Biotech’s Biggest Untapped Gold Mine

In the summer of 1944, the people of London would sometimes look up and see a strange silver streak arc across the sky.

Unlike the buzz of warplanes, these new machines arrived silently at first, because it turns out that the German V-2 rocket traveled faster than sound. By the time you heard its thunder, it had already struck. 

For those on the ground, it must have been utterly terrifying. 

However, this moment in our brutal history of war marked the beginning of a new era: We had officially entered the start of the Space Age — the V-2 rocket was the first object to enter space. It was that same technology, born in destruction, that would one day carry astronauts to the moon and launch our satellites into orbit

Just like that, what seemed like science fiction became reality almost overnight.

Now fast-forward eighty years… a quieter but no less profound milestone is passing. 

This time, it isn’t a rocket breaking the atmosphere — it’s a pill slipping past a patient’s lips. 

Somewhere in China, a man or woman living with an incurable lung disease is taking a drug called INS018_055. It looks ordinary, just another tablet in a glass of water. 

Yet this pill wasn’t conjured by a chemist sketching formulas, or by a team of researchers tinkering for countless hours in their lab. 

No, it was created by artificial intelligence — and became the first AI-designed drug to reach Phase II clinical trials.

But the mere fact that this AI drug entered into the trial phase of its development wasn’t the astounding part. 

It was the fact that it took less than 30 months. 

To give you a little idea of how mindblowing this is to the biotech sector, it’s important to know that this process typically takes a pharmaceutical company decades to reach. 

Call it Big Pharma’s V-2 moment — a signal that we’ve crossed into a new frontier. 

And for individual investors like us, it’s the unmistakable sound of a market roaring to life, announcing that the age of AI-discovered drugs has officially begun.

Look, there’s no question what the biggest hurdle facing companies hoping to develop new drugs is: Time and money. 

Up until recently, developing a new drug has been an arduous task under even the best circumstances, taking an average of 14.6 years and $2.6 billion to bring a single compound from concept to market. 

Although everyone likes to vilify Big Pharma because of how expensive drugs are for consumers, most people don’t realize that 90% of drug candidates fail along the way. 

For the record, that’s one of the major reasons why we’re left footing the bill for an industry addicted to inefficiency. Big Pharma accepted this grind as the cost of doing business — and passed it along to the rest of us.

Today, that old model is dying at the hands of AI. 

Since 2023, the world’s largest pharmaceutical companies have gone from dabbling in the AI drug discovery space to diving in headfirst. 

Pfizer struck a billion-dollar collaboration with Insilico Medicine. Sanofi pledged $1.2 billion to Exscientia. Merck pumped capital into BenevolentAI. Roche and Bristol Myers are building their own platforms. These aren’t vanity projects, folks, they’re survival plays — billion-dollar bets that the next generation of medicine won’t be created by scientists, but instead generated by algorithms.

The math explains the frenzy, too. 

Why? Because shaving 40% off development time and 30% off costs doesn’t just pad margins, it changes the economics of medicine itself. 

Drugs that would have been abandoned as unprofitable suddenly make sense to pursue. Treatments that once crawled toward approval over fifteen years can arrive in five. 

Look at the evidence. Insilico’s drug INS018_055 went from computer model to human trial in just 30 months — a quarter of the traditional timeline! 

That’s not anecdotal, dear reader, it’s a veritable blueprint for success. 

Now multiply that across an industry that spends more than $200 billion a year on research and development, and you see why Wall Street believes one-third of all new drugs will be discovered by AI within a decade.

These investments will accelerate as we move forward. Eli Lilly just launched its TuneLab platform, a dedicated AI hub designed to attract biotech partners and build an ecosystem around algorithmic discovery. Bristol Myers, Takeda, AbbVie, and Johnson & Johnson have pooled proprietary datasets in a federated AI consortium — a Big Pharma super-collaboration to train better models. 

These moves are the opening shots of a modern arms race inside the pharmaceutical industry, and each company knows that whoever controls the most powerful AI drug engine controls the future of medicine.

The shift is historic, and for patients it promises therapies that arrive faster, cheaper, and with higher odds of success. For companies, it offers a way to salvage broken pipelines and reclaim lost margins. 

Of course, it also creates one of the most asymmetric opportunities of our lifetime for investors with the foresight to see we’re still in the early innings of this opportunity: to buy into the platforms that Big Pharma will spend billions acquiring over the next decade.

This moment looks a lot like the late 1990s, when the Human Genome Project set off a gold rush in biotech. Back then, investors who recognized what was in store in the future made fortunes before the broader market caught on. 

Today, AI is running the same playbook at warp speed. Only this time, the capital flowing in dwarfs the genomics boom — and the need is even greater.

Big Pharma isn’t betting billions because it wants to innovate. It’s betting billions because the old pipeline is collapsing under its own weight. AI isn’t a supplement. It’s a lifeline. And the companies building and owning these platforms today will be tomorrow’s buyout trophies.

The only question for investors is whether you get in before the floodgates open — or after Big Pharma has already claimed the most profitable ground.

Maybe it’s time you check out the details behind this opportunity now, before the investment herd catches on.