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XAI price

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Price of XAI today

The live price of XAI is $0.{9}8674 per (XAI / USD) today with a current market cap of $0.00 USD. The 24-hour trading volume is $2.76 USD. XAI to USD price is updated in real time. XAI is 0.10% in the last 24 hours. It has a circulating supply of 0 .

What is the highest price of XAI?

XAI has an all-time high (ATH) of $0.{6}1947, recorded on 2024-05-03.

What is the lowest price of XAI?

XAI has an all-time low (ATL) of $0.{11}8855, recorded on 2024-05-03.
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XAI price prediction

When is a good time to buy XAI? Should I buy or sell XAI now?

When deciding whether to buy or sell XAI, you must first consider your own trading strategy. The trading activity of long-term traders and short-term traders will also be different. The Bitget XAI technical analysis can provide you with a reference for trading.
According to the XAI 4h technical analysis, the trading signal is Strong sell.
According to the XAI 1d technical analysis, the trading signal is Sell.
According to the XAI 1w technical analysis, the trading signal is Strong sell.

What will the price of XAI be in 2026?

Based on XAI's historical price performance prediction model, the price of XAI is projected to reach $0.{9}7571 in 2026.

What will the price of XAI be in 2031?

In 2031, the XAI price is expected to change by +10.00%. By the end of 2031, the XAI price is projected to reach $0.{8}1860, with a cumulative ROI of +115.08%.

XAI price history (USD)

The price of XAI is -68.78% over the last year. The highest price of in USD in the last year was $0.{6}1947 and the lowest price of in USD in the last year was $0.{11}8855.
TimePrice change (%)Price change (%)Lowest priceThe lowest price of {0} in the corresponding time period.Highest price Highest price
24h+0.10%$0.{9}8634$0.{9}8718
7d-28.01%$0.{9}8421$0.{8}1244
30d+63.03%$0.{9}5347$0.{8}4557
90d+16.45%$0.{9}1631$0.{8}5184
1y-68.78%$0.{11}8855$0.{6}1947
All-time-78.43%$0.{11}8855(2024-05-03, 336 days ago )$0.{6}1947(2024-05-03, 336 days ago )

XAI market information

XAI's market cap history

Market cap
--
Fully diluted market cap
$86,736.73
Market rankings
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XAI holdings by concentration

Whales
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Retail

XAI addresses by time held

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Live coinInfo.name (12) price chart
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XAI ratings

Average ratings from the community
4.4
100 ratings
This content is for informational purposes only.

About XAI (XAI)

What Is Xai?

Xai is an Arbitrum layer 3 blockchain, specifically tailored to revolutionize the gaming industry. It was conceived with the ambitious goal of enabling real economies and open trade in the next generation of video games. Xai stands out by allowing potentially billions of traditional gamers to own and trade valuable in-game items in their favorite games for the first time, without the complexities of using crypto-wallets. This approach not only simplifies the gaming experience but also opens up a world of possibilities for gamers and developers alike.

The Xai network is characterized by its open and decentralized nature, inviting anyone to participate by operating a node. This inclusivity extends beyond mere participation, as node operators are rewarded for their contributions to the network and have a say in its governance. Developed by Offchain Labs and leveraging Arbitrum technology, Xai is a testament to the evolving synergy between gaming and blockchain technology. It empowers traditional gamers to engage in open trade, thereby transforming the gaming landscape into a more dynamic and economically viable space.

Resources

Official Documents: https://xai-foundation.gitbook.io/xai-network/xai-blockchain/welcome-to-xai

Official Website: https://xai.games/

How Does Xai Work?

The Xai Blockchain, at the heart of this ecosystem, is a custom-developed solution by Offchain Labs. It's specifically designed to meet the unique demands of web3 gaming at scale. This blockchain is a specialized platform that caters to the gaming community. It offers traditional gamers a new wallet and account experience, simplifying their interaction with blockchain technology. This feature is crucial in bridging the gap between complex blockchain operations and the user-friendly experience gamers are accustomed to.

Furthermore, the Xai Blockchain enhances the capabilities for developers by providing increased gas and contract limits, thereby allowing more complex and expansive gaming worlds. This blockchain establishes a fully decentralized ecosystem, fostering trust and transparency among all participants. By leveraging Ethereum's robust security measures, the Xai Blockchain ensures resilience against potential hacks and other vulnerabilities, making it a secure and reliable platform for gaming transactions.

What Is XAI Token?

XAI is the native token of the Xai ecosystem, serving a dual purpose within this network. Firstly, it functions as the designated gas token, a critical component in facilitating transactions within the network. This role is essential in maintaining the smooth operation and efficiency of the blockchain. Secondly, Xai tokens are awarded to validator nodes as a reward for their crucial role in validating transactions, ensuring the integrity and reliability of the network.

Beyond these functional roles, the Xai token is also integral to the gaming ecosystem itself. It acts as the primary token within this space, accepted as payment for games and in-game items. This positions the Xai token at the center of economic activities within the gaming world, making it a valuable asset for gamers and developers. XAI has a maximum supply of 2,500,000,000 tokens.

What Determines Xai’s Price?

The price of Xai, like any cryptocurrency, is influenced by a complex interplay of factors, making its trajectory a subject of keen interest, especially for those eyeing price predictions in 2024. Market demand plays a pivotal role, often driven by the adoption rate of Xai in the gaming community and the broader blockchain ecosystem. As more gamers and developers integrate Xai for in-game transactions and asset trades, its value is likely to rise. Additionally, the overall performance of the cryptocurrency market, reflected in historical charts, can offer insights into Xai's price movements. Investor sentiment, often swayed by technological advancements within the Xai network and strategic partnerships, also significantly impacts its valuation. External economic factors, such as regulatory changes in the crypto space, can further influence Xai's price. Thus, understanding Xai's price requires a multifaceted approach, considering both its unique position in the gaming-blockchain interface and the broader trends in the cryptocurrency market.

For those interested in investing or trading Xai, one might wonder: Where to buy XAI? You can purchase XAI on leading exchanges, such as Bitget, which offers a secure and user-friendly platform for cryptocurrency enthusiasts.

XAI news

What Does FDV Tell Us About 2024’s Top Altcoins — Winners vs. Losers
What Does FDV Tell Us About 2024’s Top Altcoins — Winners vs. Losers

Hyperliquid’s FDV rose 2.4x as tight supply and soaring demand drove price gains. Ondo surged 3.7x in FDV despite supply growth, with demand outpacing dilution risks. Rapid unlocks caused FDV collapses in Dymension, Wormhole, StarkNet, and XAI tokens.

CoinEdition2025-03-24 16:00
Top Token Unlocks to Watch This Week: Major Crypto Projects Set for Growth
Top Token Unlocks to Watch This Week: Major Crypto Projects Set for Growth

Xai enables in-game item ownership and trade for traditional gamers without requiring crypto-wallets, expanding access to blockchain economies. Moca Network’s AIR Kit allows seamless digital identity management across platforms, providing users with a universal Web3 account. Delysium’s YKILY Network supports AI agents with decentralized financial infrastructure, ensuring security, scalability, and collaboration.

CryptoFrontNews2025-03-09 16:00
5 Token Unlocks to Watch for the Second Week of March
5 Token Unlocks to Watch for the Second Week of March

This week’s top token unlocks include XAI, MOCA, AGI, CHEEL, and XAV, with over $44 million in new tokens hitting the market.

BeInCrypto2025-03-09 02:00
More XAI updates

FAQ

What is the current price of XAI?

The live price of XAI is $0 per (XAI/USD) with a current market cap of $0 USD. XAI's value undergoes frequent fluctuations due to the continuous 24/7 activity in the crypto market. XAI's current price in real-time and its historical data is available on Bitget.

What is the 24 hour trading volume of XAI?

Over the last 24 hours, the trading volume of XAI is $2.76.

What is the all-time high of XAI?

The all-time high of XAI is $0.{6}1947. This all-time high is highest price for XAI since it was launched.

Can I buy XAI on Bitget?

Yes, XAI is currently available on Bitget’s centralized exchange. For more detailed instructions, check out our helpful How to buy guide.

Can I get a steady income from investing in XAI?

Of course, Bitget provides a strategic trading platform, with intelligent trading bots to automate your trades and earn profits.

Where can I buy XAI with the lowest fee?

Bitget offers industry-leading trading fees and depth to ensure profitable investments for traders. You can trade on the Bitget exchange.

Where can I buy crypto?

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Cryptocurrency investments, including buying XAI online via Bitget, are subject to market risk. Bitget provides easy and convenient ways for you to buy XAI, and we try our best to fully inform our users about each cryptocurrency we offer on the exchange. However, we are not responsible for the results that may arise from your XAI purchase. This page and any information included are not an endorsement of any particular cryptocurrency. Any price and other information on this page is collected from the public internet and can not be consider as an offer from Bitget.

Bitget Insights

Cointribune EN
Cointribune EN
1d
Elon Musk Is Fighting For The Privacy Of Coinbase Users
Elon Musk, via his platform X, has filed a brief with the U.S. Supreme Court challenging the IRS’s practices regarding access to Coinbase user data. This initiative is part of a broader debate on privacy protection in the crypto space. X Corp, Elon Musk’s company that manages the X platform, filed an amicus curiae brief with the U.S. Supreme Court on Friday, contesting the IRS’s methods. The company specifically denounces the use of so-called “no-suspicion” subpoenas allowing the tax authorities to access, without a judicial warrant, the financial data of users on platforms like Coinbase. The case highlights how the tax authorities obtained, through simple administrative subpoena, three years of transaction statements concerning over 14,000 Coinbase customers, including James Harper, the main plaintiff. Alongside seven advocacy groups and researchers, X Corp denounces these “no-suspicion subpoenas” as a violation of the Fourth Amendment, which protects Americans against unreasonable searches. Following this request, the Supreme Court asked the federal government on Monday to formulate an official response, highlighting the importance of this case. The dispute dates back to 2020 when James Harper sued the IRS to contest the seizure of his personal information related to cryptos. In 2023, a federal court ruled in favor of the IRS, determining that the tax agency was acting within the scope of its legal prerogatives. The current appeal before the Supreme Court thus marks a new stage in this legal battle, with potentially significant implications for the protection of digital financial data. This initiative perfectly aligns with Elon Musk’s vision regarding digital governance. The billionaire, who recently sold his platform X to his own company xAI for 33 billion dollars, has always positioned himself as an advocate for privacy and freedom of speech. By taking a stand for the protection of cryptocurrency users’ data, Musk strengthens his credibility among the tech and crypto communities, particularly sensitive to privacy issues. The Supreme Court’s verdict could redefine the limits of state power in relation to digital privacy. This case resonates with the recent case of Tornado Cash , a crypto mixing protocol ultimately removed from the OFAC blacklist after a court ruled that the agency had overstepped its authority. This case resonates with the recent case of Tornado Cash , a crypto mixing protocol ultimately removed from the OFAC blacklist after a court ruled that the agency had overstepped its authority, illustrating the growing tensions between state regulation and digital freedoms.
XAI-4.27%
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Mahnoor-Baloch007
Mahnoor-Baloch007
2d
AI agents and AI are related but distinct concepts in the field of artificial intelligence. AI (Artificial Intelligence) 1. Definition: AI refers to the broad field of study focused on creating intelligent machines that can perform tasks that typically require human intelligence. 2. Characteristics: AI systems can process and analyze large amounts of data, learn from experiences, and make decisions based on that data. 3. Examples: AI-powered chatbots, image recognition systems, and natural language processing tools. AI Agents 1. Definition: AI agents are a specific type of AI system that can autonomously perform tasks on behalf of a user or another system. 2. Characteristics: AI agents have the ability to design their own workflow, utilize available tools, and interact with external environments to achieve complex goals. 3. Examples: AI-powered trading bots, autonomous vehicles, and smart home systems. Key Differences 1. Autonomy: AI agents have a higher level of autonomy compared to traditional AI systems, allowing them to make decisions and take actions independently. 2. Interactivity: AI agents can interact with their environment and other systems, whereas traditional AI systems may only process data internally. 3. Proactivity: AI agents can anticipate and prevent problems, whereas traditional AI systems may only react to problems after they occur. 4. Complexity: AI agents often require more complex decision-making and problem-solving capabilities compared to traditional AI systems. In summary, while AI refers to the broader field of artificial intelligence, AI agents are a specific type of AI system that can autonomously perform tasks, interact with their environment, and make decisions independently. Thank you...🙂 $BTC $ETH $SOL $PI $XRP $DOGE $SHIB $SUNDOG $MEME $AI $XAI $PEPECOIN $PIPPIN $ORAI $ETC $WHY $U2U
SUNDOG-0.85%
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Crypto_inside
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3d
Machine learning ❌ Traditional learning. 🧐😵‍💫
Machine learning and traditional learning are two distinct approaches to learning and problem-solving. Traditional Learning: 1. Rule-based: Traditional learning involves explicit programming and rule-based systems. 2. Human expertise: Traditional learning relies on human expertise and manual feature engineering. 3. Fixed models: Traditional learning uses fixed models that are not updated automatically. Machine Learning: 1. Data-driven: Machine learning involves learning from data and improving over time. 2. Algorithmic: Machine learning relies on algorithms that can learn from data and make predictions. 3. Adaptive models: Machine learning uses adaptive models that can update automatically based on new data. Key Differences: 1. Learning style: Traditional learning is rule-based, while machine learning is data-driven. 2. Scalability: Machine learning can handle large datasets and complex problems, while traditional learning is limited by human expertise. 3. Accuracy: Machine learning can achieve higher accuracy than traditional learning, especially in complex domains. Advantages of Machine Learning: 1. Improved accuracy: Machine learning can achieve higher accuracy than traditional learning. 2. Increased efficiency: Machine learning can automate many tasks, freeing up human experts for more complex tasks. 3. Scalability: Machine learning can handle large datasets and complex problems. Disadvantages of Machine Learning: 1. Data quality: Machine learning requires high-quality data to learn effectively. 2. Interpretability: Machine learning models can be difficult to interpret and understand. 3. Bias: Machine learning models can perpetuate biases present in the training data. When to Use Machine Learning: 1. Complex problems: Machine learning is well-suited for complex problems that require pattern recognition and prediction. 2. Large datasets: Machine learning can handle large datasets and identify trends and patterns. 3. Automating tasks: Machine learning can automate many tasks, freeing up human experts for more complex tasks. When to Use Traditional Learning: 1. Simple problems: Traditional learning is well-suited for simple problems that require explicit programming and rule-based systems. 2. Small datasets: Traditional learning is suitable for small datasets where machine learning may not be effective. 3. Human expertise: Traditional learning relies on human expertise and manual feature engineering, making it suitable for domains where human expertise is essential. Thank you...🙂 $BTC $ETH $SOL $PI $AI $XAI $BGB $BNB $DOGE $DOGS $SHIB $BONK $MEME $XRP $ADA $U2U $WUF $PARTI $WHY
BTC-0.58%
BGB-0.28%
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3d
What is Q-learning...🤔🤔??
Q-learning is a type of reinforcement learning algorithm used in machine learning and artificial intelligence. It's a model-free, off-policy learning algorithm that helps agents learn to make decisions in complex, uncertain environments. Key Components: 1. Agent: The decision-maker that interacts with the environment. 2. Environment: The external system with which the agent interacts. 3. Actions: The decisions made by the agent. 4. Rewards: The feedback received by the agent for its actions. 5. Q-function: A mapping from states and actions to expected rewards. How Q-learning Works: 1. Initialization: The agent starts with an arbitrary Q-function. 2. Exploration: The agent selects an action and observes the resulting state and reward. 3. Update: The agent updates its Q-function based on the observed reward and the expected reward for the next state. 4. Exploitation: The agent chooses the action with the highest Q-value for the current state. Advantages: 1. Simple to implement: Q-learning is a straightforward algorithm to understand and code. 2. Effective in complex environments: Q-learning can handle complex, dynamic environments with many states and actions. Disadvantages: 1. Slow convergence: Q-learning can require many iterations to converge to an optimal policy. 2. Sensitive to hyperparameters: The performance of Q-learning is highly dependent on the choice of hyperparameters. Q-learning is a powerful algorithm for reinforcement learning, but it can be challenging to tune and may not always converge to an optimal solution. Thank you...🙂 $BTC $ETH $SOL $PI $AI $XAI $XRP $BGB $BNB $DOGE $DOGS $SHIB $BONK $FLOKI $U2U $WUF $WHY $SUNDOG $COQ $PEPE
SUNDOG-0.85%
BTC-0.58%
Crypto_inside
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3d
What is Machine learning..🤔🤔??
Machine learning is a subset of artificial intelligence (AI) that involves training algorithms to learn from data and make predictions, decisions, or recommendations without being explicitly programmed. Key Characteristics: 1. Learning from data: Machine learning algorithms learn patterns and relationships in data. 2. Improving over time: Machine learning models improve their performance as they receive more data. 3. Making predictions or decisions: Machine learning models make predictions, decisions, or recommendations based on the learned patterns. Types of Machine Learning: 1. Supervised Learning: The algorithm learns from labeled data to make predictions. 2. Unsupervised Learning: The algorithm learns from unlabeled data to identify patterns. 3. Reinforcement Learning: The algorithm learns through trial and error to achieve a goal. 4. Semi-supervised Learning: The algorithm learns from a combination of labeled and unlabeled data. 5. Deep Learning: A subset of machine learning that uses neural networks with multiple layers. Machine Learning Applications: 1. Image Recognition: Image classification, object detection, and facial recognition. 2. Natural Language Processing (NLP): Text classification, sentiment analysis, and language translation. 3. Speech Recognition: Speech-to-text and voice recognition. 4. Predictive Analytics: Forecasting, regression, and decision-making. 5. Recommendation Systems: Personalized product recommendations. Machine Learning Algorithms: 1. Linear Regression: Linear models for regression tasks. 2. Decision Trees: Tree-based models for classification and regression. 3. Random Forest: Ensemble learning for classification and regression. 4. Support Vector Machines (SVMs): Linear and non-linear models for classification and regression. 5. Neural Networks: Deep learning models for complex tasks. Machine Learning Tools and Frameworks: 1. TensorFlow: Open-source deep learning framework. 2. PyTorch: Open-source deep learning framework. 3. Scikit-learn: Open-source machine learning library. 4. Keras: High-level neural networks API. Machine learning has numerous applications across industries, including healthcare, finance, marketing, and more. Its ability to learn from data and improve over time makes it a powerful tool for solving complex problems. Thank you...🙂 $BTC $ETH $SOL $PI $AI $XAI $BGB $BNB $DOGE $SHIB $FLOKI $BONK $U2U $WUF $WHY $SUNDOG $PARTI $XRP
SUNDOG-0.85%
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