CNBC’s Deirdre Bosa joins 'Money Movers' to discuss the blind spots in AI's reasoning model.

The release of Open AI's ChatGPT has accelerated the development of artificial intelligence and warnings about its impact on society.

Rise of AI

Recent analysis highlights a significant blind spot in artificial intelligence: the limitations of reasoning models. While tech stocks and companies like NVIDIA and Microsoft have reached record highs due to optimism around AI, experts and research indicate that these models often fail at basic common sense tasks and cannot generalize beyond narrow applications. This raises concerns about the long-term productivity and return on investment for the massive resources being poured into AI development.

Industry leaders and researchers warn that if AI reasoning models cannot scale or deliver on their promise, the current surge in investment may not result in the anticipated breakthroughs or productivity gains. The debate has broad financial implications, potentially affecting major tech firms and the entire AI trade, as investors reevaluate the risks and rewards of further investment in the sector.

AI's reasoning blind spot

Tech stocks continuing to rally, powering this market to a record high on the S&P. The Nasdaq 100 hitting its own record high. NVIDIA, Microsoft, Broadcom, at or near all-time highs on their own. But could the market be overlooking a major risk popping up in the next leg of the AI trade? Deirdre Bosa digging into that in today's tech check. What are you worried about, Deirdre? Well, this is what I'm worried about. AI's next big promise is reasoning. These are models that can think through problems, make plans, even act as agents. And this is a shift that is really driving the hype around agents, artificial general intelligence. That's a promise that machines could match or even surpass human intelligence. The twist, and what is maybe cause for worry, is that these models, they do mimic reasoning, but a growing body of research suggests that they just totally break down when pushed beyond narrow tasks. For our latest tech check take, we dove into that research and we talked to some of the leading founders in artificial intelligence. Here's Databricks CEO, Ali Ghodsi. It's awful at very common sense things that, you know, you and I would do in our sleep. And that's, I think, a fundamental limitation of the reasoning models right now. That was not true in the previous paradigm. And by previous paradigm, I mean two whole years ago, when we were doing pre-training, the new model would just be better at everything. Well, that's where the market risk comes in. Wall Street is betting that smarter models, reasoning models, mean more demand for compute, more infrastructure, more upside for names like NVIDIA and the whole AI trade. But if the models cannot scale or generalize, all that spend might not lead to productivity. It could just be sunk cost. And that is something that has rattled the tech trade before, when progress stalled late last year and when deep-seek showed the limitations of Western models and narrowed the gap between U.S. and China. Now, reasoning is the new narrative. And with it, we've seen this rebound. It's also fueling Zuckerberg's recruiting drive, Jensen Huang's trillion-dollar bull case for physical AI. And it's at the crux of the Microsoft-OpenAI relationship and whether a breakthrough in reasoning could trigger artificial general intelligence, ending that partnership and redefining who controls the future of AI. Put another way, this is a technical debate that is all over Silicon Valley and tech, and it has potentially massive financial consequences for Wall Street. And that is why investors should be paying attention. We break it all down in our latest Tech Check Take, AI's multi-billion-dollar blind spot. You can catch it on YouTube or on CNBC.com, Sarah. Who are the companies that this would most impact? I mean, it could impact everyone, the whole trade. It could impact NVIDIA, AMD, the cloud players, right? It's sort of what we saw at the end of last year, when we saw that pre-training phase. It hit a wall, the data wall, and people were wondering, where's the ROI, right? Investors wondered, where do we actually make money from this thing? Reasoning is hailed as the next big breakthrough, but what an increasing number of researchers are finding out is that it's not actually that smart. So does the market take a pause again and think, hold on a second, we need to see the return on investment here before we pour hundreds of billions of dollars more into these models, developing them and buying into the promise of them? Aren't all these genius engineers going between open AI and Facebook for a hundred-million-dollar signing bonuses to figure this out? They're all working on it. And I mean, reasoning models are supposed to be getting better and better and keep the AI boom going. But you know, something that Ali Ghodsi says, who works in this space, who's very familiar with it, is that they just can't generalize. So what we need are very specific, specialized models. That might be enough, but that's not AGI, that's not super intelligence. All these things that Mark Zuckerberg and Jensen Huang and others are working on. Okay. Deirdre, thank you for flagging it for us. We'll read more about it on Deirdre's tech check letter.

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Kyiv

Ukraine Invasion

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Jerusalem

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AI's reasoning blind spot

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Rise of AI