Ask almost anyone whether AI made them faster, and the answer is an immediate yes. Ask whether it made their team faster and the answer gets vague. All that productivity is pooling inside individual chat windows — private context, private memory, private wins — while the team around each person moves at roughly the old speed. The tools got extraordinary. The seam between them and everybody else did not.
In this episode of Talking AI, Matt Paige sits down with Lane Shackleton, Head of Superhuman Docs and the product leader who spent more than a decade building Coda, now rebuilt as Superhuman Docs inside the Superhuman Suite. Lane’s diagnosis is structural rather than cultural: chat tools and collaboration tools grew up under opposite design constraints, and they have not yet unified in any meaningful way. His answer is what he calls the last mile of AI — bringing agents to where people already work, instead of waiting for someone to decide it is time to use AI.
The conversation covers why the chat window was always a soloist tool, what shared team context changes about how agents behave, the July 8 launch that turned Coda into Superhuman Docs, how product, design, and engineering roles are collapsing into one another, where the bottleneck moved once code got cheap to write, and how you partner with the same labs you compete with.
In this episode, you’ll hear about:
Why chat tools and collaboration tools grew up under opposite design constraints, and what that cost teams. The board meeting that pushed Coda into AI, and the weekend of demos that followed. Why having to decide it is time to use AI is a product failure, not a user problem. The context tax teams pay in copy/paste, and what shared context does instead. Why individual memory breaks down the moment a second person needs to see it. Using an MCP to keep a team’s decisions continuously updated from meeting notes. What it felt like to rebuild a beloved product under a new name in the middle of a platform shift. The case for a big-bang launch over a phased rollout. AI Views, and what beta users built that nobody predicted. How product, design, and engineering roles are collapsing into each other. Why the bottleneck moved to review the moment code got cheap. Let the makers make — and the point where someone has to codify what worked.
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Key Moments
- 00:01:48 — Two tool families that grew up apart: chat windows and collaboration tools
- 00:03:32 — The board meeting, Reid Hoffman, and Coda’s early look at the OpenAI API
- 00:06:11 — What a vision video is, and why prototypes beat blog posts
- 00:07:56 — Why having to think “it’s time to use AI” is a failure mode
- 00:11:20 — The context problem: copy/paste, thin slices, and memory that vanishes
- 00:13:00 — From decision logs to MCP: keeping a team’s memory continuously updated
- 00:15:01 — Coda to Grammarly to Superhuman: what the transition actually felt like
- 00:17:58 — Why July 8 was a big bang instead of a phased rollout
- 00:18:59 — AI Views explained: prompt your way on top of a dataset
- 00:20:58 — Low floor, high ceiling, and software that feels personal
- 00:24:05 — Product, design, and engineering roles collapsing into each other
- 00:26:30 — AI-native development: the team with no planning process
- 00:27:52 — Where the bottleneck moved: machines or humans reviewing the code
- 00:30:17 — Partner or threat? Working with the labs you also compete with
- 00:34:04 — Human-generated and machine-generated work have to get married somewhere
- 00:37:24 — Let the makers make, and why play beats mandates
- 00:43:31 — The end-of-day sweeper, and teaching his kids AI with homemade games
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Key Links