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Nowadays, it feels like a conversation about artificial intelligence (AI) is always a stone’s throw away. Whether deeply entrenched in technology or casually reading the news, you will likely come across headlines and stories about the latest companies and developments in the AI space. These stories talk about all the great things that AI can do locally and globally, for individuals and businesses, as well as the world’s anxieties about adopting AI systems in their homes or workplaces.
However, what is not talked about as often is who is making money in the AI industry.
Google’s (NASDAQ: GOOGL) latest earnings call brought this issue to the surface when the company revealed that its Q2 expenditure reached $13 billion, with most of that spending going toward data centers and services that support AI capabilities.
During the call, many analysts questioned whether the company’s multi-billion dollar investments in AI would turn a profit anytime soon. Google CEO Sundar Pichai said that AI is a long-term investment and that it is better to overinvest in AI now than to underinvest and fall behind. However, it is unclear when Google will begin to profit from its significant investment in AI—and they aren’t the only company in that boat.
OpenAI posted $540 million in losses the year it developed ChatGPT, attributing those losses to the spending required to train and develop GPT. Now, the company is allegedly on track to lose as much as $5 billion due to the costs associated with running the AI products it offers.
The high cost of AI: OpenAI’s financial breakdown
In OpenAI’s case, $4 billion has been earmarked for servers that maintain ChatGPT and the large-language models that power the chatbot. Another $3 billion is needed to cover the cost of training its AI models with new data, and $1.5 billion goes toward operations and the 1,500+ employees OpenAI has on staff.
Both small and large companies end up spending in these same areas—data centers, servers, and talent—when it comes to their AI operations. However, despite the large spending, these companies’ revenue is nowhere close to the costs they incur.
For example, OpenAI is estimated to generate around $3.4 billion in revenue, but that is nowhere near the ~$8.5 billion it spends to make its products possible.
AI industry’s biggest winners: How chipmakers and cloud providers benefit from AI spending
So where does all the money go that these tech giants are spending to get their AI products and infrastructure up and running? It goes to chipmakers, data centers, and cloud service providers. Every company in the AI industry needs at least one of these three items to run the AI arm of their business successfully. Very few companies can produce all of these elements in-house, and even if they can, the technology and bandwidth needed are ever-evolving, requiring the company to invest in the latest tech and infrastructure so they can grow, scale, and provide better products and services.
When it comes to chips, Nvidia (NASDAQ: NVDA) is the dominant player in that industry, with roughly 80% of the market share in the GPU space. AI companies need GPUs to train and run their models, and data centers need to accumulate GPUs so they can train and run AI models on behalf of their customers. This is why chipmakers have been the biggest winners in the AI industry so far.
When ChatGPT was released in November 2022, followed by a significant increase in competition in the AI industry, the price of Nvidia shares increased by over 500% in that two-year period. In the last year alone, Nvidia is estimated to have sold $34.5 billion in AI chips.
Simultaneously, data centers/cloud service providers such as AWS experienced significant growth in the sales of their cloud computing services, citing that their clients, especially those with businesses that had generative AI components, were a factor in Amazon’s (NASDAQ: AMZN) 17% year-over-year growth in its cloud computing service.
AI industry’s profit dilemma: High spending, low returns, and trouble on the horizon
It’s no surprise that infrastructure and ancillary services have been the biggest winners so far from a financial perspective. If a company is not fully vertically integrated, it must rely on third-party hardware, infrastructure, and service providers to deliver its goods or services. Relying on these providers isn’t unusual, but what’s giving investors in AI product providers concern is that it doesn’t look like the companies making these products will turn their big investments and expenditures into profit anytime soon.
Not only are most of the AI ventures of these companies currently not profitable, but the path to profitability remains unclear for many of them. These companies need to aggressively invest to stay competitive, pouring money into better hardware and server space so they can continue to train and run their AI models. Still, these products aren’t providing the revenue that exceeds the cost of bringing these products to life.
What is likely to make it even more difficult for many of these tech giants, who operate in silos and walled gardens, are the open-source offerings on the market, such as Meta’s (NASDAQ: META) Llama 3.1.
In an industry where many participants are not profitable yet, and the players in that industry are price-sensitive due to the high cost of operating and competing, a household name like Meta offering a high-quality free product (AI model) is likely to suppress prices further and lengthen the amount of time it takes for product providers to become profitable as players in the industry begin opting for Meta’s free, high-performing AI model to reduce their own costs and bring themselves closer to profitability.
All of this begs the question—is AI in a bubble?
The AI industry has experienced a meteoric rise in the last two years, and investment dollars have flowed into the space at a rate that does not appear to be sustainable. At the end of the day, investors want a return on their investment, and that return is nowhere in sight for most AI companies.
AI undoubtedly adds value to the world, improving business operations, leading to breakthroughs in math and science, and automating numerous workflows for individuals, freeing up time for them to focus on things that cannot be automated. However, only a few companies drive innovation in this highly saturated industry.
If these tech giants do not emerge with a clear revenue path in the AI industry, we are likely to see the industry’s rapid growth slow down and maybe even deflate to a level that reflects the real value these companies are delivering in the global economy.
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