Warning signs that basic AI research and human AI talent may be slipping through academia’s fingers

In today’s column, I address an increasingly expressed concern that our universities are losing ground in inventing the leading edge of AI advances by lacking the necessary computing resources and luring top AI talent away from academia. and in the private sector. The consequences are dire and disturbing if this trend continues unabated. A vital engine driving innovative approaches to AI will undoubtedly run out of gas and sputter at a low point, undermining the lauded and necessary pursuit of pure fundamental knowledge that drives the future of AI.

Let’s talk about it.

This analysis of the arrival of innovative AI breakthroughs is part of my ongoing Forbes column coverage on the latest trends underlying AI advancements (see link here).

Important wake-up call that needs to be heard

I recently attended an excellent workshop with Russell Wald, Executive Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and I will note various points made in his stimulating talk as part of my discussion below. The HAI seminar took place on January 15, 2025, which took place on the campus of Stanford University and covered the emerging concerns of an industry-academia divide in AI (for information about HAI events, activities and programs, see the link here).

There is also a helpful and entirely relevant white paper from HAI titled “Expanding the Role of Academia in UA in the Public Sector” that provides a close look at the growing issue (see link here), co-authored by Kevin Klyman, Aaron Bao, Caroline Meinhardt, Daniel Zhang, Elena Cryst, Russell Wald, and Fei-Fei Li (posted online December 4, 2024).

Here are some highlights from the HAI research paper (excerpts):

• “Building and deploying AI systems has become extremely resource-intensive, often requiring billions of dollars in investment, custom supercomputer clusters, and large datasets containing much of the data available on the Internet.”
• “This shift has created a significant power imbalance, with academic talent and government support flowing to private companies that now produce the vast majority of the world’s most powerful AI systems.”
• “This disparity undermines not only the future of academic research, but also the potential for a public sector AI ecosystem that serves the public interest.”
• “Academia must play a leading role in the development of frontier AI to ensure that we can safely understand and deploy the technology.”

The triad that is quietly eroding AI academic pursuits

As a former AI computer science professor who also served as the director of an AI lab, I can witness first-hand the growing phenomenon that is heavily impacting our universities when it comes to the vital pursuit of AI advances. I might add that as a founder of AI startups, I’ve definitely participated in acts that are now known as a sort of unintentional brain drain in AI academia. Yes, I ended up moving to the private sector where computing resources were more plentiful, and yes, I hired many of my best graduate students after completing their degrees. I eagerly accept this.

It’s an easy trap to fall into.

Among the many factors that influence AI academic pursuits, I tend to narrow the list down succinctly to a trio of these three:

• (1) Insufficient computing. AI advancement at the leading edge involves massive-scale GPU computing that is typically unavailable on campuses and prohibitively expensive to deploy via commercial cloud services. Academic AI researchers and their students are unable to move the needle on AI as they are hampered by a lack of iron to direct their AI efforts.
• (2) Faculty entering industry. Private industry is eager to snap up AI faculty, offering huge pay packages, seemingly endless computing resources, and appealing to a deeply felt desire to make demonstrable progress in AI. A downside is that AI discoveries often fall into the hands of the employer and become a proprietary asset. University-led AI research tends to be open source and is expected to widely disseminate innovations publicly and openly.
• (3) Graduates lured away from academia. Any student who spends a few years on their way to getting a computer science degree with an AI emphasis will be totally tempted to say goodbye to the academic world after completing their degree. This means that, for example, Ph.D. graduates are less likely to seek faculty positions. Their eyes are drawn to private industry.

A vicious cycle consumes the trio and leads to a downward spiral.

Here’s what I mean.

Faculty who cannot get the necessary computer will go out into the industry. The exit to industry leads to fewer faculty capable of covering AI classes, plus insufficient expertise in the halls of academia to adequately guide students in the latest AI research. Students who manage to complete their AI-related degrees jump right into industry endeavors. This reduces the pool of teaching assistants and research assistants. And hopes of bringing in new AI PhDs have been dashed as the pipeline is flowing directly into private industry rather than back into academia.

Rinse and repeat.

The problem has social consequences

A cursory glance might suggest that this is merely a narrow consideration and of no greater consequence. Sorry to say, there are big consequences.

I have covered extensively the belief that AI is an essential element to the future of our economy and that countries will develop geopolitical superpower dominance based on the AI ​​they use, see my analysis at the link here and the link here. In the United States, the rinse-and-repeat cycle is taking our country in a direction none of us will like. The commercial use of artificial intelligence will be the mainstay, while amazing advances in AI will be discovered elsewhere, but not here. This is bad for us.

A retort from some is that AI is moving towards smaller models anyway, moving from large language models (LLM) to small language models (SLM), as I detailed in the link here. Or that LLMs are being compressed so that they don’t require large-scale computation, such as the advent of 1-bit LLMs (see my analysis in the link here).

Let me set the record straight, we’re not going to somehow satisfy undergraduate AI research by squeezing out computing needs. Of course, more can be done as good progress evolves in reducing the resource requirements for AI. But if you’re playing at the cutting edge of AI innovation, small iron won’t get you there.

Play hard or go home.

Smart ways to move up

The good news is that we’re still in the early days of this picture of decay, so the ship can actually be righted. Don’t let despair or doom and gloom send you into a catatonic funk.

A little elbow grease and a keen sense of discretion can get things back on track.

As Russell Wald demonstrated during his presentation, now is the time to transform the emerging industry-academia AI divide into a society-saving industry-academia partnership in AI. The private and public sectors have an essential role to play in filling the gap in computing resources that are desperately needed in our universities.

Furthermore, since it takes a village to truly advance AI, the idea of ​​”team science” suggests that university AI efforts should be collaboratively interwoven with advanced commercial and government AI projects. This can shift brain drain in a way that drives us toward a brain gain.

One final thought for now.

I recently noted in my column that Generation Beta is underway — it’s the name of the generation for young people born from 2025 to 2039, so it started this month of January 2025. My eye-opening assertion is that Generation Beta will go beyond being a digital native generation, they will be what I refer to as one AI-natural generation. They will grow up with a natural and visceral familiarity with AI that will fully occur throughout their lives, as AI will be pervasive and ubiquitous (see my predictions on this phenomenon of AI naturalism at the link here ).

We must get our act together to first halt and then boldly reverse the industry-academia artificial intelligence divide. Do it for the kids. Make it for Generation Beta. Provide their generation with an ongoing and valued industry-academia partnership for AI. Heed the great words of John F. Kennedy, which are largely applicable to this day: “We have the power to make this the best generation of mankind in the history of the world or to make it the last.”

Let’s ensure that we aim high and lay a strong and lasting foundation that will ensure success for the generations here now and that follow us. I am very confident that we can.