CEO Refuses to Parse JSON, Ignites Debate Over AI and Performative Suffering

San Francisco, CA — A visibly agitated chief executive officer publicly confronted what he described as a coordinated campaign of “technological masochism” this week, arguing that widespread criticism of artificial intelligence tools reflects a fundamental misunderstanding of productive labor and, more specifically, a romanticization of suffering that serves no discernible organizational purpose.

The comments, delivered during an industry panel that had been scheduled to discuss enterprise software integration, marked an unusual departure from prepared remarks. According to multiple attendees, the CEO had grown increasingly frustrated during a preceding session where several speakers characterized AI adoption as "skill erosion" and "intellectual outsourcing."

"I am not going to fucking parse a long JSON file," the executive stated, his tone suggesting the comment represented the distillation of considerable unexpressed frustration. "I have better uses of my time."

The remark, while profane, has since been cited by technology analysts as representative of a growing divide between practitioners who view AI tools as legitimate productivity enhancements and critics who characterize their use as a form of professional weakness.

The Precipitating Incident

Sources familiar with the matter indicate that the outburst followed the CEO's receipt of what multiple witnesses described as an unusually challenging data structure. The file in question, according to internal correspondence reviewed by this publication, contained approximately four thousand lines of JavaScript Object Notation formatted without consistent indentation or meaningful key naming conventions.

Technical analysis of the file, provided on condition of anonymity by an engineer present during the incident, revealed a nested structure extending to seventeen levels of depth, with key names including "data," "data2," "dataFinal," "dataActualFinal," and "dataUseThisOne." The accompanying documentation consisted of a single comment reading "you'll figure it out."

The engineer who originally sent the file had included a note suggesting the CEO "just read through it" to understand the data model. This suggestion, according to witnesses, produced a facial expression described variously as "volcanic," "concerning from a cardiovascular perspective," and "the look of a man reconsidering every hiring decision he has ever made."

"That's not craftsmanship," the CEO reportedly stated, gesturing at his laptop screen. "That's hazing."

The Broader Critique

The executive's comments, while prompted by a specific technical frustration, quickly expanded into a more comprehensive critique of what he characterized as performative competence within technology organizations.

"People are acting like suffering is a virtue," the CEO said during subsequent remarks."It's not. It's a bug."

The statement reflects a position that has gained traction among certain technology leaders but remains controversial within engineering communities. Critics argue that manual engagement with data structures builds foundational understanding. Proponents counter that foundational understanding does not require repeated demonstration through inefficient labor.

"I understand data structures," the CEO clarified in a follow-up interview. "I don't need to prove it by eyeballing four thousand lines of nonsense. I have a computer science degree from Stanford. I have architected systems handling millions of transactions. The suggestion that I need to manually parse JSON to validate my technical credibility is insulting to me and, frankly, to the concept of tools."

When pressed on whether AI assistance might atrophy technical skills over time, the executive offered an analogy that has since been widely circulated. "You're not noble for refusing to use a calculator. You're not demonstrating mathematical superiority. You're just slower. The same principle applies here."

The Anti-AI Position

The CEO's remarks have drawn sharp criticism from advocates of what has been termed the "cognitive independence" movement, a loosely organized coalition of technologists who argue that AI tool adoption represents a dangerous abdication of professional responsibility.

Dr. Margaret Thornton, a computer science professor at Carnegie Mellon University who has published extensively on what she terms "artificial dependency syndrome," characterized the executive's position as representative of a troubling trend. "The assumption that efficiency should trump understanding reflects a fundamentally transactional view of knowledge work. When we outsource cognitive tasks to machines, we don't just save time. We erode the neural pathways that enable complex reasoning."

Thornton's research, published in the Journal of Cognitive Computing Ethics, suggests that professionals who regularly use AI assistance demonstrate measurable decline in certain problem-solving capabilities over eighteen-month periods. Critics of the research note that the decline was measured only in the specific tasks being outsourced and that overall productivity increased substantially.

A spokesperson for the Foundation for Human-Centered Computing, a nonprofit organization that advocates for limits on workplace AI adoption, released a statement following the CEO's comments. "The dismissive characterization of careful, methodical work as 'suffering' reveals a troubling attitude toward craftsmanship. Parsing JSON manually builds character and attention to detail. These are qualities that cannot be delegated to algorithms."

When this characterization was shared with the CEO, he responded with what attendees described as a prolonged exhale. "Parsing JSON manually builds character," he repeated slowly. "So does walking to work instead of driving. So does washing clothes by hand. So does performing arithmetic on an abacus. We invented cars and washing machines and calculators anyway, because the 'character' argument has always been a retrospective justification for unnecessary labor."

Historical Parallels

Technology historians note that similar debates have accompanied virtually every significant productivity enhancement in the history of professional work. The introduction of spreadsheet software in the 1980s prompted warnings that accountants would lose the ability to perform calculations. The adoption of word processors generated concerns about declining penmanship and compositional discipline.

Dr. Leonard Hirsch, a professor of technology history at MIT, observed that the current AI debate follows a predictable pattern. "Every generation identifies some form of manual labor as essential to professional identity. Then tools eliminate that labor. Then people adjust their definition of essential work. The cycle is remarkably consistent."

Hirsch's research documents similar controversies surrounding calculators (accused of destroying mathematical intuition), CAD software (accused of eliminating design understanding), and GPS navigation (accused of atrophying spatial reasoning). In each case, initial resistance eventually gave way to universal adoption, with professional standards adjusting to incorporate new baselines.

"The accountant who insisted on manual ledger calculation in 1995 was not demonstrating superior financial acumen," Hirsch noted. "They were demonstrating inability to adapt. I suspect history will view the anti-AI position similarly."

The Productivity Schism

Industry analysts have characterized the disagreement as reflecting a deeper philosophical divide about the nature of professional value. One perspective holds that value derives from demonstrable effort and visible struggle. An alternative view suggests that value derives from outcomes regardless of the process used to achieve them.

Marcus Chen, a management consultant who advises Fortune 500 companies on AI integration, has observed this tension across multiple client organizations. "There's a faction in every company that equates visible struggle with legitimate contribution. If you're not obviously suffering, you must not be working hard. AI threatens this framework because it makes certain forms of suffering unnecessary."

Chen's firm has developed what it terms a "productivity legitimacy assessment" to help organizations identify and address these cultural barriers. The assessment measures employee attitudes toward tool-assisted work and correlates them with various demographic and professional factors.

Preliminary findings suggest that resistance to AI assistance correlates strongly with tenure in specific technical roles, particularly those where manual data manipulation historically constituted a significant portion of daily tasks. "If you spent ten years becoming expert at something a machine can now do instantly, you have strong psychological incentives to argue that the machine is cheating," Chen observed.

The Labor Implications

Labor economists have entered the debate, noting that anti-AI sentiment may partly reflect legitimate concerns about job displacement disguised as philosophical objections about skill development.

Dr. Patricia Reyes of the Economic Policy Institute suggests that the "character-building" argument against AI tools often functions as a socially acceptable proxy for expressing economic anxiety. "It's more dignified to argue that manual parsing builds important skills than to admit you're worried the company won't need as many people who can do manual parsing."

This analysis does not appear to apply to the CEO in question, whose position is secure regardless of AI adoption patterns. However, Reyes notes that executives often inadvertently amplify worker anxiety by dismissing concerns without acknowledging the economic pressures underlying them.

The CEO, when presented with this perspective, expressed sympathy but maintained his position. "I understand that people are anxious about displacement. That's a real and legitimate concern that deserves serious policy attention. But the solution isn't to artificially preserve inefficient workflows. The solution is to ensure that productivity gains are broadly shared. Those are different conversations."

The Generational Dimension

Survey data suggests a significant generational component to attitudes about AI assistance. Younger workers, having entered the workforce after AI tools became widely available, demonstrate substantially less resistance to their use. Older workers, particularly those who built careers before such tools existed, show more ambivalence.

A study conducted by the Pew Research Center found that seventy-three percent of workers under thirty consider AI tool use "normal and expected," while only forty-one percent of workers over fifty share this view. The difference persists even when controlling for technical proficiency and industry sector.

Dr. Thornton, the Carnegie Mellon professor critical of AI adoption, acknowledged the generational pattern but argued it reflects a failure of education rather than a natural evolution of professional norms. "Younger workers have been conditioned to outsource cognition from an early age. This isn't adaptation; it's atrophy."

The CEO offered a different interpretation. "Younger workers recognize that the tools exist and that refusing to use them confers no advantage. They don't have decades of identity investment in doing things the hard way. They just want to accomplish tasks effectively. This seems healthy to me."

Organizational Responses

Companies have adopted varied approaches to managing the tension between AI advocates and skeptics. Some have implemented what amounts to a "don't ask, don't tell" policy, where AI tool use is neither officially sanctioned nor prohibited. Others have attempted to establish clear guidelines distinguishing appropriate from inappropriate AI assistance.

The latter approach has proven difficult to operationalize. A policy document from a major consulting firm, leaked to the technology press last month, attempted to distinguish "acceptable efficiency enhancement" from "inappropriate cognitive outsourcing." The document's criteria proved sufficiently vague that employees reported being unsure whether basic spell-checking qualified as AI assistance.

Some organizations have adopted transparency requirements, mandating that employees disclose when AI tools contributed to deliverables. Critics argue this creates a stigma around AI use, implicitly suggesting that assisted work is less valuable than unassisted work. Proponents counter that transparency enables appropriate quality review and attribution.

The CEO's company has apparently adopted what might be termed an "outcomes-only" policy. "I don't care how you get results," he stated. "I care that you get results. If you want to manually parse JSON files as a form of meditation or self-flagellation, that's your prerogative. But don't expect praise for inefficiency, and don't suggest that others should share your preferences."

The International Perspective

The debate appears largely concentrated in North American and Western European technology sectors. In other regions, AI tool adoption has proceeded with less cultural resistance.

Dr. Wei Zhang of Tsinghua University's School of Economics and Management observed that Chinese technology companies generally view AI assistance as an unambiguous competitive advantage. "The American debate about whether using efficient tools is 'cheating' is viewed with some bemusement here. It's like debating whether using electricity is unfair to candle manufacturers."

European attitudes fall somewhere between American ambivalence and Asian pragmatism. The European Union's AI regulatory framework focuses primarily on safety and transparency concerns rather than questions of professional legitimacy. Several European technology executives expressed puzzlement at the characterization of AI use as morally fraught.

"We have regulations about AI safety and bias. These are serious issues," noted Klaus Brenner, chief technology officer at a German enterprise software company. "But whether a programmer should feel guilty for using AI to understand a data structure? This is not a regulatory question. This is a question for a therapist."

The Competence Question

Central to the debate is a disagreement about what constitutes genuine technical competence. Anti-AI advocates argue that competence requires the ability to perform tasks manually, even if tools exist to automate them. AI advocates counter that competence means knowing when and how to use available tools effectively.

The CEO articulated the latter position with characteristic directness. "Competence is not about suffering. Competence is about solving problems. If I can solve a problem in five minutes using a tool or five hours doing it manually, choosing the manual approach doesn't demonstrate competence. It demonstrates poor judgment."

Dr. Thornton countered that this framing assumes equivalence between tool-assisted and manual solutions."The five-minute AI solution and the five-hour manual solution are not the same thing. The manual process builds understanding that informs future work. The AI process builds nothing except the habit of outsourcing."

Research on this question remains inconclusive. Studies demonstrating skill atrophy from AI reliance exist alongside studies showing that tool use enables deeper engagement with higher-order problems. The CEO suggested this might be because the question itself is flawed.

"We don't ask whether doctors who use MRI machines are 'really' understanding their patients' bodies. We don't question whether architects who use CAD software 'really' understand structural engineering. Tools are integrated into professional practice until they become invisible. AI is simply newer, which is why it's still visible enough to argue about."

Regulatory Considerations

Government regulators have begun examining workplace AI use, though primarily from perspectives other than professional legitimacy. The Department of Labor has issued guidance on AI use in hiring decisions. The Equal Employment Opportunity Commission has addressed AI-related discrimination concerns. The Federal Trade Commission has examined AI claims in consumer-facing products.

None of these regulatory frameworks directly address whether AI assistance in routine professional tasks constitutes legitimate work practice. Several regulatory officials, speaking on background, expressed uncertainty about whether such questions fall within any agency's jurisdiction.

"We regulate safety, discrimination, and deceptive practices," one official noted. "Whether a programmer should feel ethically comfortable using AI to understand code is not obviously a regulatory question. It seems more like a professional norm question, which industries typically work out for themselves."

Some industry groups have attempted to establish voluntary standards, but these efforts have largely failed to achieve consensus. The Association for Computing Machinery's draft guidelines on AI-assisted programming generated substantial controversy and remain under revision after three years of debate.

Market Implications

The debate carries potential implications for labor markets and compensation structures. If AI assistance becomes universally accepted, the premium for manual technical skills may decline. If resistance to AI use persists, organizations may segment into AI-embracing and AI-skeptical cultures with different productivity profiles.

Recruiting firms report early evidence of this segmentation. Some job postings explicitly encourage AI tool proficiency, while others emphasize "fundamental" skills with implicit skepticism toward tool-assisted work. Candidates increasingly report being asked about their AI use practices during interviews, with responses apparently influencing hiring decisions in both directions depending on organizational culture.

Compensation data remains limited, but preliminary analysis suggests AI-embracing organizations offer somewhat higher salaries for equivalent roles, potentially reflecting higher productivity expectations. Whether this differential will persist as AI tool use normalizes remains unclear.

The Resolution Question

Industry observers generally expect the debate to resolve through generational turnover rather than argument. As workers who entered careers before AI tools retire, the workforce will increasingly consist of professionals for whom such tools are simply part of the working environment.

This prediction offers little comfort to current workers navigating organizational cultures with conflicting norms. The CEO's company has apparently achieved internal alignment around AI acceptance, but many organizations remain divided.

"We'll look back on this debate the way we look back on debates about whether calculators should be allowed on exams," Chen, the management consultant, predicted. "The question will seem quaint. But right now, people are genuinely uncertain, and that uncertainty creates friction."

The CEO offered a more direct prediction. "If your argument against AI is that people should suffer more to prove competence, you've already lost. You just don't know it yet."

Aftermath

In the days following the public comments, the CEO's company reported a notable increase in job applications, particularly from younger engineers. Internal surveys suggested employee satisfaction had increased, with respondents citing appreciation for leadership clarity on tool use expectations.

The JSON file that precipitated the outburst was successfully parsed using AI assistance within approximately ninety seconds. The underlying data structure, once understood, revealed a customer database that could have been represented in a simple relational format requiring perhaps two hundred lines of properly documented code.

The engineer who originally created the file has not been disciplined, though sources indicate he received extensive feedback about documentation practices. His subsequent data structures have reportedly included meaningful key names and inline comments.

When asked if anyone had learned anything valuable from the manual parsing approach, the CEO's response was succinct.

"No. No one learned anything new from doing it the hard way. Time was saved. Which was the entire fucking point."

Editor's note: Following publication of this article, thirteen technology executives reached out to share similar experiences with incomprehensible data structures. All thirteen described using AI assistance to resolve the situations. None reported feeling guilty about it.