Algorithmic Musings: Things Are About to Get Weird(er)

I believe that we’ve crossed a point where our horizon of relative certainty will continually contract, while our relative uncertainty (that which is unexpected, unpredictable, and unforeseen) will expand. Dramatically.

In short, I think things are about to get weird(er).

I recognize this is an entirely subjective vantage point and a less-than-objective way to open an article. You might even be forgiven for suspecting this is some rough-handed attempt to clickbait you. It’s not. Instead, I feel compelled to acknowledge that sometimes the combined weight of all the tea leaves defies precise analysis and devolves into feeling.

So let’s read a few of those leaves together. I’ve picked three, and each one tells a different part of the story: capability, threat, and opportunity.

Leaf One: Capability

The debate over whether Astra and Fable qualify as AGI (Artificial General Intelligence) is getting louder. AGI remains an extremely ill-defined term, and I’m not going to wade into that swamp here. The more useful question for you and me is whether the machine can do your expert’s job, and the answer keeps coming back as “yes, and more of it every quarter.”

Consider GDPval, a benchmark built from real deliverables (legal memos, accounting spreadsheets, sales decks, engineering diagrams) across 44 occupations and graded blind by professionals averaging 14 years in their fields. In the fall of 2025, the best model matched or beat those experts on just under half of the tasks. By December the number was north of 70%, and we’ve seen several model generations since then.

Call it whatever you like. I’m comfortable calling it a problem for anyone who bills by the hour.

Leaf Two: Threat

One of the best open models has now been jailbroken. Somebody surgically removed its guardrails and put the result behind an API that anyone can rent for five dollars per million tokens. This means that cheap, capable models can now write proof-of-concept exploits that let small security shops do what only the big consultancies could do a year ago.

The flip side is that you’re now a target. An attacker doesn’t need to be skilled anymore, just funded to the tune of a few dollars. In my opinion, that makes mid-market companies with thin IT departments the softest targets on the board. You have enough revenue to be worth the trouble and not enough staff to notice the trouble coming.

Leaf Three: Opportunity

According to payment card processor Stripe, nearly 40% of new businesses on their platform in 2026 are in metros with fewer than one million people, up roughly ten percentage points from 2022.

Why does that belong in an article about things getting weird? Because for most of my career, serious new companies formed where the talent and the capital lived: New York, Boston, the Bay Area, Austin. AI is loosening that connection. A two-person shop in Lancaster, Chattanooga, or Boise can now rent something close to a full bench of analysts, developers, and marketers for the price of a streaming subscription. Your next best partner, or your next most dangerous competitor, may be forming this week in a town you’ve never scouted.

So, What’s a CEO to Do?

My argument isn’t that you need a better forecast. Forecasting is exactly the muscle that’s losing value.

Think about the storm chasers in Twister (1996, and yes, I’m dating myself again). Helen Hunt and Bill Paxton don’t win by predicting where the tornado will touch down. Nobody can. They win by getting close enough to put instruments into the funnel so they can learn faster than the other guys. Their edge comes from proximity and speed of learning, and that’s the posture I’d recommend for you, too. You need a better set of sensors and a shorter loop between what you sense and what you do. Practically speaking, that maps back onto our three leaves:

1. Capability: raise your floor. Stop debating whether AI is “ready.” Pick the three most expensive knowledge tasks in your business and run them through a frontier model this month, side by side with your experts. One afternoon of that will teach you more than a year of vendor demos.
2. Threat: assume you’re already in the crosshairs. Multi-factor authentication everywhere, patched systems, tested backups, and a phishing drill that includes the C-suite. Boring stuff, I know, and all of it is cheap compared to a week of downtime. If nobody in your building loses sleep over this, hire or rent someone who will. Now.
3. Opportunity: widen your scouting radius. Where are the new entrants in your industry actually forming? Who’s selling into your customers from a zip code you’ve never heard of? Put someone (or some AI) on it. The competitor you can’t see is the one that hurts.

You’ll notice that none of these steps require you to know what happens next. They only require you to shorten the distance between noticing and acting.

The Weird Turn Pro

Hunter S. Thompson famously observed that when the going gets weird, the weird turn pro. With this in mind, I suggest two additional actions. First, it’s time to bring the eccentrics out from their hiding places in your organization and lean into their unique skills. Second, I suggest that when the going gets weird, the prepared turn pro. And preparation in this environment has very little to do with a thicker strategic plan. It looks like an organization that senses sooner, decides faster, and fails cheaper than the one down the road.

I recognize you’re busy running your operation. But if you’d like to talk through what your sensor array might look like, or you simply want to compare tea leaves, give me a call. Things are about to get weird(er), and I’d rather we chase this storm together.