Amid the DeAI Craze, Is EMC the Value Oasis in This Bull Market?
Source: EMC Community Enthusiast
What kind of AI project can make waves in the crypto world? The answers can be diverse. But first, let's set aside more fantasies, fiction, and narratives, and look for answers from the world's top-tier and most valuable AI companies.
NVIDIA, one of the world's only three companies with a market cap exceeding three trillion dollars, is not only the cornerstone of global AI applications but also the undisputed overlord of GPU power. So, how does NVIDIA make money? The answer may not be complicated— the core of AI projects lies in computing power, especially in the battle for GPU power. The current logic of the AI industry is nothing more than a different form of the "selling shovels" business.
Recently, NVIDIA released its financial report for the third quarter of 2024, with data still impressive: $350.8 billion in revenue, a 94% year-on-year increase, surpassing analysts' expectations of $332.5 billion and exceeding the company's own performance guidance ($318.5 billion to $331.5 billion). Net profit reached $19.309 billion, compared to the second quarter's "300 billion in revenue, $165.9 billion in net profit," setting a new record high. These data not only exceeded market expectations but also once again affirmed NVIDIA's irreplaceability in the field of AI power.
Not only in the traditional financial sector, but back in the crypto world, the AI MEME craze is also sweeping through. From the initial AI MEME to the current AI Agents, an increasing number of large-scale AI-based applications are emerging, attracting a lot of capital and developer attention.
CZ, who was recently released from prison, is also very focused on AI. His recent tweets also mentioned the potential of combining AI and blockchain: "AI tagging (or AI data processing) is very suitable to be completed on-chain. It can leverage global low-cost labor, eliminate geographical bias, and enable instant payments through cryptocurrency." At the same time, Musk also announced that xAI will establish an AI game studio with the goal of "making games great again."

For a market like the crypto world full of "benefit mythology," replicating a 1:1 NVIDIA may be a bit too idealistic, after all, this is a behemoth with a high level of technological accumulation and market position. But if we replicate the "routine" of NVIDIA's profitability— that is, expanding the market through the core competitive advantage of powerful computing power and GPUs, perhaps it is not that difficult. Because, behind all AI Agents projects, there are massive AI models supporting them, and behind these models is the support of GPU power. Therefore, computing power remains the core driving force for the success of AI projects in the crypto world.
Crypto AI Computing Power Oasis
In this context, a project with GPU innovation that basically covers all the AI applications and possibilities you can think of in the current market: Layer 1, computing power leasing, decentralized AI+Web3 creation platform, and an AI agent project—Edge Matrix Chain, can be considered a very good ambush target, even a value oasis.
Lifting the "Heart" of AI
Traditional Layer 1 blockchains have demonstrated powerful capabilities in many areas in the past, but when you shift your focus to AI applications that require massive computational power, you will find that their computational limits cannot support these huge demands at all. AI, especially behemoths like Large Language Models (LLMs), require not just ordinary computing power but the unmatched computing power of GPUs. In the future of AI, without GPUs, nothing else matters.
EMC recognized this and thus developed a distributed GPU computing network aimed at providing sufficient computing power support for AI applications. Each EMC node not only provides ordinary computing power but offers powerful GPU support, allowing developers to run complex AI models seamlessly in a decentralized environment. What's even more astonishing is that all this computation can be done at a cost lower than traditional cluster computing. Yes, handling massive tasks is no longer the exclusive domain of cloud computing giants and no longer requires exorbitant fees.
Furthermore, EMC's network architecture further disrupts the limitations of traditional blockchains. It not only has the advantage of low latency and high throughput to ensure ultra-fast data storage and efficient retrieval, but more importantly, it provides an ideal stage for AI applications that require frequent data exchanges. In addition, EMC adopts a modular design, allowing developers to flexibly build protocol layers, network layers, application layers, and asset layers according to their needs. This design not only allows developers to focus on innovation without worrying about the limitations of the underlying architecture but also lays the foundation for the widespread application of decentralized AI.
When it comes to innovation, EMC goes beyond the technical level and brings a revolutionary product—the EMC Hub. This decentralized AI computing power scheduling platform provides unprecedented support for AI developers. Through EMC Hub, developers can build commercial AI services similar to Midjourney in just a few hours, at a cost that is only 30% of the traditional development approach.
Moreover, EMC has closely collaborated with JarvisBot to further advance decentralized AI capabilities in the market. JarvisBot, as an AI agent strongly supported by the EMC network, has successfully integrated into the TON ecosystem, offering users a diverse range of AI services: from article writing and image generation to content summarization and video creation. This AI agent was launched in June this year, putting it ahead of the market trend by almost half a year.
He Who Owns the GPU Owns the World, Possessing the Absolute Hard Currency
NVIDIA's GPU is almost the lifeblood of the entire industry, especially the NVIDIA H100 GPU, currently the latest GPU on the market.
Due to its critical importance, GPUs have also become an extremely scarce resource. Especially as AI has entered the "era of large models," the demand for GPUs has skyrocketed, making the market's supply-demand relationship increasingly tense. Obtaining high-performance GPU resources, especially top-tier chips like the NVIDIA H100, has become a form of "hard currency." For many AI companies, especially those in the early stages of startups, acquiring sufficient GPU resources is extremely challenging.
This also explains why large investment firms, such as a16z, are willing to stockpile large quantities of H100 GPUs at a high cost, renting out GPU resources to portfolio companies in exchange for equity or other benefits. It can be said that he who owns the GPU owns the world. However, as market demand is so urgent and supply is extremely limited, the "scarcity" of NVIDIA H100 GPUs has also provided investment opportunities for many industry pioneers.
Against this backdrop, EMC (Edge Matrix Chain) has utilized its innovative Layer 1 network to bridge GPU computing power with the DeFi platform, introducing a groundbreaking GPU tokenization solution. This solution not only addresses the liquidity issue of GPU computing power but also transforms these "high-performance hardware" into tradable assets, enabling more users to participate in this AI computing power ecosystem.

At the Token2049 conference on September 19, 2024, EMC showcased an incredibly innovative NVIDIA H100 SuperPOD Live Demo.
This modified machine, the NVIDIA H100 SuperPOD, consists of 1024 H100 GPUs, boasting powerful computing capabilities to provide substantial computing support for decentralized platforms. This computing power can not only sustain complex AI model training but also earn an annual income of up to $15 million through a GPU resource leasing model.

Through this demonstration, EMC not only showcased the decentralization of AI computing resources but also revealed how, through tokenizing GPU power, it is creating new economic opportunities and revenue streams for global users. What is this concept? This is not just the ultimate presentation of AI technology but also a deep exploration of the commercialization of AI computing power.
But that's not the most exciting part yet. By integrating these GPU computing resources into a decentralized platform, EMC has proposed the idea of tokenizing this high-performance hardware. In other words, global investors and developers can, through blockchain technology, break down these computing resources into small units, turning GPU power into a tradable asset. Consequently, anyone can purchase and stake this GPU power on the EMC platform, receiving approximately a 12% Annual Yield Rate (AYR) and directly participating in the AI computing wave.
Currently, the RWA assets offered by the EMC platform mainly come from top-tier high-performance GPUs such as NVIDIA A100, H100, and other models like 4090 and 3090. These GPU computing assets are provided by multiple ComputeDAO members, including significant participants like NWG and exaBITS.
This innovation not only further decentralizes AI infrastructure but also, through tokenization, breaks the traditional "high barrier to entry" of GPU resources that originally belonged to a few large enterprises, giving every ordinary user the opportunity to participate. Through EMC's technology, AI computing power has become a liquid asset rather than expensive hardware accumulated in the hands of certain institutions.
Why Is EMC Undervalued?
After introducing what EMC is doing and its unique selling points, let's return to the most crucial question: why do I think EMC is undervalued?
Let's start with the data. EMC currently spans multiple innovative fields: Layer 1 blockchain, computing power leasing, a decentralized AI+Web3 creation platform, and AI agent tools. Just looking at these concepts and business modules, its annual revenue has already reached $15 million. Considering all these highlights together, what do you think would be a reasonable market value for EMC?
If you're still having trouble giving an exact answer, let's look at the funding and valuation provided by VCs: On August 30th this year, the multi-chain infrastructure Edge Matrix Chain completed a $20 million financing round, led by P2 ventures and Amber Group.
This means that, from the perspective of these venture capital firms, EMC's valuation is approximately around 200 million US dollars. (Generally, in venture capital, when giving a valuation, a premium of about 10 times is added, and those familiar with investment know how deep the rabbit hole goes hh)
I believe this valuation is reasonable, especially considering that the current market cap of the GPU cloud mining project Ionet, which focuses solely on GPU hash power rental, is currently 300 million US dollars. And what about EMC? Not only does it have GPU hash power leasing, but it also has a whole Layer 1 blockchain network, in addition to a decentralized AI content creation platform and AI agent tools, which are four killer narrative enhancers.
Now, let's take a look at the strength behind EMC. Recently, EMC collaborated with DoraHacks to hold a high-profile hackathon, offering a direct grant of 10 million US dollars and a 1 million US dollar prize pool to support innovative projects.
With such a project, setting aside 300 million US dollars for a moment, even if we talk about just 100 million US dollars, I don't think it's unreasonable at all, especially when nowadays there are countless meme coins launching with a quick 100 million US dollars from Pumpfun. In comparison, EMC's comprehensive advantages in technology, team, market outlook, and more can easily support a higher valuation. Currently, Edge Matrix Chain has a market cap of 5 million US dollars, surely harboring hidden alpha.

What's more interesting is that as I delved into researching the EMC project, I unexpectedly discovered that EMC had appeared in an article about GPUs in The Wall Street Journal. This article had sparked widespread attention at the time, and I had read it myself, though I didn't particularly focus on the mention of EMC back then. The title was "China's AI Engineers Are Secretly Accessing Banned Nvidia Chips".

To address the shortage of hash power, Chinese AI engineers, through partnerships with international hash power suppliers, used decentralized GPU networks to access the necessary resources, and the lawyers stated that buyers, sellers, and brokers participating in accessing banned Nvidia AI chips in this manner did not violate any laws.
EMC was a key platform in this, allowing developers to access the world's top H100 GPU resources at a lower cost and with greater flexibility, widely used for training large language models (LLMs). This is undoubtedly a huge advantage for developers looking to participate in advanced AI projects.
Meanwhile, The Wall Street Journal also mentioned some technical details, such as how EMC uses smart contracts to protect transaction anonymity and ensure fair distribution of computing power. These details not only demonstrate the depth of EMC's technology but also reflect its forward-thinking approach to data privacy and security. Especially in a world where the demand for data security and privacy is increasingly growing, EMC's decentralized model appears to align more with future trends.
Moreover, The Wall Street Journal also highlighted some technical details, like how EMC safeguards transaction anonymity through smart contracts and ensures fair allocation of computing power. These details not only showcase the technical depth of EMC but also illustrate its forward-looking considerations regarding data privacy and security. Particularly in the context of a global rise in demand for data security and privacy protection, EMC's decentralized model seems even more in line with future development trends.
Nowadays, well-known AI projects have become too large in market capitalization. Once everyone realizes the potential of these projects, can they still be considered alpha?
What truly deserves exploration are often projects with lower market capitalization. Currently, EMC is undoubtedly such a hidden alpha and value oasis. If the project team collaborates on some marketing activities, waits for the right opportunity, EMC should be able to perform well.
This article is contributed content and does not represent the views of BlockBeats.
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Debunking the AI Doomsday Myth: Why Establishment Inertia and the Software Wasteland Will Save Us
Editor's Note: Citrini7's cyberpunk-themed AI doomsday prophecy has sparked widespread discussion across the internet. However, this article presents a more pragmatic counter perspective. If Citrini envisions a digital tsunami instantly engulfing civilization, this author sees the resilient resistance of the human bureaucratic system, the profoundly flawed existing software ecosystem, and the long-overlooked cornerstone of heavy industry. This is a frontal clash between Silicon Valley fantasy and the iron law of reality, reminding us that the singularity may come, but it will never happen overnight.
The following is the original content:
Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.
In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.
When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."
Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.
A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.
I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.
Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.
But everyone overlooks one thing: the current state of these software products is simply terrible.
I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.
From a broader perspective, almost all existing software is garbage, which is an undeniable fact. Every tool I've paid for is riddled with bugs; some software is so bad that I can't even pay for it (I've been unable to use Citibank's online transfer for the past three years); most web apps can't even get mobile and desktop responsiveness right; not a single product can fully deliver what you want. Silicon Valley darlings like Stripe and Linear only garner massive followings because they are not as disgustingly unusable as their competitors. If you ask a seasoned engineer, "Show me a truly perfect piece of software," all you'll get is prolonged silence and blank stares.
Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.
I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.
This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.
Of course, labor reallocation is inevitable, such as in the driving sector. As Citrini pointed out, many white-collar jobs will experience disruptions. For positions like real estate brokers that have long lost tangible value and rely solely on momentum for income, AI may be the final straw.
But our lifesaver lies in the fact that the United States has almost infinite potential and demand for reindustrialization. You may have heard of "reshoring," but it goes far beyond that. We have essentially lost the ability to manufacture the core building blocks of modern life: batteries, motors, small-scale semiconductors—the entire electricity supply chain is almost entirely dependent on overseas sources. What if there is a military conflict? What's even worse, did you know that China produces 90% of the world's synthetic ammonia? Once the supply is cut off, we can't even produce fertilizer and will face famine.
As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.
We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.
We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.
The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.
My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.
At this point, preventing the kind of market collapse Citrini imagines is actually not difficult. The U.S. government's performance during the pandemic has demonstrated its proactive and decisive crisis response. If necessary, massive stimulus policies will quickly intervene. Although I am somewhat displeased by its inefficiency, that is not the focus. The focus is on safeguarding material prosperity in people's lives—a universal well-being that gives legitimacy to a nation and upholds the social contract, rather than stubbornly adhering to past accounting metrics or economic dogma.
If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.
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