The game-changing impact of AI and cryptocurrency
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The world of cryptocurrency has seen notable changes in the past few years. In 2021, due to financial boosts from stimulus packages, venture capital firms invested a hefty $33 billion in cryptocurrency and blockchain startups.
Last year, The Federal Reserve started a series of crypto failures due to its cycle of raising interest rates. It began with the Terra (LUNA) downfall and ended with the FTX Ponzi scheme meltdown. The appeal of Decentralized Finance (DeFi) dwindled, further troubled by more than $3 billion lost in DeFi cyber attacks in 2023. The continuous growth of Bitcoin indicates the uncertainty surrounding altcoins, as the anticipated Altcoin Season still hasn’t arrived.
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In June 2023, Joseph Chalom, the head of strategic partnerships at BlackRock, said it would take a long time before institutions readily adopt DeFi. However, it’s worth considering that the up-and-coming conversation around AI might merge with blockchain technology and its uses.
The evolution of DeFi
In retrospect, firms like Celsius Network and BlockFi have transformed “DeFi” by layering tokens, effectively changing it into “CeFi.” Despite contributing to the spread of cryptocurrency, these companies have unfortunately tarnished the reputation of the term “crypto.”
DeFi v2 should shift its focus towards significantly improving the user experience, reducing the need for mediation by centralized companies. The most critical aspect that needs a boost is security. The most probable solution is the Zero-knowledge Ethereum Virtual Machine (zkEVM), which promises enhanced protection.
zkEVM makes network functions faster and cheaper by using zero-knowledge proofs. It also improves user experience by allowing different types of tokens to be used for transaction fees. Simply put, solutions like zkEVM help achieve the efficiency required for AI applications.
AI programs, by nature, handle vast amounts of data, which can slow down blockchain networks. However, with Polygon zkEVM, you can create AI artwork using the Midjourney image tool. Afterward, these artworks can be turned into low-cost NFTs
The crypto world has prepared a solid foundation for AI by combining smart contracts and other elements. This combination creates a self-operating and effective structure for financial markets.
To put it simply, imagine DeFi as Lego building blocks. Compound (COMP) allows people to add funds into collective digital agreements known as ‘smart contract’ pools. This is a groundbreaking move in DeFi because there is no need for approval from anyone to lend or borrow money. Smart contracts act as ready sources of cash, which borrowers can access by providing something of value as a guarantee or ‘collateral.’
When you provide liquidity, you receive cTokens as interest. For instance, if you supply USDC, you’ll earn cUSDC. These tokens are versatile and can be used in any protocol that accepts the ERC-20 standard.
AI revolutionizing finance
When we talk about AI, we often think of its incredible processing abilities. The complexity of financial markets has now exceeded our human skills to manage them. As a result, we lean heavily on predictive methods, automation, and personalized services.
In traditional finance, we often see robo-advisors asking users about their needs and how much risk they’re comfortable with. The robo-advisor creates a plan to handle the user’s investments. When this kind of AI is applied to blockchain’s flexible and interlinked world, it can efficiently extract high returns.
AI tools can enhance the finance market in real-time by using easily accessible smart contracts. These tools can potentially lessen market inconsistencies and human mistakes while also improving market organization. A perfect example of this would be automated market makers, who aid in determining the price of assets.
Through real-time examination of transaction orders, stability, and changeability, AI tools can be perfectly set up to ensure a steady supply of liquidity. They can also help prevent sudden loan schemes on DeFi platforms by mediating between these platforms and managing transaction volumes effectively.
As AI tools enhance market efficiency through constant market checks and learning, we could see new prediction markets with greater liquidity emerge. In this scenario, humans would control bots in their interactions with other bots.
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