23/08/2026
๐ข๐ฃ๐๐ก๐๐ข๐ก | ๐ง๐ต๐ฒ ๐ฅ๐ฒ๐๐ผ๐น๐๐๐ถ๐ผ๐ป ๐ช๐ถ๐น๐น ๐ก๐ผ๐ ๐๐ฒ ๐๐ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ฒ๐ฑ
"But what are you trying
to be free of?
The living? The miraculous
task of it?โ
โ ๐๐ผ๐๐ฒ๐ฝ๐ต ๐๐ฎ๐๐ฎ๐ป๐ผ ๐ถ๐ป โ๐๐ค๐ง ๐ ๐๐ฉ๐ช๐๐๐ฃ๐ฉ ๐๐๐ค ๐๐จ๐๐ ๐ผ๐ ๐ฉ๐ค ๐๐ง๐๐ฉ๐ ๐ ๐๐๐ฅ๐๐งโ
Hailed as this contemporaneous marvel, Tech Companies have proclaimed Artificial Intelligence (AI) as the catalyst for humanity's new golden age. The next revolution; the final frontier, and a glimpse into a possible technological utopia.
But what if I tell you that this entire scheme is a gamble in itself that's imploding from within? The future of Generative Artificial Intelligence (GenAI) is bleak, and uncertain. Overvaluation, like two fingers pointing a needle towards a balloon.
The AI bubble is popping.
An economic bubble is defined as a period wherein asset priceโor the market values of stocks and other assets, shaped by prediction and valuationโskyrocket to an unprecedented degree, far exceeding its actual intrinsic value.
A historical example would be the dot-com boom in the early 2000s. According to Investopedia, "the dot-com bubbleโalso known as the internet bubbleโepitomized a period of speculative mania that drove U.S. technology stock valuations sky-high during the late 1990s." But, as the 2000s ushered in the constant overvaluation of assets, the market suffered a dramatic correction, resulting in a staggering decline, and a subsequent market crash that led to mass lay-offs, and gluts of talent in the job market. This resulted in several, highly invested company start-ups like Pets.com and Boo.com to fail miserably, eventually shutting down.
While companies like Amazon would later recuperate from the damage, the real winners of this crash were a small coterie of individuals who took advantage of the exuberance and cashed-in early, essentially allowing a few Venture Capitalists, Entrepreneurs, and Executives to walk away from the entire fiasco unharmed. It's as if a bunch of gamblers walked out of the casino coddling a substantial amount of reward.
In today's context, the dot-com boom seems to be rearing its ugly head once again, while sneering at our general direction through the ruse of generative AI. The same cycles are repeating. Since 2023, GenAIโwith the advent of Large Language Models (LLMs) and chatbots like ChatGPT from companies like OpenAI, Alphabet, and Anthropicโhas been a rather volatile, yet lucrative business juncture highly valuated by capitalists, investors and tech executives as the next big thing that'll funnel money into their Charybdis-like pockets.
And in some transient moment spanning a few years, it did.
With the monumental hype surrounding these โoracular glimpsesโ into the future, AI slowly but surely spread like an ancient medieval plague. One could not escape its violent transmissions. Whenever you Googled something, you'd be met with a somewhat inaccurate AI overview about the given topic. Whenever you scrolled through social media, everybody raved about how they could do this, and that with the help of AI and ChatGPT. And when said social media began to assimilate GenAI into their platformsโmost notably the newly renamed Metaโit really appeared as if itโd be the next major thing that would redefine our trajectories in the modern day. Writers, artists, and the entire concept of the white-collar would supposedly be at risk of being usurped and obsoleted.
In response to that looming threat, workers have expressed their growing fears of being replaced by these tools with the initial purpose of being mere assistants. In spite of that, the advent of companies opting for mass lay-offs in lieu of an AI-generated workforce and cutting costs was at full throttle. Corporations like Cisco, Meta and Uber have supposedly cut thousands of jobs as they reorganize around AI. However, according to an article by MIT, over 80% of organizations that have explored or piloted AI into their business ventures have been met with the realization that these tools primarily enhance individual productivity above all elseโnot P&L (Profit & Loss) nor company performance.
AI was not created to handle the intricacies of a workforce. LLMs instead work as giant statistical prediction machines that repeatedly predict the next word in a sequence after taking an input text from a selected database. They learn patterns in their texts, and generate language that adheres to said patterns, hence why these LLMs were lauded as โhighly proficientโ programmers.
Meanwhile, new enterprise-grade AI systems (or products built to meet the rigorous performance, security, and scalability demands of large organizations), custom or vendor-sold, are being quietly rejected. 60% of these organizations evaluated such AI tools, but only 20% reached pilot stage, and just 5% reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.
Companies are now rehiring the same individuals they laid off because of AIโs insufficiency when executing tasksโand the fact that it has become more expensive to maintain than the very workers they fired. By 2028, over half of all these enterprises will stop paying for assistive intelligence (such as copilots and smart advisors) and instead will favor platforms that commit to workflow results, according to a forecast by Gartner, Inc.
The core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time, leading to 95% of the studied organizations getting zero return.
Still, โbillionsโ of dollars in industry revenue later, despite the caveats, makes it seem like GenAI is this untouchable war general; an entity clad in the finest armors and silks that money can buy. Unfortunately, these profits fall at a stark contrast with the trillions needed on investments and infrastructure. AIโmuch like the dot-com bubble, and all other bubbles of the pastโwas put out to be more valuable than it actually is, and is actively imploding from the scenes and burning billions of dollars. No one bought the hype more than the CEOs did. Now, it's costing them.
Everyone is scrambling to salvage what they can; Meta, like SpaceX, looking to turn excess AI compute into cash has offloaded a potential $10 billion data center space lease with Anthropic; xAIโs Grok models apparently have such an abysmal rate of demand relative to the industry that the company can sell off the capacity of entire "colossal" data centers; companies like OpenAI, Google, Microsoft, NVIDIA, and CoreWeave are investing in themselves just to delay the crash. In South Korea, the bubble has already popped. Investors are suffering losses following a 40% market plunge in the Korea Composite Stock Price Index (Kopsi) from its June highs after the Korean chipmaker, SK Hynix failed to impress investors despite reporting a sixfold increase in quarterly earnings. These losses can be narrowed down to a few factors outside of the usual causes surrounding a speculative economic bubble popping.
Things like liability towards the companies themselves, hallucinated AI information, and the hardware depreciation of NVIDIA GPUs and computer chips utilized in AI Data Centers. But conversely, one of the more notable reasons for these losses is the immense public backlash as $64 billion in U.S. data center construction projects have been blocked, delayed, or derailed by a growing wave of local opposition and protests from across the country, despite a consequent amount of arrests from the police. โWhat was once quiet infrastructure is now a national flashpoint, and communities are pushing back,โ Data Center Watch declared.
Ultimately, the question lies in what happens after the bubble finishes poppingโin what to do after the โAI revolution' crumbles. It's safe to assume that following the crash, GenAI won't completely go away and dissipateโmuch like domains and the internet thereafter the dot-com bubble; and in a few years, the prospects of AI will redevelop, similar to the ideologies that surround reignited revolutions across the globe, following a series of triggers. Eventually, AI might return larger than ever: optimized and innovated.
So what's the point of all of this? Bubbles always seem to form on the ostensibly important ideas, and when they pop, that doesn't mean that the technology immediately disappears. In a way, history is repeating itself, but with much larger implications for the workforce and humanity overall. This current GenAI bubble might look like a vain pursuit on the surface, as it dictates the market's trajectory and eventual downfall. But when the dust settles, and the companies who can survive the damages re-emerge, this whole sham might be re-contextualized as a means to force everyday people to develop an over-reliant relationship with AI in the companiesโ relentless pursuit of profit. We're slowly but surely being conditioned, and when that codependence reaches its breaking point, that's when the AI companies will decide to swoop in and monetize.
Independence is crucial in this ever-volatile zeitgeist.
Studies have shown that over-reliance on AI erodes critical-thinking skills, reduces cognitive load, deteriorates reasoning, and argumentation skills. โ[AI] could completely reorient our relationship to knowledge, prioritizing rapid, detailed, abridged answers over a deep understanding and the consideration of varied sources and viewpoints,โ said Matteo Wong, technology journalist at The Atlanticโand we are already seeing the results to that reorientation in real time.
While the sayings of CEOs, tech executives, and moguls might urge us all to keep up and adapt to the changing technological landscapeโin a way to convince themselves that they aren't in a bubbleโit's important to remember that, at the end of the day, artificial intelligence is a tool that we shouldnโt be over-reliant on.
When the time comes, it is imperative that we reclaim this revolution for ourselves.
๐ช๐ผ๐ฟ๐ฑ๐ ๐๐: John Zydryck Solina
๐๐น๐น๐๐๐๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐๐: Therese Salmo
SOURCES:
https://www.investopedia.com/terms/d/dotcom-bubble.asp
https://online.hbs.edu/blog/post/how-amazon-survived-the-dot-com-bubble
https://www.businessinsider.com/list-companies-replacing-human-employees-with-ai-layoffs-workforce-reductions
https://www.google.com/goto?url=CAESiQEB7keqTQnUeplU5L3PJ8buGP8tAoGKJlKtNW853Ci3j6fe14eIvKQY9EDOIWnTqrhGdvTDVLvsFThTaxvjNbdVAJGeW_IrgYFx940N5BOR0l-3BZr5VUuXE1pGWOMxm5_tcD2OKU_lfcF-gKsSq-TihSgoGodbqkgsBkg-GV6JHLJ2mohHnYGauQ==
https://www.ibm.com/think/topics/large-language-models
https://www.gartner.com/en/newsroom/press-releases/2026-04-02-gartner-expects-most-enterprises-to-abandon-assistive-ai-for-outcome-focused-workflow-by-2028
https://finance.yahoo.com/markets/stocks/articles/south-korean-authorities-scrambling-save-184345828.html
https://www.fastcompany.com/91537990/groks-usage-is-so-low-they-can-sell-compute-to-anthropic
https://techcrunch.com/2026/07/01/meta-like-spacex-looks-to-turn-excess-ai-compute-into-cash/
https://www.datacenterwatch.org/report
https://ctl.duke.edu/ai-ethics-learning-toolkit/does-ai-harm-critical-thinking/
