This is the fundamental Bull case for AI x Crypto.
First Let look at the current problems within the AI space:
AI models are so computationally intensive and Compute costs are expensive.
Fake AI Audios and Videos are hard to disguising for Real ones.
AI models are close source. we don’t know if the company has added any bias to its responses.
Web3 can solve most of these problems, Decentralized Compute is cheaper than Cen, if the content is human or AI generated, models are open source.
Why use Decentralized Compute?
Because it is cheap, always available, has no regulatory issues, Pays for your Use, you can also own part of the Network, and more.
From a developer's perspective, the only things they care about are cost and tooling.
If we look at Decentralized computing, it is cheaper than Centralized computing. According to recent research from UCLA, decentralized computing can offer up to 2.75x more performance-per-dollar than traditional GPU servers for large graphs. Specifically, decentralized computing can be 1.22x faster and 4.83x cheaper than GPU servers for massive sparse graphs.
Why Decentralized Compute is cheaper:
In a traditional data center, costs are broken down into servers (30%), housing (12%), networking (15%), AC (21%), power (17%), and people (5%).
Decentralized computing relies on users sharing their resources and contributing computing power in a mutually beneficial manner, reducing the need for expensive housing, networking, AC, power, and people. This provides theoretically 70% cost savings by eliminating the cost of a centralized data center.
Other areas of DePin: Decentralized Storage is cheaper than Centralized by an order of magnitude for the same reason.
What’s not great about Decentralized Compute?
Tooling is not great:
Currently, the tooling for Centralized is better than decentralized, but because of the open innovation nature of blockchain, this edge will be gone soon in a year.
Demand is not Great:
The demand for the current Decentralized Compute Network is low, with only 2% of the network being used for computing. But this is also similar to other DePin Networks like storage. We can expect this demand to rise as more developers start building more AI applications widely. This is not an issue because the operators are getting incentivized in tokens which are currently at high value because of the attention that AI has. Supply-side growth doesn't guarantee demand-side adoption. DePin projects utilize tokens to rapidly incentivize miner expansion, with onboarding fees often reinvested to enhance network value. However, a surplus of supply without corresponding usage can be problematic. To prevent this, it's crucial to ensure genuine product-market fit for each project.
No privacy: On Most of these Networks, most of the data is open, so your Training data and model are open to others.
How is the Future looking:
Data Bridge Market Research predicts that the global Artificial Intelligence (AI) infrastructure market will hit $4,225.5 billion by 2029, with a compound annual growth rate (CAGR) of 43.50% from 2022 to 2029.
Currently, the web3 Artificial Intelligence Infrastructure Market is valued at approximately 15 billion dollars, capturing less than 5% Global market share. If Web3 decentralized computing can capture 10% of the global market share, its valuation could range from 142 billion to 422 billion dollars by 2030.
In the end, blockchains are the best way to bootstrap a network, and most of the current blockchain applications like Blockspace, decentralized storage, and decentralized compute have demand that is not up to the supply. This is primarily because there are only a few applications currently. Many projects are launching, and there will be a few that are launching that will find product-market-fit will use most of the Network resources. Similar to how few applications use most of the gas on Ethereum.
In crypto, price moves faster than tech adoption in most cases.
Token price and market cap are just an attention game, and AI has the most attention now.
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