Amazon attempts to lure AI researchers with $110M in grants and credits

The ongoing competition among major cloud vendors in the realm of artificial intelligence (AI) is intensifying, with notable developments from Google, Microsoft, and Amazon Web Services (AWS). Google’s custom chip, Trillium, has recently entered preview for training and running AI models, while Microsoft’s Maia chip is expected to debut shortly. However, AWS is making headlines with its own suite of AI chips—Trainium, Inferentia, and Graviton.

To promote its Trainium chip specifically, AWS is launching a new initiative called Build on Trainium, which aims to support AI research with substantial funding. This program will distribute a total of $110 million to educational institutions, scholars, and students engaged in AI research. As part of this initiative, AWS plans to award up to $11 million in Trainium credits to select universities and offer individual grants of up to $500,000 to other AI researchers.

AWS is also setting up a “research cluster” that consists of up to 40,000 Trainium chips, which research teams and students can access through self-managed reservations. Gadi Hutt, senior director at AWS’ Annapurna Labs, emphasized that Build on Trainium seeks to provide researchers with the necessary hardware support to advance their work. The program aims to address a significant resource bottleneck in AI academic research, which has been hampered by limited access to computational infrastructure compared to large tech companies. For instance, Meta has acquired over 100,000 AI chips for its models, while Stanford’s Natural Language Processing Group operates with only 68 GPUs.

Despite the ambitious goals of the Build on Trainium program, skepticism persists regarding AWS’s intentions. Some critics, like Os Keyes, a PhD candidate at the University of Washington, view the initiative as a potential means of influencing academic research funding. Keyes points out that AWS will have the ultimate say in the allocation of grants, raising concerns about the potential commercialization of academic research. Hutt mentioned that the evaluation process would consider research merit and needs; however, details about the selection process remain somewhat opaque. An AWS spokesperson later clarified that a committee of AI experts would review proposals to identify impactful projects.

Research indicates that corporate-funded AI studies often prioritize commercially viable work over critical analyses of ethical implications. A recent paper highlighted that leading AI firms generate less output addressing AI ethics compared to more traditional research avenues. This trend further raises concerns about the narrowing scope of “responsible” AI research funded by large corporations, as it often lacks diversity in topics.

Questions remain about whether participants in the Build on Trainium program will become entrenched within the AWS ecosystem. Hutt assured that grant recipients would not be locked into AWS technologies and would only be required to publish their findings and open-source their work on GitHub under a permissive license. This aspect of the initiative aims to uphold transparency and accessibility in research outputs.

However, the Build on Trainium program may have limited influence in bridging the divide between AI academia and industry. In 2021, government agencies in the U.S., excluding the Department of Defense, allocated only $1.5 billion for academic AI research funding, while AI industry investments worldwide exceeded $340 billion during the same period. The overwhelming majority of individuals earning PhDs in AI tend to gravitate toward private sector roles, often incentivized not only by higher salaries but also by access to critical computational resources and data.

Furthermore, companies have been increasingly aggressive in recruiting AI faculty and offering substantial grants to PhD students for their research. As a result, industry now accounts for over 90% of the largest AI models produced each year, while the volume of AI research papers with industry co-authors has nearly doubled since 2000.

In response to the widening funding gap between academia and industry, policymakers have started to pursue remedies. The National Science Foundation, for instance, announced a $140 million investment in 2022 to establish several university-led National AI Research Institutes, aimed at exploring how AI can address challenges such as climate change and educational improvement. Additionally, efforts are underway to create a U.S. National AI Research Resource, a $2.6 billion initiative designed to enhance access to computational resources and datasets for AI researchers and students.

Despite these efforts, they are relatively minor compared to the extensive corporate programs shaping the current AI landscape. Given the scale and financial clout of large tech companies, the prevailing situation in the AI research funding ecosystem shows little sign of change in the near future. Thus, while AWS’s Build on Trainium program presents an opportunity for advancing AI research, its efficacy in revolutionizing the relationship between academia and industry remains uncertain.