Most hiring in business is functional, with roles tied to specific problems that executives identify. If a company has a marketing problem, executives hire a marketer. Launching a new product? Hire engineers, designers and product managers. A lot of the friction between new staff and their new companies stems from a lack of precision on exactly what problem is being solved for.
Then there’s risk and security. Risks are multiplying for all companies and in all domains, from financial risks and supply chain disruptions to climate catastrophes and pandemic-induced workforce debilitation. Ditto for security: the digital attacks on computer infrastructure, physical attacks on employees, and disinformation attacks on brands and reputation have combined to create an almost infinite ‘threat matrix’ of daily terrors.
These are all problems, but not ones that can be solely solved through functional expertise. Instead, they can and must be handled at all levels of an organization. You don’t hire for security, you create security cultures that are imbued in all decisions and strategies. You can’t throw bodies at risk, but must make resiliency and risk analysis a vital and constant concern.
Yet, companies still bring their functional, problem-solving approach and expect that with a human in place, their problems are solved. Companies hire a Chief Information Security Officer (CISO) and expect data breaches to stop, and they hire Chief Risk Officers (CRO) to stave off all those risky concerns. Problem identified; hire made. Those leaders hope to inculcate organizational cultures of course, but talk to any CISO or CRO and they will tell you horror stories about the difficulties of systematizing their thinking into the fabric of an organization.
All this was on my mind this week as I read more about Peiter Zatko (who goes by the online identity Mudge) and his whistleblowing complaint against Twitter, where he was formerly its head of security before being fired by the social network earlier this year. On Thursday, Cara Lombardo in the Wall Street Journal reported that Zatko received $7 million in compensation as part of a settlement with Twitter, that in part included a non-disclosure agreement. As a whistleblower to the SEC:
Mr. Zatko said in his complaint that he “uncovered extreme, egregious deficiencies by Twitter in every area of his mandate,” including privacy, digital and physical security, platform integrity and content moderation.
Lombardo writes that “Twitter’s team countered by describing Mr. Zatko as a disgruntled former employee with an ax to grind…” He will testify this coming Tuesday in front of the Senate Judiciary Committee.
There’s now been extensive reporting on Zatko’s firing, namely due to Twitter’s legal battle with Elon Musk to force him to buy the company, which is set for Delaware Chancery Court in October.
Twitter’s pattern of behavior is all too familiar to security and risk professionals. Twitter clearly identified that it had massive security gaps across its systems. For instance, just last month, a former Twitter employee was found guilty of conspiracy to commit wire fraud, falsifying records and money laundering while spying for Saudi Arabia in a case stemming back to 2014 and 2015. The company’s moderation of speech has been a perennial PR nightmare, and the company also disclosed a data breach in July affecting more than 5 million accounts.
Problem identified; hire made. Zatko was brought on as head of security, bringing his long-standing reputation and stature in security circles to bear on one of the most influential global social networks.
Yet, this wasn’t a hire made, it was an operating system downloaded. Fixing Twitter’s problems would require rebuilding the foundations of the entire company, from retraining engineers and prioritizing security reviews to evaluating internal threat risks and developing much more comprehensive trust and safety systems for content moderation. Security, at least for a time being, would have had to become the overriding priority of the company to rebalance a culture that by all appearances is woefully inadequate for the threats the company faces.
Unfortunately, Zatko’s work came at a time when Twitter’s business — and its products — needed extensive shoring up to meet the demands of Wall Street. It’s little wonder then that as he went about his work, he seemed to have an ax to grind.
Security and risk professionals face the daunting task of always making their work stand above daily business challenges. Profits must always be sought, new products launched, and it’s hard for security — even at the most enlightened companies — to not feel like a general tax on productivity. Something must always get the ax, and unsurprisingly, it’s often security and risk that takes the brunt of the blows.
Functional thinking simply isn’t enough anymore, particularly given the scaling up of threats against all institutions and systems the world over. Just this past week as students headed back to classrooms, the second largest school district in the United States had to shut down its entire computer system due to a ransomware cyberattack, leaving hundreds of thousands of students, teachers and parents in the lurch.
Security isn’t a job, it’s a culture. It’s not a person, it’s an organization. Security means overcoming the insecurities of leaders who would rather feign ignorance at the challenges their companies and institutions face rather than devote the resources and attention that the issue necessarily deserves.
“Securities” podcast: The geopolitics and digital future of agricultural commodities
The "Securities" podcast. Artwork by Chris Gates
Agricultural commodities is a bit like accounting: you only hear news stories about it when things go wrong. And unfortunately for the world in 2022, a lot is going wrong in agriculture. Vladimir Putin’s war on Ukraine has devastated one of the world’s great breadbaskets, and global climate disruptions are wrecking havoc on food productivity. That’s led to soaring inflation and increasingly contentious politics, particularly in the developing world.
Sadly, that’s not the only problem the industry faces. Commodities are still traded predominantly on antiquated systems, with the United Nations estimating that more than 275 million emails are exchanged annually to ship about 11,000 vessels of grain across the oceans. That’s one reason why Lux led the $7 million seed round for Vosbor earlier this summer to build the first digital agricultural commodities exchange (which we discussed in “Intel’s Malaise” and in an article).
I wanted to understand more of this extraordinarily complex industry, and so I asked two former CEOs of the largest agriculture commodities companies in the world to weigh in for a new podcast episode of “Securities”. Joining me were Chris Mahoney, former CEO of Glencore Agriculture and now known as Viterra, as well as Soren Schroder, former CEO of Bunge.
We talk about the cyclicality of agricultural markets, the cost disease of infrastructure upgrades, the geopolitical strategies of ag firms, the increasing focus on logistics capabilities, and what the future of digitalization and technology have in store for this critical industry.
With Queen Elizabeth’s sorrowful passing this week, I’ll make the obvious but sincere recommendation to watch The Crownon Netflix. No, it doesn’t have the gore and layers of intrigue of House of the Dragon or the Middle-earth fantasy of The Rings of Power, but what it offers instead is a solemn portrait of a singular person wading their way through the buffeting waves of history. Enrapturing, and deeply relevant.
Our scientist-in-residence Sam Arbesman recommends Hannah Ritchie’s thoughts on the paradoxes of being an effective environmentalist in the Works in Progress newsletter. “Microwaves are the most efficient way to cook. Local food is often no better than food shipped from continents away. Organic food often has a higher carbon footprint. And packaging is a tiny fraction of a food’s environmental footprint, and often lengthens its shelf-life. Yet it still feels wrong."
China has been facing a brewing real estate crisis that has plunged shares of major property developers to historic lows and forced one to give up its headquarters. Nearly all data point to a very turbulent period ahead for the world’s second largest economy. Ni Dandan, writing in Sixth Tone, draws our perspective to the last two decades of China’s economic history, asking “Can China Fix Its Broken Housing Market?”
As California suffered from record heat this week, there is some potential optimism from those rays of sun: recognizing the strength of the solar supply chain. David Fickling, writing in Bloomberg, argues that far from being an impossibility, net zero emissions is already within reach given current and planned supply chains. “The solar boom of the past two decades has left the world with a cumulative 971GW of panels. The polysilicon sector is now betting on hitting something like that level of installations every year.”
That’s it, folks. Have questions, comments, or ideas? This newsletter is sent from my email, so you can just click reply.
Forcing China’s AI researchers to strive for chip efficiency will ultimately shave America’s lead
In incididunt ad qui nostrud sint ullamco. Irure sint deserunt Lorem id officia dolore non. Anim dolor minim sit dolor et sint aliquip qui est. Ex in tempor laborum laboris dolor laboris ullamco quis. Enim est cupidatat consequat est culpa consequat. Fugiat officia in ea ea laborum sunt Lorem. Anim laborum labore duis ipsum mollit nisi do exercitation. Magna in pariatur anim aute.
In incididunt ad qui nostrud sint ullamco. Irure sint deserunt Lorem id officia dolore non. Anim dolor minim sit dolor et sint aliquip qui est. Ex in tempor laborum laboris dolor laboris ullamco quis. Enim est cupidatat consequat est culpa consequat. Fugiat officia in ea ea laborum sunt Lorem. Anim laborum labore duis ipsum mollit nisi do exercitation. Magna in pariatur anim aute.
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.”
