BUIDLing Your Next Great AI dApp? These 9 'Ethereum Alternatives' Should Be on Your Radar.

Piqued by the potential of AI decentralised applications (dapps) but unsure where to start? You're not alone. With the buzz around blockchain and artificial intelligence, developers are constantly searching for the most conducive platforms to bring their Eureka moment to life.

In this article, we'll guide you through the top blockchain infrastructures that offer the best environments for building AI dapps. From scalability to security, each of these platforms brings unique advantages to the table, helping you choose the perfect foundation to kickstart your AI Web3 creation.

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How Blockchain Is Conducive for AI dApp Development

One of the main reasons blockchain is conducive for AI dApp development is its inherent ability to provide a secure and transparent environment. Blockchain’s decentralised nature ensures data integrity, making it nearly impossible to alter data without detection. This feature is immensely valuable for AI applications, which often require vast amounts of data to function effectively.

Moreover, the integration of blockchain and AI can lead to significant advancements in automation and decision-making processes. For example, decentralised AI systems can use blockchain to verify transactions autonomously or even execute smart contracts based on predictive analytics. This results in more efficient and reliable dApps that can operate without much human intervention, enhancing the overall process automation.

Robust security is another major benefit. Blockchain networks, especially those integrated with AI, are highly effective at identifying and mitigating security threats. It goes further to improve resource allocation and transaction validation.

In addition, the transparency offered by blockchain is perfectly suited for AI-driven dApps. Blockchain provides a public ledger where all transactions and data exchanges are recorded. This level of transparency aids in better decision-making and fosters trust among users, which is crucial for the widespread adoption of AI technologies.

These sound good on paper, so let's put this into action for a more salient understanding. If a decentralised finance (DeFi) is built on an AI-infused blockchain, we could see a situation whereby users can better gauge the markets through AI algorithms or even enjoy a totally hands-off experience in autonomous trading, with reduced risks of fraud or system downtime.

Key Features to Look for in a Blockchain for AI dApps

When building AI dApps, it's essential to choose a blockchain infrastructure that aligns with your application's specific needs. Here are some key features to consider:

• Scalability: Scalability is paramount. The blockchain should handle high transaction volumes without compromising performance. Look for platforms that offer layer-2 solutions like ZK-rollup or sharding to improve throughput.

  
  

• Security: Ensure the blockchain employs robust consensus mechanisms like Proof of Stake (PoS) or Byzantine Fault Tolerance (BFT) to protect against attacks. AI applications often handle sensitive data, making security a critical concern.

  
  

• Interoperability: Your AI dApp might need to interact with other blockchains or traditional systems. Cross-chain interoperability features ensure smooth data exchange and integration across various platforms.

  
  

• Smart Contract Functionality: The blockchain should support smart contracts to automate processes and manage transactions efficiently. Look for platforms offering robust and secure smart contract languages and development tools. Bonus points if it can identify and rectify gaps in codes.

  
  

• Developer Ecosystem: A vibrant developer community and strong ecosystem can provide invaluable support. Access to development kits, comprehensive documentation, and active forums can significantly accelerate your development process.

  
  

• Cost-Effectiveness: Transaction fees and operational costs can impact the overall viability of your AI dApp. Evaluate the cost structure of the platform to ensure it fits within your budget.

  
  

• Data Privacy: AI applications often require access to large datasets that must remain private. Ensure the blockchain has robust data privacy and encryption features to protect user information.

  
  

• Energy Efficiency: Implementing AI algorithms can be resource-intensive. Choosing a blockchain platform that balances computational demands with energy efficiency can help minimise the environmental impact.

Ethereum Is Still the Powerhouse for dApps. But It's Not the Only One.

For developers venturing into the realm of AI dApps, Ethereum remains the go-to choice, and for good reason. It's the pioneering platform that introduced smart contracts and an established infrastructure, allowing developers to create dApps with ease.

Moreover, Ethereum boasts a substantial and vibrant ecosystem encompassing a vast array of DeFi projects, non-fungible tokens (NFTs), and DAOs — a fertile ground for integrating AI capabilities within various niches.

Ethereum’s robust and active developer community has been sustaining continuous improvements and support, making it easier to find solutions and collaborate. For instance, Ethereum’s Layer 2 solutions and sidechains cater to scalability concerns, a crucial aspect for compute-heavy AI applications.

It'll be remiss to not mention the interoperability of Ethereum too. AI dApps can benefit from a broader range of functionalities and resources, just by being able to communicate with a great variety of blockchain networks.

However, Ethereum isn't without its drawbacks. High gas fees and network congestion often plague developers, making it less ideal for dApps requiring fast and cost-effective transactions. This is especially problematic for AI dApps, where frequent data processing and micro-transactions are common.

Thankfully, Ethereum is not the only option out there. Other blockchain infrastructures are emerging, specifically tailored to overcome these challenges in AI blockchain developments.

Top 9 AI Blockchain Platforms to Build AI dApps: A Summary

NEAR Protocol

NEAR Protocol is a scalable, user-centric and developer-friendly layer 1 blockchain designed to lower the barriers to building dApps, including those that leverage AI capabilities.

It employs a unique scaling solution called Nightshade, a form of sharding that significantly boosts its throughput. This is crucial for AI dApps, which often require extensive data and computational resources. With Nightshade, the network can process thousands of transactions per second, ensuring that your AI-powered dApp runs smoothly and efficiently.

Furthermore, NEAR supports a variety of programming languages, including Rust and AssemblyScript, making it easier for developers to create sophisticated smart contracts. Its Dev Console and NEAR-CLI (Command Line Interface) are additional tools in its development suite that streamline the development process, from coding to deployment.

For those who already have existing dApps built on Ethereum, developers can simply port them over to the NEAR ecosystem through Aurora, an Ethereum Virtual Machine (EVM) built on NEAR.

A use case it is best for: NEAR's scalability makes it suitable for building decentralised machine learning platforms where models can be trained and deployed collaboratively.

Algorand

Algorand may not have AI features built directly into its protocol, but its pure proof-of-stake (PPoS) consensus mechanism ensures high throughput and low latency, making it highly efficient for processing complex AI computations rapidly, especially for dApps in DeFi, gaming, or supply chain management.

The platform's scalability is another advantage. Algorand boasts lightning-quick transaction finality, and can handle up to 6,000 transactions per second. This is ideal for AI applications that require real-time data processing. This scalability ensures that your AI dApp can grow and handle increased demand without compromising performance.

Algorand's architecture emphasises tamper-resistant and auditable transactions; this level of security is essential for maintaining trust and integrity in AI dApps, where data integrity is non-negotiable.

With a developer-friendly environment at work, developers can have a better time building, deploying, and managing AI dApps. As transaction fees are minimal, developers are incentivised to build cost-effective AI dApps that can be easily accessed and used by a wider audience.

A use case it is best for: If you're intending to build a dApp in the DeFi realm, Algorand would be your right-hand aide in realising AI-powered trading bots, prediction markets, and risk assessment tools, thanks to its speed and scalability.

aelf, Layer 1 AI Blockchain

aelf, a high-performance multi-chain AI blockchain framework, has already made leaps in extending a valuable platform for developers building AI-powered dApps. That is on top of their already successful partnership with ChainGPT to integrate cutting-edge AI chatbots for both developers' and users' benefit.

The high-performance environment is made possible by AI smart contracts capable of handling complex logics and intelligent decision-making, as well as a layer 2 solution in ZK-rollup technology to boost scalability and EVM compatibility. This goes a long way in reducing costs, improving privacy, and providing a seamless user experience for AI dApps.

Developers keen to build on aelf will be exposed to a newly upgraded playground of AI tools; they include Natural Language Processing (NLP) assists in smart contract creation, and aelf Playground, a user-friendly development environment for writing, deploying, and testing smart contracts on the aelf AI blockchain while ensuring codes are error-free.

aelf is also introducing an AI Oracle in the near future. Using a credible Web3 AI framework, it seeks to promote creation of AI-centric dApps and bring about seamless integration of AI capabilities into blockchain applications.

A use case it is best for: The sky is the limit with a sophisticated AI blockchain infrastructure. Developers across various niches can leverage aelf to springboard Web3 solutions in the areas of DeFi, GameFi, content creation, and more.

Evidently, aelf's ecosystem already houses dApps in said aspects, namely AwakenSwap (DeFi platform specialising in direct swaps), Project Schrödinger (cat adoption game), Forest (an NFT marketplace), and Portkey (Account Abstraction wallet).

Ontology

Ontology brings a set of unique features to the table, culminating in a robust infrastructure for decentralised identity and data management. This focus on trust and data aligns perfectly with the needs of many AI applications.

Ontology's ONT ID framework provides a secure and sovereign way to manage identities on the blockchain. This is crucial for AI dApps dealing with sensitive data, enabling verifiable credentials, user authentication, and data access control. Moreover, Ontology supports smart contracts and provides oracles for connecting real-world data to the blockchain. AI dApps can leverage these features to automate processes, execute agreements, and access external data feeds for analysis and decision-making.

Performance-wise, its high throughput and low transaction costs make it ideal for running complex algorithms that require many calculations and data exchanges. Coupled with cross-chain interoperability, AI dApps can effectively interact with other blockchain platforms, while running efficiently even under heavy load.

A use case it is best for: Given Ontology's niche in decentralised data management and trust infrastructure, it is well-positioned for AI dApps purposed for healthcare needs. It is sure to make inroads in tasks like AI-driven diagnostics, patient data management, and clinical trials.

Cortex

One of the standout features of Cortex is its ability to execute AI algorithms directly on the blockchain. This means AI dApp developers can integrate complex machine learning models and data analysis tasks in a decentralised environment, enhancing both transparency and security.

Cortex offers an AI-powered smart contract, known as 'Cortex Virtual Machine' (CVM), which allows you to upload models onto the blockchain. What's impressive is that these models can be used to make inferences within the smart contract execution. So, instead of fetching data off-chain or relying on external services, the blockchain itself handles AI-related tasks, reducing latency and dependency on third-party services.

Additionally, Cortex provides a well-structured ecosystem for data scientists and AI developers. It supports a wide range of AI frameworks, making it compatible with various machine learning libraries you might already be familiar with. This interoperability ensures that you can use existing tools and models with minimal modifications, simplifying the development process.

The decentralised nature of Cortex also guarantees that the data used in AI models is not controlled by a single entity. You can be confident that both your inputs and outputs remain verifiable and tamper-proof.

A use case it is best for: AI-powered prediction markets can leverage on-chain execution to ensure transparency and prevent manipulation. An honourary mention goes to gaming and virtual worlds, in which Cortex can facilitate building of complex AI characters, intelligent environments, and decentralised gaming economies.

Fetch.ai

Fetch.ai has been well documented for its autonomous economic agents (AEAs), which are AI-driven entities capable of making decisions, interacting with other agents, and autonomously executing tasks.

Besides AEAs, the next biggest appeal is arguably the sophisticated machine learning models that developers could bank on to analyse enormous datasets, discern patterns, and automate decision-making processes — a core capability for advanced AI applications. These models are maintained by a fully decentralised network, which fosters resilience and guards against potential disruptions and censorship.

The platform's high throughput and use of a Directed Acyclic Graph (DAG) structure ensure that transactions are processed swiftly without compromising on security or scalability. Moreover, Fetch.ai's compatibility with other blockchain networks makes integrating various blockchain solutions seamless, thereby broadening the horizon for AI-driven innovations.

With its decentralised network and robust consensus mechanisms, the platform ensures that AI models and data are safeguarded against tampering and breaches. This level of security is paramount for businesses and developers seeking to deploy trustworthy and reliable AI solutions.

A use case it is best for: Fetch.ai excels in optimising smart grids by using autonomous economic agents. These agents can negotiate energy prices, improving efficiency and reducing costs for both providers and consumers.

PlatON

PlatON is a decentralised network focusing on privacy-preserving computation. This makes it an ideal choice for AI dApp developers, who often deal with sensitive data.

PlatON leverages advanced cryptographic technologies like secure multi-party computation (MPC) and homomorphic encryption. This enables data sharing and collaborative computation without revealing the raw data itself, essential for AI applications handling sensitive data in healthcare, finance, or other privacy-critical fields. The reason why nodes can verify the correctness of computations without accessing the underlying data is because of the platform's verifiable computation techniques.

When talking about AI dApps, it can't go without AI smart contracts. The platform supports AI-powered smart contracts that can execute complex logic and adapt to changing conditions. This flexibility is vital for AI dApps requiring dynamic decision-making based on real-time data and AI insights.

A use case it is best for: In healthcare, AI-driven diagnostics, personalised prescriptions, and clinical trials can leverage PlatON's privacy-preserving computation to analyse sensitive patient data without compromising confidentiality.

Matrix AI

Matrix AI fuses artificial intelligence and blockchain technology to create a decentralised, autonomous network. What sets Matrix AI apart is its ability to self-optimise. It achieves this through a hybrid consensus mechanism that combines Proof of Work (PoW) and Proof of Stake (PoS) with AI algorithms to optimise network performance. This adaptability helps AI models operate efficiently even as network conditions change or evolve.

Developers interested in building AI dApps may have a field day with Matrix AI; there are intelligent contracts and no-code AI tools at their disposal, among many other features. Intelligent contracts are Matrix's AI-enhanced smart contracts. They're designed to be more flexible and adaptable than traditional smart contracts, capable of handling complex AI logic and making decisions based on real-time data and AI insights. No-code tools, as the name suggests, make it possible for those with no app development background to easily get started with their creations.

As Matrix AI progressed from its 1.0 phase to 3.0, it saw a slew of new introductions such as a decentralised AI Marketplace (MANAS), Data Sharing & Privacy (DePIN), and Morpheus (a decentralised LLM and GPT platform).

A use case it is best for: Piloting smart city initiatives such as AI-driven traffic management, energy optimisation, and public safety systems.

Sahara AI

Sahara AI is a rising AI blockchain start-up that champions collaboration, transparency, and fair compensation. By leveraging blockchain technology, it aims to empower individuals and organisations to contribute to and benefit from the creation of AI models, applications, and data.

This unique approach makes Sahara AI an exciting platform for building AI dApps that thrive on collective intelligence and ethical data practices.

The platform is committed to transparency and fairness, providing on-chain attribution for every contribution. Whether you're developing models, building applications, or providing valuable data, your efforts are recognised and rewarded, creating a sustainable and incentivised ecosystem.

On data privacy and ownership issues, Sahara AI ensures complete traceability of data contributions and model interactions through blockchain technology. Similar to Matrix AI, Sahara offers no-code/low-code platforms, allowing both technical and non-technical users to participate in creating and deploying AI solutions.

A use case it is best for: The collaborative model could be fitting for decentralised research and development platforms. Scientists and researchers could share their findings, collaborate on projects, and access diverse datasets to accelerate discoveries and breakthroughs.

Conclusion

These nine AI blockchain infrastructures offer compelling alternatives if one were to look beyond Ethereum. They bring their own strengths and features to the table, from high scalability and robust security to unique AI integrations and efficiency in handling complex computations.

After all, if you're building an AI dApp to 'save the world'’ it is only going to be as good as the platform it is being supported on. Choose wisely based on your special set of requirements; with emerging technologies like NEAR Protocol and aelf, or more niche platforms like Cortex and Sahara AI, the ecosystem is rich with opportunities to innovate and grow, but also paradox of choice.

Do shortlist a couple of contenders and follow them on their respective channels, such as X or Discord. That way, you can stay updated on their latest developments, and gauge their future sustainability to continue powering your AI dApp.

About aelf

aelf, an AI-enhanced Layer 1 blockchain network, leverages the robust C# programming language for efficiency and scalability across its sophisticated multi-layered architecture. Founded in 2017 with its global hub in Singapore, aelf is a pioneer in the industry, leading Asia in evolving blockchain with state-of-the-art AI integration and modular Layer 2 sK Rollup technology, ensuring an efficient, low-cost, and highly secure platform that is both developer and end-user friendly. Aligned with its progressive vision, aelf is committed to fostering innovation within its ecosystem and advancing Web3 and AI technology adoption.

For more information about aelf, please refer to our Whitepaper V2.0.

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