Ghost Engineers: A Deep Dive into the High-Salary, Low-Productivity Programmer Phenomenon

Ghost Engineers: A Deep Dive into the High-Salary, Low-Productivity Programmer PhenomenonA new study from Stanford University has unveiled a troubling trend in the tech industry: the "ghost engineer." These programmers command high salaries yet produce minimal meaningful code, sparking a wide-ranging discussion about industry efficiency and employee motivation

Ghost Engineers: A Deep Dive into the High-Salary, Low-Productivity Programmer Phenomenon

A new study from Stanford University has unveiled a troubling trend in the tech industry: the "ghost engineer." These programmers command high salaries yet produce minimal meaningful code, sparking a wide-ranging discussion about industry efficiency and employee motivation. Researcher Yegor Denisov-Blanch's findings quickly spread across social media, generating significant attention and debate, and leading many self-identified "ghost engineers" to share their perspectives, offering a more comprehensive understanding of the phenomenon.

Denisov-Blanch's research, based on an analysis of productivity data from hundreds of companies, revealed that approximately 9.5% of software engineers contribute negligible code. The presence of these "ghost engineers" not only wastes company resources but also creates inequities for other high-performing employees. Following the release of his findings, Denisov-Blanch's inbox was flooded with messages from individuals identifying as "ghost engineers," ranging from attempts at justification to angry outbursts, but generally acknowledging exploitation of loopholes within company culture.

Investor Deedy Das also highlighted this phenomenon on social media, noting its prevalence in several prominent tech companies, including Cisco and Salesforce. He cited common tactics employed by "ghost engineers," such as frequently setting their online status to "in a meeting" or using mouse simulators to feign continuous activity. Aaron Levie, CEO of Box, a file storage company, also weighed in on the discussion, stating that the company was addressing similar issues. While Box didn't immediately fire anyone as a result, the online debate prompted an internal reassessment of employee productivity evaluation methods. Over the past four years, as remote work became more prevalent, Box has focused on evaluating the productivity of all employees, implementing measures such as team downsizing to eliminate redundancy, reducing meeting frequency, and exercising greater caution in selecting R&D projects.

Denisov-Blanch's research was not a chance discovery. He collaborated with Stanford organizational psychology associate professor Michael Kosinski and Simon Obstbaum, former CTO of Crunchyroll, to develop a machine learning algorithm that analyzes corporate codebases to measure programmer productivity. This algorithm revealed the "ghost engineer" phenomenon. The data showed that large companies are more susceptible to harboring "ghost engineers," but the problem isn't exclusive to large firms; smaller companies also struggle to completely avoid it. Kosinski pointed out that the complexity of work and internal structures within large tech companies make assessing engineer productivity a challenge in itself. However, failing to identify and address low-performing employees not only fosters mediocrity but also undermines the rewards deserved by high-performing individuals.

Stanford's study comes at a time when some large tech companies are scaling back on remote work policies. Following massive layoffs, companies like Google, Amazon, Meta, and Microsoft are reevaluating remote work models. Amazon plans to require employees to work at least five days a week in the office starting next January, and companies like SAP, AT&T, Dell, and Zoom are also tightening flexible work policies. Denisov-Blanch's research found that while a higher percentage of top-performing programmers work remotely, "ghost engineers" also show a stronger preference for remote work. Among remote engineers, "ghost engineers" comprise 14%; this compares to 9% among engineers working at least part of the week in the office, and only 6% among those working in the office daily.

Regarding the motivation behind "ghost engineer" behavior, Denisov-Blanch, after in-depth conversations with dozens of individuals, concluded that it's not simply intentional slacking but stems from disillusionment with work and frustration over the perceived disconnect between effort and reward or recognition. Over time, motivation wanes, performance declines, and "coasting" shifts from passive to active, with employees potentially employing strategies to mask their inefficiency. Denisov-Blanch notes that managers sometimes struggle to discern the truth. Krunal Patel shared his experience of "ghosting" early in his career. Driven not by laziness but a desire to gain his manager's attention, he and a colleague meticulously reported tasks twice dailywhile accomplishing nothing. Only upon confessing did their manager realize their inaction. Honest communication led to suggestions for reducing micromanagement of daily tasks and increased involvement in impactful projects, ultimately improving both productivity and job satisfaction.

Sudheer Bandaru, while managing a software engineering team, encountered a similar situation. An engineer considered the "brightest" would eloquently speak in team meetings, yet produce minimal code. Communication revealed a mismatch between the employee's skills and their role. After a role change, this employee significantly improved performance. These cases highlight the crucial role of effective communication and role alignment in boosting employee productivity.

However, using lines of code as a sole metric for evaluating engineer productivity can easily lead to misjudgments. Tech writer and former software engineer Patrick McKenzie points out that some senior engineers "legitimately don't write code," perhaps focusing on architecture design or mentoring junior colleagues. These individuals are neither "ghosts" nor coasting. Denisov-Blanch also notes that to avoid such misinterpretations, the productivity measurement algorithm he and his collaborators developed monitors not only individual and team workloads but also assesses their impact on the overall company codebase.

With investor interest in his findings, Denisov-Blanch is considering commercializing his research, potentially aiding companies in better addressing the "ghost engineer" problem. However, Denisov-Blanch emphasizes his aim is to help "ghost engineers," not expose them. His mission is to understand the underlying causes and work towards eliminating the phenomenon. This requires companies to address management styles, employee motivation schemes, and work environments, fostering a more positive atmosphere to reduce the occurrence of "ghost engineers."

This research necessitates a deep reflection on tech industry management practices, employee motivation, and work environments. Effectively assessing engineer productivity, enhancing job satisfaction and engagement, and cultivating a fair and equitable workplace are critical considerations. Only by addressing these fundamental issues can the "ghost engineer" phenomenon be effectively countered, ultimately creating a healthier and more productive tech ecosystem.