AI Sells Urgency. Biotech Sells Necessity. What Can They Learn From Each Other?

There are two very different ways to convince people that a breakthrough technology matters.

The first is to point to a problem and say: “This is becoming unacceptable. We need a solution.”

The second is to point to the technology and say: “This is happening now. You cannot afford to be left behind.”

For decades, biotech has largely played the first game, and AI has increasingly played the second. That difference says a lot about how each industry creates urgency (and attracts capital).

Think about CRISPR. You don't need to convince someone that genetic diseases are a problem. The problem already exists and patients suffer. Families live with uncertainty, and existing treatments have limits. The story is therefore relatively intuitive:

There is a problem → the problem is serious → current solutions are insufficient → a breakthrough technology could change the equation.

CRISPR becomes exciting because of what it might allow us to do about something we already fear. The technology is the answer and this is a powerful way to finance a breakthrough.

You don't necessarily need to prove that the technology will solve everything today. You need to demonstrate that the problem is enormous, that the existing tools are insufficient, and that the new technology creates a credible possibility of doing something fundamentally different. That is how a scientific idea becomes an investment opportunity.

AI could have told a similar story. It could have said:

·       Populations are aging.

·       There will be fewer workers.

·       Healthcare systems will need more capacity.

·       Productivity growth is slowing.

·       We need machines that can help us produce more with fewer people.

That would be a very powerful argument, but it isn't the dominant AI narrative. Instead, AI has often told us something much more immediate: Your competitor is already using it.

And suddenly the question changes. It is no longer: “What problem does AI solve?”, but it becomes: “What happens to me if everyone else adopts it first?”

That is a completely different psychological mechanism. The fear has moved. In biotech, we fear not solving the problem. In AI, we fear missing the technology. Why is the second narrative so powerful? Because it compresses time.

Demographic change is real, but it happens slowly. A shrinking workforce is a 10-, 20-, or 30-year problem. A competitor adopting AI is a problem this quarter. A CEO may not change strategy because there will be fewer workers in 2045. But they might change strategy because their competitor has just announced that an AI system is doing the work of a team of analysts. The underlying economic story can be almost identical. But AI has found a way to turn a slow-moving structural problem into an immediate competitive threat. And that is incredibly powerful for selling technology. AI doesn't have to convince us that the future will be terrible. It only has to convince us that someone else will be better prepared for it.

That creates a loop: One company adopts AI. Another company notices. It becomes afraid of falling behind. It invests in AI. The next company sees that investment and becomes afraid of falling behind. And suddenly adoption itself becomes evidence that everyone needs to adopt it. So, the technology starts creating its own urgency.

This is very different from the traditional biotech model.

But there is something AI could learn from biotech. The FOMO narrative is extremely effective at generating action. But it doesn't necessarily explain why society needs the technology in the first place. That is where biotech has an advantage.

Biotech asks: What happens if we don't solve this?

AI often asks: What happens if we're not the first to adopt it?

The first question is about necessity. The second is about competition. And AI may eventually need both.

Consider the demographic transition taking place across advanced economies. The OECD projects that the old-age dependency ratio across OECD countries could rise from 31% in 2023 to 52% by 2060. At the same time, aging is expected to put pressure on economic growth and public finances. That creates a much deeper AI story: Maybe AI isn't important because it is fashionable. Maybe it becomes important because societies increasingly need more productive capacity than their human workforce can provide.

Now the narrative starts looking much more like biotech.

And biotech can learn the opposite lesson, because biotech has the opposite challenge. It has very important problems, but it can take years before that science becomes a product.

AI has become exceptionally good at creating a sense that waiting has a cost. That is something biotech could learn from.

Not: “Invest in us because everyone else is.”

But: “The technological window is opening now. The companies that build the capability today may determine what becomes possible tomorrow.”

That is a different way of thinking about a platform.

CRISPR is not only a treatment technology. It is a capability. And AI is not only a software product. It is also a capability.

And capabilities create a particular kind of strategic anxiety: what happens if somebody else has them first? The interesting future is where the two narratives meet. Perhaps the most powerful story for the next generation of technology will combine both.

Not simply: “There is a huge problem.”

And not simply: “You are going to fall behind.”

But: “There is a huge problem. A new technology can help solve it. And the window to build that capability is opening now.”

That is where AI and biotech begin to converge, and which may be the real lesson each industry has to learn from the other.