Web3 has brought a host of new possibilities for business model innovation, including commons-economies, meme-tokens, NFT-creators communities, and many more. This article explores how Web3 fundamentals could refresh a somewhat unusual model I discovered years ago and supercharge it.
Let’s start with a Web2 example of an expertise-focused business model, and then we’ll go into how Web3 can improve upon it.
Learning From The Past: Experimental Kitchens Are More Profitable Than Restaurants
I started my career in the world of high-end cuisine, and after a couple of years working 14-18 hour days in the kitchens of world-renown restaurants, I got a lucky break and managed to join what at the time was my dream job: being a development chef at the Fat Duck Group.
The Fat Duck Group had started with a single restaurant, the Fat Duck, which throughout the early 2000s, accumulated Michelin stars. They launched other (less expensive) restaurants that benefited from the main brand on the back of that success. As the success of the main restaurant relied on constant innovation to attract experience-seeking diners, they soon added to the growing empire a small ‘experimental kitchen’ where they could play around and try new dishes without getting in the way of serving customers.
Soon, the restaurant started to gain fame thanks to their innovative creations, enabling them to charge higher prices and attract talent. A few years later, this feedback loop led to them being voted ‘The World’s Best Restaurant’.
The award brought media opportunities, and the company soon branched out into books and TV shows. Once again, leveraging the infrastructure and know-how of the experimental kitchen team to showcase innovative recipes and techniques, further positioning the company as an innovation leader.
The books and TV shows paid for research on a variety of topics, and as the work continued, soon a pile of new ideas to try had accumulated. This creative explosion, in turn, enabled the company to leverage the experimental team and refactor their work into a revamped menu for the main restaurants and a concept for another restaurant that launched soon after.
Over the following years, the company repeated this trick to create three more restaurant concepts and then leveraged the accumulated expertise to partner with a large scale manufacturer and design a series of products for supermarkets. The new product line focused on new twists on popular classics.
That first partnership was followed by another deal focused on consumer electronics. This time the experimental team helped to envision, prototype, and test a range of products, including precision cooking thermometers, programmable timers, ergonomic spatulas, and more. And finally, another deal came to re-invent a range of kitchen appliances like kettles with precise temperatures for different kinds of tea and an induction cooker with a precision thermometer to perfectly cook a steak.
By then, the experimental team was no longer a couple of chefs but also included a mechanical engineer, a food scientist, and a media specialist, amongst others. Equally, the activities encompassed basic research in partnership with professors in psychology, history, and neuroscience; applied research to develop new culinary techniques and concepts; developing new dishes and products; and lending a hand in transformation projects across the group’s multiple businesses.
The more projects were undertaken, the more expertise and knowledge accumulated, enabling new business opportunities. The experimental kitchen had become a compounding business, where nurturing and marketing expertise was the core of the business model.
R&D Transitions From Web2 to Web3
The Fat Duck Group is unusual in its commitment to R&D. Most frequently, in Web2 startups, the founders carry out the research, product development, and operations in the early stages of a venture. However, as the product-market fit is found, the focus shifts into tactical execution, resulting in less space and capabilities to innovate. However, as new competitors enter the market, the incumbents need to innovate again, often attempted through acquisitions rather than home-grown ideas.
The DAO model, in contrast, offers more flexibility as DAOs can also grow like spider plants, creating multiple offshoots. For example, 1Hive diversified within less than two years of being founded into a set of products that works synergistically, including an arbitration protocol (Celeste), a DAO launcher (Gardens), and exchange with its own token (Agave).
As a DAO morphs from a ‘single product DAO’ into an ‘ecosystem of products DAO’, R&D becomes a constant activity, and there is value in organising it. We can see an early example of this in how Aragon, after acquiring another Web3 project called Vocdoni, set up a shared tech infrastructure R&D team to service the needs of multiple products and envision new ones (without endlessly reinventing the wheel).
Importantly, R&D works best when there is ample opportunity for collaboration, as we have seen with the proliferation of research networks and public-private partnerships in R&D-heavy industries such as Pharma.
DAOs have even more significant incentives to collaborate because Web3 products are largely open-source, and composability and interoperability are crucial to driving market adoption and creating network effects. For Web2 companies, the lesson was innovate or die. For DAOs, it’s innovate together or die apart.
Collaborative Research in Web3
The last year has seen a few expertise-oriented DAOs like Delphi Digital, the Governauts, or Token Engineering operate as researchers and educators. These models work by running cross-DAO research programmes (applied research) and on-demand advisory. Let’s call them on-demand expertise providers.
For time-poor but asset-rich organisations, hiring expertise on-demand enables them to quickly solve technical questions and open new problem spaces for development. While for research providers, the extra gig generates revenue and an opportunity to develop their expertise further. It’s a win-win, with some limitations.
If you have ever researched a topic and then created a report about it, you know that the report will invariably contain but a fraction of the knowledge and a few of the ideas on how to apply it.
Equally, you might have noticed that immersing oneself in a topic depends on building relationships with others in the field. Those relationships can keep bringing value long after the initial research was done. And not only that, but these relationships tend to lead to introductions to others with similar interests. Over time, expertise compounds.
From a product side, being first to market and offering better functionalities creates positive feedback loops and network effects that allow one product to become (or remain) the de-facto solution and industry leader. To enable these feedback loops, the traditional strategy from an organisational perspective has been to identify which expertise to hire from a third party and which ones to cultivate in-house.
However, Web3 enables us to rethink this dichotomy thanks to collaborative networks. We can move from an economy of competition to one where the best networks out-collaborate others. By aligning incentives and building longterm collaborative relationships, DAOs can free contributors to focus on knowledge creation and innovation, instead of guarding their piece of the puzzle behind closed doors. The result is that, compared to those who try to do everything in-house and work in silos, those who invest in collaborative R&D out-collaborate others.
Expanding the Scope of Collaborative Innovation: From R to D
Somewhat related to the model of a research agency is the idea of a Research and Product Lab. Here, the focus is on having one foot in research (thanks to partnerships) and then leveraging research discoveries and insights to create new products. Essentially, R&D labs are organisations with expertise at the core of the business model.
Doing both research and new product development requires a significant up-front investment, which is why organisations only tend to create dedicated R&D labs in later stages. The lower-hanging fruit is collaborating across DAOs to fund such activities.
Collaborative R&D in Web3 has the added benefits of composability and facilitating the creation of shared mental models and standards, all of which enable research partners to out-collaborate divided peers.
It takes time for an industry to mature to this stage, but we’re now seeing a few collaborative projects emerge as hubs for expertise applied to build products. Some interesting examples are:
- New Order - DeFi venture builder and accelerator
- SeedClub - Social Tokens incubator and venture studio
- Super Layer - DeFi incubator
- FingerprintsDAO - Smart Contract Art collector, curator and producer
- PrimeDAO - DeFi and DAO2DAO research initiative turned multi-product DAO
The above are but a few examples. And hopefully the early signs of an explosion in what will be Web3’s R&D.
Betting on this thesis, we've started RnDAO - a cross-DAO R&D lab, focused on DAO research and DAO tooling incubation, operated as a DAO and collaborating with leading players of the DAO tooling market. Our mission is to Empower Humane Collaboration.
As RnDAO, we lead and participate in research projects for clients and also incubate its own research projects, then we share insights back to our Funding Partners and incubate new DAO tooling concepts that can be incorporated into the partner’s stack or spun-out as their own ventures. R&D priorities are defined collaboratively for maximum impact. However, one area we’re particularly excited about is decision-making and sense-making tooling for DAOs (including idea management, decentralised strategy-making and visioning, collaborative problem solving, and proposal deliberation), and we're currently working on projects on Sub-DAOs, Community Health, and more.
If you’re curious about RnDAO, find a 5-minute summary on our work and ways to engage by following this link.
Written by:
@_daniel_ospina
A special thank you to Ray Kanani, Rav Sandhu, Brian Mills, Antoine Sakho, Fotis Tsiroukisfor, and Kishoraditya Chaudhari for their feedback and contribution to this article.