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Quantum-resistant tokens jump 50% as Google flags risks to Bitcoin security

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Flow Traders debuts 24/7 OTC liquidity service for tokenized stocks, gold and money market funds

The market appears to be reassessing long‑term technological risks in crypto following Google’s major quantum computing research update on Monday.

While leading coins like bitcoin and ether (ETH) have seen only modest moves in the past 24 hours, several cryptocurrencies tied to the quantum‑resistant narrative have surged sharply, with some gaining more than 50%.

This outperformance of the so-called quantum-resistant tokens shows how quickly the market is pricing in potential technological risks, even if those are still theoretical. While quantum computers capable of attacking Bitcoin are still years away, traders are already signaling an appetite for “future-proof” assets.

Late Monday, Google’s Quantum AI team suggested that quantum computers could break the elliptic‑curve cryptography used by Bitcoin, with fewer than 500,000 quantum qubits, which is significantly less than previously estimated. This prompted some analysts to cite 2029 as a potential deadline for Bitcoin and the broader blockchain ecosystem to strengthen their defenses.

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The study said that a sufficiently advanced quantum computer could attack Bitcoin within nine minutes. A separate report highlighted Ethereum’s vulnerabilities, identifying five potential attack vectors that could put an estimated $100 billion of assets at risk, including DeFi and tokenized holdings.

However, such machines do not exist and remain a threat that’s still a few years away.

Still, over the past 24 hours, the market has shown increased interest in cryptocurrencies and projects that emphasize post‑quantum cryptographic designs, research into future‑proofing security, or that appear relatively more resilient than legacy chains.

Notably, Quantum Resistant Ledger (QRL) and Cellframe (CEL) have surged 50%, reflecting growing market attention to truly post‑quantum protocols, according to data source Coingecko. Other tokens in the category, such as Abelian (ABEL), have risen 25%, while Qubic (QUBIC) and QANplatform (QANX) have each gained 10%, and even the privacy‑focused Zcash (ZEC) has added nearly 7% in the same period.

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The market cap of this group, comprising 20 coins, has increased by 8% to $4.66 billion over the past 24 hours. It’s worth noting that ZEC is not yet truly quantum-resistant but is still included in the category by data sources because of its advanced cryptographic foundations, such as zero-knowledge proofs, and ongoing research into post-quantum secure ZK-SNARKs. These factors make it part of the “quantum-aware” narrative, even if it does not currently fully implement post-quantum cryptography.

While the risks remain largely theoretical, they have been influencing market behavior since last year. According to Charles Edwards, founder of Capriole Investments, concerns over quantum attacks contributed to Bitcoin’s decoupling from the rising stock market in the second half of 2025, with the cryptocurrency sliding from $126,000 to $80,000 in the final months of the year.

“We have already started to see quantum risk be priced into Bitcoin. It’s the primary reason Bitcoin is trading -50% against the S&P 500 and -90% against gold since the inaugural Bitcoin Quantum Summit seven months ago,” Edwards said in a report in February.

Coincidentally, this was exactly the period when ZEC staged a sharp rally. ZEC surged by over 1,200% in the second half of 2025, hitting a high of $744.

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Crypto World

DeFi Is Optimizing For gas, Not For Markets

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DeFi Is Optimizing For gas, Not For Markets

Opinion by: João Garcia, DevReal lead at Cartesi.

Decentralized finance presents itself as a transparent alternative to Wall Street. Yet, what it has largely reconstructed is a simplified version of finance, engineered less around market resilience than around the constraints of gas fees. That trade-off, once treated as a technical footnote, is increasingly shaping the limits of what DeFi can become.

So long as computational minimalism remains the overriding priority, financial robustness will remain secondary, and periods of market stress will continue to expose that imbalance.

When markets move faster than the virtual machine

DeFi has rebuilt the familiar architecture of finance, including exchanges, lending markets, derivatives and stablecoins. However, the way these systems function reveals how tightly they are bound by their execution environments.

Risk parameters tend to remain static, and although collateral thresholds can adjust, they typically do so slowly, through governance processes rather than automatic recalibration. Liquidation engines currently rely on fixed formulas rather than adaptive portfolio models that account for shifting volatility or correlations. What appears as a design preference is often a concession to computational limits.

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On Ethereum and similar chains, floating-point arithmetic is absent or emulated, iterative simulations are expensive, and continuously recomputing cross-asset exposure can quickly become impractical. The outcome is that financial logic is compressed into forms that are deterministic and affordable to execute, even if that compression strips away nuance.

This architecture performs adequately in stable conditions, but volatility has a way of testing its edges. During MakerDAO’s “Black Thursday” event in March 2020, vaults were liquidated at effectively zero bids, as auction mechanics struggled under collapsing prices and network congestion. 

In later downturns, protocols such as Aave and Compound leaned on mass liquidations triggered by fixed collateral ratios, rather than dynamic portfolio recalculations. When Curve’s pools were destabilized in 2023 following a smart contract exploit, the stress radiated outward into lending protocols that treated LP tokens as static collateral, compounding systemic risk.

In each instance, decentralization itself was not the breaking point. Rather, rigid financial logic operated inside an execution layer that could not continuously recompute risk as conditions deteriorated.

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Traditional markets evolved in the opposite direction. Banks and clearinghouses simulate thousands of stress scenarios, recalculating exposure as correlations shift and volatility regimes change. Margin requirements respond dynamically to market conditions, and the response is led by substantial computational infrastructure and mature numerical tooling. Public blockchains, by contrast, were not designed with that degree of iterative financial processing in mind.

The illusion of simplicity

Constraining computational complexity reduces certain attack surfaces. Simplicity at the protocol layer, however, does not dissolve complexity in the financial system. It merely pushes it elsewhere.

When risk cannot be modeled and recomputed transparently on-chain, it migrates off-chain into dashboards, analytics teams, discretionary parameter adjustments and emergency governance coordination. The blockchain may remain the settlement layer, but the adaptive intelligence that stabilizes the system increasingly operates outside it. During volatility spikes, protocols often depend on rapid human coordination to adjust parameters, while oracles and large token holders acquire disproportionate influence over outcomes.

The system retains its decentralized base, yet its capacity to respond flexibly depends on actors operating beyond deterministic execution. What appears structurally simple at the smart contract level can conceal a more complex and less transparent operational reality.

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DeFi did not converge on simplified finance because static ratios and deterministic curves were proven superior. It converged there because richer computational models were prohibitively expensive to run. As markets deepen, leverage increases, and instruments grow more interdependent, that compromise becomes harder to ignore. Fixed thresholds and blunt liquidation engines, initially safeguards, can begin to function as amplifiers of stress.

Computation as a missing primitive

The deeper constraint, more than decentralization, is execution design.

If verifiable execution environments begin to approximate general-purpose computing systems, the financial design space expands. Native floating-point assistance, iterative algorithms and access to established numerical libraries would allow models to be expressed directly rather than translated into simplified approximations. 

Related: Wall Street will eventually submit to the rules of DeFi

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This change would allow lending protocols to incorporate scenario-based stress testing instead of relying primarily on fixed collateral ratios. Margin requirements may also adjust in response to observed volatility rather than governance cadence. It could also see credit systems recompute multivariable risk scores transparently, replacing binary heuristics with more granular assessments.

The aim is not to introduce complexity for its own sake. It is to keep financial intelligence inside the protocol, where it remains visible and enforceable, rather than externalizing it into operational layers that users cannot easily audit. This underscores the broader point that the limitations confronting DeFi are largely architectural choices, not inevitabilities of decentralization.

A credibility ceiling

DeFi now stands at a structural crossroads. One direction preserves gas-optimized minimalism, keeping base-layer execution clean while allowing increasingly sophisticated financial logic to migrate off-chain. That path may maintain clarity at the smart contract level, but it constrains how far decentralized finance can responsibly scale.

The alternative is to treat computation itself as a first-class primitive and to accept more capable execution environments in exchange for systems that can adapt, recompute and stress-test transparently. If complex risk logic cannot live on-chain, DeFi will continue to project simplicity in code while relying on discretion in practice.

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Markets will not moderate their complexity to accommodate virtual machine constraints. If decentralized finance intends to operate at a meaningful scale, its computational foundations will have to evolve alongside the financial ambitions built on top of them.

Opinion by: João Garcia, DevReal lead at Cartesi.