Big tech has pledged to increase spending on Artificial Intelligence (AI) to $300 billion in 2025. That’s a big number. Since OpenAI made headlines with its sophisticated generative AI model, businesses, non-profits, and even governments have been committing millions and billions of investments. Simultaneously, institutional and retail investors are also flocking to AI companies in hopes of riding the buzz and making profits from the success of AI projects.
These investments are crucial for various reasons. First and most importantly, developing and improving technologies takes money. Second, it helps solve some of today's most pressing issues, especially in medical research. Lastly, it’s become clear that in the future, leaders in AI will be the de facto leaders in the tech sector at large.
Much of this investment is going toward infrastructure development, which is self-explanatory. AI models are built, trained, and run on hardware, which has to meet its often stringent performance demands. IT infrastructure, as we know it today, is getting a makeover from data center expansions to network device optimizations.
The race to AI domination is very real, and that has caused companies to cough up billions for different projects. The most prominent investors in the scene are the big tech companies, with Microsoft, Meta, AWS, and X spending heavily on developing their AI models. Then, more niche players are building upon those models or developing their own to solve problems.
Companies are investing in internal research and development and forging strategic partnerships and acquisitions to accelerate their AI initiatives.
Here are some of the most significant investments in AI currently:
It’s hardly surprising that names like Alphabet and Microsoft spend top dollar on AI. Even governments worldwide have pledged investments to boost AI research and development. For instance, Hong Kong announced it would invest $128 million in creating an AI research institute.
The bulk of the AI-linked infrastructure investments are creating new data centers and expanding/upgrading existing ones. The reason is simple—the data centers will handle the lion’s share of the AI processing. So, companies with their own data centers, like the hyperscalers, and those who provide data center space to others, such as cloud providers, are ramping up investments into the right assets.
Although AI models are essentially code that relies on data, it’s the hardware that makes all the difference. AI is compute-intensive, which means lots of GPUs in servers. In the wake of cloud adoption, traditional data centers have been filled with CPU-dominated servers. That’s why data centers are investing in AI-ready servers capable of running such computations.
On the hardware side, manufacturers are working to improve the design of hardware, particularly servers, to cater to the growing demand from tech companies. Vendors like Dell EMC, HPE, and Cisco have released servers with dedicated AI accelerators that are much more efficient at computations than the good old CPU.
Of course, the most significant player in AI has been NVIDIA, whose chips have been driving the innovations. For instance, OpenAI has shelled out millions of dollars on NVIDIA’s chips to power their servers and supercomputers for AI development. At the same time, companies have also been working on their chips—companies that aren’t usually hardware players. Meta announced its in-house AI accelerator, Artemis, that is supposed to provide the right balance of computing, memory, and bandwidth.
In essence, the impact of AI across infrastructure has trickled down from the essential components like chips to the equipment used in data centers and enterprises for research and operations. The change has rippled through all stages.
This section will discuss DeepSeek's case and how it has upended all the talk about investing in new, bigger, and better infrastructure.
Since the AI wave picked up in the past few years, the idea has been that AI development projects require modern, compute-heavy infrastructure. Researchers, developers, and advocates for AI alike have been pushing for increasing investments in infrastructure to meet the computational demands. That led to billions pouring into AI investments from outside and within the companies directly involved in development.
A Chinese AI company, DeepSeek, challenged that assumption and narrative. In early 2025, it released its AI model R1, which caused havoc in the AI investment spheres. The model was created at a fraction of the cost of the famous and ubiquitous GPT. It even sent the stock market into a frenzy, with companies shedding tens of billions of dollars in value.
While the model's low cost is impressive, what’s even more crucial is that it’s open-source. That means it can be replicated and repurposed at low cost for other use cases.
That begs the question, are the billions pledged to AI advancements even necessary? Do we even need to change infrastructure entirely? Perhaps not, or at least not by the scale we thought earlier. Innovations like that of DeepSeek have paved the way for low-cost developments, which is good news if we ignore the sociopolitical implications for a second. It goes to show that unlimited resources aren’t necessary for AI innovations.
While much of the AI investments are going toward infrastructure, there are several other challenges to handle that will also cost money.
The global race for AI dominance is increasingly intertwined with geopolitical tensions. Nations are vying for technological supremacy. That could and has led to trade restrictions, export controls on critical AI components, and even the weaponization of AI technologies.
This rivalry can disrupt supply chains, prevent collaboration, and create a fragmented AI landscape. This has even led to the discussion about the centralization of AI technologies, limiting it to the hands of a few.
The rapid expansion of AI necessitates a highly skilled workforce. However, the current talent pool is struggling to keep pace with demand. There's a critical shortage of AI researchers, engineers, and data scientists. According to some estimates, the talent gap is an astounding 50%.
Some of those billions in investments must also go toward talent development. Educational programs, research initiatives, and open-source technologies can address the talent gap.
The AI sector is fiercely competitive, with established tech giants and emerging startups vying for market share. This intense competition can lead to rapid innovation, but it also challenges smaller players to compete and raises concerns about potential monopolies.
Investments should ideally create a level playing field, encourage open-source collaboration, and promote ethical competition to ensure that the benefits of AI are widely distributed. The good news is that some venture capitalists are pouring millions into smaller AI startups.
Even with significant investments and technological advancements, the widespread adoption of AI faces substantial hurdles. Businesses and individuals may be hesitant to embrace AI due to concerns about job displacement, data privacy, and the ethical implications of AI systems. That creates a challenge for getting a return on investments.
Successful adoption depends on demonstrating the value of AI in practical applications. Companies will also need to integrate the new technologies into existing workflows. In other words, AI software and models should be integrated with existing applications.
It’s clear that AI innovations require money, and money must be spent on infrastructure advancements. Although earlier notions about such investments have been challenged, the fact is that hardware is changing drastically to support AI research and development.
Investing in infrastructure should be a priority for any business that aims to take advantage of AI. Even if your enterprise isn’t involved in AI development, it can benefit from using it with the right assets to run AI models and applications.
PivIT has been an infrastructure partner for ambitious enterprises since its founding, matching innovators with the right equipment. We can help procure the next-generation servers, storage, and networking equipment ready to handle AI.
Learn more about infrastructure services at PivIT!