offshoring

/ˌɒfˈʃɔːrɪŋ/

noun

the practice of basing some of a company’s processes or services overseas, so as to take advantage of lower costs.

I recently sat on a careers panel at my alma mater. I did so both to help bright-eyed students pursue soul-destroying private sector careers and to massage my ego. In fact, when I was asked to join the panel by my former career counselor, my first thought was, “My dream has finally come true.” After years of groveling at career fairs, I have always wanted to be on the other side of that table.

But rather than making me feel my shackles are worth their weight in gold, the panel produced within me a profound sadness. Across North America, Western Europe, and even China, graduate unemployment rates are appalling . Everyone attending the panel knew this. The students did not outright say it, but these statistics encumbered each question they asked. What could us panelists possibly say to assuage their fears? How could we act like our careers were due entirely to hard work and not to a fair helping of luck that, statistically, many of them will not receive?

Of course, nobody mentioned this. Including myself.

But as the pantomime neared its end, one girl raised her proverbial hand (it was a Zoom call). “The news says that generative AI is taking away graduate private sector roles. What do you think?” she asked. “And what could students do to make themselves stand out?”

And finally, I decided to be honest. Because I may not have much to pass down to the next generation of alumni, but I know one thing very well.

“It’s not AI,” I said. “It’s outsourcing.”

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The girl was correct about one thing: the last year has seen a significant reduction in entry-level white-collar vacancies—and unemployment has risen across the board. I could not fault her for believing AI was to blame. Every day, news agencies inundate us with headlines that “AI is taking white-collar jobs”—whilst sidelining investigations that have found that AI rarely works; that when AI does work, it lessens productivity and increases burnout; and that bosses fire people with the anticipation of AI automation that never actually occurs. While AI has its “use cases”—particularly, and unfortunately, in creative sectors—it also has many demonstrable cases of being unfit for purpose. Much white-collar work is still done by humans. But hiring has flatlined. Why?

Corporate greed? Cost cutting? Sure. But given its widespread use, outsourcing—commonly utilized interchangeably with “offshoring”1—is not mentioned nearly enough.

As I work in and study technology, I have wanted to write about this topic for months. But each time I begin, I despair and relent. This is because statistics on outsourcing are far from conclusive. While many of us know it is a widespread business practice, companies do not often publicize their outsourcing contracts. Thus, research firms often rely on disparate pieces of information—often sector-specific—and their estimates of the outsourcing market’s valuation vary from 400 billion to 4 trillion U.S. dollars. But based upon surveys, onshore and offshore workers’ testimonies, and the flush state of the market itself, outsourcing is certainly enmeshed in the global economy. However, when one attempts to argue that outsourcing is driving the many workforce transformations misattributed to AI, it can be difficult to cite numbers—and you need those when going up against the AI evangelist brigade.

So I will take a different route. If AI is facilitating a white-collar wipeout and upending the economy—and, following this line of thinking, therefore paid for by many institutions across the world—why is OpenAI resorting to embedding advertisements in ChatGPT’s outputs and building an erotica feature?

Listen when I say this: OpenAI is so unable to meet its financial obligations that it is having engineers work twenty-hour days to create ChatGPT “Adult Mode”—a product aimed at people in AI psychosis who, after years of using round-about prompts to jailbreak ChatGPT’s safeguards, will pay anything2 for the convenience of getting their well-endowed, porcelain-skinned, elven-faced “partners” to have “sex” with them. Why would OpenAI—the most valuable AI startup—release this stygian cash grab if the whole wide world was already using its products? Does it sound like AI is “working”? More importantly, does it sound like as many companies are paying for it as the news implies?

As I have written previously, AI companies have displayed increasing desperation as investors, incensed at nonexistent returns, bang down their doors and pull back funding—and potential investors outright walk away. Companies that have already sunk excessive investment into dysfunctional AI products are forcing employees to use them. Big Tech firms hide infrastructure-related depreciation in their balance sheets, making their true earnings impossible to ascertain. The AI market, apparently worth nearly 400 billion U.S. dollars in 2025 and projected to be worth “trillions” by 2030, is reliant upon muddled valuations, fruitless corporate investment and circular financing—not to mention Filipino workers remotely driving WAYMO cars, Indian workers acting as chatbots, and Kenyan workers developing PTSD from sifting through violent content for LLMs. The outsourcing market is either already or will soon be worth “trillions” too, but this valuation is not based upon the promised delivery of nebulous technologies. It is based upon existing labor contracts: i.e., work actually being done.

But these contracts are signed inconspicuously. CEOs would rather tell the public that “AI is replacing jobs” than admit to their love for cheap overseas labor. They then stir up xenophobic culture war nonsense to cover what their “AI apocalypse” claims do not. But immigrants are not stealing jobs either. The people “taking your work” do not live in your country and never will.

Many people—including white-collar employees themselves—do not know that outsourcing is consuming domestic jobs because corporate overlords have taken the frog-in-boiling-water approach. Outsourcing is integrated into operations slowly, seemingly benignly—the most menial work sent overseas—and largely under the veil of secrecy. Outsourcing is sold to onshore employees as an efficiency hack. You “get time” for the things that matter, while the easy stuff “reroutes to the contractors.” But next thing you know, you are sending half your tasks to 24/7 teams in the “developing world” who, due to atrocious workplace conditions—which some laborers themselves have equated to modern slavery—often burn out and quit (of many locales, these teams are increasingly located in Gujarat, India, where the 12-hour workday was recently legalized). And when your constantly rotating cast of offshore team members sends back error-riddled work because of their excessive workloads, time-zone related miscommunications, and language differences, you have to clock in extra hours to fix it.

Outsourcing is adored by investors and by short-termist CEOS who cannot figure out other ways to make money. It cuts costs, compresses salaries, and makes for a more happy earnings report. But this dynamic is not sustainable long-term. Alongside the fact that it guts domestic economies and exhausts the onshore employees left standing, outsourcing atomizes the workplace and limits people’s skillsets. No employee—onshore or offshore—has room to grow.

And this is why entry-level white-collar vacancies are in free fall.

If one can outsource most rote tasks, while forcing one’s pre-existing onshore staff to pick up an increased burden of work with no increase in pay, one has less need for entry-level employees. Why hire a brigade of unskilled youths who must be trained when you can just have “AI” pick up a bit of slack—“AI” mostly meaning teams in Southeast Asia, Latin America, Eastern Europe, and East and South Africa paid less than two U.S. dollars per hour—and then make onshore employees fix whatever comes down the pipe? Why invest in new talent when you can invest in another holiday home?

Outsourcing statistics are unclear for a reason. Companies would like to avoid onshore employee uprisings and to seem “tech-forward” by blaming AI on mass lay-offs. Governments would like to avoid admitting that they provide companies with loopholes that directly contribute to domestic unemployment crises. They also do not want to lose the “business investment” of having these companies on their shores. Much of the think tank sphere—which produces reports in excess on the “gains” of AI with rarely a mention of outsourcing—is funded by technology companies and comprised of individuals fearful of seeming like frumpish luddites. And AI companies are riding the wave: fearmongering that AI is “taking jobs” because their spending has far outpaced their revenue, and if they repeat this phrase enough to investors, they will find salvation from the hole into which they have so rapidly sunk that sunlight is but a memory.

What is AI actually used for? Tracking down immigrants and protestors, spying on you, and deciding who to bomb and how much collateral damage is permissible. It is not taking your job any time soon. But it is taking your water and electricity. And one day, it may take your life.

Anyways, I did not say much of this to the girl. The panel did not have a revolutionary vibe, and I have a reputation to uphold. But I wish I could have.

So I am telling you instead.

People ask me to write about AI. I have avoided it because it makes me feel like my job is bleeding into my writing. However, duty called today.

It's been a big month for tech stocks. Two weeks ago, cloud behemoth Oracle saw its stock price jump 43 percent. This was after announcing a deal with OpenAI: OpenAI will pay Oracle 300 billion dollars for data center construction across the U.S. over the next five years. And then yesterday, Nvidia’s stock price jumped 4 percent after announcing a 100-billion-dollar investment in OpenAI’s computing infrastructure. This was a real “you scratch my back, I scratch yours” investment, because OpenAI will purchase an estimated 35 billion dollars in Nvidia-made graphics processing units (GPUs) per every 10 billion dollars of Nvidia’s investment. This investment added one hundred and sixty billion dollars to Nvidia’s 4 trillion dollar valuation. If you are holding Nvidia and Oracle stocks, congratulations. But also, best of luck. Because little of this money actually exists.

To explain why these massive deals are happening and why they are concerning, I will first outline some terminology. AI comes in many forms.1 But most American AI models receiving tons of investment—including large language models (LLMs) like ChatGPT, Gemini, and Claude—are basically statistical machines.2 These models do not “reason”; rather, they perform complex mathematical calculations to detect patterns in data and then produce outputs. As this is resource intensive, AI companies cannot house all of the infrastructure of their models in their offices. Thus, they use data centers, which store the physical machines and hardware that are used by AI models virtually, or via “the cloud.” An essential piece of hardware is GPUs: chips that process vast amounts of data simultaneously and therefore have the capacity to perform models’ calculations. Most American AI companies believe that the answer to improving their models is scaling up their models’ computing power. That means access to more data centers and GPUs. Thus, to develop their models, OpenAI is signing computing infrastructure deals in the hundreds of billions of dollars.

It would really be great if OpenAI had the money to pay for these things. However, the company’s financials raise perplexing questions. OpenAI has not turned a profit and will not do so until 2030 at the earliest—and that is by its own optimistic figures. Of course, the expectation that a company must turn a profit to be valuable was forever altered by the Great Recession of 2007, after which the U.S. Federal Reserve pumped tons of money into Wall Street, and Wall Street then lucratively invested in unprofitable companies (like Tesla, Uber and Netflix) via financial instruments like stock buybacks (to the detriment of the public). These companies eventually became profitable. Thus, a few years of massive unprofitability does not necessarily doom a business.

However, OpenAI’s projected profit is based upon a pixie dust business model. OpenAI is betting that its LLMs will restructure the workforce and fundamentally alter the course of humanity. However, LLMs are not actually producing revenue in the vast majority of businesses, and corporations that mass-fired workers due to “AI optimization” are now having to hire them back because it turns out AI cannot actually do their jobs. LLM evangelists say that this is just because LLMs aren’t good enough yet, but soon they will “replace humans.” Unfortunately, many AI scientists do not believe this is possible because this is not how LLMs work. LLMs are not actually intelligent: they are pattern recognition machines trained on specific data for specific kinds of tasks. That is why ChatGPT can’t do your math homework. It doesn’t matter how much you scale up their computing power, because at their core, LLMs are not built to be able to learn and reason like humans, and thus will not rearrange the economy and make all our jobs obsolete or whatever. They will not suddenly be able to do something that they are not hardwired to do—OpenAI’s LLMs included. OpenAI’s infrastructure plans are also disconcerting on an environmental level. Data centers require a vast amount energy for powering and freshwater for cooling, as saltwater is highly corrosive to metal. If the rate of OpenAI’s data center growth matches that which is laid out in its deals, U.S. power grids and freshwater sources will be massively strained, if not depleted. Investors are relying on OpenAI’s LLMs to finally being able to “do something” before that happens. They won’t.

OpenAI is not alone in these issues—all LLM-focused companies face them. And these companies are also cracking deals with computing infrastructure giants like Oracle and Nvidia. That is concerning as a whole. However, it is particularly so for Nvidia, the most valuable company in the world. As the dominant GPU provider, Nvidia uses a cyclical method to gain customers and inflate its valuation: invest in small, cash-strapped AI start-ups with the knowledge that they will then buy a bunch of chips. Some of these start-ups are employing AI in ways that are useful. But many don’t actually do anything, have valuations pulled out of the crack of some overworked venture capital analyst’s ass, and will fail. The sustainability of Nvidia’s business model is thus questionable, casting doubt upon the sustainability of our financial market that relies upon it so heavily. And to make matters more alarming, there may come a time when GPU demand falls. We got our first glimpse of it in January 2025, when AI company DeepSeek released the LLM R1, and revealed that it circumvented the need for tons of expensive GPUs by using technical workarounds to make R1 less energy intensive. The model went on to outperform many of Silicon Valley’s own, and sent U.S. financial markets into a tailspin. When LLMs don’t need as many GPUs, what happens to the massive infrastructure deals? When investors realize that LLMs cannot do what they have promised to do, what happens to the AI companies themselves? And what happens to a stock market reliant upon these valuations?

If this is all true—which it is—then why is the U.S. economy so dependent on AI? It’s simple: because people put a lot of money into AI, so they keep putting more money into it because they need it to work. This is not detached from the concept of FOMO, nor of personal pride.3 Many AI investors feel smart talking about AI, and AI CEOs appeal to this instinct. AI CEOs are successful for the same reason as their LLMs: they are sycophants. LLMs tells us how eloquent we are before we hit “Submit” on the paper it wrote that will make us fail the class; AI CEOs butter up venture capitalists with aphorisms like “you are pushing humanity forward” as the venture capitalists make investments that are completely divorced from reality.

This whole thing—putting more and more money into something until something awesome happens—is not at all dissimilar to the American approach towards AI: scale until the model hits a breakthrough. But that’s not quite how AI works. And it’s not quite how financial markets work either.

This is not to say that AI is bullshit. It is extremely effective at data collection, analysis and surveillance, and in some medical, anthropological, and technical settings, it has proven to be very useful. However, the idea that LLMs are going to take all of our jobs, much less solve the climate crisis, is absurd. And investors are slowly beginning to realize, and publicly admit, that LLMs—OpenAI’s included—are glorified chatbots. OpenAI CEO Sam Altman probably knows it too. That is why he is now running around trying to sign multi-billion dollar deals with governments to give citizens premium access to ChatGPT. What else can he do? The scaling is not working; the underwhelming performance of ChatGPT’s most recent model is proof (which Altman initially sold as having “artificial general intelligence,” whatever the fuck that means).4 LLMs can write your CV, emails and some of your code. They have their place and will not just disappear. But these investments? These valuations? These multi-billion-dollar deals made from thin air? Those, my friends, will not go on forever.

In sum, do not put all your savings into tech stocks.