The relationship between Big Tech and smaller industry players has become increasingly strained, reflecting a battle over access to resources and the pace of innovation. Consequently, startups often find themselves disadvantaged, lacking resources and market power to compete.
The same dynamic is playing out in the artificial intelligence (AI) sector. The current tech titans, known as the “magnificent seven,” which include Google, Microsoft, and Amazon, control much of the infrastructure that powers AI operations globally. However, a growing movement is advocating for decentralized AI to reduce dependence on Big Tech monopolies.
Relying solely on Big Tech risks centralizing power, but excluding them altogether impedes progress. Preventing these monopolies from controlling AI’s trajectory isn’t easy, and without a clear path, there is a risk of stifling ingenuity and creating an environment shaped by narrow, personal agendas rather than broader possibilities.
The uneven AI battlefield
With over 72 percent of businesses adopting at least one AI feature, this technology has become entrenched in our economy, enhancing how organizations operate. But behind what appears to be a seamless tool, there is a hidden reality: AI requires an obscure amount of computing power, data, and constant energy to function–resources often out of reach for smaller firms.
A couple of months ago, Meta closed a deal to give an additional 1.1 gigawatts of carbon-free power from the Clinton Clean Energy Center, enough to power its operations for the next 20 years. Google also unveiled plans to invest $25 billion over the next two years in data centers and AI infrastructure across the PJM electric grid region, which covers 13 states in the mid-Atlantic, Midwest, and South.
While these deals help secure AI’s central role in the future, they also raise important questions about who will shape its direction. When access to compute, energy, and infrastructure is concentrated in the hands of a few, so is the power to decide which problems AI addresses and who it ultimately serves.
Given this shaping reality, decentralized AI has emerged as an alternative, giving smaller startups greater access to AI resources when establishing new ventures. Just as decentralized finance disrupted traditional institutions by eliminating intermediaries, decentralized AI is now challenging Big Tech’s dominance.
By operating across multiple nodes, decentralized AI strengthens privacy, limits data exposure, and reduces the risk of system failures. Unlike centralized AI, decentralized networks allow anyone, like entrepreneurs, researchers, and individuals, to access a network of AI models and computing resources without being locked into a single provider.
Gensyn, for example, is a decentralized machine learning protocol that enables developers to train deep learning models over a network of connected devices, combining devices into a single, virtual cluster. The offering provides a cost-effective alternative to centralized cloud providers while avoiding single points of failure, safely expanding access to the infrastructure needed to power AI.
By now, it’s clear that Big Tech plays a significant role in AI’s evolution, but decentralized AI has also proven that it's creating a more open and diverse future. For AI to remain impactful, its path forward cannot and should not be shaped by individual agendas, grievances, or the quest for power.
A path to a balanced and sustainable ecosystem
Although decentralized and centralized AI models have contributed to AI’s advancement, the belief that one can independently deliver an equitable future is misguided. Clinging to one exclusive approach risks delaying progress. A forward-thinking AI ecosystem must recognize that each model fulfills separate, unique, but equally significant functions.
Without Big Tech, AI would have never progressed to what it is today. They've invested billions of dollars in R&D, and their technical resources have contributed significantly to the advancements that are used daily.
On the flip side, many AI innovations weren’t created in the buildings of top-tier tech conglomerates but came from smaller, independent teams. Startups have consistently been the birthplace of AI advancements, developing everything from novel models to more efficient techniques. Far too often, smaller companies are not granted recognition until they are purchased and absorbed into larger companies.
Take Run: AI, for example. It is a startup that built a platform to make AI workloads run more efficiently across GPUs. In December 2024, Nvidia, the most well-known and influential AI company, acquired Run: AI. Although the purchase legitimizes the company’s worth and value, it reflects a recurring theme that startups tend to be recognized only in retrospect.
Ultimately, inclusivity isn’t about sidelining Big Tech, but reducing monopolistic tendencies. Without external pressures, their dominance will only continue to grow. But if governments, universities, and independent entities embrace and invest in decentralized, open-sourced AI, the result could be a more resilient AI ecosystem that benefits all users, including Big Tech.
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