This blog post is contributed to Filecoin TL;DR by a guest writer. Catrina is an Investment Partner at Portal Ventures.

Until recently, startups led the way in technological innovation due to their speed, agility, entrepreneurial culture, and freedom from organizational inertia. However, this is no longer the case in the rapidly growing era of AI. So far, big tech incumbents like Microsoft-owned OpenAI, Nvidia, Google, and even Meta have dominated breakthrough AI products.

What happened? Why are the “Goliaths” winning over the “Davids” this time around? Startups can write great code, but they are often too hindered to compete with big tech incumbents due to several challenges:

1. Compute costs remain prohibitively high
2. AI has a reverse salient problem: a lack of necessary guardrails impedes innovation due to fear and uncertainty around societal ramifications
3. AI is a black box
4. The data “moat” of scaled players (big tech) creates a barrier to entry for emerging competitors

So, what does this have to do with blockchain technology, and where does it intersect with AI? While not a silver bullet, DePIN (Decentralized Physical Infrastructure Networks) in Web3 unlocks new possibilities for solving the aforementioned challenges. In this blog post, I will explain how AI can be enhanced with the technologies behind DePIN across four dimensions:

1. Reduction of infrastructure costs
2. Verification of creatorship and humanity
3. Infusion of Democracy & Transparency in AI
4. Installation of incentives for data contribution

In the context of this article,

• “web3” is defined as the next generation of the internet where blockchain technology is an integral part, along with other existing technologies
• “blockchain” refers to the decentralized and distributed ledger technology
• “crypto” refers to the use of tokens as a mechanism for incentivizing and decentralizing

Reduction of infra cost (compute and storage)

Every wave of technological innovation has been unleashed by something costly becoming cheap enough to waste

— Society’s Technical Debt and Software’s Gutenberg Momentby SK Ventures

The importance of infra affordability (in AI’s case, the hardware costs to compute, deliver, and store data) is highlighted by Carlota Perez’s Technological Revolution framework, which proposed that every technological breakthrough comes with two phases:

• The Installation stage is characterized by heavy VC investments, infrastructure setup, and a “push” go-to-market (GTM) approach, as customers are unclear on the value proposition of the new technology.
• The Deployment stage is characterized by a proliferation of infrastructure supply that lowers the barrier for new entrants and a “pull” GTM approach, implying a strong product-market fit from customers’ hunger for more yet-to-be-built products.

With definitive evidence of ChatGPT’s product-market fit and massive customer demand, one might think that AI has entered its deployment phase. However, there is still one piece missingstill: an excess supply of infrastructure that makes it cheap enough for price-sensitive startups to build on and experiment with.

Problem

The current market dynamic in the physical infrastructure space is largely a vertically integrated oligopoly, with companies such as AWS, GCP, Azure, Nvidia, Cloudflare, and Akamai enjoying high margins. For example, AWS has an estimated 61% gross margin on commoditized computing hardware.

Compute costs are prohibitively high for new entrants in AI, especially in LLM.

• ChatGPT costs an est. of 4M/training, and ~$700,000 per day to operate in hardware inference costs
• Version two of Bloom will likely cost $10M to train & retrain
• If ChatGPT were deployed into Google Search, it would result in $36B reduction in operating income for Google, a massive transfer of profitability from the software platform (Google) to the hardware provider (Nvidia)

Solution

DePIN networks such as Filecoin (the pioneer of DePIN since 2014 focused on amassing internet-scale hardware for decentralized data storage), Bacalhau, Gensyn.ai, Render Network, and ExaBits (the coordination layers to match the demand for CPU/GPU with supply) can deliver 75% — 90%+ cost savings in infra costs via below three levers

1. Pushing up the supply curve to create a more competitive marketplace

DePIN democratizes access for hardware suppliers to become service providers. It introduces competition to these incumbents by creating a marketplace for anyone to join the network as a “miner,” contributing their CPU/GPU or storage power in exchange for financial rewards.

While companies like AWS undoubtedly enjoy a 17-year head start in UI, operational excellence, and vertical integration, DePIN unlocks a new customer segment that was previously out-priced by centralized providers. Similar to how eBay does not compete directly with Bloomingdale but rather introduces more affordable alternatives to meet similar demand, DePIN networks do not replace centralized providers but rather aim to serve a more price-sensitive segment of users.

2. Balancing the economy of these markets with crypto-economic design

DePIN creates a subsidizing mechanism to bootstrap hardware providers’ participation in the network, thus lowering the costs to end users. To understand how let’s first compare the costs & revenue of storage providers in web2 vs. web3 using AWS and Filecoin.

Lower fees for clients: DePIN networks create competitive marketplaces that introduce Bertrand-style competition resulting in lower fees for clients. In contrast, AWS EC2 needs a mid-50% margin and 31% overall margin to sustain operations, plus

Token incentives/block rewards are emitted from DePIN networks as a new revenue source. In the context of Filecoin, hosting more real data translates to earning more block rewards (tokens) for storage providers. Consequently, storage providers are motivated to attract more clients and win more deals to maximize revenue. The token structures of several emerging compute DePIN networks are still under wraps, but will likely follow a similar pattern. Examples of such networks include:

• Bacalhau: a coordination layer to bring computing to where data is stored without moving massive amounts of data
• exaBITS: a decentralized computing network for AI and computationally intensive applications
• Gensyn.ai: a compute protocol for deep learning models

3. Reducing overhead costs

Benefits of DePIN networks like Bacalhau and exaBITS, and IPFS/content-addressed storage include:

• Creating usability from latent data: there is a significant amount of untapped data due to the high bandwidth costs of transferring large datasets. For instance, sports stadiums generate vast amounts of event data that is currently unused. DePIN projects unlock the usability of such latent data by processing data on-site and only transmitting meaningful output.
• Reducing OPEX costs such as data input, transport, and import/export by ingesting data locally.
• Minimizing manual processes to share sensitive data: for example, if hospitals A and B need to combine respective sensitive patient data for analysis, they can use Bacalhau to coordinate GPU power to directly process sensitive data on-premise instead of going through the cumbersome administrative process to handle PII (Personal Identifiable Information) exchange with counterparties.
• Removing the need to recompute foundational datasets: IPFS/content-addressed storage has built-in properties that deduplicate, trace lineage, and verify data. Here’s a further read on the functional and cost efficiencies brought about by IPFS.

Summary by AI: AI needs DePIN for affordable infrastructure, which is currently dominated by vertically integrated oligopolies. DePIN networks like Filecoin, Bacalhau, Render Network, and ExaBits can deliver cost savings of 75%-90%+ by democratizing access to hardware suppliers and introducing competition, balancing the economy of markets with cryptoeconomic design, and reducing overhead costs.

Verification of Creatorship & Humanity

Problem

According to a recent poll, 50% of A.I. scientists agree that there is at least a 10% chance of A.I. leading to the destruction of the human race.

This is a sobering thought. A.I. has already caused societal chaos for which we currently lack regulatory or technological guardrails — the so-called “reverse salient.”

To get a taste of what this means, check out this Twitter clip featuring podcaster Joe Rogan debating the movie Ratatouille with conservative commentator Ben Shapiro in an AI-generated video.

Unfortunately, the societal ramifications of AI go much deeper than just fake podcast debates & images:

• The 2024 presidential election cycle will be among the first where a deep fake AI-generated political campaign becomes indistinguishable from the real one
• An altered video of Senator Elizabeth Warren made it appear like Warren was saying Republicans should not be allowed to vote (debunked)
• The voice clone of Biden criticizing transgender women
• A group of artists filed a class-action lawsuit against Midjourney and Stability AI for unauthorized use of artists’ work to train AI imagery that infringed on those artists’ trademarks & threatened their livelihood
• A deepfake AI-generated soundtrack, “Heart on My Sleeve” featuring The Weeknd and Drake, went viral before being taken down by the streaming service. Such controversy around copyright violation is a harbinger of the complications that can arise when a new technology enters the mainstream consciousness before the necessary rules are in place. In other words, it is a Reverse Salient problem.

What if we can do better in web3 by putting some guardrails on AI?

Solution

Proof of Humanity and Creatorship with cryptographic proof of origination on-chain

This is where we can actually use Blockchain for its technology — as a distributed ledger of immutable records that contain tamper-proof history on-chain. This makes it possible to verify the authenticity of digital content by checking its cryptographic proof.

Proof of Creatorship & Humanity with Digital Signature

To prevent deep fakes, cryptographic proof can be generated using a digital signature that is unique to the original creator of the content. This signature can be created using a private key, which is only known to the creator, and can be verified using a public key that is available to everyone. By attaching this signature to the content, it becomes possible to prove that the content was created by the original creator — whether they are human or AI — and authorized/unauthorized changes to this content.

Proof of Authenticity with IPFS & Merkle Tree

IPFS is a decentralized protocol that uses content addressing and Merkle trees to reference large datasets. To prove changes to a file’s content, a Merkle proof is generated, which is a list of hashes that shows a specific data chunk in the Merkle tree. With every change, a new hash is generated and updated in the Merkle tree, providing proof of file modification.

A pushback against such a cryptographic solution may be incentive alignment: after all, catching a deep fake generator doesn’t generate as much financial gain as it reduces the negative societal externality. The responsibility will likely fall on major media distribution platforms like Twitter, Meta, Google, etc. to flag, which they are already doing. So why do we need Blockchain for this?

The answer is that these cryptographic signatures and proof of authenticity are much more effective, verifiable, and deterministic. Today, the process to detect deep fakes is largely through machine learning algorithms (such as the “Deepfake Detection Challenge”of Meta, “Asymmetric Numeral Systems” (ANS) of Google, and c2pa) to recognize patterns and anomalies in visual content, which is not only inaccurate at times but also falling behind the increasingly sophisticated deep fakes. Often, human reviewer intervention is required to assess authenticity, which is not only inefficient but also costly.

Imagine a world where each piece of content has its cryptographic signature so that everyone will be able to verifiably prove the origin of creation and flag manipulation or falsification — a brave new one.

Summary by AI: AI poses a significant threat to society, with deep fakes and unauthorized use of content being major concerns. Web3 technologies, such as Proof of Creatorship with Digital Signature and Proof of Authenticity with IPFS and Merkle Tree, can provide guardrails for AI by verifying the authenticity of digital content and preventing unauthorized changes.

Infusion of Democracy in AI

Problem

Today, AI is a black box comprised of proprietary data + proprietary algorithms. Such closed-door nature of Big Tech’s LLM precludes the possibility of what I call an “AI Democracy,” where every developer or even user should be able to contribute both algorithms and data to an LLM model, and in term receive a fraction of the future profits from the model (as discussed here).

AI Democracy = visibility(the ability to see the data & algorithm input into the model)

• contribution(the ability to contribute data or algorithm to the model).

Solution

AI Democracy aims to make generative AI models accessible for, relevant to, and owned by everyone. The below table is a comparison illustrating what is possible today vs. What will be possible, enabled by blockchain technology in Web3.

Today:

For consumers:

• One-way recipient of the LLM output
• Little control over the use of their personal data

For developers:

• Little composability possible
• Little reproducibility because there’s no traceability of the ETL performed on the data
• Single-source data contribution within the confine of the owner organization
• Close sourced only accessible through API for a charge
• 80% of data scientists’ time is wasted on performing low-level data cleansing work because of the lack of verifiability to share data output

What blockchain will enable

For consumers:

• Users can provide feedback (e.g. on bias, content moderation, granular feedback on output) as input into continuous fine-tuning
• Users can opt to contribute their data for potential profits from model monetization

For developers:

• Decentralized data curation layer: crowdsource tedious & time-consuming data preparation processes such as data labeling
• Visibility & ability to compose & fine-tune algorithms with verifiable & built-on lineage (meaning they can see a tamper-proof history of all changes in the past)
• The sovereignty of both data (enabled by content-addressing/IPFS) and algorithm(e.g. Urbitenables peer-to-peer composability and portability of data & algorithm)
• Accelerated innovation in LLM from the outpour of variants from the base open-source models
• Reproducibility of training data output via Blockchain’s immutable record of past ETL operations & queries (e.g. Kamu)

One might argue that there’s a middle ground of Web2 open source platforms, but it’s still far from optimal for reasons discussed in this blog post by exaBITS.

Summary by AI: The closed-door nature of Big Tech’s LLM precludes the possibility of an “AI Democracy,” where every developer or user should be able to contribute both algorithms and data to an LLM model, and in turn receive a fraction of the future profits from the model. AI should be accessible for, relevant to, and owned by everyone. Blockchain networks will enable users to provide feedback, contribute data for potential profits from model monetization, and enable developers to have visibility and the ability to compose and fine-tune algorithms with verifiability and built-on lineage. The sovereignty of both data and algorithm will be enabled by web3 innovations such as content-addressing/IPFS and Urbit. Reproducibility of training data output via Blockchain’s immutable record of past ETL operations and queries will also be possible.

Installation of Incentives for Data Contribution

Problem

Today, the most valuable consumer data is proprietary to big tech platforms as an integral business moat. The tech giants have little incentive to ever share that data with outside parties.

What about getting such data directly from data originators/users? Why can’t we make data a public good by contributing our data and open-source it for talented data scientists to use?

Simply put, there’s no incentive or coordination mechanism for that. The tasks of maintaining data and performing ETL (extract, transform & load) incur significant overhead costs. In fact, data storage alone will become a $777 billion industry by 2030, not even counting computing costs. Why would someone take on the data plumbing work and costs for nothing in return?

Case in point, OpenAI started off as open-source and non-profit but struggled with monetization to cover its costs. Eventually, in 2019, it had to take the capital injection from Microsoft and close off its algorithm from the public. In 2024, OpenAI is expected to generate $1 billion in revenue.

Solution

Web3 introduces a new mechanism called dataDAO that facilitates the redistribution of revenue from the AI model owners to data contributors, creating an incentive layer for crowd-sourced data contribution. Due to length constraints, I won’t elaborate further, but below are two related pieces.

• How DataDAO works by HQ Han of Protocol Labs
• How data contribution and monetization works in web3, where I dove into the mechanics, missing pieces, and emerging inevitable opportunities in dataDAOs

In conclusion, DePIN is an exciting new category that offers an alternative fuel in hardware to power today’s renaissance of innovations in web3 and AI. Although big tech companies have dominated the AI industry, there is potential for emerging players to compete by leveraging blockchain technologies: DePIN networks lower the barrier to entry in compute costs; blockchain’s verifiable & decentralized properties make true open IA possible; innovative mechanisms, such as dataDAOs, incentivize data contribution; and the immutable and tamper-proof property of Blockchain provides proof of creatorship to address concerns regarding the negative societal impact of AI.

Some good resources for DePIN deep dive

• Crypto Econ Labs
• Mechanism Institute
• Why decentralized compute by exaBITS blogs

Shoutout to below SMEs and friends for providing input and/or reviewing my draft: Jonathan Victor& HQ Han of Protocol Labs, Zackary Bof exaBITS, Professor Daniel Rock of Wharton, Evan Fisherof Portal Ventures, David Aronchickof Bacalhau, Josh Lehmanof Urbit, and more

About Author: Partner at Portal Ventures | @CuriousCatWang on Twitter

🇨🇳AI为什么离不开区块链——来看DePIN如何助力人工智能

Overview

There is an overwhelming amount of work going on in the Filecoin ecosystem, and it can be difficult to see how all the pieces fit together. In this blog post, I’m going to explain the structure of Filecoin and various components of the roadmap to hopefully simplify navigating the ecosystem. This blog is organized into the following sections:

• What is Filecoin?
• Diving into the Major Components
• Final Thoughts

This post is intended to be a primer on the major goings-on in Filecoin land; it is by no means exhaustive of everything happening! Hopefully, this post serves as a useful anchor and the embedded links are jumping-off points for the intrepid reader.

What is Filecoin?

My short answer: Filecoin is enabling open services for data, built on top of the IPFS protocol.

IPFS allows data to be uncoupled from specific servers —reducing the siloing of data to specific machines. In IPFS land, the goal is to allow permanent references to data — and do things like compute, storage, and transfer — without relying on specific devices, cloud providers, or storage networks. Why content addressing is super powerful and what CIDs unlock is a separate topic — worthy of its own blog post — that I won’t get into here.

Filecoin is an incentivized network on top of IPFS — in that it allows you to contract out services around data on an open market.

Today, Filecoin focuses primarily on storage as an open service— but the vision includes the infrastructure to store, distribute, and transform data. Looking at Filecoin through this lens, the path the project is pursuing and the bets/tradeoffs that are being taken become clearer.

It’s easier to bucket Filecoin into a few major components:

• Storage Market(s): Exists today (cold storage), improvements in progress.
• Retrieval Market(s): In progress
• Compute over Data (Off-chain Compute): In progress
• FVM (Programmable Applications): In progress
• Interplanetary Consensus (Scaling): In progress

Diving into the Major Components

Storage Market(s)

Storage is the bread and butter of the Filecoin economy. Filecoin’s storage network is an open market of storage providers — all offering capacity on which storage clients can bid. To date, there are 4000+ storage providers around the world offering 17EiB (and growing) of storage capacity.

Filecoin is unique in that it uses two types of proofs (both related to storage space and data) for its consensus: Proof-of-replication (PoRep) and Proof-of-Spacetime (PoST).

• PoRep allows a miner to prove both that they’ve allocated some amount of storage space AND that there is a unique encoding of some data (could be empty space, could be a user’s data) into that storage space. This proves that a specific replica of data is being stored on the network.
• PoST allows a miner to prove to the network that data from sets of storage space are indeed still intact (the entire network is checked every 24 hrs). This proves that said data is being stored (space) over time.

These proofs are tied to economic incentives to reward miners who reliably store data (block rewards) and severely penalize those who lose data (slashing). One can think of these incentives like a cryptographically enforced service-level agreement, except rather than relying on the reputation of a service provider — we use cryptography and protocols to ensure proper operation.

In summary, the Filecoin blockchain is a verifiable ledger of attestations about what is happening to data and storage space on the network.

A few features of the architecture that make this unique:

• The Filecoin Storage Network (total storage capacity) is 17EiB of data — yet the Filecoin blockchain is still verifiable on commodity hardware at home. This gives the Filecoin blockchain properties similar to that of an Ethereum or a Bitcoin, but with the ability to manage internet-scale capacity for the services anchoring into the blockchain.
• This ability is uniquely enabled by the fact that Filecoin uses SNARKs for its proofs — rather than storing data on-chain. In the same way zk-rollups can use proofs to assert the validity of some batched transactions, Filecoin’s proofs can be used to verify the integrity of data off-chain.
• Filecoin is able to repurpose the “work” that storage providers would normally do to secure our chain via consensus to also store data. As a result, storage users on the network are subsidized by block rewards and other fees (e.g. transaction fees for sending messages) on the network. The net result is Filecoin’s storage price is super cheap (best represented in scientific notation per TiB/year).
• Filecoin gets regular “checks” via our proofs about data integrity on the network (the entire network is checked 24 hrs!). These verifiable statements are important primitives that can lead to unique applications and programs being built on Filecoin itself.

While this architecture has many advantages (scalability! verifiability!), it comes at the cost of additional complexity — the storage providing process is more involved and writing data into the network can take time. This complexity makes Filecoin (as it is today) best suited for cold storage. Many folks using Filecoin today are likely doing so through a developer on-ramp (Estuary.tech, NFT.Storage, Web3.Storage, Chainsafe’s SDKs, Textile’s Bidbot, etc) which couples hot caching in IPFS with cold archival in Filecoin. For those using just Filecoin alone, they’re typically storing large scale archives.

However, as improvements land both to the storage providing process and the proofs, expect more hot storage use cases to be enabled. Some major advancements to keep an eye on:

• ✅ SnapDeals — coupled with the below, storage providers can turn the mining process into a pipeline, injecting data into existing capacity on the network to dramatically lessen time to data landing on-chain.
• 🔄 Sealing-as-a-service / SNARKs-as-a-service — allowing storage providers to focus on data storage and outsource expensive computations to a market of specialized providers.
• 🔄 Proofs optimizations — tuning hardware to optimize for the generation of Filecoin proofs.
• 🔄 More efficient cryptographic primitives — reducing the footprint or complexity of proof generation.

Note: All of this is separate from the “read” flow — which techniques for faster reads exist today via unsealed copies. However, for Filecoin to get to web2 latency we will need Retrieval Market(s), discussed in the next section.

Retrieval Market(s)

The thesis with retrieval markets is straightforward: at scale, caching data at the edge via an open market can solve for the speed of light problem and result in performant delivery at lower costs than traditional infrastructure.

Why might this be the case? The argument is as follows:

• The magic of content addressing (using fingerprints of content as the canonical reference) means data is verifiable.
• This maps neatly to building a permissionless CDN — meaning anyone can supply infrastructure and serve content — as end users can always verify that the content they receive back is the content they requested (even from an untrusted computer).
• If anyone can supply infrastructure into this permissionless network, a CDN can be created from a market of edge-caching nodes (rather than centrally planning where to put these nodes) and use incentive mechanisms to bootstrap hardware — leading to the optimal tradeoff on performance and cost.

The way retrieval markets are being designed on Filecoin, the aim is not to mandate a specific network to be used — rather to let an ecosystem evolve (e.g. Magmo, Ken Labs, Myel, Filecoin Saturn, and more) to solve the components involved with building a retrieval market.

This video is a good primer on the structure and approach of the working group and one can follow progress here.

Note: Given latency requirements, retrievals happen off-chain, but the settlement for payment for the services can happen on-chain.

Compute over Data (Off-chain Compute)

Compute over data is the third piece of the open services puzzle. When one thinks of what needs to be done with data, it’s typically not just storage and retrieval — users also want to be able to transform the data. The goal with these compute over data protocols are generally to perform computation over IPLD.

For the unfamiliar, IPLD aims to be the data layer for content-addressed systems. It can be used to describe a filesystem (like UnixFS which IPFS uses), Ethereum data, Git data, — really anything that is hash linked. This video might be a helpful primer.

The neat thing about IPLD being generic is that it can be an interface for all sorts of data — and by building computation tools that interact with IPLD, we reduce the complexity for teams building these tools to have their networks be compatible with a wide range of underlying types of data.

Note: This should be exciting for any network building on top of IPFS / IPLD (e.g. Celestia, Gala Games, Audius, Ceramic, etc)

Of course, not all compute is created equal — and for different use cases, different types of compute will be needed. For some use cases, there might be stricter requirements for verifiability — and one may want a zk proof along with the result to know the output was correctly calculated. For others, one might want to keep the data entirely private — and so instead might require fully homomorphic encryption. For others, one may want to just run batch processing like on a traditional cloud (and rely on economic collateral or reputational guarantees for correctness).

There are a bunch of teams working on different types of compute — from large scale parallel compute (e.g. Bacalhau), to cryptographically verifiable compute (e.g. Lurk), to everything in between.

One interesting feature of Filecoin is that the storage providers have compute resources (GPUs, CPUs — as a function of needing to run the proofs) colocated with their data. Critically, this feature sets up the network well to allow compute jobs to be moved to data — rather than moving the data to external compute nodes. Given that data has gravity, this is a necessary step to set the network up to support use cases for compute over large datasets.

One can follow the compute over data working group here.

FVM (Programmable Applications)

Up until this point, I’ve talked about three services (storage, retrieval, and compute) that are related to the data stored on the Filecoin network. These services and their composability can lead to compounding demand for the services of the network — all of which ultimately anchor into the Filecoin blockchain and generate demand for block space.

But how can these services be enhanced?

Enter the FVM — Filecoin’s Virtual Machine.

The FVM will enable computation over Filecoin’s state. This service is critical — as it gives the network all the powers of smart contracts from other networks — but with the unique ability to interact with and trigger the open services mentioned above.

With the FVM, one can build bespoke incentive systems to make more sophisticated offerings on the network:

• DataDAOs
• Retrievability Oracles
• Perpetual storage contracts / storage endowments
• Repair bounties
• Undercollateralized lending markets for storage providers
• ETL pipelines
• … and so much more

Filecoin’s virtual machine is a WebAssembly (WASM) VM designed like a hypervisor. The vision with the FVM is to support many foreign runtimes, starting with the Ethereum Virtual Machine (EVM). This interoperability means Filecoin will support multiple VMs — on the same network contracts designed for the EVM, MoveVM, and more can be deployed.

By allowing for many VMs, Filecoin developers can deploy hardened contracts from other ecosystems to build up the on-chain infrastructure in the Filecoin economy, while also making it easier for other ecosystems to natively bridge into the services on the Filecoin network. Multiple VM support also allows for more native interactions between the Filecoin economy and other L1 economies.

The FVM is critical as it provides the expressiveness for people to deploy and trigger custom data services from the Filecoin network (storage, retrieval, and compute). This feature allows for more sophisticated offerings to be built on Filecoin’s base primitives, and expand the surface area for broader adoption.

Note: For a flavor of what might be possible, this tweet thread might help elucidate how one might use smart contracts and the base primitives of Filecoin to build more sophisticated offerings.

Most importantly, the FVM also sets the stage for the last major pillar to be covered in this post: interplanetary consensus.

One can follow progress on the FVM here, and find more details on the FVM here.

Interplanetary Consensus (Scaling)

Before diving into what interplanetary consensus is, it’s worth restating what Filecoin is aiming to build: open services for data (storage, retrieval, compute) as credible alternatives to the centralized cloud.

To do this, the Filecoin network needs to operate at a scale orders of magnitude above what blockchains are currently delivering:

Looking at the above requirements, it may seem contradictory for one chain to target all of these properties. And it is! Rather than trying to force all these properties at the base layer, Filecoin is aiming to deliver these properties across the network.

With interplanetary consensus, the network allows for recursive subnets to be created on the fly. This framework allows each subnet to tune its own trade off between security and scalability (and recursively spin up subnets of its own) — while still checkpointing information to their respective parent subnets.

This setup means that while Filecoin’s base layer can be highly secure (allowing many folks to verify at home on commodity hardware) — Filecoin can have subnets that are natively connected that can make different trade offs, allowing for more use cases to be unlocked.

A few interesting properties based on how interplanetary consensus is being designed:

• Each subnet can spin up their own subnets (enabling recursive subnets)
• Native messaging up, down, and across the tree — meaning any of these subnets can communicate with each other
• Tunable tradeoffs between security and scalability (each subnet can choose their own consensus model and can choose to maintain their own state tree).
• Firewall-esque security guarantees from children to parents (think of each subnet as being like a limited liability chain up to the tokens injected from the perspective of the parent chain).

To double click on some of the things interplanetary consensus sets Filecoin up for:

• Because subnets can have different consensus mechanisms, interplanetary consensus opens the door for subnets that allow for native communication with other ecosystems (e.g. a Tendermint subnet for Cosmos).
• Enabling subnets to tune between scalability and security (and enabling communications to subnets that make different trade offs) means Filecoin can have different regions of the network with different properties. Performant subnets can get hyper fast local consensus (to enable things like chat apps) — while allowing for results to checkpoint into the highly secure (and verifiable and slow) Filecoin base layer.
• In a very high throughput subnet (a single data center, running a few nodes) — the FVM/IPVM work could be used to simply task schedule and execute computation directly “on-chain” — with native messaging and payment bubbling back up to more secure base layers.

Learn more by reading this blogpost and following the progress of ConsensusLab. This Github discussion may also be useful to contextualize IPC vs L2s.

Final Thoughts

So, after reading all the above, hopefully clearer what Filecoin is — and how it’s not exactly like any other protocol out there. Filecoin’s ambition is not just to be a storage network (as Tesla’s ambition was not to just ship the Roadster) — the goal is to facilitate a fully decentralized web powered by open services.

Compared to most other web3 infra plays, Filecoin is aiming to be substantially more than a single service. Compared to most L1s, Filecoin is targeting a set of use cases that are uniquely enabled through the architecture of the network. Excitingly, this means rather than competing for the same use cases, Filecoin can uniquely expand the pie for what can actually be done on crypto rails.

Disclaimer: Personal views, not reflective of my employer nor should be treated as “official”. This is my distillation of what Filecoin is and what makes it different based on my time in the ecosystem. Thanks to @duckie_han and @yoitsyoung for helping shape this.

🇨🇳Filecoin当前状态和发展方向总结