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| By Jurica Dujmovic |
Remember how last week, I said that social media is the next frontier primed for decentralization disruption?
That’s still true. But there’s another that’s undergoing that transition right now, and it’s causing a seismic shift.
Emad Mostaque, the founder and CEO of Stability AI — the company behind the revolutionary Stable Diffusion technology — resigned to embark on a journey toward decentralized AI.
Mostaque’s departure is not just a personnel change. Rather it signifies a growing movement within the tech community that questions the concentration of power in the hands of a few AI giants.
His new venture into decentralized AI — specifically through the Render Network (RNDR, “B”) — underscores a burgeoning trend that seeks to redistribute this power to foster innovation, transparency and general accessibility of a resource that is becoming increasingly central to our lives.
And just like with social media decentralization, the disruption that crypto AI brings to the TradFi AI sector means opportunities for investors.
That’s because traditional AI structures, which lean heavily toward centralization, are limited primarily due to the immense computational resources and data required for training sophisticated AI models.
In order to fund maintenance and scalability to support those resources, traditional AI programs can be expensive to use as costs are passed onto users.
Decentralized AI challenges this status quo by proposing a model where AI's development and deployment are spread across a network, not confined to the data centers of tech behemoths.
This act alone lowers the barrier of entry for AI use. Simply bringing down prices will almost certainly lead to an explosion of usage and experimentation and that would otherwise not occur.
And central to this discourse is the acknowledgment of decentralized AI’s potential to foster innovation. Democratic access could unleash the creativity of the best and the brightest of crypto to change the future in ways almost impossible to imagine. By distributing the power to create and refine AI models, we can tap into a broader pool of talent and perspectives, driving unfettered advancements in the field.
Moreover, decentralized AI can enhance privacy and security, mitigating the risks associated with centralized data repositories.
Yet, realizing the full potential of decentralized AI necessitates overcoming significant hurdles. These include …
- Privacy and Autonomy: This includes technical challenges related to distributed computing, data sharing and model training, and
- Transparency and Economics: These encompass broader issues related to governance, standards and collaboration among disparate stakeholders in the AI ecosystem.
The image below shows a more complete picture of the challenges decentralized AI platforms face …
To address the technical issues surrounding operating across various crypto networks — which can introduce latency and synchronization issues — edge computing is employed. This involves processing data closer to the source of data generation rather than relying on a centralized server far away to cut latency and boost the efficiency across a decentralized network.
Additionally, new algorithms are being developed that are specifically designed for distributed environments, optimizing the way data is processed and shared across nodes to enhance overall system efficiency.
When it comes to data sharing, maintaining privacy is the main concern. To uphold security a new technique — federated learning — has been developed. It trains algorithms across the multiple decentralized devices or servers that hold local data samples … all without exchanging them.
This means the data remains at its source, removing opportunities for security breaches and thus protecting users’ privacy.
Split learning is a similar technique, training each node — the individual computers that create the decentralized network — in only one aspect before it gets passed to the next for further training.
Think of it as a conveyor belt assembly line. But instead of building a car or computer, each node builds the next piece of the AI algorithm. This way, no one node can be hacked for all the data.
Moreover, techniques like differential privacy are often integrated to add further layers of security, ensuring that the data, when it is aggregated, doesn't reveal individual identities.
That’s the technical side. But when it comes to establishing effective governance in decentralized networks, the core of blockchain technology really shines.
After all, the most notable benefit of blockchain technology is the transparency of all on-chain action.
When it comes to crypto AI, that benefit is put into overdrive, providing a robust framework via an immutable ledger that records all actions and decisions within the network for everyone to see.
And bridging leadership from different stakeholders requires those transparent protocols to ensure fairness and accountability.
That transparency is also key for standardization across crypto AI organizations. These standards are crucial for ensuring that different decentralized AI systems can work together seamlessly. This will promote further innovation, which is beneficial to a broad range of stakeholders.
Already, a constellation of key players is pioneering the integration of AI with decentralized technologies, shaping a new horizon for the industry.
OpenMined stands out for its dedication to privacy-preserving AI. This is done by offering tools that allow AI models to learn from data without ever having access to it, a critical step toward secure and private AI development.
SingularityNET (AGIX, Not Yet Rated), under the guidance of Dr. Ben Goertzel — noted computer scientist and AI philosopher — has created a unique platform that democratizes AI by allowing for the creation, sharing and monetization of AI services at a global scale, fostering a more inclusive AI marketplace.
Ocean Protocol (OCEAN, “B-”) offers another layer of innovation by providing a decentralized data exchange protocol, which enables secure data sharing for AI without relinquishing control, crucial for maintaining data sovereignty.
Fetch.ai (FET, “B+”) is blending AI with blockchain to construct a decentralized digital economy, where autonomous agents can perform a variety of tasks, facilitating a more efficient and automated system.
And finally, the Render Network, now associated with Emad Mostaque, is focusing on decentralized GPU computing. While the network itself isn’t a direct AI play, it is still revolutionizing access to computational resources necessary for AI and graphics rendering, making it a definite force in this seismic shift.
This isn’t a full list of impressive new crypto AI projects, but they are some of the ones making the biggest waves and I want you to know their names.
That’s because, for crypto investors, this movement promises massive opportunities.
Especially as many crypto AI projects are just starting out. Meaning they have plenty of room to grow … especially in a bull market like this.
In fact, my colleagues Juan Villaverde and Dr. Bruce Ng have recently recommended a crypto AI pick to their New Crypto Wonder Members and are looking at more.
Their entire strategy involves maximizing returns by targeting newer, small-cap cryptos with massive growth potential. And it’s been a great success, pulling in two triple-digit wins in just a few months, with even more in their model portfolio that haven’t been harvested yet.
Juan even sat down with Weiss Ratings founder Dr. Martin Weiss to break down exactly how this strategy can turn 2x, 9x or even 10x gains into potentially 20x, 344x and even 816x winners.
They recorded that briefing, and it’s now available for you to watch for yourself.
And I suggest you do. Because without a doubt, the potential of these small-cap cryptos — especially crypto AI projects — is huge.
We’re on the cusp of a new decentralized ecosystem for AI services, data marketplaces, automated digital economies and equitable access to computing power. Put together, these new developments could revolutionize how we approach and use AI, a market set to explode to a $585 billion industry by 2028.
Early investors getting in now could reap windfalls as decentralized AI upends the centralized status quo. And as crypto networks may drive AI's future by democratizing it, savvy investors who position themselves in the leading decentralized AI protocols and platforms today stand to reap even bigger rewards.
Best,
Jurica Dujmovic
