Jason Crawford

During the process, it can guide and focus the search.

And then after the invention process, it can help us optimize the inventions that we come up with for power or efficiency, et cetera.

And it can help extend them to new domains as we extended the concept of the vaccine to other diseases.

Okay.

So that ends sort of the first half.

That is sort of what was right about the linear model, you know, the grain of or the element of truth in it.

Now let's look at, you know, where the linear model is wrong.

As we've seen, engineering gets ahead of science.

engineering can come up with inventions that were not predicted by science or that aren't explained by it.

Sometimes we don't even know how the invention works, why it works at all.

Sometimes we sort of know the basic principle, but we don't know why it has the operating characteristics it has, et cetera.

And I'm going to suggest that this is not just a historical phenomenon.

This isn't just a thing that happened in maybe in the early days of the industrial revolution, but actually engineering will always get ahead of science.

Now, one reason for this is economic.

That's where the opportunities are.

You know, if you imagine that we waited until science had fully described an area before we went in and started making inventions.

Well, first off, why wait to create economic value?

You never want to wait if you don't have to.

Also, by that point, the competition is going to be intense, right?

Everybody's going to be trying to do something once an area is understood really thoroughly.