Artificial Intelligence (AI) is currently one of the fastest-growing sectors within the emerging tech industry. According to estimates by Bloomberg Intelligence, the generative AI market could burgeon into a $1.3 trillion ecosystem by 2032, marking a decade of supercharged growth given that it was valued at only $40 billion in 2022.
To provide some context, ChatGPT recorded an average of 60 million daily visits in March 2024, one of the many milestones since this Large Language Model (LLM) made history by becoming the first application to hit 100 million users within two months of its launch.
Notably, ChatGPT’s success is not unique; the AI hype has propelled several innovations and companies to heights that were unfathomable before the hype. Nvidia, for instance, had at some point started to struggle with its video game GPUs, but with the company shifting focus to GPUs that support AI applications, it recently surpassed Microsoft, ranking as the most valuable company in the world.
Looking at the adoption rates and the success stories of the AI poster children, whether startups, individuals, blockchain-oriented AI ecosystems such as Qubic or big corporations, it is evident that AI innovations are here to stay and transform today’s computer-driven society. However, amidst these developments lies a serious danger: over-centralization of AI innovations.
AI Innovations Risk Over-Centralization
Who should control AI? This fundamental question is a hot topic of debate given the powers that innovations in this realm have to disrupt the status quo. A statement signed last year by 3 key CEOs in the space, including OpenAI’s Sam Altman, highlighted that AI risks could pose an existential crisis to the human race.
“Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war,” partly read the statement.
Fair enough, but at the same time, one cannot help but notice that AI innovations are gradually being hijacked by big tech. The very same corporations that have been notorious for exploiting user data for commercial gains over the past decade.
As of writing, Nvidia enjoys close to 90% of the specialized AI chip market, which explains the company’s newly found success. Microsoft, on the other hand, invested a whooping $10 billion into OpenAI, the startup behind ChatGPT; this investment increased MIcrosoft’s ownership to 49%.
It does not end there, cracks in AI innovations have further been exacerbated by Google’s Gemini LLM which received a backlash for generating inaccurate images. Some of the notable critics who voiced their opinions include Tesla’s founder and billionaire Elon Musk who called out Google through a tweet for advancing their social programming bias.
“I’m glad that Google overplayed their hand with their AI image generation, as it made their insane racist, anti-civilizational programming clear to all.”
While it can be argued that some of the criticisms were perhaps biased, it is a no-brainer that AI innovations in their current state are highly centralized.
Companies such as Google and Microsoft have been running the show since the dotcom bubble, and with AI now in the picture, it should come as no surprise that these corporations are leveraging their data-rich bases to steer AI development.
So, what’s the way forward to avoid a situation where big tech ends up controlling what could be the most transformative or destructive technology in human history? Of course, there are several ways that stakeholders can tackle the issue of over-centralization, one of them being proper oversight on the rules of engagement (acquisitions). But beyond the reliance on ‘central’ authorities, embracing decentralized AI innovations could be the ultimate solution.
Decentralized AI
If you’re not familiar with the concept of decentralization, it is a popular term within the crypto industry pioneered by Bitcoin’s creator, Satoshi Nakamoto.
The Bitcoin blockchain has no single point of failure as it is not controlled by a single entity but rather by a network of miners who compete to solve mathematical puzzles to include a new block. This computational mechanism is supported by what is known as the Proof-of-Work (PoW) consensus.
Similarly, AI innovations could benefit from embracing decentralization models where no central authority can have the monopoly of what to develop, the data to use in training AI, or limit more advanced innovations for mere competitional reasons. Already some projects in the Web3 industry are proving it is possible for AI technology to be integrated with decentralized infrastructures for a more productive and democratized environment.
The Qubic Layer 1 blockchain is a classic example of an AI-powered DApp building ecosystem. Unlike Bitcoin, which relies on a Proof-of-Work (PoW) consensus mechanism, Qubic introduces the Useful Proof of Work (uPoW) system. This consensus not only secures the network but also channels the computational energy toward the training of Artificial Neural Networks (ANNs). Additionally, Qubic is developing an advanced AI layer called Aigarth, which will use data from Qubic miners to create artificial neural networks.
Arguably, most of the innovations in this niche are still in the developmental phase, but what’s worth taking note of is the power they hold to decentralize AI applications.
Contrary to the gated approach that big tech wants to steer the industry towards, AI innovations that are leveraging blockchain technology have the potential to open up the space for anyone to innovate and interact with the latest technologies in the fourth industrial revolution (4IR).
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