Greek mythological figure, Prometheus, chained to a rock in Caucasus where he is constantly preyed … More
If you were lucky enough to study ancient Greek, or interested enough in history to learn from their mistakes, you’ll be familiar with the story of Prometheus. Before fire could heat homes, cook food and forge tools, legend has it that fire was the preserve of the gods who kept it selfishly to themselves. Prometheus, a Titan, climbed to the summit of Mount Olympus to steal it for humans. Zeus, furious that humans had been given something powerful without permission, chained Prometheus to a rock where an eagle would eat his liver every day.
The comparison to AI as modern Promethean fire is particularly relevant for crypto. AI has a fundamental trust problem that blockchain technology is uniquely positioned to solve. While most crypto companies are still using AI for lightweight applications, the potential for deeper integration is massive. And yet, as the crypto industry grows increasingly eager to merge these technologies, some applications make perfect sense while others threaten to burn down the very foundations of security that crypto was built upon.
Most user-facing crypto companies today are using AI in relatively harmless ways. For example, ChainGPT, Eternal AI and Virtuals Protocol all deploy social chatbots to answer user questions, create natural language interfaces for complex dashboards, or give products a fun character on socials that users can interact with.
These applications treat AI as what it fundamentally is, a sophisticated pattern-matching system that excels at understanding and generating human language. Large language models are remarkable at understanding context and generating helpful responses, but they still remain fundamentally unpredictable.
The Temptation Of Deep AI Integration
Where crypto companies begin to play with fire is when they grant AI systems direct access to sensitive operations. Some startups are experimenting with autonomous agents that can move user funds, execute smart contract calls, and tap external resources using new tooling like Model Context Protocol, all without human oversight.
AI models don’t reason through problems the way humans do. They predict what comes next based on patterns learned from training data. This makes them vulnerable to attacks that don’t exist in traditional software systems. Prompt injection attacks can trick an AI into ignoring its instructions. Data injections and jailbreaks aren’t hypothetical risks; they’re already happening. Freysa was launched as a jailbreak challenge, an autonomous agent holding funds to not be released under any circumstances. One user successfully tricked the agent to ignore its system prompt by introducing a “new session”, reinterpreted the definitions of approveTransfer and rejectTransfer, then offered a $100 “incoming transfer” which, due to the twisted logic, triggered the release of the entire prize pool (about 13.19 ETH, ~$47, 000).
The real problems begin when companies grant these models deep access, including internal tooling, sensitive data, and even on-chain signing authority. That’s a massive red flag. When you give an AI system the ability to sign transactions or access financial data, you create a fundamentally new attack surface. The crypto industry learned the hard way that “code is law” only works when the output is predictable and deterministic.
The Strategic Path To AI Innovation
Ideally, we should avoid merging AI and blockchain systems entirely. Instead, we should be surgical about what goes on-chain. The less on-chain, the better. Crypto doesn’t need to host AI software, it just needs to secure and govern AI. Blockchains should handle only the parts that genuinely need to be trustless: payments, identity, access management, and governance. The actual AI computation should happen off-chain.
This pragmatic approach unlocks massive potential. Agents can be governed by on-chain policies, become more autonomous via blockchain payment rails, and build a reputation tied to wallet addresses. The hottest topic at any crypto conference today is agent-to-agent communication, negotiation, and collaboration. Picture AI agents with their own wallet addresses, making their actions auditable and revocable.
AI computation will not be on-chain for a while, and we need ways to allow non-deterministic computation to interface with the deterministic on-chain world. Fortunately, there are multiple cryptographic methods to achieve confidential and verifiable compute: Trusted execution environments , zero-knowledge proofs, and multi-party computation. Blockchain-based reputation systems can track AI agent behavior over time, creating much-needed accountability mechanisms.
This approach solves AI’s fundamental trust problem. Instead of forced merging of technologies, we get a more elegant solution where agents have wallets for identity, permissions, and payments. The result could be trustless agents that become part of our everyday life without concerns for fraud, privacy invasion, or security issues.
The one area where crypto and AI integration shows genuine promise is in decentralized compute networks. Projects that use token incentives to coordinate distributed GPU resources address a real bottleneck in AI development. Today’s AI landscape is dominated by a handful of cloud providers who control access to the massive computing resources required for training and running large models. This isn’t about putting AI on the blockchain but rather using blockchain mechanisms to coordinate and incentivize a new kind of infrastructure layer.
The comparison between AI and Prometheus is more than a little overblown. AI is a truly remarkable achievement that comes with its own potential to grow and iterate on itself. Something we should be excited about.
The challenge isn’t to be like Prometheus, but to avoid being like Zeus. AI should be applied liberally but with caution. Poor application in crypto might not leave us with our liver being eaten out for all eternity, but it could risk the one thing the industry built over the years: trustless systems. That would turn away builders and users for good. At least trustless systems are still something worth protecting.
