Consider a modern bike’s cockpit. It’s both insanely high-tech and a bit of a mess. Power-meter pedals count the watts in each leg. A speed sensor records k/ mph. A heart-rate strap reveals how hard you’re working. A bike computer fastened to the bars, pulls all those data together and layers in GPS, a map, the climbs ahead, and on a training day, the workout you’re supposed to be suffering through enjoying. It’s a marvel!

It’s also, right now, a mess. Different sensors from different manufacturers, all supposed to talk to each other. I can tell you from experience they frequently don’t. Connections drop, syncs fail. My favorite: just as your group rolls out, the pedals suddenly won’t record your watts because of a finicky battery.

That friction — that’s part of a tell. And if you consider what’s happening outside of cycling — in physical AI, edge computing, biotech — you can spot the early signs of a convergence.

Canyon is, I think, betting on a physical AI bike. Its new Predict system builds all of that scattered technology into the bike itself: edge AI, radar, cameras, a 360-degree sensor array, and a display set cleanly into the handlebar. So that’s hardware. It also fuses the sensor feeds with your own riding dynamics, speed and steering and stability, into a live model of the road, then predicts what’s about to happen. It scores the risk of a corner, a car, or a bad patch of surface before you’ve registered it, and warns you through lights, haptics, and the screen. That’s on-bike compute. Pair it with the company’s Stingr helmet — which has a drop-down visor display — and in a real emergency the bike can act on its own, dropping your seatpost to lower your center of gravity and steady you before a crash it saw coming and you didn’t. It runs on the bike, not the cloud, which cuts the lag and keeps the data local.

For now, Canyon’s Predict is just a prototype. The peloton tomorrow will undoubtedly have silly-looking aero helmets on the first team time trial stage, but they won’t have a slick heads up display.

But, but, but… let’s think about what that bike represents. It senses the living world, interprets it, and responds in real time. That’s the exact shape of what I’ve been calling Living Intelligence, arriving in one of the oldest machines we own, and it’s the same pattern already moving toward glasses, wristbands, helmets, and cars. Physical AI doesn’t show up as a robot. It shows up as familiar objects that suddenly perceive.

This is a clear sign of a potentially catastrophic storm for everyone in the industry.

Cycling today is a hodgepodge, and every piece belongs to a different owner. Garmin makes the computer. Shimano, SRAM, and Favero make the power meters. Whoop or Apple or Garmin track your body. Strava and TrainingPeaks hold your rides. I could keep listing companies and components. There are dozens, each with a slice, none of them owning the whole picture.

Integration collapses that. Once one brand combines the sensors, the AI, the safety system, and your data into a single stack that just works every time, the fragmented ecosystem rebundles into a few walled gardens, and the value migrates with it. It stops accruing to whoever makes the best power meter and starts accruing to whoever owns the integrated model of you on the road. Convenience for the rider on one side of the ledger. Lock-in, and a situational model of your body and your movements living inside one company, on the other.

So you’re not into cycling… why should you care? Well, what's happening on that handlebar — a swarm of dumb sensors resolving into one system that perceives, predicts, and acts — is the same thing about to happen to the objects you do care about. Your glasses, your watch, your doorbell, your kitchen. Therefore, anyone in telecommunications, in media, in health, in OEMs (see what I’m doing here?) — you ought to pay close attention. The bicycle is simply early, because it's a small, contained machine with a rider who wants every possible edge. Watch what consolidates here and you're watching a rehearsal for the rest of your physical life

Since we’re under an excessive heat warning here in the northeast, I’ll be riding on my trainer indoors, watching the TDF’s first stage, happy for Campy’s vlog and sad that Wout can’t compete this year. But in the long term, the more consequential race is the one to own the handlebar. Whoever wins it won’t just be selling bikes.

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I thought about her recently, because a thief in San Francisco just made a cleaner getaway than Dillinger ever managed, and no one heard a thing.

Someone robbed a yoga studio in San Francisco, walked out with an armful of merchandise, and climbed into a driverless Waymo idling at the curb. When the thief was inside, presumably with the seatbelt on, the car pulled away. There was no driver, no chase, no witnesses. And… six months later, no suspect.

What we know to be true: a Waymo is basically a rolling surveillance system. The cars are riddled with cameras and sensors. To summon one, you need an account registered to an app on your phone, and you need a valid credit card payment on file. So this should have been an easy solve for the local police.

Instead, by the time the police got their warrant, the interior video was gone, the exterior faces had already been blurred for privacy, and the company reserved the right to push back on law enforcement to protect its riders.

The most surveilled getaway in the city’s history produced no one to arrest.

I suppose this is a win for privacy as we’re all increasingly surveilled. I’m still wondering whether this was a new form of physical AI penetration testing – someone testing the limits of security in an AI-mediated real world.

That said, we ought to think about how privacy might shield bad actors in the future. My grandmother was one of the witnesses in 1934. Today the witness is a corporation, and the record of what happened lives on its servers, under its retention schedule, released through its lawyers. Dillinger used a human shield. This thief used a privacy policy.

We assumed a city full of cameras would end anonymity. It may do the opposite: anonymity for whoever the platform decides to protect, and only for as long as the footage survives.

We're one step closer to generating human eggs without a womb, a body, or a donor. Emphasis here is on step. A biotech company just announced a milestone that’s big (if true)… but also somewhat oversold.

Conception, a biotech startup based in the US, just announced that it made human egg cells from blood. Take a donor’s blood, reprogram the cells into stem cells, coax them inside a lab-grown “mini-ovary,” and out come early human eggs. If that sounds like the opening of a story where reproduction detaches from the body entirely, it is. That’s why I’m flagging it.

But read the claim carefully, because the gap between what happened and what was announced is the whole lesson in reading a signal. Conception didn’t actually make eggs. It made primary oocytes — the earliest egg-cell precursors, nowhere near mature enough to fertilize. The news arrived by company blog and a CEO’s social post, not peer review. There’s no published data yet on efficiency, genetic normality, or safety. Clinical use is likely years and many animal studies away.

(If the miracle of “just a few X of blood” has you thinking Theranos, you’re not the only one.)

To be sure, there is a massive body of work already in progress in induced pluripotent stem cells (iPSC). Basically: researchers take a fully committed adult cell — a skin cell, a blood cell, a cell that long ago decided what it was going to be — flip a small set of genes, and reverse it into a blank slate that can become anything.

Most of this is happening in Japan, where I lived and worked for many years. I still visit regularly to meet with researchers working in biotech, robotics and AI. Mitinori Saitou’s team at Kyoto University has been pushing human stem cells toward the egg lineage inside lab-built ovaries since 2018. Recently in 2023, Katsuhiko Hayashi, who is a pioneer in generative reproduction, took skin cells from a male mouse, reprogrammed them into stem cells, got the cells to shedding their Y chromosome and to double the X, and grew them into functional eggs. Fertilized with sperm and carried by surrogates, those eggs produced healthy pups with two biological fathers. The success rate was punishing — only 7 live pups out of 630 embryos — but the team said that doing something similar in humans was likely on a decade away.

Immature cells in a dish are noise, if you’re reading one headline. They’re a signal, if you’re watching the field. What’s on our horizon: a world where an egg can come from anyone’s cells: two fathers, a sixty-year-old, a single person, a nearly unlimited supply.

When the embargo came in 1973 and prices quadrupled, nearly every major company was blindsided... except for Shell. Shell had already lived through it, on paper, and so it moved faster and more decisively than any of its rivals.

That is what a scenario is, at its core. Not an exact prediction, and not a guess dressed up in probabilities. A scenario is a disciplined, evidence-based story about a future that hasn’t happened yet, built well enough that the people who read it change how they think and what they do. The point was never to be right about a single outcome. The point was to make the unthinkable thinkable early enough to act on it.

Wack didn’t invent scenarios of nothing, and neither did I. When I wrote The Signals Are Talking, I traced foresight back through a very long lineage of people who each figured out a new way of seeing.

I’ll skip ahead to modern(ish) day and H.G. Wells, who did deliberately what no one had done before: he took the faint signals of his own moment (like, electricity becoming a general purpose technology, the machinery of modern war taking shape), asked “what if?” He used that reporting to write stories vivid enough to make readers feel a future that wasn’t yet here. I call this the Imagining Era of foresight.

After WWII, Olaf Helmer and Nicholas Rescher at RAND turned foresight into something more systematic. They built probabilistic models and made the practice rigorous. But they stopped at the edge of recommendation. Their job, as they understood it, was to show you what might happen, not to help you do anything about it. I call this the Calculating Era of foresight.

Then came Wack, and after him Peter Schwartz, who carried the method out of Shell and into the wider world, most famously in The Art of the Long View. This was I call the Exploring Era, and its insight was that the real value of a scenario isn’t the scenario itself... it’s what happens inside the mind of the person reading it. A good scenario doesn’t hand you an answer. It asks you to challenge your cherished beliefs and to updated your assumptions. That approach has governed the field for the better part of fifty years.

Now, here is the uncomfortable part, the one my own field doesn’t much like to say out loud. Exploratory scenarios are brilliant at changing minds but terrible at changing behavior. I know, because I’ve sat in the rooms where people leave with their assumptions productively shaken… and then nothing changes. Those scenarios goes into a binder, the binder goes onto a shelf, and nothing happens. Everyone may feel something, but no one does anything about it.

That gap is the problem I care about most right now, and it’s the reason this section exists on my Substack. The current era of foresight must do what the last one couldn’t: convert imagination into action you that can be measured. A scenario that expands your thinking and leaves you with nothing to do on Monday morning has done only half its job. You need to map that thinking to strategy: as in, what to accelerate, what to pause, what to build, and how you’ll know whether it worked.

So this is where I’ll do that, in the open. In this section I’ll build scenarios — when for our purposes I’m calling forecasts — that you can rehearse, and then I’ll go further than the tradition usually allows. I’ll stake claims specific enough that you and I can both check them later. Some of it will be wrong. All of it will be built to act on.

Wack didn’t just imagine the oil shock. He changed what Shell did about it, before it arrived. That was always the whole point.

Let’s rehearse the futures.

Go backward in time and you can watch a whole industry die and regenerate. Horse-drawn carriages gave way to railroads. Railroads gave way to the automobile. The personal automobile gave way to Uber. And right now, Uber is giving way to Waymo. Each wave of innovation didn’t incrementally improve upon the last, it quite literally created something net new.

There’s a name for this. In the 1940s, the economist Joseph Schumpeter called it “creative destruction,” and his idea was pretty simple: capitalism is a perpetual storm. It doesn’t drift toward equilibrium. It churns, destroying old industries and establishing new ones in the same motion. On one side of the ledger sits creation: new products, new technologies, new business models, new markets, new organizations. On the other sits destruction: incumbent firms, old jobs, outdated ways of making things, dead ways of selling them. The storm isn’t a flaw in the system as much as it is the system.

If this sounds familiar, it's probably because you've read Clayton Christensen. The late Harvard Business School professor described something adjacent, which he called "disruptive innovation”: the way a smaller, scrappier competitor slips in from below and topples an established leader. The most famous example is digital cameras causing the downfall of Kodak.

But Christensen and Schumpeter were basically studying the same weather from different distances. Christensen zoomed in: here is exactly how one competitor takes you down. Schumpeter zoomed out: here is how market forces create destruction that simply happens to you, whether or not anyone means it to happen.

Both were describing the uncomfortable truth. The storm doesn’t care about you. It isn’t personal, and it isn’t waiting for your next board meeting, or your next injection of capital, or your next policy meeting, or your product’s roadmap.

Which means you have to care about the storm before it arrives.

The thing is, if you can see the storm coming, then the destruction, and the creation, become a choice. You get to decide whether to build the new thing or defend the old one. You get agency. But only if you see it in time.

So I built a storm tracker.

Think about how weather actually works. Temperature, humidity, barometric pressure, wind speed: those are the readings. They’re useful, and you need them. But no meteorologist looks at the temperature alone and calls it a forecast. Like… nobody is going to evacuate a city because the barometer reads 29.8 inHg. (To further the point: is that number good? Bad? Numbers out of context don’t really help.)

Trends are the readings. A convergence is the storm system, the thing that emerges when all of those conditions start interacting and produce something none of them could have produced alone.

In my field — strategic foresight — we know what to do with a trend. One appears, we track it, we fold it into scenarios, we stress-test it. For us, trends are raw material. But a trend in the hands of someone who isn't running that strategic foresight process is something else: a single reading mistaken for a forecast. It tells you the temperature and lets you believe you understand the weather. That's the trap — not the trend itself, but the false confidence it hands a leader who tracks it and stops there. Which is why, if you're the one making the call rather than the one building the scenarios, the more useful thing to track isn't the trend but the convergence. You don't react to a convergence. You position ahead of it.

A trend tells you what’s changing. A convergence tells you what’s about to become inevitable, before it looks inevitable to anyone else. The window to act opens earlier, and the cost of missing it runs far higher, because by the time a convergence is obvious, the people who saw it early have already taken the ground.

Which is why a convergence is something you put to actual use. You hold it up against whatever you’re building and decide whether to accelerate, to pause, or to reframe the thing entirely.

Here is the working definition I created:

A convergence is what happens when multiple trends, forces, uncertainties, and catalysts intersect and interact to create a combined impact that is greater—and often different in kind—than the sum of their individual effects.

In practice, a real convergence has four characteristics.

1. First, it’s system-level. Convergences don’t just pile trends on top of each other.

   They operate across different domains, making them hard to see unless you are doing a specific type of cross-domain analysis.

2. Second, it creates new realities suddenly. What seemed inconceivable becomes inevitable, not gradually but suddenly, even though all the pieces were visible beforehand.

3. Third, it redistributes power and value. Convergences influence who wins, what’s valuable, and where there is leverage. They rewrite competitive dynamics, not just within industries but across them.

4. Fourth, it’s hard to reverse. Because multiple systems reinforce each other, convergences establish new market realities faster than traditional changes, making early detection increasingly critical.

Here’s a convergence I’ve been tracking. Last year I identified a convergence I call Living Intelligence: the collision of artificial intelligence, advanced sensors, and biotechnology into systems that can sense, interpret, and respond to the living world in real time. The data moves in both directions: the system reads the living world, and it acts back on it. On its own, each of those three technologies is a trend you could put on a roadmap. Together, they are a storm system, and the implications reach into nearly every industry I can name.

Here's the good news buried in all of this: convergences are only invisible until you start looking for them. The storm doesn't announce itself. It assembles from what’s already sitting in plain sight. You don't need to predict it. You need to start tracking it — early, and on purpose.

Each year my team at FTSG publishes the Convergence Outlook, a deep analysis of the convergences we're tracking most closely — what's driving them, who they reshape, and how fast. You can read highlights or download the whole Outlook for free here.

I get asked this question all the time, and my answer rarely satisfies people. If you have the right data and enough of it, and you’ve built a solid model, you can absolutely predict outcomes. These days, with ever-powerful computer systems, you can ingest a vast amount of data, run tons of simulations, and get probabilities on the other end.

The questions I get hired to answer sound impossible until you see the data behind them.

• Which US companies will see the biggest productivity gains from physical AI over the next decade, and why?

• What’s likely to make your current business model obsolete by 2031?

• Which policy choices will decide whether US AI, semiconductor, and biotechnology stay globally competitive?

• Which industries are about to become far more — or far less — insurable as climate, cyber, and geopolitical risk compound?

Every one of those questions is answerable with a good methodology, data and a model. Not a hunch.

I can do that. But given the volatility and rate of change, you’ll need to get in a habit of updating your priors. Which is to say: one-time predictions are useless without continual monitoring.

Convergence is where I do my continual monitoring in public. The weak signals, the system collisions, and the forecasts where technology, capital, geopolitics, climate, science, and human behavior collide and rewrite who holds power and what holds value.

So this is where I keep them. It’s more provisional than my books and more personal than our reports at FTSG. What you’ll find here is what I’d say to you over a meal, but definitely not in a polished deliverable.

Some of what I flag here will turn out to be noise. I write forecasts and will need to update them as needed. Occasionally (ok, often) I get irritated at business and government leaders. And, given that I keep one toe in Hollywood, I sometimes take side quests from reality into sci-fi (but you’ll know that when it’s happening).

Everything is free. If the work is valuable to you and you want to support it, just let your colleagues know. This work won’t sit behind a paywall.

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