ChatGPT's Second Anniversary: Where Does the Opportunity Lie for Decentralized AI? DeepBrain Chain (DBC) Has the Answer

By: blockbeats|2024/12/08 13:45:01

ChatGPT has just celebrated its two-year anniversary since launch. In these two years, the world has seen many changes, with the resurgence of AI technology being a significant force. OpenAI quickly became a focal point, sparking concerns and controversies. Elon Musk publicly criticized OpenAI, stating that its operations were not as "open" as its name implies. From data collection to algorithms and data usage, its processes are full of opacity, resembling a "black box." While recognizing the convenience that AI technology brings to production, we now also need to realize: Can artificial intelligence technology be open-sourced?

In the traditional AI development path, high computational costs, centralized data storage, and technological barriers have placed multiple restrictions on developers. This is where the field of cryptography, or more accurately, blockchain technology, can come into play. On November 28, Google Cloud, a tech giant with abundant resources, and the crypto project DeepBrain Chain held a decentralized AI event to specifically discuss this issue and opportunity.

ChatGPT's Second Anniversary: Where Does the Opportunity Lie for Decentralized AI? DeepBrain Chain (DBC) Has the Answer

DeepBrain Chain (referred to as DBC) was established in 2017, driven by the DeepBrain Chain Foundation and the DeepBrain Chain Council to promote DBC's development. In 2021, DBC 1.0 GPU, a distributed GPU computing power network, went online, and DBC 2.0 is the world's first AI public chain. After 7 years of development, the public chain testnet was launched in August of this year, with the mainnet scheduled to go live in mid-December.

DBC 2.0: The First Decentralized AI Public Chain

The field of artificial intelligence is already highly competitive, but decentralized AI technology still has room for development in current practical applications. DeepBrain Chain, as a pioneer in the early layout of AI technology in the crypto field, has now made initial progress in technology development, mechanism design, and ecosystem construction.

How to Balance Decentralized AI Performance, Cost, and Resource Bottlenecks?

Firstly, DBC 2.0 is compatible with the EVM smart contract standard, supporting developers to issue tokens, deploy smart contracts, and develop decentralized AI applications based on its public chain. This allows any AI project to easily achieve decentralization through the DBC ecosystem and maintain long-term stable operation.

In terms of public chain performance, DBC supports 1000 transactions per second and has a block time of only 6 seconds, providing robust support for building complex AI applications. Moreover, each transaction costs less than $0.0001 in Gas fees, significantly reducing development costs. Additionally, DBC is fully compatible with the EVM, allowing existing DApps to seamlessly migrate to the platform, further lowering the technical barrier for developers.

Secondly, DBC 2.0 adopts a decentralized AI model for operation. Ethereum's founder Vitalik has warned that relying on centralized AI models may lead to users being bound by data and algorithms, while DBC 2.0 addresses this issue by supporting the fully decentralized deployment and operation of AI models.

The traditional development of decentralized AI projects typically requires funding in the tens of millions of dollars and takes 3-4 years. In contrast, DBC's efficient development tools shorten the development cycle to 3 months and reduce costs to the million-dollar level through simple API interfaces and AI container deployment functionality. More importantly, decentralized model operation effectively protects user privacy and avoids the risk of data leakage.

Thirdly, DBC 2.0 provides GPU free trials and high-performance support. By launching a token-based mining mechanism, DBC enables developers to trial GPU resources for free, significantly shortening the development cycle. The efficient resource integration capability, reducing the traditional timeline from 3 to 4 years to just 3 months, provides strong technical support for the rapid implementation of decentralized AI.

The high cost of computing power has always been the biggest bottleneck for the AI industry, especially for small and medium-sized AI companies, as the purchase and rental costs of GPU resources deter them. DBC 2.0 offers a new solution path where developers only need to issue their own token and launch a GPU mining mechanism to freely access GPU resources, breaking away from the traditional high-cost model. Furthermore, miners receive token rewards for contributing computing power. This mining incentive model not only alleviates developers' financial burden but also attracts more computing power to join, promoting ecosystem expansion. This inclusive model allows more small and medium-sized AI projects to use high-quality computing resources at low cost, thereby driving innovation and development in the AI industry.

In summary, DBC has not only optimized resource allocation but has also provided a new development platform for AI projects with efficiency, security, and low cost through decentralization and economic incentives.

From Cloud Gaming to Decentralized Inference Network, Comprehensive Coverage in Diverse Scenarios

The above has introduced some of the core advantages of DBC 2.0, a decentralized AI public chain. Building upon this, DBC has also developed a group of high-quality ecosystem projects, showcasing how to drive AI technology beyond boundaries and bring revolutionary changes to the global market.

As one of the core projects in the DBC ecosystem, DeepLink integrates AI and blockchain technology to provide a low-latency rendering solution for cloud gaming. By involving GPU providers in the "Orion Hunt" competition, DeepLink has brought over 2000 GPU nodes to DBC, not only driving the overall ecosystem's resource expansion but also paving a low-cost, high-performance path for the cloud gaming industry.

When it comes to the layout of decentralized AI models, DecentralGPT has become a star project of DBC. Positioned against OpenAI, DecentralGPT adopts an open-source approach, emphasizing data privacy and transparency, aiming to provide users with more autonomy in AI services. Its recent launch of a multi-million dollar GPU competition has not only attracted GPU providers from around the world but also further solidified DBC's resource advantage in the decentralized AI field, helping to grow the entire ecosystem.

DecentralGPT Analyst Ze Ren Li on the Value of "DecentralGPT: Decentralized AI Large Language Model" Speech

The DBC ecosystem is not limited to core technical breakthroughs but also widely covers diverse scenarios through a series of innovative projects. SuperImage uses decentralized AI for image generation, supporting various text-to-image models, allowing users to generate high-fidelity artwork in seconds, providing a new realm of possibilities for digital creation. Meanwhile, DRCpad focuses on the primary market trading of AI nodes, laying a solid foundation for DBC's decentralized AI ecosystem through project filtering and incubation of high-quality projects.

In addition, DBC's business collaboration with HYCONS CLOUD has expanded to fields such as artificial intelligence, autonomous driving, biomedicine, and cloud gaming, providing a convenient collaboration channel for enterprises and developers in need of GPU resources. This resource-sharing model lowers the barrier to computing power for various industries and drives technological inclusivity.

At the infrastructure level, DBC also provides comprehensive tool support. DBCSCAN, as its EVM browser, has launched its testnet, supporting smart contract deployment and transaction queries. DBCWallet is feature-rich, covering POS staking, governance voting, treasury proposals, etc., providing developers and users with a complete ecosystem operation platform. These tools and platforms further enhance the user experience and development convenience of the DBC ecosystem.

DBC's ecosystem AI projects also span a wide range of areas from AI financial forecasting to AI scientific exploration. For example, AIDF positions itself as a decentralized financial forecasting platform, AITalk focuses on AI conversation interaction, DeepVideo and Hyper 3D explore the potential of video generation and 3D model generation, respectively. Whether for gaming like GameNPC and GamerGPT or for education and scientific research like MathAI and BioFold, these projects together outline various perspectives of the DBC decentralized AI ecosystem.

Overview of DBC 2.0 Tokenomics

The total supply of DBC (DeepBrainChain) tokens is 10 billion, with a fixed supply that will never increase, and is expected to be fully issued in approximately 100 years. DBC adopts a deflationary model, where the GPU leasing fee paid by users will be burned based on different proportions of the total GPU count: when the network's GPU count is below 5,000, the burn rate is 30%; when it exceeds 5,000, the rate increases to 70%; and when it reaches or exceeds 10,000, the burn rate rises to 100%.

Users need to purchase DBC tokens through a trading platform or other channels to pay for GPU leasing fees. This mechanism ensures that each GPU lease will reduce the circulating supply of DBC in the market. Additionally, miners need to stake DBC to provide GPU services, with an initial stake of 1,000 DBC per GPU (currently valued at $4). As the number of GPUs increases, the total staked DBC amount will also increase accordingly. As of now, the total DBC staked by GPU miners network-wide has reached 74,680,376 tokens, accounting for 1.33% of the total issuance.

DBC POS Supernodes are required to stake DBC to receive block rewards, with the current total staked DBC across the network being 1,466,792,420, accounting for 26.14% of the total DBC issuance.

Furthermore, DBC tokens also serve as the governance token of the DeepBrainChain network. The network elects 21 committee members through a POS mechanism to collectively manage the Ecosystem Development Fund. The Committee DAO holds elections every four months, and all candidates are ranked based on the number of votes received, with each DBC being equal to one vote. The treasury funds managed by the Committee DAO are used to support ecosystem development, further driving the sustainable operation and growth of the DeepBrainChain network.

As mentioned earlier, the DBC 2.0 Testnet was launched in August. With several months passed, how is the current performance?

According to the official information, the current DBC total hash rate has reached 259,985.16, with over 1145 GPUs, a GPU leasing rate of 92.58%, and GPU leasing consuming over 113 million DBC, demonstrating the high efficiency of its resource utilization.

The more ecological applications there are, the greater the ecosystem's demand for GPU. The more DBC transactions used per day, the more DBC destroyed, and the greater the value of DBC.

Take the cloud internet cafe application as an example. Cloud internet cafe users need to purchase coins on the transaction market to use GPU. For every additional GPU, 30% of the tokens purchased from the transaction market will be destroyed. If there are 1000 cloud internet cafes, each cafe has 100 machines, each machine is used for 10 hours a day, paying $0.1 per hour, with 30% being destroyed. Tokens worth $900,000 are destroyed every month.

Based on a coin price of $0.002USDT, over 400 million coins need to be destroyed in a month. At the same time, to support 1000 cafes, 70,000 machines are needed, and an additional 7 billion coins need to be staked.

Traditional Tech Giants and the Symphony of Decentralized AI

At the intersection of artificial intelligence and blockchain technology, the DBC AI public chain is starkly contrasting with traditional decentralized computing power projects. Decentralized computing power projects mainly target centralized AI enterprises, with competitors including giants such as Google and Microsoft, providing computing power support to centralized AI through GPU rental. However, this market competition is extremely fierce, with almost all tracks dominated by centralized enterprises, resulting in an internal competition characterized by high computing costs and limited price flexibility.

In contrast, the DBC AI public chain serves AI developers, opening up a brand-new decentralized AI market. With empowering developers at its core, DBC provides infrastructure for the decentralized AI ecosystem, helping developers rapidly refactor business models, thereby avoiding the dilemma of low user migration costs and intense price competition in the centralized AI model. This strategy not only fills the gap in the decentralized AI market but also opens up a new blue ocean for AI technology exploration and innovation.

Meanwhile, DeepBrain Chain has also partnered with tech giant Google Cloud. The conference on November 28, themed "Driving the Future of Decentralized AI," showcased the interim results of this collaboration, outlining the vision of traditional tech giants collaborating with decentralized technology platforms to forge the future.

Google Cloud Solutions Architect Leon Li on "Decentralized AI: Leveraging GPU for Distributed Inference on Google Cloud" keynote speech

First of all, Google Cloud provides powerful computing power support for the DBC ecosystem. A decentralized AI network requires a large number of GPU resources, and for miners lacking GPU devices, Google Cloud's GPU service has become an important supplement, allowing more participants to easily join the DBC ecosystem. At the same time, it has brought new user growth points and business models to Google Cloud.

Google Cloud has also reduced the barrier to entry for users through its "one-click mining" feature, allowing users to run AI model images and participate in mining various AI tokens on the DBC AI public chain without requiring a deep technical background. This convenience has attracted more developers and users, further expanding the scale of the decentralized AI ecosystem and laying the foundation for the popularization of AI technology.

It is reported that DeepBrain Chain is establishing a special fund to incubate more innovative deAI projects. The success of these projects will further drive the prosperity of the DBC chain itself, forming a virtuous cycle of mutual promotion between the ecosystem and infrastructure.

Google Cloud will continue to empower DeepBrain Chain with technology and resources, while DeepBrain Chain, through the innovation of decentralized AI technology, attracts more developers and users to join, collectively driving the rapid growth of this emerging market. This partnership model injects new momentum into the decentralized AI ecosystem and opens up new development paths for the global AI market.

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.

Never Underestimate "Institutional Inertia"

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.

The Software Industry Has "Infinite Demand" for Labor

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.

Redemption of "Reindustrialization"

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.

Towards Abundance

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.

Source: Original Post Link

Have Institutions Finally 'Entered Crypto,' but Just to Vampire?

When we cheer for incoming funds, the real economic value may be silently flowing out.

A $2 Trillion Denouement: The AI-Driven Global Economic Crisis of 2028

Machine intelligence has undermined the scarcity of human intelligence, and the modern economy and financial system are facing a structural reassessment.

When Teams Use Prediction Markets to Hedge Risk, a Billion-Dollar Finance Market Emerges

Behind every football season lies a billion-dollar market.

Cryptocurrency Market Overview and Emerging Trends

Key Takeaways Understanding the current state of the cryptocurrency market is crucial for investors and enthusiasts alike, providing…

Jane Street Market Manipulation, Stripe Considering Acquiring PayPal, What's the Overseas Crypto Community Talking About Today?

WEEX × LALIGA 2026: Trade Crypto, Take Your Shot & Win Official LALIGA Prizes

Unlock shoot attempts through futures trading, spot trading, or referrals. Turn match predictions into structured rewards with BTC, USDT, position airdrops, and LALIGA merchandise on WEEX.

a16z: Why Do AI Agents Need a Stablecoin for B2B Payments?

Smart contracts will be more like corporate entities, forming long-term relationships with their vendors and partners.