Tag: AI

As an advisor to Fortune 500 companies, here’s a pattern I’ve observed repeatedly across large businesses: They make massive investments in AI tools, and then the same outcome follows within months—employees revert back to their spreadsheets and email chains. The AI licenses sit unused. Executive teams tend to blame “change fatigue” and “technical complexity.”

They’re wrong on both counts.

The real culprit? An operating model built for 2015, trying to run 2025 technology. And they’re not alone. Despite aggressive AI investments, 95 percent of enterprise AI initiatives fail—not because the technology doesn’t work, but because the organizational infrastructure beneath it was never designed for how AI actually changes work.

Let’s talk about operating models

Before we diagnose what’s broken, let’s establish what we’re actually discussing. Your operating model is the blueprint for how work gets done. It encompasses six critical dimensions:

Structure and governance: Who makes decisions, how teams are organized, what approval mechanisms exist, and where authority lives.

Processes and workflows: How work flows across functions, where handoffs occur, what cycle times look like, and how tasks sequence.

People and talent: What skills exist, how roles are defined, where capacity is allocated, and what capabilities matter.

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As startups race to keep up with advances in artificial intelligence, some of them seem to be borrowing from China’s exacting work culture—which normalized a 72-hour workweek, or a “996” schedule of working six days a week from 9 a.m. to 9 p.m. 

While the 996 parlance and laser focus on AI may be new, hustle culture has always been embedded in Silicon Valley to some degree. Some business leaders, perhaps most famously Elon Musk, have long demanded those hours from their employees: “There are way easier places to work, but nobody ever changed the world on 40 hours a week,” he once said of the “hardcore” work ethic promoted at his companies.

Now that culture seems to be seeping into more and more workplaces, as young founders and tech workers try to capitalize on the rise of AI. The CEO of the $10 billion AI startup Cognition has talked openly about the intense work ethic expected at his company.

“Cognition has an extreme performance culture, and we’re up front about this in hiring so there are no surprises later,” he shared on X earlier this year. “We routinely are at the office through the weekend and do some of our best work late into the night. Many of us literally live where we work.”

In this environment, Karri Saarinen—an early employee at Coinbase and the former principal designer at Airbnb—has sought to do things differently. 

Saarinen founded Linear, an AI-powered enterprise software company, in 2019. It was well before the pandemic, but Saarinen believed it was important to lean into remote work—not just because Linear was creating tools for companies to use remotely for project management and product development, but also because the founders did not want to get stuck in Silicon Valley for the foreseeable future. 

“We honestly asked ourselves: Do we want to do a company here for the next 10 or 20 years? And we decided no,” Saarinen says.

Linear has raised $82 million this year—spiking its valuation to more than a billion dollars. It boasts high-profile clients like OpenAI and Perplexity. And it’s done so without blindly embracing the hustle culture spouted by people like Musk.

Avoiding an unsustainable pace

In spite of this success, Saarinen says he has tried to be deliberate in his approach to building Linear, rather than cave to the pressure so many companies seem to feel amid the rapid clip of AI innovation. (It likely also helps that Linear has been profitable for four years, per Saarinen.) 

The tone of corporate messaging on AI, both from tech giants and smaller startups, has been that the technology is moving fast—and their employees need to step up to meet the moment.

In a memo earlier this year, Amazon CEO Andy Jassy noted that the company was already using generative AI in nearly every part of the business, but that Amazon was “still at the relative beginning” and should move faster. 

“We’re going to keep pushing to operate like the world’s largest startup—customer-obsessed, inventive, fast-moving, lean, scrappy, and full of missionaries trying to build something better for customers and a business that outlasts us all,” he wrote. (Jassy also explicitly said AI adoption would necessitate job cuts, though he has denied the recent layoffs at Amazon were due to AI.) 

Other Big Tech companies have also tied AI strategy to breakneck speed and a potentially draining work culture. At Meta, senior leaders have called for employees to “go 5x faster” by using AI.

“Our goal is simple yet audacious: Make Al a habit, not a novelty,” Metaverse VP Vishal Shah wrote in an internal message, per a 404 Media report. “This means prioritizing training and adoption for everyone, so that using Al becomes second nature—just like any other tool we rely on.”

Shah added, “We expect 80% of Metaverse employees to have integrated AI into their daily work routines by the end of this year.” Meta has also invested billions of dollars in hiring—and poaching—top AI talent.

Saarinen understands why company leaders feel like they need to move fast, but he argues the pace is likely not sustainable, noting that the current AI race “is not going to end after this year. It will probably go for the next decade. So are you going to race that whole next decade?” 

As a founder, Saarinen says there can be an impulse to emulate other successful companies or keep up with peers, regardless of what might be best for your own company. 

“I think a lot of this pressure is somewhat self-created,” he says. “I don’t know if it’s even real. Companies are so focused on what all the other companies are doing, so they’re trying to build the same things or catch up to everyone.”

Taking time for test runs

Linear has intentionally taken a slower approach to growing its ranks, in stark contrast to the companies offering huge sums of money to out-hire their competitors. The company has more or less doubled its headcount each year and now employs over a hundred people. 

“At Coinbase, I was [maybe] the 12th person there,” Saarinen says. “And then in a year, there were like 60 people. Now most of the people around you are new and have been there a very short time. I think it can be useful, and it’s exciting [when] a company is growing fast. But there are a lot of situations where it gets quite chaotic, and the culture kind of suffers.”

A core part of its hiring process is what the company calls a work trial. Once a candidate gets to the final stage of the interview process at Linear, they are invited to participate in a paid trial period—typically two to five days—when they are tasked with working on a real project alongside employees at the company. 

It’s a feature that adds friction to the hiring process but helps the company understand whether someone will be a good fit. Sometimes it’s a differentiator that pushes a candidate to accept a job at Linear over other offers; in other instances, it has weeded out people who did not want to commit to a work trial. 

“The aim is trying to simulate the real working relationship as much as possible,” Saarinen says. “We can obviously see how the person gets things done, but also: What is their thinking style? What’s their communication style? For the candidate, I think it’s also a good way to know if they want to work in this company.” 

It can also help determine, for example, whether engineers are looking for a job where they are told what to do, or if they are interested in taking more ownership of their work, as is the norm at Linear.

“People should have some life outside of work”

The work trial and other atypical elements of Linear’s culture have helped attract people who are not interested in the endless grind of working at some of the hottest AI companies. 

Linear has had very little attrition, according to Saarinen, and the company usually tries to promote from within. Saarinen also firmly believes that the quality of work is compromised when you work people to the bone. 

“That quality piece that we value the most—we think that doesn’t happen if you just keep pushing people harder and harder,” he says. “People should have some life outside of work. They should get inspired by their life, and then hopefully that will kind of bleed into the work as well. If you just feel better, then I think the work you do is a little better.”

He hopes that Linear might offer a counterexample to tech workers who are building companies in the AI space at this particular juncture. 

“I want to show other founders that you can also do things differently,” he says. “You don’t always have to do what everyone else is doing. I think that’s kind of what is happening in the market, that everyone is hearing this story: ‘Those guys work really hard, so I must do it as well.’ And maybe it makes sense for you—or maybe it doesn’t.” 

By Pavithra Mohan

This article originally appeared in Inc.’s sister publication, Fast Company.

Fast Company is the world’s leading business media brand, with an editorial focus on innovation in technology, leadership, world changing ideas, creativity, and design. Written for and about the most progressive business leaders, Fast Company inspires readers to think expansively, lead with purpose, embrace change, and shape the future of business.

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