cropper
update
EDGE TECH BRIEF
update
  • Home
  • Categories
    • Future Signals
    • market signals
    • Agentic AI & Automation
    • Human + Machine
    • Tech That Moves Markets
    • AI on the Edge
    • Highlights On National Tech
    • AI Research Watch
    • Edge Case Breakdowns
    • Emerging Tech Briefs
May 29.2025
2 Minutes Read

Discover Why Your Phone Will Soon Be Obsolete as AR Takes Over

Man discusses how smartphones becoming obsolete with future tech

The Age of Obsolescence: Why Smartphones Are Losing Ground

With rapid technological advancements rewriting the playbook for communication, smartphones are on the brink of a dramatic transformation. As projections indicate, augmented reality (AR) glasses are ready to take center stage, pushing smartphones into the realm of obsolescence.

In Why Your Phone Will Be Obsolete Soon, we examine the impending technological shift towards augmented reality and its implications for communication and device usage.

How Augmented Reality Will Change Interaction

Imagine seeing your messages, directions, and media displayed in front of you without needing to pull out a device. This is the promise of AR technology, where advanced headsets can facilitate seamless interaction through voice and gesture controls. Think of the film Minority Report: familiar yet astonishingly immersive. According to industry leaders like Apple, Google, and Meta, the future relies heavily on spatial computing, where physical and digital worlds converge.

The Race for AR Dominance

The tech giants are intensifying their competition in AR device development, understanding that smartphones represent yesterday’s solution. Apple’s ARKit, Google’s ARCore, and Meta’s extensive investments in the Metaverse signal a strategic pivot towards immersive technologies. Their collective aim is clear: to capture the emerging consumer base for spatial computing.

What This Means for Tech Investors and Entrepreneurs

For tech investors and startup founders, the shift toward AR is not merely an avenue for innovation; it represents a seismic market opportunity. Investors should begin analyzing trends in AR development and exploring startups that are pushing the narrative of ‘post-smartphone’ technology. By aligning portfolios with emerging technologies, they can proactively secure a foothold in this evolving landscape.

Understanding Spatial Computing and Its Implications

The concept of spatial computing encompasses the integration of physical and digital information—all facilitated by tools like AR glasses. Venture capitalists should keep an eye on companies pioneering practical AR applications, from education and retail to healthcare and entertainment. Companies that can successfully implement AR tools into user-centric solutions will likely dominate future market trends.

Preparing for the Shift: Steps to Take Now

Experts suggest that those affected by these impending changes start by prioritizing education on spatial computing. Familiarity with AR interfaces and their potential applications can provide a competitive edge. As industries adapt to these new technologies, understanding them is crucial for future success.

In the video Why Your Phone Will Be Obsolete Soon, we delve into the remarkable shift away from traditional smartphones towards augmented realities. This pivotal discussion presents insights that are not only timely but essential for navigating an era of rapid change.

Future Signals

1 Views

0 Comments

Write A Comment

*
*
Please complete the captcha to submit your comment.
Related Posts All Posts
05.22.2026

Navigating Uncertainty: The Impact of AI on Today's Graduates

Update Understanding the Distrust: AI's Role in Graduation SentimentGraduation season brings a mixed bag of emotions, particularly for today’s graduates, who find themselves navigating an increasingly uncertain landscape marked by rapid technological advancements. During a recent episode of Mixture of Experts, discussions revolved around the ambivalence that budding professionals feel towards artificial intelligence (AI). As Eric Schmidt’s commencement speech drew boos from a crowd wary of AI’s influence, the sentiment echoed polling data indicating that approximately 70% of Americans feel AI is developing too quickly, and over half harbor negative feelings towards the technology.In AI at college graduations and why Claude blackmails, the discussion dives into the emotions of graduating students towards AI, exploring key insights that sparked deeper analysis on our end. A Generation's Anxiety: Pandemic and Professional InstabilityFor today’s graduates, the pandemic’s far-reaching effects have colored their educational experience and career outlook. Unlike previous cohorts, today’s graduates have faced a turbulent economy and shifting job expectations, leading to a pervasive sense of instability. As discussed by experts in the podcast, this generation is caught in the crosshairs of skepticism and optimism regarding AI. Marina Danilevsky articulated core issues, stating that many young people feel they lack ownership over their futures, exacerbated by anxiety around job security amid AI advancements.Navigating the Future: Ownership in AI EngagementIn a landscape where AI tools become ubiquitous, discussions on ownership are vital for graduates. Gabe Goodhart emphasized that inertia towards tech can lead to an uncritical acceptance of AI as a decision-maker rather than a tool for collaboration. This approach raises questions about how young professionals can assert control in their engagements with AI. It's vital for them to view these technologies as partners that enhance their abilities rather than detract from them. As Goodhart advised, experimenting within safe spaces using AI tools can yield personal learning and empower them against drastic sentiments about technology.Embracing the Unknown: The Human Element in AIOne of the most thought-provoking elements raised in the discussions is the necessity for a human-centric approach when dealing with AI. While the technology offers invaluable support, an over-reliance could compel young workers to forfeit their agency. Graduates are encouraged to maintain a critical perspective on AI while embracing the tools that can amplify their work processes. As Chris Hay remarked, the spectrum between embracing innovation and fearing it lies in understanding that AI should serve human needs, not vice versa.Preparing for Unknown Futures: Building New NormsThe overarching theme among experts is the anticipation of life's unpredictabilities—much of which shapes how young graduates use and perceive AI. The Moderna technology revolutionized the pharmaceutical field, a pathway for young innovators to develop personal frameworks that adapt and pivot within the fast-moving tech environment. Learning to effectively use AI tools can create pathways to success, accommodating those who aim to forge their unique routes outside mainstream corporate landscapes.With this knowledge, today’s graduates stand on the cutting edge, capable of mastering AI’s complexities while cultivating their professional identities. As technology evolves, so must the frameworks young workers adopt to foster control, adaptability, and collaboration.

05.21.2026

Exploring AI's Long Context vs. Cache Augmented Generation Innovations

Update Understanding Long Context vs. Cache Augmented Generation in AI As large language models (LLMs) evolve, the methods for enhancing their ability to access external knowledge have become increasingly vital. Two methods gaining traction are Long Context and Cache Augmented Generation (CAG). While both approaches aim to provide AI models with information beyond their initial training data, they employ fundamentally different strategies.In CAG vs Long Context: How AI Models Use and Remember Information, the discussion dives into the innovative methods AI models utilize for accessing external knowledge, exploring insights that sparked deeper analysis on our end. The Rise of Context Windows Long context is straightforward: it involves feeding all relevant information directly into the model's context window. An example of this is seen in GPT-4 Turbo, which can handle an impressive 128,000 tokens, equivalent to about 300 pages of text! However, the efficiency of this method depends on the context window's size. As the demand for more data increases, the cost associated with processing vast amounts of tokens rises concurrently, influencing both speed and expense. Introducing Cache Augmented Generation In contrast, Cache Augmented Generation offers a method of enhancing efficiency by using a Key Value Cache system. This system allows AI models to process data just once, retaining knowledge for future inquiries. Initially, relevant documents are formatted and stored. During the inference phase, instead of retracing through all information, the model pulls from this pre-computed cache, leading to quicker response times. Calculating the Benefits: Key Differences Between Long Context and CAG The primary distinction between Long Context and CAG lies in the timing of data processing. With Long Context, the model refreshes its understanding with every query, potentially leading to heightened costs and latency. In contrast, CAG processes data once, saving significant time and resources, especially for repeated queries. For example, when an HR chatbot repeatedly addresses employee questions, CAG shines due to its ability to maintain an efficient knowledge base. Practical Implications and Future Trends Moreover, the implementation of prompt caching by major LLM providers represents a significant leap in making CAG accessible to developers. By reducing costs associated with data processing, this practicality could reshape how businesses utilize LLMs, making it a feasible tool without a need for extensive infrastructure management. Concluding Insights on the Future of AI Models In conclusion, as the landscape of AI technology continues to advance, understanding the nuances between Long Context and Cache Augmented Generation is crucial for industry leaders and innovators. The ability of AI models to integrate external knowledge effectively can create more powerful applications across various domains from HR to advanced analytics. As a tech-savvy individual, staying ahead of these trends will allow for informed decision-making in adopting and implementing AI strategies.

05.20.2026

How Agentic AI Transforms Maintenance and Asset Decisions for Industries

Update Revolutionizing Asset Management with Agentic AI In an age where unplanned outages can cost businesses tremendous amounts of money—often hundreds of thousands of dollars each hour—the management of assets has never been more critical. The introduction of agentic AI presents substantial advancements in how industries handle asset maintenance and decision-making. As we transition from traditional systems of record to intelligent systems of action, the implications for manufacturing, infrastructure, and service industries are profound.In How Agentic AI Transforms Maintenance and Asset Decisions, the discussion dives into the transformative capabilities of agentic AI in optimizing asset management and maintenance tasks. Understanding Agentic AI in Maintenance Traditional asset management systems primarily focus on recording data: asset details, work orders, and inventory management. While these systems are crucial for tracking historical performance, the challenge lies in translating this data into actionable insights. Agentic AI takes this a step further, enabling operations not just to analyze past data but to plan and enact efficiency improvements proactively. The Role of Intelligent Systems of Action Imagine a technician tasked with a complex repair in a production facility. In a conventional setting, they would manually prepare work orders and coordinate logistics. However, with an intelligent system of action, an AI agent handles the preliminary work automatically. This not only optimizes maintenance schedules but also enhances the effectiveness of the technician's tasks. Enhancing Field Operations with AI Once in the field, technicians can leverage AI tools to diagnose issues rapidly. By using mobile devices or smart glasses, they can communicate their observations—such as unusual vibrations or visible leaks—while AI analyzes sensor data and overlays procedural guidance in real time. This collaboration significantly reduces the likelihood of human error and increases compliance efficiency during repairs and maintenance work. Closing the Loop: Ensuring Documentation and Compliance One of the most frequent issues in asset maintenance is incomplete documentation, often leading to rework and compliance failures. Mediating this problem, agentic AI provides real-time prompts for technicians, ensuring that all necessary documentation is completed accurately. In this way, the workflow not only ends with the repair but also includes a thorough record of the actions taken, materials used, and follow-up inspections scheduled. Why This Shift Matters This transition from systems of record to systems of intelligent action doesn’t just enhance operational capabilities; it significantly impacts financial efficiencies and operational resilience. As industries become more reliant on flexible and agile asset management solutions powered by agentic AI, the ability to anticipate and mitigate risks will determine their competitive edge in a fast-paced market. Engaging with the Future: What Lies Ahead The evolution of enterprise software illustrates a significant shift in asset management paradigms. Going forward, businesses must embrace the capabilities of agentic AI to not only keep pace but also lead within their sectors. It’s imperative for decision-makers, innovation officers, and tech-driven entrepreneurs to explore and harness these advancements. In the landscape of emerging technology, agentic AI stands out as a pivotal innovation reshaping maintenance and asset management. Are you ready to rethink the future of operational efficiencies in your field?

Terms of Service

Privacy Policy

Core Modal Title

Sorry, no results found

You Might Find These Articles Interesting

T
Please Check Your Email
We Will Be Following Up Shortly
*
*
*