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Two juggernauts are colliding, and the fallout could mint trillions. AI is rewriting the rules for everything from trading algos to meme coins, but it has issues: black-box opacity, data hoarding, and a GPU crunch that’s centralizing power. A wave of projects thinks blockchain could be the remedy. Crypto is already using AI. Could AI use crypto?
Asset manager Bitwise predicts an AI-crypto mashup could lead to a $20 trillion GDP boost by 2030. Its analysts think blockchain could supercharge data governance and LLM scalability while AI returns the favor, making crypto smarter and faster.
There’s potential edge in knowing how crypto is unclogging AI’s biggest bottlenecks. Here's why your portfolio might need an AI-blockchain upgrade.
AI’s compute layer is a monopoly mess. Just two firms, OpenAI and Anthropic, pull in 88% of AI-native revenue. Amazon (NASDAQ:AMZN), Microsoft (NASDAQ:MSFT), and Google (NASDAQ:GOOG) own 63% of cloud infra, while NVIDIA’s (NVDA) grip on 94% of data-center GPUs has the Mag7 posting double-digit earnings pops. The rest of the S&P 493? Barely beating inflation.
This isn’t just Big Tech flexing. AI model training already guzzles 2% of global electricity and the Mag7 are demanding more. AI’s concentration of power (literal and figurative) could lock out indie devs and startup firms. Blockchain’s answer? Decentralized Physical Infrastructure Networks (DePIN). It’s a bit like Airbnb for idle GPUs: gather up unused capacity and resell it through transactions that are tokenized and trustless. Projects at the bleeding edge include:
The latest? Prime Intellect’s DePIN protocol recently demonstrated 125x cheaper distributed training across continents, using blockchain staking to verify outputs. No more Big Tech gatekeepers. As senior X dev @jradoff puts it, the tech “turns GPUs, bandwidth & power into an on-chain marketplace. Payments baked in, 85% cheaper than AWS, owned by the crowd.”
But it’s not all upside. Convergence might amp-up deepfake risks, with more than $200 million already lost to AI-crypto scams this year. Solution? On-chain provenance to flag frauds.
AI’s black box is Exhibit A for trust erosion. Opaque datasets breed bias, errors, and IP theft; hence the US Generative AI Copyright Disclosure Act now snaking its way through Congress. ChatGPT’s training data? A provenance nightmare, sparking lawsuits from media outlets.
Blockchain flips the script with immutable audit trails. Store training params on-chain and anyone can see which datasets fed the model, how biases were scrubbed, who contributed, and who got paid.
Or so the thinking goes. Projects pay for quality data with tokens, curbing the “garbage in, garbage out" problem. Since users own and monetize their datasets, there’s no more free-riding on scraped web scrap.
The enabling tech is zero-knowledge machine learning (zkML), which can prove computations without spilling secrets.
Beyond black boxes, AI’s data sins can run deep. There are big concerns about LLMs and privacy, copyright, ownership, and baked-in biases.
Blockchain helps clear the air by tokenizing data as NFTs or assets, enforcing user sovereignty via smart contracts. Want to train on my health stats? Pay up, with zero-knowledge proofs hiding the goods. Leading projects include:
Immutable ledgers serve as bias detectors, identifying skewed data sources before model training begins. DePIN also has a sustainability bonus. Offloading computation from a single data center GPU to edge devices like smartphones and laptops can reduce AI's carbon footprint 4x-8x.
Infrastructure is vital but the real money could be in apps. AI agents – self-driving bots that take on tasks like trading or governance – are exploding. Capgemini says 23% of large firms are using them now, 38% by 2028.
In crypto, AI agents are on steroids. X402 protocols now enable micropayments for API calls, settling transactions without the need for banks.
Risks? Autonomous hacks could rug billions. But verifiable agents offer a solution. See Warden’s (WARD) autopilot for secure trades, audited on-chain.
Crypto and AI aren’t converging, they’re forging a partnership. Gartner sees a cumulative $30T machine economy by 2030 – and that’s not a typo. Expect zkML standards and DataDAO unicorns.
But tread smart. Do your own research, and stack accordingly.
Disclaimer: Not financial advice. DYOR.
Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy.
