Will AI Really Cause an Economic Collapse in 2028? New Metrics Reveal Another Possibility

比推Xuất bản vào 2026-02-25Cập nhật gần nhất vào 2026-02-25

Tóm tắt

A response to Citrini Research's "2028 Global Intelligence Crisis" paper argues against its prediction of an AI-induced economic collapse. The author posits that while AI will destroy the income side of the economy (wages, jobs), it will simultaneously and more rapidly destroy the cost side, leading to a "deflationary prosperity." Traditional economic metrics like GDP and unemployment rates are becoming obsolete in this new "post-human economy." The key measure of future prosperity will be "intelligence output per unit energy," which is exponentially increasing. This transition, though potentially turbulent, is not a crisis but a radical evolution. AI and automation are also seen as necessary to fill a looming demographic gap from a declining global workforce. The new economy will shift value creation towards uniquely human traits like empathy, creativity, and connection, which are not captured by traditional indicators.

Author: David Mattin

Compiled by: Deep Tide TechFlow

Original title: Refuting the '2028 Economic Collapse Theory': AI Takes Your Job, But Also Makes Everything Almost Free


Deep Tide Introduction: While the entire industry is panicking over the '2028 AI-induced global economic collapse' depicted by Citrini Research, tech thinker David Mattin steps forward with a completely different interpretation. He believes we are in the midst of a 'global intelligence transition,' where old economic indicators (such as GDP and unemployment rates) are becoming obsolete. This article delves into what happens when intelligence becomes as cheap and abundant as air: while the income side may suffer, the cost side will collapse even faster, ushering in a new era driven by 'intelligence output per unit of energy.' This is not just a crisis but a radical evolution towards a 'post-human economy.'

Full text as follows:

Everyone is talking about Citrini Research's paper, 'The 2028 Global Intelligence Crisis.' It's a great thought experiment: a speculative report from June 2028 envisioning a scenario where artificial intelligence (AI) triggers a chain-reaction economic collapse.

The following content serves as a response to that article. You can view it as a creative work in the same spirit as Citrini's original: a speculative 'reverse scenario.' It is an exploration of new ways of observing, rather than a claim to have all the answers (no one does). This article draws on years of research and analysis published by Raoul Pal and me at Global Macro Investor, as well as our jointly operated tech-focused research service, 'The Exponentialist.'

Citrini Research's paper has garnered significant attention, and for good reason. It is a brilliantly conceived thought experiment: a speculative briefing from June 2028, previewing an AI-induced chain-reaction economic meltdown. The S&P 500 drops 38%. The unemployment rate reaches 10.2%. Prime mortgages crack. The private credit complex collapses through a series of correlated bets on white-collar productivity growth.

This scenario is logically consistent, its financial mechanisms are meticulously researched, and its core argument—that an extreme abundance of intelligence destroys the consumer economy it was supposed to enhance—is highly provocative. Parts of it will likely prove prescient. There is real turbulence ahead, possibly even extreme hardship. The transition to an age of intelligence abundance will by no means be smooth.

For over five years, I have been immersed in this kind of thinking. I have been building frameworks to understand what happens when intelligence becomes abundant, the AI-energy flywheel begins to spin, and we transition from a human-centric economy to something utterly new. In related articles I've written, I have described this as a shift towards a fundamentally new type of economic system: a form of 'Post-human Economics.' From the perspective of this work, I want to offer a considered response to Citrini's argument—based on my years of analysis—that leads to a starkly different conclusion.

Citrini's argument is that abundant intelligence destroys the income side of the economy—wages, jobs, consumer spending—triggering a financial crisis. My argument is that abundant intelligence is also destroying the cost side of the economy, and potentially faster. When the prices of goods and services collapse along with wages, you are not facing a crisis. You are in the midst of a transition to a radically new system; a system in which all the old norms, rules, and metrics become incoherent.

So, what is the core error in Citrini's article? Their article is using the instruments of the 'human economy' to measure the 'post-human economy.' Then, it mistakes the instrument's erratic readings for the system's collapse.

No one has a crystal ball; no one has all the answers. We are all piecing together a seven-dimensional puzzle that no one fully understands. But I believe Citrini's article, though sophisticated, may be making a profound and instructive error. And my own work is pointing directly to it.

My timeframe is also longer than Citrini's. Their scenario unfolds over two years. I am looking at a ten to twenty-year span. I acknowledge there could be serious turbulence ahead: a 'Fourth Turning'-style moment of chaos, social unrest, and institutional collapse. Some version of what they describe might indeed come to pass. But my argument is that AI and the broader forces of the 'Exponential Age' can ultimately take us into a new kind of economy. One that actually functions. One that is, in many ways, better than anything we have known.

The Wrong Metrics

This is the core argument I want to make; if I'm right, it reframes everything.

Every data point used in Citrini's article to build its argument—the 10.2% unemployment rate, the 38% drop in the S&P 500, the surge in San Francisco mortgage delinquencies, the stagnation of money velocity—is all denominated in the old system. Every metric is native to the economy we have been living in. The one built around human labor input, conditions of material scarcity, and GDP as the scorecard.

It's understandable that the authors look at these readings and see disaster. But what if these indicators are not recording the death of the economy? What if they are recording the death of an 'economic measurement framework' that can no longer describe the reality is becoming?

Think of it another way. At the heart of Citrini's article is a powerful concept: 'Ghost GDP.' Output that appears in the national accounts but never circulates in the real economy. They present it as evidence of dysfunction. But I would flip this entirely. Ghost GDP is not a bug; it's a signal. It's telling us that GDP itself is breaking down as a meaningful indicator of the state of things. The instrument is failing, and Citrini is mistaking the failing instrument's readings for the true condition of the patient.

In my research on post-human economics, I have argued that as we transition to an economy built on automated inputs and extreme abundance, GDP becomes incoherent. It cannot capture an economy where the cost of many goods and services is trending towards zero—unevenly, across different domains, but surely. It cannot capture the massive uplift in human well-being when intelligence is hyper-abundant and nearly free. It certainly cannot capture the emergence of 'Autonomous Economic Activity'—where AIs transact with other AIs—which has no substantive link to human labor markets at all.

In the post-human economy, GDP is not a coherent measure of anything. So, what metrics should we watch?

Intelligence Output Per Unit Energy

This is my answer; this idea is at the core of my thinking about the future post-human economy.

In the coming economy, the most coherent measure of prosperity will be intelligence output per unit of energy. How efficiently is our civilization converting energy into useful intelligence?

This is the metric that resolves the paradox at the heart of the Citrini scenario. Because at the very moment their scenario shows GDP shrinking, the S&P plummeting, and unemployment soaring, intelligence output per unit of energy is going vertical.

Think about what is driving the crisis Citrini predicts. AI models are getting more powerful, compute costs are falling, and inference costs are crashing through the floor. Energy systems managed by AI are becoming more efficient. Each of these forces—the very ones destroying the old economic metrics—is simultaneously sending 'intelligence output per unit energy' soaring into the stratosphere.

This is the key insight: there are two lines on the chart. One line—GDP, employment, consumer spending—is going down; the other line—intelligence output per unit energy—is going up exponentially. Citrini's article stares only at the descending line and concludes we are in crisis. My contention is that the ascending line is the real signal, and the descending signal is just the noise of the old system dying.

In a world where intelligence becomes hyper-abundant, everything is downstream of better, more abundant intelligence. Scientific breakthroughs, new materials, advanced medicine, cheaper energy, better infrastructure, more efficient manufacturing—all of it flows from the same source: our relentlessly increasing ability to convert energy into intelligence.

Citrini's article looks at a GPU cluster in North Dakota and says: that machine just destroyed 10,000 white-collar jobs in Manhattan. I look at the same GPU cluster and say: that machine just collapsed the cost of drug discovery, materials science, legal services, education, energy management, and software development. Both observations are true, but that article stares only at the income side of the ledger and barely glances at the expenditure side.

And this is the deeper error.

Radical Prosperity

Yes, output is decoupling from the labor market. Citrini is right about that. But the same force that is destroying wages is also destroying costs. When AI pushes the price of legal services towards near-zero, you no longer need a $180,000 annual salary to afford a lawyer; when AI collapses the cost of medical diagnosis, you don't need expensive health insurance to get a diagnosis. When coding agents make software nearly free, the $500,000 annual SaaS renewal fees that Citrini frets about are not just a problem for the vendor—they are a massive saving for the buyer.

Viewed through the lens of GDP, this looks like the collapse of the consumer economy;但从另一个角度看, this is the birth of Deflationary Prosperity. It is wealth through abundance. Even as nominal incomes fall, real purchasing power explodes. The obtaining power of the average person surges in ways traditional metrics cannot capture.

If a person earns $50,000 but lives in a world where AI has pushed the cost of healthcare, education, legal advice, financial planning, software, entertainment, and creative services to near-zero, are they richer or poorer than the person earning $180,000 in 2024?

Citrini's paper never considers this. It tracks the fall in wages but not the synchronous fall in the 'cost of living.'

I can hear some readers screaming at me. I am not naive. There are important goods and services whose costs will not fall quickly, or perhaps at all, like housing, physical food, and (for a time at least) energy. This process will be wildly uneven. Some areas will see cost collapses within years, others may take a decade or more. This transition will be painful for many, a key social reality we must address, the depth of which is beyond this article, but I have written about it. I have written about the 'hairpin turn' ahead and warned that a 'Fourth Turning' moment is highly likely. There will be social and political turmoil, I do not disagree.

The Foundation Layer Flywheel: The Real Braking Mechanism

But Citrini's scenario paints this transition as a one-way spiral to ruin. They say there is no natural brake here, no bottom to the displacement loop.

I disagree. The braking mechanism is abundance itself.

This brings me to the engine I call the Foundation Layer Flywheel.

Back in 2023, I wrote about the deep symbiosis between AI and clean energy. AI needs vast amounts of energy, but AI is also the only technology that can manage the kind of incredibly complex, distributed energy systems we are building. More AI unlocks more energy, more energy powers more AI. Round and round it goes.

This flywheel is the foundation of the entire Exponential Age. It underpins everything that happens above it. This is also why there is a natural brake on Citrini's displacement spiral—and their model fails to account for it.

As intelligence output per unit energy increases, the flywheel spins faster. Cheaper, more abundant AI makes the energy system smarter; smarter energy systems provide cheaper energy; cheaper energy makes AI cheaper. Cheaper AI then permeates downstream into everything: cheaper materials science, cheaper manufacturing, cheaper healthcare, cheaper infrastructure.

Citrini's article imagines a negative feedback loop: AI destroys jobs -> unemployed workers consume less -> companies buy more AI -> repeat, with no natural brake.

But running in parallel is a positive feedback loop, and it is at least as powerful: AI gets smarter -> energy gets cheaper -> intelligence output per unit energy rises -> the cost of everything downstream of intelligence falls -> material conditions of life improve even as nominal GDP shrinks.

Which loop will dominate? That is the question. In my view, the positive loop has the laws of physics on its side. It is driven by the exponential improvement in converting energy to intelligence—a curve that has been steepening for years and shows no sign of slowing. The negative loop, by contrast, is driven by institutional and political inertia: slow-moving mortgage markets, fiscal policy, and labor market adjustments. These are real and will cause real pain, but they are not immutable laws of nature. They are human constructs, and humans can change them.

AI and Robotics Are Part of Demographics

There is another point, completely missed by Citrini's article, that is one of the most important macro forces of our time.

Demographics.

Developed nations are running out of workers. The working-age population is shrinking sharply in the US, Europe, Japan, South Korea, and China. This is the demographic doom loop I often write about. Fewer babies, longer lives, inverted population pyramids—nothing like this has existed in human history before.

As Raoul has long made clear, the golden rule is: GDP growth = population growth + productivity growth + debt growth. Population growth has vanished. It's been gone for a while. That means the only way to keep the GDP game going is to add debt. We borrow from tomorrow to keep today's party going.

Now think about what happens when AI and humanoid robots enter this environment. Citrini's article frames the arrival of machine intelligence as an invasion of a healthy labor market. AI crashes through the gates, and millions of workers are cast aside.

But that's not the reality. AI is entering a world that desperately needs it. We are running out of people. The working-age population in the Global North is shrinking fast, and without AI and robotics, GDP growth would be headed for structural decline anyway.

Kevin Kelly calls what's coming 'The Handover.' As the human population peaks and declines, billions of AI agents and tens of millions of humanoids come online to fill the gap. We are handing the economy over to non-human actors.

This doesn't erase the pain of individual transitions. Real people losing real jobs face real difficulties, and we need to face that. But at the macro level, AI and robotics are not replacing workers; they are filling a demographic hole that was about to swallow the entire economy.

Citrini's scenario imagines a world where AI destroys the job market and no one can find work. But what if by 2028 reality looks more like this: AI and humanoids fill millions of positions left vacant by labor shortages, and humans displaced from knowledge jobs—painfully, but with support—migrate into the emerging economy I'm about to describe?

The Human Residue

Because this is what Citrini's article never considers. As the old economy contracts, a new economy is self-organizing from the ground up.

I've written about the rise of the independent industrialist. Sam Altman talks about the one-person billion-dollar company. In some fields, AI tools and agents allow a single highly productive individual to output what required hundreds of employees before. We will see millions of these new economic actors—independent operators and micro-teams managing vast arrays of AI agents—creating enormous value in ways the old economic framework could not foresee.

Anthropic's research into how people use Claude reveals the contours of this future. Software development. Consulting. Financial services. Marketing. Content creation. In every field, high-capability individuals armed with AI are becoming one-person enterprises. This is new economic activity. And much of it will happen outside the structures monitored by Citrini's work.

But a deeper shift is underway. As machine intelligence takes over all the mental work—coding, legal documents, financial analysis, data processing—economic value migrates up the Maslow hierarchy to levels only humans can provide.

I call this 'The Human Residue.' The parts of value creation that require being human. The attention, empathy, and recognition from another person who truly sees you. It is art and narrative from a real, lived experience. It is the counselor who guides you through a stressful move, the guide who helps you navigate a life crisis, the community builder who creates a place you feel you belong.

When AI does all the paperwork, what becomes scarce? Feeling. Connection. Meaning. A vast new economy will form around these irreducible human outputs. It will command enormous value. But it won't be captured in GDP, nor tracked by the metrics in Citrini's article.

This is the economy emerging on the other side of the singularity. Not a dead zone of mass unemployment. But an old economy being composted to nourish something new, strange, and in many ways richer.

System Transition

Let's put this all together.

Citrini's article asks a core question: what happens when a scarce input (intelligence) becomes abundant?

This is exactly the right question. Throughout modern economic history, human intelligence has been that scarce, premium-priced input. They argue that premium is dissipating, and that is true. In more and more tasks, machine intelligence is becoming a competent and rapidly evolving substitute for human intelligence. On this, we agree.

But Citrini concludes that the dissipation of the human intelligence premium is a 'crisis.' I believe it is precisely the 'transition.' They are staring at the dissolving caterpillar and screaming that the creature is dying. In a sense, they are right—the caterpillar is dying. But inside the chrysalis, something else is forming.

What is forming is a Post-human Economy. An economy where intelligence is no longer scarce but abundant like air. An economy where the cost of knowledge work and eventually much physical production will trend towards zero—not overnight, not evenly across domains, but relentlessly. An economy where the fundamental measure of prosperity is no longer how much nominal economic output we produce, but how efficiently we convert energy into intelligence. An economy where the value humans exchange with each other migrates to a deeper place: empathy, meaning, connection, creativity, and the sheer experience of being alive with other conscious beings.

We are not heading for a 'Global Intelligence Crisis'; we are stepping into a 'Global Intelligence Transition.' We are entering a completely new economic system, one we are all struggling to understand. Yes, the transition will be bumpy, perhaps even violently turbulent. There will be chaos, pain, and political shock. A 'Fourth Turning' is likely real. Some of what Citrini describes—job losses, SaaS industry collapse, friction going to zero—is probably coming, and faster than most think.

But viewed from the longer timeframe I'm observing—ten to twenty years, not a mere two—their conclusion begins to look untenable. A Great Recession rivaling the GFC with a 57% drop and no natural brake? That conclusion depends entirely on an assumption: that those old metrics still reflect the truth of the system.

I don't think they do. There will be real pain, but that pain is characteristic of the transition process, not evidence that the destination is inevitably disaster.

There are two lines on the chart:

  • GDP is going down.

  • Intelligence output per unit energy is going up.

One of these lines is the real signal, and the other is just the noise of a dying measurement system.

If we want to understand what is happening around us now, we need to make sure we are watching both lines.


Twitter:https://twitter.com/BitpushNewsCN

Bitpush TG Discussion Group:https://t.me/BitPushCommunity

Bitpush TG Subscription: https://t.me/bitpush

Original link:https://www.bitpush.news/articles/7614423

Câu hỏi Liên quan

QWhat is the core argument of Citrini Research's paper 'The 2028 Global Intelligence Crisis'?

ACitrini Research argues that an abundance of AI-driven intelligence will destroy the income side of the economy (wages, jobs, consumer spending), triggering a chain reaction of financial collapse, including a 38% drop in the S&P 500 and 10.2% unemployment, with no natural braking mechanism to stop the downward spiral.

QAccording to David Mattin, what is the fundamental flaw in Citrini's analysis?

AMattin argues that Citrini's analysis uses outdated economic indicators from the 'human economy' (like GDP, unemployment rates) to measure the emerging 'post-human economy,' mistaking the breakdown of these old measurement tools for the breakdown of the entire economic system itself.

QWhat new metric does David Mattin propose as a more coherent measure of prosperity in a post-human economy?

AMattin proposes 'Intelligence output per unit energy' as the key metric, which measures how efficiently our civilization converts energy into useful intelligence. He argues this metric is soaring exponentially even as traditional indicators like GDP fall.

QHow does Mattin's 'Foundation Layer Flywheel' concept act as a natural brake on the economic collapse Citrini predicts?

AThe Foundation Layer Flywheel describes the symbiotic relationship between AI and energy: cheaper, more abundant AI makes energy systems more efficient, and cheaper energy makes AI even cheaper and more powerful. This creates a powerful positive feedback loop that drives down the cost of nearly everything downstream, acting as a deflationary brake and creating prosperity that offsets income destruction.

QWhat does Mattin mean by the 'human residue' in the future economy?

A'Human residue' refers to the economic value that migrates to uniquely human traits—such as empathy, connection, meaning, creativity, and the experience of conscious existence—once AI handles all intellectual and analytical tasks. A new economy will form around these irreducibly human outputs, creating value that traditional metrics cannot capture.

Nội dung Liên quan

Làm thế nào để khiến bản thân trở nên không thể bị thay thế bởi trí tuệ nhân tạo

**Tóm tắt: Làm thế nào để trở nên không thể bị thay thế bởi AI** Bài viết phản đối việc than vãn về AI và thay vào đó đề xuất một giải pháp căn cơ: trở thành một "siêu cá nhân" không thể bị thuê mướn. Mối đe dọa thực sự không phải là AI, mà là tình trạng "nô lệ lương thưởng" – phụ thuộc hoàn toàn vào người khác để sinh tồn, làm công việc nhàm chán mà không có mục đích. Để thoát khỏi vòng luẩn quẩn này và phát triển mạnh trong kỷ nguyên AI, bạn cần trau dồi 5 yếu tố then chốt: 1. **Tính tự chủ:** Khả năng hành động mà không cần chờ chỉ thị. 2. **Khiếu thẩm mỹ:** Khả năng nhận biết điều gì thực sự có giá trị. 3. **Khả năng thuyết phục:** Thu hút sự chú ý và sự công nhận. 4. **Sự kiên trì:** Không sợ thất bại, xem đó là bài học. 5. **Khả năng lặp:** Điều chỉnh dựa trên phản hồi để tiến tới mục tiêu. Giải pháp là đầu tư vào sự nghiệp của chính mình. Trong khi AI giỏi tạo ra "tài sản" (nội dung, code), nó không thể thay thế được khả năng phân biệt thứ gì đáng để tạo ra, làm cho mọi người quan tâm và kiên trì theo đuổi. Trong hai kỹ năng đòn bẩy mạnh mẽ là **Code (Lập trình)** và **Media (Nội dung)**, bài viết nhấn mạnh **Nội dung** quan trọng hơn. Giá trị của nội dung là chủ quan và đòi hỏi sự am hiểu, trải nghiệm mà AI khó có được, tạo không gian cho các cá nhân sáng tạo thực sự. **Cách bắt đầu (Bài tập 15 phút):** 1. **Khai thác nguyên liệu thô của bạn:** Xác định chủ đề bạn am hiểu sâu, vấn đề bạn tự giải quyết được, hay sở thích đặc biệt từ nhỏ. 2. **Xác định "trục phản biện" của bạn:** Tìm ra quan điểm độc đáo của bạn – những điều bạn tin là đúng nhưng số đông lại sai trong lĩnh vực của mình. 3. **Xuất bản ý tưởng đầu tiên:** Kết hợp câu trả lời từ bước 1 và 2, tạo ra một nội dung (bài đăng, video) và đăng nó lên. Hành động này mang lại phản hồi thực tế, bắt đầu quá trình học hỏi, lặp lại và phát triển kỹ năng thuyết phục. Bằng cách xây dựng một sự nghiệp xoay quanh con người thật, trải nghiệm thật và góc nhìn độc đáo của mình thông qua nội dung, bạn có thể tạo ra giá trị mà AI không thể sao chép, từ đó trở nên không thể thay thế.

marsbit1 giờ trước

Làm thế nào để khiến bản thân trở nên không thể bị thay thế bởi trí tuệ nhân tạo

marsbit1 giờ trước

Nhờ việc tung xúc xắc, chìa khóa Bitcoin được lưu trữ offline, nhưng không phải ai cũng muốn làm điều này

Cảm biến từ cuộc tranh cãi gần đây xung quanh lỗ hổng trong ví phần cứng Coldcard, bài viết thảo luận về phương pháp tạo seed (cụm từ khôi phục) cho ví Bitcoin bằng cách xúc xắc vật lý. Mỗi lần xúc xắc công bằng cung cấp khoảng 2,6 bit entropy (thước đo tính ngẫu nhiên). Để đạt mức entropy an toàn cho một seed 12 từ (128 bit), cần khoảng 50 lần xúc xắc; Coldcard khuyến nghị 99 lần để đạt mức bảo mật cao hơn. Lợi thế chính của phương pháp này là tách biệt hoàn toàn với bất kỳ lỗi phần cứng hoặc phần mềm nào trong trình tạo số ngẫu nhiên của thiết bị, từ đó bảo vệ seed chính của ví. Tuy nhiên, bài viết cảnh báo rằng trong sự cố Coldcard, các chức năng phụ khác của thiết bị (như tạo ví giấy, khóa đa chữ ký, mật mã phiên USB) vẫn có thể bị ảnh hưởng nếu chúng dựa vào trình tạo số lỗi, ngay cả khi seed chính được tạo an toàn bằng xúc xắc. Nhược điểm lớn của việc dùng xúc xắc là quá trình thủ công, dễ xảy ra sai sót, tốn thời gian và không thực tế cho đa số người dùng mới. Người dùng có thể ghi chép sai, sử dụng xúc xắc gian lận, hoặc để lộ chuỗi kết quả. Do đó, mặc dù có nền tảng toán học vững chắc, phương pháp này đòi hỏi sự tỉ mỉ cao và không phải là giải pháp khả thi cho việc áp dụng Bitcoin rộng rãi. Bài viết kết luận rằng mục tiêu dài hạn vẫn là phát triển phần cứng/phần mềm tạo số ngẫu nhiên mạnh mẽ và đáng tin cậy, trong khi vẫn giữ phương pháp thủ công như một tùy chọn cho người dùng có kinh nghiệm. Cuối cùng, bài viết đưa ra khuyến nghị cho chủ sở hữu Coldcard: cập nhật firmware, kiểm tra các chức năng phụ đã sử dụng và xem xét các biện pháp bảo mật bổ sung như ví đa chữ ký kết hợp nhiều nhà sản xuất để giảm thiểu rủi ro từ một điểm yếu đơn lẻ.

cryptonews.ru4 giờ trước

Nhờ việc tung xúc xắc, chìa khóa Bitcoin được lưu trữ offline, nhưng không phải ai cũng muốn làm điều này

cryptonews.ru4 giờ trước

Michael Saylor tuyên bố, bản cập nhật Bitcoin mà ông phản đối đã không thể được thông qua!

Michael Saylor tuyên bố rằng về mặt toán học, BIP-110 không thể đạt ngưỡng hỗ trợ 55% từ các tình nguyện viên trong chu kỳ điều chỉnh độ khó khai thác Bitcoin hiện tại. Dữ liệu của ông cho thấy, trong tổng số 946 khối được tạo ra tính đến khối 960.561, chỉ 24 khối chứa tín hiệu ủng hộ BIP-110 trong trường phiên bản tiêu đề khối. Tất cả các tín hiệu này đều đến từ thợ đào DATUM chia sẻ phần thưởng thông qua nhóm khai thác OCEAN, và không có tín hiệu nào từ các thợ đào bên ngoài OCEAN. Saylor nhấn mạnh rằng trong tình huống này, BIP-110 sẽ không đạt được mức hỗ trợ tự nguyện 55% trong chu kỳ xem xét, và các tín hiệu hiện tại không thể được coi là sự đồng thuận chung của các thợ đào. BIP-110 là một đề xuất nhằm gây khó khăn cho việc thêm ảnh, văn bản hoặc các loại dữ liệu lớn khác vào mạng Bitcoin, ngoài giao dịch chuyển tiền. Người ủng hộ cho rằng Bitcoin chỉ nên được dùng để chuyển tiền và không nên làm tắc nghẽn mạng bằng dữ liệu không cần thiết. Tuy nhiên, Michael Saylor phản đối BIP-110. Ông lập luận rằng mạng Bitcoin không nên quyết định giao dịch nào là cần thiết, luật lệ không nên thay đổi theo ý muốn của một số ít người, và tỷ lệ hỗ trợ cao có thể không phản ánh đúng sự ủng hộ thực sự của thợ đào do một phần mềm tự động hóa quá trình báo hiệu.

cryptonews.ru5 giờ trước

Michael Saylor tuyên bố, bản cập nhật Bitcoin mà ông phản đối đã không thể được thông qua!

cryptonews.ru5 giờ trước

Số lượng bình luận tiêu cực về Bitcoin đạt mức cao nhất lịch sử: Điều này có nghĩa là gì?

Công ty phân tích tiền mã hóa Santiment thông báo rằng tình cảm tiêu cực đối với Bitcoin trên mạng xã hội đã đạt mức cao kỷ lục. Tỷ lệ bình luận tích cực/tiêu cực về Bitcoin trên các nền tảng như X, Reddit, Telegram đã giảm xuống mức thấp nhất kể từ khi công ty triển khai hệ thống giám sát hiện đại. Một lỗ hổng bảo mật trong phần sụn của ví cứng Coldcard được xác định là nguyên nhân chính gây lo ngại, làm dấy lên nghi ngờ về tính an toàn của phương pháp lưu trữ lạnh vốn được coi là an toàn nhất. Santiment lưu ý rằng, khác với các cuộc khủng hoảng lớn trước đây như sự sụp đổ của FTX hay Mt. Gox, cuộc thảo luận lần này tập trung vào rủi ro bảo mật phần cứng và ví lạnh nói chung, thay vì chỉ trích các sàn giao dịch tập trung. Theo dữ liệu, hiện chỉ có 0,58 bình luận tích cực cho mỗi bình luận tiêu cực về Bitcoin, cho thấy "nỗi sợ hãi" đang áp đảo "lòng tham" ở mức đáng kể. Mặc dù dữ liệu chỉ trong một ngày, mức độ hoảng loạn hiện tại được đánh giá là cao hơn cả đỉnh lo ngại về chiến tranh đầu năm và các cuộc khủng hoảng tiền mã hóa lớn trong quá khứ.

cryptonews.ru5 giờ trước

Số lượng bình luận tiêu cực về Bitcoin đạt mức cao nhất lịch sử: Điều này có nghĩa là gì?

cryptonews.ru5 giờ trước

Giao dịch

Giao ngay
活动图片