Biotechnology Is Becoming the Next Major Tech Industry

It can be challenging to get rid of a certain feeling when strolling through a biotech campus, past the glass hallways, the sound of centrifuges, and the whiteboards covered in molecular diagrams. This no longer appears to be a specialized science facility. It appears to be the first floors of a much larger structure. Something that is still in the process of naming itself.

Software, semiconductors, and, more recently, artificial intelligence have dominated discussions about technology for the majority of the past 20 years. In that discussion, biotechnology was always mentioned, but it was almost like a footnote—important, yes, but distinct in some way. a distinct category. An alternative kind of ambition. This perception is now clearly shattering, and it is important to observe the fracture lines.

One aspect of the story is the numbers. The biotech industry, which was estimated to be worth $1 trillion in 2022, is expected to reach $3.9 trillion by 2031, growing at a rate of almost 14% per year. It is not a niche market that is slowly expanding.

Similar to how the internet gained popularity in the late 1990s, that industry is expanding, but this time the raw material is biology rather than code. The transformative potential of biotechnology is consistently underestimated, even by those who ought to know better, according to a recent statement made by Cristiano Souza, the former Dynamo executive who currently leads Zeno Equity Partners.

The scale is not the only thing that is shifting. It’s the path. Biotechnology used to advance vertically, starting with laboratory research and continuing through clinical trials and pharmaceutical products. slowly. costly. frequently in vain. The current situation is more lateral and disruptive in the more traditional and truthful sense of the word. Businesses are using biological tools to solve issues unrelated to medicine.

For example, LanzaTech uses genetically modified microbes to produce ethanol from industrial carbon emissions. TotalEnergies, Unilever, and ArcelorMittal—an energy company, a massive consumer goods company, and a steelmaker seated at the same table, linked by biology—are among their partners. It’s difficult to ignore how peculiar and important that combination is.

Moderna provides an alternative example. For years, the company’s main description was that of a manufacturer of vaccines. The framing was never big enough. With mRNA sequences created with AI support, produced by integrated automated systems, updated, and redeployed more quickly than most conventional drug pipelines could even start their paperwork, Moderna has created what is essentially a fully digitalized biological platform. It no longer functions in the manner of a pharmaceutical company. It functions more like a biology-focused technology company, which may be the best way to predict the direction of this entire industry.

Then there’s AlphaFold from DeepMind. The system did more than just improve medicine when it solved the protein-folding problem, which had baffled structural biologists for fifty years: predicting how amino acid chains fold into three-dimensional shapes. It produced new infrastructure.

In the same way that software companies used to build on top of operating systems, cloud providers, data companies, and research institutions are now building on that capability. The relationship between digital technology and biology is no longer theoretical.

In particular, four businesses—Thermo Fisher Scientific, Danaher, Sartorius, and Merck KGaA—have subtly placed themselves at the center of all of this activity. Although they don’t always make the same headlines as the more glamorous biotech startups, they provide the tools, supplies, and services that almost all significant biological research operations rely on.

With revenues close to $45 billion in 2023, Thermo Fisher alone achieved a market capitalization of $202 billion. These players are not incidental. In actuality, they are the infrastructure layer; they are comparable to chip manufacturers during the computing revolution. Additionally, they have a kind of structural advantage that tends to compound quietly over time because pharmaceutical clients find it extremely expensive to switch away from their platforms.

The extent to which this extends beyond medical facilities and pharmaceutical trials is still not entirely understood by the general public. Human clinical trials for diseases like sickle cell disease and muscular dystrophy are beginning to use CRISPR-based gene therapies. Synthetic biology is being accelerated in ways that were practically unthinkable five years ago thanks to biofoundries, automated laboratory systems powered by robotics and artificial intelligence.

Within ten years, bioprinting—the process of creating functional tissue structures in laboratories—may change the way people think about organ transplantation. These threads are all moving forward on their own. What happens when they converge is the more intriguing question.

Despite the sector’s actual risk, investors appear to think something big is on the horizon. Long development timelines, complicated regulations, and the harsh economics of clinical trials make it impossible for every company conducting serious research to survive. Numerous biotech endeavors that produced truly significant science but failed commercially can be found throughout history. It frequently takes actual money to bridge the gap between a breakthrough and a business.

However, it appears that the skepticism that previously surrounded biotech as an investment—too slow, too speculative, and too reliant on regulatory goodwill—is giving way to something more patient and structural as this industry develops over the last few years.

There are sustainable positions in the supply chain, as the leading companies have already shown. Around them, the larger ecosystem is developing. Additionally, the two fields are starting to resemble a single emerging system rather than separate industries as AI continues to speed up drug discovery, shorten timelines, and make sense of the massive amounts of biological data being generated.

Programmable biology is emerging. It used to sound like an exaggeration. It’s becoming more and more a description of what’s already taking place in factories, labs, and agricultural fields—mostly away from the headlines that follow the release of the newest chip or AI model. If it turns out to be a revolution, it has already begun. It simply hasn’t made it to the front page yet.