The Last Time This Happened Was Photonics
An unloved sector breaking out, and AI is the tailwind.
The last time I saw an entire sector move together like today was photonics.
We all know how that went.
But today isn’t about tech. It’s about an unloved, underestimated, disregarded sector.
Undercrowded, with a massive AI tailwind.
Who Buys Healthcare?
That’s a great question. If you did, you probably underperformed these last months. But things are changing.
First, the market is heated. I said yesterday it was time to slow down, to let breathers happen and prices deflate for this year’s winners. They ran a lot and now need a minute. No reason to sell those positions, but plenty of reasons to look elsewhere.
Second, artificial intelligence is becoming so powerful that governments now have to control the public release of new models - whether it is normal in a democracy or not. And one industry will thrive from having such a data-processing tool: research. Healthcare R&D, of course.
If you haven’t seen “The Thinking Game” and are curious about the early years of AI and the people who built it, go watch it. It tells the story of DeepMind’s founding founder, and how they worked for two decades to build what we have today - getting bought by Google along the way. They tried many things, but eventually found the two best subjects to test AI.
Chess - and later as it got too easy, Go.
Protein folding - when Go got too easy.
I’ll let AI explain protein folding, as I’m not a biologist - rest assured you don’t need to be one to make money.
Protein folding is how a chain of amino acids becomes a working 3D protein. It works because the chain naturally bends, twists, and packs into the shape that is most stable in its environment. That final shape matters because a protein’s function depends on its structure. If folding goes wrong, the protein may misfunction or clump together.
It is a major AI training problem because predicting the final 3D shape from the amino-acid sequence is extremely complex. The challenge comes from the huge number of possible shapes and interactions.
AI is meant to process massive amounts of data and propose solutions at a speed unthinkable less than a decade ago. Protein folding was solved and Deepmind’s team received a Nobel Prize in Chemistry for the latest version of their model. The benefits for healthcare are hard to quantify… but “game changer” about covers it.
So the real question is: what is AI’s limit in healthcare?
Hard to say. For most sectors, the bottleneck is the lack of specific data. That’s never been the case here, where the issue was enough compute to process it. That’s why healthcare should see a transformation like never before in the years to come.
But we know this doesn’t necessarily translate into market gains, because liquidity flows to the highest potential, not the best fundamentals. For years it’s been AI hardware and infrastructure, but the market is waking up to the rest. This last earnings season showed SaaS names weren’t as endangered and could generate cash from AI, and their stocks started to react.
But… isn’t healthcare even more impacted by AI?
Market Mechanisms
I’ll repeat things I’ve written many times - sorry to those reading it for the 100th time, but they matter.
You don’t need to be an IT engineer to invest in Nvidia. You don’t need to be an astrophysicist to invest in SpaceX. You don’t need to be a microbiologist to invest in healthcare research. The only thing you need is to understand why the market would get excited about it.
And the market is always excited about one thing: unlimited potential.
You know the common denominator of Micron, Nvidia, Palantir, Coherent or Nebius? The market couldn’t - and still can’t, anticipate their market size, revenues or future margins, because demand for a disruptive technology is impossible to anticipate.
The market would rather push stocks to “stupid” valuations, not because investors believe Palantir “deserves” 200x sales, but because they’d rather assume it earns it than miss out. If someone told you “this is the next Google,” would you care about the price you paid? No. You’d buy. Because you’d rather buy expensive than miss out.
Bubbles aren’t born of stupidity. They’re born of the impossibility to anticipate.
Now back to healthcare and AI. What’s the market for a company that cures a type of cancer? Degenerative diseases? Auto-immune ones? Or, less morbidly, menopausal symptoms, hair loss, and countless other “comfort” treatments?
I’m not necessarily one of those who believe humans will cure every disease on earth thanks to AI, but I am one of those who believe AI will accelerate research, not just in time and expenses, but in outcomes.
And that creates a situation where anticipating revenues, margins and market size becomes extremely complex for the market. A situation where investors are almost forced to create a bubble, because they’d rather be in it than out of it.
A 2025 study reported that using the AI system Transpara as a second reader in breast screening increased sensitivity by 8.4% and found more interval and future-detected tumors.
Another concrete case is clinical workflow automation: Chelsea and Westminster NHS Trust began piloting AI-generated discharge summaries for clinician review to speed patient throughput and reduce admin burden.
Palantir Foundry pilot: the trust reported a 28% reduction in an inpatient waiting list, theatre utilization improvement from 73% to 86%, and longer booking lead times (from ~6 to ~17 days) after using Palantir’s Foundry to unify scheduling, rostering, and waiting‑list data for operational decisions.
For drug discovery, the clearest real-world signal is that AI-designed or AI-optimized therapeutics had progressed into human trials by 2025–2026, showing that AI is already moving candidates into the clinic rather than staying in the lab.
In the rest of this article, I’ll cover the sector-wide breakout I’m seeing across 50+ charts, the proxy I’m using to play it with the least risk, and four de-risked names I’d actually buy. Plus the real risks (this is still biotech), my exact buys, and the portfolio changes I made this afternoon.
An entire sector is waking up after years asleep.
