Uncapped #42 | Bret Taylor from Sierra

Host: Jack Altman (Alt Capital)
Guest: Bret Taylor (Co-founder & CEO, Sierra; Chairman, OpenAI)
Date: February 19, 2026

Overview

In this episode, Jack Altman sits down with Bret Taylor to unpack the tumultuous state and future of enterprise software amid the AI revolution. The discussion spans the shifting center of gravity from systems of record to AI agents, defensibility in SaaS, the new shape of product and pricing, competition, and what’s next for both software teams and humanity as AI upends the industry. As both the CEO of fast-growing AI startup Sierra and chairman at OpenAI, Taylor shares his unique dual-lens perspective—covering the strategic, technical, and very human sides of the unfolding transformation.

Key Discussion Points & Insights

1. The “SaaS Apocalypse” & The Changing Moats of Software

[00:24–06:32]

• Public markets are pessimistic on software: All software stocks are trading down, not necessarily due to company-specific failings but general anxiety about the future and AI disruption.

  • “Every software stock is down, but I don't think that means every software company is equally disadvantaged. It's just basically anxiety about the future.” (Bret Taylor, 01:07)

• Historic value concentrated in “systems of record” (ERP, CRM):

  • Moats formed via ecosystem gravity, high switching costs, and partner integrations—“the sun and the solar system” model.
  • “No one gets fired for buying IBM” still explains inertia.

• AI agents question these foundations:

  • If agents take over workflows, does the database (system of record) lose centrality?
  • Rise of “invisible” workflows: The value may shift from record-keeping (the database) to the outcomes (what the agent does).
  • Existential risk: Will the value of enterprise software collapse as core features get commoditized or replaced?
    • “If you imagine you're running a sales team, how much do you value the database of leads versus the agent that generates the leads?” (Bret, 04:40)

2. Innovation, Incumbents, and the Best-of-Breed vs. Best-of-Platform Pendulum

[06:32–12:34]

• Incumbents vs upstarts:
  • Incumbents have customer trust and sales scale—but new platforms usually birth new leaders, not extensions of the old guard.
  • In moments of platform shift (web, smartphone, now AI), “best-of-breed” upstarts often move faster and get ahead.
  • Cultural and strategic inertia at large enterprises (“strategy tax”): Legacy assets and business models become anchors instead of assets.
  • “All of the advantages that you had all of a sudden become anchors that are holding you back from actually doing the right thing.” (Bret, 10:52)
• Public market investors sit out during uncertainty.

3. Competing in the AI Agent Era – Sierra’s Approach

[12:34–17:05]

• Current landscape for AI agents is both competitive and demand-rich; “too much capital” chasing too many competitors, most with undifferentiated demos.
• Market sophistication shifting:
  • Transition from educating customers (“what is an agent?”) to competing in structured RFP processes with Fortune 20 companies.
  • “Now the conversation is clearly, we need this yesterday.... You end up in more competitive conversations. And then it's a question of like, why Sierra?” (Bret, 14:43)
• Sierra’s differentiation:
  • Focus on regulated, complex industries (healthcare, banking), building “industrial-grade” agents that work in the real world.
  • Speed: Example of getting Cigna (Fortune 20) live in two months.
  • Blend of AI expertise and deep business process know-how for “extremely fast” client adoption.

4. Pricing Models for AI Agent Companies

[17:05–21:22]

• Outcome-based pricing: Sierra evangelizes charging for impact (did the agent solve the problem? did it make a sale?), not just for use.

  • “If you can measure the outcome, you want to incentivize the outcome.” (Bret, 17:32)
  • Pricing evolution: Perpetual licenses → subscriptions → usage → outcome (pay-per-click, pay-per-install analogies).
  • Looks to the history of advertising and SaaS for analogues.

• Why not token-based (model usage) pricing?

  • Inputs are not correlated with business value (quality/quantity of leads, service outcomes), so charging for tokens is inefficient.
  • “The closer you get to a business outcome, like it's actually you should charge for the business outcome, which is uncorrelated with tokens.” (Bret, 19:40)
  • Applied AI: Can you describe your product's value proposition without mentioning models or tokens?

5. Technical Edge Cases & The Future of Agent Innovation

[24:22–28:21]

• Many support/voice agent challenges remain unsolved:

  • Supporting global languages (e.g., Cantonese, Tagalog) and messy real-world environments (car horns, kids in background).
  • Building proprietary tech is required now, but will get commoditized fast.
  • The durable “asset” may shift from code itself to system prompts and decision logs:
    • “When generating the code is easy, it's almost like the system and the prompt that are actually the durable asset.” (Bret, 27:01)

• The AI-agent product cycle parallels previous tech waves: raw technical advantage gets commoditized; product and business process integration becomes the differentiator.

6. The “Young Founder Advantage” & The Role of Experience

[28:21–31:03]

• Debate: Does AI favor young, “naïve” founders with first-principles thinking?
  • Bret: Naivete can be an advantage (“too naive to know it couldn't be done”), but experience matters, especially in enterprise.
  • Sierra’s edge lies in combining next-gen AI knowledge with deep business/process acumen, especially for regulated clients.
  • “There's a circle: People understand the next generation of AI and people who understand business. We're the company in the middle.” (Bret, 29:41)

7. Strategic Choices That Powered Sierra’s Lead

[31:03–34:12]

• Two key investment areas:

  1. Product: Maximum extensibility for complex enterprise environments—can go live quickly and handle enterprise messiness (mainframes, lots of integrations).
  2. Go-to-market & partnerships: Unique “forward deployed” model with technical advisors working directly with customers.
     • Outcome-based pricing creates alignment: Sierra only gets paid if the deployment works.

• Attracts world-class technical talent by empowering them to drive real industry transformations.

8. Agent Use Cases & Commoditization Debate

[34:12–38:47]

• Expanding beyond support:

  • Example of Rocket companies (Redfin, Rocket Mortgage) having agents for everything from home search, mortgage origination, to servicing.
  • Agents are becoming the customer-facing “front door”—all transactional communications.
  • For telcos: Sierra’s AI agents are negotiating billions of dollars of telecommunications contracts.

• “Agent builders” (tooling) vs. solution agents:

  • Generic agent-building tools and horizontal tech will be commoditized/open sourced (like website builders).
  • True value is in specialized agents that do something meaningful: “Agent building is not a product. Agent building is a technology.” (Bret, 38:31)
  • Likes focused companies (like Harvey for law/antitrust)—productizing meaningful workflows.

9. OpenAI, Codex, and the New Software Team

[38:47–44:41]

• Codex (AI for code) has reached an inflection point—“an emotional experience” for Taylor as an engineer.

  • “...the first time you one-shot something and it turns out like really good… it's an emotional experience.” (Bret, 39:54)

• Software engineering teams and best practices will completely reorganize to take advantage of AI:

  • Those who adapt fastest will move fastest.
  • “My hypothesis is the companies that figure it out first will move the fastest and…the companies that don't will move much more slowly… It's fascinating to me.” (Bret, 40:46)

• AI productivity will support smaller teams, but industry competition ensures not every company will become a “10-person, billion-dollar company.”

  • AI may free up engineers for higher-value work, but complex real-world sectors remain resistant to full automation.
  • Not all jobs will be disrupted equally; “software and finance” are just one (digital) segment of the bigger economy.

10. Human Identity, Taste, & the Limits of AI

[46:38–51:49]

• Taste and identity likely remain human—even if intelligence is automated.

  • Relative status, local context, and unique taste are less fungible.
  • “I don't know if taste is necessarily related to intelligence… I don't think they would consider ChatGPT's opinion. They care more about what the person in class next to them is wearing.” (Bret, 47:17)

• Psychology of disruption:

  • Coders may feel threatened, but adaptation re-establishes value and identity.
  • Historical analogies: Accountants before/after Excel—same job outcome, different “craft.”
  • “It's not like what you did, like the value you provided didn't change, but actually the act of doing it is completely different.” (Bret, 51:18)

• Renaissance of being offline as a new status—possible backlash to digital/AI overload.

11. Business Models for AI Labs—OpenAI, Ads, Safety, and Distribution

[51:49–54:59]

• Taylor is “very optimistic” about tasteful, clearly labeled ads as a way to fund universal AI access (Google’s model as positive precedent).
  • Safety comes first, but breadth of access is core to OpenAI’s mission.
  • Recognizes the importance of offering AI for free to the world so that “artificial general intelligence benefits humanity.”
  • “Our mission is to ensure artificial general intelligence benefits humanity. Obviously, the most important part of that mission is safety. But…how do we widely distribute it?...Being able to offer it for free widely is a huge part of that and we need to be able to afford that.” (Bret, 53:39)
  • Stresses the necessity of making AI accessible to those who can't pay—ad-based business model is pragmatic.

12. Financing Sierra, Board Dynamics, and the Art of Strategic Board Management

[54:59–59:48]

• Chose board members (Peter Fenton of Benchmark, Ravi Gupta, Neil Mehta) for long-term trust and partnership, not just capital.
• Board meetings: Prefers written docs over presentations—encourages deep, real debate, and reflection.
  • “The process of the writing is a process of clarifying your thoughts.” (Bret, 58:10)
• Diversity of board experience is key—choose people your management team wants to consult, beyond investor representation.

13. Closing Thoughts and Predictions

[59:48–End]

• Biggest excitement: Adoption expanding into regulated industries—“doing the hard stuff.”

  • “If you want a hot take, I think my intuition is regulators will start asking for agents. The idea that you have a human set of controls over a regulated process will start to feel like a risk, rather than the risk being AI.” (Bret, 60:07)

• A return invite for a future episode promised.

Notable Quotes

• “Every software stock is down, but I don't think that means every software company is equally disadvantaged. It's just basically anxiety about the future.” (Bret Taylor, 01:07)
• “All of the advantages that you had all of a sudden become anchors that are holding you back from actually doing the right thing.” (Bret Taylor, 10:52)
• “Outcome based pricing feels like the secular business model for agents. And I think it's quite both disruptive and I think a huge step forward.” (Bret Taylor, 18:21)
• “Agent building is not a product. Agent building is a technology.” (Bret Taylor, 38:31)
• “My hypothesis is the companies that figure it out first will move the fastest and…the companies that don't will move much more slowly… It's fascinating to me.” (Bret Taylor, 40:46)
• “Taste is not intelligence.” (paraphrased, Bret Taylor, 47:17)
• “How you do it every day doesn't define you.” (Bret Taylor, 50:53)
• “Our mission is to ensure artificial general intelligence benefits humanity... being able to offer it for free widely is a huge part of that.” (Bret Taylor, 53:39)

Suggested Listening Timestamps

• Systems of record & the SaaS apocalypse: [00:24–06:32]
• Agent-centric future & competitive dynamics: [12:34–17:05]
• Outcome-based pricing model: [17:31–21:22]
• Technology commoditization & durable edge: [24:30–28:21]
• On young founders vs experience: [28:21–31:03]
• Agent building vs solution agents: [37:00–38:47]
• OpenAI, Codex & disruption of software teams: [38:47–44:41]
• Business models for future AI labs (ads): [51:49–54:59]
• The future of regulation & AI adoption: [59:48–End]

A fascinating deep dive into how the disruptive force of AI can flip not just technology, but organizations, business models, and even human aspirations on their head. For founders, investors, and builders, Taylor’s perspective is a front-row seat to history in motion.