Introduction to AI and Mobile Networks

During congressional hearing in the House of Representatives’ Energy & Commerce Committee Subcommittee of Communication and Technology, Ronnie Vasishta, Senior VP of telecom at Nvidia said that mobile networks will be called upon to support a new kind of traffic—AI traffic. This AI traffic includes the delivery of AI services to the edge, or inferencing at the edge. Such growth in AI data could reverse the general trend towards lower growth in traffic on mobile networks.

The Rise of AI Traffic

Many AI-enabled applications will require mobile connectivity including autonomous vehicles, smart glasses, generative AI services and many other applications. He said that the transmission of this massive increase in data needs to be resilient, fit for purpose, and secure. Supporting this creation of data from AI will require large amount of memory, particularly very high bandwidth memory, such as HBM. This will result in great demand for memory that supports AI applications.

Micron’s HBM4 Memory

Micron announced that it is now shipping HBM4 memory to key customers, these are for early qualification efforts. The Micron HBM4 provides up to 2.0TB/s bandwidth and 24GB capacity per 12-high die stack. The company says that their HBM4 uses its 1-beta DRAM node, advanced through silicon via technologies, and has a highly capable built-in self-test.

HBM Memory and AI Applications

HBM memory consisting of stacks of DRAM die with massively parallel interconnects to provide high bandwidth are combined GPU’s such as those from Nvidia. This memory close to the processor allows training and inference of various AI models. The current generation of HBM memory used in current GPUs use HBM3e memory. At the 2025 March GTC in San Jose, Jensen Huang said that Micron HBM memory was being used in some of their GPU platforms.

HBM Memory Manufacturers

The manufacturers of HBM memories are SK Hynix, Samsung and Micron with SK Hynix and Samsung providing the majority of supply and with Micron coming in third. SK hynix was the first to announce HBM memory in 2013, which was adopted as an industry standard by JEDEC that same year. Samsung followed in 2016 and in 2020 Micron said that it would create its own HBM memory. All of these companies expect to be shipping HBM4 memories in volume by sometime in 2026.

Emerging Memory Technologies

Numen, a company involved in magnetic random access memory applications, recently talked about how traditional memories used in AI applications, such as DRAM and SRAM have limitations in power, bandwidth and storage density. They said that processing performance has skyrocketed by 60,000X over the past 20 years but DRAM bandwidth has improved only 100X, creating a “memory wall.”

AI Memory Engine

The company says that its AI Memory Engine is a highly configurable memory subsystem IP that enables significant improvements in power efficiency, performance, intelligence, and endurance. This is not only for Numem’s MRAM-based architecture, but also third-party MRAMs, RRAM, PCRAM, and Flash Memory.

Future of Memory Technologies

Numem said that it has developed next-generation MRAM supporting die densities up to 1GB which can deliver SRAM-class performance with up to 2.5X higher memory density in embedded applications and 100X lower standby power consumption. The company says that its solutions are foundry-ready and production-capable today.

Projections for Emerging Memories

Coughlin Associates and Objective Analysis in their Deep Look at New Memories report predict that AI and other memory-intensive applications, including the use of AI inference in embedded devices such as smart watches, hearing aids and other applications are already using MRAM, RRAM and other emerging memory technologies will decrease the costs and increase production of these memories.

Conclusion

AI will generate increased demand for memory to support training and inference. It will also increase the demand for data over mobile networks. This will drive demand for HBM memory but also increase demand for new emerging memory technologies.

FAQs

Q: What is AI traffic?
A: AI traffic refers to the delivery of AI services to the edge, or inferencing at the edge, over mobile networks.
Q: What is HBM memory?
A: HBM (High-Bandwidth Memory) is a type of memory that provides high bandwidth and is used in applications such as AI and machine learning.
Q: Who are the manufacturers of HBM memory?
A: The manufacturers of HBM memories are SK Hynix, Samsung, and Micron.
Q: What are emerging memory technologies?
A: Emerging memory technologies include MRAM, RRAM, PCRAM, and Flash Memory, which offer improvements in power efficiency, performance, and storage density compared to traditional memories.
Q: What is the projected market size for emerging memories?
A: The projected market size for emerging memories is $100B, with NOR and SRAM expected to be replaced by new memories within the next decade.

Introduction to the AI Revolution

The AI revolution is coming, and it’s going to change everything. From the way we work to the way we live, artificial intelligence is set to have a profound impact on our daily lives. But are we prepared for the changes that are coming? The answer, unfortunately, is no. Most of us are not prepared for the AI revolution, and it’s going to take some getting used to.

What is the AI Revolution?

The AI revolution refers to the rapid development and deployment of artificial intelligence technologies across various industries and aspects of life. This includes machine learning, natural language processing, computer vision, and other forms of AI that are being used to automate tasks, make decisions, and interact with humans.

Key Technologies Driving the AI Revolution

Several key technologies are driving the AI revolution, including:

• Machine learning: This is a type of AI that allows systems to learn from data and improve their performance over time.
• Natural language processing: This is a type of AI that allows systems to understand and generate human language.
• Computer vision: This is a type of AI that allows systems to interpret and understand visual data from images and videos.

Impact of the AI Revolution

The AI revolution is going to have a significant impact on our lives, and it’s not all positive. While AI has the potential to bring about many benefits, such as increased efficiency and productivity, it also poses significant risks, such as job displacement and bias.

Positive Impacts of the AI Revolution

Some of the positive impacts of the AI revolution include:

• Increased efficiency: AI can automate many tasks, freeing up humans to focus on more creative and high-value work.
• Improved decision-making: AI can analyze large amounts of data and provide insights that humans may miss.
• Enhanced customer experience: AI can be used to personalize customer interactions and provide 24/7 support.

Negative Impacts of the AI Revolution

Some of the negative impacts of the AI revolution include:

• Job displacement: AI has the potential to automate many jobs, leaving millions of people without work.
• Bias: AI systems can perpetuate existing biases and discriminate against certain groups of people.
• Loss of privacy: AI can be used to collect and analyze large amounts of personal data, threatening our privacy and security.

Preparing for the AI Revolution

So, how can we prepare for the AI revolution? The answer is not simple, but there are several steps we can take to get ready.

• Educate yourself: Learn about AI and its applications, and stay up to date with the latest developments.
• Develop new skills: As AI automates many tasks, it’s essential to develop new skills that are complementary to AI.
• Support AI research and development: Encourage and support research and development in AI, and advocate for responsible AI development.

Strategies for Businesses

Businesses can also take several steps to prepare for the AI revolution, including:

• Investing in AI research and development: Businesses can invest in AI research and development to stay ahead of the competition.
• Upskilling and reskilling: Businesses can provide training and development programs to help their employees develop new skills.
• Implementing AI responsibly: Businesses can implement AI in a responsible and transparent way, ensuring that it is fair, secure, and respectful of human rights.

Conclusion

The AI revolution is coming, and it’s going to change everything. While there are many benefits to AI, there are also significant risks. To prepare for the AI revolution, we need to educate ourselves, develop new skills, and support responsible AI research and development. By working together, we can ensure that the AI revolution benefits everyone, and that we are all prepared for the changes that are coming.

FAQs

Q: What is the AI revolution?
A: The AI revolution refers to the rapid development and deployment of artificial intelligence technologies across various industries and aspects of life.
Q: What are the key technologies driving the AI revolution?
A: The key technologies driving the AI revolution include machine learning, natural language processing, and computer vision.
Q: What are the positive impacts of the AI revolution?
A: The positive impacts of the AI revolution include increased efficiency, improved decision-making, and enhanced customer experience.
Q: What are the negative impacts of the AI revolution?
A: The negative impacts of the AI revolution include job displacement, bias, and loss of privacy.
Q: How can we prepare for the AI revolution?
A: We can prepare for the AI revolution by educating ourselves, developing new skills, and supporting responsible AI research and development.

Introduction to the Quantum Threat

Bitcoin and other cryptocurrencies are now embedded in the global financial system. Countries are creating strategic reserves, and institutional investors, from hedge funds to pension schemes, are allocating capital to digital assets.

Many individuals, businesses, and even governments are exposed to price fluctuations in this notoriously volatile market. But could it all collapse overnight if quantum computing renders the technology behind cryptocurrencies obsolete, potentially causing trillions of dollars in value to vanish?

That’s the risk some experts associate with quantum computing. These futuristic machines harness the strange properties of quantum mechanics to perform specific types of calculations exponentially faster than even the most powerful supercomputers. Given enough power, quantum computers could one day break the cryptographic foundations of blockchain systems like Bitcoin.

The Threat of Quantum Computing

At the start of 2024, an estimated 500 million people globally held Bitcoin or other cryptocurrencies, a 34% increase from the year before. The majority of holders reside in Asia and North America. In many cases, these assets represent a substantial portion of personal wealth or national reserves.

If a technological advance were to render these assets insecure, the consequences could be severe.

Cryptocurrencies function by ensuring that only authorized parties can modify the blockchain ledger. In Bitcoin’s case, this means that only someone with the correct private key can spend a given amount of Bitcoin.

Bitcoin currently uses cryptographic schemes such as the Elliptic Curve Digital Signature Algorithm (ECDSA) and Schnorr signatures to verify ownership and authorize transactions. These systems rely on the difficulty of deriving a private key from a public key, a task that is computationally infeasible for classical computers.

This infeasibility is what makes “brute-force” attacks, trying every possible key, impractical. Classical computers must test each possibility one by one, which could take millions of years.

Quantum computers, however, operate on different principles. Thanks to phenomena like superposition and entanglement, they can perform many calculations in parallel. In 1994, mathematician Peter Shor developed a quantum algorithm capable of factoring large numbers exponentially faster than classical methods. This algorithm, if run on a sufficiently powerful quantum computer, could undermine encryption systems like ECDSA.

Understanding Quantum Computers

The core difference lies in how quantum and classical computers handle data. Classical computers process data as binary digits (bits), either 0s or 1s. Quantum computers use qubits, which can exist in multiple states simultaneously.

As of 2024, the most advanced quantum computers can process around 1,000 qubits, but estimates suggest that breaking Bitcoin’s ECDSA encryption would require a machine with 10 million to 300 million fault-tolerant qubits, a goal that remains years or even decades away.

Nonetheless, technology often advances unpredictably, especially now that AI tools are accelerating research and development across fields, including quantum computing.

Counter-Measures and Preparations

This is why work on quantum-safe (or post-quantum) cryptography is already well underway. The U.S. National Institute of Standards and Technology (NIST) is leading efforts to standardize cryptographic algorithms that are secure against quantum attacks, not just to protect cryptocurrencies but to safeguard the entire digital ecosystem, from banking systems to classified government data.

Once quantum-safe standards are finalized, Bitcoin and other blockchains could adapt accordingly. Bitcoin’s open-source software is managed by a global community of developers with clear governance protocols for implementing updates. In other words, Bitcoin is not static; it can evolve to meet new threats.

The Future of Bitcoin and Quantum Computing

Could quantum computing kill Bitcoin? In theory, yes, if Bitcoin failed to adapt and quantum computers suddenly became powerful enough to break its encryption, its value would plummet.

But this scenario assumes crypto stands still while quantum computing advances, which is highly unlikely. The cryptographic community is already preparing, and the financial incentives to preserve the integrity of Bitcoin are enormous.

Moreover, if quantum computers become capable of breaking current encryption methods, the consequences would extend far beyond Bitcoin. Secure communications, financial transactions, digital identities, and national security all depend on encryption. In such a world, the collapse of Bitcoin would be just one of many crises.

The quantum threat is real, but so is the work being done to prevent it.

So, if you’re among the millions with a bit of Bitcoin tucked away in the hope it will one day make you rich, well, I can’t guarantee that will happen. But I don’t think you need to worry that quantum computing is going to make it worthless any time soon.

Conclusion

In conclusion, while the threat of quantum computing to Bitcoin and other cryptocurrencies is real, it is not imminent. The development of quantum computers capable of breaking current encryption methods is still in its early stages, and the cryptographic community is already working on counter-measures. Bitcoin and other blockchains have the potential to adapt and evolve to meet new threats, ensuring their continued security and integrity.

Frequently Asked Questions

Q: Can quantum computers break Bitcoin’s encryption?

A: Theoretically, yes, but it would require a quantum computer with a large number of fault-tolerant qubits, which is still years or decades away.

Q: What is being done to prevent quantum computers from breaking Bitcoin’s encryption?

A: The cryptographic community is working on developing quantum-safe (or post-quantum) cryptography, and the U.S. National Institute of Standards and Technology (NIST) is leading efforts to standardize cryptographic algorithms that are secure against quantum attacks.

Q: Will quantum computing kill Bitcoin?

A: It’s unlikely, as Bitcoin and other blockchains have the potential to adapt and evolve to meet new threats, and the financial incentives to preserve their integrity are enormous.