When I put together the core concepts of PowerShell, I was committed to solving the boof-a-rama that is Windows CLI syntax.  Prior to PowerShell, any developer that got at least a ‘D’ in a course on parsing was allowed to inflict their damage on the user community.  This incoherence caused a great deal of confusion as users struggled to navigate at least four distinct syntax groupings:

I have a lot of conversations where teams toss around the concept of Product Market Fit (PMF) like it’s a vibe. We all pretend to get the gestalt of it, but usually, we’re just guessing. Here are explanations my teams have used over the years:

Is your Hair on Fire?You have PMF when the customers are buying the product as fast as you can make it—or usage is growing as fast as you can add more servers.

How would you feel if you could no longer use this product?If at least 40% of them answer “very disappointed,” you have reached the threshold. Anything less means you are building a toy, not a necessity.

Since I got involved in angel investing, I learned the cold truth from my fellow investors:  PMF is MATH!  

In response to my blog Welcome to the Room, Davor Vukovic asked the question:

I might be mistaken, but don’t the required resources result from defining a theory of success (i.e., the solution)? My understanding is that you first define a plausible theory of success and then evaluate it against the available resources, not the other way around; then adjust.

A lesson in leadership by Satya Nadella

When I was Promoted to Technical Fellow, I was “invited to the room”, joining Microsoft’s other Senior Executives.  It was really something. Achieving the Senior Executive status is often mistaken for a comfortable reward, a final destination with enhanced perks and support. A more  fitting analogy is reaching the NFL Super Bowl. You are now part of an elite team where nothing less than peak performance is acceptable. As the Navy SEALs put it, “The only easy day was yesterday”. You can feel that energy when you walk in the room. 

I didn’t know what to expect but what I got changed my worldview and my life.

Bad things happen when you are grounded in the real world.  Ergo, in systems architecture, we constantly look for feedback loops. Negative feedback loops provide stability (like a thermostat). Positive feedback loops provide amplification, but without a governor, they inevitably lead to system runaway and collapse.

You know the story of the Golem of Prague? The Rabbi builds a protector out of clay. It works great until it doesn’t. In engineering terms, the Golem failed because it lacked a governor—a negative feedback loop. In systems architecture, positive feedback loops without governors don’t just grow; they run away and tear themselves apart. That is exactly what it appears we are building with this circular financing of AI where capital flows from Big Tech balance sheets into startups and immediately flows back as revenue.

I’ve spent a lot of time, and acquired a lot of scar tissue, in this industry, and if there is one thing I’ve learned, it’s that most people are terrified of looking different. People hug the center of the road because it feels safe. But as Jim Hightower once said, 

“There’s nothing in the middle of the road but yellow stripes and dead armadillos.”

When we were building the early versions of PowerShell (back when it was Monad), we had a massive fight over a single word. It seemed trivial to everyone else, but I knew it was a hill worth dying on. It was the fight over the word Cmdlet.

The disagreement wasn’t a matter of wordsmithing; it was a foundational issue about establishing the cognitive contours of the product – how people would think, learn, find things, and form a community. It was an issue of soul.

I’ve been doing Office Hours and the topic of AI has been in almost every one of the conversations. Everyone is wondering what the right play is with AI. A number of people are concerned about it replacing them. I’ve been giving the following advice, “Treat AI as an awesome teammate”

I was telling someone recently that I studied Physics & Philosophy and that Philosophy taught me the art of asking great QUESTIONS, while Physics taught the art of creating great ANSWERS. So let’s start with a great question:  What is a Team?

We don’t build teams just to have more bodies in the room. We build teams because we are all flawed. Every single one of us has peaks of high capability and deep valleys of incompetence. The theory of a team is that it is a structure designed to amplify our strengths and compensate for our weaknesses. Putting my SRE hat on, you can view it as an architectural pattern for resilience and error correction.

If you stop looking at AI as a threat and start looking at it as a teammate, the dynamic changes. You figure out how to use it to compensate for your weaknesses. As I’ve said many times, I am a deeply flawed human (that is why you can use wildcards everywhere in PowerShell) so I need awesome teammates to function.

I am not a finance guy. I am an engineer. But I’ve been in this industry for forty+ years, and I’ve got enough scar tissue to look at a P&L sheet and ask some questions. And frankly, I don’t like what I’m seeing.

When I look at the current AI landscape, I see a trillion-dollar venture. As I wrote recently in AI Bubble? : AI Skeptics Hard Look at the Value Equation, the math of this investment requires scrutiny, not just optimism. You have reports like Leopold Aschenbrenner’s “Situational Awareness” paper arguing that AGI by 2027 is a certainty based on straight lines on a graph. You have accelerationists claiming that winning AI is a geostrategic categorical imperative—that the U.S. must secure superintelligence before China does to ensure national security. (If you want to lose some sleep, watch the AI 2027 video).  The narrative is that it is effectively a winner take all race.  But breathy narratives rarely hold up in the real world. We need to focus on Getting the AI Hype Cycle Right – distinguishing between inevitable progress and the economic reality.

I always like to look at the physics of a situation. And the physics suggest that this massive spending isn’t building a defensible fortress. It’s building a money pit.

Because here is the hard truth: AI ages like fresh fish.

I got a lot of feedback on my AI Bubble? : AI Skeptics Hard Look at the Value Equation, and the reactions were fascinating. A lot of you read my analysis of the economics and put me in the “AI Skeptic” box.

That’s a mistake. It misses the physics of what is actually happening.

While I agree with many of the skeptical points regarding the current financial bubble—because let’s be honest, the math on some of these valuations is ‘no bueno’—my long-term view aligns much more closely with Jim Keller.

There is an ongoing conversation happening right now between three camps: the AI Accelerationists, who want ‘all gas no brakes”; the AI Safetyists, who want to slam on the brakes; and the AI Skeptics.

The first two camps get all the press because they deal in extremes—utopia or apocalypse and extremes drive the attention economy. But we need to pay attention to the Skeptics. There is a taxonomy to the camp of skeptics; some don’t think AI is going to do what people say it will; others believe that it is a distraction from more pressing issues; and some are all about the economics and the math. The skeptics in this discussion aren’t arguing about the soul of the machine or killer bot swarms; they are arguing about the math. And from their perspective, the math is no bueno. MUY no bueno.