We’re Reaching the Physical Limits of Chips

Summary

• AI’s investable bottlenecks now center on power, memory, cooling, and data transfer. Adam identifies these constraints; Stephen says physical components are harder to improve overnight. Micron, SK hynix, and Samsung account for 95% of memory, whose price rose 700% this year as hyperscalers absorbed supply.
• Moore’s Law is reaching a physical plateau after decades of transistor scaling. Stephen puts the transistor’s dimension near 1 nanometer—roughly 100,000 times smaller than a human hair—forcing hardware solutions to become “significantly more inventive.”
• The apparent hardware renaissance reflects software’s current dependence on “brute force.” Adam says the software market is still immature: hardware is filling the pyramid’s base, while software that couples tightly to new architectures should create more value.
• Ever-larger models may be approaching their practical endpoint. Stephen cites open-source Kimi K3 as having more parameters than the human brain, while the chip needed to operate it is about a million times less energy-efficient; memory bottlenecks should push developers beyond the “scaling hypothesis.”
• Photonics is a leading candidate to relieve several infrastructure constraints simultaneously. Stephen flags optical-to-electrical conversion as a bottleneck, while Adam says light can improve power consumption, cooling, and data transfer. Optical links are expected to move from between clusters toward “co-packaged optics” beside GPUs within five years.
• Today’s LLM era may eventually resemble dial-up internet: transformative, but primitive in hindsight. Adam predicts that in 5–10 years “we will laugh at how old-fashioned AI was today.” He also favors hiring and investing in people who combine European academic and technical training with U.S. experience and business acumen.

Deep dive

1. Moore’s Law has run into physics

• Stephen calls IMEC the semiconductor industry’s “most unknown hidden gem”: its unique Belgian clean rooms help develop chips that may reach market seven, eight, or ten years later.
• IMEC has operated for 40 years, helped define the chip-development roadmap, and, in Stephen’s view, has had a hand in almost every chip in the world in some way.
• Hardware development takes 5–10 years, not “30 seconds.” With the transistor’s dimension near 1 nanometer—about 100,000 times smaller than a human hair—continued performance gains demand hardware solutions that are much more inventive than simple shrinking.

2. Scarcity is concentrating value in physical infrastructure

• Adam’s bottleneck map covers energy, memory, cooling, and data transfer. Stephen identifies memory as the current number-one problem: Micron, SK hynix, and Samsung account for 95% of the market, while hyperscaler buying helped drive memory prices up 700% this year.
• Stephen says the bottleneck is production of the right components, not necessarily the number of chip companies; he highlights TSMC and expects many distinct designs. Over the next 5–10 years, Adam expects cooling fluid to move closer to the GPU, shifting “a significant part of the value” toward manufacturing.

3. Hardware’s renaissance does not eliminate software upside

• The host frames the moment as an “Iron Age renaissance,” with chip economics strengthening while AI and token costs eat into software profitability.
• Adam’s pushback: the apparent inversion reflects an immature software market still using computational “brute force.” Hardware is filling the pyramid’s base; software tightly coupled to that hardware should follow and create more value.

4. Model scaling is giving way to specialization

• Stephen cites the latest open-source models, including Kimi K3, as having more parameters than the human brain, yet says the chip needed to operate K3 is about a million times less energy-efficient.
• After moving from 175 billion to roughly 10 trillion parameters in five years, Stephen expects ever-larger-model scaling to stop. More diverse algorithms should require more diversified hardware—not merely additional GPUs—and memory bottlenecks are prompting companies to move beyond the “scaling hypothesis.”

5. Photonics could relieve several bottlenecks at once

• Stephen notes that copper remains convenient because it is fully electrical and digital, but flags the optical-to-electrical transition as photonics’ bottleneck. Adam argues that photonics can reduce power consumption and cooling needs while addressing data-transfer constraints; he calls it the technology most likely to win.
• Within five years, optics should move from linear interconnects between clusters toward co-packaged components beside GPUs.
• Stephen connects that thesis to the brain’s efficient three-dimensional structure, noting that software may diversify into paths such as world models and reinforcement learning, while every plausible AI path still requires colossal data movement.
• Adam’s boldest forecast is that today’s LLMs will look like dial-up internet within 5–10 years: an early, mostly “brute force” approach remembered with amusement. Acknowledging he may be biased, he also favors hiring and investing in people who combine European academic and technical education with U.S. experience and business acumen.