No Priors Podcast — "Meet Snowflake Intelligence: A Personalized Enterprise Intelligence Agent with Sridhar Ramaswamy"

Date: November 6, 2025
Host: Sarah Guo (Conviction)
Guest: Sridhar Ramaswamy (CEO, Snowflake; former founder of Neeva, ex-SVP of Google Ads)

Episode Overview

This episode features an in-depth conversation with Sridhar Ramaswamy about his first 18 months as CEO of Snowflake and the company's strategic pivot to become an "AI-first" enterprise. The hosts and Sridhar explore the launch of Snowflake Intelligence (SI), a new agentic platform, discuss tactics for organizational change at scale, and offer Sridhar's firsthand takes on enterprise AI adoption, product-market fit, cloud provider dynamics, and the future of advertising in an AI-driven world.

Key Discussion Points & Insights

1. Transformation of Snowflake under Sridhar (00:44–05:44)

• Transition to CEO: Sridhar stepped in as CEO after Frank Slootman, as Snowflake confronted rapid technological change, especially in AI. The transition was driven by a need for product-first leadership in volatile times.
• Company Reorientation: Initial months focused on breaking down traditional silos and driving direct accountability from product engineering to customer outcomes.
  • Quote: “There was a very long distance between the engineer that did a feature and the customer that made use of that feature... That works fine when you have perfect product market fit... but in the world of AI... this is the wrong structure.” (03:24, Sridhar)
• Culture of Iteration: Emphasis on "speed wins" and rapid iteration over slow, bureaucratic processes.

2. Vision and Launch of Snowflake Intelligence (05:44–11:55)

• AI Strategy Shift: Attempted to build foundation models in 2024, but quickly pivoted to focus on integrating AI to accelerate data value within Snowflake, leveraging its unique position as the home of valuable enterprise data.
  • Quote: “A lot of our AI product strategy was actually quite humble. It didn't say we were going to rethink everything.” (05:50, Sridhar)
• Opinionated Agentic Platform: SI is not a general-purpose agent but tightly focused on extracting value from structured and unstructured data—prioritizing immediate utility over aspirational completeness. - Use cases emerged from internal tools like “Raven,” Snowflake’s sales data assistant, and expanded to customer pilots with companies like Cisco and Fanatics.
  • Quote: “We wanted to create something that freed people from the 2D style of thinking... but also knew its place. This is not a general-purpose agentic platform to do it all.” (08:10, Sridhar)

3. How Snowflake Intelligence Works (09:00–11:55)

• User Experience: Interactive, natural language interface. Designed for all employees, not just technical users.
  • Provides context, data summaries, and recommendations—integrated with workflow tools and identity providers.
• Trust and Governance: Rigorous evaluation for every new feature or model. Strong focus on reliability and adherence to enterprise governance norms.
  • Quote: “It cannot be this mode of like YOLO AI. You can get some good answers, some terrible answers—it's your problem.” (10:54, Sridhar)

4. Boundary Between Apps, Agents, and Value (11:55–13:30)

• On Differentiating Agents vs. Apps: Snowflake isn’t trying to be SAP or Salesforce, recognizing natural boundaries, but sees convergences for agentic systems to handle routine data tasks.
  • Flexible approach: "Be opportunistic, but operate from a position of value and strength and not just on naked ambition." (12:09, Sridhar)

5. Organizational Change and Leadership at Scale (13:30–16:18)

• Driving Change: Emphasizes tactical leadership—incremental rollouts, clear accountability, and combining top-down directives with bottom-up initiative.
  • Champions early adopters within the org, using them to influence broader change. Example: Accelerating coding agent adoption among engineers.
• Personal Growth: Sridhar draws on his research, startup, and big-tech experience to shape culture and leadership style.
  • Quote: “You need to find them, you need to encourage them, you need to elevate and use that to drive change. Top-down change can be helpful but really needs to come from a bottoms-up perspective.” (15:26, Sridhar)

6. Product-Market Fit, Defensibility, and AI Platform Moats (18:50–22:45)

• Success Factors: Even with hyperscalers as potential competitors, durable product-market fit remains a “lightning in a bottle.”
• Model Company Trajectory: Sridhar observes today's foundation model companies (OpenAI, Anthropic) as "empires that have not yet met their oceans."
• Staying Ahead: Infinite budgets and patience of CSPs requires relentless innovation—defensibility is built daily, not strategized in the abstract.
  • Quote: “Defensibility is built, not strategized. It's built every single day. You have to keep moving.” (23:13, Sridhar)

7. The Future of Snowflake—Ecosystem and Partnerships (23:45–30:20)

• Vision: Becoming a “data platform” supporting customers “from inception to insight.” AI accelerates this—moving beyond basic data warehousing to closed feedback loops and decision automation.
• Platform Integration: Pursuing deep, bidirectional data partnerships with players like SAP, Microsoft, Workday, cited as a “maturing of how we think about partnerships.”
  • Importance of creating 1+1=3 value, especially through analytics and agentic services rooted in partners' data.
  • Snowflake aspires to be a higher-level abstraction above raw compute/storage.

8. ROI of Enterprise AI – Practical Use Cases (30:20–34:44)

• Highest ROI Use Cases:
  • Coding agents (for developer productivity and demystification)
  • Customer support (leveraging internal knowledge bases)
  • Self-serve data access (democratization and cost reduction)
• Iteration over Big Bets: Advocates for many small, quick projects—many “shots on goal”—rather than high-risk, high-cost moonshots.
  • Quote: “Obsessing about a lot of ROI too quickly is also a bad idea for many companies because you don't want your first step to be a hundred feet. You want to do a lot of little things that sort of prove value.” (30:46, Sridhar)
• Product Evolution: SI’s flagship sales assistant evolved through multiple iterations/prototypes before current form.

9. AI, Ads, and the Evolution of the Internet (35:08–37:10)

• Digital Advertising in AI Era: Advertising remains powerful but will change form—potential for more nuanced or even insidious embedding within conversational/agentic UIs.
  • Transparency and user agency are vital.
    • “The ad model is here to stay. It will just come in different forms and I think as long as it's done well, it's a reasonable model. And as a consumer you also have to be smart about what's in these things for you.” (35:23, Sridhar)
• Citations and Trust: Growth of citation/sourcing features in LLMs and agentic experiences is encouraging for information reliability.

10. The Ongoing Role of Search and Traditional Information Retrieval (38:15–41:47)

• Integration, Not Replacement: Sridhar maintains that information retrieval/indexing remains essential, even as LLMs become more capable.
  • Best-practice AI will “use reliable tools wherever it is available” (41:05, Guo).
  • Search APIs and external tools add value by supplying up-to-date, verifiable information the model does not retain.
  • Quote: “At least at this point in time there's enough value from these outside tools, including search, that I don't see the point of trying to dismiss it right now.” (41:19, Sridhar)

Notable Quotes & Memorable Moments

• On Product-Market Fit & AI Industry Disruption:
  • “There's a reason that Snowflake exists. The three hyperscalers would love to just own the data space…but yet there's Snowflake, there's Databricks.” (19:28, Sridhar)
• On Building Defensibility:
  • “Defensibility is built, not strategized. It's built every single day. You have to keep moving.” (23:13, Sridhar)
• On Trust in Enterprise AI:
  • “You need an eval for every single new thing… If you want to change the underlying model, you need to be able to quickly verify that you didn’t blow up on the things that you are already doing.” (10:54, Sridhar)
• On the Pace of Change:
  • “It always feels entirely too slow… but for scale seems pretty fast.” (13:36, Sarah)
  • “Change is hard. You have to acknowledge that. And driving behavioral changes from lots of people is incredibly difficult.” (14:02, Sridhar)
• On Using the “Best Tools”:
  • “A maximal intelligence will use reliable tools wherever it is available. You cannot be so smart that you don’t use the computer.” (41:05, Sarah and Sridhar)

Timestamps for Important Segments

• 00:44 — Sridhar's journey as CEO, Snowflake's transformation
• 03:24 — Organizational structure overhaul, product iteration philosophy
• 05:50 — Snowflake's pivot in AI strategy, humility in product thinking
• 08:10 — Design philosophy of Snowflake Intelligence (SI), focus on data value
• 09:10 — Example use cases (Raven), product aspirations
• 10:54 — AI trust, security, and evaluation standards
• 12:09 — Lines between apps, agents, and agentic platforms
• 14:02 — Implementing organizational change
• 18:50 — Product-market fit and defensibility in an AI-dominated market
• 23:45 — Snowflake's forward-looking vision and competitive stance
• 27:28 — Role of partnerships (e.g., SAP, Microsoft)
• 30:46 — ROI-driven use cases for enterprise AI
• 33:10 — SI product iteration journey
• 35:23 — Future of advertising in an AI/chatbot world
• 37:10 — Trend toward sourcing/citation for information reliability
• 38:15 — Ongoing importance of search and information retrieval vs. LLM overreach
• 41:05 — “A maximal intelligence will use reliable tools wherever it is available”

Summary

In this engaging conversation, Sridhar Ramaswamy shares a candid and granular look at how Snowflake rapidly adapted to the AI revolution, culminating in the launch of Snowflake Intelligence—a focused, trustworthy agentic platform for enterprise data. He offers practical advice on enterprise AI adoption, leadership, and innovation, all while sharing a nuanced, pragmatic vision for staying competitive in a market threatened by hyperscalers and rapid technological shifts.

Listeners gain actionable insights into how major software companies can—and must—balance humility, speed, and constant reinvention to remain at the cutting edge during a period of historic disruption.