Inside Anthropic: How Safety Became the Business Model

How Anthropic's Co-Founders Turned AI Safety Into Competitive Advantage

In a rare fireside conversation, Anthropic's co-founding team — Dario Amodei (CEO), Daniela Amodei (President), Chris Olah, and Jared Kaplan — sit down to discuss why they started the company, how safety drives every decision, and why the Responsible Scaling Policy (RSP) has become their defining document.

On why Anthropic had to exist: "We just felt like it was our duty." The co-founders describe the moment when staying at OpenAI no longer felt viable. After working on GPT-2 and GPT-3, the scaling trajectory became clear — and so did the urgency of building safety into the process rather than bolting it on later.

On the culture that makes it work: "It's because of low ego." Daniela Amodei credits the company's unusual cohesion to a deliberate hiring philosophy they call "keeping out the clowns" — prioritizing people who are both technically brilliant and genuinely collaborative. The result is a culture where safety teams and product teams aren't adversarial but aligned.

On the RSP as organizational backbone: "It's like the holy document for Anthropic." The Responsible Scaling Policy — Anthropic's framework for measuring AI capability thresholds and triggering safety requirements — has gone through more drafts than any other internal document. It creates clear accountability: at each capability level, specific safety measures must be met before deployment.

On evals driving everything: "Evals, evals, evals. Every team produces evals." Jared Kaplan describes how evaluation has become embedded in every team's workflow — not just the safety team. Engineers working on inference talk about safety. Product teams build evals into their planning process. This isn't a separate department's job; it's a company-wide muscle.

On interpretability as the long game: Chris Olah's work on mechanistic interpretability — understanding what's actually happening inside neural networks — represents Anthropic's deepest bet. Rather than treating models as black boxes, the team is beginning to crack open how these systems actually think, with implications for both safety and capability.

6 Takeaways From Anthropic's Co-Founders on Safety-First AI

• Safety is the business model, not a constraint — Customers don't want models that are easy to jailbreak or that hallucinate. Safety research directly improves product quality, creating a "race to the top" where competitors are incentivized to match Anthropic's standards.
• The RSP creates healthy incentives — By publishing specific capability thresholds and corresponding safety requirements, Anthropic makes its commitments legible to employees, customers, regulators, and competitors alike. Other labs have since adopted similar frameworks.
• Constitutional AI was born from iteration — The idea of giving models a set of principles rather than relying solely on human feedback went through extensive drafting. It started as a consensus-building exercise and became one of Anthropic's core alignment techniques.
• Culture scales through mission clarity — With hundreds of employees, the co-founders credit unity to the fact that everyone shares the same mission. People frequently join because they care about safety, not despite it.
• Interpretability could be Nobel-worthy — Dario Amodei publicly stated that Chris Olah's interpretability work could lead to a future Nobel Prize in Medicine, drawing parallels to how understanding neural networks could unlock breakthroughs in biological research.
• Claude for work is the vision — The team expressed excitement about Claude becoming a tool that can genuinely help with professional tasks — from coding to research to biology — making AI useful in ways that are safe, reliable, and trustworthy.

What This Means for Organizations Building With AI

Anthropic's co-founders make a compelling case that safety isn't the opposite of capability — it's the path to it. For organizations evaluating AI partners, the lesson is clear: the companies investing most deeply in understanding how their models work are also the ones building the most reliable products. The RSP framework offers a template for how any organization can think about AI governance — not as bureaucratic overhead, but as a competitive advantage that builds trust with customers, regulators, and employees alike.