David Gan is a university and business leader with extensive international experience in innovation strategy and technology management,
is professor of Innovation and Technology Management, Imperial College Business School,
and now is a local guy because he's the chairman of the UK Atomic Energy Authority.
So he's just down the road in Oxfordshire.
His research explores why and how innovation happens and the way conciliation transforms the world and how it can be managed.
And so we've asked him to come and talk today about the business of science and the science of business.
David's straight over to. Thank you, Phil and Tony, for inviting me, and good afternoon, ladies and gentlemen.
There's a title in the programme, which is a draught title when I try to put it on the slide, it was too long,
so I've shortened it to the business of science and the science of business because I think that's what we're here to talk about.
And I want to flesh back a little way in history and say, Where have we come from?
Take some insights and is rather partial, and we can just take a few little nuggets out and say, Where are we going to?
And perhaps the previous discussions already prefaced some of some of the issues that are drawn.
You'll notice a picture here that I felt I had to use,
given it's the 50th anniversary and in the title in the programme, it says, I think back 30 years,
but allow me to to celebrate with you that science and business did come together to an extraordinary effect,
actually, and we still use the word moonshot to mean an audacious project.
And with that in mind, let let me start off and I feel you kindly gave my academic credentials and leadership a column for those of you don't know me,
I also work in business a lot. I've had five start-ups, one relatively successful one, and I'm on a number of boards and have been for a while.
So you'll excuse me if I start off with business the first insight and I'm taking you back one hundred and forty years to Thomas Edison,
who was extraordinary in many ways.
So one thousand ninety three patents is a lot of patents to have your name on, and that number is only exceeded in two thousand and three.
So you held the world record for the best part of one hundred years in terms of the number of patents,
thus productivity in spewing out new ideas and logging them in the world.
And of course, we've all benefited from the electric light bulb.
The Phonogram, whatever it was called at the time, he and the reason I put him here is not for those patents, it's for the mechanisms he put in place.
So arguably some of you know this. Edison created the first research laboratory that provided ideas on contract in third party form to others.
And this was the invention laboratory at Menlo Park, and this is Menlo Park in New Jersey.
So don't get your geographies too confused at this point in history.
First Industrial Research Laboratory A small thing every 10 days and the big thing every six months.
That was the mantra that was running through this,
and it worked because he selected technologists and scientists and universities that have started to
educate people with the right skills to go and work in line and solve problems for other people.
And within a short seven year period, it produced 400 patents.
And if you will, that's the tipping point between hero individuals who invented things and sometimes change the world.
And a systematic approach to deploying science and technology to solve problems for commercial gain.
And we're talking about technology transfer and all of that gubbins, the relationship stuff.
So we go back one hundred and forty years to see the person who arguably started it all.
And so that's the first insight. Where did it come from? And then I think, you know, we connect into how do universities get funded?
What do governments do about all of this? So the second thing I wanted to mention was the emergence of science policy.
Now you have to go back to about nineteen hundred, you could probably go back further if you really want to.
But I've taken all dean just as a little inflexion point here because the whole principle which I have, you know,
was that researchers are better at determining what research should be funded than politicians, right?
And that gave us in this country in the UK at least the arm's length relationship between the funding agencies and the political process.
And I think in the US and many other countries, we have similar formations,
which means that the rhythm of how we fund the flywheel, the science base is determined by peer review and so on.
And of course, that was very good,
except if US dotty scientists go off in a direction and you can't see much impact for a while and coming into the The Second World War,
I've chosen J.D. Burnell for several reasons. But, you know, a great historian of science, by the way.
Huge number of books and articles, he wrote, as well as being an Irish X-ray crystallography molecular.
Biologist and a communist in the Second World War, he was in the British government and he said,
Actually, come on, scientists, we've got to think about social purpose.
We need to think about some direction here, putting our great minds and infrastructure together.
He was one of the people who masterminded Overlord, by the way, at the end of the Second World War.
Systems Engineer. So here we have the freedom of the academic and here a wake up call from government saying, hang on a minute.
What are you going to do with this stuff? Is it going to be useful? And of course, that time around, about that time in the US,
we had the most fabulous piece of work by Vannevar Bush and Science, The Endless Frontier and for all you scientists,
and you're just going to love this if you haven't read it before opening basic research on a massive scale,
that's what he advocated science, the seed corn of technological advance research.
The pacemaker of technological progress, chiefly in academic institutions.
Scientists may work in an atmosphere relatively free from adverse pressure of convention prejudice or commercial necessity.
Yeah, give us the money. Scientists need maximum autonomy to pursue research free from the influence of pressure groups,
free from the necessity of producing immediate results free from any central board.
Now he got away with that.
And not only that, he really lifted the budget for science in the US and, you know, by implication, for other countries too.
And one thing you must note, and Matt Wolpert made a special mention of engagement in his opening remarks.
I'm not sure that any piece of science policy is ever had so much engagement with this space.
This became a bestseller. This document people were walking around Times Square reading it in Fortune magazine.
So here we get science policy. Big, audacious.
Give us the money. And this really unlocked a [INAUDIBLE] of a lot of new activities coming out of the Second World War,
the military industrial complex and so on in the US and soon after.
In many of our countries, we started thinking, how do we connect this to industrial strategy?
And so we put the T word with science, policy, science and technology policy.
And Solly Zuckerman actually came out with a very clear statement saying,
Let's not to get too hung up between applied and basic research because these things had gone off in different directions.
And you can see many, many policy documents, too many to put up on the screen since that time.
But there's one that was influential for me particularly.
And I think for many of us in the UK system in nineteen ninety three realising our potential.
William Aldergrove. And in that document, it really makes a link to industrial strategy and it's a strategy for science, engineering and technology.
But an interesting to hear the discussion about manufacturing just now because this was the beginning of what we had in the UK,
the innovative manufacturing initiative.
And it was a 1996 that I won the Royal Academy of Engineering in Innovative Manufacturing Initiative Chair and worked in this area.
So I thank William for the funding. But these were the insights, really and how arguments morphed.
And actually, if we went back to nineteen ninety three or nineteen seventy one roomful of people like this from all over the world,
we might be having quite similar discussions. So one of the reasons saying this is not a lot new under the Sun when it comes to this,
and maybe that's my reflection on the past, and I'll just tilt in a little bit into the future.
And there are many ways of cutting up history.
But if you take this sort of large 50 year chunks, you come out of the 19th century with Edison here.
The great entrepreneur turned systematise of science and technology gave us the first research lab, which of course grew in a 50 year period.
There's Bell Labs, but it could be any one of the huge labs from the big corporates into the corporate research and development machine.
And our universities, of course, were educating the talent that would feed these huge audacious machines and great companies like in this country.
GSK and others invest heavily in research and employ a lot of scientists with a bet on the future.
You know, that's money that could go out as a dividend at the end of the year,
but it has to be thought through with a business case for its research investment and go across the world and you.
You come in to companies contentious these days like Huawei. Seventy thousand people working in R&D.
I've been into every one of their major labs and seen some extraordinary things that they do.
Of course, that really was a fairly settled system,
and that industrial mechanism reflected back in on how we funded science and how the universities responded to them.
And we had corporate partnerships and so on, and we kind of knew that entrepreneurship was important,
but we thought it was the 19th century sort of thing that Thomas Edison and people did until around 15, 20 years ago.
The World Wide Web and internet became really useful. And I worked with Procter and Gamble and Eli Lilly on some studies that resulted in over part of
the creation of what became a sort of eBay on the web where you could trade intellectual property,
you could trade licences. And that really opened up this whole discussion about what is open innovation.
How can we reach out for new ideas? And since then, we ask companies, what's your strategy for search?
And they say, Well, what are you talking about? Well, if the ideas can be everywhere, you better have a strategy for searching.
So it's opened up a whole lot of new ways of thinking about how you manage the innovation process.
And of course, the corporate lab hasn't gone away and entrepreneurs haven't come away.
In many senses they've come back in, and I'll end on that point in just a minute.
So there are a few new things and some of this, not all of it, but some of it is driven by technology.
I hope Simon Sciences of the artificial, you know, artificial intelligence and artificiality,
how science and technology creates the modern world go back a very long way.
This is nineteen sixty nine publication that links to some work.
I'm very much in favour of Thomas Crump, and many others have written good histories on the evolution of scientific instruments.
Right. So one thing we often don't talk about enough is what the science contribute in terms
of ways of seeing the world that allow new industries to emerge and then flourish,
the electron tunnelling microscope and nanotechnology, for example.
And so the sciences that create the instrumentation that allow us to do new things and speed up increased productivity are incredibly important.
And they just happen to be getting very good.
So this AI stuff that's been around in the research lab for 50 years is now coming out and British government, other governments are publishing on it.
I wrote a book fifteen years ago about this think play do with colleagues looking at how digitisation in the research lab was speeding things up.
And the main ingredients the intensification of science and innovation come from our ability to model and simulate the real world in a virtual world.
Now, engineers have done that forever in mathematical models, but the power has a force multiplier on it.
When you can use a digital toolkits that we've got,
we know that we're talking here about machine learning where we put the ability to capture and harness data, keep it somewhere and analyse it.
Robotics to do it automatically. Yeah, some curious new things coming in like blockchain security of that data,
the parcels and the image I really like is the one of the car crash test dummies.
You know, they were ubiquitous in every car company 25 years ago.
And what we call time to market, the lead time it takes to create a new product and sell it to us was partly conditioned
by the time it took to prove cabin safety by crashing these poor little mannequins.
And, you know, as human beings, different shapes and sizes, it takes a bit of time to work out whether we've got a safe design or not.
I've got time to get into the history of the software, except it came from US military designs of trying to understand explosions at the tip of a
nuclear warhead when that came out of licence and was developed for looking at impact assessment.
Eventually, we managed to have a working tool for modelling car crash tests.
And of course, you can do thousands of these iterations a day. There's not a car company in the world that relies upon mannequins.
You go out of business straightaway, you have to do it in silica. You still have a few mannequins to test your model.
So the message from this is it's a lot cheaper to fail in bits rather.
Is in that and the other lesson, by the way. What is it?
Cars have got a lot safer.
The learning curve is gone up almost exponentially, so every time an iteration happens, there's a learning curve in the computer.
And so we have much safer vehicles than we would have had. It was incremental before. We've had a step change in the last 20 years.
That's happened everywhere. You can go online to a body map and design your own fashion.
It's the same sort of tool kit. It's really accelerating the innovation process and it's transforming the way we do science.
So true, and let me just walk through a few questions. We still love discovery.
Science. OK, curiosity. We've heard a bit about that today.
We still need to have people who want to detect solar flares or cosmic microwave.
Some of these things come back an incredibly important, you know,
our communications systems would fall apart if there's a cosmic flare of a certain size.
We need to know about this linking curiosity with practicality, as we did in the UK.
In the cosmic microwave detector that was made here, sponsored by UK, our RFI Initiative C has resulted in a new way of doing security detection.
And the first implication application of that was in Los Angeles rail system in 2018.
So discovery. Yes. Let's see how we can continue our curiosity and having people doing extraordinary things.
I mean, CERN is the obvious example, isn't it, because trying to understand the Higgs boson or the nature of the universe is a big discovery quest.
But the fact that on the left, our friend Tim Berners-Lee could produce a paper and you can just about see the date March nineteen ninety nine.
I reckon that's about 30 years and a few months ago. This is really not ambitiously titled information management a proposal.
OK? And his boss said vague but exciting.
That was the only note that was written on it. Well, that's the paper that set out the World Wide Web.
And look what it's done for science.
The things you may not know, but you would guess certainly is that if you're detecting hadrons, you can create pet scans.
And if you've got to build a great big tunnel 27 seven kilometres with the sort of accuracy to put those beams in,
you better have a tunnel boring machine with some new sensors on it,
which has led to much better tunnel boring machines for civil engineering, the ones we used in Crossrail.
So some of these areas of discovery have extraordinary power later, and perhaps we can harness those better.
The other way around, of course, is mission oriented, and most of our language these days is about mission.
And here is the existential threat, one of them that we've got to get real about.
And it takes a 16 year old at the World Economic Forum this year to wake the world up yet again.
And the clock is ticking.
And there's a lot of science that needs to be done here to understand the ramifications and the mechanisms of global warming and what to do about it.
And there are a lot of scientists working in this field,
and I am chairman of the Atomic Energy Authority and our mission in the research field is to develop fusion energy.
And all the way from that mission, we've had to look at the exhaust of very hot plasma just down the road.
We've created a plasma at 350 million degrees that's nearly 20 times hotter than the centre of the Sun.
That's pretty incredible. From an engineering point of view, trying to exhaust it led us into something which is equally incredible.
And a company called Reaction Engines is on the campus, and it has a mechanism that we think will produce a hypersonic jet engine and give us flight
times from London to New York or wherever you want to go in the matter of narrative.
So I'm just helping you understand the things you already know the world's largest scientific collaboration.
This is the E2 fusion reactor in the south of France.
One of the other benefits we don't talk about enough is the engineering supply chain that is required.
This is a 20 billion behemoth dollar, euro pound.
They're all equal now. He's created a new industrial supply chain.
I was down there the other week and the stainless stainless steel containment for this vessel,
which weighs twenty three thousand tons, it's going to sit in.
The middle is thirty mm thick and there's one company in the world that reckons it can weld that using robotic welding, it's a German company.
And the amount of lessons that are coming out of that because it's a problem solving approach in engineering will spill over into many,
many other areas. I'm sure. Of course, medicine.
And we've already heard a bit from Mark and others. Antibiotic resistance, another existential threat.
And the Francis Crick Institute, which I think in this country actually melts together nicely.
Discovery mission. You know, Paul Nurse will talk about wanting to discover the meaning of life or what life is.
The mission is improving human health. And so these are the big, big questions that we are looking at and wanting industrial collaboration.
But they're also throwing up new dilemmas.
In fact, that digital world is really creating all sorts of questions that scientists on their own ill equipped to answer.
I don't want to pick on anyone, particularly but Cambridge University and Cambridge Analytica,
Airbnb and residents who can't find any way to live in the centre of New York because everyone's renting out the French love of protest.
This is Uber and taxi drivers in France, you know, going for it with a bit of a right.
And here we have a Chinese bicycle rental online company business that's gone bankrupt and everyone just there.
The manifestation of a digital bankruptcy has physical reality in the streets of Shanghai with these mountains of bicycles.
And so it goes on. And so the lesson from from this looking to the future is and we heard it a little bit steam art.
We need all the social sciences here.
A huge opportunity, of course, with Conte, because social media is quantifying the social world in one dimension, at least.
But also, we need to build theory.
So we do need people with cool heads to sit in places like Oxford or York over if they sit and think and philosophise about what's happening.
And if we don't do that, we might be riddled with all sorts of questions that we can't understand.
So just to summarise, and this is a, I hope,
a slightly provocative challenge given the discussion just now about place is that quite obviously innovation isn't evenly distributed.
I happen to think it never will be evenly distributed.
And that's partly the nature of the way in which ideas are developed, and it's partly the economic systems that we live in and play with, you know,
the return on investment, which is a very important side to think about from some of our science, the funding and the mechanisms of where people are.
So his five thoughts for you specialism?
Well, I think the knowledge economy, whatever that is, really took off from about 1980.
And if you were an academic in the knowledge economy, you better specialise because you've got to publish.
Otherwise, you're not going to get visibility for promotion.
And if you're very specialised, you have limitations in terms of how much impact you can have on your own.
You need to buddy up with other specialists.
And what we've seen, I think in the last 40 years is a concentration of specialists working with other specialists.
So an agglomeration scale effect. Indeed, I've shown you big science today.
I'll show you some very big equipment, $20 billion worth of equipment just to do one set of experiments.
No country can afford that, typically. So we're starting to see scale and agglomeration, which counts against some of the spread out.
And there's a big question mark. How far are we happy with that?
What does it really look like? What can it look like in the future? Who's going to pay for it?
What talent going to do? Instrumentation? Who's creating it?
Mark talked about improbable. What about DeepMind?
Know the private sector now, perhaps controlling even more the infrastructure that science needs to be unfettered in Vancouver, Bush's words with?
And how do we get into that data and analytics collaboration?
This does pretty well too big for any of the mighty players to do on their own, so we know we need to collaborate.
But what does that mean? And we need supply chains. We need engineering and technology companies that can produce and feed our labs and our systems.
It's good money in that.
I mean, in the last three years, the Atomic Energy Authority, which leads for the U.K. in the technical supply chain to build beta,
has already won half a billion pounds worth of contracts from that international community back into the U.K.
So this business there? But how? How do we do it? How do we understand timescales?
So of course, the investor wants the return pretty well now.
We need patient money for some of this stuff and some of it. Serendipitously, we can't even see the outcome, perhaps in half a lifetime.
So. Somehow, we've got to manage expectations. And lastly, we've got to think about how do new areas emerge?
And it's something I'm particularly concerned about because I spent six years
on the leadership of Imperial College and we invested in some good new things,
some of them that I hardly heard of before the last six years, things like synthetic biology.
But we don't really have a plan for the next things that will keep the flywheel of science and business working,
and I don't think we're very strategic about that. And who's going to pay for it and how do we and fitter ourselves in that regard?
So going back to business business and there are some of you here represent mighty companies that have done very, very well with science.
Increasingly, I think is looking for new ideas. And at Imperial, we held a conference recently.
One of the first on corporate accelerators, two companies.
They're trying to learn How do we get outside of our R&D box and see what's happening in the street?
And all of them we're talking about how do we learn from entrepreneurs, particularly deep tech entrepreneurs, these people who take risks?
And I was writing, sorry, I'm plugging to my post this one, a recent one, the playful entrepreneur writing this book.
And I was talking to Lord Skidelsky, who, if you note, wrote the best biography of Keynes three volumes massive task about entrepreneurs.
And he said the more unstable the parameters in the world you find yourself in, the more the insights or intuition of the entrepreneur matter.
And I'll just leave you with that thought because I think I'm bringing back Thomas
Edison and that spirit of entrepreneurship into that science business mix,
and I think we need to find the right conditions for that. Thank you very much.