Leveraging the Give-to-Get Model: How the Emerging G2G Model Empowers Businesses and Consumers Alike

TL;DR

Since the 2008 financial crisis, the exponential growth of the technology sector has commodified consumer data, resulting in some of the biggest and most profitable companies in the world building their business models around data monetization (Meta, Google, Apple Microsoft, etc.). However, as the tech industry embarks on its next digital transformation from Web 2 to Web 3, the existing “Purchase vs Protect” (PvP) model which prioritizes data aggregation only in silos at the expense of data privacy and collaboration is at risk of being disrupted. Especially as consumer and legal scrutiny over data privacy as well as technologies that offer data transparency and security (blockchain, AI, etc.) become more prevalent, corporate decisions on how to aggregate and leverage data will define the next group of winners in the technology sector. In the face of evolving consumer preferences, data policy, and technological capabilities, I recommend investing in companies that prioritize a “Give-to-Get” (G2G) model—ideally on the blockchain—for data usage rather than a PvP model. I believe the G2G model offers a higher probability of superior returns in the venture space over the long term as this model emphasizes a mutually symbiotic relationship with end consumers rather than the existing exploitative relationship attributable to the PvP model. As the G2G model also maintains the same ability to generate substantial incremental operating margins (via lower CAC) as the PvP model, there should be no significant dropoff in long term profitability when rotating capital allocation from PvP to G2G.

The Problem

In today’s digitalized world, data aggregators seek to obtain as much data as possible, given the importance of a broad dataset as a source of revenue. Reliable data sources are scarce, opaque and centralized, however, because they are difficult to protect. The high risk of, and penalties for, any data loss incentivizes collectors to wall off what data they can. This data aggregation/protection market structure places companies and creators in direct competition—and gives the market power to the data owners. We can call this the traditional “Purchase vs Protect” (PvP) model, in which Big Tech firms and banks seek to maximize the consumer data obtained from individual customers by undermining data privacy, while inhibiting access to that data in silos by compromising data sharing. These twin dynamics threaten efforts to maximize business and social value from data and analytics, and create monopolistic power within certain markets. For example, Meta (Facebook) explains, in language buried deep within its user agreements, that it will own user information, but the only “payment” users receive comes in the form of “catered” ads. Instead, Meta uses data protection provisions to preserve its own centralized data silos and discourage competition and the use of other platforms by centralizing the benefit—but not the one we choose. This dynamic forms extreme barriers to entry for new market participants and limits the benefits for customers that they don’t necessarily choose in the use of their data—which is precisely why organizations like Epic and the Department of Justice are suing Apple and Meta, respectively.

The Solution

Significant opportunities to generate alpha in the VC industry exist when investing in companies that find ways to reverse this power imbalance and try to build digital trust (Companies vs. Creators → Companies & Creators). Gartner, for example, predicts that through 2023, “organizations that can instill digital trust will be able to participate in 50% more ecosystems, expanding revenue-generation opportunities.” In other words, if digital trust exists, data creators can propose a G2G approach, whereby access to a large quantity of a customers’ data becomes one form of payment made in exchange for a good or service.

I believe the G2G model is the solution to the digital trust problem as the model replaces the “don’t share data unless” mindset with “must share data unless,” by recasting data sharing as a business necessity. G2G presents an opportunity for mutual symbiosis: both parties can use an aggregated pool of data to garner more prospective customers and clients and generate more revenue. In retail and consumer contexts, this G2G approach retains targeted ads but also gives those who intentionally share their data the transparency they desire in how that data is being used. And, in the aggregate, the data becomes more valuable to third parties and can provide better analytics and insights through the use of AI and ML tools. 

Thus, a significant disruptive opportunity exists for those firms that can develop and leverage a G2G strategy, in which through mutual data protection safeguards, market inequities are removed and data creators negotiate with data aggregators at parity. AI and especially blockchain technology offer the safeguards to make this change possible.

Why “Give-to-Get” Model Works

So what’s the incentive for a customer, or investor to participate in this ecosystem? For one thing: cost. For customers demanding the service, they only have to contribute data in order to access the aggregated database, software, and analytics tools that can be generated from it. For the suppliers, this lowers the Cost of Acquiring Customers (CAC). As a G2G business begins adding data-sharing customers, this, in turn, increases customer Lifetime Value (LTV); customers are more likely to remain using the service and contributing valuable data to it as more data is aggregated. The more customers, the more benefit and profit that can be derived from the data value chain that the G2G model engenders.

Similarly, with only fixed costs in developers’ salaries and low variable costs, G2G businesses perform with efficiently high operating leverage, meaning that their break-even point is typically much lower than competitors who do not use a G2G model. G2G businesses are therefore able to set better selling prices to cover costs and generate a profit, with better incremental and contribution margins and return on investment in the infrastructure necessary for their G2G model to work. In other words, by increasing the number of customers who add and contribute to their service and thus increasing their revenue, G2G businesses can increase their operating income and gross margins, leading to higher net profit margins as well.

G2G and similar data sharing models also power rapid business growth, because their use scales easily to other asset and product classes. If one party can automatically leverage its own data to gain access to an even larger and more valuable database of pertinent information, then all parties can leverage that data to profit even further. Institutions and customers that participate in this system are supposed to profit and be onboarded cost-effectively by participating in the revenue sharing of sales made to third party actors outside of this network. To get the bigger clients and partners on board, a DataTech or other tech company with a G2G model can offer them a bigger slice of the shared revenue pie. As Chris White, Founder and CEO of FinTech data startup BondCliQ, told me: “When you sell to folks outside this network, it’s almost like streams on any one of these music sites. If you are Beyonce, for example, you should be getting a bigger piece of the pie than if you’re some garage band.”

Structured incentives and collaboration are why the Give-to-Get model has proved to be such a vital resource in many industries. In a report by the National Academy of Sciences, researchers found that in multiplayer social dilemmas, like the G2G model entails, Tit-for-Tat and Win-Stay Lose-Shift strategies engender and sustain cooperation. Without cooperation, G2G and other data sharing models fall apart. And any data sharing model is unlikely to gain that cooperation and, therefore, traction without the proper incentives and infrastructure in place. 

Early Adopters of the G2G Model

Early adopters of G2G are already demonstrating the disruptive capabilities of symbiotic data sharing strategy that focuses on consumer inclusiveness. For example, FinTech startup DataLend explains on its website that “clients must supply their securities finance transaction data in order to access DataLend’s aggregated industry data in return.” Glimpse takes this one step further in the bond markets: the FinTech firm empowers its buy-side users—mostly asset managers like Invesco—to control and own the data they produce through data sharing while also monetizing their data with a “data dividend.” This data dividend is generated when Glimpse sells agreed data sets to customers outside of their closed network. In other words, Glimpse and its data providers are not just sharing data; they are sharing revenue as well. This data aggregation and revenue-sharing, in turn, lowers the cost of being an effective trader in the bond markets by removing the buy-side’s necessity of buying the data directly and only from disparate, thirty-party vendors.

BondCliQ likewise relies on G2G as it seeks to consolidate pre-trade and post-trade information to provide dealer prices for corporate bonds. The FinTech startup ensures that dealers are compensated properly for their proprietary data with shared data and revenue streams from external data sales. They are similarly able to see where other bond dealers are making markets using BondCliQ’s browser-based app, which allows them to efficiently and effectively update their quotes to make them more competitive. This, in turn, leads to more market making and higher revenue generation overall for their partner firms with increased margins. According to Chris White, the G2G model “is the only way you can move forward if you think the information you’re dealing with is quasi-proprietary, which includes knowing how an individual dealer has priced something.” Chris and his team focus on a G2G model “in which dealers in the marketplace get more information, because you can’t have a market where dealers are trying to make markets with inadequate information, which really compromises liquidity.” 

There are other market players outside of the financial markets who are experimenting with a G2G or a similar data sharing model. For example, an investor at Base10 recently told me that Tegus, which offers thousands of call transcripts covering companies across sizes, stages, and geographies and then publishes those transcripts as data points to their platform after a delay so that the folks participating in the calls can act on insights before Tegus shares their work. Subscribers can choose to delay call publication further in cases of preemptive research or ongoing deals, and they choose to participate in this system in order to gain access to all of the consolidated data points, experts, and market research within Tegus’s network. Base10 then uses such data sharing firms like Tegus to build Base11, the internal software it uses to “empower data-driven research of the fastest growing companies and trends in the Real Economy.”

In similar fashion, Delphia has “built a Wall Street-caliber algorithm designed to help [its users] achieve superior investment returns,” by using the combined data of its members to see trends in the market before others have caught on. Those willing to share their data to improve [their] predictions are rewarded with chances to win cash each week.” Delphia never sells or shares their data; instead, they charge fees to large, institutional investors, letting them make Delphia free to all those using their app. 

The algorithm is specifically powered by the insights it can make when members contribute proprietary data by connecting their social media, banking, and other accounts to Delphia, which it uses to predict public opinion and consumer behavior. The algorithm also uses a suite of other AI tools that corroborate and calibrate publicly available data that impacts companies, brands, and sectors across the globe to better inform its investment advisory services to some of its Model Portfolios. Delphia’s Co-Founder and CTO, Cameron Westland, told me in an interview that “Delphia sprung from the idea that data is really valuable (in investing) but the people who are helping to aggregate data, don’t get exposure to the aggregate benefit of that data. Once you solve that problem of building these high trust partnerships, then you need to combine the data in ways that are more predictive than any individual data source… We want you to invest your data with us. We acknowledge your asset, which is called data. And these other companies do not. And therefore we’re going to be able to offer you more value. It’s going to lower costs, especially because we’re very transparent about it. And it’s gonna bring people who normally wouldn’t serve the consumer audience to bear.”

Similarly, Second Measure (recently acquired by Bloomberg) tracks credit card purchasing data to give institutional investors an idea of which companies are performing well. App Annie (now known as data.ai) collects data and tracks app downloads by distributing high-quality free apps, such as VPN software. In exchange for the free product you, as a customer, give permission to App Annie to monitor the other apps you have downloaded and use—a pretty elegant solution for sharing and tracking consumer data. Even Glassdoor explicitly uses a “give to get” policy: in order to obtain access to all the valuable information on their site, you must first submit a piece of content based on your personal experience at a company.

Many would assume that the big tech and finance firms, which hold most of the power over users and other businesses’ data today, would be hurt the most by these new arrangements. Counterintuitively, though, they actually stand to gain from it. For example, if Goldman Sachs could also see where other bond dealers are making markets, then they can properly update their quotes to make them more competitive. This, in turn, can lead to more market making for their firm and higher revenue generation overall. And Google, for instance, stands to benefit by preventing the outflow of old and new customers to competitors, like DuckDuckGo, in search of promises to prioritize protecting users’ data privacy and ownership. Market participants who do not take part in these new arrangements, however, are left out of the data and revenue sharing that the G2G model offers. Without access to these aggregated, collaborative databases, they stand to gain much less and offer less competitive products compared to their competitors who do take part.

The Risks to the Model

There are certain risks to incorporating or adopting the Give-to-Get model. For electronic trading, for example, there is a risk that a significant number of traders can come up with the same data or analysis and pile in on trades. But aggregated databases should provide the capacity to derive key insights that others cannot by sifting through disparate data alone. And even if such traders can get access to the same data or analysis, there are always arbitrage opportunities in being able to act on the key insights sooner. 

Likewise, adopting G2G might present a long path to significant revenue generation and profitability. Similar to how it took Facebook years to achieve profitability after offering its product for free for so long, G2G companies must gain solid traction with customers to derive the insights necessary to generate revenue or market data aggregations to third parties. At bottom, the G2G firm must establish its knowledge base as truly differentiated in a given market, or orchestrate proprietary data pipelines so that any customer can use their data in their own data ecosystem. 

There also is a significant risk of defensive competition from established firms. In the corporate bond markets, for instance, big banks fight the efforts of companies like DataLend, Glimpse, and BondCliQ, as they seek to maintain their monopoly status over their own proprietary data silos and pipelines. These big firms naturally resist new market participants and the possibility of data sharing and transparency, especially because they believe these efforts reduce the benefits of leveraging the data from their own deal flows and market making practices (which isn’t necessarily true as discussed above).

According to White, the reason why most G2G models and OTC products might initially struggle is because “the data sharing doesn’t mean much if you don't have the infrastructure to make that data actually useful.” For this reason, BondCliQ uses Sigma for its data visualization tools to offer dashboards that provide corporate bond dealers with the necessary business intelligence, reports, and analytics to help them improve their quote rates and market making. In that regard, Sigma, and other data analytics and infrastructure companies like it, act as the last straw to tip the scale in favor of adoption.

What the Future Holds: Further Use Cases

Private Market Data. Once the scales of adoption have started to tip in favor of give to get and similar data sharing models, there are some further buildable and investible use cases. In regards to consumers, they could voluntarily share their credit card transaction data with an app in order for the company that runs the app to get access to the same data VCs pay Second Measure for. Similarly, since DataTechs like Tegus, Pitchbook, and Crunchbase source a lot of their data from conversations with very early-stage startups and VCs, they could offer those same SMBs discounts to their subscription services in order to entice more of these small companies into onboarding their valuable aggregated information, as well as themselves as paying customers. (That annual $16K price tag on Pitchbook is pretty pricey for early-stage startups and VCs!)

Government. In terms of government, it would be easier to pass popular policies if voters could both easily vote and share their policy and voting desires with a centralized (or decentralized) system, in return for more applicable and appropriate state, local, and federal governance. This could also solve the polling issues around inaccuracy we have had in recent years by allowing the data to be meaningfully aggregated and analyzed.

Collecting Data. According to a series of reports on data sharing models by Boston Consulting Group, “citizen sensing” is an emerging way to crowdsource data by allowing individuals or institutions to cooperatively collect data for the purpose of fact-finding and policy-making. Pilot programs in Europe and the US, such as the Making Sense Project, have captured data on pollution, noise, and radiation, among other data types. Similarly, UN World Data Forum touched on the value of distributed sensors, both for scaling up data capture and for encouraging participatory policy-making.

Machine Learning. Federated learning is an approach to collaborative data sharing in which the underlying data doesn’t leave its owners’ control; instead, algorithms are remotely trained with their cooperation, and insights are then centralized for the owners’ use. An example of a company that uses collaborative federated machine learning is Devron. In speaking with their Founder and CEO, Kartik Chopra, he told me that Devron uses FML to train machine learning models at the location of data sources, by only communicating the trained models from individual data sources, without exchanging them, to reach a consensus for a global model. By giving your AI and ML models from decentralized edge devices or servers holding local data samples to a centralized intermediary like Devron, you can get them trained to achieve the most efficient and effective models possible. Give-to-get, right?

EdTech, BioTech, and ClimateTech. Shared data on energy consumption and efficiency can help pinpoint issues in our energy sector and redefine competition in the space. In the education sector, we could secure better insights on students’ educational needs if schools were willing to share collected data points and achievements with a consolidated, artificial and human intelligence-driven database, in exchange for more plentiful and effective resources. For the BioTech industry, a study on the genome biology sector demonstrated that data sharing models designed to facilitate global business provide insights for improving transborder genomic data sharing. In particular, “a flexible, externally endorsed, multilateral arrangement, combined with an objective third-party assurance mechanism, can effectively balance privacy with the need to share genomic data globally.”

InsurTech. For the insurance industry, digital innovation and data sharing models are being designed to reduce costs and develop new, innovative products, services, and distribution channels throughout the insurance value chain. While the current incremental approach to data sharing is impeding the ability of the insurance industry as a whole to improve its profitability, the relatively slow uptake of data standards to overcome the barriers to sharing make insurance increasingly attractive to radical change from a new entrant such as a tech giant and the myriad of AI startups launching novel products and services. An article published by Willis Towers Watson suggests that “a combination of smart business networks orchestrated by incumbent insurance firms, electronic markets, and data trusts, combined with leading use of digital technology and data standards, enable a way forward for the insurance industry to successfully evolve from its current position, and build competitive and technology barriers to new entrants.” Specifically, this could look like an AI startup using shared access to customers’ credit scores to offer them a plethora of new insurance policies, on both digital and physical assets. This could likewise make offering a host of derivatives on insurance policies much easier and more proliferated, with access to a shared, aggregated database establishing extra layers of trust in the fundamentals and financials underpinning these derivatives.

This could, theoretically, erode competition in the current market systems. But as always with new entrants and market participants, competitive edges will inevitably appear, in turn creating further competition and driving further innovation.

Anonymity and the Healthcare Industry. All these data sharing innovations could be even more adoptable if the reporting is done anonymously. The healthcare industry, in particular, could use this efficiency. There currently is no standardized process in healthcare to share information across insurers, healthcare providers, hospital networks, and specialty clinics. If patients anonymously could share data with their doctors and the doctors could then share that proprietary information with consolidated data sources, the latter could catch diseases earlier and conduct more robust research on them. 

The Untapped Opportunity: How Decentralization and Blockchain Technology Can Help

Adopting digital trust technologies, such as smart contracts, to securely collect data and efficiently transfer and share assets of monetary or nonmonetary value provides the untapped opportunity to leverage the G2G approach.

In Chris White’s words: “I think this is actually where ideas on blockchain make a lot of sense—where the data can be shared in such a way where it is still anonymized on an individual basis, but you can see what the aggregate calculations are for certain things. One example would be looking at cancer rates and then working backwards to see what the common lifestyle choices are of those people that have cancer. For a very long time, there was a huge fight against the idea that smoking cigarettes causes cancer. And all of the cigarette companies used to try to bury the data or manipulate the data, but it became so obvious that it did cause lung cancer that eventually the research was accepted, but it probably took 15 years longer than it should. So what are the other things in society right now that are causing issues?”

Logistics and Supply Chain. Other decentralized G2G models directly utilize blockchain tech to protect privacy, transparency, and speed up the fluidity and efficiency of data sharing. For the supply chain logistics industry, in freight transportation applications, moving freight is a complicated process involving many parties with varying priorities. According to an Internet-of-Things industry report: “An IoT-enabled blockchain can record the arrival times, the status of shipping containers, temperatures, and position, when in a movable state, as well as transparently and privately provide the infrastructure for efficient payment rails.” Similarly, in the component tracking and compliance application, IoT data kept in shared blockchain ledgers allows all parties to track component provenance throughout the product life cycle. For smart cities, connected devices, including smart homes and buildings, sensors, lights, manufacturing, industry and other infrastructures help in improving the functions and efficiency of infrastructure and related services. In short, blockchain and shared data tech could help improve the scalability and security concerns associated with connected technology, but we must first give access to those devices in order to get the desired utilities.

Dune is one example of these blockchain applications with G2G data-sharing incentives built in. The on-chain data analytics tool is a collaborative effort, in that users can fork and remix the blockchain data queries of other creators and build on top of their knowledge. On the other side, every time users write a new query, they contribute to the collection of queries that help people query for data on Dune. That way, the whole Dune Community succeeds together through an ever improving range of queries that allow users to easily query for just the stats they need. In a few words, give data queries, get access to even more data queries.

Helium offers users an opportunity to deploy a “simple device in their home or office to provide their city with miles of low-power [wireless] network coverage for billions of devices” and earn the blockchain’s native cryptocurrency, HNT.

Data Storage and Sharing. Distributed ledger technology can also enable trusted, distributed data storage and sharing. IOTA, a non-profit organization based in Germany, is building a blockchain-based data platform for IoT applications and has the support of large industrial players like Bosch, Volkswagen, and Schneider Electric. In IOTA’s case, there are no blocks and no miners; when you send an IOTA transaction you validate the two other transactions. This allows IOTA to overcome the cost and scalability limitations of a standalone blockchain. It is also a viable G2G solution because you are giving up access to your data—in this case transaction data—to a decentralized system in order to allow it to be privately and securely stored in a distributed ledger. 

Arweave is a global, permanent, hard drive built on the blockchain that uses a “new type of storage that backs data with sustainable and perpetual endowments, allowing users and developers to truly store data forever—for the very first time.” On top of the Arweave network is the permaweb: “a global, community-owned web that anyone can contribute to or get paid to maintain.” According to Arweave’s white papers, their economic mechanism is similar to a traditional economic endowment structure. When a piece of data is added to their network, the user pays a “principle” upfront, on which “interest” in the form of storage purchasing power is accrued. Over time, interest on this one-time upfront payment is given to those that offer hard drive space so that they can profit from their storage contributions. By using extremely conservative estimates for storage pricing, “Arweave ensures the long-term viability of the network’s endowment.” The blockchain startup is notably backed by a16z, Union Square Ventures, Multicoin, and Coinbase Ventures.

Art Market Using G2G Blockchain Tech. Artists and gallerists are left out of their own markets when artworks resell on the secondary market. Collectors are susceptible to forgeries, scams, and disputes. Fairchain establishes new revenue streams for artists and galleries while protecting buyers from fakes, fraud, and disputes. They are developing a new way to securely manage digital certificates of esoteric collectibles, while simplifying transactions and granting artists tradable residual rights to their creative product. Fairchain’s platform delivers a more trustworthy, equitable, and sustainable approach to art transactions. They allow artists, gallerists, and collectors to prove title, record provenance, authenticate, and transact art works with ease, all while supporting working artists, and they use blockchain technology’s public digital ledger system to establish trust in the veracity of artwork and their transactions in art marketplaces.

Data Ownership. One person who is putting distributed technology to good use is MIT professor Tim Berners-Lee, who invented the World Wide Web. In particular, Berners-Lee is trying to reinvent the internet by storing consumer data in highly decentralized “pods” that are controlled by the individuals who generate the data. Inrupt is an IoT startup that is currently trying to pursue this concept, called the “solid framework.” It specifically helps prevent data decay in silos and allows enterprises “to unite user data with users, kicking off a cycle of mutual trust and innovation.” As another viable G2G solution, this leaves the benefits of owning one’s data—and the choices of what to do with that data and whom to share it with—still up to the user. There is always a risk to losing access to this data—through theft or loss of one’s private keys—but the fault would have to lie in the individual user or the whole system, rather than a centralized authority, for an erosion of utility and trust in it to occur. Another MIT professor, Sandy Pentland, who created the MIT Media Lab, is exploring citizens’ data cooperatives, which help people keep control of their data while maintaining the benefits of pooling and aggregation.

Insurance Using G2G Blockchain Tech. Insurance can also be brought to the blockchain. Using a G2G model, a digital credit system can be created, wherein, through blockchain tech, people’s—that is, wallets’—financial transaction history is transparent. This allows a centralized intermediary to accurately determine whether certain wallets are over-levered, return the necessary collateral to lending protocols, and otherwise not participate in the crypto ecosystem through illicit means or in the pursuit of illicit ends. Accordingly, an algorithm can correctly determine one’s credit worthiness and how likely they are to pay back a loan or become liquidated if not. With established digital credit scores and an open and transparent ecosystem, crypto users can give up their transaction history and their established credit information in order to get very liquid and trustworthy loans on the blockchain from trusted (and also transparent) financial intermediaries, protocols, and institutions. This also leads to greater trust, security, and adaptability when it comes to blockchain tech being brought to the greater landscape of institutional financial infrastructure, all through an accurate, transparent, and predictable G2G, data sharing model. 

Our Opportunity

Companies that build systems which vertically integrate and distribute data necessary to accelerate the adoption of G2G-based data aggregation and democratization. As you know now, there are direct paths to unique product-market-fits, revenue generation, and profitability as the adoption of data-sharing and G2G business models only accelerate over time. Recognizing how the incentive structure is baked into the G2G model can also help you determine how investible other opportunities are in a wide variety of spaces. G2G infrastructure companies for data ecosystems, many of the use cases outlined here, and many of the startups mentioned in this paper are also all investable opportunities. If you are on the founder side of startups, using the Give-to-Get and similar models can help you build a larger store of data and analytics by participating in the ever-growing distributed data ecosystem. Whether you are on the founder or investor side, though, it is important to think about the different verticals and spaces in which these innovations have not yet occurred. 

Thank you to Henry Berry, Drake Hicks, and Harrison Lapides for their helpful suggestions on this thought piece. A special thanks to Chris White, Cameron Westland, Kartik Chopra, and Paul Gover for taking the time to speak with me and answer my questions for this piece. 

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