Exclusive Interview: TEN Protocol on Privacy, Verifiability, and the Next Phase of Ethereum Applications

Q1. For readers who may only know TEN from recent headlines, how do you explain TEN Protocol’s core mission and the problem it is fundamentally designed to solve within Ethereum’s execution landscape?

Ethereum did something radical: it made computation globally verifiable by making everything public. That trade-off unlocked trustless finance – but it also quietly broke a huge class of real applications.

Today, when you use most Ethereum L2s, you’re not just executing a transaction. You’re broadcasting your intent, your strategy, your timing, and often your economic reasoning to every bot, competitor, and adversary watching the chain. That visibility enables verification – but it also enables front-running, strategy extraction, behavioural surveillance, and entire attack markets built on copying intent faster than humans can react.

TEN exists to break that false binary.

Our mission is simple to state but hard to execute: let people use Ethereum applications without revealing what they’re trying to do, while still preserving Ethereum-grade verifiability. With the right cryptography and execution model, you can prove that computation was correct without revealing the inputs, intermediate steps, or private logic behind it.

In practice, this changes everything. Node operators can’t front-run. AI agents can safely hold secrets. Games can exist on-chain without exposing hidden state. Bids don’t get copied. Applications don’t have to leak sensitive information just to be provable.

TEN is about restoring something blockchains accidentally removed: the ability to compute in confidence.

Q2. TEN positions “compute in confidence” as a missing primitive in today’s blockchain stack. Why is selective confidentiality increasingly necessary for real-world DeFi, AI, gaming, and enterprise use cases?

Every successful software system in the world relies on access control.On Facebook, you don’t see every post – only what you’re allowed to see. In banking, your balance isn’t public. In games, opponents don’t see your hand. In businesses, internal logic and data are guarded because exposure destroys value.

Blockchains inverted this model. They made total transparency the default – which is great for auditability, but catastrophic for many real applications.

In DeFi, users leak strategies and become predictable prey. In gaming, hidden information, randomness, and fair play are impossible to implement correctly. In AI and enterprise, exposing data, models, or internal decision logic either violates regulation or eliminates competitive advantage entirely.

What’s missing isn’t trust – it’s programmable confidentiality with cryptographic guarantees. Not privacy bolted on via centralized servers or legal promises, but access control enforced by the protocol itself.

That’s what “compute in confidence” restores: the ability to decide who can see what, while keeping the system verifiable.

Q3. Your architecture relies on Trusted Execution Environments rather than ZK-only or MPC-based approaches. What trade-offs did you make in choosing this design, and how do you mitigate the associated trust assumptions?

From day one, our constraint was clear: builders should be able to deploy real EVM applications without rewriting the world.

Running the full EVM inside a Trusted Execution Environment lets developers use the same languages, tooling, and mental models they already know – while gaining selective confidentiality. Settlement, liquidity, and composability remain anchored to Ethereum.

ZK and MPC approaches are powerful and improving fast, but today they often impose serious trade-offs: circuit complexity, performance bottlenecks, limited programmability, or operational overhead that makes general-purpose apps hard to build and harder to scale.

Using TEEs introduces a hardware-rooted trust assumption – and we’re explicit about that. TEN mitigates it through layered design: cloud-only hosting to reduce physical attack vectors, mandatory remote attestation, redundancy, governance constraints, and rigorous security engineering.

The result is a hybrid model. Public where it should be public – settlement, auditability, outcomes. Confidential where it must be – inputs, order flow, and sensitive state. It’s not ideological purity; it’s engineering pragmatism.

Q4. How does TEN preserve Ethereum-grade verifiability and composability while allowing parts of execution, such as inputs, order flow, or strategies, to remain confidential?

TEN separates what must be provable from what must be visible.

Smart contract rules remain public. Anyone can inspect them. Execution happens inside an attested TEE, and the network can cryptographically verify that the correct code ran on valid inputs – even if those inputs were encrypted.

As a Layer 2, TEN still posts rollups and state transitions back to Ethereum. Finality, settlement, and composability remain exactly where users expect them to be.

What disappears is unnecessary exposure. Intermediate strategies, private thresholds, and sensitive logic don’t need to leak just to prove correctness.

Confidentiality becomes a first-class capability, not a workaround.

Q5. From a user experience standpoint, how does interacting with a TEN-powered application differ from using a typical Ethereum L2 today?

The biggest difference is psychological – and it’s immediate.

Users no longer feel watched. There’s no mempool anxiety, no defensive slippage settings, no private RPC gymnastics just to avoid being exploited. Intent is private by default.

You submit a bid, a strategy, or a move assuming it won’t be copied in real time – because it won’t be. That single shift makes Web3 feel closer to how normal software actually behaves.

Privacy stops being an advanced feature for power users and becomes an invisible property of the application itself.

Q6. One of TEN’s flagship narratives is reducing MEV and market exploitation. How do mechanisms like sealed bids, hidden order flow, or private routing work in practice, and what measurable improvements do they enable?

TEN changes what is visible during execution.

In a sealed-bid auction, bids are encrypted and processed inside a TEE. No one sees individual bids in real time. Depending on the design, bids may never be revealed at all – only the final outcome.

Hidden order flow follows the same principle. Strategies aren’t broadcast to the world, so there’s nothing to copy, simulate, or sandwich. MEV doesn’t need to be “fought” – it simply has nothing to feed on.

Crucially, this doesn’t sacrifice trust. The rules are public, the execution is attested, and outcomes are verifiable. You can prove fairness without exposing intent.

Q7. TEN has highlighted use cases such as verifiable AI agents and provably fair iGaming. Which of these do you see as the earliest drivers of real adoption, and why are they better suited to TEN than to transparent-by-default chains?

Real-money gaming is the clearest near-term fit.

Gaming requires hidden information, fast randomness, and low latency. Transparent chains break those assumptions. On TEN’s testnet, we saw tens of thousands of unique wallets and over a million wagers – orders of magnitude more engagement than typical testnets.

House of TEN, a world first with onchain poker played by AI agents proved to be a huge hit during the time we released it for beta.

Verifiable AI agents are equally transformative but slightly longer-cycle. They enable confidential treasury management, private decision-making, and AI systems that can prove rule-compliance without exposing proprietary models or data.

Both categories benefit directly from selective confidentiality – and both are impossible to do properly on transparent-by-default chains.

Q8. Trusted hardware introduces a different class of operational risk. How is TEN designed to ensure failures are detectable, contained, and recoverable rather than systemic?

Trusted hardware changes the failure mode – it doesn’t eliminate it.

TEN assumes things can go wrong and designs for detectability and containment. Remote attestation ensures incorrect execution is observable. Redundant operators prevent single-node failures from becoming systemic. Governance mechanisms allow compromised components to be isolated or replaced.

The goal isn’t blind trust – it’s bounded trust with strong guarantees.

Q9. Shifting briefly to network operations: what does the current operator model look like, and how does the roadmap move from a bootstrap phase toward greater decentralization and resilience?

TEN begins with a constrained operator set to ensure security and performance, then progressively expands participation as tooling, monitoring, and governance mature.

Decentralization is not a checkbox – it’s a sequence. Each phase increases resilience without compromising confidentiality guarantees.

Q10. Token launches are often conflated with product readiness. How do you internally separate market events from protocol development, and what milestones matter most for evaluating TEN’s technical progress over the next 6–12 months?

Very deliberately.

Token events don’t define readiness. Shipping does.

Internally, progress is measured by audited releases, live applications, operator expansion, developer activity, and real revenue-generating use cases that require confidentiality.

Over the next 6–12 months, success is about capabilities delivered – not narratives maintained.

Q11. In hindsight, what operational lessons has the team taken from launching a complex infrastructure protocol in a highly reflexive market environment?

That technology alone isn’t enough.

Execution, communication, and timing compound each other – especially in markets where perception feeds directly back into reality. Even strong systems suffer if expectations aren’t aligned.

The lesson is simple but unforgiving: trust is rebuilt by delivery, not explanation. Working infrastructure beats perfect messaging every time.

Q12. Looking forward, what would success for TEN look like one year from now in terms of shipped capabilities, developer adoption, and real applications running in production?

Success means applications running in production that simply couldn’t exist on transparent chains.

Live iGaming. Protected DeFi workflows. Verifiable AI agents managing real value. Developers using confidentiality as a core design primitive, not an afterthought.

At that point, TEN isn’t “a privacy project.” It’s foundational infrastructure – the missing layer that lets Ethereum finally support the full spectrum of real applications.