Crypto World
CoinFello Launches OpenClaw Skill for AI Agent Transactions
[PRESS RELEASE – Fort Worth, Texas, March 11th, 2026]
CoinFello, an AI agent optimized for interacting directly with any EVM smart contract, today announced the release of its open source OpenClaw skill in partnership with MetaMask. The new integration enables Moltbots, personal AI agents running on OpenClaw, to securely execute on-chain transactions using delegated smart wallet permissions.
The launch introduces a new framework for connecting AI agents with crypto wallets while maintaining user custody of private keys. By leveraging ERC-4337 smart accounts and ERC-7710 delegations through the MetaMask Smart Accounts Kit, the CoinFello OpenClaw skill allows Moltbots to grant other agents, such as CoinFello, narrowly defined delegations to act on their behalf. This represents a significant advancement in agentic wallet security compared to the current status quo, where agents typically store private keys or API credentials in plain text.
The system follows the principle of least privilege. A user’s Moltbot can grant CoinFello, and eventually other compatible agents, only the permissions required to complete a specific task, ensuring no agent receives broader wallet access than necessary. When a user submits a natural-language request, CoinFello converts the instruction into a delegated transaction and validates it in an evaluation layer before execution.
“If we want agents to participate meaningfully in the on-chain economy, we need a security model that is better than handing an autonomous system a private key,” said Brett Cleary, CTO at CoinFello. “The CoinFello Skill introduces hardware-isolated keys and fine-grained delegations, giving AI agents a secure way to execute transactions while helping bootstrap on-chain capabilities for the broader agent ecosystem.”
The release comes amid the rapid growth of the OpenClaw ecosystem. Over the past two months, the OpenClaw GitHub repository has surpassed 150,000 stars and 22,000 forks, while npm downloads exceeded 416,000 in the previous 30 days.
Until now, many AI agent wallets have given the agent direct access to a private key or API credential, exposing sensitive secrets within the agent’s runtime and creating a large attack surface.
Some newer designs attempt to mitigate this risk by using server-side trusted execution environments (TEEs), but they still rely on centralized infrastructure.
The CoinFello skill takes a different approach. The signing key stays on the user’s device, while tasks are carried out through fine-grained ERC-7710 delegations. Agents can execute actions without ever accessing the private key.
Using the CoinFello skill, Moltbots can perform a wide range of blockchain actions via natural-language prompts. Supported capabilities include swapping between ERC-20 assets, bridging across EVM networks, interacting with NFTs such as ERC-721 or ERC-1155 tokens, staking, lending, automatic rebalancing of token portfolios, and executing multi-step trading strategies.
The CoinFello OpenClaw skill is built on the Agent Skills specification and is compatible with OpenClaw environments and Claude Code. The implementation is released under the MIT license, allowing developers to freely deploy, modify, and contribute to the skill in their own AI agent environments.
CoinFello notes that the system is designed to remain open and configurable. While CoinFello acts as the default Web3 agent, Moltbots can delegate permissions to any compatible on-chain agent. The company says future development will focus on expanding permissions frameworks and deeper integrations with the MetaMask Smart Accounts Kit to support broader portfolio management features.
Interest in the intersection of AI agents and crypto infrastructure has accelerated in recent months as developers experiment with autonomous software agents capable of interacting with decentralized networks. The CoinFello OpenClaw skill aims to provide a secure foundation for this emerging category by bridging natural language interfaces with on-chain execution.
About CoinFello
CoinFello is an AI agent designed to explain, execute, and automate interactions with smart contracts. Built for self-custody, the platform is currently available in private alpha for end users, with developer versions expected soon. CoinFello supports EVM-compatible networks, leverages EigenAI to enable a self-custodied AI environment, and integrates the MetaMask Smart Accounts Kit to give users control over their assets.
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A misconfigured Oracle system in Aave triggered $27 million in forced liquidations on March 10, undervaluing wrapped staked Ether by 2.85% against its actual market rate.
According to the post-mortem by Chaos Labs, the CAPO oracle error caused Aave V3 Ethereum Core and Prime instances to apply an exchange rate of roughly 1.1939 wstETH-per-ETH when the live onchain rate was approximately 1.228, enough of a gap to push 34 high-leverage E-Mode positions below their liquidation thresholds automatically.
It resulted in the liquidation of 10,938 wstETH. The protocol says it incurred no bad debt and is moving to compensate all affected users.
The Damage: 34 Users, $27M in Liquidations, and 499 ETH in Bot Profits
The oracle glitch liquidated 34 users, with the total volume reaching $27 million in wstETH positions.
Liquidation bots moved quickly, capturing 499 ETH in bonuses, approximately $1.2 million, by executing against positions that should not have been eligible for liquidation at that moment.
Aave founder and CEO Stani Kulechov confirmed in a Wednesday post that the protocol generated no bad debt from the incident.
Of the 499 ETH that went to liquidators, Aave recaptured 141 ETH ($285,000) through BuilderNet refunds and an additional 13 ETH in liquidation fees.
Those recovered funds will flow directly to affected users as compensation, with DAO treasury funds covering any remaining shortfall up to the full 345 ETH identified as the excess liquidation windfall.
Lido contributors confirmed the event had no connection to wstETH or the Lido staking protocol itself; the issue originated entirely within Aave’s oracle configuration layer.
With Ethereum price defending the $2,000 support zone around the time of the incident, the liquidation values were amplified by the broader market context for ETH-denominated collateral.
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Chaos Labs Confirms Aave CAPO Oracle Misconfiguration: Here Is What They Found
Chaos Labs, Aave’s external risk management partner, confirmed the incident stemmed from what it described as an onchain configuration misalignment under differing onchain update constraints, not a design flaw in the CAPO system or in the core oracle infrastructure of Aave.
The team emphasized that Chaos Risk Oracles had processed over 1,200 payloads and more than 3,000 parameters across Aave markets without incident prior to March 10.
Chaos Labs quickly contained the situation: borrow caps on wstETH were reduced immediately, and snapshot parameters were manually realigned to restore oracle accuracy. Kulechov noted in his public statement that the configuration issue had already been remediated by the time the post-mortem was published, and praised the team’s response speed in limiting broader DeFi risk contagion.
The Aave governance post-mortem marks this as the first operational failure in CAPO’s deployment history on Aave V3, despite more than a year of live operation across multiple markets.
What Traders and Aave Users Are Watching Next
The immediate focus is on the full reimbursement timeline. Aave DAO service providers are finalizing compensation for all 34 affected users following the initial 141 ETH refund via BuilderNet, with a formal governance announcement expected shortly.
Beyond compensation, governance teams are conducting a broader review of CAPO parameters across all Aave markets, updating stale snapshots and building out enhanced monitoring to flag rate divergences before they reach liquidation-threshold proximity.
Whether that review produces binding parameter update standards or remains advisory is the governance question to watch.
If the DAO formalizes automated CAPO sync requirements and publishes updated risk oracle documentation, the incident may ultimately strengthen Aave’s operational credibility. If the review stalls at the discussion stage, the reputational cost will compound the financial one.
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The post Aave Oracle Glitch Causes $27M Liquidations: CAPO Misconfiguration Confirmed appeared first on Cryptonews.
India’s Central Bureau of Investigation (CBI) has arrested Ayush Varshney, co-founder and chief technology officer of Darwin Labs Private Limited, in connection with the long-running GainBitcoin cryptocurrency fraud investigation.
According to a Wednesday press release shared via the CBIs official X account, Varshney was detained at Mumbai airport on Monday while attempting to leave India after a Look Out Circular had been issued against him. He was formally arrested and handed over to the CBI on Tuesday.
The CBI said Darwin Labs played a central role in building the technological infrastructure used by the alleged scheme, including the GainBitcoin investor platform and associated tools used to manage payments and wallets.
The arrest is the latest development in India’s investigation into the multi-million-dollar GainBitcoin scheme, one of the country’s largest alleged cryptocurrency investment frauds.
Investigators link developer to infrastructure behind alleged scheme
According to the CBI, the GainBitcoin scheme was promoted through Variabletech Pte. Ltd. and allegedly promised investors monthly returns of about 10% in Bitcoin (BTC) for up to 18 months. “The funds collected from investors were subsequently misappropriated,” the CBI said.
Related: India’s central bank proposes linking BRICS digital currencies for trade: Reuters
The agency said Darwin Labs and its co-founders, including Varshney, Sahil Baghla and Nikunj Jain, were involved in designing and deploying a cryptocurrency token known as MCAP along with its associated ERC-20 smart contract.
CBI added that the company also helped develop key components of the platform’s technical infrastructure, including the GBMiners.com mining pool, a Bitcoin payment gateway, the Coin Bank Bitcoin wallet, and the GainBitcoin investor website used to interact with participants.
Decade-old case involved 8,000 investors and $790 million
GainBitcoin emerged in the mid-2010s as a cloud-mining investment platform that encouraged users to purchase Bitcoin and deposit it with the service in exchange for promised fixed returns.
The CBI alleged that the scheme eventually relied on a multi-level marketing structure in which payouts were tied to recruiting new investors. As new deposits slowed, the platform reportedly shifted payouts from Bitcoin to its in-house MCAP token, which had a significantly lower value.
The operation was allegedly masterminded by Amit Bhardwaj, a prominent early Bitcoin promoter in India who died in 2022 while out on bail.
On Feb. 26, 2025, Indian authorities conducted searches at more than 60 locations as part of the investigation into the GainBitcoin scheme.
Investigators have previously said the case involves 8,000 investors, with estimated losses reported by authorities ranging from about 6,606 crore Indian rupees (roughly $790 million).
Magazine: DAT panic dumps 73,000 ETH, India’s crypto tax stays: Asia Express
US Democratic Party Senator Adam Schiff introduced legislation Tuesday that would explicitly bar federally regulated prediction-market platforms from listing contracts tied to war, terrorism, assassination and individual deaths.
The bill, called the DEATH BETS Act, would amend the Commodity Exchange Act to make those contracts prohibited for entities overseen by the US Commodity Futures Trading Commission (CFTC).
In a statement announcing the bill, Schiff said markets that let traders profit from violent events create incentives for the misuse of classified information, threaten national security and encourage violence. He said prediction markets had become a “Wild West” and called for Congress and the CFTC to make clear that such “death bets” are not allowed.
The bill seeks to ban prediction market contracts that involve references to “terrorism, assassination, war, or any similar activity,” or that are related to an “individual’s death.” The bill was referred to the Senate Committee on Agriculture, Nutrition, and Forestry for consideration, where Schiff is a member.
US-Israel war with Iran ignites military insider concerns
The legislation comes after renewed scrutiny of event-contract platforms during the recent US and Israeli military confrontation with Iran, when war-related markets drew heavy trading and fresh allegations of insider activity.
Six Polymarket traders netted $1 million by accurately betting on the US strike against Iran.
Related: Suspected insider wallets rack up $1.2M betting on ZachXBT’s Axiom exposé
The six wallets were all created in February and placed all their bets on the contracts predicting the timing of a potential US attack, with several shares purchased only hours before the first reported explosions in Iran’s capital, Tehran.
On Tuesday, a new wallet spent $32,900 to bet on US forces entering Iran by Saturday, despite the odds continuing to decline, according to blockchain data platform Lookonchain.
Related: Kalshi, Polymarket face trading halt in Nevada after court rulings
In February, Israeli authorities arrested and indicted two people suspected of using secret information about Israel striking Iran for insider trading on Polymarket.
Insider concerns grew in January after a Polymarket account profited $400,000 after it placed a bet on a contract predicting that Venezuelan President Nicholas Maduro would be captured, wagering the funds just hours before US forces captured him.
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Crypto World
Binance WSJ Lawsuit: The Crypto Exchange Sues Wall Street Journal Over ‘Defamatory’ Iran Sanctions Report
The Binance crypto exchange has officially filed a defamation lawsuit against the Wall Street Journal, or known as WSJ, in the Southern District of New York. The complaint, filed today (March 11), alleges the newspaper published false claims regarding the exchange’s compliance controls and handling of Iran sanctions data.
At the center of the dispute is a February report claiming Binance knowingly processed over $1Bn for sanctioned entities.
This news has led to the BNB price dropping 1% in the past hours, to $640, as investors are seemingly spooked at yet another potential legal dispute involving Binance.
CEO Richard Teng has condemned the reporting as inaccurate, stating the outlet ignored documented evidence provided before publication.
— Litest (@Litest) March 11, 2026
BREAKING:
Binance launches defamation lawsuit against the Wall Street Journal over Iran sanctions report. pic.twitter.com/8qdrf9rXcD
What’s the WSJ Report Actually Alleged And Why Binance Says It’s Wrong
The Wall Street Journal article, titled “Binance Fired Staff Who Flagged $1 Billion Moving to Sanctioned Iran Entities,” depicted a chaotic internal struggle at the world’s largest crypto exchange.
It is alleged that compliance staff were fired not for policy breaches, but for doing their jobs identifying illicit flows.
Specifically, the report claimed Binance processed $1.7Bn in transactions linked to Iranian entities, including a Hong Kong-based fiat-to-crypto converter called “Blessed Trust.”
According to the Journal, this activity continued despite internal red flags. The report immediately triggered a regulatory inquiry.
US Senator Richard Blumenthal cited the article as grounds for demanding a formal investigation into the exchange’s operations, which Binance CEO Richard Teng responded to on March 6, denying all claims.
The allegations arrived during a sensitive period for crypto regulation, mirroring the pressure seen as Democrats introduce bills to ban platforms like Polymarket over compliance concerns.
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Binance Fires Back: 19 Ignored Responses and a 96.8% Compliance Claim
Binance’s defense hinges on what it calls willful disregard for the facts. The exchange claims it sent the WSJ 19 detailed responses and answered 27 specific questions before the publication deadline, none of which appeared in the final story.
Richard Teng publicly rejected the narrative, emphasizing that the employees in question were dismissed for data policy violations, not for flagging sanctions evasion.
The exchange cited hard numbers to counter the defamation claims. Binance states it has achieved a -96.8% reduction in sanctions exposure risks through upgraded protocols. Currently, more than 1,500 employees, nearly a quarter of the workforce within Binance, work in compliance.
Regarding the specific “Blessed Trust” account, Binance clarified that the entity was offboarded and reported to law enforcement in 2025, long before the WSJ report suggested the activity was ongoing.
What This Means for Binance and the Broader Crypto-Media Relationship
This lawsuit seeks compensatory and punitive damages, arguing the report caused harm that no simple correction can fix. The legal action follows a significant win for Binance on March 7, when a federal judge dismissed a separate lawsuit alleging the exchange facilitated terrorist financing.
That court found no material support was provided, strengthening Binance’s position that it is not liable for the actions of bad actors who might attempt to access the platform.
Traders are watching this case closely as a test of the “actual malice” standard in crypto reporting. While the exchange settled with the DOJ in 2023 for $4.3Bn over historical failures, this aggressive legal stance signals a refusal to accept what it deems false narratives about its current operations.
The focus now shifts to the WSJ’s response and whether the regulatory inquiry sparked by the article will sustain momentum without the supporting media narrative.
We will continue to update this story as more details emerge over the coming days and weeks.
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The post Binance WSJ Lawsuit: The Crypto Exchange Sues Wall Street Journal Over ‘Defamatory’ Iran Sanctions Report appeared first on Cryptonews.
CoinDesk Indices presents its daily market update, highlighting the performance of leaders and laggards in the CoinDesk 20 Index.
The CoinDesk 20 is currently trading at 1980.55, down 0.6% (-12.31) since 4 p.m. ET on Tuesday.
Eight of 20 assets are trading higher.
Leaders: ICP (+11.9%) and DOT (+2.2%).
Laggards: HBAR (-1.8%) and XLM (-1.6%).
The CoinDesk 20 is a broad-based index traded on multiple platforms in several regions globally.
TLDR
- February’s Consumer Price Index increased 2.4% year-over-year, aligned with analyst predictions
- Core inflation (stripping out food and energy costs) registered at 2.5% annually, meeting forecasts
- Report captures timeframe prior to U.S.-Israel coordinated strikes against Iran
- Crude oil has jumped approximately 18% since late February, while pump prices climbed 20%
- Federal Reserve anticipated to maintain current interest rate range of 3.5%–3.75% at upcoming meeting
While February’s inflation report appeared reassuring at first glance, the underlying narrative reveals a more complex situation unfolding.
The Consumer Price Index advanced 0.3% month-over-month in February and climbed 2.4% on an annual basis. These metrics aligned precisely with economist projections. Meanwhile, core CPI—which excludes volatile food and energy categories—increased 0.2% monthly and 2.5% yearly, similarly matching consensus estimates.
The Bureau of Labor Statistics published these figures on Wednesday, March 11.
Both energy and food categories showed increases during February, though these changes were relatively contained compared to subsequent developments following the data collection period.
Crucially, this report reflects conditions that existed before coordinated U.S. and Israeli military operations against Iran commenced in late February. Those hostilities have subsequently created significant disruptions throughout global energy markets.
Iran Crisis Delivers Major Shock to Energy Sector
The Strait of Hormuz—a critical chokepoint handling approximately 20% of worldwide oil shipments—has experienced a dramatic reduction in tanker movement. Intelligence reports suggest Iran has deployed naval mines throughout the waterway, prompting President Trump to warn of potential additional military responses.
Brent crude futures stood near $92 per barrel at press time, following an earlier spike to almost $120 this week. Motorists across America have seen gasoline costs surge 20% as a direct consequence.
Bank of America’s economist Stephen Juneau noted that petroleum prices have climbed roughly 18% since February concluded. He indicated that sustained conflict would probably generate upward pressure on both headline and underlying inflation measures in coming months.
The International Energy Agency has put forward its most substantial strategic reserve release proposal to date aimed at market stabilization, the Wall Street Journal reported. IEA member countries were scheduled to vote on this initiative Wednesday. The prior record stood at 182 million barrels, authorized following Russia’s 2022 Ukraine invasion.
Implications for Federal Reserve Policy
The Fed’s favored inflation metric—the Personal Consumption Expenditures index—registered 2.9% annually in December. This remains substantially above the central bank’s 2% objective. January’s PCE figures are scheduled for Friday release, with forecasters anticipating a 3.1% annual rate.
Market indicators suggest the Federal Reserve will almost certainly maintain its current rate posture during next week’s policy meeting, preserving the 3.5%–3.75% band, per CME FedWatch tracking data.
Employment trends add another dimension of complexity to the Fed’s calculus. The U.S. economy surprisingly shed 92,000 positions last month, elevating the unemployment rate to 4.4%.
President Trump indicated earlier this week the military operations might conclude “very soon,” though U.S. and Israeli forces have maintained strikes across multiple Iranian targets throughout the Middle East region.
Ghana’s Securities and Exchange Commission (SEC) said 11 companies have been granted access to a regulatory sandbox to test cryptocurrency and digital asset services under the country’s Virtual Asset Service Providers Act, 2025.
The program allows companies to run their products in a controlled environment while regulators monitor risks and compliance.
The sandbox will run for 12 months and sits at the center of Ghana’s early efforts to bring oversight to the crypto sector, according to a press release.
Companies in the first cohort include asset tokenization firms like Africoin, Blu Penguin, Vaulta, XChain and Goldbod as well as cryptocurrency exchanges like Hyro Exchange, HanyPay and WhiteBit.
The commission said firms whose products are market-ready and meet regulatory requirements could transition to a full license after six months. Others may remain in the sandbox for the remaining period to refine their services.
The SEC said the exercise will also help it shape detailed licensing guidelines for different types of crypto businesses. Data gathered during the pilot will inform rules covering areas such as investor protection, market integrity and anti-money laundering controls.
Once the sandbox closes, the regulator plans to publish the final guidelines and open the licensing process to a broader set of virtual asset service providers.
Artificial intelligence has long been defined by scale—larger models, faster processing, and sprawling data centers. Yet a growing cohort of researchers, investors, and practitioners is suggesting the traditional growth path is hitting a ceiling. AI is increasingly capital-intensive and tethered to physical limits, with diminishing returns appearing sooner than many anticipated. The latest data underscore the shift: electricity demand from global data centers is projected to more than double by 2030, a surge comparable to expanding entire industrial sectors; in the United States, data-center power usage is forecast to rise well over 100% by the end of the decade. As the economics of AI tighten, trillions of dollars in new investment and substantial grid upgrades loom, coinciding with the way the technology embeds itself into finance, law, and crypto workflows.
Key takeaways
- Energy demand tied to AI is accelerating, with the IEA projecting data-center electricity use will more than double by 2030, highlighting a fundamental constraint in the current scaling paradigm.
- The United States could see data-center power consumption surge by more than 100% before the 2030s, signaling a major resource and infrastructure challenge for AI-enabled sectors.
- Frontier AI training costs are skyrocketing, with estimates suggesting single training runs could exceed $1 billion, making inference and ongoing operation the dominant long-term expense.
- The verification burden grows with scale: as AI outputs proliferate, human oversight becomes increasingly critical to prevent errors from propagating, such as false positives in automated AML flagging.
- Architectural shifts toward cognitive or neurosymbolic systems—emphasizing reasoning, verifiability, and localized deployment—offer a path to reduce energy use and improve reliability versus brute-force scaling.
- Blockchain-enabled, decentralized AI concepts may distribute data, models, and computing resources more broadly, potentially lowering concentration risk and aligning deployment with local needs.
Sentiment: Neutral
Market context: The convergence of AI with crypto analytics and DeFi tooling sits amid broader questions about energy consumption, regulation, and the governance of automated decision-making. As AI tools increasingly monitor on-chain activity, assess sentiment, and assist in smart-contract development, the industry faces a tighter coupling between performance, verification, and accountability.
Why it matters
The debate over AI scaling is not a theoretical one—it touches the core of how and where AI is deployed in high-stakes sectors. Large language models (LLMs) have grown fluent by pattern-matching across vast text corpora, enabling impressive capabilities but not necessarily robust, reliable reasoning. As these systems become embedded in legal workflows, financial risk management, and crypto operations, the consequences of incorrect outputs become less tolerable and more costly.
Training frontier AI models remains a mission-critical and expensive endeavor. Independent analyses suggest that the cumulative cost of training can be immense, with credible voices estimating that a single training run could cross the $1 billion threshold in the near future. Yet even more consequential is the ongoing cost of inference—running models at scale with low latency, high uptime, and rigorous verification requirements. Each query consumes energy, and each deployment necessitates infrastructure. As usage expands, energy use compounds, pressuring both operators and grids alike. In crypto contexts, AI systems increasingly monitor on-chain activity, analyze sentiment, generate code for smart contracts, flag suspicious transactions, and automate decision-making; missteps here can move capital and undermine trust across markets.
The industry is beginning to recognize that fluency alone is insufficient. When AI can produce convincing but incorrect conclusions, verification burdens intensify. False positives in AML flagging, for instance, have been documented as a practical drag on resources, diverting investigators from genuine activity. This dynamic underscores why a shift toward architectures that integrate cause-and-effect reasoning, explicit rules, and self-checking mechanisms is gaining traction. Cognitive AI and neurosymbolic approaches—where knowledge is structured into interrelated concepts and reasoning can be revisited and audited—promise higher reliability with lower energy demands than brute-force scaling.
Beyond the architecture, there is a broader trend toward decentralization of AI development itself. Some platforms explore blockchain-enabled models for contributing data, models, and computing resources, reducing concentration risk and aligning deployment with local needs. In a field where room for error is small and the stakes are high, the ability to inspect, audit, and shape AI systems matters just as much as the outputs they produce. The turning point is clear: scaling for the sake of scale may no longer be sufficient. The industry must invest in architectures that make intelligence more reliable, verifiable, and controlled by communities rather than distant, centralized infrastructure.
As AI considerations bleed into crypto workflows, the stakes grow sharper. On-chain monitoring, sentiment analysis for market signals, automated code generation for smart contracts, and risk-management automation are all increasingly dependent on AI, yet they demand a higher standard of trust. The tension between speed and accuracy—between fast, automated decisions and verifiable reasoning—will shape the next wave of crypto tooling and governance. The upshot is not simply bigger models; it is better systems that can reason about their own steps, explain conclusions, and operate within clear constraints.
Ultimately, the industry faces an inflection point. If architecture and reasoning take precedence over sheer scale, AI could become more affordable to operate, while remaining safer and more controllable. The era of growth-at-any-cost may yield to a more deliberate phase where wealth creation in AI and crypto hinges on transparent verification, resilient design, and decentralized collaboration. The author argues that the path forward lies in rethinking how intelligence is built and deployed—prioritizing robust reasoning and governance over incremental increases in parameter counts.
What to watch next
- Regulatory and policy developments around AI safety, auditing, and accountability in finance and crypto.
- Advances in cognitive AI and neurosymbolic architectures, including practical deployments on edge devices and local servers.
- Decentralized AI initiatives that use blockchain-inspired models to distribute data, models, and computing resources.
- Shifts in data-center capacity, energy pricing, and grid infrastructure tied to AI-enabled demand.
- New benchmarks or case studies illustrating the trade-offs between scale, reasoning, and verification in real-world crypto applications.
Sources & verification
- Energy demand from AI: IEA, Energy and AI — energy demand from AI.
- U.S. data-center power demand projections: Pew Research Center / energy use at US data centers amid the AI boom.
- UK legal AI cautionary note: Guardian article on the High Court warning against AI-generated fabricated case law in legal filings (June 2025).
- AML false positives and AI risk: IBM Think topics on AI fraud detection in banking and related AML flagging issues.
- Costs to train frontier AI models and ongoing inference costs: Epoch AI blog and Digital Experience Live analyses.
- On-chain and crypto AI applications: efforts around Ethereum and on-chain tooling that leverage AI signals (as referenced in industry coverage).
Rethinking AI scaling: energy, reasoning, and the crypto interface
Artificial intelligence has long scaled on a simple premise—more data, bigger models, faster hardware would continually unlock better performance and lower costs. The latest economic and technical signals, however, suggest a pivot. Energy and capital intensity are rising faster than anticipated, with global data-center electricity demand projected to more than double by 2030. In the United States alone, data-center power consumption is expected to rise by more than 100% before the decade ends, a trajectory that will require massive investments in grid capacity and infrastructure as AI becomes embedded in critical sectors, including markets, compliance, and on-chain activity monitoring.
Training frontier AI models remains extraordinarily expensive, with credible estimates pointing to costs that could top $1 billion per training run. Yet even more consequential is the ongoing cost of inference—sustained, low-latency operation that must deliver results with high reliability. In markets and crypto, AI systems are increasingly used to monitor on-chain activity, analyze sentiment, generate smart-contract code, flag suspicious transactions, and automate governance decisions. The result is a double exposure: the potential for rapid, data-driven signals coupled with the risk of false signals that can misallocate capital or mischaracterize risk. Notably, false positives in automated AML flagging illustrate how unreliable outputs can waste human resources and erode trust when deployed widely.
To address these pressures, the narrative is shifting away from sheer scale toward architectures that emphasize reasoning and verifiability. Cognitive AI and neurosymbolic approaches seek to braid pattern recognition with structured knowledge, rules, and self-checks. These systems aim to deliver usable reasoning traces and transparent decision processes, reducing the need for brute-force computation and enabling more predictable energy use. Early demonstrations suggest that local or edge deployments, supported by knowledge representations, could keep control with users and organizations rather than entrusting cognition to centralized, opaque infrastructure.
Decentralized AI models—where data, models, and computation can be contributed by diverse participants—offer another path to resilience. By distributing the workload and oversight, communities can mitigate concentration risk and tailor AI deployments to local needs. In this ecosystem, the role of governance becomes more pronounced: platforms must enable auditing, adjustment, and interoperability without compromising security or performance. The shift toward more sophisticated reasoning, coupled with a commitment to verifiable outcomes, marks a meaningful departure from scaling solely for scale’s sake. If the industry can operationalize cognitive architectures at scale, the economics of AI may improve—reducing both energy consumption per decision and the verification burden on human operators.
In the crypto arena, this evolution matters. The reliability of AI-assisted on-chain analytics, fraud detection, and smart-contract tooling will influence investor confidence and market integrity. The path forward requires not only bigger systems but smarter ones—systems whose inner workings can be inspected, challenged, and improved by a broad community. The debate is no longer about whether AI should grow, but how to grow it in a way that is auditable, trustworthy, and aligned with the needs of decentralized finance and broader digital markets.
U.S. inflation data met expectations on Wednesday, reinforcing anticipation that the Federal Reserve will keep interest rates steady not just at its March 18 meeting, but likely at the bank’s April meeting as well.
The Consumer Price Index (CPI) rose 0.3% in February, according to a report from the Bureau of Labor Statistics. Economist forecasts had been for a rise of 0.3% and January’s increase was 0.2%.
On a year-over-year basis, CPI was higher by 2.4% against expectations of 2.4% and January’s 2.4%.
Core CPI, which excludes food and energy costs, rose 0.2% in February versus forecasts of 0.2% and January’s 0.3%. Year-over-year core CPI was higher by 2.5% versus forecasts of 2.5% and January’s 2.5%.
Under modest pressure for the morning, bitcoin was trading at $69,500 in the minutes following the report, lower by 1.2% over the past 24 hours.
U.S. stock index futures were slightly lower across the board and the 10-year Treasury yield ticked up to 4.18%. The main actor in markets this week, WTI crude oil was higher by 4.2% to $87 per barrel.
Ahead of the data, markets were pricing in a 99% probability that the Federal Reserve would leave interest rates unchanged at its March meeting next week, according to the CME FedWatch tool. For the April meeting, rate cut odds were at just 11% versus 21% one month ago.
February’s inflation numbers, of course, are somewhat old news given the events that have transpired since, namely the war in Iran and spiking oil prices. How much this plays into the Fed’s thinking on interest rates should become more evident following next week’s policy meeting.
Foundry Digital, one of largest Bitcoin mining pools by hashrate, said it plans to introduce a zcash (ZEC) mining pool by next month, expanding beyond BTC and bringing a large institutional operator into the privacy-focused network.
With the new pool, Foundry aims to offer zcash miners a U.S.-based platform designed around compliance checks, reporting standards and operational controls often required by public companies and large firms.
The move addresses what Foundry describes as a gap in Zcash infrastructure. While the cryptocurrency has existed for nearly a decade, much of its mining ecosystem still consists of smaller global pools that often operate outside formal compliance frameworks.
“Zcash has matured into an institutional-grade asset, but the mining infrastructure supporting it hasn’t kept pace,” Foundry CEO Mike Colyer said in a statement shared with CoinDesk.
Betting on privacy
The expansion comes as privacy-focused cryptocurrencies regain attention across the market as new crypto tax reporting rules, with threat of asset seizure, kicked in across the European Union at the turn of the year and as onchain analysis keeps developing, leading to growing demand for financial anonymity.
Zcash, along with other privacy coins including monero (XMR) and dash (DASH) has seen renewed interest that has helped their prices surge. ZEC has seen significant outperformance, up more than 670% in the last 12 month period, compared XMR’s 72% rise in the same period, while DASH is up 51%.
ZEC’s outperformance can likely be attributed to its hybrid privacy model, which makes shielded – completely anonymous – transactions optional with selective disclosure. This means that transactions can be transparent for custody and exchanges, and attracted accumulation from a Winklevoss-backed treasury firm as well as into the Grayscale Zcash Trust.
Foundry’s shift toward zcash also likely reflects broader changes in mining economics. Bitcoin mining profitability has tightened following the 2024 halving, which cut block rewards in half while mining difficulty surged.
Speaking to CoinDesk, Coyler pushed back on the idea the move is primarily a response to lowering bitcoin margins.
“We evaluate opportunities based on where institutional infrastructure is needed, not on bitcoin margins at any given moment,” he said. “Foundry’s bitcoin mining business is strong and remains our core foundation.”
The expansion, Coyler said, was over an identified gap in compliant Zcash infrastructure. “Institutional and public miners who want exposure to zcash have had no US-based, compliant, purpose-built infrastructure to do it through,” he added.
As for whether the move shows a broader multi-chain strategy, Coyler said the company’s focus is “squarely on bitcoin and zcash” for now, though he added that Foundry is “always evaluating opportunities” that align with its mission and the demands of institutional miners.
While the price of bitcoin saw a major rise to near $125,000 late last year, its price has since corrected to now stand at $69,500. That has seen hashprice, a measure of expected value of 1TH/s of hashing power a day, drop from over $60 to $30 per petahash.
As margins shrink, many large mining firms have begun exploring other proof-of-work networks to diversify revenue.
Zcash mining infrastructure
Zcash launched in 2016 as a privacy-focused cryptocurrency built on zero-knowledge proof technology. The network allows users to send transactions on a public blockchain while keeping key details private. Using a cryptographic method known as zk-SNARKs, Zcash can verify that a transaction is valid without revealing the sender, receiver or amount involved.
Like Bitcoin, the Zcash network relies on proof-of-work mining to secure its blockchain and miners use specialized hardware to solve complex mathematical puzzles to help secure the network. When a miner or mining pool solves one of these puzzles, it adds a new block of transactions to the chain and earns a reward in newly issued ZEC tokens along with transaction fees.
Zcash blocks are produced about every 75 seconds, faster than bitcoin’s blocks which are produced every 10 minutes. Still, both shared a supply cap of 21 million coins. The mining process uses an algorithm called Equihash, which differs from Bitcoin’s SHA-256 and was designed to require large amounts of memory during computation.
Network difficulty, which helps the time between block production remain consistent, means the probability of solving a block alone is low. As a result miners bundle together in what are known as mining pools, in which participants combine computing power and share rewards based on how much work they contribute. Large pools can influence the stability and decentralization of a network because they control significant portions of its total hashrate.
Foundry’s zcash pool
Foundry said its zcash pool will include identity verification checks for participants through rigorous know-your-customer and anti-money laundering compliance, transparent payout calculations and reporting tools aimed at institutional users. It’ll feature a dedicated support team and its operations will be based in the United States.
The company plans to apply the same operational framework used by its bitcoin pool, which has undergone SOC 1 Type 2 and SOC 2 Type 2 compliance audits, it said.
Mining rewards will be distributed through transparent Zcash addresses, not shielded ones, the company said. The pool will be paying miners on a Pay Per Last N Shares (PPLNS) model, which Coyler said is “fully auditable” and provides detailed data supporting daily payment reconciliation.
Foundry didn’t disclose the fee for miners, saying only it will offer “competitive pool fee rates.” There will be no minimum hashrate threshold to join the pool, Coyler said, noting that the Zcash mining ecosystem is still emerging.
The company expects demand from miners that already operate in regulated environments such as North America. Many of those firms rely on formal reporting systems and compliance programs to meet corporate governance requirements.
If the zcash pool launches on schedule in 2026, it would mark one of the largest institutional entries into the Zcash mining ecosystem to date. Other major mining pools operating within it include F2Pool, 2Miners, and ViaBTC.
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