Tara Seshan

1. Key Themes

The Three Eras of AI Products: Chat → Agents → Persistent Coworkers

Tara frames the evolution of AI products in three distinct eras, and positions us at the cusp of the third. The framing matters because it gives product builders a mental model for where to build.

"If you think about the first era of AI products as chat, the second era of these products working with agents, that third era that might come soon is how do you work with a persistent coworker who is able to get things done with you?" 00:00:00

"We're introducing agents to a billion people who may not have experienced them yet. How do you do so in the easiest, most natural and most usable way possible?" 00:35:36

Build for Two to Three Months Out — Not Now, Not a Year From Now

Tara identifies a precise and non-obvious failure mode: building for where models are today is wrong, but so is building for where they'll be in a year. The only viable horizon is two to three months.

"You fail if you build for where the models are now. You fail if you build for where you think the models will be in a year. Both outcomes are equally wrong. The only way to build is two to three months." 00:00:19

"How do I ensure that this is right for the model in two to three months time?" 00:27:57

Empiricism Over Theory: The PM Role Has Fundamentally Shifted

In fast-moving, emergent markets, rigorous strategy documents are less valuable than rapid hypothesis testing. The core PM discipline has sharpened, but many of the traditional trappings have fallen away.

"Rather than writing out some long reasoning doc, almost like a PhD thesis of what I think should be the plan for the next N amount of time. Instead, it's like how do I get to something I can try out and test with users as fast as possible?" 00:00:38

"What is actually, to use the Shashir phrase, the eigen question? What is that specific most important thing to test? And everything else, like any other grand strategy you concoct, is not relevant." 00:08:35

The Future of Work Is Steering, Not Rowing

Tara articulates a durable mental model for human-AI collaboration: humans steer at ever-higher levels of abstraction while agents do the rowing. The competitive edge becomes the quality of human direction.

"Increasingly the future of work will look more like steering than rowing. In the sense that there will be agents that you'll be able to work with that do a lot of the rowing. And your role increasingly becomes steering the ship in the right direction." 00:11:22

"Work will also look like steering with other people over a group of agents that you guys work with together. Bringing in other teammates into that interaction between you and the agent where it's rowing and you're steering feels also incredibly valuable." 00:12:42

Knowledge Work and Coding Require Fundamentally Different AI Product Designs

This is one of the most underappreciated product insights in the conversation. Coding is output-verifiable; knowledge work requires process transparency. This reshapes how AI products must be built for each domain.

"Coding is so output oriented that when you ask it to do a coding task, you can verify whether it did the task correctly or well via tests. But knowledge work is different in that I can't simply look at the deck in the end and see the numbers and actually believe that. I really need to think about the process and the inputs and the reasoning and how it went along the way." 01:05:08

"A lot of work that we have done and have to continue to do is continue to adapt the product to knowledge work, which means way more focus on making ChatGPT your collaborator, allowing you to see all the in-progress work, see its citations and inputs, help you go on the journey with the model to get to that end output." 01:05:52

Ambition Is Now the Scarce Resource

The capabilities ceiling has risen so dramatically that the limiting factor is no longer execution — it's imagination. Elevating others' ambitions has become a core PM function.

"The capabilities have expanded so dramatically. It is really expanding your thinking of what's possible in an unreasonably short timeframe." 00:22:50

"Elevating others' ambitions or reminding them of what's possible here is a huge part of the product management role." 00:00:58

"Tyler Cowen has this statement on his site, which is that most people underrate the impact of going to someone else and saying, hey, couldn't you try this faster or couldn't you try this at a 10x bigger scale?" 00:24:08

Writing as Thinking vs. Writing as Reporting — A Critical Distinction

Tara draws a sharp line between two types of writing that should be treated completely differently in the AI era. Conflating them leads to either over-relying on AI or under-using it.

"Writing as thinking is me writing a brief about why we should build a certain product or why we should take a certain strategy. Writing as reporting is things like summarizing the status of what our team has been up to this week. Writing as reporting, I happily automate. But writing as thinking is something I never will automate." 00:53:12

"I still write hundreds of docs all the time, but I do it for me. And I no longer do it for other people, really. That no longer is the best way to talk and communicate. That is probably the biggest change I've experienced personally in this era versus the previous era." 00:56:37

Product Marketing Fit Precedes Product-Market Fit

Drawing from her time at Sutter Hill Ventures, Tara shares a counterintuitive insight: the narrative and pitch should be tested and refined before the product is even built.

"Product marketing fit, that narrative, that positioning is actually even before you build a product experience, the right thing to test. So you should go pitch 100 people, figure out how to refine that pitch as much as possible, get the marketing narrative of why this thing is transformative right. And then and only then go commit to this is exactly the product shape." 01:03:27

2. Contrarian Perspectives

The "Polish First" Doctrine Is Dead

The conventional wisdom at established companies is that you don't ship until it's perfect. Tara argues the opposite — in AI, transformative value shipped imperfectly beats polished work shipped late.

"At previous companies, polish was king. Getting every UI interaction or every little thing completely right was way more important than shipping something early because time didn't make as much of a difference in terms of the outcome. But getting the product in the hands of users when you have so much conviction that it's transformative is way better than perfect." 00:36:20

Grand Strategy Documents Are Now a Liability, Not an Asset

In traditional product development, long strategic documents signaled rigor and thoughtfulness. Tara argues the opposite is now true — a long doc is no longer proof of deep thinking.

"A long doc is not a signal that you thought through something because you could easily produce a long doc that indicates that you haven't." 00:55:50

"I am way more on mocks, not docs or prototypes, not docs. And if I have something that people can try and interact with, or even better, I have results where we tried this, we ran an A/B — that is a way better communication tool than the doc itself." 00:56:11

OpenAI Has No Secret Master Plan — and That's a Feature, Not a Bug

Most outsiders assume frontier labs have deeply guarded strategic doctrine. Tara reveals the opposite: OpenAI is structurally decentralized and founder-mentality driven, which is both disorienting and powerful.

"I came into the company expecting that there was a treasure trove of OpenAI secret strategy. And actually, OpenAI is open. Every sort of thought that exists in terms of this is how the world should look like or this is how products should be built very, very quickly becomes a part of the public product." 00:01:07

Product Market Fit Is Repeatable — It's Not a Dark Art

The startup mythology treats product market fit as partly luck. Tara's time at Sutter Hill Ventures challenged this directly.

"People look at finding product market fit as a dark art or building a tens-of-billion-dollar company as a dark art. Like, oh, it's luck. Yet Mike Speiser has done it multiple times. There is clearly a way to do it. There's clearly a roadmap. It's not just luck. It's not just a dark art. There is a playbook." 01:01:06

Software Is More Like Filmmaking Than Real Estate

Against the common intuition that more investment equals better product outcome, Tara invokes a reframe that has significant implications for how AI-era products will be differentiated.

"Patrick Halsey has this really nice statement about software, which is that software is not like real estate. You don't put money in and get value out. It is a little bit more like filmmaking, where you can put a lot of money into a film, but that doesn't guarantee that the film is successful or good. There is some auteur statement or some opinionation and artistry that goes along with it." 00:14:06

3. Companies Identified

OpenAI

AI research lab and product company behind ChatGPT, Codex, and related products. Discussed extensively as Tara's current employer. Notable for its founder-mentality culture, fast iteration cycles, and the product strategy of bringing agentic capabilities to a billion ChatGPT users.

"So much of what OpenAI does immediately becomes something that users can touch and feel in the product. And that cycle is faster than anywhere else I've seen." 00:05:14

Stripe

Payments infrastructure company. Tara spent six years there as one of the first five PMs, was repeatedly named one of the top three employees company-wide. Cited as an exemplar of writing culture, rigorous strategy in established markets, and role fluidity.

"Stripe is incredibly oriented as a writing culture. And there are many people like Jeff Weinstein who are also very into writing and sharing briefs at Stripe. Stripe is one of the few places where a brief will go viral inside the company." 00:55:19

Sutter Hill Ventures

Iconic, deliberately low-profile VC firm known for incubating companies like Snowflake. Praised for having a systematic, repeatable playbook for finding B2B product-market fit rather than relying on luck.

"They have this very unusual incubation model, which Mike Speiser, who is one of the amazing partners there, started and has rolled out success after success." 01:01:06

Watershed

Climate software company. Tara led product there as part of her journey of repeatedly finding product-market fit across companies.

"I wanted to learn what I could from them... whether that was as a founder or in starting new products at Stripe or in joining a startup like Watershed." 01:02:32

WorkOS

B2B SaaS infrastructure company providing enterprise features (SSO, SCIM, RBAC, audit logs) as drop-in APIs. Sponsor of the episode, described as the enterprise-readiness layer for companies like OpenAI, Anthropic, Cursor, Replit, Sierra, and Clay.

"WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS." 00:05:56

Mercury

Business banking and spend management platform. Sponsor of the episode, praised for being built by product people rather than bankers.

"It's what online banking feels like when it's built by product people, not by bankers." 00:37:55

Figma

Design tool company. Mentioned in context of Dylan Field, a co-Thiel fellow with Tara, as an example of exceptional Thiel fellowship alumni success.

Notion

Productivity tool. Cited as an example of prior attempts to create configurable personal software, contrasted with what AI-powered Sites now makes possible.

"Certainly people with tools like Notion try with all these blocks to configure what that could be. But with a site, it is literally a prompt." 00:49:36

GATS

A private social network app built by Tara's friend Sebastian for a small group of friends. Cited as an exemplar of the "cozy software" movement — making personal software for small groups of friends.

"It is exactly what I think the future should be, which is people should make software that exactly meets their and their friends' needs." 01:14:01

Sky (acquired by OpenAI)

Company founded by Ari Weinstein, a fellow Thiel fellow, acquired by OpenAI. Ari now leads computer use work at OpenAI.

"One of the Thiel fellows that I get to work with all the time now is Ari Weinstein, who founded a company called Sky that was acquired by OpenAI." 01:16:30

4. People Identified

Product lead at OpenAI for Codex and ChatGPT Work. Former Stripe PM (one of first five, repeatedly top-3 employee company-wide), product lead at Watershed, founder, Thiel fellow, and Lenny's Newsletter fellow. Named for exceptional product instincts, writing culture, and deep thinking on the AI product era.

"Everyone on that team, the desktop team especially, acts like founders and cares about every piece and every detail." 00:41:39

Andrew Ambrosino

Engineering manager at OpenAI working alongside Tara on Codex and ChatGPT Work. Referenced as a key cultural carrier of the team's operating philosophy.

"Are you mainlining it yet? Which is like, are you using this product all day, every day to get your thing done. Andrew Ambrosino and I love to ask the team that." 00:25:24

Mike Speiser

Partner at Sutter Hill Ventures. Named specifically for his repeatable incubation model and extraordinary track record of finding B2B product-market fit.

"Mike Speiser has done it multiple times. And so there is clearly a way to do it... Mike Speiser is unbeatable at this art." 01:01:34

Dylan Field

Co-founder and CEO of Figma. Thiel fellow from the same cohort as Tara. Named for being both an exceptional talent and a kind person.

"One of the most amazing and successful people who came out of that batch of the fellowship is Dylan Field, who is not only an incredible talent, but also a very kind person." 01:18:40

Ari Weinstein

Thiel fellow, founder of Sky (acquired by OpenAI), previously had a company acquired by Apple. Now leads computer use work at OpenAI. Named for exceptional creativity and craft.

"Ari is just one of the most creative thinkers I've ever seen and is truly the expert on what are all the cool things you can do on a Mac... his creativity and his joy in what he does and his love of his craft really inspires me." 01:17:00

Jeff Weinstein

PM at Stripe. Named as an exemplar of writing culture and brief-sharing within Stripe's internal ecosystem.

"There are many people like Jeff Weinstein who are also very into writing and sharing briefs at Stripe." 00:55:19

Nick Turley

Former product lead for ChatGPT at OpenAI, now working on enterprise. Named for originating the internal cultural meme "Is this maximally accelerated?"

"Nick Turley was on the podcast who was maybe had the role before you. He had this meme internally: Is this maximally accelerated?" 00:25:08

Kevin Weil

Former Chief Product Officer at OpenAI. Named for the memorable insight that the current models are the worst they will ever be.

"Kevin Weil was on the podcast. He said that this is the worst the models will ever be. And it sounds so simple, but it's hard to just wrap your head around that." 00:28:54

Shashir Mehrotra

Named for coining the concept of the "eigen question" — the single most important hypothesis to test — cited by Tara as a key operating framework.

"What is actually, to use the Shashir phrase, the eigen question? What is that specific most important thing to test?" 00:08:35

Shreyas Doshi

PM thought leader. Named alongside Marty Cagan and Shashir as exemplars of PM craft that Tara values, even as AI shifts the nature of that craft.

"There are so many aspects to PM craft that I know folks like Shreyas or maybe Marty Cagan or Shashir, all these people have really espoused that I think are wonderful." 00:44:12

Marty Cagan

Product thought leader and author. Cited for the principle that the initial product idea is rarely what ends up being built — there is always a discovery process.

"Marty Cagan is big on this idea that when you have an idea for a product or feature, rarely is that idea the thing that ends up being. There's this whole process you go through to figure out what it actually should be." 00:14:53

Patrick Halsey (Patrick Collison)

Note: Tara appears to be referencing Patrick Collison (Stripe CEO), citing his website's list of unreasonably fast and ambitious projects as inspiration for expanding what's possible.

"Patrick Halsey has on his website, patrickhalsen.com slash fast, I think, which is all of these projects that were unreasonably ambitious that were executed in a really, really short time period." 00:22:50

Tyler Cowen

Economist and writer. Cited for his insight that most people underrate the power of simply asking someone to be more ambitious or move faster.

"Tyler Cowen has this statement on his site, which is that most people underrate the impact of going to someone else and saying, hey, couldn't you try this faster or couldn't you try this at a 10x bigger scale?" 00:24:08

Kevin Kwok

Named in passing as the person who secured tickets to Christopher Nolan's The Odyssey at the Metreon.

"Kevin Kwok got us tickets at 10 p.m. at the Metreon earlier this week." 01:12:53

Brie Wolfson

Named as someone who knows Tara well and suggested interview questions, and who also contributed to a previous Cursor-related podcast episode.

"I asked Brie Wolfson, who knows you well, what to ask you." 00:52:34

5. Operating Insights

Write Docs to 70% Completion, Then Bring in Stakeholders

Rather than presenting a polished final document, Tara's manager taught her to deliberately stop at 70% and bring in the people whose buy-in you need to complete the remaining 30% together. This tactically transforms a pitch into a collaboration and dramatically improves adoption.

"The right thing to always do is write a doc to 70% completion and then take it to the people that you need buy-in from and get it from 70% to 100%. Very few great people want to interact with a perfectly polished, finished idea. Their new ideas just bounce off of it versus something that has more crags and more rough edges that they too can polish with you together." 00:57:30

The Three Cultural Memes That Should Run Every AI Product Team

Tara distills OpenAI's product operating philosophy into three questions that every team should be asking constantly. These are practical stand-ins for an entire operating system.

"Is this maximally accelerated? Are we moving as fast as possible on it? And then are you mainlining it yet? Are you using it? And are you bringing all your tastes to bear on whether this thing works and is something that people really want and tightening that feedback loop as much as possible? Those are the three memes of product development that we just have to spread as much as possible now." 00:25:24

Use the Meeting Prep Rule to Protect Cognitive Sharpness

Tara applies a specific discipline to both meetings and documents: invest at least as much time preparing as the collective time others will spend consuming. This also directly combats AI brain rot by keeping her own thinking engaged.

"If I'm going to make someone read my document, I have to at least read it first that number of times. If I'm going to call a meeting with a set of people, I need to have prepped the collective amount of time that people are going to spend in that meeting before the meeting." 00:58:27

Use Sites Instead of Decks and Docs for Presentations and Internal Tools

Tara has replaced a significant portion of her document and slide creation workflow with AI-generated Sites — shareable, database-backed, dynamic web applications built from a single prompt. This is an immediately actionable workflow shift.

"The easy reach of building a site all the time has changed what my day-to-day looks like, which often in previous worlds used to look like creating lots of artifacts, like docs, sheets, and whatever it might be. Now I just make sites all the time." 00:50:54

Test Your Pitch 100 Times Before Committing to the Product Shape

Particularly for B2B, the product marketing narrative should be stress-tested through repeated pitching before product investment is made. This insight from Sutter Hill Ventures directly inverts the typical build-then-market sequence.

"You should go pitch 100 people, figure out how to refine that pitch as much as possible, get the marketing narrative of why this thing is transformative right. And then and only then go commit to this is exactly the product shape." 01:03:27

6. Overlooked Insights

Sutter Hill's "Reticle" — A Secret Proprietary Recruiting Graph That May Be Their Real Moat

Tara briefly mentions a specific internal tool at Sutter Hill Ventures called Reticle — a mapped database of every person they've interacted with and the ten best people those people know. This was mentioned almost in passing, but it describes what may be the actual compounding moat behind Sutter Hill's repeated success at incubating category-defining companies. Most VCs discuss deal flow and pattern recognition as their edge; a systematized, proprietary talent graph that maps second-degree relationships is a structurally different and deeply defensible advantage that rarely gets named explicitly.

"They have a secret tool called Reticle where they have a map of everyone that they've interacted with and the 10 best people that those people have interacted with. That helps them be so, so effective at this." 01:02:02

Cloud Infrastructure for Agents — Not Intelligence — Is the Real Near-Term Bottleneck

Tara briefly but specifically names data access, cloud infrastructure, and third-party system integrations as equally important to model intelligence for making agents actually useful. This reframes the agent opportunity: the companies solving the "plumbing" problem — reliable cloud execution, data access pipelines, third-party integrations — may be just as critical to the agentic future as those improving model reasoning. This is an underappreciated infrastructure investment thesis.

"To make an agent successful in the cloud, there is a ton of cloud infrastructure that you have to build to make that possible and just like access to your systems. Like how can agents talk to all these third-party systems that have all of your data? Just like a colleague who you lock into a room and never give them access to Google Docs and Slack and the company database would not be that useful to you. A huge part of making these agents useful are on the intelligence side, certainly. But a lot of it is also just really tactical like data access, cloud infrastructure and reliability pieces that feel much more prosaic than some of the broader intelligence questions, but matter in some ways just as much for end effectiveness." 00:19:14