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Electron Band Structure in Germanium, My Ass

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Kovar/Hall

          Abstract: The exponential dependence of resistivity on temperature in germanium is found to be a great big lie. My careful theoretical modeling and painstaking experimentation reveal 1) that my equipment is crap, as are all the available texts on the subject and 2) that this whole exercise was a complete waste of my time.

Introduction

          Electrons in germanium are confined to well-defined energy bands that are separated by "forbidden regions" of zero charge-carrier density. You can read about it yourself if you want to, although I don't recommend it. You'll have to wade through an obtuse, convoluted discussion about considering an arbitrary number of non-coupled harmonic-oscillator potentials and taking limits and so on. The upshot is that if you heat up a sample of germanium, electrons will jump from a non-conductive energy band to a conductive one, thereby creating a measurable change in resistivity. This relation between temperature and resistivity can be shown to be exponential in certain temperature regimes by waving your hands and chanting "to first order".

Experiment procedure

      I sifted through the box of germanium crystals and chose the one that appeared to be the least cracked. Then I soldered wires onto the crystal in the spots shown in figure 2b of Lab Handout 32. Do you have any idea how hard it is to solder wires to germanium? I'll tell you: real goddamn hard. The solder simply won't stick, and you can forget about getting any of the grad students in the solid state labs to help you out.
      Once the wires were in place, I attached them as appropriate to the second-rate equipment I scavenged from the back of the lab, none of which worked properly. I soon wised up and swiped replacements from the well-stocked research labs. This is how they treat undergrads around here: they give you broken tools and then don't understand why you don't get any results.

 
Fig. 1: Check this shit out.
      In order to control the temperature of the germanium, I attached the crystal to a copper rod, the upper end of which was attached to a heating coil and the lower end of which was dipped in a thermos of liquid nitrogen. Midway through the project, the thermos began leaking. That's right: I pay a cool ten grand a quarter to come here, and yet they can't spare the five bucks to ensure that I have a working thermos.

Results

      Check this shit out (Fig. 1). That's bonafide, 100%-real data, my friends. I took it myself over the course of two weeks. And this was not a leisurely two weeks, either; I busted my ass day and night in order to provide you with nothing but the best data possible. Now, let's look a bit more closely at this data, remembering that it is absolutely first-rate. Do you see the exponential dependence? I sure don't. I see a bunch of crap.
      Christ, this was such a waste of my time.
      Banking on my hopes that whoever grades this will just look at the pictures, I drew an exponential through my noise. I believe the apparent legitimacy is enhanced by the fact that I used a complicated computer program to make the fit. I understand this is the same process by which the top quark was discovered.

Conclusion

      Going into physics was the biggest mistake of my life. I should've declared CS. I still wouldn't have any women, but at least I'd be rolling in cash.

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GaryBIshop
4 hours ago
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Ha! Working with physical things is indeed a pain!
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Microsoft uses AI to find flaws in GRUB2, U-Boot, Barebox bootloaders

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Microsoft used its AI-powered Security Copilot to discover 20 previously unknown vulnerabilities in the GRUB2, U-Boot, and Barebox open-source bootloaders. [...]
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GaryBIshop
22 hours ago
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Wow
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Saturday Morning Breakfast Cereal - Bean

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Click here to go see the bonus panel!

Hovertext:
Shouting BLOOOOOOOD is similarly frustrating.


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GaryBIshop
4 days ago
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This is a great strategy. One enormous bean!
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An AI bubble threatens Silicon Valley, and all of us

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This article appears in the April 2025 issue of The American Prospect magazine. Subscribe here.

The week of Donald Trump’s inauguration, Sam Altman, the CEO of OpenAI, stood tall next to the president as he made a dramatic announcement: the launch of Project Stargate, a $500 billion supercluster in the rolling plains of Texas that would run OpenAI’s massive artificial-intelligence models. Befitting its name, Stargate would dwarf most megaprojects in human history. Even the $100 billion that Altman promised would be deployed “immediately” would be much more expensive than the Manhattan Project ($30 billion in current dollars) and the COVID vaccine’s Operation Warp Speed ($18 billion), rivaling the multiyear construction of the Interstate Highway System ($114 billion). OpenAI would have all the computing infrastructure it needed to complete its ultimate goal of building humanity’s last invention: artificial general intelligence (AGI).


Art for this story was created with Midjourney 6.1, an AI image generator.


But the reaction to Stargate was muted as Silicon Valley had turned its attention west. A new generative AI model called DeepSeek R1, released by the Chinese hedge fund High-Flyer, sent a threatening tremor through the balance sheets and investment portfolios of the tech industry. DeepSeek’s latest version, allegedly trained for just $6 million (though this has been contested), matched the performance of OpenAI’s flagship reasoning model o1 at 95 percent lower cost. R1 even learned o1 reasoning techniques, OpenAI’s much-hyped “secret sauce” to allow it to maintain a wide technical lead over other models. Best of all, R1 is open-source down to the model weights, so anyone can download and modify the details of the model themselves for free.

It’s an existential threat to OpenAI’s business model, which depends on using its technical lead to sell the most expensive subscriptions in the industry. It also threatens to pop a speculative bubble around generative AI inflated by the Silicon Valley hype machine, with hundreds of billions at stake.

Venture capital (VC) funds, drunk on a decade of “growth at all costs,” have poured about $200 billion into generative AI. Making matters worse, the stock market’s bull run is deeply dependent on the growth of the Big Tech companies fueling the AI bubble. In 2023, 71 percent of the total gains in the S&P 500 were attributable to the “Magnificent Seven”—Apple, Nvidia, Tesla, Alphabet, Meta, Amazon, and Microsoft—all of which are among the biggest spenders on AI. Just four—Microsoft, Alphabet, Amazon, and Meta—combined for $246 billion of capital expenditure in 2024 to support the AI build-out. Goldman Sachs expects Big Tech to spend over $1 trillion on chips and data centers to power AI over the next five years. Yet OpenAI, the current market leader, expects to lose $5 billion this year, and its annual losses to swell to $11 billion by 2026. If the AI bubble bursts, it not only threatens to wipe out VC firms in the Valley but also blow a gaping hole in the public markets and cause an economy-wide meltdown.

OpenAI’s Ever-Increasing Costs

The basic problem facing Silicon Valley today is, ironically, one of growth. There are no more digital frontiers to conquer. The young, pioneering upstarts—Facebook, Google, Amazon—that struck out toward the digital wilderness are now the monopolists, constraining growth with onerous rentier fees they can charge because of their market-making size. The software industry’s spectacular returns from the launch of the internet in the ’90s to the end of the 2010s would never come back, but venture capitalists still chased the chance to invest in the next Facebook or Google. This has led to what AI critic Ed Zitron calls the “rot economy,” in which VCs overhype a series of digital technologies—the blockchain, then cryptocurrencies, then NFTs, and then the metaverse—promising the limitless growth of the early internet companies. According to Zitron, each of these innovations failed to either transform existing industries or become sustainable industries themselves, because the business case at the heart of these technologies was rotten, pushed forward by wasteful, bloated venture investments still selling an endless digital frontier of growth that no longer existed. Enter AGI, the proposed creation of an AI with an intelligence that dwarfs any single person’s and possibly the collective intelligence of humanity. Once AGI is built, we can easily solve many of the toughest challenges facing humanity: climate change, cancer, new net-zero energy sources.

And no company has pushed the coming of AGI more than OpenAI, which has ridden the hype to incredible heights since its release of generative chatbot ChatGPT. Last year, OpenAI completed a blockbuster funding round, raising $6.6 billion at a valuation of $157 billion, making it the third most valuable startup in the world at the time after SpaceX and ByteDance, TikTok’s parent company. OpenAI, which released ChatGPT in November 2022, now sees 250 million weekly active users and about 11 million paying subscribers for its AI tools. The startup’s monthly revenue hit $300 million in August, up more than 1,700 percent since the start of 2023, and it expects to clear $3.7 billion for the year. By all accounts, this is another world-changing startup on a meteoric rise. Yet take a deeper look at OpenAI’s financial situation and expected future growth, and cracks begin to show.

To start, OpenAI is burning money at an impressive but unsustainable pace. The latest funding round is its third in the last two years, atypical for a startup, that also included a $4 billion revolving line of credit—a loan on tap, essentially—on top of the $6.6 billion of equity, revealing an insatiable need for investor cash to survive. Despite $3.7 billion in sales this year, OpenAI expects to lose $5 billion due to the stratospheric costs of building and running generative AI models, which includes $4 billion in cloud computing to run their AI models, $3 billion in computing to train the next generation of models, and $1.5 billion for its staff. According to its own numbers, OpenAI loses $2 for every $1 it makes, a red flag for the sustainability of any business. Worse, these costs are expected to increase as ChatGPT gains users and OpenAI seeks to upgrade its foundation model from GPT-4 to GPT-5 sometime in the next six months.

Financial documents reviewed by The Information confirm this trajectory as the startup predicts its annual losses will hit $14 billion by 2026. Further, OpenAI sees $100 billion in annual revenue—a number that would rival Nestlé and Target’s returns—as the point at which it will finally break even. For comparison, Google’s parent company, Alphabet, only cleared $100 billion in sales in 2021, 23 years after its founding, yet boasted a portfolio of money-making products, including Google Search, the Android operating system, Gmail, and cloud computing.

OpenAI is deeply dependent on hypothetical breakthroughs from future models that unlock more capabilities to boost its subscription price and grow its user base. Its GPT-5 class models and beyond must pull godlike capacities for AI out of the algorithmic ether to create a user base of hundreds of millions of paid subscribers. Yet, with the release of the open-source DeepSeek R1 model earlier this month, OpenAI has no moat for its increasingly expensive products. The R1 matched its performance across math, chemistry, and coding tasks, independently learned OpenAI’s reasoning techniques, and can be downloaded, modified, and deployed for free. Why would people continue to pay $20, let alone the $200 tier OpenAI reserves for its latest, greatest models, rather than use something that can deliver the same performance at a 95 percent lower price?

Venture capital funds, drunk on a decade of “growth at all costs,” have poured about $200 billion into generative AI.

Silicon Valley Is All In on AI

Wall Street asked itself the same question after the release of DeepSeek R1 and panicked, wiping more than 15 percent ($600 billion) off Nvidia’s stock price, the largest single-day loss for a company ever. And that’s not the only bad sign Altman received about OpenAI’s future. OpenAI is in talks again to raise more money (less than a year after raising $10 billion) at a proposed $340 billion valuation. In most cases, a startup doubling its valuation would be great news. But for OpenAI, the money may make things worse. It signals a desperate need for cash and puts more pressure on a company that today loses $2 for every dollar it makes. As Zitron pointed out, at $340 billion, few companies have the liquidity to acquire OpenAI, and public investors expect strong returns and profitability to justify an IPO anywhere near that price. Plus, the latest round of funding is being led by Masayoshi Son, a billionaire investor known more for losing money than making it. Given Son’s Vision Fund’s disastrous investing record, Zitron said, it’s as bearish a signal as you could find. Hanging over all this for OpenAI is the fact that Microsoft’s investments in the company, which run north of $10 billion, are not standard equity investments but “profit participation units” that will convert to debt in a year and a half.

It’s not just OpenAI that’s burning through billions. Silicon Valley has hyped AI as the next internet or iPhone, and has invested like it cannot afford to miss out on the next big tech revolution. In 2021, with the last gasp of zero-interest-rate loans paired with trillions in COVID relief spending, venture capitalists poured a record $78.5 billion into the AI space. And, despite a broader slowdown in venture activity, the second quarter of 2024 set the record for quarterly venture investing in AI at $23.3 billion. In fact, 33 percent of VC portfolios are committed to AI, another worrying sign of concentration.

Even so, Big Tech companies are the biggest spenders on AI. While VCs dropped approximately $200 billion into AI between 2021 and 2024, Big Tech is on pace to surpass that amount this year alone. According to Goldman Sachs research, cloud computing giants are expected to plow over $1 trillion over the next five years into graphics processing units (GPUs) and to build data centers to power generative AI. AI is an expensive technology like few before it.

All those racks of GPUs and supercluster data centers need power, and the power industry is also embarking on a once-in-a-generation investment spree to keep up. The scale of expected data centers to power generative AI is difficult to wrap your head around. Oracle recently announced plans to build a gigawatt-scale data center just for AI, powered by a trio of nuclear reactors, while OpenAI pitched the White House on the necessity of five-gigawatt data centers, which would be enough power for about three million homes consumed by a single AI data center. A recent report from McKinsey expects the electricity going to fuel AI data centers will triple from 3 to 4 percent of the country’s electricity to 11 to 12 percent by 2030. The power industry typically grows 2 to 3 percent a year, far too little to meet the predicted jump in demand. McKinsey estimates that power utilities would have to spend $500 billion on top of their planned capital expenditure to keep up with AI needs. If true, this presents a serious bottleneck for not just OpenAI but the expected growth of the AI industry.

Where’s the Money, Lebowski?

Between VCs, Big Tech, and power utilities, the bill for generative AI comes out to close to $2 trillion in spending over the next five years alone. Adding all this up, some are starting to question the economic fundamentals of generative AI. Jim Covello, head of global equity research at Goldman Sachs, doubts the technology can recoup what’s been invested as, unlike the internet, it fails to solve complex business problems at a lower cost than what’s available today. Plus, he argues, the most expensive inputs for generative AI, GPUs and energy, are unlikely to decline meaningfully for the tech industry over time, given how far demand outstrips supply for both. While AI-fueled coding could definitely boost productivity, it’s hard to see how it could become a multitrillion-dollar industry.

Surveys confirm that for many workers, AI tools like ChatGPT reduce their productivity by increasing the volume of content and steps needed to complete a given task, and by frequently introducing errors that have to be checked and corrected. A study by Uplevel Data Labs tracked 800 software engineers using Copilot on GitHub and found no measurable increase in coding productivity, despite this exact use case being the one pointed to the most by AI companies. And even productivity gains may come at a cost: Microsoft researchers concluded that workers became more productive using generative AI tools but their critical thinking skills declined, presumably because they were offloading the thinking to AI. Looking past the hype, the business case for generative AI two years after the stunning success of ChatGPT appears weaker by the day.

Even worse, as AI expert Gary Marcus pointed out, DeepSeek’s R1 model spells serious trouble for OpenAI and the cloud giants. The only way OpenAI could hope to recoup the billions it was spending on GPUs to train bigger and bigger models was to maintain a large enough technical lead over other AI companies to justify charging up to $200 for paid subscriptions to its models. That lead just vaporized and was given to the entire industry for free. In response, Altman has already twice cut the prices of his subscriptions in an effort to stay competitive. But without millions of paid subscriptions, it’s difficult to see the pathway to profitability for a company that loses $2 for every $1 it brings in and expects costs to continue to grow approximately tenfold in five years. OpenAI has set $100 billion as its break-even point, which would require it to increase its revenue by a factor of 25 in just five years, an incredible feat of scale that its current business model does not justify.

OpenAI is, however, the perfect kind of growth-at-all-costs story investors need to think still exists—capable of not only achieving Meta- or Amazon-like growth again, but becoming an indispensable part of growth and innovation in every industry in the future, too. No industry could escape, and software would close its jaws around the world, finally, as Marc Andreessen predicted in 2011.

For his part, Gary Marcus has taken to calling OpenAI the WeWork of AI—WeWork, of course, is the poster boy for wasteful, nearly fraudulent, growth-at-all-costs investing that led to a spectacular downfall. Marcus is so confident current approaches cannot take us to the promised land of AGI that he bet Anthropic CEO Dario Amodei $100,000 that AGI would not be achieved by the end of 2027. Without AGI, the valuations of leading AI startups like OpenAI ($340 billion) and Anthropic ($61.5 billion) stop making sense. If GPUs are no longer the most capital-efficient or effective way to build better AI models, then the expected AI computing “supercycle” that the hundreds of billions in capital expenditure is premised on never arrives. Instead, the underlying asset bubble of a multitrillion-dollar bet on GPUs as the necessary component to an internet-like era of growth vanishes into thin air.

For many workers, AI tools reduce their productivity by increasing the volume of steps needed to complete a given task.

Just How Big Will the Blast Be?

OpenAI’s incredible burn rate, the trillions in capital expenditure by cloud giants and utilities to build out the infrastructure necessary to support AI, the supply bottlenecks ahead from the power and semiconductor industries, and the questionable economic gains from these tools all point to a generative AI bubble. Should the bubble burst, startups and venture funds alike face possible extinction, and a big enough drop from the Magnificent Seven could spark skittish markets to panic, leading to wider economic contagion, given how dependent on the growth of the top technology companies the public markets have become.

In 2024, the Magnificent Seven were responsible for the lion’s share of the growth of the S&P 500, with the returns of the other 493 companies flat. When Nvidia hit its peak valuation of $3 trillion over the summer, just five of the seven—Microsoft, Apple, Nvidia, Alphabet, and Amazon—accounted for 29 percent of the total index’s value, surpassing the concentration of the five top technology companies just before the dot-com crash. Nvidia has been on an incredible bull run over the last five years, its shares gaining a dizzying 4,300 percent, reminiscent of how network equipment maker Cisco grew about 4,500 percent in the five years leading up to its peak just before the dot-com crash in 2000.

Nvidia and the other Magnificent Seven members are in a codependent relationship when it comes to AI hype. They are Nvidia’s biggest customers, feeding the bull run by pushing demand for GPUs beyond even what chipmaker TSMC can supply. At the moment, Nvidia can pass those prices on to their customers, the only clusters big enough for AI computing. But should demand for AI fall, all seven will tumble with it.

For the tech industry, DeepSeek is a threat to its incredible bull run because it proved three things. First, frontier AI models could be trained much more cheaply and efficiently than the current Silicon Valley approach of building massive models requiring hundreds of thousands of GPUs to train. From a capital perspective, the U.S. strategy is wasteful, relying on at least ten times the investment to make similar model progress. Second, DeepSeek showed you could train a state-of-the-art model without the latest GPUs, calling into question the current demand for the latest GPUs that is so hot customers have been facing delays of six months to a year to get their hands on them. Finally, the high valuations of leading AI startups depend on a technical lead in their models to charge prices anywhere near what they need to recoup their computing costs, but that technical lead, enabled by a combination of closed-source models, billions in capital expenditure, and export controls blocking Chinese companies like DeepSeek from accessing the latest GPUs, is gone. Should demand for GPUs fall or even not hit the exponential increases the billions invested are betting on, the bubble will pop.

Given the stock market’s dependence on tech companies for growth, the trigger may not come from the AI industry itself, but any pullback in spending will crater the current trajectory of the AI industry. Many potential triggers abound: a crypto crash; President Trump’s trade wars with Canada, Mexico, and China; the stated goal to cut more than $1 trillion of government spending by Elon Musk’s Department of Government Efficiency; or a Chinese invasion of Taiwan, where nearly 70 percent of the world’s advanced computer chips are manufactured. You can tell Wall Street is worried about a bubble, because Nvidia is hit the hardest by any bearish AI news, and even when the market panic has nothing to do with the tech industry, like when the Japan currency trade happened last summer, the Magnificent Seven suffer punishing losses.

The AI bubble wobbles more precariously by the day. Some bubbles, like that of the dot-com crash, end up being positive in the long run, despite the short-term economic pain of it bursting. But some, like the 2008 housing bubble, leave permanent scars on the economy and can knock an entire industry off its growth trajectory for years. To date, the U.S. housing industry has not recovered to pre-2008 growth trend lines, a major contributor to the housing crisis gripping the U.S. That is the fire that the tech industry is playing with today.

This is not the Silicon Valley of lore. Venture investors, for all their tech manifestos celebrating “little tech” and entrepreneurship, have come to resemble more traditional financial firms, raising money from pension funds, hedge funds, and sovereign wealth funds. Silicon Valley has gone corporate and managerial; even private equity invests in the Valley today. The fusion of venture capital and Wall Street threatens to bring the unbridled speculation of unregulated finance and the breathless tech industry hype together in a single, massive bubble. Inimical to the old ethos of the Valley and emblematic of a bloated, rotten investing strategy, in Silicon Valley now the money chases founders rather than founders chasing money. Maybe, after the fallout of the AI bubble is felt and the sun sets on Silicon Valley for a bit, the tech world can do a hard reset and return to its more innovative days again.

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GaryBIshop
7 days ago
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Scary read. We're doomed! Doomed I tells ya!
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Don't bring slop to a slop fight

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Whenever I talk about generative AI slop being sent into every conceivable communication platform I see a common suggestion on how to stop the slop from reaching human eyes:

“Just use AI to detect the AI”

We're already seeing companies offer this arrangement as a service. Just a few days ago Cloudflare announced they would use generative AI to create an infinite "labyrinth" for trapping AI crawlers in pages of content and links.

This suggestion is flawed because doing so props up the real problem: generative AI is heavily subsidized. In reality generative AI is so expensive we're talking about restarting nuclear and coal power plants and reopening copper mines, people. There is no universe that this service should allow users to run queries without even a credit card on file.

Today this subsidization is mostly done by venture capital who want to see the technology integrated into as many verticals as possible. The same strategy was used for Uber and WeWork where venture capital allowed those companies to undercut competition to have wider adoption and put competitors out of business.

So using AI to detect and filter AI content just means that there'll be even more generative AI in use, not less. This isn't the signal we want to send to the venture capitalists who are deciding whether to offer these companies more investment money. We want that "monthly active user" (MAU) graph to be flattening or decreasing.

We got a sneak peek at the real price of generative AI from OpenAI where a future top-tier model (as of March 5th, 2025) is supposedly going to be $20,000 USD per month.

That's sounds more like it. The sooner we get to unsubsidized generative AI pricing the better we'll all be, including the planet. So let's hold out for that future and think asymmetrically, not symmetrically, on methods to make generative AI slop not viable until we get there.

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GaryBIshop
7 days ago
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I think he's right. This stuff should not be free.
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Antarctica’s bases are hotbeds of stress and violence – expert

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Earlier this week, reports emerged that a scientist at South Africa’s SANAE IV Antarctic research base had accused a colleague of physical assault.

We research Antarctic governance and crime in isolated, confined and extreme environments such as Antarctic and space stations. Rebecca specifically investigates how station cultures evolve in isolation and what factors significantly influence conflict – and what can be done to improve safety in these environments.

What happened on SANAE IV?

SANAE IV is located on the edge of a steep cliff in Vesleskarvet in east Antarctica. The alleged assault stemmed from a dispute over a task the team leader wanted the team to do. In an email published by the South African Sunday Times, the alleged victim said the alleged attacker had also:

threatened to kill [name withheld], creating an environment of fear and intimidation. I remain deeply concerned about my own safety, constantly wondering if I might become the next victim.

Psychologists are now in touch with the research team. They aren’t due to leave the extremely isolated and remote base until December.

file 20250320 56 22mhep.jpg?ixlib=rb 4.1
The South African National Antarctica Expedition research base, SANAE IV, at Vesleskarvet, Queen Maud Land, Antarctica. Dr Ross Hofmeyr/Wikimedia, CC BY-SA

This latest incident fits within a broader pattern of crime and misconduct in Antarctica. Research stations on the icy continent are often portrayed as hubs of scientific cooperation. But history has shown they can also become pressure cookers of psychological strain and violence.

Misconduct in Antarctica over the years.

In 1959, a scientist at Russia’s Vostok Station allegedly attacked his colleague with an ice axe after losing a game of chess. In 2018, another Russian research station became the site of a stabbing. The alleged cause? Spoiled book endings.

In 1984, the leader of Argentina’s Almirante Brown Station set fire to the facility after being ordered to stay through the winter. This resulted in the station’s evacuation.

The 2000 death of an astrophysicist at the Amundsen-Scott South Pole Station was a suspected murder.

And recent investigations into sexual harassment at multiple Antarctic stations highlight ongoing safety concerns.

Drivers of conflict

Research suggests several psychological and social factors contribute to conflict in remote locations such as Antarctica. These include prolonged isolation, extreme environmental conditions, and the necessity of constant close contact.

In combination, these factors can amplify even minor frustrations. And over time, the lack of external social support, the monotony of daily routines, and the psychological weight of confinement can lead to heightened emotional responses and conflict.

Without structured outlets for stress relief and effective de-escalation mechanisms (such as gyms, libraries, or quiet spaces where mediation between people can happen), tensions can reach breaking points.

Power dynamics also play a crucial role. With limited external oversight, leadership structures and informal hierarchies take on an outsized influence. Those in positions of authority have significant control over how disputes are resolved. This has the potential to exacerbate tensions rather than reducing them.

The process for reporting and responding to incidents in these kinds of environments also remains inconsistent. There’s a lack of policing, and traditional justice systems are also largely absent. Many stations rely on administrative action and internal conflict resolution mechanisms, rather than legal enforcement.

But these mechanisms can be biased or inadequate. In turn, this can leave victims of harassment or violence with few options. It can also lead to more conflict.

From Antarctica to space

As Antarctica and space become more accessible for research and commercial ventures, proactive approaches to crime and conflict prevention in these remote and extreme environments is vital.

The psychological and social challenges observed in Antarctic stations provide a valuable model for understanding potential conflicts in long-duration space missions. Lessons learned from incidents in Antarctica can inform astronaut selection, training, and onboard conflict resolution strategies.

A key area requiring refinement is psychological screening for personnel.

Current screening methods may not fully account for how individuals will react to the social shift that takes place in a remote environment. This includes the altering of attitudes, personal priorities and tolerances.

More advanced stress tolerance assessments and social adaptability training could improve candidate selection. It could also reduce the likelihood of conflicts escalating to violence.

It’s also vital that we gain a better understanding of the unique conflict dynamics that evolve in these equally unique environments.

Research can help. So too can thorough investigations of incidents, such as the one that allegedly occurred at SANAE IV.

This knowledge can be used to recognise early signs of potential conflicts. It can also be integrated into case study-based training modules for expeditioners prior to their deployment. These training modules should include role-playing scenarios, crisis intervention techniques, and integrating the lived experiences of past expeditioners.

This would better equip personnel to navigate interpersonal challenges.

Going to extremes

The recent alleged events at SANAE IV are indicative of a broader pattern of human behaviour in extreme environments.

If we are to successfully expand scientific exploration and habitation in these settings, we must acknowledge the realities of human conflict and develop strategies to ensure the safety and wellbeing of those who live and work in these challenging conditions.

Studying crime and conflict in environments such as Antarctica is not just about understanding the past. It’s about safeguarding the future of exploration – whether on Earth’s harshest frontier or in the depths of space.

Rebecca Kaiser, PhD Candidate, School of Social Sciences, University of Tasmania and Hanne E F Nielsen, Senior lecturer, Institute for Marine and Antarctic Studies, University of Tasmania

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The Conversation



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GaryBIshop
11 days ago
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Mars will be much worse.
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