This article was originally published on Fool.com. All figures quoted in US dollars unless otherwise stated.
yKey Points
- Nvidia stock could come under pressure for a few reasons.
- Generative AI pilots are failing to produce results, even with artificial intelligence (AI) model providers burning cash.
- AI models aren't improving as quickly, raising questions about the value of higher spending on AI training.
Nvidia (NASDAQ: NVDA) dominates the market for AI accelerators. The company's powerful data center GPUs have made the AI boom possible, and the stock has exploded since ChatGPT kicked off the AI revolution.
While Nvidia is still growing and generating massive profits, some cracks in the AI growth story are starting to emerge. While it's impossible to predict the future, there are multiple reasons to believe that Nvidia stock could plunge as the AI industry matures. Here are three of them.
1. Generative AI projects are just not working out
Businesses big and small are testing out generative AI for a wide variety of purposes. AI assistants can help employees work more efficiently. AI coding tools can boost programmer productivity; AI chatbots can handle customer service tasks. Back-office processes can be automated by AI agents. The list goes on.
The only problem is that many of these projects aren't delivering results. A recent report from MIT found that an incredible 95% of generative AI pilots initiated by businesses failed to produce positive results. It's not that AI technology isn't useful -- it certainly is. The issue is that businesses seem to be struggling to translate AI investments into meaningful revenue increases or cost reductions.
AI is expensive, and if it's not producing results for a business, justifying the cost becomes impossible. As businesses start to think about returns on investment and move away from implementing AI just to keep up with the times, the immense demand for AI computing services could start to weaken. AI is useful, but it's not the answer to every problem.
For Nvidia, any slowdown in demand for its powerful data center GPUs would be a disaster for the stock. With a market capitalization above $4 trillion and a historically high price-to-sales ratio around 25, Nvidia needs to keep growing at a brisk pace. If businesses pull back on AI investments, the company's growth story would take a serious hit.
2. AI start-ups are hemorrhaging cash
The fact that so many generative AI pilots are failing is even more concerning, given the fact that AI companies, including OpenAI, are burning cash at an alarming rate. OpenAI was reportedly expecting to lose $5 billion on $3.7 billion in revenue last year. This year, the company expects to hit $20 billion in recurring revenue, but it still won't be profitable.
Even with AI companies selling their services at prices that are unsustainable, the businesses buying those services still generally aren't realizing meaningful benefits. One reason why AI companies are losing so much money is the high cost of Nvidia's GPUs. This can't go on indefinitely. OpenAI and others will need to turn a profit eventually to justify their sky-high valuations, and one path is greatly reducing the cost to train and run AI models.
While Nvidia is dominant today in the AI accelerator market, lower-cost competition will eventually come for the market leader, putting pressure on its profit margins.
3. AI models may be hitting a ceiling
AI models are still improving, but gains are slowing down. OpenAI's GPT-5 AI model was hyped up by the company, with CEO Sam Altman at one point claiming it would provide PhD-level expertise. When GPT-5 finally launched, it wasn't meaningfully better than top-tier models from other providers.
There are still plenty of opportunities to apply powerful AI models to new use cases and applications, but the models themselves may be hitting a ceiling. If that's the case, Nvidia is in for a world of pain. Nvidia's growth story depends on companies being able to justify the ever-increasing cost of training AI models. If companies can't justify increasing spending on training because the output isn't meaningfully better, a big chunk of demand for Nvidia's GPUs will take a serious hit.
No matter what happens, AI will still be an extremely useful technology that will continue to find new applications. But as businesses struggle to make AI investments pay off, AI start-ups operate with heavy losses, and AI model improvements slow down, the writing is on the wall: Nvidia's best days may very well be behind it.
This article was originally published on Fool.com. All figures quoted in US dollars unless otherwise stated.
