AI Revives Chipmaking as Tech’s Core Engine

A century ago, 391 San Antonio Road in Mountain View, California, housed an apricot-packing shed. Today, it’s marked by sculptures of diodes and a transistor, commemorating the 1956 founding of Shockley Semiconductor Laboratory—the birthplace of Silicon Valley. William Shockley, co-inventor of the transistor, aimed to craft components from silicon, but his firm flopped. Yet, his “traitorous eight” employees defected in 1957 to launch Fairchild Semiconductor nearby. This group included Gordon Moore and Robert Noyce, who later co-founded Intel, and Eugene Kleiner, pioneer of venture capital firm Kleiner Perkins. Most Silicon Valley giants trace their lineage to these early innovators.

Semiconductors revolutionized computing. Pre-semiconductor era computers relied on bulky, unreliable vacuum tubes. Semiconductors, solid materials controlling electrical flow, offered durable, versatile alternatives. Silicon enabled mass production of transistors, diodes, and integrated circuits on single chips, processing and storing data efficiently.

In 1965, Moore observed that transistor density on chips doubled annually (later adjusted to every two years), an observation dubbed Moore’s Law. This drove exponential progress: from 200 transistors per square millimeter in 1971 to 150 million in AMD’s 2023 MI300 processor. Smaller transistors switched faster, fueling breakthroughs like personal computers, the internet, smartphones, and artificial intelligence.

Chipmakers once dominated tech. In the 1970s, IBM integrated chips, hardware, and software into unparalleled dominance. The 1980s saw Microsoft’s software-only model thrive, but Intel’s processors remained vital. By 2000, Intel ranked sixth globally by market cap. Post-dotcom bust, however, firms like Google and Meta overshadowed hardware, commoditizing chips. Venture capitalist Marc Andreessen famously declared software was “eating the world” in 2011.

AI’s surge has reversed this. Training LLMs demands immense computation. Pre-2010, AI training compute doubled every 20 months, aligning with Moore’s Law. Since then, it doubles every six months, spiking chip demand. Nvidia, specializing in AI-suited GPUs, now ranks third-most valuable globally. Since late 2023, chipmaker stocks have outpaced software firms for the first time in over a decade.

Beyond training, AI inference, responding to queries, requires efficient, bespoke chips. General-purpose processors fall short, prompting tech giants to design custom silicon. Apple, Amazon, Microsoft, and Meta invest heavily; Google deploys more proprietary data-center processors than anyone except Nvidia and Intel. Seven of the world’s top ten firms now make chips.

Sophistication hinges on process nodes—feature sizes under 7nm define cutting-edge AI chips. Yet, over 90% of manufacturing uses 7nm or larger nodes for everyday devices like TVs, fridges, cars, and tools.

The 2021 COVID-19 chip shortage exposed vulnerabilities in global supply chains: design in America, equipment in Europe/Japan, fabrication in Taiwan/South Korea, packaging in China/Malaysia. Governments responded with subsidies—America’s $50 billion CHIPS Act in 2022, followed by $94 billion from the EU, Japan, and South Korea. Geopolitics complicates matters: U.S. export bans limit China’s access to advanced tech, prompting Beijing’s restrictions on key materials like gallium and germanium.

Yet, technological hurdles loom larger than political ones, argues Economist Global Business Writer Shailesh Chitnis. For decades, shrinking transistors boosted performance without proportional energy hikes. Now, denser chips and massive AI models drive soaring power use. Data centers could consume 8% of U.S. electricity by 2030.

To sustain exponential gains, innovations are essential. Incremental steps include hardware-software integration, like optimizing algorithms for specific chips. Radical shifts involve alternatives to silicon, such as gallium nitride for efficiency, or neuromorphic computing mimicking brain analog processes over digital ones. Optical computing, using light for faster data transfer, and quantum chips for complex simulations also promise breakthroughs.

AI’s demands are putting silicon back at tech’s heart, echoing Valley origins. As computation needs explode, chipmaking’s evolution will dictate future innovation, balancing efficiency, geopolitics, and sustainability. The apricot shed’s legacy endures—silicon’s story is far from over.

You can read the full article here.