Businesses

Apps may fade into the background. Software won’t. Wall Street has a new consensus: AI is about to kill the software industry. Software stocks are down nearly 30% since the start of the year; pundits call it the SaaSpocalypse. But the story is wrong. AI is rewriting who builds software and how we pay for it—not eliminating it. This piece looks at why the real moats (data, workflows, habits) are getting deeper, how “Software as a Service” is turning into “Service as Software,” and what that means for builders and buyers. Two 19-year-old high schoolers built an AI calorie-tracking app called Cal AI that brought in over $30 million a year; it was recently acquired by MyFitnessPal. The deal size was not disclosed, but the two clearly came out on top. On another front, Cursor, the fastest-growing AI coding company in history and less than five years old, was reported in February to have passed $2 billion in annualized revenue. Whether we talk about AI companies that build apps or the AI-powered apps already in the world, the outlook seems bright. ...

For the past few years, most conversations about AI products have centered on the model. Which model is better. Which benchmark is higher. Which company has the next breakthrough. But as more AI products reach real users, a different question has come into focus: What actually drives value in AI products may have less to do with model strength and more to do with the environment the product lives in. OpenClaw is a clear example. Its underlying capabilities are comparable to other agent tools, yet it quickly gained attention and discussion in user communities. That forces a sharper question: What has to be true for an AI product to create real value? Break it down and three conditions have to hold: Context: The AI understands what the user is doing. Delivery: The AI’s output can turn directly into outcomes (not just text to copy elsewhere). Collaboration: The human and the AI settle into a stable way of working together. When these three happen naturally, the AI is operating inside an effective environment. ...

Inside AI labs, researchers believe the most explosive phase of progress may still be ahead. For the past year, a common narrative has taken hold across the AI industry: Scaling is slowing down. Larger models are producing smaller gains. Benchmarks are improving more gradually. Some researchers have begun to argue that the explosive phase of large language models may already be behind us. But inside the labs building these systems, the story looks very different. At a recent Morgan Stanley Technology, Media & Telecom Conference, Anthropic CEO Dario Amodei dismissed the idea outright: “We do not see hitting the wall.” If anything, he suggested the opposite. The most dramatic phase of AI progress may still lie ahead — and it could arrive sooner than most people expect. If almost anyone else made that claim, it might sound like hype. But Anthropic sits at the center of the current race to build more capable AI systems. Its Claude models power a growing number of enterprise applications, and the company is widely considered one of the three or four organizations operating at the frontier of AI development. ...

Over the past two decades, something subtle but profound has been happening in the history of war. Wars are ending less often with grand campaigns or sweeping territorial conquest. Increasingly, they conclude with the physical removal of a single critical individual. At the same time, two different models of conflict have been unfolding in parallel. One resembles traditional industrial-age warfare — armored divisions, territorial lines, attrition. The other looks entirely different: precise, intelligence-driven, node-focused. I do not study warfare professionally. My work focuses on how AI reshapes organizations. But it is impossible to ignore how similar structural shifts are now transforming conflict. When organizational forms change, warfare changes with them. And once the efficiency gap becomes clear, the shift is irreversible. From a purely operational standpoint, the performance difference between these two paradigms is staggering. What follows is not a moral argument about right or wrong. It is an attempt to describe a transformation in the structure of power. From “Destroying Systems” to “Deleting Nodes” Modern military operations increasingly follow a pattern: Persistent surveillance → continuous modeling → anomaly detection → instantaneous strike. ...

“Singularity Crossing” — that’s probably the most accurate way to describe where AI development stands right now. AGI may not be here yet. But after humanity invented Claude Code, Opus 4.5, and OpenClaw, the singularity effectively arrived. The word “singularity” comes from mathematics and physics. In math, a singularity is the point where a function blows up — like 1/x at x=0, where the value shoots toward infinity and the rules that governed everything before suddenly stop working. The center of a black hole is also a singularity, where all known laws of physics break down. Once the singularity hits, every rule we knew becomes void. All of humanity’s accumulated experience, institutions, and instincts — none of it can tell us what comes next. It’s like standing outside a black hole’s event horizon: no information escapes from inside. Every rule fails. Every prediction fails. No science fiction writer ever imagined a world where intelligence is no longer scarce. Just as humans can’t picture what the inside of a black hole looks like. What happens next? No one knows. It can’t be predicted. ...

A Critical Analysis of Citrini Research’s Viral 2028 Crisis Scenario The financial world is currently grappling with a thought experiment that feels uncomfortably close to reality. In a viral research piece titled “THE 2028 GLOBAL INTELLIGENCE CRISIS” published by Citrini Research (co-authored with Alap Shah), the authors paint a chilling picture: unemployment at 10.2%, the S&P 500 down 38% from its October 2026 peak, and an economy where AI’s productivity gains have paradoxically triggered the deepest structural crisis since the Great Depression. Written as if from June 2028, this speculative scenario has exploded across investment communities, racking up millions of reads within days of publication. What makes it particularly unsettling is not its dystopian framing, but its logical coherence. This isn’t science fiction—it’s financial analysis written in the language of cause and effect. Source: Citrini Research - THE 2028 GLOBAL INTELLIGENCE CRISIS The Core Mechanism: A Self-Reinforcing Doom Loop At the heart of Citrini’s crisis scenario lies a deceptively simple feedback loop: AI capability improves → Companies lay off workers → Consumer spending falls → Corporate profits compress → Companies buy more AI to cut costs → AI capability improves ...

Most AI failures don’t look like failures. They simply fade away. The Real Failure Isn’t Technical AI projects usually fail because: No one owns outcomes Data is messier than expected Goals are vague or political Technology is rarely the bottleneck. Start Where Friction Already Exists The most effective AI use cases often start with: Internal documentation Repetitive communication Manual reporting These areas don’t require perfection—just consistency. Human-in-the-Loop Is a Feature Keeping humans involved isn’t a compromise. It’s risk management. Well-designed systems: Flag uncertainty Invite review Escalate exceptions AI works best as an assistant, not a replacement. Boring AI Wins Flashy demos impress leadership. Boring improvements survive budgets. In business, AI succeeds when it becomes invisible—and reliable.