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!

• 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 open
• 00:02:06 Jonathan’s backstory: his experience at Stanford
• 00:06:37 Lessons from Rover
• 00:08:39 Early Turing: incubation at Foundation Capital and finding PMF
• 00:13:52 Why Turing took off
• 00:15:12 Evolving from developer cloud to AGI partner for frontier labs
• 00:16:49 How coding improved reasoning - and why Turing became essential
• 00:20:38 Founder lessons: building org speed and intensity
• 00:23:33 Why work-life balance is a false dichotomy
• 00:24:17 Daily standups, flat orgs, and Formula One culture
• 00:25:15 Confrontational energy and Frank Slootman’s influence
• 00:29:50 Positioning Turing as “Switzerland” in the AI arms race
• 00:34:32 The four pillars of superintelligence: multimodality, reasoning, tool use, coding
• 00:37:39 From copilots to agents: the 100x improvement
• 00:40:00 Why enterprise hasn’t had its “ChatGPT moment” yet
• 00:43:09 Jonathan’s thoughts on RL gyms, algorithmic techniques, and evals
• 00:46:32 The blurring line between model providers and AI apps
• 00:47:35 Why defensibility depends on proprietary data and evals
• 00:55:20 RL gyms: how enterprises train agents in simulated environments
• 00:57:39 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 open
• 00:01:14 Ramesh’s backstory
• 00:04:34 Things that shaped Ramesh’s leadership philosophy
• 00:05:19 The big idea behind his book: The Journey of Leadership
• 00:09:12 Building empathy: For your team, your customers, your market
• 00:11:00 Lessons from Frank D’Souza at Cognizant
• 00:14:20 Lessons from Stéphane Bancel at Moderna
• 00:17:15 Trust, vulnerability, and the power of asking for help
• 00:19:40 Finding purpose: Starting from life’s crucible moments
• 00:22:00 Renewal: How great leaders evolve over time
• 00:24:00 Common mistakes founders make on the leadership journey
• 00:26:10 Resilience: The ultimate test of a founder’s staying power
• 00:30:20 The impact of AI on leadership and organizational change
• 00:34:00 Where AI is reshaping healthcare today
• 00:36:00 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 open
• 04:42 How Arvind began his journey in search
• 06:59 Arvind on Glean's mission
• 08:50 The evolution of enterprise search
• 12:56 How AI unlocks a new dimension for search
• 16:56 Lessons for AI startup founders
• 21:23 Navigating the AI startup landscape
• 25:44 The "build vs. buy" decision with AI models
• 31:09 Defining the role of AI in business
• 34:57 The future of work with AI agents
• 39:30 The shift from SaaS to Service-as-Software
• 41:21 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

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