AI and Blockchain Disruption: Unveiling Perfect Synergy Use Cases

AI and blockchain—two equally disruptive technologies, but only one is stealing the show! What if we combined them to leverage each other's transformational powers? This report investigates projects that sense the potential in using one to enhance the other. Find out how the synergies work, where they are most useful and whether they have a future.



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    ORA enables verifiable AI inference on any blockchain.

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    AI company within life science, exploring onchain market developments for breast cancer data.

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    AI model designed to provide solutions and applications for the web3 industry.

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Key Takeaways

  • It’s not about assessing whether or not combining AI and blockchain makes sense. The powerful synergy between the two became apparent from the very first industry report we analyzed. The question is, where will it be most beneficial?
  • Currently, the largest group of Web3 project types provides other companies with AI-based tools. For example, and Autonolas facilitate decentralized crypto-AI agents, while ORA Protocol gives them access to open-source AI development communities.
  • The adoption of AI x blockchain convergence companies remains mediocre at best. Surprisingly, the industries with the highest adoption rate are healthcare and finance. In general, Web3 projects are more open to integrating AI solutions than the other way around.
  • We found four application sectors leveraging this alliance the most: generative AI, battling fake news, micropayments, and smart contract auditing.
  • AI & blockchain are used in synergy, mainly to address internal processes and enhance technological efficiency. We have yet to see use cases that provide solutions for more worldly concerns.

Where does blockchain meet AI?

Can blockchain be perceived as disruptive as artificial intelligence? Will rapid AI developments cause VCs to forget entirely about Web3? And does the blockchain world need AI only because of the energy created through the huge hype?

Despite the uneven attention the two technologies have received over the past few months, their disruptive power is quite comparable. However, there is no competition between them. Quite the opposite is true. We believe AI and blockchain can and should coexist and cooperate.

This research report reveals why and explores how.


Our initial research for this report led us in a distinct direction. We quickly realized that it’s not about assessing whether or not combining AI and blockchain makes sense. The powerful synergy between the two became obvious from the very first industry report we analyzed.

The data collected from the survey respondents (including people familiar with AI, blockchain, or both) further confirmed this assessment. This convergence’s transformative potential became apparent in real-world challenges and its ability to improve businesses.

Hence, this paper focuses on when and where the combination of AI and blockchain can bring the most value

Is it particularly useful for a specific industry? Can it enhance or create a unique business model? Or are there internal company processes that are particularly “revolutionizable” by the convergence?

Stay with us as we dig deep into these critical entrepreneurial considerations.

Everything you’ve wanted to know about AI (but were afraid to ask?)

Before we embark on a journey to explore the most useful, promising, and popular synergies between AI and blockchain, let’s clarify the term that you are probably less familiar with: AI. We believe you already know a thing or two about blockchain. 

What is artificial intelligence, anyway?

Artificial intelligence is a field of computer science that started in the late 1950s. It is dedicated to creating intelligent machines that can think, learn, and act autonomously – similar to humans. The idea is to make computers smart enough to carry out simple tasks that usually require human intelligence, like understanding language, recognizing patterns, or making decisions.

This is how AI operates in a nutshell: AI gathers data from various sources and preprocesses it for analysis and selection of appropriate algorithms for the task at hand. It trains machine learning models if needed, makes predictions or decisions based on the analyzed data, and incorporates feedback to improve performance over time. This enables AI systems to emulate human-like intelligence and autonomously perform tasks ranging from image recognition to recommendation systems and self-driving cars.

ChatGPT isn’t all that artificial intelligence has to offer

ChatGPT is a specific type of AI, like a powerful tool in a toolbox. It’s a large language model (LLM) that excels at generating human-quality text. But artificial intelligence is much broader. It encompasses everything from self-driving cars that learn to navigate to medical diagnosis tools that identify patterns in patient data. 

What’s the difference between artificial intelligence and machine learning? 

This is a common question, so how can we stop confusing the two? AI is responsible for achieving the overarching goal of creating intelligent machines, and machine learning is a technique used to fulfill it. Think of it this way: AI is the destination, and machine learning is one of the vehicles that can take us there. 


Where does blockchain technology fit into the picture? 

Blockchain technology serves as the underlying technology for the infrastructure required for many AI tools. At the same time, blockchain adds value to AI projects and AI-driven tools by providing an additional security layer, transparency, decentralization, automated mining processes, data analysis, secure storage, and data management. 

In simpler terms, blockchain creates a secure, transparent, and decentralized infrastructure needed for AI tools to function effectively in the world of open-source projects. And we’re happy to elaborate more on this in the following sections. 

If you’d like to make yourself familiar with some of the critical terms surrounding blockchain x AI intersections before we dive in, please check out our short glossary.

Web3 AI x blockchain market

Let’s start by reviewing the current state of the AI x blockchain market, giving you an overview of the projects we’ll discuss here.

Our first research step was an initial screening of the AI part of the Web3 market. We analyzed 107 companies that fit this description. We then categorized them into the following 13 sectors (this is explained in more detail in the “AI x blockchain – synergies overview” section):

We broke the categories down further based on the value projects bring to the market. We identified the following three main types:

  • Suppliers – Web3 projects that provide other companies with resources necessary for creating or maintaining AI tools. Good examples are Akash and OpSec, which use DePIN to deliver computational power, which is essential for AI algorithms.
  • Providers – Web3 projects that provide other companies with AI-based tools. For example, and Autonolas facilitate decentralized crypto-AI agents. 
  • Users – Web3 projects that implement AI solutions to improve already existing blockchain-based initiatives. For instance, Web3 games like and Sidus use artificial intelligence to optimize in-game experiences.

The obvious question now is: How are the Web3 projects distributed among the three types?  

It’s safe to say that it’s relatively balanced. The most universal group are the providers, and they’re also the largest, with the abject sides of the spectrum almost identical in size. Note that some companies supply AI tools with data, computational power, etc., and provide solutions on their own. That’s why the sum of the projects in the below graph exceeds 107.

The last part of this initial analysis focused on the services provided by such companies. We investigated the specific utilities Web3 AI projects offer for both users and businesses, starting with the aforementioned decentralized computing, moving through decentralized generative AI models, and ending with smart contract auditing and generation.

The following graph shows the number of projects in each service category.

And if you’d like to see more concrete examples of projects operating in specific niches, here is a blockchain x AI sector map for you:

You must be keen to grasp what these niches, utilities, and numbers mean for you as an entrepreneur. Well, now that we are on the same page regarding the projects that capitalize on or provide the most value from AI x blockchain convergence, we can dive into specific use cases of various synergies.

AI x blockchain – synergies overview

To pinpoint the potential impact, it’s best to look at this union from two different angles:

  1. How does blockchain already help AI? Use cases related to AI problems that blockchain technology addresses, e.g., the lack of computational power to run AI algorithms → utilizing the unused GPU power from personal computers thanks to DePIN technology.
  2. How does AI already help blockchain? Use cases related to blockchain problems that artificial intelligence addresses, e.g., vulnerabilities in smart contracts → automated AI-based smart contract auditing.

We also included an analysis of specific business implications (e.g., the changes in revenue streams, cost structure, and accessible markets thanks to implementing AI x blockchain synergies). You’ll find it all in the following three sections of the report. 

How does blockchain already help AI?

Decentralized computing power

AI is currently not in a position to overtake the world. It’s way more focused on ordinary, playful, or routine tasks. Like this one: finding enough computational power to create yet another image based on the “Dwayne The Rock Johnson as a Rock” prompt.


Source: TBC

Regardless of the task, AI algorithms always need graphic processing units (GPUs) to perform them. The sudden increase in demand for advanced chips such as Nvidia’s H100 has caused a shortage, driving up the costs of such computational power. Therefore, it may become impossible for individuals or smaller businesses to use AI at full scale, leaving it in the hands of well-funded corporations.

That’s where DePIN, a blockchain-based technology covered in our previous report, comes in, along with projects like Akash, OpSec, and the Golem Network. They serve as a type of Uber or Airbnb for computational power and create a two-sided marketplace. Companies needing GPUs can “rent” them from businesses and individuals who share them in exchange for token-based incentives.

Such an approach significantly decreases the cost of computational power. Look at a simple comparison of Akash pricing with the largest representatives of a non-Web3 computing market:

It’s also important to mention that the DePIN x AI intersection benefits aren’t limited to businesses or people using high-end GPUs such as Nvidia A100s. The recently well-funded GPU network ($30 million in Series A) allows the sharing of the power coming from the Apple’s M1 or M2 chips. 

So, if you’re currently reading this report on your Macbook, you may consider an additional source of income. 

Security risks in AI

As artificial intelligence systems become increasingly sophisticated and integrated into our lives, they also introduce potential security risks that need careful consideration. 

  • One major concern is the trust problem surrounding AI data. How can we ensure the data used to train these systems is accurate, unbiased, and ethically sourced?
  • Additionally, the centralized nature of many AI platforms raises a red flag about the potential misuse of data and lack of control for individuals. 

Therefore, safeguarding privacy and establishing security measures are crucial to maintaining trust and ensuring the ethical and responsible use of AI technology.

The fundamental trust problem for AI data

AI adoption expands at an extraordinary pace – especially when fueled by Big Data, the resource that enables AI to learn, become smarter, and unlock insights from an ocean of information.


However, as AI advances, the trust problem grows proportionately. Discerning between genuine and AI-generated content becomes increasingly difficult. Generative AI, while capable of stunning creativity, lowers the barrier for misinformation and deepfakes, demanding a renewed focus on content authentication. For example, an AI system trained primarily on images of white men performs poorly when identifying people of color or women, leading to biases and discrimination.

This erosion of trust fuels the need for solutions, and blockchain can offer some. Take these four as examples:

1. For those who are already tired of fake news and misleading AI-generated media (all of us?), Fox Corporation has taken a bold step. The news company launched Verify, a tool built on Polygon where publishers establish undeniable proof of content ownership. Each piece is secured on the blockchain, empowering consumers to easily identify trustworthy sources using the Verify Tool.



2. Teams from the Arweave blockchain and the provenance layer, Irys (formerly Bundlr), have followed suit and developed the Digital Content Provenance Record (DCPR) standard. This innovative solution utilizes the Arweave blockchain to timestamp and verify digital content. By providing reliable metadata, DCPR empowers users to assess the credibility of online information with greater confidence.

3. Similarly, skincare firm Clarins combats counterfeit ingredients with cutting-edge blockchain verification. Partnering with NeuroChain, an ML-based consensus blockchain and Community, they built the Clarins T.R.U.S.T. solution. Suppliers log ingredients into the system, while the blockchain provides proof of authenticity, ensuring only genuine materials enter their supply chain.

A scalability test carried out by Clarins produced the following results: They were able to open 160,000 pages of their website/software interface within 1 minute without any degradation and without any negative effects on response times or availability.

4. The last example comes from the world of global enterprises. The Geoscience Team of the Abu Dhabi National Oil Company (ADNOC) used AI to augment and accelerate the visual photography process. ADNOC has partnered with software giant IBM to pilot blockchain-powered transaction management for its oil and gas operations. This combined solution helps the organization automatically track, validate, and execute transactions throughout the supply chain.

Centralization risk of AI
While open-source AI promises democratization, a closer look reveals a troubling reality: the control seems to remain in the hands of a few tech leaders. Google, Amazon, and Microsoft appear to dominate AI development by concentrating the necessary data.

Centralized data storage raises the following concerns:

  • A single point of vulnerability: breaches at large data holders expose vast amounts of information.
  • Lack of transparency: users often have little control or visibility over how their data is used.

To combat these challenges, companies are leveraging blockchain and AI to revolutionize how data is stored, managed, and used, with profound implications for healthcare and beyond. One such example is the partnership between Akash Network and Solve.Care, which is using these technologies to change how patient data is handled.

Due to current industry regulations, patient data must be stored in environments such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). Under this system, patients lack sovereignty over their own data. Additionally, there is no guarantee of digital permanence if data is lost.

Akash Network and Solve.Care’s partnership allows patients to own and control their health information securely through blockchain technology. Akash provides the computing power necessary to deploy Solve.Care’s proprietary node infrastructure (Care.Nodes) to patients globally. As a distributed network with compute providers worldwide, Akash allows Solve.Care to access computing resources without permission in the regions where their enterprise clients and patients reside and receive care.

Another inspiring example of this approach is Thermaiscan, which explores blockchain to improve healthcare data security:

Arby Leonian

Combining AI and blockchain makes achieving a good level of healthcare data security way more possible. It’s a completely different approach to databases compared to how it’s being done today in Web2

Arby Leonian, CEO & Co-founder of Thermaiscan

However, such a transition doesn’t happen without challenges. Especially in an “analog” industry like healthcare. As stated by Arby, “Many countries and healthcare systems still use paper and pens. Moving into digital systems powered by blockchain will require them to make a “jump” compared to some developed western countries that use standard databases provided and hosted by big cloud server companies.

But according to Thermaiscan, it’s still achievable. Especially in emerging markets: “If we take Swedish healthcare providers, they still use hard disks to store data – since they don’t want to contract international cloud providers. However, low and middle-income countries where Thermaiscan has been engaged have a higher adoption and acceptance rate of new technologies. There, it’s way easier to find right-minded, healthcare-oriented people and convince them to at least start testing such solutions.

Healthcare is by far not the only industry experimenting with this synergy of technologies. In a joint effort, (partnering with peaq), Deutsche Telekom, and Bosch use the AI x blockchain convergence to transform IoT devices.

At the heart of their collaboration is the Bosch XDK110 sensor kit, which serves as a self-contained data acquisition solution and is capable of measuring environmental factors (such as seismic activity, humidity, pressure, etc.) for various applications. Each sensor has a secure, blockchain-powered identity that enables the identification of a machine by giving it a unique identifier generated by the blockchain protocol of peaq ID.’s AI agent allows the sensor to learn, function independently, and even earn revenue by selling its data. At the same time,  the sensor data can improve traffic management or air quality monitoring. You’ll learn more about AI agents in the “Crypto Infrastructure for Agents” section.

Machine learning privacy

AI systems are hungry for vast datasets, regardless of whether they contain sensitive information or not.

Let’s stick to the example of health data because privacy is particularly critical here. The personal genomics company Nebula Genomics teamed up with Web3 company Oasis Labs to give people more control over their genetic information. They use a new tool called Parcel which is designed to let users decide who can access their data based on a complete history of information usage stored on the blockchain. This way, patients can see who has looked at their genetic information and stop such activities whenever they want. 



In another example, iExec, a marketplace for AI models, leverages blockchain technology and Intel’s SGX hardware to ensure secure sharing and utilization of AI models. When these models are rented on iExec, they remain encrypted until reaching a secure environment provided by Intel SGX. This protects the intellectual property of AI developers and promotes trust within the AI ecosystem, where concerns about model theft and misuse are prevalent.

AI data decentralization

Research on AI is flourishing. Based on the results derived from the EBSCO database, since 2021, over 1,300,000 academic papers have been published on this topic. That’s more than throughout the entire last decade (1,100,000). 

Does the quality meet the quantity, though? Not quite. Research is done repetitively by a small group of authors, and there is a growing number of flashy journals and biased reviews. This suggests that there’s a need for change. Another argument is that “data” remains the most frequently cited challenge in discussions surrounding AI, as our survey indicates (you can see it from the following graphic).

That’s where decentralized AI research fits in. It’s an innovation highly focused on incentivizing data sharing. Projects like Bittensor and Allora use the weighing system to encourage participants to co-create ML models by focusing on the most useful data. This approach is based on analyzing a specific task (e.g., predicting the weather), weighing the importance of specific data when building a model to conduct it, and – most importantly – incentivizing participants through economic rewards to share the most vital data. 

If you read our previous report on DePIN business opportunities, you discovered that decentralized AI research is often combined with DePIN mechanisms. A simple example is Grass, which pays its users to share data scraped from the public web. This is essential for even basic AI tools such as ChatGPT.

The casual gaming company NeuroMesh invests significant resources in building distributed training networks. Its executives believe in the potential that incentivization brings to building AI models.

Diego Hong, CTO_NeuroMesh

I see blockchain itself - or, let's say, Web3 itself as a very good provider of computing power. It's perfect for incentivizing GPU provision, data training, or addressing cross-country demands. It's basically the infrastructure upon which we can build larger AI applications and models.

Diego Hong, CTO at NeuroMesh

Similarly, the Chief Scientist of the AI-powered oracle ORA Protocol sees it as one of the most powerful convergences of both technologies. 

Cathie So, PhD

Especially when we consider the latest development by ORA (optimistic machine learning), one of the most promising intersections would probably be the open source movement, based on the incentives. I think we’ve kind of solved this aspect in Web3, which means we know how to use crypto-economics to help this open-source ecosystem grow. Whereas in AI, I think we're still figuring it out. But that’s basically why we need this synergy so much.

Cathie So, Chief Scientist at ORA Protocol

It’s also worth mentioning that, in the case of AI research, decentralization goes beyond expanding the number of data sources through incentivization. Cere Network takes it one step further and adds a layer of shared storage. 

A single point of failure is perceived as something blockchain can successfully address; therefore, securing AI data by decentralizing serves as a viable option. It’s another intersection between AI and DePIN – Cere Network compensates its users for providing storage in the same way as Filecoin and Arveawe. 

All these factors contribute to the shared view among people familiar with blockchain, AI, or either of these areas that decentralized AI is the synergy with the highest potential for the future.

How does ORA Protocol contribute to the Web3 x AI landscape?

Ora Protocol

The ORA Protocol contributed significantly to our research. Read how the ORA team sees its role in the field of decentralized open-source AI:

Open-source AI startups face a major hurdle in securing the funding that would enable them to stay competitive in the rapidly growing industry. ORA’s recently developed Initial Model Offering (IMO) framework acts as an expansive tool for funding, allowing open-source AI to compete with closed-source AI and combat the emerging AI monopoly.

AI development is an extremely resource-intensive undertaking for a business, and open-source is not viewed as attractive to investors. OpenAI itself has adjusted its capital structure three times over its lifetime in order to adopt a sustainable business approach in this industry. From a non-profit to a for-profit to a limited partnership (LP), OpenAI has strategically positioned itself to raise the capital it needs to remain competitive without giving up equity. As a LP, OpenAI exchanges a percentage of future revenue and future asset appreciation for capital via limited partner deals.

For open-source AI, the head start that the incumbent giants have is vast and leaves little opportunity for capital raising. The Initial Model Offering (IMO), pioneered by ORA, provides open-source AI development communities with a framework to fundraise rapidly from an international market. Through the IMO framework, the value of the model and its applications are represented onchain as a verifiable tokenized model, which can be used to incentivize the continued development of the model. Moreover, the IMO includes an automated revenue-sharing mechanism so that a percentage of future revenue generated by the model flows back to the token holders. IMO provides open-source AI communities with the same powers as the capital structure of OpenAI.

Crypto infrastructure for AI agents

A dystopian vision of an army of autonomous AI-powered individuals is already coming to fruition. And blockchain can make it even more frightening – or promising.

A so-called AI agent is a software program that acts on your behalf based on provided data. The data can come from a variety of sources, including manual input from humans, sensors, or cameras. An AI agent is designed to make an informed decision and act according to specifically defined requirements.

It may sound a bit vague and complicated, but examples of AI agents are already surrounding us. Innovations such as autonomous vehicles, virtual assistants, or even good old chatbots in the form of ChatGPT all rely on AI agent principles. 

However, despite a vast set of use cases already present, such solutions lack one important aspect to become truly powerful: built-in economics. AI agents are unable to open or use a bank account and spend money on your behalf. Let’s be honest; they’d immediately fail the KYC process. And even if we provided them with the necessary permissions (you can imagine the endless back-and-forth with the bank or its chatbot AI agent), such a tandem would likely become cumbersome, inefficient, and dangerous for your personal finances.

Providing crypto-AI agents with a blockchain infrastructure adds an economic layer to their work while also addressing security concerns. 

One of the most promising examples of this intersection is On the one hand, it enables developers to build secure and token-governed autonomous agents with the software. On the other hand, it provides a marketplace called agentverse, where AI deputies can transact, negotiate, and chain with each other, forming “working groups.” 

As a result, AI agents become more autonomous and accomplish tasks in a collective manner. When an agent requires data or a specific activity, it can purchase it from another participant in the agentverse. At the same time, the entire network is still governed by humans and token holders. They need to ensure their agents focus on assigned duties, such as building real-time EV charging station maps (an actual use case), instead of taking over the entire world.



Ocean Protocol also explores this particular use case. Ocean sees blockchain as a decentralized substrate to support sovereign AI agents, i.e., “AI DAOs”:

Trent McConaghy

In such a case, the agent itself is not owned or controlled by anyone. It literally owns itself. We can basically imagine a smart contract that simply has “more intelligence” and takes the AI agent shape. It can then sense the world, build a model of the world (a model relevant for itself), take actions, get feedback and update its model, or even accumulate resources (coins, data, compute, data storage, bandwidth, etc.).

Trent McConaghy, founder of Ocean Protocol.

Another project that utilizes the AI DAO concept is Autonolas. Here, a group of OLAS token holders facilitates governance over the AI agents, enabling more complex cooperation methods between contracts. By doing so, the project helps businesses and developers build an army of autonomous agents that work collectively towards an outlined goal. Companies, teams, people… who needs them anyway? Scrap that; we never said that!

Addressing the AI carbon footprint

It’s estimated that data center energy consumption in Europe will grow by 28% by 2030. Needless to say, AI is a major contributor to this massive increase. To put things in perspective, training ChatGPT-3 generated 552 metric tonnes of carbon, which is equivalent to driving 112 petrol-powered cars for a year.

How did we approach the research?

The research was based on the "grounded theory." It means we had no specific presumptions before staring the work. The "business" part of the analysis relied on the chosen elements of the Business Model Canvas (A. Osterwalder, Y. Pigneur) and was primarily based on the qualitative research methods.

Research limitation

  • General: It was sometimes challenging to clearly indicate whether a project applies to the “blockchain for AI” or “AI for blockchain” category. Some of the use cases may be included in both. Several projects started their PoC, but there was no up-to-date information on whether they made any progress in mainnet development. 

  • Quantitative research: The research on AI companies included only a portion of Web3 AI companies (the ones that appeared in the TOP1500 of CMC at the time of the research, i.e., April 2024). It also didn’t include companies that don’t use tokens (besides CertiK). The results of the quantitative survey on AI and blockchain experts may be slightly biased due to the unequal structure of the sample (e.g., it included more AI than Web3 experts; the majority of respondents represented technological industries). However, considering the nature of the research, which has a distinct focus on technology, the effect on the final results should be minor.    

  • AI experiments: The source code that was tested and audited was a simple, smart contract example, and the output is just to demonstrate generative AI systems for blockchain and smart contract development. Generative AI systems are still in the early stages of smart contract development and auditing. Hence, it is not recommended to use them on a larger scale beyond such simple experiments.  


  • The research was conducted using a grounded theory approach. However, before starting it, we had one presumption: AI x blockchain synergy is useful for both Web3 and non-Web3 companies. The presented use cases, industries, or technologies in which the combination of the two technologies fits best were selected based on further research. 
  • The quantitative research on Web3 AI projects (from the “Web3 AI x blockchain market” section) was done on the first 1500 projects from CoinMarketCap (as of April 2024). The choice of the 107 Web3 AI projects was based on (1) AI services – we looked only for the companies that implemented them or solutions that aim at helping AI; (2) traction – we looked only for projects with working use cases; and (3) the stage of development – we looked only for projects that have at least a PoC in place.
  • The quantitative survey was conducted with a diverse group of 464 respondents. A significant portion (41.4%) indicated AI as their area of expertise, while 25% identified with blockchain. The remaining respondents represented a mix of both or other completely different industries. The majority of respondents (32%) came from technological industries, with job roles primarily in Lead/Managerial (36.4%) or C-level/Founding (22.4%).


  • Deep Learning (DL) – a subset of machine learning where algorithms with multiple successive layers (known as neural networks) can extract features from the input data and make predictions like classic machine learning algorithms.
  • Machine Learning (ML) – a core AI technique in which algorithms learn from data to make predictions or decisions without explicit programming.
  • Decentralized Artificial Intelligence (DAI) – AI models trained and executed on decentralized networks, that eliminate reliance on a single entity for control or data storage.
  • Natural Language Processing (NLP) – enables AI to understand and generate human language. NLP can be used to analyze sentiment in social media data on blockchains or to create chatbots for interacting with decentralized applications (dApps).
  • Generative AI – models that can create new data, like images, video, audio, or text. While not directly used in core blockchain functions, generative AI has the potential to create new user experiences within Web3 environments.

Lead by

  • Michał Moneta

    Michał Moneta

    Head of Onchain

Conducted by

  • Ananya Shrivastava

    Ananya Shrivastava

    Research Analyst

  • Ambreen Khral

    Ambreen Khral

    Market Researcher

  • Arin Soleymani

    Arin Soleymani

    Senior Business Developer

  • Boris Agatić

    Boris Agatić

    Data Scientist


  • Ruth M. Trucks

    Ruth M. Trucks

    Senior Content Manager

  • Lucas de Melo

    Lucas De Melo

    UX Designer

  • Chris Braithwaite

    Chris Braithwaite

    Content & Technical Writer

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What will you discover:

  • Diverse use cases of AI enhancing blockchain

  • The results of our own Ochain AI audit experiment

  • Which industries are most impacted by the synergy

  • Where to expect market growth, how much and how fast

  • How to address related technology and ethical concerns

  • And more!

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