Outlook theme 3 of 5

Health, longevity & bioscience

The biggest return on AI may not be measured in dollars, but in healthy years of life.

The big picture

Biology is the most complex information system we know. Every cell runs on a code of about three billion DNA letters, which instructs the building of tens of thousands of proteins, each folding into a precise 3D shape that decides what it does. For most of history, that complexity was simply too much for humans to read.

AI changes that. The same kind of models that learned language are now learning the language of life: reading genomes, predicting protein shapes, designing new molecules and spotting disease earlier than any doctor could alone. I believe this is where AI will have its most profound impact on people, and one of the largest long-term investment opportunities of all.

Why now

Three things have come together. Sequencing a whole human genome has fallen from billions of dollars to a few hundred. AI can now predict and design protein structures that once took years to work out in a lab. And the first drugs discovered and designed with AI are producing results in human clinical trials. Biology is becoming an information science.

Where the opportunity sits

The layers I’m researching

Tools & sequencing

Companies making the sequencers, lab instruments and reagents every genomics and proteomics lab needs: the picks and shovels of bioscience.

AI drug discovery

Platforms and biotech firms using AI to find targets and design molecules, often partnered with large pharmaceutical companies.

Diagnostics & imaging

AI that reads scans, pathology slides and blood tests to catch disease earlier and more accurately.

Compute for biology

Biology models need enormous computing power, linking this theme straight back to chips, cloud and data centres.

Deep dives

Where AI meets biology

Genomics

Reading the code of life

What it is

Genomics is the study of a person’s complete DNA, the instruction manual inside every cell. Differences in that code influence disease risk, how we respond to medicines and how we age.

What AI changes

AI finds the patterns in billions of DNA letters that matter: which variants cause disease, which patients will respond to which treatment, and which cancers are driven by which mutations. As sequencing becomes cheap, AI is what turns the data into decisions.

In practice

Routine genome sequencing for newborns, cancer patients and rare-disease diagnosis, with AI flagging the handful of changes that matter out of millions.

Proteomics

Understanding the machines of the body

What it is

Proteomics is the study of proteins, the molecular machines that do almost all the work in our bodies. A protein’s 3D shape determines its function, and misshapen or missing proteins are behind many diseases.

What AI changes

Working out a single protein’s shape used to take years. AI systems such as AlphaFold can now predict it in minutes, and have done so for virtually every known protein. New AI tools go further, designing entirely new proteins that nature never made.

In practice

Designing new enzymes, vaccines and antibodies from scratch, and understanding exactly how a drug fits the protein it targets.

Drug discovery

From a decade to a few years

What it is

Bringing a new medicine to market traditionally takes 10 to 15 years and billions of dollars, and most candidates fail along the way.

What AI changes

AI can identify promising disease targets, design candidate molecules, predict their safety and choose the right patients for trials. That shortens early research from years to months and could raise the odds of success.

In practice

AI-designed drug candidates are already in human trials. One, for a serious lung-scarring disease, published positive mid-stage trial results in 2025.

Longevity

Adding healthy years, not just years

What it is

Ageing is the single biggest risk factor for cancer, heart disease and dementia. Longevity science aims to slow the biological processes of ageing itself, extending ‘healthspan’: the years we live in good health.

What AI changes

AI combines genomic, protein, blood-test and wearable data to measure a person’s biological age, predict disease years before symptoms, and personalise prevention. It also speeds up research into therapies that target ageing directly.

In practice

Preventive check-ups that combine genetics, blood biomarkers and AI-read imaging to catch disease early, with personalised plans to stay healthy for longer.

The evidence so far

Nobel

The 2024 Nobel Prize in Chemistry was awarded for AI protein-structure prediction (AlphaFold) and computational protein design, a sign of how central AI has become to biology.

Source: EMBL ↗

~$100

Sequencing companies now advertise a whole human genome for around US$100 to US$200, down from about US$3 billion for the first one.

Source: STAT News ↗

Phase 2

Rentosertib, a drug whose target and molecule were both found using AI, reported positive Phase 2a results in Nature Medicine for idiopathic pulmonary fibrosis.

Source: BioSpace ↗

Signals I watch

How I’ll know it’s working

↗AI-discovered drugs progressing through later-stage clinical trials and gaining approval
↗The falling cost of genome and protein sequencing, and growth in test volumes
↗Partnerships between AI companies and major pharmaceutical firms
↗Health systems and insurers adopting AI for diagnosis and preventive care

Risks

What could go wrong

!Drug development is slow and most candidates still fail, AI or not
!Regulation, privacy rules and ethics around genetic data are rightly strict
!Biotech valuations can be extremely volatile and driven by single trial results
!Longevity is a young field and some claims are ahead of the science

How I’m approaching it

I see health and bioscience as a long-horizon theme. I favour the picks and shovels (sequencing, lab tools and the compute behind biology models) plus established healthcare companies adopting AI, and keep any single-drug biotech bets small.

“AI learned to read our language first. Now it’s learning to read our biology, and that could change how long and how well we live.”

These are personal, forward-looking views, not predictions or recommendations. They may turn out to be wrong.