US research universities are the training ground for many of the PhD students who power private labs, yet these universities are not at the forefront of setting the AI agenda.
A key reason is the scale of private AI investment. The largest academic computing commitment in US history, the 10-year $457 million National Science Foundation (NSF) Leadership-Class Computing Facility (LCCF) at The University of Texas at Austin (UT Austin) is roughly what a single hyperscaler now spends in a matter of weeks.
To succeed, universities do not need to match industry’s capital or attempt to compete at the cutting edge of development. In fact, many already have the capability to work across specific domains of foundational AI research, often anchored by standout faculty or a long-standing lab collaboration with an industry partner. They also have the opportunity to make gains in the broader arena of applied AI research, a rapidly expanding source of value focused on developing models across real-world systems. (See sidebar, “Which AI Strategy to Pursue?”)
Which AI Strategy to Pursue?
Foundational AI Research. The university develops AI models, with research spanning generative and foundational models, natural language processing, reinforcement learning, and probabilistic machine-learning frameworks.
- We estimate about 10 universities globally have the technical depth, capital, and scale to compete with leading private AI players at the frontier of foundational model development, while also engaging deeply at the applied AI research layer.
- Another 20–30 universities can differentiate within specific domains of the foundational stack, often anchored by standout faculty or long-standing industry collaboration. Most, if not all, will also partner at the applied layer.
- Over 100 universities will compete primarily at the applied layer, leveraging domain strengths in medicine, agriculture, or aerospace.
This article proposes a four-layer framework to enable universities to position themselves as indispensable partners to industry and realize the potential value of such partnerships. (See Exhibit 1.) It’s a reinforcing cycle of steps: world-class research attracts talent, talent requires compute to do their research, compute enables corporate partnerships, and partnerships fund the next research cycle. Universities that build a coherent strategy across all four layers can do what neither corporate labs nor isolated academic departments can achieve alone.
Layer 1: The AI-Forward Research Lab
The balance of AI research has decisively shifted. Industry now produces more than 90% of notable frontier models.
The authors propose an ambitious path forward, termed the AI-Forward Research Lab, focused on interdisciplinary, field-building outputs. (See Exhibit 2.)
There are three models worth watching.
Model 1: Independent Nonprofit Labs and Focused Research Organizations. A new class of research institutions, built for speed and insulated from grant cycles, is emerging outside the university. The Arc Institute, founded with $650 million in philanthropic funding in partnership with Stanford, UCSF, and UC Berkeley, provides researchers renewable eight-year appointments with full salary and lab support, freeing them from grant cycles. In a short period, the model has produced technology breakthroughs (e.g., bridge recombinases, a programmable tool that can insert, delete, or flip large sections of DNA in a single step), companies raising hundreds of millions of dollars, and public goods co-developed with industry partners such as the Evo 2 DNA foundational model, developed with Nvidia. Similarly, focused research organizations (FROs), time-limited, nonprofit entities gaining traction through NSF’s newly created $1 billion X-Labs program are often able to move with greater speed and flexibility than traditional universities.
Universities that become founding partners are best placed to benefit from FROs, for example by structuring pre-negotiated IP terms with aligned FRO entities, establishing predetermined slots for recurring sabbaticals and exchanges, creating post-doc externships at FROs, and negotiating master service agreements for FROs to use university facilities and equipment to perform their research. Universities that do not risk having their best researchers recruited away.
Model 2: For-Profit Labs with Strong University Ties. For-profit labs with university ties show how porous the boundary between university and commercial labs has become. The 2024 Nobel Prize in Chemistry, awarded to the University of Washington’s David Baker for computational protein design and jointly to Google DeepMind’s Demis Hassabis and John Jumper for AI-based protein structure prediction, shows how the leading edge of science now spans academic and corporate labs. Genesis Therapeutics (Stanford) and Xaira Therapeutics (co-founded by Baker Lab alumni) reflect the same fluidity. (See sidebar, “The Baker Lab Alumni Network.”) Anthropic’s co-founder and chief science officer, Jared Kaplan, remains on leave from a Johns Hopkins physics professorship.
The Baker Lab Alumni Network
- Just under half (roughly 45%–50%) remained in academia: faculty appointments, postdoctoral roles, and research institutes.
- Roughly one-third to 40% moved into industry, including Microsoft, Google X, and biotech firms such as Ginkgo Bioworks, Generate:Biomedicines, and Xaira Therapeutics.
- The balance went into medicine, national laboratories, and other roles.
Flexible leave and IP policies matter more than ever. Universities that make it easy for faculty to move between academic and commercial roles, and cleanly negotiate equity participation in ventures that emerge, can capture more value than those that force binary choices.
Model 3: New Schools of Computing and AI Institutes. Increasingly, universities are turning toward creating entirely new schools dedicated to exploring the foundation and frontier of data science and computing. (See Exhibit 3.) Johns Hopkins is building the Data Science and AI Institute (DSAI) with 80 new tenure-track faculty positions as part of a broader expansion, which is expected to generate $1.6 billion in economic impact. Vanderbilt, the University of Chicago, and others are following. Federal funding is evolving toward large-scale funding and nimble, team-based efforts: NSF’s X-Labs funding call and the Department of Energy’s Genesis Mission both signal a preference for cross-sector collaborations, often led by full-time teams over traditional single-PI grants and research centers.
At the same time, IP disputes in AI research partnerships are rising. Who owns a model trained on university data with corporate compute is unsettled in law and practice. Therefore, it is important that universities establish clear frameworks before entering these arrangements. We expect a likely shift toward co-investment and co-development partnerships rather than traditional licensing-first models.
Layer 2: Securing and Deploying Compute
The scale of the gap is considerable. Big Tech’s four largest hyperscalers (Amazon, Google, Meta, and Microsoft) spent approximately $410 billion on AI infrastructure capital expenditure in 2025, with projections approaching $785 billion and about $1 trillion in 2026 and 2027, respectively.
- Model 1: The Single-Institution Powerhouse. UT Austin’s approach has been to become a single-institution powerhouse by assembling the most concentrated academic computing infrastructure through the $457 million NSF LCCF grant and partnerships with Dell and Nvidia.
9 9 Data Center Dynamics, “National Science Foundation to Invest $457m in Computing Facility to House Horizon Supercomputer,” July 30, 2024. The Horizon supercomputer entered production in 2026 and will offer roughly 100x the AI performance of Frontera, until now the leading US academic supercomputer. - Model 2: The State-Convened Consortium. New York’s Empire AI is the most ambitious multi-university compute consortium in the country, backed by over $500 million in public and private funding, including up to $340 million in state capital.
10 10 New York State Office of the Governor, “Governor Hochul Announces $40M to Launch Empire AI Beta Supercomputer,” June 26, 2025. The consortium includes 10 institutions (Columbia, Cornell, NYU, RPI, SUNY, CUNY, University of Rochester, RIT, Flatiron Institute, and Icahn School of Medicine at Mount Sinai) and has more than 320 active users across more than 120 projects. Its Beta system, featuring Nvidia’s Blackwell chips and slated to come online in 2026, will be 11 times more powerful than the current Alpha system. California has followed with CalCompute, housed in the UC system. The Empire AI model has also seeded adjacent talent efforts such as the Empire AI postdoctoral fellowship at Cornell Tech, funded with $10.5 million from Bloomberg co-founder Tom Secunda.11 11 Cornell Tech, “Tom Secunda Gives $10.5M to Cornell Tech for Empire AI Research,” March 5, 2025. - Model 3: Federal Supercomputing. The Department of Energy’s Genesis Mission and NSF’s LCCF represent a new model for allocating supercomputing time to academic researchers. DoE’s proposal to offer federal lands for private data center development in exchange for shared compute access points to an emerging model that states could replicate within their research universities.
12 12 US Department of Energy, “DOE Identifies 16 Federal Sites Across the Country for Data Center and AI Infrastructure Development,” April 3, 2025.
Leaders should be aware that a compute investment dependent on a single funding source is inherently fragile; diversified models leveraging multiple funding sources tend to be more resilient. This is especially the case in times of federal budget uncertainty. Universities should also account for capital expenditure cycle risk: compute investments made today reflect current GPU architectures, and there is limited clarity on how institutions will refresh infrastructure at the pace that technology is advancing. A $100 million cluster that becomes obsolete within three years before generating its intended research output represents significant stranded-asset risk. Compute access is also increasingly a recruiting tool, with commitments to compute becoming a standard element of faculty startup package negotiations.
Layer 3: Reimagining Corporate Partnerships
Many corporate partnership offices were built for an era in which companies sought brand affiliation, sponsored hackathons, and recruited graduates. Two important developments are prompting university leaders to reevaluate how they approach these partnerships.
- AI companies need and often cannot easily acquire or replicate the curated datasets that universities hold. While especially common in universities with affiliated hospital systems (e.g., datasets on longitudinal health studies and genomic databases), academic datasets span industries and include decades of data on topics from agricultural field trials to robotics testing, as well as detailed data on infrastructure and energy systems and more. There is no proven, replicable model for extracting full strategic value from these assets. The institution that develops one, covering governance, pricing, access terms, and model ownership, can seize durable competitive advantage.
- Manufacturers, Fortune 500 companies, and local corporations are seeking academic AI expertise for product design, pipeline, and deployment. For example, Indiana-based Eli Lilly and Purdue University announced a $250 million partnership in 2025 to accelerate AI-powered drug discovery and AI-enabled manufacturing scale-up.
13 13 Wes Mills, “A Year of Advancing the Lilly-Purdue $250M Research Partnership to Accelerate Medicine Discovery and Manufacturing,” Purdue University News, May 11, 2026. Universities have the potential to become preferred AI implementation partners for regional and national industry, not just model developers. (See Exhibit 4.)
There is the concern that corporate partnerships can become talent pipelines in the wrong direction: close collaboration with an industry lab can accelerate a faculty member’s departure. To address this, it is important to structure partnerships to deepen engagement rather than facilitate exits. Health data, student records, and research subject information carry significant privacy obligations that need proper oversight. Finally, convincing individual researchers and schools that there is value in expanding corporate partnership scope requires both a compelling institutional vision and an incentive structure that rewards faculty who bring partners to the table for institution-wide collaborations. OpenAI, Microsoft, Salesforce, and AWS are among the companies expanding collaborations with universities through research funding, compute and model access, and support for applied AI projects.
Layer 4: Winning the R&D Talent Competition
The AI talent landscape is rapidly evolving. Roughly 70% of AI-relevant PhD graduates now take industry positions post-graduation, up from 20% two decades ago.
Federal funding uncertainty has contributed to what is a paradoxical market for talent. Admissions to PhD programs at 55 leading US research universities fell 15% for the Fall 2026 semester, and several top universities have imposed hiring freezes.
Universities cannot match industry salaries. But they can compete with industry when it comes to:
- Freedom to publish openly and pursue curiosity-driven agendas unconstrained by product roadmaps or earnings calls.
- Multi-year research endowments that insulate researchers from grant cycles.
- Flexible leave policies that allow faculty to move between academic and commercial roles, including dual appointments with affiliated research entities.
- The opportunity to train and mentor the next generation.
Universities could also do much more to go beyond the school-level tracking they currently do. One step is to begin mapping their AI lab alumni networks more systematically—identifying where a lab’s alumni have landed, maintaining those connections, and creating pathways for philanthropic giving and co-founding opportunities.
Another development worth noting is that China now produces significantly more AI-relevant PhDs than the US and is responsible for 74.2% of all AI patents granted worldwide.
Scaling with State Partners
The strategic choices about AI sit largely within universities, but they are difficult to execute at scale without state partnership. Increasingly, states are moving beyond general innovation strategies to capitalize on shared research infrastructure and organize universities, companies, and public agencies around AI. New York’s Empire AI, Pennsylvania’s Keystone AI + Quantum Factory, and the University of Florida’s HiPerGator AI supercomputer illustrate the range of models emerging, from multi-university compute consortia to concentrated investments in flagship institutions and statewide shared infrastructure. University leaders can engage proactively in shaping these investments through active collaboration with their state partners. (See Exhibit 5.)
Seven Actions to Turn Ambition into Action
Every research university must decide where it can lead, what capabilities it must build, and which partnerships it needs to succeed through a cohesive, integrated approach to AI. (See Exhibit 6.)
The window to shape that position is narrowing. Talent, corporate partnerships, philanthropic capital, and public investment are already concentrating around institutions that have a clear AI ambition they are executing. University leaders that act now will be better able to attract and retain talent, maintain influence, and set the direction of research.
