A significant portion of LinkedIn content from Western executives is not their own. It's crafted by virtual assistants in the Philippines who use AI to generate posts and comments, creating a false perception of expertise and engagement for a very low cost.
The strategy of using cheap human labor combined with AI to manufacture posts and fake engagement is the same recipe used by state actors to manipulate public opinion online. The only difference is the initial intent: selling 'humblebrags' versus eroding democracy.
There's a deep irony in the AI boom: the same leaders who publicly claim AI will automate jobs are heavily dependent on humans, often in low-wage countries, to manage, edit, and pilot the AI tools. The 'human in the loop' is essential but often hidden.
With over half of long-form LinkedIn posts being AI-generated and engagement being faked by bots and coordinated groups, metrics like likes and generic comments ('so true') are no longer reliable indicators of audience agreement or content quality. Treat it as manufactured volume, not proof.
Many tasks branded as 'AI automated' secretly rely on human intervention. To reveal this dependency and identify the real accountability structure, simply ask who is responsible for errors produced by the system. This forces the organization to name the person still in the loop.
The system of cheap labor, AI drafting, and fake accounts is topic-agnostic. It was built for commercial purposes but can be easily repurposed for malicious intent. The machine doesn't care if it's amplifying a product launch or state-sponsored disinformation; it just works.
The primary goal of many influence operations isn't to change hardened minds, but to create the illusion of an overwhelming consensus. This manufactured majority causes individuals with dissenting views to stay silent for fear of social isolation, effectively suppressing real debate.
Instead of debating the truth of a widely-held belief, a more effective analysis is to question the financial or political incentives behind it. Identifying who stands to gain from a particular consensus reveals the machine's purpose and its operators, regardless of the message's validity.