VCs like to swing for the fences, so they strike out a lot. PEs prefer singles and doubles since they’re less risky. But they’re both missing something important.

Systemizing high-growth by consistently hitting triples. Very few can do it.

We’ve yet to see people using this exact phrase, but it represents the core need of enterprise software companies and their investors.

They simultaneously want profitable growth (i.e., driving EBITDA), but also AI Defensibility.

The way you maintain defensibility from frontier AI is, as you might have suspected, to build frontier AI. If you just use someone else’s, and get locked in, by definition your business operates at the whims of the Emperor.

I grew up in a small town in Iowa. In fact, I spent half my life there. What a magical place to grow up playing baseball. The second half of my life was spent living and working in the biggest cities on the planet: Chicago, New York, LA, Dallas, Nashville, London, Paris, Milan, Tokyo, the list goes on.

In short, I put in the time and did the research. The truth about what I found may surprise you.

The only real way you fail is if you stop.

Keep movin’.

We see many people make the same mistake on the road to creating product.

We’ll hear things like “People will love this”. And so we see a lot of market research, and surveys, and customer advisory boards take over. Sure, this will help you get part of the way there, but not all the way.

Inside Silicon Valley, you hear terms like “AGI-pilled”, which means you drink the kool-aid, you’re on the frontier, and you understand what’s coming with Recursive Self-Improvement (e.g., automate the automation).

But outside Silicon Valley, the feeling is different. Fear, Terminator, Job Loss, Apocalypse. It’s not great. And with the next presidential and political elections right around the corner, it means campaigning for popularity is about to begin.

The U.S. AI labs on the frontier used a three-fold pricing ladder as a business model:

1. Free to get you started

2. Subscription to get you retained (once you reach the free limit)

3. Usage to get you paying (once you reach the subscription limit)

Over the last 6 months, product development within enterprise software changed. We transitioned from 0% of new code written by AI to nearly 100%. And more recently, every company’s board and management team we work with is already there.

By far the most frustrating part of using frontier agentic coding models, even with state of the art harnesses, is that they don’t check their own work. It’s basic, first day of work stuff, but for some reason these trillion-dollar AI labs don’t seem to care much about quality.

How was your weekend?

Mine was cool, had a couple SaaS vendors send me automated emails saying they were increasing prices significantly, but without any communication about product features they are adding for that extra price.

There’s about 5 bits of information per token.

But do tokens = value?

No, it’s just raw information. Raw data. Raw binary of 0s or 1s.

Finding and removing bottlenecks is an optimization strategy as old as time. But optimization is not invention.

I can checkmate you in a single move, before the chess match begins. The matchup? Your proprietary data against my algorithm that adapts in real time.

When most companies, hiring managers, leaders, purchasing departments, and recruiters go looking for talent or service providers, 99.999% are looking for the exact same thing.

This is one of the best repeatable sound bites I’ve heard from the Big AI space. It’s Microsoft AI’s positioning statement used at their recent Build conference.

ERPs are catnip for PEs. Private equity loved those enterprise software businesses because they had low customer churn and long-duration recurring revenue.

We’re witnessing a planetary-scale, decentralized drunk driving experiment, and boy is it getting bad out there. To describe what I mean, let’s use an analogy.

Taste If you spend any time in the Valley, you've heard the discussion about how, in the age of AI, the only thing left is "taste". But no one has defined what that means.

How do you ensure a global Fortune 200 consumer products company maintains relevance during this planetary-scale AI disruption event? You hire evergence to build an AI-native marketplace and an AI-native Business Intelligence Agent for use by all employees, in every country, in every department, to

In my last post, I wrote about recursive self-improvement and the end of static software. The core idea was: The next durable advantage in software will not come from having access to the same AI models, cloud platforms, engineers, or tools everyone else can buy.