The office has a new loyalty test, and it doesn't involve the company mission statement or quarterly earnings. It's whether you love ChatGPT as much as the C-suite does.

According to reporting from the New York Times, a growing number of workers face an unexpected dilemma: their bosses have become AI evangelists, and tepid responses to the latest large language model aren't playing well in performance reviews. The question isn't whether the technology works for your actual job. It's whether you're sufficiently excited about it.

This represents a peculiar evolution in workplace dynamics. Historically, employees were expected to adopt new tools — email, Slack, project management platforms — with varying degrees of enthusiasm. But AI has triggered something different: a quasi-religious fervor among some executives that treats skepticism as heresy rather than prudent evaluation.

The Performance of Technological Optimism

The pressure to perform enthusiasm creates an awkward theater. Employees who find ChatGPT occasionally useful but hardly transformative must now calibrate their public stance. Too much honesty risks being labeled a Luddite. Too much enthusiasm without substance looks like transparent pandering.

The dynamic mirrors earlier tech-adoption cycles, but with higher stakes. When smartphones arrived, nobody questioned your commitment to the company if you preferred email on a laptop. When Salesforce rolled out, resistance was seen as inconvenient, not ideological.

AI is different because it's been marketed — and in many cases, genuinely believed — to represent a fundamental shift in how knowledge work happens. Executives who've bought into this narrative don't just want adoption. They want converts.

What the Research Actually Shows

The disconnect between executive enthusiasm and worker experience has empirical backing. Multiple studies on AI adoption in white-collar work show a consistent pattern: the technology delivers uneven results depending on task type, user expertise, and implementation quality.

For certain narrowly defined tasks — drafting initial email responses, summarizing documents, generating code snippets — AI tools demonstrate clear value. For complex reasoning, strategic thinking, or work requiring deep institutional knowledge, the results range from mediocre to actively counterproductive.

Yet these nuances rarely survive the journey to the executive suite. What arrives instead is a simplified narrative: AI is the future, early adopters will thrive, skeptics will be left behind.

This creates a rational incentive for employees to overstate AI's impact on their work. If the boss believes ChatGPT saved you ten hours this week, why complicate the story by explaining that you spent three of those hours fixing its mistakes?

The Authenticity Tax

The pressure to fake enthusiasm carries costs beyond personal discomfort. It distorts feedback loops that organizations need to make good decisions about technology investment.

When everyone performs optimism, executives receive no accurate signal about what's actually working. The company doubles down on tools that employees have learned to route around. Training programs multiply for technology that people use primarily to satisfy management expectations.

Meanwhile, workers who might offer genuine insights about how to deploy AI more effectively stay quiet. The risk of being labeled resistant to change outweighs the benefit of providing honest assessment.

This dynamic isn't unique to AI — it's plagued every major technology rollout in corporate history. But the current AI moment has intensified it because the technology arrives wrapped in existential rhetoric. This isn't just a new tool. It's supposedly the difference between obsolescence and relevance.

The Age Factor

The Times reporting also touches on a related tension: age-based assumptions about technological aptitude. Older workers face particular pressure to demonstrate AI fluency as a way of countering stereotypes about their adaptability.

This adds another layer of performance to the mix. It's not enough to use the tools competently. You must use them enthusiastically, and in ways that signal you're not stuck in outdated modes of work.

The irony is that experience often provides exactly the judgment needed to evaluate new tools effectively. Knowing what's genuinely novel versus repackaged hype requires having seen previous cycles of technological disruption. But that experience can be reframed as baggage rather than wisdom.

What Healthy Adoption Looks Like

Organizations that successfully integrate new technology typically do so by creating space for honest assessment. They distinguish between experimentation (which should be encouraged) and mandated enthusiasm (which produces performance rather than insight).

They also recognize that different roles will benefit differently from new tools. A marketing team's use case for AI differs fundamentally from an engineering team's, which differs from finance's. Blanket enthusiasm makes no more sense than blanket skepticism.

The best implementations involve workers in shaping how tools get deployed rather than treating them as passive recipients of executive vision. This requires psychological safety to say "this isn't working for my workflow" without career consequences.

The Bigger Question

Beneath the specific question of AI enthusiasm lies a more fundamental tension about authenticity at work. How much performance is reasonable to expect? When does healthy professionalism shade into exhausting theater?

Every workplace requires some degree of managing up and calibrating your public stance to organizational realities. But when that extends to faking excitement about technology you find mediocre, something has shifted.

The current moment asks workers to be early adopters, cheerleaders, and success stories simultaneously — regardless of their actual experience. That's not change management. It's loyalty testing with a chatbot.

For employees navigating this landscape, the calculus is straightforward if uncomfortable: assess how much your workplace values authentic feedback versus performed enthusiasm, then act accordingly. Some organizations genuinely want to know what's working. Others want confirmation that their technology bets were wise.

The challenge is that you often can't tell which kind you work for until you've already made the wrong guess.