Episode Transcript
[00:00:00] Speaker A: Welcome to Infinite Future. I'm Todd Thomas, and today we're exploring the innovations shaping tomorrow's world. You're watching Now Media tv.
Welcome to Infinite Future. I'm your host, Todd Thomas. Infinite Future is the show for people who don't want to just talk about the future, they want to engineer it.
Today, we're cutting straight through a problem that's quietly slowing down progress everywhere. The way most organizations talk about innovation versus the way real innovation actually happens.
My guest today is Elliot Parker. Elliot is the CEO of Alloy Partners. He's the founder of the venture builder High Alpha Innovation and author of the Illusion of Innovation. A deep Dive into why efficiency thinking can block radical progress. Elliot, welcome to the show.
[00:00:45] Speaker B: Thanks. Thanks, Todd. Good to be here.
[00:00:48] Speaker A: The world is obsessed with AI, but adding AI is not the same as building a durable advantage.
This segment sets the foundation of why innovation often becomes theater, what real innovation looks like, and how leaders can stop confusing activity with progress.
Elliot, before we go deep, what's your lane today and what should viewers know about the work you're doing through Alloy Partners and the venture builder model?
[00:01:17] Speaker B: Sure. Yeah. At Alloy Partners, we build startups, and our thesis as venture builders that we can build really compelling, successful startups if we do that in partnership with corporations that you see corporations are designed and optimized for scaled execution, for doing what they've done for a long time to get them to their success, and for continuing that. Everything's built around that. Learning, doing new things is very hard for large corporations. And so we come to them and say, let's drive some of that innovation through external startups. We can build startups that work really well. That's what we do. We are 43 companies in now and counting.
[00:01:55] Speaker A: What was the moment in your career that made you realize most companies don't actually innovate, they optimize?
[00:02:03] Speaker B: Yeah. I spent many years.
One of the most amazing experiences of my life was being able to work closely with Clayton Christensen for many years. And Clay was the thinker, great business thinker, professor at Harvard Business School, who came up with the idea of disruptive innovation, created a theory about disruptive innovation, wrote a book called the Innovator's dilemma back in 1997 where he explained that corporations are essentially optimized for efficiency.
Over time, he developed this idea that corporations.
We've determined collectively that the way we measure the success of companies is by their return on invested capital.
It turns out that if you're a CFO or a CEO running a large company, you can boost your Return on invested capital by reducing the amount of capital you're putting to work inside the business.
And so it's much easier to boost your ROIC by shutting down a factory or closing a business line than it is to go build a new business unit or new venture or launch a new product.
And over time, as companies have gotten really good at earning profit and becoming more capital efficient, they've become worse at innovating and at surviving. In fact, companies, we see the amount of money on their balance sheets increasing and we see the lifespans of companies decreasing at the same time. That's counterintuitive. And so for me, it was. I look back to some conversations, lots of conversations with Clay Christiansen over the years trying to investigate this problem.
So many companies, they know they need to innovate, they know what's getting in the way. They're very smart and capable people. But for some reason the organizational structures get in the way. And so a lot of the activity inside these companies ends up looking like theater.
We need to be doing something that looks like innovation, but it produces very little in terms of results. That's not good for anybody.
[00:03:55] Speaker A: So your book frames innovation as an illusion in many organizations. What are the most common signals that a team is performing innovation instead of producing it?
[00:04:07] Speaker B: Yeah, really good question.
A few things to look out for.
What we'll see often is if innovation teams are asked to achieve lofty revenue goals, for example, that's not what innovation teams do. Well, think about inside a corporation. Corporation to survive a long time, needs to do a couple things, needs to gather wealth over time, and needs to gather knowledge over time. Innovation teams are really good at gathering knowledge. Operating businesses are really good at gathering wealth. When the two get conflated or confused, problems arise. The operating team fails if it's focused on gathering knowledge. It's not good at learning innovation. Innovation teams fail when they get focused on gathering revenue or wealth. They're not good at that. And that's where we see a big problem. A lot of times these teams inside of large companies are focused on opportunities that might generate $100 million in first year revenue. And that's the cutoff. We won't look at anything below that. And as a result, some of these disruptive ideas that look less attractive right now in the near term become things that the corporation can't pursue. So that's a big one.
Number two, a lot of times we see related to this, we see innovation teams being asked to develop really stringent forecasts for their performance so they'll Be asked, hey, over the next 12 months when you see the ROI on your activity, it's impossible to calculate a true ROI forecast when you're doing something nobody's done before. And so if ROI and those forecasts become the focus, the innovation team by default ends up doing things that look like things people have already done because we can predict the roi.
And so that type of top down ROI focused approach actually drives the wrong kind of behavior. It leads to spreadsheets that become operating models rather than assumption testing kind of scenario plans, which they're much better at doing at that stage. So those are a couple things to watch out for. Oftentimes where the innovation reports up to is a question. And then right now what we're seeing with the deployment of AI, of course everywhere, it's having a kind of a, a magnifier effect on all of this.
Lots of AI theater going on in big companies right now.
[00:06:19] Speaker A: That was actually the very next thing I wanted to talk about. So that's a great segue in this new AI first world. A lot of people talk about innovation and AI and combine the two.
What's the fastest way for leaders accidentally to turn AI into automation theater instead of real transformation?
[00:06:41] Speaker B: Well, in the end, what you want is business results, right?
You think about AI can be used to do three different things inside a company, just like any new technology can. It can be used to drive higher efficiency, to cut costs, it can be used to improve the customer experience, or it can be used to build entirely new business models.
And large corporations are actually pretty good at doing the first two.
They're pretty good at figuring out how to deploy AI to drive more efficiency. I'll come back to that in a second because there's certainly room for improvement.
Improving the customer experience can also be done. We're all now getting used to AI driven customer support, which hit or miss, sometimes it's better than the human call center, sometimes not.
The third area, launching new business models that are enabled by AI is really, really hard for existing businesses to do.
And the reason why is that when a new technology comes onto the scene, what we do is we apply it to the things that we already know. A quick example of this. Back when we first started making movies, films were a thing people. This was a new technology. It was hard to imagine how this new technology might be used.
And so if you go back and look at early films, in the very beginning, they look like recorded stage plays because that's what people were used to. There was a camera that sat in the middle People entered and exited the stage just like they would in a theater. And that's how we use the new technology. It took a little bit of time for people to realize that you could mount a camera on a train and capture moving images, or you could do things differently. This technology opened up new ways to imagine what a movie could be inside our companies right now, we're in that stage often when it comes to deploying AI, we're taking AI and we're layering it on top of the things that we already know. And it's very hard for us to come up with those new models that might exist. And so the last few years, what you see is most if not all of the kind of groundbreaking AI enabled business models are coming out of startups, not incumbent companies and industries. And if you're running a large company or working in one, that's a problem. You need to find ways to get serious. Driving efficiency is important. Improving customer experience is important.
That's not enough to help the company survive over the long run. You've got to find new business models and it's an amazing time to do that right now.
[00:09:05] Speaker A: When people talk about AI companies, I often think of kind of two different camps. There are companies that are building new AI technologies, new models, new LLMs.
There's also a whole other group of companies that are taking existing AI and redeploying it to new verticals, to new business opportunities, oftentimes in verticals that are really ripe for disruption.
So you've got companies building brand new AI technology, companies that are using that technology to disrupt other industries.
Do you see what are the trends that you see in that area?
[00:09:46] Speaker B: Yeah, that's an interesting question. It's kind of a version of the question, where's the value going to go and where's it going to migrate to in this new AI enabled world? Is it going to be on the infrastructure providers? Are they going to be the ones who win? Is it going to be on the application layer providers? Are they going to be the ones that ultimately win?
And I think there are good arguments on both sides right now. What I think clearly is when you think about software and traditional software enabled business models, that's where AI is going to have the most impact in the near term and in the past, I'd argue that product mattered a lot. Software, how the product was built and how hard it was to build was really important.
Other ways that software companies generated and captured value was through their distribution or access to customers. It was through the level of customer service they provided. It was maybe through some unique access to data or integration with other systems. All of those have always been really important.
I think in this AI enabled world. When it comes to software businesses in particular, less of the value is going to be attributed to the product. In a world where anybody can build products, that becomes less important.
We've been experimenting here at Alloy. We've launched new or we've developed AI tools that could very well, five years ago would have been great products to sell in the market. But if it's something that took you a few hours to code, is there really value in that from a product level? No, probably not, because it's easily replicable.
So the things that really matter then are distribution, integration with other things. That's where the value gets captured. I think that's true across a lot of businesses. We're going to see shifts in terms of the old way of where value was created and captured is going to shift to new ways and new models for capturing value. The one thing I do know that's important in an AI enabled world is that AI can help us do things faster.
LLMs right now, large language models are not good at creating new knowledge. They're good at packaging up existing knowledge that might change over time. As AI develops, we have new world models that help us actually generate new knowledge. But for now, judgment matters a lot and the human in the loop matters a lot. And if you want to do things that succeed in business, you have to have contrarian views that turn out to be right. You're unlikely to get contrarian views that judgment from a large language model. Large language models can tell you the same thing as telling everybody else that everybody else is thinking. And that's not going to help you do things that are different from everybody else, ultimately succeed.
[00:12:31] Speaker A: Thank you so much, Elliot. Coming up in the next segment, we're going to map the physics of building innovation that survives contact with the real world.
Incentives, governance, speed and strategy. I'm Todd Thomas, this is Infinite Future. Stay with us.
We'll be right back with more conversations at the edge of technology and transformation. Stay tuned. Every week on Infinite Future, we explore breakthrough innovation across every major frontier. We talk to AI architects, biotech pioneers, space entrepreneurs, clean energy disruptors, and the thinkers redesigning global systems.
We don't just talk trends, we examine scalability, ethics, economic impact and real world implementation.
If you're building, investing in or leading the future, join me on Infinite Future only on NOW Media tv. Because the future isn't predicted, it's engineered.
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Welcome back to Infinite Future. We're here with Elliot Parker. And now we're going to get practical. If a leader wants innovation that actually ships, especially in an AI first world, what systems do they need behind it?
Innovation fails less from lack of ideas and more from misaligned incentives and low quality. Context.
This segment explores context windows in business, what to measure, and how to design a system that rewards learning speed without gambling the company.
Elliot, you've talked about context as a differentiator, almost like a business equivalent of an AI models context window. What does that mean operationally, not metaphorically?
[00:14:51] Speaker B: Yeah, operationally it means that large organizations or companies that exist inside of industries have a right to win.
They know better than anybody else, better than anybody forming a new startup, for example, where the opportunities and the problems are in their space. They've got the context that they can act on to do new things. The hard part is the acting, and that's where the existing companies run into trouble.
And that's where startups have an edge in those early days of acting, especially in ambiguous settings where it's not clear what to do and you've got to go out and try things and make mistakes and learn. That is not a capital efficient kind of activity. Corporations are good at capital efficiency, they're really bad at learning. Startups are great at learning. Not so good with capital efficiency.
[00:15:42] Speaker A: In your experience, what's the number one incentive problem that causes innovation portfolios to fail?
[00:15:49] Speaker B: Yeah, I think it's interesting to contrast how this works inside of large companies with innovation portfolios versus versus in a startup. And one example I like to think about is Sarah Blakely, the inventor of Spanx. Great story, right? When she came up with the idea for her product, she made her own prototype by cutting off the feet of some nylons. And she went down to meet with the team at a retailer in Dallas, got on a plane, met with them face to face and convinced them to take her product and involve some crazy things along the way. But she Pulled it off.
And she did that because she was incentivized to make it work. And she became wildly successful, her company smashing success. And everyone associated with that business had a good outcome.
That's great. Now imagine Sarah operating inside a big company. How different it would have been to get approval for her new product. Idea would have been layers of decision makers to be able to just hop on a plane and go meet with a retailer, Marcus in Texas, and convince them by the product. That probably would have been other people involved. Maybe she wouldn't have even been able to be the one to make the trip because somebody else owned the relationship and on and on. And these things get stuck in the, what we call the corporate goo. The problem is incentives.
Inside of large organizations, people are incentivized to be frequently right to not make mistakes. Inside of startups, entrepreneurs are incentivized to around magnitude of correctness. So inside of a large corporation, frequency of correctness is how we set the incentives inside a startup, magnitude of correctness is how we set the incentives inside a startup.
You can be wrong a lot because when you're right, it really pays off. Inside a large corporation, don't ever be wrong. It doesn't matter how right you are, just be right. Don't make mistakes. As much as we say no failure is okay, the truth is that the incentives don't, don't align with that. And when you, if you want outsize outcomes, you need, you want wild outcomes, you need wild incentives in place, right? And you think about inside of our companies, what we'll often do is we will cap the incentives on the high end and cap them on the low end. In other words, if you give someone a responsibility to go launch a new product or venture inside a company, you'll say, if this succeeds, we'll give you a nice bonus. But you can't make more money than the CEO does. I mean, that would be weird. So you cap the upper bound of what's possible.
And then you also say, you know, if this doesn't work out, that's fine, we'll find another job for you inside the company. So don't worry about it too much. You cap the incentives at the low end.
Now it's very different for entrepreneurs and startups. They know that if it works, if the business succeeds, they may never have to work again for the rest of their life. There is it's unbounded upside opportunity. And then on the downside, there's also tremendous risk. If the business doesn't work, I may not be able to pay my mortgage in a few months.
That's also very motivating. And that that kind of extremity of outcomes is what drives entrepreneurs to figure out things so quickly they can move mountains. It's like it's magic what a, what a good entrepreneur can do. And so question for these big companies is how do you tap into that? You're not going to be able to restructure your incentive systems, but how do you, how do you tap in the magic, the power of entrepreneurs to make some of these things happen.
[00:19:07] Speaker A: So we've seen a trend with large corporations trying to build their own innovation shops in house. And they talk about insulating them from the corporate goofy so that they can operate like true entrepreneurs.
But we haven't seen many of those with much success.
Why is that so hard for big corporations to insulate and create an in house innovation shop?
[00:19:29] Speaker B: Yeah, I'll give you a good example of this. Just a quick story. We launched a startup once with a corporation and you know all the right, the best of intentions this, this startup got going. The CEO launched the business at the launch party, one of the board members who was from the corporation said, hey, when you've got the executive to CEO team and team, when you've got your new brand ready, let me know. I will run it by our marketing department inside the corporation for approval.
Now that is not what a board member does, but inside the corporation it makes perfect sense to mitigate the risks and check things. But every single thing like that that a corporation does comes with a cost.
And so for someone who may be running an innovation team or a new venture inside a corporation, it's important to recognize that sometimes the cost is worth it. Sometimes, for example, you want the brand of the corporation associated with the new thing you're working on. Just recognize that there is a cost that is not free. Sometimes it's worth it. You want to make the deal.
What happens most of the time is that these aren't deliberate choices. They kind of happen by default. So an innovation team will get set up with some distance from the operating business.
Maybe they get a separate physical office even, but they've still got to use the internal lawyers, they've still got to use the HR system.
And all of these things end up being death by a thousand cuts. And the end result of their activity, if there is any end result at all, it ends up looking a lot like what the corporation already does. That defeats the purpose. If you're a business, you're running experiments to reinforce what you already know.
That's a waste of time and money.
You want to be running experiments that challenge the status quo. And you only do that by going further in terms of distance.
[00:21:14] Speaker A: So if you could redesign a company's innovation stack in 90 days, what are the first three moves that you would make?
[00:21:23] Speaker B: Well, let's set the premise, kind of the foundation for a corporation. And thinking about this, if you accept the premise that the fundamental atomic unit of innovation is an entrepreneur inside a startup with urgency, ownership, autonomy, the right incentives and so on, you need to find ways to tap into that type of that atomic unit. It's impossible to recreate inside a corporation. You can go out and find it and bring it to you, but you cannot create it. So if you can accept that premise that determines the structure of how you go, who would go after this? Right. It's also important to recognize there are different forms of innovation that a corporation can pursue.
Most of the innovation that a corporation is going to do well on its own is what we might call efficiency innovation. It's doing things a little bit faster, less expensive.
That is an important part of our innovation ecosystem. Most of the innovation that we benefit from in the world is that type of innovations. These incremental improvements over things corporations do that well should continue to do that well. That innovation is extremely valuable. What I'm talking about is with a more transformative type of innovation, the new kinds of business models, that's where you need that entrepreneurial atomic unit of innovation.
There are four ways to access that atomic unit of innovation. If you're running an innovation system or you're running a company, number one is that you can invest in outside startups. Number two, you can acquire them. Number three, you can partner with them. Or number four, you can build them from scratch outside the corporation. That's it. Those are the four ways you access that atomic unit of innovation. So you need to organize your program and approach around those things and how much acquisitions you need versus building versus investment versus partnership. It depends on how much innovation you need and how quickly you need it.
If you're in a perilous time and you need lots of innovation now, well, you probably need to go do a large scale bet, the farm acquisition. If you've got more time, you might want to lean more toward building or other types of kind of slower burn experiments.
[00:23:26] Speaker A: So if you're a large corporation, if you're on the board or an executive and you're looking at metrics, what measures should they use instead of say, number of pilots or number of ideas, what's the right metric.
[00:23:39] Speaker B: Yeah, definitely not the inputs you want to be tracking the outputs, the results, not the activity.
And the way I think about it, I'll start with one premise, but then I'll give something that's a little more practical as the one thing that I really like doesn't apply yet to most companies. The very best metric I've ever seen for determining innovation successes is the extent to which outside investors are clamoring to put capital to work inside the things that you are inventing or creating.
And when we launch startups with large corporations, it's very easy to measure the value that the market attributes to these things because they exist outside and other investors are participating and collaborating with corporation inside these startups and they're assigning a value to them.
And when a market assigns value in that way, that what they're saying is the entrepreneur. This startup has discovered some truth about the world that nobody knows yet, some secret. And this is what that secret is worth.
Now we can go back to the CEO of the corporation and say, look, this thing has value. It's not us saying it, it's the outside market that is the best metric.
That takes a whole system to be able to implement that type of metric. And so what I would argue get back to the premise. What is innovation for an innovation team inside a large corporation is designed for should be optimized for converting assumptions about the future into knowledge on behalf of the organization.
Therefore, all of the metrics that are used to determine the success of the team should be oriented around the pace and extent to which that innovation team is learning on behalf of the organization.
So metrics can be focused on.
There are ways to track the amount assumptions, the learning that happens. We have a system we built internally at Alloy that tracks the number of assumptions, the frequency of assumptions. At the end of a quarter can give us a report on here's what the innovation team learned over the last 90 days and it puts it in a list. Here are all the things we didn't know before as a corporation that now we do because of what the innovation team is doing. So the very best form of metrics are all oriented around learning.
[00:25:45] Speaker A: Thanks so much, Elliot. For leaders that are listening that want to go deeper into your work and your frameworks, where can they find you and where can they find the illusion of innovation?
[00:25:56] Speaker B: So the illusion of innovation is available where all books are sold. You can find it audio and paperback versions or hard copy versions.
And you can find me on Twitter or XerParker or on LinkedIn. Elliot, Parker with two L's, two T's.
[00:26:16] Speaker A: Thanks so much. Up next, we'll talk about the venture builder model. How corporations, universities and entrepreneurs can co create startups that aren't innovation side projects, but real market moving companies.
We'll be right back with more conversations at the edge of technology and transformation.
Stay tuned. Every week on Infinite Future, we explore breakthrough innovation across every major frontier. We talk to AI architects, biotech pioneers, space entrepreneurs, clean energy disruptors, and the thinkers redesigning global systems.
We don't just talk trends, we examine scalability, ethics, economic impact and real world implementation.
If you're building, investing in or leading the future, join me on Infinite Future only on NOW Media tv. Because the future isn't predicted, it's engineered. And we're back. I'm Todd Thomas and this is Infinite Future on NOW Media tv. Let's look ahead.
Welcome back to Infinite Future. We're continuing with Elliot Parker. And now we're stepping into a model that's showing up more and more venture studios and venture builders. Not just funding startups, but co creating them.
This segment focuses on how to build advantaged startups by combining corporate assets, distribution data, domain expertise with startup speed without killing the startup with corporate gravity.
Elliot, what problem is the venture builder model solving that traditional corporate innovation teams often can't?
[00:27:54] Speaker B: Well, it's producing results.
I don't mean that as too much of a dig on traditional corporate innovation teams, but the truth is, if you look back, let's go back to 1997 when Clay Christensen wrote the Innovators Dilemma. What he proposed as a solution to the dilemma? The first of all, the dilemma, what is it? It's the idea that corporations struggle to innovate because the innovation often starts out as improvements that look unattractive to the incumbent. These improvements are good enough along some performance dimension for some segment of customers that often the incumbent companies look at and say we don't want those customers anyway. That product's unattractive, it doesn't make enough money. Those low end entrants gain a foothold in the market and then ultimately move up market themselves and displace the incumbents over time. It's a very difficult problem to solve.
And what, what Clay proposes a solution is, and he was right, is that corporations need need to innovate as far from their core business as they can. In other words, give that innovation, as people working on innovation some distance so that they can do the things that look fundamentally unattractive to the corporation. Those things can take hold and move up, essentially disrupt yourselves now, over the last 30 years, every company, every large executive has looked at that problem. That idea of the, the disruptive innovation is better known than it's ever been and yet somehow it's a harder problem to solve. Every corporation has set up an innovation team and you would think that 30 years in we would all have examples of these innovation teams producing transformative innovations for the corporations that they're associated with.
And I can't name a single one and I have been looking for 20 years.
And so I, I think that that's indicative that this approach doesn't work. Everybody keeps trying it, thinking we're going to put the first ones to transform our corporation through an innovation team setup. It just doesn't work. And so I think it's, it's high time to recognize that innovation teams are important. I'm not saying scrap them, I'm saying just let's just be honest about what they're good at and what they're not good at and make sure we're directing their time, energy and resources to the best use.
And what I am saying is that transformative innovation is very, very unlikely to come from that traditional innovation team structure. It's more likely to come from entrepreneurs operating outside the corporations.
[00:30:21] Speaker A: So when you co create a startup with a large organization, as Alloy does, what's the no go zone? That behavior that instantly slows velocity?
[00:30:33] Speaker B: That's a good question. So yeah, we, we believe that as venture builders, corporations as mentioned can, can provide amazing advantage to these startups and then benefit as a result in terms of the learning, the new opportunities that it opens up, the optionality that these startups create.
But you've got to get the incentives and the governance right. Those are the two things that matter most. And where we see things slow down the most is questions of governance.
It's where in governance, how a large corporation makes decisions. Right? And so what often happens is the corporation as it scales up, decisions get made by committee. There are layers of people making decisions and that is a feature of a large corporation, not a bug that helps the corporation manage risk and operate carefully and preserve what exists.
Nothing can be done rashly inside a corporation most of the time and that is good.
But when you try and apply that same decision making approach process to a young startup that is learning, it turns out it's not fast enough.
It doesn't give the amount of autonomy that these startups and these entrepreneurs need to go figure things out. And so where we see things slow down is around applying that committee structure for making decisions or Authority that's dispersed across a large group of people where any one person can say no and kill a project.
It doesn't work when you're doing something new and different. And that's why when we set up these startups with corporations, we're very careful to design governance systems that ensure that the startup has the freedom to operate and ability to succeed and to do so very quickly.
[00:32:10] Speaker A: So when Alloy partners partners with a large corporation to launch a startup, you talk about your advantage startups, what types of corporate assets actually translate into startup advantage and which ones look valuable but aren't?
[00:32:26] Speaker B: Yeah, I'll start on the latter.
You know, when we began this, we thought that that brand affiliation with the corporation would be a great source of advantage. In other words, a startup that the market recognizes as being somehow affiliated with a respected corporation in the space could help that startup move faster.
You know, it certainly there are, there are instances where can be of help, but it never seems to materialize in terms of real market advantage in the way that we would hope. And so of all the possible ways to drive advantage in the company, that is probably the least valuable.
When a corporation, what can a corporation do to provide advantage to a startup and to boost the chance of success in that startup? There are a number of things.
The easy ones top of mind are that corporation becomes a first customer of that startup. Another one is that corporation becomes a distribution partner to that startup. In other words, helping that startup get access to customers.
Another one that matters a lot is bringing credibility in terms of bringing other investors into the startups ecosystem, introducing to other players that might strategically help that startup, that can matter a lot. So it's not quite the brand thing, but it's actual introductions and personal relationships that come from the networks that these corporations have developed. So corporations can do amazing work to help these startups move quickly in their markets, gain a foothold and in the end produce a strategic and financial return to the corporations that are associated with them.
[00:33:59] Speaker A: So for startups, iteration speed is vital.
AI is largely rewriting how fast companies can innovate, how quickly they can iterate.
How do you keep speed high while keeping the real world constraints visible?
Regulation, safety, trust, security.
[00:34:18] Speaker B: Yeah, yeah, really good question. A lot of the startups we build are in highly regulated industries.
And take healthcare for example.
Health systems, our hospital systems are correctly and wisely optimized for safety. We do not want our health systems taking risks, undue risks, right.
And so as a result innovation, it turns out, is really hard inside of health systems systems because they are so safe.
It's a trade off. And so how do you innovate inside a health system when safety is the number one concern?
Innovation is not necessarily, you know, it involves taking risk, it involves making mistakes, it involves doing things nobody's done before and we don't know how it's going to turn out. And so you have to find ways to do that innovation outside the existing structures.
You've got to find ways to make it in a walled environment safe for these small fledgling organizations to go out and make those magnitude of correctness bets, to do errors, to try things and have it not work. It's so, so critical.
[00:35:30] Speaker A: Is there a specific metric or a specific indicator that tells you when a venture is ready to scale?
[00:35:41] Speaker B: The best metric I've ever heard is that we call this product market fit. Right when do you know a startup is ready to just pour gas on the fire and let it go? The best definition I've ever heard for product market fit is when you wear a meat suit to a dog park. You have product market fit. In other words, the customers are just flocking to you. You can't, almost to the point where it's bothersome, right? You can't handle all the customers, partners. You've got product market fit. And that is a, once you know it, it flips. And those companies are now ready to scale. Often we, we confuse and fool ourselves into thinking that's come sooner. And I'll add a thing. When you think about the relationship between corporations and startups, this is where corporations often make the mistake.
Corporations are really good at execution problems where we, we know what we need to do, we just need to go do it.
Startups are really good at learning problems. We don't know what to do, we need to go try things. We're going to operate in some degree of ambiguity where corporations make the mistake. You see this often in acquisitions, for example, is they acquire young startups that are still in learning problem mode, assuming that they're now ready for execution problems. They bring them in ready to scale and they, it turns out the startups haven't entirely figured out the, the learning problems yet. They're still learning, they still can't predict the future and how these things are going to play out. And that's, that's an important thing to keep in mind if you're working inside a large corporation dealing with some of these issues or some of the opportunities you want to pursue. If it's a, if it's a learning problem, the corporation is not going to be good at it if it's an execution problem, the corporation is going to be fantastic at it and going to be able to to beat startups almost all the time. Time.
[00:37:26] Speaker A: So do you have a particular startup that you've helped launch that you're really proud of that really exemplifies the things you're talking about?
[00:37:36] Speaker B: It's hard to pick one. It's like my kids ask me all the time which one of them is my favorite.
And I don't know. I might say that it depends on the moment. But the truth is I love them all. It's the same with the companies that we've launched. But there is one that Todd, both you and I are fond of, called Chuck, that is a wonderful example of how this works. And just let me give you the quick story from my point of view on this business that you're obviously intimately involved in.
This is a company that we launched with a utility where the utility had made a bet saying we're going to convert some of our old coal plants into biomass plants and quickly ran into a problem where there was not enough biomass supply to run these power plants.
Came to us and said, can we figure out a solution to this?
And to be honest, I wasn't sure we could at the beginning because it was a really hard problem to solve. We came up with a kind of a rough hypothesis of an idea that perhaps there was a way to get waste from construction projects. You could create a supply chain to get that wood waste to these power plants to have a form of high quality supply to run these power plants.
We weren't sure in the beginning would there be enough interest from construction companies, would there even be enough wood ways to do something like that?
And you came into that business to help drive it and figured it out and within six months found that you were generating more supply than those power plants could handle. Gave that utility the confidence to say we can convert more of our power, our corporate plants, to biomass if we want to. But then the important thing is along the way you identified all kinds of other ways to take what you've learned and apply it in new markets or new opportunities. And it turned out the initial opportunity that we saw was just a small piece of the big puzzle. The opportunity was much, much larger than we could have imagined. And the only way you do that is by taking action, by going and doing things and figuring it out. We could have sat in a room together and done all kinds of research and thought and debated in the end, what we tell our teams all the time is there is no data about the future as much as we'd like there to be. The only way you get data about the future is by creating it. And you get that data by taking action. Action creates data. Doing stuff creates results. It creates learning. Action creates data. And that's what entrepreneurs like you do, do, do extraordinarily well.
[00:40:08] Speaker A: All right, that was my favorite response ever on the show. That was fantastic.
Coming up in our final segment, we're going to zoom out. We're going to look at ethics, long term impact and what it means to build innovation that improves the world instead of just accelerating consumption. I'm Todd Thomas. This is Infinite Future. We'll be right back.
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Welcome back to our final segment of Infinite Future. We've talked about the illusion, the system and the venture building model. Now let's go to the deeper question. Not just can we build it, but should we build it and what happens when we do?
Innovation at scale shapes society.
This segment will explore ethical design, second order effects and the leadership maturity required to build breakthroughs that are resilient, trustworthy and sustainable.
Elliot, what ethical failure mode worries you most right now in the AI driven innovation race? Is it bias, misinformation, labor displacement, security, or something else entirely?
[00:42:36] Speaker B: It's probably something else entirely. Let me give you kind of where I'm thinking on this right now. There's this really interesting idea called the automation paradox.
And the automation paradox is the notion that as the systems become more automated, the errors that these systems produce become stranger and harder to deal with. In other words, our Automated systems are designed to deal with all the problems that would have happened at that point. And so if a new problem does pop up or arise, it's something weird that we haven't seen before and the system can't handle it.
The paradox is then that in those highly automated systems, the human becomes more important because you need that human intervention, that human judgment to deal with these strange problems that arise. The challenge is that in highly automated systems, the humans also become less capable of dealing with those weird problems because they're not getting the training, they're not getting the reps.
The thing I worry about most right now is how do we ensure that the humans in the loop. I'm not too worried about job displacement.
The economy over time has shown that all kinds of new opportunities will be opened up because of this new technology. I'm incredibly bullish on the future and the impact this is going to have on wealth and our, you know, kind of comfort and happiness and all the things that matter. But what I am worried about is making sure that we don't forget the humans in the loop and ensure that we're finding ways that those humans can continue to develop the judgment and not become overly reliant on the automated system to the point of losing their edge and their capacity for dealing with the weird problems that are certainly going to come our way.
[00:44:19] Speaker A: So as we continue to innovate and optimize for efficiency, what human outcomes get lost and how do leaders bake human value back into that system?
[00:44:33] Speaker B: Yeah, the greatest innovations. Why I talked earlier about the importance of efficiency innovations, those matter a lot because it helps people to do more with less. And that's important.
But the things that really change our world are what Clay Christiansen used to call market creating innovations is where disruptive innovation comes in and makes some technology or product or tool available to the masses that previously was unattainable because they lacked the knowledge or the wealth to have that as they might have before.
And think about all the disruptive innovations that have come our way that have enabled that. Think about the way that we take care of our health, for example, and all the disruptive innovations that have come along that have enabled the, as Clay used to call them, the weak and simple of the world to be able to do challenging things.
Those innovations matter so much. And what I worry about inside our corporations when I see all this optimism around return on invested capital is that we're at risk of kind of losing the thread and remembering what matters is these market creating innovations that create long term resilience for the corporations, which should matter to them. But more importantly, these are the things that advance society and create better outcomes for the world. And corporations are these amazing inventions. They're a fantastic way that we come together as humans to solve really big important problems. And if we're going to direct all that energy to just efficiency innovation, it's a gross misuse of what's what we've got and what we're stewards over and what's possible.
And so part of our mission at aloy and launching startups with corporations, a very important part of our focus is that we believe in a future where we're supposed startups and large organizations matter a lot. We depend on corporations.
We need them to the extent that they're generating surplus for society. We need them to continue existing, to continue doing that. And they do that by pursuing these market creating innovations.
Efficiency innovation is not a long term path to resilience. It ends up turning into the status quo. And status quo is failure.
[00:46:44] Speaker A: So you've talked about efficiency innovation or you know, perhaps that's incremental improvements and that's important, but it's not transformative innovation, it's not market creating innovation.
What are the risks or the temptations that will seduce a company to be satisfied with efficiency innovations?
[00:47:06] Speaker B: Well, in the end it is this over optimization. As a society that we have on return on invested capital capital, we've decided over the last 50 years, 50 years, that the primary measure of a company's success and impact in the world is their ability to return invested capital.
And what I would argue is that there ought to be some room for long term longevity, this resilience in the conversation we saw, for example, before COVID If you look at companies operating before COVID they were extremely capital efficient.
And Covid came along and one piece of their infrastructure system failed and these companies came crashing down. Think about our automotive parts supply chains and how parts of that broke down. The whole thing came crashing. These systems were extraordinarily capital efficient, yet at the same time very fragile. And when a piece of it broke, the whole thing came crashing down. And so what was capital efficient in the near term turned out to not be capital efficient in the long term.
I think we ought to be thinking about our companies and the impact over decades or even centuries, not quarters or years.
And when you do that, it actually produces a better return, if that's the thing that matters most to you. For investors, I would argue that the total profit under the curve over 100 years matters more than the individual Profit in any single quarter or year that a company might experience. And so we need to be optimizing our companies for long term resilience.
It's not just a financial markets problem. And I think it's also a cultural problem. I don't know what the solution is other than helping people to see that long term impact matters a lot. I think inside of all of us, we have a desire to be good ancestors for those yet to come.
And we need to tap into that and build things that endure and last a long time.
[00:49:05] Speaker A: Is there an uncomfortable truth that innovators need to accept about scaling technology into real communities?
[00:49:15] Speaker B: An uncomfortable truth?
You know, it's one of those things, it's the uncomfortable truth that I'm glad most entrepreneurs don't realize is how hard it's going to be to scale.
That naive optimism that entrepreneurs often have is what makes the world move, what leads to things. Everything that we see around us was created by entrepreneurs. Or somebody once said that everything you see is, was somebody's passion project.
And there's a funny slogan I saw once printed on a sign that said we do this not because it's easy, but because we thought it would be easy.
Isn't that true?
It's much harder to scale new technologies than people think. And often when you talk to entrepreneurs who are on the other side and extraordinarily successful and you ask them, would you go back 30 years and do it all over again? Surprisingly many of them will say no.
And that is a thing that I'm glad most entrepreneurs don't recognize or in the beginning, because that, that optimism is what enables them to accomplish amazing things. It's hard.
I wish it were easier. We need more entrepreneurs and more of them to be successful.
[00:50:29] Speaker A: So then, is optimism a required trait for an entrepreneur?
[00:50:35] Speaker B: 100% it is. It ought to be a required trait for everybody. And I don't think that, you know, by optimism it's not a sense that things are going to be perfect from here on out. That's not optimism. That's a disconnection from reality.
But when I say optimism, what I mean is the understanding that there will be big problems that come our way. In fact, those problems will only get worse. Every new solution that we can come up with creates new problems. So there will be problems in our future. Problems are inevitable.
All problems are solvable and problems are beneficial drivers of progress.
Optimism means that we recognize problems are coming, but we also believe that our capacity to address those problems is going to increase.
That's optimism. And if you look back through history, the optimists were more likely to be right than the pessimists. If you want to be correct about the future, you're more likely to be correct if you're optimistic than if you're pessimistic. And so we should all, I think we have a moral obligation actually to be optimists.
[00:51:34] Speaker A: Wow. I love that answer. Fantastic.
Ten years from now, what would you be proud to say that you helped build not as a company outcome, but as a societal outcome?
[00:51:48] Speaker B: Yeah. Good, good, good question.
To answer that question, I'll give you a quick story about my first job out of business school. I was first week on the job, I was sitting in the CEO's office looking out the window of this company, company where I was working, large company. And in the parking lot there were thousands of cars.
And it occurred to me that each of those cars represented an employee and their loved ones who were doing things, accomplishing things in their life. The things, you know, living at home, having experiences as a family that they wanted to have because of the employment, their employment of that company enabled them to do that.
And I realized that that business that I was benefit, that I was an employee, I've been benefiting from contributing to have been started by one guy in his garage 30 years prior.
I thought that's amazing to have that kind of impact where you can have a parking lot full of cars, each representing people and their loved ones who are doing the things in life they want to do because one person started a company.
Now this company also did a lot of good in the world.
They create medical devices that help people live longer and more, more fruitful lives. And so you can do both things with businesses. And that's why I love entrepreneurship. That's why I love being in business, is that on the one hand, you can create opportunity and good outcomes for everybody involved. Number two, you can leverage the power of people coming together to do amazing things that make the world a better place.
That's the miracle of it all. So if I were to say 10 years from now, success would be creating thousands of jobs for people through the businesses that we launch, but also at the same time solving really important, sticky, hard to solve problems that are holding society back from advancement.
[00:53:36] Speaker A: Wow. I love your viewpoint on optimism, grounded in pragmatism and real world problems. That's really refreshing.
[00:53:45] Speaker B: I love that.
[00:53:47] Speaker A: Elliot, where can people follow your work, book you for speaking, and dive deeper into the illusion of innovation?
[00:53:55] Speaker B: You can reach out through our website, alloypartners.com or you find me on X Twitter, ER Parker is probably the easiest, most memorable way to get a hold of me. And in the book Google it. You can find it on Audible, Amazon or in bookstores. It's available in bookstores around the world, places as far away as China and Malaysia. And so it's good chance you look for it.
[00:54:16] Speaker A: You'll find it fantastic. Elliot, thank you so much for bringing clarity to a noisy field innovation.
Today we unpacked the illusion, rebuilt the system behind real progress, and explored how venture building can turn big Org advantages into startups that actually ship to everyone. Watching the future isn't a mystery, it's a design problem.
And the leaders who win won't be the ones who chase hype, they'll be the ones who build what matters with discipline and intent and optimism.
I'm Todd Thomas. Thanks for joining us on Infinite Future. We'll see you next time.
Thank you for watching Infinite Future. I'm Todd Thomas. Join us next week as we continue exploring the ideas and innovators building what's next Only on NOW Media TV.
