Today is International Women’s Day, and now more than ever, we need to clearly see the common and unique challenges that women globally face across a range of securities. While important strides have been made and should be applauded, we need to double down on building a world where women take full and equal access to the full bounty of modern society.
Women face multiple layers of securities challenges, from physical and health to economic and even climate-related. There are unique physical security challenges for women in regions all around the world. Restrictions on movement enforced by laws, societal customs and patriarchal norms can make it nearly impossible for women to build an independent life outside the context of a family or home. Gender-based violence — both in rapidly expanding urban areas as well as in rural villages — are particularly pernicious for women, with studies showing that nearly one in three women globally have suffered sexual or violent crime. Then there’s the extreme risk of human trafficking, a crisis where the vast majority of victims are female.
Those physical security challenges can bleed right over to health security, with women often struggling to acquire the unique health services they need. In the United States, women can find it difficult to access a range of services from obstetrics and gynecology to the comprehensive help needed to fight breast and ovarian cancers. But even outside of these specific health services, women encounter significant barriers to accessing all healthcare services. Outside of wealthy nations, the quality of health care can decline dramatically, with acute declines for women who are often overlooked by public health systems.
Finally (although sadly not comprehensively), women face disproportionately large impacts from the on-going climate emergency threatening Earth. Resource scarcity can force women to do more work to provide food and water for their families while restricting the nutrition they receive. Women are often forced by social custom to stay put in the aftermath of climate disasters, endangering their health and particularly maternal health.
The quality of these securities affects all of us, but the lack of any one layer of security is felt more acutely by women. Physical security is a challenge for billions of people, but the deleterious effects of violence fall heavier on women. A lack of health security is a plague that affects billions of people, but women face uniquely daunting barriers to the services they need. Economic precarity applies to all but a small sliver of the richest countries, but that precarity particularly affects women. Even more ominously, these barriers to securities don’t just apply to individual women, but by extension, to their families, communities, societies and even future generations. Every security challenge is more acute and intense for women, and that’s what International Women’s Day and the broader global women’s movement is meant to highlight.
There are positive developments on many of these fronts. Human trafficking has become a potent issue and significant international work has been conducted to stamp it out. The education of girls has improved remarkably over the past decade, and access to maternal care in many developing nations has expanded. All of these improvements should be applauded.
Yet, any disparity — and particularly the yawning disparities we still see across much of the world — remains a crisis that we must mobilize to solve. Those disparities exist between men and women as well as with people of color. This is a generational fight, and one that we need to keep building momentum so that all people around the world can live a fair and equitable life.
What solutions are needed? Better safety, health services, and economic security can improve the lives of anyone, but too often, international benchmarks fail to properly weigh challenges for women as well as other minority groups. As the classic statistical problem of Simpson’s Paradox shows, individual groups can have radically divergent outcomes even as aggregate statistics show improvement. While development institutions like the World Bank have done more to highlight the unique challenges of women the past two decades, many development indicators fail to capture equity in these various securities. Updating our metrics and improving measurement is a great place to improve the visibility of women in international conversations.
With better data comes the need for better governance and services. Greater investment and attention need to be offered on the securities of women, ensuring that the gap between women and men is narrowed and ultimately eliminated. That includes ensuring better representation of women in city councils, national legislatures, executive branches, and in corporate C-suites and board rooms, to ensure that women’s voices are heard in centers of power globally.
Finally, there is the potential for entrepreneurial innovation to ameliorate some — although not all — securities problems. Women’s health security, for instance, is particularly ripe for improvement, and as a firm, Lux Capital has made broad investments across startups like Alife Health, Maven Clinic, Gameto and many others to improve access to health for all women. As my partner Deena Shakir wrote in Forbes late last year, “Transformative innovations in women’s and family health not only advance health equity, but can also improve individual family finances and contribute more broadly to economic productivity.”
With each flip of the calendar, International Women’s Day is a moment to memorialize challenges, recognize large and small victories, and build community to strike forth for a better and more secure world for all women. We’ve done so much, and yet, so much more work is waiting to be done. Even as we continue to talk about securities on “Securities,” we must be alert to the visibility of women in these conversations and ensure that our analysis always encompasses everyone.
Forcing China’s AI researchers to strive for chip efficiency will ultimately shave America’s lead
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Right now, pathbreaking AI foundation models follow an inverse Moore’s law (sometimes quipped “Eroom’s Law”). Each new generation is becoming more and more expensive to train as researchers exponentially increase the number of parameters used and overall model complexity. Sam Altman of OpenAI said that the cost of training GPT-4 was over $100 million, and some AI computational specialists believe that the first $1 billion model is currently or will shortly be developed.
As semiconductor chips rise in complexity, costs come down because transistors are packed more densely on silicon, cutting the cost per transistor during fabrication as well as lowering operational costs for energy and heat dissipation. That miracle of performance is the inverse with AI today. To increase the complexity (and therefore hopefully quality) of an AI model, researchers have attempted to pack in more and more parameters, each one of which demands more computation both for training and for usage. A 1 million parameter model can be trained for a few bucks and run on a $15 Raspberry Pi Zero 2 W, but Google’s PaLM with 540 billion parameters requires full-scale data centers to operate and is estimated to have cost millions of dollars to train.
Admittedly, simply having more parameters isn’t a magic recipe for better AI end performance. One recalls Steve Jobs’s marketing of the so-called “Megahertz Myth” to attempt to persuade the public that headline megahertz numbers weren't the right way to judge the performance of a personal computer. Performance in most fields is a complicated problem to judge, and just adding more inputs doesn't necessarily translate into a better output.
And indeed, there is an efficiency curve underway in AI outside of the leading-edge foundation models from OpenAI and Google. Researchers over the past two years have discovered better training techniques (as well as recipes to bundle these techniques together), developed best practices for spending on reinforcement learning from human feedback (RLHF), and curated better training data to improve model quality even while shaving parameter counts. Far from surpassing $1 billion, training new models that are equally performant might well cost only tens or hundreds of thousands of dollars.
This AI performance envelope between dollars invested and quality of model trained is a huge area of debate for the trajectory of the field (and was the most important theme to emanate from our AI Summit). And it’s absolutely vital to understand, since where the efficiency story ends up will determine the sustained market structure of the AI industry.
If foundation models cost billions of dollars to train, all the value and leverage of AI will accrue and centralize to the big tech companies like Microsoft (through OpenAI), Google and others who have the means and teams to lavish. But if the performance envelope reaches a significantly better dollar-to-quality ratio in the future, that means the whole field opens up to startups and novel experiments, while the leverage of the big tech companies would be much reduced.
The U.S. right now is parallelizing both approaches toward AI. Big tech is hurling billions of dollars on the field, while startups are exploring and developing more efficient models given their relatively meagre resources and limited access to Nvidia’s flagship chip, the H100. Talent — on balance — is heading as it typically does to big tech. Why work on efficiency when a big tech behemoth has money to burn on theoretical ideas emanating from university AI labs?
Without access to the highest-performance chips, China is limited in the work it can do on the cutting-edge frontiers of AI development. Without more chips (and in the future, the next generations of GPUs), it won’t have the competitive compute power to push the AI field to its limits like American companies. That leaves China with the only other path available, which is to follow the parallel course for improving AI through efficiency.
For those looking to prevent the decline of American economic power, this is an alarming development. Model efficiency is what will ultimately allow foundation models to be preloaded onto our devices and open up the consumer market to cheap and rapid AI interactions. Whoever builds an advantage in model efficiency will open up a range of applications that remain impractical or too expensive for the most complex AI models.
Given U.S. export controls, China is now (by assumption, and yes, it’s a big assumption) putting its entire weight behind building the AI models it can, which are focused on efficiency. Which means that its resources are arrayed for building the platforms to capture end-user applications — the exact opposite goal of American policymakers. It’s a classic result: restricting access to technology forces engineers to be more creative in building their products, the exact intensified creativity that typically leads to the next great startup or scientific breakthrough.
If America was serious about slowing the growth of China’s still-nascent semiconductor market, it really should have taken a page from the Chinese industrial policy handbook and just dumped chips on the market, just as China has done for years from solar panel manufacturing to electronics. Cheaper chips, faster chips, chips so competitive that no domestic manufacturer — even under Beijing direction — could have effectively competed. Instead we are attempting to decouple from the second largest chips market in the world, turning a competitive field where America is the clear leader into a bountiful green field of opportunity for domestic national champions to usurp market share and profits.
There were of course other goals outside of economic growth for restricting China’s access to chips. America is deeply concerned about the country’s AI integration into its military, and it wants to slow the evolution of its autonomous weaponry and intelligence gathering. Export controls do that, but they are likely to come at an extremely exorbitant long-term cost: the loss of leadership in the most important technological development so far this decade. It’s not a trade off I would have built trade policy on.
The life and death of air conditioning
Photo by Ashkan Forouzani on Unsplash
Across six years of working at TechCrunch, no article triggered an avalanche of readership or inbox vitriol quite like Air conditioning is one of the greatest inventions of the 20th Century. It’s also killing the 21st. It was an interview with Eric Dean Wilson, the author of After Cooling, about the complex feedback loops between global climate disruption and the increasing need for air conditioning to sustain life on Earth. The article was read by millions and millions of people, and hundreds of people wrote in with hot air about the importance of their cold air.
Demand for air conditioners is surging in markets where both incomes and temperatures are rising, populous places like India, China, Indonesia and the Philippines. By one estimate, the world will add 1 billion ACs before the end of the decade. The market is projected to before 2040. That’s good for measures of public health and economic productivity; it’s unquestionably bad for the climate, and a global agreement to phase out the most harmful coolants could keep the appliances out of reach of many of the people who need them most.
This is a classic feedback loop, where the increasing temperatures of the planet, particularly in South Asia, lead to increased demand for climate resilience tools like air conditioning and climate-adapted housing, leading to further climate change ad infinitum.
Josh Wolfe speaking at Nvidia Auditorium at Stanford. Photo by Chris Gates.
Josh Wolfe gave a talk at Stanford this week as part of the school’s long-running Entrepreneurial Thought Leaders series, talking all things Lux, defense tech and scientific innovation. The .
Lux Recommends
As Henry Kissinger turns 100, Grace Isford recommends “Henry Kissinger explains how to avoid world war three.” “In his view, the fate of humanity depends on whether America and China can get along. He believes the rapid progress of AI, in particular, leaves them only five-to-ten years to find a way.”
Our scientist-in-residence Sam Arbesman recommends Blindsight by Peter Watts, a first contact, hard science fiction novel that made quite a splash when it was published back in 2006.
Mohammed bin Rashid Al Maktoum, and just how far he has been willing to go to keep his daughter tranquilized and imprisoned. “When the yacht was located, off the Goa coast, Sheikh Mohammed spoke with the Indian Prime Minister, Narendra Modi, and agreed to extradite a Dubai-based arms dealer in exchange for his daughter’s capture. The Indian government deployed boats, helicopters, and a team of armed commandos to storm Nostromo and carry Latifa away.”
Sam recommends Ada Palmer’s article for Microsoft’s AI Anthology, “We are an information revolution species.” “If we pour a precious new elixir into a leaky cup and it leaks, we need to fix the cup, not fear the elixir.”
I love complex international security stories, and few areas are as complex or wild as the international trade in exotic animals. Tad Friend, who generally covers Silicon Valley for The New Yorker, has a great story about an NGO focused on infiltrating and exposing the networks that allow the trade to continue in “Earth League International Hunts the Hunters.” "At times, rhino horn has been worth more than gold—so South African rhinos are often killed with Czech-made rifles sold by Portuguese arms dealers to poachers from Mozambique, who send the horns by courier to Qatar or Vietnam, or have them bundled with elephant ivory in Maputo or Mombasa or Lagos or Luanda and delivered to China via Malaysia or Hong Kong.”
