The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny? The article discusses the growing divide in computing resources between academic and industrial AI labs, which has significantly impacted the landscape of machine learning research. Academic labs, once dominant in developing large-scale machine learning models, are now outpaced by industry labs, which have access to more powerful computing resources. This divide has led to a reduction in academic contributions to compute-intensive research areas, particularly in foundation models. Industry labs are increasingly responsible for developing large-scale models, while academic labs focus on lower-compute intensity research and open-source pre-trained models. The article highlights the challenges faced by academic researchers due to limited funding, engineering expertise, and access to computing clusters. Industry labs, with better infrastructure and access to cloud resources, are better positioned to handle the high computational demands of modern machine learning. This has resulted in a shift towards open-source models developed by industry, which are more accessible to academic researchers. The compute divide also affects the scrutiny and evaluation of machine learning models. Industry labs are less likely to share their models and code, which limits the ability of academic researchers to test and evaluate these models. This reduces the overall transparency and accountability in AI development. The article suggests that national computing infrastructure and open science initiatives could help bridge this gap by providing more equitable access to computing resources. To address these challenges, the article recommends policies that promote responsible compute provision, open science, structured access, and third-party auditing. These measures aim to ensure that academic researchers can contribute to the development and evaluation of AI systems, even as industry labs dominate the field. The article emphasizes the importance of balancing intellectual property rights with the need for public accountability and transparency in AI research.The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny? The article discusses the growing divide in computing resources between academic and industrial AI labs, which has significantly impacted the landscape of machine learning research. Academic labs, once dominant in developing large-scale machine learning models, are now outpaced by industry labs, which have access to more powerful computing resources. This divide has led to a reduction in academic contributions to compute-intensive research areas, particularly in foundation models. Industry labs are increasingly responsible for developing large-scale models, while academic labs focus on lower-compute intensity research and open-source pre-trained models. The article highlights the challenges faced by academic researchers due to limited funding, engineering expertise, and access to computing clusters. Industry labs, with better infrastructure and access to cloud resources, are better positioned to handle the high computational demands of modern machine learning. This has resulted in a shift towards open-source models developed by industry, which are more accessible to academic researchers. The compute divide also affects the scrutiny and evaluation of machine learning models. Industry labs are less likely to share their models and code, which limits the ability of academic researchers to test and evaluate these models. This reduces the overall transparency and accountability in AI development. The article suggests that national computing infrastructure and open science initiatives could help bridge this gap by providing more equitable access to computing resources. To address these challenges, the article recommends policies that promote responsible compute provision, open science, structured access, and third-party auditing. These measures aim to ensure that academic researchers can contribute to the development and evaluation of AI systems, even as industry labs dominate the field. The article emphasizes the importance of balancing intellectual property rights with the need for public accountability and transparency in AI research.