Ethics: Who gets a cure, and who doesn’t?

AI can now generate millions of molecular candidates in weeks. AlphaFold 3 takes us further, predicting how proteins interact with DNA, RNA, and small molecules. That’s revolutionary for discovery. But who benefits? If incentives remain profit-driven, rare diseases and those affecting poorer regions may still be ignored.

There’s also the dual-use dilemma: the same AI that designs a life-saving drug could be misused to generate harmful compounds. Ethics here isn’t just about dataset bias—it’s about responsibility for outcomes. And it’s not abstract. Imagine a parent in a low-income country watching global headlines about “AI-discovered cures” while their own child has no access. The technology promises universality, but the distribution risks being anything but.

Governance: Science sprints, law walks

Regulation is scrambling to keep up. The EU AI Act now classifies biotech applications as “high risk,” demanding strict oversight. In the U.S., the FDA has introduced Predetermined Change Control Plans (PCCPs), allowing developers to declare in advance how AI systems may evolve post-approval.

These are steps forward, but they reveal a deeper challenge: science is sprinting, while law is jogging behind. Worse, regulations are fragmented. The EU enforces precaution; the U.S. experiments with flexibility; China pushes rapid state-backed adoption. Companies may exploit these gaps, moving data and trials to whichever jurisdiction offers the lightest touch. Governance isn’t just slow—it’s uneven.

Also Read: AI, advanced therapeutics, and the geopolitical balancing act in biotech

Economics: The monopoly question

AI should lower the cost of early-stage discovery. In theory, that democratises innovation. In practice, it risks concentrating power. Training and deploying frontier models requires massive compute and proprietary datasets—resources controlled by a handful of tech and pharma giants. Smaller labs risk becoming dependent, licensing access rather than innovating independently.

The economic implications ripple outward. Will insurers reimburse AI-designed treatments differently? Will costs actually fall for patients, or will monopoly pricing persist? Alphabet’s Isomorphic Labs, which has already signed multi-billion-dollar partnerships with major drugmakers, embodies this tension: breakthrough efficiency paired with concentrated control.

Politics: Biotech as geopolitical strategy

COVID-19 taught nations that control over vaccines and treatments is not just a health issue—it’s sovereignty. Governments are now pouring billions into AI-biotech ecosystems. Whoever leads in this space doesn’t just sell cures; they wield geopolitical leverage.

Emerging technologies like quantum computing add another layer. Quantum promises to simulate molecules and chemical interactions at scales classical computers can’t touch. Partnerships such as Boehringer Ingelheim with Google Quantum AI are early signals. For countries, this isn’t just about innovation—it’s about future control over health, defense, and economic power.

Social impacts: Access, trust, and equity

Let’s say AI shortens discovery cycles tenfold. Who gets the new drugs first? Wealthy nations with purchasing power? Patients with premium insurance? Without careful policy, AI risks widening global health inequities between the Global North and South.

Trust is another fault line. How comfortable will people be taking a treatment designed largely by machines? In healthcare, perception shapes adoption as much as efficacy. Review articles note that even when accuracy improves, social acceptance cannot be assumed.

There’s also the question of data. AI-driven biotech relies on genomic and clinical datasets. Who owns this data? Are patients fully consenting to its use? And when genomic data flows across borders, what protections follow it? Without clear answers, privacy and trust will become major bottlenecks.

Also Read: Asia-Pacific governments step in as private biotech investors pull back

Workforce and culture: Who gets left behind?

AI’s acceleration raises uncomfortable questions about the people inside the system. If algorithms handle much of early-stage drug design, what happens to thousands of researchers who once did that work manually? There’s a risk of deskilling, where human expertise erodes as machines take over the hardest parts of the pipeline.

Yet there’s also an opportunity. New roles are emerging: data stewards, model auditors, bioethics officers. The biotech workforce could shift from “pipette in hand” to “oversight of AI-wet lab loops.” Whether this is empowering or displacing depends on how institutions prepare now.

The real blueprint

The future of AI in biotechnology isn’t just about algorithms—it’s about design. A resilient blueprint must embed ethics, governance, economics, politics, and social equity from the start.

Because the promise is enormous: faster cures, more resilient health systems, breakthroughs against diseases that have long eluded us. But the risks are just as stark: monopolies, inequity, regulatory arbitrage, public mistrust, and workforce disruption.

For innovators, leaders, and policymakers, the central question is simple but urgent:

Can we design a biotech future where AI serves not only markets, but humanity?

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When we talk about AI in biotechnology, the conversation usually revolves around science: faster drug discovery, protein folding breakthroughs, or personalised medicine. But the real story is bigger than science. It’s about how ethics, governance, economics, politics, and society intersect at this new frontier. Ethics: Who gets a cure, and who doesn’t? AI can now
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