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3 Jun 2026, 14:21

DeFi TVL Stress: Why Falling Liquidity Could Hurt Smaller Protocols First

In the 48 hours after the KelpDAO rsETH exploit in mid-April, on-chain dashboards lit up with red. Billions in TVL sprinted to safety, and the thinnest order books blinked first as prices gapped and utilization spiked. Some lending markets re-priced overnight. Periphery pools saw spreads widen. A few small protocols paused features , others began quiet wind-downs. The long tail of DeFi discovered the hard truth: when liquidity retreats, it doesn’t do so evenly. By early May, industry trackers counted dozens of projects shutting down or moving to wind-down mode in 2026—an unmistakable signal of stress across the stack. The Big Picture Editor's note: The most useful tells weren’t headline TVL but depth at 1–2% on major pools, LST discounts, and bridge queue times. I also saw how quickly incentive budgets broke when token prices slipped; smaller teams couldn’t defend ranges for more than a few days. The market coordination around the rsETH recap was encouraging, yet it took weeks to fully operationalize. My takeaway from talking with risk folks and LPs: pre-wiring pause tiers and oracle bounds matters more than any single incentive program. — Elliot Veynor DeFi is coping with a synchronized liquidity squeeze. After a high-profile exploit hit KelpDAO’s rsETH on April 18, trackers reported an estimated $13+ billion of TVL withdrawals within roughly 48 hours, including about $8.4 billion leaving Aave CryptoTimes . In the same stretch of early 2026, more than 40 DeFi protocols reportedly shut down or began wind-downs and hack losses reached roughly $770 million through April CryptoTimes . In a liquidity shock, depth concentrates in the largest venues and collateral markets; smaller protocols face a double bind of higher volatility and thinner exit lanes. Not all the news is bleak. A recovery coalition led by major protocols—including Aave—mobilized commitments exceeding $320 million in ETH to recapitalize rsETH and contain bad-debt spillovers BYDFi . And on May 25–26, Kelp DAO marked the operational completion of its rsETH recovery: the final 20,373.72 rsETH tranche was moved to the rsETH OFT adapter, closing that chapter operationally CoinLaw . Still, the episode exposed structural dependencies that place smaller protocols at the front line when TVL pulls back. How TVL Evaporates in Practice TVL isn’t a single pool; it’s a network of interlocking positions. When a shock hits, withdrawals ripple along predictable paths. Common sequence of a liquidity flight Stablecoin preference shifts: users rotate to top-cap stables and exit riskier LPs or synthetic pegs. Blue-chip refuge: liquidity concentrates in large DEX pools and lending markets with deeper reserves and better oracle coverage. Collateral de-leveraging: elevated volatility triggers LTV haircuts, creating forced unwinds and reducing protocol-side liquidity. Incentive decay: token price drawdowns make emissions less effective, accelerating LP attrition in smaller pools. Governance risk-off: emergency parameters (lower LTVs, higher reserves) tighten credit, further shrinking usable liquidity. Why it accelerates Because liquidity providers are paid on a risk-adjusted basis, they demand more yield to stay. If a small protocol can’t compensate quickly—either due to treasury limits or token price pressure—depth thins and price impact worsens, feeding back into more exits. Why Smaller Protocols Are Exposed First Size brings buffers: diversified collateral, thick markets, robust oracles, and a wider base of market makers. Smaller protocols often rely on a few whales, concentrated LPs, or mercenary incentives. That concentration amplifies drawdowns. Structural differences that matter in a drawdown CharacteristicLarge, established protocolsSmaller or emerging protocolsLiquidity depthMultiple deep pools across chains and venuesOne or two primary pools; thin depth off-peakOracle coverageDiverse oracles, tighter bounds, longer historyLimited feeds; higher risk of stale or thin pricesIncentive budgetLarge treasuries; flexible emissions and gaugesFinite runway; incentive cuts hit LPs quicklyCollateral diversityMultiple blue-chip assets and LSTsConcentrated in a few correlated tokensUser baseSticky integrators, market makers, institutionsMore retail, mercenary capital, whale-dependentGovernance agilityBattle-tested risk frameworks and delegatesAd hoc changes; slower or politically fragile Feedback loops Once spreads widen, slippage increases. Traders price in higher execution risk, which reduces volumes and fees for LPs. With lower fees and weaker token incentives, LPs leave—further widening spreads. Smaller venues can spiral into illiquidity faster than they can adjust parameters. Case Study: rsETH Shock and the Liquidity Cascade The rsETH incident offered a live-fire test of DeFi’s resilience. Following the April exploit, liquidity migrated rapidly toward the safest perceived venues and collateral types. Within roughly two days, an estimated $13+ billion in TVL exited DeFi positions, with about $8.4 billion reportedly leaving Aave CryptoTimes . Smaller protocols tied to LST/LRT collateral—rsETH included—faced price dislocations and utilization spikes. Emergency backstops and the recap channel As the dust settled, a “DeFi United” coalition led by Aave and peers coordinated over $320 million in ETH commitments to recapitalize rsETH and patch bad-debt exposures, according to aggregated reporting and on-chain tracking in mid-May BYDFi . This response aimed to stabilize collateral confidence and restore orderly markets. Operational closure and what it signals On May 25–26, Kelp DAO confirmed the operational completion of its rsETH recovery, transferring the final 20,373.72 rsETH to the rsETH OFT adapter CoinLaw . That milestone matters for optics and mechanics: it reduces uncertainty premiums and helps normalize LRT pricing. But it also underlines that repair cycles take weeks, not hours—an interval that can be existential for smaller protocols dependent on continuous liquidity. Lessons for smaller venues Dependency risk: if your top collateral or routing venue is shocked, your protocol inherits its stress instantly. Exit pressure: concentrated LPs or whales can drain a pool faster than governance can react. Bridge and wrapper complexity: multi-hop wrappers (LST/LRT/OFT) add operational steps to recovery and redemption. Stablecoins: The Load-Bearing Beam Stablecoin liquidity is DeFi’s primary settlement rail. As of June 1, 2026, industry statistics put the stablecoin market around $320 billion in total, with roughly $160.95 billion on Ethereum alone—concentrating a large share of settlement liquidity on one chain Datawallet . Concentration cuts both ways When flows are positive, Ethereum’s depth helps. When flows reverse, the same concentration can starve smaller chains and niche L2s of dollars-on-chain. Cross-chain AMMs and bridges then face widening spreads, higher fees, and time-to-finality constraints that slow rebalancing when it’s needed most. Stablecoin tiers and sensitivity Tier 1: large-cap, widely integrated stables with native liquidity across blue-chip venues. Tier 2: programmatic or newer issuers with fewer deep markets and thinner periphery liquidity. Wrapped or cross-chain representations: depend on bridge solvency and liveness assumptions. Smaller protocols leaning on Tier 2 or wrapped stable liquidity are typically the first to feel the pinch when redemptions surge. Builders’ Playbook for Surviving a Liquidity Squeeze There’s no silver bullet, but operators can pre-wire defenses and response plans. Before a shock Diversify collateral: limit correlated assets and cap exposure to a single LST/LRT or bridge representation. Right-size oracles: use multi-source feeds with bounded deviations and circuit breakers for thin markets. Tiered risk buckets: segment markets so riskier assets can be paused or haircut without freezing safer pairs. Treasury liquidity buffers: maintain stablecoin reserves to support incentives when token price weakens. Whale risk mapping: identify top LPs and lenders; simulate their exit impact and pre-negotiate standby MM lines. During a shock Communicate quickly: publish parameter changes, redemption paths, and bridge statuses in one place. Throttle risk: tighten LTVs, raise reserves, and pause fringe markets first; keep core rails live when safe. Reroute liquidity: concentrate incentives into the deepest pools to minimize slippage where users actually trade. Coordinate publicly: align with integrators, oracles, and market makers to reduce information asymmetry. Snapshot and rectify: document affected accounts and propose transparent remediation if losses occur. After the event Audit the entire chain of dependencies—wrappers, oracles, governance timelines—and publish a postmortem with measurable follow-ups. Where relevant, consider external recap channels or coalitions; the rsETH response showed the market can coordinate capital when the remediation path is credible BYDFi . Market Structure Signals to Watch Users and operators can monitor a handful of leading indicators that tend to move before TVL data prints. Pricing and liquidity microstructure AMM imbalances: sustained skew in concentrated-liquidity ranges on major pairs indicates LP retreat. Depth at 1%: thinning bids/offers within 1% on blue-chip pools can precede outsized price impact elsewhere. LST/LRT discounts: persistent dislocations (e.g., staked ETH wrappers vs ETH) flag collateral stress. Cross-chain and bridge telemetry Outbound queue buildup: longer waits or higher fees signal stressed bridge capacity. Wrapped-stable premiums/discounts: indicate redemption frictions or trust differentials. Credit and risk parameters Protocol-wide LTV cuts: multiple protocols tightening simultaneously suggest system-wide risk-off. Reserve factor hikes: lenders preserving treasuries at the expense of borrowers denote a safety pivot. Macro rails Stablecoin net issuance: shrinking supply on Ethereum can foreshadow broad TVL drawdowns Datawallet . Funding/borrowing spreads: wide gaps between centralized exchanges and on-chain lending attract arbitrage that drains marginal liquidity from smaller venues. Risks & What Could Go Wrong Oracle distortions: thin markets or manipulations can cascade through lending and derivatives. Stablecoin depegs: redemption waves or blacklist events can freeze settlement rails. Bridge outages: validator failures or exploits can trap wrapped liquidity cross-chain. Governance latency: slow quorums or contentious votes delay vital parameter changes. Incentive exhaustion: token drawdowns make emissions ineffective, accelerating LP exits. Cross-collateral contagion: correlated collateral haircuts cause simultaneous liquidations. Regulatory shocks: sanctions, KYC shifts, or banking rails disruptions reduce fiat on-ramps. In a crunch, the absence of depth is itself a risk amplifier—price discovery becomes path-dependent and exit costs climb with every minute of delay. If you track this space daily, outlets like Crypto Daily aggregate research, governance proposals, and security updates that often surface early warning signs—especially around parameter changes and cross-protocol dependencies. Frequently Asked Questions Does TVL always equal usable liquidity? No. TVL measures value deposited, not how easily that value can be converted or rehypothecated without slippage. In stress, much of TVL becomes “sticky” due to withdrawal queues, fees, or collateral haircuts. Why do smaller protocols feel the pain first? They rely on fewer market makers, more concentrated LPs, and often one or two collateral types. When shocks hit, incentives and treasuries can’t scale quickly enough to retain depth, so price impact rises and users rush to exit. What metrics better capture real liquidity than TVL? Depth at 1–2% price impact on major pairs, time-to-exit for top LPs, borrow utilization rates under stress scenarios, and stablecoin net issuance by chain are more telling than headline TVL. Can recapitalization coalitions solve systemic drawdowns? They can contain specific failures if governance is aligned and the remediation path is credible—as seen with the rsETH commitments exceeding $320 million in ETH BYDFi . But they’re not a cure-all if multiple large protocols are impaired simultaneously. Is rotating to blue-chip venues always safer during stress? Blue-chip venues typically have deeper liquidity and stronger risk controls, which can reduce execution risk. However, they are not immune to oracle issues, parameter changes, or collateral-specific events. Evaluate venue- and asset-level risks. How does stablecoin concentration affect smaller chains? With roughly $160.95 billion of stablecoins on Ethereum alone Datawallet , reversals on Ethereum can drain cross-chain liquidity fast, raising spreads and slowing exit times for smaller ecosystems. What signs suggest a protocol might wind down? Persistent liquidity outflows, emergency pauses extending beyond 48–72 hours, governance gridlock, and disappearing incentive budgets are red flags. In 2026, trackers reported over 40 such wind-downs or closures by early May CryptoTimes . Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

3 Jun 2026, 14:10

Aave Says Operations Back to Normal as $300M Backstop Replaces Drained Assets

Decentralized finance protocol Aave recently revealed that it has fully restored liquidity to its lending pools following a $300 million cross-chain exploit. The Anatomy of the Exploit Decentralized finance ( DeFi) pioneer Aave has successfully restored full liquidity to its lending pools, capping off an aggressive multi-week stabilization effort following a $300 million cross-chain exploit

3 Jun 2026, 08:41

TAO’s Subnet Test: Why Bittensor Needs Utility Beyond AI Rotation

AI narratives can attract capital, but they rarely sustain it. TAO’s recent swings have reinforced a hard truth for Bittensor: long-term value must come from subnets that deliver tangible, repeatable utility, not from rotation alone. This article maps how to evaluate that utility and what the latest governance changes mean in practice. If you build on, operate, or allocate to Bittensor, your decision now is less about “AI exposure” and more about subnet economics: who pays, for what, and how value returns to TAO. We outline the mechanics, a practical playbook, and the red flags to avoid. We also integrate new governance and market context—from convective locking changes to a sharp price move —so you can translate on-chain signals into better choices. AspectWhat to KnowMarket backdropOn 2026-06-03, CMC AI flagged TAO down 12.70% to $221.07 (24h), with elevated derivatives activity underscoring event-driven volatility ( CoinMarketCap ).Governance shiftSubtensor Conviction v2 moved to devnet-ready with decaying locks; PRs #2687 and #2696 merged, setting 648,000 blocks (~60-day half-life). Mainnet PR #2643 remained open/blocked as of late May ( Taostats documentation (Conviction) ).Commitment signalsA SubnetRadar snapshot showed ~4.58M α locked, ~4.14M α counted as conviction, 16 active lockers; top convict SN79 (MVTRX) held 1.27M α—early evidence of operator commitment ( Tao Outsider (SubnetRadar snapshot) ).Stress eventCovenant AI’s April exit involved selling ~37,000 TAO of α tokens and sparked a sharp selloff and governance urgency across the network ( Tao.media ).Core questionCan subnets generate durable, paid demand (inference, data, compute routing) that feeds TAO value beyond short-lived AI rotations?Who should careSubnet owners/operators, data/model providers, validators, allocators, and enterprises testing decentralized AI services.Action nowTrack Conviction v2 rollout, read per-subnet demand metrics, and back operators with clear customers and verifiable performance. Core concepts that matter for TAO’s next phase Bittensor coordinates open, competitive markets (subnets) where miners provide AI-related services—such as inference, dataset curation, or retrieval—and validators score their usefulness. Rewards flow to the most useful work. That design is elegant, but the investment thesis only compounds if subnets meet real demand and route value back to TAO holders and builders. AI token rotations can lift all boats temporarily. The sustainability test is different: do end users—startups, data teams, model engineers—rely on a subnet because it is cheaper, faster, or more resilient than centralized alternatives? If yes, usage should translate into pricing power for providers, clearer validator economics, and more predictable returns for capital that locks into subnet ecosystems. Governance is evolving to align that capital. Conviction v2 introduces decaying locks aimed at longer-term commitment without permanent bondage. In theory, that stabilizes subnet stewardship and dampens mercenary churn; in practice, it depends on the lock parameters, distribution of lockers, and whether commitment correlates with service quality. For allocators, the key is to evaluate subnets like early-stage platforms: identify a paying user base, verify the throughput and latency they require, and map token mechanics (α-to-TAO pathways, emissions, fees) to a plausible return profile. For builders, the mandate is simpler: deliver a service people repeatedly pay for. Glossary: Bittensor and subnet economy TAO — The native token that secures the network and underpins staking, rewards, and governance across subnets. Subnet — A specialized market inside Bittensor where miners provide a focused service (e.g., inference) and validators score outputs. α (alpha) tokens — Per-subnet accounting units or derivatives used in some governance and economic mechanisms around subnet participation. Conviction v2 — An upgraded locking and voting model with decaying locks to align long-term commitment while allowing gradual liquidity return. Validator — Node that assesses miners’ outputs and influences reward allocation according to usefulness. Emissions/fees — Token flows that reward useful work or accrue from paid usage, forming the economic backbone of each subnet. A practical playbook for builders, operators, and allocators Define the user and job-to-be-done. Write a one-line user story (e.g., “LLM ops team needs low-latency inference with predictable throughput”) and verify it with at least two real prospects. Quantify demand-side metrics. Track request counts, latency SLOs, error budgets, and willingness to pay. If a subnet can’t publish these, assume demand is unproven. Map the value path to TAO. Diagram how fees, emissions, or α mechanics link usage to TAO accrual or reduced sell pressure; if the path is hand-wavy, pass. Audit governance and locks. Review Conviction v2 parameters and current lockers per subnet. Decaying locks (e.g., 648k blocks ≈ 60-day half-life in devnet updates) change liquidity timing and control. Stress-test operator concentration. Check whether a few lockers or validators can gatekeep upgrades or capture emissions. Concentration raises governance risk. Pilot with staged exposure. Start with a small allocation or limited deployment, measure outcomes for 2–4 weeks, then scale only if KPIs improve. Hedge event risk. Expect volatility around governance and subnet events; size positions accordingly and consider derivatives hedges off-chain if available. Set pre-committed exits. Define objective thresholds (latency, user growth, governance transparency) that trigger a scale-up or unwind, and stick to them. The “Subnet Test”: Turning AI buzz into durable demand To justify TAO at scale, subnets need customers, not just miners and validators. The durable-demand checklist looks like this: a repeatable workload; clear latency and cost advantages over centralized providers; and credible, verifiable performance data. If a subnet can demonstrate those consistently, emission subsidies matter less over time and the economics can tilt positive. Consider three archetypes likely to pass the test sooner: Inference marketplaces for LLMs and niche models. They win if they beat centralized APIs on price/performance or offer censorship resistance and uptime diversity (multi-provider routing). Retrieval and data curation layers. If they generate demonstrably higher model quality or faster iteration cycles for fine-tuning, data teams will pay. Compute orchestration and routing. If a subnet reliably finds cheap, available GPUs and allocates jobs with SLAs, it can undercut cloud burst pricing. By contrast, speculative subnets without real workloads become reflexive: token incentives attract supply, validators score outputs of limited external value, and the flywheel spins until emissions fade. The moment macro AI rotation cools, these markets unwind fast. Pro tip: Treat every subnet like a startup. Demand diligence outranks token design. Ask to see real dashboards: request volume, p95 latency, paying logos, and incident reports. Conviction v2, decaying locks, and what to read on-chain Late May brought meaningful progress on Bittensor’s governance mechanics. Subtensor PR #2687 (Conviction v2 updates) and PR #2696 (setting unlock/maturity to 648,000 blocks, about a 60-day half-life) were merged, moving Conviction v2 to devnet-ready status with decaying locks; the mainnet deployment PR #2643 remained open/blocked at that time ( Taostats documentation (Conviction) ). Why it matters: decaying locks alter the incentive for long-term stewardship without freezing capital indefinitely. A locker’s influence and liquidity both change predictably over time, creating a gradient instead of a cliff. Subnets where owners/operators publicly lock and maintain rising conviction signal skin in the game. We already have early on-chain signals. A SubnetRadar snapshot cited by Tao Outsider showed roughly 4.58M α locked, about 4.14M α counted as conviction, with 16 active lockers; the top convict leader, SN79 (MVTRX), held 1.27M α—suggesting concentrated, but visible, commitment in the early phase ( Tao Outsider (SubnetRadar snapshot) ). Balance that against tail risk. In April, Covenant AI exited Bittensor, reportedly selling approximately 37,000 TAO of α tokens; the episode triggered a sharp selloff and immediate governance focus across the ecosystem ( Tao.media ). Coupled with price and derivatives activity flagged on June 3 by CMC AI, these events illustrate how governance and subnet developments can transmit quickly to markets ( CoinMarketCap ). How to interpret: watch the distribution of conviction across lockers and the cadence of new lockers joining. A healthy pattern is broadening participation, steady or rising conviction totals, and sustained endpoint performance. A fragile pattern is one or two dominant lockers, falling conviction, and widening spreads between promised and observed service quality. Builders vs. backers: choosing your exposure Exposure to Bittensor can range from passive to deeply operational. Match your choice to your edge—capital, engineering, distribution, or governance fluency—and to your tolerance for event-driven volatility. Exposure pathCapital/skill needsMain risksUpside driversTypical horizonHold TAOLow ops; portfolio risk managementMarket and governance shocks; rotation cyclesNetwork-wide utility growth; improved token sinksMedium–longLock α in selected subnetsGovernance reading; on-chain trackingConcentration of lockers; parameter changes; liquidity decaySubnet-specific demand; aligned operatorsMediumOperate a subnetEngineering, DevOps, BD, and communitySLA failures; validator capture; regulatory questionsFee revenue; emissions; reputation moatLongProvide inference/data servicesModel quality; GPU capacity; monitoringPerformance drift; cost spikes; competitionThroughput and reliability; customer retentionShort–medium For allocators, the differentiator is diligence on the demand side. For builders, it’s operational excellence and transparent reporting. Both groups benefit from reading governance repos, tracking conviction, and correlating it with real service metrics. When these line up, TAO has a shot at escaping the gravity of AI rotation. SubnetRadar Conviction leaderboard (snapshot May 30, 2026) showing total alpha locked and the top subnet (SN79 MVTRX) with 1.27M α — a concrete on‑chain visualization of Conviction locks and early alignment signals. — Source: SubnetRadar (screenshot hosted on Tao Outsider) Pitfalls and red flags to avoid Top-heavy conviction. If one or two lockers dominate, governance capture risk rises and exit cascades can be brutal. Unverified usage claims. Screenshots aren’t data. Ask for raw request counts, latency percentiles, and uptime history. Parameter complacency. Treat Conviction v2 as evolving; mainnet timing and details matter. Model liquidity with current block assumptions. Event-blind sizing. Governance and subnet events have translated to sharp price/derivatives moves; size positions accordingly. Opaque cost structures. If a subnet can’t explain GPU, storage, and bandwidth costs, margins likely vanish at scale. Validator quality drift. Weak or misaligned validators can inflate “usefulness” without real-world benefit. For ongoing coverage and contextual analysis around decentralized AI markets, Crypto Daily tracks governance shifts, builder activity, and cross-market flows in one place. Visit Crypto Daily for updates. Frequently Asked Questions What does Conviction v2 change for subnet participants? Conviction v2 introduces decaying locks designed to align long-term commitment while gradually returning liquidity. Recent devnet-ready updates set unlock/maturity to 648,000 blocks (about a 60-day half-life), with mainnet deployment still pending as of late May per public repos and documentation. This shifts governance power and exit timing for lockers and should reduce abrupt cliffs. How did Covenant AI’s exit impact Bittensor? According to reporting, Covenant AI sold roughly 37,000 TAO of α tokens during its April 9–10 exit. The episode coincided with a sharp selloff and catalyzed governance urgency across the ecosystem, reinforcing how concentrated positions and liquidity profiles can translate into fast market moves. Why is TAO so sensitive to governance and subnet news? Because Bittensor’s value accrues through subnet performance and community governance, changes to locks, validator rules, or operator composition can materially alter expected cash flows and risk. Recent price/derivatives activity highlighted by CMC AI shows how such events transmit quickly to TAO’s market. What on-chain signals best indicate real commitment? Look for broadening conviction (more lockers, rising totals), stable or improving service KPIs, and public, auditable disclosures from subnet operators. Early snapshots showing millions of α locked with identifiable leaders provide context, but the trend and dispersion over time matter more. How do I evaluate a subnet’s demand without insider access? Start with public dashboards and independent latency tests. Ask for anonymized customer counts, case studies, and incident reports. Compare cost per 1,000 requests to centralized benchmarks, and verify consistent p95 latency under load. Is holding TAO enough exposure to “decentralized AI”? It offers network-wide exposure but also event-driven volatility. If you have an edge in evaluating or operating specific subnets, targeted α exposure or running services may offer differentiated outcomes—at the cost of higher operational and governance risk. What could prove that utility has arrived beyond AI rotation? Evidence would include named paying customers, stable or rising request volumes, tight latency SLOs, transparent fee flows, and measurable TAO sinks (e.g., buy-and-burns, staking demand, or fee-denominated usage) that persist across broader market cycles. Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

3 Jun 2026, 05:00

Bitcoin’s Longest-Running Bottom Signal Is Back In Focus: Capitulation Fears Grow

Bitcoin has lost the $69,000 level as selling pressure and market uncertainty combine to test the resilience of a market that has now given back a significant portion of its recovery from the cycle lows. The breakdown is uncomfortable — and analyst MorenoDV has identified a signal in the supply data that places the current moment in a long-term structural context that spans a decade of Bitcoin market cycles. Bitcoin’s Supply in Loss currently sits at 40.6% — meaning more than four in ten units of Bitcoin’s circulating value are held by participants whose cost basis is above the current price. The metric measures the share of circulating supply that is underwater at any given moment, and its current reading reflects the pain that the correction from the cycle highs has distributed across the holder base. But the raw percentage is not the most important element of what MorenoDV’s analysis reveals. The real story is the long-term pattern behind the metric’s peaks — a structural observation that requires looking at the entire history of Bitcoin’s major cycle bottoms rather than any single reading in isolation. Since 2015, every major Bitcoin cycle low has occurred when Supply in Loss pushed into the upper band of a descending trendline. And crucially, each successive cycle bottom has required a lower percentage of supply in loss than the one before it — a pattern of diminishing pain at successive lows that describes how Bitcoin’s market structure has evolved as the asset has matured and its holder base has deepened. Each Cycle Bottom Needed Less Pain Than the Last The MorenoDV analysis traces the descending loss threshold across Bitcoin’s entire modern market history to reveal the structural evolution that makes the current 40.6% reading more significant than the raw number suggests. Early Bitcoin cycles required extreme pain to form genuine bottoms — more than 60% of the circulating supply underwater before capitulation created the conditions for recovery. The 2018 to 2019 and 2020 to 2022 cycle lows formed with progressively lower loss thresholds as the holder base matured and conviction deepened. The same structural trendline now sits closer to the high-40% area — reflecting a market where ETFs, institutions, long-term holders, and high-conviction participants have replaced the weaker hands that previously needed to be fully exhausted before bottoms could form. The current 40.6% reading places Bitcoin in meaningful stress territory without yet reaching the historical maximum opportunity zone. A continuation of weakness or extended consolidation that pushes Supply in Loss into a retest of the descending trendline would place the market in a region that has repeatedly marked significant accumulation windows across a decade of cycles. The psychological mechanism behind the signal is what gives it its forward relevance. Rising supply in loss moves markets from optimism to doubt and from doubt to forced patience — the sequence that exhausts reactive sellers and creates the conditions where long-term capital begins absorbing supply at scale. Bottoms do not form immediately when this zone is reached. Historical precedent includes volatility, false breakdowns, and emotional exhaustion before recovery begins. But from a risk and reward perspective, a retest of this decade-long structure represents one of the most important signals Bitcoin can generate — and MorenoDV’s analysis suggests the market is approaching rather than departing from that territory. Bitcoin Loses Major Weekly Support As Bears Target Lower Demand Zone Bitcoin is trading near $69,600 on the weekly timeframe after losing the critical $72,000–$75,000 support region that had acted as the foundation of the recovery rally from the March lows. The breakdown is technically important because this zone served as both resistance and support during the past three months, making its loss a clear deterioration in market structure. The weekly chart shows BTC rejecting from the $82,000 area before reversing sharply lower. That rejection established a lower high relative to the cycle peak near $123,000 and reinforced the broader downtrend that has been in place since late 2025. More concerning for bulls, the price has now fallen below the 50-week and 100-week moving averages, both of which are beginning to flatten after months of weakness. From a structural perspective, the next major support sits between $64,000 and $66,000, highlighted by the lower yellow zone on the chart. This area acted as a key accumulation range following February’s capitulation event and represents the most important demand zone on the weekly timeframe. For Bitcoin to stabilize, bulls must quickly reclaim the lost $72,000–$75,000 range. Until that happens, the path of least resistance remains lower, with the market increasingly focused on whether the $64,000–$66,000 region can provide the foundation for a durable bottom. Featured image from ChatGPT, chart from TradingView.com

2 Jun 2026, 23:55

Bittensor Co-Founder: Bitcoin Network Outperforms Top 100 Supercomputers by 600,000x

BitcoinWorld Bittensor Co-Founder: Bitcoin Network Outperforms Top 100 Supercomputers by 600,000x At the Proof of Talk event in Paris, Bittensor co-founder Ala Shaabana made a striking comparison: the Bitcoin network’s hashrate is more than 600,000 times greater than the combined computing power of the world’s top 100 supercomputers. The statement highlights the vast, decentralized computational resources that Bitcoin has secured through its incentive-based structure. Applying Bitcoin’s Model to Artificial Intelligence Shaabana argued that the same principle that made Bitcoin’s network so powerful can be applied to artificial intelligence (AI). He explained that Bittensor operates with 128 subnets, where participants are rewarded with TAO tokens for contributing to specific tasks such as AI training and validation. This model, he believes, can pool global hardware and intelligence more efficiently than centralized big tech companies. The Role of Incentive Design and Network Effects According to Shaabana, the future competitiveness of AI will depend less on the underlying technology itself and more on incentive design and network effects. He emphasized that open networks can aggregate resources from around the world, creating a more dynamic and scalable infrastructure for AI development. This perspective challenges the prevailing notion that only large, centralized corporations can lead in AI innovation. Implications for the Crypto and AI Sectors The comparison underscores the growing intersection between blockchain technology and artificial intelligence. As AI models require increasingly massive computational power, decentralized networks like Bittensor offer an alternative to traditional cloud computing providers. The use of token-based incentives could democratize access to high-performance computing, potentially lowering costs and accelerating innovation. Conclusion Shaabana’s remarks at the Paris event provide a compelling vision for the future of AI infrastructure. By leveraging the same incentive mechanisms that made Bitcoin the world’s most powerful computing network, projects like Bittensor aim to create a more open and efficient ecosystem for AI development. The key takeaway for industry observers is that the battle for AI supremacy may be won not by the fastest chips, but by the most effective networks. FAQs Q1: What is the Bitcoin hashrate and why is it compared to supercomputers? The Bitcoin hashrate measures the total computational power used to mine and secure the Bitcoin network. Comparing it to supercomputers illustrates the massive scale of decentralized computing resources that Bitcoin has aggregated through its incentive system. Q2: How does Bittensor use TAO tokens to support AI development? Bittensor operates 128 subnets where participants earn TAO tokens for contributing computing power to AI training, validation, and other tasks. This creates a decentralized marketplace for AI computation, rewarding contributors based on the value of their work. Q3: Why might decentralized AI networks be more efficient than centralized ones? Decentralized networks can pool hardware and expertise from a global, diverse set of participants, reducing reliance on single providers. This can lead to lower costs, greater resilience, and faster innovation, as the network is not limited by the resources of any single entity. This post Bittensor Co-Founder: Bitcoin Network Outperforms Top 100 Supercomputers by 600,000x first appeared on BitcoinWorld .

2 Jun 2026, 16:30

Georgia’s illegal BTC mining spikes electricity use 13 times

⚡ Electricity usage in Mestia hit 133 million kilowatt-hours due to illegal $BTC mining. The projected cost reached up to 25 million lari this year. ⛏️ Georgia will install meters to pinpoint unauthorized mining hotspots. Continue Reading: Georgia’s illegal BTC mining spikes electricity use 13 times The post Georgia’s illegal BTC mining spikes electricity use 13 times appeared first on COINTURK NEWS .