Young, successful AI researchers are increasingly choosing to leave academia for industry.Credit: Costfoto/NurPhoto via Getty
In 2025, Google, Amazon, Microsoft and Meta collectively spent US$380 billion on building artificial-intelligence tools. That number is expected to surge still higher this year, to $650 billion, to fund the building of physical infrastructure, such as data centres (see go.nature.com/3lzf79q). Moreover, these firms are spending lavishly on one particular segment: top technical talent.
Meta reportedly offered a single AI researcher, who had co-founded a start-up firm focused on training AI agents to use computers, a compensation package of $250 million over four years (see go.nature.com/4qznsq1). Technology firms are also spending billions on ‘reverse-acquihires’ — poaching the star staff members of start-ups without acquiring the companies themselves. Eyeing these generous payouts, technical experts earning more modest-salaries might well reconsider their career choices (see ‘Academic brain drain’).
Academia is already losing out. Since the launch of ChatGPT in 2022, concerns have grown in academia about an ‘AI brain drain’. Studies point to a sharp rise in university machine-learning and AI researchers moving to industry roles. A 2025 paper reported that this was especially true for young, highly cited scholars: researchers who were about five years into their careers and whose work ranked among the most cited were 100 times more likely to move to industry the following year than were ten-year veterans whose work received an average number of citations, according to a model based on data from nearly seven million papers1.
Source: Ref. 1
This outflow threatens the distinct roles of academic research in the scientific enterprise: innovation driven by curiosity rather than profit, as well as providing independent critique and ethical scrutiny. The fixation of ‘big tech’ firms on skimming the very top talent also risks eroding the idea of science as a collaborative endeavour, in which teams — not individuals — do the most consequential work.
Here, we explore the broader implications for science and suggest alternative visions of the future.
Myth of lone genius
Astronomical salaries for AI talent buy into a legend as old as the software industry: the 10x engineer. This is someone who is supposedly capable of ten times the impact of their peers. Why hire and manage an entire group of scientists or software engineers when one genius — or an AI agent — can outperform them?
That proposition is increasingly attractive to tech firms that are betting that a large number of entry-level and even mid-level engineering jobs will be replaced by AI. It’s no coincidence that Google’s Gemini 3 Pro AI model was launched with boasts of ‘PhD-level reasoning’, a marketing strategy that is appealing to executives seeking to replace people with AI.
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