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

AI Programming Tools Promise to Free Engineers, But Instead Spark 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—in fact, they are increasing. AI, which should be a time-saving tool, has become a new source of workplace pressure.

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

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

Executives Coding, Efficiency Anxiety Spreading Top-Down

The term “vibe coding” initially carried a sense of relaxed optimism. Introduced to the public by former OpenAI researcher Andrej Karpathy in February 2025, it described a new programming paradigm where engineers only need to chat with AI to complete development—“completely immersed in the vibe.”

But a year later, the vibe has already shifted.

Alex Balazs, CTO of Intuit, describes his recent routine: his wife leaves for work at 8 a.m., and he’s already been working for hours. “She asked me how long I’d been up, and I said I got up at 5 a.m. to write code.” In reality, he’s guiding AI agents 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 cascading downward. Greg Brockman, President of OpenAI, recently posted on X that “every moment your agent isn’t running feels like a missed opportunity.” This statement hits a nerve in the already workaholic tech culture.

Alex Salazar, co-founder and CEO of AI startup Arcade.dev, is more direct. He regularly checks the company’s Claude Code billing—since the bill directly correlates with how often engineers use the tool—and criticizes employees who “don’t spend enough.” “I tell them, ‘You need to hustle more,’” he says. He notes that after the first “faith meeting,” the company’s AI coding bill skyrocketed tenfold, and he views this expenditure as a sign of progress.

Quantified Management of Employees, “AI Fatigue” Quietly Spreading

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

DocuSketch, a software company specializing in property restoration, tracks how many times engineers interact with AI coding tools daily. Andrew Wirick, VP of Product, says the company assumes that higher interaction counts mean higher productivity. Claude Code even generates weekly reports for each engineer, listing all the patterns of unproductive AI loops and offering suggestions for improvement.

Wirick admits he’s developed a kind of “addiction.” “I feel like I have to interact more every day, even thinking about how to do a few 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, which autonomously broke down and completed the task in 20 minutes—“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 as AI takes over many tasks, people’s working hours are not decreasing. Some engineers openly admit they are experiencing “AI fatigue”—constantly worried about missing the next breakthrough, which always seems just one prompt away.

Growing Cognitive Gap Between Executives and Employees

The enthusiasm of executives largely stems from the novelty of creating with AI themselves. Salazar admits that building prototypes with AI himself feels more “productive” than routine 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. Balazs says, “At least now, product managers can bring a concrete example to engineers and say, ‘I want something like this.’”

However, Section’s survey shows a significant perception gap.

The perceived benefits of AI among top executives are vastly different from the experiences of frontline employees. Salazar believes this partly results from the higher transition costs employees face when adapting to new tools: “They’re implicitly asked to find time to explore and experiment, but their daily workload expectations haven’t changed to allow for that.”

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

“Task Expansion” and False Prosperity, the Other Side of the Efficiency Myth

Researchers at Berkeley call this phenomenon “task expansion”: when non-technical colleagues start generating code with AI, engineers must spend time cleaning up these semi-finished outputs, increasing their workload. Balazs admits this is reshaping clear-cut roles, leading to more “hybrid” positions and complicating collaboration.

More fundamentally, the question is: Is this wave of construction creating valuable things, or just producing more stuff?

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

Balazs states that, measured by code production and delivery speed, the productivity of company engineers has increased by about 30%. But in this increasingly “one-time” coding future, the real efficiency gain may lie elsewhere: in identifying what shouldn’t be built at all.

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