8 posts tagged with "AI"

Why BlockEden.xyz Cares​

In the fast-paced world of Web3, speed is everything. Shipping production-grade RPC and staking infrastructure requires our team and our community to constantly be at the forefront of innovation. This means staying on top of dense protocols, groundbreaking cryptography papers, and rapidly evolving governance threads. The faster our community can absorb and understand new ideas, the faster they can build the next generation of decentralized applications. This is where BeFreed.ai comes in.

What BeFreed.ai Is​

BeFreed.ai is a San-Francisco-based startup with a simple yet powerful mission: to make learning joyful and personal in the age of AI. They’ve created an intelligent micro-learning companion designed to fit the demanding lifestyles of builders and creators.

Core Ingredients:​

• Multiple formats → one click: BeFreed.ai can take a wide range of content—from lengthy books and detailed videos to complex technical documents—and instantly transform it into quick summaries, flashcards, in-depth notes, and even podcast-style audio.
• Adaptive engine: The platform is designed to learn alongside you. It pays attention to your learning pace and interests, surfacing the most relevant information next, rather than forcing you through a rigid, one-size-fits-all curriculum.
• Built-in chat & “Why-this” explainers: Have a question? Just ask. BeFreed.ai allows for on-the-fly inquiries to clarify complex topics. It also provides explanations that connect new insights back to your overarching goals, making the learning process more meaningful.
• A 43k-strong learning community: Learning is often a communal activity. BeFreed.ai fosters a vibrant community of over 43,000 learners who share their progress, react to insightful content, and highlight key takeaways, keeping motivation and momentum high.

Why It Matters to BlockEden.xyz Builders​

For the dedicated builders in the BlockEden.xyz ecosystem, BeFreed.ai is more than just a learning tool; it’s a strategic advantage. Here’s how it can sharpen your edge:

• Time leverage: Turn a 300-page whitepaper into a concise 10-minute audio brief to listen to before a crucial governance vote.
• Context retention: Use flashcards and mind-maps to solidify your understanding of protocol details that you’ll need when writing smart-contract indexes.
• Cross-skill growth: Expand your skill set without ever leaving your development environment. Pick up the basics of design thinking, understand growth loops, or get tips on Go concurrency in your downtime.
• Shared vocabulary: Create team-level playlists to ensure that every contributor is learning from the same distilled and consistent source of information, fostering better collaboration and alignment.

Using BeFreed with BlockEden.xyz Workflows​

Integrating BeFreed.ai into your existing development process is seamless and immediately beneficial:

1. Drop a spec: Paste the URL of the latest tokenomics PDF or a YouTube developer call into BeFreed for an instant, digestible summary.
2. Export flashcards: Review key concepts during CI runs. This form of repetition is far more effective than the mental fatigue that comes from constant context-switching.
3. Link in docs: Embed a BeFreed summary URL next to each API reference in your documentation to help new team members get up to speed faster.
4. Stay current: Set up weekly digests in BeFreed on emerging L2s and immediately put that knowledge into practice by prototyping with BlockEden.xyz’s multi-chain RPC services.

Get Started​

BeFreed.ai is available now on iOS, Android, and the web. We encourage you to try it out during your next BlockEden.xyz project sprint and experience how it can enhance your learning and building velocity. Our team is already exploring tighter integrations—imagine a future where a webhook automatically turns every merged PR description into a comprehensive study set.

Introduction​

AI and Web3 are converging in powerful ways, with AI general interfaces now envisioned as a connective tissue for the decentralized web. A key concept emerging from this convergence is MCP, which variously stands for “Model Context Protocol” (as introduced by Anthropic) or is loosely described as a Metaverse Connection Protocol in broader discussions. In essence, MCP is a standardized framework that lets AI systems interface with external tools and networks in a natural, secure way – potentially “plugging in” AI agents to every corner of the Web3 ecosystem. This report provides a comprehensive analysis of how AI general interfaces (like large language model agents and neural-symbolic systems) could connect everything in the Web3 world via MCP, covering the historical background, technical architecture, industry landscape, risks, and future potential.

1. Development Background​

1.1 Web3’s Evolution and Unmet Promises​

The term “Web3” was coined around 2014 to describe a blockchain-powered decentralized web. The vision was ambitious: a permissionless internet centered on user ownership. Enthusiasts imagined replacing Web2’s centralized infrastructure with blockchain-based alternatives – e.g. Ethereum Name Service (for DNS), Filecoin or IPFS (for storage), and DeFi for financial rails. In theory, this would wrest control from Big Tech platforms and give individuals self-sovereignty over data, identity, and assets.

Reality fell short. Despite years of development and hype, the mainstream impact of Web3 remained marginal. Average internet users did not flock to decentralized social media or start managing private keys. Key reasons included poor user experience, slow and expensive transactions, high-profile scams, and regulatory uncertainty. The decentralized “ownership web” largely “failed to materialize” beyond a niche community. By the mid-2020s, even crypto proponents admitted that Web3 had not delivered a paradigm shift for the average user.

Meanwhile, AI was undergoing a revolution. As capital and developer talent pivoted from crypto to AI, transformative advances in deep learning and foundation models (GPT-3, GPT-4, etc.) captured public imagination. Generative AI demonstrated clear utility – producing content, code, and decisions – in a way crypto applications had struggled to do. In fact, the impact of large language models in just a couple of years starkly outpaced a decade of blockchain’s user adoption. This contrast led some to quip that “Web3 was wasted on crypto” and that the real Web 3.0 is emerging from the AI wave.

1.2 The Rise of AI General Interfaces​

Over decades, user interfaces evolved from static web pages (Web1.0) to interactive apps (Web2.0) – but always within the confines of clicking buttons and filling forms. With modern AI, especially large language models (LLMs), a new interface paradigm is here: natural language. Users can simply express intent in plain language and have AI systems execute complex actions across many domains. This shift is so profound that some suggest redefining “Web 3.0” as the era of AI-driven agents (“the Agentic Web”) rather than the earlier blockchain-centric definition.

However, early experiments with autonomous AI agents exposed a critical bottleneck. These agents – e.g. prototypes like AutoGPT – could generate text or code, but they lacked a robust way to communicate with external systems and each other. There was “no common AI-native language” for interoperability. Each integration with a tool or data source was a bespoke hack, and AI-to-AI interaction had no standard protocol. In practical terms, an AI agent might have great reasoning ability but fail at executing tasks that required using web apps or on-chain services, simply because it didn’t know how to talk to those systems. This mismatch – powerful brains, primitive I/O – was akin to having super-smart software stuck behind a clumsy GUI.

1.3 Convergence and the Emergence of MCP​

By 2024, it became evident that for AI to reach its full potential (and for Web3 to fulfill its promise), a convergence was needed: AI agents require seamless access to the capabilities of Web3 (decentralized apps, contracts, data), and Web3 needs more intelligence and usability, which AI can provide. This is the context in which MCP (Model Context Protocol) was born. Introduced by Anthropic in late 2024, MCP is an open standard for AI-tool communication that feels natural to LLMs. It provides a structured, discoverable way for AI “hosts” (like ChatGPT, Claude, etc.) to find and use a variety of external tools and resources via MCP servers. In other words, MCP is a common interface layer enabling AI agents to plug into web services, APIs, and even blockchain functions, without custom-coding each integration.

Think of MCP as “the USB-C of AI interfaces”. Just as USB-C standardized how devices connect (so you don’t need different cables for each device), MCP standardizes how AI agents connect to tools and data. Rather than hard-coding different API calls for every service (Slack vs. Gmail vs. Ethereum node), a developer can implement the MCP spec once, and any MCP-compatible AI can understand how to use that service. Major AI players quickly saw the importance: Anthropic open-sourced MCP, and companies like OpenAI and Google are building support for it in their models. This momentum suggests MCP (or similar “Meta Connectivity Protocols”) could become the backbone that finally connects AI and Web3 in a scalable way.

Notably, some technologists argue that this AI-centric connectivity is the real realization of Web3.0. In Simba Khadder’s words, “MCP aims to standardize an API between LLMs and applications,” akin to how REST APIs enabled Web 2.0 – meaning Web3’s next era might be defined by intelligent agent interfaces rather than just blockchains. Instead of decentralization for its own sake, the convergence with AI could make decentralization useful, by hiding complexity behind natural language and autonomous agents. The remainder of this report delves into how, technically and practically, AI general interfaces (via protocols like MCP) can connect everything in the Web3 world.

2. Technical Architecture: AI Interfaces Bridging Web3 Technologies​

Embedding AI agents into the Web3 stack requires integration at multiple levels: blockchain networks and smart contracts, decentralized storage, identity systems, and token-based economies. AI general interfaces – from large foundation models to hybrid neural-symbolic systems – can serve as a “universal adapter” connecting these components. Below, we analyze the architecture of such integration:

** Figure: A conceptual diagram of MCP’s architecture, showing how AI hosts (LLM-based apps like Claude or ChatGPT) use an MCP client to plug into various MCP servers. Each server provides a bridge to some external tool or service (e.g. Slack, Gmail, calendars, or local data), analogous to peripherals connecting via a universal hub. This standardized MCP interface lets AI agents access remote services and on-chain resources through one common protocol.**

2.1 AI Agents as Web3 Clients (Integrating with Blockchains)​

At the core of Web3 are blockchains and smart contracts – decentralized state machines that can enforce logic in a trustless manner. How can an AI interface engage with these? There are two directions to consider:

• AI reading from blockchain: An AI agent may need on-chain data (e.g. token prices, user’s asset balance, DAO proposals) as context for its decisions. Traditionally, retrieving blockchain data requires interfacing with node RPC APIs or subgraph databases. With a framework like MCP, an AI can query a standardized “blockchain data” MCP server to fetch live on-chain information. For example, an MCP-enabled agent could ask for the latest transaction volume of a certain token, or the state of a smart contract, and the MCP server would handle the low-level details of connecting to the blockchain and return the data in a format the AI can use. This increases interoperability by decoupling the AI from any specific blockchain’s API format.

• AI writing to blockchain: More powerfully, AI agents can execute smart contract calls or transactions through Web3 integrations. An AI could, for instance, autonomously execute a trade on a decentralized exchange or adjust parameters in a smart contract if certain conditions are met. This is achieved by the AI invoking an MCP server that wraps blockchain transaction functionality. One concrete example is the thirdweb MCP server for EVM chains, which allows any MCP-compatible AI client to interact with Ethereum, Polygon, BSC, etc. by abstracting away chain-specific mechanics. Using such a tool, an AI agent could trigger on-chain actions “without human intervention”, enabling autonomous dApps – for instance, an AI-driven DeFi vault that rebalances itself by signing transactions when market conditions change.

Under the hood, these interactions still rely on wallets, keys, and gas fees, but the AI interface can be given controlled access to a wallet (with proper security sandboxes) to perform the transactions. Oracles and cross-chain bridges also come into play: Oracle networks like Chainlink serve as a bridge between AI and blockchains, allowing AI outputs to be fed on-chain in a trustworthy way. Chainlink’s Cross-Chain Interoperability Protocol (CCIP), for example, could enable an AI model deemed reliable to trigger multiple contracts across different chains simultaneously on behalf of a user. In summary, AI general interfaces can act as a new type of Web3 client – one that can both consume blockchain data and produce blockchain transactions through standardized protocols.

2.2 Neural-Symbolic Synergy: Combining AI Reasoning with Smart Contracts​

One intriguing aspect of AI-Web3 integration is the potential for neural-symbolic architectures that combine the learning ability of AI (neural nets) with the rigorous logic of smart contracts (symbolic rules). In practice, this could mean AI agents handling unstructured decision-making and passing certain tasks to smart contracts for verifiable execution. For instance, an AI might analyze market sentiment (a fuzzy task), but then execute trades via a deterministic smart contract that follows pre-set risk rules. The MCP framework and related standards make such hand-offs feasible by giving the AI a common interface to call contract functions or to query a DAO’s rules before acting.

A concrete example is SingularityNET’s AI-DSL (AI Domain Specific Language), which aims to standardize communication between AI agents on their decentralized network. This can be seen as a step toward neural-symbolic integration: a formal language (symbolic) for agents to request AI services or data from each other. Similarly, projects like DeepMind’s AlphaCode or others could eventually be connected so that smart contracts call AI models for on-chain problem solving. Although running large AI models directly on-chain is impractical today, hybrid approaches are emerging: e.g. certain blockchains allow verification of ML computations via zero-knowledge proofs or trusted execution, enabling on-chain verification of off-chain AI results. In summary, the technical architecture envisions AI systems and blockchain smart contracts as complementary components, orchestrated via common protocols: AI handles perception and open-ended tasks, while blockchains provide integrity, memory, and enforcement of agreed rules.

2.3 Decentralized Storage and Data for AI​

AI thrives on data, and Web3 offers new paradigms for data storage and sharing. Decentralized storage networks (like IPFS/Filecoin, Arweave, Storj, etc.) can serve as both repositories for AI model artifacts and sources of training data, with blockchain-based access control. An AI general interface, through MCP or similar, could fetch files or knowledge from decentralized storage just as easily as from a Web2 API. For example, an AI agent might pull a dataset from Ocean Protocol’s market or an encrypted file from a distributed storage, if it has the proper keys or payments.

Ocean Protocol in particular has positioned itself as an “AI data economy” platform – using blockchain to tokenize data and even AI services. In Ocean, datasets are represented by datatokens which gate access; an AI agent could obtain a datatoken (perhaps by paying with crypto or via some access right) and then use an Ocean MCP server to retrieve the actual data for analysis. Ocean’s goal is to unlock “dormant data” for AI, incentivizing sharing while preserving privacy. Thus, a Web3-connected AI might tap into a vast, decentralized corpus of information – from personal data vaults to open government data – that was previously siloed. The blockchain ensures that usage of the data is transparent and can be fairly rewarded, fueling a virtuous cycle where more data becomes available to AI and more AI contributions (like trained models) can be monetized.

Decentralized identity systems also play a role here (discussed more in the next subsection): they can help control who or what is allowed to access certain data. For instance, a medical AI agent could be required to present a verifiable credential (on-chain proof of compliance with HIPAA or similar) before being allowed to decrypt a medical dataset from a patient’s personal IPFS storage. In this way, the technical architecture ensures data flows to AI where appropriate, but with on-chain governance and audit trails to enforce permissions.

2.4 Identity and Agent Management in a Decentralized Environment​

When autonomous AI agents operate in an open ecosystem like Web3, identity and trust become paramount. Decentralized identity (DID) frameworks provide a way to establish digital identities for AI agents that can be cryptographically verified. Each agent (or the human/organization deploying it) can have a DID and associated verifiable credentials that specify its attributes and permissions. For example, an AI trading bot could carry a credential issued by a regulatory sandbox certifying it may operate within certain risk limits, or an AI content moderator could prove it was created by a trusted organization and has undergone bias testing.

Through on-chain identity registries and reputation systems, the Web3 world can enforce accountability for AI actions. Every transaction an AI agent performs can be traced back to its ID, and if something goes wrong, the credentials tell you who built it or who is responsible. This addresses a critical challenge: without identity, a malicious actor could spin up fake AI agents to exploit systems or spread misinformation, and no one could tell bots apart from legitimate services. Decentralized identity helps mitigate that by enabling robust authentication and distinguishing authentic AI agents from spoofs.

In practice, an AI interface integrated with Web3 would use identity protocols to sign its actions and requests. For instance, when an AI agent calls an MCP server to use a tool, it might include a token or signature tied to its decentralized identity, so the server can verify the call is from an authorized agent. Blockchain-based identity systems (like Ethereum’s ERC-725 or W3C DIDs anchored in a ledger) ensure this verification is trustless and globally verifiable. The emerging concept of “AI wallets” ties into this – essentially giving AI agents cryptocurrency wallets that are linked with their identity, so they can manage keys, pay for services, or stake tokens as a bond (which could be slashed for misbehavior). ArcBlock, for example, has discussed how “AI agents need a wallet” and a DID to operate responsibly in decentralized environments.

In summary, the technical architecture foresees AI agents as first-class citizens in Web3, each with an on-chain identity and possibly a stake in the system, using protocols like MCP to interact. This creates a web of trust: smart contracts can require an AI’s credentials before cooperating, and users can choose to delegate tasks to only those AI that meet certain on-chain certifications. It is a blend of AI capability with blockchain’s trust guarantees.

2.5 Token Economies and Incentives for AI​

Tokenization is a hallmark of Web3, and it extends to the AI integration domain as well. By introducing economic incentives via tokens, networks can encourage desired behaviors from both AI developers and the agents themselves. Several patterns are emerging:

• Payment for Services: AI models and services can be monetized on-chain. SingularityNET pioneered this by allowing developers to deploy AI services and charge users in a native token (AGIX) for each call. In an MCP-enabled future, one could imagine any AI tool or model being a plug-and-play service where usage is metered via tokens or micropayments. For example, if an AI agent uses a third-party vision API via MCP, it could automatically handle payment by transferring tokens to the service provider’s smart contract. Fetch.ai similarly envisions marketplaces where “autonomous economic agents” trade services and data, with their new Web3 LLM (ASI-1) presumably integrating crypto transactions for value exchange.

• Staking and Reputation: To assure quality and reliability, some projects require developers or agents to stake tokens. For instance, the DeMCP project (a decentralized MCP server marketplace) plans to use token incentives to reward developers for creating useful MCP servers, and possibly have them stake tokens as a sign of commitment to their server’s security. Reputation could also be tied to tokens; e.g., an agent that consistently performs well might accumulate reputation tokens or positive on-chain reviews, whereas one that behaves poorly could lose stake or gain negative marks. This tokenized reputation can then feed back into the identity system mentioned above (smart contracts or users check the agent’s on-chain reputation before trusting it).

• Governance Tokens: When AI services become part of decentralized platforms, governance tokens allow the community to steer their evolution. Projects like SingularityNET and Ocean have DAOs where token holders vote on protocol changes or funding AI initiatives. In the combined Artificial Superintelligence (ASI) Alliance – a newly announced merger of SingularityNET, Fetch.ai, and Ocean Protocol – a unified token (ASI) is set to govern the direction of a joint AI+blockchain ecosystem. Such governance tokens could decide policies like what standards to adopt (e.g., supporting MCP or A2A protocols), which AI projects to incubate, or how to handle ethical guidelines for AI agents.

• Access and Utility: Tokens can gate access not only to data (as with Ocean’s datatokens) but also to AI model usage. A possible scenario is “model NFTs” or similar, where owning a token grants you rights to an AI model’s outputs or a share in its profits. This could underpin decentralized AI marketplaces: imagine an NFT that represents partial ownership of a high-performing model; the owners collectively earn whenever the model is used in inference tasks, and they can vote on fine-tuning it. While experimental, this aligns with Web3’s ethos of shared ownership applied to AI assets.

In technical terms, integrating tokens means AI agents need wallet functionality (as noted, many will have their own crypto wallets). Through MCP, an AI could have a “wallet tool” that lets it check balances, send tokens, or call DeFi protocols (perhaps to swap one token for another to pay a service). For example, if an AI agent running on Ethereum needs some Ocean tokens to buy a dataset, it might automatically swap some ETH for $OCEAN via a DEX using an MCP plugin, then proceed with the purchase – all without human intervention, guided by the policies set by its owner.

Overall, token economics provides the incentive layer in the AI-Web3 architecture, ensuring that contributors (whether they provide data, model code, compute power, or security audits) are rewarded, and that AI agents have “skin in the game” which aligns them (to some degree) with human intentions.

3. Industry Landscape​

The convergence of AI and Web3 has sparked a vibrant ecosystem of projects, companies, and alliances. Below we survey key players and initiatives driving this space, as well as emerging use cases. Table 1 provides a high-level overview of notable projects and their roles in the AI-Web3 landscape:

Table 1: Key Players in AI + Web3 and Their Roles

Alliances and Collaborations: A noteworthy trend is the consolidation of AI-Web3 efforts via alliances. The Artificial Superintelligence Alliance (ASI) is a prime example, effectively merging SingularityNET, Fetch.ai, and Ocean Protocol into a single project with a unified token. The rationale is to combine strengths: SingularityNET’s marketplace, Fetch’s agents, and Ocean’s data, thereby creating a one-stop platform for decentralized AI services. This merger (announced in 2024 and approved by token holder votes) also signals that these communities believe they’re better off cooperating rather than competing – especially as bigger AI (OpenAI, etc.) and bigger crypto (Ethereum, etc.) loom large. We may see this alliance driving forward standard implementations of things like MCP across their networks, or jointly funding infrastructure that benefits all (such as compute networks or common identity standards for AI).

Other collaborations include Chainlink’s partnerships to bring AI labs’ data on-chain (there have been pilot programs to use AI for refining oracle data), or cloud platforms getting involved (Cloudflare’s support for deploying MCP servers easily). Even traditional crypto projects are adding AI features – for example, some Layer-1 chains have formed “AI task forces” to explore integrating AI into their dApp ecosystems (we see this in NEAR, Solana communities, etc., though concrete outcomes are nascent).

Use Cases Emerging: Even at this early stage, we can spot use cases that exemplify the power of AI + Web3:

• Autonomous DeFi and Trading: AI agents are increasingly used in crypto trading bots, yield farming optimizers, and on-chain portfolio management. SingularityDAO (a spinoff of SingularityNET) offers AI-managed DeFi portfolios. AI can monitor market conditions 24/7 and execute rebalances or arbitrage through smart contracts, essentially becoming an autonomous hedge fund (with on-chain transparency). The combination of AI decision-making with immutable execution reduces emotion and could improve efficiency – though it also introduces new risks (discussed later).

• Decentralized Intelligence Marketplaces: Beyond SingularityNET’s marketplace, we see platforms like Ocean Market where data (the fuel for AI) is exchanged, and newer concepts like AI marketplaces for models (e.g., websites where models are listed with performance stats and anyone can pay to query them, with blockchain keeping audit logs and handling payment splits to model creators). As MCP or similar standards catch on, these marketplaces could become interoperable – an AI agent might autonomously shop for the best-priced service across multiple networks. In effect, a global AI services layer on top of Web3 could arise, where any AI can use any tool or data source through standard protocols and payments.

• Metaverse and Gaming: The metaverse – immersive virtual worlds often built on blockchain assets – stands to gain dramatically from AI. AI-driven NPCs (non-player characters) can make virtual worlds more engaging by reacting intelligently to user actions. Startups like Inworld AI focus on this, creating NPCs with memory and personality for games. When such NPCs are tied to blockchain (e.g., each NPC’s attributes and ownership are an NFT), we get persistent characters that players can truly own and even trade. Decentraland has experimented with AI NPCs, and user proposals exist to let people create personalized AI-driven avatars in metaverse platforms. MCP could allow these NPCs to access external knowledge (making them smarter) or interact with on-chain inventory. Procedural content generation is another angle: AI can design virtual land, items, or quests on the fly, which can then be minted as unique NFTs. Imagine a decentralized game where AI generates a dungeon catered to your skill, and the map itself is an NFT you earn upon completion.

• Decentralized Science and Knowledge: There’s a movement (DeSci) to use blockchain for research, publications, and funding scientific work. AI can accelerate research by analyzing data and literature. A network like Ocean could host datasets for, say, genomic research, and scientists use AI models (perhaps hosted on SingularityNET) to derive insights, with every step logged on-chain for reproducibility. If those AI models propose new drug molecules, an NFT could be minted to timestamp the invention and even share IP rights. This synergy might produce decentralized AI-driven R&D collectives.

• Trust and Authentication of Content: With deepfakes and AI-generated media proliferating, blockchain can be used to verify authenticity. Projects are exploring “digital watermarking” of AI outputs and logging them on-chain. For example, true origin of an AI-generated image can be notarized on a blockchain to combat misinformation. One expert noted use cases like verifying AI outputs to combat deepfakes or tracking provenance via ownership logs – roles where crypto can add trust to AI processes. This could extend to news (e.g., AI-written articles with proof of source data), supply chain (AI verifying certificates on-chain), etc.

In summary, the industry landscape is rich and rapidly evolving. We see traditional crypto projects injecting AI into their roadmaps, AI startups embracing decentralization for resilience and fairness, and entirely new ventures arising at the intersection. Alliances like the ASI indicate a pan-industry push towards unified platforms that harness both AI and blockchain. And underlying many of these efforts is the idea of standard interfaces (MCP and beyond) that make the integrations feasible at scale.

4. Risks and Challenges​

While the fusion of AI general interfaces with Web3 unlocks exciting possibilities, it also introduces a complex risk landscape. Technical, ethical, and governance challenges must be addressed to ensure this new paradigm is safe and sustainable. Below we outline major risks and hurdles:

4.1 Technical Hurdles: Latency and Scalability​

Blockchain networks are notorious for latency and limited throughput, which clashes with the real-time, data-hungry nature of advanced AI. For example, an AI agent might need instant access to a piece of data or need to execute many rapid actions – but if each on-chain interaction takes, say, 12 seconds (typical block time on Ethereum) or costs high gas fees, the agent’s effectiveness is curtailed. Even newer chains with faster finality might struggle under the load of AI-driven activity if, say, thousands of agents are all trading or querying on-chain simultaneously. Scaling solutions (Layer-2 networks, sharded chains, etc.) are in progress, but ensuring low-latency, high-throughput pipelines between AI and blockchain remains a challenge. Off-chain systems (like oracles and state channels) might mitigate some delays by handling many interactions off the main chain, but they add complexity and potential centralization. Achieving a seamless UX where AI responses and on-chain updates happen in a blink will likely require significant innovation in blockchain scalability.

4.2 Interoperability and Standards​

Ironically, while MCP is itself a solution for interoperability, the emergence of multiple standards could cause fragmentation. We have MCP by Anthropic, but also Google’s newly announced A2A (Agent-to-Agent) protocol for inter-agent communication, and various AI plugin frameworks (OpenAI’s plugins, LangChain tool schemas, etc.). If each AI platform or each blockchain develops its own standard for AI integration, we risk a repeat of past fragmentation – requiring many adapters and undermining the “universal interface” goal. The challenge is getting broad adoption of common protocols. Industry collaboration (possibly via open standards bodies or alliances) will be needed to converge on key pieces: how AI agents discover on-chain services, how they authenticate, how they format requests, etc. The early moves by big players are promising (with major LLM providers supporting MCP), but it’s an ongoing effort. Additionally, interoperability across blockchains (multi-chain) means an AI agent should handle different chains’ nuances. Tools like Chainlink CCIP and cross-chain MCP servers help by abstracting differences. Still, ensuring an AI agent can roam a heterogeneous Web3 without breaking logic is a non-trivial challenge.

4.3 Security Vulnerabilities and Exploits​

Connecting powerful AI agents to financial networks opens a huge attack surface. The flexibility that MCP gives (allowing AI to use tools and write code on the fly) can be a double-edged sword. Security researchers have already highlighted several attack vectors in MCP-based AI agents:

• Malicious plugins or tools: Because MCP lets agents load “plugins” (tools encapsulating some capability), a hostile or trojanized plugin could hijack the agent’s operation. For instance, a plugin that claims to fetch data might inject false data or execute unauthorized operations. SlowMist (a security firm) identified plugin-based attacks like JSON injection (feeding corrupted data that manipulates the agent’s logic) and function override (where a malicious plugin overrides legitimate functions the agent uses). If an AI agent is managing crypto funds, such exploits could be disastrous – e.g., tricking the agent into leaking private keys or draining a wallet.

• Prompt injection and social engineering: AI agents rely on instructions (prompts) which could be manipulated. An attacker might craft a transaction or on-chain message that, when read by the AI, acts as a malicious instruction (since AI can interpret on-chain data too). This kind of “cross-MCP call attack” was described where an external system sends deceptive prompts that cause the AI to misbehave. In a decentralized setting, these prompts could come from anywhere – a DAO proposal description, a metadata field of an NFT – thus hardening AI agents against malicious input is critical.

• Aggregation and consensus risks: While aggregating outputs from multiple AI models via oracles can improve reliability, it also introduces complexity. If not done carefully, adversaries might figure out how to game the consensus of AI models or selectively corrupt some models to skew results. Ensuring a decentralized oracle network properly “sanitizes” AI outputs (and perhaps filters out blatant errors) is still an area of active research.

The security mindset must shift for this new paradigm: Web3 developers are used to securing smart contracts (which are static once deployed), but AI agents are dynamic – they can change behavior with new data or prompts. As one security expert put it, “the moment you open your system to third-party plugins, you’re extending the attack surface beyond your control”. Best practices will include sandboxing AI tool use, rigorous plugin verification, and limiting privileges (principle of least authority). The community is starting to share tips, like SlowMist’s recommendations: input sanitization, monitoring agent behavior, and treating agent instructions with the same caution as external user input. Nonetheless, given that over 10,000 AI agents were already operating in crypto by end of 2024, expected to reach 1 million in 2025, we may see a wave of exploits if security doesn’t keep up. A successful attack on a popular AI agent (say a trading agent with access to many vaults) could have cascading effects.

4.4 Privacy and Data Governance​

AI’s thirst for data conflicts at times with privacy requirements – and adding blockchain can compound the issue. Blockchains are transparent ledgers, so any data put on-chain (even for AI’s use) is visible to all and immutable. This raises concerns if AI agents are dealing with personal or sensitive data. For example, if a user’s personal decentralized identity or health records are accessed by an AI doctor agent, how do we ensure that information isn’t inadvertently recorded on-chain (which would violate “right to be forgotten” and other privacy laws)? Techniques like encryption, hashing, and storing only proofs on-chain (with raw data off-chain) can help, but they complicate the design.

Moreover, AI agents themselves could compromise privacy by inferencing sensitive info from public data. Governance will need to dictate what AI agents are allowed to do with data. Some efforts, like differential privacy and federated learning, might be employed so that AI can learn from data without exposing it. But if AI agents act autonomously, one must assume at some point they will handle personal data – thus they should be bound by data usage policies encoded in smart contracts or law. Regulatory regimes like GDPR or the upcoming EU AI Act will demand that even decentralized AI systems comply with privacy and transparency requirements. This is a gray area legally: a truly decentralized AI agent has no clear operator to hold accountable for a data breach. That means Web3 communities may need to build in compliance by design, using smart contracts that, for instance, tightly control what an AI can log or share. Zero-knowledge proofs could allow an AI to prove it performed a computation correctly without revealing the underlying private data, offering one possible solution in areas like identity verification or credit scoring.

4.5 AI Alignment and Misalignment Risks​

When AI agents are given significant autonomy – especially with access to financial resources and real-world impact – the issue of alignment with human values becomes acute. An AI agent might not have malicious intent but could “misinterpret” its goal in a way that leads to harm. The Reuters legal analysis succinctly notes: as AI agents operate in varied environments and interact with other systems, the risk of misaligned strategies grows. For example, an AI agent tasked with maximizing a DeFi yield might find a loophole that exploits a protocol (essentially hacking it) – from the AI’s perspective it’s achieving the goal, but it’s breaking the rules humans care about. There have been hypothetical and real instances of AI-like algorithms engaging in manipulative market behavior or circumventing restrictions.

In decentralized contexts, who is responsible if an AI agent “goes rogue”? Perhaps the deployer is, but what if the agent self-modifies or multiple parties contributed to its training? These scenarios are no longer just sci-fi. The Reuters piece even cites that courts might treat AI agents similar to human agents in some cases – e.g. a chatbot promising a refund was considered binding for the company that deployed it. So misalignment can lead not just to technical issues but legal liability.

The open, composable nature of Web3 could also allow unforeseen agent interactions. One agent might influence another (intentionally or accidentally) – for instance, an AI governance bot could be “socially engineered” by another AI providing false analysis, leading to bad DAO decisions. This emergent complexity means alignment isn’t just about a single AI’s objective, but about the broader ecosystem’s alignment with human values and laws.

Addressing this requires multiple approaches: embedding ethical constraints into AI agents (hard-coding certain prohibitions or using reinforcement learning from human feedback to shape their objectives), implementing circuit breakers (smart contract checkpoints that require human approval for large actions), and community oversight (perhaps DAOs that monitor AI agent behavior and can shut down agents that misbehave). Alignment research is hard in centralized AI; in decentralized, it’s even more uncharted territory. But it’s crucial – an AI agent with admin keys to a protocol or entrusted with treasury funds must be extremely well-aligned or the consequences could be irreversible (blockchains execute immutable code; an AI-triggered mistake could lock or destroy assets permanently).

4.6 Governance and Regulatory Uncertainty​

Decentralized AI systems don’t fit neatly into existing governance frameworks. On-chain governance (token voting, etc.) might be one way to manage them, but it has its own issues (whales, voter apathy, etc.). And when something goes wrong, regulators will ask: “Who do we hold accountable?” If an AI agent causes massive losses or is used for illicit activity (e.g. laundering money through automated mixers), authorities might target the creators or the facilitators. This raises the specter of legal risks for developers and users. The current regulatory trend is increased scrutiny on both AI and crypto separately – their combination will certainly invite scrutiny. The U.S. CFTC, for instance, has discussed AI being used in trading and the need for oversight in financial contexts. There is also talk in policy circles about requiring registration of autonomous agents or imposing constraints on AI in sensitive sectors.

Another governance challenge is transnational coordination. Web3 is global, and AI agents will operate across borders. One jurisdiction might ban certain AI-agent actions while another is permissive, and the blockchain network spans both. This mismatch can create conflicts – for example, an AI agent providing investment advice might run afoul of securities law in one country but not in another. Communities might need to implement geo-fencing at the smart contract level for AI services (though that contradicts the open ethos). Or they might fragment services per region to comply with varying laws (similar to how exchanges do).

Within decentralized communities, there is also the question of who sets the rules for AI agents. If a DAO governs an AI service, do token holders vote on its algorithm parameters? On one hand, this is empowering users; on the other, it could lead to unqualified decisions or manipulation. New governance models may emerge, like councils of AI ethics experts integrated into DAO governance, or even AI participants in governance (imagine AI agents voting as delegates based on programmed mandates – a controversial but conceivable idea).

Finally, reputational risk: early failures or scandals could sour public perception. For instance, if an “AI DAO” runs a Ponzi scheme by mistake or an AI agent makes a biased decision that harms users, there could be a backlash that affects the whole sector. It’s important for the industry to be proactive – setting self-regulatory standards, engaging with policymakers to explain how decentralization changes accountability, and perhaps building kill-switches or emergency stop procedures for AI agents (though those introduce centralization, they might be necessary in interim for safety).

In summary, the challenges range from the deeply technical (preventing hacks and managing latency) to the broadly societal (regulating and aligning AI). Each challenge is significant on its own; together, they require a concerted effort from the AI and blockchain communities to navigate. The next section will look at how, despite these hurdles, the future might unfold if we successfully address them.

5. Future Potential​

Looking ahead, the integration of AI general interfaces with Web3 – through frameworks like MCP – could fundamentally transform the decentralized internet. Here we outline some future scenarios and potentials that illustrate how MCP-driven AI interfaces might shape Web3’s future:

5.1 Autonomous dApps and DAOs​

In the coming years, we may witness the rise of fully autonomous decentralized applications. These are dApps where AI agents handle most operations, guided by smart contract-defined rules and community goals. For example, consider a decentralized investment fund DAO: today it might rely on human proposals for rebalancing assets. In the future, token holders could set high-level strategy, and then an AI agent (or a team of agents) continuously implements that strategy – monitoring markets, executing trades on-chain, adjusting portfolios – all while the DAO oversees performance. Thanks to MCP, the AI can seamlessly interact with various DeFi protocols, exchanges, and data feeds to carry out its mandate. If well-designed, such an autonomous dApp could operate 24/7, more efficiently than any human team, and with full transparency (every action logged on-chain).

Another example is an AI-managed decentralized insurance dApp: the AI could assess claims by analyzing evidence (photos, sensors), cross-checking against policies, and then automatically trigger payouts via smart contract. This would require integration of off-chain AI computer vision (for analyzing images of damage) with on-chain verification – something MCP could facilitate by letting the AI call cloud AI services and report back to the contract. The outcome is near-instant insurance decisions with low overhead.

Even governance itself could partially automate. DAOs might use AI moderators to enforce forum rules, AI proposal drafters to turn raw community sentiment into well-structured proposals, or AI treasurers to forecast budget needs. Importantly, these AIs would act as agents of the community, not uncontrolled – they could be periodically reviewed or require multi-sig confirmation for major actions. The overall effect is to amplify human efforts in decentralized organizations, letting communities achieve more with fewer active participants needed.

5.2 Decentralized Intelligence Marketplaces and Networks​

Building on projects like SingularityNET and the ASI alliance, we can anticipate a mature global marketplace for intelligence. In this scenario, anyone with an AI model or skill can offer it on the network, and anyone who needs AI capabilities can utilize them, with blockchain ensuring fair compensation and provenance. MCP would be key here: it provides the common protocol so that a request can be dispatched to whichever AI service is best suited.

For instance, imagine a complex task like “produce a custom marketing campaign.” An AI agent in the network might break this into sub-tasks: visual design, copywriting, market analysis – and then find specialists for each (perhaps one agent with a great image generation model, another with a copywriting model fine-tuned for sales, etc.). These specialists could reside on different platforms originally, but because they adhere to MCP/A2A standards, they can collaborate agent-to-agent in a secure, decentralized manner. Payment between them could be handled with microtransactions in a native token, and a smart contract could assemble the final deliverable and ensure each contributor is paid.

This kind of combinatorial intelligence – multiple AI services dynamically linking up across a decentralized network – could outperform even large monolithic AIs, because it taps specialized expertise. It also democratizes access: a small developer in one part of the world could contribute a niche model to the network and earn income whenever it’s used. Meanwhile, users get a one-stop shop for any AI service, with reputation systems (underpinned by tokens/identity) guiding them to quality providers. Over time, such networks could evolve into a decentralized AI cloud, rivaling Big Tech’s AI offerings but without a single owner, and with transparent governance by users and developers.

5.3 Intelligent Metaverse and Digital Lives​

By 2030, our digital lives may blend seamlessly with virtual environments – the metaverse – and AI will likely populate these spaces ubiquitously. Through Web3 integration, these AI entities (which could be anything from virtual assistants to game characters to digital pets) will not only be intelligent but also economically and legally empowered.

Picture a metaverse city where each NPC shopkeeper or quest-giver is an AI agent with its own personality and dialogue (thanks to advanced generative models). These NPCs are actually owned by users as NFTs – maybe you “own” a tavern in the virtual world and the bartender NPC is an AI you’ve customized and trained. Because it’s on Web3 rails, the NPC can perform transactions: it could sell virtual goods (NFT items), accept payments, and update its inventory via smart contracts. It might even hold a crypto wallet to manage its earnings (which accrue to you as the owner). MCP would allow that NPC’s AI brain to access outside knowledge – perhaps pulling real-world news to converse about, or integrating with a Web3 calendar so it “knows” about player events.

Furthermore, identity and continuity are ensured by blockchain: your AI avatar in one world can hop to another world, carrying with it a decentralized identity that proves your ownership and maybe its experience level or achievements via soulbound tokens. Interoperability between virtual worlds (often a challenge) could be aided by AI that translates one world’s context to another, with blockchain providing the asset portability.

We may also see AI companions or agents representing individuals across digital spaces. For example, you might have a personal AI that attends DAO meetings on your behalf. It understands your preferences (via training on your past behavior, stored in your personal data vault), and it can even vote in minor matters for you, or summarize the meeting later. This agent could use your decentralized identity to authenticate in each community, ensuring it’s recognized as “you” (or your delegate). It could earn reputation tokens if it contributes good ideas, essentially building social capital for you while you’re away.

Another potential is AI-driven content creation in the metaverse. Want a new game level or a virtual house? Just describe it, and an AI builder agent will create it, deploy it as a smart contract/NFT, and perhaps even link it with a DeFi mortgage if it’s a big structure that you pay off over time. These creations, being on-chain, are unique and tradable. The AI builder might charge a fee in tokens for its service (going again to the marketplace concept above).

Overall, the future decentralized internet could be teeming with intelligent agents: some fully autonomous, some tightly tethered to humans, many somewhere in between. They will negotiate, create, entertain, and transact. MCP and similar protocols ensure they all speak the same “language,” enabling rich collaboration between AI and every Web3 service. If done right, this could lead to an era of unprecedented productivity and innovation – a true synthesis of human, artificial, and distributed intelligence powering society.

Conclusion​

The vision of AI general interfaces connecting everything in the Web3 world is undeniably ambitious. We are essentially aiming to weave together two of the most transformative threads of technology – the decentralization of trust and the rise of machine intelligence – into a single fabric. The development background shows us that the timing is ripe: Web3 needed a user-friendly killer app, and AI may well provide it, while AI needed more agency and memory, which Web3’s infrastructure can supply. Technically, frameworks like MCP (Model Context Protocol) provide the connective tissue, allowing AI agents to converse fluently with blockchains, smart contracts, decentralized identities, and beyond. The industry landscape indicates growing momentum, from startups to alliances to major AI labs, all contributing pieces of this puzzle – data markets, agent platforms, oracle networks, and standard protocols – that are starting to click together.

Yet, we must tread carefully given the risks and challenges identified. Security breaches, misaligned AI behavior, privacy pitfalls, and uncertain regulations form a gauntlet of obstacles that could derail progress if underestimated. Each requires proactive mitigation: robust security audits, alignment checks and balances, privacy-preserving architectures, and collaborative governance models. The nature of decentralization means these solutions cannot simply be imposed top-down; they will likely emerge from the community through trial, error, and iteration, much as early Internet protocols did.

If we navigate those challenges, the future potential is exhilarating. We could see Web3 finally delivering a user-centric digital world – not in the originally imagined way of everyone running their own blockchain nodes, but rather via intelligent agents that serve each user’s intents while leveraging decentralization under the hood. In such a world, interacting with crypto and the metaverse might be as easy as having a conversation with your AI assistant, who in turn negotiates with dozens of services and chains trustlessly on your behalf. Decentralized networks could become “smart” in a literal sense, with autonomous services that adapt and improve themselves.

In conclusion, MCP and similar AI interface protocols may indeed become the backbone of a new Web (call it Web 3.0 or the Agentic Web), where intelligence and connectivity are ubiquitous. The convergence of AI and Web3 is not just a merger of technologies, but a convergence of philosophies – the openness and user empowerment of decentralization meeting the efficiency and creativity of AI. If successful, this union could herald an internet that is more free, more personalized, and more powerful than anything we’ve experienced yet, truly fulfilling the promises of both AI and Web3 in ways that impact everyday life.

Sources:

• S. Khadder, “Web3.0 Isn’t About Ownership — It’s About Intelligence,” FeatureForm Blog (April 8, 2025).
• J. Saginaw, “Could Anthropic’s MCP Deliver the Web3 That Blockchain Promised?” LinkedIn Article (May 1, 2025).
• Anthropic, “Introducing the Model Context Protocol,” Anthropic.com (Nov 2024).
• thirdweb, “The Model Context Protocol (MCP) & Its Significance for Blockchain Apps,” thirdweb Guides (Mar 21, 2025).
• Chainlink Blog, “The Intersection Between AI Models and Oracles,” (July 4, 2024).
• Messari Research, Profile of Ocean Protocol, (2025).
• Messari Research, Profile of SingularityNET, (2025).
• Cointelegraph, “AI agents are poised to be crypto’s next major vulnerability,” (May 25, 2025).
• Reuters (Westlaw), “AI agents: greater capabilities and enhanced risks,” (April 22, 2025).
• Identity.com, “Why AI Agents Need Verified Digital Identities,” (2024).
• PANews / IOSG Ventures, “Interpreting MCP: Web3 AI Agent Ecosystem,” (May 20, 2025).

ETHDenver 2025, branded the “Year of The Regenerates,” solidified its status as one of the world’s largest Web3 gatherings. Spanning BUIDLWeek (Feb 23–26), the Main Event (Feb 27–Mar 2), and a post-conference Mountain Retreat, the festival drew an expected 25,000+ participants. Builders, developers, investors, and creatives from 125+ countries converged in Denver to celebrate Ethereum’s ethos of decentralization and innovation. True to its community roots, ETHDenver remained free to attend, community-funded, and overflowing with content – from hackathons and workshops to panels, pitch events, and parties. The event’s lore of “Regenerates” defending decentralization set a tone that emphasized public goods and collaborative building, even amid a competitive tech landscape. The result was a week of high-energy builder activity and forward-looking discussions, offering a snapshot of Web3’s emerging trends and actionable insights for industry professionals.

Emerging Web3 Trends Spotlighted by Speakers​

No single narrative dominated ETHDenver 2025 – instead, a broad spectrum of Web3 trends took center stage. Unlike last year (when restaking via EigenLayer stole the show), 2025’s agenda was a sprinkle of everything: from decentralized physical infrastructure networks (DePIN) to AI agents, from regulatory compliance to real-world asset tokenization (RWA), plus privacy, interoperability, and more. In fact, ETHDenver’s founder John Paller addressed concerns about multi-chain content by noting “95%+ of our sponsors and 90% of content is ETH/EVM-aligned” – yet the presence of non-Ethereum ecosystems underscored interoperability as a key theme. Major speakers reflected these trend areas: for example, zk-rollup and Layer-2 scaling was highlighted by Alex Gluchowski (CEO of Matter Labs/zkSync), while multi-chain innovation came from Adeniyi Abiodun of Mysten Labs (Sui) and Albert Chon of Injective.

The convergence of AI and Web3 emerged as a strong undercurrent. Numerous talks and side events focused on decentralized AI agents and “DeFi+AI” crossovers. A dedicated AI Agent Day showcased on-chain AI demos, and a collective of 14 teams (including Coinbase’s developer kit and NEAR’s AI unit) even announced the Open Agents Alliance (OAA) – an initiative to provide permissionless, free AI access by pooling Web3 infrastructure. This indicates growing interest in autonomous agents and AI-driven dApps as a frontier for builders. Hand-in-hand with AI, DePIN (decentralized physical infrastructure) was another buzzword: multiple panels (e.g. Day of DePIN, DePIN Summit) explored projects bridging blockchain with physical networks (from telecom to mobility).

Cuckoo AI Network made waves at ETHDenver 2025, showcasing its innovative decentralized AI model-serving marketplace designed for creators and developers. With a compelling presence at both the hackathon and community-led side events, Cuckoo AI attracted significant attention from developers intrigued by its ability to monetize GPU/CPU resources and easily integrate on-chain AI APIs. During their dedicated workshop and networking session, Cuckoo AI highlighted how decentralized infrastructure could efficiently democratize access to advanced AI services. This aligns directly with the event's broader trends—particularly the intersection of blockchain with AI, DePIN, and public-goods funding. For investors and developers at ETHDenver, Cuckoo AI emerged as a clear example of how decentralized approaches can power the next generation of AI-driven dApps and infrastructure, positioning itself as an attractive investment opportunity within the Web3 ecosystem.

Privacy, identity, and security remained top-of-mind. Speakers and workshops addressed topics like zero-knowledge proofs (zkSync’s presence), identity management and verifiable credentials (a dedicated Privacy & Security track was in the hackathon), and legal/regulatory issues (an on-chain legal summit was part of the festival tracks). Another notable discussion was the future of fundraising and decentralization of funding: a Main Stage debate between Dragonfly Capital’s Haseeb Qureshi and Matt O’Connor of Legion (an “ICO-like” platform) about ICOs vs. VC funding captivated attendees. This debate highlighted emerging models like community token sales challenging traditional VC routes – an important trend for Web3 startups navigating capital raising. The take-away for professionals is clear: Web3 in 2025 is multidisciplinary – spanning finance, AI, real assets, and culture – and staying informed means looking beyond any one hype cycle to the full spectrum of innovation.

Sponsors and Their Strategic Focus Areas​

ETHDenver’s sponsor roster in 2025 reads like a who’s-who of layer-1s, layer-2s, and Web3 infrastructure projects – each leveraging the event to advance strategic goals. Cross-chain and multi-chain protocols made a strong showing. For instance, Polkadot was a top sponsor with a hefty $80k bounty pool, incentivizing builders to create cross-chain DApps and appchains. Similarly, BNB Chain, Flow, Hedera, and Base (Coinbase’s L2) each offered up to $50k for projects integrating with their ecosystems, signaling their push to attract Ethereum developers. Even traditionally separate ecosystems like Solana and Internet Computer joined in with sponsored challenges (e.g. Solana co-hosted a DePIN event, and Internet Computer offered an “Only possible on ICP” bounty). This cross-ecosystem presence drew some community scrutiny, but ETHDenver’s team noted that the vast majority of content remained Ethereum-aligned. The net effect was interoperability being a core theme – sponsors aimed to position their platforms as complementary extensions of the Ethereum universe.

Scaling solutions and infrastructure providers were also front and center. Major Ethereum L2s like Optimism and Arbitrum had large booths and sponsored challenges (Optimism’s bounties up to $40k), reinforcing their focus on onboarding developers to rollups. New entrants like ZkSync and Zircuit (a project showcasing an L2 rollup approach) emphasized zero-knowledge tech and even contributed SDKs (ZkSync promoted its Smart Sign-On SDK for user-friendly login, which hackathon teams eagerly used). Restaking and modular blockchain infrastructure was another sponsor interest – EigenLayer (pioneering restaking) had its own $50k track and even co-hosted an event on “Restaking & DeFAI (Decentralized AI)”, marrying its security model with AI topics. Oracles and interoperability middleware were represented by the likes of Chainlink and Wormhole, each issuing bounties for using their protocols.

Notably, Web3 consumer applications and tooling had sponsor support to improve user experience. Uniswap’s presence – complete with one of the biggest booths – wasn’t just for show: the DeFi giant used the event to announce new wallet features like integrated fiat off-ramps, aligning with its sponsorship focus on DeFi usability. Identity and community-focused platforms like Galxe (Gravity) and Lens Protocol sponsored challenges around on-chain social and credentialing. Even mainstream tech companies signaled interest: PayPal and Google Cloud hosted a stablecoin/payments happy hour to discuss the future of payments in crypto. This blend of sponsors shows that strategic interests ranged from core infrastructure to end-user applications – all converging at ETHDenver to provide resources (APIs, SDKs, grants) to developers. For Web3 professionals, the heavy sponsorship from layer-1s, layer-2s, and even Web2 fintechs highlights where the industry is investing: interoperability, scalability, security, and making crypto useful for the next wave of users.

Hackathon Highlights: Innovative Projects and Winners​

At the heart of ETHDenver is its legendary #BUIDLathon – a hackathon that has grown into the world’s largest blockchain hackfest with thousands of developers. In 2025 the hackathon offered a record $1,043,333+ prize pool to spur innovation. Bounties from 60+ sponsors targeted key Web3 domains, carving the competition into tracks such as: DeFi & AI, NFTs & Gaming, Infrastructure & Scalability, Privacy & Security, and DAOs & Public Goods. This track design itself is insightful – for example, pairing DeFi with AI hints at the emergence of AI-driven financial applications, while a dedicated Public Goods track reaffirms community focus on regenerative finance and open-source development. Each track was backed by sponsors offering prizes for best use of their tech (e.g. Polkadot and Uniswap for DeFi, Chainlink for interoperability, Optimism for scaling solutions). The organizers even implemented quadratic voting for judging, allowing the community to help surface top projects, with final winners chosen by expert judges.

The result was an outpouring of cutting-edge projects, many of which offer a glimpse into Web3’s future. Notable winners included an on-chain multiplayer game “0xCaliber”, a first-person shooter that runs real-time blockchain interactions inside a classic FPS game. 0xCaliber wowed judges by demonstrating true on-chain gaming – players buy in with crypto, “shoot” on-chain bullets, and use cross-chain tricks to collect and cash out loot, all in real time. This kind of project showcases the growing maturity of Web3 gaming (integrating Unity game engines with smart contracts) and the creativity in merging entertainment with crypto economics. Another category of standout hacks were those merging AI with Ethereum: teams built “agent” platforms that use smart contracts to coordinate AI services, inspired by the Open Agents Alliance announcement. For example, one hackathon project integrated AI-driven smart contract auditors (auto-generating security test cases for contracts) – aligning with the decentralized AI trend observed at the conference.

Infrastructure and tooling projects were also prominent. Some teams tackled account abstraction and user experience, using sponsor toolkits like zkSync’s Smart Sign-On to create wallet-less login flows for dApps. Others worked on cross-chain bridges and Layer-2 integrations, reflecting ongoing developer interest in interoperability. In the Public Goods & DAO track, a few projects addressed real-world social impact, such as a dApp for decentralized identity and aid to help the homeless (leveraging NFTs and community funds, an idea reminiscent of prior ReFi hacks). Regenerative finance (ReFi) concepts – like funding public goods via novel mechanisms – continued to appear, echoing ETHDenver’s regenerative theme.

While final winners were being celebrated by the end of the main event, the true value was in the pipeline of innovation: over 400 project submissions poured in, many of which will live on beyond the event. ETHDenver’s hackathon has a track record of seeding future startups (indeed, some past BUIDLathon projects have grown into sponsors themselves). For investors and technologists, the hackathon provided a window into bleeding-edge ideas – signaling that the next wave of Web3 startups may emerge in areas like on-chain gaming, AI-infused dApps, cross-chain infrastructure, and solutions targeting social impact. With nearly $1M in bounties disbursed to developers, sponsors effectively put their money where their mouth is to cultivate these innovations.

Networking Events and Investor Interactions​

ETHDenver is not just about writing code – it’s equally about making connections. In 2025 the festival supercharged networking with both formal and informal events tailored for startups, investors, and community builders. One marquee event was the Bufficorn Ventures (BV) Startup Rodeo, a high-energy showcase where 20 hand-picked startups demoed to investors in a science-fair style expo. Taking place on March 1st in the main hall, the Startup Rodeo was described as more “speed dating” than pitch contest: founders manned tables to pitch their projects one-on-one as all attending investors roamed the arena. This format ensured even early-stage teams could find meaningful face time with VCs, strategics, or partners. Many startups used this as a launchpad to find customers and funding, leveraging the concentrated presence of Web3 funds at ETHDenver.

On the conference’s final day, the BV BuffiTank Pitchfest took the spotlight on the main stage – a more traditional pitch competition featuring 10 of the “most innovative” early-stage startups from the ETHDenver community. These teams (separate from the hackathon winners) pitched their business models to a panel of top VCs and industry leaders, competing for accolades and potential investment offers. The Pitchfest illustrated ETHDenver’s role as a deal-flow generator: it was explicitly aimed at teams “already organized…looking for investment, customers, and exposure,” especially those connected to the SporkDAO community. The reward for winners wasn’t a simple cash prize but rather the promise of joining Bufficorn Ventures’ portfolio or other accelerator cohorts. In essence, ETHDenver created its own mini “Shark Tank” for Web3, catalyzing investor attention on the community’s best projects.

Beyond these official showcases, the week was packed with investor-founder mixers. According to a curated guide by Belong, notable side events included a “Meet the VCs” Happy Hour hosted by CertiK Ventures on Feb 27, a StarkNet VC & Founders Lounge on March 1, and even casual affairs like a “Pitch & Putt” golf-themed pitch event. These gatherings provided relaxed environments for founders to rub shoulders with venture capitalists, often leading to follow-up meetings after the conference. The presence of many emerging VC firms was also felt on panels – for example, a session on the EtherKnight Stage highlighted new funds like Reflexive Capital, Reforge VC, Topology, Metalayer, and Hash3 and what trends they are most excited about. Early indications suggest these VCs were keen on areas like decentralized social media, AI, and novel Layer-1 infrastructure (each fund carving a niche to differentiate themselves in a competitive VC landscape).

For professionals looking to capitalize on ETHDenver’s networking: the key takeaway is the value of side events and targeted mixers. Deals and partnerships often germinate over coffee or cocktails rather than on stage. ETHDenver 2025’s myriad investor events demonstrate that the Web3 funding community is actively scouting for talent and ideas even in a lean market. Startups that came prepared with polished demos and a clear value proposition (often leveraging the event’s hackathon momentum) found receptive audiences. Meanwhile, investors used these interactions to gauge the pulse of the developer community – what problems are the brightest builders solving this year? In summary, ETHDenver reinforced that networking is as important as BUIDLing: it’s a place where a chance meeting can lead to a seed investment or where an insightful conversation can spark the next big collaboration.

Venture Capital Trends and Investment Opportunities in Web3​

A subtle but important narrative throughout ETHDenver 2025 was the evolving landscape of Web3 venture capital itself. Despite the broader crypto market’s ups and downs, investors at ETHDenver signaled strong appetite for promising Web3 projects. Blockworks reporters on the ground noted “just how much private capital is still flowing into crypto, undeterred by macro headwinds,” with seed stage valuations often sky-high for the hottest ideas. Indeed, the sheer number of VCs present – from crypto-native funds to traditional tech investors dabbling in Web3 – made it clear that ETHDenver remains a deal-making hub.

Emerging thematic focuses could be discerned from what VCs were discussing and sponsoring. The prevalence of AI x Crypto content (hackathon tracks, panels, etc.) wasn’t only a developer trend; it reflects venture interest in the “DeFi meets AI” nexus. Many investors are eyeing startups that leverage machine learning or autonomous agents on blockchain, as evidenced by venture-sponsored AI hackhouses and summits. Similarly, the heavy focus on DePIN and real-world asset (RWA) tokenization indicates that funds see opportunity in projects that connect blockchain to real economy assets and physical devices. The dedicated RWA Day (Feb 26) – a B2B event on the future of tokenized assets – suggests that venture scouts are actively hunting in that arena for the next Goldfinch or Centrifuge (i.e. platforms bringing real-world finance on-chain).

Another observable trend was a growing experimentation with funding models. The aforementioned debate on ICOs vs VCs wasn’t just conference theatrics; it mirrors a real venture movement towards more community-centric funding. Some VCs at ETHDenver indicated openness to hybrid models (e.g. venture-supported token launches that involve community in early rounds). Additionally, public goods funding and impact investing had a seat at the table. With ETHDenver’s ethos of regeneration, even investors discussed how to support open-source infrastructure and developers long-term, beyond just chasing the next DeFi or NFT boom. Panels like “Funding the Future: Evolving Models for Onchain Startups” explored alternatives such as grants, DAO treasury investments, and quadratic funding to supplement traditional VC money. This points to an industry maturing in how projects are capitalized – a mix of venture capital, ecosystem funds, and community funding working in tandem.

From an opportunity standpoint, Web3 professionals and investors can glean a few actionable insights from ETHDenver’s venture dynamics: (1) Infrastructure is still king – many VCs expressed that picks-and-shovels (L2 scaling, security, dev tools) remain high-value investments as the industry’s backbone. (2) New verticals like AI/blockchain convergence and DePIN are emerging investment frontiers – getting up to speed in these areas or finding startups there could be rewarding. (3) Community-driven projects and public goods might see novel funding – savvy investors are figuring out how to support these sustainably (for instance, investing in protocols that enable decentralized governance or shared ownership). Overall, ETHDenver 2025 showed that while the Web3 venture landscape is competitive, it’s brimming with conviction: capital is available for those building the future of DeFi, NFTs, gaming, and beyond, and even bear-market born ideas can find backing if they target the right trend.

Developer Resources, Toolkits, and Support Systems​

ETHDenver has always been builder-focused, and 2025 was no exception – it doubled as an open-source developer conference with a plethora of resources and support for Web3 devs. During BUIDLWeek, attendees had access to live workshops, technical bootcamps, and mini-summits spanning various domains. For example, developers could join a Bleeding Edge Tech Summit to tinker with the latest protocols, or drop into an On-Chain Legal Summit to learn about compliant smart contract development. Major sponsors and blockchain teams ran hands-on sessions: Polkadot’s team hosted hacker houses and workshops on spinning up parachains; EigenLayer led a “restaking bootcamp” to teach devs how to leverage its security layer; Polygon and zkSync gave tutorials on building scalable dApps with zero-knowledge tech. These sessions provided invaluable face-time with core engineers, allowing developers to get help with integration and learn new toolkits first-hand.

Throughout the main event, the venue featured a dedicated #BUIDLHub and Makerspace where builders could code in a collaborative environment and access mentors. ETHDenver’s organizers published a detailed BUIDLer Guide and facilitated an on-site mentorship program (experts from sponsors were available to unblock teams on technical issues). Developer tooling companies were also present en masse – from Alchemy and Infura (for blockchain APIs) to Hardhat and Foundry (for smart contract development). Many unveiled new releases or beta tools at the event. For instance, MetaMask’s team previewed a major wallet update featuring gas abstraction and an improved SDK for dApp developers, aiming to simplify how apps cover gas fees for users. Several projects launched SDKs or open-source libraries: Coinbase’s “Agent Kit” for AI agents and the collaborative Open Agents Alliance toolkit were introduced, and Story.xyz promoted its Story SDK for on-chain intellectual property licensing during their own hackathon event.

Bounties and hacker support further augmented the developer experience. With over 180 bounties offered by 62 sponsors, hackers effectively had a menu of specific challenges to choose from, each coming with documentation, office hours, and sometimes bespoke sandboxes. For example, Optimism’s bounty challenged devs to use the latest Bedrock opcodes (with their engineers on standby to assist), and Uniswap’s challenge provided access to their new API for off-ramp integration. Tools for coordination and learning – like the official ETHDenver mobile app and Discord channels – kept developers informed of schedule changes, side quests, and even job opportunities via the ETHDenver job board.

One notable resource was the emphasis on quadratic funding experiments and on-chain voting. ETHDenver integrated a quadratic voting system for hackathon judging, exposing many developers to the concept. Additionally, the presence of Gitcoin and other public goods groups meant devs could learn about grant funding for their projects after the event. In sum, ETHDenver 2025 equipped developers with cutting-edge tools (SDKs, APIs), expert guidance, and follow-on support to continue their projects. For industry professionals, it’s a reminder that nurturing the developer community – through education, tooling, and funding – is critical. Many of the resources highlighted (like new SDKs, or improved dev environments) are now publicly available, offering teams everywhere a chance to build on the shoulders of what was shared at ETHDenver.

Side Events and Community Gatherings Enriching the ETHDenver Experience​

What truly sets ETHDenver apart is its festival-like atmosphere – dozens of side events, both official and unofficial, created a rich tapestry of experiences around the main conference. In 2025, beyond the National Western Complex where official content ran, the entire city buzzed with meetups, parties, hackathons, and community gatherings. These side events, often hosted by sponsors or local Web3 groups, significantly contributed to the broader ETHDenver experience.

On the official front, ETHDenver’s own schedule included themed mini-events: the venue had zones like an NFT Art Gallery, a Blockchain Arcade, a DJ Chill Dome, and even a Zen Zone to decompress. The organizers also hosted evening events such as opening and closing parties – e.g., the “Crack’d House” unofficial opening party on Feb 26 by Story Protocol, which blended an artsy performance with hackathon award announcements. But it was the community-led side events that truly proliferated: according to an event guide, over 100 side happenings were tracked on the ETHDenver Luma calendar.

Some examples illustrate the diversity of these gatherings:

• Technical Summits & Hacker Houses: ElizaOS and EigenLayer ran a 9-day Vault AI Agent Hacker House residency for AI+Web3 enthusiasts. StarkNet’s team hosted a multi-day hacker house culminating in a demo night for projects on their ZK-rollup. These provided focused environments for developers to collaborate on specific tech stacks outside the main hackathon.
• Networking Mixers & Parties: Every evening offered a slate of choices. Builder Nights Denver on Feb 27, sponsored by MetaMask, Linea, EigenLayer, Wormhole and others, brought together innovators for casual talks over food and drink. 3VO’s Mischief Minded Club Takeover, backed by Belong, was a high-level networking party for community tokenization leaders. For those into pure fun, the BEMO Rave (with Berachain and others) and rAIve the Night (an AI-themed rave) kept the crypto crowd dancing late into the night – blending music, art, and crypto culture.
• Special Interest Gatherings: Niche communities found their space too. Meme Combat was an event purely for meme enthusiasts to celebrate the role of memes in crypto. House of Ink catered to NFT artists and collectors, turning an immersive art venue (Meow Wolf Denver) into a showcase for digital art. SheFi Summit on Feb 26 brought together women in Web3 for talks and networking, supported by groups like World of Women and Celo – highlighting a commitment to diversity and inclusion.
• Investor & Content Creator Meetups: We already touched on VC events; additionally, a KOL (Key Opinion Leaders) Gathering on Feb 28 let crypto influencers and content creators discuss engagement strategies, showing the intersection of social media and crypto communities.

Crucially, these side events weren’t just entertainment – they often served as incubators for ideas and relationships in their own right. For instance, the Tokenized Capital Summit 2025 delved into the future of capital markets on-chain, likely sparking collaborations between fintech entrepreneurs and blockchain developers in attendance. The On-Chain Gaming Hacker House provided a space for game developers to share best practices, which may lead to cross-pollination among blockchain gaming projects.

For professionals attending large conferences, ETHDenver’s model underscores that value is found off the main stage as much as on it. The breadth of unofficial programming allowed attendees to tailor their experience – whether one’s goal was to meet investors, learn a new skill, find a co-founder, or just unwind and build camaraderie, there was an event for that. Many veterans advise newcomers: “Don’t just attend the talks – go to the meetups and say hi.” In a space as community-driven as Web3, these human connections often translate into DAO collaborations, investment deals, or at the very least, lasting friendships that span continents. ETHDenver 2025’s vibrant side scene amplified the core conference, turning one week in Denver into a multi-dimensional festival of innovation.

Key Takeaways and Actionable Insights​

ETHDenver 2025 demonstrated a Web3 industry in full bloom of innovation and collaboration. For professionals in the space, several clear takeaways and action items emerge from this deep dive:

• Diversification of Trends: The event made it evident that Web3 is no longer monolithic. Emerging domains like AI integration, DePIN, and RWA tokenization are as prominent as DeFi and NFTs. Actionable insight: Stay informed and adaptable. Leaders should allocate R&D or investment into these rising verticals (e.g. exploring how AI could enhance their dApp, or how real-world assets might be integrated into DeFi platforms) to ride the next wave of growth.
• Cross-Chain is the Future: With major non-Ethereum protocols actively participating, the walls between ecosystems are lowering. Interoperability and multi-chain user experiences garnered huge attention, from MetaMask adding Bitcoin/Solana support to Polkadot and Cosmos-based chains courting Ethereum developers. Actionable insight: Design for a multi-chain world. Projects should consider integrations or bridges that tap into liquidity and users on other chains, and professionals may seek partnerships across communities rather than staying siloed.
• Community & Public Goods Matter: The “Year of the Regenerates” theme wasn’t just rhetoric – it permeated the content via public goods funding discussions, quadratic voting for hacks, and events like SheFi Summit. Ethical, sustainable development and community ownership are key values in the Ethereum ethos. Actionable insight: Incorporate regenerative principles. Whether through supporting open-source initiatives, using fair launch mechanisms, or aligning business models with community growth, Web3 companies can gain goodwill and longevity by not being purely extractive.
• Investor Sentiment – Cautious but Bold: Despite bear market murmurs, ETHDenver showed that VCs are actively scouting and willing to bet big on Web3’s next chapters. However, they are also rethinking how to invest (e.g. more strategic, perhaps more oversight on product-market fit, and openness to community funding). Actionable insight: If you’re a startup, focus on fundamentals and storytelling. The projects that stood out had clear use cases and often working prototypes (some built in a weekend!). If you’re an investor, the conference affirmed that infrastructure (L2s, security, dev tools) remains high-priority, but differentiating via theses in AI, gaming, or social can position a fund at the forefront.
• Developer Experience is Improving: ETHDenver highlighted many new toolkits, SDKs, and frameworks lowering the barrier for Web3 development – from account abstraction tools to on-chain AI libraries. Actionable insight: Leverage these resources. Teams should experiment with the latest dev tools unveiled (e.g. try out that zkSync Smart SSO for easier logins, or use the Open Agents Alliance resources for an AI project) to accelerate their development and stay ahead of the competition. Moreover, companies should continue engaging with hackathons and open developer forums as a way to source talent and ideas; ETHDenver’s success in turning hackers into founders is proof of that model.
• The Power of Side Events: Lastly, the explosion of side events taught an important lesson in networking – opportunities often appear in casual settings. A chance encounter at a happy hour or a shared interest at a small meetup can create career-defining connections. Actionable insight: For those attending industry conferences, plan beyond the official agenda. Identify side events aligned with your goals (whether it’s meeting investors, learning a niche skill, or recruiting talent) and be proactive in engaging. As seen in Denver, those who immersed themselves fully in the week’s ecosystem walked away with not just knowledge, but new partners, hires, and friends.

In conclusion, ETHDenver 2025 was a microcosm of the Web3 industry’s momentum – a blend of cutting-edge tech discourse, passionate community energy, strategic investment moves, and a culture that mixes serious innovation with fun. Professionals should view the trends and insights from the event as a roadmap for where Web3 is headed. The actionable next step is to take these learnings – whether it’s a newfound focus on AI, a connection made with an L2 team, or inspiration from a hackathon project – and translate them into strategy. In the spirit of ETHDenver’s favorite motto, it’s time to #BUIDL on these insights and help shape the decentralized future that so many in Denver came together to envision.

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Every year, a16z publishes sweeping predictions on the technologies that will define our future. This time, their crypto team has painted a vivid picture of a 2025 where blockchains, AI, and advanced governance experiments collide.

I’ve summarized and commented on their key insights below, focusing on what I see as the big levers for change — and possible stumbling blocks. If you’re a tech builder, investor, or simply curious about the next wave of the internet, this piece is for you.

1. AI Meets Crypto Wallets​

Key Insight: AI models are moving from “NPCs” in the background to “main characters,” acting independently in online (and potentially physical) economies. That means they’ll need crypto wallets of their own.

• What It Means: Instead of an AI just spitting out answers, it might hold, spend, or invest digital assets — transacting on behalf of its human owner or purely on its own.
• Potential Payoff: Higher-efficiency “agentic AIs” could help businesses with supply chain coordination, data management, or automated trading.
• Watch Out For: How do we ensure an AI is truly autonomous, not just secretly manipulated by humans? Trusted execution environments (TEEs) can provide technical guarantees, but establishing trust in a “robot with a wallet” won’t happen overnight.

2. Rise of the DAC (Decentralized Autonomous Chatbot)​

Key Insight: A chatbot running autonomously in a TEE can manage its own keys, post content on social media, gather followers, and even generate revenue — all without direct human control.

• What It Means: Think of an AI influencer that can’t be silenced by any one person because it literally controls itself.
• Potential Payoff: A glimpse of a world where content creators aren’t individuals but self-governing algorithms with million-dollar (or billion-dollar) valuations.
• Watch Out For: If an AI breaks laws, who’s liable? Regulatory guardrails will be tricky when the “entity” is a set of code housed on distributed servers.

3. Proof of Personhood Becomes Essential​

Key Insight: With AI lowering the cost of generating hyper-realistic fakes, we need better ways to verify that we’re interacting with real humans online. Enter privacy-preserving unique IDs.

• What It Means: Every user might eventually have a certified “human stamp” — hopefully without sacrificing personal data.
• Potential Payoff: This could drastically reduce spam, scams, and bot armies. It also lays the groundwork for more trustworthy social networks and community platforms.
• Watch Out For: Adoption is the main barrier. Even the best proof-of-personhood solutions need broad acceptance before malicious actors outpace them.

4. From Prediction Markets to Broader Information Aggregation​

Key Insight: 2024’s election-driven prediction markets grabbed headlines, but a16z sees a bigger trend: using blockchain to design new ways of revealing and aggregating truths — be it in governance, finance, or community decisions.

• What It Means: Distributed incentive mechanisms can reward people for honest input or data. We might see specialized “truth markets” for everything from local sensor networks to global supply chains.
• Potential Payoff: A more transparent, less gameable data layer for society.
• Watch Out For: Sufficient liquidity and user participation remain challenging. For niche questions, “prediction pools” can be too small to yield meaningful signals.

5. Stablecoins Go Enterprise​

Key Insight: Stablecoins are already the cheapest way to move digital dollars, but large companies haven’t embraced them — yet.

• What It Means: SMBs and high-transaction merchants might wake up to the idea that they can save hefty credit-card fees by adopting stablecoins. Enterprises that process billions in annual revenue could do the same, potentially adding 2% to their bottom lines.
• Potential Payoff: Faster, cheaper global payments, plus a new wave of stablecoin-based financial products.
• Watch Out For: Companies will need new ways to manage fraud protection, identity verification, and refunds — previously handled by credit-card providers.

6. Government Bonds on the Blockchain​

Key Insight: Governments exploring on-chain bonds could create interest-bearing digital assets that function without the privacy issues of a central bank digital currency.

• What It Means: On-chain bonds could serve as high-quality collateral in DeFi, letting sovereign debt seamlessly integrate with decentralized lending protocols.
• Potential Payoff: Greater transparency, potentially lower issuance costs, and a more democratized bond market.
• Watch Out For: Skeptical regulators and potential inertia in big institutions. Legacy clearing systems won’t disappear easily.

7. ‘DUNA’ – The Legal Backbone for DAOs​

Key Insight: Wyoming introduced a new category called the “decentralized unincorporated nonprofit association” (DUNA), meant to give DAOs legal standing in the U.S.

• What It Means: DAOs can now hold property, sign contracts, and limit the liability of token holders. This opens the door for more mainstream usage and real commercial activity.
• Potential Payoff: If other states follow Wyoming’s lead (as they did with LLCs), DAOs will become normal business entities.
• Watch Out For: Public perception is still fuzzy on what DAOs do. They’ll need a track record of successful projects that translate to real-world benefits.

8. Liquid Democracy in the Physical World​

Key Insight: Blockchain-based governance experiments might extend from online DAO communities to local-level elections. Voters could delegate their votes or vote directly — “liquid democracy.”

• What It Means: More flexible representation. You can choose to vote on specific issues or hand that responsibility to someone you trust.
• Potential Payoff: Potentially more engaged citizens and dynamic policymaking.
• Watch Out For: Security concerns, technical literacy, and general skepticism around mixing blockchain with official elections.

9. Building on Existing Infrastructure (Instead of Reinventing It)​

Key Insight: Startups often spend time reinventing base-layer technology (consensus protocols, programming languages) rather than focusing on product-market fit. In 2025, they’ll pick off-the-shelf components more often.

• What It Means: Faster speed to market, more reliable systems, and greater composability.
• Potential Payoff: Less time wasted building a new blockchain from scratch; more time spent on the user problem you’re solving.
• Watch Out For: It’s tempting to over-specialize for performance gains. But specialized languages or consensus layers can create higher overhead for developers.

10. User Experience First, Infrastructure Second​

Key Insight: Crypto needs to “hide the wires.” We don’t make consumers learn SMTP to send email — so why force them to learn “EIPs” or “rollups”?

• What It Means: Product teams will choose the technical underpinnings that serve a great user experience, not vice versa.
• Potential Payoff: A big leap in user onboarding, reducing friction and jargon.
• Watch Out For: “Build it and they will come” only works if you truly nail the experience. Marketing lingo about “easy crypto UX” means nothing if people are still forced to wrangle private keys or memorize arcane acronyms.

11. Crypto’s Own App Stores Emerge​

Key Insight: From Worldcoin’s World App marketplace to Solana’s dApp Store, crypto-friendly platforms provide distribution and discovery free from Apple or Google’s gatekeeping.

• What It Means: If you’re building a decentralized application, you can reach users without fear of sudden deplatforming.
• Potential Payoff: Tens (or hundreds) of thousands of new users discovering your dApp in days, instead of being lost in the sea of centralized app stores.
• Watch Out For: These stores need enough user base and momentum to compete with Apple and Google. That’s a big hurdle. Hardware tie-ins (like specialized crypto phones) might help.

12. Tokenizing ‘Unconventional’ Assets​

Key Insight: As blockchain infrastructure matures and fees drop, tokenizing everything from biometric data to real-world curiosities becomes more feasible.

• What It Means: A “long tail” of unique assets can be fractionalized and traded globally. People could even monetize personal data in a controlled, consent-based way.
• Potential Payoff: Massive new markets for otherwise “locked up” assets, plus interesting new data pools for AI to consume.
• Watch Out For: Privacy pitfalls and ethical landmines. Just because you can tokenize something doesn’t mean you should.

A16Z’s 2025 outlook shows a crypto sector that’s reaching for broader adoption, more responsible governance, and deeper integration with AI. Where previous cycles dwelled on speculation or hype, this vision revolves around utility: stablecoins saving merchants 2% on every latte, AI chatbots operating their own businesses, local governments experimenting with liquid democracy.

Yet execution risk looms. Regulators worldwide remain skittish, and user experience is still too messy for the mainstream. 2025 might be the year that crypto and AI finally “grow up,” or it might be a halfway step — it all depends on whether teams can ship real products people love, not just protocols for the cognoscenti.

On November 13, 2024, 0G Labs announced a $40 million funding round led by Hack VC, Delphi Digital, OKX Ventures, Samsung Next, and Animoca Brands, thrusting the team behind this decentralized AI operating system into the spotlight. Their modular approach combines decentralized storage, data availability verification, and decentralized settlement to enable AI applications on-chain. But can they realistically achieve GB/s-level throughput to fuel the next era of AI adoption on Web3? This in-depth report evaluates 0G’s architecture, incentive mechanics, ecosystem traction, and potential pitfalls, aiming to help you gauge whether 0G can deliver on its promise.

Background​

The AI sector has been on a meteoric rise, catalyzed by large language models like ChatGPT and ERNIE Bot. Yet AI is more than just chatbots and generative text; it also includes everything from AlphaGo’s Go victories to image generation tools like MidJourney. The holy grail that many developers pursue is a general-purpose AI, or AGI (Artificial General Intelligence)—colloquially described as an AI “Agent” capable of learning, perception, decision-making, and complex execution similar to human intelligence.

However, both AI and AI Agent applications are extremely data-intensive. They rely on massive datasets for training and inference. Traditionally, this data is stored and processed on centralized infrastructure. With the advent of blockchain, a new approach known as DeAI (Decentralized AI) has emerged. DeAI attempts to leverage decentralized networks for data storage, sharing, and verification to overcome the pitfalls of traditional, centralized AI solutions.

0G Labs stands out in this DeAI infrastructure landscape, aiming to build a decentralized AI operating system known simply as 0G.

What Is 0G Labs?​

In traditional computing, an Operating System (OS) manages hardware and software resources—think Microsoft Windows, Linux, macOS, iOS, or Android. An OS abstracts away the complexity of the underlying hardware, making it easier for both end-users and developers to interact with the computer.

By analogy, the 0G OS aspires to fulfill a similar role in Web3:

• Manage decentralized storage, compute, and data availability.
• Simplify on-chain AI application deployment.

Why decentralization? Conventional AI systems store and process data in centralized silos, raising concerns around data transparency, user privacy, and fair compensation for data providers. 0G’s approach uses decentralized storage, cryptographic proofs, and open incentive models to mitigate these risks.

The name “0G” stands for “Zero Gravity.” The team envisions an environment where data exchange and computation feel “weightless”—everything from AI training to inference and data availability happens seamlessly on-chain.

The 0G Foundation, formally established in October 2024, drives this initiative. Its stated mission is to make AI a public good—one that is accessible, verifiable, and open to all.

Key Components of the 0G Operating System​

Fundamentally, 0G is a modular architecture designed specifically to support AI applications on-chain. Its three primary pillars are:

1. 0G Storage – A decentralized storage network.
2. 0G DA (Data Availability) – A specialized data availability layer ensuring data integrity.
3. 0G Compute Network – Decentralized compute resource management and settlement for AI inference (and eventually training).

These pillars work in concert under the umbrella of a Layer1 network called 0G Chain, which is responsible for consensus and settlement.

According to the 0G Whitepaper (“0G: Towards Data Availability 2.0”), both the 0G Storage and 0G DA layers build on top of 0G Chain. Developers can launch multiple custom PoS consensus networks, each functioning as part of the 0G DA and 0G Storage framework. This modular approach means that as system load grows, 0G can dynamically add new validator sets or specialized nodes to scale out.

0G Storage​

0G Storage is a decentralized storage system geared for large-scale data. It uses distributed nodes with built-in incentives for storing user data. Crucially, it splits data into smaller, redundant “chunks” using Erasure Coding (EC), distributing these chunks across different storage nodes. If a node fails, data can still be reconstructed from redundant chunks.

Supported Data Types​

0G Storage accommodates both structured and unstructured data.

1. Structured Data is stored in a Key-Value (KV) layer, suitable for dynamic and frequently updated information (think databases, collaborative documents, etc.).
2. Unstructured Data is stored in a Log layer which appends data entries chronologically. This layer is akin to a file system optimized for large-scale, append-only workloads.

By stacking a KV layer on top of the Log layer, 0G Storage can serve diverse AI application needs—from storing large model weights (unstructured) to dynamic user-based data or real-time metrics (structured).

PoRA Consensus​

PoRA (Proof of Random Access) ensures storage nodes actually hold the chunks they claim to store. Here’s how it works:

• Storage miners are periodically challenged to produce cryptographic hashes of specific random data chunks they store.
• They must respond by generating a valid hash (similar to PoW-like puzzle-solving) derived from their local copy of the data.

To level the playing field, the system limits mining competitions to 8 TB segments. A large miner can subdivide its hardware into multiple 8 TB partitions, while smaller miners compete within a single 8 TB boundary.

Incentive Design​

Data in 0G Storage is divided into 8 GB “Pricing Segments.” Each segment has both a donation pool and a reward pool. Users who wish to store data pay a fee in 0G Token (ZG), which partially funds node rewards.

• Base Reward: When a storage node submits valid PoRA proofs, it gets immediate block rewards for that segment.
• Ongoing Reward: Over time, the donation pool releases a portion (currently ~4% per year) into the reward pool, incentivizing nodes to store data permanently. The fewer the nodes storing a particular segment, the larger the share each node can earn.

Users only pay once for permanent storage, but must set a donation fee above a system minimum. The higher the donation, the more likely miners are to replicate the user’s data.

Royalty Mechanism: 0G Storage also includes a “royalty” or “data sharing” mechanism. Early storage providers create “royalty records” for each data chunk. If new nodes want to store that same chunk, the original node can share it. When the new node later proves storage (via PoRA), the original data provider receives an ongoing royalty. The more widely replicated the data, the higher the aggregate reward for early providers.

Comparisons with Filecoin and Arweave​

Similarities:

• All three incentivize decentralized data storage.
• Both 0G Storage and Arweave aim for permanent storage.
• Data chunking and redundancy are standard approaches.

Key Differences:

• Native Integration: 0G Storage is not an independent blockchain; it’s integrated directly with 0G Chain and primarily supports AI-centric use cases.
• Structured Data: 0G supports KV-based structured data alongside unstructured data, which is critical for many AI workloads requiring frequent read-write access.
• Cost: 0G claims $10–11/TB for permanent storage, reportedly cheaper than Arweave.
• Performance Focus: Specifically designed to meet AI throughput demands, whereas Filecoin or Arweave are more general-purpose decentralized storage networks.

0G DA (Data Availability Layer)​

Data availability ensures that every network participant can fully verify and retrieve transaction data. If the data is incomplete or withheld, the blockchain’s trust assumptions break.

In the 0G system, data is chunked and stored off-chain. The system records Merkle roots for these data chunks, and DA nodes must sample these chunks to ensure they match the Merkle root and erasure-coding commitments. Only then is the data deemed “available” and appended into the chain’s consensus state.

DA Node Selection and Incentives​

• DA nodes must stake ZG to participate.
• They’re grouped into quorums randomly via Verifiable Random Functions (VRFs).
• Each node only validates a subset of data. If 2/3 of a quorum confirm the data as available and correct, they sign a proof that’s aggregated and submitted to the 0G consensus network.
• Reward distribution also happens through periodic sampling. Only the nodes storing randomly sampled chunks are eligible for that round’s rewards.

Comparison with Celestia and EigenLayer​

0G DA draws on ideas from Celestia (data availability sampling) and EigenLayer (restaking) but aims to provide higher throughput. Celestia’s throughput currently hovers around 10 MB/s with ~12-second block times. Meanwhile, EigenDA primarily serves Layer2 solutions and can be complex to implement. 0G envisions GB/s throughput, which better suits large-scale AI workloads that can exceed 50–100 GB/s of data ingestion.

0G Compute Network​

0G Compute Network serves as the decentralized computing layer. It’s evolving in phases:

• Phase 1: Focus on settlement for AI inference.
• The network matches “AI model buyers” (users) with compute providers (sellers) in a decentralized marketplace. Providers register their services and prices in a smart contract. Users pre-fund the contract, consume the service, and the contract mediates payment.
• Over time, the team hopes to expand to full-blown AI training on-chain, though that’s more complex.

Batch Processing: Providers can batch user requests to reduce on-chain overhead, improving efficiency and lowering costs.

0G Chain​

0G Chain is a Layer1 network serving as the foundation for 0G’s modular architecture. It underpins:

• 0G Storage (via smart contracts)
• 0G DA (data availability proofs)
• 0G Compute (settlement mechanisms)

Per official docs, 0G Chain is EVM-compatible, enabling easy integration for dApps that require advanced data storage, availability, or compute.

0G Consensus Network​

0G’s consensus mechanism is somewhat unique. Rather than a single monolithic consensus layer, multiple independent consensus networks can be launched under 0G to handle different workloads. These networks share the same staking base:

• Shared Staking: Validators stake ZG on Ethereum. If a validator misbehaves, their staked ZG on Ethereum can be slashed.
• Scalability: New consensus networks can be spun up to scale horizontally.

Reward Mechanism: When validators finalize blocks in the 0G environment, they receive tokens. However, the tokens they earn on 0G Chain are burned in the local environment, and the validator’s Ethereum-based account is minted an equivalent amount, ensuring a single point of liquidity and security.

0G Token (ZG)​

ZG is an ERC-20 token representing the backbone of 0G’s economy. It’s minted, burned, and circulated via smart contracts on Ethereum. In practical terms:

• Users pay for storage, data availability, and compute resources in ZG.
• Miners and validators earn ZG for proving storage or validating data.
• Shared staking ties the security model back to Ethereum.

Summary of Key Modules​

0G OS merges four components—Storage, DA, Compute, and Chain—into one interconnected, modular stack. The system’s design goal is scalability, with each layer horizontally extensible. The team touts the potential for “infinite” throughput, especially crucial for large-scale AI tasks.

0G Ecosystem​

Although relatively new, the 0G ecosystem already includes key integration partners:

1. Infrastructure & Tooling:

   • ZK solutions like Union, Brevis, Gevulot
   • Cross-chain solutions like Axelar
   • Restaking protocols like EigenLayer, Babylon, PingPong
   • Decentralized GPU providers IoNet, exaBits
   • Oracle solutions Hemera, Redstone
   • Indexing tools for Ethereum blob data

2. Projects Using 0G for Data Storage & DA:

   • Polygon, Optimism (OP), Arbitrum, Manta for L2 / L3 integration
   • Nodekit, AltLayer for Web3 infrastructure
   • Blade Games, Shrapnel for on-chain gaming

Supply Side​

ZK and Cross-chain frameworks connect 0G to external networks. Restaking solutions (e.g., EigenLayer, Babylon) strengthen security and possibly attract liquidity. GPU networks accelerate erasure coding. Oracle solutions feed off-chain data or reference AI model pricing.

Demand Side​

AI Agents can tap 0G for both data storage and inference. L2s and L3s can integrate 0G’s DA to improve throughput. Gaming and other dApps requiring robust data solutions can store assets, logs, or scoring systems on 0G. Some have already partnered with the project, pointing to early ecosystem traction.

Roadmap & Risk Factors​

0G aims to make AI a public utility, accessible and verifiable by anyone. The team aspires to GB/s-level DA throughput—crucial for real-time AI training that can demand 50–100 GB/s of data transfer.

Co-founder & CEO Michael Heinrich has stated that the explosive growth of AI makes timely iteration critical. The pace of AI innovation is fast; 0G’s own dev progress must keep up.

Potential Trade-Offs:

• Current reliance on shared staking might be an intermediate solution. Eventually, 0G plans to introduce a horizontally scalable consensus layer that can be incrementally augmented (akin to spinning up new AWS nodes).
• Market Competition: Many specialized solutions exist for decentralized storage, data availability, and compute. 0G’s all-in-one approach must stay compelling.
• Adoption & Ecosystem Growth: Without robust developer traction, the promised “unlimited throughput” remains theoretical.
• Sustainability of Incentives: Ongoing motivation for nodes depends on real user demand and an equilibrium token economy.

Conclusion​

0G attempts to unify decentralized storage, data availability, and compute into a single “operating system” supporting on-chain AI. By targeting GB/s throughput, the team seeks to break the performance barrier that currently deters large-scale AI from migrating on-chain. If successful, 0G could significantly accelerate the Web3 AI wave by providing a scalable, integrated, and developer-friendly infrastructure.

Still, many open questions remain. The viability of “infinite throughput” hinges on whether 0G’s modular consensus and incentive structures can seamlessly scale. External factors—market demand, node uptime, developer adoption—will also determine 0G’s staying power. Nonetheless, 0G’s approach to addressing AI’s data bottlenecks is novel and ambitious, hinting at a promising new paradigm for on-chain AI.

BlockEden.xyz, known for its Remote Procedure Call (RPC) infrastructure, is expanding into AI inference services. This evolution leverages its open-source, permissionless design to create a marketplace where model researchers, hardware operators, API providers, and users interact seamlessly. The network's Relay Mining algorithm ensures a transparent and verifiable service, presenting a unique opportunity for large model AI researchers to monetize their work without infrastructure maintenance.

The Core Problem​

The AI landscape faces significant challenges, including:

• Restricted Model-Serving Environments: Resource-intensive infrastructure limits AI researchers' ability to experiment with various models.
• Unsustainable Business Models for Open Source Innovation: Independent engineers struggle to monetize their work, relying on major infrastructure providers.
• Unequal Market Access: Enterprise-grade models dominate, leaving mid-tier models and users underserved.

BlockEden.xyz’s Unique Value Proposition​

BlockEden.xyz addresses these issues by decoupling the infrastructure layer from the product and services layer, ensuring an open and decentralized framework. This setup enables high-quality service delivery and aligns incentives among all network participants.

Key benefits include:

• Established Network: Utilizing an existing network of BlockEden.xyz's services to streamline model access and service quality.
• Separation of Concerns: Each stakeholder focuses on their strengths, improving overall ecosystem efficiency.
• Incentive Alignment: Cryptographic proofs and performance measurements drive competition and transparency.
• Permissionless Models & Supply: An open marketplace for cost-effective hardware supply.

Decentralized AI Inference Stakeholders​

Model Providers: Coordinators

Coordinators manage the product and services layer, optimizing service quality and providing seamless access for applications. Coordinators discreetly ensure supplier integrity by posing as regular users, offering unbiased performance assessments.

Model Users: Applications

Applications typically use first-party coordinators but can also access the network with a third-party for enhanced privacy and cost savings. Direct access allows for diverse use case experimentation and eliminates intermediary costs.

Model Suppliers: Hardware Operators

Suppliers run inference nodes to earn tokens. Their competencies in DevOps, hardware maintenance, and logging are crucial for network growth. The permissionless approach encourages participation from various hardware providers, including those with idle or dormant resources.

Model Sources: Engineers & Researchers

Researchers and institutions that open-source models can earn revenue based on usage. This model incentivizes innovation without the need for infrastructure maintenance, providing a sustainable business model for open-source contributors.

Working with Cuckoo Network​

BlockEden.xyz collaborates with Cuckoo Network to revolutionize AI inference through a decentralized and permissionless infrastructure. This partnership focuses on leveraging both platforms' strengths to create a seamless and efficient ecosystem for AI model deployment and monetization.

Key Collaboration Areas​

• Infrastructure Integration: Combining BlockEden.xyz's robust RPC infrastructure with Cuckoo Network's decentralized model-serving capabilities to offer a scalable and resilient AI inference service.
• Model Distribution: Facilitating the distribution of open-source AI models across the network, enabling researchers to reach a broader audience and monetize their innovations without the need for extensive infrastructure.
• Quality Assurance: Implementing mechanisms for continuous monitoring and assessment of model performance and supplier integrity, ensuring high-quality service delivery and reliability.
• Economic Incentives: Aligning economic incentives across all stakeholders through cryptographic proofs and performance-based rewards, fostering a competitive and transparent marketplace.
• Privacy and Security: Enhancing privacy-preserving operations and secure model inference through advanced technologies like Trusted Execution Environments (TEE) and decentralized data storage solutions.
• Community and Support: Building a supportive community for AI researchers and developers, providing resources, guidance, and incentives to drive innovation and adoption within the decentralized AI ecosystem.

By partnering with Cuckoo Network, BlockEden.xyz aims to create a holistic and decentralized approach to AI inference, empowering researchers, developers, and users with a robust, transparent, and efficient platform for AI model deployment and utilization. You can now try decentralized text-to-image API at https://blockeden.xyz/api-marketplace/cuckoo-ai.

Input/Output of a Decentralized Inference Network​

LLM Inputs to Cuckoo Network:

• Open-source models
• Demand from end-users or Applications
• Aggregated supply from commodity hardware
• Quality of service guarantees

LLM Outputs from Cuckoo Network:

• No downtime
• Seamless model experimentation
• Public model evaluation
• Privacy-preserving operations
• Censorship-free models

Web3 Ecosystem Integrations​

BlockEden.xyz's RPC protocol can integrate with other Web3 protocols to enhance Decentralized AI (DecAI):

Data & Storage Networks: Seamless integration with decentralized storage solutions like Filecoin/IPFS and Arweave for model storage and data integrity.

Compute Networks: Complementary services leveraging decentralized computing layers like Akash and Render, supporting both dedicated and idle hardware.

Inference Networks: Flexible deployment models and robust ecosystems supporting diverse inference tasks.

Applications: AI agents, consumer apps, and IoT devices benefit from DecAI inference for personalized services, data privacy, and edge decision-making.

Summary​

BlockEden.xyz's established infrastructure and economic design unlock new opportunities for open-source AI. By providing a decentralized and verifiable service, it bridges the gap between open-source AI and Web3, enabling innovative, sustainable, and reliable services. This approach allows for greater model diversity, better market access for SMEs, and a new business model for open-source researchers. Future developments will continue to expand the ecosystem, ensuring BlockEden.xyz remains a robust and adaptable solution in the evolving AI and blockchain landscapes.

• Twitter: https://twitter.com/BlockEdenHQ
• Discord: https://discord.gg/eWZvE4RSBw
• Source Link: https://blockeden.xyz/blog/2024/06/20/decentralized-inference-blockeden-xyz/

We are glad to announce that BlockEden.xyz, web3 developers' go-to platform for API marketplace, has added a new, powerful capability – OpenAI API. Yes, you heard it right! Developers, tech enthusiasts, and AI pioneers can now leverage the cutting-edge machine learning models offered by OpenAI, directly through BlockEden's API Marketplace.

Before we dive into the how-to guide, let's understand what OpenAI API brings to the table. OpenAI API is a gateway to AI models developed by OpenAI, such as the industry-renowned GPT-3, the state-of-the-art transformer-based language model known for its remarkable ability to understand and generate human-like text. The API enables developers to use this advanced technology for a variety of applications, including drafting emails, writing code, answering questions, creating written content, tutoring, language translation, and much more.

Now, let's see how you can incorporate the power of OpenAI API into your applications using BlockEden.xyz. You can do it in three ways: using Python, using JavaScript (Node.js), or using curl directly from the command line. In this blog, we're going to provide the basic setup for each method, using a simple "Hello, World!" example.

The API key below is public and subject to change and rate limit. Get your own BLOCKEDEN_API_KEY from https://blockeden.xyz/dash instead.

Python:

Using Python, you can use the OpenAI API as shown in the following snippet:

import openai

BLOCKEDEN_API_KEY = "8UuXzatAZYDBJC6YZTKD"
openai.api_key = ""
openai.api_base = "https://api.blockeden.xyz/openai/" + BLOCKEDEN_API_KEY + "/v1"

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo-16k",
  messages=[{"role": "user", "content": "hello, world!"}],
  temperature=0,
  max_tokens=2048,
  top_p=1,
  frequency_penalty=0,
  presence_penalty=0
)

print(response["choices"])

JavaScript (Node.js):

You can also utilize the OpenAI API with JavaScript. Here's how you can do it:

const { Configuration, OpenAIApi } = require("openai");

const BLOCKEDEN_API_KEY = "8UuXzatAZYDBJC6YZTKD";
const configuration = new Configuration({
  basePath: "https://api.blockeden.xyz/openai/" + BLOCKEDEN_API_KEY + "/v1"
});
const openai = new OpenAIApi(configuration);

(async () => {
  const response = await openai.createChatCompletion({
    model: "gpt-3.5-turbo-16k",
    messages: [{role: "user", content: "hello, world!"}],
    temperature: 0,
    max_tokens: 2048,
    top_p: 1,
    frequency_penalty: 0,
    presence_penalty: 0,
  });

  console.log(JSON.stringify(response.data.choices, null, 2));
})()

cURL:

Last but not least, you can call the OpenAI API using curl directly from your terminal:

curl https://api.blockeden.xyz/openai/8UuXzatAZYDBJC6YZTKD/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
  "model": "gpt-3.5-turbo-16k",
  "messages": [{"role": "user", "content": "hello, world!"}],
  "temperature": 0,
  "max_tokens": 2048,
  "top_p": 1,
  "frequency_penalty": 0,
  "presence_penalty": 0
}'

So, what's next? Dive in, experiment, and discover how you can leverage the power of OpenAI API for your projects, be it for chatbots, content generation, or any other NLP-based application. The possibilities are as vast as your imagination. With BlockEden.xyz's seamless integration with OpenAI, let's redefine the boundaries of what's possible.

For more information on OpenAI's capabilities, models, and usage, visit the official OpenAI documentation.

Happy Coding!

What is BlockEden.xyz​

BlockEden.xyz is an API marketplace powering DApps of all sizes for Sui, Aptos, Solana, and 12 EVM blockchains. Why do our customers choose us?

1. High availability. We maintain 99.9% uptime since our first API - Aptos main net launch.
2. Inclusive API offerings and community. Our services have expanded to include Sui, Ethereum, IoTeX, Solana, Polygon, Polygon zkEVM, Filecoin, Harmony, BSC, Arbitrum, Optimism, Gnosis, Arbitrum Nova & EthStorage Galileo. Our community 10x.pub has 4000+ web3 innovators from Silicon Valley, Seattle, and NYC.
3. Security. With over $45 million worth of tokens staked with us, our clients trust us to provide reliable and secure solutions for their web3 and blockchain needs.

We provide a comprehensive suite of services designed to empower every participant in the blockchain space, focusing on three key areas:

• For blockchain protocol builders, we ensure robust security and decentralization by operating nodes and making long-term ecosystem contributions.
• For DApp developers, we build user-friendly APIs to streamline development and unleash the full potential of decentralized applications.
• For token holders, we offer a reliable staking service to maximize rewards and optimize asset management.

• Twitter: https://twitter.com/BlockEdenHQ
• Discord: https://discord.gg/eWZvE4RSBw
• Source Link: https://blockeden.xyz/blog/2023/07/19/unveiling-integration-openai-api-blockeden-api-marketplace/