An interview with Bret Taylor and Clay Bavor, co-founders of conversational AI startup Sierra, on using several AI models at once, building AI agents, and more
basically the AI version of that company—will be just as important as their website,” says Taylor. “It's going to completely change the way companies exist digitally.” - Wired https://www.wired.com/... [image] LinkedIn: Steven Levy : I have known Bret Taylor and Clay Bavor for years, and hold them in high regard. So I was fascinated to hear about their startup, Sierra … Mark Oehlert : First, the pedigrees here if you don't know them: Taylor, former co-CEO of Salesforce and was a key developer of Google Maps in the aughts and Bavor headed Google's VR efforts. …
Context & Ripple Effects
Sierra’s founders are outlining the product logic behind an enterprise conversational-AI company that had just emerged with a $110M launch backed by Sequoia and Benchmark. The emphasis on combining models positions the company around orchestration and agent-building rather than a single underlying model.
That framing matters because Bret Taylor’s prior leadership at Salesforce ties Sierra’s ambitions to how businesses present and operate through digital customer interactions. Later coverage of a fundraising round at a valuation above $4B indicates investors treated that enterprise-agent thesis as consequential.
First-order effects
- Sierra can design its agents around multiple models, allowing it to select or combine model capabilities rather than make its product dependent on one provider.
- Enterprise buyers evaluating Sierra are being offered an agent layer intended to represent the company digitally, not merely a standalone chatbot.
Second-order effects
- Competing enterprise-agent vendors face pressure to demonstrate model flexibility and a credible way to manage the complexity of multi-model systems.
- Model providers become components within a higher-level customer-service and workflow product, increasing the importance of the agent platform’s orchestration and integration layer.
Third-order effects
- If enterprises adopt this architecture, competitive advantage may shift from owning a single model toward embedding reliable agents in business workflows and managing the model stack behind them.
- The pattern points to a more layered AI market: foundation-model suppliers underneath, with enterprise agent companies competing for the customer relationship and operational control.
The trend: Sierra is one data point in the shift from standalone generative-AI tools toward workflow-embedded agents that orchestrate multiple underlying models.