Decentralization and AI: Bridging the Gap between Rapid Software Advances and Hardware Limitations
AI capabilities have expanded significantly in the last couple of years, with major language models like ChatGPT, Dall-E, and Midjourney becoming integral tools for a variety of applications. Currently, generative AI solutions are handling tasks such as replying to emails, crafting marketing content, producing music, and generating images from basic descriptions.
The adoption rate of the AI ecosystem by both individuals and corporations is equally astounding. A recent survey conducted by McKinsey shows that the percentage of companies implementing generative AI in at least one segment of their operation has increased to 65% this year, a substantial rise from 33% at the start of 2023.
Despite these advances, the burgeoning field of AI is facing several hurdles. The training and execution of AI programs require significant resources, which currently gives major tech companies a considerable advantage, potentially leading to the centralization of AI.
An article by the World Economic Forum points out the growing necessity for AI computing power. It indicates that the demand for computational capabilities to support ongoing AI developments is increasing annually by between 26% and 36%.
Recent analysis by Epoch AI indicates that training and operating AI programs may soon incur billions of dollars in expenses.
“Since 2016, the expense for major AI training sessions has increased two to threefold annually, indicating billion-dollar costs could emerge as soon as 2027,” stated Ben Cottier, a researcher at Epoch AI.
From my perspective, we have reached that milestone already. For instance, last year Microsoft invested $10 billion in OpenAI and they are now developing a data center intended to support a supercomputer using millions of specialized chips with an estimated cost of $100 billion, which is significantly higher than their initial investment.
Microsoft is not alone in significantly funding AI computing capabilities; other major technology corporations like Google, Alphabet, and Nvidia are equally committed to advancing their AI research and development endeavors.
While we can agree that the outcome could match the amount of money being invested, it is hard to ignore the fact that AI development is currently a ‘big tech’ sport. Only these deep-pocketed companies have the ability to fund AI projects to the tune of tens or hundreds of billions.
It begs the question; what can be done to avoid the same pitfalls that Web2 innovations are facing as a result of a handful of companies controlling innovation?
Stanford’s HAI Vice Director and Faculty Director of Research, James Landay, is one of the experts who has previously weighed in on this scenario. According to Landay, the rush for GPU resources and the prioritisation by big tech companies to use their AI computational power in-house will trigger the demand for computing power, ultimately pushing stakeholders to develop cheaper hardware solutions.
In China, the government is already stepping up to support AI startups following the chip wars with the US that have limited Chinese companies from seamlessly accessing crucial chips. Local governments within China introduced subsidies earlier this year, pledging to offer computing vouchers for AI startups ranging between $140,000 and $280,000. This effort is aimed at reducing the costs associated with computing power.
Examining the present landscape of AI technology reveals a consistent theme – it’s predominantly centralized. Major tech corporations hold the reins over substantial AI computing resources and applications. Yet, despite changes, the overarching scenario remains largely the same.
However, there’s potential for a positive shift through innovations like the Qubic Layer 1 blockchain. Unlike conventional blockchain models such as Bitcoin, which rely on energy-consuming Proof-of-Work (PoW) for network security, Qubic employs a novel form of PoW called “useful Proof-of-Work” (uPoW). This technique leverages computational power for valuable AI tasks, including training neural networks.
Simplifying it, Qubic’s approach decentralizes the acquisition of AI computational power, moving away from the traditional dependence on privately owned or leased hardware. By capitalizing on a vast network of miners, potentially numbering in the thousands, Qubic enhances access to necessary resources.
Shifting away from the status quo, where major tech entities manage the technical aspects, towards a decentralized model not only proves to be cost-effective but also equitable. It is crucial for AI innovations to be powered by a broader base of contributors rather than a handful of dominant players.
What if every major tech provider suddenly collapsed? Moreover, these corporations have demonstrated a concerning lack of reliability in handling revolutionary technologies that impact lives significantly.
Current public discourse largely revolves around breaches in data privacy, along with other significant concerns like societal influence. With the introduction of decentralized AI technologies, monitoring advancements becomes simpler and less costly.
AI technology is only beginning to unfold, yet accessing sufficient computational power remains a significant barrier. Big tech’s dominance over necessary resources poses a formidable obstacle to the pace of innovation, raising concerns about these corporations’ increasing control over our data—often considered the digital equivalent of gold.
However, the development of decentralized frameworks presents a promising opportunity to cut computational expenses and curtail the influence of large tech entities over one of this century’s crucial innovations.
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