The Data Revolution in DNA

In 2003, sequencing a single human genome cost more than $1 billion.

Today a startup claims it can do the same thing for about $100.

That price collapse might be the most important technological curve you’ve never heard of.

But it should seem familiar. Because for decades now, we’ve measured technological progress with one deceptively simple concept.

Moore’s Law.

It tells us that the number of transistors on a computer chip doubles roughly every two years. Which means as costs fall, computing power compounds.

This compounding power is what turned room-sized mainframes into pocket-sized smartphones. It built the internet and gave us cloud computing. And it made artificial intelligence possible.

Entire industries have reshaped themselves around this trajectory.

Now, something similar seems to be happening in biology.

And if the latest claims out of San Diego are even close to right, we might have just crossed a threshold that changes medicine, insurance and even how we think about ourselves.

From $1 Billion to $100

When the Human Genome Project began in 1990, it was a scientific moonshot.

It took 13 years, more than $1 billion and a global consortium of research institutions to sequence a single human genome.

Today, a private startup says it can sequence a whole genome for about the price of a dinner for two.

Even if the real all-in costs are higher, it’s an incredible achievement. The price curve has collapsed from billions of dollars… to millions… to thousands.

And now the cost of sequencing a full human genome might have just dropped to $100.

To me, this looks a lot like Moore’s Law applied to biology. And if the trend continues, it could profoundly change the economics of modern health care.

Because at $1 billion per genome, sequencing was a scientific milestone. But at $100 per genome, it starts to become something akin to infrastructure.

You see, the human genome is the biological instruction manual that builds and maintains your body. It contains roughly three billion base pairs.

Image: Wikipedia Commons

For years, the challenge was simply being able to read this incredibly dense manual.

But now that it’s far faster, cheaper and easier to do, the only limiting factor is whether we can make sense of all that data.

Because that’s exactly what human genomes have become.

Data.

Fortunately for us, this is happening at the same time artificial intelligence is becoming powerful enough to do something useful with all that data.

Modern AI systems are built to analyze large, structured datasets. The larger the dataset, the better the models tend to perform.

If sequencing becomes inexpensive enough to become widely adopted, we move from a world where genetic information is scarce to one where it’s abundant. Instead of thousands of sequenced individuals, we could be talking about tens of millions, each tied to years of medical history.

This means that once DNA sequencing becomes routine, genetic information becomes just another type of health data. And biology starts to look less like specialized research and more like a data problem.

But sequencing alone won’t cure disease.

What changes things is what we learn from the data.

Think about what happened when information moved online. First, it was digitized. Then the cloud made it easy to store, share and analyze. That not only sped up the flow of information, it changed who had access to it and how entire industries operated.

Cheap DNA sequencing could play a similar role in health care.

Machine learning models are already being trained to identify disease risk, predict how proteins fold and flag genetic mutations linked to cancer and rare disorders.

But as more people have their DNA sequenced, AI systems will be able to spot additional patterns across large groups of patients. This should help doctors to be able to detect health risks earlier. Treatments will be better matched to the individual. And some conditions could be flagged years before symptoms appear.

And it doesn’t stop there. Drug companies will be able to design therapies for smaller, more specific groups of patients. Insurers will adjust how they think about long-term risk. And hospitals will be able to focus more on prevention instead of waiting for problems to become severe.

All these improvements could significantly impact the economics of a health care market that’s already measured in the trillions.

After all, we’ve seen this shift happen before in other industries like finance, transportation and retail.

We know that when large amounts of data become available, software can help guide decisions that used to rely on broad averages and intuition.

Health care might be nearing this same turning point.

Here’s My Take

Put simply, if the Human Genome Project decoded the alphabet of life, then AI is starting to understand the grammar.

This means we’ve reached a moment with biology that we’ve seen before in other industries. When information becomes digital and abundant, and innovation accelerates.

But there’s another side to this story.

If sequencing costs $100, what stops employers from asking for it? Or insurers from adjusting premiums based on genetic risk? Or governments from building national genomic databases in the name of public health?

The United States has laws like the Genetic Information Nondiscrimination Act (GINA), which was designed to limit genetic discrimination. But those laws were written when sequencing was rare and expensive.

They need to be updated for a world where it’s routine.

I believe we are entering a decade when biology begins to follow the same pattern that upended computing. As genome sequencing becomes more common and AI keeps getting better, medicine will move from reacting to illness to predicting it.

That transition will create enormous opportunities. In fact, it’s one of the reasons I highlighted Illumina (Nasdaq: ILMN) for my Strategic Fortunes readers last November.

It builds the sequencing platforms used by labs around the world. If genome sequencing becomes as routine as some researchers now expect, companies like Illumina will be right at the center of the genomic economy. And that could make it one of the biggest winners.

Because Moore’s Law didn’t just give us faster computers.

It rewired the modern economy.

And if biology is now following a similar exponential path, we should expect it to do the same.

Regards,

Ian KingChief Strategist, Banyan Hill Publishing

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