ASI Merger Is Here: SingularityNET, Fetch.AI and Ocean Protocol’s Crypto AI Super Alliance Fights Back!

Posted on by Werner

The Decentralized Resistance joins forces and fights back. Our bid to keep artificial intelligence development democratic and open is about to get supercharged thanks to the merger of three of the biggest crypto AI projects: SingularityNET (AGIX), Fetch.ai (FET), and Ocean Protocol (OCEAN). 

It’s the hottest thing in crypto AI this year and it’s called the Artificial Superintelligence Alliance (ASI). Like its name suggests, it’s big and ready to fight. 

Fetch.ai, Ocean Protocol Foundation, and SingularityNET Foundation will continue operating independently but will collaborate closely within the ASI tokenomic ecosystem. The merger brings together the expertise of renowned AI pioneers, ensuring ethical and innovative AI development.

Together, the three ASI partners make up a market cap of nearly 3 billion dollar and represent some of the smartest and innovative minds in the space. It’s super-ambitious and is aiming for a 7.5 billion dollar market cap post-launch.

If this flew under your radar, fret not, but keep reading this article. Let’s take a look at the entities behind it, why it’s been created, what each party brings to the table, and what essential information that AGIX investors and supporters should know. 

Artificial General (AGI) vs Super Intelligence (ASI)

Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) are both advanced forms of artificial intelligence, but they differ in their capabilities and scope: the term ‘AGI’ emphasizes matching human intelligence in flexibility and generality, while the term ‘ASI’ emphasizes capabilities beyond the greatest human geniuses: a machine-mind that can whip up new theoretical physics in an hour,

  • AGI is a hypothetical AI system that can match human-level cognitive abilities, including self-teaching and problem-solving across various domains. It is considered strong AI, as it can perform any intellectual task that a human can. 
  • ASI however is a hypothetical AI system that surpasses human intelligence. A mind of superhuman genius. It would have thinking skills never before seen in the known universe: able to ingest millions of scientific data in seconds, and understand them with the nuance and common sense of a real intelligence. 

While AGI is focused on matching human intelligence, ASI aims to exceed it. 

What is the Artificial Superintelligence (ASI) Alliance?

ASI is a unified network aimed at accelerating the development of decentralized artificial superintelligence (ASI). The new partners had to put the merger proposal to a vote with their respective communities, and have now received the community greenlight to join forces. 

ASI wants to reshape the landscape of AI research and development, challenging the dominance of centralized tech giants and promoting an open, democratic, and ethical AI ecosystem.

The alliance is not a merger of the three technology platforms. It is a merger of their three tokens.

Overview of SingularityNET, Fetch.ai, and Ocean Protocol

SingularityNET

Founded by Dr. Ben Goertzel, SingularityNET is a decentralized marketplace for AI services. Its mission is to create inclusive, democratic, and beneficial Artificial General Intelligence (AGI), a concept coined or popularized by Dr. Goertzel. The platform allows companies and developers to trade algorithms at scale without a central controller, reducing costs and barriers to entry. SingularityNET is developing its own AGI system, the OpenCog Hyperon AGI framework, and using OpenCog Hyperon and the AI marketplace, SingularityNET aims to leverage its decentralized tools for emergent superintelligence.

Fetch.ai

Fetch.ai is building a decentralized machine learning network that enables users to access secure datasets and execute tasks using autonomous AI agents. This empowers consumers to create and deploy versatile AI for various tasks. The Fetch.ai tech stack includes a Cosmos-based Layer 1 network and a multi-agent framework, facilitating rapid deployment of commercial AI applications.

Ocean Protocol

Ocean Protocol provides a secure, privacy-preserving platform for trading tokenized data assets, giving a way to financially support AI models through their development lifecycle. One of its flagship tools is ‘Predictoor’ for crowdfunded predictions, which has gained traction in the crypto-finance community. Ocean Protocol aims to  manage data requirements for both small-scale and large-scale AI systems ethically and securely.

Credit: Tesfu Assefa

Why Is ASI Needed? 

In the race towards AGI and ASI, the choice is stark in 2024: a choice between either centralized control by Big Tech and the military, or else a decentralized, open network. As most new entrants have found, AI’s barriers to entry are now incredibly high, and a few Web2 behemoths like Microsoft, Alphabet and Amazon are very far ahead thanks to financial power. 

As a result, the leaders of SingularityNET, Ocean and Fetch had to make a necessary choice: adapt or die.

The mission of the ASI Alliance is to ensure that decentralized ASI is in the race, benefiting humanity as a whole. 

This alliance aims to make a real-world impact with decentralized AI, to guide the public and the industry towards decentralized AGI and ASI with continuously improving AI applications. By uniting their strengths, Fetch.ai, SingularityNET, and Ocean Protocol are poised to create synergies that exceed their individual capabilities.

This begins with the merger of AGIX, FET, and OCEAN tokens into the new ASI token. The total supply of ASI will be 2,630,547,141 dollar tokens, distributed among AGIX, FET, and OCEAN holders.

Leadership and Governance

A governing council will guide the Superintelligence Alliance, with Dr. Ben Goertzel as CEO and Humayun Sheikh as Chairman. Bruce Pon and Trent McConaghy will represent Ocean Protocol. 

Key Benefits of the Partnership

Here are five good reasons why the ASI alliance is a great idea:

  • The merger creates the largest open-source, decentralized player in AI research and development.
  • It provides an unprecedented opportunity to challenge Big Tech’s control over AI. 
  • It leaves the technology platforms and development operations of the three partners largely intact and independent, while merging their tokens on the market.
  • By combining their research, brands, technologies, and products, Fetch.ai, SingularityNET, and Ocean Protocol lay the foundation for a scalable decentralized AI infrastructure.
  • The merger facilitates the commercialization of each company’s technology, offering universal access to cutting-edge AI platforms and large databases.

Essential Q&A for AGIX Holders

1. Why are these tokens merging?

The merger aims to consolidate resources and accelerate the development of decentralized ASI. Rather than try to merge the technologies of the three platforms, it aims to merge their economic muscle.

2. How does this benefit token holders?

Tokenholders will benefit from a streamlined ecosystem, greater interoperability, and accelerated progress towards decentralized AGI and ASI.

3. How will this transaction work?

Existing AGIX, FET, and OCEAN tokens will be converted into ASI tokens at specified conversion rates.

4. What are the token conversion rates?

AGIX holders will receive 0.433350 ASI per AGIX token, FET holders will receive 1 ASI per FET token, and OCEAN holders will receive 0.433226 ASI per OCEAN token. This aligns pro-rata with their respective crypto asset prices at the time the announcement was made.

5. Do I need to do anything?

No immediate action is required. Conversion tools will be provided if necessary.

6. What does this mean for my AGIX tokens?

Your AGIX tokens will become ASI tokens, integrating into a larger new asset.

7. How do I exchange my AGIX token for a merged ASI token?

Conversion tools will be provided. Follow the instructions from official channels like the SingularityNET blog and Telegram.

8. My AGIX tokens are staked and I won’t have access to them to participate in the merger. What should I do?

A migration tool will be available. Tokens can be converted once they are unstaked.

9. What happens to my tokens on centralized exchanges?

Tokens on centralized exchanges will be automatically converted to ASI. Holders do not need to take any action.

Conclusion

The ASI merger certainly marks an exciting but challenging new chapter in the AI landscape and the battle for little guys to shape the development of Artificial Superintelligence. By merging Fetch.ai, SingularityNET, and Ocean Protocol, a multi-billion dollar alliance is created. This has the strength to drive progress towards decentralized AGI and ASI, ensuring ethical and trustworthy practices. 

ASI’s formation accelerates AI development but also democratizes access to AI technologies, challenging the dominance of centralized tech giants. As the world watches, the ASI Alliance stands ready to lead the next wave of innovation in decentralized AI.

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How Large Language Models Anticipate Future Words

Posted on by Bezawit Abebaw

Humans are renowned for their ability to think ahead while speaking, predicting upcoming language input with remarkable accuracy. But do language models exhibit a similar foresight? Recent research delves into this intriguing question, uncovering two potential explanations for why transformer language models prepare information in advance: pre-caching and breadcrumbs.

Pre-caching involves the model computing features at the current time step that may not be immediately needed but will prove useful for future steps. Conversely, breadcrumbs suggest that the features most relevant at the current time step inherently benefit future inference.

To test these hypotheses, researchers conducted “myopic training,” limiting language models from considering gradients from past time steps. In synthetic data settings, clear evidence for pre-caching emerged, indicating that successful models prepare information for the next word in advance. However, in autoregressive language modeling experiments, the breadcrumbs hypothesis appeared more applicable, suggesting that relevant features at any time step naturally benefit future inference.

Credit: Tesfu Assefa

Examples of Pre-caching and Breadcrumbs in Action

Pre-caching 

Consider a language model trained on a dataset of simple arithmetic problems. When given the input “2 + 3 =,” the model needs to predict the next token, which should be “5.” In this case, the model pre-caches the information that “2 + 3” will result in “5” even before seeing the “=” symbol. Here, the model computes and stores intermediate arithmetic results in advance, ensuring that it can predict the correct answer once the full equation is presented. This pre-caching behavior is crucial in synthetic data settings where specific future outcomes need preparation.

Breadcrumbs

Now, consider a language model trained on natural language text, such as a news article. When the model processes the sentence, “The stock market saw a significant rise today as investors showed confidence in the new economic policies,” it might need to predict the next word “policies” after reading “new economic.” Here, the breadcrumbs hypothesis is at play. The model uses the context from the current and preceding words to make an informed prediction. The features relevant to “new” and “economic” are naturally beneficial for predicting “policies” without deliberate preparation, as they all relate to the same context.

In the arithmetic example, the model benefits from pre-caching because it needs to prepare specific future outcomes based on the current input. In contrast, the news article example showcases the breadcrumbs hypothesis, where relevant features at the current time step (e.g., “new” and “economic”) inherently aid future predictions (e.g., “policies”) without additional pre-computation.

Conclusion

When performing gradient descent, the off-diagonal terms in the gradient of the expected loss with respect to the model’s parameters reveal how weights at one position influence predictions at future positions. This insight underpins the distinction between myopic and non-myopic models, where myopic models prioritize immediate predictions over future ones.

The study provides evidence that while transformers do pre-cache information in synthetic tasks, in natural language settings, they likely operate under the breadcrumbs hypothesis, using features relevant to both current and future tokens without deliberate preparation. This understanding enhances our comprehension of how language models process and anticipate linguistic input, drawing a fascinating parallel between human and artificial cognitive processes.

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The Future of Lifelike Audio-Driven Talking Faces: Microsoft Research Asia and VASA-1

Posted on by Abdulmunim Jundurahman

In the digital age where multimedia and communication technologies continue to impress the masses with their dramatic advancement, Microsoft Research Asia introduces VASA-1, a transformative model designed to generate real-time, lifelike talking faces from a single static image and a speech audio clip. This technology pushes the boundaries of audio-visual synchronization and enhances the realism and effectiveness of human-computer interactions across various domains.

Comprehensive Overview of VASA-1 Technology

VASA-1 stands out for its ability to produce synchronized lip movements, natural facial expressions, and head movements.

Core Innovations:

  • Holistic Facial Dynamics Modeling: Unlike traditional methods that treat different facial features separately, VASA-1 models all aspects of facial dynamics—including lip movements, eye gaze, and other expressions—as a single latent variable. This approach ensures seamless integration and fluid motion, contributing to the model’s lifelike outputs.
  • Diffusion Transformer Model: At the heart of VASA-1’s capability is a Diffusion Transformer model that enhances the generative process. This model is trained on a vast dataset of face videos, allowing it to accurately replicate human-like nuances in facial dynamics and head movements based on audio inputs alone.

Expanding the Horizons of Digital Communication

VASA-1’s application potential is vast and varied:

  • Enhanced Accessibility: VASA-1 can facilitate more expressive interactions for individuals with communicative impairments, providing a platform for clearer and more empathetic communication.
  • Education and Learning: In educational settings, VASA-1 can serve as an interactive tool for AI-driven tutoring, capable of delivering instructional content with engaging and responsive facial expressions that mimic human tutors.
  • Therapeutic Use: The technology also holds promise in healthcare, particularly in therapeutic settings where lifelike avatars can offer social interaction and emotional support to patients.

Credit: Tesfu Assefa

Technical Specifications and Performance Metrics

VASA-1 delivers high-resolution videos (512×512 pixels) at up to 40 frames per second, with negligible starting latency, making it ideal for real-time applications. The model’s efficiency and quality are evidenced by its performance across several newly developed metrics for evaluating lifelike digital animations, where it significantly outperforms existing methods.

Future Directions and Ethical Considerations

Looking ahead, the development team aims to refine VASA-1’s capabilities by:

  • Broadening Emotional Range: Incorporating a wider array of emotions and talking styles to cover more nuanced interactions.
  • Full-Body Dynamics: Expanding the model to include full-body dynamics for complete digital persona creation.
  • Multi-Lingual and Non-Speech Sounds: Enhancing the model’s responsiveness to a broader spectrum of audio inputs, including multiple languages and non-verbal sounds.

The ongoing development will focus on safeguarding against misuse, particularly in impersonation or deceptive uses.

Conclusion

VASA-1 by Microsoft Research Asia represents a significant step forward in the convergence of AI and human interaction. By delivering real-time, high-fidelity talking faces, VASA-1 opens new pathways for making digital interactions as rich and engaging as face-to-face conversations. It promises not only to transform user experiences but also to foster connections that transcend the digital divide.

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The Rise of Graph Foundation Models: How Large Language Models are Revolutionizing GML

Posted on by Naod

The world around us is inherently interconnected. Social networks connect people, molecules form chemical compounds, and knowledge graphs organize information. Capturing these relationships is crucial for various tasks, from drug discovery to recommender systems. This is where Graph Machine Learning (GML) comes in. GML excels at analyzing these interconnected structures, called graphs, to extract insights and make predictions.

Despite its strengths, traditional Graph Machine Learning (GML) struggles with limited data and diverse real-world graphs. Large Language Models (LLMs), on the other hand, excel at learning complex patterns from massive amounts of text data. This exciting convergence paves the way for Graph Foundation Models (GFMs), a promising new direction that merges GML’s graph processing with LLMs’ language understanding, potentially revolutionizing how we handle complex data.

Graph ML has progressed from traditional algorithms to advanced models like Graph Neural Networks (GNNs) that learn representations directly from graph data. This evolution has set the stage for integrating LLMs to further enhance Graph ML’s capabilities, providing new methods to handle large and complex graph structures.

LLMs can significantly augment Graph ML by leveraging their superior language understanding capabilities. Techniques such as prompt-based learning, where LLMs are given graph-related tasks, show great promise.

The Power of LLMs in Graph Learning

LLMs bring several advantages to the table:

  • Improved Feature Quality: LLMs can analyze textual descriptions of graphs, extracting rich features that capture the relationships and context within the data. This can significantly improve the quality of features used by GML models, leading to more accurate predictions.
  • Addressing Limited Labeled Data: Labeling data for graph tasks can be expensive and time-consuming. LLMs can leverage their pre-trained knowledge to learn from unlabeled graphs, alleviating the need for vast amounts of labeled data.
  • Tackling Graph Heterogeneity: Real-world graphs come in all shapes and sizes, with varying densities and node/edge types. LLMs, with their flexible learning capabilities, can potentially adapt to this heterogeneity and perform well on diverse graph structures.

Credit: Tesfu Assefa

Graphs Empowering LLMs

The benefits are not one-sided. Graphs can also empower LLMs by providing a structured knowledge representation for pre-training and inference. This allows LLMs to not only process textual data but also reason about the relationships within a graph, leading to a more comprehensive understanding of the information.

Applications of LLM-Enhanced GML

The potential applications of LLM-enhanced GML are vast and span various domains:

  • Recommender Systems: Imagine a recommender system that not only considers your past purchases but also understands the relationships between different products based on reviews and product descriptions. LLM-enhanced GML can achieve this, leading to more personalized and accurate recommendations.
  • Knowledge Graphs: Knowledge graphs store information about entities and their relationships. LLMs can improve reasoning and question answering tasks on knowledge graphs by leveraging their understanding of language and the structured knowledge within the graph.
  • Drug Discovery: Molecules can be represented as graphs, where nodes are atoms and edges are bonds. LLM-enhanced GML can analyze these graphs to identify potential drug candidates or predict their properties.
  • Robot Task Planning: Robots need to understand their environment to perform tasks. By integrating scene graphs (representing objects and their spatial relationships) with LLMs, robots can generate more efficient and safe task plans.

Looking Forward: Challenges and Opportunities

While the potential of LLM-enhanced GML is exciting, there are challenges to address:

  • Generalization and Transferability: Can models trained on one type of graph perform well on another? Future research needs to focus on developing models that generalize across different graph domains.
  • Multi-modal Graph Learning: Many real-world graphs contain not just text data but also images and videos. Research on incorporating these multi-modal data formats into LLM-enhanced GML is crucial.
  • Trustworthy Models: Ensuring the robustness, explainability, fairness, and privacy of LLM-enhanced GML models is essential for their responsible deployment in critical applications.
  • Efficiency: LLM computations can be resource-intensive. Developing more efficient LLM architectures specifically tailored for graph tasks is necessary for practical applications.

Conclusion

The intersection of GML and LLMs opens a new chapter in graph learning. By combining the strengths of both approaches, GFMs have the potential to revolutionize various fields that rely on analyzing interconnected data. While challenges remain, ongoing research efforts hold the promise of unlocking the full potential of this exciting new direction.

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BlackRock’s RWA Vision: Pioneering the Future of Tokenized Assets and Securities

Posted on by Werner

BlackRock, the world’s largest asset manager with over $10 trillion in assets under management, first stirred the crypto pot in August 2022 when it announced that it would use Coinbase to service its Aladdin clients. In late 2023, it came to the rescue when the SEC besieged the crypto industry by announcing its iShares spot Bitcoin ETF application. That sent BTC on a rocket ride from $25,000 to nearly $75,000 this year, as I predicted last year.

The ETF approvals in January 2024 finally made the world’s biggest cryptocurrency an accessible mainstream investment that anyone and their grandmother could own without fear of security or regulatory risks. 

Almost immediately after the SEC greenlit Bitcoin spot ETFs, BlackRock execs began to endorse Ethereum and unveiled plans for an ETH spot ETF, which has now also come to fruition and will start trading imminently (2 July) if analysts are to be believed. 

The ETFs are all roads however that legitimize blockchain and smart contract technology to lead to BlackRock’s Grand Plan: 

Tokenizing the world’s assets, or real world assets (RWA). Why trade Monday to Friday in US office hours only, if you could allow the whole globe to trade the US stock market and treasuries anywhere, anytime? 

BlackRock goes all out on RWAs

To that effect, BlackRock already announced in April 2024 that it would be launching a new RWA fund called the BlackRock USD Institutional Digital Liquidity Fund (BUIDL) that will invest in tokenized treasuries and repos. Coinbase, yet again, will play an important role as a key infrastructure provider. 

The TradFi behemoth doubled down on RWAs by investing $47 million into Securitize, an RWA tokenization firm. With a significant portion of its portfolio dedicated to real estate assets, approximately $39 billion, the potential impact of tokenizing property investment is immense. 

Larry Fink, CEO of BlackRock (and of Bitcoin as well? just kidding maxis, put down the pitchforks) sees the big picture very clearly:

I believe the next generation for markets, the next generation for securities will be tokenization of securities, and if we could have a distributed ledger that we know every beneficial owner, every beneficial seller, we all have our code of who’s buying, who’s selling – instantaneous settlement – think about it! It changes the whole ecosystem!

What exactly are Real World Assets (RWAs), and why is this significant for BlackRock?

Credit: Tesfu Assefa

What are RWAs?

As we discussed in a previous article, Real World Assets (RWAs) is a very hot new crypto narrative about converting ownership rights of various tangible and intangible assets into digital tokens using blockchain technology. These assets can be bonds, equity, and real estate, or even cultural assets like art and collectibles. 

Tokenizing RWAs offers several compelling benefits:

  1. Enhanced liquidity: Traditional assets, particularly real estate and fine art, are often illiquid. Tokenization allows these assets to be broken down into smaller, tradable units, increasing their liquidity.
  2. Transparency: Blockchain technology provides a transparent and immutable ledger, ensuring that ownership and transaction histories are easily verifiable.
  3. Access to investment opportunities: By lowering the barriers to entry, tokenization makes it possible for a broader range of investors to participate in markets that were previously inaccessible.

BlackRock USD Institutional Digital Liquidity Fund (BUIDL Fund)

The launch of the BUIDL fund in March 2024 on the Ethereum blockchain represents BlackRock’s first tokenized offering, and is a crucial component of its broader strategy. The BUIDL fund attracted $245 million in its first week of operations, showing massive investor interest and confidence in tokenized assets.

The fund is represented by the BUIDL token, which is fully backed by cash, U.S. Treasury bills, and repurchase agreements to ensure its stability and confidence among investors.

Token holders receive daily yield payouts via blockchain rails, providing a seamless and efficient way to distribute earnings. 

BlackRock’s partnership with Coinbase is notable because it represents a collaboration between a traditional financial institution and a cryptocurrency exchange. Cross-industry collaboration between crypto and TradFi is crucial for growing and developing the RWA market, bringing together the traditional financial world and the emerging world of digital assets.

Secondly, BlackRock’s involvement is likely to get RWAs adopted into the mainstream quicker. With its vast network of institutional clients and its reputation as a trusted asset manager, BlackRock has the potential to bring significant capital and credibility to the RWA market, helping to drive growth and attract new investors.

BlackRock’s Partnerships and Ecosystem

BlackRock’s strategy is to create a robust ecosystem through strategic partnerships, ensuring the seamless operation and security of its tokenized assets. Partners include:

  • Securitize: As the transfer agent and tokenization platform for the BUIDL fund, Securitize plays a pivotal role in managing the token issuance and lifecycle. BlackRock’s investment in Securitize, coupled with Joseph Chalom’s appointment to Securitize’s board, underscores its commitment to this partnership.
  • BNY Mellon: Serving as the custodian of the BUIDL fund’s assets, BNY Mellon ensures the safekeeping and proper management of the underlying assets.
  • Other Key Participants: The fund’s ecosystem includes prominent names like Anchorage Digital Bank, BitGo, and Fireblocks, each contributing to the security and functionality of the tokenized assets.
  • Circle’s Smart Contract Feature: This feature enables BUIDL holders to exchange shares for the USDC stablecoin, adding another layer of flexibility and liquidity.
  • Ondo Finance’s OUSG Token: By moving $95 million of assets to BlackRock’s BUIDL fund, Ondo Finance’s OUSG token achieves instant settlement, showcasing the efficiency of tokenized assets.

Future of Tokenized MMFs and RWAs

The future of tokenized Money Market Funds (MMFs) and other securities is not confined to centralized platforms. BlackRock envisions a broader distribution across various trading platforms and decentralized finance (DeFi) protocols. 

This decentralized approach can unlock numerous opportunities:

  • Collateral for lending: Tokenized MMFs can serve as collateral in smart contract loans, providing additional security for borrowers and lenders.
  • Liquidity pool deposits: These tokens can be deposited into the liquidity pools of automated market makers, enhancing market efficiency and liquidity.

What is the ERC-3643 Token Standard?

Compliance with existing regulations is key to the success of tokenized assets. The ERC-3643 token standard is designed to address this requirement by embedding compliance at the token level.

ERC-3643 ensures that tokens adhere to regulatory requirements, providing a level of assurance that is crucial for institutional investors.The standard is being spearheaded by a non-profit community, and has been presented to regulators worldwide. It’s steadily gaining recognition for its ability to uphold security laws while facilitating innovation.

Cogito Moves Into RWA Space

Cogito, Mindplex’s partner in the SingularityNET ecosystem, is making waves in the RWA market with its innovative approach. The platform recently obtained a tokenized fund license from CIMA, allowing it to offer fully regulated RWA investment products. 

Cogito’s unique offerings include TFUND (tokenized treasury bonds), GFUND (tokenized green bonds), and XFUND (an AI-managed basket of tech stocks), catering to various investor preferences. 

By leveraging AI to manage investments and providing crypto and fiat on-ramps, Cogito is making RWAs accessible to a wide range of investors, driving adoption and growth in the market.

Conclusion

No matter what you think about their powerful financial and political influence in our world, BlackRock’s involvement in crypto has so far helped to champion the sector to new regulatory and market highs. Its vision for the RWA market is a forward-thinking strategy that promises to revolutionize the investment landscape and bring crypto and TradFi closer together than ever before. 

By tokenizing a significant portion of its assets, BlackRock is enhancing liquidity and transparency, and also opening access to traditionally exclusive investment opportunities. The successful launch of the BUIDL fund and the strong assembly of partners will give it a precedence that other traditional firms will find irresistible. 

BlackRock, as usual, can never be counted out.

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Real World Assets (RWAs) Primer: Crypto’s Ultimate Use Case?

Posted on by Werner

Introduction

With the Bitcoin Halving and Bitcoin and Ethereum spot ETF approval in the rear view mirror in mid 2024, crypto investors’ are now asking what’s next for the world of Decentralized Finance (DeFi).

The recent regulatory approval of Ethereum can help spread one of the biggest crypto narratives for 2024, known as Real World Assets, or RWAs in short. These hybrid financial assets are offering investors a unique opportunity to diversify their portfolios and tap into the potential of tokenizing traditional financial assets. 

The RWA market is expected to experience significant growth in the coming years; some predict a 5-10 trillion USD market cap by 2030. As more investors seek to bridge the gap between traditional finance and the digital realm, RWAs are emerging as an attractive option for those looking to invest in tangible assets while enjoying the benefits of blockchain technology. It’s easier to understand, less volatile and can unlock all the benefits that come with decentralized networks. 

Let’s dive in with a primer on RWAs. 

What are RWAs?

RWAs are physical or non-physical assets from the real world that are tokenized and represented on a blockchain. This can include a wide range of assets, such as

  • real estate, 
  • precious metals, 
  • stocks, 
  • bonds, 
  • commodities, 
  • art, and 
  • collectibles

When these assets become tokens, they become more accessible, tradable, and divisible within the crypto ecosystem, opening up new opportunities for investors who may have previously been excluded from certain markets by high barriers to entry, or by lack of liquidity. They can be traded 24/7 from anywhere in the world, without geographical or technological barriers. 

How are Real World Assets tokenized?

The process of tokenizing real-world assets can get very complex, but these are the broad strokes:

  • First, the asset must be appraised to determine its value. 
  • Next, a legal framework is established to define the rights and responsibilities of token holders. 
  • The token is then issued on a blockchain platform, making it available for trading. 

Platforms like Cogito, a SingularityNET ecosystem partner, are simplifying this process for investors, providing user-friendly interfaces and streamlined workflows for creating and managing RWA tokens.

Why are RWAs Gaining Popularity?

While they’re not as sexy as other crypto trends such as AI cryptocurrencies, GameFi and memecoins (and even DePIN), there are several reasons why RWAs are touted as the ultimate use case for blockchain technology.  

  1. They’re stable

RWAs introduce real, tangible value into the often volatile crypto market, providing a level of stability that pure cryptocurrencies may lack.

  1. They bridge the gap with TradFi 

The involvement of major players like BlackRock is bringing massive growing institutional interest in RWAs, potentially leading to mainstream adoption.

  1. They bring liquidity to assets

Tokenization allows previously illiquid assets to be traded 24/7 on crypto exchanges, giving investors greater flexibility and liquidity.

  1. They make TradFi assets more accessible

RWAs lower the barriers to entry for high-value investments by enabling fractional ownership, making these assets more accessible to a wider range of investors.

BlackRock’s RWA Plans

Earlier this year, BlackRock, the world’s largest asset manager, doubled down on its long-term plans for crypto by announcing it was entering the RWA market. Through a partnership with Coinbase, BlackRock launched BUIDL (BlackRock’s USD Institutional Digital Liquidity Fund) a fund investing in tokenized treasuries and repos.

This move validates RWAs as a viable investment opportunity, and could potentially accelerate mainstream adoption. In previous years US regulators like the SEC have come down hard on previous attempts to tokenize securities, such as securitized token offerings (STOs), and prosecuted exchanges like Binance and Kraken.

With BlackRock’s vast network and massive influence on Capitol Hill, the company could bring significant capital and credibility to the space, driving growth and attracting new investors. The collaboration between a traditional financial giant and a crypto exchange is crucial to bridging the gap between the two worlds.

In fact, with the Ethereum spot ETF now approved, many crypto experts believe that BlackRock’s real ambitions for crypto stretch far beyond just ETFs – that they ultimately aim to tokenize all real world assets! Just listen to how their CEO Larry Fink waxes lyrical on tokenization here.

Credit: Tesfu Assefa

Top RWA Projects in 2024

Several notable projects and platforms are leading the way in the RWA space, each offering unique features and benefits for investors.

Ondo Finance: Tokenizing Traditional Assets

Ondo Finance is currently the biggest player in the RWA space with a market cap of $1.7 billion. It leads the pack thanks to its ability to bridge TradFi and DeFi. Ondo’s partner with established financial institutions to help ensure the security and stability of tokenized assets. 

Ondo focuses on tokenizing traditional assets like corporate bonds and treasuries, and works with BlackRock, moving nearly $100 million over to the BUIDL network. The platform aims to provide investors with access to these assets through a user-friendly interface, making it easier for them to diversify their portfolios. 

Mantle: Bringing Real Estate to the Blockchain

Mantle is a platform that focuses on tokenizing real estate assets, allowing investors to gain exposure to this traditionally illiquid market through fractional ownership. The platform uses blockchain technology to ensure the security and transparency of these investments, providing investors with a new way to participate in the real estate market without the large upfront capital or extensive paperwork that would be required to buy houses or commercial buildings.

RealT

RealT is another platform that tokenizes real estate, turning ownership of properties into digital tokens, allowing people to invest in fractional ownership, and trade with less friction. Like Mantle, RealT is making it easier for investors to access the real estate market without the need for large upfront investments or lengthy paperwork processes.

Pendle: Unlocking Yield Opportunities

Pendle is a platform that is focused on unlocking yield opportunities in the RWA market. The platform achieves this by tokenizing future yield, allowing investors to buy and sell the rights to future cash flows from various assets. 

This innovative approach provides investors with a new way to generate passive income and potentially earn higher returns than traditional fixed-income investments. Pendle’s platform is designed to be user-friendly and accessible, making it easier for investors to participate in the RWA market and take advantage of the unique yield opportunities it offers.

MakerDAO

Stablecoins are a core component of a resilient RWA market. As such, MakerDAO is playing an important role by using RWAs as collateral to stabilize the value of its DAI stablecoin. 

By accepting real-world assets as collateral, MakerDAO is able to create a more stable and reliable stablecoin that is backed by tangible value. This innovative approach has helped to establish MakerDAO as a leader in both the RWA and stablecoin spaces, paving the way for other projects to follow suit. MakerDAO has restored much-needed trust in algorithmic stablecoins following the brutal 2022 demise of Terra Luna that wiped tens of billions from investor accounts. 

Centrifuge

Centrifuge is another notable player in the RWA market, offering a platform for tokenizing and financing real-world assets like invoices and loans. It unlocks liquidity and provides new financing options for businesses and investors alike.

SingularityNET’s Cogito Gets RWA License

Cogito, a SingularityNET ecosystem partner, is another sleeping giant in the RWA market that is starting to wake up, making huge strides in tokenizing real-world assets in 2024. Recently, Cogito obtained a tokenized fund license from the Cayman Islands Monetary Authority (CIMA), becoming one of the first companies to offer fully regulated RWA investment products.

Cogito is committed to making RWAs accessible to a wide range of investors, including both institutional and retail investors. The platform offers both crypto and fiat on-ramps, making it easy for investors to purchase RWA tokens using their preferred currency. This accessibility is key to driving adoption and growing the overall market for RWAs.

Cogito’s approach to RWAs is unique in several ways:

  • The platform offers a range of innovative investment products, including the TFUND (tokenized treasury bonds), GFUND (tokenized green bonds), and XFUND (an AI-managed basket of tech stocks). 
  • These products are designed to cater to different investor preferences and risk profiles, providing a diverse range of options for those looking to invest in RWAs.
  • Importantly, Cogito is leveraging the power of artificial intelligence to help manage and optimize its RWA investments. By using AI algorithms to analyze market data and identify investment opportunities, Cogito is able to make more informed decisions and potentially generate higher returns for its investors.

Conclusion

There’s no room left for doubt: Real World Assets are here to stay. In fact, they might dominate the crypto sector by the end of the decade. With the potential to introduce real, tangible value into the digital asset ecosystem, RWAs offer investors a compelling opportunity to diversify their portfolios and gain exposure to a wide range of assets.

As more institutional behemoths like BlackRock enter the market and innovative platforms like Cogito continue to develop new investment products and services, the future looks bright for RWAs. While there are certainly challenges and risks to be aware of, such as regulatory uncertainty and the need for robust security measures, the potential benefits of RWAs are simply too great to ignore.

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