Sridhar Ramaswamy

Ramaswamy’s 18-month reset treated Snowflake’s AI lag as an organizational-speed problem: shorten the seven-to-10-layer distance between engineers and customers, assign accountable product leaders, and connect them closely to go-to-market teams. His operating maxim is “speed wins. The ability to iterate always trumps carefully laid-out strategies,” particularly when AI’s next month is barely predictable.

Snowflake pivoted away from foundation-model development after recognizing it lacked the capital to compete meaningfully with OpenAI or Anthropic, then repositioned from the Data Cloud to the “AI Data Cloud.” The narrower bet is to compound its installed-base advantage—“something like half” of qualifying Fortune 2000 companies—by applying search, text-to-SQL and agents to valuable customer data already in Snowflake.

Snowflake Intelligence is an opinionated enterprise-data agent, not a universal agent framework or a replacement for SAP, Salesforce, Tableau or Sigma. Its Raven sales assistant combines contracts, consumption, conversations and outstanding issues in one interface, while required evals reject “YOLO AI”: changing a model must not silently break existing answers.

The moat must be rebuilt continuously because foundation-model companies are “empires that have not met their oceans just yet,” while cloud providers possess “infinite budgets” and “infinite patience.” Thin prompt layers look exposed; Snowflake’s defense is a cross-cloud, governed data platform plus deeper Microsoft, AWS, GCP and SAP integration. As Ramaswamy warns, merely being ahead is insufficient: fail to stay ahead and “you will be Intel.”

Ramaswamy identifies coding agents, customer support and easier data access as AI’s clearest near-term enterprise returns. But he rejects giant first bets: take more “shots on goal,” iterate toward product fit, and spend with Snowflake “a thousand bucks at a time” until demonstrated value justifies scaling.

Internet advertising will survive chat interfaces, but disclosure and user agency become more important as chat narrows what is presented and commercial influence becomes harder to see. His deliberately creepy failure case is a psychiatrist biased toward one medication; the counterweight is visible sourcing, citations and easy cross-checking between systems such as Gemini and ChatGPT.

Search and other reliable external tools remain relevant even as LLMs grow more capable. Google’s advantage moved from PageRank to behavioral feedback, just as AI products can improve through eval loops; asking an LLM to internalize everything is like refusing two lines of Python for arithmetic because “you cannot be so smart that you don’t use the computer.”

🔗 Original source & video: No Priors Ep. 139 | With Snowflake CEO Sridhar Ramaswamy

The most exposed AI startups build directly on foundation-model providers whose application boundary keeps moving. Sridhar Ramaswamy calls building on OpenAI “terrifying”: OpenAI, Anthropic, Microsoft, or Google can enter any promising coding, legal, or workflow category. Defensibility instead requires established customer relationships, clear delivered value, and embracing AI fast enough that a disruptor cannot unseat the incumbent.

DeepSeek may puncture claims about model scarcity without necessarily displacing ChatGPT’s consumer distribution. Harry Stebbings pushes back that DeepSeek reached No. 1 in the charts and is free. Sridhar answers, “It’s a product. It’s not a model”: ChatGPT bundles image creation, uploads, and code execution. Harry also suggests OpenAI could host DeepSeek to power part of ChatGPT.

OpenAI’s moat is approaching consumer-platform scale, not permanent model supremacy. By some accounts, ChatGPT has roughly 500 million loyal users without really paying for advertising in recent years; Sridhar compares that reach with Meta and Google. OpenAI may not always build the best or cheapest foundation model, but on the consumer side he bets specialized-model value will accrue to the incumbent entry point.

Established software companies can defend themselves if they combine embedded relationships with rapid self-disruption. Snowflake’s wager is that an AI-native entrant starting from zero will not be better than Snowflake, while Salesforce’s Agentforce illustrates the same playbook. “All-new value creation,” however, looks “very murky” where those advantages are absent.

Enterprise AI is producing real utility now, although adoption should be gentler than a frictionless hockey stick. Sridhar cites compressing notes from 30 Davos meetings—25 pages—into one-line summaries, querying structured data conversationally, and describing how parts of building-insurance underwriting could be automated by combining structured and unstructured information. The CEO message he heard was: “Help us create utility; tell us what is possible.”

The AI-capex arms race will end with a bubble bursting, but the residual value depends on what gets built. Harry points to Meta’s $65 billion data-center investment and the $500 billion Stargate announcement. Sridhar distinguishes a productive 1990s-style bubble that leaves power, buildings, and fiber from a Webvan-style burn—or rapidly depreciating hardware whose value disappears “in a puff.”

Snowflake accepts public-market constraints because accountability can sharpen innovation and expose narrative games. Private Databricks can spend more freely and has doubled the number of Snowflake’s salespeople, but Sridhar argues that constraints force clarity: “Having rich uncles is not always a good thing.” Public liquidity, free-cash-flow reality, and having to “show your work” outweigh the temptation to go private.

🔗 Original source & video: Sridhar Ramaswamy, CEO @Snowflake: Deepseek is Not a Threat to OpenAI & OpenAI Beats Anthropic|E1258