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Forbes
5 days ago
- Business
- Forbes
Beyond Prompts: Agentic AI And The Dawn Of Self-Directed Intelligence
Daniel A. Keller, CEO and President of InFlux Technologies Limited and Flux. From a conceptual perspective, agentic AI represents a shift from traditional AI or even the more recent and widely adopted generative AI. In what may be the shortest timeline in the history of proliferative technology, AI systems have evolved from struggling with essential speech recognition to executing complex, well-defined tasks with intelligence levels that often surpass human capabilities in scope and speed. Agentic AI introduces a unique form of autonomous intelligence. This enables AI systems to operate with a degree of independence that outpaces the predefined restrictions of older AI frameworks. Instead of using rule-based systems that generate content from prompts like older AI systems, agentic AI systems are designed to plan and act almost independently to achieve goals. What Agentic AI Is Agentic AI broadly refers to AI systems that can act almost independently, make decisions and adapt to evolving circumstances without requiring constant human intervention. These systems are characterized by their capacity to reason and plan. Agentic AI can evaluate situations, consider multiple options and devise strategies to achieve objectives like a human professional would. Unlike static models, agentic AI systems continuously learn from their data and improve their performance over time. They can also interface with digital systems or data warehouses to make real-time decisions. Their approach is goal-driven, which affords them the flexibility to tackle problems by "thinking" outside the proverbial box. This shift is fueled primarily by advancements in machine learning, reinforcement learning and multi-agent systems. For instance, frameworks like DeepMind's AlphaGo demonstrated early forms of agentic behavior. The system mastered the game of Go and developed its own unique tactics, defeating reigning European champion Fan Hui 5-0 in a tournament match. Modern agentic AI builds on these foundations, integrating large language models (LLMs), sensory processing and decision-making algorithms to create far more versatile systems. From Generative AI To Agentic AI Generative AI, made popular by models like ChatGPT and, more recently, Deepseek, excels at producing human-like text, images or other outputs based on user prompts. However, its limitations are obvious; it operates within the confines of the user's scope and cannot autonomously pursue broader objectives. Agentic AI, on the other hand, can move beyond prompt-driven responses to proactive problem-solving. For example, while a generative AI might draft an email when prompted, an agentic AI could manage an entire communication workflow, going as far as to schedule email campaigns based on responses without human input. This transition from passive to active intelligence marks the dawn of autonomous systems capable of functioning as agencies rather than mere AI tools. Revolutionizing Cloud Infrastructure Management Traditionally, cloud infrastructure management relies on human engineers and monitoring tools to handle everyday tasks, ranging from resource allocation to incident response. Yes, these systems are designed to be robust; however, they often struggle with real-time optimization and scalability, especially under unpredictable workloads. With agentic AI, the AI could autonomously manage the cloud infrastructure, optimizing performance, reducing costs and enhancing reliability. For example, it can monitor real-time metrics from servers, virtual machines and containers. When a sudden spike in traffic occurs, the AI can dynamically reallocate resources, scaling up compute instances or redistributing workloads across regions, thus preventing bottlenecks and lags. Using reinforcement learning, agentic AI can predict potential failures, such as hardware degradation or network congestion, by analyzing historical performance data and external factors like global internet traffic trends. It can then proactively schedule maintenance or reroute traffic to avoid downtime. Typically, agentic AI operates as a network of specialized agents, each managing a different component in the system. These agents collaborate to ensure seamless operations, such as prioritizing low-latency resources for real-time applications. While agentic AI can handle routine optimizations, it does not entirely exclude the human touch. It can escalate complex situations to human engineers, providing data-driven recommendations to streamline decision-making. Implications And Challenges The rise of agentic AI holds profound implications for industries ranging from healthcare to finance to urban planning. Agentic AI could manage patient care in healthcare based on real-time health data. It could autonomously detect fraud, adjust investment portfolios or negotiate contracts in finance. However, this autonomy also raises critical challenges: Who is responsible when an agentic AI makes a suboptimal decision, such as misallocating cloud resources during a critical outage? Ensuring accountability requires robust governance frameworks like the OECD AI Principles. Autonomous systems must be designed with fail-safes to prevent unintended consequences. Since agentic AI systems, like their predecessors, can inherit biases from training data, rigorous testing and transparency are necessities. Deploying agentic AI in legacy cloud systems requires significant infrastructural upgrades, posing serious challenges for smaller organizations. The Future Of Agentic AI The dawn of agentic AI signals a future where intelligent systems are tools and partners in decision-making. As these systems become more sophisticated, they could redefine industries, reshape economies and even alter societal structures. For instance, urban planning could leverage agentic AI to reduce energy consumption and enhance public safety. However, realizing this potential requires addressing technical, ethical and regulatory hurdles. Collaborative efforts between researchers, policymakers and industry leaders will be crucial to ensure agentic AI serves our best interests. Rounding Off We are entering a new era where prompt-based interfaces are no longer the peak experience of AI utility. Agentic AI represents the first real step toward machines that can initiate, adapt and evolve. These systems won't just answer our questions; they can pursue objectives, solve novel problems and collaborate with humans as autonomous partners. As we stand on the verge of this new frontier, the question is not whether agentic AI will shape the future, but how we will shape it to ensure an innovative yet ethical world. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
5 days ago
- Business
- Forbes
How Business Leaders Can Leverage Blockchain And AI To Unlock New Opportunities
Daniel A. Keller, CEO and President of InFlux Technologies Limited and Flux. Blockchain and AI are increasingly becoming more integrated—the duo can work symbiotically to bolster one another. At its core, blockchain provides a decentralized, consensus-based infrastructure that enables AI solutions to operate without third parties controlling the data and algorithms. There's the privacy element as well. Blockchain can help companies address data privacy issues inherent in AI solutions that run on centralized Web2 platforms. Both of these technologies are continuously evolving. However, business leaders should embrace them sooner rather than later to avoid falling behind. The Key Business Benefits Of Using Blockchain And AI In Tandem Why should leaders embrace blockchain and AI sooner rather than later? Consider the benefits that both technologies can offer companies when used in tandem. Blockchain gives businesses more control and ownership over their data. Third-party platforms—cloud providers, social networks, etc.—can be fickle. Overnight, a third-party platform could change the rules of engagement, such as by raising costs or adding new content restrictions, that make it difficult, if not impossible, for companies to control their costs, maintain their operations and share their narratives. Blockchain breaks that grip of control from third parties. With blockchain, leaders can create cost-effective infrastructure that runs on their terms. As for AI, it can help companies streamline their operations, pinpoint issues in real time and personalize customer service, to name a few of the many use cases. However, AI comes with various risks, namely, data privacy issues and concerns about centralized data control and training when using publicly available platforms. In certain industries, such as healthcare and finance, the consequences that can stem from those risks are magnified. By using the decentralized, open-source infrastructure and consensus mechanisms that blockchain provides, leaders can more effectively safeguard their data—both at the input and output stages. Best Practices For Implementing Blockchain And AI Together Business leaders should adopt blockchain and AI before these technologies mature. The more they delay adoption, the further behind they risk falling. To effectively leverage both technologies, leaders should start by identifying how blockchain and AI can serve their business needs. They should focus on their strategic vision for the next six months to a year and then evaluate where blockchain and AI can fit in. Short iterations are vital, given how quickly both technologies are evolving; long planning cycles could render them obsolete before implementation. Once leaders have identified their strategic vision for the next six months to a year, they can research vendors and find one that aligns with their business needs. From there, they proceed to the implementation stage. There's room for flexibility here. Leaders shouldn't go all-in on adopting both technologies at once. In most cases, an incremental, scalable approach to implementing blockchain and AI will be more manageable. For instance, the executives of a local consulting firm might opt to stay in Web2 and keep 50% of their company's infrastructure there. It could move the other half of its infrastructure to Web3 and then gradually start migrating customers there. On that decentralized infrastructure, it could begin running AI tools that refine certain processes, such as client scheduling and communication. Over time, the consulting firm can move more of its infrastructure to Web3, increase the number of AI tools it runs and shift more customers. Following implementation, leaders should remain proactive in keeping their systems current. Blockchain and AI are rapidly changing, and by staying informed about those changes, leaders can pinpoint how they factor into their business needs. Risks—And How Business Leaders Can Navigate Them Business leaders should be aware that adopting blockchain and AI comes with risks. For instance, aside from technical complexity, another prominent risk is that both operate in an uncertain regulatory environment. Consider recent regulatory activity in the United States. According to TheStreet, on May 29, 2025, lawmakers 'introduced the Digital Asset Market Clarity (CLARITY) Act—a bill designed to finally bring clear regulations to the crypto and digital asset industry.' A June 3, 2025, StateScoop article noted that 'A bipartisan coalition of more than 260 state legislators from all 50 states on Tuesday sent a letter to Congress opposing a provision in the federal budget reconciliation bill that would impose a 10-year ban on state and local regulation of artificial intelligence.' The outcomes of these regulatory activities can have serious ramifications for businesses implementing blockchain and AI, making it paramount for leaders to stay informed about developments on the policy side. A new law could render a company's adoption of blockchain and AI noncompliant, requiring a costly overhaul to get back on track. Another significant risk is workforce disruptions. When a company switches to Web3 and starts implementing AI, its existing workforce will likely be restructured or cut. Leaders must carefully consider the potential workforce disruptions that may arise from leveraging blockchain and AI. However, now is the time for leaders to explore blockchain and AI. Acting proactively, rather than reactively, gives leaders the best chance at mitigating risks, leveraging blockchain and AI symbiotically to drive business results and staying ahead of their competitors. Ultimately, it's by embracing open-source, decentralized platforms and AI solutions that leaders can safeguard their costs, operations and narratives. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
07-05-2025
- Business
- Forbes
DeepSeek: Smarter Software Vs. More Compute
Daniel A. Keller, CEO and President of InFlux Technologies Limited. Cofounder of Flux. Getty Images When ChatGPT was released by OpenAI in 2022, it was the peak expression of AI chatbots built on large language models (LLMs). With an accessible interface and absolutely no need for external gadgets, it was the power of interactive AI in the palms of users, literally! Barely five days after its launch, ChatGPT broke the 1 million download milestone. (For context, that took Facebook 10 months to achieve.) Of course, there were a few problems, like the occasional lags and hallucinations, but version after version, ChatGPT continued to expand its frontiers. There were also apprehensions about the development cost of ChatGPT-4, somewhere between $48 to $71 million. But it was all completely justifiable. Sixteen thousand H100s GPUs don't come cheap, and salaries have to be paid. Or was it? Rise Of The Deep On January 20, 2025, the world woke up to news that would change the trajectory of AI technology. A little-known Chinese company had launched DeepSeek R1, an AI with capabilities comparable to OpenAI's ChatGPT. And the shocker? The initial reports claimed it did it with fewer, cheaper and older GPUs at a development cost of only $5.6 million. The ripple effect sent shock waves across the markets. By Monday, Nvidia, the biggest supplier of AI GPU chips, lost almost $600 billion in market value as investors started reconsidering their options. Indexes and corporations like Nasdaq, Microsoft and Alphabet also plummeted. Within a week, Deepseek had overtaken ChatGPT to become the most downloaded application on the Apple App Store. But since then, DeepSeek has come under scrutiny, with the head of Google's DeepMind calling its claims "exaggerated" and one critic suggesting it actually cost DeepSeek over $1 billion to create its AI model. Nevertheless, DeepSeek's arrival has caused a shift. The investment rationale for the supply chain had been quite simple: more spending and better outcomes for AI. Until now. The Paradigm Shift Deepseek's story is exceptional for several reasons. First, due to the United States' efforts to stem the flow of advanced AI technology to competing nations, the Biden administration restricted the export of GPUs to China, limiting the availability of advanced AI GPUs like the A100s and the H100s. As a result, Deepseek presumably had to rely on less sophisticated but more available GPUs like the H800. The ability of Deepseek to turn this crippling limitation into one of the marvels of AI innovation highlights a very critical question: Is ingenuity and better software architecture a more sustainable alternative to advanced but expensive GPUs? GPU availability (significantly advanced chips like the H100s) is one of the rate-limiting steps for AI research and development; even in the U.S., Nvidia, the top producer of GPUs globally, continues to grapple with meeting its high demand. A breakthrough that demonstrates that companies and research labs can maximize their computing power and cut down costs is a game-changer for the entire industry, but how exactly did DeepSeek achieve this? Flipping The Game Before Deepseek's emergence in AI, it had always been a game of who was bigger. Bigger financial investments translate into bigger LLM Models, which in turn require more compute resources and, hopefully, bigger innovative strides. However, DeepSeek's approach was counterintuitive. Instead of slapping on more compute and developing bigger models, the Chinese company focused on optimizing for a more efficient use of available resources. This included enhancing its model abilities through reinforcement learning, leveraging improved software architecture and optimizing its algorithm. Rather than dwarfing prevailing challenges with sheer brute power, Deepseek turned the game on its head. Early benchmarks showed it was 20 times more efficient and far less compute-intensive than its more pronounced competitors. Since it relied on reinforcement learning, Deepseek-R1 also eliminated the need for large teams of human reviewers and supervised fine-tuning, keeping operating costs to a minimum. Another important paradigm that Deepseek adopted was its incorporation of MOE (mixture of experts) architecture. MOE leverages multiple expert sub-models and uses selective gating to activate only the most relevant parameters for each input. For context, the Deepseek MoE framework comprises around 671 billion parameters; however, less than 0.5% of these parameters are used during any input. Picture a diverse team of seasoned experts across different disciplines. When needed, the gating mechanism dynamically selects the best combination of experts to solve the problem. The result? Dynamic routing and allocation lowers the amount of computation the model requires by reducing unnecessary computation. This approach also improves efficiency, promotes seamless scalability and supports progressive fine-tuning of different expert system components for specific problems. Implications For The Broader AI Industry Compute-efficient AI solutions encourage democratization, allowing for dynamic innovations from different quarters. This could, in turn, promote cheaper access to AI resources, breaking Big Tech's monopoly on AI innovation. Deepseek's open-source nature provides a level playing field for researchers to engage in deep R&D without breaking the bank. Its lower energy requirements and smaller carbon footprint can also positively drive environmentally sustainable designs for data centers in the near future. However, as revolutionary as the emergence of Deepseek has been, there are also a few drawbacks (on top of the dubiousness of its claims). First, while DeepSeek's open-source nature encourages technology sharing and participation, it also means malicious actors can repurpose it, raising fresh concerns about heightened misinformation, deepfakes and other sinister possibilities. Another danger hinges on data sovereignty and the possibility of the Chinese government mining users' data. Rounding Off While DeepSeek has demonstrated capabilities that are comparable to OpenAI ChatGPT in many ways, its long-term effect on repositioning AI technology, compute and market dynamics still remains to be seen. Whatever the future might hold, Deepseek's successful deployment of a powerful open-source model has introduced a new level playing field for innovation in the AI industry. As this distills into the mainstream, its ripple effect could determine the face of the next iteration of artificial intelligence. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
21-03-2025
- Business
- Yahoo
This Week In AI Chips - NVIDIA Collaboration Redefines Distributed AI Computing Landscape
Recent developments in the AI chip sector highlight a notable collaboration between InFlux Technologies and NexGen Cloud to enhance accessibility and performance of cloud-based AI solutions. This partnership focuses on deploying NVIDIA's Blackwell and other data center GPUs through InFlux's Hyperstack solution, marking its inclusion in the NVIDIA Partner Network (NPN) as a solution advisor. Leveraging NexGen's expertise in high-performance GPU-as-a-Service, the collaboration aims to redefine the landscape of distributed AI computing. By offering scalable and innovative GPU-accelerated resources, it seeks to empower businesses worldwide to harness advanced AI technologies seamlessly. last closed at $118.53 up 0.9%. Elsewhere in the market, was trading firmly up 4.5% and ending the day at NT$69.50, not far from its 52-week high. In the meantime, lagged, down 7.5% to close at HK$46.95. NVIDIA's Blackwell architecture offers a compelling growth opportunity in AI and data centers amidst regulatory challenges. Click to explore this potential and understand market dynamics further. Be sure to revisit our Market Insights article "DeepSeek and Trump's EOs: The Winners and Losers," which uncovers the game-changing impact of DeepSeek's AI advancements on the chip investment landscape—act fast before the market shifts! finished trading at $107.14 up 0.9%. This week, AMD presented at the AI Health World Summit 2025, focusing on AI solutions in the ASEAN region. settled at $157.95 down 0.2%. Two days ago, Qualcomm filed a Shelf Registration for $3.63 billion related to an ESOP offering. ended the day at $23.96 down 0.7%. Dive into all 54 of the AI Chip Stocks we have identified, like MediaTek, ASE Technology Holding and Microchip Technology, right here. Seeking Other Investments? We've found 19 US stocks that are forecast to pay a dividend yeild of over 6% next year. See the full list for free. This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned. Sources: Simply Wall St "InFlux Technologies Teams Up with NexGen Cloud to Deliver Hyperstack Solutions Built with NVIDIA AI Accelerated Computing Platform" from InFlux Technologies Ltd. on GlobeNewswire (published 20 March 2025) Companies discussed in this article include TPEX:5314 NasdaqGS:NVDA NasdaqGS:AMD NasdaqGS:QCOM NasdaqGS:INTC and SEHK:981. Have feedback on this article? Concerned about the content? with us directly. Alternatively, email editorial-team@
Yahoo
20-03-2025
- Business
- Yahoo
InFlux Technologies Teams Up with NexGen Cloud to Deliver Hyperstack Solutions Built with NVIDIA AI Accelerated Computing Platform
CAMBRIDGE, United Kingdom, March 20, 2025 (GLOBE NEWSWIRE) -- InFlux Technologies (Flux), a leading global decentralized technology company specializing in cloud infrastructure, artificial intelligence, and decentralized cloud computing services, has collaborated with NexGen Cloud to deploy NVIDIA Blackwell and additional data center GPUs through its Hyperstack solution. This is part of Flux's new inclusion as a Solution Advisor in the NVIDIA Partner Network (NPN), enabling NVIDIA to showcase how its GPUs power distributed computing workloads, including AI, machine learning and rendering, within a decentralized environment. Flux's collaboration with NexGen underscores its dedication to advancing cutting-edge AI solutions. This partnership leverages NexGen's expertise in high-performance computing, GPU-as-a-Service (GPUaaS) technology, and Flux's decentralized infrastructure to deliver scalable, efficient, and innovative AI solutions to businesses worldwide. Together, the two companies are redefining the landscape of cloud-based AI by providing seamless access to premium GPU-accelerated resources, ensuring organizations can unlock the full potential of next-generation technologies. 'NexGen Cloud has deployed a significant fleet of enterprise-grade GPUs—approximately 13,000 units—through the Hyperstack platform across Europe, North America, and the Asia-Pacific region," said Chris Starkey, CEO and co-founder of NexGen Cloud. "Our expanding infrastructure of NVIDIA H100s, NVIDIA A100s, and NVIDIA RTX A6000s will be strengthened by next-generation NVIDIA H100 and NVIDIA H200 deployments, with plans to introduce NVIDIA Blackwell GPUs throughout the year. We sought a partner with transformative solutions and deep industry insights, and in InFlux Technologies, we found an ideal collaborator. Their expertise and strong enterprise relationships open up incredible opportunities for businesses looking to harness the power of AI.' As an NVIDIA NPN Solution Advisor, InFlux Technologies transforms businesses by enabling them to build advanced AI agents. With its decentralized marketplace for computing resources, FluxEdge, businesses can leverage the latest optimized AI models through NVIDIA NIM™ microservices and connect AI agents to data through NVIDIA NeMo, both part of the NVIDIA AI Enterprise software platform for streamlined development and deployment of production-grade, end-to-end enterprise AI workflows. The FluxEdge platform empowers cloud providers and others to integrate seamlessly, driving adoption among small and enterprise providers who might otherwise focus on centralized solutions. This simplifies the process for customers to access and rent a wide range of GPUs, including the cutting-edge NVIDIA Blackwell. FluxEdge offers regional deployment options and a flexible marketplace, making it easy for users to tailor their compute setups to fit their needs. By providing expert guidance, InFlux Technologies helps organizations confidently navigate their AI journeys, unlocking the full potential of NVIDIA's innovative technologies. "Our roadmap for 2025 focuses on expanding our rendering capabilities and enhancing our core infrastructure to meet the growing demands of AI and ML workloads," said InFlux Technologies' CEO and Co-founder, Daniel Keller. "With our enhanced platform capabilities, we're well-positioned to continue leading the transformation of decentralized computing infrastructure." Through this collaboration, InFlux Technologies and NVIDIA will focus on leveraging GPU-accelerated computing platforms for enterprise IT, covering on-premise and cloud solutions, and accelerating data science and AI lifecycle management through NVIDIA AI enterprise software. Customers can effortlessly deploy their AI computing on FluxEdge, selecting from various locations and machine configurations. This integration allows users to quickly and easily run NVIDIA NIM™ inference microservices on the machine of their choice. For more information about InFlux Technologies, visit About NexGenNexGen Cloud is a sustainable European cloud IaaS, specialising in building large-scale HPC and GPU infrastructure, commanding a global presence with a first-mover advantage in Europe. Since its inception in 2020, NexGen Cloud has built one of the largest GPU fleets on the continent, fortified by the ownership of the most in-demand chips in the world, including NVIDIA H100 Tensor Core GPUs. NexGen Cloud is on a mission to democratise the accessibility of accelerated compute on a global scale by building a safer, greener, and more affordable cloud. The company's vision is to become the world's number one supplier of GPUaaS solutions through its cutting-edge platform, Hyperstack, whilst continuously supporting and expanding future technologies. All of NexGen Cloud's solutions are built with the aim of tackling three of the main concerns in the current cloud market – cost, transparency, and accessibility. For more information, visit About InFlux TechnologiesInFlux Technologies (Flux) powers a decentralized Web3 cloud infrastructure composed of user-operated, scalable, and globally distributed computational nodes. Flux provides the critical, high-availability infrastructure for the New Internet. The Flux service offers a fully decentralized alternative to some of the world's largest cloud infrastructure providers while offering competitive pricing. Flux is committed to developing disruptive solutions that empower individuals and businesses in the blockchain industry, emerging technologies like AI, and the broader technology space worldwide. For more information, visit Media Contact:Shannon BloodChief Marketing Officershannon@ 208.216.9180
