Here’s How Much Michael Saylor’s Strategy Has Lost On Bitcoin

bitcoinistОпубликовано 2026-04-09Обновлено 2026-04-09

Введение

Michael Saylor's company, Strategy (formerly MicroStrategy), has incurred massive losses on its multi-billion dollar Bitcoin bet. According to its Q1 2026 filing, the company's Bitcoin holdings lost over $14 billion in value during the quarter. Despite this, the firm continued its aggressive accumulation strategy, spending over $7 billion to purchase an additional 89,000 BTC. This brought its total expenditure on Bitcoin to over $57 billion. The company has since made further purchases, with its total spend now at $58.02 billion. However, with a cost basis of $75,644 per coin, its entire holdings remain underwater as long as the Bitcoin price stays below that level. The company's stock price has also fallen by more than 50% from its 2025 peak. Saylor has stated the company has no plans to sell and will continue to accumulate Bitcoin.

Strategy (formerly MicroStrategy) started buying Bitcoin back in 2020, kickstarted by founder and CEO at the time, Michael Saylor. What began as a small buying trend quickly exploded over the years, and now, the company has claimed the title of the public company with the largest Bitcoin holdings in the world. The company has now spent tens of billions of dollars buying Bitcoin, but with the price decline, the holdings have now moved into losses.

Strategy’s Bitcoin Bet Records Billions Of Dollars In Losses

Strategy recently filed its Form 8-K for the first quarter of the year 2026, and it showed major losses for the company and its Bitcoin strategy. According to the form, the company’s Bitcoin holdings were down over $14 billion in Q1 alone, reflecting the decline that the btc price has suffered during this time.

Despite the Bitcoin price and its holdings moving underwater, Strategy had continued to purchase Bitcoin. Throughout the first quarter of the year, the company made a total of 12 different BTC purchases, with the lowest being a $40 million haul.

By the time the company was done in Q1, it had spent over $7 billion buying BTC in three months, adding more than 89,000 BTC to its already massive stash. This brought the total spend that the company has made over the years buying BTC to over $57 billion.

Despite the filing showing billions of dollars in losses, the company is right back to buying Bitcoin again. On April 6, the company reported another purchase of 4,871 BTC at an average price of $67,718. This cost the compass $329.9 million, bringing its total BTC spend so far to $58.02 billion.

Source: Strategy.com

The Bitcoin price has since rebounded from its Q1 lows and is trending high again, but the company is still underwater. Its current cost basis sits at $75,644 per coin, so as long as the BTC price stays below this level, then the company’s BTC holdings remain underwater.

As for the company’s stock price, it has followed the downward trajectory of Bitcoin. The MSTR stock price is sitting round $163 at the time of this report, down by more than 50% from its 2025 peak above $400. As for its BTC holdings, Saylor has previously said that the company has no plans to sell its BTC, and in fact will keep accumulating BTC for the foreseeable future.

BTC price begins reversal trend | Source: BTCUSD on Tradingview.com

Связанные с этим вопросы

QHow much did Strategy's Bitcoin holdings lose in Q1 2026 according to its 8-K filing?

AStrategy's Bitcoin holdings were down over $14 billion in Q1 2026.

QWhat is the total amount of money Strategy has spent on purchasing Bitcoin so far?

AStrategy has spent a total of $58.02 billion on purchasing Bitcoin so far.

QWhat is Strategy's current cost basis per Bitcoin coin, making its holdings 'underwater'?

AStrategy's current cost basis is $75,644 per Bitcoin coin.

QDespite the losses, what is the company's stated plan for its Bitcoin strategy according to Michael Saylor?

AThe company has no plans to sell its BTC and will keep accumulating it for the foreseeable future.

QHow many Bitcoins did Strategy purchase in its most reported transaction on April 6th, and at what average price?

AOn April 6, Strategy purchased 4,871 BTC at an average price of $67,718.

Похожее

It Took Me a Year to See the Bitter Truth About Agent Payments

After a year building infrastructure for the Agent economy, engaging with major players like Stripe, Visa, and Coinbase, the author shares a sobering analysis of the current state of Agent payments. The core finding is a stark lack of genuine, immediate demand across most envisioned use cases. The article breaks down four key market segments: 1. **Agent-to-Merchant (Consumer Shopping):** For most product categories (e.g., clothing, electronics), conversational AI shopping is a step backwards from visual e-commerce interfaces. While agents excel at understanding needs, they can't replace side-by-side product comparison. Real merchant interest is defensive "Agent Engine Optimization," not driven by current customer demand. Potential exists for high-frequency, low-decision purchases (like food delivery) or navigating complex store UIs, but these require massive B2C distribution channels dominated by giants like Amazon. 2. **Agent-to-API (Developer Services):** Developers already have subscriptions and billing relationships for APIs (compute, data). Prepaid balances solve micro-payment issues for low transaction volumes. A deeper structural problem is that major SaaS vendors' business models rely on enterprise contracts, resisting granular pay-per-call pricing. While protocols like MPP and x402 serve the long tail of niche services, this market is small and developers are historically low-willingness-to-pay. 3. **Agent-to-Agent:** This remains largely theoretical with minimal transaction volume. While it represents a long-term bet on a fundamentally new transaction infrastructure (sub-second, micro-penny to million-dollar, multi-party settlements), it does not constitute a present market. 4. **Agent-to-Finance:** This is the only category with existing, paying demand. Integrating AI into financial workflows (trading, portfolio management) is a natural evolution and enables new capabilities like autonomous rebalancing. However, competition favors established, regulated institutions. The "real problem" is not moving money between agents, but the broader challenge of **coordination**—orchestrating work between agents and humans, verifying outcomes, and settling results. Payment is just one component of settlement, which is itself part of coordination. Companies that solve the coordination layer will subsume payment, not the other way around. While well-funded incumbents build defensively for a long-term future, startups must find where the market is today—which, for the author's team, lies outside these four categories in an area of real, growing, and underserved activity.

marsbit12 мин. назад

It Took Me a Year to See the Bitter Truth About Agent Payments

marsbit12 мин. назад

It Took Me a Year to See the Hard Truth About Agent Payments

**Title: It Took Me a Year to See the Hard Truth About Agent Payments** Over the past year, I've worked on infrastructure for the Agent economy, engaging with major players like Stripe, Visa, Coinbase, and numerous startups. The findings reveal a stark reality: genuine, widespread demand for Agent-based payments does not yet exist. **Key Observations:** * **Agent-to-Merchant (Shopping):** The user experience for AI shopping often falls short, especially for visual product discovery. While AI excels at understanding needs, conversational interfaces can't yet replace browsing and comparing multiple products visually. Current merchant interest is largely defensive ("Agent Engine Optimization") for a future that hasn't arrived. High-frequency, low-friction purchases (like food delivery) are potential fits, but lack open APIs and face high AI inference costs. Simpler, more affordable, or cross-language interactions for complex UIs are a niche opportunity but require massive consumer distribution to scale. * **Agent-to-API (Developer Tools):** Developer payment needs for APIs (computing, data, models) are already met through subscriptions and prepaid credits. The core challenge is not payment friction but supplier economics: most large SaaS providers prefer enterprise contracts over micropayments for API calls. Protocols like MPP and x402 suit the long-tail of smaller services but cater to a developer market historically reluctant to pay for these tools. Major infrastructure needs at the top of the stack are already being addressed. * **Agent-to-Agent (Machine Commerce):** This is a long-term vision with almost no current transaction volume. While a future with high-speed, high-frequency, multi-party machine-to-machine transactions would require novel infrastructure, it remains theoretical. The market is not here yet. * **Agent-to-Finance:** This is the only category with clear, present demand. Financial professionals and DeFi users already pay for tools, and AI augmentation is a natural evolution. Autonomous AI agents can enable entirely new financial strategies. However, competition is fierce from established, regulated incumbents who can more easily layer AI onto their existing products. **The Core Insight:** Companies, especially giants with long time horizons, are building defensively for a potential future of mass machine commerce. For them, early investment is a low-cost hedge. For startups, the current market reality is different. The primary challenge isn't just moving money between agents (payments). The larger, unsolved problem is **orchestration** – coordinating work between agents and humans, verifying outcomes, and then settling. Payment is just a part of settlement, which is just a part of orchestration. Companies that solve the orchestration problem will subsume payments, not the other way around. After a year of building, we see the real, growing, and underserved market opportunity lies in this broader domain of orchestration.

链捕手36 мин. назад

It Took Me a Year to See the Hard Truth About Agent Payments

链捕手36 мин. назад

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

A researcher discovered a critical "infinite mint" vulnerability in the Zcash cryptocurrency's Orchard protocol using Claude Opus 4.8, leading to a swift fix but also a 50% market drop, erasing billions in value. This incident highlights a new era where powerful, accessible AI models are dramatically lowering the barrier to finding software vulnerabilities. Previously, the security community feared specialized models like Claude Mythos Preview, capable of finding decades-old zero-day exploits. The Zcash case, however, involved a publicly available, general-purpose model. This shift makes advanced security auditing—and attack capabilities—accessible to far more people, not just experts. The mass democratization of vulnerability discovery brings a dual challenge: a flood of low-quality, AI-generated false reports that overwhelm maintainers, and the real, rapid uncovering of deep, dangerous bugs. Open-source projects, often understaffed and unfunded, are particularly vulnerable to this "attention DDoS." The article cites examples like curl shutting down its bug bounty program due to the unsustainable workload. Our perceived digital safety has often been luck, relying on the high cost and effort required to find deeply hidden flaws in complex systems, as seen with historical vulnerabilities like Heartbleed or Baron Samedit. AI changes this cost structure, effectively "mass-producing flashlights" to illuminate every corner of our codebase. While large companies operate extensive security chains involving external white-hat hackers and massive defensive operations, the global cybersecurity workforce faces a severe shortage, especially of experienced personnel capable of analyzing complex threats and coordinating fixes. The core dilemma emerges: AI makes *finding* bugs cheap and scalable, but *fixing* them remains a slow, expensive, and human-intensive process. The article concludes that AI won't destroy the internet but acts as a bright light, revealing that our digital existence is not inherently secure but is precariously maintained by ongoing human effort. The true cost in the AI era may not be discovery, but whether there will be enough people left willing and able to do the hard work of repair.

marsbit1 ч. назад

Claude Opus 4.8 Finds a $4.5 Billion Bug: The AI Era is Mass-Producing Hackers

marsbit1 ч. назад

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

"Codex Goal Mode: How to Make AI Work Continuously Toward a Specific Goal" OpenAI's Codex "goal mode" (/goal) transforms the AI from a reactive code assistant into a proactive execution agent capable of working autonomously for hours or even days to achieve a defined objective. To maximize its effectiveness, follow these key principles: 1. **Define Clear, Verifiable Exit Criteria:** The goal prompt should be a concise, measurable success condition, not a lengthy specification. Use quantifiable metrics like "reduce build time by 30%" or "achieve 100% test parity." 2. **Provide Initial Guidance and Tools:** Direct Codex toward likely problem areas and specify available tools (e.g., browsers, testing environments) to prevent it from exploring unproductive paths. 3. **Enable Progress Measurement:** Equip Codex with ways to track advancement, such as creating comparison tools for visual tasks or evaluation sets, ensuring it can gauge its own progress. 4. **Use a Realistic Execution Environment:** For tasks like performance optimization, provide access to environments that closely mimic production (e.g., similar configs, databases) to yield valid results. 5. **Be Cautious with Visual Goals:** Avoid vague "pixel-perfect" instructions. Instead, supplement visual references with functional checklists or design system specifications to prevent Codex from obsessing over minor details. 6. **Implement Progress Tracking:** For long-running tasks, have Codex commit code to draft PRs, update progress documents, or send Slack updates to maintain visibility into its work. 7. **Review and Consolidate Results:** Once the goal is met, instruct Codex to review its work, clean up ineffective experimental code, and reflect on what strategies succeeded or failed. Ultimately, using goal mode shifts the developer's role from writing prompts to managing a persistent engineering agent—defining objectives, establishing metrics, configuring environments, and conducting final reviews.

marsbit2 ч. назад

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

marsbit2 ч. назад

Торговля

Спот
Фьючерсы

Популярные статьи

Как купить S

Добро пожаловать на HTX.com! Мы сделали приобретение Sonic (S) простым и удобным. Следуйте нашему пошаговому руководству и отправляйтесь в свое крипто-путешествие.Шаг 1: Создайте аккаунт на HTXИспользуйте свой адрес электронной почты или номер телефона, чтобы зарегистрироваться и бесплатно создать аккаунт на HTX. Пройдите удобную регистрацию и откройте для себя весь функционал.Создать аккаунтШаг 2: Перейдите в Купить криптовалюту и выберите свой способ оплатыКредитная/Дебетовая Карта: Используйте свою карту Visa или Mastercard для мгновенной покупки Sonic (S).Баланс: Используйте средства с баланса вашего аккаунта HTX для простой торговли.Третьи Лица: Мы добавили популярные способы оплаты, такие как Google Pay и Apple Pay, для повышения удобства.P2P: Торгуйте напрямую с другими пользователями на HTX.Внебиржевая Торговля (OTC): Мы предлагаем индивидуальные услуги и конкурентоспособные обменные курсы для трейдеров.Шаг 3: Хранение Sonic (S)После приобретения вами Sonic (S) храните их в своем аккаунте на HTX. В качестве альтернативы вы можете отправить их куда-либо с помощью перевода в блокчейне или использовать для торговли с другими криптовалютами.Шаг 4: Торговля Sonic (S)С легкостью торгуйте Sonic (S) на спотовом рынке HTX. Просто зайдите в свой аккаунт, выберите торговую пару, совершайте сделки и следите за ними в режиме реального времени. Мы предлагаем удобный интерфейс как для начинающих, так и для опытных трейдеров.

1.4k просмотров всегоОпубликовано 2025.01.15Обновлено 2026.06.02

Как купить S

Sonic: Обновления под руководством Андре Кронье – новая звезда Layer-1 на фоне спада рынка

Он решает проблемы масштабируемости, совместимости между блокчейнами и стимулов для разработчиков с помощью технологических инноваций.

2.3k просмотров всегоОпубликовано 2025.04.09Обновлено 2025.04.09

Sonic: Обновления под руководством Андре Кронье – новая звезда Layer-1 на фоне спада рынка

HTX Learn: Пройдите обучение по "Sonic" и разделите 1000 USDT

HTX Learn — ваш проводник в мир перспективных проектов, и мы запускаем специальное мероприятие "Учитесь и Зарабатывайте", посвящённое этим проектам. Наше новое направление .

1.8k просмотров всегоОпубликовано 2025.04.10Обновлено 2025.04.10

HTX Learn: Пройдите обучение по "Sonic" и разделите 1000 USDT

Обсуждения

Добро пожаловать в Сообщество HTX. Здесь вы сможете быть в курсе последних новостей о развитии платформы и получить доступ к профессиональной аналитической информации о рынке. Мнения пользователей о цене на S (S) представлены ниже.

活动图片