Right now, the minds of many investors are occupied with one important question: What industry will artificial intelligence (AI) turn on its head next? Investors are looking for a new point of capital allocation, and the biotech space seems to be a potentially promising candidate.
If we analyze the fundamentals of the pharmaceutical business and overlay them with the capabilities of generative AI, the logic leads us in a certain direction. Of course, I don't claim to know exactly how AI will definitively improve or affect the biotech industry, as the future is always unpredictable. But if we conduct an analysis and consider some hypotheticals, we can start to identify what might happen next in the world of AI.
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Let's take a closer look at what AI could have in store for biotech.
The Mechanics of an AI Breakthrough
We don't have to dive into complex molecular biology to understand the essence of what AI could bring to the table for biotech. Instead, we can look at the situation in simple terms.
The whole essence of modern pharmacology boils down to working with proteins. Proteins in the human body, as well as in the structures of viruses or bacteria, are a universal set of parts. How a protein chain folds in space determines its function — whether it will break down food, carry oxygen, or become a receptor through which a disease enters a cell.
Until recently, creating a new drug was a relatively blind and lengthy, search-like process. Scientists could understand which part was broken or which target on a cancer cell needed to be blocked, but finding the correct key to the puzzle required years of essentially manual sorting. Researchers tested thousands of variants in laboratories, spending significant amounts of money, time, and resources to find working formulas by trial and error.
This is where AI enters the scene. What some tech giants have recently done — particularly divisions of Alphabet's (GOOGL) Google — changes the rules of the game. Modern neural networks haven't just learned to predict the shape of existing proteins. Generative AI in biotech has appeared, and it works on the same principle as neural networks creating images from text prompts, only it operates with atoms instead of pixels.
Now, a scientist can set parameters for the system, and AI can provide a ready-made blueprint. Moreover, AI is capable of generating proteins de novo — that is, constructing molecules from scratch that have never existed in nature before.
Tech Giants Enter the Pharma Space
Previously, the barrier to entry in the drug development market was extremely high. Entering this market without hundreds of laboratories and significant investments seemed practically impossible. But the emergence of AI is forming a new segment in the industry.
One of the main drivers here is Alphabet. The company's DeepMind division first made an important step by creating the AlphaFold 3 neural network, which learned to model the interaction of proteins with DNA, RNA, and chemical compounds. Then, under the influence of the scientific community, Google open-sourced this model, making it available to a wide range of researchers.
But some of the most interesting things in this space are now happening on the commercial front. In 2021, Alphabet created a subsidiary called Isomorphic Labs, an AI-first pharma company with serious computing power. The latest Isomorphic Labs Drug Design Engine (IsoDDE) closed system can model molecular bonds twice as accurately as AlphaFold 3.
In essence, Google and similar tech platforms are becoming the new architects of medicine. Isomorphic Labs has already signed large contracts with traditional giants like Eli Lilly (LLY) and Novartis (NVS), and the firm is starting to develop its own drug programs in fields like oncology.
The emergence of a player like Isomorphic Labs changes the biotech landscape in two ways. First, the barrier to entry in drug discovery is lowering. Powerful AI from tech leaders can make drug development accessible to firms without their own laboratories. Second, a new alignment of forces is taking shape. Traditional pharma companies risk losing exclusive rights to knowledge. If before, large companies were the sole holders of the secrets of drug creation, now they are being forced to share the market and future profits with Silicon Valley.
The Golden Age of Uniqueness: How the Economics of Big Pharma Worked
Before assessing the consequences of this breakthrough, let's recall the basic economics of the largest pharmaceutical firms. These business models are based on a critically important concept: uniqueness.
Creating a high-demand drug is a process that lasts 10 to 15 years and costs companies billions of dollars. Most drugs in development are rejected at various stages amid their clinical trials. But if a company manages to reach the finish line — for example, creating a drug for a severe form of cancer or an effective weight-loss treatment — the win ultimately brings in substantial profit. As a result, the company patents its formula, and the patent grants it the exclusive right to produce and sell its drug for many years. That secures the company a strong position in a narrow medical segment.
Because there are no analogs on the market, and the barrier to entry for competitors is measured in years of research and high costs, these drug developers have traditionally had the opportunity to set their prices. This exclusivity is exactly what provides pharmaceutical giants with high margins and stable cash flows. The entire market has relied on the fact that creating a drug is a long and bespoke process, with exclusivity serving as the main driver of business value.
The AI Revolution: The End of the Exclusivity Era
That brings us to an important aspect that the broader market has ignored so far. What will happen to this model when generative AI becomes an everyday tool for all major players?
Let's imagine a hypothetical situation. A leading corporation, after many years of research, releases a drug for a specific type of cancer or an innovative drug that slows down cell aging. In the old paradigm, this company would occupy a dominant position in its niche for a decade or more. Competitors would need years to understand the mechanism, find workarounds, and conduct their own lengthy tests. But in a world with AI, the situation will soon change. When the target becomes known — a receptor or a protein that needs to be acted upon — competitors may soon be able to just plug in their computing power.
In our hypothetical, by setting the necessary parameters for their AI, competitors will be able to receive a ready-made blueprint for an alternative molecule — one serving the same function but with a completely different chemical structure — in a fraction of the time. Essentially, in the near future, AI could make it possible to bypass previously insurmountable patent restrictions.
With that in mind, we are apparently on the verge of a situation where the technical realization of a drug will cease to be the main obstacle. In the foreseeable future, every major pharmaceutical company could have a full spectrum of necessary drugs. Corporation "A" will have its own cancer pill, its own obesity pill, and so forth — and exactly the same set of drugs, generated by AI but with different chemical formulas, will lie in the portfolios of corporations “B,” “C,” and “D.”
The concept of a unique drug without analogs may soon disappear. Exclusivity risks becoming a relic of the past. In the presence of one effective drug, AI will be able to practically spam the market with similar blueprints for alternatives.
Economic Consequences and the Triumph of the Consumer
To assess the economic impact of this shift, let's recall the basic laws of the market. What happens to the price of a product when it turns from bespoke and unique to easily reproduced? The price naturally falls.
When there is only one life-saving drug on the market, insurance companies and state healthcare systems are forced to pay the asking price for it. We could be talking about tens or hundreds of thousands of dollars per course, all because there is simply no alternative.
But imagine that five or ten drugs of similar efficacy from different corporations appear on the market. What happens in that scenario? Well, serious price competition ensues. Manufacturers will lower prices so that exactly their drug is included in insurance protocols.
For ordinary people and patients, this is positive news. The medicine of the future will become effective and accessible. This technological shift is truly capable of saving lives by lowering the cost of treating complex diseases.
However, for investors accustomed to the high profitability of the pharmaceutical sector, this is a reason for caution. The biotech industry risks going through a process of standardization and commoditization. Just as the production of steel or basic chemicals went from an innovation to a standard industrial process with moderate margins, so too could drug creation. Companies will produce large volumes of drugs, but their profit on every bottle sold will shrink due to competitive pressure.
What Can Investors Expect?
How will this affect the stock market? We may see a divergence of expectations and reality, with two distinct phases.
The first phase would be associated with growing optimism. Right now, we are on the threshold of this stage. The market is beginning to realize the scale of AI's capabilities in drug development. In the coming years, the headlines could be full of news that yet another company has found a solution to a complex medical problem using artificial intelligence. Amid this wave, we will most likely see an influx of capital into the sector. Biotech and pharma stocks may show active growth, fueled by the belief that AI will increase their profits by cutting research costs.
The second phase, however, will bring practical implementation and a balanced assessment. When hundreds of new, AI-generated drugs pass clinical trials and simultaneously enter the market, the market will face the effect of overproduction.
Investors will likely see that lower development costs did not lead to an automatic increase in profits. Drug prices themselves will also fall under the pressure of competition. The first phase — a period of inflated expectations — will be replaced by a correction.
What conclusion should investors draw here? A technological shift in biotech is coming, and it will change the world. But technologies do not always work to increase the capitalizations of companies. Sometimes, they transform old business models to the benefit of the end consumer.
With that in mind, you can invest in this sector, but it should be done with a cool head. In time, investors may need to recognize when optimism turns into an overheated market — and manage to lock in profits before AI triggers price competition, changing the profitability structure of large pharmaceutical companies.
On the date of publication, Mikhail Fedorov did not have (either directly or indirectly) positions in any of the securities mentioned in this article. All information and data in this article is solely for informational purposes. For more information please view the Barchart Disclosure Policy here.
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