Ai

Time Between Disengagements is a concept I came across in a recent article from Gitpod, and it offered an interesting new way to think about AI’s role in software development. It compares the evolution of AI in engineering to the progression of self-driving cars—where the key metric is how long an autonomous system can operate before a human needs to step in. That simple but powerful analogy really clicked with me. It reframes how we should think about the future of AI-assisted development—not just in terms of raw capability, but in how independently and safely these systems can work. ...

As the agent ecosystem rapidly evolves, interoperability is emerging as the foundation for scalable, secure, and collaborative AI. Industry leaders—including Google, Anthropic, Cisco, and others—are actively building open standards like Agent2Agent (A2A), Model Context Protocol (MCP), and AGNTCY to ensure agents from different vendors and platforms can discover, communicate, and work together seamlessly We’re at a pivotal moment where these standards are becoming the essential building blocks for the “Internet of Agents,” much like HTTP and TCP/IP did for the web. This collaborative push for interoperability aims to break down silos, accelerate innovation, and create a robust, trustworthy AI ecosystem that benefits enterprises and developers alike ...

Despite a $40 billion funding round, only $10 billion is secured, with the rest contingent on converting to a for-profit model. OpenAI is burning billions, potentially $14 billion in 2025 alone, due to high compute costs and infrastructure investments like the Stargate data center. SoftBank’s financial health is also at risk due to its significant investments in OpenAI, leading to potential asset sales. OpenAI’s high spending and increasing costs raise concerns about its long-term sustainability. ...

Digital industries are booming. Data storage business alone are projected to grow by nearly 18% anually, reaching $778 billion by 2030.

The Model Context Protocol (MCP) is an open standard created by Anthropic to facilitate seamless integration of AI assistants with external data sources, tools, and systems. It addresses the challenge of delivering real-time, structured, and relevant information to AI models while ensuring security, privacy, and modularity. MCP is rapidly gaining traction, transforming the way AI connects with external systems. Businesses can now use AI to manage real-time operations, drive data-informed decisions, and automate processes seamlessly. MCP acts like a universal adapter that lets AI models connect to any system using a standard method. Instead of building custom connections for every data source, MCP provides a single plug-and-play interface that any AI model can use to fetch information or execute tasks. ...

The software observability landscape is radically transforming as artificial intelligence becomes deeply woven into our digital infrastructure. Traditional monitoring paradigms—once sufficient for predictable, human-written code—now face unprecedented challenges in an era where system behavior emerges from complex AI interactions rather than explicit programming. From Reactive to Predictive: Entering the AI Era of Observability

Text-to-SQL technology promised a revolution: natural language seamlessly converting into precise database interactions. However, the practical implementation has revealed significant challenges, particularly with agent meshes—the multi-agent systems designed to parse, optimize, and execute queries. Key Technical Challenges Orchestration Complexity: Coordinating specialized AI agents creates potential bottlenecks and critical failure points in the query processing pipeline. Context & Memory Management: Maintaining coherent awareness across different agents becomes particularly difficult with ambiguous or evolving queries that require nuanced interpretation. Performance Limitations: Delivering real-time, accurate responses in enterprise-scale databases remains challenging, even with advanced LLMs powering the system. Troubleshooting Difficulties: Unlike traditional SQL’s deterministic nature, agent mesh systems often produce inconsistent results, making root cause analysis exceptionally difficult. Path Forward For Text-to-SQL and agent-based approaches to fulfill their potential, we need fundamental improvements in agent coordination frameworks, better model grounding in database contexts, and enhanced database adaptability to different query patterns. ...

AI bots are reshaping the Internet at its core. What was once primarily a human-driven space is now increasingly navigated by intelligent, autonomous bots that explore, interact and even make decisions… Bots Evolution: From Search Indexing to AI During the Internet’s early period, web crawlers functioned mainly within the framework of search engines such as Google and Bing. These bots had a straightforward yet impactful purpose: traversed websites, catalogued content, and structured data to enhance user accessibility. Their function was clearly established—these automated tools classified and prioritized web pages according to relevance, search terms, and visitor interaction. This period depended on programmed algorithms that determined how information was found and displayed. ...

In the article “Another Sputnik Moment,” Louis-Vincent Gave discusses China’s recent release of DeepSeek, an open-source AI large language model (LLM) developed at a fraction of the cost of its U.S. counterparts. This development challenges the prevailing belief that significant capital investment is essential for technological advancement in AI. Gave draws a parallel to the original “Sputnik moment,” suggesting that DeepSeek could signal a shift in technological leadership and prompt a reevaluation of current investment strategies in the tech sector. ...