Editorial
The Gist
- Universities lack AI resources. Stanford University leaders highlight that academia cannot compete with private companies in AI research due to limited access to GPUs and funding.
- Empire AI coalition offers a glimmer of hope. A New York-based coalition is pooling resources to create a shared AI computing center for academic research.
- Government action could level the field. The proposed "Create AI" bill seeks federal funding to provide universities with shared resources for large-scale AI experiments.
Stanford University has 300 GPUs. Microsoft will have 1.8 million. Here's what's needed to make academia relevant in AI research.
In front of a packed room inside the Dirksen Senate Office Building in Washington, D.C. last Tuesday, the leadership of Stanford’s Institute for Human-Centered AI made a plea.
Co-director Fei-Fei Li and executive director Russell Wald told the approximately 200 congressional staffers gathered that universities today simply don’t have the resources to do basic generative AI research. The chips, data centers and energy costs are not in scope for university budgets. And they need serious help.
Universities Struggle to Compete in AI Research
“All U.S. universities combined could not build a version of ChatGPT right now,” Wald told me. “That's pretty problematic.”
The dire situation is reaching a boiling point. Microsoft is aiming to have 1.8 million GPUs by the end next month while Stanford University has approximately 300 of these chips, which are the critical components for testing and training generative AI.
Related Article: Decoding the Differences of Traditional AI vs. Generative AI in Marketing
Private Sector vs. Academia: The Growing Divide
Wald then recited some stunning statistics showing how far universities have fallen behind the private sector in an area where they previously led.
In 2022, Wald said, there were 22 significant AI breakthroughs that came from industry, compared to only three from academia. In 2011, AI PhDs would go into private industry and academia in about equal numbers, now 70% go into private industry, he said. Professors and grad students interested in doing cutting edge research also struggle to choose universities because the resources available will not allow them to do the work.
“It's deeply concerning about the future of the technology,” Wald said, “because it begs the question — Does the academy belong in frontier AI research? We at HAI would argue yes it does.”
University technology research, Wald said, is necessary because it’s freed from the intense product-focused work that is done within companies, helping it spark fundamental breakthroughs like GPS, MRIs and the internet.
“These are things that no investor would ever put any money into, because they wouldn't see a return on investment,” he said. “They'd be dead by the time they saw any really true profitable margin out of this.”
The Role of Academia in Advancing Technology
Wald also noted university AI research can help train regulators to understand AI, so that knowledge extends beyond those within the companies themselves.
And it can serve the public good by publishing research and open-sourcing models given that companies like OpenAI and Google have stopped sharing their cutting-edge research as the field has grown ultra-competitive.
What Are the Solutions?
So, universities have a serious AI resource problem right now. Along with not having the money to invest in AI data centers (an NVIDIA GPU can cost up to $40,000 per chip) they are facing challenges in setting up facilities with enough space and cooling capacity to operate. And the energy costs add up as well.
But seeing the problem, some academic institutions such as Stanford and a new group in New York are fighting to fix it.
Empire AI: A Collaborative Step Forward
In New York, a first-of-its-kind coalition of universities, the state and private philanthropy has just built an artificial intelligence computing center that started running experiments last week. The initiative, called Empire AI, is set up as a shared resource at The University of Buffalo and includes NYU, Cornell, Columbia, RPI, SUNY and CUNY. The group received a donation of GPUs from the Simons Foundation and support from Empire State Development.
“We're just in the process of testing Empire AI; we’re starting to run the first jobs,” said Stacie Grossman Bloom, NYU’s chief research officer, vice provost and vice chancellor for global research and innovation.
NYU researchers are already using the machine to detect deep fakes. The university, Grossman Bloom said, has 19 work order requests in, with more to come. “The idea is for the machine to not be idle at all. But to really run it, run it hard and, run it at capacity,” she said.
Empire AI currently has 96 NVIDIA H100 GPUS, which, even at a multi-million dollar value, don’t come close to what companies like Amazon, xAI, Meta, Microsoft, OpenAI are using to build, interrogate and run their models. But the coalition does plan to meaningfully expand the compute resources within Empire AI as it moves beyond its ‘alpha’ phase.
Such initiatives ultimately won’t put universities in position to do foundational research without a massive increase in resources — and that’s where the federal government might come in.
Related Article: Confronting AI and Inequality in the Big Tech Era
The Create AI Act: A Solution on the Horizon
Stanford’s Wald and Li were in DC advocating for Congress to pass the “Create AI” act, a bill that would make permanent a shared resource that would enable large scale AI research. This “National Artificial Intelligence Research Resource,” which has a pilot established by the Biden administration through executive order, would make compute and data sets available to universities and students to run AI experiments.
The U.S. government and partner companies like NVIDIA and Microsoft have already put tens of millions of dollars into the project, but the bill would establish it in law and help it get funded through federal government appropriations, via the National Science Foundation. The current funding number discussed within Congress is about $400 to $500 million per year, Wald said, over the course of six years. It would be a paradigm changing bill if passed.
A Race Against Time in Congress
Wald said that he and Li set up last week’s gathering with Congressional staffers last Friday. Initially, only ten signups came in, and they began to worry. But the event became so popular that a line wrapped around the hall as people scrambled to get in.
The Create AI bill has momentum within Congress today. There’s bipartisan support in both houses. Seventy eight companies, universities and institutions, from Princeton to Google, just wrote leadership last week supporting it. And it could pass during the lame duck session.
Webinar
Sep
25
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Register
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
Webinar
On demand
From Hype to High-Impact CX Strategies That Actually Scale
Turn buzzworthy AI and outsourcing trends into measurable CX wins with fresh 2025 data.
Watch Now
Webinar
On demand
Insights to Action Rethinking the Contact Center for Real Business Impact
Join our exclusive webinar to hear CX executives share their innovative strategies for transforming service delivery.
Watch Now
Webinar
On demand
Accelerating Healthcare Ops with AI
Sign up to see how your peers are putting AI to work — and making it stick.
Watch Now
But Wald said the bill’s fate hinges on legislators agreeing to get it done in a very busy period, effectively giving it a six-week window before Congress turns over.
“It is probably one of the top easy things to pass on any AI agenda,” Wald said.
And now, the only question is whether the will is there to do it.
Learn how you can join our contributor community.
