The Modern Innovation Chasm: Rethinking How Agrifood Technologies Scale

Elnaz Shabani: Welcome to the webinar, The Modern Innovation Chasm: Rethinking How agri-food Technologies Scale. My name is Elnaz Shabani, analyst here at Lux Research, and I will be moderating today’s session. Presenting today is my colleague, Josh Haslun, Senior Director 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 that we can. If your question does not get answered, please do not hesitate to email 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 strong. Now let’s jump into the discussion. Over to you, Josh.

Joshua Haslun:

Thanks, Elnaz. Hi, everybody. So again, today we’re going to be talking about the modern… thinking about how we need to consider the future of agri-food technologies. And why is that? The reason is we’re awash in first-of-a-kind innovation. And just like any challenging endeavor, whether that’s moving to space, the reality is failure is an option always, but fear is not. And we know this. We know that Innovation is difficult, but we’ve learned A lot. agri-food as a whole has come through this first-of-a-kind innovation period. A lot of the technologies we’ve covered and followed have been first-of-a-kind. They have had their first application or perhaps even their first reimagining or use case application. We’ve learned a lot during that time, and now we need to take those learnings and understand better what we’ve got wrong, what we continue to get wrong, and how we can improve how we assess these innovations as we look to the future where we have perhaps fewer resources for innovation and we have to make every decision count.

So to do that today, we’re going to talk more about the modern innovation chasm, this idea from first-of-a-kind to mature. And we’re going to build from there and then say, how does Lux assess first-of-a-kind futures, first-of-a-kind failures, and get to the point that when you begin to layer on additional context, the signals aren’t weak. I want to move away from thinking about everything as weak signals because we see that those signals are much stronger than expected in most cases. And then we’re going to tie all that together and really think about how we can take these learnings and assess an agri-food innovation gap and assess whether or not a particular opportunity the future is going to succeed or not with an example.

So let’s dive into the meat here. As I said, first-of-a-kind and reimagined failures are mounting across agri-food. And really this shouldn’t be surprising, as I said, as we get out of first-of-a-kind innovation cycles. You can look at the right here and look at everything from vertical farming to cell-based meat, alternative protein, food and delivery meal kits. And we can look at the bankruptcies and closures over the last three years and look at how much funding was associated with those companies.

For vertical farming, we have over 12 bankruptcies and closures in just the last 12 years, $3 billion. Cell-based meat for those that are particularly producing products, 7 closures, $700 million. Alternative protein, 13, another billion dollars. Food delivery and meal kits over 12, $3.1 billion. Again, as we continue to make new business models, make new products, use new technologies, the failures are mounting. So how can we do better? And do we need to do better? Do we need to even rethink what that innovation chasm is these days? This is the question we want to address. So to do that, let’s set the stage here. We all probably if you’re thinking about innovation, you often think about the innovation chasm, and that is the movement from being an early adopters to the early majority. This is what we want to jump because it is the major tension point as we think about any technology becoming important in the world. And there’s a couple ways that we think about this today, different data, different ways. And the first one that you’ve probably seen is We often assess early adopter and investor enthusiasm, right? We get a curve that sort of looks like this. You have early innovators. As that innovation gets more mature, you see a lot more excitement, right? This purple line showing a lot more excitement and investor enthusiasm. We get a dip as things try to maybe come to that innovation chasm and maybe begin to fail, and then some things succeed and you have this increase, right? And that’s great. It’s an interesting notion, but the reality is it’s highly limited and highly biased. This data is based on hope. It’s based on innovator enthusiasm and investor enthusiasm, not market readiness, not technology readiness, not a lot of the other data sets that we actually need to make these choices. So when we use something like this, we’re basing it on hope and it’s heavily biased. There’s other things that we can use too. Often you see a diffusion curve, technology diffusion curve. And what we hope to see in this particular curve is that initial inflection point as diffusion passes the innovation chasm there, where we see that initial uptick in the S-curve here. And that typically happens right around the early majority period. Now, again, When we think about innovation maturity, it’s often about particular use cases. So adoption of a technology may not align well to market readiness. And again, that’s a big challenge. So this again is a severely limited metric to use and align to innovation maturity as we think about this. And I would argue that The fact that we’ve focused on these is one of the reasons why we continue to make poor choices with resources when it comes to innovation, particularly at early stage innovations. And we need to come to terms with that and begin to think about the data sets we’re using and begin to change and build in more comprehensive, diverse, and quality context to build and improve that decision making. Because we can actually look at that. We’ll take a look at each of the things on the right and show how they plot out and show how these inconsistencies mount between different data sets. We’ll start with vertical farming. We can look at where we would place vertical farming and really this is an early adopter technology. Some may think it’s at the early majority, but the reality is vertical farming. We’re talking about pilots, pilots with retailers, pilots with different types of grocery outlets, things like that, but never really become mainstream. We haven’t taken to the point where vertical farming can out-compete basic controlled environment agriculture. And if we look at that, what happens here? Well, does this position on the curve align to where vertical farming is? The reality is vertical farming uses innovations and the adoption curve that are highly mature. It’s just using them in a different way in most cases. as far as the sort of hope and innovation curve goes for those who are investing. This one’s actually in the right place, but you can see that these two things are biased. The innovation, diffusion, vertical farming would be far to the right, but here it’s positioned. So again, two things on bias, not really working together, telling two different stories around innovation maturity. Makes it really hard to make a decision.

For early stage technologies, things like cell-based meat, we can think, okay, this one looks pretty good. It’s at the early-innovator stage. Great, we all know that. Products aren’t on the market yet. It’s moving up this hope curve here and perhaps even falling back down it a little bit. But let’s look at the technologies being applied. Again, innovation maturity. We’re talking about technologies that have long been applied on the diffusion curve in the space of cell production, cell proliferation, right? These are the technologies used and they’re being addressed and scaled in different ways. But overall, the technology has been developed and it was available. Again, a major issue with understanding diffusion, technology diffusion, and sort of this hype cycle. That’s challenging.

We can see perhaps something the same for alternative proteins. This is more of an average alternative protein curve, right? There’s things like single cell protein, even insect protein production, things that have been available, but maybe technology that is being applied a little more recently. But overall, we see that diffusion beginning to come up. early adoption is happening, right, for many of these. And again, this is the middle ground. Some proteins are much later in the innovation maturity curve, but others are much earlier. So, okay, so this one fits to some degree. I would still argue that technologies are further down the diffusion curve overall and are being applied in a lot of different industries. So again, we still have some differences there, making it difficult to really say together, do these things tell us where something’s positioned? Not really.

And then we have other challenging scenarios, things like meal kits, where we have opportunities that are growing. Let’s face it, meal kits and meal delivery services don’t typically have a challenge getting consumers to buy in. They do have a challenge in profitability. So when you think about these opportunities, they’re using technologies that are widely available overall, but they’re thinking about business model opportunities, ways to scale and affect the economics of the actual business model itself and tend to fail. But is that really an issue with overcoming and understanding if a technology is already past the innovation chasm? Not really. This is something a little bit further on. And you actually see that. Folks are beginning to say, oh, We need to think about the modern innovation chasm in a different way. Do we really need to change it though? That’s my question. For instance, has the innovation chasm shifted later stage? I think I’ve already shown you that that’s not the case. We see the innovation chasm is still where it is. The technology has been around a long time. For instance, to some degree like vertical farming, still hasn’t passed that. We do see other cases though where there’s challenges maybe later downstream. But is that really about the innovation chasm? Is it about additional chasms? I would argue that innovation chasm is still there and that those things pointed out as different innovation chasms are simply the nature of developing businesses and working on economics. It’s not necessarily an innovation chasm. It is about business development and how we build those later on.

So overall, has the innovation chasm shifted? I think this shows us that the innovation chasm isn’t shifting. Reality is we’re still being really hopeful with what we are thinking about, particularly market readiness, things like that. So we need to improve how we identify alignment with innovation scenarios, the scenarios that we care about, and the factors that we’re including to say, has this technology or opportunity checked the boxes it needs to check in order to really move forward in our minds? And that’s where we get into assessing first-of-a-kind innovation failures, futures, and noting to ourselves, okay, if we look at this, how strong were those signals? Should we have been ready for it? Should we not? And then we carry that forward and begin to change how we think about the signals we look at.

So let’s get into some examples here. And at Lux, we think about quality, diverse contexts, and we think that leads to clear signals. Strong or weak, but clear. We focus on three types of information. Focus on innovators themselves and innovation. We focus on emerging policy. We focus on consumer insights. And we take those and we assess them in a lot of different ways. We look at the product level. What is the status quo out there today and how do technologies compare to that? We then look at those technologies and we say, how ready are they for the market? How ready are they to solving the needs of the industry at large? And then market organization, who has those? Who has those? Who owns the IP? Where is there room to enter? And is there room for your business to enter? And of course, next is market readiness. So we think about policy, as we think about consumer insights. Is that market really ready for those technologies to make an impact, for those companies to succeed and grow? Now that’s all focused on innovation, disruption, but what about the incremental things? We also think about that a lot. We consider production and processes, those internal metrics you need to think about, whether it has to do with your carbon initiatives, right? Greenhouse gas reduction, a lot of other things, water use, How do you decrease those as well? And of course, that overlaps all the things I talked about earlier. So each of these, innovation, emerging policy, consumer insights, are diverse contexts where you need expert opinion to really understand and qualify what those are, to come out the other end with good decisions to hopefully avoid what I brought up before, leading to those biases. We want to avoid those biases that I think are part of some of the existing metrics that have been used in the past to assess whether or not innovations actually mature or not. And one way we’ve been doing that is looking at different industries where we’ve seen failures, for instance.

So I’m going to start by talking about a first-of-a-kind failure and that is cannabinoid biosynthesis. And I’m going to start by just kind of building some groundwork and then talking about how we’re going to assess that. If we look at this particular one, we see, for instance, that why was this interesting? Well, we had 37 billion in growth from 2018 to 2023. That’s noteworthy. We also saw parallel waves of innovation emerge, things in agriculture, things in biochemical delivery, things in processing. Lots of different innovations were going on. But for this, I want to kind of focus on the cannabinoid synthesis. And so on your right, you can see our tech signal here. This is a data reduction technique where we take summary of trends and patents, papers, funding, partnerships, a lot of different data that we ingest on a daily basis and provide a single metric that is a leading indicator of innovation. And we can see for cannabinoid synthesis, right, there’s two different innovation points here that you can see on the curve, one early, 26, 2008, that was kind of when the initial legalization happened. And then a second one. And the second one is where we’re going to focus. The second one was all about synthesis in this particular case. And what we had happen was cannabinoids became a biotech signal. There was during this period from sort of 2017 to 2022, 2023, there was enough funding to support 30 different cannabinoid startups. And they were doing all sorts of things, producing directly from cells, a lot of different opportunities here. But at the same time, in just under two years, That momentum completely dissolved. By 2025, only two companies were actively pursuing cannabinoid biosynthesis. So the question is, should we have seen this? And can we learn from that moving forward?

So how can we do that? As I said before, what we need to do is build quality context, quality, diverse context. And I’m going to start with a basic version of this, and then we’re going to go to sort of Lux’s more advanced versions of this moving forward. So a basic version is where we look at drivers. And we consider their impact and drivers, I include everything from sort of a pest outlook, right? Political, economic, social, and technological. Great. And so we can accumulate all these different drivers across those areas and we can assess them for impact, severity, and prevalence. So severity being, you know, how severe is this market change going to be or prevalence, how widespread is this driver going to impact? And persistence. How long is that driver going to last? And how, again, how much interest is there in the industry for their businesses? Interesting. Now, that sets one tone and one bit, but you also have to think about the context and confidence in each driver. How confident are we that that’s going to be that impactful? That’s typically not presented in most cases. But at Lux, we have our Lux take and our analysts who are experts in these fields are thinking about the confidence they have in the metrics that they’re seeing and the signals that they’re seeing. What we can do is we can change the bubble size of each of these as we plot them and get that. We can also plot whether or not these are positive or negative drivers. So positive in this case being green, negative in this case being pink. And what you end up with when you do this is something that looks like what we have on the right. So it’s a busy figure, but it’s important to walk through this figure. Again, Driver impacts on the y-axis, driver persistence on the x-axis, things in the top right quadrant are going to be the most impactful and most prevalent. And then negative drivers are pink, positive drivers are green. So if we take that in mind, the first thing we see is that most of these drivers are in the top right quadrant. They are both impactful and persistent. That’s really important to consider. First strong signal that we’ve seen already. A lot going on here and a lot of powerful movement going on. Now we begin to look at what’s actually in that upper right quadrant. We see a prevalence of pink and kind of a dearth of green. Another strong signal saying there’s a lot of negative drivers and very few positive drivers. We also see that as far as Lux’s confidence from its analysts in these, we see a lot of large points on here, meaning that we have a lot of confidence that these are drivers that are powerful and our analysts have the data and information to back that up. So that again is a third strike. Now we begin to look at the details. So if we look at the green things here, we have positive drivers. We have I bolded one or two here. Restricted and disjointed regional legalization is a negative driver. We have rising legalization momentum is a positive driver. Okay, great. Rising cannabinoid demand. Great. Now let’s think about those with respect to the negative drivers. Okay, we have competition, improved plant production efficiencies. Great. We have fragmented supply chains. for cannabinoid production. That’s a challenge that then affects that. We even have rising biotech skepticism, the ability to do it and accomplish it at a scale and cost that matters. Again, regulatory constraints, cost structure dynamics, highly impactful drug drivers. This is a really strong signal that indicates that the biosynthesis market for cannabinoid was very unlikely to succeed. this time we like to think about weak signals. This in itself is not a weak signal. This is a very clear signal that there is a lot of risk. And I think it’s something that the industry missed as a whole. And we actually did this particular data set not from a retroactive view, but we only looked at information up until 2024 to say, would we see this going forward? And that’s exactly what we saw. Now, as a result, right, cannabinoid was, this fall was inevitable. We had a lot of hope, but too much hope. We weren’t really looking at the market readiness for this, particularly overcoming some key challenges.
What about other examples? Let’s look at one that’s maybe closer to agri-food overall, which is cell-based meat, one we’re all well aware of, most likely. And again, were these signals there hiding in plain sight? How strong were they? You can see on the right our Lux Tech Signal, very indicative of first-of-a-kind innovation. You don’t see any early bumps. It’s a straight increase, right? Exponential increase to the top there. And then trickling off showing that innovation momentum decreased. And we had early movers generating 1.25 billion in a single year in funding. But you can see we had this drop-off. And what did that drop-off include? Well, it included things like companies beginning to introduce products, and those products not really aligning to what consumers want and suffering launch setbacks. and timelines that had to do with policy, a lot of other things. So we initially have a big drop off here. And then we see a more abrupt drop off moving into 2026 as we see funding evaporate, moving from, again, billions to tens of millions of dollars. And again, the question is, if we take more diverse information to account, is this something we should have seen ahead of time?

Again, we would posit that we should. And then again, the signals are strong if you apply diverse quality context. And so let’s take a look at cell-based meat. Again, same type of figure, impact on the left, persistence on the y-axis, sorry, persistence on the x-axis. And then we have the negative and positive drivers, and the size of the dots indicates our confidence. Again, signals in the top right. So there’s highly impactful drivers going on. So this means there’s a lot happening and we need to now evaluate what that looks like. If you look again, we do see a little bit more green than in the previous example. However, if we look at a majority of those green dots, they’re of smaller confidence. Whereas when we look at the negative drivers, they are both large, concentrated on top right, and prevalent there. So that, again, big, important, clear signals to say that there are many more negative drivers than positive drivers for this. And we need to then say, well, how significant are those? So let’s look at some of the positive drivers. We have things like animal-based seafood depletion. Okay, that’s a big signal. It’s not going away, but we do have other options for proteins that compete. We also have things like rising ethical concerns with existing meat production, legalization momentum, which let’s face it now, that’s changed and shifted in the opposite way. But we even have things like collaborative manufacturing models, which are beginning to be developed. Okay, those are interesting signs and signals, but also as far as the potential impact, the ones at the highest, we’re less considering them to be as impactful as other things as far as our confidence in that signal. However, when we look at things associated with cost, processing, legalization, we see that we have strong confidence in those, that they’re highly impactful, highly persistent, and that they’re all creating a strong negative narrative that is going to be difficult to overcome. Together, again, this is a clear signal versus a weak signal that cell-based meat was incredibly challenged, very unlikely. Now, I don’t think this one is a surprise per se, but it’s important to note the things that we’ve seen and say to ourselves, okay, we continue to fund this. Now, what do we do moving forward? How do we think about leveraging innovation in the future, or at least calling out technologies correctly and saying that this one is really going to struggle and we need to push the buttons in the right locations in order to, or pull levers, right, the correct levers in order to make that thing scale. And this was going to be exceptionally challenging and continues to be.

So with that, we kind of lead into the last thing where we’ve talked a lot about the potential to leverage diverse quality context to make and at least call out decisions, particularly when it comes to first-of-a-kind innovation or reimagined first-of-a-kind innovation. But we can’t stop innovating. That’s what we’ve learned. Markets are changing rapidly. There’s a lot of competition. And really we need to continue to move ahead. And at the same time, how much are we spending? Well, we tend to spend a little less on innovation than we have in the future. So we need to make that count. So again, failure is an option, but fear is not. So what do we do moving forward? How do we call out some technology using diverse quality context to say whether or not a company is going to succeed based on its products, based on its technology? And I think there’s a really interesting example for this. So again, we’re going to use the same kind of framework I brought up before for Lux, where we look at innovation. We look at emerging policy. We look at consumer insights, bring these things together to assess options at the product, technology readiness, market-organization levels, things like that, really matter when making those decisions. And you’ll see that as you take this in, having diverse experts that can help you understand it, and I leverage those from our team, is absolutely critical to do this.

So the example I want to talk through is a recent one. So trait development, has kind of just come through its final piece of first-of-a-kind innovation cycle. And what have we had now? We’ve had a lot of companies shifting. And I think one of the most prevalent ones or noteworthy ones is the fact that Benson Hill, an initial driver of next generation breeding, recently went bankrupt after going all the way to become a seed company from a trait development company. and was recently acquired by private equity. And now you see the company Confluence Ag beginning to emerge and trying to sell this stuff. So is there going to be a confluence of sales from these Benson Hill assets? That’s the question. But what are they actually doing here? Well, they’re selling the same products using the same platforms and trying to bring to the market. Animal feed, 90% less anti-nutrients, high protein feeds, things like that. It’s what Benson Hill had before. They’re bringing food ingredients to the market, ultra high protein, high oleic, low linoleic, and even the potential for tailored fuel crops, ultra high oil fuel crops that they can develop in an ongoing basis. For this talk, I’m going to focus on the middle one because I think this is where a lot of tension lies and kind of go through and say, if we were to assess the ability of this company to push this forward and maybe how it’s shifting and changing its rhetoric around the products it has, will it be successful? So let’s dive in here.

We’re going to start on the innovation. So just as we saw before, I want to start by saying How is this technology developed? And here we have the tech signal that we use for next generation breeding, includes everything from gene editing to predictive breeding to phenotyping, a lot of different information here. We see as we look at this, right, as I brought up, while there was an initial jet, this is really a first-of-a-kind innovation height, as we see. But we also see that After that peak in around 2021, we’ve kind of begun to head an interesting downswing. And why is that? Well, a couple of things happen. We’ve had innovations begin to come to market. So consumer and processing facing traits like we showed protein increases or ease of extraction, things like that are entering commercial stage. I’ll say I even grew the purple tomato in my garden here in California not too long ago. Did I love it? was okay. But was it enough to maybe satisfy the dollar per seed? not quite. And I think this is what we’re seeing kind of broadly across these traits is they’re great ideas and we pushed forward and developed them. But is there market for them? Should we begin to think about market readiness? And we’ll do that in a little bit as we continue to examine these. And in this particular case, if we think about high protein or high oleic, low linoleic oils, what we see is that Benson Hill wasn’t the only company to fail and these other companies have failed. And instead we see this sort of transition from market readiness to market organization where these innovators are now becoming innovation engines versus seed companies. Most of these companies have not succeeded in outcompeting existing commercial activity. Instead, they’re tending to be acquired as innovation arms for existing seed companies. So that technology is being ingested. Question here again is, will Confluence Ag be any different? Will it be able to escape this? So let’s continue our assessment forward.
So that’s our innovation assessment. A lot of red flags there. Let’s look at our assessment of emerging policy.

So in this particular case, Benson Hill and Confluence Ag, I should say Confluence Ag in this case, is positioning its products for high protein and high oleic, low linoleic. Great. These are things associated with health, associated with largely processed foods. And right now we have sort of an interesting paradigm change. We have a world where ultra-processed foods are facing quite a bit of increased scrutiny, whether it means what products have on the label or consumers looking to achieve different health outcomes that are firsthand visible. Reality is policy and national promotion of particular products is beginning to be challenged. And whether that’s the US, whether that’s the EU, whether that’s APAC, Each one’s taking slightly different challenges, but the reality is these things are under scrutiny. So bringing something to market may be supported, but it’s going to be under more scrutiny. The products that they include, the decisions companies make to use those ingredients, farmers’ choices to grow a seed, are going to be impacted by these policies. So again, This is an uncertain, sort of a yellow flag in this case for emerging policy. As far as seeds go, great. If you’re developing something that’s non-GMO, you can bring it to market. And we saw Benson Hill didn’t have any trouble bringing its products to market. It indeed did.

Now comes the final test, right? We’ve said, well, there’s some yellow kind of flags for market readiness insofar as emerging policies coming, but what about the consumers, the end users of these protein and high oleic, low linoleic products that they’re going to develop? Well, if we look at this just from the perspective of protein and we look at, let’s say, the EU and the US consumers’ perspectives on this is Confluence Ag supporting their product in a way that aligns to those. So that if you’re a supplier, you’re going to say, yeah, this is the source of soy that we’re going to use in the future, a source of oil that we want to use in the future. So let’s take a look. When it comes to protein in the EU, we think about practical, cost-effective, good-tasting protein that supports nutrition. So health is clearly a headline, practicality is a headline. But when we get into the details about what consumers come to consensus on, we see that it’s not only about just protein itself and the protein amount. It’s about the amino acid profile. Now, Confluence Ag does not talk about its amino acid profile as being any different or unique. Bioavailability, again, soy is bioavailability overall, but is their product differentiated there? No. Does it have a clean taste? Sure, but again, soy in general is aligned to that. Traceable sourcing, you could argue that Benson Hill or Confluence Ag’s product does have potential for traceable sourcing, but it would need to have enough demand to support a business’s entire choice for protein. Lastly, minimal processing. Overall, we’re talking about the same amount of processing for these products as other products. And then lastly, competition that aligns with milk. So is it comparing its protein source to any of this? the reality is no. So again, what we have here is a protein source that is high and potentially healthy, aligns to some of those things, but does it match what consumers think about and what they want as you make your products? Not quite. Again, red flag for Europe in this particular case.

But let’s look at the more direct market for Confluence Ag, which is the US. As we think about this, we see Again, practical plant protein is important. In this case, overall, it starts with saying, okay, we want to hit daily targets. So let’s see if there’s any confirmation and differences between the US and EU here. So again, not only about protein when you dive into the weeds here. First we see again, top of mind, amino acid profile is important. Now in this case, instead of comparing to milks, we have a comparison to whey and other meats. Is that amino profile different? Is it unique? Not something, again, that Confluence Ag is saying as the winning factor for their product. What about digestive trade-offs? So these don’t claim to have better digestive trade-offs, anything like that. It’s just high protein. Can it fit protein for every occasion? Sure. Soy is great for that. And does their amount of protein that they can produce is substantial. It is much more concentrated. Lastly, clear nutrition information, possible. and the unpleasant flavors and odd aftertaste, not something typically associated with these. So again, does this product fit what the market really wants in this case? Is it standing out as that? The answer is no. It has some interesting opportunities, but as far as being better than other elite varieties that also have different pest control or other things, The answer here is really not. It’s going to be challenging, right, to enter this area where you are targeting a, you have some maybe elite variety included, but is this as good as something from one of the other majors out there? That’s a steep hill to climb in this particular case.

And so, again, as we look at Confluence Ag and its future, and well, again, particularly around food ingredients, the outlook is relatively grim here. It’s probably unlikely that Confluence is going to be able to make headway without really shifting its rhetoric and providing the information that supports its differentiation beyond just high protein. Right now, just having high protein is really not enough at this point. So instead, really there are opportunities probably to focus more fully on the animal feed and potential for tailored field crops if possible. So likely animal feed might be the best opportunity because when we think about anti-nutrients and productivity, these are things that can actually greatly influence livestock feed if they can come in at a low cost. Tailored field crops, that’s going to be up to the energy industry to determine whether or not ethanol budgets, things like that are going to continue moving forward. So overall, When we bring in this layered context, we can see again that there is a clear signal here, a clear red flag for many of this business units, and perhaps a signal to say if you want to think about acquiring some interesting assets in the future, you may want to watch Confluence Ag because you may have an opportunity to do that at a relatively low cost and then bring those traits into your market. And that’s exactly what we want to do. We want to make sure these resources in innovation are going to the right opportunity, but timing is critical. By stacking these layers of context, we’re really able to achieve that.

So with that, I want to leave you with a few takeaways here as I close out. The first thing I want to start with is you just hear a lot of chatter about the innovation chasm is shifting. I think hopefully you’re convinced that the innovation chasm isn’t shifting. It still exists. It’s still there. And while when we have these first-of-a-kind failures that are observed and going to be prevalent, we really need to then think about what’s next for these, because there is technology that’s valuable, but what that market readiness looks like, and what do we need to push as far as information to understand how to position those in the future?

Second, again, when we look at first-of-a-kind innovation signals and we begin to assess them on multiple different layers with quality context, we see that those signals are strong. I mean, to some degree, tired of hearing weak signals because these signals tend to be quite strong. We just have a lot of hope. We need to kind of dash that hope a little bit and come to reality and say, Here’s what we’ve learned over the past 15 years. We know that a lot of technologies aren’t going to succeed and that we can’t win on, let’s face it, sustainability alone or things like that. We need to be thinking about performance and we really need to be assessing not only that, but whether the markets are actually ready to take on those technologies. And that’s where we’re beginning to really layer that context and understand that end outcome much better. And so learn from our first-of-a-kind failures, so to speak, and understand, especially as we get into things like health, that are big, important areas of opportunity for businesses, that we really need to understand market readiness, technology readiness, outcomes, and how we assess outcomes and position our products to balance what’s happening on the innovation side, the policy side, as well as the consumer angle and bring that context together. That’s where we can make much stronger decisions. So with that, I want to say thank you to everybody and appreciate you all listening in today.

Elnaz Shabani: Thanks, Josh. We will now be taking questions that you may have on the presentation, which you can type into the questions box.

Given the time constraint, we might be able to get to one question today. But if we do not get to your question on this call, someone from Lux will be in touch after the webinar.
Let’s go for this question. Josh, does Lux have any kind of explicit methods to connect consumer policy and innovation insights beyond what you presented today to us?

Joshua Haslun: Yeah, so we’re beginning to do several things at Lux actually. On just the consumer and policy side, we’re beginning to assess how consumers think about policy. So that’s one interesting area. And it’s very interesting to say, will consumers go along with this policy? Do they care about it as much? that can help you understand how to act and behave and work.
We also are connecting understandings of unmet needs or jobs to be done from consumers to technologies. The method is called the Helix and the goal for this is to help you sort of understand this common connection across your organization.

Most orgs have a marketing team, a product team, an R&D team, all these different teams. And while we do talk with one another, often what happens is one team passes information to the other team and the other team says, okay, we’re going to move forward. There’s not enough tension between the two. And so what we’re doing is in our teams actually having our anthropologists and tech scouts work right alongside each other to identify those sort of hidden connections that once made obvious are really helpful to beginning to assess kind of just like what I said today, whether or not a technology and an unmet challenge are well aligned for future opportunities or not.

You can really assess it at the portfolio level, you can assess it at the capability level, or you can assess it at the unmet need level.
So yeah, it’s a really exciting area. We’re getting a lot of positive feedback there if folks are interested. definitely reach out and we can have a broader discussion about that method.

Elnaz Shabani: Right, thank you, Josh. It’s definitely very, very important to be able to create that connection. Thanks for adding that.

And that concludes our webinar for today.

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