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There’s a lot of AI news this week from Google, Microsoft, OpenAI, and Anthropic, which we’ll cover in the news section below. Most of the product innovations these companies are rolling out are built on top of a few key “foundation” models. These are large AI models that, once trained, can perform all kinds of different tasks. Today’s large language models are trained to just predict the next word in a sentence, but after that training can perform many language tasks—from translation to answering questions like virtual encyclopedias to summarization.
But there are still some advantages in training more narrowly-tailored foundation models for specific areas. For instance, Google DeepMind’s AlphaFold 3 is a foundation model for biology. It can’t write poetry. But it can predict the structure of proteins and the interactions between two proteins or between any protein and any small molecule. That makes it super useful for tasks like drug design. Wayve, a U.K. self-driving car startup, has built foundation models that can handle many different aspects of driving—identifying objects, deciding the best way to steer the car, and working the accelerator and brake, for instance. Robotics company Physical Intelligence has built foundation models for robotics that can help any kind of robot perform all kinds of different tasks without any additional training.
For businesses, it is often a lot easier to see a path to ROI from these somewhat more narrow foundation models than it is from the completely generalist LLMs. A Swiss Army knife is great. But you probably wouldn’t want to use it to perform surgery. In today’s Eye on AI, I want to introduce you to Kumo, a Silicon Valley company that has built a foundation model that is supposed to make it easy to do something that sits at the heart of business decisions: making accurate predictions.
Saving time, data—and money
Normally, making predictions from data requires painstaking work by data scientists, over days, weeks, or even months. Machine learning and deep learning—the sub-branch of machine learning most closely affiliated with today’s AI—has been applied to predictive analytics for years. But these models were usually tailored to make just one particular kind of prediction in one specific context and have to be trained on a large dataset specific to that use case before they can render accurate predictions. Big technology companies and major retailers often have the reams of data needed to train these kinds of predictive AI models. But a lot of smaller enterprises do not.
Kumo’s new RFM model—which they are announcing and making available to customers today—on the other hand, can handle all kinds of different predictions. From customer churn to credit default risk to the chances that a patient discharged from the hospital will need to be readmitted within 24 hours—KumoRFM can handle all of these different predictions, and can do so almost instantly, without any additional training. “With the foundation model, you point it to your data, you define what you mean by churn, and a second later, you get the prediction,” Jure Leskovec, the Stanford University computer scientist who cofounded Kumo three years ago and serves as its chief scientist, told me, using the example of creating a customer churn model. He said a customer could further fine tune the model on its own data and get about a 10% improvement in the accuracy of its predictions.
Using graph neural networks to discover key correlations
Kumo’s model is based on Leskovec’s research into graph neural networks, which can encode the relationships between things in the structure of the network, and applying this method to data listed across different tables and understanding how the data in those tables changes across time. (RFM, the name of Kumo’s model, stands for Relational Foundation Model.) The model also couples a graph structure with the same kind of Transformer architecture—which is good at figuring what data to pay attention to in order to make an accurate prediction, even if the crucial, predictive data occurs far back in a sequence. The foundation model has been trained on publicly-available data as well as what Leskovec said is a large amount of synthetic data.
As long as the time stamps across a user’s tables are correct, Kumo’s model is able to make highly accurate predictions, he said. On benchmark tests that Kumo conducted, RFM without any fine-tuning performs better than some traditional machine learning methods, on par or better than a human data scientist who has hand-crafted a model, and only a bit worse than a graph neural network that has been specifically trained for that task. With additional fine-tuning, RFM performed on par or, for some tasks, significantly better than the graph neural network trained in the traditional way for a single task. And, critically, when compared with trying to use Meta’s Llama 3.2 B large language model and asking it to try to make predictions based on a prompt, RFM performed significantly better. (Kumo’s benchmark results have not been independently replicated and verified.)
KumoRFM’s results can also be more interpretable than many of the hand-engineered models that data analysts construct. That’s because human data analysts sometimes develop signals that they think are predictive—for instance, saying a customer might be more likely to purchase a particular product if they see an advertisement for it after 10 p.m.—but which turn out to be spurious. “Today’s models can only explain through the signals you generated. But in our case, we can go all the way down to the raw data and say, because of these events, because of this information, we made this decision,” Leskovec said.
Kumo has received $37 million in venture capital funding to date from investors including Sequoia Capital, and currently employs a team of around 50 people split between Silicon Valley and Europe. Its models have been used so far by companies including food delivery app DoorDash, Reddit, and U.K. grocery chain Sainsbury’s, among others.
For enterprises struggling to extract value from their data and frustrated by the lengthy process of building predictive models, Kumo’s approach could represent a significant efficiency breakthrough. (Amazon Web Services offers a foundation model called Chronos for making predictions about things that occur in a timed sequence, but it still requires fine-tuning to achieve accurate results. The data monitoring software company Datadog also offers a similar foundation model called Toto.) It also suggests that while much attention focuses on general-purpose AI, there remains enormous potential in more specialized frontier models that solve specific, high-value business problems. With that, here’s the rest of this week’s AI news.
With that, here’s more AI news.
Jeremy Kahn
jeremy.kahn@fortune.com
@jeremyakahn
Before we get to the news, the latest Fortune Most Powerful Women list is out today and it includes a number of important figures to the AI industry, including AMD CEO Lisa Su, Huawei deputy chairwoman Meng Wanzhou, Anthropic president Daniela Amodei, and Thinking Machines Lab founder and CEO Mira Murati. You can check out the list here. There’s also a great interview with New York Times’ CEO Meredith Kopit Levien by Fortune’s Ruth Umoh that touches on how the publisher sees AI as both an opportunity and a threat, and why it’s suing OpenAI. You can check that out here.
This story was originally featured on Fortune.com
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https://fortune.com/2025/05/20/kumo-ai-rfm-foundation-model-for-predictions-shows-power-of-smaller-foundation-models-eye-on-ai/
Jeremy Kahn
