The AI blockchain market has grown tremendously in the past few years, and it’s expected to reach USD 3,414.46 million by 2032. Such growth impact is clearly visible in the realm of sovereign L1 chains, especially Avalanche L1 ecosystem. The most recent addition of AI-powered L1s like KiteAI and Turf Network is a testimony to this. So, what’s the motivation for these projects to build Avalanche L1 for AI? Let’s get into the details.
How are Avalanche L1s powering innovative AI use cases?
Avalanche L1s is already popular due to its features like endless scalability, independence from main Avalanche L1, customizability, enhanced customizability, and interoperability. Due to this, Avalanche L1s have become a home for 64+ mainnet Avalanche L1s, and hundreds of testnets that are custom and purely use case-specific. Now, Avalanche Ecosystem is joining the AI boom, and Avalanche L1s are also seeing a surge in AI-powered projects from different verticals.
Let’s figure out the motivation
1.Customization at the validator and consensus level:
AI projects seek end-to-end customization to make their chain use-case fit. While this feature has already been offered by Avalanche’s previous architecture, now, post-Etna upgrade and Avalanche9000 have even extended this customization to the validators and consensus level.
This means each L1 can independently manage its own validators and use a custom consensus to define rules about who can be validators, the token used, and structure its own reward. Additionally, AI projects can choose Proof-of-Stake (PoS) or Proof-of-Authority to better align their L1 network with the designated goals. For example, AI-powered Layer1s can create high-trust chains that can be private and permissioned chains with 100% compliance from day one.
TurfGG– the on-demand data network for specialized AI, is utilizing the customizability, sovereign performance, and below features from Avalanche L1:
- Custom economics and absolute sovereignty.
- Composability to manage plug-in analytics, prediction markets, and other dev tools in e-sports.
- Deterministic finality for real-time AI learning and e-sport processing.
- Fast finality, quick state updates, and full ecosystem control.
2. Suitability for projects with high-validator counts:
Advanced AI applications—such as generative AI chatbots, large-scale training models, and autonomous decision-making systems / federated learning models—often require sophisticated consensus mechanisms and custom validator logic to ensure reliability, scalability, and trust. This is particularly important as these systems grow in complexity and demand more nuanced control over execution environments.
Avalanche Layer 1 (L1) chains are uniquely positioned to support such needs. Their architecture enables the integration of customizable validation logic directly into smart contracts, allowing for application-specific consensus mechanisms. Additionally, Avalanche’s infrastructure is designed to scale efficiently with a growing number of validators, making it a robust choice for high-throughput AI workloads.
Examples include Coqnet and Beam L1, two L1 chains that efficiently manage 772 and 323 active validators along with 7000+ and 1000+ daily active addresses respectively. These L1s guarantee that it is possible to support a large and decentralized validator set without compromising the performance or security even a bit. This level of scalability and flexibility is particularly well-suited for AI-driven platforms, which usually require dynamic validator sets to handle distributed compute, data verification, and governance at scale.
3. Powerful tech stack:
That’s a great summary of Rizenet- a 2B+ in asset, AI-ready, institutional RWA chain on why they chose the Avalanche L1 stack.
Tbh, Avalanche9000 has made Avalanche L1’s tech stack a lot stronger than before. This empowers user-friendly upgrades like a fully independent incentive model, support for native tokens on each L1, and more. Highlighting this, KiteAI- the purpose-built Layer1’s CEO, Chi Zhang, shared their motivation to choose to build Avalanche L1. “Avalanche L1 is the best option for innovators to build AI-powered use cases”. This statement is from Zhang, who chose Avalanche L1 after an in-depth research and multi-chain analysis.
Their biggest motivation is Avalanche’s core tech stack consisting of service like seamless L1 launch at L2 speed, Avalanche9000 upgrade, and several tech advancement (such as ability to launch a permissioned subnet within a permissionless sovereign L1), and user-friendly upgrades to lower the stacking requirement for L1s, and strong features like Social Login, Account Abstraction. Chi Zang also highlighted the fact that AI agents and specific AI models demand some core capability in the tech stack.For example, each blocks on KITE’s blockchain consists AI computation and AI Task details compared to standard details like tokens, transfer type, etc.
4. Seamless migration for existing AI projects:
Avalanche’s unique consensus, a combination of classical and Nakamoto consensus, is designed to employ a probabilistic consensus to offer a setup for seamless integration of different virtual machines, including the dApps and L2s looking to migrate from other protocols / public chains to Avalanche L1 or maybe Avalanche Subnet -> L1.
- For example, Memoria, an AI powered gaming platform in mainnet, migrated all their Albert NFTs and Albert memory spores to the new L1 chain from Ethereum & Avalanche C-chain. They got a Retro9000 grant also recently, btw.
- Though not from AI, The Binary Holdings, a web3 Telecom company, could be another great example of such a migration trend. They migrated their mainnet from OP Stack to Avalanche L1.
- TRIZE we mentioned earlier was also running as a subnet previously and recently they migrated to Avalanche L1
to Lower costs for validators, smart contract-based validator management, and enhanced cross-chain security.
There Could be many more such examples.
To further simplify this, various managed service providers are there to simplify the migration–end–to–end process to build, deploy, manage, and scale sovereign Layer1 networks. This service includes a no-code, automated platform, which has comprehensive support to fully manage the underlying infrastructure for AI L1s, allowing owners to focus on innovation, not on performance. However, migration expertise only comes with a managed service provider who has extensive experience working with multiple stacks or who is already assisting multiple complex L1 projects.
5. InfraBUILDL (AI) Program:
The Avalanche foundation recently announced infraBUIDL(AI); a program to encourage development of AI-driven use cases and sovereign L1s.
Through a $15M funding and a vast community engagement initiative, InfraBUILDL(AI) is supporting worldwide AI developers to build AI-specific use cases like intelligent tooling, AI-based launchpads, coin-operated agents (COAs), and several future-ready solutions to foster AI innovation in the Avalanche ecosystem.
AI projects with the potential will simply fill out a form with all the specific details and get approval from a committee of AI experts, such as AiFRED, our AI Infra-Agent.
6. Super strong ecosystem:
Avalanche offers a super strong ecosystem, consisting of highly-feasible integration solutions, funding support, computation storage, and a lot more. A recent example is Atheir, which has brought $100M ecosystem fund to support development of next-gen AI projects. Aethir already lead the cloud computing industry with 400,000+ GPU containers, 3000+ NVIDIA sH100s & H200s, and with 61k+ devices powered by Aethir Edge cloud.
Now with $100M ecosystem funding, Athier is allowing sovereign L1 chains to build high-performance AI projects like AI Agents, training models, or AI gaming projects to utilize computational resources efficiently and come up with never-seen-before AI innovations, for example an AI project to process higher of graphics or AI NPCs.
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