The social science of Wagner Group’s past and future
I am on vacation, so this column is written by Michael Magnani, a Political Risk Researcher based in NYC. He graduated from NYU’s Center for Global Affairs with a concentration in Transnational Security.
If I had a dollar for each time I heard the words ‘Wagner’ or ‘Prigozhin’ this summer, I’d be able to enjoy more than a few meals at West Village’s Via Carota. That’s self-inflicted, partially: I chose to write my graduate thesis on the Wagner Group, only to find that my quiet days in the library were shattered by a staccato of news flashes. The mercenary group failed in its mutiny in late June that arguably presented the greatest domestic threat to Vladimir Putin’s two decade-plus hold on power.
Suddenly, what was Wagner’s future? How would their successful and lucrative operations continue in Syria and across Africa? Would Yevgeny Prigozhin mysteriously defenestrate himself, like so many others that Putin no longer had use for? Ultimately, Prigozhin, along with other key Wagner leaders such as founder Dmitry Utkin, met their fate in late August when the former’s private jet went down over the Russian countryside, a crash U.S. intelligence attributes to the Kremlin.
For all the focus on the group’s future, what is profoundly interesting is its past. Wagner’s history and relationship with its chief sponsor, Russia, can explain how both the state and the non-state will transform in the years ahead.
Georgetown professor Daniel Byman suggests that states, typically weaker ones, are motivated to sponsor terror groups for a variety of reasons including strategic concerns, ideology and domestic politics. Each of these motivations have sub-motivations as well (see Figure 1). While Wagner isn’t universally considered a terror group in the West, proposed and passed legislation originating from the EU, UK and U.S. have suggested this designation is shifting. On top of this, Wagner is an unconventional organization like most terror groups, and engages in many similar activities such as attacks on civilians and natural resource extraction/extortion.
Figure 1: A chart showing the motivations of non-state actors like Wagner Group
Russia has long held imperialistic strategic concerns that include projecting power globally. Former prime minister Yevgeny Primakov described Russia’s intentions, in what would come to be known as the Primakov Doctrine, as seeking to counter a U.S.-led unipolar world with a multipolar one in which Russia continues to play the role of a great power.
There’s just one challenge: Russia is not a great power. As its ‘special military operation’ in Ukraine has shown, Russia’s military capabilities are a far cry from what the world once thought of them. Its economy isn’t much better, smaller than all but a handful of individual U.S. states in gross terms.
So how does a wannabe great power act as one on the international stage? Enter Wagner.
As Byman explains, weaker states with limited conventional military forces tend to rely on unconventional groups to carry out their strategic goals. Russia used Wagner for exactly this purpose during the Syrian Civil War in support of Bashar al-Assad’s regime, allowing Russia to carve out a key sphere of influence in western Syria that closely follows territories liberated from ISIS/rebel control by Wagner forces. These territories include a significant portion of Syria’s energy infrastructure, where Wagner was extracting a 25% commission on future revenues. Russia’s sphere runs parallel with the American sphere of influence in the country’s east, stalemating any progress.
In Libya, Russia has used Wagner as a more conventional military force in order to augment Field Marshal Khalifa Haftar and his Libyan National Army (LNA) forces in their campaign against the internationally-backed Government of National Accord (GNA). If Haftar and the LNA won control of Libya, Russia would hold a key seat at the table regarding the future of Libya and could potentially turn the country into a useful client state. On top of this, they could use Wagner’s control of energy installations there as leverage against both Haftar and Europe while also carving out a key sphere of influence along the Mediterranean immediately due south of NATO’s southern flank.
The story repeats elsewhere in Africa and as far flung as South America, but step back for a moment. Among early-stage startups, it’s often hard to get inexperienced CEOs to properly delegate an activity as simple as writing a press release. Putin has delegated whole theaters of combat to a non-state organization to prosecute.
This is an extreme example of a well-studied problem in the social sciences known as the principal-agent problem. No person, organization or government (the principal) can do it all, so they have to delegate tasks to agents — but those agents don’t necessarily have the same motivations or goals as the principal. Hire a consultant, and they suggest hiring more consultants, not solving the problem.
The relationship between principal and agent evolves over time as well, often complicating the original hierarchy. Returning to Byman alongside Cornell professor Sarah Kreps, the two describe how principal-agent dynamics with states and non-state terror organizations break down over time. As Russia has delegated more autonomy to Wagner, Wagner developed its skills and power independent of the Kremlin. As Wagner developed independence, the relationship between state and non-state declines into a host of so-called agency losses that eventually cause the relationship between the principal and the agent to collapse (see Figure 2).
Figure 2: Principal-agent dynamics and how agency losses can come about.
Syria encapsulates this principal-agent collapse, where Russia saw Wagner as having a comparative advantage against more unconventional groups such as ISIS. Putin delegated autonomy to Wagner, which alongside Syrian forces, led offensives that liberated key cities such as Palmyra from ISIS control. Successes such as these may have emboldened the group and furthered their independent nature.
This independence culminated in the Battle of Khasham, an unsuccessful assault in 2018 by Wagner and Syrian forces against a Kurdish/American-held gas facility in eastern Syria. While it is not known whether Russia knew about Wagner’s plans beforehand, they did little to stop American forces from decimating Wagner forces using air power. Russia denied that any of the casualties were theirs (plausible deniability when it suits their interests, another key point of Byman and Kreps’ theory), perhaps hoping a big defeat would bring Wagner back into their orbit.
It was not to be. Emboldened by success in Syria, Wagner became ever more independent and operationally capable. They believed that instead of being an asset of the Kremlin that they were in fact an asset to the Kremlin. This line of thinking was further exemplified by Wagner’s actions during the Battle of Bakhmut in Ukraine last year, where the group largely fought on its own and very publicly came out against Russian military leadership.
As early as 2018, key fissures existed in the relationship between Wagner and Russia that eventually split entirely in Wagner’s attempted mutiny and the ultimate assassination of the group’s leadership in August 2023. Russia relied on Wagner across the globe for years, only taking action against the group when it attempted to carve out an even bigger slice of power.
Wagner’s summer in the limelight has led to much speculation on the group’s next moves. A majority of this speculation can largely be ignored, as Wagner, like many other unconventional groups, has proven notoriously unpredictable and secretive in its actions. Russia is now trying to cobble Wagner’s forces into its own armed forces, merging the principal and agent into one. Synergy, I believe they call it in business, but like any hostile takeover, the culture of the acquisition rarely expires in a quiet death. The fissure between Wagner and Russia still exists, just internally rather than externally. I’m not going to lie, I won’t miss a whirl of words about Wagner or Prigozhin, at least for a few months.
Lux Recommends
Tess Van Stekelenburg enjoyed a post from Lux portfolio founder Viswa Colluru of Enveda Biosciences, who argues that there is an expansive opportunity in mapping the chemistry of life. “If we reframe life as metabolism, then our map of life is woefully incomplete. Today, even with our most sophisticated tools to annotate (i.e., identify or name) the masses obtained from an untargeted metabolomics experiment, we can barely annotate 6% of the metabolites.”
Amman, Jordan has grown from a small desert crossroads into a sizable and sprawling metropolis. Local writer Ursula Lindseydescribes the extreme water stress that Amman’s growth has placed on its infrastructure, and how desert cities can flourish in the age of climate disruption. “In daily domestic life, the average Jordanian uses 80 liters per day or less — in some cases as few as 35. (For perspective, Europeans on average consume about 150 liters per day, and Americans double that. An average seven-minute shower is 55 liters, or more than half a Jordanian’s daily usage, and an average washing-machine cycle is 70 liters. Handwashing a day’s worth of dirty dishes uses around 60 liters.)”
Our scientist-in-residence Sam Arbesman recommends Jeremy B. Merrill’s description of “champagne phrases” — phrases that can be rewritten to sound equally positive or negative and can confuse natural language processing models.
In the department of the alarming, Stephanie Nolen writes in The New York Times about how a new species of mosquito is upending the boundaries around malaria and dengue fever, threatening to spread these diseases throughout urban Africa and beyond. "Public health experts say [*anopheles stephensi*] might be less of a threat now if it had been taken more seriously when it was first discovered in Africa — in 2012, in the seaport at Djibouti, a tiny nation on the Horn of Africa. The country is so small that no one paid much attention — except for a handful of entomologists who anticipated potential disaster. It wasn’t until their warnings began to come true a decade later that governments and major international funders of mosquito-control efforts started to grapple with this new reality.”
Finally, Tess and Sam recently talked about the failed experiment of Fordlandia, Henry Ford’s quixotic quest to own a vertical supply chain for rubber via a jungle utopia. “Fordlandia isn't just the story of a plantation; it's a story about Ford's ego. As disaster after disaster struck, Ford continued to pour money into the project. Not one drop of latex from Fordlandia ever made it into a Ford car. But the more it failed, the more Ford justified the project in idealistic terms. ‘It increasingly was justified as a work of civilization, or as a sociological experiment,’ [**greg grandin**] says. One newspaper article even reported that Ford's intent wasn't just to cultivate rubber, but to cultivate workers and human beings.”
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.”
