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Aakash GuptaAakash 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.

Aakash GuptahostNan Yuguest
Aug 3, 20251h 0mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Linear’s direct, high-momentum product method behind its unicorn rise

  1. Linear’s popularity with top AI companies is driven by speed of operations: fast UI interactions, direct workflows, and minimized friction so teams can stay focused on building.
  2. The Linear Method centers on directness—avoiding indirect artifacts like performative user stories or misused OKRs—and replacing them with clear problem statements and falsifiable points of view.
  3. Linear treats speed and quality as mutually reinforcing: strong code quality and abstractions reduce the need for hacks, enabling fast shipping without corner-cutting.
  4. Planning is “always-on” with a rolling set of accepted problem areas and lightweight roadmaps (roughly up to three quarters), designed to change quickly as new information arrives.
  5. Linear builds in public via a changelog every 2–3 weeks, while being cautious about public roadmaps due to anchoring effects and incentive distortion.

IDEAS WORTH REMEMBERING

5 ideas

Optimize for operational speed, not just feature breadth.

Linear wins in a crowded project-management market by sanding off daily workflow friction (fast interactions, obvious actions, dev-native primitives like branch naming), which compounds across teams.

Directness beats process theater.

Nan argues many common artifacts (over-formatted user stories, cascaded OKRs) are indirect translations of simple asks; Linear prefers stating the real task plainly and aligning around it.

Treat “speed vs. quality” as a false trade-off by fixing upstream quality.

If the codebase and abstractions are strong, teams feel less pressure to ship hacks; the real risk is letting low quality accumulate until shortcuts become necessary.

Maintain momentum with aggressive scope reduction.

Linear’s “one weird trick” is to shrink scope to the smallest solvable subproblem, ship it quickly at high quality, then expand iteratively based on what reality teaches.

Say no to busywork by acting on falsifiable models.

Busywork (excessive analysis, endless A/B tests) often signals unclear intent; Linear prefers a clear mental model, an aggressive test in the product, and rapid learning from user reaction.

WORDS WORTH SAVING

5 quotes

The core of the Linear method is, uh, is just directness, right? If you, if you look at a lot of the practices, um, that, uh, you know, have been very prevalent in the software industry, they're, they're all strangely indirect, right?

— Nan Yu

"Yeah, yeah, but, like, what do you actually want me to do?" "Well, I want you to, you know, when you click a button, this, it sends an email." It's like, "Why didn't you just say that?"

— Nan Yu

I, you know, I, I, I think, like, to me, that's something of a, it's almost of, like, a false dichotomy, right?

— Nan Yu

It, it's like, honestly, it's our one weird trick, right? And like I, I promise you it's not more complicated than that, which is just shrink the scope as aggressively as you can so that you can ship it quickly with high quality at the same time.

— Nan Yu

No customer has ever churned because of the lack of a single feature. That's never happened, right?

— Nan Yu

Speed of operations as product strategyDirectness vs. indirect process artifacts (user stories, OKRs)Momentum without sprint exhaustionAlways-on planning and idea backlog escalationRoadmaps as intent, not commitmentsQuality rituals: “roasts” and internal-first rolloutAI agents as workflow-native, not flashy AIAvoiding feature bloat (serious, not literal user input)Small-team leverage and org design (PM + PMM together)Work-trial hiring and remote-first expectations

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