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Compute Maximalism: The Symbiosis Between Bitcoin Mining and AI

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The story of human progress can be simplified into the story of increasing energy utilization. We harness energy to create order, both in terms of biology and society. Energy surpluses allow for every form of wealth creation, which in turn produce new technologies to effectively harness yet more energy. This truth has inspired famous concepts such as the Kardashev scale, which measures civilizations by their ability to harness energy resources towards useful ends.

Compute is a natural continuation of this endeavor. Modern digital technology transforms ever greater quantities of electricity into advanced value-creating processes. The most recent surge in demand for compute has come from two technologies in particular: bitcoin mining and, more recently, high performance compute (“HPC,”) in particular the use of Graphics Processing Units (“GPUs”) for artificial intelligence. The meteoric rise in energy consumption by these technologies have raised many questions: What impact will these power-hungry technologies have on our energy systems? Given their mutually voracious use of energy, what interplay will they have with each other? What do these developments mean for humanity?

We explore the essential characteristics of these respective technologies, and how they offer alternative markets for excess power that can in fact improve the efficiency of energy systems. Based on this exploration, we also argue that bitcoin mining and HPC are complementary rather than competitive. As we’ll see, their respective trade offs offer a symbiotic ability to maximize the value created from energy resources, which in turn benefits society as a whole.

In short, we argue for compute maximalism.

Energy

Modern technology depends on energy converted from a wide array of sources into electricity, and this comes with certain challenges and tradeoffs. The primary of which is limited portability.

This is due to several simple realities. Electricity requires a grid, essentially a massive series of circuits which transports energy in real time. The grid must remain in balance, meaning the generation must roughly equal demand at any point in time.

This is difficult for two reasons:

First, energy resources are not always conveniently distributed, have long lead times to develop, and have varying degrees of dispatchability.

Second, transmission as well as storage are both expensive, have similarly long lead times, and experience inherent inefficiencies. An estimated 8-15% of electricity is lost to transmission and distribution losses by the time it reaches local consumers, and this figure is even higher for long term battery storage.

The result is that it will always be cheaper and more efficient to consume generated electricity immediately at the source, than to transport it over time or space. As such, the most efficient solution isn’t to more widely and inefficiently transport electricity to where it can be used, but to move use cases to the electricity. Compute is an ideal use case for such excess electricity because it is power dense, largely portable, and scalable; we are yet to find a limit on our demand for compute. Meanwhile “meatspace” constraints are strong limiting factors for legacy forms of energy sinks like aluminum smelting and manufacturing.

Bitcoin mining in particular has emerged as such an ideal use case for local surplus power, providing a dispatchable and revenue-generating load to balance the grid. More recently the demand for High Performance Compute, in particular GPUs, is also having unignorable impacts on the utilization of energy as well. Many are expecting these two technologies to compete over the same energy resources, but as we explore the characteristics of each, the potential symbiosis will become self-evident.

Bitcoin Mining

Bitcoin mining can be thought of as a permissionless energy sink. Bitcoin’s proof-of-work consensus mechanism amounts to proof of energy-intensive computation. Miners must perform this energy-intensive computation to create new blocks of transactions, thereby earning bitcoin as a reward. It is this proof-of-work that provides global settlement assurances in a decentralized and permissionless way.

In practice, this looks like millions of computers (these days, application specific integrated circuits or “ASICs”) running in bare bones data centers around the world. One of the beautiful things about bitcoin mining is its permissionless nature; anyone anywhere in the world can plug in an ASIC. In effect, Bitcoin allows miners around the world to participate in a global energy market; whoever has the lowest cost of power has the highest margin.

This global decentralized network is part of the reason why Bitcoin’s adoption has steadily continued, as people seek a new monetary and financial system that is active 24/7, lacks a single point of failure, and sidesteps the perverse incentives of politically captured central bank monopolies.

Bitcoin mining is distinguished by the following characteristics relative to GPU/HPC infrastructure:

  • No customers
    • No customer acquisition
    • No support
  • High interruptibility
  • Low operational complexity
  • Low connectivity requirements (Less than 100MB/s)
  • Low margin (generally)

HPC

data center GPUs are the latest form of HPC, the demand for which has exploded in the previous 2 years due to quickly escalating interest in AI/ML breakthroughs which rely on it. These technologies have unlocked whole new categories of digital operations and functions which were not previously possible, with the resulting use cases only just beginning to be explored. The sudden explosion in interest in these technologies has quickly made NVIDIA, the leading manufacturer of the underlying GPUs, the most valuable company in the world.

Initially this sudden spike in demand created an intense bottleneck in the production of sufficient units of GPU itself. This however was temporary and over time continues to be alleviated by increased production, with focus quickly switching to a new bottleneck: Data center rack space with cheap power. The result has been an explosion in new data center build outs, wherever a large amount of steady power can be sourced. This has brought GPU infrastructure into competition with Bitcoin mining in many areas with excess local power.

Relative to Bitcoin mining, GPU/HPC has these is distinguished by these characteristics:

  • Customers
    • Customer acquisition
    • Customer support
  • Low interruptibility
  • High operational complexity
  • High connectivity requirements (10 – 100GBs)
  • High margin (generally)

Complimentary Competition

The demand for both Bitcoin and AI/ML technologies has taken off in the last decade, a testament to their utility to society. This demand has led to the proliferation of their respective compute resources.

To reduce operating costs, both markets seek excess power to utilize as it tends to be cheaper. This naturally resolves some of the grid inefficiencies discussed above, but it does mean that data center builders and operators will find themselves asking which form of compute to support and invest in for the same amount of available power.

Both forms of compute are energy intensive and relatively location-agnostic (barring legal or jurisdictional considerations beyond the scope of this paper) bringing them into seeming competition, but they can in fact be highly complementary tools for maximizing utilization and profit from such excess or stranded electricity.

GPU workloads have higher operational complexity, and low interruptibility, as well as higher upfront capital investment. That makes it a poor choice for taking advantage of transient surpluses of power, such as the peak window of energy production by solar panels for instance. Unlike bitcoin mining, GPUs have customers, who are typically sensitive to issues such as uptime and availability. There are exceptions, such as spot instances and frameworks which can failover from such instances, but generally speaking due to the existence of a customer the interruptibility tolerance of GPU infrastructure will never match that of bitcoin mining. Coupled with the higher capital costs and complexity, in these situations we can expect bitcoin mining to continue to grow and dominate as a highly flexible, dispatchable load to the grid.

Consistent excesses in power on the other hand, such as a largely fixed delta between the base generation capacity of hydropower or nuclear sites and their surrounding consumption, are ideal opportunities for GPU infrastructure to close the gap and establish new baseline consumption and equilibrium. These situations favor the low interruptibility of GPU infrastructure, and justify the added expenditure and operational complexity in order to secure substantially higher revenues. So long as the supporting bandwidth is available to facilitate GPU workloads (at least 10GB/s, ideally 100GB/s), these sites will always provide more profit opportunity than if allocated exclusively for bitcoin mining.

Hybrid data center Strategies

There are also strategies which can utilize both technologies in tandem to maximize revenue and return on investment.

First, bitcoin mining could be used as an initial load for energy resources before the site is suitable for high performance compute. Examples include: (1) using semi-portable modular bitcoin mining data centers to monetize power while the remaining infrastructure for an HPC data center (redundant power/internet lines, buildings, backup energy systems, etc.) is built; or (2) pioneering stranded energy resources with bitcoin mining, some of which may eventually be used for HPC. In fact, Core Scientific’s recently announced deal with CoreWeave could be viewed as an example of this occurring in the wild, as bitcoin mining led to the development of a large substation and data center shell that would eventually be used for HPC.

A second, more advanced strategy is to co-mingle HPC and Bitcoin mining workloads in tandem, using Bitcoin mining as a counter weight to balance fluctuations in HPC workload power draws. While HPC loads require reliable power, “inferencing workloads” which host production AI/ML models can fluctuate based on levels of real time use by users, leading to typical cycles of high activity and power consumption and low activity and lower power consumption. To date, the value for such HPC has substantially outweighed any inefficiencies from fluctuating power use, but the highly flexible and interruptible nature of Bitcoin mining can be used to provide stable power draw and in turn lower effective power rates, in addition to providing additional revenue for the data center overall. Some are describing this strategy as a “mullet data center,” with AI in the front and bitcoin in the back. While it is still early, this approach promises to utilize the best of both HPC and bitcoin mining to offer the most value maximized data center deployments possible with current technology.

Industry Implications

Until recently, the data center industry has been dominated by colocation providers. These providers build the facilities used to host industrial servers, and lease out space, power, connectivity, and sometimes the servers themselves to tenants. Traditionally, the majority of these tenants have been large enterprise and hyperscale cloud providers. In many cases these hyperscale and enterprise tenants have also built their own data centers to support their own growth.

Since roughly 2017 bitcoin mining has truly entered the picture at an industrial level, with entire data center complexes being built solely to support Bitcoin mining in areas with extreme deltas in produced and consumed electricity. Now in 2023 and 2024 we’ve seen shifts in the market even more notable and disruptive. With the explosion in demand for GPU infrastructure, many former colocation focused data centers have ventured into buying and hosting this GPU infrastructure themselves. Meanwhile hyperscalers are moving behind-the-meter to co-locate with large baseload power plants, seeking cheap reliable power for the new surge in HPC demand. This is particularly notable as intermittent renewables have been the most popular form of generation in recent years, primarily due to government subsidies.

We anticipate the following:

1. Continued increase in energy demand for both forms of compute.

2. New data center construction as the next bottleneck of expanding HPC footprints, with large swaths of bitcoin mining facilities being repurposed for higher margin use cases.

3. Mining hardware will relocate to the fringes, seeking remote locations and variable inefficiencies that HPC workloads are ill-suited to monetize.

4. Co-mingling of both bitcoin mining and HPC in “mullet data centers” will leverage the high revenue potential of HPC and the flexible nature of bitcoin mining to effectively balance power draw and local grids, while outcompeting traditional data center strategies.

Conclusion

When new power-hungry technologies emerge, there is often concern about their energy utilization and its externalities. Bitcoin mining and HPC are no exceptions with politicians and arm-chair technologists alike crying for their mitigation or control. But such voracious technologies represent the natural trend of human progress. In addition to the self-evident utility provided by the Bitcoin settlement network and AI/ML workloads, we can demonstrate that they can be deployed in ways that efficiently maximize new and existing energy resources to useful economic ends. 

This is a guest post by Drew Armstrong and Ariel Deschapell. Opinions expressed are entirely their own and do not necessarily reflect those of BTC Inc or Bitcoin Magazine.



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Coinbase Unveils On-Chain AI Agents On Ethereum L2 Base

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Coinbase announced a new set of fully on-chain AI agents users can create in under three minutes on its Ethereum L2 network Base.

Built with tools from Coinbase, OpenAI, and Replit, these agents can manage crypto wallets, connect with X (formerly Twitter), and perform other tasks.

This marks a significant step toward the convergence of AI and blockchain technology.

Coinbase’s Vision: A Future Where AI Agents Drive DeFi

Recently, Coinbase and its CEO Brian Armstrong showed a far-reaching vision for the new era of AI and blockchain integration. In this world, AI agents have the financial independence to spend and transact through cryptocurrency wallets.

For Armstrong, this is how DeFi becomes a game-changing place. Digital economies are reshaped through AI-driven systems autonomously without human interference.

One major limitation that really holds back AI systems from widely usage today, is financial autonomy. Opening bank accounts or keeping credit cards for AI agents is not possible. They are not able to handle resources or purchase things on their own.

That really hinders their use of important services, like cloud computing in AWS, paid APIs, and subscription-based digital tools. The lack of independent transaction capabilities greatly restricts AI system’s real-world applications.

Cryptocurrency wallets for AI agents remove various barriers that would otherwise be in place. The crypto wallet allows AI agents to interact with open marketplaces, transacting with stablecoins on Base and Coinbase’s Layer 2 blockchain.

Financial independence means that they can pay bills, subscribe to things, or buy digital assets. This capability is a breakthrough that will grant AIs the ability to act as autonomous economic entities across industries.

Because of that, Armstrong recently offered the AI agent a new crypto wallet. He acknowledged that Truth Terminal already had a crypto wallet but insinuated that its human creator controls it.

AI Agents to Drive Crypto Innovation

The integration of crypto wallets with AI agents represents one of those points of inflection in integrating AI with blockchain. This is where the dream of an AI-to-AI economy is being trailed by platforms such as Coinbase. There, even financial transactions and asset management-participation in decentralized governance, is done autonomously between AI agents, independently of human intervention.

For crypto investors, this shift in dynamics translates into an opening of new opportunities. As financial freedom is slowly bestowed upon AI agents, their ability to operate freely, independently in decentralized ecosystems unravels new ways of growth, innovation, and investment. It also promises long-term value for infrastructural and consumer use cases.

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Teuta

Teuta is a seasoned writer and editor with over 15 years of experience in macroeconomics, technology, and the cryptocurrency and blockchain industries. Starting her career in 2005 as a lifestyle writer for Cosmopolitan in Croatia, she expanded into covering business and economy for several esteemed publications like Forbes and Bloomberg. Influenced by figures like Don Tapscott and Bruce Dickinson, Teuta embraced the blockchain revolution, believing crypto to be one of humanity’s most crucial inventions. Her fintech involvement began in 2014, focusing on crypto, blockchain, NFTs, and Web3. Known for her excellent teamwork and communication skills, Teuta holds a double MA in Political Science and Law, enjoys punk rock, chablis, and has a passion for shoes.

Disclaimer: The presented content may include the personal opinion of the author and is subject to market condition. Do your market research before investing in cryptocurrencies. The author or the publication does not hold any responsibility for your personal financial loss.





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The AI compute craze for retail investors in web3

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Disclosure: The views and opinions expressed here belong solely to the author and do not represent the views and opinions of crypto.news’ editorial.

As we approach the end of 2024 and reflect on the technological advancements it brought, the buzz surrounding artificial intelligence and high-performance computing continues to overshadow all other web3 developments. As such, this year saw an overwhelming customer demand for AI products and even greater pressure on data centers to deliver AI infrastructure to boost efficiency. 

With companies racing to adopt these technologies, many have considered investing in compute resources like graphic processing unit chips, commonly used for training AI models, blockchains, autonomous vehicles, and other emerging applications. But before organizations fully embrace the exciting potential of this hardware, we need to carefully consider the complexities and challenges that come with them.

It’s true that the promise of AI is indeed enticing. Just look at the stats from OpenAI’s ChatGPT, which garners over 200 million active weekly users. From automating mundane tasks to driving sophisticated analytics, the potential of AI and large language models is vast, and these technologies are here to stay. 

The growth has just started 

Unsurprisingly, organizations are eager to gain a competitive edge through AI, leading major players like Meta and Apple to invest in the software that supports this technology. 

A recent report from Bain & Company—a management consulting company—revealed that AI workloads are expected to grow 25 to 35 percent annually over the next several years, pushing the AI-related hardware and software market to between $780 billion and $990 billion by 2027. 

However, investing in compute resources involves more than just purchasing hardware or subscribing to a cloud service. If we’re assessing some of the barriers to investing in this software, one of the biggest hurdles investors face is the initial cost.

The costs of advanced GPUs like NVIDIA’s A100 or H100 can be upwards of millions of dollars, with additional costs for servers, cooling systems, or the electricity needed to power the devices. This presents a challenge for retail investors looking to add this technology to their portfolios, often limiting investment opportunities to powerful corporations.  

Beyond the hefty price tag, the hardware itself isn’t for the faint of heart. It requires a thorough understanding of optimizing and managing these resources effectively. Investors should have specialized knowledge in the hardware and software, making technical expertise a prerequisite. 

Even if affordability and technical challenges weren’t barriers to investing, a significant obstacle remains: Supply or lack thereof. The Bain & Company report reveals that demand for AI components could grow by 30 percent or more, outpacing supply capabilities. 

While investing in compute may seem out of reach, there are new models making it more accessible to everyday investors, allowing them to tap into the potential of advanced computing despite existing barriers.  

Tokenization as a solution

Through the tokenization of high-compute GPU resources, Exabits offers users an opportunity to become stakeholders in the AI compute economy, allowing them to earn rewards and revenue without needing to manage the complexities of hardware ownership. With affordable entry points and reward systems, Exabits allows individuals to participate in the demand for GPU resources while avoiding the risks associated with direct investment, making investing in AI compute more accessible. 

Exabits has coined its business model, “The Four Seasons of GPU,” emphasizing quality assurance and consistency across its GPU offerings. Just as the Four Seasons is world renowned for its high service standards, “The Four Seasons of GPU” provides quality-guaranteed hardware that investors can trust. Investors can rely on Exabits for personalized assistance, similar to the hotel’s commitment to customer satisfaction. As a platform and a business, Exabits aims to provide equal opportunities for investors to participate in this growing AI compute economy.

As demand for computation rises, so does the appetite for investment opportunities within this rapidly emerging space. With the ongoing growth of AI, blockchain, and other tech trends, the future of GPU development will depend on the industry’s ability to meet these demands and create opportunities that continue to broaden access to this esteemed technology. 



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Io.net and Phala Network partner to improve decentralized AI

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As part of a new agreement, Phala Network will enhance its AI capabilities using io.net’s GPU cloud infrastructure, making advanced computation more accessible.

GPU cloud network io.net (IO) has partnered with Phala Network to improve secure computation and decentralized AI capabilities, according to a press release shared with crypto.news. 

The IO Cloud service provided by io.net allows users to access high-performance GPUs on demand, reducing costs compared to traditional cloud services by up to 90%. Machine learning engineers and developers can access the computational power needed to train and deploy AI models without investing in expensive hardware.

The collaboration will allow Phala Network to tap into io.net’s cloud network, IO Cloud, for powerful GPU hardware, expanding the reach of Phala’s decentralized AI ecosystem.

This partnership strengthens Phala’s ability to run complex AI applications securely by leveraging Nvidia H100 and H200 GPUs, known for their advanced cryptographic protections. In other words, this collaboration will make it easier and more cost-effective for developers to run complex AI tasks while ensuring data security.

Phala Network will use io.net’s GPU resources to support its Trusted Execution Environment CPU nodes. TEEs are isolated sections of a computer’s hardware designed to securely handle sensitive data.

AI workloads 

According to the press release, Phala introduced the first benchmark for TEE-enabled GPUs in August. Through io.net’s network, Phala ensures that AI workloads are processed securely with Nvidia’s confidential computing features, such as encrypted memory and secure boot.

Phala Network is known for offering a decentralized platform where developers can execute complex tasks outside of traditional blockchain networks while maintaining privacy. Its infrastructure of over 40,000 TEE CPU nodes enables Web3 applications to handle computationally intensive tasks while safeguarding data privacy.

Io.net and Phala Network will conduct research and benchmarking, starting with Nvidia’s H100 and H200 GPUs. 

They plan to integrate Phala’s AI agents and hardware into the IO Network, enhancing both parties’ ability to support secure and decentralized AI operations, according to the release. This could lead to more accessible and efficient AI-driven Web3 applications.



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