“Nvidia opens investigation after CEO Jensen Huang’s affair with alleged Chinese spy” screamed an 8 November 2026 NBC headline. The story noted that after U.S. intelligence agencies determined that Alice Huang, a businesswoman whose company was buying Nvidia’s advanced chips, was a Chinese spy, she had a relationship with Jensen. The story caused consternation among lawmakers and the public, leading President Donald Trump to claim that the Federal Bureau of Investigation (FBI) was investigating the allegation. This marked a sharp about-face from an administration that had previously allowed the Nvidia CEO to participate in talks with China, despite knowing U.S. intelligence believed he had grown close to a Chinese spy. Nvidia’s spy saga serves as a poignant reminder of the democratic risks inherent in relinquishing control over powerful technologies to private industry. Entrusting artificial intelligence (AI) to commercial entities insulates critical national intelligence and military considerations from public oversight and democratic accountability.
Whether you love, hate, or feel indifferent toward AI, Big Tech’s economic health directly affects you. Indeed, AI accounted for one-third of U.S. economic growth in 2026. The immense wealth accrued by major technology firms is widely attributed to market concentration and anti-competitive practices. By leveraging network effects, strategic acquisitions, and ecosystem lock-in, these corporations consolidate market power to extract economic rents while stifling potential competition. In fact, only a handful of tech companies accounted for more than one-third of the nation’s GDP growth over the preceding decade. These mega-corporations have changed in name over time, previously known as the FAANGs (Meta, Amazon, Apple, Netflix, Google) or GAFAMs (Google, Apple, Facebook/Meta, Amazon, Microsoft). Historically, these companies maintained their economic hegemony by purchasing potential competitors in their startup phases before they could ever become a threat.
Trump is only the latest in a long line of presidents dating back to the 1980s who believe national economic viability depends on catering to Big Tech oligarchs through minimal regulation and low taxes. Yet the reality is far different. Contrary to the myth of smart college kids taking large economic risks while tinkering in garages, Tech’s dominance stems from wealthy founders with parental seed money—such as Jeff Bezos of Amazon, Elon Musk of X, and Bill Gates of Microsoft—and massive public investment. Core technologies like the internet, GPS, and touchscreens, all of which made the iPhone possible, were funded by Cold War military spending. However, in the 1980s, neoliberal philosophy came to dominate lawmakers’ thinking, resulting in policies that assumed private industry was more efficient and innovative, so public assets should be privatized. Consequently, during the late 20th century , publicly funded technologies were systematically transitioned out of government stewardship and transferred to private enterprise. This shift effectively privatized state-sponsored innovation, reallocating publicly capitalized assets into proprietary commercial domains
Despite political rhetoric extolling privatization, these enterprise transformations occurred largely in name only. Today, Google, Amazon, Nvidia, and many leading social media companies actively collect billions of dollars in government tax breaks, local subsidies, and federal contracts. In fact, despite positioning himself as a fiscal conservative, Elon Musk’s corporate empire, from Tesla to SpaceX, relies on extensive government contracts. Far from saving taxpayers money, his tenure at the Department of Government Efficiency (DOGE) was focused on dismantling the regulatory state that threatens his company profits.
The government, for its part, can use these contracts, deregulation, low taxation, and subsidies as leverage to compel platforms to do things the state cannot do directly. For example, while the government is constitutionally forbidden from censoring speech, restricting the press, or spying on citizens without a warrant, tech platforms can censor speech or content under the guise of moderation or hand over user data to law enforcement. If tech firms refuse, they risk being regulated, taxed, or worse. Take TikTok , which refused to censor content regarding the U.S.-funded Israeli genocide in Gaza, the government responded by outlawing the platform.
Compliance with the political priorities of the incumbent administration yields significant regulatory captures: lawmakers cultivate public skepticism toward taxation and oversight, ultimately enacting legislative protections that shield compliant technology firms from legal and market liability. Take Section 230 of the 1996 Communications Decency Act—the 26 words that created the modern internet. It shielded platforms from lawsuits over third-party posts, essentially granting social media broad legal immunity. Such deference to the tech industry has continued into the AI era. For example, The New York Times reports that a federal probe into whether a company was led by a Chinese spy was halted a year ago after Nvidia intervened.
Novelty and enthusiasm surrounding the internet fueled intense financial speculation in the 1990s, drawing massive venture capital into the sector. This speculative influx accelerated economic growth and ultimately generated an asset bubble that burst in 2000. Between March 2000 and October 2002, the tech heavy NASDAQ fell by more than 75 percent, wiping out more than $5 trillion in market value. In response, Big Tech reinvented itself as “Web 2.0,” defined by smart devices, social media, and a new economic order that scholars referred to as “surveillance capitalism.” In the surveillance capitalist economy, Big Tech generates revenue by collecting and analyzing user data to inform decisions across industry and government. “Data” in this context refers to every digital trace, action, and preference we leave behind.
Just as Web 2.0 platforms weaponized gambling industry techniques, like variable reward schedules of likes and shares, to maximize data collection through extended screen time, modern AI models employ subtle manipulation tactics like sycophancy , constantly praising users to keep them engaged and feeding the system fresh data. As smart devices transformed everyday consumer goods into vectors for pervasive corporate surveillance, the absence of democratic oversight allowed Big Tech to harvest intimate human data across every sector of society, from targeted warfare and risk insurance to public education. Today, this systemic extraction reaches its apex in the AI domain, as evidenced by Google effectively holding schools hostage, coercing educational institutions into adopting its AI products under the implicit threat of losing access to its ubiquitous search engine. Ultimately, citizens remain the mined product, stripped of foundational privacy while tech conglomerates leverage institutional dependence to solidify their domain over both classic Web 2.0 channels and emerging AI frontiers.
By the 2010s, the bottom began falling out of Web 2.0. In 2020, Big Tech rebranded yet again, promising that the industry’s next savior was AI. However, “AI” is just an industry buzzword that misleads users into believing the technology currently being deployed is genuine intelligence. In fact, “intelligence ‘ is a term that lacks a strict universal definition, making true AI an impossibility until an agreed-upon definition is reached. When most people hear ‘AI,’ they picture human-level intellect, known as artificial general intelligence (AGI), or software far exceeding human capabilities, known as artificial superintelligence (ASI). We have neither.
What industry refers to as “AI” are actually large language models (LLMs): systems that make statistical predictions about what word comes next. Because they process massive datasets at extreme speeds, relying on data centers that waste water, spike electricity bills, and generate severe noise pollution, they create a deceptive illusion of thought and intelligence when they are actually fundamentally mindless.
Yet, the industry has unleashed massive marketing campaigns to convince the public that AI is the inevitable future destined to replace all human labor. Current research demonstrates the opposite: nearly 95% of corporate AI pilots fail, and those deployed often generate added costs and complications due to errors, such as botched surgeries in hospitals. Furthermore, these AI systems clearly lack emotional intelligence as they allegedly aid teenagers experiencing suicidal ideation . They are also frequently flat wrong—with research indicating that AI models summarize articles correctly only about half the time. Finally, they inherit the biases of their creators and training data; studies consistently show that AI perpetuates racist, sexist, and heteronormative tropes in both images and text. Most notably, Elon Musk’s Grok AI has gained notoriety for spreading baseless white supremacist content, while AI-driven applicant screening systems systematically marginalize people of color, and image and content generators routinely objectify women.
Despite these flaws, private industry retains absolute control over the creation and distribution of tools with profound societal implications. Some researchers warn that deploying AI is dangerous because superintelligence might eventually be achieved and result in human extinction. However, the more immediate and profound concern is that AI is fundamentally stupid: if we delegate critical decisions about healthcare, warfare, water supplies, hygiene, and public infrastructure to automated platforms, we risk granting systems prone to reckless errors the direct power to destroy us.
Whether the threat is environmental destruction, Chinese espionage, biological warfare, fake news, or AI-generated child sexual abuse material (CSAM), democratic processes—not profit-driven corporations—must govern these tools and their societal impact. When the sovereign power over humanity’s most sensitive infrastructure is outsourced to unelected executives, we shouldn’t be surprised when national security reads like a tabloid headline about a CEO and a foreign spy.
