Aakash Gupta

$1.25 billion Unicorn. Only 2 Product Managers. The Linear Method:

How did Linear scale to a $1.25B valuation with just 40 engineers and 2 PMs while becoming the tool of choice for OpenAI, Perplexity, and Cursor? This is part 2 of the podcast with Nan Yu, Head of Product at Linear. In the first episode, we dove into building AI agents inside Linear: https://youtu.be/e_T8Sn8s46M In this episode, we sit down to unpack the Linear Method that’s redefining how high-velocity teams build software without bloat. We talk about: - How Linear became the backbone for top AI companies like OpenAI and Perplexity. - The $1.25B Linear Method: directness, momentum, and shrinking scope to ship with speed and quality. - Why most startups misuse OKRs (and what to do instead). - The secret behind scaling with only 40 engineers and 2 PMs. - Culture and craft: feature “roasts,” public changelogs, and avoiding feature bloat. - Nan Yu’s personal career story Timestamps: Preview – 00:00:00 Why are all the GIANTS using Linear? – 00:02:09 Ad (AI Evals) – 00:04:44 Ad (Vanta) – 00:05:44 Linear Method: How They Build Product – 00:06:36 Principles of the Linear Method – 00:09:16 Saying No to Busy Work / Busy Work Is a Result of “Lack of Clarity” – 00:12:14 Ad (AI PM) – 00:15:06 Ad (Maven) – 00:15:53 Planning Process – 00:16:41 Linear's Take on OKRs – 00:19:13 Setting The Bar High – 00:26:06 Story: Building The Smallest Version Possible – 00:28:49 Public Roadmaps are DANGEROUS – 00:34:20 Applying Linear Method to AI Features (Agents as Users) – 00:38:18 Mantra: Solve Real Problems for Real People – 00:43:08 Landing a Head of Product Role at Linear – 00:46:08 Story: How the Initial Contact with Linear Happened – 00:48:16 Advice for Aspiring PMs at Linear – 00:49:50 Making it to unicorn status with 2 PMs & 40 engineers – 00:53:18 Everlane's Pricing Strategy (Cost-Plus) – 00:53:44 Shift from B2C Retail to Product-Led B2B SaaS – 00:55:10 Can Today's PMs Shift Industries? – 00:57:22 Closing Notes – 00:59:49 💼 Check out our sponsors: 1. The AI Evals Course for PMs & Engineers :Get $800 off with this link - https://maven.com/parlance-labs/evals?promoCode=ag-product-growth 2. Vanta: Automate compliance, security, and trust with AI (Get $1,000 with our link) - https://www.vanta.com/lp/demo-1k?utm_campaign=1k_offer&utm_source=product-growth&utm_medium=podcast 3. Product Faculty: Get $500 off the AI PM certification with code AAKASH25 - https://maven.com/product-faculty/ai-product-management-certification?promoCode=AAKASH25 4. Maven: Get $100 off my curation of their top courses - http://maven.com/x/aakash 👀 Where to Find Nan Yu: LinkedIn: https://www.linkedin.com/in/thenanyu/ X: https://x.com/thenanyu Personal website: https://thenanyu.com Linear: https://linear.app/partners/aakash 👨‍💻 Where to find Aakash: Twitter: https://www.twitter.com/aakashg0 LinkedIn: https://www.linkedin.com/in/aagupta/ Instagram: https://www.instagram.com/aakashg0/ 🔑 Key Takeaways: 1. How to build products with speed and quality: Everyone thinks speed and quality are opposite ends of a spectrum. At Linear, they've learned they’re actually dance partners. The trick isn’t to move faster by cutting corners. It’s to design the system so cleanly that you don’t need hacks in the first place. 2. Why the smallest possible scope is your superpower: When you’re staring at a big ambitious idea, it’s tempting to swing for the fences immediately. The problem? Big scope creates drag. Ship the smallest version that works, then let reality guide the next cut. That’s how you keep velocity without losing control of quality. 3. The truth about OKRs: OKRs are like prescription glasses: they work beautifully if you actually need them and give you headaches if you don’t. For huge, multi-layered orgs, they align chaos. But most startups try to wear someone else’s prescription. 4. Scaling smart vs. scaling headcount: Linear hit a $1.25B valuation with ~40 engineers and 2 PMs. That’s not because they were allergic to hiring, it’s because they treated people as force multipliers, not just bodies to throw at problems. 5. Breaking into top product roles: His own career shift - from Everlane CTO in apparel to SaaS PM leadership - taught me a hard truth: sometimes you have to take a step sideways (or even down) to move forward. When I pivoted industries, I took an IC engineering role and a comp hit. But eventually it all worked! #ai #linear 🧠 About Product Growth: The world's largest podcast focused solely on product + growth, with over 180K listeners. Hosted by Aakash Gupta, who spent 16 years in PM, rising to VP of product, this 2x/ week show covers product and growth topics in depth. 🔔 Subscribe and like the video to support our content! And turn on the bell for notifications.

AGAakash GuptahostNYNan Yuguest

Aug 4, 2025·1h 0m·Watch on YouTube ↗

📖 CHAPTERS

1. 1

   Why AI startups standardize on Linear: speed as a product feature

   Aakash opens by noting Linear’s $1.25B valuation and its adoption by leading AI companies (OpenAI, Perplexity, Cursor). Nan explains that Linear wins early with teams that prioritize speed of operations and minimal friction in day-to-day execution.

2. 2

   A new generation tool stack: building for teams that already know the basics

   Nan frames Linear as a “rebase” on modern assumptions: software teams today have strong baseline skills and don’t need heavy scaffolding. Linear is built assuming fluency with tools like Git and modern dev workflows.

3. 3

   What Linear is (and why the details matter): dev-first project management primitives

   Nan defines Linear as a project management tool purpose-built for software development. He highlights small workflow optimizations (like auto branch naming) that remove daily “rough edges” and reduce cognitive overhead.

4. 4

   The Linear Method in one idea: radical directness over process theater

   Nan gives the core pitch for the Linear Method: be direct. He critiques common “indirect” industry practices (like overly formal user stories) as legacy scaffolding from an era when stakeholders didn’t understand software.

5. 5

   Small-company advantage: designing an org to stay tiny and effective

   Aakash and Nan discuss how newer companies aim to stay small, and Linear embodies that philosophy. Nan contrasts past eras where growth meant headcount with today’s pride in operating efficiently.

6. 6

   Principles into practice: momentum without burnout, speed without chaos

   Using the Linear Method principles page as a guide, Nan explains two through-lines: directness and maintaining momentum. Momentum is about steady, sustainable pace—fast enough to respond to reality without sprint/exhaust cycles.

7. 7

   Speed vs quality is a false dichotomy: fix upstream so hacks aren’t tempting

   Nan reframes the speed-quality tradeoff: teams fear corner-cutting, but high-quality foundations reduce the need for hacks. If you feel forced into shortcuts, it signals upstream issues in abstractions and system design.

8. 8

   Saying no to busywork: act on falsifiable beliefs instead of endless analysis

   Nan argues busywork often stems from indecision and lack of clarity. Instead of heavy testing/analysis as a default, Linear favors strong mental models and aggressive, falsifiable actions that reveal truth quickly.

9. 9

   Roadmaps that breathe: plan ~3 quarters, hold intent lightly

   Nan supports roadmaps as alignment tools but warns against treating them as sacred. Linear plans a few quarters out with decreasing certainty and updates plans readily when new information changes the best path.

10. 10

    Always-on planning: idea backlogs, continuous discovery, and fast escalation loops

    Linear avoids massive planning offsites by continuously learning and refining priorities. They maintain an “accepted ideas” backlog (dozens of areas) and use customer-facing teams to escalate signals back into product when relevant topics arise.

11. 11

    OKRs: useful at the right altitude, harmful when forced onto IC workflows

    Nan calls OKRs overused and inherently indirect, best suited for large orgs or budget-owning leaders with measurable outcomes. Cascading OKRs down to ICs often becomes “job description theater” and can distort incentives away from craft.

12. 12

    How Linear evaluates PMs/designers: strong POV, clear story, and fast feedback loops

    Instead of numeric OKRs for craft roles, Linear looks for a falsifiable point of view that resonates with customers. Performance is judged by clarity of narrative, ability to learn what’s needed, and consistent “landing” of high-quality outcomes over time.

13. 13

    Quality gates that don’t kill speed: “roasts,” internal-first rollouts, and aggressive scoping

    Nan describes Linear’s quality process: team-wide “roasts” to break features and surface usability issues, plus incremental rollout starting with internal use for months. The key speed lever is aggressive scope shrinking—ship a small, high-quality slice, then expand.

14. 14

    Building AI the Linear way: agents as users, emergent learnings, and avoiding flashy bloat

    Nan explains Linear’s agent platform: start with a clear model (agents are users) and let reality reveal gaps. They learned agents aren’t accountable, are chatty, and need the right context—leading to UX and responsibility-system adjustments. The broader AI philosophy: solve real workflow problems already happening, not trend-chasing chatbots.

15. 15

    Hiring and operating with tiny product teams: work trials, remote clarity, and PM org design

    Nan shares how he joined via a paid work-trial/consulting engagement and what Linear values in remote environments: async communication, clarity, and initiative. He also explains the PM org structure (PM + product marketing together), the small current PM headcount, and what candidates should do to succeed in trials.

16. 16

    Nan’s broader career lessons: B2C→B2B shifts, storytelling, and getting in the door

    In the closing segment, Nan discusses his Everlane background (cost-plus model and materials-driven pricing), then contrasts B2C vs B2B skill shifts like handling sales and enterprise stakeholders. He argues industry pivots are possible by getting a foot in the door (even via level/comp hits) and letting contribution expand from there.