LinkedIn has become a natural home for corporate jargon, and the platform seems increasingly aware of the problem. A couple of months ago, the Microsoft-owned social network introduced a new “seems like AI slop” feedback button for posts. The move comes amid research from Pangram suggesting that nearly half of all LinkedIn posts were AI-generated, as well as growing complaints from users about the proliferation of automated hot air.
It’s tempting to laugh at the expansion of AI slop on LinkedIn. But the phenomenon raises a more serious question for businesses: Is generative AI making corporate jargon even more pervasive?
On LinkedIn, at least, the answer appears to be yes. What’s less clear is whether ChatGPT and other generative AI tools are having the same effect on communication within organisations. And if they are, what does that mean for workplace relationships, culture and productivity?
Research into the question remains limited, but emerging evidence suggests that AI could accelerate the spread of corporate jargon, potentially compounding its documented effects on communication and collaboration.
Corporate-speak: A working definition
In a paper published in Personality and Individual Differences, Cornell University’s Shane Littrell introduces a “Corporate Bullshit Receptivity Scale,” which measures people’s susceptibility to meaningless corporate language. He defines “corporate bullshit” as “a semantically empty and often confusing style of communication in organizational contexts that leverages abstruse corporate buzzwords and jargon in a functionally misleading way.”
Littrell’s paper also finds that a higher score on the scale is negatively correlated with analytic thinking and decision making. Corporate-speak could decrease employee engagement, undermine a company’s reputation, and even expose businesses to legal or financial risks.
So, is the proliferation of AI increasing the incidence or severity of corporate jargon? Despite a lack of research, it’s a definite possibility, Littrell says — “especially as the use of generative AI/LLM becomes more popular among the public.” Some researchers go a little further, suggesting a demonstrable link between AI and corporate-speak.
Professor Ian P. McCarthy of Simon Fraser University says there is “some emerging evidence” that AI is increasing homogenised corporate-sounding language, reducing “stylistic variance” and “pushing text toward dominant, formulaic patterns.”
Amplifying the causes and effects of jargon
McCarthy suggests that AI may be not just interacting with but magnifying some of the causes of corporate jargon, which he notes is often used to persuade, evade, envision, imagine and strategise. But it can also serve to facilitate group cohesion and signal status, something AI seems well-equipped to do.
“AI is trained on many things, including jargon-heavy corporate communications,” he says, which makes it easier for humans to generate and spread more of it. If that AI-generated language is subsequently incorporated into training data, it could create a feedback loop that reinforces the same patterns.
Littrell also identifies persuasion, evasion and social bonding as key motivations, and argues that corporate environments were already ripe for excessive jargon prior to AI, including company-wide emails, employee feedback sessions, official press releases and mission statements, and shareholder conference calls.
“I think it’s clear that AI-generated text is becoming more prevalent in general,” he says. “Not just online, but in organizational contexts too. This seems especially true on the so-called ‘professional networking’ sites like LinkedIn.” Littrell says AI red flags include “cataphoric teasers” in the form of phrases such as “Here’s the part nobody tells you,” negative parallelisms like “‘that’s not X, it’s Y,” and overuse of em-dashes and colons.
“I think this type of ‘AI slop’ … will likely increase, at least in the short term, particularly among business professionals who ‘leverage’ AI to make their writing sound superficially more insightful and impressive,” he says, pointing out that ‘leverage’ is itself an excellent example of the type of corporate jargon he means.
There’s evidence to suggest that corporate-speak can have a negative, if subtle, effect on the health of a business. McCarthy cites a 2025 study which found that workplace jargon makes information harder to understand, undermines employees’ confidence in their abilities and discourages them from sharing information or collaborating. His own work argues that organisational jargon leads to lower job satisfaction — while it may offer short-term impression-management benefits, it worsens cohesion, trust and productivity when revealed for what it is.
How AI contributes to the problem
More broadly, Littrell warns that one of the key issues for the corporate world won’t merely be the increased risk of corporate jargon, but the larger risk of AI being used in increasingly unhelpful ways that contribute further to the problem. As an example, he points to how much of the recruitment process is being outsourced to AI, from the submission of AI-drafted cover letters to the use of AI-assisted applicant tracking systems.
“If someone asks any of the major AI/LLM agents to write an impressive cover letter or polish up the career biography on their LinkedIn page,” he says, “it will usually spit out text that’s saturated with corporate bullshit in the form of superficially impressive, but arguably vapid, buzzwords and jargon. Since that type of language is already quite common on professional networking sites, the AI agent will naturally mimic it when creating business-oriented materials.”
For Littrell, one of the big dangers is that AI is used heavily by both parties, with employers’ AI-assisted tracking systems being fed info by prospective employees’ AI applications — including applicants using real-time advice from their own AI assistants. In theory, no humans need to directly communicate across virtually the entire process. Ultimately, such a situation would negate the whole purpose of job screening, which Littrell says is “to reliably and authentically signal” an applicant’s skills and qualities.
Job seekers who use AI to generate their most important application materials are essentially creating more corporate speak. On the other side of that scenario, he says, employers who use AI for the recruitment, selection and hiring processes may worsen the problem if their AI screening systems favour AI-generated applicant materials.
This unfortunate dynamic can create an AI-mediated “ouroboros of bullshit,” Littrell says, referring to the circular symbol of a snake eating its own tail. The result could be a recruitment process that consumes time without providing employers with meaningful information about candidates. Moreover, if applicants do land a job on the basis of automating their applications, it could increase the likelihood of AI-enabled jargon and corporate-speak permeating the workplace.
Time for self-reflection
The upshot of all this is that, in a scenario where corporate-speak becomes excessive, business performance can suffer. Ambiguous corporate language can weaken decision-making, according to a 2017 study published in Theory and Decision. Similarly, a 2021 article by researchers at Columbia Business School found that jargon can cause workplace exclusion, disengagement and mistrust.
This same article advises organisations to take certain steps if they’re concerned that jargon could be having a negative impact on their operations. This includes monitoring the use of jargon and checking for negative consequences, finding simpler ways of communicating, ensuring that leaders are setting a good example, and conveying that your organisation prioritises clear communication.
Of course, the article was published prior to the launch of ChatGPT in late 2022. The risk for businesses is not simply that AI will produce more jargon, but that employees and employers will increasingly mistake polished language for meaningful communication. As AI becomes embedded in everyday working life, the ability to recognise the difference may become more important than ever.
LinkedIn has become a natural home for corporate jargon, and the platform seems increasingly aware of the problem. A couple of months ago, the Microsoft-owned social network introduced a new “seems like AI slop” feedback button for posts. The move comes amid research from Pangram suggesting that nearly half of all LinkedIn posts were AI-generated, as well as growing complaints from users about the proliferation of automated hot air.
It's tempting to laugh at the expansion of AI slop on LinkedIn. But the phenomenon raises a more serious question for businesses: Is generative AI making corporate jargon even more pervasive?
On LinkedIn, at least, the answer appears to be yes. What's less clear is whether ChatGPT and other generative AI tools are having the same effect on communication within organisations. And if they are, what does that mean for workplace relationships, culture and productivity?
