The Automated New Deal: Unlocking Billions By Giving Data Context via the blockchain

With blockchain-powered communal data infrastructure, we can turn toll booths into the new social order we've been looking for.

(Mahesh is co-founder of Escape Velocity Ventures, a thesis-driven venture capital firm focused on early stage decentralized networks. He previously held roles at Apollo and Goldman Sachs, after graduating with honors from Harvard College with an Economics degree.)

Giving people the ability to monetize their own data could drive the most significant social transformation since the New Deal, as crypto-economic platforms allow people to unlock hundreds of billions of dollars by enriching their own data with context.

For as much value as our Web 2 overlords extract from data, they undoubtedly leave much more on the table. Stringent regulation aimed at safeguarding personal privacy allows companies to do less and less with your data. And with even the pro-business right-wing staunchly against the most prominent businesses of our age, expect corporate data moats to get weaker. The making the world a better place ethos of 2015 tech is genuinely dead, for better or worse. But one can't argue Web 2 platforms haven't created millions of jobs, income, and best-in-class technical capabilities for the country.

History shows that corporate hegemony extracts every last drop out of everyday people, even for their labor. Physical labor is the most evident human value-added to quantify: Americans make on average ~$42k for it. But even fair pay for work is a recent western development: for hundreds of years, serfs and slaves were exploited out of the reward for their labor. Labor contributions become harder to ignore when combined with technological progress (agricultural technology, cars, and automation) that amplify a worker's contribution and allow them to measure it.

But we contribute in other ways, too. If the ordinary person could distribute and contextualize their own data, they'd supplement their income significantly. But how much is that data worth? One simple approach is to look at the advertising revenue generated by America's biggest tech companies - they capture the lion's share of our personal data across every aspect of our physical and digital lives.

Ad revenue is the toll payment big tech charges to access their captive consumer base's data. The toll payment is variable and changes depending on how profitable the tech behemoth's customer is. For small businesses, advertising revenue is CAC, and businesses buying ads must make LTV/CAC work for their product. By enriching data, these behemoths can price discriminate more effectively, selling ads to the highest bidder vs. just selling generic ads. Big tech companies are obsessed with finding roundabout ways of adding context to their generic data. That is especially true today, given cookies (invisible bots that track you across the internet) are being phased out.

Without rich context, advertising is the easiest way to monetize this partial data, and it's the only standardized system today with a clear ROI. Let's see how much these companies earn off their users:

2021 US Advertising Revenue ($bn) 2020 US Advertising Revenue 2021 Global Ad Revenue % US Google 96,000 68,000 208,000 46% Facebook 60,000 42,000 120,000 50% Amazon 20,000 13,000 31,000 65% Microsoft 5,807 5,082 11,615 50% Snapchat 2,831 1,649 4,000 71% Pinterest 2,000 1,300 2,500 80% Total $186,638 $131,031 $377,115.

We can comfortably assume that Facebook's numbers for daily active users in the US (195mm) represent the US population participating in the data economy covered by the companies above. That's ~60% of Americans, which makes sense when you consider ~50mm of Americans are under 11 years old and ~80mm are over 60.

Total US Advertising Revenue (2021) $186,638

Total Number of DAUs (FB Estimate) 195

Ad Revenue Per Capita $957.12

Doing the math, six companies monetize each US user to the tune of ~$1,000, with advertising the only monetization avenue. Tech behemoths today are creating $200bn of US advertising revenue of 200mm users, using frameworks requiring them to be extremely sneaky in capturing your data and severely limited in how much they can enrich that data with context.

The scale achieved by the digital advertising industry is incredible. However, growth expectations are tempering in the face of increasing barriers to data capture, led by the EU but likely to leak towards the US eventually. Headwinds like these and Apple's increasingly privacy-focused stance have led to core changes in the direction many of these companies are taking, led by Meta's radical pivot towards the digital asset world.

So then, what of Facebook's metaverse? Well, in a world where everything you see/touch/interact with is optimized for you, platforms can price discriminate massively because they can direct traffic to the highest value advertiser and charge a massive toll.

Crypto's vision is a public metaverse (i.e., one that doesn't need to earn excess rents). Instead of price discriminating for toll booth access, the applicable mental model is entropy in a chemical system. The more composable and recent data is, the more chemical reactions it can spark (i.e., interactions, where insight from high-context data allows 1+1 = 3). We provide examples of this later below.

So how do you enrich data? Provide context. There are three simple ways you can empower data:

Proximity: Data is more valuable in the hands of someone who values it more. Your healthcare data is extremely valuable to your doctor, moderately valuable to an advertiser, and entirely useless for your plumber. By getting data into the hands of the person able to make the most out of it, you enrich the data's monetary value.

Composability: Data that can be contributed neatly as a piece towards other supersets can be easily leveraged by others to derive critical insights is very valuable. This underlies many of the DataDAO approaches taking off recently —millions of sets of Amazon, Facebook, and Google data can be aggregated to predict the onset of severe diseases, power massive hedge funds to invest behind consumer behavior, or even understand the development of society entertainment preferences. Super setting data breeds insights for which many will pay a premium.

Recency: Data is more valuable the more recent it is, and the fewer people have seen it. This is more obvious for certain forms of data than others. A sales lead for a paper salesperson in Scranton is far more valuable ten seconds after it's delivered vs. ten days. An inflation reading is more valuable to JP Morgan's fixed-income department if they see it before anyone else. People are happier to get a warning of a meteor headed for their city sooner rather than later. There's a premium for getting relevant data to people who need it faster.

Aided by self-empowering technology, we can give data appropriate context by getting it into the right hands, at the right time, and in the right format. Many platforms are working towards this end goal, and while approaches differ radically, they center around the theme of empowering people to capture their own data in a composable and immediate format and eventually connecting them with a proximate user of that data. Two contrasting blockchain-native approaches that may unlock this trapped value at scale are Machine Networks and Data Funds.

MachineFi is an excellent example of a Machine Network. MachineFi is a developer platform built on the IoTeX blockchain that empowers the creation of autonomous machine networks. These are networks of connected IoT devices (think Fitbits, sensors, phones) that relay real-time data about their subject, which is captured on IoTeX's secure blockchain. Developers can spin up different applications to reward participants for their data in real-time. As a simple example, Aetna could use MachineFi to spin up a network that pays people for contributing their health data from their fitbits. With context-rich data, Aetna can better price healthcare across all patients and offer you better pricing individually if your on-chain data is linked to an identity layer. Here, 1+1=3. The result is more dollars in your pocket from providing data to the most proximate counterparty and savings derived from analysis of composable data. MachineFi traffics in real-world data and monetizes that data for users.

Delphia is a Data Fund structured as a DAO that rewards users directly from contributing datasets they already provide to Web 2 companies. The protocol incentivizes people to contribute Amazon, Facebook, Google, and other data to their DAO in return for a token payment. That's immediate value to the user but is compounded even more so by Delphia's strategy, which uses that data to power a robo-advisor that will invest with the benefit of that data, with some winnings distributed to providers of the data. Delphia benefits from the composable nature of data provided (they can superset Amazon data for unique insights) and the recency of that data (real-time data allows enhanced portfolio management capabilities). Again, 1+1=3. Delphia empowers users to make more out of data already being collected and unlocks significant additional value by providing context.

Data is money, and money is power, so finding ways to give people ownership of their own data could be one of the great equalizers of the 21st century. A 10x increase in data value

through providing detailed context (vs. advertising) would be a gamechanger for the average American. $10,000 of annual income from data sales could be the new basis for a universal basic income and would drag tens of millions of Americans back above the poverty line. You could drive even more radical insights: the value of context-rich data could imply that, if hardware costs continue to deflate, it may make policy sense to subsidize buying smartphones for homeless people. That insight then empowers governments to spend aggressively to build smart cities: Data collection can juice the ROI of national infrastructure projects significantly, creating a virtuous investment cycle driven by the insight provided by new data capabilities.

Investing in a business that increases the context around data has the rare quality of benefitting from increasing returns to scale. We believe adopting and normalizing crypto economic incentives will unlock a whole new breed of businesses that get better as they get bigger.