1. Platform conversion

In this era, the transformation of the world is shaped by dramatic changes in technology and infrastructure. When these changes occur, they unleash the power of generational innovation and bring us a whole new world. Think back to the birth of the telegraph and the railway, the spread of fiber optic cables and the Internet, and the revolution of mobile phones and 3G networks, all of which have brought about earth-shaking changes in human life.

Now, we are in a similarly transformative moment at the intersection of two groundbreaking fields: artificial intelligence (AI) and blockchain. The rapid development of artificial intelligence and the breakthrough of blockchain technology have allowed us to witness the arrival of a technological revolution. Artificial intelligence is changing our work, life, and social structure at an alarming rate, and blockchain technology provides new solutions for data security and trust establishment.

Advances in artificial intelligence bring us many new applications and possibilities. From smart assistants to self-driving cars, from speech recognition to machine translation, artificial intelligence is gradually penetrating into various fields. Its emergence not only improves production efficiency, but also provides us with more convenience and comfort. At the same time, the rise of blockchain technology has also triggered people to rethink data exchange and trust mechanisms. Through a decentralized approach, blockchain can ensure the security and credibility of data, break the limitations of the traditional trust system, and bring the possibility of innovation to many industries.

The intersection of artificial intelligence and blockchain will bring huge changes to society. Their combination can give artificial intelligence more intelligence and autonomy, improving its performance in various fields. At the same time, the decentralized characteristics of blockchain can also provide more secure and reliable underlying support for artificial intelligence, ensuring data accuracy and privacy protection. This integration of technologies will promote the development of human society in a more intelligent, efficient and sustainable direction.

Just like other technological revolutions in history, the transformative moment of artificial intelligence and blockchain will change our world. They will redefine our lifestyles, professional development, and social structures. We are standing at a new starting point, facing unprecedented opportunities and challenges. As artificial intelligence and blockchain continue to develop, we can look forward to a more intelligent and prosperous future. Let us embrace this moment of change together and build a better world with an open mind and innovative spirit. The three theoretical pillars of this article are as follows:

① The rise of AI will increase the demand for blockchain technology

② AI will accelerate the maturity and adoption of decentralized applications

③ Open source innovation in decentralized infrastructure will shape the future of AI

2. Blockchain can provide better design space

There are many areas where AI has high impact, but they can be roughly summarized into three main categories:

① User-oriented intelligent systems, products or applications

② Improve the operational and/or capital efficiency of the enterprise

③ Eliminates the marginal cost of content creation (and idea generation)

In particular, generative AI presents unique challenges and opportunities, where we believe blockchain technology can play an advantage.

To understand why, it's important to consider the core inputs that drive the evolution of intelligent systems. Machine learning (ML) is fundamentally driven by data (lots of it, but increasingly of higher quality), feedback mechanisms, and computing power.

Currently, major players in artificial intelligence and machine learning, such as OpenAI (backed by Microsoft) and Anthropic (backed by Google and Amazon), are pooling resources and building barriers around their own models and data. However, this approach may have undermined the collaborative development cycle that gave birth to the industry in the first place, inhibiting its momentum despite their early advantages in computing, data and distribution.

Blockchains, such as Ethereum, offer a viable solution and have become reliable neutral data and computing systems. It drives open source innovation and supports a range of digitally native primitives. These primitives have an important place in a world increasingly shaped by generative AI, so blockchain is expected to become a major force in open source research and development in artificial intelligence.

The decentralized nature of blockchain provides a more secure, transparent and trustworthy way to manage data. By storing data in a distributed network and using encryption algorithms to ensure the integrity and authenticity of the data, blockchain provides a basis of trust and a mechanism for data sharing for artificial intelligence research and development.

In the field of artificial intelligence, data is crucial. However, data acquisition and management often face many challenges, such as data security, data privacy, and data distribution. Blockchain allows participants to share and access data while maintaining data security and privacy by establishing a distributed data storage and management system. This provides a more open and collaborative environment for AI research and development.

In addition, blockchain can also solve the problems of transparency and explainability of artificial intelligence algorithms. Artificial intelligence algorithms are often black boxes, making it difficult to understand their decision-making process and basis for judgment. Blockchain can record and trace the training process and data input of the algorithm, making the decision-making process of artificial intelligence more transparent and explainable.

All in all, blockchain has broad application prospects, especially in open source research and development in the field of artificial intelligence. It provides solutions for data management, data sharing, algorithm transparency and explainability, promoting collaboration and innovation in the field of artificial intelligence. We believe that blockchain will become an important driving force for development in the field of artificial intelligence and promote the development of the industry in a more open and cooperative direction.

3. Current market conditions

This year, significant funding has been invested in AI’s core infrastructure, model layer, and even user-facing applications such as chatbots, customer support, and coding assistants. However, in traditional fields, it is not obvious where the value generated by artificial intelligence is accumulated.

In the current scenario, artificial intelligence is likely to become a centralizing force and continue to expand its position as the dominant player in the Web2 market. Particularly at the infrastructure and model layer, players have invested in expanding hardware and capital resources, data access, distribution channels and unique partnerships.

Many leading giants are developing towards a full-stack model through mergers and acquisitions or patent cooperation, from cloud service providers such as AWS to hardware manufacturers such as Nvidia, to established giants such as Microsoft. The giants are competing for scale and profits, but the market for ultra-expensive, high-precision enterprise API models may be limited by economics, open source performance convergence, and even trends in low-latency workload demand.

At the same time, a large portion of the mid-range market has seen a commoditization trend similar to “OpenAI API wrapper” products that are fully functional but difficult to differentiate. These products provide users with the flexibility and value of custom APIs, but also present problems of lack of funding and insufficient scalability.

In this market environment, open source solutions are particularly important. Open source software can provide better performance, lower cost, better repeatability and scalability. Based on open source models, tools and libraries, developers can build their own applications more flexibly and respond quickly to market and user needs.

In short, the future development trend of the artificial intelligence market is inseparable from open source and open cooperation. A decentralized open source ecosystem can lower barriers to the development of artificial intelligence and provide opportunities for more people, thus promoting the development of the industry and technological advancement.

4. Open source construction momentum

Currently, open source systems and tools are encouraging enterprises large and small to directly utilize pre-trained, trained, and fine-tuned open source data sets, as well as freely accessible basic models and tools, allowing creativity to be fully unleashed.

Notably, the gap between the worlds of closed-source and open-source code is closing rapidly, as outlined in a leaked article from Google. Currently, 96% of code bases use open source software, a trend that is particularly evident in the fields of big data, artificial intelligence, and machine learning.

At the same time, the time may be ripe to disrupt the cloud services oligopoly. In the past, the three giants AWS, Google Cloud and Azure dominated the market by layering tools and services to deeply consolidate their positions in enterprise competition. However, this dominance creates many challenges for enterprises, from restrictive operational dependencies to the exorbitant costs associated with cloud infrastructure, especially given the premiums charged by the major providers.

In this case, the application of open source systems and tools will be a powerful disruptor. Open source systems and tools can save businesses costs, reduce dependencies, and provide more flexible solutions. Enterprises can choose open source systems and tools based on their own needs, and they can customize and optimize them to meet their business needs.

In short, the widespread application of open source systems and tools will subvert the cloud service oligopoly market environment and bring more flexible and economical solutions to enterprises.

Expense pressure on existing companies to restructure their operations, coupled with attempts to experiment and integrate more and more open source AI, will create a window to restructure businesses using decentralized alternatives.

The emerging intersection of open source AI and blockchain technology therefore provides an extraordinary area for experimentation and investment.

5. Encryption and AI: Two-way value relationship

We are incredibly excited about the potential symbiotic relationship between artificial intelligence and blockchain.

Crypto middleware can greatly improve information input on the supply side of AI by establishing efficient computing and data markets (supply, labeling or fine-tuning) and attestation or privacy tools.

In turn, decentralized applications and protocols will reach new heights by assimilating the fruits of this labor.

There is no denying that cryptography has come a long way, but protocols and applications are still hampered by tools and user interfaces used by mainstream users that are still unintuitive. Likewise, smart contracts themselves may have limitations, both in terms of manual workload demands on developers and in terms of the liquidity of the overall functionality.

Web3 developers are a very productive bunch. At its peak, just 75,000 full-time developers created an industry worth trillions of dollars. Coding assistants and ML-enhanced DevOps promise to empower existing efforts, while no-code tools are rapidly empowering a new class of builders.

As machine learning capabilities are integrated into smart contracts and brought on-chain, developers will be able to design more fluid and expressive user experiences, and ultimately new killer apps. This step-change in functionality for the on-chain experience will attract new—and potentially larger—audiences, catalyzing an important flywheel of adoption feedback.

Generative AI may be the missing link for cryptocurrencies that will transform UI/UX and catalyze a new wave of technology development. In turn, blockchain technology will harness, generalize and accelerate the potential of artificial intelligence.

6. Use blockchain to build a better data market

Using blockchain technology to build a better data market is a promising direction. Data plays the role of basic information input in machine learning. Huge databases such as Common Crawl and The Pile have enabled basic models to gain global attention.

Companies can use this data to refine basic models of product offerings or to establish future competitive advantages. Data will ultimately become the bridge between users and personal models, which can run locally and continuously adapt to individual needs.

The competition for data therefore becomes an essential frontier where blockchain technology can take advantage, especially as quality becomes an important attribute shaping the data market.

Data quality is more important than quantity. Early research suggests that up to 90% of online content in the future may be synthetic. While synthetic training data has certain advantages, it also carries significant risks of deteriorating model quality and reinforcing biases.

In the coming years, machine learning models may risk running out of non-synthetic data sources. Blockchain technology, through its coordination mechanisms and proof primitives, offers optimized possibilities to support decentralized markets, enabling users to share, own or monetize data used to train or fine-tune domain-specific models.

As a result, Web3 may become a better and more efficient source of artificially generated training and fine-tuning data.

Currently, blockchain technology has made progress in supporting decentralized training, fine-tuning and inference processes, while also enabling better preservation and utilization of open source intelligence.

The smaller open source model is improved through an efficient fine-tuning process, and its output accuracy is already comparable to that of the larger model. As a result, the trend has begun to shift from quantity to quality when it comes to sourcing and fine-tuning data.

The ability to track and verify the lifecycle of raw and derived data can promote model reproducibility and transparency, thereby driving the development of higher quality models and inputs.

Blockchain can build a durable moat and become a prime domain with diverse, verifiable and tailored data sets. This is particularly valuable in situations where traditional solutions over-index algorithm progress in response to insufficient data.

Content imitation breaks out

The coming wave of copycat content is another area where cryptocurrency’s first-mover advantage will come into play.

This new technology paradigm will empower digital content creators at an unprecedented scale, and Web3's plug-and-play infrastructure makes it simple and straightforward. Cryptocurrencies have a home field advantage, thanks to years of development around primitives that establish ownership and immutable provenance of digital assets and content in the form of NFTs.

NFTs can capture the entire content creation lifecycle, but can also represent digitally native identities, virtual assets, and even cash flow.

As a result, NFTs enable new user experiences such as digital asset markets (OpenSea, Blur), while also rethinking written content (Mirror), social media (Farcaster, Lens), gaming (Dapper Labs, Immutable), and even the fundamentals of finance Business models such as facilities (Upshot, NFTFi).

This technology could even combat deepfakes and computational manipulation more reliably than the other option - using algorithms. An obvious example is that OpenAI's detection tool was shut down due to accuracy failure.

One final note: Advances in concise and verifiable computing will also upgrade the dynamic landscape of NFTs as they incorporate ML output to drive smarter, evolving metadata. We believe that AI tools and interfaces based on blockchain technology will unleash comprehensive value and reshape the digital content landscape.

Using zero-knowledge proofs to achieve unlimited knowledge in machine learning

The blockchain industry is looking for technical solutions that can achieve resource-efficient computing while maintaining trustless characteristics, and zero-knowledge proofs (ZK) have made significant progress in this regard.

Although originally designed to solve the resource bottleneck problem of systems such as the Ethereum Virtual Machine, ZK proofs actually provide many valuable use cases related to artificial intelligence.

An obvious example is a simple extension of an existing use case: validating a computationally intensive process, such as running a machine learning model off-chain, in an efficient and concise way, so that the final product (such as model inference) can be used in the form of a ZK proof. On-chain integration.

By combining proof of storage with co-processing, we can make on-chain applications more flexible and agile, greatly enhancing their functionality without introducing new trust assumptions.

When calling an API using ZK proofs, we can verify that a specific model or data pool was actually used to generate inferences. It can also hide specific weights or data used by models in sensitive industries like healthcare or insurance.

Companies can even collaborate more effectively by exchanging data or intellectual property and benefit from shared learning, while still maintaining ownership of their own resources.

Finally, ZK proves to have real applicability in the increasingly relevant and challenging field of distinguishing artificial and synthetic data.

Some of these use cases will require further technology development and finding ways to sustain economies of scale, but zkML has the potential to have a unique impact on the trajectory of artificial intelligence.

8. Long-tail assets and potential value

Cryptocurrency has proven its role as a preeminent architect of traditional market value streams such as music and art. In the past few years, on-chain liquidity markets representing off-chain tangible assets such as wine and sneakers have also emerged.

The successor will naturally involve advanced ML capabilities, as artificial intelligence is brought on-chain and made accessible to smart contracts.

ML models combined with blockchain rails will redesign the underwriting process behind illiquid assets that were previously inaccessible due to lack of data or buyer depth.

One approach is for machine learning algorithms to query large numbers of variables to evaluate hidden relationships and minimize the attack surface for manipulators. Web3 is already trying to create markets around new concepts like social media connections and wallet usernames.

Similar to the impact of AMMs on unlocking liquidity for long-tail tokens, ML will revolutionize price discovery by capturing large amounts of quantitative and qualitative data to obtain implicit patterns. These new insights could form the basis of smart contract-based markets.

The analytical capabilities of artificial intelligence will be embedded in decentralized financial infrastructure to discover the potential value in long-tail assets.

9. Decentralized infrastructure layer

Not only do cryptocurrencies have advantages in attracting and monetizing high-quality data, but they also hold similar promise in terms of the infrastructure support behind artificial intelligence.

Some decentralized physical infrastructure networks (DePINs), such as Filecoin and Arweave, have built systems for storage that themselves incorporate blockchain technology.

There are other companies, such as Gensyn and Together, working to solve the challenges of distributed network model training, while Akash has launched an impressive P2P marketplace that connects excess computing resources with demand.

In addition, Ritual provides the foundation for the construction of open source AI infrastructure in the form of incentive networks and model suites, connecting distributed computing devices for users to perform inference and fine-tuning.

The most important of these is that DePIN like Ritual, Filecoin or Akash can also create a larger and more efficient market. They do this by opening up to a broader supply side, including passive suppliers who can unlock potential economic value, or by consolidating lower-performing hardware into pools that compete with high-level peers.

Each part of the technology stack involves different constraints and value preferences, and there is still a lot of work to be done in operationally testing these layers at scale, especially in the emerging area of ​​decentralized model training and computation.

However, the foundations already exist for blockchain-based computing, storage and even model training solutions that can finally compete with traditional markets.

10. In summary, the combination of encryption technology and artificial intelligence has become one of the most promising design fields, affecting everything from content creation to corporate workflows and financial infrastructure.

We believe these technologies will reshape the world in the coming decades. The best teams will combine technologies such as infrastructure, cryptoeconomics, and artificial intelligence to improve product/service performance, enable new behaviors, or achieve a competitive cost structure.

Encryption introduces unprecedented scale, depth, and standardized data granularity to collaborative networks, while AI transforms pools of information into vectors of relevant context or relationships. When these two fields come together, a unique and mutually beneficial relationship can form, laying the foundation for builders of a decentralized future.