelines, though their value depends on strong metadata. QA agents catch broken links, design flaws, and compliance issues before launch, shrinking review cycles from days to minutes. An AI hivemind memory curator that records every experiment and outcome, giving teams durable knowledge instead of relying on long-tenured employees. Documentation agents close the loop, with AI browsers hinting at a future where SOPs and playbooks stay accurate by default. About AboliAboli Gangreddiwar is the Senior Director of Lifecycle and Product Marketing at Credible, where she leads growth, retention, and product adoption for the personal finance marketplace. She has previously led lifecycle and product marketing at Sundae, helping scale the business from Series A to Series C, and held senior roles at Prosper Marketplace and Wells Fargo. Aboli has built and managed high-performing teams across acquisition, lifecycle, and product marketing, with a track record of driving customer growth through a data-driven, customer-first approach.Agentic Infrastructure Components in Marketing OperationsAgentic infrastructure depends on layers that work together instead of one-off experiments. Aboli starts with the data layer because every agent needs the same source of truth. If your data is fragmented, agents will fail before they even start. Choosing whether Snowflake, Databricks, or another warehouse becomes less about vendor preference and more about creating a system where every agent reads from the same place. That way you can avoid rework and inconsistencies before anything gets deployed.Orchestration follows as the layer that turns isolated tools into workflows. Most teams play with a single agent at a time, like one that generates subject lines or one that codes email templates. Those agents may produce something useful, but orchestration connects them into a process that runs without human babysitting. In lifecycle marketing, that could mean a copy agent handing text to a Figma agent for design, which then passes to a coding agent for HTML. The difference is night and day: disconnected experiments versus a relay where agents actually collaborate.“If I am sending out an email campaign, I could have a copy agent, a Figma agent, and a coding agent. Right now, teams are building those individually, but at some point you need orchestration so they can pass work back and forth.”Execution is where many experiments stall. An agent cannot just generate outputs in a vacuum. It needs an environment where the work lives and runs. Sometimes this looks like a custom GPT creating copy inside OpenAI. Other times it connects directly to a marketing automation platform to publish campaigns. Execution means wiring agents into systems that already matter for your business. That way you can turn novelty into production-level work.Feedback and human oversight close the loop. Feedback ensures agents learn from results instead of repeating the same mistakes, and human review protects brand standards, compliance, and legal requirements. Tools like Zapier already help agents talk across systems, and protocols like MCP push the idea even further. These pieces are developing quickly, but most teams still treat them as experiments. Building infrastructure means treating feedback and oversight as required layers, not extras.Key takeaway: Agentic infrastructure requires more than a handful of isolated agents. Build it in five layers: a unified data warehouse, orchestration to coordinate handoffs, execution inside production tools, feedback loops that improve performance, and human oversight for brand safety. Draw this stack for your own team and map what exists today. That way you can see the gaps clearly and design the next layer with intention instead of chasing hype.Self Healing Data Quality AgentsAutonomous data quality agents are being pitched as plug-and-play custodians for your warehouse. Vendors claim they can auto-fix more than 200 common data problems using patterns they have already mapped from other customers. Instead of ripping apart your stack, you “plug in” the agent to your warehouse or existing data layer. From there, the system runs on the execution layer, watching data as it flows in, cleaning and correcting records without waiting for human approval. The promise is speed and proactivity: problems handled in real time rather than reports generated after the damage is already done.The mechanics are ambitious. These agents rely on pre-mapped patterns, best practices, and the accumulated experience of diverse customer sources. Their features go beyond simple alerts. Vendors market capabilities like:Data issue detection that flags anomalies as records arrive.Auto-generated rules so you do not have to write manual SQL for every edge case.Auto-resolution workflows that decide which record wins in conflict scenarios.Self-healing pipelines that reroute or repair flows before they break downstream dashboards.Aboli noted that the concept makes sense in theory but still depends heavily on the quality of metadata. She recalled using Snowflake Copilot and asking it for user lists by specific criteria. The model understood her intent, but it pulled from the wrong tables.“If it had the right metadata, the right dictionary, or if I had access to the documentation, I could have navigated it better and corrected the tables it was looking at,” Aboli said.Phil highlighted how this overlaps with data observability tools. Companies like Informatica, Qlik, and Ataccama already dominate Gartner's “augmented data quality” quadrant, while newcomers are rebranding the category as “agentic data management.” DQ Labs markets itself as a leader in this space. Startups like Acceldata in India and Delpha in France are pitching autonomous agents as the future, while Alation has gone further by releasing a suite of agents under an “Agentic Data Intelligence” platform. The buzz is loud, but the mechanics echo tools that ops teams have worked with for years.Aboli stressed that marketers and ops leaders should resist jumping straight to procurement. Demoing these tools can spark useful ideas, and sometimes the exposure itself inspires practical fixes in-house. The key is to connect adoption to a specific pain point. If your team loses days untangling duplicates and broken joins, the ROI might be obvious. If your pipelines already hold together through strict governance, then the spend may not pay off.Key takeaway: Autonomous data quality agents can detect issues, generate rules, resolve conflicts, and even heal pipelines in real time. Their effectiveness depends on metadata discipline and the actual pain of bad data in your org. Use vendor demos as a scouting tool, then match the investment to measurable business problems. That way you can avoid buzzword chasing and apply agentic tools where they drive the most immediate value.Data Activation Agents

Ben Horowitz founded Loudcloud in the middle of the dot-com bust and sold it for $1.6 billion, then led Andreessen Horowitz from its founding to $46 billion in committed capital. Ali Ghodsi co-founded Databricks, stepped in as CEO during a crisis, and led it to a valuation of over $100 billion.In this episode of “Boss Talk”, Ben and Ali join a16z General Partners Sarah Wang and Erik Torenberg to share founder war stories, how to hire and make deals, how to keep culture intense without burning employees out, and why founders should raise their ambitions even higher. ResourcesFollow Ali on X: https://x.com/alighodsiLearn more about Databricks: https://www.databricks.com/Follow Ben on X: https://x.com/bhorowitzFollow Sarah on X: https://x.com/sarahdingwangFollow Erik on X: https://x.com/eriktorenberg Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Resources:Find a16z on X: https://x.com/a16zSubscribe to a16z on Substack: https://a16z.substack.com/Find a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenberg Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Chris Degnan is the former Chief Revenue Officer at Snowflake, where he was instrumental in scaling the company from less than $1M in ARR to over $3B in annual revenue. He joined as the first sales hires and built Snowflake's go-to-market engine from scratch, growing the team from to more than 6,000 globally. Under his leadership, Snowflake became one of the fastest-growing enterprise software companies in history, achieving a record-breaking IPO in 2020. AGENDA: 04:34 How to Build a Sales Team from Scratch 07:49 How to Hire and Evaluate Sales Leaders 15:23 Four Big Lessons Scaling Snowflake to $3BN ARR 31:30 Comparing Snowflake and Databricks: What Databricks Did Better? 35:26 How to Manage Sales Team Morale in Competitive Markets 43:53 Why Customer Success is BS and What To Do With It 48:31 How Every Sales Leader Needs to Change in An AI World 49:37 Biggest Reflections on Sales Leadership 54:38 Quick Fire Questions and Final Thoughts   20Sales: Scaling Snowflake from $0-$3BN in ARR | Snowflake vs Databricks: My Biggest Lessons | Why Customer Success is BS and What Replaces It with Chris Chris Degnan

Nike's Principal Data Engineer Ashok Singamaneni joins Benjamin and Eldad to discuss his open-source data quality framework, Spark Expectations. Ashok explains how the tool, which was inspired by Databricks DLT Expectations, shifts data quality checks to before the data is written to a final table. This proactive approach uses row-level, aggregation-level, and query data quality checks to fail jobs, drop bad records, or alert teams - ultimately saving huge costs on recompute and engineering effort in mission-critical data pipelines.

Mal Vivek, founder and CEO of Zeb, discusses the rapid growth of her firm, which has become a leader in digital and AI transformation. Zeb has established itself as one of the fastest-growing AWS Premier Tier and Databricks partners, primarily by addressing the challenges small and medium businesses face in implementing AI solutions. Vivek emphasizes the importance of understanding each client's unique business model and tailoring AI solutions to meet their specific needs, rather than offering a one-size-fits-all approach.Vivek highlights the significant shift in lead generation strategies among their clients, who are increasingly utilizing AI to create more targeted and high-quality leads. This change reflects a broader trend where businesses are moving away from traditional lead lists and instead developing custom AI systems that align with their ideal customer profiles. Additionally, she notes the importance of training and upskilling employees through AI, enabling them to access vast amounts of knowledge quickly and efficiently.The conversation also touches on the concept of an "AI-first strategy," which varies in definition across different organizations. For Zeb, this strategy involves identifying repetitive tasks that can be enhanced through AI while ensuring that the human touch remains integral to the customer experience. Vivek stresses the need for restraint in digital transformation, arguing that not every process should be automated, especially when personal interaction is a key component of a business's success.Finally, Vivek discusses the evolving business models driven by AI, including a shift towards value-based pricing. She explains how Zeb structures its pricing around measurable outcomes and mutual agreements on success metrics, ensuring that both the firm and its clients benefit from the results achieved. This approach not only fosters accountability but also aligns the interests of Zeb with those of its customers, ultimately driving better business outcomes. All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us a textThe enterprise software ecosystem continues to evolve rapidly as vendors double down on AI, automation, and data-driven orchestration across business functions. Nintex unveiled new generative AI capabilities to enhance business automation, while Circana introduced its Liquid Supply Chain solution to drive agility and responsiveness in complex networks. CloudBolt extended its optimization capabilities to Kubernetes through the acquisition of StormForge, signaling a stronger focus on cloud efficiency. Kinaxis partnered with Databricks to accelerate AI-powered supply chain orchestration and also announced a collaboration with Infor, further expanding its ecosystem reach. Rootstock's Spring '25 release reimagined the modern ERP experience for manufacturers, and Tempo launched a Strategic Portfolio Management platform to strengthen business alignment. Meanwhile, Tricentis rolled out new testing innovations in its spring update, Velixo secured Series A funding from Elephant to fuel growth, and FloQast introduced auditable AI agents to help finance teams bridge the growing talent gap—illustrating how innovation is reshaping every layer of the enterprise stack.In today's episode, we invited a panel of industry analysts for a live discussion on LinkedIn to analyze current enterprise software stories. We covered many grounds including the direction and roadmaps of each enterprise software vendors. Finally, we analyzed future trends and how they might shape the enterprise software industry.Background Soundtrack: Away From You – Mauro SommFor more information on growth strategies for SMBs using ERP and digital transformation, visit our community at wbs. rocks or elevatiq.com. To ensure that you never miss an episode of the WBS podcast, subscribe on your favorite podcasting platform. 

Join Accenture's Chief AI Officer Lan Guan and Naveen Rao, Former VP of AI at Databricks, for an open and engaging conversation about the realities of scaling AI within large organizations. Hear about Databricks journey from pioneering the first AI chip company to shaping AI strategy and discover the technical hurdles and organizational complexities involved in deploying AI at scale.

"85% of AI use cases are being evaluated by the engineer who built it saying, 'yep, seemed to work pretty well.' If you're gonna build a system that's going to be critical to the business, that's going to be important that it gets it right, then you can't do that without evaluations." - Craig Wiley Fresh out of the studio, Craig Wiley, Senior Director of Product Management at Databricks who leads Mosaic AI, joins us to discuss the forefront of enterprise AI from model development to deployment at scale. Beginning with his career journey in ML operations, Craig explained how he recognized the critical connection between data and AI layers that could deliver order-of-magnitude acceleration in development cycles. Emphasizing the transition from classical ML operations to LLM operations, he showcased how Databricks' unified platform eliminates training-serving skew through data lineage capabilities and supports both fine-tuning and RAG approaches depending on industrial use case requirements. Highlighting compelling customer success stories including Suncorp's employee productivity platform and AstraZeneca's transformation of 400,000 clinical trial documents into queryable insights, Craig revealed a striking reality about enterprise AI evaluation - that 85% of AI use cases are being evaluated only by the engineers who built them, reinforcing that proper evaluation frameworks remain foundational for trustworthy AI implementation. He concluded by introducing Agent Bricks as Databricks' evaluation-centric approach to building production agents, emphasizing that model flexibility and rigorous testing are essential for enterprises moving from experimentation to production, while sharing his vision that the industry must evolve from the "year of agents" to the "year of evaluation and quality." Episode Highlights: [00:00] Quote of the Day by Craig Wiley [01:21] How Craig Wiley started his work in ML Ops that led him to Databricks [02:43] Data and AI layer connection creates order-of-magnitude acceleration [03:47] Mosaic AI acquisition expanded Gen AI solution capabilities [04:38] Classical ML statistics versus Gen AI evaluation challenges [05:48] Mosaic AI covers end-to-end from data ingestion [07:12] Training-serving skew eliminated through unified platform lineage [08:51] Fine tuning versus RAG depends on use case [10:49] Industrial agents benefit from fine-tuned smaller models [12:44] Common governance scheme covers tables through model access [13:52] Agent Bricks prioritizes accuracy over simplicity alone [15:44] Model flexibility crucial for speed and accuracy optimization [16:54] AB testing different models shows immediate performance differences [17:59] Suncorp and AstraZeneca demonstrate diverse AI applications [19:37] Asia Pacific shows aggressive AI adoption strategies [20:59] CFO approval requires proven agent effectiveness evaluation [22:00] 85% of AI cases evaluated only by building engineer [23:20] Model agnostic approach beats single-vendor AI strategies [24:12] Industry terminology evolves rapidly from RAG to agents [25:39] Customer creativity with governance capabilities inspires product development Profile: Craig Wiley, Senior Director of Product Management at Databricks and Mosaic AI LinkedIn: https://www.linkedin.com/in/craigwiley/ Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. Here are the links to watch or listen to our podcast. Analyse Asia Main Site: https://analyse.asia Analyse Asia Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Asia Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Asia LinkedIn: https://www.linkedin.com/company/analyse-asia/ Analyse Asia X (formerly known as Twitter): https://twitter.com/analyseasia Sign Up for Our This Week in Asia Newsletter: https://www.analyse.asia/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

Plus: OpenAI and Databricks strike a $100 million deal to sell AI agents. U.S. senators send letters to big tech companies demanding information on H-1B visa usage. Nvidia-backed AI startup Nscale raises $1.1 billion for data-center rollout. Zoe Kuhlkin hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Amazon has reached a $2.5 settlement with the FTC over accusations of deceptive practices locking consumers into Prime subscriptions. Plus, we talk to Databricks CEO Ali Ghodsi in an exclusive interview on the company's $100M OpenAI deal. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

"I think the biggest trap to potentially fall into is, "Hey, it's moving so fast, so much is changing. Let's just wait it out." Completely the wrong approach. You just gotta get started." Nick Eayrs from Databricks "As tech people within the shipping industry, how do we explain, how do we make it accessible to all our users? So that's where we came up with the idea of a data supermarket, with in mind really the target of enabling self-service for our business. So by giving the analogy of a supermarket, it was much easier at the beginning to explain our business." - Simon Fassot from Hafnia Fresh out of the studio, Nick Eayrs, Vice President of Field Engineering for Asia Pacific and Japan at Databricks, and Simon Fassot, General Manager and Head of Global Data and Analytics at Hafnia, join us to explore how data intelligence is transforming enterprise AI across diverse industries in Asia. Nick explained the fundamental distinction between general intelligence and data intelligence - emphasizing how enterprises gain competitive advantage by training AI on their proprietary data rather than public knowledge. Nick showcased customer success stories including Standard Chartered Bank and TechComBank and shared his perspectives on how senior executives can take advantage of AI by moving fast rather than wait and see. Last but not least, Nick offered what great would look like for Databricks in Asia Pacific and Japan in serving their customers. Adding the lens of the customer, Simon shared Hafnia's transformation from legacy SQL Server systems to a unified Databricks architecture serving their global shipping operations and elaborated on how the company is breaking down silos with their data supermarket and "Marvis" AI copilot for maritime operations based on retrieval augmented generation. This is Part 1 from Databricks Data + AI Event Singapore.  Episode Highlights: [00:00] QOTD by Nick Eayrs and Simon Fassot [00:49] Introduction: Nick Eayrs from Databricks [03:32] Customer obsession means deeply understanding their business context [05:22] Data intelligence versus artificial general intelligence explanation begins [06:42] AI trained on your data creates competitive advantage [08:17] Only 15% of companies have correct AI infrastructure ready [11:17] Don't wait for AI perfection, just get started now [12:30] Agent Bricks simplify AI development using natural language [13:49] Standard Chartered Bank cybersecurity use case with SIEM [16:22] TechCom Bank in Vietnam customer brain with 12,000 customer attributes [18:32] Shared responsibility model for ethical AI deployment [25:24] Asia Pacific psychology focuses on future, not past [26:28] Most important question: How do you get started? [30:18] What does great look like for Databricks? [33:16] Introduction: Simon Fassot from Hafnia [35:18] How Hafnia transformed to full cloud architecture centralizes data through Databricks [36:28] Self-service access needed for 300 onshore, 4000 vessel employees [37:00] Three user types: operations, business intelligence, domain experts and Use Cases for Hafnia [41:32] Unity catalog controls data quality for AI cases [42:21] Two-phase Gen AI: ingest unstructured, then consume data [44:25] How to implement Generative AI: One bad AI answer loses all user trust [45:31] How reports in Hafnia use RAG embedded in workflows [46:47] Data supermarket analogy simplifies self-service for business [48:39] Marvis AI personalizes Gen AI within company context [49:46] Neo4j partnership adds graph capabilities to ecosystem [53:33] DNA Port platform unifies scattered dashboards and applications [54:22] Databricks enables focus on business value over operations Profiles: Nick Eayrs, Vice President of Field Engineering, Asia Pacific & Japan at Databricks LinkedIn: https://www.linkedin.com/in/nick-eayrs/ Simon Fassot, General Manager and Head of Global Data and Analytics at Hafnia LinkedIn: https://www.linkedin.com/in/simon-fassot-68b95135/ Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. Here are the links to watch or listen to our podcast. Analyse Asia Main Site: https://analyse.asia Analyse Asia Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Asia Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Asia LinkedIn: https://www.linkedin.com/company/analyse-asia/ Analyse Asia X (formerly known as Twitter): https://twitter.com/analyseasia Sign Up for Our This Week in Asia Newsletter: https://www.analyse.asia/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

Welcome to episode 323 of The Cloud Pod, where the forecast is always cloudy! Justin, Matt and Ryan are in the studio tonight to bring you all the latest in cloud and AI news! This week we have a close call from Entra, some DeepSeek news, Firestore, and even an acquisition! Make sure to stay tuned for the aftershow – and Matt obviously falling asleep on the job. Let's get started!  Titles we almost went with this week When One Key Opens Every Door: Microsoft's Close Call with Cloud Catastrophe Bedrock Goes Qwen-tum: Alibaba's Models Join the AWS Party DeepSeek and You Shall Find V3.1 in Bedrock GPUs of Unusual Size? I Don't Think They Exist (Narrator: They Do) Kubernetes Without the Kubernightmares Firestore and Forget: AI Takes the Wheel SCPs Get Their Full License: IAM Language Edition Do What I Meant, Not What I Prompted Atlassian Pays a Billion to DX the Developer Experience Entra at Your Own Risk: The Azure Identity Crisis That Almost Was Oracle Intelligence: The AI Nobody Asked For Wisconsin Gets Cheesy with AI: Microsoft’s Dairy State Datacenter  Azure Opens the Data Floodgates (But Only in Europe) PostgreSQL Gets a Security Blanket and Won’t Share Its TEEs Microsoft’s New Cooling System Has Veins Like a Leaf and Runs Hotter Than Your Gaming PC Azure Gets Cold Feet About Hot Chips, Decides to Go With the Flow AI Is Going Great – Or How ML Makes Money  00:58 Google and Kaggle launch AI Agents Intensive course Google and Kaggle are launching a 5-day intensive course on AI agents from November 10-14.  This follows their GenAI course that attracted 280,000 learners, with curriculum covering agent architectures, tools, memory systems, and production deployment. The course focuses on building autonomous AI agents and multi-agent systems, which represents a shift from traditional single-model AI to systems that can independently perform tasks, make decisions, and interact with tools and APIs. This development signals growing enterprise interest in AI agents for cloud environments, where autonomous systems can manage infrastructure, optimize resources, and handle complex workflows without constant human intervention. The hands-on approach includes codelabs and a capstone project, indicating Google’s push to democratize agent development skills as businesses increasingly need engineers who can build production-ready autonomous systems. The timing aligns with major cloud providers racing to offer agent-based services, as AI agents become essential for automating cloud operations, customer service, and business processes at scale. Interested in registering? You can do that here.  Cloud Tools  03:21 Atlassian acquires DX, a developer productivity platform, for $1B

In this episode, Bob Evans chats with Gerrit Kazmaier, President, Products and Technology, Workday. They explore how Workday is evolving into a platform company, the role of AI agents in reshaping enterprise workflows, and why trust, accuracy, and extensibility are key to future-ready business solutions. Kazmaier also discusses Workday's approach to ecosystem innovation and composable ERP.Workday's AI FutureThe Big Themes:AI at the Core: Workday is reshaping how enterprises operate by embedding AI into the core of their business processes. This isn't about slapping AI onto legacy systems as a side panel or assistant. It's about redefining how people work, with AI-led experiences, purpose-built agents, and intelligent orchestration. From onboarding to payroll, Workday is transforming each layer of the enterprise with tools that understand business context.Open Platform and Data Integration: Customers demand flexibility and interoperability. Workday is responding by making openness a foundational principle — not just a tagline. Through partnerships with Snowflake, Databricks, Microsoft, and Salesforce, Workday ensures that enterprise data is not locked away but is seamlessly integrated across platforms. Whether you're building a forecasting model in Snowflake or enriching financials in Workday, the data now flows freely.Workday's Focus: Kazmaier referenced a quote: “Technology evolves from primitive to complex to simple.” Today's ERP systems sit in the “complex” phase — bloated, hard to manage, and expensive. Workday's goal is to move ERP into the “simple” era. That means intuitive, intelligent systems that just work — powered by AI, open by design, and personalized for each user. The aim is to empower CEOs to drive outcomes, and employees to thrive at work, without wading through process chaos or outdated tools.The Big Quote: “I frankly think that today, the default is that vendors have a slew of generic agents, they hand them over to their customers, and wish them good luck in figuring out how it's supposed to work. When we say, open AI platform, I talk about purpose-built frameworks and tools like our new Agent Builder . . . so that you can seamlessly compose, you know, workflows in the definition and context of your business and expect them to work with high accuracy and reliability, without becoming an AI expert yourself."Learn more:Follow Gerrit on LinkedIn, and read more about Workday and agentic AI. Visit Cloud Wars for more.

In today's Cloud Wars Minute, I explore Workday's bold entry into the ERP space and share insights from my interview with Gerrit Kazmaier on how AI and data are reshaping enterprise software.Highlights00:24 — Last week, 30,000 people were at Workday's big Rising event in San Francisco. I had a chance to sit down with Product and Technology President Gerrit Kazmaier to talk about his views on how the Workday approach to ERP is going to be different from what we see from other players.01:08 — Kazmaier brings enterprise applications, data, data cloud, hyperscale — all those different backgrounds, expertise, and experiences — to Workday. And now he's taken a very aggressive agenda in these first six or seven months, leading up to this notion of ERP. Workday moved into the ERP space with a lot of new introductions, agents, and more at last week's Rising event.01:48 —And a couple of things that Kazmaier talks about: Kazmaier believes the ERP concept is right — giving business leaders a chance to see what's going on inside their companies from multiple perspectives with fully integrated applications. But he feels that the tools have been outdated, too difficult, too slow, too fragmented.02:08 —So Workday, although for its first 20 years had avoided getting into ERP, now feels that the time is right to give huge value to customers. Also, for the Data Cloud, it's now got partnerships to enhance the way it's able to give customers better use and value from the data they have. These include partnerships with Databricks, Snowflake, Microsoft, and Salesforce.02:54 —So that full interview with Gerrit Kazmaier, President of Products and Technology at Workday, is coming up here. It's got not just him in a new role, but also Rob Enslin, over the last several months, as Chief Commercial President and Chief Commercial Officer, and a new Chief Technology Officer, Peter Bayless, who came to Workday from Google Cloud. Visit Cloud Wars for more.

Ashu Garg has backed companies like Databricks, Turing, Cohesity, Jasper, and Eightfold.ai as General Partner at Foundation Capital. Over the years, he's seen multiple waves of innovation but in his words, nothing in the last 45 years comes close to the transformation AI is bringing right now.Ashu discusses how the next wave of AI products will be driven by combining reasoning with reinforcement learning, and cautions every startup building on top of foundation models: that their vendors will also be their competitors.He also talks about how agents are moving from simple copilots to autonomous workers, how the internet itself will have to be reinvented for an agentic world, and what happens when your agent can not only draft emails but also buy plane tickets or make payments on your behalf.We also get into the realities of building AI companies today: why your competitor isn't GPT-5 but GPT-7, where startups can actually outcompete big tech, whether geography still matters, and how relationships and access still shape outcomes in an age that feels completely digital.This is one of the most insightful conversations you'll hear on what it takes to build durable AI companies in this era and where the next generation of billion-dollar startups will come from.0:00- Trailer0:42 – Foundation models as biggest competitor of AI startups4:19 – Agents are visible; reasoning is underneath6:20 – The leap of AI from autonomous to automation9:27 – Why the internet must be reinvented for AI10:49 – What if agents act (and do payments) on your behalf? 13:06 – Is Ashu using agents for himself?13:54 – No tech shift in 45 years compares to today15:38 – Who is accountable for what your agent does?17:57 – Who has advantage: first-time or repeat founders?19:27 – Does geography matter for founders anymore?21:19 – Whose AI will become the user's default?25:44 – Where do startups have an edge in AI?28:25 – How can startups outdo their model providers31:21 – Does distribution still matter in the Agentic era?33:29 – Why experience and access will always matter35:36 – Startups today must compete with GPT-7, not GPT-537:09 – Why Dollars on talent poaching in AI makes sense42:20 – Are only 1,000 people at AI's cutting edge?43:32 – What does Ashu garg look for in a founder?45:15 – How to build more billion-dollar companies?-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

In this episode of the Tech World Human Skills podcast, Ben Pearce and guest Liping Huang discuss the importance of technical folks building rapport and relationships with customers. We chat through practical tips for establishing strong connections, the significance of empathy, effective communication, and that technical skills alone are not enough. Liping Huang has huge experience working with customer in tech roles. She has been a solutions architect at Microsoft, solutions architect at Databricks and now runs Dataleaps. Show Links Ben Pearce LinkedIn - https://www.linkedin.com/in/benpthoughts/ Tech World Human Skills Home - https://www.techworldhumanskills.com Liping Linktree - https://linktr.ee/liping_dataleaps Takeaways Building rapport is crucial for success in customer interactions. Technical knowledge is essential before engaging in pre-sales. Empathy helps in understanding customer challenges and needs. Effective communication involves making complex topics easy to understand. Being likable and helpful fosters strong customer relationships. Self-assessment is key to identifying areas for improvement. Follow up on commitments to build trust with customers. Creating reusable content can streamline customer interactions. Understanding different audiences is vital for effective communication. Continuous development is necessary to adapt to changing technologies. Keywords rapport, relationships, customer service, empathy, communication skills, technical skills, pre-sales, business relationships, customer interactions, professional development

This episode of the OnBase Podcast features a compelling discussion with Nick Webb on the power of a modern go-to-market strategy. Host Paul Gibson and Nick explore the challenges of navigating organizational change and the critical shift from high-volume, low-quality lead generation to a targeted ABM/ABX approach. Nick shares the story of how CloudPay transformed its pipeline by moving from "net fishing" to "spear fishing," a move that quadrupled its sales pipeline.The conversation reveals why sales and marketing alignment is non-negotiable and how data-driven decisions provide the confidence needed to make bold changes. Nick details the hurdles, the mindset shifts, and the specific KPIs that were essential to driving this monumental transformation. This episode is a masterclass for any B2B leader looking to build a scalable and effective growth engine.Key TakeawaysQuality Over QuantityGenerating thousands of leads is meaningless if it doesn't translate to pipeline. Focusing on an agreed-upon ICP is the foundation of a successful GTM strategy.Shared KPIs Drive AlignmentShifting marketing's core KPI from lead volume to dollar-value pipeline ensures both sales and marketing are working toward the same goal.Data is Your Ally in ChangeUse data to prove the need for change and validate new strategies. Data-backed insights overcome resistance and build trust across teamsIt's a Partnership Not a HandoffThe old model of marketing throwing leads over the fence is broken. A modern GTM requires genuine collaboration where sales and marketing are fully integrated.Rethink Your TerminologyCalling leads "signals" reframes the follow-up process, shifting focus from pursuing an individual to understanding account-level interest.Quotes"Gone are the days where marketing people could get away with not knowing their numbers. We have to carry a number just like sales people do."Best Moments (07:22) – The Damascene Moment Nick details the realization that generating 3x more leads was actually causing the sales pipeline to fall.(09:38) – From Net Fishing to Spear Fishing The core analogy that drove CloudPay's strategic shift to a targeted ABM/ABX model.(14:25) – The New Playbook How CloudPay revolutionized its operations by changing KPIs, moving BDRs into marketing, and renaming leads to "signals."(20:00) – Overcoming Resistance Nick outlines the three groups of people in any change scenario and how to build momentum with advocates and data.(33:27) – Stopping the Attribution Wars The decision to stop attributing leads to specific departments and why it immediately ended internal friction.Shout-OutsKate Cox - CEO, Bray Leino.Tim Johnson - Field CTO, Gaming, Databricks.Andy McFarlane - VP of Marketing, Morse Micro.About the GuestNick Webb has more than 25 years of Marketing experience in world-class technology and fintech organisations, including Vodafone, Microsoft and WorldFirst. Now, as Chief Marketing Officer of CloudPay, Nick leads the Marketing team to build market awareness and drive business growth through the creation of a pipeline of leads and prospects for the Sales teams.Connect with Nick.

Money flood - insane revenue and valuation growth, AI impacting every industry, Open AI and Microsoft deal, new time compute records are changing the game, the first AI government member, and more important AI news for the week ending on September 12 2025Is AI on the verge of world domination… or an economic meltdown?This week's AI headlines weren't about shiny new model releases and that's a good thing. It gave us time to zoom out and examine the billion-dollar chess game shaping our future.From OpenAI's $115B spend-fest to the first AI government cabinet member, and from Replit's code-writing agents to copyright lawsuits with a twist — this episode is a crash course in just how *wild* and *wide* AI's reach has become.Here's your witty but grounded executive summary of the week's most impactful AI news — handpicked and broken down by your host, Isar Meitis, with direct implications for how business leaders should think, adapt, and move.In this session, you'll discover:- OpenAI's capital-intensive moonshot and why it may still not be profitable in 2030- Microsoft's unexpected pivot: From exclusive OpenAI integration to paying AWS for Claude- The first AI cabinet member in Albania here's why it might be brilliant (or backfire)- AI-made movies & TV are no longer a fantasy, OpenAI is backing a full-length feature- Funding frenzy decoded: Databricks, Replit, Perplexity, and others are raising billions- "Thinking" AI that works for hours: How new models are pushing past past limitations- 5,000 AI podcasts a week for $1 each?! The scary-fascinating rise of mass-produced audio- FTC probes AI's influence on kids and what it means for regulation & trust- AI-powered AR glasses from Amazon — coming to delivery drivers and consumers near you- Duke gives GPT-4o to all students what this means for the future of higher education- Why Apple is strangely silent on AI this year, and what it could cost themGoogle Cloud AI Agent Handbook (PDF) - https://services.google.com/fh/files/misc/ai_agents_handbook.pdfAbout Leveraging AI The Ultimate AI Course for Business People: https://multiplai.ai/ai-course/ YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/ Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/events If you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

In this conversation from Lenny's Podcast, Ben Horowitz joins Lenny to discuss the psychological muscle every founder needs, why hesitation can be fatal for CEOs, when it's time to replace a founder, and how to normalize failure while building confidence. They also explore the Databricks founding story, investing in Adam Neumann after WeWork, whether AI is in a bubble, where the real opportunities lie, and Ben's work with the Paid in Full Foundation supporting hip-hop pioneers. The result is a candid look at leadership, product management, and what it takes to build enduring companies. Timecodes: 00:00 Introduction 00:22 The Psychology of Leadership & Decision-Making02:41 Leadership lessons from Shaka Senghor07.56 Struggle, Pain, and Growth as a CEO 10:15 Running toward fear and why hesitation kills companies19:35 Who shouldn't start a company22:36 The Databricks story: thinking bigger24:54 Managerial leverage and CEO psychology28:06 When founders should be replaced as CEOs31:20 Normalizing failure for CEOs37:57 Counterintuitive lessons about building companies42:31 “Good Product Manager/Bad Product Manager”48:21 Product managers as leaders51:16 Why a16z invested in Adam Neumann after WeWork56:23 Is AI in a bubble?01:02:43 The biggest opportunities in AI01:12:51 Why U.S. leadership in AI matters01:18:53 The Paid in Full Foundation for hip-hop pioneers01:23:18 Lightning round: book recommendations, products, and life mottos Resources: Find Ben on X: https://x.com/bhorowitzFind Ben on LinkedIn: https://www.linkedin.com/in/behorowitz/Watch more of Lenny's Podcast: https://www.youtube.com/@LennysPodcastCheck out Lenny's newsletter here: https://www.lennysnewsletter.com Stay Updated: Find a16z on X: https://x.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX?si=3E8B3qT9TyiwAHJ7JnaKbgListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://twitter.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

Send us a text00:00 - Intro00:51 - Klarna's $15.1B IPO + Up in Public Markets (as of Thu, Sep 11)01:51 - Cognition's $400M Raise at $10.2B Valuation02:43 - ElevenLabs' $100M Tender at Doubled $6.6B Valuation03:05 - Replit's $250M Funding at $3B Valuation04:05 - X Square Robot's $140M Raise, New Robot OS Released04:41 - Mistral Finalizes $1.5B Funding at $11.7B Valuation05:12 - Perplexity Finalizes $200M Round at $20B Valuation05:32 - Databricks >$4B ARR in Jul 2025, up 50% YoY 06:07 - Ramp's $1B ARR, +43% in 6 Months06:51 - SpaceX's $17B Spectrum Deal with EchoStar08:01 - Anduril's $1.26B of New Contracts09:06 - AlterEgo's Silent Sense Wearable Launch10:02 - OpenAI's $300B Oracle Data Center Deal10:39 - OpenAI + Microsoft Agree on Nonprofit to For-profit Shift11:05 - Thinking Machines' $2B Seed at $12B Valuation

Ben Horowitz is the co-founder of Andreessen Horowitz, Silicon Valley's largest and most influential venture capital firm, with over $46B in committed capital across multiple funds. He took Loudcloud public with just $2 million in revenue (dubbed “the IPO from hell”), sold it for $1.6 billion, and has backed companies from Facebook to Stripe to Airbnb to OpenAI to Databricks (now worth more than $100 billion). His management philosophy—forged through near-death experiences and refined through coaching hundreds of CEOs—contradicts most conventional startup wisdom.In our conversation, Ben shares:1. Why “founder mode” is half right and half dangerously wrong2. The story behind “Good Product Manager/Bad Product Manager” and why it went viral despite being written in anger3. Where the biggest AI startup opportunities remain4. Why you need to run toward fear, never away5. The one trait that predicts that a founder will fail as CEO6. Inside Paid in Full, Ben's nonprofit awarding pensions to pioneering hip-hop artists—Brought to you by:DX—The developer intelligence platform designed by leading researchers: http://getdx.com/lennyBasecamp—The famously straightforward project management system from 37signals: https://www.basecamp.com/lennyMiro—A collaborative visual platform where your best work comes to life: https://miro.com/lenny—Transcript: https://www.lennysnewsletter.com/p/46b-of-hard-truths-from-ben-horowitz—My biggest takeaways (for paid newsletter subscribers): ⁠https://www.lennysnewsletter.com/i/172439345/my-biggest-takeaways-from-this-conversation—Where to find Ben Horowitz:• X: https://x.com/bhorowitz• LinkedIn: https://www.linkedin.com/in/behorowitz/• Website: https://benhorowitz.com/• Andreessen Horowitz's website: https://a16z.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Ben Horowitz(04:09) Important leadership lessons from Shaka Senghor(10:15) Running toward fear and why hesitation kills companies(19:35) Who shouldn't start a company(22:36) The Databricks story: thinking bigger(24:54) Managerial leverage and CEO psychology(28:06) When founders should be replaced as CEOs(31:20) Normalizing failure for CEOs(37:57) Counterintuitive lessons about building companies(42:31) “Good Product Manager/Bad Product Manager”(48:21) Product managers as leaders(51:16) Why a16z invested in Adam Neumann after WeWork(56:23) Is AI in a bubble?(01:02:43) The biggest opportunities in AI(01:12:51) Why U.S. leadership in AI matters(01:18:53) The Paid in Full Foundation for hip-hop pioneers(01:23:18) Lightning round: book recommendations, products, and life mottos—References: https://www.lennysnewsletter.com/p/46b-of-hard-truths-from-ben-horowitz—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Today on the AI Daily Brief: AI isn't just helping engineers anymore—it's writing nearly half the code at companies like Robinhood and Coinbase, with some leaders saying human-written code is now in the minority. We explore what this milestone means for software development, the rise of agentic coding tools, and why investors are pouring billions into the space. In the headlines: OpenAI's AI-powered feature film project, Microsoft's $17.4B cloud deal, and fresh moves from Databricks, 11Labs, and Anthropic.Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠Vanta - Simplify compliance - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://vanta.com/nlw⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? nlw@aidailybrief.ai

Connor Group's Jim Neesen goes in-depth on the IPO market, including one of his clients that debuted Wednesday: Klarna. He says the fintech company has a "great growth story" in one of the year's most-anticipated public debuts. Jim adds that 2025 is shaping up to be the best year for IPOs since 2021 and explains how the seeds of this year's "cherry blossoms" for the IPO spring were set in place. He tells investors to watch for debuts down the road from names like Databricks, Cerebras, and Stubhub.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

```html i'm wall-e, welcoming you to today's tech briefing for tuesday, september 9th! dive into today's top tech stories: databricks' milestone: exceeds $100 billion valuation post $1 billion funding round, driven by ai-centric databases and $4 billion in annual recurring revenue. netskope's ipo plans: cloud security platform aiming for $6.5 billion valuation with ipo pricing set between $15-$17 per share. cognition ai's valuation boost: secures $400 million funding, skyrocketing valuation to $10.2 billion with strong growth in ai coding agent devin. intel's leadership revamps: ceo lip-bu tan initiates strategic appointments to advance innovation and product delivery. snap's internal transformation: pivot to “startup squads” to spur growth amidst declining ad revenues, while snapchat+ subscriptions rise. stay tuned for tomorrow's tech updates! ```

Partners Group CIO Anastasia Amoroso joins for comprehensive market analysis. Databricks CEO Ali Ghodsi joins after the company announced some key revenue and AI milestones. Evercore Partners Chairman Emeritus Ralph Schlosstein discusses the heating M&A and IPO landscape. Eunice Yoon provides the latest China market perspective while Morningstar Analyst William Kerwin previews Apple's upcoming event and what it means for investors. 

Dave Herrald, Global Head of Cybersecurity GTM at Databricks, tells Jack about transforming security operations through modern data lake architectures and strategic AI implementation. He discusses the practical benefits of separating storage from compute, giving security teams direct control over data retention while maintaining operational flexibility. The conversation explores how organizations can move beyond traditional SIEM limitations by leveraging cost-effective data lake storage with advanced analytics capabilities. They touch on AI agents in security, where Dave advocates for focused agents over broad analyst replacement approaches. He also addresses common concerns about hallucinations, framing them as engineering challenges rather than insurmountable obstacles, and shares real-world examples of successful agent implementations. Topics discussed: Moving from traditional SIEM architectures to modern data lake approaches for cost-effective security analytics and data control. Implementing focused AI agents for specific security tasks like context gathering rather than attempting broad analyst replacement. Leveraging graph analytics for security operations including CMDB visualization, breach scoping, and vulnerability prioritization across enterprise environments. Addressing AI hallucinations through prompt engineering and proper context management rather than avoiding AI implementation entirely. Building detection capabilities using SQL and Python for analytics that provide supersets of traditional SIEM query languages. Creating normalization frameworks using standards like OCSF to enable consistent data analytics across diverse security data sources. Developing career resilience in security through mission-focused thinking, continuous AI learning, and building practical skills. Comparing modern AI agents to traditional SOAR platforms for automation effectiveness and maintenance requirements. Establishing data governance and access controls in security data lakes while maintaining operational flexibility and cost effectiveness. Listen to more episodes:  Apple  Spotify  YouTube Website

The Information's Sylvia Varnham O'Regan talks with TITV Host Akash Pasricha about the SEC's use of AI. We also talk with Mercor CEO Brendan Foody about how AI could overhaul recruiting and Chalk CEO Marc Freed-Finnegan about his company's strategy to take on Databricks. Lastly, we get into AI data licensing with Reporter Natasha Mascarenhas.Articles discussed on this episode: https://www.theinformation.com/articles/replits-margins-illustrate-high-costs-coding-agents TITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

Marketing is changing forever.    In this episode of Eye on AI, host Craig Smith sits down with Chris O'Neill, CEO of GrowthLoop and board member at Gap, to explore how agentic AI and GrowthLoop's Compound Marketing Engine are transforming the way brands connect with their customers.   Chris shares how GrowthLoop applies AI on top of modern data clouds like Snowflake, BigQuery, and Databricks to automate audience targeting, personalize campaigns in real time, and accelerate experimentation loops.    He explains why speed and iteration matter more than ever, how companies like Allegro doubled their return on ad spend with GrowthLoop, and why the future of marketing belongs to brands that embrace agentic AI.   If you're a marketer, technologist, or business leader looking to stay ahead in the age of AI, this conversation is packed with practical insights you can't afford to miss. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI

In today's Cloud Wars Minute, I dive into Snowflake's record-breaking Q2 performance and explore how CEO Sridhar Ramaswamy is positioning the company for long-term AI-driven success.Highlights00:14 — Snowflake has been a remarkable story of growth and achievement—category creation here around the AI Data Cloud—and it reported last week a very strong Q2 with product revenue up 32% to $1.09 billion. That's the first time it has topped $1 billion. That's the good news. The bad news is that the competition is really intensifying.01:16 — CEO Sridhar Ramaswamy said, “We're also trying to continue to push out new AI solutions and technology as rapidly as possible.” There are startups and similarly sized companies, such as Databricks and Palantir, coming after it in that core market. But also bigger players (SAP, Oracle, Microsoft, Google Cloud, and others) are getting more deeply into this AI Data Cloud space.02:06 — Ramaswamy feels that Snowflake is very well positioned around this end-to-end data lifecycle spot that it has. He has been repeating relentlessly over the 18 months he's been CEO that businesses cannot have a successful AI strategy unless they first have a successful data strategy and are able to execute on that data very, very forcefully and consistently.03:30 — I mentioned Palantir, talked about them some last week—and the phenomenal Q1 it had. It grew 48%. So I really applaud Snowflake for 32% growth. But here's Palantir in a similar space—analytics and AI—growing 48%, which is a 50% higher growth rate than what Snowflake just posted. Databricks is growing very rapidly as well, doing some good things.03:54 — Lots of competition there, but—as always in the Cloud Wars—the biggest winners are always, always the customers. Later, we'll have a long, detailed article on Cloud Wars about Snowflake's Q2 results, perspectives from Ramaswamy, and some of my own thoughts about how this all shapes out. Visit Cloud Wars for more.

Host Chris Moody sits down with Jillian Lellis to explore how AI is revolutionizing account-based marketing and the critical importance of sales-marketing alignment in modern B2B strategies. The conversation dives deep into the practical challenges of building effective target account lists, the role of AI in account prioritization, and why data quality remains the foundation of successful marketing operations. Jillian shares real-world examples of how her team at Algolia successfully transitioned from SQL-focused metrics to ABX qualified accounts, emphasizing the change management and collaborative processes required for success. The discussion also tackles the balance between leveraging AI for efficiency while maintaining the human creativity and strategic thinking that drives competitive advantage.Listen to discover actionable insights on building unified account strategies and practical approaches to AI implementation in B2B marketing.Key TakeawaysTarget Account List AlignmentSuccess requires early sales involvement and approval before going live with any target account strategy.AI as Enhancement, Not ReplacementAI improves account scoring and prioritization but requires human oversight and verification to avoid costly mistakes.Data Foundation FirstYou cannot automate or AI-optimize broken processes - solid data quality and operational foundations are essential.Start Small with AIBegin with specific use cases rather than trying to automate everything at once.Sales-Marketing CollaborationRegular check-ins and clear communication remain critical, even as AI handles more routine tasks.Quotes"Trust, but verify. AI can be wrong, and you can't just blindly trust it."Best Moments (02:31) – Data Analytics Evolution Jillian's unique journey from heavy equipment to FinTech to marketing operations(05:49) – Target Account List Strategy The difference between ICP and TAL, and why sales alignment is non-negotiable(12:12) – AI in Action How predictive models and journey stages are transforming account prioritization(18:57) – AI Gone Wrong Real examples of when AI provides incorrect data and the importance of verification(25:11) – Sales-Marketing Collaboration A manager's creative AI pilot project for contact prioritization(29:33) – The Future of ABM How AI is changing account scoring beyond traditional data modelsTech Recommendations:Hex – Data analytics and coding platformChatGPT – AI assistance for documentation and workflow optimizationClaude – AI tool particularly effective for coding tasksResource RecommendationsPodcastsMorbid – True crime podcast (Jillian's personal favorite for non-work listening)Shout-OutsSarah McNamara - Founder @ #samsalesCarly Taylor - Field CTO, Gaming, Databricks.About the GuestJillian Lellis is a Data-driven GTM operator with 15+ years of experience spanning marketing ops, analytics, and data science. Jillian started her career building predictive models and experimenting with customer segmentation—and now she builds scalable systems that align GTM strategy, pipeline health, and operational excellence. Currently making AI-powered search smarter at Algolia.Website: ⁠www.algolia.com⁠⁠Connect with Jillian⁠.

Send us a text00:00 - Databricks Targets $100b in New Round08:42 - Canva Launches Tender at $42b Valuation15:27 - Eight Sleep Raises $100m at $1.5bNick Fusco = CEO at PM Insights, a pre-IPO secondary market pricing company…X - @TheFuscoKid…LinkedIn - www.linkedin.com/in/nickfuscoEvan Cohen = Founder/COO of withVincent.com, a media company focused on alternative investments…X - @evvcohen…LinkedIn - www.linkedin.com/in/evcohenClint Sorenson = Chief Investment Officer at WealthShield, an outsourced CIO and investment research company…X - @clint_sorenson…LinkedIn - www.linkedin.com/in/csorensoncfacmtAaron Dillon = Managing Director of AG Dillon Funds, pre-IPO stock investing for RIAs…X - @AaronGDillon…LinkedIn - www.linkedin.com/in/aarondillonnyc

Agenda: 00:00 – Databricks hits $100B: Bubble or just the beginning? 03:15 – Is Databricks actually undervalued at 25x revenue? 07:40 – Are we on the verge of the biggest IPO wave ever? 11:30 – Can Andreessen's Databricks bet return $30B+? 18:10 – Who really gets rich when mega-unicorns IPO? 19:30 – Is the return of Chamath's SPACs the ultimate bubble signal? 28:00 – Should OpenAI staff be cashing out billions in secondaries? 33:30 – Founder raises $130M… then walks away. Is this the new normal? 36:30 – Nubank's $2.5B profit: The best FinTech in the world? 48:00 – On Running at $15B: Can consumer brands still be VC-backed rockets? 52:00 – CoreWeave takes on $11B in debt: smart bet or ticking time bomb? 1:11:00 – Will AI spend really hit trillions—or is it all hype?  

Today's show:In this TWiST 500 double feature, Alex sits down with two breakout founders: Chalk's Marc Freed-Finnegan & Tollbit's Toshit Panigrahi!First, Chalk's CEO Marc Freed-Finnegan is tackling one of AI's biggest bottlenecks—data freshness. Instead of relying on stale batch jobs, Chalk delivers real-time pipelines for inference compute, automatically transpiling Python into C++/Rust so it can run blazing fast in production. Investors are calling it the ‘next Databricks'—and after hearing this convo, you'll see why.Then, Tollbit's CEO Toshit Panigrahi returns after raising a $24M Series A and signing up 1,400+ publishers. With RAG traffic exploding and robots.txt losing its teeth, their ‘bot paywall' could reshape how AI agents pay for the content they consume. Are we heading for a Spotify moment for data licensing?A must-watch if you care about the economics of AI, from data pipelines to publisher monetization.Timestamps:(0:00) Intro + sponsors(1:03) Alex tees up two Twist 500 interviews: Chalk & Tolbert(2:47) Why AI is shifting from training compute to inference compute(4:41) Data freshness & real-time inference explained(10:43) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(12:00) How Chalk transpiles Python into C++/Rust for speed(18:52) Chalk's business model, margins & $500M valuation(20:11) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist(21:24) Show Continues…(28:02) Tollbit returns: $24M Series A & the rise of RAG bot traffic(30:37) .TECH: Say it without saying it. Head to get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today.(31:41) Show Continues…(36:53) Robots.txt losing relevance & the case for bot paywalls(39:19)Publishers, AI agents & the future economics of the webSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(10:43) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(20:11) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist(30:37) .TECH: Say it without saying it. Head to get.tech/twist or your favorite registrar to get a clean, sharp .tech domain today.Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

In this episode of The Investor Professor Podcast (Ep. 172), Ryan and Cameron dive deep into the latest market movers. They break down Palantir's blockbuster earnings, Alex Karp's bold comments to short sellers, and the stock's meteoric 134% year-to-date rise. The conversation explores Palantir's growing influence as a talent magnet in tech, its valuation risks, and the competitive landscape with Databricks. The hosts also analyze Figma's roller-coaster IPO performance, the challenges of timing IPO investments, and why investor psychology plays such a crucial role in early trading dynamics.The discussion expands to CoreWeave's volatile lockup expiration, employee stock decisions, and how leverage often amplifies risk in frothy markets. Ryan and Cameron also preview upcoming earnings from Home Depot and Target—highlighting the consumer backdrop, Amazon's aggressive expansion into grocery delivery, and what it means for retail. Wrapping up, they touch on Buffett's new bets in UnitedHealth and DR Horton, Michael Burry's surprising long positions (including Mercado Libre), and how Fed policy and Jackson Hole may shape the next market leg. It's a timely mix of earnings, behavioral finance, and market insights that investors won't want to miss.*This podcast contains general information that may not be suitable for everyone. The information contained herein should not be construed as personalized investment advice. There is no guarantee that the views and opinions expressed in this podcast will come to pass. Investing in the stock market involves gains and losses and may not be suitable for all investors. Information presented herein is subject to change without notice and should not be considered as a solicitation to buy or sell any security. Rydar Equities, Inc. does not offer legal or tax advice. Please consult the appropriate professional regarding your individual circumstance.  Past performance is no guarantee of future results.

Skip The Interview Prep, Lose the Job. Here's How To Fix It I Varun Puri I CEO of Yoodli Most people treat interviews like pop quizzes. They wing it. Then wonder why they keep coming in second. Here's the thing: If  you don't rise to the occasion. You sink to the level of your practice. And even for most execs? That level can be none. Varun Puri—ex-Google, debate shark, and co-founder of Yoodli—is done watching smart people blow it because they never practiced the one thing that matters: how they sound when it counts. Yoodli isn't a coach. And, It's not “content.” It's your AI interview  sparring partner— built to catch every “um,” dodge every ramble, and turn your half-baked answers into mic-drop moments. That's why Korn Ferry, Google, Snowflake, Databricks, and use it—because nothing kills a deal faster than a boring, muddled interview.   In this episode: The painful reason brilliant execs still tank interviews The three invisible habits that scream “don't hire me” How to stack 15,000 practice reps without begging for anyone's time Why public speaking fear beats fear of death (and what to do about it) What actually changes when feedback is instant and can't be ignored Listen now. Or keep losing to the person who did.

On the latest episode of the Financial Samurai podcast, I sat down with Ben Miller, cofounder and CEO of Fundrise, for a deep dive into AI, venture capital, and what it really takes to get into the best deals. Key Takeaways from the Podcast 1. AI Growth and Market Dynamics Revenue growth is accelerating in big AI companies like Anthropic. There's an AI benchmarking race where many products seem similar, but differentiation still matters—Ben Miller doesn't believe AI is a commodity at all. The biggest AI players continue to extend their lead, creating a “winner-take-most” dynamic. 2. Venture Capital Strategy and Concentration How much concentration is acceptable in a venture fund: up to 50% of the portfolio can be concentrated in just two companies. Importance of building a pipeline (“bench”) of potential giants like OpenAI, Anthropic, and Databricks. Leveraging scouts in key hubs like San Francisco to source the next wave of private growth companies. 3. Valuation and Economic Concepts Growth-Adjusted Revenue Multiple as a more nuanced valuation approach for high-growth companies. Baumol Effect – rising costs in labor-intensive sectors despite limited productivity gains, and how this might influence AI adoption and consumer behavior. 4. Access and Allocation Challenges Figma IPO: Allocation was difficult even for well-connected investors; demand for strong growth companies far outstrips supply. Innovation Fund's approach: invested in 6 of the top 50 companies on the CNBC Disruptor list. The battle of connections and wealth—strong networks often determine who gets into the best deals. See related post:  The Futility Of Chasing Allocation In A Hot IPO Company 5. Strategic Advantages for Investors Directing a 2M+ user base to both invest in and use portfolio company products (examples: Ramp, Flywheel) as a growth driver. Using product adoption to create a feedback loop of higher valuations and more capital access. 6. Macro Perspectives on AI China's optimistic, aggressive push into AI contrasts with America's more cautious and sometimes pessimistic stance. Why I'm personally increasing my allocation to AI—both as a long-term growth opportunity and as a hedge against missing the next big wave. Invest in Private Growth Companies With Fundrise Companies are staying private longer, which means more gains go to early private investors rather than the public. If you don't want to fight in the IPO “Hunger Games” for scraps, consider Fundrise Venture. About 80% of the Fundrise venture portfolio is in artificial intelligence—an area I'm extremely bullish on. In 20 years, I don't want my kids asking why I ignored AI when it was still early. The investment minimum is just $10, compared with $200,000+ for most traditional venture funds (if you can even get in). You can also see exactly what the fund holds before you invest, and you don't need to be an accredited investor. Subscribe To Financial Samurai  Pick up a copy of my USA TODAY national bestseller, Millionaire Milestones: Simple Steps to Seven Figures. I've distilled over 30 years of financial experience to help you build more wealth than 94% of the population—and break free sooner. To expedite your journey to financial freedom, join over 60,000 others and subscribe to the free Financial Samurai newsletter. Financial Samurai is among the largest independently-owned personal finance websites, established in 2009. Everything is written based on firsthand experience and expertise. To Your Financial Freedom, Sam

Ron Gabrisko is the Chief Revenue Officer at Databricks, where he joined in 2016. Under his leadership, Databricks has scaled from $0 to over $4BN in annual revenue. He has grown the sales team from 0 to over 1,000 globally, leading expansion into enterprise, government, and international markets. Ron previously held senior sales roles at Cloudera and IBM, bringing deep experience in data and AI infrastructure. His tenure at Databricks has been defined by hypergrowth, multi-product adoption, and world-class GTM execution. Agenda for Today: 00:04 – The Databricks Origin Story: Ali, Ben Horowitz & 7 PhDs 00:08 – Ali vs JPMorgan: Turning Down $10M to Stay Cloud-First 00:13 – Prospecting Day: How Ron Scaled the GTM Culture 00:16 – Why Databricks' Pricing Model Was Its Secret Weapon 00:19 – Enterprise vs SMB: The Risky Bet That Paid Off 00:23 – From $2M to $13M ARR: How Ron Built the First Sales Engine 00:29 – Can AI Replace Salespeople? Ron's Brutally Honest Take 00:36 – How to Get Your First Million-Dollar Rep (and Keep Them) 00:42 – The Culture Secret Behind Scaling to 5,000 Sales Reps 00:45 – Why Databricks Waited Until $500M ARR to Go International 00:52 – What Makes a Great Sales Meeting? Ron's Gold Standard 00:58 – The Snowflake Wars: Why Ron Says Databricks Is 5 Years Ahead  

Bret Taylor's legendary career includes being CTO of Meta, co-CEO of Salesforce, chairman of the board at OpenAI (yes, during that drama), co-creating both Google Maps and the Like button, and founding three companies. Today he's the founder and CEO of Sierra, an AI agent company transforming customer service. He's one of the few people I've met who's been wildly successful at every level—from engineer to C-suite executive to founder—and across almost every discipline, including PM, engineer, CTO, COO, CPO, CEO, and board member.In this conversation, you'll learn:1. The brutal product review that nearly ended his Google career—and how that failure led to creating Google Maps2. The question Sheryl Sandberg taught him to ask every morning (“What's the most impactful thing I can do today?”) that transformed how he approached every role3. The three AI market segments that matter4. Why AI agents will replace SaaS products5. His framework for knowing whose advice to actually listen to—and how that came in handy during the OpenAI board drama6. The counterintuitive go-to-market strategy most AI startups get wrong7. Sierra's outcome-based pricing model that's transforming how enterprise software is sold (and why every SaaS company should adopt it)8. What he's teaching his kids about AI that every parent should know—Brought to you by:CodeRabbit—Cut code review time and bugs in half. Instantly: https://coderabbit.link/lennyBasecamp—The famously straightforward project management system from 37signals: https://www.basecamp.com/lennyVanta—Automate compliance. Simplify security: https://vanta.com/lenny—Where to find Bret Taylor:• X: https://x.com/btaylor• LinkedIn: https://www.linkedin.com/in/brettaylor/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Bret Taylor(04:10) Bret's early career and first major mistake(08:24) The birth of Google Maps(11:57) Lessons from FriendFeed and the importance of honest feedback(31:30) The future of coding and AI's role(45:26) Preparing the next generation for an AI-driven world(48:46) AI in education(52:05) Business strategies in the AI market(01:04:38) Outcome-based pricing in AI(01:09:15) Productivity gains and AI(01:17:35) Go-to-market strategies for AI products(01:21:49) Lightning round and final thoughts—Referenced:• Marissa Mayer on LinkedIn: https://www.linkedin.com/in/marissamayer/• “Lazy Sunday”—SNL: https://www.youtube.com/watch?v=sRhTeaa_B98• Quip: https://quip.com/• Sierra: https://sierra.ai/• FriendFeed: https://en.wikipedia.org/wiki/FriendFeed• Sheryl Sandberg on LinkedIn: https://www.linkedin.com/in/sheryl-sandberg-5126652/• Jim Norris on LinkedIn: https://www.linkedin.com/in/halfspin/• Paul Buchheit on X: https://x.com/paultoo• Sanjeev Singh on LinkedIn: https://www.linkedin.com/in/sanjeev-singh-20a1b72/• Barack Obama: https://www.obamalibrary.gov/obamas/president-barack-obama• Oprah Winfrey: https://en.wikipedia.org/wiki/Oprah_Winfrey• Ashton Kutcher: https://en.wikipedia.org/wiki/Ashton_Kutcher• PayPal Mafia: https://en.wikipedia.org/wiki/PayPal_Mafia• Sam Altman on X: https://x.com/sama• Warren Buffett on X: https://x.com/warrenbuffett• Unix: https://en.wikipedia.org/wiki/Unix• Fortran: https://en.wikipedia.org/wiki/Fortran• C: https://en.wikipedia.org/wiki/C_(programming_language)• Python: https://www.python.org/• Perl: https://www.perl.org/• Rust: https://www.rust-lang.org/• Eleven Labs: https://elevenlabs.io/• The exact AI playbook (using MCPs, custom GPTs, Granola) that saved ElevenLabs $100k+ and helps them ship daily | Luke Harries (Head of Growth): https://www.lennysnewsletter.com/p/the-ai-marketing-stack• Confluent: https://www.confluent.io/• Databricks: https://www.databricks.com/• Snowflake: https://www.snowflake.com• Harvey: https://www.harvey.ai/• Behind the founder: Marc Benioff: https://www.lennysnewsletter.com/p/behind-the-founder-marc-benioff• Larry Summers's website: https://larrysummers.com/• AutoCAD: https://www.autodesk.com/products/autocad/overview• Revit: https://www.autodesk.com/products/revit/• The art and science of pricing | Madhavan Ramanujam (Monetizing Innovation, Simon-Kucher): https://www.amazon.com/Monetizing-Innovation-Companies-Design-Product/dp/1119240867• Pricing your AI product: Lessons from 400+ companies and 50 unicorns | Madhavan Ramanujam: https://lenny.substack.com/p/pricing-and-scaling-your-ai-product-madhavan-ramanujam• Cursor: https://cursor.com/• CodeX: https://openai.com/codex/• Claude Code: https://www.anthropic.com/claude-code• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• DirecTV: https://www.directv.com/• SiriusXM: https://www.siriusxm.com/• Wayfair: https://www.wayfair.com/• Akai: https://www.akaipro.com/• Chubbies Shorts: https://www.chubbiesshorts.com/• Weight Watchers: https://www.weightwatchers.com/• CLEAR: https://www.clearme.com/• Stripe: https://stripe.com/• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• Twilio: https://www.twilio.com/• ServiceNow: https://www.servicenow.com/• Adobe: https://www.adobe.com/• Jobs to be done: https://jobs-to-be-done.com/jobs-to-be-done-a-framework-for-customer-needs-c883cbf61c90• The ultimate guide to JTBD | Bob Moesta (co-creator of the framework): https://www.lennysnewsletter.com/p/the-ultimate-guide-to-jtbd-bob-moesta• Inception: https://www.imdb.com/title/tt1375666/• Alan Kay's quote: https://www.brainyquote.com/quotes/alan_kay_100831• Jobs at Sierra: https://sierra.ai/careers—Recommended books:• Monetizing Innovation: How Smart Companies Design the Product Around the Price: https://www.amazon.com/Monetizing-Innovation-Companies-Design-Product/dp/1119240867• Competing Against Luck: The Story of Innovation and Customer Choice: https://www.amazon.com/Competing-Against-Luck-Innovation-Customer/dp/0062435612• Endurance: Shackleton's Incredible Voyage: https://www.amazon.com/Endurance-Shackletons-Incredible-Alfred-Lansing/dp/0465062881—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Episode 729: Sam Parr ( https://x.com/theSamParr ) talks to Zapier founder Wade Foster ( https://x.com/wadefoster ) about how to build AI Agents. — Show Notes: (0:00) Intro (5:57) DEMO: Instant Dossier (10:49) Model Context Protocol (19:30) DEMO: Read Strategy memos like a Harvard MBA (29:13) DEMO: Inbox Zero Agent (36:00) Getting your team to use AI (40:00) DEMO: Employee fraud detector — Links: • Zapier - zapier.com • Claude - Claude.ai • Glean - https://www.glean.com/ • Databricks - https://www.databricks.com/ • Superwhisper - https://superwhisper.com/ • Wisprflow - https://wisprflow.ai/ • N8n - https://n8n.io/ — Check Out Shaan's Stuff: • Shaan's weekly email - https://www.shaanpuri.com • Visit https://www.somewhere.com/mfm to hire worldwide talent like Shaan and get $500 off for being an MFM listener. Hire developers, assistants, marketing pros, sales teams and more for 80% less than US equivalents. • Mercury - Need a bank for your company? Go check out Mercury (mercury.com). Shaan uses it for all of his companies! Mercury is a financial technology company, not an FDIC-insured bank. Banking services provided by Choice Financial Group, Column, N.A., and Evolve Bank & Trust, Members FDIC — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano