Strategy’s common stock has fallen below $100, prompting renewed criticism from Bitcoin skeptic Peter Schiff, who argues that further declines could leave the company facing difficult decisions over its Bitcoin treasury strategy.

According to comments posted by Schiff on X, sustained pressure from short sellers could push Strategy into a situation where repurchasing its own shares becomes more attractive than continuing to accumulate Bitcoin.

He suggested that selling some Bitcoin to fund stock buybacks could help narrow the discount between the company’s market value and its underlying assets, though he questioned whether such a move would restore investor confidence.

Schiff also claimed that any forced sale of Bitcoin by Strategy could have consequences for the broader market, arguing that liquidating part of its holdings would likely weigh on Bitcoin prices.

The warning comes as Strategy shares continue to slide. MSTR traded at $96.27 on June 24, down 7.2% during the session and near its lowest level in two years. Regulatory filings show the stock has lost nearly 20% over the past five trading days and more than 38% over the last six months.

Cash reserves face growing scrutiny

Recent capital allocation decisions have added to the debate surrounding Strategy’s balance sheet. Company disclosures show that Strategy sold approximately 2.71 million MSTR shares last week, generating about $335.5 million in proceeds.

Executive Chairman Michael Saylor later disclosed that the company used roughly $35 million of that capital to purchase 520 Bitcoin. At the same time, Strategy increased its U.S. dollar reserves by about $300 million, bringing its cash balance to approximately $1.4 billion.

Saylor stated that the larger cash position is intended to support the credit quality of Strategy’s Digital Credit securities, a group of products that has become increasingly important to the company’s financing structure.

Separate concerns have emerged from on-chain analytics firm CryptoQuant, which recently urged Strategy to slow its Bitcoin purchases and focus on rebuilding liquidity. According to CryptoQuant, annualized dividend obligations tied to the company’s preferred stock products have climbed to roughly $1.2 billion.

Preferred stock obligations draw attention

CryptoQuant’s concerns center on STRC, Strategy’s perpetual preferred stock product. The firm reported that the company’s cash reserves have fallen 38% in 2026, while dividend coverage has dropped from more than seven years to about 14 months.

According to CryptoQuant, restoring coverage to 24 months would require roughly $2.8 billion in cash, nearly double Strategy’s current reserves.

CryptoQuant CEO Ki Young Ju also argued that Strategy’s Bitcoin purchases are no longer a major price catalyst. He said buying during periods of strong selling pressure may help defend Bitcoin’s range but is unlikely to spark a new rally.

Given those conditions, Ju recommended pausing additional Bitcoin purchases, rebuilding cash reserves, and adopting a more structured buying strategy.

In a follow-up X post on June 24, Schiff expanded his criticism to Strategy’s STRC preferred stock. He argued that the security had been marketed to risk-averse retirees as a lower-volatility way to gain exposure to the company’s Bitcoin strategy, despite falling more than 5% on the day and over 17% below levels where many investors reportedly purchased shares the previous month.

Schiff further claimed that the decline had erased nearly two years of dividend income and accused Saylor of making “material misrepresentations” when describing the preferred stock.

If congressional Republicans pivot toward further work on the elections-focused legislation, that may squeeze their bandwidth for other legislation. GOP lawmakers were already pushing again for Senate action on Trump’s favored bill on Wednesday.

“There is no path for the SAVE Act becoming law,” said Jaret Seiberg, a policy analyst at TD Cowen, in a Wednesday research note. “Senate GOP would need to eliminate the filibuster, a step they already have rejected. Even absent the filibuster, it is not clear the bill has the support of 50 senators, given worries about have to prove citizenship.”

Before his cancellation of the housing bill signing, Trump had posted on his social-media platform that the housing bill is of “minor importance compared to lower interest rates” and other congressional priorities, and he criticized the involvement of Democratic Senator Elizabeth Warren.

The president has a constitutionally designated 10-day window to weigh approved bills for signature once they land on his desk. If he were to veto it, the bill did pass with enough of a margin to reject that veto, though Republican allies of the president would have to agree to override his sentiment.

UPDATE (June 24, 2026, 16:01 UTC): Adds comment from TD Cowen.

This trend is becoming even more relevant as real-world assets enter the digital landscape. Stablecoins have already demonstrated the power of blockchain-based representations of traditional value, becoming the most successful digital asset use case to date. Tokenized deposits, bonds, funds, and other real-world assets are poised to follow, expanding the range of opportunities available to businesses and individuals worldwide.

For the end user, however, the underlying asset may become increasingly irrelevant. Most people are unlikely to care about the blockchain protocol, token standard, or settlement mechanism powering a transaction. What matters is accessibility, speed, security, and trust. Users want to access global opportunities using their local resources, through partners they know and platforms they can rely on.

In this environment, the long-term competitive advantage belongs to those who build and operate the infrastructure connecting participants, assets, and markets. Coins may evolve, protocols may change, and new forms of digital value will continue to emerge. But the institutions that enable trust, connectivity, and seamless access will remain at the center of the ecosystem.

The prevailing currency in digital assets may change over time. Infrastructure, however, is what endures.

Principled Perspectives

Bitcoin’s liquidation cascade peaked before the bottom

– By Alen Pavlović, Portfolio Manager, Liquibit Capital

Using CoinDesk’s liquidation feed, the forced selling flushed early and high. By the time Bitcoin bottomed on 5 June, the cascade was already over.

Key Takeaways

• Reflection AI, an emerging AI firm, has entered into an agreement with SpaceX for Nvidia GB300 chip access at the Colossus 2 facility, costing $150 million monthly.
• The contract extends through 2029, potentially generating approximately $6.3 billion in total revenue over its duration.
• Despite lacking any released products or revenue streams, Reflection AI secured $2 billion in funding last October, achieving an $8 billion valuation with Nvidia’s backing.
• Following the announcement, SpaceX stock (SPCX) experienced a decline of approximately 10.6%, even though the contract represents substantial recurring income.
• The Colossus data center portfolio now includes major clients such as Anthropic, Google, Cursor, and Reflection AI.

SpaceX (SPCX) stock experienced a significant decline of roughly 10.6% following news of a massive $6.3 billion computing agreement with Reflection AI — an AI startup that has yet to launch a commercial product or generate any revenue.

Space Exploration Technologies Corp, SPCX

CNBC broke the story on June 22. According to the agreement’s structure, Reflection AI will gain prompt access to Nvidia GB300 processors located within SpaceX’s Colossus 2 Memphis data center. Monthly installments of $150 million commence on July 1, 2026, with the contract extending until 2029.

If executed in full, the arrangement would generate roughly $6.3 billion in total payments. Both parties retain the option to terminate with 90 days’ notice following an initial three-month period.

Neither SpaceX nor Reflection AI provided responses to Reuters’ inquiries.

Reflection shared on LinkedIn that “additional compute capacity enables us to further advance the boundaries of open models,” offering no additional specifics.

Expanding Client Portfolio

SpaceX has been methodically assembling an impressive collection of high-profile computing clients at its Colossus facilities. Anthropic secured exclusive access to all of Colossus 1 for approximately $1.25 billion monthly. Google subsequently committed to $920 million per month for transitional capacity while constructing its proprietary data centers, with service beginning October this year and continuing through June 2029. Reflection represents the fourth major tenant in a portfolio that emerged from nothing within the past year.

Reflection was established in early 2024 by co-founders Misha Laskin and Ioannis Antonoglou, both alumni of Google DeepMind. Laskin previously directed reward modeling efforts for Gemini. Antonoglou co-developed AlphaGo. The startup completed a $2 billion funding round last October at an $8 billion valuation, with Nvidia serving as the lead investor. By spring 2026, industry reports suggested its valuation had climbed toward $20 billion. The company has not yet released any public model.

The organization has established itself as an open frontier laboratory concentrating on government and national security applications, including initiatives connected to the Department of Energy’s Genesis Mission and various Pentagon AI contracts.

Understanding the Stock Movement

Despite securing billions in guaranteed recurring revenue from an additional tenant, SPCX shares fell approximately 10.6% on announcement day — marking the sharpest single-day decline since the company’s June 11 public debut at a $1.77 trillion valuation.

The market reaction surprised several analysts. SpaceX is securing guaranteed, recurring payments against existing infrastructure assets. The Colossus 2 agreement alone contributes $1.8 billion in annual contracted revenue. This financial dynamic doesn’t immediately explain the negative market response.

Looking Ahead

The agreement features a 90-day termination provision following the initial three-month period, meaning the critical evaluation point arrives around late October. Should Reflection choose not to exercise this exit option, the lease essentially transitions from potentially temporary to confirmed long-term demand.

Reflection’s LinkedIn communication mentioned advancing the frontier on “open models.” The company has not disclosed a timeline for any public product release.

SpaceX, Reflection AI, and Nvidia had not provided additional comments by publication time.

Bitcoin BTC$60,542.37 dropped to the $60,000 area on Wednesday for the second time this month, continuing its poor price action in the face of risk market rallies elsewhere.

Also continuing to lose ground on Wednesday were gold and oil, each falling below key levels — gold $4,000 per ounce and oil $70 per barrel.

Read more: Gold, silver and bitcoin tumble as ‘debasement’ trade unwinds

The declines in crypto, precious metals, and oil came as tech stocks rebounded following Tuesday’s modest one-day slump, with the AI trade continuing to draw investor interest and dollars.

South Korean memory chip giant SK Hynix on Wednesday filed to raise nearly $30 billion in a U.S. share offering, in what would be the overseas company capital raise since Saudi Aramco’s mammoth $26 billion sale in 2019.

The Nasdaq at midday Wednesday was up 0.8% against bitcoin’s 3.2% slump.

Bitcoin has lost the plot

Billionaire hedge fund manager Philippe Laffont succinctly summed up investor sentiment Tuesday, telling CNBC he has become “a little bit more worried” about bitcoin’s future, arguing that investors now have a wider range of opportunities to choose from than in previous years.

Today’s blockchains already treat consensus as a matter of two properties: nodes must agree on the same history (consistency) and the system must keep processing transactions (liveness). But that framing leaves a crucial gap—what users ultimately care about is not only whether transactions get confirmed, but whether their relative ordering is meaningfully fair when multiple parties submit transactions that can interact economically.

A new line of research is trying to formalize “transaction order fairness” and map out what is possible under real-world networking constraints. The core takeaway: perfect “first-come, first-served” ordering is mathematically out of reach in asynchronous distributed systems, even before considering adversaries. The practical question becomes how to approximate fairness while keeping liveness and minimizing opportunities for extractive behavior.

Key takeaways

• Perfect receive-order fairness (“first-seen, first-executed”) cannot be guaranteed on public networks because messages arrive at different times and there is no shared clock.
• Even when each node has a clear local arrival order, group preferences can conflict—captured by the Condorcet paradox—making a single linear order impossible to satisfy.
• Hashgraph’s fairness model uses a DAG of events with median timestamps to respect causal relationships while bounding how far adversarial influence can shift ordering.
• BOF-style protocols (from the Aequitas/Themis line of work) relax fairness by ordering transaction “batches” derived from Condorcet cycles, enabling stronger liveness guarantees.

Why “fair ordering” is harder than it sounds

In public blockchains, ordering isn’t just an implementation detail—it can decide who captures value and who pays. When privileged roles like block builders or sequencers determine execution order, they can potentially exploit that power through strategies that front-run, back-run, or sandwich transactions. Research on maximal extractable value (MEV) describes this as a direct consequence of who can influence ordering.

To counteract this, some proposals treat transaction ordering fairness as a third consensus objective alongside consistency and liveness. The general idea is to constrain the block producer’s ability to bias ordering beyond what the network conditions and protocol rules imply—making execution more predictable and less vulnerable to systematic exploitation.

But the most intuitive fairness notion runs into a structural limitation. In an asynchronous distributed system, there is no globally defined reception order because different nodes observe transaction messages at different times. Without a shared clock and with arbitrary message delays, no protocol can ensure that every node’s “arrival order” maps perfectly onto a single network-wide execution order.

The Condorcet paradox: why majority “first” can loop

The strongest form of fairness is often described as Receive-Order-Fairness (ROF): if most nodes receive transaction A before transaction B, then A should be processed before B. ROF sounds straightforward, but the network reality undermines it. Nodes see messages at different speeds, so different nodes can legitimately observe different pairwise “firsts.” Even if those local observations are consistent for each node, the collective can still become inconsistent.

This is where the Condorcet paradox comes in from voting theory, and it translates cleanly to distributed ordering. Even when each participant has an internal preference for which of two items comes first, the majority preference across multiple pairs can form a cycle:

• Most nodes see A before B
• Most nodes see B before C
• Most nodes see C before A

When that happens, there is no single linear ordering that satisfies all majority pairwise preferences. The implication for blockchain consensus is direct: if fairness is defined too strictly in terms of majority “first-seen” comparisons, the protocol may be unable to produce any ordering that matches the majority view across all pairs.

Because of this impossibility, systems aiming for “fairness” must adopt weaker—but more achievable—guarantees.

Hashgraph’s approach: DAG causality plus median timestamps

Hedera’s hashgraph algorithm tackles transaction ordering fairness through a leaderless, event-driven model. According to the described model, transactions are transformed into cryptographically linked events inside a directed acyclic graph (DAG). Consensus ordering then emerges from how nodes collectively observe and sign those events, rather than from a single proposer unilaterally choosing a sequence.

Operationally, when a node receives a transaction, it creates an event and gossips it to peers. Subsequent events record hashes of earlier events they have seen, and nodes digitally sign the result. This creates a provable causal structure: if one event is an ancestor (direct or indirect) of another, the protocol provides a cryptographic guarantee about which event was created first by some node.

The ordering logic then distinguishes between events with causal relationships and those that are concurrent. Events connected by DAG ancestry are ordered according to their causal dependencies. For concurrent events (those without ancestor relationships), the protocol resolves relative ordering using a “round-received” concept and then refines that using median timestamps.

Median timestamps, as described, are derived from a set of node-reported local receive times, but constrained by the hashgraph’s ancestry. That constraint matters: nodes cannot claim to have observed an event before its causal predecessors without creating detectable inconsistency in the DAG. Under the standard assumption used in Byzantine fault tolerance—fewer than one-third of nodes are Byzantine—the median timestamp should remain within a bounded range of honest timing reports, limiting adversarial ability to arbitrarily skew ordering.

However, hashgraph’s fairness is not infinite. The described research emphasizes that fairness is bounded by an adversarial “surface” where a node can still influence its gossip behavior: which events it relays first and whether it delays relaying. While the DAG cannot fabricate a false causal history, strategic propagation patterns can reshape the inputs that ultimately feed into median timestamp computation.

There is also the Condorcet paradox risk for concurrent events. The DAG eliminates ambiguity for causally linked events because the ancestry is fixed at creation. But concurrent events can still be observed in different orders by different nodes, leaving some ordering tension that is then handled by the protocol’s round and median mechanisms.

BOF protocols: fairness by collapsing Condorcet cycles

Another line of work frames fairness differently—by explicitly embracing cycles. BOF (Batch-based Order Fairness) protocols define “blocks” as sets of transactions that form a Condorcet cycle, then enforce fairness at the level of how those blocks relate, while allowing arbitrary internal ordering inside each block.

In the BOF formulation described, fairness is controlled by a parameter γ: if a sufficient fraction γ of nodes observe block b before block b′, then honest nodes cannot output b after b′. When fairness constraints induce a cyclic relation, the protocol collapses the strongly connected component (SCC) into a single batch/block, because no linear order can satisfy all the directed constraints simultaneously.

A key practical point is that this approach relaxes strict ROF requirements. When a cycle occurs, internal ordering becomes irrelevant to the fairness guarantee, since the protocol treats the entire cycle participation as atomic at the batch level. The research description notes that deterministic rules (such as a hash-based rule) may then sort transactions within the batch, but the fairness criterion does not attempt to make those internal orders correspond to any global first-seen preference.

The Aequitas protocol line is described as having weaker liveness: its strict fairness constraints require waiting for complete Condorcet cycles, and if cycles can chain indefinitely, finalization delays could grow without bound—creating a “freeze” risk.

Themis is introduced as a refinement intended to preserve γ-BOF while improving liveness. As described, Themis also builds a dependency graph and collapses SCCs during a “FairFinalize” stage, but it avoids waiting for the full cycle to close. Instead, it uses deferred ordering and “batch unspooling” so SCCs can be output incrementally while new transactions keep flowing. The result, as presented, upgrades Aequitas’ weak liveness into standard liveness with a delay bound.

Themis also addresses communication scaling concerns. In its basic form, participants exchange messages with most other nodes, leading to communication growth roughly proportional to the square of the network size. An optimized variant, SNARK-Themis, replaces much of that direct exchange with succinct cryptographic proofs, so verification can scale more efficiently as the node count increases.

Finally, the protocol design includes a mechanism to prevent denial-style manipulation. If a malicious proposer tries to exploit the system by proposing an empty block, Themis’s deferred ordering accepts a partially ordered batch and leaves exact finalization to a subsequent honest proposer, based on verifiable transaction relationships rather than discretionary choices by the current proposer. This is framed as a way to tie finalization to bounded network delay rather than arbitrary proposer behavior.

What to watch next

The central unresolved question across these approaches is how to balance fairness guarantees against the operational costs—especially complexity, communication overhead, and the practical handling of concurrency. As more consensus designs incorporate formal ordering fairness ideas, investors and builders should watch for implementations that demonstrate bounded delays in real network conditions while maintaining robustness against adversarial reordering.