One of my favorite crypto use cases is borrowing for one-off expenses.
I recently paid a large deposit. Instead of selling my savings account assets, I borrowed stables on Morpho to offramp via Coinbase. Et voilà.
In a few minutes, a crypto loan replenishes my bank account. This is a more seamless experience than anything outside of crypto. I realised it was so good because it was instantaneous because it did not require any human verification.
In crypto, loans are secured by assets worth more than the loan, similar to the mechanics of a mortgage. As long as I remain solvent, meaning my collateral’s value exceeds the loan value by a margin of security, my credit score or risk doesn’t matter. When I borrow, I don’t need permission to someone who’d verify my claims - everything is in a smart contract that will enforce those rules. There is no need for verification and the operation is thus instant.
A major cost in lending is verifying borrower information. The industry relies on underwriting models that use personal information (age, salary, employment, etc.) to make risk assessments and lending decisions. For the model to be accurate, the information must be verified - self reporting isn’t enough. Every additional user has a high marginal cost due to verification.
I argue that crypto and zero-knowledge proofs bring verification costs to zero. You don’t need to pay a credit score provider to know if I’m employed if I present a cryptographic proof generated from my payroll provider’s website. Lenders don’t need to know your salary if you locked collateral in a contract to borrow.
What happens when verification costs approach zero? I anticipate several outcomes:
The user experience is improved because loans are instantaneous. Lending companies on crypto rails will offer a better user experience.
Loan costs decrease. Traditional lenders must shrink to remain competitive and pass savings to consumers, or new players emerge with lean cost structures to compete on price. A similar situation occurred with traditional banks that lost revenue from net interest margin to neo-banks passing those savings to users.
Verification companies either disappear or get integrated with the risk assessment function. The top 5 credit scoring companies have a combined market capitalization of $120 billion. They sell verified data. If users can self-authenticate the data, their margin will be pressured.
The market grows as previously unprofitable borrowers due to verification costs become a profit center.
Crypto and zero-knowledge change lending economics. As verification costs evaporate, you can operate with leaner organizations, offer instant loans at better rates, and service underserved customer segments.
My goal in this piece is to develop a view on web3 social, that justifies beyond ideology why a decentralised open graph can be superior to a constellation of closed graphs, and what are the reasons to believe that there's a credible path to their adoption.
My thesis is that the web3 social networks can enable better experiences and stronger network effects than existing social products. Their core advantage is incentivising an ecosystem of developers that build experiences that benefit users and the third-party applications building on top. Open systems attract developers, and in consequence, create emergent behaviours.
Open systems that support emergent behavior are way more likely to become platforms and we are excited by the possibilities of new consumer facing web platforms.
It’s not a new idea - it was part of the investment thesis for Twitter. And it was spot on: their mobile client, a big driver of growth in the mobile age, was developed by a third-party developer.
I've been reading more about the future of blockchains. I just came across this great piece that looks at it from first principles.
The thesis is that blockchains as we currently use them are a intermediary step, towards future where computation/block production is centralised, and verifications is highly decentralised. In that world zero-knowledge proofs are the enabling technology, and would be the primary way in which we access trustless compute.
We currently use blockchains as distributed computer, with the execution environment acting like a CPU and the state acting as memory.
The computer ensures the property of trustlessness by running all transactions through all nodes in the network. That makes the network as performant as its weakest link, and seriously limits the complexity of applications leveraging trustless computing. Forget about anything that requires complex maths like ML, or many transactions like games.
One path to improvement is to increase node requirements. The problem is that raising the price of the hardware needed excludes validators, and leads to more centralisation.
Another path is to break down the computer into specialised components. That's been the Ethereum roadmap, through rollups. These are cheaper (and more centralised) execution environments that keep their own state and occasionally settle on Ethereum. They achieve scaling by bundling many transactions into a single one, posted to an L1 like Ethereum.
One of my favorite crypto use cases is borrowing for one-off expenses.
I recently paid a large deposit. Instead of selling my savings account assets, I borrowed stables on Morpho to offramp via Coinbase. Et voilà.
In a few minutes, a crypto loan replenishes my bank account. This is a more seamless experience than anything outside of crypto. I realised it was so good because it was instantaneous because it did not require any human verification.
In crypto, loans are secured by assets worth more than the loan, similar to the mechanics of a mortgage. As long as I remain solvent, meaning my collateral’s value exceeds the loan value by a margin of security, my credit score or risk doesn’t matter. When I borrow, I don’t need permission to someone who’d verify my claims - everything is in a smart contract that will enforce those rules. There is no need for verification and the operation is thus instant.
A major cost in lending is verifying borrower information. The industry relies on underwriting models that use personal information (age, salary, employment, etc.) to make risk assessments and lending decisions. For the model to be accurate, the information must be verified - self reporting isn’t enough. Every additional user has a high marginal cost due to verification.
I argue that crypto and zero-knowledge proofs bring verification costs to zero. You don’t need to pay a credit score provider to know if I’m employed if I present a cryptographic proof generated from my payroll provider’s website. Lenders don’t need to know your salary if you locked collateral in a contract to borrow.
What happens when verification costs approach zero? I anticipate several outcomes:
The user experience is improved because loans are instantaneous. Lending companies on crypto rails will offer a better user experience.
Loan costs decrease. Traditional lenders must shrink to remain competitive and pass savings to consumers, or new players emerge with lean cost structures to compete on price. A similar situation occurred with traditional banks that lost revenue from net interest margin to neo-banks passing those savings to users.
Verification companies either disappear or get integrated with the risk assessment function. The top 5 credit scoring companies have a combined market capitalization of $120 billion. They sell verified data. If users can self-authenticate the data, their margin will be pressured.
The market grows as previously unprofitable borrowers due to verification costs become a profit center.
Crypto and zero-knowledge change lending economics. As verification costs evaporate, you can operate with leaner organizations, offer instant loans at better rates, and service underserved customer segments.
My goal in this piece is to develop a view on web3 social, that justifies beyond ideology why a decentralised open graph can be superior to a constellation of closed graphs, and what are the reasons to believe that there's a credible path to their adoption.
My thesis is that the web3 social networks can enable better experiences and stronger network effects than existing social products. Their core advantage is incentivising an ecosystem of developers that build experiences that benefit users and the third-party applications building on top. Open systems attract developers, and in consequence, create emergent behaviours.
Open systems that support emergent behavior are way more likely to become platforms and we are excited by the possibilities of new consumer facing web platforms.
It’s not a new idea - it was part of the investment thesis for Twitter. And it was spot on: their mobile client, a big driver of growth in the mobile age, was developed by a third-party developer.
I've been reading more about the future of blockchains. I just came across this great piece that looks at it from first principles.
The thesis is that blockchains as we currently use them are a intermediary step, towards future where computation/block production is centralised, and verifications is highly decentralised. In that world zero-knowledge proofs are the enabling technology, and would be the primary way in which we access trustless compute.
We currently use blockchains as distributed computer, with the execution environment acting like a CPU and the state acting as memory.
The computer ensures the property of trustlessness by running all transactions through all nodes in the network. That makes the network as performant as its weakest link, and seriously limits the complexity of applications leveraging trustless computing. Forget about anything that requires complex maths like ML, or many transactions like games.
One path to improvement is to increase node requirements. The problem is that raising the price of the hardware needed excludes validators, and leads to more centralisation.
Another path is to break down the computer into specialised components. That's been the Ethereum roadmap, through rollups. These are cheaper (and more centralised) execution environments that keep their own state and occasionally settle on Ethereum. They achieve scaling by bundling many transactions into a single one, posted to an L1 like Ethereum.
. Key behaviours in the app like threads came from the experiments of hobbyist and tinkering users.
The promise with web 3 is that the social protocols we’ll use won’t be able to close off the graph and API like all previous social networks eventually did. Twitterific, the client that gave us the bird and the verb, lost their access to Twitter when the firm did an unannounced and undocumented policy change. The second promise is that that value will flow fairly from the protocol to the third party application developers, therefore supercharging the cycle that encourages more them to tap into the user base and build companies.
This being said, Facebook’s early days traction is sobering for web3 social - within a few months it had reached millions of users. We can’t say the same of web3 social. It’s hard to point to unique and differentiating features with a massive pull on users. If we believe that an open ecosystem can create those unique experiences, we need to explore how social products built on open graphs can compete.
I see three vectors of significant user experience improvement, and some early signs of what they can look like:
Open ecosystems of apps:
Guaranteeing platform access to developers promotes the number of apps being built and increases the rate of innovation.
Social media have often been platforms for developers to build third-party apps (and sometimes clients) and it has often contributed to their success (Zynga representing 12+% of FB revenue in 2011, Twitter mobile client).
Open data graphs guarantee third-party developers access to the data which lowers the barriers to entry for new developers because a) they skip the cold start problem of having no data/content/users b) it reduces the friction for users to adopt the new app where both users' profiles and social following are pre-loaded c) they are natively social/tailored to the users preferences.
There are some early signs of this happening in the Farcaster with:
Integration with apps building social features: commenting system for existing systems like Mirror, social networks/aggregators adding content/distribution likeInterface, Yup, or Buidler, and recommendation engines with data from many contexts.
While it is too early to call them hits, they show that a number of high caliber builders are committing months of full-time work to building the experience that might in turn attract more users.
Cross context network effects:
The internet’s 'one-app one identity' architecture leads to a fragmentation of data across apps with little to no integrations among them and user lock-in.
An open graph creates the native possibility to opt-in to share data across contexts. In terms of UX, it could look like:
starting on a new app with a pre-loaded profile and follow-list
Seing actions taken by the people you follow in a third-party app (e.g. on an exchange, or see their restaurant ratings on Google Maps).
This increases the potential strength of the network effect of sharing a backend for a social graph: the number of nodes in the network scales much faster than in a setting with closed and split user databases.
We can expect a couple of large scale successes to exert a strong pull for other platforms to build on top of the same open graph. That would be a process similar to how Uniswap significantly improved the UX of using Ethereum, attracting more users, thus making it more attractive for developers to build on the platform.
Granular curation:
The ad-based business model of social media dictates engagement to be the variable to optimise, often at the expense of quality content.
With the content and user graph in open storage, there's a proliferation of options to tailor content recommendation with a high level of granularity.
There are some early experiments in the Farcaster ecosystem that either focus on certain types of contents (Alphacaster for DAO-related content) or giving users the flexibility of building and sharing custom feeds like Jam or Discove.
Broader possibilities could include: restricting content only from verified humans, surfacing niche content shared within a subpart of the graph, creating new types of content (from the graph’s metadata)
These vectors open the possibility for users and developers to build apps, experiences, or surface knowledge that
What exactly this 'social layer' will look like is hard to predict but here are some traits I think we can expect:
"The last handle you'll create":
In its steady state, it could be the canonical online identity layer - the address that represents all your connections to other profiles and authorship over any content.
A good example of what that might look like is ENS that are used by most crypto users as their global ID.
That profile controlled by a private key would serve as the primary identifier pointing to a number of alternative context-specific profiles.
Serving as a single sign-on would immediately makes apps aware of all that existing data (if users want to).
The big mental shift here is that rather than having apps have all the context, the profiles are the ultimate container of data and bring it to the dApps.
Some UX possibilities to visualise: connecting to an app with tailored recommendations based on other apps you've used or your following; anonymous contributions with provable reputation (anonymous reply with proof of number of followers)
A web 2 to web 3 continuum
I find it unhelpful to oppose web 2 and web 3 especially in the context of social - it’s a continuum based on how much is stored on decentralised infrastructure.
The experience is likely to be opt-in interoperability - users are presented with the option to share as much or as little context with the apps they use.
Some UX possible to visualise: connecting to GMaps and toggling "see ratings from my circle", using a web3 key to sign and timestamp publications on web2 social platforms for authenticity, storing content on decentralised storage solutions
Immutable APIs
Developers incentive to build on top of social networks is that out of the box you get a) rich data about the users for personalisation, b) an existing user base, c) a network of existing integrations in various platforms, d) out of the box social features like follows and comments, and e) a monetisation model.
That explains why building on top of Facebook or any other social network was great, until it wasn't.
At some point, it became in the economic interest of platforms to close access, and so they did.
What's changed is that blockchains can be viewed as computers that can make commitments, commitments of the sort - here's irrevocable access to the social graph.
There are different ways to do this, but the most promising seems to be zero-knowledge proofs - they are a mathematical tool that guarantees that an given computation was performed according to a certain set of rules, without having to perform the computation again. Instead, a prover creates a mathematical proof that can then be verified much more cheaply than it would take to run the computations themselves.
Bringing it back to how we ensure trustlessness in blockchains, this creates an alternative for nodes. Instead of running all transactions, they could simply run proof verifications to the same effect, but more more cheaply. In that new architecture, state still exists on a blockchain, where all nodes store a record of it at all times. But state transactions occur as a result of computations that are executed off-chain and verified on-chain. Once all nodes have verified, state can be updated.
I like to think of this as hybrid architectures, where the computation happens offchain in a provable way, and state is available at all times to all participants. That's a model where block production is centralised, but trustlessness is guaranteed by the zero-knowledge proofs and the number of nodes (lower requirements = more nodes).
This is what Vitalik envisions in his ethereum endgame, and a future I see as increasingly probable as developer tooling brings down the costs of using proofs. At some point, using proofs becomes 100x cheaper and enables developers to build much more complex applications.
If we reach that point, zero-knowledge proofs will be the dominant mode of accessing trustless computing.
. Key behaviours in the app like threads came from the experiments of hobbyist and tinkering users.
The promise with web 3 is that the social protocols we’ll use won’t be able to close off the graph and API like all previous social networks eventually did. Twitterific, the client that gave us the bird and the verb, lost their access to Twitter when the firm did an unannounced and undocumented policy change. The second promise is that that value will flow fairly from the protocol to the third party application developers, therefore supercharging the cycle that encourages more them to tap into the user base and build companies.
This being said, Facebook’s early days traction is sobering for web3 social - within a few months it had reached millions of users. We can’t say the same of web3 social. It’s hard to point to unique and differentiating features with a massive pull on users. If we believe that an open ecosystem can create those unique experiences, we need to explore how social products built on open graphs can compete.
I see three vectors of significant user experience improvement, and some early signs of what they can look like:
Open ecosystems of apps:
Guaranteeing platform access to developers promotes the number of apps being built and increases the rate of innovation.
Social media have often been platforms for developers to build third-party apps (and sometimes clients) and it has often contributed to their success (Zynga representing 12+% of FB revenue in 2011, Twitter mobile client).
Open data graphs guarantee third-party developers access to the data which lowers the barriers to entry for new developers because a) they skip the cold start problem of having no data/content/users b) it reduces the friction for users to adopt the new app where both users' profiles and social following are pre-loaded c) they are natively social/tailored to the users preferences.
There are some early signs of this happening in the Farcaster with:
Integration with apps building social features: commenting system for existing systems like Mirror, social networks/aggregators adding content/distribution likeInterface, Yup, or Buidler, and recommendation engines with data from many contexts.
While it is too early to call them hits, they show that a number of high caliber builders are committing months of full-time work to building the experience that might in turn attract more users.
Cross context network effects:
The internet’s 'one-app one identity' architecture leads to a fragmentation of data across apps with little to no integrations among them and user lock-in.
An open graph creates the native possibility to opt-in to share data across contexts. In terms of UX, it could look like:
starting on a new app with a pre-loaded profile and follow-list
Seing actions taken by the people you follow in a third-party app (e.g. on an exchange, or see their restaurant ratings on Google Maps).
This increases the potential strength of the network effect of sharing a backend for a social graph: the number of nodes in the network scales much faster than in a setting with closed and split user databases.
We can expect a couple of large scale successes to exert a strong pull for other platforms to build on top of the same open graph. That would be a process similar to how Uniswap significantly improved the UX of using Ethereum, attracting more users, thus making it more attractive for developers to build on the platform.
Granular curation:
The ad-based business model of social media dictates engagement to be the variable to optimise, often at the expense of quality content.
With the content and user graph in open storage, there's a proliferation of options to tailor content recommendation with a high level of granularity.
There are some early experiments in the Farcaster ecosystem that either focus on certain types of contents (Alphacaster for DAO-related content) or giving users the flexibility of building and sharing custom feeds like Jam or Discove.
Broader possibilities could include: restricting content only from verified humans, surfacing niche content shared within a subpart of the graph, creating new types of content (from the graph’s metadata)
These vectors open the possibility for users and developers to build apps, experiences, or surface knowledge that
What exactly this 'social layer' will look like is hard to predict but here are some traits I think we can expect:
"The last handle you'll create":
In its steady state, it could be the canonical online identity layer - the address that represents all your connections to other profiles and authorship over any content.
A good example of what that might look like is ENS that are used by most crypto users as their global ID.
That profile controlled by a private key would serve as the primary identifier pointing to a number of alternative context-specific profiles.
Serving as a single sign-on would immediately makes apps aware of all that existing data (if users want to).
The big mental shift here is that rather than having apps have all the context, the profiles are the ultimate container of data and bring it to the dApps.
Some UX possibilities to visualise: connecting to an app with tailored recommendations based on other apps you've used or your following; anonymous contributions with provable reputation (anonymous reply with proof of number of followers)
A web 2 to web 3 continuum
I find it unhelpful to oppose web 2 and web 3 especially in the context of social - it’s a continuum based on how much is stored on decentralised infrastructure.
The experience is likely to be opt-in interoperability - users are presented with the option to share as much or as little context with the apps they use.
Some UX possible to visualise: connecting to GMaps and toggling "see ratings from my circle", using a web3 key to sign and timestamp publications on web2 social platforms for authenticity, storing content on decentralised storage solutions
Immutable APIs
Developers incentive to build on top of social networks is that out of the box you get a) rich data about the users for personalisation, b) an existing user base, c) a network of existing integrations in various platforms, d) out of the box social features like follows and comments, and e) a monetisation model.
That explains why building on top of Facebook or any other social network was great, until it wasn't.
At some point, it became in the economic interest of platforms to close access, and so they did.
What's changed is that blockchains can be viewed as computers that can make commitments, commitments of the sort - here's irrevocable access to the social graph.
There are different ways to do this, but the most promising seems to be zero-knowledge proofs - they are a mathematical tool that guarantees that an given computation was performed according to a certain set of rules, without having to perform the computation again. Instead, a prover creates a mathematical proof that can then be verified much more cheaply than it would take to run the computations themselves.
Bringing it back to how we ensure trustlessness in blockchains, this creates an alternative for nodes. Instead of running all transactions, they could simply run proof verifications to the same effect, but more more cheaply. In that new architecture, state still exists on a blockchain, where all nodes store a record of it at all times. But state transactions occur as a result of computations that are executed off-chain and verified on-chain. Once all nodes have verified, state can be updated.
I like to think of this as hybrid architectures, where the computation happens offchain in a provable way, and state is available at all times to all participants. That's a model where block production is centralised, but trustlessness is guaranteed by the zero-knowledge proofs and the number of nodes (lower requirements = more nodes).
This is what Vitalik envisions in his ethereum endgame, and a future I see as increasingly probable as developer tooling brings down the costs of using proofs. At some point, using proofs becomes 100x cheaper and enables developers to build much more complex applications.
If we reach that point, zero-knowledge proofs will be the dominant mode of accessing trustless computing.
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one small idea
weekly (mostly) publication. I write to learn, sharing ideas and notes for what I find interesting
one small idea
weekly (mostly) publication. I write to learn, sharing ideas and notes for what I find interesting
