Find the lead domino: the one initiative that, when pursued, makes it easier to accomplish everything else. Or identify the constraint — the choke point or bottleneck — and address it first. For more on how to choose this “critical” constraint, read my favorite biz book of all time titled The Goal by the late Eli Goldratt. Scaling up is all about eliminating constraints — in the business and for customers.

At ProService Hawaii, a human resources firm based in Honolulu, President Ben Godsey determined that in the 2014 fiscal year, his Critical Number was getting 600 referrals. This was a major stretch goal. The company, which has $311 million in annual sales (2019), had previously averaged fewer than 200 referrals a year, despite its focus on developing a great service culture and innovative products — indicated by a Net Promoter Score (NPS) consistently above 70% (on par with Apple).

At the time, Schmidt’s prediction made me wonder whether the second time around would still be coincidence or whether data-annotation firms would make a playbook out of what had worked there. Scouting workers in crisis could become a surefire way to continue driving down the costs of the labor that serves as the lifeblood of the AI industry. Looking back several years later, that’s exactly what happened—and what has become one of the most stunning parallels between empires of old and empires of AI. One of the defining features that drives an empire’s rapid accumulation of wealth is its ability to pay very little or nothing at all to reap the economic benefits of a broad base of human labor.

Where MTurk’s workforce primarily came from the US and India, Scale went hunting first in Kenya and the Philippines, English-speaking former colonies with a long history of servicing American companies through call centers and digital work. The startup’s worker-scouting teams searched for the areas in each country that struck the very same balance of factors that would converge in Venezuela: a high density of people with good education and good internet yet who were poor and thus willing to work hard for very little money. The thesis was guided not only by the company’s cutthroat business practices but also a compelling story they told themselves: that these were the people who could benefit most from the economic opportunity and be happier because of it.

Scale downgraded Kenya to a Group 5 designation: blacklisted. There was also another reason to exit Kenya. By then, Scale was moving on to a new focus, following the demands of the AI industry. OpenAI and its competitors were increasingly searching for highly educated workers to perform RLHF—doctors, coders, physicists, people with PhDs. So went the profit-chasing progression of chatbot development. Those willing to pay money for chatbots were not casual consumers but businesses that expected tools to perform complex tasks such as in science and software development. Kenya did not fulfill the new labor demand. Scale was now recruiting a fresh workforce primarily in the US with a new worker-facing platform called Outlier, offering as much as forty dollars an hour.

It was yet another stark illustration of the logic of AI empires. Behind promises of their technologies enhancing productivity, unlocking economic freedom, and creating new jobs that would ameliorate automation, the present-day reality has been the opposite. Companies pad their bottom lines, while the most economically vulnerable lose out and more and more highly educated people become ventriloquists for chatbots.

The empire’s devaluing of the human labor that serves it is also just a canary: It foretells how the technologies produced atop this logic will devalue the labor of everyone else. In fact, for the artists, writers, and coders whose labor the empires of AI turned into free training data, that is already happening.

Scale’s decision would send Winnie and her family spiraling. By then Millicent had lost her job and Remotasks had been the only thing keeping them afloat. Now they were struggling to feed their kids. Winnie was terrified they would soon be evicted. In her inbox, the email Scale sent to inform workers of the shutdown was cold and clinical: “We are discontinuing operations in your current location,” it read. “You have been off-boarded from your current project.”

With every team stretched dangerously thin, managers begged Altman for more head count. There was no shortage of candidates. After ChatGPT, the number of job applicants clamoring to join the rocket ship had rapidly multiplied. But Altman worried about what would happen to company culture and mission alignment if the company scaled up its staff too quickly. He believed firmly in maintaining a small staff and high talent density. “We are now in a position where it’s tempting to let the organization grow extremely large,” he had written in his 2020 vision memo, in reference to Microsoft’s investment. “We should try very hard to resist this—what has worked for us so far is being small, focused, high-trust, low-bullshit, and intense.

“The overhead of too many people and too much bureaucracy can easily kill great ideas or result in sclerosis. Unlike the big-iron engineering projects of the past, we could fulfill our mission with a surprisingly small number of great people.”