Author: 0xJeff, Crypto KOL

Compiled by: Felix, PANews

Prediction has always been a core capability of human evolution—since ancient times, humans have relied on their senses and instincts to predict threats and opportunities in their environment, including detecting predator activity patterns, opportunities for prey, and seasonal food supply situations, all of which are crucial for survival.

Since then, this predictive pattern has gradually evolved into the use of tools and planning (such as predicting the need for planting crops, slaughtering, and preserving meat), predicting social cues (intentions, emotions, behaviors), leading to the development of writing, science, mathematics, and modern tools like statistics, computing, machine learning, and artificial intelligence, all aimed at enhancing human predictive capabilities.

Prediction markets, in particular, have evolved into an economic tool—they leverage human predictive abilities to forecast economic, political, and cultural outcomes. Unlike traditional polls, prediction markets like Polymarket and Kalshi use economic incentives to obtain accurate predictions, as participants bet real money.

Polymarket attracted nearly $4 billion in bets in the 2024 U.S. election market, outperforming polls in predicting Trump's victory, reflecting the economic value of crowdsourced predictions.

The same evolution applies to spot and perpetual contract trading, from the rise of CEXs to meet the growing global demand for cryptocurrencies, to Hyperliquid's recent disruptive developments, offering self-custody and no KYC services while providing a CEX-like trading experience.

Prediction is a core capability of human evolution, and with the rise of AI/machine learning predictive models, the ability to predict events, asset prices, and volatility is significantly improving.

This brings humanity into the next stage of evolution.

DeFi 3.0

DeFi 1.0 introduced smart contracts and decentralized applications, allowing anyone to transfer, buy, sell, stake, lend, and yield farm anytime and anywhere, essentially putting crypto assets on-chain to create economic value, such as Uniswap, AAVE, Compound, Curve, Yearn, and Maker.

DeFi 2.0 expanded on 1.0, introducing novel token economics and incentive distribution mechanisms aimed at aligning the interests of different stakeholders within protocols (e.g., Olympus/Wonderland, Solidly/Aerodrome) and spawning emerging markets that provide alternative sources of yield (such as Maple, Pendle, Ethena, Ondo, Clearpool, Solv, USDai, etc.).

DeFi 3.0 brings artificial intelligence into DeFi. Some call it DeFAI, while others refer to it as AiFi. It means integrating large language models (LLM) and/or machine learning models (ML) into DeFi products.

From simple LLM integrations (acting as customer support/co-pilot to help users navigate protocols) to multi-agent/clustering and machine learning systems that fundamentally improve products (increasing trading profits, reducing impermanent loss, enhancing LP yields, lowering liquidation risks in perpetual trading, etc.).

In addition to the DeFAI abstraction layer and fully autonomous financial agents, today we will discuss the role of AI/machine learning systems and predictive models in transforming DeFi and other verticals.

Predictive Systems

Neural networks and decision trees have been around since the 2000s, and these systems were used by hedge funds to predict stock and commodity prices. Early stock predictions were quite informative, with short-term prediction accuracy reaching 50% - 60%, but their application was limited due to overfitting and limited data.

Then came the rise of deep learning and big data, enabling models to handle larger datasets (time series data, unstructured data from news and social media, etc.), leading to more accurate predictions and broader applications.

Breakthrough developments occurred in the past five years, where Transformer models and multimodal AI integrated more diverse datasets, such as Twitter sentiment, blockchain transactions, oracle data, real-time news, and crowdsourced predictions (Polymarket, Kalshi). This has allowed some AI models to achieve 80% - 90% accuracy in predicting event outcomes and asset prices.

As these models continue to improve, the demand to integrate predictive capabilities into DeFi systems has surged. We are currently in the early stages of DeFi 3.0, witnessing some market participants combining AI/machine learning systems with Web3 application scenarios in real-time.

DeFi x AI/ML Systems

Allora

Allora may be the most widely used decentralized predictive model network currently. Allora has achieved numerous integrations with DeFi protocols and AI agent teams, endowing it with predictive capabilities (primarily focusing on cryptocurrency price predictions, such as BTC, ETH, SOL).

Its short-term cryptocurrency price prediction accuracy is reportedly around 80%.

Some major applications include:

• Vectis Finance's AI-driven treasury based on USDC, utilizing Allora's reasoning technology to maximize SOL trading returns. Since April 23, its cumulative return rate has been 2.4%, with an annual interest rate of about 10%.
• Steer Protocol's AI LP treasury, leveraging Allora's predicted price data to better position liquidity ahead of price fluctuations, thus avoiding impermanent loss.
• Allora collaborates with numerous teams such as Cod3x, Axal, Brahma, and Virtuals Protocol to support trading strategies and execution for AI agents.

Bittensor Subnet

Bittensor's dTAO incentive distribution mechanism helps startups (subnets) offset development costs, allowing teams to use Bittensor to kickstart their product development by outsourcing significant development work to miners, where higher incentives lead to better quality miners.

Given that machine learning models and predictive systems are among the easiest tasks to quantify (building models that can accurately predict certain outcomes), this is one of the verticals that subnets focus on the most.

Subnets focused on prediction:

• SN6 @Playinfgames
• SN8 @taoshiio
• SN18 @zeussubnet
• SN41 @sportstensor
• SN44 @webuildscore
• SN50 @SynthdataCo

Since SN6, SN18, SN41, and SN44 have been detailed previously, we will skip these subnets but want to emphasize again:

➔ SN6's @aion5100 (the AI agent/predictive hedge fund layer of SN6) is about to launch a DeFi treasury that will automatically allocate user deposits to high-confidence events/markets for betting. This treasury is set to launch soon, with early testing APY reportedly exceeding four digits.

➔ SN44's @thedkingdao has shown continuous improvement in signals related to football/soccer. Recent performances in the Club World Cup demonstrated that aggressive betting scales yielded a 232% return on investment. The team is also working on developing a DeFi treasury product that will adopt a more risk-adjusted approach.

The AI agents/tokens representing these two application layers on CreatorBid have excelled in showcasing the capabilities of SN6 and SN44. This has inspired many other subnet teams to follow suit, launching AI agent tokens to demonstrate the functionalities of their subnets.

➔ SN50 Synth is particularly interesting. This subnet is built around a highly versatile volatility prediction model. It can be used to cover various probabilities of price occurrences (not just predicting future prices), such as predicting liquidation probabilities, survival/liquidation times of perpetual positions, setting Univ3 LP ranges and predicting impermanent loss, predicting option strike prices and expiration times within a window, etc.

• Synth reportedly outperforms traditional benchmark models (geometric Brownian motion) by 25% - 30%.

There is immense demand for L1/L2 ecosystems looking to integrate such engines into their DeFi ecosystems.

So far, Synth has integrated with the following platforms:

• Arbitrum, supporting AI trader competitions
• Chainrisk, understanding volatility so that partner protocols can better cope with drastic changes in volatility
• A major liquid staking protocol on Solana for unknown use cases (the team claims an official announcement will be made within 1-2 days)

The team positions Mode L2 (their own L2) as an application layer, enabling traders to leverage Synth to predict asset prices and trade better by combining Synth reasoning with Mode AI terminals + Mode Perp products.

SN6, SN44, SN50, and many other subnets are so compelling because they attract miners to continuously improve their predictive models with dTAO tokens incentivizing between $2 million to over $10 million annually.

Their goal is to use dTAO incentives as capital expenditure to guide product development and achieve commercialization/productization as soon as possible, thereby earning actual returns and offsetting the sell pressure of dTAO. Some of these subnets have begun to move towards commercialization (as evidenced by DKING providing $300 million deployment support for a top sports hedge fund).

What’s Next?

The pursuit of higher yields and lower risks will continue, prompting builders to bring more RWAs on-chain. Existing DeFi yield sources will continue to be optimized and will become increasingly accessible.

Prediction markets will become a primary source of information, with AI acting as market makers, while experienced participants further stimulate collective intelligence. Tools are becoming smarter, and models are becoming more precise, with some results already visible.

The more these systems learn, the greater their value. Moreover, the stronger their composability with other parts of Web3, the more unstoppable the overall trend becomes.

What this means is… ultimately, everything in the crypto space is a bet on the future.

Therefore, any infrastructure and applications/agents that can foresee the future—whether through collective intelligence, higher quality data, or more precise models—will have a significant advantage.

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