The one about complexity science
What does this all entail?
Friends,
I hope that all is well with you and yours, and that this e-mail finds you on a boat with shoddy connection, in the tropics, three months after I sent it.
Today, our look into complexity science continues as we begin to break down what it is and how it applies to business, strategy, and life. Also, as ever, a quick look at financial markets and the latest AI insights.
Now accepting keynotes for 24Q4-25Q2
Every year for the last decade or so, I have created three main presentation decks. For 2025, however, I have (for the first time) added a fourth due to popular demand. They are:
What to Do When You Don’t Know What to Do: How to turn change into a competitive advantage. (Based on the new book by the same name.)
Leadership in a Time of Change: How to steer an organization through a sea of uncertainty.
Resilient Retail: How to build a profitable retail business in the modern marketplace. (Based on the 2025 follow-up to the highly praised 2022 white paper The Gravity of e-Commerce.)
Artificial Intelligence Beyond the Hype: How to understand the narratives, risks, opportunities, and best uses of a new technology.
If you want to book me for your event, corporate speaking slot, or workshop, merely send me an email. To make sure I am available, please do so at your earliest convenience; my availability is limited and the schedule tends to fill up fast. More information may be found here.
A couple of updates before we go-go
Not a great week for climate change deniers as the latest set of extreme weather phenomena hit the globe. If you are in the US particularly, please, stay safe.
Work on the new book update - yes, a book update! - is progressing very nicely at the moment. We are finally starting to hit the last mile. The end product will be slightly more extensive than we had originally thought (we are adding instruction on how to build adaptive capacities in a number fields from budgeting to marketing), but hopefully that is all for the better. Steve and I are really giving it our all.
One of the positives of doing extensive research is discovering brilliant work that you were previously unaware of, or at least did not know the extent of. To illustrate, I think that Ralph Stacey deserves immense praise for doing what most balk at, i.e., completely rethink his previous work after making a major discovery (in this case related to complexity). I also believe that William Deming gets a lot of undeserved flack; he is a far better thinker than most, for some reason, seem to believe.
Of course, the downside is discovering that some people who are held in high regard do not necessarily deserve their lofty status. I was never particularly impressed by Michael Porter and Tom Peters, as most know, but I recognize Porter’s role in broader strategy development while Peters appears to be improving. Perhaps more surprisingly, I am starting to view Henry Mintzberg as somewhat overrated, at least across his entire body of work.
I admit that I may be a bit too harsh (there is some evidence to suggest that he might have suffered the same fate as Peter Drucker and felt the need shoehorn his work into the old paradigm), but beyond his excellent work on emergent strategy and the practical issues with strategic planning, he habitually makes the same assumptions as the people he critiques. And I cannot lie: some of his late career output borders on the nonsensical.
Moving on to a couple of thoughts on the markets and artificial intelligence.
Markets
We are about to hit Q3 earnings season with JP Morgan and Wells Fargo reporting results later today. For the purposes of strategy, it is worth looking at the following in particular: lending profits, unrealized gains and losses, savings data, and credit card delinquencies.
The US Department of Justice is pondering whether to break up Google. Predictably, this has garnered a lot of big headlines, but I would recommend a bit of caution. Yes, the company has been ruled to be an illegal monopoly. But splitting the company into Android, Youtube, etc. will not actually do that much to the overall revenue picture for Alphabet. A legal process would also constitute the largest antitrust case in decades. It will take many years to complete.
A number of companies - including UBS, PepsiCo, Progressive, and TikTok - have begun scrapping DEI specific grants, many of which were created in response to events such as the George Floyd murder and the Black Lives Matter movement. Suddenly, all those efforts that had nothing to do with public pressure but all to do with brand positioning, values, and whatever else marketers claimed at the time, are pulled back because of, well, public pressure.
On one hand, their behavior reveals the ridiculous hypocrisy in many companies; they will hold whatever values the public desires and, no matter what they are, will say that they have been there since the beginning. On the other, it is another sign of a growing moral chasm between the US and the EU. In the home of the brave and the land of the free, DEI efforts are facing legal challenges. In Europe, it is all but becoming law (sustainability already is).
AI
Apple’s Worldwide Developers Conference next week will be one of the more important ones in recent memory. As some may recall, Apple has been trying to prove it is not lagging behind in the AI arms race, yet so far not much has actually materialized. On Monday, that is supposed to change as the firm has promised to reveal its strategy. Stay tuned.
As we discussed last week, OpenAI recently finished its latest funding round at a stratospheric $156B valuation; the $6.6B raised constitutes the largest round in history. Even though, as Benedict Evans recently pointed out, some of the investors are effectively paying themselves (e.g., Microsoft, Nvidia), and industry insiders consider the multiple to be reasonable (far from convinced myself), their product is not exactly safe behind a mile wide moat.
AI is starting to move from the exigent quadrant towards the exposed quadrant in the Castlin 4E model, much as we predicted it would. One part of the reason is that the technology is becoming more widely accessible, but another is that AI has become such a fashion word that the entire industry has become a story eerily reminiscent of that, as Alison Gopnik recently wrote, of stone soup.
Indeed, upon closer inspection, the grand promises of artificial intelligence simply fail to match even mundane reality. As we have seen with numerous gen-AI solutions, not least from OpenAI, users are initially impressed but the novelty soon wears off. Consequently, many of the assumptions of recurring revenue are going to be tested. And even though Sam Altman & Co. may be ahead of the pack at the moment, the question is not only whether they will be able to hold the position, but also what the long-term value of that is. At long last, people are starting to wonder.
Moving on.
Complexity science
A scientific revolution
One of the things most important to understand about complexity theory is that, as we established last week, it is not based on some fringe idea or oddball hypothesis. On the contrary, complexity science represents a genuine revolution in scientific understanding and knowledge. More and more Nobel prize winners use what one might call complexity theories in their work (including the winners in physics and chemistry this year). Laureates who were not necessarily awarded for their work in complexity are also increasingly shifting their focus to the field.
Importantly, however, complexity science applied in a corporate context is not an academic undertaking by the proverbial nerds in the classroom; it is something which stands to become the new golden standard. The hard sciences are leading the way. Eventually, inevitably, businesses will follow suit.
So, what really is complexity science?
Well, unlike the many classical scientific fields that were influenced by the Newtonian focus on components, it concerns itself with how components within a system are related to one another and the patterns that emerge due to their interactions.