10x Genomics, a publicly traded company with roughly $2.5 billion in market capitalization, is building the technological foundation that will allow artificial intelligence to finally understand biology at scale. While headlines focus on AI drug discovery breakthroughs, a quieter revolution is happening in the tools that measure biological systems. The convergence of these two forces, according to the company's leadership, will fundamentally reshape medicine .

Why Can't We Understand Most of Biology Yet?

The answer is surprisingly simple: we haven't had the right tools to measure it. For decades, researchers relied on technologies that blurred critical details, treating thousands of cells as a single sample rather than understanding what each individual cell was doing. This limitation has constrained scientific progress far more than most people realize .

"Fundamentally, we still don't understand most of biology. And that's something people consistently underestimate, even within the field," said Serge Saxonov, co-founder and CEO of 10x Genomics.

Serge Saxonov, Co-founder and CEO at 10x Genomics

Founded in 2012 by Saxonov, Ben Hindson, and Kevin Ness, 10x Genomics has grown into one of the most influential toolmakers for biology. The company now generates around $600 million in annual revenue and its platforms are used widely across academia and industry to profile individual cells and map molecular activity inside tissues .

How Did Single-Cell Technology Transform Research?

When 10x Genomics launched its Chromium platform in 2016, the technology allowed researchers to monitor gene expression in tens of thousands of individual cells simultaneously. Today, researchers can routinely analyze millions of cells in a single experiment. This shift from measuring cell populations to measuring individual cells has been described by many researchers as a single-cell revolution .

The company has since pushed further into spatial biology, a rapidly growing field that maps molecular activity within intact tissues while preserving the physical context. Platforms such as Visium and Xenium allow scientists to see not only which genes are active in a cell, but exactly where those cells sit in a tissue and how they interact with their neighbors .

"Spatial gives you the ultimate combination of cell biology, pathology, and genomics. Instead of measuring one biomarker at a time, you can look at hundreds or thousands while preserving the tissue context," explained Saxonov.

Serge Saxonov, Co-founder and CEO at 10x Genomics

How to Build AI Models That Actually Understand Biology

• Generate massive, high-quality datasets: Large biological datasets must be created at scales that were unimaginable five years ago. The Biohub's Billion Cells Project aims to generate a dataset of one billion single cells using 10x technology to fuel new AI models in biology.
• Combine multiple data types into unified datasets: The UK-based PharosAI consortium is converting decades of archived cancer samples into one of the world's largest multimodal cancer datasets by combining spatial biology, genomics, imaging data, and AI models to uncover hidden patterns in tumor biology.
• Annotate samples with clinical context: To unlock disease biology, researchers need well-annotated biological samples, technologies that measure those samples at scale, and AI capable of interpreting the resulting data in clinical context.
• Create perturbation datasets for predictive models: Collaborations with groups such as the Arc Institute aim to generate massive perturbation datasets that could enable "virtual cell" models capable of predicting how cells respond to genetic or chemical changes.

The scale of these efforts is staggering. In Singapore, 10x is collaborating with the A*STAR Genome Institute of Singapore on the TISHUMAP initiative, which will analyze thousands of tumor samples to uncover new biomarkers and therapeutic targets. Meanwhile, PharosAI is bringing together King's College London, Queen Mary University of London, and major NHS research hospitals to convert archived cancer samples into datasets that could enable earlier diagnoses and more precise therapies .

What Does the Convergence of Biology and AI Actually Mean?

The real transformation happens when measurement technology meets artificial intelligence. Large biological datasets are now being generated at scales that enable AI systems to learn patterns that humans cannot see. But this only works if the data is accurate, comprehensive, and properly annotated .

"Everyone talks about AI. But in parallel a more quiet revolution has been happening in technology to measure biology. The convergence of these two will absolutely transform the world," noted Saxonov.

Serge Saxonov, Co-founder and CEO at 10x Genomics

The long-term vision is predictive models of biology that guide drug discovery, diagnostics, and personalized medicine. These advances could eventually enable therapies tailored to individual patients based on their unique genetic and cellular profiles. But reaching that future requires solving a fundamental problem: extracting meaning from massive amounts of biological data .

"The answers to disease are locked inside clinical samples. To unlock that biology you need the samples, the technology to measure them, and the AI to make sense of the measurements," stated Saxonov.

Serge Saxonov, Co-founder and CEO at 10x Genomics

10x Genomics is not positioning itself as an AI company. Instead, the company is focused on building the infrastructure that makes AI possible in biology. By generating massive, high-quality datasets at the right resolution and scale, 10x is creating the foundation upon which the next generation of biological understanding will be built. For now, the mission remains focused on accelerating fundamental science, with the belief that impact on human health will follow naturally from that progress .