The traditional approach to AI development is to focus on a single, monolithic AI system that is designed to perform a specific task. This approach has been successful in certain areas, such as natural language processing and computer vision, but it is not scalable for complex tasks that require multiple AI systems working together.

The Traditional Approach to AI Development

The traditional approach to AI development involves designing a single, monolithic AI system that is optimized for a specific task. This approach has been successful in certain areas, such as natural language processing and computer vision. For example, Google’s AlphaGo system was able to defeat a human world champion in Go, a game that requires complex decision-making and strategy. However, this approach is not scalable for complex tasks that require multiple AI systems working together.

The Limitations of the Traditional Approach

The traditional approach to AI development has several limitations. Secondly, it is not flexible enough to adapt to changing requirements or new data. Finally, it is not cost-effective, as it requires significant investment in hardware and software.

The Emergence of Massively Parallel Computing

Massively parallel computing is a new approach to deploying AI that is changing the industry focus. This approach involves using multiple, specialized computers to perform complex tasks in parallel. This allows for faster processing times and greater scalability than traditional approaches.

The Generative Artificial Intelligence Awakening

The generative artificial intelligence awakening refers to the development of AI systems that can generate new content, such as text, images, or music.

The survey results are then analyzed by theCUBE Research team to provide insights into customer spending patterns.

The Rise of Gen AI Awakening

The term “Gen AI awakening” refers to the growing awareness and acceptance of artificial intelligence (AI) among consumers. This phenomenon is characterized by a shift in customer behavior, where individuals are increasingly embracing AI-powered technologies and services.

The red line at 60% on the vertical axis indicates a highly elevated spending velocity.

This is a significant shift in behavior, indicating a growing awareness of the potential benefits of AI.

The Rise of AI-Driven Business Decisions

The increasing adoption of AI and machine learning (ML) technologies has led to a seismic shift in the way businesses operate.

We’ll also have a panel discussion on the future of work and the role of AI in shaping it.

The Future of Large Language Models: A Deep Dive with Scott

As we approach the 2025 predictions, one area that’s sure to be a hot topic is the future of large language models.

Deep learning and AI drive market demand for specialized hardware and software.

The Rise of Deep Learning and AI

The rapid advancement of deep learning and artificial intelligence (AI) has led to a significant increase in demand for specialized hardware and software.

IBM Corp’s Granite is a closed-source LLM, but it has a unique feature that allows it to dynamically adjust its capacity to match the user’s needs. This feature, called “Granite’s Adaptive Capacity,” enables the flexibility to scale up or down depending on the user’s input, making it more efficient and effective. While Granite is a closed-source LLM, its adaptive capacity feature makes it more comparable to the DeepSeek trend in terms of flexibility and scalability.

The Flexibility of DeepSeek Trend

The DeepSeek trend is characterized by its ability to flex capacity up or down as needed. This flexibility is achieved through a combination of advanced algorithms and machine learning techniques that enable the model to adapt to changing user needs. The DeepSeek trend is neutral for energy consumption because it can adjust its capacity to match the user’s requirements, reducing the need for over-provisioning and minimizing waste. Key benefits of the DeepSeek trend: + Flexibility to scale up or down depending on user needs + Reduced energy consumption through efficient capacity management + Improved performance and effectiveness

The Challenges for Closed-Source LLMs

The DeepSeek trend presents a challenge for closed-source Large Language Models (LLMs) like Anthropic PBC’s. Closed-source LLMs are typically designed to operate within a fixed capacity, without the ability to dynamically adjust their capacity to match user needs.

The future of AI will be shaped by the convergence of hardware and software innovations.

The Rise of Edge-Based Inference

Edge-based inference refers to the process of processing data at the edge of the network, rather than in the cloud or data center. This approach has gained significant attention in recent years due to its potential to reduce latency, increase efficiency, and improve real-time decision-making. Key benefits of edge-based inference include:

• Reduced latency: By processing data closer to the source, edge-based inference can reduce the time it takes to receive and process data, resulting in faster decision-making. Increased efficiency: Edge-based inference can reduce the amount of data that needs to be transmitted over the network, resulting in lower bandwidth costs and improved performance. Improved real-time decision-making: Edge-based inference enables real-time processing of data, allowing for faster and more accurate decision-making.
  

  The Rise of Generative AI: A Double-Edged Sword

  The advent of generative AI has brought about a new era of innovation, with applications in various industries, from art and design to healthcare and finance. However, as with any emerging technology, the benefits and drawbacks of generative AI must be carefully weighed.

  The Promise of Low Training Costs

  Vendors often tout the low training costs of generative AI, making it an attractive option for organizations looking to reduce expenses. However, a closer examination of the costs reveals a more complex picture.

  Hidden Costs of Hardware and Infrastructure

• Hardware requirements: Generative AI models require significant computational power, which can lead to substantial investments in hardware upgrades. Infrastructure costs: The need for specialized infrastructure, such as high-performance computing clusters, can add to the overall cost. Energy consumption: The energy required to power these systems can be substantial, leading to increased energy costs and environmental concerns. #### Elusive ROI**

Elusive ROI

Despite the initial cost savings, the return on investment (ROI) for generative AI remains elusive for many organizations. Several factors contribute to this:

• Energy consumption: The high energy requirements of generative AI models can offset any cost savings, making it difficult to achieve a positive ROI. Privacy concerns: The use of generative AI raises significant privacy concerns, which can lead to increased costs associated with data protection and compliance.

  This shift will also lead to increased competition among AI vendors, driving innovation and advancements in the field.

  The Rise of Inference

  Inference is a critical component of AI, enabling machines to make decisions and draw conclusions from data. As AI adoption continues to grow, the demand for inference-capable hardware and software will only increase.

  The Evolution of Networking: From Basic Plumbing to Strategic Enabler

  In the past, networking has been perceived as a necessary but mundane aspect of business operations. However, with the rapid advancement of artificial intelligence (AI) and the increasing reliance on digital technologies, the role of networking is poised to undergo a significant transformation.

  The Rise of AI-Driven Initiatives

  As AI continues to play a more prominent role in various industries, the need for robust and reliable networking infrastructure will become increasingly critical. In 2025, we predict that networking will evolve from being viewed as basic ‘plumbing’ to a strategic enabler of AI-driven initiatives.

  Key Drivers of this Evolution

• Increased demand for high-speed and low-latency networks: The growing need for fast and reliable data transfer will drive the development of more advanced networking technologies. Rise of edge computing: The proliferation of edge computing will require more efficient and secure networking solutions to manage data transfer between devices and cloud infrastructure. Growing importance of cybersecurity: As AI-driven initiatives become more widespread, the need for robust cybersecurity measures will become increasingly important.
  

  High-Bandwidth Networking Revolutionizes Data Centers with AI and ML Growth.

  The Rise of High-Bandwidth Networking in Data Centers

  The rapid growth of artificial intelligence (AI) and machine learning (ML) has led to an unprecedented demand for high-bandwidth connections in data centers. As the number of devices and applications continues to increase, the need for reliable and fast data transfer has become a top priority for large enterprises. In response, major networking equipment manufacturers are investing heavily in the development of ultra-reliable, high-bandwidth connections.

  Key Players in High-Bandwidth Networking

  Several leading companies are at the forefront of this revolution, including:

• Arista Networks
• Nvidia
• Cisco Systems Inc. Juniper Networks Inc.
  

  The Rise of Converged Access Points

  In recent years, the traditional network infrastructure has undergone significant changes. The proliferation of IoT devices, 5G networks, and the increasing adoption of AI and edge computing have created a pressing need for more efficient and secure network architectures. This has led to the emergence of converged access points, which aim to simplify network management and improve overall performance.

  Key Benefits of Converged Access Points

• Simplified Network Management: Converged access points integrate multiple network functions into a single device, reducing the complexity of network management and increasing efficiency. Improved Performance: By consolidating multiple network functions, converged access points can provide faster data transfer rates and lower latency, making them ideal for applications that require high-speed connectivity. Enhanced Security: Converged access points often include advanced security features, such as encryption and firewalls, to protect sensitive data and prevent unauthorized access. ### The Role of Smaller Players**

The Role of Smaller Players

Smaller players, such as Meter, Ericsson, HPE Athonet, Celona, Federated Wireless, and Highway9, are leading the charge on converged access points.

A combination of machine learning, deep learning, and symbolic AI will be used to develop more robust and transparent models.

The Rise of Hybrid AI Approaches

The increasing complexity of AI systems has led to a growing need for more sophisticated and integrated approaches. Traditional machine learning (ML) models have limitations in terms of accuracy, explainability, and trustworthiness. To address these challenges, organizations are turning to hybrid AI approaches that combine multiple techniques to achieve better results.

Key Benefits of Hybrid AI

• Improved accuracy: Hybrid AI models can learn from multiple sources and data types, leading to more accurate predictions and decisions. Increased explainability: By incorporating symbolic AI, hybrid models can provide more transparent and interpretable results, helping to build trust with stakeholders. Enhanced trustworthiness: Hybrid AI approaches can address concerns around bias and fairness, leading to more reliable and trustworthy outcomes. ## The Role of Symbolic AI**

The Role of Symbolic AI

Symbolic AI is a key component of hybrid AI approaches.

LLM adoption gaps: While 70% of enterprises report using LLMs as part of their AI strategies, only 24% have successfully deployed them at scale. Many pilot projects remain stalled, and ROI remains elusive — Harvard Business School data suggests only 18% of firms see a high-impact return. Correlation versus causation: LLMs often confuse correlation with causation, undermining confidence in their predictions. This shortfall becomes acute when making critical decisions that require interpretability and clear reasoning. Explainability and trust: Without transparency into how AI arrives at a conclusion, businesses hesitate to place mission-critical decisions under AI control. This “black box” factor is particularly concerning as organizations consider using AI for autonomous actions.

The Rise of Predictive Analytics and Specialized LLMs

Predictive analytics and specialized Large Language Models (LLMs) have been gaining traction in recent years, with many organizations leveraging these technologies to gain a competitive edge. However, despite their growing popularity, the results suggest that a more tailored and context-aware approach is needed to unlock their full potential.

The Limitations of Current Approaches

Current predictive analytics and LLMs often rely on generic, one-size-fits-all solutions that fail to account for the unique complexities and nuances of individual organizations. This can lead to suboptimal results, as these systems are not designed to adapt to the specific needs and context of each organization. Key limitations of current approaches include: + Lack of contextual understanding + Insufficient domain knowledge + Inability to adapt to changing circumstances + Overreliance on generic algorithms

The Need for Multi-Agent Reasoning

To overcome these limitations, organizations are turning to multi-agent reasoning, a technology that enables multiple agents to collaborate and make decisions in a coordinated manner.

Harnessing the Power of AI-Powered Hardware to Revolutionize Industries and Daily Life.

This shift will have significant implications for various industries and individuals.

The Rise of AI-Powered Hardware

The year 2025 is expected to be a game-changer for the tech industry, as it marks the beginning of a new era in AI adoption. The proliferation of AI-powered hardware will revolutionize the way we live, work, and interact with technology. This shift will be driven by the increasing availability and affordability of AI-enabled devices, making it possible for consumers to harness the power of AI in their daily lives.

Key Features of AI-Powered Hardware

• On-Device Inference Capabilities: AI-powered hardware will be equipped with on-device inference capabilities, allowing for faster and more efficient processing of AI tasks. This will enable devices to perform complex AI tasks without relying on centralized data centers. Edge Computing: The shift to edge computing will enable devices to process data closer to the source, reducing latency and improving real-time decision-making. Enhanced Security: AI-powered hardware will incorporate advanced security features, such as encryption and secure boot mechanisms, to protect user data and prevent unauthorized access. ## Implications for Industries and Individuals**

Implications for Industries and Individuals

The rise of AI-powered hardware will have far-reaching implications for various industries and individuals.

The Rise of On-Device AI

The advent of on-device AI represents a significant shift in the way we interact with artificial intelligence. No longer will we need to rely on cloud-based services to access advanced AI capabilities.

Devices are becoming increasingly intelligent, transforming the way we interact with technology and enabling new applications and innovations.

Here are some key points to consider:

The Rise of AI-Optimized Devices

The proliferation of AI-optimized devices is transforming the way we interact with technology. These devices, such as smartphones, laptops, and smart home devices, are being designed with AI capabilities that enable them to learn, adapt, and respond to user behavior. AI-optimized devices are equipped with specialized hardware and software that allow them to process and analyze large amounts of data in real-time. This enables them to provide personalized experiences, anticipate user needs, and make decisions based on patterns and trends. AI-optimized devices are also more energy-efficient, as they can perform tasks without relying on cloud-based services.

The Benefits of Local Processing

The shift from cloud-dominated AI to local processing offers several benefits, including:

• Improved privacy: By processing data locally, AI-optimized devices can reduce the amount of sensitive information that needs to be transmitted to the cloud. Increased security: Local processing can also reduce the risk of data breaches and cyber attacks, as sensitive information is not transmitted over the internet. Faster performance: Local processing enables AI-optimized devices to respond quickly to user input, providing a more seamless and intuitive experience. ## The Future of AI at the Edge**

The Future of AI at the Edge

As AI-optimized devices become more prevalent, we can expect to see a range of new applications and innovations emerge. Some potential examples include:

• Smart homes: AI-optimized devices can be used to create smart homes that are tailored to individual preferences and needs. Personalized healthcare: AI-optimized devices can be used to provide personalized healthcare recommendations and monitoring.

  Bertrand, on the other hand, believes that these changes are necessary to address the growing national debt and the need for fiscal responsibility. Both experts agree that the policy changes are part of a larger trend of reducing government spending and shifting the burden to the private sector.

  The Shift in Government Spending: A Debate Among Experts

  The recent policy changes in the U.S. have sparked a heated debate among experts, with some arguing that gutting certain agencies and shifting authority to others is a recipe for disaster, while others believe it is a necessary step towards fiscal responsibility.

  The Risks of Gutting Agencies

  Jackie McGuire, a renowned expert in public policy, warns that gutting certain agencies and shifting authority to others is a recipe for disaster. According to McGuire, this approach can lead to a range of negative consequences, including:

• Critical infrastructure vulnerabilities: By gutting agencies responsible for maintaining critical infrastructure, such as transportation systems and public health services, the country may be left exposed to potential failures and disruptions. Exposure of other vulnerabilities: Shifting authority to other agencies may also expose new vulnerabilities, as these agencies may not have the necessary expertise or resources to handle the new responsibilities. Loss of expertise and knowledge: Gutting agencies can also result in the loss of valuable expertise and knowledge, which can be difficult to replace.
  

  Interconnected insurers and reinsurers face systemic cyber risk, requiring enhanced cybersecurity measures to prevent devastating consequences.

  The Interconnectedness of Insurers and Reinsurers

  The insurance industry is characterized by a complex web of interconnectedness, with many insurers and reinsurers tied to common market players. This interconnectedness creates a vulnerability that can have far-reaching consequences if a large-scale attack were to occur. Insurers and reinsurers often share common risk management practices, use similar technology, and have overlapping business models, making them susceptible to a cascading failure. Key characteristics of the interconnectedness: + Shared risk management practices + Similar technology + Overlapping business models + Common market players

  The Potential for Systemic Cyber Risk

  A large-scale attack on the insurance sector could have devastating consequences, not only for the targeted companies but also for the entire industry. Systemic cyber risk could require federal intervention, similar to the mortgage-backed securities crisis. The potential for widespread disruption and financial loss is significant, and it is essential to understand the risks and vulnerabilities that exist within the industry. Potential consequences of a large-scale attack: + Widespread disruption + Financial loss + Potential for systemic cyber risk + Federal intervention

  The Need for Enhanced Cybersecurity Measures

  To mitigate the risks associated with systemic cyber risk, insurers and reinsurers must prioritize enhanced cybersecurity measures.

  dismantling cybersecurity oversight structures poses significant risks to the US economy and society.

  The Risks of Dismantling Established Cybersecurity Oversight Structures

  The notion of dismantling established cybersecurity oversight structures has sparked intense debate in recent years. While some argue that this approach could lead to greater flexibility and innovation, others warn of the potential consequences. In this article, we will delve into the risks associated with dismantling these structures and explore the importance of robust public-private collaboration.

  The Importance of Public-Private Collaboration

  In the face of an increasingly complex and dynamic cyber threat landscape, public-private collaboration has become essential. The U.S. government, private sector organizations, and other stakeholders must work together to share intelligence, best practices, and resources. This collaboration enables the development of more effective cybersecurity strategies and helps to mitigate the risks associated with dismantling established oversight structures. Key benefits of public-private collaboration include: + Enhanced threat intelligence sharing + Improved incident response capabilities + Increased investment in cybersecurity research and development + Better alignment of cybersecurity policies and regulations

  The Consequences of Dismantling Oversight Structures

  Dismantling established cybersecurity oversight structures could have severe consequences for the U.S. economy and society. A cyber event that cripples critical infrastructure and the insurance market could have far-reaching and devastating effects.

  Cyber Resiliency is no longer a luxury, but a necessity in today’s digital landscape.

  He is a renowned cybersecurity expert with over 20 years of experience in the field. Bertrand has worked with top organizations, including IBM, Intel, and Cisco, to develop and implement robust cybersecurity strategies.

  The Importance of Cyber Resiliency

  Cyber resiliency is a critical aspect of modern cybersecurity. It refers to the ability of an organization to withstand and recover from cyber attacks, data breaches, and other security incidents.

  Companies like Google, Amazon, and Microsoft are already investing heavily in AI-related technologies.

  AI Workloads: The Emerging Data Protection Battleground

  The Rise of AI-Driven Workloads

  Artificial intelligence (AI) is transforming the way we live and work, and its impact on data protection is becoming increasingly significant. As AI workloads continue to grow in complexity and scale, the need for robust data protection measures is becoming more pressing. In 2025, we predict that AI workloads will become a major battleground for data protection, with vendors racing to add AI-specific backup and resilience features.

  The Challenges of AI-Driven Data Protection

  AI workloads pose unique challenges for data protection. Unlike traditional workloads, AI workloads are often characterized by:

• High-speed data processing: AI workloads generate vast amounts of data at incredible speeds, making it difficult to keep up with the pace of data creation. Unpredictable data patterns: AI workloads often involve complex, dynamic data patterns that are difficult to anticipate and protect. High-stakes data sensitivity: AI workloads often involve sensitive data, such as personal identifiable information (PII) or confidential business data, which must be protected from unauthorized access or breaches. ### The Response from Vendors**

The Response from Vendors

To address the challenges posed by AI workloads, vendors are investing heavily in AI-specific backup and resilience features.

AI is transforming the cybersecurity landscape with automation and efficiency, enabling faster and more effective security operations.

AI will also be used to enhance the security posture of organizations by identifying vulnerabilities and providing recommendations for remediation.

AI-Driven Automation in Cybersecurity

The Rise of AI-Driven Automation

Artificial intelligence (AI) is transforming the cybersecurity landscape by introducing automation and efficiency to the traditional security operations center (SOC). AI-driven automation is revolutionizing the way security teams respond to threats, detect vulnerabilities, and remediate incidents. This shift is driven by the need for faster and more effective security operations, as well as the increasing complexity of modern cyber threats.

Key Benefits of AI-Driven Automation

• Improved Response Times: AI-driven automation enables security teams to respond to threats and incidents faster, reducing the mean time to detect (MTTD) and mean time to respond (MTTR). Enhanced Detection Capabilities: AI-powered systems can analyze vast amounts of data, identifying patterns and anomalies that may indicate a security threat. Increased Efficiency: Automation reduces the workload of security teams, allowing them to focus on higher-level tasks and improving overall productivity. * Reduced False Positives: AI-driven automation can help reduce false positives, minimizing the noise and distractions that can slow down security teams. ### AI-Driven Automation in Proactive Threat Detection**

AI-Driven Automation in Proactive Threat Detection

Proactive threat detection is a critical aspect of cybersecurity, and AI-driven automation is playing a key role in this area.

Data classification and lineage are crucial for protecting sensitive information and ensuring regulatory compliance.

Understanding the Challenges of Data Classification and Lineage

The increasing reliance on artificial intelligence (AI) has brought about a new set of challenges for enterprises. One of the most pressing issues is the lack of effective data classification and lineage. Data classification is the process of assigning labels or categories to data based on its sensitivity, importance, or other relevant factors. Lineage, on the other hand, refers to the tracking of data origin, processing, and movement throughout an organization.

This prediction is based on the developer workforce being the most vulnerable to automation.

The Rise of AI in the Developer Workforce

The developer workforce is considered the most vulnerable to automation due to the repetitive and routine nature of many coding tasks. As AI technology advances, it is becoming increasingly capable of performing tasks that were previously thought to be the exclusive domain of humans.

Human developers bring creativity, intuition, and critical thinking skills that AI cannot replicate.

While AI can assist with routine tasks, it cannot replace the creativity, intuition, and critical thinking skills that human developers bring to the table.

The Rise of AI-Driven Development

In recent years, the use of Artificial Intelligence (AI) in software development has gained significant traction. Many organizations have started to adopt AI-driven development tools to automate routine tasks, improve efficiency, and enhance productivity. However, the notion that AI can completely replace human developers is a misconception.

The Limitations of AI-Driven Development

While AI can excel in certain areas, such as:

• *Data analysis and pattern recognition**
• *Predictive modeling and forecasting**
• *Automated testing and debugging**

it falls short in other critical areas, including:

• *Complex problem-solving and critical thinking**
• *Innovation and creativity**
• *Collaboration and communication**

These limitations highlight the importance of human developers in the development process.

The Role of Human Developers

Human developers bring a unique set of skills and qualities to the table, including:

• Creativity and intuition: Human developers can think outside the box and come up with innovative solutions to complex problems.

  The rest of their time is spent on other tasks such as testing, debugging, and maintenance. While these tasks are essential to the development process, they often take away from the actual coding time. This is a common problem in the software development industry, and it’s essential to address it to improve productivity and efficiency.

  The Problem of Non-Coding Time

  The issue of non-coding time is a significant concern for developers. According to a study, developers spend around 70% of their time on non-coding tasks.

  The Rise of Low-Code/No-Code Adoption

  The low-code/no-code revolution is transforming the way businesses approach application development. With the projected 30% growth in adoption, organizations are now empowered to build routine applications without extensive coding knowledge. This shift has significant implications for data protection and regulatory compliance.

  The Benefits of Low-Code/No-Code Adoption

• Increased Productivity: Low-code/no-code platforms enable business stakeholders to build applications quickly, without relying on IT teams. Improved Collaboration: Stakeholders from various departments can work together to develop applications, fostering a more collaborative environment.
  

  The Future of Artificial Intelligence: Expert Predictions

  The future of artificial intelligence (AI) is a topic of great interest and debate. As AI continues to advance and become increasingly integrated into our daily lives, it’s essential to consider the potential implications and predictions of experts in the field. In this article, we’ll delve into the predictions of six analysts from theCUBE Research, exploring the potential future of AI and its potential impact on various industries.

  Predictions on AI Advancements

  The analysts from theCUBE Research have made several predictions regarding AI advancements. Here are some of the key predictions:

• Increased use of AI in healthcare: AI is expected to play a significant role in healthcare, with predictions that AI-powered systems will be used to diagnose diseases more accurately and develop personalized treatment plans. Advancements in natural language processing: The analysts predict that natural language processing (NLP) will continue to improve, enabling AI systems to better understand and generate human-like language. Growing importance of explainability: As AI becomes more pervasive, there will be a growing need for explainability, with predictions that AI systems will need to provide transparent and interpretable results.
  

  The Companies Behind the Analysis

• The companies featured in Breaking Analysis are likely to have a vested interest in the publication, which could influence the content. This potential bias could be mitigated by ensuring that the analysis is based on publicly available data and that the companies do not have direct access to the content prior to publication.