Hiding will be harder than ever and finding will be easier than ever

Monday, April 11th, 2022

As sensors of all kinds become ubiquitous, Christian Brose notes (in The Kill Chain), hiding will be harder than ever and finding will be easier than ever, making it more difficult and riskier to penetrate another country’s territory:

In 2014, for example, the Russian government emphatically denied what most of the world knew to be true: that Russia’s Little Green Men had actively intervened in Ukraine. What revealed the truth (though Moscow never admitted it) was a flurry of pictures and videos of Russian forces and equipment that had been captured and shared on social media, including by Russian soldiers posing for selfies.

This was also how it was revealed that Russia had supplied Ukrainian separatists with the surface-to-air missile system that shot down Malaysian Airlines Flight 17 on July 17, 2014. Civilians with smartphones captured the weapon moving away from the scene of the crime, which revealed its Russian military markings, and then they photographed the same system on its way back toward the border of Russia.

Similarly, when the Chinese government denied in 2016 that it was installing military capabilities on reclaimed islands in the South China Sea, commercial satellite imagery showed the truth in high resolution.

Militaries in the future will have little hope of hiding large traditional ships, aircraft, or ground force movements.

An economy is a system for generating and trading solutions to problems

Sunday, April 10th, 2022

Robin Hanson once wrote about how intelligent people tend to overestimate how smart everyone else is, and Anatoly Karlin elaborates on this, with support from PISA test scores:

Fortunately, the PISA website has sample math questions from the 2012 assessment, corresponding to each of the six different levels of difficulty, as well as statistics on the percentage of 15-16 year old students from each of the participating countries that is capable of correctly answering it.

Here is the sample question from Level 6, the hardest level:

Helen rode her bike from home to the river, which is 4 km away. It took her 9 minutes. She rode home using a shorter route of 3 km. This only took her 6 minutes.

What was Helen’s average speed, in km/h, for the trip to the river and back?

Karlin notes how few people get this right:

This problem requires a multi-step approach, an understanding of rates, and the intelligence to complete it in the correct order.

Though not especially hard, even at this level. I suspect that many of you can do it in your heads within a minute.

But a majority of all the tested teens begged to differ.

OECD average: 3% (!!). Korea: 12%, Japan: 8%, Germany: 5%. The US, Italy, Sweden, and Russia were all at 2%; the Mediterranean was at 1%.

Some countries where a big fat 100% (rounded up) were unable to do this problem: Argentina, Brazil, Chile, Colombia, Indonesia, Jordan, Kazakhstan, Malaysia, Mexico, Peru, Qatar, Tunisia, Uruguay.

The number of people at this level, the highest measured by PISA, is dwindling away into insignificance in Latin America and the Middle East.

And yet this only translates to an IQ of 120-125. We’re nowhere even near genius level yet.

This matters:

The classical definition of an economy is a system for the production and exchange of goods and services. However, I will argue that you can view it even more fundamentally as a system for generating and trading solutions to problems.


Some of these problems, such as subsistence farming and trucking, are pretty simple and can be accomplished with reasonable efficiency even by relatively dull workers. This is because problems in this “Foolproof sector” (as Garett Jones calls it) require few steps and have only a minimal threshold difficulty, so production in this sector is governed by the standard Cobb-Douglas equation. More highly skilled workers are only modestly more productive, and are thus awarded with modestly higher salaries. Labor differs by productivity, but is substitutable — one experienced waiter is worth two novice ones.

Other problems are very complex and require teams of competent workers to perform multiple complicated steps to create a successful solution. The best are paired with the best for maximum productivity. Moreover, many O-Ring problems might have a threshold limit for IQ, below which no productive work can be done on them in principle (as per the Ushakov-Kulivets model). To be commercially viable, the risk of failure on any one link of a long production chain needs to be kept low. Examples of these “O-Ring” tasks may include: Aircraft manufacturing; corporate merger planning; computer chip design; machine building; open-heart surgeries.

What Bloch foresaw with stunning prescience was a future battlefield that would be far more lethal than most of his contemporaries imagined

Saturday, April 9th, 2022

In The Kill Chain, Christian Brose tells the story of Jan Bloch:

Jan Bloch was not a soldier. He was a banker who was born into poverty in Warsaw in 1836 but worked his way up to become a wealthy railroad financier in Russian-controlled Poland. He never served a day of his life in uniform. But he was passionate about military issues and for years obsessively studied how the new technologies of his era would change warfare.

Bloch examined the introduction of the machine gun, smokeless gunpowder, long-range artillery, new types of explosives, railroads, telegraphs, steamships, and other innovations. And he traced their increasingly devastating effects from the Crimean War in the 1850s through the American Civil War a decade later, the Austro-Prussian War in 1866, the Franco-Prussian War in 1870, the Russo-Turkish War that began in 1877, and the start of the Boer Wars in 1880. He poured the results of his lifelong study of technology and warfare into a six-volume doorstop of a book that he published in 1898, four years before his death. He called it The Future of War.

What Bloch foresaw with stunning prescience was a future battlefield that would be far more lethal than most of his contemporaries imagined. The invention of smokeless gunpowder would literally lift the fog of war that had hung thick over past conflicts so that, unlike in previous skirmishes, opposing armies would remain dangerously exposed after the initial volleys of gunfire. Rifles could shoot farther, faster, and more accurately than ever. For centuries, the best professional soldiers could fire a few accurate shots per minute. At the end of the nineteenth century, average conscripts could fire dozens of accurate shots per second. And because bullets had become smaller, soldiers could carry more of them into combat.

Modern fast-loading artillery, equipped with range finders and high-explosive shells, were 116 times deadlier, by Bloch’s calculation, than guns from just a few decades prior.


For Bloch, this meant that battlefields would become killing fields, where combatants would never “get within one hundred yards of one another.” War would cease to be “a hand-to-hand contest in which the combatants measure their physical and moral superiority.” Instead, Bloch predicted, “the next war will be a great war of entrenchments.”


Much of the war was waged with modern technology but antiquated doctrine.


Another factor that made the war so calamitous was the military technological parity that existed between the great powers.

How is China turning deserts into arable lands?

Friday, April 8th, 2022

China has a total land area of 3.5 million square miles, but only 12% of that land is arable. Researchers there claim to have developed a novel technology that can convert desert to arable land:

The technology developed by the researchers at Chongqing Jiaotong University involves a paste made from plant cellulose, that can greatly improve the ability of desert sands to hold water, minerals, air, microbes, and nutrients essential for plant growth.

This paste was applied to a sandy 1.6-hectare plot in the Ulan Buh Desert, in the Mongolian Autonomous Region. Over time, the plot was transformed into fertile cropland capable of producing tomatoes, rice, watermelon, sunflowers, and corn.

Professor Yang Qingguo, of Jiaotong University, explained that “The costs of artificial materials and machines for transforming sand into the soil is lower compared with controlled environmental agriculture and reclamation”

According to the Chinese researchers, the plants grown in the sandy plot delivered higher crop yields, using the same amount of water needed for growing crops in normally arable soils. Moreover, the amount of fertilizer needed to produce the crops was lower than what is generally required for the growth of vegetables in other soils.

Lethal autonomous weapons have existed for a long time

Thursday, April 7th, 2022

Lethal autonomous weapons have existed for a long time, Christian Brose explains (in The Kill Chain):

Such systems, with varying degrees of capability, are currently in use by at least thirty different states. The US Navy, for example, has used the Phalanx gun and Aegis missile defense systems to defend its ships for decades. Though far less capable than the intelligent machines of today and tomorrow, these systems can be switched into a fully automatic mode that enables them to close the kill chain against incoming missiles without human involvement. The decision to trust those machines to do so was born of necessity: it was unlikely humans could respond fast enough to counter incoming missiles. That inability was deemed a greater danger than the option of turning the kill chain over to a machine that could shoot down missiles in time-sensitive situations more effectively than humans could.

Is financial innovation a good thing?

Wednesday, April 6th, 2022

Is financial innovation a good thing?

In the context of a free market, innovation is a positive-sum game. The innovations that survive — most don’t — are the ones that conserve resources and improve quality. In the case of financial innovation, improving quality could mean better risk management.

But financial innovation does not take place in the context of a free market. Our financial system is permeated with government guarantees. Some guarantees, like deposit insurance or pension guarantees, are explicit. Other guarantees, like “too big to fail,” are implicit.

These guarantees can be exploited by firms that take on excessive risk. If a gamble pays off, the gains go to owners and managers of the firm. If the gamble turns out badly, some of the losses go to taxpayers. Even though managers might not consciously be searching for ways to game the system, the competition for returns will push them in the direction of doing so.

Innovative financial instruments and practices can facilitate gaming the system, without regulators realizing it. Clever innovations can enable a bank to comply with the letter of a regulation while violating its spirit. Sometimes, even the executives of the bank are fooled. They do not realize that their profits are coming from this “regulatory arbitrage,” rather than from real business skill.

Both militaries had tanks, radios, and airplanes

Tuesday, April 5th, 2022

History is replete with examples of military rivals that had the same technologies, and what set them apart is how they used them, Christian Brose explains (in The Kill Chain):

The archetypal case is that of France and Germany in the 1930s. Both militaries had tanks, radios, and airplanes. But whereas the French chose to employ those technologies as part of their effort to build better versions of the fortifications they had relied upon in World War I, Germany combined those capabilities into a new concept called blitzkrieg, which enabled the German army to maneuver rapidly through France’s defensive positions, capturing Paris in roughly one month in 1940.

The Psychology of Your Scrolling Addiction

Monday, April 4th, 2022

To better understand why people fall into (metaphorical) rabbit holes, researchers conducted a series of studies with a total of 6,445 U.S.-based students and working adults:

Through this research, we identified three factors that influence whether people choose to continue viewing photos and videos rather than switch to another activity: the amount of media the person has already viewed, the similarity of the media they’ve viewed, and the manner in which they viewed the media.

In the first part of our research, we were interested in exploring whether the pull of the rabbit hole would grow stronger or weaker once people had already viewed several videos. We had participants view either five different music videos or just one music video, and then we asked them if they’d rather watch another video or complete a work-related task. In theory, one might expect that people would get tired of watching music videos after watching five in a row, reducing their desire to watch more of them. But in fact, we found that the opposite was true: Watching five videos made people 10% more likely to choose to watch an additional music video than if they only watched one video.

Next, we examined the impact of framing the videos people watched as similar to one another. We showed participants the same two videos, but for half of the participants, we explicitly labelled the videos with the same category label (“educational videos”), while for the other half of the participants, we didn’t include a category label. We found that simply framing the videos as more similar via the category label made people 21% more likely to choose to watch another related video.

Finally, we looked at how people acted after watching several videos consecutively, versus when they watched the same number of videos with some interruptions. We had one group of participants complete two work tasks and then watch two similar videos, while the other group completed the same four tasks, but alternated between them (i.e., work, video, work, video). Despite having done exactly the same activities, the order made a big difference: The participants whose video consumption was uninterrupted were 22% more likely to choose to watch another video than those who alternated between work tasks and videos.

The information that most US military machines collect is not actually processed onboard the machine itself

Sunday, April 3rd, 2022

The information that most US military machines collect is not actually processed onboard the machine itself, Christian Brose explains (in The Kill Chain):

It is either stored on the system and then processed hours or even days later when the machine returns from its mission. Or it is streamed back to an operations center in real time, terabyte by terabyte, which places a huge burden on military communications networks.

Either way, it is the job of humans, not machines, to comb through most of that data and find the relevant bits of information. In 2020, that is the full-time job of literally tens of thousands of members of the US military.


In reality, these supposedly “unmanned” systems require dozens of people to pilot each one remotely, steer its sensors, maintain it on the ground, and analyze the information that it collects, much of which is discarded because there are simply not enough people to process all of it.

Indeed, for years, the US military has supplied only a fraction of the drone missions that its commanders in combat have demanded. The problem has not been a lack of drones, but a lack of people.


In the absence of machines that can share information directly with other machines, this is how the United States connects its battle networks: a lot of people sitting in a lot of large rooms.


More often they use a computer-based instant messaging program called mIRC chat. I have watched individual servicemembers juggling a dozen separate chat windows, which can often involve taking information generated by one machine and manually transferring it to another machine. They call it “hand jamming” or “fat fingering.” It is slow and prone to human error.

A friend of mine who recently did targeting in the US military told me that the best way his unit could get on one page in identifying a target was with Google Maps. They had to gather up all of their different streams of information about the target from their assorted sensor platforms, come to a time-consuming decision on where the target actually was located, and literally drop a pin in Google Maps to direct their shooters where on earth to fire their weapons.

This shows up as a stripe of interference perpendicular to the orbital path of the satellite

Saturday, April 2nd, 2022

While most satellite imagery is optical, meaning it captures sunlight reflected by the earth’s surface, Synthetic Aperture Radar (SAR) satellites such as Sentinel-1 work by emitting pulses of radio waves and measuring how much of the signal is reflected back:

Coincidentally, the radars on some missile defence batteries and other military radars operate using frequencies in the NATO G-band (4,000 to 6,000 Gigahertz) which overlaps with the civilian C-band (4,000, to 8,000 Gigahertz), commonly used by open source SAR satellites.

In the simplest terms, this means that when the radar on the likes of a Patriot battery is turned on, Sentinel-1 picks up both the echo from its own pulse of radio waves, as well as a powerful blast of radio waves from the ground-based radar. This shows up as a stripe of interference perpendicular to the orbital path of the satellite.

Patriot missiles are not the only system that create this type of interference. Other military radars that operate on the same C-band frequency include naval radars such as the Japanese FCS-3, the Chinese Type-381 and the Russian S-400 surface-to-air missile system. All should be detectable when switched on and in view of Sentinel-1.

Dan confirmed the site of the radars he discovered during his initial research by using other open sources such as imagery on Google Maps and even data from the Strava running app.

He also highlighted other interesting missile battery locations, such as the Swedish STRIL array which acts as the country’s early warning system against Russian aircraft and missiles.

This was how America acted when it was serious

Friday, April 1st, 2022

It is difficult to overstate the all-encompassing sense of urgency that Washington felt in the early years of the Cold War, Christian Brose explains (in The Kill Chain):

The way Eisenhower saw it, Washington’s primary role was to get the big things right. That started with picking the right people—not necessarily good people or nice people, but exceptional people, the kinds of people who might today be called “founders.” Eisenhower believed in empowering these founders by giving them broad authority to solve clearly defined problems, providing them all of the resources and support they needed to be successful, and then holding them strictly accountable for delivering results. In short, it was a strategy of concentration—of priorities, money, effort, and, most importantly, people.


He awarded gigantic contracts with fat margins to companies and technologists and integrated them into one military-industrial team. He scraped a space launch center out of a boggy stretch of Florida wetland called Cape Canaveral. He repeatedly blew up rocket engines and missile prototypes on the launchpad. But along the way, Eisenhower defended Schriever, got him more money when he needed it, and protected him from bureaucrats and staunch rivals, such as fellow Air Force general Curtis LeMay, who tried to kill the project at every turn…

Eventually, Schriever and his team did the impossible: they developed the Thor, Atlas, Titan, and Minuteman missiles that could deliver nuclear weapons to precise locations on the other side of the planet in minutes.


This was how America acted when it was serious. The paramount concern was picking winners: the priorities that were more important than anything else, the people who could succeed where others could not, and the industrialists who could quickly build amazing technology that worked.


This is how Silicon Valley originated: as a start-up incubated by the Department of Defense. Margaret O’Mara, a historian and former staffer for Vice President Al Gore, has observed, “Defense contracts during and after World War II turned Silicon Valley from a somnolent landscape of fruit orchards into a hub of electronics production and innovations ranging from mainframes to microprocessors to the internet.”


A sprawling bureaucracy materialized in the 1960s to administer and discipline the military-industrial complex. Eisenhower’s more personalized approach to military acquisition and innovation, which was based on picking winners and holding them accountable, became bureaucratized amid the broader adoption of the industrial age management practices that had come into vogue in leading companies.

No one did more to further this trend than Robert McNamara, a veteran of Ford Motor Company who ran the Pentagon for much of the 1960s. Under his tenure, in the spirit of improving efficiency, new layers of oversight, analysis, and management were added, and these grew and began choking off the ability to develop breakthrough technologies quickly.


The result was that the process of developing military technology became harder, slower, and less creative. This outcome only intensified in the early 1970s, when many engineers in Silicon Valley began growing uncomfortable working for the US government as the Vietnam War grew more divisive.