I just got back from a data conference in Chicago, and I left honestly stunned. Not by the technology — by the people. Panel after panel, supposedly “leaders” in data science, logistics, and analytics — all of them dancing around the same idea: “We’re being cautious about AI.” Cautious. That word kept coming up like a reflex, a corporate mantra. “We’re waiting for regulation.” “We’re concerned about bias.” “We don’t trust the outputs.” It was like watching a room full of data

It used to be the one with the title. The corner office. The authority. Not anymore. Today, the most dangerous person in any room isn’t chasing applause, approval, or perception. They’re the one sitting quietly in the back — calm, confident, and completely underestimated — because they’re running a system in their head no one else can see. That’s operational power — and it’s rewriting everything you think you know about hierarchy, influence, and control. The Old Game: Power T

This all started with a phone call. An old friend of mine, Boris — a fellow IBM alum and one of the few people who still remembers what IRQ conflicts felt like — called me out of the blue a few weeks back. He had a question about AI. Simple enough. But if you’ve ever talked to two lifelong tech guys, you know how that goes. Five minutes in, we were no longer talking about AI — we were talking about everything that led to AI . We fell straight down the nostalgia rabbit hole: A

Let’s start with a truth that’s equal parts uncomfortable and undeniable: If you’ve got deep expertise in anything  — consulting, medicine, engineering, marketing, welding, whatever — you’re training your replacement right now. And no, not the human one. The AI one. Projects like Argentum  (Bloomberg’s scoop about hundreds of ex-McKinsey, Bain, and BCG consultants training AI to do entry-level consulting) and Project Mercury  (ex-bankers teaching financial modeling to models)

Let’s be honest—process mapping used to suck. It was the equivalent of eating your vegetables before you could touch the steak. Necessary? Sure. Fun? Not remotely. Whether it was defining database structures before writing a single line of code, or sketching out user journeys before building an app, process mapping always felt like the preamble to the “real” work. But here’s the thing: it was always the most important part. If you've ever written a business plan, mapped out a

Let me tell you something strange: We don’t know how to build the Space Shuttle anymore. NASA had the plans. The blueprints. The specs.What they didn’t have was the team —the network of minds, habits, and shared mental models that made it all work. That knowledge didn’t live in one document. It lived in hallway conversations. In hand gestures. In intuition built through repetition and failure. When those engineers retired or moved on, the Shuttle didn’t just become obsolete.

There’s a hard truth nobody wants to say out loud: if you’re not adapting to modern tools — if you’re not using AI, automating your workflows, or streamlining with the tech that’s already freely available — you are the problem. Not the market. Not the system. You. We’re living in an adapt or die  era. Technology isn’t creeping forward anymore — it’s sprinting. AI has hit escape velocity. What used to take teams of people and days of work can now be done in minutes. For free.

If you’ve been on the internet in the last 48 hours, you’ve probably seen that  photo — a massive white-roofed facility in Memphis with MACROHARD  painted across it in letters so huge you can literally see them from space. And of course, it came straight from Elon Musk’s X account. Classic Elon: half-joke, half-omen, and somehow both at the same time. This is the same man who named a car line “S3XY,” sent a sports car into orbit, and made flamethrowers a consumer product. So

Let’s be clear: the cloud isn’t dying. But the age of unquestioned cloud supremacy  — that decade-long reign where every ounce of intelligence had to pass through someone else’s API — that’s beginning to crack. We’re entering the post-imperial  phase of compute. The Great Inversion For twenty years, the cloud has been the empire of cognition: centralized, industrial, and rented by the teraflop. It made sense. The economics of scale were brutal. Training frontier models requir

There’s a strange kind of silence that comes right before a revolution—not the loud kind filled with slogans and sirens, but the quiet...

I keep coming back to this: my whole life, I’ve heard people throw around the term “paradigm shift.” The internet was a paradigm shift....

In Q2 of 2025, the “Magnificent Seven” tech giants spent over $100 billion  on AI infrastructure . One quarter. Three months. That’s $1.1...

Okay, stop me if you’ve heard this one: the guy who designed the iPhone, Apple Watch, iMac, and probably your favorite power adapter just...

In the span of 24 hours, I had two conversations that made this whole thing snap into focus. First: a younger guy—maybe ten years my...

Let me tell you a weird story: we don’t know how to build the Space Shuttle anymore. Seriously. NASA built the Shuttle with some of the...

It’s the best of times—and the worst of times. AI is accelerating faster than ever. Models are getting better, smarter, and cheaper by...

Almost two years ago, I wrote about a future where our operating systems wouldn’t just sit there waiting for input—they’d morph . They’d...

Let’s set the scene: you're not just building an AI system anymore—you're designing infrastructure for a new kind of intelligence...

Let’s get right to it: Manus AI  is the latest agent out of China that’s stirring up serious noise in the AI world. Launched in March...

In today’s workplace, it’s often the hidden inefficiencies—what I like to call “loose screws”—that quietly eat away at productivity....