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Watch out Bitcoin devs. Google says post-quantum migration needs to happen by 2029.

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(CoinDesk)

The crypto industry’s reaction was that a quantum computing threat was still distant when Google unveiled its Willow quantum chip in December 2024.

Bitcoin uses SHA-256 for mining and ECDSA for signatures, both of which are theoretically vulnerable to quantum decryption, but the consensus was that the threat was decades away. Breaking encryption would require millions of physical qubits (a unit of information in quantum systems). Willow had just 105.

That story has marginally changed sixteen months later, and Google isn’t dismissing anything.

The company announced this week that it is setting a 2029 deadline to migrate its authentication services to post-quantum cryptography, citing progress in quantum hardware, error correction, and factoring resource estimates.

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Google’s security engineering team wrote that quantum computers “will pose a significant threat to current cryptographic standards, and specifically to encryption and digital signatures,” and that the threat to digital signatures specifically “requires the transition to PQC prior to a cryptographically relevant quantum computer.”

These risks are not theoretical. The Android 17 mobile operating system is already integrating post-quantum digital signature protection. Chrome already supports post-quantum key exchange. Google Cloud offers post-quantum solutions to enterprise customers.

(CoinDesk)

Here’s why it matters

Classical computers process information as bits, each one either a 0 or a 1, and solve problems by checking possibilities one at a time. Quantum computers use qubits that can exist as both 0 and 1 simultaneously, a property called superposition, which lets them explore vast numbers of possibilities in parallel.

For most everyday tasks, the advantage is negligible. But for specific problems like factoring the large prime numbers that underpin modern encryption, a sufficiently powerful quantum computer could solve in minutes what would take a classical machine longer than the age of the universe.

Bitcoin uses ECDSA (Elliptic Curve Digital Signature Algorithm) to sign transactions, which is exactly the category of cryptography Google flagged as requiring migration before a quantum computer capable of breaking it arrives.

A sufficiently powerful quantum computer running Shor’s algorithm could derive private keys from public keys, allowing an attacker to spend any bitcoin whose public key has been exposed on the blockchain.

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Shor’s is a quantum computing method that can crack the math protecting passwords and wallets exponentially faster than normal computers.

(CoinDesk)

When CoinDesk wrote about Willow in December 2024, the math was reassuring. Chris Osborn, founder of Solana ecosystem project Dialect, laid it out clearly at the time: roughly 5,000 logical qubits are needed to run Shor’s algorithm against current encryption, and each logical qubit requires thousands of physical qubits for error correction.

That meant millions of physical qubits, against Willow’s 105. The gap seemed enormous.

What’s changed isn’t the qubit count. It’s the error correction trajectory and the institutional response. Google went from demonstrating “below threshold” error correction, meaning they could turn noisy physical qubits into usable logical ones for the first time, to setting a corporate migration deadline in 16 months.

When the company that builds the quantum computers urges developers to migrate by 2029, that’s a signal that the gap is closing faster than the public timeline suggests.

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Ethereum co-founder Vitalik Buterin was already calling for urgency in October 2024, a month before the Willow announcement.

“Quantum computing experts such as Scott Aaronson have also recently started taking the possibility of quantum computers actually working in the medium term much more seriously,” Buterin wrote at the time.

“This has consequences across the entire Ethereum roadmap: it means that each piece of the Ethereum protocol that currently depends on elliptic curves will need to have some hash-based or otherwise quantum-resistant replacement.”

How Ethereum and Bitcoin developers are responding

The contrast with how the two largest blockchain networks are responding could not be sharper.

The Ethereum Foundation treated that as a directive and built accordingly. Eight years of work, now visible in weekly shipping devnets and a public roadmap with fork-level specificity.

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Bitcoin’s governance model makes this kind of coordinated response structurally harder. There is no Ethereum Foundation equivalent to fund and direct a multi-year engineering effort.

Protocol changes require broad consensus among a decentralized developer community that has historically moved slowly and deliberately, a feature for stability but a liability when facing a deadline.

The last major cryptographic upgrade to Bitcoin, Taproot, took years of discussion before activation in 2021.

Ethereum launched pq.ethereum.org this week, a dedicated hub for its post-quantum security effort that has been underway since 2018. The Ethereum Foundation’s post-quantum team, cryptography team, protocol architecture team, and protocol coordination team have spent eight years building toward a migration that touches every layer of the protocol.

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More than 10 client teams are shipping weekly devnets through what the foundation calls PQ Interop. The roadmap maps specific milestones across four upcoming hard forks, from a post-quantum key registry to full PQ consensus.

Bitcoin, on the other hand, has no equivalent effort. No coordinated roadmap. No multi-team engineering program. No fork milestones.

(CoinDesk)

Nic Carter, one of Bitcoin’s most prominent advocates and co-founder of crypto fund Castle Island Ventures, said the quiet part out loud this week.

“Elliptic curve cryptography is on the brink of obsolescence,” he wrote on X. “Whether it’s 3 or 10 years, it’s over and we need to accept that. The only thing that matters is how quickly blockchain developers recognize that they need to bake in cryptographic mutability into their networks.”

Carter contrasted the two approaches directly. Ethereum’s approach, he said, was “best in class,” describing how the network “gets together and announces a specific, detailed PQ roadmap by 2029, sets it as top strategic priority, folds PQ into ongoing roadmap, detailed FAQ, no fear, just action.”

Bitcoin’s approach, Carter said, was “worst in class.” He noted there is currently one group working on a quantum-related proposal that has “received zero buy-in from top devs,” with developers pointing to isolated pieces of research as evidence of progress while having “no coherent strategy, no roadmap.”

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“Everyone knows I’m a bitcoiner and would like bitcoin to win,” Carter added. “Not saying this to hurt feelings. Saying this to spur action.”

The urgency isn’t universally shared, however.

Firms such as CoinShares argue that fears of an imminent quantum threat to bitcoin are overstated, and it estimates that only about 10,200 BTC is concentrated enough in vulnerable legacy address types that its theft could cause “appreciable market disruption.”

The remaining exposed supply, roughly 1.6 million BTC in older Pay-to-Public-Key addresses, is scattered across more than 32,000 separate wallets averaging about 50 BTC each, making them slow and unprofitable to crack individually, as CoinDesk reported at the time.

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But the question isn’t whether quantum computing will eventually threaten blockchain cryptography. Google, the Ethereum Foundation, NIST, and now prominent Bitcoin advocates all agree it will.

It is whether three years is enough time to migrate a global, decentralized protocol that has no central authority to set deadlines, no coordinated engineering team to execute them, and a culture that treats urgency with suspicion.

Ethereum’s answer is that eight years of preparation put it in a position to execute the migration across four hard forks. Google’s answer is that 2029 is the deadline, and the migration is already underway in its products.

Bitcoin’s answer, so far, is silence. And as Carter warned, “ETHBTC will start to reflect the divergence in prioritization” if that silence continues.

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Rising XRP Whales Tighten Risk-Reward, Foreshadow Price Move

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Crypto Breaking News

XRP’s risk-adjusted performance turned modestly positive on March 26, marking a shift after months of flat-to-negative readings. A 30-day average return of 0.00063 accompanies a Sharpe ratio of 0.0267, suggesting that current gains are modest but still outpaced by risk. On-chain data shows persistent accumulation by large holders, implying underlying demand even as price action remains subdued.

Analysts point to a broader pattern: on-chain buying and a slowly improving risk profile could set the stage for a steadier path higher, even if price upside remains constrained in the near term.

Key takeaways

  • The XRP Sharpe ratio moved into positive territory for the first time in months on March 26, supported by a 30-day average return of 0.00063.
  • Whale activity has remained firm, with CryptoQuant data showing XRP inflows averaging about $9 million per day over the last 30 days, continuing a pronounced accumulation phase that began in late February.
  • Open interest surged 14.8% in the 24 hours to March 26, signaling renewed trader participation, alongside repeated long-liquidation spikes above $2 million in recent sessions.
  • XRP’s price structure has shifted to a bearish bias: the asset invalidated its previously bullish ascending triangle and shed about 13.63% over ten days, with near-term support at $1.27 and a yearly low near $1.11 in focus.
  • Past patterns suggest that prolonged accumulation can precede stronger upside, as seen in Q2 2025 when accumulation preceded a rally to a $3.65 high on July 18, 2025; watchers will want to see if the current phase leads to a similar outcome.

Positive risk-adjusted returns amid on-chain demand

CryptoQuant-derived data indicate that XRP’s improved risk-adjusted profile aligns with a pickup in trading activity. Arab Chain, in a CryptoQuant quicktake, framed the recent Sharpe ratio improvement as part of a gradual rebalancing that could limit downside for holders. However, the analyst cautioned that a return to negative territory would signal renewed volatility and fading momentum.

“If the indicator falls back into negative territory, it could signal a return of volatility and weakening momentum.”

While the short-term signals point to hedged risk, the long-run picture suggests a more constructive tilt if accumulation continues. The last substantial accumulation wave in Q2 2025 culminated in XRP’s expansion rally to an all-time high of $3.65 on July 18, 2025, underscoring how inflows can precede meaningful upside in subsequent months.

Whale flows and market momentum

On-chain trackers show that XRP whale inflows have remained robust, with the 30-day moving average holding around $9 million per day. The sustained demand has persisted since February 27, marking the longest accumulation stretch in months and echoing a broader pattern seen during prior cycles when whales stepped in ahead of bigger price moves.

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That trend matters for investors because it points to durable demand that could underpin market returns even if price volatility remains elevated. The question for traders is whether this accumulation translates into sustained upside or simply supports a slower drift higher as macro and liquidity conditions evolve.

Open interest and near-term technicals

Open interest figures reinforce a market where risk is being actively recycled. CryptoQuant data show a 14.8% rise in 24-hour open interest on March 26, the strongest such move since March 4, reflecting renewed participation from long and short positions and a pattern of consecutive long liquidations—$2.5 million on March 18, roughly $2.45 million on March 21, and about $2.15 million on March 26.

From a price-structure perspective, XRP has broken from a bullish ascending triangle, and the prior ten-day slide of around 13.6% points to a bearish bias in the near term. If the current dynamic persists, traders will likely test support around $1.27, with a deeper look toward the yearly low near $1.11 in the weeks ahead.

The combination of a positive risk-adjusted metric and steady whale inflows paints a nuanced picture: a market where demand is accumulating even as prices wobble, potentially laying a groundwork for a more durable move if buyers sustain their activity.

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Looking ahead, buyers will want to see whether the positive risk-adjusted read holds and whether whale demand remains steady. The next critical junctures to watch include whether XRP can sustain levels above near-term support and whether accumulation pulses continue to shape the risk landscape in the coming weeks.

Past patterns offer a useful lens: the accumulation phase seen in Q2 2025 preceded a rally to an all-time high later that year, suggesting that continued demand could precede stronger upside if sustained by shifting market dynamics.

Looking ahead, traders will watch if the positive risk-adjusted reads endure and whether whale accumulation remains steady; a sustained move higher will depend on whether demand translates into durable upside beyond the near-term support.

Risk & affiliate notice: Crypto assets are volatile and capital is at risk. This article may contain affiliate links. Read full disclosure

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MetaComp Upgrades StableX for AI-Driven Hybrid Finance

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Crypto Breaking News

Key Insights

  • MetaComp launches AI-driven StableX upgrade to unify compliance, payments, and digital asset operations
  • VisionX engine strengthens AML/CFT with multi-layer analytics and near-zero false clean rates
  • AgentX and KYA enable regulated AI automation across payments, treasury, and compliance workflows

Singapore-based MetaComp has introduced major upgrades to its StableX Network, aiming to strengthen compliance, payments, and wealth management across fiat and stablecoin systems. The move positions StableX as a compliance-first platform designed to bridge traditional finance and digital assets.

The upgrade integrates three core components: VisionX Engine, AgentX AI layer, and the KYA governance framework, focused on enabling regulated, AI-driven financial infrastructure.

VisionX Engine Enhances AML/CFT Monitoring

The Web2.5 VisionX Engine delivers multi-layered risk monitoring across identity, behavior, and network levels. Identity screening combines traditional KYC data with Web3 wallet intelligence, while behavioral analysis detects transaction anomalies.

Network screening highlights the concealed counterparty risks, offering a closer supervision of the flow of transactions. MetaComp said parallel screening across four blockchain analytics providers reduces false clean rates from around 25% to near zero.

The system supports both cross-border payments and digital asset transactions, allowing institutions to maintain compliance with global AML/CFT requirements.

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AgentX Powers AI Financial Execution

AgentX serves as the platform’s AI execution layer, enabling autonomous financial operations. AI agents can handle transactions, detect risks and perform operations on fiat and crypto systems.

The layer enables AI-to-AI communication, enabling automated processes in the payment, treasury and compliance operations. The most important characteristics are real-time transaction intelligence, wallet screening, compliance integration, and a modular and protocol-agnostic infrastructure.

The initial implementation, Agentic KYT, is concerned with the monitoring of transactions as an AML/CFT compliance, which expands the automation of regulation.

KYA Framework Governs AI Activity

The KYA (Know Your Agent) framework provides a regulatory mechanism over AI agents in financial systems. It ensures that AI-based processes are auditable and compliant with regulatory standards.

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MetaComp observed that Singapore Model AI Governance Framework is consistent with the given framework, and it helps to responsibly deploy agentic AI in financial services.

AI-Native Automation and Expansion Plans

Together, AgentX and KYA enable AI-native financial automation, allowing intelligent agents to independently manage payments, treasury, and compliance while remaining regulated.

The upgrade is after the $35 million Pre-A round at MetaComp. The company will increase the penetration of StableX in the Asian, Middle East, African and Latin American markets to attract the use by institutions.

MetaComp also published a whitepaper called Cross-Border Payments for SMEs: Voices in ASEAN and the Rise of Stablecoins, which states that the stablecoin is increasingly becoming an important part of enhancing the efficiency of payment.

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Risk & affiliate notice: Crypto assets are volatile and capital is at risk. This article may contain affiliate links. Read full disclosure

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How AI Agents Can Reshape Arbitrage in Prediction Markets

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How AI Agents Can Reshape Arbitrage in Prediction Markets

Prediction markets aggregate human judgment in theory, but some of their consistent trading opportunities may end up captured by systems that move faster than any person can.

Arbitrage opportunities can show up as brief mispricings, from outcomes that temporarily fail to sum up to 100%, to short delays in how quickly markets react to new information.

Rodrigo Coelho, CEO of Edge & Node, said bots are already scanning hundreds of markets per second, a role that increasingly overlaps with more advanced AI-driven agents.

“Capturing those opportunities requires monitoring thousands of markets and executing trades almost instantly, which is why they’re largely dominated by automated systems,” Coelho told Cointelegraph.

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That makes prediction markets a natural next step for AI-driven systems built to exploit short-lived pricing gaps without human input.

AI agents can target brief gaps in prediction markets. Source: Rohan Paul

Arbitrage mechanics in prediction markets

Bitcoin and crypto prices haven’t been performing well recently, with BitMine’s Tom Lee calling the current sentiment a “mini-crypto winter.” Meanwhile, prediction markets have emerged as venues where users can bet to profit independently of broader economic conditions.

The rise of prediction markets has also seen opportunities such as what Coelho calls “latency arbitrage,” which rely on short windows too narrow for humans to manually target. He told Cointelegraph:

If there’s even a few-second delay between an event happening and the market updating, bots scan for that and place bets on the correct outcome. For that window, they have a 100% guaranteed win.”

A recent study found that Polymarket exhibits frequent pricing inconsistencies, allowing traders to construct arbitrage positions. These opportunities arise both within individual markets, where probabilities don’t sum to 100%, and across related markets with inconsistent pricing. The researchers estimated that roughly $40 million has been extracted from these inefficiencies.

Academic researchers present their findings at the International Conference on Advances in Financial Technologies. Source: CyLab/YouTube

Prediction markets are still nascent, but their technology has been improving as well. For example, Polymarket recently introduced taker fees to increase trading costs. Outcomes aren’t finalized immediately, making these strategies less reliable and not always profitable.

AI agents could amplify market manipulation risks

Aside from arbitrage, AI agents could increasingly take over activity in prediction markets, raising concerns that automated systems may replicate the same behaviors seen from humans. They are trained on human activity, after all. 

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Coelho pointed out that large players can influence outcomes by placing sizable bets on one side, and that more advanced agents could exploit similar dynamics at scale.

“If you have a large pool of money and the market is thin, you can bet on one side and sway the market, like we saw in the election when some French guy put in like [$45 million] on Donald Trump winning,” he said.

Polymarket’s open interest was highest around October and early November of 2024, during the US elections, according to Dune Analytics data. Following a sharp initial decline, it has continued to surge in popularity, with politics leading as the most popular topic, followed by sports and crypto.

Polymarket’s open interest is nearing 2024 election levels. Source: datadashboards/Dune Analytics

Related: Federal regulation looms as 11 states go after prediction markets

Pranav Maheshwari, engineer at Edge & Node, said the rapid improvement of AI agents alongside prediction markets makes such risks more urgent and called for guardrails.

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“Up until now, AI agents have medium capability and we give them a lot of permissions. With this medium capability, they have already started acting autonomously,” Maheshwari told Cointelegraph.

But in the future, AI agents will have really high capabilities. When it has really high capabilities as humans, you have to restrict their permissions.”

From execution bots to AI-driven systems

Trading itself is undergoing a shift, as automation moves from simple execution bots to more advanced, AI-assisted systems capable of identifying and acting on opportunities in real time.

The systems currently used to exploit market inefficiencies remain largely rule-based, but the tools behind them are evolving.

Archie Chaudhury, CEO of LayerLens, said most retail participants are not using AI agents directly, relying instead on chatbot interfaces like ChatGPT or Gemini for research, while more advanced users are beginning to experiment with automation.

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“Some of us simply use coding agents such as Claude Code to create automated bots or algorithms for executing trades, while others take it a step further, using autonomous tools such as OpenClaw to enable the automatic execution of trades and other policies,” he told Cointelegraph.

Related: Do Super Bowl ads predict a bubble? Dot-coms, crypto and now AI

As AI literacy among retail traders rises, agents could broaden access to strategies that were previously limited to institutions, according to Chaudhury. However, this does not eliminate competition, and large institutions are already using AI, though not always publicly.

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He added that existing large language model architectures are well suited to interpreting structured financial data, which could lower the technical barrier for building trading systems that would have previously required specialized quantitative expertise.

The same dynamics are already visible across crypto markets, where arbitrage increasingly depends on automation rather than human judgment. As these systems evolve, the edge is shifting execution speed. Those leaning on AI and automation have a clear edge over those that don’t.

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