Hook
Over 40 crypto firms—exchanges, miners, custodians—just submitted a joint request to the largest AI laboratories. They are not asking for faster transactions or cheaper fees. They are demanding pre-release access to the strongest AI models. The market sees a security plea. I see a liquidity cascade in the making.
This is not a charitable handshake. It is a liability management event. Crypto assets are not tokens; they are liabilities issued by security infrastructure. When an AI model capable of autonomously identifying smart contract vulnerabilities or generating phishing scripts at scale is released, the entire crypto balance sheet shifts. The firms that signed this request understand that the only way to price that risk is to see the model before the market does.
Context
To understand what this request actually means, we need to strip away the industry jargon. The mechanism is called “pre-deployment red-teaming.” It is a standard practice in AI safety: before a model like GPT-5 or Claude 4 is made public, external researchers are invited to stress-test it. OpenAI did it for GPT-4. Anthropic does it for Claude. The UK AI Safety Institute has built its entire mandate around this model.
What is novel here is the scope and the demand side. Until now, the red-teaming has been initiated by AI labs themselves. They choose the researchers, define the scope, and control the timeline. This request flips the script: the crypto industry, as a collective, is demanding to be the red team. The signal is not just about security—it is about power. They want to be inside the room before the weapon is released.
The crypto industry has a specific vulnerability. AI-enhanced attacks are not theoretical. In 2023 alone, crypto hacks exceeded $1.7 billion, with a growing fraction attributed to automated tools. A large language model can scan for reentrancy bugs in minutes, generate synthetic identities for sybil attacks, or simulate cross-chain bridge exploits in hours. The attack surface is not just code; it is the entire economic loop of staking, lending, and arbitrage. The request is a preemptive attempt to map that surface before the black hats do.
Core
Let me decompose this request into its structural components. Crypto assets, as I have argued in my previous analyses, are liabilities. A Bitcoin is a liability of the network’s security budget. A stablecoin is a liability of the issuer’s reserve management. An exchange token is a liability of the platform’s liquidity depth. When an AI model that can simultaneously attack all three layers is released, the entire liability structure re-prices.
This is a liquidity cascade in the making. The cascade begins with the model’s release. If the model can detect a vulnerability in a widely used smart contract library, the immediate reaction is not a patch—it is a withdrawal. Users see the exploit, they panic, they pull liquidity. The cascade then propagates: from the exploited protocol to its lending partners, to the DEX aggregators, to the cross-chain bridges. The total value locked in the ecosystem can evaporate within hours, as we saw with Terra in 2022.
But here is the technical insight that most analysts miss: the request is not just about blocking attacks. It is about simulating the cascade before it happens. Independent security researchers, armed with the pre-release model, can run hypothetical scenarios: “What if we ask the model to find a vulnerability in the Uniswap v3 router? What if we ask it to generate a front-running script for a new memecoin? What if we ask it to design a synthetic identity that passes all KYC checks?” By running these simulations, the firms can estimate the magnitude of the potential cascade and allocate capital buffers accordingly.
This is exactly the kind of work I did during my 2022 DeFi liquidity forensic. When I analyzed Terra’s collapse, I calculated the $60 billion evaporation as a function of algorithmic de-pegging feedback loops. The underlying cause was not a single exploit but a structural vulnerability in the stablecoin’s balance sheet. The same logic applies here. An AI model does not need to steal funds directly. It only needs to trigger a small, credible signal that the system is untrustworthy. The cascade does the rest.
From my experience auditing the 0x Protocol v2 smart contracts in 2018, I learned that security is not about trust but about mathematical proof. The crypto industry has spent years building trustless systems based on code audits and formal verification. But AI models are not trustless. They are black boxes. The only way to verify their safety is to run them against the actual systems they will interact with. The request is an attempt to extend the trustless paradigm to the AI layer.
Liquidity doesn’t lie, but it can be manipulated. Security is the new liquidity. The firms that signed this request understand that the value of their assets is directly proportional to the security of the AI models that can attack them. They are not asking for charity. They are asking for the raw material of their own risk management.
Contrarian
Now, the contrarian angle. Most observers will interpret this request as a defensive move. “Crypto firms are scared of AI-powered hacks, so they want to test the models early.” That is the surface narrative. But I see a different dynamic: this is a power play, a subtle attempt to shape the regulatory architecture of AI governance.
Consider the timing. The European Union’s AI Act is being finalized. The US Executive Order on AI Safety is facing implementation challenges. Regulators are struggling to define what “safe” means for large language models. The crypto industry, by inserting itself into the pre-release testing process, is positioning itself as a de facto regulatory body. They are saying: “We will tell you if the model is safe enough to release for financial applications.” This is a classic regulatory arbitrage move—influence the standard before it is codified.
But there is a blind spot. The request assumes that independent researchers are benevolent. What if the researchers themselves are compromised? The crypto industry has a long history of “white hat” hackers who turn into “black hats” when the bounty is large enough. Giving a researcher access to a state-of-the-art model is like giving a teenager the keys to a Ferrari. The temptation to use the model for personal gain—or to sell the access to a malicious actor—is real.
Furthermore, the request exposes a decoupling thesis. The market assumes that AI and crypto will converge in a positive-sum way. But this request suggests the opposite: the convergence is a zero-sum game. The firms that gain early access to the model will have an information advantage over those that do not. This creates a new asymmetry in the market. The firms that are not part of the 40+ list will be at a disadvantage. They will be exposed to the same AI risks but without the pre-release mitigation. This is not a collective security blanket; it is a privilege for the connected few.
Regulation follows architecture. If the request is successful, the architecture of AI safety in finance will be shaped by the crypto industry. But if it fails—if the AI labs refuse, or if the researchers leak the model—the result could be a fragmentation of security standards. The crypto industry might split into two tiers: those with pre-release access and those without. The latter will be forced to rely on open-source models, which may be less capable but also less controlled. The decoupling is not between crypto and AI, but between the haves and the have-nots within crypto.
Takeaway
The preemptive cascade is already in motion. The request is not a single event but a signal of a structural shift. The crypto industry is no longer a passive victim of technological change. It is actively trying to shape the tools that will define its security landscape.
The question is not whether AI will be used to attack crypto. It will. The question is who gets to define the rules of engagement. The 40+ firms are betting that they can be inside the room, influencing the test cases, the timelines, and the disclosure policies. If they succeed, they will have created a new asset class: pre-release AI security intelligence. If they fail, they will have handed the attackers a playbook.
Code audits, not prayers. The crypto industry has always relied on proof, not promises. This request is an attempt to extend that principle to the AI layer. But the proof is only as good as the testers. And the testers are only as good as their incentives.
Liquidity is a weapon. The firms that secure pre-release access will wield it. The rest will be targets.