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The great chip crisis threatens the promise of Moore’s Law



The great chip crisis threatens the promise of Moore’s Law

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.”


Why I became a TechTrekker



group jumps into the air with snowy mountains in the background

My senior spring in high school, I decided to defer my MIT enrollment by a year. I had always planned to take a gap year, but after receiving the silver tube in the mail and seeing all my college-bound friends plan out their classes and dorm decor, I got cold feet. Every time I mentioned my plans, I was met with questions like “But what about school?” and “MIT is cool with this?”

Yeah. MIT totally is. Postponing your MIT start date is as simple as clicking a checkbox. 

Sofia Pronina (right) was among those who hiked to the Katla Glacier during this year’s TechTrek to Iceland.


Now, having finished my first year of classes, I’m really grateful that I stuck with my decision to delay MIT, as I realized that having a full year of unstructured time is a gift. I could let my creative juices run. Pick up hobbies for fun. Do cool things like work at an AI startup and teach myself how to create latte art. My favorite part of the year, however, was backpacking across Europe. I traveled through Austria, Slovakia, Russia, Spain, France, the UK, Greece, Italy, Germany, Poland, Romania, and Hungary. 

Moreover, despite my fear that I’d be losing a valuable year, traveling turned out to be the most productive thing I could have done with my time. I got to explore different cultures, meet new people from all over the world, and gain unique perspectives that I couldn’t have gotten otherwise. My travels throughout Europe allowed me to leave my comfort zone and expand my understanding of the greater human experience. 

“In Iceland there’s less focus on hustle culture, and this relaxed approach to work-life balance ends up fostering creativity. This was a wild revelation to a bunch of MIT students.”

When I became a full-time student last fall, I realized that StartLabs, the premier undergraduate entrepreneurship club on campus, gives MIT undergrads a similar opportunity to expand their horizons and experience new things. I immediately signed up. At StartLabs, we host fireside chats and ideathons throughout the year. But our flagship event is our annual TechTrek over spring break. In previous years, StartLabs has gone on TechTrek trips to Germany, Switzerland, and Israel. On these fully funded trips, StartLabs members have visited and collaborated with industry leaders, incubators, startups, and academic institutions. They take these treks both to connect with the global startup sphere and to build closer relationships within the club itself.

Most important, however, the process of organizing the TechTrek is itself an expedited introduction to entrepreneurship. The trip is entirely planned by StartLabs members; we figure out travel logistics, find sponsors, and then discover ways to optimize our funding. 

two students soaking in a hot spring in Iceland


In organizing this year’s trip to Iceland, we had to learn how to delegate roles to all the planners and how to maintain morale when making this trip a reality seemed to be an impossible task. We woke up extra early to take 6 a.m. calls with Icelandic founders and sponsors. We came up with options for different levels of sponsorship, used pattern recognition to deduce the email addresses of hundreds of potential contacts at organizations we wanted to visit, and all got scrappy with utilizing our LinkedIn connections.

And as any good entrepreneur must, we had to learn how to be lean and maximize our resources. To stretch our food budget, we planned all our incubator and company visits around lunchtime in hopes of getting fed, played human Tetris as we fit 16 people into a six-person Airbnb, and emailed grocery stores to get their nearly expired foods for a discount. We even made a deal with the local bus company to give us free tickets in exchange for a story post on our Instagram account. 

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The Download: spying keyboard software, and why boring AI is best




This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How ubiquitous keyboard software puts hundreds of millions of Chinese users at risk

For millions of Chinese people, the first software they download onto devices is always the same: a keyboard app. Yet few of them are aware that it may make everything they type vulnerable to spying eyes. 

QWERTY keyboards are inefficient as many Chinese characters share the same latinized spelling. As a result, many switch to smart, localized keyboard apps to save time and frustration. Today, over 800 million Chinese people use third-party keyboard apps on their PCs, laptops, and mobile phones. 

But a recent report by the Citizen Lab, a University of Toronto–affiliated research group, revealed that Sogou, one of the most popular Chinese keyboard apps, had a massive security loophole. Read the full story. 

—Zeyi Yang

Why we should all be rooting for boring AI

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. It hopes they could improve intelligence and operational planning. 

But those might not be the right use cases, writes our senior AI reporter Melissa Heikkila. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases. 

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. The DoD’s best bet is to apply generative AI to more mundane things like Excel, email, or word processing. Read the full story. 

This story is from The Algorithm, Melissa’s weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The ice cores that will let us look 1.5 million years into the past

To better understand the role atmospheric carbon dioxide plays in Earth’s climate cycles, scientists have long turned to ice cores drilled in Antarctica, where snow layers accumulate and compact over hundreds of thousands of years, trapping samples of ancient air in a lattice of bubbles that serve as tiny time capsules. 

By analyzing those cores, scientists can connect greenhouse-gas concentrations with temperatures going back 800,000 years. Now, a new European-led initiative hopes to eventually retrieve the oldest core yet, dating back 1.5 million years. But that impressive feat is still only the first step. Once they’ve done that, they’ll have to figure out how they’re going to extract the air from the ice. Read the full story.

—Christian Elliott

This story is from the latest edition of our print magazine, set to go live tomorrow. Subscribe today for as low as $8/month to ensure you receive full access to the new Ethics issue and in-depth stories on experimental drugs, AI assisted warfare, microfinance, and more.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 How AI got dragged into the culture wars
Fears about ‘woke’ AI fundamentally misunderstand how it works. Yet they’re gaining traction. (The Guardian
+ Why it’s impossible to build an unbiased AI language model. (MIT Technology Review)
2 Researchers are racing to understand a new coronavirus variant 
It’s unlikely to be cause for concern, but it shows this virus still has plenty of tricks up its sleeve. (Nature)
Covid hasn’t entirely gone away—here’s where we stand. (MIT Technology Review)
+ Why we can’t afford to stop monitoring it. (Ars Technica)
3 How Hilary became such a monster storm
Much of it is down to unusually hot sea surface temperatures. (Wired $)
+ The era of simultaneous climate disasters is here to stay. (Axios)
People are donning cooling vests so they can work through the heat. (Wired $)
4 Brain privacy is set to become important 
Scientists are getting better at decoding our brain data. It’s surely only a matter of time before others want a peek. (The Atlantic $)
How your brain data could be used against you. (MIT Technology Review)
5 How Nvidia built such a big competitive advantage in AI chips
Today it accounts for 70% of all AI chip sales—and an even greater share for training generative models. (NYT $)
The chips it’s selling to China are less effective due to US export controls. (Ars Technica)
+ These simple design rules could turn the chip industry on its head. (MIT Technology Review)
6 Inside the complex world of dissociative identity disorder on TikTok 
Reducing stigma is great, but doctors fear people are self-diagnosing or even imitating the disorder. (The Verge)
7 What TikTok might have to give up to keep operating in the US
This shows just how hollow the authorities’ purported data-collection concerns really are. (Forbes)
8 Soldiers in Ukraine are playing World of Tanks on their phones
It’s eerily similar to the war they are themselves fighting, but they say it helps them to dissociate from the horror. (NYT $)
9 Conspiracy theorists are sharing mad ideas on what causes wildfires
But it’s all just a convoluted way to try to avoid having to tackle climate change. (Slate $)
10 Christie’s accidentally leaked the location of tons of valuable art 🖼📍
Seemingly thanks to the metadata that often automatically attaches to smartphone photos. (WP $)

Quote of the day

“Is it going to take people dying for something to move forward?”

—An anonymous air traffic controller warns that staffing shortages in their industry, plus other factors, are starting to threaten passenger safety, the New York Times reports.

The big story

Inside effective altruism, where the far future counts a lot more than the present

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October 2022

Since its birth in the late 2000s, effective altruism has aimed to answer the question “How can those with means have the most impact on the world in a quantifiable way?”—and supplied methods for calculating the answer.

It’s no surprise that effective altruisms’ ideas have long faced criticism for reflecting white Western saviorism, alongside an avoidance of structural problems in favor of abstract math. And as believers pour even greater amounts of money into the movement’s increasingly sci-fi ideals, such charges are only intensifying. Read the full story.

—Rebecca Ackermann

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Watch Andrew Scott’s electrifying reading of the 1965 commencement address ‘Choose One of Five’ by Edith Sampson.
+ Here’s how Metallica makes sure its live performances ROCK. ($)
+ Cannot deal with this utterly ludicrous wooden vehicle
+ Learn about a weird and wonderful new instrument called a harpejji.

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Why we should all be rooting for boring AI



Why we should all be rooting for boring AI

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

I’m back from a wholesome week off picking blueberries in a forest. So this story we published last week about the messy ethics of AI in warfare is just the antidote, bringing my blood pressure right back up again. 

Arthur Holland Michel does a great job looking at the complicated and nuanced ethical questions around warfare and the military’s increasing use of artificial-intelligence tools. There are myriad ways AI could fail catastrophically or be abused in conflict situations, and there don’t seem to be any real rules constraining it yet. Holland Michel’s story illustrates how little there is to hold people accountable when things go wrong.  

Last year I wrote about how the war in Ukraine kick-started a new boom in business for defense AI startups. The latest hype cycle has only added to that, as companies—and now the military too—race to embed generative AI in products and services. 

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. 

The department sees tons of potential to “improve intelligence, operational planning, and administrative and business processes.” 

But Holland Michel’s story highlights why the first two use cases might be a bad idea. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases.  

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. Everyone agrees that humans should make the final call, but that is made harder by technology that acts unpredictably, especially in fast-moving conflict situations. 

Some worry that the people lowest on the hierarchy will pay the highest price when things go wrong: “In the event of an accident—regardless of whether the human was wrong, the computer was wrong, or they were wrong together—the person who made the ‘decision’ will absorb the blame and protect everyone else along the chain of command from the full impact of accountability,” Holland Michel writes. 

The only ones who seem likely to face no consequences when AI fails in war are the companies supplying the technology.

It helps companies when the rules the US has set to govern AI in warfare are mere recommendations, not laws. That makes it really hard to hold anyone accountable. Even the AI Act, the EU’s sweeping upcoming regulation for high-risk AI systems, exempts military uses, which arguably are the highest-risk applications of them all. 

While everyone is looking for exciting new uses for generative AI, I personally can’t wait for it to become boring. 

Amid early signs that people are starting to lose interest in the technology, companies might find that these sorts of tools are better suited for mundane, low-risk applications than solving humanity’s biggest problems.

Applying AI in, for example, productivity software such as Excel, email, or word processing might not be the sexiest idea, but compared to warfare it’s a relatively low-stakes application, and simple enough to have the potential to actually work as advertised. It could help us do the tedious bits of our jobs faster and better.

Boring AI is unlikely to break as easily and, most important, won’t kill anyone. Hopefully, soon we’ll forget we’re interacting with AI at all. (It wasn’t that long ago when machine translation was an exciting new thing in AI. Now most people don’t even think about its role in powering Google Translate.) 

That’s why I’m more confident that organizations like the DoD will find success applying generative AI in administrative and business processes. 

Boring AI is not morally complex. It’s not magic. But it works. 

Deeper Learning

AI isn’t great at decoding human emotions. So why are regulators targeting the tech?

Amid all the chatter about ChatGPT, artificial general intelligence, and the prospect of robots taking people’s jobs, regulators in the EU and the US have been ramping up warnings against AI and emotion recognition. Emotion recognition is the attempt to identify a person’s feelings or state of mind using AI analysis of video, facial images, or audio recordings. 

But why is this a top concern? Western regulators are particularly concerned about China’s use of the technology, and its potential to enable social control. And there’s also evidence that it simply does not work properly. Tate Ryan-Mosley dissected the thorny questions around the technology in last week’s edition of The Technocrat, our weekly newsletter on tech policy.

Bits and Bytes

Meta is preparing to launch free code-generating software
A version of its new LLaMA 2 language model that is able to generate programming code will pose a stiff challenge to similar proprietary code-generating programs from rivals such as OpenAI, Microsoft, and Google. The open-source program is called Code Llama, and its launch is imminent, according to The Information. (The Information

OpenAI is testing GPT-4 for content moderation
Using the language model to moderate online content could really help alleviate the mental toll content moderation takes on humans. OpenAI says it’s seen some promising first results, although the tech does not outperform highly trained humans. A lot of big, open questions remain, such as whether the tool can be attuned to different cultures and pick up context and nuance. (OpenAI)

Google is working on an AI assistant that offers life advice
The generative AI tools could function as a life coach, offering up ideas, planning instructions, and tutoring tips. (The New York Times)

Two tech luminaries have quit their jobs to build AI systems inspired by bees
Sakana, a new AI research lab, draws inspiration from the animal kingdom. Founded by two prominent industry researchers and former Googlers, the company plans to make multiple smaller AI models that work together, the idea being that a “swarm” of programs could be as powerful as a single large AI model. (Bloomberg)

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