Squawk Pod: Davos 2026 – Anthropic CEO Dario Amodei

Episode Date: January 22, 2026
Guests: Dario Amodei (CEO & Co-founder, Anthropic)
Host Panel: Andrew Ross Sorkin, Becky Quick, Joe Kernan

Episode Overview

In this episode, CNBC’s Squawk Pod speaks with Dario Amodei, CEO and co-founder of Anthropic, at the World Economic Forum in Davos, Switzerland. The conversation explores Anthropic’s philosophy and approach to AI development, the company’s competitive strategy, enterprise-focused business model, exponential revenue growth, and the future of AI adoption globally. Amodei also addresses the strategic challenges of scaling, moats in the AI industry, and discusses regulatory and geopolitical dynamics affecting the sector.

Key Discussion Points & Insights

Anthropic’s Distinct Path in AI

• Focus on Safety and Reliability:

  • From inception, Anthropic’s guiding principle has been to ensure the safety and reliability of AI systems, positioning them as a trusted partner for enterprises.
    • Quote: “Anthropic has thought in terms of safety and reliability of AI systems. And one of the things we realized is that that was very synergistic with working with enterprises as compared to consumers. Because enterprises really, they value the reliability, they value the trust...” – Dario Amodei [03:44]

• Enterprise vs. Consumer Prioritization:

  • The company has a roughly 80/20 split, primarily serving enterprises rather than focusing on consumer-facing AI models. (03:44, 01:19)
  • By serving enterprise clients, Anthropic can focus on capability and long-term value rather than engagement metrics prevalent in consumer applications.
    • Quote: “We don't have to do ads, we don't have to focus on short form video... Instead we actually just focus on making our models as smart and as capable as possible. Because that's what enterprises are looking for.” – Dario Amodei [04:20]

Anthropic’s Competitive Edge

• Differentiating from Other Models:

  • Amodei notes that rivals like OpenAI, Google, Meta, and XAI are heavily optimizing for consumer engagement and entertainment, possibly diverting incentives away from building ever-smarter, more capable models.
    • Quote: “If you're optimizing for consumers, right, you're trying to be as engaging as possible… I actually don't think some of these other models have enough incentive to get smarter, have enough incentive to become capable of more tasks across the board.” – Dario Amodei [05:12]

• Enterprise Needs Drive Technological Depth:

  • Anthropic’s emphasis is helping businesses optimize complex, high-value processes (finance, pharmaceuticals, manufacturing, energy, etc.), directly contributing to real economic growth.
    • Quote: “We think of the work we do as most directly connected to directly driving GDP and not all this kind of indirect or complex like maximizing engagement.” – Dario Amodei [04:50]

Revenue Growth and Global Diffusion

• Explosive Revenue Trajectory:

  • Anthropic’s reported revenue growth:
    • $0 (2023) → $100M (2024) → $1B (2025) → “almost $10B” (2026).
  • Amodei acknowledges these numbers sound “crazy,” but says such growth is plausible given the massive economic potential of AI for labor and productivity.
    • Quote: “If you look at all labor in the economy, that's something like $50 trillion a year. So I could easily imagine that the revenue of the industry or even single companies, if it's even 10% of that, could be $5 trillion a year. Now, that's something we haven't seen in the history of the world...” – Dario Amodei [07:29]

• Widening Access & Responsibility:

  • Both panel and Amodei acknowledge that for AI to truly impact society and economy, benefits must reach beyond early-tech adopters and the developed world.
    • Quote: “Diffusing this technology across, across not just the developed but, but the developing world, I think that's going to be very important. You know, we've done, we're starting to do work with folks like the Gates Foundation.” – Dario Amodei [06:32]

Scaling, Capital, and Uncertainty

• “Cone of Uncertainty” in Forecasting:

  • With explosive but unpredictable growth, Amodei discusses the challenge of heavy, up-front capital investment in data centers amid vast uncertainty about next year’s revenue.
    • Quote: “There's this kind of difficult problem, this cone of uncertainty, right, which is the revenue is growing very fast... and yet you have to buy the data center.” – Dario Amodei [08:07]

• Enterprise Model Advantages:

  • Higher margins and more predictable revenues in enterprise over volatile consumer spending.
    • Quote: “One of the fortunate things about being in the enterprise business is that we have relatively higher margins and enterprise spending is relatively predictable in comparison to consumer spending, which is very fickle.” – Dario Amodei [08:57]

• Fundraising Outlook:

  • Anthropic expects to continue raising “very large amounts of money” as they scale, but anticipates revenue will catch up or outpace investment needs over time. [09:36]

The Nature of Moats in AI

• What Protects Anthropic’s Position?
  • Enterprise focus and model intelligence: Only a few companies can plausibly claim “smartest” status for specialized domains (e.g., coding).
    • Quote: “We simply try to have the smartest model and there are maybe only three or four companies that are plausible for having the smartest model... models specialize in different things.” – Dario Amodei [10:32]
  • Specialization: Anthropic’s Claude is considered best-in-class for coding; Anthropic is building out moats in other areas (e.g., agentic tasks). [10:32]
  • Sticky customer relationships: Enterprises are slow to switch vendors due to complex, costly adoption and integration cycles.
    • Quote: “Enterprise relationships are formed slowly over a lot of time and then are difficult to disrupt... if they manage to go through that slow process with one company, it is again slow to try to do that with another company.” – Dario Amodei [11:30]

Geopolitical Tensions and Selling Globally

• Selling Amid Global “Splintering”:
  • While geopolitical tensions exist, Anthropic aims to remain “independent,” selling models worldwide and responding to policy issues in a technocratic, not politicized, way.
    • Quote: “We just have models to sell. We're willing to sell them to businesses across the world. When policy matters come up, we do speak on them… we try to be very technocratic in the way that we address these things.” – Dario Amodei [12:08]

Memorable Quotes

• On AI industry incentives:

  • “I actually don't think some of these other models have enough incentive to get smarter, have enough incentive to become capable of more tasks across the board and we've been able to focus on that more.” – Dario Amodei [05:12]

• On the scale of the opportunity / future revenue:

  • “If it's even 10% of that [$50T in global labor], could be $5 trillion a year. Now, that's something we haven't seen in the history of the world that creates all kinds of problems as well as creating all kinds of growth.” – Dario Amodei [07:29]

Notable Segments & Timestamps

• [03:44] – Dario Amodei on Anthropic’s enterprise focus
• [04:20] – Why Anthropic avoids consumer-centric engagement tactics
• [05:12] – Divergence of incentives between consumer- and enterprise-focused AI
• [06:32] – Importance of global and equitable AI dissemination
• [07:29] – Revenue growth, scale, and the $5T industry forecast
• [08:07] – Challenges of capital investment amid high-growth unpredictability
• [10:32] – The “moat” around Anthropic’s business and model specialization
• [12:08] – Anthropic’s stance on geopolitics and global AI markets

Episode Tone & Language

The tone is conversational, candid, and analytical—blending business pragmatism with long-term industry vision. Amodei is direct but optimistic, focusing on transparency and measured ambition rather than hype.

For Listeners: What You’ll Walk Away With

• A clear picture of how Anthropic positions itself differently from consumer-facing AI giants
• An understanding of why enterprise AI is seen as “stickier” and potentially more lucrative
• A sense of the massive economic scale AI leaders now contemplate
• Perspective on the challenges of capital, forecasting, and technological differentiation in AI
• Insight into business leadership in the age of rapid technological change and global uncertainty

Note: This summary skips commercial breaks, episode intros/outros, and focuses exclusively on the core discussion.