From Lab to Market: Identifying the Scientific Breakthroughs You Should Care About

Anthony Schiavo: Hello and welcome to the webinar, From Lab to Market, Identifying the Scientific Breakthroughs You Should Care About. My name is Anthony Schiavo, Senior Director and Principal Analyst here at Lux Research, and I’ll be moderating today’s session. Presenting today is my colleague, Arij van Berkel, Chief Product Officer here at Lux Research. Throughout the webinar, you can type any questions you have in the questions box on your screen. Time permitting, we will answer all the questions we can. If your question does not get answered, please do not hesitate to e-mail it to [email protected] and we will respond. If at any point you experience technical difficulties such as a frozen screen, simply refresh your browser and check that your internet connection is still strong. Before we start the webinar, a quick word about who we are. Lux Research, excuse me. At Lux Research, we help our clients make more confident innovation decisions about what matters next. As an independent research and advisory firm, we work with many of the world’s leading companies to identify emerging opportunities, evaluate technologies, and make high-impact innovation decisions. Our scientists, engineers, analysts, and industry experts combine original research methodologies with practical decision frameworks to help their organization separate signal from the noise. We help our clients answer three critical questions: Where should we focus our innovation efforts? Which technologies deserve investment? And which partners can accelerate innovation? And today on this webinar, we’re going to be talking about some of that. And in particular, we’re going to be talking about the Nobel Prize. This is a topic we discussed last year and there’s been a new round of Nobel Prizes, and so we’re back to talk about it again this year. And with that, I want to hand it over to you, Arij.

Arij van Berkel: All right, thank you, Anthony. Yes, so it’s the second time in a row, so I don’t think we can quite call it a tradition, but I would like it to be a tradition. Nobel Prizes have just been announced, but not yet presented, right? The ceremony is still to come. But it’s a good prompt for us to talk about scientific breakthroughs and what they mean for you as an innovator. So we’re going to talk about that. We’re going to take the Nobel Prize as a reason to talk about it. And we’ll share with you our model, which allows you to monitor how scientific breakthroughs are progressing towards application, towards where it becomes an innovation rather than only a scientific breakthrough. And then we’ll talk a little bit about the breakthroughs that we think you should be monitoring in the year to come. But before we do all that, obviously, we need to congratulate the winners of the Nobel Prizes this year, and for chemistry, Henri Kagan and Kenso Soai won for their discovery of nonlinear effects and autocatalysis, particularly with stereo molecules, right, asymmetric organic synthesis. They didn’t discover asymmetric organic synthesis, but they discovered a lot of the things you can do with it. Then Francis Halzen, who came up with the IceCube Neutrino Observatory. Neutrinos are notoriously hard to capture. So his work in physics on doing that, he deserves a Nobel Prize for that this year. And we will also look at the Nobel Prize for physiology or medicine, which was awarded to discoveries in optogenetics. And that was done by Karl Deisseroth, Peter Hegemann, and Georg Nagel. So congratulations to all of you. And we’ll be looking– OK, this is me presuming that we’ll watch our webinar, obviously. I don’t know if that’s actually true. But even if they’re not, if you know them, convey our congratulations to them. OK. What do we do with that? So these are big and now recognized at the highest level scientific breakthroughs. What does it mean for you as an innovator? Do you need to do something with it? Can you do something with it? What does it mean for innovation in a corporate or a startup environment? And that is very hard to tell. So a lot of those scientific breakthroughs are not predictable in the sense that you cannot say we should really fund research in the topic of optogenetics because that is going to be a breakthrough that will generate a lot of new applications and new products. So this is a quote from a corporate funder who was funding hundreds of researchers by his account and I do believe him. So you’re funding research at universities because you find it’s interesting or relevant to your business and then turns out that some of that wins the Nobel Prize. But that doesn’t mean that Nobel Prizes are a good way of managing your research inputs, your front end of the innovation funnel. When we look at innovation activity at Lux, we look at our technology signal. And by the way, you should keep an eye out. We’ll be introducing a major innovation ourselves where we are upgrading our innovation signal. Not replacing it, but really making it a lot more functional. We’ve been working hard on the metrics and the algorithms behind our innovation signal. But for now, for this webinar, since we wanted to be close to the Nobel prizes, we still used our current tech signal, and this allows you to compare it to last year as well. So our tech signal measures all the innovation activity in a certain area. So in this case, the area of asymmetric catalysis and all the innovation activity means we’re looking at patenting, scientific publications, investments, grants, government grants, and also just the activity that we’re aware of with our clients and on our platform. If you look at that, you can see the key publications for asymmetric catalysis happened in the 1980s and 1990s. And you can see that after that, there was quite a bit of activity already happening in this area. And then there were two Nobel Prizes, actually one for asymmetric catalysis to begin with, and then the other one, which was awarded this year. But you can see that they are quite a while after the key publications and when they were awarded, a lot of research was already happening. There was already about 20 years of research happening before the first Nobel Prize became apparent. And if you look at the history of chirality, you can see that the industry was roughly 27 years ahead of the Nobel Prize. Our timeline here stretches a little bit further back because it’s always good to mention Louis Pasteur, who already was aware of mirror image crystals and all that. But we put it really at Monsanto, who was doing the first industrial asymmetric synthesis. And that was L-DOPA, which, of course, you also have D-DOPA. 1986, Kagan. published his paper on non-linear effects, right? Where a slightly impure catalyst can give a much purer product than a much more purified catalyst. Then Soai published his paper on asymmetric autocatalysis was published in 1995. And then the Nobel Prize, the first one for asymmetric catalysis was awarded in 2001 to Knowles, Noyori, and Sharpless, and then we have this year’s. Nobel Prize. But as you can see, people were already working on it in industry, even before some of the main discoveries and before the Nobel Prizes were awarded. You look at optogenetics, you can again see the Nobel Prize was awarded this year, but there was a lot of activity and a lot of increase in activity already happening around, it really took off in 2010. But even before that, there were people working on it at a somewhat lower level was kind of a niche, small community. And the key publications, of course, happened at the turn of the century. So again, there’s like a 25 year lag where a lot of things are happening already. So scientific breakthroughs are hard to track and their trajectory is hard to understand. They all seem to have their own way of eventually working their way towards some sort of impact in applications. So how do you deal with that? Well, here’s our proposal. We are looking at the 3Ds of innovation. The 3Ds, actually, you could also say it’s a 3G model. But it ended up being a 3D model because we couldn’t come up with appropriate words starting with a G. But why is it a 3G model? Because you need to think about this in terms of generations. So when a new discovery is made, a new scientific breakthrough, then the first generation is just busy discovering what all of this actually means. So the first generation is just figuring out what this optogenetic discovery actually entails. How does the mechanism work? What are true statements about it? What are maybe mistakes about it? How can we model this? How can we predict this? And they hand over their knowledge to generation two. So they start teaching at universities and they show people this new great discovery. Generation two, standing on the shoulders of the giants of generation one starts to dabble. So they start to do things, they tinker, they say, okay, this is great. What can we do with it? Can we try this? Can we try this work? Now that we know this, can we do this thing? And this can take quite a long time, so sometimes generation three and four and five are also still dabbling because the job for generation two and any subsequent generation until they hit on the right application is to really create engineering guidelines and to figure out what really good applications are for this newly acquired knowledge. And then we get to generation three who starts to deploy, who starts to look at some of the successful applications and starts building production capability and starts extending the limits, starts optimizing, starts also scaling because at that point there’s much more money involved. And so the number of people that are capable and also enabled to work on this starts to grow. So it really is a model in three stages that we’re proposing here, where the dabble stage can be one generation, if you’re lucky, and we’ll see one generation of people being educated at the university, right? Generation takes about between four and eight years, right? That’s what you should imagine. So a really quick Scaling would take about 16 years, 16 to maybe 20 years. But it could take much longer if the dabble phase turns out to take many generations, which could be due to not finding the right applications. It could also be due to economic circumstances. The dabble phase really is the phase where you need to be watching. So where are our Nobel laureates this year? For optogenetics, I think we’re in the dabble phase, but there are already some attempts to deploy. For asymmetric autocatalysis, we’re still in the discover phase. For neutrino astronomy, obviously, we’re still in the discover phase. But I think what’s interesting there, and we’ll get to that in a moment, is really not so much neutrinos. I have a very hard time coming up with good applications for neutrinos right now. Maybe they exist, but it seems very far out. But the fact that science is working on this results in a lot of spin-off. It results in really precise photon detection. It results in really deep hot water drilling. results in tomography with cosmic rays. So there’s a lot of things that are spin-offs from this kind of development that are also interesting to watch. And I would include those in the dabble phase. So in dabbling, you’re also dabbling with all of the tools, the things that you need to create to work on this science. So optogenetics, just a bit more. background, 2002 to 2003, maybe, we found the first light-gated ion channels. And we, not me, but science found that it also works in other cells. Then 2005, so this is the first generation. The second generation quickly behind it already starts to dabble. And starts to look at, for example, experiments in mice and things like that. And then already in 2021, we can see the first real applications. First partial vision restoration in a blind patient, gene therapies. They’re still in clinical trials, but it’s the onset of deployment. And of course, those things take a while. But the first patient was in 2021. That’s why we put the marker here at 2021. And you can see that took about 20 years, give or take. So this is a really fast deployment of a scientific breakthrough into application. Autocatalysis is quite a bit slower, right? discoveries in the 1980s, starting to dabble with those discoveries in around 1995. But today it really doesn’t, it’s the actual effect, the actual breakthrough is not really deployed in chemical production. However, it is used in explaining things. It is used in modeling and in discovery of new molecules and nowadays also it’s used enzymes are used a lot of course to produce chiral molecules mostly in the production of drugs. So we’d say probably that for the actual scientific breakthrough itself. We’re still kind of dabbling, but there’s already quite a bit of spin-off that you could say is deployed. Now, obviously, neutrinos discovery 1988 till 2013, and this is an interesting case where discover actually takes a long time, much longer than a generation. simply because of the complexity and the scale of the project. So this is normally, I would say 90% of the cases, the way from a scientific breakthrough to application is rate limited by those generations. Sometimes it’s rate limited simply by the sheer size of the project, that’s what you’re seeing here. So another example, just going a little bit back, and you have RNA interference. This was a 2006 Nobel Prize awarded to Craig Mello and Andrew Fire. And they did their foundational research in the late 1990s, right? they could show that RNA switches genes off and on in worms in this case. Dabbling started pretty quickly and in 2006, so we’re talking just eight years after discovery, you can see that there are already some initial drugs being approved. There are people working with it and nowadays 2024 is when, for example, sprayable RNA pesticides came on the market. So this is again, this is slightly more, I say, 26 years from discovery to deploy, but a pretty quick runway from scientific discoveries to deployment. And we can track it, right? So this kind of transition, we can reasonably track it with our tech signal. So here you can see the discover phase, dabbling, and now we see deploy, and you can see how from discover to dabble, you typically see a steep rise in activity. So now lots of people have learned about this and are starting to do fun stuff with it. And then typically from dabble to deploy, you see another spike in activity. So this is where people finally have zoomed in, focused on a great application. And so more people start to work towards that particular application. They see the pot of gold at the end of the rainbow down. Also, And this may be a prediction for us for future Nobel Prizes, right? Don’t just look at Nobel Prizes. Obviously, there’s other big discoveries, scientific breakthroughs that have not been awarded a Nobel Prize, or maybe not yet, that also lead to interesting applications and a lot of growth. And of course here, What’s interesting here in GLP-1, I’m not going to elaborate much on GLP-1. I think everyone has heard about it by now. What’s interesting here is in the dabble phase, that weight loss effect was discovered at some point. So while these were already being developed, for example, for diabetics, At one point during Dabble, people discovered something really interesting about it that unlocked a much bigger market. And so that’s we all know the story by now, right? This is in the news quite regularly. Let’s look back at the Nobel prizes of last year. What happened just as just tracking what we presented last year, right? So one of the Nobel Prizes was awarded to Metal Organic Frameworks. And they were first synthesized 1990, 1989, right? And around 2003, we could make stable ones, but that’s still about 25 years ago. Around 2012, we saw the first startups around MOFs. And in 2016, we sold the first commercial products announced, but they didn’t quite make it. Last year, Nobel Prize was awarded. And this year, very interesting, we see a pilot production, which is a joint venture between Kettle and AirJewel. And this is a self-regenerating dehumidifier, which is very– it’s interesting. among others, because that is a relatively quote unquote cheap market to get into. So you really need to know what you’re doing there in terms of synthesizing the MOFs. But it’s also a big market. So it’s interesting to see people going in there. I don’t want to imply there’s a relationship between this happening and the Nobel Prize. I think this project was long in the works before the Nobel Prize was announced. But nevertheless, it is a– probably makes it a little easier to raise money for this kind of thing and to get a decision for this kind of thing if there’s a Nobel Prize linked to it. We also looked at quantum computing, right? The Nobel Prize for physics last year was for macroscopic quantum tunneling, which really helps to build superconducting qubits. So how are they doing? Well, There is a hardware race. There’s a race to figure out what is the best way to produce quantum computers. However, if you’re looking to get involved in quantum now, you should be looking at sensing. So really sensors, quantum sensors is where the commercial action is right now. And you can see there a couple of things that are happening there right now. So on our 3D model, what we did is we scored all of the Nobel Prizes since 1985. You could have come further back, of course, but you have to stop somewhere. And like I said, in our generations model, it takes minimum 16 years. If you’re lucky, 12 years, but let’s say 16 years from Nobel Prize application, but it could take as long as 30, 40 years. So this is kind of a good cutoff to look about 40 years back. And here you can see all of the Nobel Prizes mapped on our 3D stages. Using a scoring methodology, I won’t elaborate on that here, but this is kind of the fields and obviously what you can see is that physics is still relatively low. So physics discoveries are very fundamental, take a long time, multiple generations. The dabbling phase is quite long for physics. For chemistry, you can see that that’s much quicker. And you can also see that about 30% of the Nobel prizes, the discoveries attached to Nobel prizes are still actively dabbling. You may wonder why, if we’re looking back to 1985, why is there a 1925 in the chart here? Because we’re looking at not so much at when the Nobel Prize was awarded, but we’re looking at when the key publication was made. If you look at the statistics, you can see that of the chemistry Nobel prizes, of the discoveries that were awarded with the chemistry Nobel Prize, None of them actually is still in discovery. And there’s about 35% still in the dabbling phase and 65% of them are already in deploy. Physics picture is quite different. Over 40% still in discovery. And then there’s about 30%, one third still dabbling, and then the rest is in deploy. Whereas physiology or medicine looks very similar to chemistry, slightly behind, but quite similar to the chemistry prices. So again, the big difference really is how long discovery and dabbling take. And for physics, before you can start to tinker and do stuff, discovery phases tend to be a lot longer. So you need more research, and the research itself takes longer is bigger projects, more complicated experiments. So last year, we presented you with a watch list. So out of the 40 years of Nobel Prize, or by now 41 years, we looked at what are things to watch? What should you be looking into where people are now dabbling or close to deployment, where you could see new interesting things happening? And so we updated the list. So obviously optogenetics was not on the list last year. It couldn’t have been. It’s there now. And what you can see here, of course, is that the whole vision restoration gene therapies that are now in trials. And so there are interesting things coming. Molecular machines, right? Still worth watching. I think it was a 2016 Nobel Prize, but of course the research is older and there’s lots of interesting applications that you should be watching for. I won’t read the entire list to you. By now you’ve been able to read it, but I would like to point out the quasicrystals, right? Because they could help with PFAS. And obviously the Bose-Einstein condensation. We are very bullish on photonics. We think you should really be watching photonics. So what are some of the breakthroughs you should care about in the coming 12 months? Well, first, let’s take a look at what we said in 2025, and we picked four breakthroughs. These are breakthroughs, whether there’s a Nobel Prize attached to them or not, these are just scientific breakthroughs that we think were worth your attention and watching. So you can see our 2025 list here. And if we look back, what happened, you can see how they developed across the past 12 months. For superconductors, I think we can clearly see a move to deploy now. We are seeing a demonstration of a three megawatt high temperature superconducting cable to be used in data centers. So there’s a big market, a very dynamic market behind it right now. So that is interesting to watch. You can see the recyclable curing agents. I think they’re still dabbling. So they’re still looking for the real application that will move them into deploying. We have interesting results, but not quite the killer app yet. Superlattices, new electronic materials. I think this is still in discovery. People are still working out how to make them reliable, how to produce them in a way that is repeatable and can be done. And of course, in the meantime, The applications they’re targeting are also still very much in development. Aluminium, nickel, cobalt magnets. I think that has a lot of push behind it because of concerns of critical minerals. Also, there is a lot of attention to recycling and maybe design for recycling. Still dabbling, still not a killer application inside, but the dabbling has broadened. A lot more people are thinking about it than a year ago. So what’s for this year? We picked four. We could have picked eight, to be frank, but we think we should limit ourselves a little bit, so we picked four. And our pick for this year is synthetic cells. We’ve been talking– I remember talking about this at Lux even as far back as 10 years ago. But I think right now, we’re seeing some really good developments that might lead this to become deployed at some point. Photonic materials, I told you specifically meta photonic materials. We’re quite optimistic about them. We think they will play a big role in the future. Electrochemicals, ammonia synthesis. I think recent events have spurred more interest in local ammonia generation, small scale in situ ammonia generation. And then this is really interesting because it will also be a first scaling of electrochemical manufacturing. So it has a wider impact beyond ammonia. It also has an impact in just building an electrochemistry industry. And of course, we have to talk about AI and we think AI driven materials discovery is about to experience a next phase, maybe driving it beyond dabbling. It’s a little too early to say, but we think it might. So just quickly, our tech signals, right? Synthetic cells, you can see a very long discovery stage, basically dating back more or less to the Human Genome Project. Then as the tools became available, say around 2004, 2005, people really were finally enabled to dabble and of course with some of the gene editing technologies nowadays that is only accelerating. We think we can see the first deployments, first signs of deployments around this time. It’s a little early to say. Metamaterials, photonic metamaterials, we’d say really now dabbling. The abilities are there to produce them at a relative scale. And so people are now figuring out what are good applications for them. But you can see the really steep ramp up in activity here. This is, as far as tech signals are concerned, a really consistent, very steep climb in activity. Electrochemical ammonia synthesis, pretty Quite a bit of discovery, I’d say, and people are now starting to dabble trying to find good initial applications. And I think that the main problem here really is about economics. So you will see this switching to deploy really rapidly depending on economics. AI-driven materials discovery is really interesting where you see, again, a large discovery. People have been talking about expert systems and later about materials informatics and things like that. So I would put that in discovery right now. The databases are there, the information is there. AI is providing the tools to combine information really quickly and find new combinations really quickly. So people are dabbling and I think dabbling just got way more interesting in the past two or three years where people are now closing the loop by linking dabbling to synthesis. So we’re not only theoretically discovering materials, there are some labs that are now closing the loop by actually producing the materials, testing their abilities, testing their properties and feeding it back into the system. And that’s the steep increase you can see as of say 2024 when we see the first patents around that technology emerging. Now, We couldn’t possibly discuss all scientific breakthroughs that you should be interested in. So we’ve made a selection. But if we zoom out and we look at this kind of analysis, what can we learn? How should you be looking when you get the next request to fund university research, for example, how should you be looking at that? Well, first, you really need to track the start of the conversion from science to invention, to innovation. Our tech signal, our soon to be improved tech signal, really provides you with the information you need to do that. But then also, you really need to watch for that dabbling phase. Can you see more people doing stuff, figuring things out, just trying things? And that’s where you want to watch. So if people are proposing to you proposals, research proposals where they’re trying to see what they can do with this new knowledge, you should seriously consider that. You need to look at all of those breakthroughs, not invest in all of those breakthroughs, but really just look at what breakthroughs you see and really make a selection and you need to have a system to make those selections. And then finally, you need to look at what’s happening around these discoveries. So especially in physics, if you look there, a lot of the spin-off from physics discoveries is not about the physics, or at least not in the short run, but it’s about the research, the tools that were developed to do the research, which find applications elsewhere. Just look at the breakthrough, look at the entire activity of doing research in those areas. With that, thank you for your attention. I hope to see you next year discussing maybe next year’s Nobel Prize winners. Anthony, do you think we have any questions?

Anthony Schiavo: I think we have a couple of questions. I think we will have to make it a tradition. So if you have questions, wait till next year. Now, we will be taking questions. And if you have them, you can type them in the question box, right? So if we don’t get to your question on this call, we will be in touch much sooner than next year for this one. There’s a couple of questions here that have already come in, and there’s one that’s kind of on my mind as well. You mentioned with the MOFs, that we talked about last year and then this year, there’s been some progress. You also mentioned one of your key takeaways. Don’t wait for the Nobel Prize to investigate a technology. I guess I’m curious, and there was a question along this line as well, how much impact does the Nobel Prize actually have, in your opinion? You know, you mentioned some of the follow-up work that’s been happening in MOFS. Do you think that would have happened without the Nobel Prize? And is that really something that is a game changer for these technologies now that we’ve been looking at it for two years?

Arij van Berkel: Yeah. So I think for MOFs, no, it’s not a game changer. And that’s because MOFs have been in the spotlight regardless. But there is an aspect where it’s actually somewhat fortunate that Nobel prizes are typically awarded quite a while after the key publications. So I didn’t put any of that in this presentation, but you can find, especially in physics, you can find quite a few scientific breakthroughs that create a lot of attention when they happen. People are quite excited about them and you see like maybe six, eight years of increasing innovation activity, lots of people working on it, publications and whatnot. And then you see it dying down. And then the Nobel Prize comes along and kind of reminds people, hey, this thing happens like 20 years ago. And that is sometimes a good thing because sometimes things that 20 years ago we didn’t have the tools, there were maybe no applications yet. A lot of things in the context of this discovery can happen in 20 years that will make it a very different consideration whether you should work on it and what kind of things you could develop with it. So I think there is no direct relation between Nobel prizes and investments in innovation. I don’t think it works like that. I do think there are some discoveries that kind of get a nudge get a reminder to everyone and are reinvigorated through the Nobel Prize. Most, not so much, people kept working on them. So there’s not really a measurable effect, I think, of the Nobel Prize there.

Anthony Schiavo: Yeah, it’s interesting you brought that up because we certainly saw that with something like graphene, right? Where the Nobel Prize was a big part of the research taking off when it did. You really see that in the data. It makes me think of some of the more abstract work, like the neutrino research here, where maybe we’ll see more research, we will see more sort of fundamental understanding as a result of this Nobel Prize inspiring more research there. But you know, you made a kind of an important point, I think, in that third key takeaway below that you had, and there were some questions around this. These breakthroughs or scientific work that is pretty far afield of what our clients do in their commercial innovation world. How should they be thinking about that? I mean, you said it’s for observing, it’s to increase our understanding, but can you say a little more about what that means in practice and how companies can think about these technologies?

Arij van Berkel: So this is really, I’m going to quote Mariana Mazzucato on this. This really is about moonshots. So the neutrino detector thing is like the Large Hadron Collider or like the big fusion project in Cadarache, right? They’re moonshot projects. They’re man on the moon projects where you’re doing big things and solving problems that are difficult to solve and that have no solution when you start the project. For scientific reasons, because you want to detect neutrinos or something like that, but those are because you want to put someone on the moon and have them walk around there. But while doing that, you create a lot of innovation that has application elsewhere. Looking at those things, look at those scientific endeavors that create a Moonshot project that take things to maybe, to quote the kickoff of the original Moonshot projects, things that we’re doing not because they’re easy, but because they’re hard. And those tend to result in spin-offs that are interesting for businesses to look at. For example, in the large fusion project, this is explicitly one of the goals. The governments are funding this project to create nuclear fusion, but also along the way to create a lot of spin-offs in material technology, in sensing, in control technology, all of those kind of things.

Anthony Schiavo: Yeah, I mean, even some of the neutrino detecting work, just like the water purification systems that are necessary to go into that type of thing are pretty impressive scientifically. And there’s a lot to understand there. Well, Arij, I want to say thank you. And I want to say thank you to everyone who joined us today. I think that’s going to conclude our webinar for today. The slides and recording from this webinar will be sent to all attendees via email today. After leaving the webinar, you will be prompted to complete a survey on today’s presentation. We always appreciate the feedback. It really helps us inform and improve our future webinars. And you can also go to our website. There’s a link here and you can check out our upcoming webinars. We encourage you to do that. Thanks for joining us and have a great day.