Quilibrium : the path to achieving scale for AI
According to IDC, only 25% of AI projects actually "Scale Successfully" while the rest either stay in the pilot phase or get abandoned:
A recent McKinsey's report states AI software and services alone could generate up to $23 trillion in annual economic value by 2040. The report identifies 18 future arenas that could "Reshape The Global Economy" with AI Software and Services being one of the high-growth sectors expected to have a significant impact.
Ever hear of the "Pilot Phase Trap"? It’s when companies get some initial success with a project in a small, "Controlled Environment" but when they try to scale it up to a real-world environment, everything falls apart. According to another report from McKinsey, 78% of AI projects get stuck at the pilot phase and never move forward.
Challenges For AI To Succeed In Real-World Environments:
Scaling AI is no small feat. It’s one thing to get it working in a sandbox, but when you need to integrate it into larger systems and infrastructure, that’s when things get tricky.
Every connection added to an AI system multiplies "Complexity".
An AI system connecting to three other systems on a network isn't 3x more complex – it's closer to 8x more complex.
Most current network architecture available is "Fragmented With Integrated Systems" and many can have to rely on other "Centralized" third party "Middleware Applications" for core functionality. This fragmentation creates extra layers of complexity and friction that can lead to software compatibility issues, vulnerabilities to cyber attacks, multiple single points of failure. If one middleware provider has issues, it can cause an outage for the whole network.
Network Hosting Costs:
The cost of hosting AI can vary widely depending on the type of AI solution and its complexity. Specialized AI platforms can cost between $5,000 to $50,000+ per month.
Cloud-based AI solutions can incur significant monthly fees. For instance, hosting a (LLM) could cost approximately $1,000 per month for 1,000 requests per day.
The cost for initial deployment of AI models onto a complex network can be expensive.
Unforeseen maintenance costs can spiral on networks with integrated applications meshed into them for interoperability.
All these costs can significantly impact a business's budget and long-term sustainability, making it crucial for them to carefully plan and assess for the network they use.
The System Throughput Speed Needed:
AI drives bandwidth demands in data centers, it needs to process data inputs and outputs quickly, often within milliseconds.
Internet traffic is expected to rise significantly over the next decade, driven by "Data-Intensive" activities such as,
Video streaming,
Cloud gaming,
Augmented and virtual reality applications,
Mobile data traffic is also projected to continue growing quickly, quadrupling by 2028.
High-speed connectivity is essential for optimal performance in AI. "Blockchain Technology" will not scale (Even With Added Layers / Connected Systems Etc) and cannot achieve the needed throughput speed to "Constantly" send and receive millions of transactions simultaneously from AI / AI agent systems and other users of a network to the same specifications Quilibrium can.
In Summary
Organizations currently lack the use of a suitable network with the combined functionalities to host AI systems on. It is now well documented from several "Credible Sources In The Industry" that the lack of networks with the "Correct Architecture" is one of the main reasons AI systems are "Failing To Scale" in real-world environments.
AI Systems Need "Enhanced Seamless Networks"
Hosting systems on Quilibrium's unique "Seamless Architecture" that leverages a mix of advanced technologies will allow them to fully scale unhindered at "Constant High Throughput Speeds" on its standardized embedded network that does not rely on third party middleware applications for its core functionality.
Quilibrium's network design offers the desired cost-effective "Uncomplex Foundation" for developers to build / host AI systems on and combines the numerous functions and enhancements that are required for,
Fluid interoperability,
Robust security,
Unlimited scalability,
Constant High Throughput Speeds,
Quilibrium's fully decentralized network has been designed to facilitate the real-world operability conditions needed for AI systems to keep pushing boundaries and "Succeed At Scale"
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