54. Simon Hill - Hybrid Collective Intelligence: The Next Big Leap

In this season finale, Simon Hill looks back at more than 20 years of Innocentive and the enduring power of solving problems by going wide, not just deep.

From breakthrough ideas generated by unexpected human solvers to Wazoku’s emerging synthetic crowd, the episode explores the next big leap in innovation: hybrid collective intelligence. What happens when human experience, judgment and creativity are combined with the speed, scale and breadth of machine intelligence?

It is not humans versus AI. It is about orchestrating both to explore more possibilities, solve harder problems and move from discovery to impact faster.

This episode brings Season 4 to a close. We are taking a short summer break, but the Total Innovation Podcast will return with Season 5 later this year, featuring a new line-up of conversations and guests.

Transcript

unknown 0:00

What's up, uh-uh?

Intro 0:03

Uh-uh, uh-uh. What's up, uh-uh, uh-uh.

Simon Hill 0:09

Welcome to the Total Innovation Podcast. As always, I'm your host, Simon Hill. In 2006, ExxonMobil had a problem that had sat unsolved for almost 20 years. Crude oil frozen to the inside of tankers up in Alaska following one of the world's largest oil spills at the time. Every time they tried to offload the oil, it had thickened in the cold and wouldn't move. Scrapers didn't work, heaters didn't work, they had chemical engineers, petroleum engineers, some of the best mines in the oil and gas industry and environmental recovery industry, and none of them could crack the problem. And so eventually they stopped asking the oil people and the experts, and they posted the problem to a network of complete strangers. No idea who'd even see it. And a few weeks later, the answer came back from a man in New York. He didn't work in oil and gas, he didn't even work in concrete, where the solution ultimately came from. Not really. Years earlier, as a summer job on a building site, he'd seen something that had just sat in his mind, or the back of his mind ever since. The trick that people used to keep concrete wet and workable in cold weather was exactly the same trick that this frozen oil problem needed. He wasn't a construction professional, he wasn't a chemist, he was just a guy who'd spent one summer on a building site years before and happened to remember something nobody in the oil industry had ever thought to ask a concrete crew about. That serendipitous network of strangers was innocentive. And that story isn't a fluke or a one-off headline. It is basically the entire idea behind the last 20 years of the company. And it's the entire idea behind this episode. Because today we're going to do something a little bit different. We're going to go back 20 plus years back to where Innocentive actually came from, six years back to when Wazoku acquired it. And then we're going to go forward into something genuinely new, a second kind of crowd that showed up in the last couple of years that isn't human at all. And what happens when you put the two of them to work side by side? Before I go any further, I want to say a proper thank you. This whole season, this whole show exists because of our sponsor, Wizoku. I genuinely mean this. So quickly, what does Wizoku actually do? Well, here's the short version. Wizoku builds hybrid collective intelligence, human minds and machine intelligence connected and working the same problem together. Ecosystems, not silos, innovation systems, not single ideas. The job is simple to say and hard to do. Take the path from discovery to value and make it fast. Find the right problem, find the right people, human or synthetic, to solve it and get it into the world with impact quickly. And the mission behind that is bigger than any one client logo. Wizoku is trying to bring the cost of doing innovation down towards zero, to democratize it. Wizoku wants any organization to be able to play SMEs, third sector, government, and enterprise, the same tools, same access, same shot at a breakthrough. To change the world one idea at a time. And inside that whole system, there's one brand older than Wizoku itself, one that's already changed how entire industries solved their hardest problems. In this episode, we're going to take a look back and a look forward into the pioneering work of Innocentive. There is no guest today, it is just me and reflecting on some of the incredible stories, past, present, and future, emerging from this incredible business. So first of all, let's rewind. All the way back to the early 2000s. Innocentive, unlike many startups, didn't start as some scrappy startup in a garage. It started inside one of the biggest pharmaceutical companies in the world, Eli Lilly. Lilly's own RD teams were sitting on problems they couldn't or wouldn't solve internally. And somebody had the idea, what if we just asked outside of the building? Not outside just of the sector, but also outside of the industry even. Just let's look wider. That experiment worked well, so well that in 2005 it was spun out as its own independent company. Not a side product project anymore, a real business built entirely around one idea. Post your hardest, most stubborn problems to a global network of strangers and let whoever, whoever, is actually equipped to solve it to find it for you, instead of you trying to find them. At the time, that was a genuinely radical idea. RD was something you did with it with your own scientists, in your own labs, protected by your own patterns and your own walls. In a sentive said, what if the answer to your problem is sitting with someone who's never worked in your industry, never read your journals, never been in the room with you? What if that's actually the point? And it turns out, time and time again, it was exactly the point. I mentioned the Exxon story a minute ago, and that's probably the single most told in a sensitive story, and for good reason. It is a perfect illustration. A problem that sat unsolved for close to two decades inside the oil and gas industry, solved in a matter of weeks once it was opened up to a crowd that had absolutely nothing to do with oil and gas, or indeed with environmental disasters and environmental recovery. But NASA has a similar story, as do many, many others. NASA ran a long-standing partnership within Ascentive for many, many years. One of the more charming examples is stuck around in company folklore is solving how astronauts do laundry in zero gravity. Not a headline grabbing grabbing scientific breakthrough, just a genuinely hard practical problem that a crowd outside of NASA's own engineers was more equipped to help crack. And then there's 2010, the deep water horizon disaster in the Gulf of Mexico, oil rig explosion, catastrophic spill, and inaccentive did something a little different that time. They put out an open call with no prize money attached, just an urgent ask to the entire crowd. If you have an idea that could help contain or mitigate this disaster, please tell us. Because in that moment, speed mattered more than intellectual property, more than exclusivity, more than anything else. And that's just a different inner sense of instincts, using the crowd not just for clever RD, but as an emergency response network. And over the years, the same model has been picked up by mission-led organizations around the world. Organizations trying to move from we have a national scale problem to we have a deployable solution faster than traditional procurement usually allows. There are many examples with the COVID pandemic and response to that being one that sticks in the memory. But why does all this actually work? Why does someone with a summer job on a building site years in their past solve the problem that experts with all their training, information, and budget simply could not? Put most simply, we all know the answer. Many minds are better than one. The wisdom of crowds is real. And the power of a heterogeneous group is better than that of a skilled, expert, homogenous group. This isn't about individual shortcomings or one individual being bad at their job. It is a structural thing. If you put 10 oil engineers in a room, you get 10 very deep, very similar perspectives on an oil problem. That room is in sense is in a sense too similar to itself to see outside of itself. Homogeneous rooms of experts are a structural design flaw in how most organizations do RD. Not a people problem, an architecture problem. And that is the whole premise Innocentive was built on 20 plus years ago. In essence, go wide, not just deep. Now let's fast forward 2020, and Wizoku acquires Innocentive. It's actually the sixth anniversary of that acquisition this year, which is part of why I wanted to do this episode now. Innocentive was the anchor deal in a run of five acquisitions that Wizoku made from 2020 onwards, picking up along the way change in Colombia, Idea Drop in the UK, Mindpool in Copenhagen, and Poster Lab in Germany, all with the same underlying goal to pull together idea management, open innovation, and collective intelligence into one connected platform instead of a patchwork of separate tools. And I'll be honest with you about something because I think it's a good story in itself. For a long stretch after we acquired Inaccentive, the Innocentive name actually carried more recognition in the market than the Wizoku name. We'd worked hard for many, many years to build, did. People who'd never heard of us as Wazoku absolutely knew Inaccentive, which, if I'm being honest, used to sting a little. You buy a company, and the thing people remember is the name you bought, not the business that you've been building for many years. But here's the thing about pride versus what's actually true and what's actually good for customers. The name recognition wasn't a problem to fix, it was an asset that Wazoku was sitting on. Let me give you a sense of scale because I think the numbers are genuinely worth hearing. Today, that was that solver network in an incentive is over a million people around the world. Across it, more than 200,000 innovations have been delivered. More than 200,000 innovations have been delivered. More than two and a half thousand challenges solved at roughly an 80% success rate, hundreds of million dollars paid out in awards. And impressively, almost 60% of that global community holds an advanced degree. Where are you going to find such an incredible network? That's why the client list reads like a who's who, from NASA to Novartis, to Microsoft, to brands like Porsche, to nonprofits like the Gates Foundation and government institutions like DARPA and beyond. Genuinely across every sector you can think of: energy, life sciences, defense and security, consumer goods, financial services, and beyond, the impact is felt daily. And so last year in June 2025, Wizoku did something that I think was actually the right emotional call, even if it took them a little while to get there. They formally brought back the inner center brand name for the crowdsourcing community itself, timed quite deliberately to the 25th anniversary of that community's existence, a quarter of a century. And alongside that, they repositioned the whole business, not just as a software platform, but as what they're calling an innovation intelligence ecosystem, built around the concept of hybrid collective intelligence. And that wasn't a rebrand for the sake of a rebrand. It wasn't reorganizing something that customers had been telling them for years. It was the thing that they trusted, the thing they actually valued was that inner sensitive brand. So they stopped fighting that and put it back at the center where it belonged of that global community. So that's 20 plus years of human crowds solving problems by going wide instead of deep. Now I want to talk to you about something that's really only become something of substance in the last year, maybe two. And it changes the story in a way I find really quite exciting. Not just as a business person, but honestly as someone who spent a career thinking about this stuff deeply. Wizoku ran an experiment, the Chicken Sexing Challenge, a real scientific and ethical problem that impacts seven billion chickens globally each year. The point of the experiment was simple to describe, even though what's underneath it is pretty sophisticated. Wizoku wanted to directly compare how the normal human crowd of real people submitting real solutions against something they'd been building called a synthetic crowd. Let me explain a little bit what this synthetic crowd is, because I think it's easy to misunderstand if you just hear the word AI and assume that it means one chatbot answering a question. It's not that. What was OQ built was a contextual synthetic crowd with unique synthetic skills built through a proprietary combination of specific variables, lots of words, but in essence, each combination of those variables became one distinct simulated solver. In the chicken sexing experiment, there were over 600 such synthetic solvers. And they were able to generate around 3,000 synthetic ideas in this small experiment, which were then clustered down into about 30 core solution themes. And critically, every one of those was evaluated on exactly the same scoring rubric that was used for human solvers. Not a different softer bar, the same bar. In parallel, the human crowd was also given the same chicken sexing challenge. And so what happened? Well, in aggregate, the synthetic solution scored marginally higher than the human ones. And when we looked at the clustering, the themes that the synthetic crowd surfaced, optimal optical approaches, electromagnetic approaches, acoustic, chemical, thermal, some partly overlapped with what the humans came up with and partly they didn't. Similarly, from the human side, some overlapped with what the synthetic crowd came up with and some didn't. Both found genuinely different corners of the solution space. I presented some of this work back in May at MIT's Innovation Lab. There was a line that came out of that session that I've thought about a lot since. When we looked at what the synthetic crowd actually gave us, it wasn't a single clean, correct answer the way you might expect from a search or from the human crowd. It was something else. It didn't give us the answer. It gave us the map, or at least a big part of the map. I think that's exactly right. And I think it's the way to think about where all this is heading. The synthetic crowd isn't there to replace the human solver who actually has the expertise, the judgment, the lived experience, the context to know whether a solution is genuinely workable in the real world. What it's there to do is to show you how the whole space of possibility may look, to give you that quickly, fast, cheaply, and at scale that no human campaign could ever match, or at least not at the speed and cost that we can do here. So this helps the humans know where to actually go and look. There was another line that's come from presenting this from a completely different conversation that I think pairs perfectly with it. Someone described what's happening with AI and organizations right now, not as role replacement, but role augmentation. Something that listeners to this podcast and readers of my newsletter will know is something that I support, or a context that I support absolutely. So here's how I tie all this back to where we started this episode. Remember that solver who cracked Exxon's problem purely because a summer job years earlier had happened to leave him standing far enough outside the oil industry to see it differently. A synthetic crowd might be the fastest way we've ever had to manufacture that kind of distance on demand whenever we need it. You don't have to wait 20 years and hope a stranger with the right, completely unrelated memory happens to see the question that you're posting. You can simulate an acoustics engineer, a biomimicry expert, a material scientist pointed at your problem this afternoon. It doesn't replace the human crowd. If anything, I think it makes the human crowd more valuable, not less, because it means the humans get pulled in at exactly the point where their judgment actually matters most. Deciding what's real, what's feasible, what's actually worth building, instead of spending all their time just searching for where to look in the first place. And that I think is genuinely the next 20 years of this, or maybe not 20. Not humans versus machines, organizations that get good at orchestrating both kinds of ecosystems together, human and synthetic, side by side. These are going to move faster than the ones still trying to solve every problem with just their own four walls or just their own experts. So let's bring this all the way back around. Twenty years ago, a stranger with no oil and gas training solved Exxon Valdez's problem, carrying nothing more than a memory from one summer on a building site, because that was enough distance to see something the experts could not. This year, an AI-simulated acoustics solver that never existed as a person at all did something recognizably similar. The tools changed completely. The underlying bet has not changed even slightly. Distance from a problem isn't a liability, it's an asset. It always has been. We've just got a lot faster and a lot more creative at manufacturing that distance on purpose. And that's in a sentive. In fact, that's Wazoku in one sentence. Over 20 years running now, put simply, go wide, not just deep. And now go wide with both humans and machines together. Hybrid collective intelligence. And that's today's episode. A little bit of a reflection, a little bit of a look forward. This is the last episode of season four. Thank you genuinely for spending this season with me. It's been a good one. We're now going to take a little bit of a summer break, recharge, and come back properly for season five. And I can tell you already, without giving too much away, we've already got some incredible guests lined up for when we're back. So thank you for listening. Enjoy your summer if you're in the north. Enjoy the break if you're not. And I'll see you back here for season five later this year. Thank you, everybody.

Intro 19:03

What's up? Uh uh. Uh uh. Uh uh uh what's up, uh uh, uh uh.