Author: Xu Chao, Wall Street Insights
AI programming tools promise to free 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 personally coding, employees are being asked to interact with AI more frequently, yet 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 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 UC Berkeley of a 200-person organization found that even after delegating much work to AI, actual working hours are still lengthening.
This spread of anxiety has structural reasons. When CTOs code at 5 a.m. and CEOs measure team effort by billable amounts, the entire industry’s concept of “efficiency” has been redefined—and the cost of this redefinition is borne by ordinary employees.
Executives coding themselves, top-down spread of efficiency anxiety
The term “Vibe coding” initially carried a sense of relaxed anticipation. In February 2025, former OpenAI researcher Andrej Karpathy introduced this concept to the public, describing a new programming mode where engineers only chat with AI to complete development—“completely immersed in the atmosphere.”
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 got up at 5 a.m. to write code.” More precisely, he’s guiding AI 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, increasing pressure. OpenAI President Greg Brockman recently posted on X, saying, “Every moment your agent isn’t running feels like a waste of opportunity.” This statement perfectly triggers the already prevalent workaholic culture in tech.
Alex Salazar, co-founder and CEO of AI startup Arcade.dev, 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 bill skyrocketed tenfold, which he views as a sign of progress.
Quantified management of employees, “AI fatigue” quietly spreading
In this atmosphere, how employees are evaluated is also subtly changing.
DocuSketch, a software company focused on property repair,’s VP Andrew Wirick says they now track 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 patterns of unproductive 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” when trying out Anthropic’s latest model, Opus 4.5, last November—when he handed a typical prototype task to the model, and 20 minutes later, saw it autonomously break down and implement the task. “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 do not decrease. Some engineers are openly admitting 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
Executives’ enthusiasm largely stems from the novelty of creating with AI themselves. Salazar admits that building prototypes with AI personally gives him a stronger sense of “productivity” 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 themselves. 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 executives are vastly different from the experiences of frontline employees. Salazar believes this partly results from employees bearing higher transition costs 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 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—another side of the efficiency myth
Berkeley researchers 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 from Intuit admits this is reshaping the once-clear division of roles, leading more roles toward “hybridization” and making collaboration more complex.
The deeper issue is: is this wave of enthusiasm creating truly 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 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 from Intuit states that, measured by code production and delivery speed, engineer productivity has increased by about 30%. But in this future of increasingly “one-time” code, the real efficiency gain may lie in another question: what are the things that shouldn’t be built at all?
