Written by: Deep Tide TechFlow
Crypto AI is rapidly approaching the 'Gartner Hype Cycle' development curve:
After the emergence of ChatGPT, the innovation narrative of Crypto + AI quickly attracted significant attention, especially with the appearance of GOAT, which ignited market sentiment. We witnessed the AI Meme Summer of 2024 unfold together, where Crypto + AI entered a peak period mainly characterized by conceptual speculation.
Subsequently, under the multiple pressures of factors such as TRUMP Meme, Deepseek, and tariffs, the AI Meme bubble burst prematurely, and the market capitalization of Crypto + AI saw a significant pullback, pushing the 'high heat' market back to a brief 'calm'. However, the burst of the bubble does not mean the end but rather clears the stage for genuine value creators.
As Crypto + AI enters the pragmatic exploration stage of application landing, infrastructure projects aiming to promote Crypto AI into the fifth phase of stable production become longer-term wealth hotspots following AI Meme, and as the countdown to the mainnet and TGE begins, the self-improving decentralized AI network Allora Network further enters the public eye.
Having secured $35 million in funding, Allora Network has seen continuous expansion of its ecosystem since announcing the launch of the Beta version of the mainnet in February: not only has the number of Workers in the network exceeded 288,000, but over 690 million inferences have been generated so far, and the ecological landscape has expanded into multiple fields such as DeFAI, RWA, and GameFi, increasingly highlighting its importance as infrastructure empowering the practical application of AI across multiple scenarios.
'Self-improvement' means stronger reasoning and judgment capabilities, more efficient and intelligent decision-making systems, and more powerful service capabilities in complex scenarios. How does Allora achieve all of this?
As we are approaching the official launch of the mainnet, is Allora worth paying attention to and how to participate more effectively?
This article aims to explore.
More efficient AI model collaboration, broader AI application scenarios.
The AI products we are familiar with, whether ChatGPT, Claude, or Gemini, seem to be tirelessly striving for 'universality'. Everyone hopes to create an all-knowing and all-powerful AI product that meets all users' needs and captures a larger market share.
However, building behind closed doors not only costs time, effort, and money but also limits a single AI's capabilities.
In comparison to single model AI inference results, the synthetic inference results obtained through multi-AI model collaboration, as a crystallization of collective wisdom, are evidently more advantageous.
Collaboration among different models avoids the one-sidedness of single model output results, thereby obtaining more comprehensive and precise results; the cross-validation method of multiple models also reduces the errors that a single model may produce; when facing complex problems in complex scenarios, multi-model collaboration can also provide higher flexibility and adaptability, promoting better real-world applications of AI.
It can be said that closer AI collaboration brings higher-level intelligence, and higher-level intelligence endows AI with broader application scenarios.
However, how to achieve efficient collaboration is indeed a problem that must be faced:
On one hand, different AI models often operate within their own closed systems, lacking a unified collaboration mechanism, which leads to isolation and restricts the potential of AI.
On the other hand, under the existing technological and business environment, the incentive mechanisms to promote collaboration among AI models have yet to mature. How to achieve win-win cooperation among different models is an urgent problem to be solved.
At the critical stage where Crypto AI is truly moving from speculation to value-oriented, how will Allora break the deadlock facing the pain points and difficulties of multi-AI model collaboration?
Empowering AI with contextual awareness, optimal solutions for AI inference under collective intelligence.
Dual-input weight system: Empowering AI with contextual awareness to make results more accurate.
Simply put, Allora does not train any AI; it merely acts as a dispatcher between AI models.
For example, when a user initiates a request to 'predict tomorrow's weather,' Allora acts like a control center, mobilizing AI focused on different dimensions such as temperature, wind speed, air humidity, and UV intensity, ultimately aggregating all feedback and providing the user with an optimal result derived from collective input.
In this process, Allora Chain serves as a consensus layer, a platform for different users to participate in the network, built on the Cosmos SDK based on CometBFT and DPoS consensus mechanisms.
Facing different reasoning demands, Allora classifies them through Topics. For example, one topic may focus on predicting future asset prices, while another may focus on social sentiment analysis, managed by a coordinator (Topic Coordinator) who plays an important role in interaction and task distribution.
Allora Chain has three main participants:
Consumers
Workers
Reputers
Specifically:
When consumers wish to obtain an inference result, they need to initiate a request to the network and pay a fee.
After the network receives a request, the coordinator will create a new Topic or mobilize different Topics to complete the task based on the request.
Workers need to pay a certain fee to register a Topic to become an inference participant for that Topic. This introduces the first major innovation of the Allora mechanism: upon receiving a task, workers not only need to submit inferences based on consumer demands but also evaluate the accuracy of other workers' inferences. Simply put, workers must not only submit answers but also predict the accuracy of other workers' answers. This dual-input system forms the basis of contextual awareness.
Around these two outputs, the coordinator will perform weighted calculations based on the different weights of workers, generating a context-aware comprehensive result, which is finally fed back to the consumer.
Indeed, you may have already noticed that different workers have different weights; this is another important innovation of the Allora system.
Allora does not simply collect feedback from every worker for average calculation but is evaluated in real-time by evaluators. If a worker can infer accurately and predict the accuracy of other workers' inferences, they will receive higher rewards and greater weight.
At the same time, to ensure the accuracy of the evaluation process, evaluators not only need to pay a certain fee to register a Topic but also need to invest a certain amount of tokens. If malicious behavior is detected, they will face the risk of asset slashing.
For instance, when a user initiates a request to 'predict tomorrow's weather':
Worker A's average inference accuracy for the weather is 90%, but it will decrease in summer.
Worker B's average inference accuracy for the weather is 88%, but it will increase in summer.
If it is currently summer, multiple workers predict that 'Worker A has about a 10% error in summer' and simultaneously predict that 'Worker B has about a 5% error in summer.' Even though Worker A has a higher average inference accuracy, Allora will still assign higher weight to Worker B.
In this way, Allora can dynamically adjust the weight of each prediction based on the current environment rather than relying on static or historical data. This contextually aware collective reasoning enables Allora to provide users with fairer, more accurate outputs that adapt to complex needs, while laying the foundation for Allora's differentiated reward mechanism.
Differentiated reward mechanism: Empowering every participant in the ecosystem.
As the native token of Allora, $ALLO is the core carrier of network incentives and has multiple utilities within the network:
Purchase inference result: Consumers use $ALLO to pay for fees to obtain more accurate inference results. Allora adopts a flexible 'PWYW - Pay What You Want' model, allowing consumers to decide the $ALLO fee they pay for inference themselves.
Participation fee payment: Use $ALLO tokens to pay for creating Topics, registering Topics, and other fees in Allora to better participate in the network.
Staking: Evaluators and network validators can stake $ALLO tokens to earn $ALLO staking rewards, and other token holders can delegate their tokens to evaluators or network validators.
Reward payment: The network uses $ALLO tokens to pay rewards to participants. The higher the accuracy of the workers, the more generous the rewards. The rewards for evaluators and network validators are proportional to their staking and consensus.
The differentiated reward mechanism, as another major innovation of Allora, provides customized incentives for different network participants based on a real-time adjusted weighting system, ensuring that rewards are allocated to higher-quality contributions and maintaining peak performance of the entire operating system.
Moreover, the Allora system will also calculate the counterfactual value of 'what would happen without a certain worker's input,' ensuring that the rewards align with the true information gain of contributions.
Building AI multi-scenario application infrastructure: from DeFAI, RWAFi to GameFi.
After discussing the products, the ecosystem is even more worth discussing.
When discussing the ecosystem, Allora is indeed all-encompassing:
From the user's perspective, Allora can provide consumers with higher quality AI services.
From a product perspective, developers can build more powerful applications based on the decentralized, self-improving ML model network and other infrastructures provided by Allora, deploying models on Allora to respond to user needs and earn rewards, achieving tokenization of model value and continuously enhancing model capabilities. They can also connect existing platforms to Allora, integrating AI into their applications.
The rapid expansion of Allora's multi-track ecological collaboration strongly proves this.
According to the official ecosystem page, more than 100 projects have established cooperation with Allora, covering multiple sectors such as DeFi, RWA, GameFi, and public chains, with an increasingly rich and complete ecological landscape.
In cooperative projects, there are also well-known Web2/Web3 projects such as Monad, Berachain, Story Protocol, 0xScope, Virtuals Protocol, Eliza OS, and Alibaba Cloud.
DeFAI is one of the most important sectors in the Allora ecosystem. Efficient collaboration among different AI models can integrate on-chain transaction data, social media sentiment, and macroeconomic indicators to achieve more accurate market trend predictions, higher levels of risk management, more complex investment strategy optimization, and smarter execution of trading strategies.
Previously, multiple projects have achieved cooperation with Allora to explore more possibilities of DeFAI:
PancakeSwap announced the launch of an AI-driven prediction market on Arbitrum, supported by AI price predictions from Allora, allowing users to predict token price trends every 10 minutes.
Joule Finance announced the integration of Allora's advanced price forecasting capabilities into its Move AI agent toolkit, enabling AI agents to execute intelligent leveraged cycles and yield optimization strategies, further enhancing the efficiency and intelligence of the DeFAI ecosystem.
After the collaboration between Drift Protocol and Allora, AI-driven cyclical strategies were introduced within Agents deployed together with RoboNet, capable of dynamically optimizing returns, reducing risks and adjusting leverage based on predicted market conditions, aiming to provide users with more efficient and intelligent DeFi solutions.
The development team of Virtuals AI Agent Game announced a partnership with Allora Network, supporting Virtuals developers to use Allora AI technology for trading strategies of AI Agents.
Mind Network and Allora Network jointly launched the first privacy-protecting price oracle FHE TrustPrice Index for DeFAI scenarios, ensuring input data confidentiality, verifiable processes, and tamper-proof results.
Additionally, Allora announced support for DeepSeek as an AI Agent for LLM trading judgment, using Allora Network as an interactive platform to implement transaction management in Hyperliquid treasury, facilitating smarter trading strategies.
Beyond DeFAI, the self-improving decentralized AI network built by Allora has powerful capabilities for the RWAFi sector:
The core of RWAFi lies in the digitization and financialization of real assets, where precise assessment and pricing of assets are crucial. Multi-AI model collaboration can significantly enhance this capability.
Based on this, Allora announced a partnership with RWAFi leading project Plume, integrating Allora's collective intelligence network into Plume's ecosystem to provide advanced AI-driven insights for RWA valuation, pricing, and risk management for projects built on Plume. Future collaboration directions between Plume and Allora will also include real-time AI-driven valuation models for various asset classes, advanced annualized yield (APY) predictions using AI oversampling technology, dynamic risk management systems with adaptive thresholds, and smart liquidity optimization strategies.
In the GameFi sector, Allora also performs well:
Collaboration among multiple AI models can significantly enhance the intelligence level of GameFi platforms, providing players with a more personalized gaming experience. At the same time, achieving dynamic economic balance in games, asset valuation and dynamic pricing, yield optimization and distribution through collaboration among multiple AI models will further strengthen the stability and security of the GameFi economic system.
Allora's partnership with Japanese digital entertainment company Gumi serves as a great testament to this, as both aim to explore how decentralized AI can reshape the future of gaming, and in the future, they will delve into areas such as AI-driven reasoning, intelligent in-game agents, and AI-driven anti-cheat systems.
When looking at the Allora application scenarios from the perspective of the AI grand narrative, you will find that Allora's self-improving decentralized AI network empowers Allora as an AI infrastructure with strong capabilities to promote AI towards practical applications across multiple scenarios.
Because theoretically, more accurate inference results lead to more intelligent AI, empowering AI with strong service capabilities in various complex scenarios. As long as there is a need for more efficient collaboration, more precise inference results, and higher quality AI services, Allora can provide corresponding services.
In the future, as Allora continues to make strides in ecological construction, more partners will join, further encompassing multiple sectors such as DeFi, social, and healthcare in the Allora ecosystem, solidifying its important position as an infrastructure driving the development of Crypto AI.
With TGE + mainnet milestones approaching, how to participate in Allora more efficiently?
Various trends have indicated that Allora is on the eve of the official launch of the mainnet + TGE:
On January 10, 2025, the Allora Foundation was established, and an official Twitter account was opened, signaling the release of TGE.
On January 17, 2025, Allora announced the launch of the Engineer Forge competition. Within one month, Allora ML engineers will compete to build a 12-hour ETH/USD volatility prediction model, a 12-hour ETH/USDC trading volume prediction model, and a 5-minute ETH/USD price prediction model, aiming to select AI model creators for the upcoming mainnet launch.
In February 2025, Allora announced the launch of the Beta version of the mainnet and clearly stated that this version is the last version before the mainnet launch.
At the same time, the continuously expanding scale of workers and the inclusion of heavyweight node partners also demonstrate Allora's solid preparations for the mainnet launch. It is reported that the Allora network currently has over 55 Topics, with the number of Workers exceeding 288,000, including Bahrain's telecom operator stc Bahrain and energy giant EDF Group's subsidiary Exaion, both of which have announced their participation in the Allora Network node program.
At such an important moment, how can one participate in Allora more effectively?
Recently, the most discussed topic is that Allora is currently conducting the Kaito event.
As early as March 20, Kaito announced that Allora was selected as the next Pre-TGE project from Yapper Launchpad.
Currently, the Allora Yapper leaderboard has been announced: Users can improve their rankings by posting quality content and interacting with KOL comments, serving as an important means of identifying core opinion leaders and supporters for the project, with higher-ranking users likely to receive airdrop rewards in the future.
Aside from the Kaito event, the points program is the most important way for users to accumulate airdrop tokens.
According to official documentation, users can earn points rewards through both on-chain and off-chain methods.
On-chain activities include:
Creating Topics: Identifying and defining specific problems or areas of interest within the network to attract other participants to develop and provide solutions.
Introducing machine learning models: Adding machine learning models to the network for others to use.
Using applications supported by Allora: Participating in applications and services that utilize Allora's machine intelligence capabilities.
Off-chain activities include:
Community participation: Follow Allora on Twitter, join Discord and Telegram groups.
Participate in community activities: Engage in selected community events and activities to support the Allora network.
Users can connect their wallets to complete corresponding tasks on the Allora Points Program page and check their points on the leaderboard.
Although the documentation currently does not explicitly indicate that points are linked to airdrops, the official points introduction hints that 'exciting rewards are waiting for our active contributors.' Perhaps now is the last opportunity to participate before TGE.
Additionally, for developers, Allora recently launched the Allora Agent Accelerator program:
This plan will gather a selection of Agent teams, some from community developer applications and others nominated by foundation teams. The selected Agents will undergo evaluations across various dimensions, including on-chain performance, user interaction and usage, integration quality with Allora, and overall value of the Agents. To ensure fairness, a snapshot of all participating Agents will be taken on the day the project launches, establishing an initial performance baseline so that different projects have an equal opportunity to demonstrate their value during the accelerator period.
Allora releases real-time leaderboards weekly, sharing updates on outstanding Agents, with better performers receiving higher points.
During the event period: Projects will receive technical support, product guidance, and community exposure from the Allora ecosystem, helping participants attract users, partners, and investors. Additionally, Allora releases real-time leaderboards for participating projects weekly, sharing updates on outstanding Agents, with better performers receiving higher points. After the event, the best-performing Agents will receive funding in ALLO tokens.
The entire program lasts for 6 weeks, aiming to discover, refine, and amplify those intelligent Agents that demonstrate significant effectiveness and deep integration within the Allora network, reserving excellent talent and products for the prosperous development of the Allora ecosystem.
Conclusion
Following the curve of the 'Gartner Hype Cycle', we are standing at a critical moment where Crypto AI is transitioning from noise to rationality, achieving a solid foundation.
As a project of a self-improving decentralized AI network, Allora not only possesses a solid technical foundation to achieve more accurate inference results through multi-AI model collaboration but also demonstrates its strong capabilities in ecological construction through remarkable performances in DeFAI, RWAFi, and GameFi, pushing Crypto AI towards practical applications. With more users and developers joining the ecosystem, will Allora truly develop into an AI control center that mobilizes the power of collective AI wisdom and promotes a boom in AI practicality?
Amid the ongoing recovery of Crypto AI, with TGE and the mainnet approaching, let’s witness Allora's subsequent performance in this wave of AI.
