The stronger the AI, the more people feel exhausted, and "anxiety" becomes the norm for companies and employees.

AI was supposed to be a productivity tool, but in many workplaces, it has become a new source of pressure.

By Xu Chao

Source: Wall Street Insights

AI programming tools promise to free up engineers, but in reality, they have sparked a new wave of efficiency anxiety.

As AI coding assistants like Anthropic’s Claude Code and OpenAI’s Codex continue to improve, tech companies are caught in a top-down “productivity obsession.” Executives are coding themselves, employees are being asked to interact with AI more frequently, and overtime hours are not decreasing but increasing. AI was meant to save effort, yet in many workplaces, it has become a new source of stress.

Survey data reveal a clear perception gap: a study by consulting firm Section shows over 40% of C-level executives believe AI tools save them at least 8 hours a week, while 67% of non-management employees say AI helps them save less than two hours or none at all. A continuous study at the University of California, Berkeley, involving 200 employees, found that even after delegating much work to AI, actual working hours are still lengthening.

This anxiety spread has structural reasons. When CTOs code at 5 a.m., and CEOs measure team effort by billable hours, the entire industry’s concept of “efficiency” has been redefined—and the cost of this redefinition is borne by ordinary employees.

Top executives coding themselves, efficiency anxiety spreading from top to bottom

The term “Vibe coding” initially carried a sense of relaxed anticipation. In February 2025, former OpenAI researcher Andrej Karpathy brought this concept into the public eye, describing a new programming mode where engineers only need to chat with AI to complete development—“completely immersed in the vibe.”

But a year later, the vibe had already shifted.

Alex Balazs, CTO of Intuit, describes his recent routine: his wife comes downstairs at 8 a.m., and he’s already been working for hours. “She asked how long I’d been up, and I said I was up at 5 a.m. working on code.” More precisely, he’s guiding AI assistants to write code for him, which has allowed him to reconnect with low-level code he hadn’t touched in years.

This behavior among executives is now passing down pressure to lower levels. OpenAI President Greg Brockman recently posted on X, saying, “Every moment your AI isn’t running feels like a missed opportunity.” This statement precisely triggers the already prevalent workaholic culture in tech.

AI startup Arcade.dev’s co-founder and CEO, Alex Salazar, is more direct. He regularly checks the company’s Claude Code bills—linked directly to how often engineers use the tools—and criticizes employees who “don’t spend enough.” “I tell them, ‘You need to hustle more,’” he said. After the first such “faith meeting,” the company’s AI coding bills skyrocketed tenfold, which he sees as a sign of progress.

Employees quantified and managed, “AI fatigue” quietly spreading

In this environment, how employees are evaluated is also subtly changing.

DocuSketch, a software company focused on property repair, has its VP Andrew Wirick tracking how many times engineers interact with AI coding tools daily, assuming higher numbers mean greater productivity. Claude Code also generates weekly reports for each engineer, listing all the ineffective loops with AI and offering suggestions for improvement.

Wirick admits he’s developed a kind of “addiction.” “I feel like I need to do more interactions every day, even thinking about how to do more before bed.” He attributes this to an “epiphany” he had in November last year when trying out Anthropic’s latest model, Opus 4.5—he handed a typical prototype task to the model, and 20 minutes later, saw it decompose and implement the task on its own. “It felt like my brain was rebooted.”

This all-accelerating mindset is eroding the boundaries between work and life. Berkeley’s research shows that even when AI takes over many tasks, people’s working hours have not shortened. Some engineers are openly admitting they are experiencing “AI fatigue”—constantly worried about missing the next breakthrough, which always seems just one prompt away.

The widening perception gap between executives and employees

Much of the executives’ enthusiasm comes from the novelty of creating with AI themselves. Salazar admits that building prototypes with AI himself feels more “productive” than handling approvals and decisions. Recently, he even responded directly to a major financial client’s request by building a demo app from scratch.

At Intuit, product managers and designers are now encouraged to use “vibe coding” to build prototypes in QuickBooks themselves. Balazs says, “At least now, product managers can bring something concrete to engineers and say, ‘I want something like this.’”

However, Section’s survey shows this perception gap is significant.

There’s a huge disconnect between how executives perceive AI’s benefits and how frontline employees experience it. Salazar believes this partly stems from employees bearing higher transformation costs when adapting to new tools: “They’re implicitly asked to find time to explore and experiment, but their daily work expectations haven’t changed to free up that time.”

Job security concerns are also real. Salazar admits he planned to switch to a third-party cloud provider, but now the marketing team can update the company website using AI tools themselves, so the outsourcing expense was cut.

“Task expansion” and false prosperity—the other side of the efficiency myth

Berkeley researchers call this phenomenon “task expansion”: when non-technical colleagues start generating code with AI, engineers have to spend time cleaning up these semi-finished products, increasing their workload. Balazs admits this is reshaping the once-clear division of roles, leading to more “hybrid” roles and making collaboration more complex.

Deeper still, the question remains: Is this wave of building truly creating valuable things, or just producing more stuff?

Analysts warn that if this AI-driven productivity obsession isn’t restrained, it could lead to a proliferation of “busyware”—superfluous software like minor website tweaks, custom dashboards for a single user, or half-finished prototypes abandoned by marketing—ultimately all handed over to engineers. While each seems justified at the moment, most will likely end up as discarded code.

Balazs from Intuit states that, measured by code production and delivery speed, engineer productivity has increased by about 30%. But in this future where code becomes more “one-time use,” the real efficiency gain may lie in the answer to another question: what should never have been built in the first place.