Has artificial intelligence truly outgrown its “Global South data sweatshop” phase? The recent deluge of “AI tutor” job advertisements on LinkedIn targeting highly qualified candidates with advanced degrees might suggest so. When Sam Altman claims his chatbot is “PhD-level smart,” one might assume this reflects a genuine shift toward elite expertise in AI training. However, groundbreaking investigative reporting published by Africa Uncensored reveals a more troubling reality: these recruitment campaigns represent elaborate investor-facing theatrics rather than meaningful industry evolution. DiPLab applauds the exceptional work of data journalists and Pulitzer Center Artificial Intelligence Accountability fellows Kathryn Cleary and Marché Arends, whose year-long investigation exposed a curious case study in modern AI labor practices. Their research focused on companies like Mindrift and Scale AI’s Outlier, which have been flooding professional networks with advertisements for highly qualified and relatively well-compensated “AI tutors” and “trainers,” primarily targeting workers in high-income countries across North America and Europe. These positions appeared to target elite specialists rather than the typical pool of low-paid data annotators traditionally associated with AI training. The recruitment campaigns seems to suggest that major tech companies, in their aggressive push toward Artificial General Intelligence (AGI), are now seeking only the most brilliant minds to train sophisticated chain-of-thought models. The Africa Uncensored investigation reveals a starkly different reality. Once recruited, these qualified workers—many holding advanced degrees in physics, philology, and other specialized fields—were left idle for months, barely managing to earn double-digit wages. They were essentially serving as props in an elaborate performance of AI progress, carefully staged to impress investors and signal scalability to potential big tech clients. Meanwhile, on platforms targeting workers in the Global South, such as Mindrift’s sister platform Toloka, recruitment for poorly paid microtasks continued under largely exploitative conditions. This parallel system reveals the persistent nature of what researchers have termed “digital sweatshops.” For DiPLab and its research community, these findings represent “old wine in new bottles.” For nearly a decade, DiPLab researchers have been encountering and interviewing data workers who hold Master’s and Doctoral degrees—experts in their own right across diverse disciplines. Many of these highly qualified individuals remain unemployed due to dysfunction in traditional job markets, or find themselves forced to accept data work that neither matches their specialization nor provides adequate compensation. According to DiPLab co-founder Antonio Casilli, interviewed along prof. Edemilson Paranà and dr. Adio Dinika, in the exposé: “This is the biggest waste of social capital in human history. These people would be, should be, destined to the best jobs because they are probably the best and the brightest of their generation.” The mass recruitment strategy serves a specific economic function within what researchers call “labor hedging”—a tactic where companies amass large pools of workers primarily to signal scalability and attract major contracts. As the investigation revealed, Mindrift alone posted over 5,770 job listings across 62 countries in just four months, yet provided minimal actual work opportunities. This approach allows platforms to maintain what they euphemistically term “talent pools”—readily available workforces that can be presented to potential clients as evidence of operational capacity. When a major tech company inquires about access to specialized expertise, these platforms can point to their extensive databases of pre-vetted candidates as proof of their ability to deliver at scale. DiPLab’s research situates these practices within the broader context of platform capitalism surrounding AI development. The current AI boom and the associated recruitment theater serve as crucial signals in this speculative environment. As Casilli noted, “Investors are on LinkedIn too, they see this [mass recruitment], it is a signal for them. This looks more like a communications operation.” These platforms understand that LinkedIn functions not merely as a talent acquisition tool, but as a visibility platform for investor audiences. The courageous reporting by Cleary and Arends, supported by Africa Uncensored, an outlet willing to publish investigations that major US and European media often avoid, highlights the critical need for continued scrutiny of AI labor practices. --------------------------------------------------------------------------------