AI server growth could boost PCB and copper-clad laminate demand more than 200% by 2027, straining global materials supply.
The global excitement surrounding AI has certainly caught my attention for many reasons. However, I was taken aback by an analysis indicating that increasing demand for new infrastructure will significantly boost growth rates for copper-clad laminates (CCL), which are expected to rise by over 200% in 2027, as reported by Goldman Sachs in January.
From x-ray hype to AI black boxes, progress works best when curiosity is paired with caution.
Throughout history, our enthusiasm for new technology has often outpaced our ability to fully comprehend its risks. From the industrial revolution to the digital age, we have repeatedly embraced innovations with excitement while ignoring caution, sometimes overlooking potential hazards in our eagerness to advance. In healthcare, where technological breakthroughs promise transformative benefits, new capabilities can come with severe risks that demand careful scrutiny.
Under Foundry 2.0, the semiconductor value chain is moving back into strategic focus.
When Joni Mitchell recorded Big Yellow Taxi, singing “You don’t know what you’ve got till it’s gone,” she was reiterating the proverbial warning that we often appreciate things properly only after losing them. It’s an observation that transcends context and can be applied even in today’s electronics industry. For decades, Western companies have outsourced significant parts of their value chain to achieve cost-down and to focus on core competencies in pursuit of efficiency. Today’s geopolitical tensions are drawing attention to the loss of sovereignty that results from exporting control of critical processes like packaging and testing as part of the semiconductor value chain.
AI investment shows no signs of slowing, prompting concern about what happens when it eventually does.
Victor Huang has described ChatGPT’s arrival as AI’s “iPhone moment,” when the technology’s potential to change the way we live became clear to all. Indeed, the publication of large language models (LLMs) is arguably the most powerful innovation we have seen so far, enabling widespread user engagement spanning personal and professional purposes. Resulting from this, acceptance has snowballed and more and more of us have come to trust and rely on AI assistants. In turn, mainstream use has driven further improvements, as successive updates have delivered more humanlike interactions and additional capabilities.