The big names in AI are fighting over neuroscientists like they were soccer stars

AI companies have found their new hiring obsession. After the engineers prompts and multimodal model designers, now they are looking for neuroscientists at the stroke of a checkbook.

Why is it important. Language models have become common territory for all technology companies. The competitive advantage is no longer in having a LLMbut in making it more efficient and predictable. And to do that, they need to better understand how the human brain works.

The Battista case. Aldo Battista At New York University, he was researching brain decision processes when faced with subjective options. In September he made the leap to Meta, according to what he says Semafor, to apply that knowledge to content recommendation systems on social networks.

• The most notable change: the speed of impact. Instead of publishing papers that perhaps no one will read, the changes in algorithms show immediate results in the behavior of millions of users.
• His academic research on how we choose what to have for dinner, for example, now helps predict which video will hook us on Instagram.

There are more examples:

• OpenAI indeed approached Merge Labs a few months agoa brain implant firm competing with Neuralink.
• Akshay Jagadeesh joined OpenAI as research resident after almost ten years studying the brain and visual perception, focused on using his experience in computational neuroscience to improve AI models.
• At the ‘EBRAINS Summit 2025 – Neuroscience, AI & Technology’, a European event that brings together neuroscientists, technologists and industry, several biographies highlighted the jump from academic profiles to advice on AI startups.
• Ruslan Salakhutdinov is part of Apple AI Research. Although he is best known for Machine Learninghas worked for years on models inspired by biological systems and as a university professor, but Apple hired him as Director of AI Research.

The logic of the signing. The basics of artificial neural networks are decades old, but taking them further requires looking to biology. Two specific areas are of particular interest to companies:

1. Energy consumption.
2. Interpretability.

A human brain performs almost unlimited operations with just 20 wattsbut AI systems require much more energy for equivalent tasks. That gap is the Holy Grail: whoever reduces it will immediately gain an advantage.

The money trail. In the offers You can see the logic of the level they are reaching economically:

• A researcher position at OpenAI, in the area of ​​mathematical sciences and applied to AI, announces base salaries ranging from approximately $178,000 to $342,000 annually, not counting bonuses or stock packages.
• In other private AI labs, the ranges for researchers with a mix of AI and neuroscience move in a similar range, from about $150,000 to $350,000 a year.
• OpenAI has come to offer total packages that reach the range of millions of dollarsincluding salary, bonus and stocks. It’s not the norm for everyone, but it helps explain why some leading neuroscience researchers are negotiating contracts that look more like those of sports stars than those of a university professor.

Between the lines. Understanding why a model decides something matters more and more. For decades, neuroscience has developed methods to interpret complex decision processes. Those same tools can be applied to algorithmic black boxes.

Yes, but. The phenomenon is not new, it has only intensified. Apple, Google or Neuralink have been hiring these profiles for years. The difference is in the scale and current urgency.

Matthew Law works at OpenAI after studying at Stanford. Your diagnosis: AI companies have expanded their recruiting focus beyond traditional computer science graduates. They search the entire available scientific base. And the pool of pure developers is beginning to dry up.

The background. This race says something without having to say it: there is a certain desperation in the AI ​​industry to find differential advantages. If the next breakthrough innovation is in university neuroscience labs, Silicon Valley will not hesitate to empty them. Exorbitant salaries and practically unlimited funding are weapons that universities will hardly be able to counter.

In Xataka | Technology companies no longer even pretend to seek general artificial intelligence. And the “godfather” of AI has gotten tired

Featured image | Josh Riemer