How Linear became the new Jira

Welcome to Runtime! Today: An interview with Linear CEO Karri Saarinen on the role of AI in product-management software, CoreWeave's up and down year has an up and down week, and the latest enterprise moves.

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Marked as promising

There are a handful of tools that almost every software developer on the planet has grudgingly used as part of their day-to-day workflow over the last decade or so, and Atlassian's Jira bug-tracking system ranks near the top of that list in both daily usage and off-the-record complaints. Linear has raised over $50 million to build a modern alternative, and it counts some of today's fastest-growing tech companies as customers.

"We have this fairly simple idea that engineering is really the front line of all this information," Saarinen said in a recent interview with Runtime. "If we can build a tool that they're happy to use, they use it more frequently, all of their information is more up to date, and this kind of tool that is supposed to be a system of record is more valid for the companies" that use it, he said.

In the interview, Saarinen discussed Linear's role within engineering organizations, the company's initial hesitancy around building generative AI technology into its product, and the tipping point that prompted it to jump on the agentic AI bandwagon. Selected excerpts follow below.

On Linear's product strategy:

Saarinen: I come from a design background, so I always believed that form follows the function, not the other way around. Every company has to do some kind of bug triage, but no company has implemented any kind of system for it; usually it means that you have to build some kind of custom thing where there's some kind of backlog and a certain way of managing it.

But we actually built a triage feature that is basically like an inbox queue that you can set to have new stuff automatically going there. You can also set someone to monitor that queue so you can have a rotation based on your on-call responsibilities. And those people get notified [with] specific actions they can take, like, "Hey, we should accept this, we should reroute this, we should cancel this, because this is not actually a bug."

We try to figure out, "what are the main things people actually need to do," and then build the features around it, versus having this kind of endless platform or some kind of product that can do anything, but it's not really designed for anything specifically.

On generative AI in Linear:

Saarinen: What we've been working on is changing the API in such a way that it allows these agentic users, so that you can register [them] and they kind of have similar capabilities that normal users would have. But then the interesting thing that comes from that is if there's going to be a lot of agents, or if you are farming out a lot of the work to these agents, then you might have like tens or hundreds of different conversations going with the agents as they're working on your issues. So then that, again, is a UI or UX problem; how do you manage that?

Another interesting challenge is as a company, or if you're the owner or admin of this workspace, how do you then track or manage what these agents are doing? Or manage the scope of these agents, or even know which ones are installed? I think there's going to be a third category of problems that come from this that we need to solve to help companies manage them, budget them, maybe set certain rules or security controls, which is something we can help with.

On the land grab for AI agents:

Saarinen: Our goal is to be this best-in-class tool for building products and planning. I don't think we'll be the platform for all kinds of agents, but we want to be the platform for building products. So when you think about any of these workflows of discovery, planning, building, and tracking, we want agents to be deployed in those workflows and those kinds of instances.

I would also say our customer base and developers are actually quite powerful. Our customers are the customers that are the first ones to use something. They're more like the pioneers; they're not like most legacy enterprises out there, they're more like the growth companies that can make decisions fast. We actually have an impactful customer base who is also willing to adopt new tools fairly quickly if they want to.

Read the full interview on Runtime here.

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Strikes and gutters

CoreWeave became a bellwether for the generative AI boom several years ago, parlaying a losing hand in cryptocurrency mining into becoming one of Nvidia's closest partners and a second source of computing infrastructure for companies like Microsoft and OpenAI. Its first earnings report as a public company was also a litmus test for perceptions of generative AI in 2025; growing fast, but at quite the cost.

First-quarter revenue at CoreWeave increased 420% (nice) compared to last year, coming in far ahead of Wall Street expectations at $986 million. However, the company is still losing a ton of money, posting a first-quarter net loss of $315 million according to CNBC.

The good news: CoreWeave signed a new $4 billion infrastructure deal with OpenAI, which is looking to new cloud providers after its relationship with Microsoft hit the rocks last year. The bad news: CoreWeave will need to spend between $20 billion to $23 billion this year to build enough capacity to take on those deals, which sent its stock down 2.5% on Wednesday as investors started to wonder about its ability to find that money.

Enterprise moves

Jeetu Patel and Mark Patterson are the new president and chief product officer, and chief financial officer, respectively, at Cisco, following promotions in the wake of the retirement of chief financial officer Scott Herren.

Saurabh Giri is the new chief product and technology officer at Voltage Park, following almost a decade at AWS during which he built and launched its Amazon Bedrock AI service.

John Newton is the new chief innovation strategist at Hyland, joining the content-management company after founding Alfresco and Documentum.

Stephen Orban is the new senior vice president of product ecosystem and partnerships at Databricks, following more than a decade in partner leadership roles at Google Cloud and AWS.

The Runtime roundup

Salesforce introduced a new, more flexible pricing strategy for its AI agents, and announced plans to acquire Convergence.ai.

Databricks acquired Neon, which built a version of the PostgreSQL database for AI applications, for $1 billion.

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Thanks for reading — see you Saturday!