Bitcoin Is The Standard Now: Outperform It, Or Get Left Behind – CEO

bitcoinistPublished on 2025-10-09Last updated on 2025-10-09

Abstract

Bitcoin’s climb shows no signs of stopping, and one of crypto’s loudest bulls says the rally could keep running as...

Trusted Editorial content, reviewed by leading industry experts and seasoned editors. Ad Disclosure

Bitcoin’s climb shows no signs of stopping, and one of crypto’s loudest bulls says the rally could keep running as long as governments keep expanding the money supply.

According to his CNBC interview, Anthony Pompliano called Bitcoin a “savings technology” and argued that people can protect their earnings by putting part of their money into BTC.

Related Reading: Momentum Builds: SBR And Bitcoin Act Are Closer Than You Think

Reports have disclosed that Bitcoin recently hit an all-time high of $126,100 and traded around $122,500 at press time, figures that form the backdrop for Pompliano’s comments.

Pompliano Frames Bitcoin As Savings Technology

Pompliano told CNBC that the core idea is simple: work, save, and put part of your savings into crypto to preserve value as fiat currencies weaken.

He said that as long as governments and central banks keep printing money, demand for a scarce asset like Bitcoin should remain strong.

Based on his on-camera remarks, he expects the trend to push adoption higher and to reshape how investors think about storing wealth.

BTC market cap currently at $2.45 trillion. Chart: TradingView

The New ‘Hurdle Rate’

Pompliano went further, describing the top digital asset as the “hurdle rate” of modern finance — a baseline investors must beat before choosing other assets.

He contrasted Bitcoin’s performance with traditional markets, arguing that the S&P 500 has risen by more than a hundred percent since 2020 in fiat terms but has fallen roughly 90% when priced in BTC, a comparison he used to stress BTC’s long-term outperformance. This framing explains why he and some others say, “If you can’t beat Bitcoin, buy it.”

Further Gains Ahead

Based on projections, BTC could climb about 20% to $148,500 by the end of the year. The same forecasts sees a jump in market infrastructure: the number of crypto exchange-traded funds could double to 80, and stablecoin circulation is predicted to reach $500 billion as more money moves onchain.

Those observations realistically bolster an argument that the market is maturing beyond the realm of a short-term speculation.

Source: Defillama
Related Reading: After Nearly $80 Billion In BTC, Saylor Says It’s Time To Pause – For Now

Market Size And Stablecoin Liquidity Here To Stay

Market intelligence reveals that the total cryptocurrency market is sizeable at roughly $4.3 trillion, according to CoinGecko.

In addition, another market data source, DeFiLlama, reports that the stablecoin supply has exceeded $300 billion as an indication that there is a lot of liquidity onchain and it could flow into risk assets like Bitcoin.

Featured image from Kitco, chart from TradingView

Editorial Process for bitcoinist is centered on delivering thoroughly researched, accurate, and unbiased content. We uphold strict sourcing standards, and each page undergoes diligent review by our team of top technology experts and seasoned editors. This process ensures the integrity, relevance, and value of our content for our readers.
Christian Encila

Christian Encila

Follow

Christian, a journalist and editor with leadership roles in Philippine and Canadian media, is fueled by his love for writing and cryptocurrency. Off-screen, he's a cook and cinephile who's constantly intrigued by the size of the universe.

Full Profile

Related Posts

Michael Saylor bitcoin microstrategy

New SEC Filing Shows Michael Saylor’s $78 Billion Bitcoin Strategy Faces A Major Danger

14 hours ago
Bitcoin

BlackRock Bitcoin Fund Hits ‘Absurd’ Growth, Eyes $100B AUM

1 day ago
Bitcoin

Buying Homes With Bitcoin? $6-B Firm Says It’s Making That A ‘Priority’

1 day ago
Bitcoin

Momentum Builds: SBR And Bitcoin Act Are Closer Than You Think

1 day ago
Bitcoin

After Nearly $80 Billion In Bitcoin, Saylor Says It’s Time To Pause – For Now

2 days ago
Bitcoin

Are Bitcoin Whales Back In Accumulation Mode? On-Chain Data Suggests So

3 days ago

Related Reads

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbit6h ago

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbit6h ago

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手8h ago

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手8h ago

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbit9h ago

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbit9h ago

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbit9h ago

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbit9h ago

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

The price of Xiaomi's MiMo-V2.5 series API has been permanently reduced by up to 99%, specifically for the "Input (Cache Hit)" cost, which covers users re-reading historical context in long conversations. MiMo's head, Luo Fuli, published a detailed technical blog to clarify that this drastic price cut stems from genuine engineering breakthroughs, not a marketing stunt or a simple price war. The core of the achievement lies in six key engineering optimizations. First, the model architecture adopts a Hybrid Sliding Window Attention (SWA), reducing the memory footprint (KVCache) to 1/7th of a traditional model. Second, a dual-pool memory management system actually utilizes these savings, allowing a single GPU to handle over 5 times more concurrent users. Third, an upgraded prefix caching mechanism achieves a cache hit rate of 93-95% for repeated reads, meaning most such requests bypass GPU computation entirely. Fourth, a self-developed distributed cache (GCache) utilizes idle SSD space on existing GPU servers, eliminating additional storage costs. Fifth, an intelligent scheduling system (LLM-Router) efficiently routes requests to maximize cache reuse and performance. Sixth, Multi-Token Prediction (MTP) accelerates the model's text generation ("output") side. Together, these systemic optimizations dramatically lower the real computational cost per request, enabling the 99% price reduction for cached inputs while reportedly maintaining positive gross margins. Luo Fuli's disclosure aims to shift the narrative from "price war" to a demonstration of substantive AI engineering progress.

marsbit11h ago

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

marsbit11h ago

Trading

Spot
Futures

Hot Articles

What is $BITCOIN

DIGITAL GOLD ($BITCOIN): A Comprehensive Analysis Introduction to DIGITAL GOLD ($BITCOIN) DIGITAL GOLD ($BITCOIN) is a blockchain-based project operating on the Solana network, which aims to combine the characteristics of traditional precious metals with the innovation of decentralized technologies. While it shares a name with Bitcoin, often referred to as “digital gold” due to its perception as a store of value, DIGITAL GOLD is a separate token designed to create a unique ecosystem within the Web3 landscape. Its goal is to position itself as a viable alternative digital asset, although specifics regarding its applications and functionalities are still developing. What is DIGITAL GOLD ($BITCOIN)? DIGITAL GOLD ($BITCOIN) is a cryptocurrency token explicitly designed for use on the Solana blockchain. In contrast to Bitcoin, which provides a widely recognized value storage role, this token appears to focus on broader applications and characteristics. Notable aspects include: Blockchain Infrastructure: The token is built on the Solana blockchain, known for its capacity to handle high-speed and low-cost transactions. Supply Dynamics: DIGITAL GOLD has a maximum supply capped at 100 quadrillion tokens (100P $BITCOIN), although details regarding its circulating supply are currently undisclosed. Utility: While precise functionalities are not explicitly outlined, there are indications that the token could be utilized for various applications, potentially involving decentralized applications (dApps) or asset tokenization strategies. Who is the Creator of DIGITAL GOLD ($BITCOIN)? At present, the identity of the creators and development team behind DIGITAL GOLD ($BITCOIN) remains unknown. This situation is typical among many innovative projects within the blockchain space, particularly those aligning with decentralized finance and meme coin phenomena. While such anonymity may foster a community-driven culture, it intensifies concerns about governance and accountability. Who are the Investors of DIGITAL GOLD ($BITCOIN)? The available information indicates that DIGITAL GOLD ($BITCOIN) does not have any known institutional backers or prominent venture capital investments. The project seems to operate on a peer-to-peer model focused on community support and adoption rather than traditional funding routes. Its activity and liquidity are primarily situated on decentralized exchanges (DEXs), such as PumpSwap, rather than established centralized trading platforms, further highlighting its grassroots approach. How DIGITAL GOLD ($BITCOIN) Works The operational mechanics of DIGITAL GOLD ($BITCOIN) can be elaborated on based on its blockchain design and network attributes: Consensus Mechanism: By leveraging Solana’s unique proof-of-history (PoH) combined with a proof-of-stake (PoS) model, the project ensures efficient transaction validation contributing to the network's high performance. Tokenomics: While specific deflationary mechanisms have not been extensively detailed, the vast maximum token supply implies that it may cater to microtransactions or niche use cases that are still to be defined. Interoperability: There exists the potential for integration with Solana’s broader ecosystem, including various decentralized finance (DeFi) platforms. However, the details regarding specific integrations remain unspecified. Timeline of Key Events Here is a timeline that highlights significant milestones concerning DIGITAL GOLD ($BITCOIN): 2023: The initial deployment of the token occurs on the Solana blockchain, marked by its contract address. 2024: DIGITAL GOLD gains visibility as it becomes available for trading on decentralized exchanges like PumpSwap, allowing users to trade it against SOL. 2025: The project witnesses sporadic trading activity and potential interest in community-led engagements, although no noteworthy partnerships or technical advancements have been documented as of yet. Critical Analysis Strengths Scalability: The underlying Solana infrastructure supports high transaction volumes, which could enhance the utility of $BITCOIN in various transaction scenarios. Accessibility: The potential low trading price per token could attract retail investors, facilitating wider participation due to fractional ownership opportunities. Risks Lack of Transparency: The absence of publicly known backers, developers, or an audit process may yield skepticism regarding the project's sustainability and trustworthiness. Market Volatility: The trading activity is heavily reliant on speculative behavior, which can result in significant price volatility and uncertainty for investors. Conclusion DIGITAL GOLD ($BITCOIN) emerges as an intriguing yet ambiguous project within the rapidly evolving Solana ecosystem. While it attempts to leverage the “digital gold” narrative, its departure from Bitcoin's established role as a store of value underscores the need for a clearer differentiation of its intended utility and governance structure. Future acceptance and adoption will likely depend on addressing the current opacity and defining its operational and economic strategies more explicitly. Note: This report encompasses synthesised information available as of October 2023, and developments may have transpired beyond the research period.

363 Total ViewsPublished 2025.05.13Updated 2025.05.13

What is $BITCOIN

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of BTC (BTC) are presented below.

活动图片

Top Questions

Hot Categoriesright-arrow

Hot Tagsright-arrow