Michael Saylor Says Quantum Threat to Bitcoin Is Not Immediate

TheNewsCryptoОпубликовано 2026-02-24Обновлено 2026-02-24

Введение

Michael Saylor, founder of MicroStrategy, addressed concerns about quantum computing as a potential threat to Bitcoin, stating it is not an immediate risk and is at least a decade away. He emphasized that the broader cybersecurity community views quantum risk as a long-term issue rather than a current threat. Saylor expressed confidence in the Bitcoin ecosystem’s ability to adapt, noting that upgrades would be implemented across global systems if such a risk emerged. He also highlighted MicroStrategy’s significant Bitcoin holdings, which now total 717,722 BTC worth approximately $54.56 billion, despite recent market declines.

Michael Saylor, founder of Strategy, formerly MicroStrategy, discussed concerns about the quantum threat to Bitcoin in an interview and clarified that it is not an immediate security risk at this time, as it is ten years away.

​On February 23, Natalie Brunell, who shared a recent episode of her Coin Stories podcast on X with Saylor, in which they discussed several topics around Bitcoin and Strategy. In the middle of the conversation, Brunell asked whether quantum computing poses an existential threat to Bitcoin.

​She noted that many people are not technical enough to independently verify the seriousness of the risk and referenced Strategy’s earlier statement suggesting Bitcoin is “quantum-proof.” She asked Saylor why this is not considered a bigger threat.

​In response, Saylor stressed that quantum risk is not seen as imminent by the larger cybersecurity community. He insisted that the “consensus of the cyber security community broadly held is that quantum risk, if it exists, is more than ten years out. It’s not a this-decade thing.”

Quantum Threat Would Trigger Upgrades

Saylor stated, “The crypto community is the most sophisticated cybersecurity community,” he added, adding that it already makes use of innovative authentication techniques like hardware keys. He proposed that Bitcoin uses extremely sophisticated security measures in comparison compared to traditional banking systems.

​Then, Saylor went on and said that if a quantum risk materialized, it would lead to upgrades in the software that runs the Bitcoin network, the global banking system, the global internet, consumer devices, and all crypto networks. Eventually, post-quantum-resistant cryptography would replace all digital devices. In his view, quantum risk is currently in the spotlight largely because other anticipated risks have not materialized.

​He said, “You’ll see it coming. We’ll all see it coming, as the crypto security community will be the first to identify any real quantum threat, perceive it, and lead the way. Also, it can have enough time to implement necessary upgrades in response to emerging threats.

Strategy’s Bitcoin Accumulation and Market Performance

Saylor had previously posted “The Orange Century” on his X account, hinting at Strategy’s 100th Bitcoin buy. As the company began to accumulate Bitcoin in 2020 and has since expanded to become the biggest corporate owner in the world. The company owns 717,722 Bitcoin, which is worth around $54.56 billion.

​While writing this article, Bitcoin is down over 3% in the past 24 hours, and is trading at $62,884, which is actually down over 29% over the past month. Also, Bitcoin is down beyond 50% from its last all-time high of $126,198.07 in October 2025

Highlighted Crypto News:

Crypto Funds Shed $4B as Outflows Hit Five-Week Streak

TagsBitcoinMicheal Saylorquantum

Связанные с этим вопросы

QAccording to Michael Saylor, when is the quantum threat to Bitcoin expected to become a significant risk?

AAccording to Michael Saylor, the consensus of the cybersecurity community is that the quantum risk, if it exists, is more than ten years away and is not a threat for this decade.

QWhat did Michael Sayer suggest would happen if a quantum risk to cryptography did materialize?

ASaylor stated that a materialized quantum risk would trigger upgrades to the Bitcoin network's software, the global banking system, the internet, consumer devices, and all crypto networks, leading to their replacement with post-quantum-resistant cryptography.

QHow does Michael Saylor describe the Bitcoin and crypto community in terms of cybersecurity?

ASaylor described the crypto community as 'the most sophisticated cybersecurity community,' which already employs innovative authentication techniques like hardware keys and uses extremely sophisticated security measures compared to traditional banking systems.

QHow many Bitcoins does MicroStrategy (Strategy) own, and what is their approximate value?

AMicroStrategy owns 717,722 Bitcoins, which are worth approximately $54.56 billion.

QWhat was the price of Bitcoin and its performance at the time the article was written?

AAt the time of writing, Bitcoin was trading at $62,884, down over 3% in the past 24 hours and down over 29% over the past month. It was also down more than 50% from its all-time high of $126,198.07 in October 2025.

Похожее

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

The article "a16z: AI's 'Amnesia' – Can Continual Learning Cure It?" explores the limitations of current large language models (LLMs), which, like the protagonist in the film *Memento*, are trapped in a perpetual present—unable to form new memories after training. While methods like in-context learning (ICL), retrieval-augmented generation (RAG), and external scaffolding (e.g., chat history, prompts) provide temporary solutions, they fail to enable true internalization of new knowledge. The authors argue that compression—the core of learning during training—is halted at deployment, preventing models from generalizing, discovering novel solutions (e.g., mathematical proofs), or handling adversarial scenarios. The piece introduces *continual learning* as a critical research direction to address this, categorizing approaches into three paths: 1. **Context**: Scaling external memory via longer context windows, multi-agent systems, and smarter retrieval. 2. **Modules**: Using pluggable adapters or external memory layers for specialization without full retraining. 3. **Weights**: Enabling parameter updates through sparse training, test-time training, meta-learning, distillation, and reinforcement learning from feedback. Challenges include catastrophic forgetting, safety risks, and auditability, but overcoming these could unlock models that learn iteratively from experience. The conclusion emphasizes that while context-based methods are effective, true breakthroughs require models to compress new information into weights post-deployment, moving from mere retrieval to genuine learning.

marsbit52 мин. назад

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

marsbit52 мин. назад

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

An individual manipulated a weather sensor at Paris Charles de Gaulle Airport with a portable heat source, causing a Polymarket weather market to settle at 22°C and earning $34,000. This incident highlights a fundamental issue in prediction markets: when a market aims to reflect reality, it also incentivizes participants to influence that reality. Prediction markets operate on two layers: platform rules (what outcome counts as a win) and data sources (what actually happened). While most focus on rules, the real vulnerability lies in the data source. If reality is recorded through a specific source, influencing that source directly affects market settlement. The article categorizes markets by their vulnerability: 1. **Single-point physical data sources** (e.g., weather stations): Easily manipulated through physical interference. 2. **Insider information markets** (e.g., MrBeast video details): Insiders like team members use non-public information to trade. Kalshi fined a剪辑师 $20,000 for insider trading. 3. **Actor-manipulated markets** (e.g., Andrew Tate’s tweet counts): The subject of the market can control the outcome. Evidence suggests Tate’sociated accounts coordinated to profit. 4. **Individual-action markets** (e.g., WNBA disruptions): A single person can execute an event to profit from their pre-placed bets. Kalshi and Polymarket handle these issues differently. Kalshi enforces strict KYC, publicly penalizes insider trading, and reports to regulators. Polymarket, with its anonymous wallet-based system, has historically been more permissive, arguing that insider information improves market accuracy. However, it cooperated with authorities in the "Van Dyke case," where a user traded on classified government information. The core paradox is reflexivity: prediction markets are designed to discover truth, but their financial incentives can distort reality. The more valuable a prediction becomes, the more likely participants are to influence the event itself. The market ceases to be a mirror of reality and instead shapes it.

marsbit1 ч. назад

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

marsbit1 ч. назад

Торговля

Спот
Фьючерсы
活动图片