Tipping the Scales of Reality: How AI Research Exposes the Industry’s Billion-Dollar Illusion
In a recent report that has sent ripples through Silicon Valley’s meticulously cultivated reality distortion field, AI researchers have delivered what can only be described as an inconvenient truth: throwing obscene amounts of money at a problem doesn’t necessarily solve it. Who knew?
The Association for the Advancement of Artificial Intelligence’s survey of 475 AI researchers revealed that a staggering 76% believe “scaling up” current AI approaches is “unlikely” or “very unlikely” to achieve artificial general intelligence (AGI). This collective academic eye-roll comes at a particularly awkward moment for tech giants currently building nuclear-powered monuments to their AGI ambitions.
Technology companies have spent years convincing investors, the public, and perhaps themselves that AGI – the holy grail of human-level artificial intelligence – is just a matter of more: more data centers, more processing power, more electricity, more billions. Microsoft alone has committed to spending $80 billion on AI infrastructure in 2025, an amount that could solve numerous pressing global problems but will instead be sacrificed at the altar of corporate techno-optimism.
The scaling dogma has always had a beautiful simplicity to it: if my model is smarter with 100 billion parameters, imagine how brilliant it will be with 500 billion! This logic, reminiscent of a teenager convinced their basketball skills would improve exponentially if only they had more expensive shoes, has driven investment strategies across the industry.
As Stuart Russell, computer scientist at UC Berkeley, eloquently puts it: “The vast investments in scaling, unaccompanied by any comparable efforts to understand what was going on, always seemed to me to be misplaced.” Translation: perhaps we should have spent some time understanding the technology before building data centers the size of small countries to power it.
Plateau of Diminishing Returns
Signs that the emperor might be underdressed have been emerging for some time. When OpenAI researchers discovered their next GPT iteration showed little improvement over its predecessor, one might have expected a moment of reflection. Instead, the industry doubled down, with Google CEO Sundar Pichai asserting there was no reason they “couldn’t just keep scaling up,” a statement with the same energy as a gambler convinced the next hand will definitely recover all previous losses.
Meanwhile, Chinese startup DeepSeek managed to create an AI model comparable to Western flagships at a fraction of the cost, using a “mixture of experts” approach that leverages multiple specialized neural networks rather than a single massive one. This is the algorithmic equivalent of realizing that assembling a team of specialists might be more effective than training one person to be mediocre at everything – a concept apparently revolutionary in AI development.
The Nuclear-Powered Elephant in the Room
Perhaps the most darkly humorous aspect of this scaling obsession is the energy consumption. Tech giants are literally signing deals to redirect entire nuclear power plants to fuel their data centers. One can’t help but picture future historians documenting this era: “And then, faced with climate crisis and energy insecurity, they decided to use nuclear energy to power machines that could write slightly better marketing copy and generate images of cats wearing hats.”
The irony thickens when considering that 80% of survey respondents believe current perceptions of AI capabilities don’t match reality. As Thomas Dietterich of Oregon State University notes, systems proclaimed to match human performance “still make bone-headed mistakes.” Yet somehow, these obvious limitations haven’t deterred companies from investing sums that would make the GDP of small nations blush.
Defining Success Through Moving Goalposts
Even the definition of AGI remains conveniently fluid. Google DeepMind describes it as a system outperforming humans on cognitive tests. Huawei suggests it requires a physical body. Perhaps most tellingly, Microsoft and OpenAI reportedly consider AGI achieved only when OpenAI develops a model generating $100 billion in profit – a definition that subtly shifts the goal from “human-level intelligence” to “unprecedented corporate profit machine.”
This definitional flexibility ensures the AGI carrot can always remain tantalizingly out of reach, justifying ever-increasing investments while providing a ready-made excuse for why true AGI hasn’t been achieved yet. “We just need more scale” becomes the technological equivalent of “the check is in the mail”.
The Scaling Bubble
As tech titans continue pouring billions into data centers that may well be elaborate monuments to a fundamentally flawed approach, smaller companies explore alternatives that do more with less. The situation bears an uncanny resemblance to other historical bubbles, where massive investment flowed into ventures based more on hype than substance.
Perhaps the most biting irony is that while AI models struggle to achieve genuine understanding, they have perfectly replicated one quintessentially human trait: the stubborn refusal to admit when a chosen path might be fundamentally wrong.
In this light, the tech industry’s continued commitment to scaling might represent the most expensive example of sunk cost fallacy in human history – a cognitive bias that even their own AI systems could probably identify, if only they were asked the right question.
Founder and Managing Partner of Skarbiec Law Firm, recognized by Dziennik Gazeta Prawna as one of the best tax advisory firms in Poland (2023, 2024). Legal advisor with 19 years of experience, serving Forbes-listed entrepreneurs and innovative start-ups. One of the most frequently quoted experts on commercial and tax law in the Polish media, regularly publishing in Rzeczpospolita, Gazeta Wyborcza, and Dziennik Gazeta Prawna. Author of the publication “AI Decoding Satoshi Nakamoto. Artificial Intelligence on the Trail of Bitcoin’s Creator” and co-author of the award-winning book “Bezpieczeństwo współczesnej firmy” (Security of a Modern Company). LinkedIn profile: 18 500 followers, 4 million views per year. Awards: 4-time winner of the European Medal, Golden Statuette of the Polish Business Leader, title of “International Tax Planning Law Firm of the Year in Poland.” He specializes in strategic legal consulting, tax planning, and crisis management for business.
