Product development tool for software teams
Product development tool for software teams
Product development tool for software teams
summary
How Linear designs with AI
As software becomes easier to make, how do design teams protect the work of understanding what should be built
Founded in 2019, Linear set out to rethink how software teams plan and build by replacing slow, fragmented project management tools with a faster, more opinionated system. Today, more than 33,000 companies, including OpenAI, Coinbase, and Ramp, use Linear to bring clarity and momentum to their product development workflows.
As AI changes how software is built, Linear is expanding beyond issue tracking into a shared workspace where teams can turn ideas, feedback, context, and code into products. But when execution becomes easier, the harder question becomes more important: how do teams decide what is worth building in the first place?
Conversations with co-founder & CEO Karri Saarinen alongside product designers Charlie Aufmann and Isha Kumar revealed a shared philosophy: AI should expand a designer’s capacity to think rather than replace it. While tools may change by the day, Linear holds fast to the belief that setting the quality bar, exercising taste, and understanding problems must remain in human hands.
summary
How Linear designs with AI
As software becomes easier to make, how do design teams protect the work of understanding what should be built
Founded in 2019, Linear set out to rethink how software teams plan and build by replacing slow, fragmented project management tools with a faster, more opinionated system. Today, more than 33,000 companies, including OpenAI, Coinbase, and Ramp, use Linear to bring clarity and momentum to their product development workflows.
As AI changes how software is built, Linear is expanding beyond issue tracking into a shared workspace where teams can turn ideas, feedback, context, and code into products. But when execution becomes easier, the harder question becomes more important: how do teams decide what is worth building in the first place?
Conversations with co-founder & CEO Karri Saarinen alongside product designers Charlie Aufmann and Isha Kumar revealed a shared philosophy: AI should expand a designer’s capacity to think rather than replace it. While tools may change by the day, Linear holds fast to the belief that setting the quality bar, exercising taste, and understanding problems must remain in human hands.
summary
How Linear designs with AI
As software becomes easier to make, how do design teams protect the work of understanding what should be built
Founded in 2019, Linear set out to rethink how software teams plan and build by replacing slow, fragmented project management tools with a faster, more opinionated system. Today, more than 33,000 companies, including OpenAI, Coinbase, and Ramp, use Linear to bring clarity and momentum to their product development workflows.
As AI changes how software is built, Linear is expanding beyond issue tracking into a shared workspace where teams can turn ideas, feedback, context, and code into products. But when execution becomes easier, the harder question becomes more important: how do teams decide what is worth building in the first place?
Conversations with co-founder & CEO Karri Saarinen alongside product designers Charlie Aufmann and Isha Kumar revealed a shared philosophy: AI should expand a designer’s capacity to think rather than replace it. While tools may change by the day, Linear holds fast to the belief that setting the quality bar, exercising taste, and understanding problems must remain in human hands.
The hard part of design is rarely generating the form. It is understanding the problem well enough to know what and how something should exist at all.
Karri Saarinen
Co-founder & CEO, Linear
The hard part of design is rarely generating the form. It is understanding the problem well enough to know what and how something should exist at all.
Karri Saarinen
Co-founder & CEO, Linear
The hard part of design is rarely generating the form. It is understanding the problem well enough to know what and how something should exist at all.
Karri Saarinen
Co-founder & CEO, Linear
KEY INSIGHTS
What we learned
01
Design is search
Karri has often described design as search: the act of exploring many possibilities before arriving at a solution. As AI makes it easier to take an idea and turn it directly into working code, the team actively guards against collapsing that important search process too early. Code is a medium of commitment, and jumping into production too quickly locks in assumptions and narrows the problem space. Instead of using AI to generate finished UI and offer solutions, they deploy agents as exploratory probes. These tools surface customer context, synthesize feedback, and pressure-test different directions (including ones that never ship), so the problem becomes clear long before anyone commits to building.
02
Stay close to the work
For product designer Isha Kumar, good design happens through prolonged contact with the work: testing ideas, refining details, and discovering solutions through the act of designing. That hands-on, visual flow is also where she finds the most joy and creativity in her job. While prompting an agent might be faster, it also tends to flatten that rhythm, turning design into a sequence of managing prompts and responses, rather than a process of thinking through making. Instead, she uses AI selectively to explore edge cases or spin up quick prototypes, keeping it out of her core creative loop.
03
Let humans set the bar
Karri believes one of the designer's most important jobs is deciding what deserves to exist in the first place, all the more so now. While AI may be able to instantly generate interfaces and prototypes, it can't determine whether a product solves the right problem or meets Linear’s standards. He sees an opportunity for designers to move closer to the business: understanding customers, identifying the problems worth solving, and helping teams decide where to go. “There’s more value you could add on the thinking level,” Karri says. “The best designers produce clarity in problems that aren’t clear.”
04
Use AI to create leverage
AI lets Linear’s compact team punch well above its weight. It handles tasks that are tedious to simulate in Figma, like generating custom shaders, testing interaction variations, or fixing backlogged UI paper cuts. For product designer Charlie Aufmann, AI serves as an organizational memory engine. Before starting new work, he prompts agents to query the codebase and meeting history to surface past architectural trade-offs, giving the team access to context that might otherwise be difficult to reconstruct.
KEY INSIGHTS
What we learned
01
Design is search
Karri has often described design as search: the act of exploring many possibilities before arriving at a solution. As AI makes it easier to take an idea and turn it directly into working code, the team actively guards against collapsing that important search process too early. Code is a medium of commitment, and jumping into production too quickly locks in assumptions and narrows the problem space. Instead of using AI to generate finished UI and offer solutions, they deploy agents as exploratory probes. These tools surface customer context, synthesize feedback, and pressure-test different directions (including ones that never ship), so the problem becomes clear long before anyone commits to building.
02
Stay close to the work
For product designer Isha Kumar, good design happens through prolonged contact with the work: testing ideas, refining details, and discovering solutions through the act of designing. That hands-on, visual flow is also where she finds the most joy and creativity in her job. While prompting an agent might be faster, it also tends to flatten that rhythm, turning design into a sequence of managing prompts and responses, rather than a process of thinking through making. Instead, she uses AI selectively to explore edge cases or spin up quick prototypes, keeping it out of her core creative loop.
03
Let humans set the bar
Karri believes one of the designer's most important jobs is deciding what deserves to exist in the first place, all the more so now. While AI may be able to instantly generate interfaces and prototypes, it can't determine whether a product solves the right problem or meets Linear’s standards. He sees an opportunity for designers to move closer to the business: understanding customers, identifying the problems worth solving, and helping teams decide where to go. “There’s more value you could add on the thinking level,” Karri says. “The best designers produce clarity in problems that aren’t clear.”
04
Use AI to create leverage
AI lets Linear’s compact team punch well above its weight. It handles tasks that are tedious to simulate in Figma, like generating custom shaders, testing interaction variations, or fixing backlogged UI paper cuts. For product designer Charlie Aufmann, AI serves as an organizational memory engine. Before starting new work, he prompts agents to query the codebase and meeting history to surface past architectural trade-offs, giving the team access to context that might otherwise be difficult to reconstruct.
KEY INSIGHTS
What we learned
01
Design is search
Karri has often described design as search: the act of exploring many possibilities before arriving at a solution. As AI makes it easier to take an idea and turn it directly into working code, the team actively guards against collapsing that important search process too early. Code is a medium of commitment, and jumping into production too quickly locks in assumptions and narrows the problem space. Instead of using AI to generate finished UI and offer solutions, they deploy agents as exploratory probes. These tools surface customer context, synthesize feedback, and pressure-test different directions (including ones that never ship), so the problem becomes clear long before anyone commits to building.
02
Stay close to the work
For product designer Isha Kumar, good design happens through prolonged contact with the work: testing ideas, refining details, and discovering solutions through the act of designing. That hands-on, visual flow is also where she finds the most joy and creativity in her job. While prompting an agent might be faster, it also tends to flatten that rhythm, turning design into a sequence of managing prompts and responses, rather than a process of thinking through making. Instead, she uses AI selectively to explore edge cases or spin up quick prototypes, keeping it out of her core creative loop.
03
Let humans set the bar
Karri believes one of the designer's most important jobs is deciding what deserves to exist in the first place, all the more so now. While AI may be able to instantly generate interfaces and prototypes, it can't determine whether a product solves the right problem or meets Linear’s standards. He sees an opportunity for designers to move closer to the business: understanding customers, identifying the problems worth solving, and helping teams decide where to go. “There’s more value you could add on the thinking level,” Karri says. “The best designers produce clarity in problems that aren’t clear.”
04
Use AI to create leverage
AI lets Linear’s compact team punch well above its weight. It handles tasks that are tedious to simulate in Figma, like generating custom shaders, testing interaction variations, or fixing backlogged UI paper cuts. For product designer Charlie Aufmann, AI serves as an organizational memory engine. Before starting new work, he prompts agents to query the codebase and meeting history to surface past architectural trade-offs, giving the team access to context that might otherwise be difficult to reconstruct.
in action
Refreshing Linear's UI
Joining Linear brought a real sense of weight for Charlie. “This is one of the best design teams in the world and the craft is through the roof,” he recalls. “Will I meet the bar?” That question hit home on his first major project: an overhaul of Linear’s core interface. Years of rapid feature releases had gradually cluttered the UI, diluting the product's visual hierarchy. By 2026, it was time for a refresh.
Charlie started by investigating the underlying problems. After customers pushed back on a new issue layout, he used Linear’s AI agent to pull together feedback from Twitter, Slack, and support calls to better understand the underlying problem. He then sketched out three possible approaches into Figma for a design review.
From there, AI became a partner in execution. Charlie translated the research into issue descriptions and proposals that an agent could turn into working previews using real product data. He collaborated with Staff Engineer Maxime Heckel to bring the concepts into the live product, using coding agents to understand the existing system and build custom tools along the way, including an in-app editor for tuning Linear’s new color palette.
The final result was a two-person effort that shipped a major interface refresh. For Charlie, AI didn’t replace the design process; it accelerated it. “I want it to be my thought partner, co-navigating me through the same design process I’ve always used. That doesn’t change the process. It speeds it up.”
I want it to be my thought partner, co-navigating me through the same design process I've always used. That doesn't change the process. It speeds it up.
Charlie Aufmann
Product Designer, Linear
in action
Refreshing Linear's UI
Joining Linear brought a real sense of weight for Charlie. “This is one of the best design teams in the world and the craft is through the roof,” he recalls. “Will I meet the bar?” That question hit home on his first major project: an overhaul of Linear’s core interface. Years of rapid feature releases had gradually cluttered the UI, diluting the product's visual hierarchy. By 2026, it was time for a refresh.
Charlie started by investigating the underlying problems. After customers pushed back on a new issue layout, he used Linear’s AI agent to pull together feedback from Twitter, Slack, and support calls to better understand the underlying problem. He then sketched out three possible approaches into Figma for a design review.
From there, AI became a partner in execution. Charlie translated the research into issue descriptions and proposals that an agent could turn into working previews using real product data. He collaborated with Staff Engineer Maxime Heckel to bring the concepts into the live product, using coding agents to understand the existing system and build custom tools along the way, including an in-app editor for tuning Linear’s new color palette.
The final result was a two-person effort that shipped a major interface refresh. For Charlie, AI didn’t replace the design process; it accelerated it. “I want it to be my thought partner, co-navigating me through the same design process I’ve always used. That doesn’t change the process. It speeds it up.”
I want it to be my thought partner, co-navigating me through the same design process I've always used. That doesn't change the process. It speeds it up.
Charlie Aufmann
Product Designer, Linear
in action
Refreshing Linear's UI
Joining Linear brought a real sense of weight for Charlie. “This is one of the best design teams in the world and the craft is through the roof,” he recalls. “Will I meet the bar?” That question hit home on his first major project: an overhaul of Linear’s core interface. Years of rapid feature releases had gradually cluttered the UI, diluting the product's visual hierarchy. By 2026, it was time for a refresh.
Charlie started by investigating the underlying problems. After customers pushed back on a new issue layout, he used Linear’s AI agent to pull together feedback from Twitter, Slack, and support calls to better understand the underlying problem. He then sketched out three possible approaches into Figma for a design review.
From there, AI became a partner in execution. Charlie translated the research into issue descriptions and proposals that an agent could turn into working previews using real product data. He collaborated with Staff Engineer Maxime Heckel to bring the concepts into the live product, using coding agents to understand the existing system and build custom tools along the way, including an in-app editor for tuning Linear’s new color palette.
The final result was a two-person effort that shipped a major interface refresh. For Charlie, AI didn’t replace the design process; it accelerated it. “I want it to be my thought partner, co-navigating me through the same design process I’ve always used. That doesn’t change the process. It speeds it up.”
I want it to be my thought partner, co-navigating me through the same design process I've always used. That doesn't change the process. It speeds it up.
Charlie Aufmann
Product Designer, Linear
in action
Using AI with purpose
After seeing how constant AI use flattened both her output and judgment, Isha started to use it more selectively.
For example, when rethinking how a coding session opens from an issue, she didn’t jump straight into prompting. Instead, she first used Linear’s agent to ask why the current behavior existed and whether it was intentional—treating AI as a way to surface context before making any decisions herself. Once it was clear the behavior wasn’t intentional, she implemented the fix in Conductor, running it alongside her other sessions. She then reviewed the generated code carefully, rewrote the agent’s pull request description in her own words, and nominated a teammate for review.
She applies the same pattern when exploring states she couldn't easily prototype before, like spinning up a quick visualizer to test how a profile card behaves with long names and varied roles. AI helps her get to these edge cases faster, but she still decides what's good. In her estimate, it now handles most of her “paper cuts,” but only about a tenth of net-new design work.
There's a big difference between using something as a tool and using it as a crutch. AI invites infinite usage, and that makes you trust yourself less. If AI is the only thing you use, that points to blind spots that are preventing you from using it well.
Isha Kumar
Product Designer, Linear
in action
Using AI with purpose
After seeing how constant AI use flattened both her output and judgment, Isha started to use it more selectively.
For example, when rethinking how a coding session opens from an issue, she didn’t jump straight into prompting. Instead, she first used Linear’s agent to ask why the current behavior existed and whether it was intentional—treating AI as a way to surface context before making any decisions herself. Once it was clear the behavior wasn’t intentional, she implemented the fix in Conductor, running it alongside her other sessions. She then reviewed the generated code carefully, rewrote the agent’s pull request description in her own words, and nominated a teammate for review.
She applies the same pattern when exploring states she couldn't easily prototype before, like spinning up a quick visualizer to test how a profile card behaves with long names and varied roles. AI helps her get to these edge cases faster, but she still decides what's good. In her estimate, it now handles most of her “paper cuts,” but only about a tenth of net-new design work.
There's a big difference between using something as a tool and using it as a crutch. AI invites infinite usage, and that makes you trust yourself less. If AI is the only thing you use, that points to blind spots that are preventing you from using it well.
Isha Kumar
Product Designer, Linear
in action
Using AI with purpose
After seeing how constant AI use flattened both her output and judgment, Isha started to use it more selectively.
For example, when rethinking how a coding session opens from an issue, she didn’t jump straight into prompting. Instead, she first used Linear’s agent to ask why the current behavior existed and whether it was intentional—treating AI as a way to surface context before making any decisions herself. Once it was clear the behavior wasn’t intentional, she implemented the fix in Conductor, running it alongside her other sessions. She then reviewed the generated code carefully, rewrote the agent’s pull request description in her own words, and nominated a teammate for review.
She applies the same pattern when exploring states she couldn't easily prototype before, like spinning up a quick visualizer to test how a profile card behaves with long names and varied roles. AI helps her get to these edge cases faster, but she still decides what's good. In her estimate, it now handles most of her “paper cuts,” but only about a tenth of net-new design work.
There's a big difference between using something as a tool and using it as a crutch. AI invites infinite usage, and that makes you trust yourself less. If AI is the only thing you use, that points to blind spots that are preventing you from using it well.
Isha Kumar
Product Designer, Linear
FEATURED LINEAR TEAM MEMBERS
NAME
POSITION
Karri Saarinen
Co-founder & CEO
Charlie Aufmann
Product Designer
Isha Kumar
Product Designer
FEATURED LINEAR TEAM MEMBERS
NAME
POSITION
Karri Saarinen
Co-founder & CEO
Charlie Aufmann
Product Designer
Isha Kumar
Product Designer
FEATURED LINEAR TEAM MEMBERS
NAME
POSITION
Karri Saarinen
Co-founder & CEO
Charlie Aufmann
Product Designer
Isha Kumar
Product Designer
Join the team
Linear Design
Linear works in small, autonomous project teams, where designers are paired tightly with engineers to explore ideas, build prototypes, deploy internal builds, and ultimately ship to customers. They’re a remote-first company, with optional co-working offices in San Francisco, New York, and London.
Join the team
Linear Design
Linear works in small, autonomous project teams, where designers are paired tightly with engineers to explore ideas, build prototypes, deploy internal builds, and ultimately ship to customers. They’re a remote-first company, with optional co-working offices in San Francisco, New York, and London.
Join the team
Linear Design
Linear works in small, autonomous project teams, where designers are paired tightly with engineers to explore ideas, build prototypes, deploy internal builds, and ultimately ship to customers. They’re a remote-first company, with optional co-working offices in San Francisco, New York, and London.
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