Part of the reason the company has focused its initial efforts on Canada is that the nation has large amounts of survey data in the public domain, including narrative field reports, timeworn geologic maps, geochemical data on drill hole samples, airborne magnetic and electromagnetic survey data, lidar readings, and satellite imagery spanning many decades of exploration.
“We have a system where we can ingest all this data and store it in standard formats, quality-control all of the data, make it searchable, and be able to programmatically access it,” Goldman says.
Once it has compiled all the available information for a site, KoBold’s team explores the data using machine learning. The company might, for instance, build a model to predict which parts of ore deposits have the highest concentrations of cobalt, or create a new geologic map of a region showing all the different rock types and fault structures. It can add new data to these models as it’s collected, allowing KoBold to adaptively change its exploration strategy “almost in real time,” Goldman says.
KoBold has already used insights from machine-learning models to acquire its Canadian mining claims and develop its field programs. Its partnership with Stanford’s Center for Earth Resources Forecasting, under way since February, adds an additional layer of analytics to the mix in the form of an AI “decision agent” that can map out an entire exploration plan.
Stanford geoscientist Jef Caers, who is overseeing the collaboration, explains that this digital decision-maker quantifies the uncertainty in KoBold’s model results and then designs a data collection plan to sequentially reduce that uncertainty. Like a chess player trying to win a game in as few moves as possible, the AI will aim to help KoBold reach a decision about a prospect with minimal wasted effort—whether that decision is to drill in a particular spot or walk away.
Why can’t tech fix its gender problem?
Not competing in this Olympics, but still contributing to the industry’s success, were the thousands of women who worked in the Valley’s microchip fabrication plants and other manufacturing facilities from the 1960s to the early 1980s. Some were working-class Asian- and Mexican-Americans whose mothers and grandmothers had worked in the orchards and fruit canneries of the prewar Valley. Others were recent migrants from the East and Midwest, white and often college educated, needing income and interested in technical work.
With few other technical jobs available to them in the Valley, women would work for less. The preponderance of women on the lines helped keep the region’s factory wages among the lowest in the country. Women continue to dominate high-tech assembly lines, though now most of the factories are located thousands of miles away. In 1970, one early American-owned Mexican production line employed 600 workers, nearly 90% of whom were female. Half a century later the pattern continued: in 2019, women made up 90% of the workforce in one enormous iPhone assembly plant in India. Female production workers make up 80% of the entire tech workforce of Vietnam.
Venture: “The Boys Club”
Chipmaking’s fiercely competitive and unusually demanding managerial culture proved to be highly influential, filtering down through the millionaires of the first semiconductor generation as they deployed their wealth and managerial experience in other companies. But venture capital was where semiconductor culture cast its longest shadow.
The Valley’s original venture capitalists were a tight-knit bunch, mostly young men managing older, much richer men’s money. At first there were so few of them that they’d book a table at a San Francisco restaurant, summoning founders to pitch everyone at once. So many opportunities were flowing it didn’t much matter if a deal went to someone else. Charter members like Silicon Valley venture capitalist Reid Dennis called it “The Group.” Other observers, like journalist John W. Wilson, called it “The Boys Club.”
The venture business was expanding by the early 1970s, even though down markets made it a terrible time to raise money. But the firms founded and led by semiconductor veterans during this period became industry-defining ones. Gene Kleiner left Fairchild Semiconductor to cofound Kleiner Perkins, whose long list of hits included Genentech, Sun Microsystems, AOL, Google, and Amazon. Master intimidator Don Valentine founded Sequoia Capital, making early-stage investments in Atari and Apple, and later in Cisco, Google, Instagram, Airbnb, and many others.
Generations: “Pattern recognition”
Silicon Valley venture capitalists left their mark not only by choosing whom to invest in, but by advising and shaping the business sensibility of those they funded. They were more than bankers. They were mentors, professors, and father figures to young, inexperienced men who often knew a lot about technology and nothing about how to start and grow a business.
“This model of one generation succeeding and then turning around to offer the next generation of entrepreneurs financial support and managerial expertise,” Silicon Valley historian Leslie Berlin writes, “is one of the most important and under-recognized secrets to Silicon Valley’s ongoing success.” Tech leaders agree with Berlin’s assessment. Apple cofounder Steve Jobs—who learned most of what he knew about business from the men of the semiconductor industry—likened it to passing a baton in a relay race.
Predicting the climate bill’s effects is harder than you might think
Human decision-making can also cause models and reality to misalign. “People don’t necessarily always do what is, on paper, the most economic,” says Robbie Orvis, who leads the energy policy solutions program at Energy Innovation.
This is a common issue for consumer tax credits, like those for electric vehicles or home energy efficiency upgrades. Often people don’t have the information or funds needed to take advantage of tax credits.
Likewise, there are no assurances that credits in the power sectors will have the impact that modelers expect. Finding sites for new power projects and getting permits for them can be challenging, potentially derailing progress. Some of this friction is factored into the models, Orvis says. But there’s still potential for more challenges than modelers expect.
Putting too much stock in results from models can be problematic, says James Bushnell, an economist at the University of California, Davis. For one thing, models could overestimate how much behavior change is because of tax credits. Some of the projects that are claiming tax credits would probably have been built anyway, Bushnell says, especially solar and wind installations, which are already becoming more widespread and cheaper to build.
Still, whether or not the bill meets the expectations of the modelers, it’s a step forward in providing climate-friendly incentives, since it replaces solar- and wind-specific credits with broader clean-energy credits that will be more flexible for developers in choosing which technologies to deploy.
Another positive of the legislation is all its long-term investments, whose potential impacts aren’t fully captured in the economic models. The bill includes money for research and development of new technologies like direct air capture and clean hydrogen, which are still unproven but could have major impacts on emissions in the coming decades if they prove to be efficient and practical.
Whatever the effectiveness of the Inflation Reduction Act, however, it’s clear that more climate action is still needed to meet emissions goals in 2030 and beyond. Indeed, even if the predictions of the modelers are correct, the bill is still not sufficient for the US to meet its stated goals under the Paris agreement of cutting emissions to half of 2005 levels by 2030.
The path ahead for US climate action isn’t as certain as some might wish it were. But with the Inflation Reduction Act, the country has taken a big step. Exactly how big is still an open question.
China has censored a top health information platform
The suspension has met with a gleeful social reaction among nationalist bloggers, who accuse DXY of receiving foreign funding, bashing traditional Chinese medicine, and criticizing China’s health-care system.
DXY is one of the front-runners in China’s digital health startup scene. It hosts the largest online community Chinese doctors use to discuss professional topics and socialize. It also provides a medical news service for a general audience, and it is widely seen as the most influential popular science publication in health care.
“I think no one, as long as they are somewhat related to the medical profession, doesn’t follow these accounts [of DXY],” says Zhao Yingxi, a global health researcher and PhD candidate at Oxford University, who says he followed DXY’s accounts on WeChat too.
But in the increasingly polarized social media environment in China, health care is becoming a target for controversy. The swift conclusion that DXY’s demise was triggered by its foreign ties and critical work illustrates how politicized health topics have become.
Since its launch in 2000, DXY has raised five rounds of funding from prominent companies like Tencent and venture capital firms. But even that commercial success has caused it trouble this week. One of its major investors, Trustbridge Partners, raises funds from sources like Columbia University’s endowments and Singapore’s state holding company Temasek. After DXY’s accounts were suspended, bloggers used that fact to try to back up their claim that DXY has been under foreign influence all along.
Part of the reason the suspension is so shocking is that DXY is widely seen as one of the most trusted online sources for health education in China. During the early days of the covid-19 pandemic, it compiled case numbers and published a case map that was updated every day, becoming the go-to source for Chinese people seeking to follow covid trends in the country. DXY also made its name by taking down several high-profile fraudulent health products in China.
It also hasn’t shied away from sensitive issues. For example, on the International Day Against Homophobia, Transphobia, and Biphobia in 2019, it published the accounts of several victims of conversion therapy and argued that the practice is not backed by medical consensus.
“The article put survivors’ voices front and center and didn’t tiptoe around the disturbing reality that conversion therapy is still prevalent and even pushed by highly ranked public hospitals and academics,” says Darius Longarino, a senior fellow at Yale Law School’s Paul Tsai China Center.