The world is experiencing an artificial intelligence boom, but not everyone is benefiting equally. Companies that build AI models often struggle to make profits, while hardware suppliers like Nvidia are reaping huge rewards. Nvidia’s market value recently surpassed $5 trillion, showing that the real gains are going to those providing the chips, data centers, and infrastructure that make AI possible.
However, Prof. Amit Joshi at IMD Business School says there is a growing risk that the world may be overinvesting in the wrong kind of AI infrastructure. Building and powering the massive systems required for large language models, such as OpenAI’s ChatGPT, costs trillions of dollars. If AI development shifts toward smaller or more efficient models, much of this expensive infrastructure could end up unused or uneconomic. Advanced models now require enormous computing resources but often deliver only modest improvements, which raises concerns about whether current investments will pay off.
He says funding and research are also concentrated on transformer-based models, which power most large language models. This focus has left other approaches, such as liquid neural networks or neuro-symbolic AI, underfunded. Experts warn that this narrowing of AI research could limit innovation just as the field could benefit from more diversity in approaches. History provides lessons in overbuilding. In the past, railroads and fiber-optic networks were expanded based on overly optimistic forecasts, leaving much capacity idle and leading to financial losses.
Joshi cautions that the AI market is also heavily concentrated among a few major tech companies, sometimes called the “Magnificent 7,” including Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla. Investors tied too closely to these companies may face sharp losses if the market changes. OpenAI’s reliance on partnerships with big tech to fund its computing needs also creates a network of financial dependence that could lock the AI industry into specific technologies and suppliers.
So far, most AI projects deliver little measurable return, yet funding continues to pour in as companies do not want to miss out on what could be the next industrial revolution. He says governments, investors, and businesses need to hedge their bets by not putting all resources into the same suppliers or technologies. Flexible infrastructure, such as modular data centers, and support for alternative AI approaches could help reduce risk, he adds.
Open-source AI and smaller research labs deserve more attention. By sharing code and training data, these projects lower costs, speed up innovation, and make AI more accessible. Initiatives like Europe’s Mistral or China-backed open models are promising, but they remain far smaller than U.S. tech giants.
Investors should be cautious. While AI offers enormous potential, the rush to invest in the current infrastructure could lead to a costly correction if technology or market conditions change. Diversifying investments and supporting varied approaches may be the safest path forward in this rapidly evolving field.
A number of firms, such as Core AI Holdings Inc. (NASDAQ: CHAI), that are leveraging AI to add value to their core solutions could offer investors some opportunities to get a hedge for their AI holdings so that portfolios are shielded from overexposure to the main AI firms like Nvidia and other tech giants.
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