Binance Research: Latest Data and Developments in AI+Crypto

Original Title: "AI × Crypto: Latest Data and Developments"
Original Source: JieXuan Chua, CFA, Binance Research
Original Translation: Kate, Mars Finance

Key Points

• Interest in artificial intelligence ("AI") has risen over the past few months, as evidenced by Google search trends and the surge in prices of AI-related tokens.

• In 2023, funding for AI-related web3 projects skyrocketed to $298 million. This is more than the total funding amount for AI projects from 2016 to 2022 ($148.5 million).

• AI-related tokens performed well in 2023, with the top five AI tokens by market capitalization significantly outperforming BTC and ETH, with increases ranging from 200% to 650% in 2023.

• We have observed several trends and practical use cases emerging from the fusion of AI and cryptocurrency. From driving the growth of decentralized physical infrastructure networks ("DePIN") to creating more interactive consumer-facing applications, we highlight some notable developments in this report.

Introduction

2023 has proven to be a milestone year for artificial intelligence ("AI"), as the transformative power of AI has become increasingly evident, particularly with the widespread use of AI chatbots like OpenAI's ChatGPT, Google's Bard, and Microsoft's Bing Chat. ChatGPT, in particular, has highlighted the potential of AI, reaching the milestone of 100 million users in just two months.

This achievement surpassed major social media platforms like TikTok and YouTube.

Figure 1: ChatGPT is one of the fastest-growing applications, reaching 100 million users just two months after its launch

Source: demandsage, Binance Research

More importantly, AI has also begun to reshape the cryptocurrency space, both in practical use cases and in the heightened interest in AI-related tokens. The fusion of these two disruptive technologies has quickly become a prominent topic within the industry. Building on our previous report that revealed the use cases of AI in the crypto space, we now revisit this evolving landscape. Given the recent rekindled interest in the field, we will examine the current market conditions and explore new developments.

Market Conditions

In 2023, public interest in AI has significantly increased, as evidenced by a notable rise in global Google searches for "AI." This surge in interest indicates a growing public engagement with AI-related topics. This spike is largely attributed to the popularity of AI chatbots, the launch of new AI tools, and increased media coverage and desire to understand AI.

Figure 2: Google search interest in AI surged significantly in 2023, far exceeding "crypto" and "bitcoin."

Source: Google Trends, Binance Research, as of December 31, 2023
Note: The numbers represent search interest relative to the highest point on the chart for the given region and time.

In contrast, search interest in "crypto" remained relatively stable throughout the year. There was a slight decline from January to May, followed by a period of stability, with a slight uptick towards the end of the year. The search trend for "bitcoin" mirrored that of "crypto," but with more pronounced fluctuations. The volatility in bitcoin interest may be related to several hot topics surrounding bitcoin, including Ordinals/BRC-20, potential spot ETFs, and the upcoming bitcoin halving in 2024. These events led to a rise in bitcoin prices, reigniting public interest.

Overall, the search trends reveal a clear divergence between the growing interest in AI and the relatively stable interest in bitcoin and cryptocurrencies, indicating that AI has been capturing public attention at an accelerating pace, with no apparent signs of waning interest thus far.

Strong Investor Interest

The AI sector has also shown strong investor interest in 2023. Despite a general decrease in funding amounts, the share of AI in U.S. startup funding grew by 230%, accounting for approximately 26%. This growth occurred against a backdrop of funding slowdowns in both AI and non-AI sectors. However, AI has demonstrated particular resilience compared to the overall market.

Figure 3: In 2023, the share of AI in U.S. startup funding doubled

Source: Crunchbase, Binance Research, as of August 29, 2023
Note: The latest data for 2023 has not yet been published. Readers are advised to consider this limitation when interpreting the analysis.

Compared to 2022, absolute funding in non-AI sectors decreased by 65%, while funding in the AI sector saw a relatively small decline of only 6%.

Additionally, when considering the number of funding rounds, the non-AI sector experienced a reduction of 55%, while the AI sector saw a decrease of 45%. The relatively small decline in AI funding and funding rounds indicates that, despite an overall downward trend in funding amounts since peaking in 2021, investor interest in AI applications remains relatively high. This may also suggest a sustained belief in the long-term potential and viability of AI technologies and applications.

Furthermore, the AI sector within Web3 experienced explosive growth in funding in 2023. Data from Rootdata shows that the total funding for AI projects from 2016 to 2022 was $148.5 million, while funding for just 2023 reached $298 million. This figure for 2023 is double the total funding amount for the previous seven years, reflecting a surge in interest in AI this year.

Figure 4: Funding for AI projects in 2023 reached $298 million, ranking seventh and accounting for 3.7% of total Web3 project funding

Source: Rootdata, Binance Research, as of December 31, 2023

Compared to other areas within the Web3 space, the $298 million funding for AI projects in 2023 ranked seventh, surpassing the $293 million for NFTs and $42 million for DAOs. This funding represents approximately 3.7% of the total funding for Web3 projects in 2023. While 3.7% may seem small, considering that AI only began to gain significant traction in 2023, this substantial funding growth highlights the increasing recognition and value of the sector.

Strong Performance

From a price perspective, AI tokens have also outperformed the overall market, experiencing significant surges over the past quarter and year. The increased interest in the sector has contributed to the strong price performance of AI-related tokens.

Figure 5: Over the past three months, AI tokens have been rated as the second-best performing category

Source: Dune Analytics (@cryptokoryo_research), as of January 2, 2023, AI tokens include: AGIX, CTXC, FET, OCEAN, ORAI, RNDR

According to the Dune dashboard summarizing the performance of representative tokens across different narratives/sectors, AI tokens ranked second in performance over the past three months. Note that while the initial dashboard included MEME coins, we have excluded them from our analysis due to their relatively low market capitalization leading to disproportionately large percentage performance gains.

When comparing the top five AI tokens by market capitalization with BTC and ETH, it is clear that AI tokens have significantly outperformed the major tokens in 2023.

These AI tokens saw annual performance increases ranging from 200% to as high as 650%. In contrast, BTC rose by 150% by the end of the year, while ETH increased by 44%.

However, it is important to note that the market capitalization of BTC and ETH is much larger compared to these AI tokens. Therefore, it is natural for BTC and ETH to have smaller percentage gains. This comparison primarily serves to highlight the strong performance and growing traction of AI tokens in recent months.

Figure 6: In 2023, the performance of the top five AI tokens by market capitalization significantly outperformed BTC and ETH, with increases ranging from 200% to as high as 650%

Source: CoinMarketCap, Binance Research, as of December 31, 2023

Overall, AI has gained tremendous traction. The adoption of AI applications has been accelerating, attracting sustained interest from both investors and retail participants. Additionally, the performance of AI tokens has remained strong. Beyond these trends, there are several emerging AI x crypto innovations worth discussing, detailed in the next section.

AI x Crypto Developments

The surge in interest in AI has driven the growth of AI-related crypto applications, paving the way for continued innovation in the field. In this section, we will delve into some of the trends and practical use cases arising from the fusion of AI and cryptocurrency technology. From driving the growth of decentralized physical infrastructure networks ("DePIN") to creating more interactive consumer-facing applications, we highlight some notable developments in this sector.

AI x DePIN

Large language models, deep learning, and various AI applications heavily rely on the computational power of graphics processing units ("GPUs"). However, over the past year, the surge in interest in AI has led to an overwhelming demand for GPUs, resulting in a chip shortage. If GPUs are not readily available, the high costs of computation may deter researchers and startups engaged in AI-related research. This is where decentralized computing networks (a subset of DePIN) come into play. They offer an alternative to existing solutions dominated by centralized cloud providers and hardware manufacturers. As a result, we have also witnessed strong growth in the industry driven by GPU demand.

Considering that GPUs do not always operate at 100% capacity, decentralized computing networks seek to connect those with idle computing power to those who need it. This is achieved by establishing a bilateral marketplace that allows suppliers of computing power to earn rewards from buyers. Examples of such networks include Akash, Render, Gensyn, and io.net. Additionally, the pricing of decentralized computing networks is competitive, as suppliers provide computing power to the network without significant additional costs.

Figure 7: The pricing of decentralized computing networks is competitive

Source: Cloudmos, as of January 2, 2024
Note: Pricing is for 1 CPU, 1GB RAM, and 1GB disk

By providing potential solutions to real-world problems, decentralized computing networks are riding the wave of AI growth, with increasing activity on their platforms.

Figure 8: The number of rendering scenes on the Render Network increased in 2023

Source: Dune Analytics (@lviswang), as of December 31, 2023

Figure 9: Active leases on the Akash Network surged in Q4 2023

Source: Cloudmos, as of January 3, 2024

AI x Zero-Knowledge

Smart contracts are known for their efficiency due to their code-based automation capabilities. However, their predefined nature can sometimes lead to a lack of adaptability, especially in unforeseen complex situations. This is where a subfield of AI, machine learning (ML), can provide significant improvements. Machine learning models are trained on extensive datasets, enabling them to learn, adapt, and make highly accurate predictions. Integrating these models into smart contracts can open up a wide range of adaptability and flexible capabilities.

One major challenge of this integration is the high computational overhead of on-chain ML computation. This leads to the concept of zero-knowledge machine learning ("ZKML"). ZKML combines zero-knowledge proofs with machine learning. In this setup, ML computations are processed off-chain, while ZK proofs are used to verify the integrity of these computations without revealing the actual data. By leveraging ZKML, smart contracts can effectively harness the power of AI while maintaining the security and transparency of blockchain technology.

Figure 10: ZKML combines zero-knowledge proofs with machine learning, performing off-chain computations followed by on-chain verification

Source: Binance Research

A notable development is the ZK Predictor launched by Upshot in collaboration with Modulus Labs. This tool enables Upshot to leverage Modulus ZK circuits to verify asset valuations without disclosing proprietary intellectual property. It can assist in developing automated market makers ("AMMs") that optimize long-tail asset pricing, AI-driven on-chain index funds with on-chain cryptographic proofs of their operations, or prediction markets focused on specific themes that can enhance and validate the accuracy of crowd-sourced pricing signals. Other products utilizing ZKML include price oracles. For example, Upshot provides complex market data to its AI models to assess the value of long-tail assets like NFTs. Modulus's technology then verifies the correctness of these AI computations, encapsulating them in proofs and submitting them to Ethereum for final validation.

These examples are just the beginning of the countless applications that ZKML can support. As this technology is still in its infancy, more mature and widespread ZKML applications are expected to emerge in the coming years.

AI x Consumer dApps

Over the past year, we have observed an increase in the integration of AI in consumer-facing decentralized applications ("dApps") to enhance interactivity and promote user engagement. This trend is changing the way users interact with platforms, providing personalization and interactivity. By leveraging AI, these dApps enable users to transition from mere users to active participants.

One example is AI user-generated content ("UGC") platforms, such as NFPrompt. As the name suggests, AI UGC refers to content created by users with the assistance of autonomous systems. This can be achieved by setting a series of rules that can be automatically outputted, embedding some form of randomness in the algorithm. In other words, users can input a set of rules or constraints (e.g., patterns, colors, shapes), and the AI will generate content based on this framework. By involving users in the creative process, AI UGC platforms establish a more participatory relationship between users and the platform while allowing users to propose unique, one-of-a-kind, and infinitely scalable content.

Figure 11: Generating NFTs using text prompts on NFPrompt

Source: NFPrompt

In addition to content generation, the integration of AI could have profound implications for web3 games or virtual worlds, where character interactivity is enhanced and dialogues are more realistic. The games "Him" and "Her" by Sleepless AI are excellent examples. By utilizing AI, the gameplay focuses on customized and realistic interactions. This provides a more personalized experience and fosters more genuine emotional connections, thereby increasing user engagement.

Figure 12: "Him" and "Her" provide immersive experiences using AI

Source: Sleepless AI

AI x Data Analytics

Accurate market data is crucial for understanding industry trends and is essential for investors to make informed investment decisions. However, real trading instances, such as wash trading, can artificially inflate sales and distort true sales volumes. By integrating AI into analytics to filter out noise, more accurate data outputs can be achieved. This is widely accomplished through AI and machine learning ("ML"), where large amounts of data are used as input to identify wash trading patterns or trends. The end result is a more accurate depiction of market activity.

Take BitsCrunch as an example, an AI-based NFT data analytics platform that utilizes AI and machine learning to detect fraudulent or suspicious trading patterns in real-time, providing accurate data. The use of AI/ML enables the platform to analyze large volumes of data relatively easily, allowing it to distinguish between genuine trading volume and inorganic trading volume. This, in turn, aids in making informed decisions.

Figure 13: Wash trading metrics analyzed by BitsCrunch

Conclusion

The fusion of AI and cryptocurrency technology has sparked tremendous excitement about the potential of these cutting-edge technologies to redefine the digital landscape. AI-centric tokens are becoming increasingly popular, and the growing interest reflected in online search trends highlights the ongoing acceleration of the AI narrative.

Admittedly, we have not yet reached a stage of widespread adoption. Many AI-driven crypto projects are still in their early developmental stages, while others may primarily cater to niche audiences. However, the increasing tangible use cases is an encouraging trend that is positive for long-term growth. Given these factors, investors need to navigate the risks of investing in such cutting-edge technologies while capitalizing on the AI hype.