Bitcoin Mining: Pioneering Innovations in Data Center Technologies

In the intricate world of data centers, managing the heat generated by servers is a pivotal challenge, shaping the core of their design and operation. This heat, a byproduct of relentless data processing, poses a significant risk to the delicate electronic components housed within these technological hubs. Bitcoin mining is the first to adopt pioneering technologies to address this challenge.

Efficiency Evolution: Cooling Innovations in Bitcoin Mining

The quest to maintain an optimal operating environment not only motivates energy conservation but also drives innovation in cooling technologies. Ensuring efficiency and reliability in data processing while mitigating environmental impact and operational costs is a balancing act with high stakes. As we delve deeper into the realm of high-density data center cooling technologies, it becomes clear why mastering this thermal conundrum is more than a necessity; it’s an ongoing evolution in the face of ever-increasing digital demands.

As processing power has exponentially increased through the years, cooling technology has become ever more essential. From the outset, data centers have relied upon air cooling, and the basic concept has remained the same. Air-cooled compute systems utilize ventilation fans and atmospheric air to lower the temperature.

Experts in the field of Bitcoin mining are shifting to more efficient methods, placing them at the forefront of the data center industry. Bitcoin mining is an industry that uses massive amounts of computational processing power, but one that is highly focused on efficiency. This has led miners to seek groundbreaking cooling processes to maintain the health and efficiency of the mining rigs, as well as ways to recycle the intense heat that is generated.

Through the use of innovative cooling and heat distribution techniques, Bitcoin miners are showing their ability to optimize performance, reduce downtime, and maximize return on investment.

Bitcoin Miners Pioneering New Cooling Technologies

Out of necessity, Bitcoin miners are among the first adopters of new cooling technologies, paving the way for the entire data center industry. Miners have discovered that hydro-cooling and immersion cooling have emerged as two promising methods to cool larger, high-density systems. These cooling systems can help push performance while allowing miners to manage costs.

Hydro-cooling, also called liquid cooling, utilizes deionized water as the heat transfer medium. This cold plate water cooling technology uses a closed-loop system, circulating water through heat exchangers without coming into contact with the electrical components. By promoting efficient heat transfer – due to the higher heat capacity of water compared to air – hydro-cooling’s advantages include improved scalability, flexibility, efficiency, and lower operating costs compared to air cooling systems.

One of the world’s fastest supercomputers has turned to hydro-cooling, helping it achieve up to five times the density of a traditional, air-cooled system. The result has been a reduction in cabling requirements and operational expenses.

Another, newer mechanism known as immersion cooling has emerged, offering improved cooling efficiency, reduced thermal stress, increased performance, and an extended equipment lifespan for Bitcoin miners. It does so by submerging electronic components, such as servers or application-specific integrated circuit (ASIC) chips, into a non-conductive liquid or coolant. By enabling direct contact between the components and the coolant, immersion cooling provides heightened heat dissipation. Immersion cooling allows for higher-density deployments and reduces noise pollution in computing environments.

Immersion cooling, therefore, helps accommodate compute-intensive applications by significantly reducing energy consumption, reducing OPEX, and creating a more sustainable data center.

Large-scale immersion mining is set to affect the Bitcoin industry, according to mining consultant BlocksBridge. A recent partnership between a global distributor of enterprise data center infrastructure and a mining operator will bring immersion cooling technology to more Bitcoin miners. The collaboration is promising a power density improvement of up to 100%.

Shaping Future Bitcoin Mining Hardware

Companies that can leverage immersion cooling effectively stand to benefit over others. Launched this year, Chain Reaction’s EL3CTRUM product line is future-ready with its best-in-class ASIC for Bitcoin mining, including high performance and energy efficiency. By offering air-cooled and immersion-ready models, customers have a selection of products that are optimized to power the next generation of Bitcoin miners by providing superior hashing, higher throughput, and sustainable use of energy.

With the ability to optimize power and performance, EL3CTRUM is providing the industry with another way to accelerate computing capabilities while keeping energy efficiency in mind. These qualities are essential to the future of disruptive blockchain and supercomputing.

Fueling the Future: Bitcoin Miners and Oil Giants Forge a New Path

Bitcoin miners are pioneering an energy revolution, transforming the energy landscape by harnessing excess gas for mining, and driving a sustainable future for all.

Oil companies like Exxon Mobil Corp and ConocoPhillips see cryptocurrency as part of the solution to the regulatory crisis facing the industry because of climate concerns. By using Bitcoin mining to offload some of their carbon emission footprint, companies such as these can reduce taxation and limit penalties from state and federal governments, bringing in extra revenue in the process.

Transferring Energy

Partnerships between energy companies and Bitcoin miners have proven successful for both parties. In 2021, Exxon launched a pilot project that is geared toward mining Bitcoin in North Dakota’s Bakken oil fields. The largest oil and gas company in the United States is considering doing the same in Alaska and parts of Nigeria, Argentina, Guyana and Germany.

Tecpetrol, headquartered in Buenos Aires, is also converting excess gas into energy for cryptocurrency mining. Tecpetrol announced in September the launch of its first gas-powered crypto mining facility north of Vaca Muerta in Argentine Patagonia.

What was the reason behind these moves?

Along with oil, drilling an oil well also results in the extraction of natural gas. Consequently, drilling companies are faced with the challenge of preventing this methane gas from escaping into the atmosphere. Oftentimes, gas flaring – the burning of the gas associated with oil extraction – is the way companies manage this. Flaring has endured since the start of oil production and is said to create more CO2 emissions than cars. Oil companies using this practice are wasting a valuable natural resource that others, such as Bitcoin miners, can put to good use.

Rather than flaring the gas, energy companies have started transferring it toward power generators for the Bitcoin mining network. Bitcoin mining rigs need little more than an affordable, abundant power source, meaning they can easily be set up near remote oil fields. Redirecting the unused gas to adjacent mining centers helps optimize gas utilization and reduces waste. Not only is this better for the environment, but it will also help oil and gas companies avoid flaring fines, which, under the Clean Air Act, can cost companies up to $37,500 per violation. Rather than wasting gas, energy companies can reduce fines, curb emissions, and create an additional revenue stream.

Thus, Bitcoin mining can serve as a demand response mechanism during periods of excess energy production. Instead of allowing excess electricity to go to waste, miners convert that would-be wasted energy into economic value.

This has proven to be an effective mechanism in states like Colorado that are rich in pipelines but have banned flaring. Under state rules, operators that cannot connect to a gas pipeline or find another use for the gas must shut down their wells. In Colorado, cryptocurrency mining operators have stepped in to form partnerships with pipeline operators at least six times, according to the Colorado Oil and Gas Conservation Commission.

Blazing Trails

When it comes to sustainable energy solutions, Bitcoin mining is emerging as a trailblazer, revolutionizing the way industries generate and consume power. This is creating a highly productive synergy that could transform the energy landscape.

Studies show that Bitcoin mining stabilizes power grids while leveraging underutilized renewable energy sources. A recent report by KPMG found that Bitcoin can reduce CO2 emissions by converting unused gas into electricity. Another recent paper, published by the Institute of Risk Management, found that Bitcoin mining could reduce global emissions by up to 8% by 2030 – simply by converting wasted CO2 emissions into less harmful ones.

Several oil and gas companies have already jumped on board. Beyond Exxon Mobil, ConocoPhillips, and Tecpetrol, Marathon Oil and EOG Resources are also among the energy companies turning to Bitcoin mining to curb emissions.

Teaming up with Bitcoin miners — like Texas Pacific Land recently did after signing a deal with two mining companies — represents an effective strategy for oil & gas companies to turn wasted gas into profit.

This new green alliance further develops and diversifies the Bitcoin mining industry, calling for new and flexible mining solutions. Bitcoin miners that demonstrate their ability to innovate can contribute to the creation of a sustainable and interconnected energy framework.

Product lines like EL3CTRUM by Chain Reaction are embracing these sustainable options. Thanks to its diverse form factors and modular design, EL3CTRUM allows energy companies to tailor a solution that best meets their specific needs. This adaptability, combined with the ability to optimize power and performance, positions EL3CTRUM as a prominent solution in providing the industry with enhanced computing capabilities while prioritizing energy efficiency.

Why Is the Cloud Still Not Trusted by Many Large Organizations?

Cloud computing is here to stay, and the numbers speak volumes. With worldwide end-user spending on public cloud services reaching a staggering $591.8 billion, and governments across the globe investing hundreds of millions, it’s clear that the realms of supercomputing, Big Data, and artificial intelligence are firmly entrenched in our future.

This technology has become an indispensable pillar in maintaining an effective and competitive information infrastructure, offering unmatched flexibility, scalability, and accessibility to businesses and governments.

Cloud computing has been known to reduce downtime by 57% and reduce the average time-to-market for new product features by 37%, so there is ample reason to invest in cloud infrastructure.

Still, while countless enterprises have already jumped on board, entire industries cannot move their data to the cloud, whether due to cloud security, privacy, compliance, or regulatory concerns. While such reticence is certainly justified, there is a solution on the immediate horizon.

When Privacy Keeps Organizations Out of the Cloud

The decision to avoid the cloud rarely stems from a fundamental aversion to technology. Many organizations in industries that are highly regulated opt not to trust online storage solutions because they could face liability and public confidence issues if their data were to be compromised.

Government agencies, such as the Internal Revenue Service or the Social Security Administration in the United States and their respective counterparts in other countries, handle a vast amount of sensitive personal data, making them subject to a range of privacy regulations. Given that the IRS relies on outdated software and hardware despite the vast amounts of personal information that they handle, it is inconceivable that a move to the cloud is forthcoming.

Similarly, the financial services sector has been hesitant about moving to the cloud due to questions regarding security and regulatory compliance. One example of a financial institution that initially waited to adopt the cloud is Bank of America. The bank struggled to develop a scalable solution after initial investments in private cloud infrastructures. Meanwhile, Capital One felt comfortable trusting Amazon Web Services with all its data.

The reason for these often seemingly arbitrary decisions lies in the stringent regulations that govern data handling in finance — the Privacy Act of 1974 and the PCI Security Standards Council’s standards offer two examples. While the total workloads moved to the cloud increased from 8% to 15% just in 2022, there is a lot of room for improvement, and artificial intelligence will only complicate the situation, as recent measures to prohibit chatbots show.

Oil and gas enterprises have always invested heavily in exploring unconventional reservoir sites, which is one of their most computationally intensive tasks. While this industry’s choice to perform data crunching on-site may often come down to practical necessity during seismic data analysis, such companies seem to be ardent believers in on-site computing. They may not be affected by as many laws and regulations as other industries, but it seems obvious that maintaining control over strategically relevant and proprietary data is part of their decision-making process.

However, despite the tendency for oil and gas companies to retain their data in local repositories, there are advantages to be gained from moving to the cloud, especially the potential to harness even greater efficiency and profitability through the use of shared supercomputing resources. To this end, two recent examples of oil and communication services companies joining forces to benefit from the power of the cloud while addressing the industry’s inherent challenges are Aramco’s recent partnership and Petrobras’ investment in supercomputers.

Judging by these cases, it is clear that even the most vulnerable industries want to move to the cloud. They have only been held back because current solutions fail to account for their regulatory needs or competitive strategies.

Fully Homomorphic Encryption (FHE) and the Future of Cloud Security

While encryption of data has long been the foundation of cybersecurity, it has traditionally only been effective on data at rest and data in transit. It has never been possible to process encrypted data without having to decrypt it first, which makes the data vulnerable to a security breach. Fully homomorphic encryption, though, allows analysis and sampling of encrypted data without having to decrypt it. This means that proprietary data and customer information can remain fully encrypted throughout the entire data lifecycle.

With FHE, the tasks of protecting sensitive data and analyzing that data for business purposes are no longer mutually exclusive. An enterprise can leverage cloud storage and computing solutions while reducing the risk of exposing its proprietary code or its customers’ personal data to regulatory issues or privacy breaches. Due to its lattice-based cryptography schemes, FHE is even resistant to quantum computing attacks, making it a great choice for post-quantum cybersecurity.

While there are tremendous opportunities for FHE across multiple high-profile industries, it has yet to be commercially viable. This is because FHE requires computational processing acceleration on a scale of between 100,000 and 1 million times the current performance of standard CPUs, rendering them impractical for the deployment at scale as required to protect data in the cloud.

This endeavor will be made a reality with Chain Reaction’s 3PUTM privacy processor – presenting significant magnitude of compute and bandwidth, reducing the million-fold performance overhead associated with FHE, and enabling real-time processing on encrypted data.

With 3PUTM, whether the data is oil reservoir analysis, election balloting systems, or patient healthcare records, businesses most sensitive IP and data remains encrypted throughout the processing lifecycle, ensuring that personal and proprietary corporate information is always safe.

Privacy-Enhancing Technologies (PETs): A Brief Guide

Privacy-enhancing technologies, or PETs, are tools designed to protect and preserve user privacy in a world where more platforms process our personal information than ever before. These technologies employ different methods to safeguard private data, preventing unauthorized access and maintaining anonymity.

Put simply, you can think of them as a shield against breaches, surveillance and invasive data collection practices.

Popular Examples of PETs

PETs come in various forms, each serving a distinct purpose in fortifying our digital privacy.  Four pivotal PETs have reshaped the landscape of online security and personal data protection:

Zero-Knowledge Proofs

Zero Knowledge Proofs (ZKP) enable one party to prove to another that a piece of data is true or correct without revealing the information itself. They use a series of interactions or a string of information to validate the data, offering a new level of privacy to transactions involving confidential information. Zero-Knowledge Proofs are used to securely vote, share health care record information, and provide salary ranges for mortgage loans.

While these allow for secure verification without exposing sensitive data, they are often complex to implement and are not suitable for all types of data or use cases. They require significant computational power for setup and verification, and they often require multiple rounds of interaction, which can be a hurdle for large-scale applications in the cloud.

Differential Privacy

Operating on the principle of injecting noise into datasets, differential privacy shields individual data points while still allowing data analysis. It safeguards privacy by preventing the identification of specific individuals within a dataset, maintaining anonymity. Differential privacy is applied in scenarios like health care research, where researchers can analyze aggregated data to derive valuable insights without compromising individual privacy, ensuring anonymity while studying trends and behavioral or reaction patterns.

This method ensures anonymity in datasets, but it also introduces a trade-off between privacy and accuracy. The ‘noise’ added to protect individual data points can sometimes lead to less accurate results, especially in smaller datasets. This makes it less ideal for situations where precision is critical. Differential privacy also offers no real guarantees that real data will always be safeguarded, and it is therefore vulnerable to slow leaks.

Confidential Computing

Confidential Computing addresses the need to safeguard sensitive information while actively processing it by enabling data processing within a secure environment, known as a Trusted Execution Environment (TEE). This technology is particularly valuable in cloud computing, where it ensures the confidentiality and integrity of data throughout its lifecycle, providing assurance that data remains protected during processing even from the cloud providers.

Although Confidential Computing offers robust protection for data in use, its implementation can be complex and resource-intensive. The necessity for specialized hardware and software to establish and maintain secure environments can result in increased costs and complexity. Additionally, Confidential Computing does not inherently protect against all types of vulnerabilities, such as side-channel attacks. Consequently, its application may be limited in situations where the required infrastructure is not readily available, where the operational overheads are not justified by the benefits, or in use cases where the data security must be impregnable.

Fully Homomorphic Encryption (FHE)

This revolutionary technology allows computations to be performed on encrypted data without decrypting it first. FHE is most practical in cloud computing, which, in turn, affects many of the aforementioned examples. By allowing data analysis to run on encrypted data, it is especially important for confidential analysis in industries like defense, healthcare, and financial services.

Historically, the primary limitation of FHE has been its computational intensity, making it impractical for large-scale use. However, with Chain Reaction’s 3PU™ privacy processor, FHE is becoming a more viable option, offering unparalleled data privacy without compromising on functionality.

These PETs operate on diverse principles, from cryptographic techniques to secure processing areas, all with the common goal of preserving user privacy and data integrity in an increasingly interconnected world.

PETs and Your Personal Data

With the recent spotlight on data privacy concerns, major players like Meta (formerly Facebook) have come under scrutiny for their handling of personal data. Meta’s ad-targeting algorithms rely on user data, raising concerns about privacy infringement. While the company claims to use this data in an anonymized manner, there have been instances of breaches and mismanagement.

As a response, Meta has already shifted toward prioritizing research about secure FHE, multi-party computation, on-device learning, and differential privacy as part of its content marketing strategy.

The use of personal data for targeted advertising underscores the vulnerability of user information in the digital space. As concerns about data misuse persist, it is imperative to explore robust solutions that ensure data confidentiality without compromising functionality.

One promising solution in this landscape we have already mentioned is FHE. Currently, Meta relies on extensive user data to power its targeted advertising, which involves analyzing and processing user information to match ads with user preferences. By implementing FHE, companies like Meta could perform complex data operations while preserving user privacy. The confidential data would remain encrypted throughout the entire analysis process, ensuring that user privacy is preserved at all stages of ad targeting.

Implementing FHE at Scale Requires a New Approach

While Fully Homomorphic Encryption (FHE) has always been a promising technology, its widespread adoption has been slowed by practical challenges, primarily in computational efficiency. Historically, the computational resources needed to protect your personal data, let alone Meta’s datasets, have simply made it impossible to use FHE on a massive scale.

At Chain Reaction, we understand these challenges and are committed to overcoming them. Our pioneering 3PU™ privacy processor is a game changer in this arena. This advanced processor is specifically designed to handle the intense computational demands of FHE, enabling it to process data at speeds and at a scale previously unattainable.

The 3PU™ technology revolutionizes how data is processed. By leveraging cutting-edge algorithms and hardware optimizations, it dramatically reduces the time and resources required to perform complex calculations on encrypted data. This means that tasks that were once deemed too resource-intensive for FHE are now not only possible but efficient and practical.

What is Fully Homomorphic Encryption?

Imagine you had to put together a jigsaw puzzle blindfolded. You wouldn’t see the individual puzzle pieces, but you’d still be able to figure out where they lie on the table and could figure out how they relate to each other.  

In a way, that’s how fully homomorphic encryption, or FHE for short, works. With FHE, you don’t need to decrypt data to analyze or process it. You just run your computations on information while it remains encrypted. Gartner predicts that by 2025, 60% of large organizations will use at least one privacy-enhancing computation technique, but FHE is the “holy grail” of privacy for cloud computing and artificial intelligence, turning implicitly untrusted environments into trusted ones. 

Let’s investigate why this technology is so valuable and what new opportunities it creates. 

How Is Fully Homomorphic Encryption Used? 

The ability to analyze encrypted data without compromising its integrity makes FHE a natural fit for industries that regularly process private or confidential data. Typical examples include highly regulated businesses and industries that must comply with stringent security and privacy of data regulations, in addition to the conventional targets such as government agencies, financial institutions, and healthcare businesses, all of whom handle highly-sensitive information as a matter of course, and therefore are prevented from accessing and adopting the latest AI tools and the cloud. 

For financial institutions, leveraging machine learning could streamline credit assessments, fraud detection, forecasting, and risk management. At the same time, they also have to comply with countless regulations that require them to manage sensitive financial data securely while working toward limiting bias. With FHE, it is possible to achieve the former while respecting the latter. 

Similarly, healthcare organizations that plan to use artificial intelligence to automate parts of their revenue cycle management or for clinical predictive algorithms must consider their patients’ privacy. An existing strategy in this sector is to de-identify data before it’s fed into the algorithm, which requires constant monitoring of current legislation and additional processing steps, just to start analyzing data. FHE offers a more efficient and stronger option for such organizations. 

These examples show us that the experts responsible for handling our most sensitive data tend to be risk-averse and will always prioritize privacy over performance. But with FHE, there’s no need to decide between the two anymore because it enables you to analyze and process confidential data in a trusted environment with no chance of data leakage because it always remains encrypted. FHE is also the only post-quantum secure privacy technology, meaning even a quantum computer can’t breach your privacy. 

Considering that FHE has been around for over a decade, what’s holding industries back? That brings us to the big hurdle and the most recent changes. 

What is the Primary Challenge of Fully Homomorphic Encryption? 

Naturally, FHE’s advantages make it a great solution for private and public cloud scenarios, This is why we’ve witnessed such massive resources being allocated by the biggest hyperscalers toward R&D, software implementation and public libraries based on FHE. However, the one obstacle that has traditionally stood in the way of this scenario, similar to that of AI, machine learning, deep learning, and more, is overcoming computational overhead. Given how complex the data analysis involved is, FHE requires up to one million times the processing power that a typical CPU expends on plain-text analysis, making it entirely unfeasible today for real-time analysis. For example, Homeland Security cannot make use of AI-driven facial recognition software in real-time and share it with other governmental agencies if FHE cannot be implemented on a scale that ensures that the data remains private and encrypted throughout its lifespan. 

Unless a new approach to implementing FHE is developed, for instance by way of a dedicated accelerated processor, FHE will be limited to individual use cases that are not time sensitive and that are limited in scope. 

Luckily, this issue is already being addressed. 

What Is the Future of Fully Homomorphic Encryption? 

Once every programmer and chip designer is aware of the problem, the race is on. You’ll find a range of tech giants and specialist startups working on an FHE solution, whether in software or hardware. There are various issues, though. Software solutions simply cannot scale to the level that is required for cloud computing and AI. With hardware, many startups are looking at long development cycles, and for most of the tech giants and hyperscalers, hardware is not the core competency and expertise.  

Meanwhile, our team at Chain Reaction is developing the first dedicated fully homomorphic encryption chip, the 3PU™ privacy processor, which is specifically designed for artificial intelligence platforms, enterprise data centers, and cloud service providers. Only 3PU™ has approached the million-times performance over the CPU and GPU chips that are currently available on the market.  

What does that mean for FHE? Here’s what our co-founder and CEO, Alon Webman, had to say about it: 

“We think our solution will make homomorphic encryption viable. We have a unique architecture, and we also understand the limitations on compute and memory among processors today. We have the solution needed to make it possible.” 

With the 3PU™ privacy processor, fully homomorphic encryption is no longer a distant fantasy. It’s here today. 

Specialized Compute Infrastructure, Part 2

Compute infrastructure, the collection of hardware and software elements needed to enable cloud computing, has been developing at an astonishing pace (see here for Part 1 of Specialized Compute Infrastructure).

Silicon Processes

The semiconductor industry is continuously fine-tuning processes for manufacturing ASICs, and silicon fabrication is becoming increasingly centralized and capital intensive. TSMC, the leading pure-play foundry, and Samsung have the biggest market shares. They are advancing towards increasingly smaller processes – 3nm is currently in production, with plans for 2nm in 2025 and 1.4nm in 2027.

Supply & Demand

As industries move towards more application-specific hardware, and compute infrastructures shift from homogenous to heterogeneous computing, the need for novel next-gen hardware infrastructure is growing exponentially, while supply lags.

The CapEx and OpEx involved in manufacturing semiconductors are increasing in line with the complexity of the product. In the early 2000s there were over 30 foundries manufacturing semiconductors, and most were integrated with chip design companies (like Texas Instruments). Due to this consolidation, today there are only four advanced chip foundries with high volume manufacturing capacity – TSMC, Samsung, Intel, and Global Foundries.

In response to the astronomical costs of building chip manufacture infrastructure, coupled with the US’s interest in moving more processes and supply chain nodes to US soil, the Biden administration’s 2022 CHIPs and Science Act allocated a $208B investment in the US semiconductor industry for manufacture and R&D. This came at the same time that TSMC announced an allotment of $40B towards fab in Arizona, Samsung announced $17B for fab in Texas, and Intel is also targeting building a new fab in Arizona.

Specialized Compute: Modernizing Infrastructure

In recent years, the biggest data centers, hyperscalers, and cloud service providers have been looking at their supply chain for opportunities to add specialized hardware innovations to their offerings. Their aim is to differentiate, compete and gain market share in this $500B annual cloud services market, expected to more than double by 2030.

Specialization is the driving factor in winning massive markets.  The emerging, ever-changing tech landscape has proven in many cases to represent a tipping point for many giants. ChatGPT is a most recent example of this.

We see that aggressive M&A strategies that add specialized compute infrastructures to their product serve as a huge value added. AWS acquired Annapurna Labs in 2016, fueling growth by integrating ASICs into AWS’s EC2 offering. AMD acquired Xilinx in 2020 and Pensando in 2022 to add FPGAs and smartNICs to its product line. Qualcomm acquired Nuvia, headed by the Chief Architect of Apple’s revolutionary M1 chip. As businesses increasingly rely on TTM solutions to be competitive, compute capabilities become more competitive and specialized solutions are winning market share.

Building ASICs

While hyperscalers and enterprise companies traditionally focused on, and excelled in software solutions, today they are aggressively developing their own specialized hardware – some more successfully than others.

Companies including Block, Meta, Google, Apple, and Amazon are expanding their in-house teams of chip design engineers dedicated to hardware infrastructure. Though many companies struggle with building hardware, success can translate into massive revenues; developing the right hardware can be the ‘make it or break it’ for expansion. For example, Google Cloud’s TPU accelerates dense vector and matrix computations (for ML) and contributed to Google Cloud increasing its cloud computing market share in 2022.

Cloud providers such as Microsoft Azure, Oracle and others seek next-gen compute solutions that will help them diversify and maximize their offerings and grow market share. Security and privacy are a top concern for them as they seek to appeal to sectors that cannot modernize their workloads to the cloud but that represent hundreds of billions of dollars of potential revenue. Once robust hardware and software solutions are available to implement scalable solutions for complex operations, such as FHE, ZKP and others, this market will quadruple in size.

Conclusion

Disruptive new compute technologies will continue to reshape our present and future, affecting every aspect of our lives. With a barrage of novel software and hardware breakthroughs across Blockchain, Quantum, AI and Privacy, we’re certain to see almost every global industry affected, hopefully for the better.