Decentralized Knowledge Networks

What are Knowledge Networks?

Curation assembles information through crawling/automated discovery and/or crowd-sourced actions and contributions.

Ranking scores each piece of information to assess its intrinsic value to users. It takes into account multiple types of signals and updates it dynamically over time based on actual user behavior.

Matching makes it easy for users to access the knowledge on-demand through intelligent matching against explicit and implicitly stated user needs and intents.

Information turns into knowledge and insight when tagged, scored, ranked, and matched in context.

What are the Examples of Centralized Knowledge Networks?

Horizontal networks are broad and use generic and simple structures for buyer needs (e.g. keywords) and seller offerings (e.g. web links). But they operate at massive scale with hundreds of millions to billions of users.

Google is the largest horizontal knowledge network there is: It crawls for links, scores and ranks them based on page rank algorithms, and makes it easy for anyone to find and access the right content by matching keywords with links and content therein.

Vertical networks are deep and use industry specific structures to model buyer needs and seller offerings. They also tend to go deeper in terms of decision support on specific jobs to be done.

NerdWallet is one of the largest knowledge networks in Financial Services. It provides tools, expert information, and insights for consumers to discover the right service providers for credit cards, mortgages, personal loans, etc. and make better financial decisions.

G2 Crowd is the largest knowledge network for Software as a Service. It aggregates seller information, reviews of validated customers, and provides easy ways for buyer to access this data on demand.

42 Floors is a knowledge network for corporate real estate. It helps buyers express needs and connect with the right brokers, listings, and content.

Disadvantages of Centralized Knowledge Networks

They provide a more cohesive and integrated user experience but also suffer from several drawbacks.

  1. The intermediary can change its curation, ranking, and matching algorithms on a whim with no notice and no remediation. One small change in code can make or break companies overnight.
  2. The intermediary almost always charges a rent to the supply side in the form of listing fees, advertising, or subscription fees while sharing none of the profits with the community.
  3. Buyer’s personal data belongs to the intermediary (notwithstanding the legal fineprint) and is sold and resold 24*7 on the Internet through Ad-tech middlemen. For some, it is the price to pay for a zero-cost utility, but has significant long-term negative effects (both visible and invisible).
  4. Buyers do not get paid anything even though they are the heart of the network.
  5. Experts who contribute content do not get paid anything even though their content is pivotal to the success of the network.
  6. In contrast to the halcyon days of digital marketing, more sellers are now concerned about fraud, lack of transparency, and marketing ROI, and wondering if there is a better way.

Can the knowledge networks of today be disrupted and decentralized?

Can it be operated as an open and transparent meritocracy?

Can it deliver equivalent or better outcomes to users than status quo?

Can it create better knowledge through distributed crypto-economic incentives?

While the answers are TBD to many of these questions and ones we grapple with every day as we build Pulse, there are reasons to be hopeful and optimistic.

The gravitational pull of the opportunity is immense. In the digital age, knowledge is the lifeline of every horizontal function and industry vertical. Helping create permissionless, censorless, trustless, open, non intermediated knowledge networks is a massive opportunity to redistribute value from middlemen to edges.

To succeed, a decentralized knowledge network needs to accomplish the following:

  • Security: Provide buyers full control and ownership of their data. Buyers can take the data with them wherever they want and provide personal APIs to third parties / dApps.
  • Privacy: Buyers can share data on their terms with specific and revocable permissions.
  • Open and Transparent Meritocracy: There is no intermediary, but the work being done (curation and ranking of content, origination of user intent, intelligent matching, relaying, seller bidding, and engagement) is measured and rewarded objectively and transparently.
  • Censorless: No party can shut it down. The content and algorithms live forever and are modified only by the community.
  • Trustless: Parties can exchange value with each other in a peer-to-peer fashion knowing that all value exchanges are secured and validated.
  • Better User Outcomes: The network needs to deliver better FIND outcomes to buyers vs. status quo in terms of a) Expression of Intent, b) Accuracy and Relevance of Results, and c) Engagement with Counter Parties. This is indeed the primary and toughest litmus test.

Can Decentralized Knowledge Networks Change the World?

  1. At scale, decentralized knowledge networks will make demand more transparent reducing cost of acquisition; they will make it easier for buyers to express intent and get access to expertise to make good decisions; experts can monetize their knowledge instead of getting used. It is a fairer and more meritocratic model that rewards users than middle-men.
  2. And when decentralized knowledge networks truly take off, tens of billions of dollars spent on digital marketing intermediaries and Ad-tech will dwindle dramatically. They will instead be spent on incentivizing communities, rewarding creators, and building and delivering better products.

Yes, there is some way to go from here to there. Blockchain technology and developer tools are still early, incumbents are powerful, and distribution is a bitch.

But the prize and the impact are so game-changing that we will be foolish to bet against it.

As any keen observer of the history of technology and revolutions knows, new ways of doing things often seem impossible at the start; change slowly, and eventually suddenly.

Also refer to Tweet Storm for a crisper summary.

Anandan (AJ)

A protocol and decentralized app suite seeking to transform marketing and discovery for Web 3.0

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