If 2022 was the beginning of a period of collective and magical mania with the launch of ChatGPT, 2025 is the year of the decline of hype; The year we realized that many companies’ promises about artificial intelligence might be empty. CEOs of tech giants who promised that artificial intelligence would soon cure all ills, replace office workers and usher in an age of abundance are now faced with meaningful market silence and investor skepticism. This is what MIT experts call it The big overhaul of artificial intelligence They call Below, we take a look at all the details that 2025 showed us about the limitations, hidden economy, and realities of artificial intelligence.
The main problem with large language models
One of the biggest issues that became clear about artificial intelligence this year was that simply making language models bigger is not the way to achieve artificial general intelligence (AGI). Even Ilya Satskiver, the co-founder of OpenAI and one of the main creators of the technology, who now runs the startup Safe Superintelligence, admitted that language models have fundamental weaknesses.
Artificial intelligence can memorize thousands of algebraic problems and learn how to solve them, but it does not necessarily understand the principles of algebra. “These models generalize significantly worse than humans,” Satskewer says.
On the other hand, we humans have hardware in our brain that tends to see a “mind” in anything that exhibits intelligent behavior such as speaking. Marketers exploited this feature to make us believe that there was a living, thinking being behind these chatbots, when we were just dealing with a word prediction machine.
The issue of artificial intelligence in businesses
Artificial intelligence was promised to be the savior of the economy and the killer of boring bureaucracy. But new MIT research found that 95 percent of businesses that tried to implement proprietary AI systems failed or got stuck in the pilot phase. But why?
A report by Upwork found that AI agents (even with the GPT-5 engine) are incapable of performing many simple administrative tasks without human supervision, and cannot manage a chain of complex tasks.
Andrei Karpati, a famous artificial intelligence researcher, explains the reason for this in an interesting way. Chatbots are better than the average employee at many tasks (e.g. writing emails or simple coding), but worse than experts, he says. For this reason, these tools are attractive to ordinary consumers (who have little knowledge), but cannot replace skilled employees in companies.
Although the official statistics of companies tell about the failure of artificial intelligence projects, there is a hidden reality: employees are using chatbots in secret. MIT research showed that in 90% of companies, a kind of “shadow economy” has been formed. Workers use ChatGPT to do things without the knowledge of managers and with their personal accounts. This means that AI is useful, but not in the way that managers had hoped.
The problem of the economic bubble of artificial intelligence
The debate about the artificial intelligence bubble is hot. But the important question is: which historical crisis is this bubble similar to? Is it like the 2008 housing crisis, which left nothing but debt and destruction, or the 2000 dot-com bubble, which, although it caused many bankruptcies, left important infrastructure (such as fiber optics) on which the modern Internet was built.
AI seems like the dotcom bubble. Huge investments in data centers may not pay off in the short term, but they build the infrastructure of the future.
Of course, the main concern of economists is circular transactions; For example, Nvidia invests in cloud companies, and those companies buy chips from Nvidia with the same money. This cycle makes earnings look artificially high. However, “Glenn Hutchins”, a reliable investor, believes that there is nothing to worry about, because the accounts of these data centers are powerful companies like Microsoft that have the necessary financial credit to pay the bills and will not go bankrupt.
The GPT-5 issue and general frustration
One of the high points of AI disappointment in 2025 was when OpenAI released the long-awaited GPT-5 model. After months of advertising and promising that this model is “PhD-level expertise in any field”, users were faced with a product that was not much different from the previous generation. This incident made Yannick Kilcher, an artificial intelligence researcher, to declare: “The era of revolutionary developments is over. We have entered the era of Samsung Galaxy; “Every year a new model comes out with minor changes but nothing amazing.”
The rise of artificial intelligence unicorns
In the midst of this frustration, some companies found the right way. Startup Synthesia, which focuses on creating video avatars for corporate training, is a clear example of success away from hype. While everyone was worried about Deepfake, the company recognized a real market need (cheap video content production). Now this company has 55,000 corporate clients, earns $150 million annually, and its value has reached $4 billion. This shows that the companies that solve the real problem will win even when the bubble bursts.
The good news is that the end of artificial intelligence hype does not mean the end of progress. “We are back to the age of research,” says Ilya Satskiver. This means that progress is no longer achieved by splashing money and enlarging data centers, but requires scientific innovation and new architectures. The hype may be bad, but it had one benefit: attracting the world’s brightest talent and big capital to the industry. Now, these talents have the opportunity to focus on solving real problems away from publicity controversies.
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