Big Technology Podcast
Episode: Who’s Winning The AI Race? + Software’s Future — With Sridhar Ramaswamy
Host: Alex Kantrowitz
Guest: Sridhar Ramaswamy, CEO of Snowflake
Date: February 11, 2026

Overview

This episode explores the rapidly shifting dynamics in the AI race, focusing on the competition between OpenAI, Google (Gemini/DeepMind), and Anthropic, and delves into the transformative future of software through AI agents. Sridhar Ramaswamy brings his perspective as Snowflake’s CEO and a former Google SVP of Ads and Commerce. The conversation covers competitive strategy, enterprise adoption, agentic AI, open source models, market impacts on software companies, and the democratization of AI tools.

Key Discussion Points & Insights

1. The Evolving AI Race: OpenAI, Google, Anthropic, and Beyond

• AI Leadership is Fluid and Competitive
  • The field changes monthly: “The AI race changes every month. ... The gap between the truly great model makers ... and everyone else is quite staggering.” (Sridhar, 02:49)
  • Recent years saw OpenAI take a substantial lead in public perception and usage, particularly with ChatGPT, but Google is catching up rapidly with Gemini and its deep integration advantages.
  • Web visit growth: OpenAI’s chatbot traffic grew 50% from Jan 2025 to Jan 2026, while Google’s Gemini chatbot visits surged 647% in the same period. (Alex, 05:26)
• Moat and Momentum
  • “OpenAI has become the Google of choice when it comes to chat for most of us ... that's actually a durable advantage.” (Sridhar, 06:06)
  • But features like Google’s faster/more accurate image generation (“nanobanana”) can quickly tip the scales, showing how even small product improvements matter immensely.
• Competition Accelerates Innovation
  • Sridhar notes how advances take time—for example, it took Anthropic about two years to catch up to GPT-4’s quality, and the lead narrows quickly.
  • “Leads are shrinking and there's going to be more and more competition. ... Open source models have turned this into a whole other ball game.” (Sridhar, 09:19)

2. Inside Google’s Response & Culture

• Crisis Mode Drives Action
  • Google is portrayed as highly adaptive in moments of crisis (“cordialogue” or “code red”), shifting the whole company’s focus:

    “Every year that I have been at Google, I can think of one or more crises that required us to operate very differently. ... What looks like a placid company from outside is very motivated, very driven.”
    (Sridhar, 10:21 & 11:00)

  • The success of Gemini marks a return to hero status after past criticisms of lack of focus.

3. Enterprise AI Adoption & Agents

• OpenAI’s Bet on Enterprise

  • OpenAI is betting on a broad range of efforts (consumer, video, device, and enterprise). Their $200 million partnership with Snowflake is highlighted as indicative of enterprise AI’s importance.
  • Winning in enterprise is about making AI an “agentic platform” that can provide actionable, decision-supporting tools atop company data. Snowflake’s customers can now leverage these systems, e.g., to optimize pricing on millions of products using data and AI recommendations.

  • “We’ve created an agentic platform called Snowflake Intelligence ... over 2,000 customers are using it pretty much, you know, three months after we released the product to GA.”
    (Sridhar, 13:50-15:30)

• Agentic Systems — What Work Looks Like

  • The vision: work moves away from dashboards and manual queries to systems where users “describe what you want systems to do” and agents compile, analyze, and recommend actions automatically.

    “Your work very much becomes: these are the five topics that you should be paying attention to. And here is a brief for these five topics and potentially even recommendations.”
    (Sridhar, 18:02)

• Demonstrated 10x Productivity Gains

  • Snowflake’s support team is already seeing a 10x reduction in time to resolve complex cases using agentic tools.

    “Already we are seeing 10x, not 10%, 10x reductions in the amount of time that it takes to debug complex cases.”
    (Sridhar, 20:56)

4. Real vs. Hype: Enterprise AI in Use

• Hype vs. Reality
  • While much of agentic AI remains conceptual for many, Snowflake is deploying these tools and requiring execs to use them:

    “All of the people that are in the camp that you’re describing have never had useful products built for them that deliver meaningful value. I speak as somebody that lives this.”
    (Sridhar, 22:11)

  • True adoption requires not just demos but useful, value-generating integrations (e.g., sales intelligence, ops monitoring).

5. Software’s Moats and the Platform Battle

• Software Competition Shifts

  • With AI making app-building cheap and fast, traditional SaaS moats based on narrow feature sets are eroding.

    “If every software company can write infinite code cheaply, the competitive dynamics change. ... Now everyone can build into adjacencies.”
    (Alex quoting Ben Thompson, 26:49-27:48)

  • There will be “a concentration towards platform players,” says Sridhar, but warns “No one has an insurmountable moat.”

• Platforms vs. Features

  • The future may see specialized SaaS products relegated to “dumb backends” for agentic platforms; control is shifting from application frontends to AI/data layers.

6. Market Uncertainty & Software Valuation

• Software Multiples Under Pressure

  • Despite software companies beating expectations, their valuations have compressed significantly—likely due to market uncertainty over who prevails in an AI-driven future.

    “AI as a bolt on to SaaS software does not feel like a winning strategy.”
    (Sridhar, 34:45)

• Winning Is About Customer Value

  • Snowflake positions itself as a differentiated, consumption-based platform focused on agents, integrations, and actionable data. “It’s all about creating value really fast for your customers.” (Sridhar, 28:19)

7. The Great Battle: Centralized vs. Specialized AI Agents

• Who Controls Enterprise Data & Decisioning?
  • Centralized AI (ChatGPT/Gemini as the master agent) vs. specialized agents connected to data platforms (Snowflake, etc.).

    “The vision that they would like to see come true [model-makers] is the world is just a dumb data pipe that feeds into that big brain. … The vision that I would like to see come true is hey, we host the most important data for every company and the most important predictive models for every company.”
    (Sridhar, 36:12-36:49)

  • Ultimately, customer choice and product value will determine who wins these battles.

8. Shadow AI and the Bottom-Up Adoption Wave

• Shadow AI Will Drive Adoption

  • Individuals deploying consumer AI tools (e.g., Open-Source agents like OpenClaw), pushing companies to catch up and formalize adoption.

    “Employees who select their own free AI tools will remain the primary driver of enterprise AI adoption in 2026.”
    (Alex, 46:06)

  • Companies must adapt, balancing security (“openclaw on a Snowflake laptop—please don’t do that!”) with the need to empower and learn from internal innovators.

• AI Champions as Internal Drivers

  • Snowflake’s approach is to find early adopters (“AI champions”), let them experiment, and have them help scale adoption internally.

    “Change in any large company is not going to come from top down mandates… you need to create an environment in which the most progressive of the ideas... have a way to quickly surface the idea up.”
    (Sridhar, 49:46)

9. The Rise of Open Source & Global AI Competition

• Big Tech’s Grip Loosening
  • New training methods (DeepSeek, smaller efficient models) lower the barrier; the market for model innovation is getting more diverse.

    “Foundation models became very expensive to build. ... But a new QEN model came out yesterday that is shockingly close to the best sonnet model that there is.”
    (Sridhar, 53:38)

• Academic & Geo-Political Implications
  • “What is happening right now is that it's the Chinese companies that are publishing their work. ... Academia is diverging from what's happening in the research labs. That's part of the danger of this moment.” (Sridhar, 55:08)
  • Transparency and open source are vital for broad-based innovation.

Notable Quotes & Moments

• “The gap between the truly great model makers ... and everyone else is quite staggering.”
  — Sridhar, 02:49

• On rapid change in AI:
  “It's very, very early. ... The AI race changes every month.”
  — Sridhar, 02:49

• Regarding Google’s competitive culture:
  “What looks like a placid company from outside is very motivated, very driven.”
  — Sridhar, 11:00

• On the reality of agentic AI in the enterprise:
  “All of the people that are in the camp that you're describing have never had useful products built for them that deliver meaningful value. I speak as somebody that lives this.”
  — Sridhar, 22:11

• On the risk of SaaS apps becoming “dumb backends”:
  “That's a very dangerous place to be. ... This current moment is pointing out ... a lot of these players risk becoming dumb backends to the models.”
  — Sridhar, 31:08

• On shadow AI adoption:
  “I talked to you about how with something like a cortex code, you can get a job that you need to do on Snowflake ... in less than a tenth of the time.”
  — Sridhar, 46:32

• On AI’s democratization:
  “The general purpose nature of this is truly, truly mind blowing. Took [my son] a few hours to set up. ... That’s the wildness of the moment.”
  — Sridhar, 45:00

• On open source and geopolitics:
  “We should understand that much of their work has effectively become walled off from the rest of the world. … Academia is diverging from what's happening in the research labs. That's part of the danger of, of this moment.”
  — Sridhar, 55:08

Important Timestamps

• 02:49: Sridhar on the ever-changing AI race and shrinking lead times.
• 05:26: Data illustrating shifts in web traffic to AI chatbots.
• 10:21–11:00: Sridhar’s anecdotes on Google’s “cordialogue”/crisis mode.
• 13:50–19:23: Deep dive into agentic enterprise AI and real-world use cases.
• 20:56: Concrete 10x productivity gain example from Snowflake.
• 27:48: Discussion on changing software economics; SaaS moats under threat.
• 31:08: Sridhar on the risk of SaaS products being commoditized into model inputs.
• 46:06: Shadow AI as the driver of enterprise adoption.
• 53:38: Discussion on the open-source models making state-of-the-art more accessible.
• 55:08: Academic openness vs. commercial secrecy in AI research.

Conclusion

This episode provides a rich examination of the present and near future of the AI race, arguing that flexibility, integration, and value creation for customers—rather than static competitive moats—will define the winners. Innovations in agentic AI and open source model development are placing power in the hands of both enterprises and individuals, eroding traditional boundaries and shifting paradigms across tech, enterprise, and the global AI market.

For further insights or to listen to the episode, visit [Big Technology Podcast].