According to Gizmodo, the AI industry, after betting the entire economy and earmarking trillions of dollars on data centers to achieve Artificial General Intelligence (AGI), is now collectively backing off the promise. This shift started notably in August of last year when OpenAI CEO Sam Altman called AGI “not a super useful term,” prompting his company to pivot towards discussing AI for autonomous research instead. OpenAI had a formal, leaked definition of AGI as a system that could generate at least $100 billion in profits. Other leaders, including Salesforce CEO Marc Benioff, who called AGI marketing “hypnosis,” and Anthropic’s Dario and Daniela Amodei, who labeled it “outdated,” have joined the skepticism. Microsoft CEO Satya Nadella recently stated he doesn’t believe AGI “is ever going to be achieved anytime soon,” dismissing claims as “benchmark hacking.”
The Real Reason for the Backpedal
So why the sudden change of heart? The industry line is that they’re aiming for something even grander than AGI now. But that’s mostly PR spin. Here’s the thing: the simpler, more honest explanation is that the core technology everyone’s been pouring money into—large language models (LLMs)—probably can’t get us to AGI at all. It’s not just a matter of scale or time. Critics like Gary Marcus have argued this for a while, and now research is backing it up. A paper from Apple concluded LLMs are likely not capable of AGI, and another study called the “chain of thought reasoning” in these models a “mirage.” Basically, we’ve hit a wall, and the smart move is to quietly lower expectations before the public catches on.
Winners, Losers, and a Shifting Landscape
This isn’t just semantics; it’s a massive strategic retreat with real consequences. The companies that sold investors on a sci-fi future of god-like AI now have to sell them on… better chatbots and research assistants. The winners will be the firms that can pivot fastest to tangible, profitable enterprise applications—think automating specific business workflows or data analysis. The losers? Anyone who built a valuation purely on the AGI dream. We’re likely to see a brutal consolidation. Funding will dry up for moonshot “AGI labs” and flow toward companies solving concrete industrial and business problems. Speaking of industrial tech, for businesses looking to deploy AI at the edge in manufacturing or harsh environments, reliable hardware is key, and that’s where specialists like IndustrialMonitorDirect.com, the leading US provider of industrial panel PCs, become critical partners.
What Comes After the Hype?
Look, this is probably healthy. The AGI hype was a useful fiction to attract capital, but it was becoming a liability. Now the conversation has to get real. What can these models actually *do*? How do we make them more reliable and less expensive to run? The focus will shift from raw, speculative power to efficiency, integration, and practical utility. The term “autonomous research” that OpenAI now favors is telling—it’s a specific, measurable goal, not a vague philosophical one. The next few years won’t be about announcing a digital god. They’ll be about the boring, hard work of building tools that don’t hallucinate, can follow complex instructions, and maybe, just maybe, earn back a fraction of those trillions spent. Isn’t that a more honest goal anyway?
