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Thoughts on Compute Power Lately, I’ve been thinking about how rapidly computing power is advancing and how accessible it’s become. It’s pretty amazing to think about the leaps we’ve made over the past few decades. There was a time when powerful computing was reserved for big companies or research labs, but now, high-performance devices are in our homes, our pockets, and even on our wrists.
Affordable High-Performance Computing
Think about storage, for example. It wasn’t long ago that we relied on bulky hard drives with limited space that cost a small fortune. Fast-forward to today, and we have high-speed SSDs that are not only affordable but hold far more data. In the processor world, Apple Silicon has been a real game changer. Apple’s M1 chip has redefined the standards of performance and efficiency, packing incredible power into a compact, energy-efficient design. And the latest M4 models are pushing the envelope even further, blending more cores and specialized neural engines directly into our everyday devices.
The same progress is happening in mobile technology. Modern smartphones are incredibly powerful, able to handle tasks like photo editing, gaming, and even video production that used to require a full desktop setup. It’s safe to say that affordable, powerful computing is no longer a luxury but a part of daily life.
But We Still Need More Compute Power
Despite these advancements, the demand for compute power keeps climbing, driven by the explosive growth in artificial intelligence and large language models. Training a model like GPT-4, for instance, requires enormous resources—far beyond what a single machine can handle. Companies invest in massive GPU clusters and specialized data centers, sometimes spending millions just to train and deploy these models.
And it’s not just about training. Once these models are in use, they require a powerful infrastructure to deliver results in real-time, which is why companies are building huge, specialized hardware setups to keep everything running smoothly. As AI continues to expand into areas like healthcare, robotics, and personalized services, the need for even greater compute power will only increase.
Quantum Computers
Now, let’s talk about quantum computing. It’s still in the experimental stages, but big companies like Google, IBM, and Rigetti are already making breakthroughs. Quantum computers operate fundamentally differently from classical computers, which allows them to tackle certain problems that are incredibly difficult for traditional machines. For example, Google’s Sycamore processor performed a task in just 200 seconds that could take a classical supercomputer thousands of years.
Quantum computers are already being used in specialized fields like molecular modeling, which has big implications for drug discovery and materials science. They’re also beginning to address complex optimization problems in logistics and supply chain management. Although they aren’t ready to replace our laptops anytime soon, quantum computers are showing us what’s possible—and hinting at a future where they’ll handle tasks that classical computers can’t efficiently tackle.
Harvest Now, Decrypt Later
However, with all this power, there’s a catch: the security risks. One emerging threat is the “harvest now, decrypt later” approach, where attackers collect encrypted data today, hoping they’ll be able to decrypt it in the future when technology advances. This approach is particularly concerning in light of quantum computing, which has the potential to break traditional encryption methods.
Certain types of data are especially vulnerable in this scenario:
- Government and Military Information: Classified files and secure communications could be exposed, affecting national security.
- Personal Identification and Financial Records: Social security numbers, financial transactions, and even biometric data could be decrypted, leading to privacy breaches and financial fraud.
- Healthcare Data: Medical records and genetic information could be revealed, risking patient privacy and potential misuse.
- Intellectual Property: Proprietary corporate data, including research, formulas, and legal contracts, could be stolen, impacting businesses and industries.
To address this, post-quantum cryptography (PQC) is being developed. PQC refers to encryption techniques designed to be secure against both classical and quantum attacks. It’s a field that’s gaining a lot of attention, and some companies are already taking action. Earlier this year, Apple, for instance, has integrated post-quantum cryptography in iMessage to secure messages now and in the future.
PQC is built on new types of mathematical problems that are resistant to quantum computing threats, you can read about it in the Apple blog I linked. These new algorithms are complex and typically require more processing power, but they’re necessary for ensuring the long-term security of sensitive data. As quantum computing advances, PQC will likely play an essential role in keeping data secure.
References
Lately, I’ve been thinking about how rapidly computing power is advancing and how accessible it’s become. It’s pretty amazing to think about the leaps we’ve made over the past few decades. There was a time when powerful computing was reserved for big companies or research labs, but now, high-performance devices are in our homes, our pockets, and even on our wrists.
Affordable High-Performance Computing
Think about storage, for example. It wasn’t long ago that we relied on bulky hard drives with limited space that cost a small fortune. Fast-forward to today, and we have high-speed SSDs that are not only affordable but hold far more data. In the processor world, Apple Silicon has been a real game changer. Apple’s M1 chip has redefined the standards of performance and efficiency, packing incredible power into a compact, energy-efficient design. And the latest M4 models are pushing the envelope even further, blending more cores and specialized neural engines directly into our everyday devices.
The same progress is happening in mobile technology. Modern smartphones are incredibly powerful, able to handle tasks like photo editing, gaming, and even video production that used to require a full desktop setup. It’s safe to say that affordable, powerful computing is no longer a luxury but a part of daily life.
But We Still Need More Compute Power
Despite these advancements, the demand for compute power keeps climbing, driven by the explosive growth in artificial intelligence and large language models. Training a model like GPT-4, for instance, requires enormous resources—far beyond what a single machine can handle. Companies invest in massive GPU clusters and specialized data centers, sometimes spending millions just to train and deploy these models.
And it’s not just about training. Once these models are in use, they require a powerful infrastructure to deliver results in real-time, which is why companies are building huge, specialized hardware setups to keep everything running smoothly. As AI continues to expand into areas like healthcare, robotics, and personalized services, the need for even greater compute power will only increase.
Quantum Computers
Now, let’s talk about quantum computing. It’s still in the experimental stages, but big companies like Google, IBM, and Rigetti are already making breakthroughs. Quantum computers operate fundamentally differently from classical computers, which allows them to tackle certain problems that are incredibly difficult for traditional machines. For example, Google’s Sycamore processor performed a task in just 200 seconds that could take a classical supercomputer thousands of years.
Quantum computers are already being used in specialized fields like molecular modeling, which has big implications for drug discovery and materials science. They’re also beginning to address complex optimization problems in logistics and supply chain management. Although they aren’t ready to replace our laptops anytime soon, quantum computers are showing us what’s possible—and hinting at a future where they’ll handle tasks that classical computers can’t efficiently tackle.
Harvest Now, Decrypt Later
However, with all this power, there’s a catch: the security risks. One emerging threat is the “harvest now, decrypt later” approach, where attackers collect encrypted data today, hoping they’ll be able to decrypt it in the future when technology advances. This approach is particularly concerning in light of quantum computing, which has the potential to break traditional encryption methods.
Certain types of data are especially vulnerable in this scenario:
- Government and Military Information: Classified files and secure communications could be exposed, affecting national security.
- Personal Identification and Financial Records: Social security numbers, financial transactions, and even biometric data could be decrypted, leading to privacy breaches and financial fraud.
- Healthcare Data: Medical records and genetic information could be revealed, risking patient privacy and potential misuse.
- Intellectual Property: Proprietary corporate data, including research, formulas, and legal contracts, could be stolen, impacting businesses and industries.
To address this, post-quantum cryptography (PQC) is being developed. PQC refers to encryption techniques designed to be secure against both classical and quantum attacks. It’s a field that’s gaining a lot of attention, and some companies are already taking action. Earlier this year, Apple, for instance, has integrated post-quantum cryptography in iMessage to secure messages now and in the future.
PQC is built on new types of mathematical problems that are resistant to quantum computing threats, you can read about it in the Apple blog I linked. These new algorithms are complex and typically require more processing power, but they’re necessary for ensuring the long-term security of sensitive data. As quantum computing advances, PQC will likely play an essential role in keeping data secure.