77% Of Bitcoin Treasury Firms Sitting Underwater—Highest Since 2023

bitcoinistОпубліковано о 2026-03-10Востаннє оновлено о 2026-03-10

Анотація

A recent analysis reveals that 77.4% of Bitcoin treasury companies are currently underwater on their Bitcoin holdings, the highest level since 2023. These firms, which hold BTC as a reserve asset, are facing significant losses due to the recent price decline. Notably, 65.6% of these companies are experiencing losses exceeding 20% below their cost basis. MicroStrategy, a major player, has an average acquisition price of $75,985, placing it over 12% in the red. The trend is reminiscent of the May 2022 bear market. Despite this, U.S. spot Bitcoin ETFs have recently seen a return of inflows, suggesting a potential resurgence in demand. Bitcoin's price currently hovers around $67,600.

Data shows the Bitcoin price decline has left the majority of treasury companies in a state of loss, with 65% sitting more than 20% below cost basis.

Over 77% Of Bitcoin Treasury Firms Are Underwater On Their Buys

As pointed out by Capriole Investments founder Charles Edwards in a new post on X, a high amount of Bitcoin treasury companies are sitting on losses at the moment. Treasury companies refer to firms that keep BTC on their balance sheet as a reserve asset. Companies of this type that are publicly traded do so to allow their investors indirect exposure to the digital asset via their stock.

The approach was popularized by Michael Saylor’s Strategy (previously MicroStrategy), which has amassed a humongous Bitcoin stack after its consistent accumulation over the years. During the past few months, BTC has observed a bearish shift, so these firms have naturally been impacted. Below is the chart shared by Edwards that shows the trend in the percentage of such companies that are underwater on their BTC buys.

Companies have increasingly gone underwater as the bearish momentum has advanced | Source: @caprioleio on X

As is visible in the graph, the total percentage of Bitcoin treasury firms in loss has gone up recently, with its value today sitting at 77.4%. Thus, it would appear that a strong majority of the companies have their holdings below their cost basis. This includes Strategy, which has an average acquisition level of $75,985, more than 12% above the current spot price.

A large percentage of the firms are in even worse losses than Strategy. In the same chart, data for the treasuries with holdings sitting more than 20% below their cost basis is also displayed. It would appear that this metric has a value of 65.6%, implying that less than 12% of the underwater companies are in losses smaller than 20%.

From the graph, it’s also apparent that the recent trend in the treasury firms resembles that of May 2022, when the bear market of that year was in full swing. Back then, the percentage figure eventually went on to touch even higher highs.

Like how public treasury companies provide for an indirect route into Bitcoin, there is also another such indirect means in the market available today: the spot exchange-traded funds (ETFs). These funds buy and hold the asset on behalf of their users, allowing them to get exposure to BTC’s price movements without having to deal with blockchain elements.

The bearish market shift also caused the US spot ETFs to face net outflows, as data from SoSoValue shows. During the last couple of weeks, however, inflows have poured into these funds, implying that demand for Bitcoin may be starting to return.

How the weekly netflow related to the spot BTC ETFs has changed over the last couple of years | Source: SoSoValue

BTC Price

Bitcoin has retraced its recovery during the past few days as its price is back at the $67,600 mark.

The trend in the price of the coin over the last five days | Source: BTCUSDT on TradingView

Пов'язані питання

QWhat percentage of Bitcoin treasury firms are currently underwater on their Bitcoin buys according to the article?

A77.4% of Bitcoin treasury firms are currently underwater on their Bitcoin buys.

QWhat is the average acquisition price of Bitcoin for the company Strategy (MicroStrategy) mentioned in the article?

AStrategy (MicroStrategy) has an average Bitcoin acquisition price of $75,985.

QWhat percentage of the underwater treasury firms are sitting on losses greater than 20% below their cost basis?

A65.6% of the treasury firms are sitting on losses more than 20% below their cost basis.

QAccording to the article, what recent trend in US spot ETFs suggests that demand for Bitcoin may be returning?

AThe article states that inflows have poured into US spot Bitcoin ETFs over the last couple of weeks, suggesting demand may be returning.

QWhat was the price of Bitcoin at the time the article was written?

AAt the time the article was written, the price of Bitcoin was at the $67,600 mark.

Пов'язані матеріали

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

OpenAI engineer Weng Jiayi's "Heuristic Learning" experiments propose a new paradigm for Agentic AI, suggesting that intelligent agents can improve not just by training neural networks, but also by autonomously writing and refining code based on environmental feedback. In the experiment, a coding agent (powered by Codex) was tasked with developing and maintaining a programmatic strategy for the Atari game Breakout. Starting from a basic prompt, the agent iteratively wrote code, ran the game, analyzed logs and video replays to identify failures, and then modified the code. Through this engineering loop of "code-run-debug-update," it evolved a pure Python heuristic strategy that achieved a perfect score of 864 in Breakout and performed competitively with deep reinforcement learning (RL) algorithms in MuJoCo control tasks like Ant and HalfCheetah. This approach, termed Heuristic Learning (HL), contrasts with Deep RL. In HL, experience is captured in readable, modifiable code, tests, logs, and configurations—a software system—rather than being encoded solely into opaque neural network weights. This offers potential advantages in explainability, auditability for safety-critical applications, easier integration of regression tests to combat catastrophic forgetting, and more efficient sample use in early learning stages, as demonstrated in broader tests on 57 Atari games. However, the blog acknowledges clear limitations. Programmatic strategies struggle with tasks requiring long-horizon planning or complex perception (e.g., Montezuma's Revenge), areas where neural networks excel. The future vision is a hybrid architecture: specialized neural networks for fast perception (System 1), HL systems for rules, safety, and local recovery (also System 1), and LLM agents providing high-level feedback and learning from the HL system's data (System 2). The core proposition is that in the era of capable coding agents, a significant portion of an AI's learned experience could be maintained as an auditable, evolving software system.

marsbit24 хв тому

OpenAI Post-Training Engineer Weng Jiayi Proposes a New Paradigm Hypothesis for Agentic AI

marsbit24 хв тому

Your Claude Will Dream Tonight, Don't Disturb It

This article explores the recent phenomenon of AI companies increasingly using anthropomorphic language—like "thinking," "memory," "hallucination," and now "dreaming"—to describe machine learning processes. Focusing on Anthropic's newly announced "Dreaming" feature for its Claude Agent platform, the piece explains that this function is essentially an automated, offline batch processing of an agent's operational logs. It analyzes past task sessions to identify patterns, optimize future actions, and consolidate learnings into a persistent memory system, akin to a form of reinforcement learning and self-correction. The article draws parallels to similar features in other AI agent systems like Hermes Agent and OpenClaw, which also implement mechanisms for reviewing historical data, extracting reusable "skills," and strengthening long-term memory. It notes a key difference from human dreaming: these AI "dreams" still consume computational resources and user tokens. Further context is provided by discussing the technical challenges of managing AI "memory" or context, highlighting the computational expense of large context windows and innovations like Subquadratic's new model claiming drastically longer contexts. The core critique argues that this strategic use of human-centric vocabulary does more than market products; it subtly reshapes user perception. By framing algorithms with terms associated with consciousness, companies blur the line between tool and autonomous entity. This linguistic shift can influence user expectations, tolerance for errors, and even perceptions of responsibility when systems fail, potentially diverting scrutiny from the companies and engineers behind the technology. The article concludes by speculating that terms like "daydreaming" for predictive task simulation might be next, continuing this trend of embedding the idea of an "inner life" into computational processes.

marsbit26 хв тому

Your Claude Will Dream Tonight, Don't Disturb It

marsbit26 хв тому

Торгівля

Спот
Ф'ючерси
活动图片