Advertise on podcast: B2BaCEO (with Ashu Garg)

Today, raw enterprise data can't simply be fed into a model: it's full of personal information that has to be transformed so it protects privacy while preserving meaning. Skyflow controls what data reaches a model, enforces policy on what an agent is allowed to do with it, and manages data sovereignty across borders.Anshu has been chasing this problem for over two decades.At Salesforce, he had to convince early enterprise customers like JPMorgan and Cisco to trust the cloud with their most sensitive data; work that led him to help build Salesforce's identity and app-exchange infrastructure from scratch.That same trust problem kept resurfacing across two more startups before he founded Skyflow in 2019. “At some point you realize, you know what? There's probably a $100B company to be built just protecting the most sensitive data for customers at all these companies,” he explains.As a founder, Anshu draws lessons from leaders like Marc Benioff, noting the way companies position themselves against rivals and partners can shape their success in the market. He also argues that enduring companies start with a meaningful problem and leaders who can build a culture around the shared mission to solve it.We close with his advice for technical founders: learn to respect marketing as a craft. The best sales and marketing comes from understanding your audience so well that it doesn’t feel like sales or marketing at all. What we covered:0:00 – Cold open: Anshu on the biggest misconception in AI security1:00 – What Skyflow does2:35 – Why AI agents raise the stakes on data privacy3:55 – Anshu’s story: from Salesforce to Skyflow8:05 – How AI is reshaping SaaS and infrastructure10:44 – Skyflow's role in the token economy15:59 – The biggest misconception about AI security18:22 – Is cybersecurity a failed industry?19:08 – How Skyflow's product and go-to-market evolved with the AI shift20:57 – Building culture in a company competing with OpenAI and Anthropic for talent22:40 – Lessons from Marc Benioff: beginner's mind and breaking the rules26:12 – The open-weights debate: trust vs. transparency30:16 – Advice for technical founders: respect the art of marketing31:18 – Closing thoughts Links:Skyflow: https://www.skyflow.com/

Aaron Levie has been on the podcast twice before. After we published our context graphs thesis, he wrote a response - so we invited him on to continue the conversation. A context graph is institutional memory for how an organization actually makes decisions: not how the process doc says it should, but how it works in practice. Enterprise software is very good at recording outcomes - the final price, the approved discount, the escalated ticket - but not the reasoning behind them. Which exceptions applied? What precedent mattered? Who approved what, and why? We call these missing records decision traces. Over time, they accumulate into a context graph: a living, queryable map of how an enterprise actually makes decisions, stitched across systems and time so precedent becomes searchable. We think the companies that capture that layer will define the next generation of enterprise software. Aaron read the piece and joined us to push it further. We get into how the services as software opportunity unfolds as agents scale, and what it actually takes to move them out of the sandbox and into production. Chapters: 00:00 Intro: Aaron’s third time on the podcast! 00:43 What is a context graph? 01:35 Aaron’s take: this won’t be zero-sum 04:10 Why systems of record may become more valuable in a world with 100x more agents 05:26 The difference between data and context 06:28 The moat incumbents have: workflow wiring, permissions, access controls 10:35 Which functions are most vulnerable to disruption 15:43 The trillion-dollar greenfield: high headcount, exception-heavy workflows 16:20 Ops as a wedge: RevOps, DevOps, SecOps, and glue work between systems 19:27 How PlayerZero is building context graphs for real engineering workflows 20:48 Is permissioned inference possible? 21:27 “Agents can’t keep secrets”: why access controls are so important 27:45 What Box is building 29:33 Multimodal context: screenshots, audio, and video 32:50 Why vibe coding won’t change the software industry’s power-law 35:03 Aaron’s advice to founders

My guests today are Animesh Koratana and Jamin Ball.  Animesh is the founder and CEO of our portfolio company PlayerZero, which is building AI production engineers that operate complex enterprise software autonomously - resolving production incidents, catching defects before release, and building durable models of how systems actually behave. Jamin is a partner at Altimeter Capital and the writer behind Clouded Judgement, a Substack where he analyzes emerging trends in enterprise software.  Jamin recently sparked a debate with an essay titled “Long Live Systems of Record.”  His core argument is that while agents are changing how software is used and where value accrues, they still depend on ground truth. Systems of record won't disappear so much as get pushed down the stack as new agent-native interfaces emerge on top. My partner Jaya and I felt compelled to respond, with Animesh contributing insights based on what he's seeing on the ground as he builds PlayerZero.  From our perspective, the missing layer is what happens inside the workflow itself: the judgment, exceptions, and reasoning that agents and humans apply as work gets done. We call these decision traces, and we believe the context graph they form over time will become the most valuable asset for companies building and deploying AI systems. It's a genuine debate - and one that's only going to matter more as agents move from demos to production. Looking forward to keeping the conversation going! Chapters 00:00 Why Jamin’s essay sparked debate  00:35 Jamin’s argument: agents need ground truth  02:00 Animesh on why context graphs matter  07:58 What today's systems of record are missing  08:28 How PlayerZero thinks about context graphs  10:00 How context graphs could change org structures  11:10 How do you capture decisions without making people log everything?  14:35 Which systems of record are most at risk  17:04 Two workflows ripe for disruption: GTM and software development  22:31 Animesh on where context graphs can add most value  28:50 Why context graphs create moats for startups  30:00 Will context graphs be industry-specific or universal?  34:00 Bear case: do context graphs fail like semantic layers?  43:27 2026 predictions: big AI IPOs, world models, enterprise agent adoption  45:00 Hot takes: point solutions die; AI job-loss discourse hits a fever pitch  47:30 Jevons paradox: why agents create more work, not less

My guest today is Jonathan Siddharth, co-founder and CEO of Turing. Jonathan incubated Turing in Foundation Capital’s Palo Alto office in 2018. Since then, it has grown into a multi-billion dollar company that powers nearly every frontier AI lab: OpenAI, Anthropic, Google, Meta, Microsoft, and others. If you’ve seen a breakthrough in how AI reasons or codes, odds are Turing had a hand in it. Jonathan has a provocative thesis: within three years, every white-collar job, including the CEO’s, will be automated. In this episode, we talk about what it will take to reach artificial superintelligence, why this goal matters, and how the agentic era will fundamentally reshape work. We also dig into his founder journey: what he learned from his first startup Rover, how he built Turing from day one, and how his leadership style has evolved to emphasize speed, intensity, and staying in the details. Jonathan has been at the edge of AI for years, and he has the rare ability to translate what’s happening at the frontier into lessons for builders today. Hope you enjoy the conversation!  Chapters:  00:00 Cold open00:02:06 Jonathan’s backstory: his experience at Stanford00:06:37 Lessons from Rover00:08:39 Early Turing: incubation at Foundation Capital and finding PMF00:13:52 Why Turing took off00:15:12 Evolving from developer cloud to AGI partner for frontier labs00:16:49 How coding improved reasoning - and why Turing became essential00:20:38 Founder lessons: building org speed and intensity00:23:33 Why work-life balance is a false dichotomy00:24:17 Daily standups, flat orgs, and Formula One culture00:25:15 Confrontational energy and Frank Slootman’s influence00:29:50 Positioning Turing as “Switzerland” in the AI arms race00:34:32 The four pillars of superintelligence: multimodality, reasoning, tool use, coding00:37:39 From copilots to agents: the 100x improvement00:40:00 Why enterprise hasn’t had its “ChatGPT moment” yet00:43:09 Jonathan’s thoughts on RL gyms, algorithmic techniques, and evals00:46:32 The blurring line between model providers and AI apps00:47:35 Why defensibility depends on proprietary data and evals00:55:20 RL gyms: how enterprises train agents in simulated environments00:57:39 Underhyped: $30T of white-collar work will be automated

My guest today is Ion Stoica, professor of computer science at UC Berkeley and the co-founder of Conviva, Databricks, and Anyscale. Over the last two decades, Ion’s research labs - the AMP Lab, the RISE Lab, and now the Sky Computing Lab - have seeded a generation of category-defining companies.  Ion has the unique ability to turn non-consensus ideas into durable businesses. He applied machine learning to video optimization with Conviva before AI became mainstream. He scaled Apache Spark into a $60B platform with Databricks. And now, with Anyscale, he’s betting on Ray as the foundation for distributed AI workloads.  In this episode, we dig into both sides of Ion’s work: how to build world-class research labs, and how to turn research into real companies. His clarity of thought makes the future feel legible, and his track record suggests he’s very often right.  Hope you enjoy the conversation!  Chapters:  00:00 The Spark thesis: win the ecosystem first, monetize later 01:00 Intro: From lab to company - Ion’s repeatable playbook 03:00 Did you always plan to become a founder, or did it just happen? 05:23 Let’s start with Spark - how did the project come about? 13:04 What were the most important early decisions at Databricks? 23:49 You were the first CEO - what did you have to learn (or unlearn)? 30:01 How was building Anyscale different from building Databricks? 33:53 What’s obvious to you about the future of AI that others miss? 37:31 Why AI works so well for code 41:00 The thesis behind OPAQUE Systems 44:06 Future infra will be heterogeneous, distributed, and vertically integrated 49:03 China’s edge: faster diffusion from lab to market 53:19 Platform companies still work, but only with the right investors 55:57 What role did the Databricks Unit (DBU) play in value capture? 58:02 AI progress is plateauing, but adoption is just beginning

My guest today is Ramesh Srinivasan, a senior partner at McKinsey and trusted advisor to some of the world’s top CEOs. Over his career, Ramesh has worked with leaders at companies like Cognizant, Moderna, Nissan, and Delta, helping them navigate tough challenges and scale high-performing teams. Ramesh just published a new book, The Journey of Leadership, which distills lessons from thousands of hours spent alongside top executives. In our conversation, he shares practical insights for founders on how to discover their natural leadership style, why empathy is a non-negotiable leadership skill, and what it really takes to inspire people at scale. Hope you find this conversation valuable! Chapters:  00:00:00 Cold open00:01:14 Ramesh’s backstory00:04:34 Things that shaped Ramesh’s leadership philosophy00:05:19 The big idea behind his book: The Journey of Leadership00:09:12 Building empathy: For your team, your customers, your market00:11:00 Lessons from Frank D’Souza at Cognizant00:14:20 Lessons from Stéphane Bancel at Moderna00:17:15 Trust, vulnerability, and the power of asking for help00:19:40 Finding purpose: Starting from life’s crucible moments00:22:00 Renewal: How great leaders evolve over time00:24:00 Common mistakes founders make on the leadership journey00:26:10 Resilience: The ultimate test of a founder’s staying power00:30:20 The impact of AI on leadership and organizational change00:34:00 Where AI is reshaping healthcare today00:36:00 Advice for AI + healthcare founders

My guest today is Arvind Jain, the founder and CEO of Glean. Before Glean, Arvind spent over a decade building Google's search infrastructure. He then co-founded Rubrik, which recently passed $1B ARR. With Glean, Arvind is tackling the longstanding challenge of enterprise search. Yet his vision goes beyond this. He believes every employee should have their own team of AI agents to help them work smarter and achieve more.  In our conversation, Arvind shares his journey as a technical founder and offers his unique perspective on what it takes to build a successful startup today. We also discuss where AI is heading, and where he sees the biggest opportunities for founders.  Hope you find this conversation valuable!  Chapters: 00:00 Cold open04:42 How Arvind began his journey in search06:59 Arvind on Glean's mission08:50 The evolution of enterprise search12:56 How AI unlocks a new dimension for search16:56 Lessons for AI startup founders21:23 Navigating the AI startup landscape25:44 The "build vs. buy" decision with AI models31:09 Defining the role of AI in business34:57 The future of work with AI agents39:30 The shift from SaaS to Service-as-Software41:21 Concluding thoughts

Frank Slootman turns the 'founder mode vs. manager mode’ debate on its head.  Frank’s track record in B2B land is iconic: He took Data Domain from pre-revenues to a $2.5B acquisition by EMC. He led the IPO at ServiceNow, and when he left the company, it was worth $34B. Frank then took Snowflake public, and the company was worth over $70B when he retired earlier this year.  After three successful CEO stints, Frank isn’t buying Silicon Valley’s fairytales about founders. His leadership style combines a manager’s prowess with a founder’s passion. Frank epitomizes what some might call “owner mode!”   (00:07) Frank's thoughts on 'founder mode' vs. 'manager mode'  (00:47) The role of non-founder managers and CEOs  (09:59) How to manage effectively without micro-managing  (17:11) The importance of intellectual honesty (18:32) Frank's thoughts on being 'in the arena'  (21:04) What it really takes to build a viable business  (28:34) Contrasting ServiceNow and Snowflake  (33:40) The impact of AI on business  (39:01) The future of app ecosystems  (44:50) Becoming a student of leadership  (46:31) Managing investor relationships  (48:04) Why Frank doesn't think about his legacy  (50:17) Closing Thoughts

Aaron Levie, co-founder and CEO of Box, has guided the cloud content management platform from a dorm room project into a publicly traded company with over $1B in annual revenue. In his second appearance on B2BaCEO, Aaron reflects on his founder journey, sharing how Box capitalized on cloud computing and their recent push to integrate generative AI. But our conversation goes far beyond Box. Aaron’s role has given him a unique vantage point on what the latest advances in AI mean for founders. We explore the AI applications that excite him most, where he sees opportunities for startups over incumbents, and the potential areas in AI that founders might be overlooking. (0:00) Intro (2:26) The Box journey (4:23) Transitioning to enterprise (8:26) Building a GTM flywheel (11:45) Lessons from the enterprise journey (15:16) Where AI is heading (18:14) Facing the innovator's dilemma (20:54) AI agents (26:15) Why AI is positive sum for the economy (30:24) The AI doomer debate (34:22) The evolving model ecosystem (40:30) Parting advice for founders

In this episode, I talk with Srinath Sridhar, CEO & Cofounder of Regie.ai, who has always been ahead of the AI curve. Sri and his co-founder Matt Millen started Regie.ai in 2019 with the idea that GPT-3 would transform how all of us write emails. Today, Regie uses AI to automate sales prospecting for the enterprise. The company's Auto-Pilot automates most of the repetitive tasks involved in demand generation, including writing sequences, scheduling calls and responding to emails. Sri knew in early 2019 that LLMs would be a game-changer. What he didn’t know was exactly what product to build. In this episode, we’ll dig into the details of how he did it.

In this episode, I'm excited to welcome Mohit Aron back to B2BaCEO for the second time. As the founder of Cohesity and co-founder of Nutanix, Mohit is a titan in the world of enterprise GTM and infrastructure software. With two wildly successful companies under his belt, he's a true expert when it comes to building enterprise software businesses from the ground up. In our conversation, Mohit shares his proven frameworks for validating startup ideas. He reveals hard-won lessons from starting Nutanix and Cohesity, with real-world examples that bring his advice to life. We explore product-market fit—what it really looks like in practice—as well as how to build a team and manage performance in a high-growth startup. We wrap up by discussing the topic du jour, generative AI, and the opportunities it opens for startups. This episode is full of insights for technical founders. I hope you enjoy it! (00:00) Intro (00:21) Mohit's framework for a bulletproof startup hypothesis document (07:53) Why your MVP shouldn't be your full vision (10:39) Cohesity's journey from 0 to 1, 1 to 10, and 10 to 100+ (17:19) Examples of founders not being intellectually honest about their hypotheses (20:35) How to accurately size your startup's market (TAM) (23:55) Balancing founder conviction with naysayer feedback (31:02) Adapting the hypothesis document for the generative AI era (34:19) Mohit's definition of product-market fit (39:05) When to hit the gas on sales hiring (and when not to) (44:59) Mohit's system for competency-based hiring (53:17) Implementing performance management via quarterly calibrations (56:00) What Mohit would do differently as a technical founder (58:07) Mohit's top advice for founders (60:09) The industries ripe for disruption by generative AI (62:04) Book recommendations for founders

My guest today is Sanjit Biswas, the co-founder and CEO of Samsara, a platform that helps companies digitize their physical operations. In 2023, Samsara reached $1 billion in ARR, making it one of the fastest startups in history to hit this milestone.  But this wasn't Sanjit's first major success. Before starting Samsara, Sanjit left his Ph.D at MIT to build Meraki, a cloud networking company that Cisco acquired for $1.2 billion in 2012. In our conversation, Sanjit shares his journey from grad school research to bootstrapping Meraki in the early days. He reflects on the lessons he learned from Meraki's pivot to the enterprise, the decision to sell to Cisco, and the insights that led him to start Samsara.  We then turn to how Samsara found product-market fit, Sanjit's philosophy on allocating capital, and the areas where he believes generative AI will have the biggest impact.  It was a really fun and insightful conversation that I think you'll enjoy! (00:00) Intro (00:16) Sanjit's background and the genesis of Meraki (06:25) Meraki's pivot to the enterprise and rapid growth (09:45) Lessons from building Meraki's enterprise sales motion (16:15) The decision to sell Meraki to Cisco (20:05) Founding Samsara and its mission (27:11) Defining Samsara's product strategy based on customer feedback (31:00) Samsara's unique journey to $1B in revenue (39:16) Sanjit’s "70/20/10” framework for allocating capital (43:13) The importance of board alignment and company culture (50:58) Sanjit's thoughts on AI and hype cycles (54:22) Sanjit's advice for aspiring entrepreneurs

My guest today is Doug Winter, the founder and CEO of Seismic, a leading enterprise sales enablement platform. Today, Seismic has a team of 1,500 people and nearly $400 million in recurring revenue. We start the conversation with Doug explaining what sales enablement actually means. From there, we turn to the early days of Seismic and unpack Doug’s approach to finding product-market fit, positioning, and targeting enterprise customers from day one. He speaks candidly about the challenges of scaling and closes with actionable advice for founders in 2024. Fewer than 1% of B2B software companies reach Seismic’s scale. Doug’s story offers a valuable blueprint for founders with similarly audacious ambitions. I hope you find this conversation as inspiring as I did. Let’s dive in! (00:00) Cold open (2:21) Doug explains what sales enablement is (3:39) Genesis of the idea for Seismic (7:08) Seismic's scrappy early days (13:10) Lessons from fundraising (18:23) Targeting large enterprises from day one (22:32) Scaling a GTM engine (28:23) Making proactive leadership changes (37:12) Running great board meetings (40:59) Impact of AI on Seismic's business (44:42) Advice for founders starting in 2024

My guest this month is Naveen Rao, the co-founder of MosaicML and current head of Generative AI at Databricks. Naveen's journey is unique, as it echoes the evolution of AI itself. He’s best known for founding and selling two successful companies. The first, Nervana, an AI-focused chip company, was acquired by Intel for $400 million in 2016. The second, MosaicML, was acquired by Databricks in June for $1.3 billion. In our conversation, we unpack the insights and frameworks that led Naveen to make these bets in the first place. We begin by exploring his long history in AI research and startups, from his early days at Qualcomm, his founding of Nervana, and the genesis of MosaicML. We then turn to the complexities of the ever-changing AI landscape and go behind the scenes of MosaicML’s acquisition by Databricks. We close with Naveen’s takes on the most urgent questions in AI, including the recent tumult at OpenAI, the road to AGI, the role of regulation, and where he thinks generative AI will go next. What struck me most is Naveen’s remarkable ability to not only anticipate the future but also actively pave the path toward it. For founders looking to navigate our current AI moment, this episode is full of valuable lessons.

In this episode of B2BaCEO, I speak with Robert Nishihara, co-founder and CEO of Anyscale. Anyscale’s aspiration is to build the fastest, most cost-efficient infrastructure for running LLMs and AI workloads. When it is successful, Anyscale will be to the AI era what Microsoft was for the PC era: the underlying operating system on which all AI applications are developed and run. Anyscale is built on Ray, an open-source compute framework that Robert and his co-founders developed as PhD students at UC Berkeley. Under the guidance of Professor Ion Stoica, who also co-founded Conviva and Databricks, the team sought to make distributed computing broadly accessible. Anyscale was then launched as a fully managed platform for Ray, making even the toughest problems in distributed computing easy for developers to tackle. Today, Anyscale is a billion-dollar business powering mission-critical AI use cases at companies like Amazon, Cohere, Hugging Face, NVIDIA, OpenAI, and Visa. If you’ve been a PhD student at UC Berkeley, created a popular open-source framework, and built a billion-dollar business on top of it, you’ve likely learned a thing or two along the way. Robert’s story offers valuable lessons for fellow founders and builders at all stages of the startup journey.