The one about the adjacent possible
What can come to be?
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 move on to something which explains not only how markets and economies grow, but also how innovation works and why strategic planning is fatally flawed a priori.
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
As many of you are aware, at least going by the amount of emails I have received over the last few days, an Oxford professor is once more in the news attempting to make a name for himself by criticizing the work of the Ehrenberg-Bass Institute. No, it is not his first rodeo. A couple of years ago, he gave it a go alongside two colleagues - and failed spectacularly. It would appear that he is the only one not to have become gun-shy.
For whatever it may be worth, I welcome all critical inquiry; it is how we improve and progress. Having said that, I do wish that the quality of the challenge would be significantly higher. For all the talk of peer review (a topic for another time), I struggle to see why serious people would take it, well, seriously. It just is not very good.
Either way, at the end of the clichéd day, the point of praxis is to take a theory, apply it to practice, and then continuously update said theory according to discoveries made in said practice. For those of us who actually do the work, the end product is all that matters. I leave the academic bickering to academics who have nothing better to do than bicker.
Another set of keynotes have been booked for the spring, with more dates in the summer already in the pipeline. If you are interested in getting me in to talk, now is the time to let me know. My availability is diminishing fast.
Moving on to markets and artificial intelligence:
Markets
The FTC has mandated that companies must make it easier to cancel subscriptions. This should make Rory happy.
Amazon is investing heavily into nuclear technology to secure its future needs; it recently made a down payment on new reactors in Washington State to power its data centers. Alphabet, Google, and Microsoft, have announced similar intentions. While it all makes obvious sense, it does make one ponder the broader implications of corporations not only being able to buy direct, but also to set up their own plants. Not entirely convinced all of them are good.
Although it is not in my place to comment upon politics, it would be remiss of me not to highlight the potential implications of another Trump presidency within the context of financial markets. Based on promises made during his campaign trail, tariffs could reach their highest level since before WWII, which would have massive implications for world trade. On a more immediate local level, his policies are expected to add further inflation, deficits, and higher interest rates. Even if you (somehow) ignore all the other crap, it makes little fiscal sense to elect the guy.
Earnings weeks are currently underway. Thus far, the large banks have all proven to do very well indeed. The big tech companies are following.
Speaking of which…
AI
The latest financial report from ASML, a Dutch company that makes machinery critical for manufacturing advanced semiconductors (with customers like Taiwan Semiconductor Manufacturing, Samsung Electronics, and Intel), included disappointing guidance figures in their latest quarterly report. This caused some concern that the AI bubble is about to burst. I am not entirely sure that is the case; a closer look at the report reveals that other segments (PCs and smartphones in particular) were to blame.
What will be significantly more telling is whether firms such as Microsoft, Google, Meta, and Amazon, are cutting back AI investments. We will soon see, but I doubt it. In H1, the companies combined to dole out $106.2B in capital spending; an increase of 49% YoY. Most of the addendum went to Nvidia. Taiwan Semiconductor Manufacturing, seen as a bellwether for the relevant markets, also smashed expectations in Q3. If anything, AI chip demand appears to be surging.
A few more nuggets from OpenAI’s latest funding round have been released. As detailed by The Information and later Benedict Evans, apparently the company imagines becoming profitable by 2029 at $100B revenue, with $44B of cumulative losses to get there. While it is not out of the realm of imagination - Azure had $70B revenue over the previous twelve months and AWS came in at $99B - a lot of capex will be required. And as we discussed last week, the models are becoming increasingly commodified.
Shifting from a growth-first strategy to a profit-first strategy is also, not least due to path dependencies, a lot more difficult than commonly realized. Most who attempt it fail. I am not saying that OpenAI will; I am just saying it is anything but guaranteed that they will succeed.
Moving on.
The adjacent possible
What can come to be?
In last week’s newsletter, we established why complex systems such as firms and markets cannot be understood by observation of their parts. Of particular note was the concept of emergence, which explained why so many conventional frameworks, tools, and methods fail in practice. We also began to look at patterns and proved that despite the indefinite variables that affected the micro, the macro often proved to be remarkably stable. This had direct implications for strategic decision-making.
Today, we are going to delve deeper into yet another revelation from complexity science that has proven to hold immense explanatory power. Not only does it demonstrate why and how markets and economies grow, but also how innovation works and why strategic planning is doomed from the get-go.