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How to Reorg After AI Changes Everything | Block's Owen Jennings on the a16z Show

David Haber speaks with Owen Jennings, executive officer and business lead at Block, about how the company rebuilt itself around AI agents, small squads, and internal tools like Goose and Builder Bot after restructuring more than 40% of its workforce. They discuss what it took to execute a major restructuring, how teams of three are now doing what teams of 14 used to, and how Block is shipping AI-native products like Money Bot and Manager Bot that generate custom interfaces on the fly for tens of millions of users. Timestamps: 0:00—Introduction 6:06—How the RIF Actually Executed 9:08—The Most Meaningful Difference in How We're Operating 12:57—AI Infrastructure Build Across the Org 17:09—The Shape of the Business: Square, Cash App, Afterpay 20:00—From Static UI to Generative UI 23:23—Defensibility in the AI Era Read the full transcript here: https://www.a16z.news/s/podcast Resources: Follow Owen Jennings on X: https://twitter.com/owenbjennings Follow David Haber on X: https://twitter.com/dhaber Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

OJOwen JenningsguestDHDavid Haberhost

Apr 1, 2026·27m·Watch on YouTube ↗

📖 CHAPTERS

1. 1

   Why Block made a 40% RIF: the December “capability jump” in AI coding

   Jennings explains that Block’s workforce reduction was driven primarily by a sudden step-change in AI’s ability to work inside large, complex existing codebases—not just greenfield projects. He argues that the long-standing relationship between headcount and output “broke” as AI-enabled developers became dramatically more productive.

2. 2

   Was it just ZIRP overhiring? Jennings’ rebuttal and the “we don’t write code by hand” claim

   Pressed on whether the cut was simply a correction from 2021-era bloat, Jennings points to productivity metrics and the composition of cuts. He asserts the move reflects a fundamental shift in how software is produced at Block.

3. 3

   How the RIF was executed: principles, guardrails, and operational tactics

   Jennings describes designing the new org from first principles rather than targeting a financial percentage. Execution focused on avoiding outages, maintaining regulatory trust, and keeping the growth roadmap moving—while handling the human aspects of a large transition.

4. 4

   Day-to-day operating changes: fewer meetings, fewer layers, and an “agent-driven” workflow

   Post-RIF operations emphasize speed and building: meetings are cut dramatically and the org is flatter with wider spans. Work shifts from linear task flow to supervising many parallel AI agents producing outputs simultaneously.

5. 5

   What it takes for others to replicate: groundwork, agent substrate, and Jevons paradox

   Jennings cautions that not every company can copy Block’s approach without prior tooling and cultural groundwork. He also argues reduced headcount per roadmap doesn’t necessarily mean fewer total tech jobs—more could be built overall.

6. 6

   AI infrastructure across Block: Goose, G2, and Builder Bot

   Jennings outlines the internal AI stack: Goose as a model-agnostic agent harness, G2 as an internal automation layer, and Builder Bot as a more autonomous coding system. The emphasis is on compressing time from idea to production and embedding AI deeply into internal operations.

7. 7

   Org redesign enabled by AI: tiny squads, fluid staffing, and flatter structures

   The org moves away from classic hierarchical functional teams tied to a single domain. Instead, Block uses small squads that can move between products quickly, with fewer management layers to increase information flow and speed.

8. 8

   AI beyond engineering: automating queues in support, ops, risk, and compliance (with human-in-loop)

   Outside of software development, Block uses AI to automate deterministic, queue-based work common in scaled companies. Jennings notes current emphasis on human-in-the-loop for regulators and partners, while suggesting long-term automation will surpass human performance in many decision workflows.

9. 9

   The shape of the business: functionalizing Square, Cash App, and Afterpay into one platform strategy

   Jennings explains Block’s move from separate business units (with separate CEOs) to a functional structure that shares engineering, design, and product across the company. This shift supports building cross-product infrastructure and experiences that connect Square, Cash App, and Afterpay.

10. 10

    From static UI to generative UI: Money Bot and Manager Bot as agentic products

    Block is pushing AI into customer-facing experiences via proactive bots built on Goose. Jennings argues the industry is moving quickly from fixed interfaces to generative experiences where UI and workflows are created dynamically for each user’s needs.

11. 11

    Practical implications and risks of generative UI: QA, non-determinism, and building custom mini-apps

    Jennings highlights that dynamic, model-generated outputs create new quality and safety challenges. He shares examples of AI assembling workflows and even generating app-like experiences that aren’t embedded in the shipped source code, raising questions about testing and reliability at scale.

12. 12

    Defensibility in the AI era: distribution, regulation, and the deeper moat of “hard-to-learn understanding”

    Jennings acknowledges traditional moats—distribution, network effects, regulatory posture, and hardware—but argues the lasting advantage will be a company’s unique understanding of complex systems. He describes Block’s direction as becoming an “intelligent system” built on rich signals about commerce, iterating rapidly through agentic feedback loops.