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Forbes
24-03-2025
- Business
- Forbes
DeepSeek And ASI-1 Mini: A Closer Look At AI Computing Optimization
AI is advancing faster than ever—that much is clear. But what's often overlooked is the knock on effect on computing power, which is struggling to keep up with demand. With models like DeepSeek and ASI-Mini 1 introducing smarter architectures, it might seem like we're on the verge of a solution. Yet, this opens up a bigger question—are we solving the compute crisis, or are we actually accelerating it? The common denominator between DeepSeek and ASI-Mini 1 is their use of Mixture of Experts (MoE)—an architecture incorporating multiple expert sub-models. Rather than engaging the entire model for every request, MoE selectively activates specialised expert models, reducing computational strain while maintaining performance. This approach enhances compute efficiency, scalability, and specialisation, making AI systems more adaptable and resource-conscious. This breakthrough has highlighted the growing importance of MoE in AI development. While both models employ MoE, ASI-Mini 1, built by takes this even further by incorporating Mixture of Agents (MoA) or Mixture of Models (MoM). As an example, MoA allows multiple autonomous AI agents to collaborate, optimising resource use and making AI more adaptable. Not only excelling in expansion, but also becoming the world's first Web3 large language model. Optimised compute usage should, in theory, reduce overall computing demand. However, it's not that simple. Jevons Paradox suggests that efficiency gains often lead to greater adoption, ultimately driving demand even higher. DeepSeek's ability to deliver high-performance AI at lower costs is a prime example—by making AI more accessible, it fuels greater investment in AI projects, intensifying the need for infrastructure. As a result, the focus shifts toward ensuring solutions are not only cost-efficient, but also scalable and adaptable to sustain AI's rapid growth. Both LLMs and AI Agents are intensifying this demand, requiring substantial computing power for training, inference, and real-time decision-making. LLMs, particularly the latest iterations with billions of parameters are computationally expensive not just during training but also where they process massive datasets and in inference, where generating responses at scale remains resource-intensive. AI Agents, operating in dynamic environments, introduce continuous workloads, constantly analysing incoming data and making autonomous decisions in real time. This sustained computational demand places additional strain on infrastructure, requiring consistent access to high-performance compute resources. As highlighted in Aethir's analysis, GPUs remain the foundation of AI infrastructure, yet their high costs, supply chain constraints, and availability pose significant challenges for businesses scaling AI operations. This surge in AI adoption makes high-performance, cost-efficient, and scalable infrastructure an imperative, particularly as businesses seek flexible, transparent, and globally distributed compute solutions to maintain a competitive edge. The market isn't just seeing incremental advancements. What we're experiencing is an infrastructural shift, where companies must rethink how they build, deploy, and sustain AI systems. That's the new status quo. One of the biggest shifts is the broadening of AI applications which are no longer limited to research labs or enterprise automation, AI is embedding itself into consumer products, financial systems, and real-time decision-making engines. AI agents, once a niche concept, are now being deployed in autonomous trading, customer interactions, creative fields, and decentralised networks, all of which require constant, real-time compute power. At the same time, we're witnessing an evolution in how AI infrastructure is funded and scaled. SingularityNET's $53M investment in AI infrastructure reflects a broader trend: businesses aren't just developing better models—they're strategising around compute access itself. The scarcity of GPUs, the need for decentralised compute solutions, and the rising costs of cloud AI infrastructure are becoming as critical as AI model improvements themselves. But, how will companies sustain this level of growth? Even with MoE and its extensions reducing computational inefficiencies, the demand isn't shrinking—it's accelerating. Companies that once focused solely on AI capabilities now must navigate compute economics just as carefully. Those who fail to plan for infrastructure growth risk being left behind.
Yahoo
07-02-2025
- Business
- Yahoo
Meta Breaks Record with 14-Day Rally Streak, Gains 16.5%
The Meta Platforms Inc (META, Financial) stock had the widest 14-day winning streak in its history, surpassing the 2015 streak of 11 days. This period saw the stock's ascension by 16.5 percent until hitting an all-time record price of $718.90 on February 6, 2025, for its lifetime. After its new free speech approach, Meta enjoyed more faith from investors, which also led to higher trading activity, with the daily numbers falling to 25 million shares from 18 million in the previous month. Warning! GuruFocus has detected 5 Warning Sign with META. Following the February 6 RSI indicator reaching 72, market participants saw the $META/USD direction towards the positive side, but they also monitored for future signals of overbought conditions. And upward market value shifts in the entire artificial intelligence market sector arise from Meta's excellent financial results. AGIX soars from VisionNET due to the Chinese investor's 5.2%, $0.52, and users raise FET by 3.8%, $0.78. Once the META token is strategically transitioned, its use parallels that of traditional market stock prices as well as the usage of crypto assets. It is the current market value of Meta's new strategic approach, which finds positive technological signals. Market analysts currently claim that a continuous evaluation is necessary to measure the results of a high momentum signal plus technical indicators because this cocktail might prelude market consolidation between the traditional equity market and the AI cryptographic market. This article first appeared on GuruFocus.
