AI workers at other Big Tech companies, including Google and Microsoft, told CNBC about the pressure they are similarly under to roll out tools at breakneck speeds due to the internal fear of falling behind the competition in a technology that, according to Nvidia CEO Jensen Huang, is having its “iPhone moment.” (View Highlight)
They spoke of accelerated timelines, chasing rivals’ AI announcements and an overall lack of concern from their superiors about real-world effects, themes that appear common across a broad spectrum of the biggest tech companies — from Apple to Amazon to Google. (View Highlight)
Engineers and those with other roles in the field said an increasingly large part of their job was focused on satisfying investors and not falling behind the competition rather than solving actual problems for users. Some said they were switched over to AI teams to help support fast-paced rollouts without having adequate time to train or learn about AI, even if they are new to the technology. (View Highlight)
A common feeling they described is burnout from immense pressure, long hours and mandates that are constantly changing. Many said their employers are looking past surveillance concerns, AI’s effect on the climate and other potential harms, all in the name of speed. Some said they or their colleagues were looking for other jobs or switching out of AI departments, due to an untenable pace. (View Highlight)
This is the dark underbelly of the generative AI gold rush. Tech companies are racing to build chatbots, agents and image generators, and they’re spending billions of dollars training their own large language models to ensure their relevance in a market that’s predicted to top $1 trillion in revenue within a decade. (View Highlight)
Microsoft Chief Financial Officer Amy Hood, on an earnings call earlier this year, said the software company is “repivoting our workforce toward the AI-first work we’re doing without adding material number of people to the workforce,” and said Microsoft will continue to prioritize investing in AI as “the thing that’s going to shape the next decade.” (View Highlight)
“This leads me to believe that we should invest significantly more over the coming years to build even more advanced models and the largest scale AI services in the world,” Zuckerberg said. (View Highlight)
In an emailed statement to CNBC, an Amazon spokesperson said, the company is “focused on building and deploying useful, reliable, and secure generative AI innovations that reinvent and enhance customers’ experiences,” and that Amazon is supporting its employees to “deliver those innovations.” (View Highlight)
When it comes to ethics and safeguards, he said, Microsoft has cut corners in favor of speed, leading to rushed rollouts without sufficient concerns about what could follow. The engineer said there’s a recognition that because all of the large tech companies have access to most of the same data, there’s no real moat in AI. (View Highlight)
At Google, an AI team member said the burnout is the result of competitive pressure, shorter timelines and a lack of resources, particularly budget and headcount. Although many top tech companies have said they are redirecting resources to AI, the required headcount, especially on a rushed timeline, doesn’t always materialize. That is certainly the case at Google, the AI staffer said. (View Highlight)
The Google AI engineer, who has over a decade of experience in tech, said she understands the pressure to move fast, given the intense competition in generative AI, but it’s all happening as the industry is in cost-cutting mode, with companies slashing their workforce to meet investor demands and “increase their bottom line,” she said. (View Highlight)
An AI researcher at a government agency reported feeling rushed to keep up. Even though the government is notorious for moving slower than companies, the pressure “trickles down everywhere,” since everyone wants to get in on generative AI, the person said. (View Highlight)
Regardless of the employer, AI workers said much of their jobs involve working on AI for the sake of AI, rather than to solve a business problem or to serve customers directly. (View Highlight)
“A lot of times, it’s being asked to provide a solution to a problem that doesn’t exist with a tool that you don’t want to use,” independent software engineer Kolman told CNBC. (View Highlight)
The Microsoft AI engineer said a lot of tasks are about “trying to create AI hype” with no practical use. He recalled instances when a software engineer on his team would come up with an algorithm to solve a particular problem that didn’t involve generative AI. That solution would be pushed aside in favor of one that used a large language model, even if it were less efficient, more expensive and slower, the person said. He described the irony of using an “inferior solution” just because it involved an AI model. (View Highlight)
The engineer has worked in machine learning for years, and described much of the work in generative AI today as an “extreme amount of vaporware and hype.” Every two weeks, the engineer said, there’s some sort of big pivot, but ultimately there’s the sense that everyone is building the same thing. (View Highlight)
He said he often has to put together demos of AI products for the company’s board of directors on three-week timelines, even though the products are “a big pile of nonsense.” There’s a constant effort to appease investors and fight for money, he said. He gave one example of building a web app to show investors even though it wasn’t related to the team’s actual work. After the presentation, “We never touched it again,” he said. (View Highlight)
An AI engineer who works at a retail surveillance startup told CNBC that he’s the only AI engineer at a company of 40 people and that he handles any responsibility related to AI, which is an overwhelming task. (View Highlight)
He said the company’s investors have inaccurate views on the capabilities of AI, often asking him to build certain things that are “impossible for me to deliver.” He said he hopes to leave for graduate school and to publish research independently. (View Highlight)
