Verifiability as a Market (2025)

Decentralized AI: Builders Love It. Do Buyers?

Model verifiability and decentralized inference are theoretically elegant but not mission-critical. It’s often a case of people falling in love with the solution more than the problem. Most users and enterprises are fine trusting OpenAI (just like they trust AWS).

We didn’t convince the world to move from AWS to Filecoin, so what makes us think they’ll abandon OpenAI for a slower, less-performant crypto-native model?

I get the appeal… but adoption still feels speculative. The global market for AI governance and compliance tools is projected to reach $20B by 2030, and the EU AI Act could force enterprises to invest in verifiability infrastructure. But innovation always outpaces regulation, and regulation is the only clear path to top-down demand here.

So the question becomes: are there sectors where this pain is already pressing? Who are the actual consumers of decentralized inference and computational verifiability today? What is the market size? Because right now, very few companies are using this in production day-to-day.

I love nerding out about infrastructure, but it’s hard not to notice that these solutions seem to appeal more to builders than buyers. The real need might emerge in regulated verticals : finance, healthcare, legal but usage today is experimental.

It's been years since ChatGPT launched. It’s time to ask hard questions. If users didn’t migrate from AWS to Filecoin, why would they leave OpenAI for an overengineered alternative unless there’s a strong regulatory or economic forcing function?

That said, we often say innovation moves faster than regulation but in reality, regulation moves markets. If the output of a model impacts a person’s life and you can’t prove where it came from, that becomes a liability. Even if just 5% of use cases require provenance, that’s still billions of dollars at stake.

Some argue decentralized inference enables new coordination mechanisms. You can’t build composable, trustless AI systems without verifying each step. That’s compelling but does verification need a ZK proof? Does it have to involve blockchain? Or are we chasing elegance over necessity?

Technically, you can verify inference without blockchain:

Log inputs/outputs and model hashes

Use TPMs like Intel SGX for trusted execution

Store hashes in centralized systems or use Merkle chaining

This works well for internal auditing or compliance workflows: banks, hospitals, and enterprise AI pipelines. But it lacks trustless guarantees, public composability, and enforcement.

If you want verifiability without trusted third parties, open APIs that autonomous agents can call, and execution that can be enforced onchain, you start needing crypto rails. That’s where projects like Modulus, EZKL, Gensyn, Akash, and Bittensor are playing.

In that world — where multiple agents, dapps, or protocols coordinate over shared model outputs — you need more than logging. You need economic incentives, execution guarantees, and cryptographic proofs.

So the question is both technical and philosophical:

What kind of verification do we actually need…. also who needs it?

Who Holds the Keys When the Agent Acts Alone?

Imagine this: your AI agent just submitted a DAO proposal, traded on Uniswap, then updated your Notion board with your new net worth, all while you were in the shower. Sounds efficient, right?

Now imagine the same agent leaks your wallet key. That’s not just annoying. That’s catastrophic.

This is the dark frontier of autonomous agents: not just thinking and doing, but signing. Holding funds. Moving value. Making commitments on your behalf, sometimes without human oversight. Which begs the question:

How do you let an agent act like a sovereign without giving it a loaded gun?

The answer lies in one core challenge: key management. And the ecosystem is splitting into camps — each solving this problem with its own flavor of tradeoffs: speed vs safety, practicality vs purity, abstraction vs attestation.

The NEAR Perspective: Let Agents Act, But Set Their Boundaries

NEAR’s philosophy is simple: autonomous agents should be usable. They should work out of the box, launch quickly, and integrate easily with smart contracts. With projects like Shade Protocol, NEAR enables agents to operate DAO governance, perform DeFi strategies, or self-update over time.

Key access here is managed through smart contract wallets, programmable accounts that don’t need raw private keys. Instead, NEAR lets developers define logic: who can act, when, under what conditions. Agents aren’t holding a key in a vault; they’re operating within a sandboxed permission system. It’s the app-first approach: give people tools they can use now, even if it means leaning more on platform logic than cryptographic guarantees.

But the tradeoff? It’s not fully trustless. You’re trusting NEAR’s execution layer, the contract logic, and the assumptions of the runtime. It’s fast. It works. But it's not unbreakable.

The EigenLayer Camp: Don’t Trust the Agent, Slash It

EigenLayer enters the picture with a radically different posture: “Don’t trust the agent. Prove it. And punish it if it lies.”

Here, key management becomes part of a bigger security game. Agents don’t just sign transactions — they interact with Autonomous Verifiable Services (AVSs): restaked validators that verify every step of the agent’s reasoning, memory, tool usage, and yes, its signing behavior.

An agent running inside Eigen’s ecosystem may use a key, but that key is guarded by slashing conditions, attestations, and real cryptoeconomic consequences. Whether the agent computes inside a TEE, produces a ZK proof, or delegates signing through MPC. The system is watching. It’s not about trusting the vault. It’s about making lying expensive.

This isn’t the fastest way to ship a product. But if the agent’s holding real money, real voting power, or access to onchain systems, Eigen is betting we’ll demand this level of security.

TEE: The Physical Vault in the Digital Brain

Enter the Trusted Execution Environment (the hardware vault). This is where projects like Autonome are operating. Instead of treating verification as a protocol-level challenge, TEEs handle it in the guts of the machine.

The agent runs inside the processor’s secure enclave. Keys never leave. If the OS is compromised, the TEE still holds. It’s like building a panic room inside your laptop, complete with a cryptographic receipt that says, “I ran the code as expected, and no one tampered with me.”

TEEs offer a pragmatic bridge: more secure than raw cloud compute, faster than ZK proofs. But they come with baggage: centralized attestation (you have to trust Intel or ARM), hardware exploits, and opaque performance tradeoffs.

Still, if you want your agent to sign things quickly and securely, TEEs are a compelling bet.

MPC: Divide and Don't Trust

Then there’s Multi-Party Computation (for people who don't trust anyone). Not even the machine.

MPC splits the private key into shards, spreads them across multiple parties, and requires a quorum to sign. No single party ever sees the full key. It’s like giving your house key to five roommates and requiring three of them to show up to unlock the door.

This is perfect for adversarial settings: agents operating across chains, controlled by DAOs, or executing in volatile governance environments. The downside? Coordination is a pain, latency is real, and UX suffers. But if you want your agent to act like a committee of guards, MPC delivers.

ZK + Smart Contracts: No Key At All — Just Logic

The final group wants to skip keys entirely. With Zero-Knowledge SNARKs and smart contract wallets, the agent doesn’t hold a key. It holds a proof. It sends that proof to the contract. The contract checks the logic. If the logic matches the pre-agreed rules, the transaction goes through.

This is the cleanest model. No keys. No cloud. No risk of compromise. But it’s also the most expensive and rigid. Complex behavior is hard to prove in ZK, and gas costs can spike. Still, for high-value actions, this model is elegant. It's trustless, deterministic, and doesn't require trusting the agent at all — just the math.

So Who Wins?

Nobody (yet). And that’s the point.

Different agents, different risks, different trust models.

NEAR is betting on simplicity and developer adoption.

EigenLayer is optimizing for cryptoeconomic guarantees.

Autonome brings verifiability to runtime environments via TEEs.

MPC and ZK offer the paranoid paths — expensive, but robust.

The only real mistake? Pretending this stuff is solved.

We’re still in the early innings of figuring out what it means for an agent to hold power. To act for us. To act without us.

And until we can verify every decision, every signature, and every safeguard —

We’re not giving them the damn keys.

Most of crypto is building for imagined futures. The only part that’s actually leapfrogging anything today is borderless payments.

People don’t need trustless ZK-proven inference to send money to their mom. They just need something that works better than their bank and crypto, in this use case, already does.