Decentralization of AI
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SkillfulAI_Docs_v1.0
Last updated
The current global AI market’s rapid expansion is largely driven by a handful of dominant players in the tech industry, including Microsoft, Google, and Amazon, which control the majority of AI resources.
For instance, as per S&P Global, 63% of organizations rely on public cloud services for AI model training, while 68% store their data in these centralized environment
However, such centralization of AI is a significant risk that warrant urgent attention as the technology continues to evolve. The growing dependence of companies on a limited number of third-party providers for hardware, cloud services, specialized software, and advanced generative AI models increases single-provider risks.
The CrowdStrike outage in July 2024 shows the challenges associated with technology concentration and the interconnectedness of critical systems and software.
The World Economic Forum has also raised alarms about the geopolitical implications of AI centralization, suggesting that overreliance on a limited set of AI models could lead to cybersecurity risks affecting global infrastructure
Although Centralized AI have achieved many breakthroughs, including generative AI models that can understand and generate text, images, and even complex simulations, their dominance raises several concerns:
Bias and Manipulation: Centralized AI systems can inadvertently or deliberately reflect the biases of their creators, leading to skewed outcomes that may favor certain groups or ideologies.
Transparency and Accountability: The opaque nature of centralized AI systems makes it difficult to understand decision-making processes or hold entities accountable for errors or unethical practices.
Innovation Stagnation: Centralization often leads to a monopoly over AI development, hindering diverse and innovative approaches in the field.
Privacy and Data Security: Centralized AI systems rely heavily on data collection, raising significant privacy concerns and the potential for misuse.
Exclusivity: Nations and communities with limited resources may be excluded from the benefits of AI advancements. Who decides when AGI gets to Pakistan or Bangladesh? Closed models could lead to unchecked power and misuse, further exacerbating global inequalities.
The adoption of decentralized alternatives, such as blockchain and federated learning, can offer solutions to mitigate above risks by democratizing access to AI technologies and reducing dependency on a few dominant players.
Spread intelligence across many points instead of a single fortress, and you move closer to openness, composability, transparency, and more distributed ownership of the AI models – an outcome that developers with past bruises and monopoly rents have craved ever since they first saw small circles squeeze the rest for profit.
As Crypto x AI fusion gains ground, everyday people and blockchain coded clusters will compete with big centralized techs in holding and shielding critical data. With such competition, private details will no longer be spilled into centralized servers; but stored locally.
One of the other benefit of blockchain is that crypto incentives can rally new pools of computing power and programming talent, pushing back against once-unchallenged giants. With the rise of decentralized personal assistants, we’ll see AI agents - fluid meshes of cooperative code-elements - pop up wherever needed.
Another big edge of crypto-tech is how its pieces click together easily (i.e., composability), letting developers create new building blocks without a nod from centralized entity, scaling across the globe. In short, it ignites non-stop growth in code creation.
As coding shifts from expert developers into everyday hands, we’ll see rival blueprints emerge on how to advance tomorrow’s tech stakes.
Such decentralized AI (DeAI) represents a new vision with fragmented data sets connected with each other to create and fine tune new models. This will lead to a final vision where compossibility links countless agents. Together, they can handle jobs smartly, waste less power, and shape a more accessible AI environment no single player can cage. In many ways, this is similar to modular vs monolithic blockchain debate.
Early on in any tech cycle, unified centralized approach wins because they streamline everything, making life easy for users and handing them a neat, polished bundle because they own every layer of the tech stack —from services to specialized chips. Google has its own apps, Vertex as its own platform, Gemini as LLM, Google Cloud Platform (GCP) as Cloud solution, and Tensor Processing Units (TPUs - AI accelerator chips developed by Google) for chips.
But eventually, cracks appear in tightly-held control, and as the tech mature, “decentralization” becomes the name of the game.
This entire decentralized evolution run on the idea of modular structure and composability, something that many see as the core goal behind web3.
DeAI moves the AI slider from integrated to the modular one. From the one model to rule them all to Foundational Model + RAG (user-specific context in prompt) to Mixture of Experts to LLM Synthesis (combines multiple LLMs) to LLM Routing (auto-select the optimal LLM) and to finally Group of AI Agents.
In other words, commoditizing each piece of the tech stack invites more players to join, and thus more competition, and ultimately more reliability.
Within this environment, clusters of AI agents can convert intelligence itself into a marketplace of interchangeable building blocks— skills, tools, memory, and characters —pushing past the monolithic approaches that tried to do everything in one place.
Decentralized AI is all about “Build AI for the people by the people" —or risk a future ruled by unelected machine lords.
Without DeAI, we risk creating a caste system of digital haves and have-nots. Even the best-intentioned centralized AI will default to ‘control, control, control.’
A future where AI replaces workers but not leaders is dangerously imbalanced.
Historically, letting both money and labor flow freely drove explosive growth in living standards. But in recent times, wealth pools at the top. Blockchain first caught attention as a way to move hard-earned resources out of reach of wasteful authorities who kept tilting the scales. Now, by wresting the basic tools of production from a few central nodes, decentralized intelligence puts forth a different vision—one where people matter not as burdens, but as vital contributors. They can supply fresh data, run efficient computing rigs, and feed the rising minds that will shape the planet’s future economy.
TL;DR
The key components of decentralized AI:
Self-Sovereign Identity and AI: Individuals and organizations retain control over their data and AI systems. Models tailored for specific needs could operate locally, ensuring data privacy.
Privacy and Security: Decentralized systems can offer better privacy controls and data security, as they do not rely on centralized data repositories.
Diversity and Innovation: By distributing AI development across a wider range of creators, decentralized AI fosters a more diverse and innovative environment.
Distributed Governance: Inspired by blockchain and decentralized autonomous organizations (DAOs), governance mechanisms would allow collective decision-making, minimizing risks associated with centralization.
Accessible Infrastructure: Leveraging decentralized compute networks, individuals with basic hardware can contribute to and benefit from AI advancements.
