The Forecasting Fallacy
Meteorologists can predict weather five days out with decent reliability. Seven days? Broad guidance only. Beyond ten days? Forget it. And this is achieved using some of the most sophisticated forecasting technology on the planet.
Economic forecasters use equally sophisticated models, yet we still expect them to predict market conditions, technological disruptions, and career trajectories months or years in advance. The delusion is remarkable.
I think we all sense the economic weather is changing. The AI revolution isn't coming; it's here, even if we do not yet understand it. Traditional career paths are fragmenting. Organisational hierarchies are flattening, hollowing out, or becoming irrelevant. The old rules of professional advancement no longer apply.
In the torrent of information we receive, it is easy to forget that all weather is local. A hurricane in Florida affects your weather days later, but you don't experience the hurricane. If Elon gets a trillion-dollar payday, that's economic (and social) weather somewhere else. The AI hype cycle, the political turbulence, the breathless predictions about job displacement; most of it is weather happening to other people, in other places. It doesn’t make it irrelevant, but it does make it a distraction.
Our weather is what we actually experience, adapt to, and respond to. And just like literal weather, we need to dress appropriately for the conditions we actually face.
Weather is a System.
In his classic "The Goal," Eli Goldratt argued that lasting business success comes from identifying and managing bottlenecks. Identify the constraint in your system, optimise it, and everything else falls into place more smoothly.
Forty years later, the constraint has shifted. It's no longer about optimising production lines or supply chains. As
argues in "Reshuffle," the real constraints now lie in how roles, relationships, and information flows are structured across networks.Constraints are more human than ever, as a more profound shift is happening. AI has created a new kind of constraint, or rather, revealed one that was always there: the boundary between what machines do well and what humans do irreplaceably well.
This isn't the familiar "humans vs. robots" narrative. It's more subtle and more important. It's about learning to work above the algorithm rather than being replaced by it.
Above and Below the Algorithm
Working "below the algorithm" means competing with machines on their terms: speed, consistency, processing power, and pattern recognition in well-defined domains. This is a game that tech now plays better than humans (and cheaper mostly - but that’s a secondary point)
Working "above the algorithm" is different. It's about the things that still require human judgment, contextual understanding, and creative synthesis: recognising when the brief itself is wrong, translating fuzzy human concerns into workable frameworks, knowing when to ignore the data, seeing patterns that transcend categories, and building relationships that enable new understanding.
It isn't about technical prowess; it's more about craft. The artist's disposition to see and the artisan’s ability to apply what others miss. The confidence to constructively reframe problems rather than just solve them as presented, and the judgment to know when the algorithm has missed the point entirely.
Colin Chapman, founder of Lotus Engineering, had a principle: "First simplify, then add lightness." Anyone with enough money can make a car go fast in a straight line; add more power. Making it go fast around corners requires craft.
The same applies now. AI can add power to almost any process and make it go faster, but it’s not good at going round unexpected corners. Knowing which processes matter, which problems are worth solving, and which solutions will actually work in the messy reality of human organisations is a craft.
The Augean Stables Problem
One of the most interesting aspects of AI is how it feeds off the excessive complexity we've built into our working lives. David Graeber's "bullshit jobs" aren't just inefficient; they're systems designed in a way that create more complexity, more process, more coordination overhead.
AI promises to do for organisational complexity what Heracles did for the Augean Stables. Heracles didn't jump in with a shovel, he redirected rivers to clean them out. That's systems thinking. The opportunity isn't to optimise every inefficiency by hand, but to shift processes so the system clears itself.
Suppose you've recently attended a routine leadership program, anything with "agile" in the title, or a course on prompt engineering. In that case, you might want to consider whether you're working in an “Augean Stables” organisation. Up to your neck in bullshit, and wondering whether a technological Heracles isn't already on the way.
This is where craft becomes crucial. While others are optimising processes that may disappear entirely, craftspeople are developing capabilities that become more valuable as everything else gets automated.
It is a craft where logic and creativity play equal parts, because..
The Margins Are Where Possibility Lives
The old career advice was to move toward the centre: the big company, the prestigious role, the established industry. But centres are exactly where AI will have the most impact, where standardisation is highest, where human judgment matters least.
The margins are different. At the edges of organisations, industries, and disciplines, the problems are messier, the contexts more varied, the need for human insight more acute. This is where craftspeople have always thrived.
The economic weather is changing, but weather systems create opportunities as well as challenges. Storms bring destruction, but they also bring energy, movement, and the possibility of something entirely new.
The question isn't whether you can predict precisely what's coming. The question is whether you're dressed appropriately for whatever weather actually arrives.
Dressing for Local Weather
So how do you dress for economic autumn? How do you prepare for technological winter while positioning for the inevitable spring?
The same way artisans have always adapted to changing conditions:
Choose a domain to master. Something that involves creation rather than extraction. Something you'd be proud to explain to your grandchildren. Something that gets stronger when paired with AI rather than replaced by it.
Choose your company carefully. The people and information sources that will shape your thinking matter more than ever. In a world of infinite information, belonging to something beyond it is an anchor.
Don't rush. Craft takes time. Working with AI, really partnering with it, above the algorithm, not just prompting it, seems likely to be a craft practice for the next era. Those who master this collaboration will gain sustainable advantages.
Be a Curator. Provide signal, not noise. Be a source of clarity and wisdom for those you serve. In a world where AI can generate infinite content, the ability to synthesise, judge, and curate becomes exponentially more valuable.
Work where you have agency. Focus on the spaces where you can actually influence outcomes. Let others worry about the hurricanes in Florida. Dress for your own weather.
All weather is local. Dress accordingly.
Over the next couple of months, I am going to be following my own advice to simplify and lighten. I am considering how to align what I do here and at more effectively, and provide more focus for what promises to be a challenging 2026. Whilst I’m doing that. I have paused paid subscriptions to both blogs, and will make all content free whilst I think out loud in your company….
Have a great week.
A couple of posts I liked during the week.
A sign of retreat from mindless scale?
I have always liked
work on ‘Spectacle”
And
on another lens on peak tech…