Atlases have long guided us through uncharted territory. Now, an AI-generated atlas by Google DeepMind seeks to do the same for the vast landscape of our DNA.

Ever since the Human Genome Project, scientists have painstakingly traced the myriad DNA mutations that contribute to health and disease. But that quest has largely been stymied by the genome’s vast scale. Only two percent encodes the proteins that make our bodies work; the rest may control how genes are turned on or off or be junk left over from evolution.

With roughly nine billion possible DNA letter swaps, testing each one in the lab is impossible. Making sense of their interactions is an even tougher challenge. Yet these changes often contribute to differences in risk for cancer, dementia, and other medical scourges.

DeepMind’s new atlas could lend researchers a hand. Generated from the company’s AlphaGenome AI released last year, the searchable database predicts the effects of every possible DNA letter swap. Thousands of researchers have already experimented with AlphaGenome, but those studies required some coding prowess, raising the barrier to entry.

AlphaGenome Atlas may make the AI more accessible. Analysis of individual DNA changes, down to the level of specific tissues, is readily available through a web portal for non-commercial use. As the most comprehensive catalog of how genetic mutations might affect molecules in the body, it could help uncover the mutations underlying traits and illnesses. By charting the genome’s “dark matter”—regions that don’t encode proteins— it might also reveal hidden rules that direct gene activity. The details are described in a paper.

“This represents the first time that any researcher in the world can access a comprehensive map of the human genome and its variations by simply opening a browser,” said Pushmeet Kohli, DeepMind’s vice president of science, in a press briefing.

The Language of Life

With just four DNA letters—A, T, C, and G—our genomic instructions seem simple. But the actual genetic playbook is far more complex. After piecing together the first draft of the human genome at the turn of the century, scientists were surprised by how little of it guided protein manufacturing. A staggering 98 percent didn’t seem to do much, earning the nickname junk DNA.

Long overlooked, these non-coding sections have increasingly captured attention for their role in regulating gene expression. Some DNA snippets can even operate thousands of letters away from the genes they control, making their involvement tough to decipher.

Non-coding DNA is also highly dynamic. Some genetic chunks can be duplicated or cut out as cells divide. Others jump to distant locations, reverse their sequences, or elbow their way into protein-coding genes.

Single-letter swaps are among the most prevalent DNA mutation. These can be relatively harmless. But they also can lead to diseases such as sickle cell anemia or raise a person’s “bad cholesterol” levels, increasing the risk of heart attacks. Gene-editing clinical trials are already underway to tackle these problems. But engineering a safe and effective treatment requires knowing which DNA swaps to make, and that’s been a roadblock.

Here’s where AlphaGenome comes in. Formally released early this year, the AI works in three steps. First, it spots short patterns in DNA sequence. Then it shares that information across a larger region of the DNA strand, letting it connect local patterns to distant letters. Finally, AlphaGenome translates those patterns into predictions of downstream biological effects.

The AI is customizable for different projects, allowing researchers to home in on DNA changes related to their specific questions. But it can only be accessed through an automated programing interface (API) which requires writing code and makes the data harder to access.

“AlphaGenome is helpful for analyzing specific variants and has found widespread use in research, but we wanted to show researchers a big-picture view of variants across the entire genome,” wrote the DeepMind team in a blog post.

Genome Cartographer

The new atlas does away with much of the coding and analysis, allowing researchers to search for DNA variants across the genome to see their potential effects.

To build the database, the team computed predictions for all three possible swaps at every DNA letter—for example, changing A to T, C, or G—resulting in a whopping petabyte of data.

As with AlphaGenome itself, the atlas generates thousands of predictions about how DNA changes affect molecular processes in different tissues. These include what happens when a nearby gene is switched on or how changes in the shape of chromatin, the tightly folded form of DNA, alter its biological activity.

“Just as an atlas is a collection of maps, linking together features of the land like altitude and location, AlphaGenome Atlas charts the molecular effects of DNA variants across the genome,” wrote the team.

But interpreting the atlas takes more work. With billions of potential changes, which ones should researchers prioritize?

To help them navigate the most promising variants, the team also developed a single metric to measure their predicted effects. Called the AlphaGenome Variant Impact (AVI) score, it combines AlphaGenome with AlphaMissense, a model that predicts the effects of mutations in protein-coding regions. Together, these two tools help distinguish harmless mutations from those more likely to play a role in disease.

In collaboration with the Broad Institute, the score has already helped researchers find and prioritize a non-coding DNA variant that may contribute to severe epilepsy. Rare disease researchers, who often lack the funding and computing resources needed to run genomic AI models directly, could particularly benefit from the atlas.

“If somebody is studying a disease, and they don’t have any idea about what cell types to look for or what molecular processes are impacted, then starting with an AVI score…is a great starting point to help you prioritize variants and try to find that needle in the haystack,” said genomic lead and study author Žiga Avsec in a press conference.

Beyond tackling genetic diseases, the atlas could also help decode mysterious non-coding motifs, or snippets of DNA scattered across the genome. Some motifs control the production of messenger RNA, which carries genetic instructions to the cell’s protein-making factories. Others alter the activity of individual genes. But most remain poorly understood, if they have a function at all.

Linking these motifs to large health databases, such as the UK Biobank, could map the gene interactions and resulting proteins underlying height and other complex traits. The atlas could also help AI agents rapidly generate hypotheses for human collaborators to explore in the lab.

AlphaGenome Atlas isn’t meant to replace real-world experiments. And unlike AlphaFold, DeepMind’s protein structure-predicting AI that garnered a Nobel Prize, DeepMind needs to further boost its accuracy. But the atlas is shaping up to be a valuable guide for genomic explorers navigating the vast DNA landscape that makes us human.

The post Google’s Genome Atlas Predicts the Effect of Every Possible DNA Mutation appeared first on SingularityHub.

For many established companies, the AI conversation starts with tools: Where can we deploy AI pilots? What processes can we automate? How much time or money can we save?

Meanwhile, a new generation of companies is starting with a different question: If we use AI from the ground up, how would we design this business?

Incumbents are largely using AI to improve organizations built for an earlier era. AI-native competitors can rethink the organization itself: its workflows, staffing, management layers, products, and cost structure.

An established company might use AI to make an existing process more efficient. An AI-native company can ask whether that process, or the organizational structure around it, needs to exist at all.

This raises a much harder question than how to adopt AI: How do you keep running the business that works today while simultaneously building the one that might replace it tomorrow?

The Threat Is Structural

For more than two centuries, companies have been designed around assumptions inherited from the industrial age.

As organizations grow, they add specialization, management layers, processes, controls, budgets, and systems intended to make performance more predictable. Successful companies become very good at serving known customers, forecasting demand, improving efficiency, and scaling what already works.

AI does not suddenly make those capabilities obsolete. But it does make some of the assumptions behind them worth questioning.

A startup built today can assume from the beginning that significant amounts of knowledge work can be automated or augmented. It can organize teams differently. It can build workflows around collaboration between humans and AI. It can operate with less human intervention and, potentially, a very different cost structure.

The advantage is not simply that these companies can do the same work faster. It is that they have permission to question whether the work, roles, processes, and organizational structures should look the same in the first place.

Why Successful Companies Struggle to Reinvent Themselves

This problem predates artificial intelligence.

Most successful businesses are optimized for the markets they already understand. They know their customers, their margins, their products, and their operating models. They have learned how to make all of those things more efficient over time. Progress comes through experimentation, failure, feedback, and iteration.

That is the logic of sustaining innovation. Disruptive innovation behaves differently.

Singularity expert Jody Medich describes the resulting resistance as corporate antibodies: the internal forces that protect the existing business but can inadvertently attack the experiments intended to create its future.

A promising initiative may be asked to meet the same revenue expectations as an established product. A team trying to experiment rapidly may encounter budgeting, procurement, legal, or approval processes designed for predictable operations. A new idea may gradually be pulled back toward the core business until what was supposed to be disruptive becomes merely incremental.

None of this requires hostile executives or shortsighted employees. The organization is often doing exactly what it was designed to do.

Running the Business and Reinventing It

If disruptive innovation behaves differently from the core business, companies may need to create different conditions for it to survive.

That can mean giving teams protected space to experiment without immediately subjecting them to the metrics of mature products. It can mean more flexible budgets, faster legal and operational support, and career paths that reward people who can work across disciplines and navigate uncertainty.

The point is not to isolate innovation permanently. It is to give new ideas enough distance from the core business to develop before the organization pulls them back toward familiar assumptions.

In some cases, the separation may need to go further. A subsidiary or other independent structure can give teams the freedom to experiment with different incentives, cost structures, workflows, and cultures. Instead of retrofitting AI into legacy systems, leaders can explore what an AI-native version of the business might actually look like.

That does not mean abandoning the advantages of being an incumbent. Large companies often have assets startups desperately want: capital, customers, distribution, data, brand recognition, and deep industry expertise.

The challenge is giving new ventures access to those strengths without forcing them to inherit every constraint of the existing organization.

The Workforce Has to Change Too

Organizational design is only part of the equation.

AI will change what many jobs require, eliminate some tasks, and create new ones. Companies that treat those shifts purely as a headcount exercise may miss an important source of competitive advantage.

Medich argues that established companies should invest in reskilling and internal mobility, helping employees learn to work with emerging tools and move into higher-value roles as parts of their existing work become automated.

Innovation teams also benefit from people who can move between specialties rather than staying inside conventional corporate silos.

Deep expertise still matters. But so does the ability to connect ideas across domains, translate between disciplines, and challenge assumptions that insiders have stopped noticing.

Becoming AI-Native Is Not a Technology Project

Eventually, the distinction between an “AI company” and an ordinary company will become meaningless. AI will simply become part of how organizations operate.

But getting there requires much more than adopting better software. Companies will have to reconsider how teams are organized, how experimentation is funded, how employees develop new skills, how success is measured, and which parts of the organization should be rebuilt rather than optimized.

Most importantly, leaders will need to become comfortable operating in two modes at once: improving the business they have while creating space for a fundamentally different business to emerge.

This article draws on insights from Singularity expert Jody Medich. The full report, How Companies Can Compete in an AI-Native World, explores the Medich model for disruptive innovation, common pitfalls in enterprise AI, and how organizations can build the structures, teams, and culture needed for continual reinvention.

The post The Real AI Disruption Isn’t the Technology. It’s the Company. appeared first on SingularityHub.