Even as microchips have become essential in so many products, their development and manufacturing have come to be dominated by a small number of producers with limited capacity—and appetite—for churning out the commodity chips that are a staple for today’s technologies. And because making chips requires hundreds of manufacturing steps and months of production time, the semiconductor industry cannot quickly pivot to satisfy the pandemic-fueled surge in demand.
After decades of fretting about how we will carve out features as small as a few nanometers on silicon wafers, the spirit of Moore’s Law—the expectation that cheap, powerful chips will be readily available—is now being threatened by something far more mundane: inflexible supply chains.
A lonely frontier
Twenty years ago, the world had 25 manufacturers making leading-edge chips. Today, only Taiwan Semiconductor Manufacturing Company (TSMC) in Taiwan, Intel in the United States, and Samsung in South Korea have the facilities, or fabs, that produce the most advanced chips. And Intel, long a technology leader, is struggling to keep up, having repeatedly missed deadlines for producing its latest generations.
One reason for the consolidation is that building a facility to make the most advanced chips costs between $5 billion and $20 billion. These fabs make chips with features as small as a few nanometers; in industry jargon they’re called 5-nanometer and 7-nanometer nodes. Much of the cost of new fabs goes toward buying the latest equipment, such as a tool called an extreme ultraviolet lithography (EUV) machine that costs more than $100 million. Made solely by ASML in the Netherlands, EUV machines are used to etch detailed circuit patterns with nanometer-size features.
Chipmakers have been working on EUV technology for more than two decades. After billions of dollars of investment, EUV machines were first used in commercial chip production in 2018. “That tool is 20 years late, 10x over budget, because it’s amazing,” says David Kanter, executive director of an open engineering consortium focused on machine learning. “It’s almost magical that it even works. It’s totally like science fiction.”
Such gargantuan effort made it possible to create the billions of tiny transistors in Apple’s M1 chip, which was made by TSMC; it’s among the first generation of leading-edge chips to rely fully on EUV.
Only the largest tech companies are willing to pay hundreds of millions of dollars to design a chip for leading-edge nodes.
Paying for the best chips makes sense for Apple because these chips go into the latest MacBook and iPhone models, which sell by the millions at luxury-brand prices. “The only company that is actually using EUV in high volume is Apple, and they sell $1,000 smartphones for which they have insane margin,” Kanter says.
Not only are the fabs for manufacturing such chips expensive, but the cost of designing the immensely complex circuits is now beyond the reach of many companies. In addition to Apple, only the largest tech companies that require the highest computing performance, such as Qualcomm, AMD, and Nvidia, are willing to pay hundreds of millions of dollars to design a chip for leading–edge nodes, says Sri Samavedam, senior vice president of CMOS technologies at Imec, an international research institute based in Leuven, Belgium.
Many more companies are producing laptops, TVs, and cars that use chips made with older technologies, and a spike in demand for these is at the heart of the current chip shortage. Simply put, a majority of chip customers can’t afford—or don’t want to pay for—the latest chips; a typical car today uses dozens of microchips, while an electric vehicle uses many more. It quickly adds up. Instead, makers of things like cars have stuck with chips made using older technologies.
What’s more, many of today’s most popular electronics simply don’t require leading-edge chips. “It doesn’t make sense to put, for example, an A14 [iPhone and iPad] chip in every single computer that we have in the world,” says Hassan Khan, a former doctoral researcher at Carnegie Mellon University who studied the public policy implications of the end of Moore’s Law and currently works at Apple. “You don’t need it in your smart thermometer at home, and you don’t need 15 of them in your car, because it’s very power hungry and it’s very expensive.”
The problem is that even as more users rely on older and cheaper chip technologies, the giants of the semiconductor industry have focused on building new leading-edge fabs. TSMC, Samsung, and Intel have all recently announced billions of dollars in investments for the latest manufacturing facilities. Yes, they’re expensive, but that’s where the profits are—and for the last 50 years, it has been where the future is.
TSMC, the world’s largest contract manufacturer for chips, earned almost 60% of its 2020 revenue from making leading-edge chips with features 16 nanometers and smaller, including Apple’s M1 chip made with the 5-nanometer manufacturing process.
Making the problem worse is that “nobody is building semiconductor manufacturing equipment to support older technologies,” says Dale Ford, chief analyst at the Electronic Components Industry Association, a trade association based in Alpharetta, Georgia. “And so we’re kind of stuck between a rock and a hard spot here.”
Low-end chips
All this matters to users of technology not only because of the supply disruption it’s causing today, but also because it threatens the development of many potential innovations. In addition to being harder to come by, cheaper commodity chips are also becoming relatively more expensive, since each chip generation has required more costly equipment and facilities than the generations before.
Some consumer products will simply demand more powerful chips. The buildout of faster 5G mobile networks and the rise of computing applications reliant on 5G speeds could compel investment in specialized chips designed for networking equipment that talks to dozens or hundreds of Internet-connected devices. Automotive features such as advanced driver-assistance systems and in-vehicle “infotainment” systems may also benefit from leading-edge chips, as evidenced by electric-vehicle maker Tesla’s reported partnerships with both TSMC and Samsung on chip development for future self-driving cars.
But buying the latest leading-edge chips or investing in specialized chip designs may not be practical for many companies when developing products for an “intelligence everywhere” future. Makers of consumer devices such as a Wi-Fi-enabled sous vide machine are unlikely to spend the money to develop specialized chips on their own for the sake of adding even fancier features, Kanter says. Instead, they will likely fall back on whatever chips made using older technologies can provide.
The majority of today’s chip customers make do with the cheaper commodity chips that represent a trade-off between cost and performance.
And lower-cost items such as clothing, he says, have “razor-thin margins” that leave little wiggle room for more expensive chips that would add a dollar—let alone $10 or $20—to each item’s price tag. That means the climbing price of computing power may prevent the development of clothing that could, for example, detect and respond to voice commands or changes in the weather.
The world can probably live without fancier sous vide machines, but the lack of ever cheaper and more powerful chips would come with a real cost: the end of an era of inventions fueled by Moore’s Law and its decades-old promise that increasingly affordable computation power will be available for the next innovation.
The majority of today’s chip customers make do with the cheaper commodity chips that represent a trade-off between cost and performance. And it’s the supply of such commodity chips that appears far from adequate as the global demand for computing power grows.
“It is still the case that semiconductor usage in vehicles is going up, semiconductor usage in your toaster oven and for all kinds of things is going up,” says Willy Shih, a professor of management practice at Harvard Business School. “So then the question is, where is the shortage going to hit next?”
A global concern
In early 2021, President Joe Biden signed an executive order mandating supply chain reviews for chips and threw his support behind a bipartisan push in Congress to approve at least $50 billion for semiconductor manufacturing and research. Biden also held two White House summits with leaders from the semiconductor and auto industries, including an April 12 meeting during which he prominently displayed a silicon wafer.
The actions won’t solve the imbalance between chip demand and supply anytime soon. But at the very least, experts say, today’s crisis represents an opportunity for the US government to try to finally fix the supply chain and reverse the overall slowdown in semiconductor innovation—and perhaps shore up the US’s capacity to make the badly needed chips.
An estimated 75% of all chip manufacturing capacity was based in East Asia as of 2019, with the US share sitting at approximately 13%. Taiwan’s TSMC alone has nearly 55% of the foundry market that handles consumer chip manufacturing orders.
Looming over everything is the US-China rivalry. China’s national champion firm SMIC has been building fabs that are still five or six years behind the cutting edge in chip technologies. But it’s possible that Chinese foundries could help meet the global demand for chips built on older nodes in the coming years. “Given the state subsidies they receive, it’s possible Chinese foundries will be the lowest-cost manufacturers as they stand up fabs at the 22-nanometer and 14-nanometer nodes,” Khan says. “Chinese fabs may not be competitive at the frontier, but they could supply a growing portion of demand.”
Boston Metal’s strategy is to try to make the transition as digestible as possible for steelmakers. “We won’t own and operate steel plants,” says Adam Rauwerdink, who heads business development at the company. Instead, it plans to license the technology for electrochemical units that are designed to be a simple drop-in replacement for blast furnaces; the liquid iron that flows out of the electrochemical cells can be handled just as if it were coming out of a blast furnace, with the same equipment.
Working with industrial investors including ArcelorMittal, says Rauwerdink, allows the startup to learn “how to integrate our technology into their plants—how to handle the raw materials coming in, the metal products coming out of our systems, and how to integrate downstream into their established processes.”
The startup’s headquarters in a business park about 15 miles outside Boston is far from any steel manufacturing, but these days it’s drawing frequent visitors from the industry. There, the startup’s pilot-scale electrochemical unit, the size of a large furnace, is intentionally designed to be familiar to those potential customers. If you ignore the hordes of electrical cables running in and out of it, and the boxes of electric equipment surrounding it, it’s easy to forget that the unit is not just another part of the standard steelmaking process. And that’s exactly what Boston Metal is hoping for.
The company expects to have an industrial-scale unit ready for use by 2025 or 2026. The deadline is key, because Boston Metal is counting on commitments that many large steelmakers have made to reach zero carbon emissions by 2050. Given that the life of an average blast furnace is around 20 years, that means having the technology ready to license before 2030, as steelmakers plan their long-term capital expenditures. But even now, says Rauwerdink, demand is growing for green steel, especially in Europe, where it’s selling for a few hundred dollars a metric ton more than the conventional product.
It’s that kind of blossoming market for clean technologies that many of today’s startups are depending on. The recent corporate commitments to decarbonize, and the IRA and other federal spending initiatives, are creating significant demand in markets “that previously didn’t exist,” says Michael Kearney, a partner at Engine Ventures.
One wild card, however, will be just how aggressively and faithfully corporations pursue ways to transform their core businesses and to meet their publicly stated goals. Funding a small pilot-scale project, says Kearney, “looks more like greenwashing if you have no intention of scaling those projects.” Watching which companies move from pilot plants to full-scale commercial facilities will tell you “who’s really serious,” he says. Putting aside the fears of greenwashing, Kearney says it’s essential to engage these large corporations in the transition to cleaner technologies.
Susan Schofer, a partner at the venture firm SOSV, has some advice for those VCs and startups reluctant to work with existing companies in traditionally heavily polluting industries: Get over it. “We need to partner with them. These incumbents have important knowledge that we all need to get in order to effect change. So there needs to be healthy respect on both sides,” she says. Too often, she says, there is “an attitude that we don’t want to do that because it’s helping an incumbent industry.” But the reality, she says, is that finding ways for such industries to save energy or use cleaner technologies “can make the biggest difference in the near term.”
Getting lucky
It’s tempting to dismiss the history of cleantech 1.0. It was more than a decade ago, and there’s a new generation of startups and investors. Far more money is around today, along with a broader range of financing options. Surely we’re savvier these days.
“If you’re doing a specific application, like searching through email … do you really need these big models that are capable of anything? I would say no,” Luccioni says.
The energy consumption associated with using AI tools has been a missing piece in understanding their true carbon footprint, says Jesse Dodge, a research scientist at the Allen Institute for AI, who was not part of the study.
Comparing the carbon emissions from newer, larger generative models and older AI models is also important, Dodge adds. “It highlights this idea that the new wave of AI systems are much more carbon intensive than what we had even two or five years ago,” he says.
Google once estimated that an average online search used 0.3 watt-hours of electricity, equivalent to driving 0.0003 miles in a car. Today, that number is likely much higher, because Google has integrated generative AI models into its search, says Vijay Gadepally, a research scientist at the MIT Lincoln lab, who did not participate in the research.
Not only did the researchers find emissions for each task to be much higher than they expected, but they discovered that the day-to-day emissions associated with using AI far exceeded the emissions from training large models. Luccioni tested different versions of Hugging Face’s multilingual AI model BLOOM to see how many uses would be needed to overtake training costs. It took over 590 million uses to reach the carbon cost of training its biggest model. For very popular models, such as ChatGPT, it could take just a couple of weeks for such a model’s usage emissions to exceed its training emissions, Luccioni says.
This is because large AI models get trained just once, but then they can be used billions of times. According to some estimates, popular models such as ChatGPT have up to 10 million users a day, many of whom prompt the model more than once.
Studies like these make the energy consumption and emissions related to AI more tangible and help raise awareness that there is a carbon footprint associated with using AI, says Gadepally, adding, “I would love it if this became something that consumers started to ask about.”
Dodge says he hopes studies like this will help us to hold companies more accountable about their energy usage and emissions.
“The responsibility here lies with a company that is creating the models and is earning a profit off of them,” he says.
And really, what’s the point of such a hard-won triumph unless it’s to enforce your rights? “Honestly, this train has been coming down the track since at least 2014, if not earlier. We’re at the collision point. I struggle to imagine there’s going to be a diversion,” says Sherkow. “Brace for impact.”
The Broad Institute didn’t answer any of my questions, and a spokesperson for MIT didn’t even reply to my email. That’s not a surprise. Private universities can be exceedingly obtuse when it comes to acknowledging their commercial activities. They are supposed to be centers of free inquiry and humanitarian intentions, so if employees get rich from biotechnology—and they do—they try to do it discreetly.
There are also strong reasons not to sue. Suing could make a nonprofit like the Broad Institute look bad. Really bad. That’s because it could get in the way of cures.
“It seems unlikely and undesirable, [as] legal challenges at this late date would delay saving patients,” says George Church, a Harvard professor and one of the original scientific founders of Editas, though he’s no longer closely involved with the company.
If a patent infringement lawsuit does get filed, it will happen sometime after Vertex notifies regulators it’s starting to sell the treatment. “That’s the starting gun,” says Sherkow. “There are no hypothetical lawsuits in the patent system, so one must wait until it’s sufficiently clear that an act of infringement is about to occur.”
How much money is at stake? It remains unclear what the demand for the Vertex treatment will be, but it could eventually prove a blockbuster. There are about 20,000 people with severe sickle-cell in the US who might benefit. And assuming a price of $3 million (my educated guess), that’s a total potential market of around $60 billion. A patent holder could potentially demand 10% of the take, or more.
Vertex can certainly defend itself. It’s a big, rich company, and through its partnership with the Swiss firm CRISPR Therapeutics, a biotech co-founded by Charpentier, Vertex has access to the competing set of intellectual-property claims—including those of UC Berkeley, which (though bested by Broad in the US) hold force in Europe and could be used to throw up a thicket of counterarguments.
Vertex could also choose to pay royalties. To do that, it would have to approach Editas, the biotech cofounded by Zhang and Church in Cambridge, Massachusetts, which previously bought exclusive rights to the Broad patents on CRISPR in the arena of human treatments, including sickle-cell therapies.
