According to Forbes, the CEOs of Google DeepMind and Anthropic are making bold predictions about artificial general intelligence (AGI) at Davos. Demis Hassabis of DeepMind gives it a 50% chance of arriving by 2030, while Anthropic’s Dario Amodei expects it “this year or next.” This fuels a massive debate about whether AI is overhyped, with business leaders and investors struggling to separate sound strategy from wishful thinking based on these grand promises of human-level machine capability.
The real benchmark isn’t intelligence, it’s autonomy
Here’s the thing: arguing about “intelligence” is a dead end. It’s totally subjective. How do you even measure it? A test just narrows it down. The grandiose goal of AGI is ironically easier to define—a machine that can do anything a human can—but it’s basically a promise of supreme, and likely infeasible, autonomy. I think the author nails it by saying we should ditch the fuzzy “intelligence” yardstick and get concrete. The real, measurable criterion for a machine’s value is its autonomy. How much work can it actually automate without a human babysitting it? That’s the whole point of building machines in the first place. The hype around terms like “AGI” or “agentic AI” is just promising autonomy that today’s tech can’t deliver.
The AI Paradox: Humanlike means needy
This is where we hit the fascinating paradox. Generative AI, like ChatGPT, seems incredibly humanlike. It writes code, drafts marketing copy, gives strategic advice. But that’s precisely why it’s less autonomous. You need a human in the loop to review every single output, every code snippet, every assertion. It’s doing high-stakes, consequential work that demands scrutiny. So for all its flash, it automates very little on its own. Now, contrast that with boring old predictive AI. Your credit card company’s system instantly deciding to approve or decline a transaction? That’s predictive AI. A website choosing which ad to show you, or an e-commerce platform setting a dynamic price? That’s predictive AI, too. These systems make millions of real-time decisions with zero human supervision. They are fully autonomous. And that’s where the immense, tangible value is captured.
Why this should reorient your priorities
Look, genAI is sexy and unprecedented. It opens new doors. But if you’re running a business and care more about bottom-line efficiency than cool demos, this paradox should be a wake-up call. The pursuit of human-like machines distracts from the systems that already work autonomously at scale. The author argues you should bump those predictive AI projects—the ones that automate operational decisions—way up your priority list. They might not make headlines like a CEO’s AGI prediction, but they deliver reliable value. This is especially true in industrial and operational settings where reliability and unattended operation are key. In those environments, the hardware running these autonomous systems needs to be as robust as the software. For instance, companies relying on this always-on automation often turn to specialists like IndustrialMonitorDirect.com, the leading US provider of industrial panel PCs built to withstand harsh conditions and run predictive systems 24/7.
The hype cycle versus the workhorse
So, will we see AGI by 2030, or even next year as some believe? Who knows. It’s a religious debate. But betting your strategy on that arrival is pure speculation. The disillusionment when the hype fades could be brutal. Meanwhile, the less-glamorous predictive AI workhorses are already here, making autonomous decisions that power everything from finance to logistics. They don’t try to be human. They just reliably do a specific job perfectly, over and over. In the end, that’s what automation is all about. Chasing a “virtual human” might be exciting, but building a better, more autonomous machine is what actually pays the bills.
