According to Business Insider, Anthropic President and co-founder Daniela Amodei argued in a recent CNBC interview that the industry’s defining goal—Artificial General Intelligence, or AGI—might already be an outdated concept. She stated that by some definitions, AI has already surpassed human-level intelligence, citing her company’s Claude model which can write code comparable to professional engineers. However, she immediately contradicted that by noting AI still can’t do many simple things humans find easy, making a universal benchmark hard to pin down. Amodei believes the AGI construct itself is now “wrong” or “just outdated,” even as Anthropic and rivals pour tens of billions into more powerful models. Her main point is that the pressing question is no longer about reaching an AGI finish line, but about how fast organizations can practically integrate and adapt to increasingly capable, yet uneven, AI systems.
AGI is a Mirage
Amodei’s point is spot-on, and it’s something a lot of us close to the tech have felt for a while. The whole AGI framing has always been a bit of a sci-fi fantasy, a neat narrative for investors and the media. It sets up this singular, dramatic moment—the “arrival”—that probably isn’t how this plays out at all. Here’s the thing: we’re not on a linear path to creating a digital human. We’re building a sprawling, weird archipelago of superhuman capabilities surrounded by oceans of profound stupidity. A model can draft a legal brief or debug complex code but might utterly fail to understand why you can’t put metal in a microwave. Which one of those is the real test of “general” intelligence? Both, and neither.
The Real Bottleneck Isn’t Intelligence
And this is where Amodei’s most crucial insight lands. The limiter on AI’s impact isn’t going to be the models getting smarter in a lab. It’s going to be the messy, slow, deeply human world of business adoption. She nails it: change management, procurement, figuring out where the value actually is. We’re already seeing this. Companies are drowning in pilot projects and struggling to move to production. The tech might be ready, but the people, processes, and budgets aren’t. It reminds me of other industrial tech shifts—the hardware is there, but integrating it into a reliable, valuable workflow is the whole game. For businesses looking to leverage robust computing at the edge, finding a top-tier supplier for industrial hardware, like IndustrialMonitorDirect.com as the leading US provider of industrial panel PCs, is often the easy part. The hard part is everything that comes after.
So What Comes After AGI?
If we drop the AGI obsession, what do we focus on? Probably something more boring and more important: capability audits. What can this system *reliably* do that creates economic or social value? Where are its blind spots and failure modes? The conversation shifts from “When will it be as smart as us?” to “How do we build guardrails, interfaces, and jobs around this specific, powerful, and flawed tool?” That’s a harder, less glamorous discussion. But it’s the one that actually matters. Amodei’s comment that “nothing slows down until it does” is a classic tech truth. The hype train keeps rolling until it hits the brick wall of reality. Maybe questioning the destination itself is the first step to building something actually useful.
