Ai

Over the last year the workflows vs. agents debate has turned from a niche engineering question into something every product team seems to argue about. New tooling lowers the barrier to spin up an AI agent that can call tools in a loop, so leaders start asking whether they should rebuild processes around these agents, or stick with the safer structure of step-by-step workflows. The conversation heats up because both sides have real wins and real failure modes: agents feel magical when they solve fuzzy, open-ended tasks, but they can be slow, costly, and unpredictable at scale; workflows are efficient and auditable, but can feel rigid when the job needs exploration. That tension between flexibility and control is why this topic is hot and why teams keep getting stuck. ...

Bloomberg just published this fascinating map of the AI power network, showing how companies like NVIDIA, OpenAI, Microsoft, AMD, Oracle, and Intel are now intertwined through billions in deals, compute, and equity. It’s not just a supply chain anymore. It’s a feedback loop — where hardware, software, and capital keep feeding each other. NVIDIA sits at the center with a $4.5 trillion market cap, investing up to $100 billion in OpenAI, while selling GPUs to Oracle, AMD, xAI, and everyone else. OpenAI, in turn, signs a $300 billion cloud deal with Oracle, deploys 6 gigawatts of AMD GPUs, and gives AMD an option to buy 160 million OpenAI shares. Microsoft is still the connective tissue, part investor, part service provider, part enabler.

I came across a video by Dr. John Padfield, a former engineer and state representative, talking about something that stuck with me. He recalls being told, “never get in a fight with people who buy ink by the barrel.” It was a warning for politicians — don’t mess with newspapers. Then he updates it for our time: “Never get in a fight with people who buy network servers by the acre.” ...

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