COO at @linear. previously building at @stripe, @NotionHQ & @firstround. angel investor to ~80 early-stage startups
San Francisco, CA
Joined March 2009
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People used to say a company has two jobs (1) build and (2) sell.
AI is making building easier. That means selling may become more valuable, not less. When everyone can make at least a fine/mediocre product quickly, you need to get the right people to care, trust you, and choose it over something else.
An AI agent can find candidates and send a recruiting email. But the people you most want to hire won't join because an agent reached out. They'll join because a person made them believe in the team, the ambition, and the work.
An agent can answer questions in your homepage chat bot. But when a purchase has to get through finance, procurement, and a room full of stakeholders, someone still has to understand what each person needs, make the case, and keep the decision moving.
AI can make building cheaper. It can make selling more important.
When everyone can build something, the advantage goes to the people who can persuade.
Linear has officially crossed the pond. 🇬🇧
New London office, standing-room-only launch event, and a lot more to come.
Turns out good software travels well.
standing room only at tonight’s @linear launch event with @Pragmatic_Eng @GergelyOrosz @artman @raycast @thomaspaulmann @meetgranola @NikolaOtasevic @swissbeatz 🖤
First we gave agents intelligence. Now @inkbox_ai is giving them computers, phones, and inboxes.
I first started using Inkbox because I was missing agent messages in a sea of push notifications. Now my most critical messages come through the right inbox for my needs. 📫
We raised $5.3M to give AI agents a computer to run on, with their own email, phone, iMessage, and WhatsApp
Led by @peakxvpartners, with @rauchg, @sternhenri, @jefrankle, @cjc, and @daniel_levine
Today we’re launching hosted agents on @inkbox_ai. Your agent runs next to its communication channels, so it never misses a reply. It sleeps when idle, so you don’t pay for downtime.
In the 4 months since our YC launch, we’ve processed millions of messages for AI agents.
I know far too many startups that have spent countless hours translating strings so their products could work in multiple languages.
Imagine translating a video pixel by pixel with an API -- it's now possible with what @archiemckenzie_ and the @generaltxn team are building!
General Translation has raised $27m led by @martin_casado at @a16z to localize the world’s best companies.
Today, @generaltxn serves Ramp, Cursor, and Sierra by translating apps, sites, docs, slide decks, and even product videos into dozens of languages.
a16z invested alongside @upfrontvc, Peter Thiel, Fei-Fei Li, and more. 🧵
I used to think sales was icky. What I actually disliked wasn’t sales. It was *bad* sales. Selling an undifferentiated product through increasingly desperate outbound tactics is icky.
Selling a great product to a large company is very different work than doing PLG to small companies.
The product doesn’t sell itself. Someone has to navigate procurement, convince finance, win over skeptical executives, and keep a deal alive through a dozen people who can kill it but can’t approve it.
Even the best products encounter this.
Founders sometimes think a great product makes sales unnecessary. In reality, a great product makes a great sales team much more valuable.
Today I was offered Dom Pérignon, caviar, an Ooni, and a Ninja Creami to take sales calls with software companies.
One perk of building something people want... you never have to learn what kitchen appliance they want first.
At this point I’m not sure if we’re organizing a conference or assembling an unfair concentration of product taste.
Excited to have leaders from @nothing, @Meta, @stripe and @SierraPlatform at Orbit!
Announcing the next round of speakers for Orbit (by Linear):
> Carl Pei (@getpeid)
> Alex Cornell (@alexcornell)
> Michelle Bu (@hazelcough)
> Zack Reneau-Wedeen (@ZackRW)
See the full agenda and request your ticket: orbit.linear.app
There’s a long American tradition of industry titans turning their fortunes into public goods. Carnegie built libraries and Rockefeller funded universities and public health. Others built parks, museums, and civic institutions.
Great to see @bchesky carrying that tradition forward in SF.
New: Airbnb CEO Brian Chesky is committing $100 million to SF parks in what Mayor Lurie's administration says will be the largest contribution the city has ever received from 1 donor. sfchronicle.com/sf/article/a…
Cristina Cordova retweeted
Announcing the next round of speakers for Orbit (by Linear):
> Carl Pei (@getpeid)
> Alex Cornell (@alexcornell)
> Michelle Bu (@hazelcough)
> Zack Reneau-Wedeen (@ZackRW)
See the full agenda and request your ticket: orbit.linear.app
Cristina Cordova retweeted
People keep asking how companies like Ramp, Linear, and Anthropic ship so much software with agents.
There is no new SDLC or secret set of tools. The tools are increasingly available to everyone. You can build your own, use Linear, Claude, or whatever works.
The bigger difference is ambition. These companies encourage their teams to build more, experiment more, and let agents do more of the work. That's the main different between "AI native" or not. You try to use the technology to the max.
But don't get there by adopting a tool. You get there by changing what you believe your teams are capable of.
So in the very simplest way:
- let agents build
- make it easier and faster to contribute
- increase the ambition of what your teams attempt.
In practice, that means things like:
- Standardize around cloud agents. Most of our code (5M lines last quater) is now written in Linear with our Linear cloud coding agent. Cloud sandboxes make standardizing the system and environment easier for everyone and enables faster contributions
- Linear agent (with code access and cloud agent) operates in Slack too. If there is a discussion or someone notices a problem just say "@linear fix this" (decrease the time from something noticed to fixed)
- As bugs, errors etc arrive in Linear, send them to Triage, and have Loop to auto-investigate (connect to tools like Sentry, Datadog), and write a fix
- Let designers, PMs, non-technical people contribute code where useful. If they notice a problem, tell them they can submit a fix. If they want to try something, ask them to build a prototype.
- Set up code review agent and system to make sure PRs are safe to merge.
- Run security as an automation, have agents constantly try to find security issues in your codebase, as they find, send it to a coding agent
- Instead of working on issue level, increase ambition, make a whole project, ask the agent to ask questions, investigate code base, create issues and have the agent work on the whole project at once. Set up goals like "give me proof that what you build works". All our PRs now come with screenshots and videos and preview build you can
- Bring customer feedback to the system like Linear, and make it available for your team. At Linear every project now starts with 10-500 customer notes that influence the direction of the work, and which customers we should ask to test the solution first.
- Set up Loops/automations to constantly update reference documents for agents, like product area/code owners so work can be routed to right humans
Then what is left is how do you set direction for your teams (initiatives, projects they should work on) and how do you review results (better code review, Linear does it with Guided Reviews), how you maintain quality/cohesion which is not a new problem either.
Basically most of the problems you always had remain, but the execution bit is now more and more solved.
At @linear we can help with automation, cloud agents, and code review, customer feedback and planning bits.
But we can't change your mindset or how your company operates.
If your company is still spending energy debating jira workflow steps, you're probably solving the wrong problems.
THINK DIFF
THINK DIFF
THINK DIFF
(now on mobile)
Introducing Mobile Diffs.
• Understand changes with Guided Reviews
• Start on mobile, continue on desktop
• See what needs review in your inbox
• View preview links
• Reply to diff discussions
• Iterate with coding agents
• Merge approved changes
All from your phone.
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With the Grok Bot Guide for Linear, you can now put @bot to work for you.
Our new guide shows how you can use the Linear Product Ops bot to investigate customer feedback, plan a project, and coordinate coding agents.
x.ai/bot/guides/grok-bot-for…
Cristina Cordova retweeted
Introducing Mobile Diffs.
• Understand changes with Guided Reviews
• Start on mobile, continue on desktop
• See what needs review in your inbox
• View preview links
• Reply to diff discussions
• Iterate with coding agents
• Merge approved changes
All from your phone.
Cristina Cordova retweeted
Can a designer-founder win the VC game by ignoring all of its rules?
Season 3, Episode 3 is here:
How @karrisaarinen built @linear to win through quality above all else – now available on 𝕏, YouTube, and Instagram
Powered by @framer @noondesign @bot @tryramp @vercel @meetgranola @profound
00:00 Winning by Your Own Rules
03:12 Quality Is Directional
06:24 Do You Design in Code?
15:45 Owning the Problem
22:45 Beyond the Issue Tracker
26:36 The Bull Case for Design
28:45 Do We Still Need UI?
33:02 The Car Designer of Software
37:37 School Feels Fake
40:59 Never Build the MVP
43:56 Clarity of Scope
45:37 Linear Recalibrates the Palate
Hosted by @soleio and coproduced with @Room3NYC
Keith Rabois has never met a prediction he couldn't make with 100% confidence and 0% accuracy.
🤖 Made with AI
I think @morganlinton is right here. Companies truly changing how they build software are changing more than just how they write code.
At first, they add an AI coding tool and keep everything else the same. The old process still decides what work exists and how it gets handed off. So the system is effectively the same.
Then, the mismatch starts to look obvious. Agents can take on more of the work, but people are still moving tasks between tools and keeping the process together manually by hand. That’s when some teams move to Linear. The work needs to flow from an idea to an agent to a reviewed change without someone translating every step (issue tracking is dead: linear.app/next).
The interesting thing is that this isn't just happening at AI startups. Large, established companies are making the same shift. They want to use agents inside the way their teams already work, with enough structure to see what's happening and enough flexibility to keep improving it.
Also, the best teams don't think they've figured it all out. We're still learning what agents are good at, where humans still need to be involved, and how the process should change as the models improve.
I see such a wide range of AI adoption curves in engineering orgs, it's fascinating. This could be the widest gap we'll ever have, which also makes sense given how big of a shift we're seeing.
Right now, I think companies are in one of five stages. And you'll have to take off your X glasses, because all of us yapping on here aren't quite living in reality.
Which btw, I like. Many of us are the crazies living in a future that could be a decade or more away, and that's fun to do, together.
That being said, here's the stages I see engineering orgs in today. And before you read this know, this is just my (loosely held) opinion.
And as I've said time and time again on here, but feel I always need to repeat. I'm not an expert, I'm just an insanely curious guy who loves sharing what I think.
So on that note, here's my thoughts on the five stages eng orgs are in right now:
Stage 1: Humans still writing code like it's the stone age.
- there are less and less companies in this stage by the week, people are moving out of this stage quickly. But yes, believe it or not, some companies still only trust humans to write the code.
Stage 2: One lab, one model, one effort level, and usually JIRA for ticketing.
- this is the stage where an engineering leader has either decided their team needs to use AI, or an exec told an engineering leader they need to. They usually just go straight for Claude Code, and everyone on the team uses Opus High. Tickets are managed in JIRA, just like they always have been, and humans manage those tickets.
Stage 3: Multi-model, driven by a third party model router that picks the model and effort level. Actively building a software factory.
- usually people in Stage 2 graduate to this. They start getting crazy high bills from their model provider, and realize they have no idea how to tell their team which model or effort level they need to use for their work. They get pitched the concept of saving 30% or 40% if they switch to a company that offers a model router and decides which model and effort level to use per task. And, in this process, they start building a software factory.
Stage 4: Multi-model, driven by internal benchmarks based on real PRs and their actual codebase. Software factory is mostly built.
- usually tried auto model routing, but realized it wasn't really optimized for the work each team does. Started doing internal benchmarking and realized that the model and effort level the data science team needs for their python coding, is different from what the data engineering team needs for their python coding. Comes up with model/effort combos by team, but has a hard time keeping up with it. Software factory is mostly built, either moved from JIRA to Linear and now actively hands off tasks to Linear, or moved off a formal ticketing system entirely.
Stage 5: feels the most behind, constantly optimizing models and effort levels, most easy/medium tasks are done entirely end-to-end by agents. Fully functioning software factory, but constantly updating and trying to adjust to new model releases.
- yes, the first few words here are important. The most advanced stage feels the most behind. Kinda like Einstein with Math. He was quoted as saying, "the more I learn about math, the more I realize I don't know about math." The engineering orgs that are the most ahead, often feel behind. Terra 6.1 comes out, they're scrambling to figure out what they should use it for, where it will save them money/time. Some teams love this excitement, want to constantly change, for others its dizzying.
Okay, Wednesday morning brain dump complete, time to play some WOW Forever. Zug Zug.
Scout24 is a publicly listed company behind the marketplace millions use to find a home in Germany and Austria.
They switched to @linear to improve software delivery and to give AI agents the context to take on more of it.
Today, teams across many different functions use Linear. Requests become scoped issues straight from Slack, agents can pick them up, and engineers review the pull requests in Linear.
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At Linear, we appreciate good design. We also appreciate a good excuse to eat bingsu.
Design Gallery is back. This time co-hosted w/ @linear
Hundreds of talented designers showing off work they're proud of
Good food, wine, live music and bingsu
7 PM, Nov 19th in Jackson Square
(luma in comments to exhibit or attend)