Lathrop had spent years looking through microscopes to make something small look bigger. As he puzzled over how to miniaturize transistors, he and Nall wondered whether microscope optics turned upside down could let something big—a pattern for a transistor—be miniaturized. To find out, they covered a piece of germanium material with a type of chemical called a photoresist, which they acquired from Eastman Kodak, the camera company. Light reacts with photoresist, making it either harder or weaker. Lathrop took advantage of this feature and created a “mask” in the shape of a mesa, placing it on the microscope with upside-down optics. Light that passed through holes in the mask was shrunk by the microscope’s lens and projected onto the photoresist chemicals. Where the light struck, the chemicals hardened. Where light was blocked by the mask, they could be washed away, leaving a precise, miniature mesa of germanium. A way to manufacture miniaturized transistors had been found.
Lathrop named the process photolithography—printing with light—and he and Nall filed for a patent. They delivered a paper on the topic at the annual International Electron Devices Meeting in 1957, and the Army awarded him a $25,000 prize for the invention. Lathrop bought his family a new station wagon with the money.
In the midst of the Cold War, the market for mortar fuzes was growing, but Lathrop’s lithography process took off because companies producing transistors for civilian electronics realized its transformative potential. Lithography not only produced transistors with unprecedented precision but also opened the door to further miniaturization. The two companies leading the race to commercial transistors—Fairchild Semiconductor and Texas Instruments—understood the implications early on. Lithography was the tool they needed to manufacture transistors by the millions, turning them into a mass-market good.
Painting with light
Robert Noyce, one of the cofounders of Fairchild, had studied alongside Lathrop when both had been PhD students in physics at MIT. The two of them had spent their weekends in graduate school hiking New Hampshire’s mountains, and they had stayed in touch after graduating. At Fairchild, Noyce moved quickly to hire Nall, Lathrop’s lab partner, and spearheaded his company’s lithography efforts by jury-rigging his own device with a set of 20-millimeter camera lenses he’d bought from a Bay Area photography shop.
Lathrop, meanwhile, took a job at Fairchild’s competitor, Texas Instruments, driving his new station wagon down to Dallas. He arrived just as his new colleague and lifelong friend Jack Kilby was on the brink of creating a piece of semiconductor material with multiple electronic components built—or integrated—into it. These integrated circuits, it soon became clear, could be efficiently produced only with Lathrop’s lithography method. As chip firms strove to shrink transistors to cram more of them onto chips, photolithography provided the precision that miniaturized manufacturing required.
Fairchild and Texas Instruments made their first lithography machines in house, but the growing complexity of the machines soon attracted new entrants. As the scale of transistors declined from centimeters to millimeters to microns, the importance of precision optics increased. Perkin-Elmer was a Connecticut-based firm that produced specialized optics for the US military, from bombsights to spy satellites. In the late 1960s, it realized that this expertise could be used for lithography, too. It developed a scanner that could project the mask pattern onto a silicon wafer while aligning them with almost flawless precision. The scanner then moved a light across the wafer like a copy machine, painting it with lines of light. This tool proved capable of fabricating transistors as small as a micron—one millionth of a meter.
Robert Noyce, who later cofounded Intel, launched Fairchild Semiconductor’s lithography program with lenses purchased from a Bay Area camera shop.
TED STRESHINSKY/GETTY IMAGES
But the approach wasn’t practical as chip features got still smaller. By the late 1970s, scanners began to be replaced with steppers, machines that moved light in discrete steps across a wafer. The challenge with a stepper was to move the light with micron-scale precision, so that each flash was perfectly aligned with the chip. GCA, a Boston-based firm that had its origins in spy balloons, devised the first stepper tool, reportedly on the advice of Texas Instruments executive Morris Chang—later the founder of TSMC, which is today the world’s largest chipmaker.
New England’s specialist lithography firms soon faced steep competition. In the 1980s, as Japanese chipmakers began winning major market share in the production of memory chips, they started buying from Nikon and Canon, two homegrown producers of lithography tools. Around the same time, the Dutch chipmaker Philips spun out its own unit that made lithography tools, calling the new company ASML.
The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania.
Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.
Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.
A changing way of life
The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.
Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.
One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.
Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.
The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.
Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.
Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost.
But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.
—Jessica Hamzelou
Eric Schmidt has a 6-point plan for fighting election misinformation
—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures
The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.
Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.
According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.
Mapping out a generative AI pathway
One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.
The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.
Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.
All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.
The benefits of third-party experience
Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).
Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”
