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Commoncog |
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a newsletter on accelerating business expertise |
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Commoncog This Week
This week's Commoncog case is free; next week's piece will be members-only.
A New Series about Folks Handling Disruption Well (and Badly)
Sometime back I said that Commoncog will, as a service to members and readers, begin publishing cases of people and businesses who have had to go through disruption. We'll look at cases where folks handled such disruption well, as well as cases where they failed and handled such disruption badly.
Hopefully it's clear why we're doing this: given what's going on with AI, it's probably useful to fill one's head with such cases of past disruption. That is, we want to calibrate ourselves for what may or may not come.
I've actually already started a concept sequence for this: Navigating Disruptive Shifts. The most recent addition was The Swatch Case, which I originally commissioned because I thought it was a clear case of technological disruption. (We now know, of course, that it wasn't nearly that simple.) But no matter: there are other cases that I've queued up for this sequence, and we're going to publish them over the next few months, starting today.
Later this year, Commoncog will publish increasingly complex cases about prior technological revolutions. (We haven't started doing this because writing cases about entire technological revolutions are a lot more complicated than writing cases about specific companies or specific events).
But this series is here, and it’s about dealing with disruption.
Note that ‘dealing with disruption’ is not limited to technological disruption. Disruption, after all, can come from many places. As a trivial example, you could build a business selling fast food, and then suffer as a consumer fad for salads take over.
This week, we’re adding a new case to the sequence, that will serve as the official ‘kickoff’ for the entire series:
How Amazon Handled The Dotcom Bust (freely available)
This case — on Amazon’s experience through the dotcom bust — might be relevant if you’re — I don't know — building with new technology in an equity market that’s excited about said technology. And then that market tanks.
Why Do We Want to Read About Previous Cases of Dealing With Disruption?
As we’re kicking off the entire series, though, I want to address a question that some of you will have. Inevitably, someone will say something like the following: “Artificial Intelligence is new, we’ve never seen anything like this before, everyone will lose their jobs, this disruption will be unlike any other that has come before, therefore we know nothing, therefore history can teach us nothing.”
This is a fair objection.
My answer shouldn’t surprise longtime readers. It overlaps somewhat with my objection to reading history for lessons, or against skipping history because survivorship bias. (To be clear: a) we shouldn’t read history for lessons; b) survivorship bias is not a problem if you know how to read history in a particular way. You may click both links above if you're not familiar with either argument because they are both useful, but you don’t need to — those arguments are not critical to the one I’m about to make here)
The argument I’m going to make is this, and I should note that it’s not mine (it was originally from the researchers who published Cognitive Flexibility Theory). First, let’s assume that every revolutionary technology was revolutionary for its time. That is, when the technology was new, nobody could’ve predicted what those technologies would do to their companies, their markets, or their lives.
Let’s dump all such cases into a set of its own. Let’s call this the “100% unique set". Yes, this is a thing you can do. Notice that the meta-pattern uniting all of these cases is “how folks respond to this completely novel situation”. Everything else will differ — naturally, because everything else is ‘revolutionary’. But we may anchor ourselves on the reactions of the folks caught in the middle of the storm.
So what you may then do is to examine this set and ask: “When folks were facing something completely novel and did well, what did they do? What happened? When folks did badly and got disrupted, what did they do?” And then, on a case by case basis: what are the surprising similarities and dissimilarities with other cases?”
This is, of course, Commoncog’s Calibration Case Method in action. Note that we’re not building a causal model here. We’re not looking for universal patterns because reality is complex; things that work in one situation don’t necessarily work in another. But if you do this exercise — and you absolutely can do this already: we’ve got 12 case studies in the Navigating Disruptive Shifts concept sequence right now, so there’s more than enough to start — you’ll find that there are already interesting patterns to the cases. Not in all of them. But in enough that it’ll make you go hmm.
I’m not going to reveal what I think they are. I want you to read the cases in the concept sequence and then record a voice note: what did you notice? What leaps out at you? Dwell on it a little.
We’ve got a few more cases to publish. and then I’ll give you my reaction. In a few weeks.
Note: Members may leave a comment at the bottom of the case. This entire section is published as a blog post here; you may leave comments at the bottom of that post too. If you don't know how to log into the forum, here are some instructions.
💡 The Commoncog Membership: for the discerning businessperson. Get full access to paywalled articles, curated events, a rich and growing case library, and an exclusive, members-only forum.
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📆 Events
Two big events in the coming weeks:
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(Free to all readers) Singapore Commoncog Meetup on 3rd August 2026, 7pm SGT! I am doing an experiment! A number of folks have organised unofficial Commoncog meetups in the past, and I thought I’d give it a try. Also, this was motivated by a number of different members telling me that they thought Commoncog’s value as a selection mechanism was quite useful, and they would love to meet other serious folks who read Commoncog.
- If you are a member, indicate your interest in the forum event here.
- If you are NOT a member, you may sign up here (note that the bar I've chosen for this meetup is rather small, so this may be a ... terrible idea LOL).
- Note that there are other Commoncog meetups cooking; see below in the Members Discussions section to keep track of them.
- (Members Only) Tactical Decision Game with John Schmitt. A TDG is an accelerated expertise training approach developed for the US Marine Corps. John Schmitt created TDGs when he was in the Corps. He was also the Colonel who wrote MCDP1 Warfighting — the foundational doctrinal document for Marine Corps manoeuvre warfare. If you want to know what that is, and why TDGs were invented, I give a longer explanation here. Schmitt has agreed to run a TDG with Commoncog members. The event will be two hours long: the first hour is the TDG itself, and the next hour will be a debrief about the methodology. I know some participants will want to adapt this to their own contexts. If you're a member, you may discuss the event in the forums here.
Member Discussions
The Commoncog members-only forum is a private place for sensemaking on business and markets.
Here are a couple of members-only discussions I'd like to draw attention to:
- A member gives a follow-up to a request for coaching clients. The thread then evolves into a discussion about the coaching business.
- A member offers up another case fragment to make sense of AI: apparently, Dupont was the first to computerise materials requirements planning (MRP). They turned what used to take a week into a night of computation. Then instead of going "ok we've saved costs! yay let's boast about this
on LinkedIn" they used this new capability to replan more often and order production inputs in smaller batches, and thus increased delivery speed, increased quality, retained customers and took market share away from their competitors, etc etc. This is further evidence that you can't just "throw AI at a process" and expect dollars to rain on you. You need to do something ... more with that. Anyway: if you're reading this, I'd like more fragments like this please.
- NDM researcher Jared Peterson ran a TDG last week, which was well attended by Commoncog members. The forum topic lit up with reflections on the experience afterwards. We follow Chatham House rules, so I'm going to refrain from citing specific folks. Instead, I'll quote from my own reflection: "One of the most striking things Jared said was that ‘expertise should feel like common sense’. And by the end of the exercise, that’s exactly what it felt like — we had developed a new bit of common sense, one that would apply to exactly that sort of situation. Not that any of us are likely to be dealing with a hostage situation any time soon."
- More discussion in the What Does Writing Expertise Look Like thread.
- An Ode to the Ricoh GR — this draws a lot of members who are great photographers out of the woodwork. Wow! The number of members who shoot film is mind-boggling to me. Also, the post about how to remain unnoticed when shooting on the streets is great.
- A member links to Dexter Horthy's article on 'Why Software Factories Fail'. A response: because Horthy never reached out to the StrongDM folks, nor watched their demo, nor asked them questions directly ... this is more in the "man who says it cannot be done should not get in the way of man who is doing" category. (The Software Dark Factory folks's private demo and Q&A with Commoncog members is available here).
- I'm planning a Commoncog meetup in London in late October. If you're a Commoncog member in the UK, please chime in! I need help! I don't know anything about the UK except that
steak and kidney is the best dish ever invented.
- There's also an Unofficial Silicon Valley meetup being planned. (Unofficial = no Commoncog team member will be present. Official = someone from Commoncog will be present and we will be responsible for the shenanigans)
- I don't know if I mentioned this in a prior newsletter, but the most amazing thing about the Cultivating Agency in Kids thread is, to me, various members chiming in with stories of how they were unagentic when they were kids, and had to learn to get better at agency.
- Speaking of agency, some members have finished Cate Hall's You Can Just Do Things. Their thoughts are here. One of the members got a massive paper cut from his copy. Apparently this book — like its author — can also just do things.
- Members discuss Jason Cohen's recent SaaS coaching video recording.
- A member chimes in with an update to Sam Shillace's "The Network Always Beats the Castle" essay. This is the same member that wrote what I thought was the best response to the post. Seriously. Go read it. It's awesome. It seems there are many, many people who believe that AI will finally unlock the 'networked, non-hierarchical organisation' — a dream from the early days of the cybernetics movement.
- Bill Bensley and the Luxury Strategy — I talk a lot about Bill Bensley's ridiculous hotel designs and then learn a lot about the hotel business over the course of this thread.
- Just a heads-up that the Ample Hills Creamery thread has turned into a top-tier "here's how to analyse a four-wall F&B business" thread, thanks to a few members with real expertise in investing / investment banking for such businesses.
- There are more Personal Experience Reports with AI! A member uses AI to name things in their code. Another came up with their own grill-me skill. And then there's a fascinating conversation about context management and compacting.
Note that you'll have to be logged in as a member to view many of these threads. Here are instructions for logging into the forum. You may login here.
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Elsewhere On The Web
The Global Battle for the Ultimate Luxury Hotel Chain (archive.is link) — This is an old piece, from back in 2014, and it tells the story of Aman. Aman is the most luxurious of luxury hotel brands. I like this piece because it ties back to two things: first, the luxury hotel reading I've been spending my down time digging into, and second, because it ties back to one of my favourite cases from last year: Kwek Leng Beng and Winning At The Hotel Game.
In that case, Kwek's father famously tells him, basically "Why do you want to be a deluxe developer? You should do whatever makes money. Spread your risk." Kwek himself notes that four star hotels generate a better return than five star hotels.
And that's exactly what's going on here.
Aman, which in the eyes of many lies at the literal top of the luxury hotel hierarchy, doesn't make that much money — and never has. This is basically the story of how its founder — the legendary Adrian Zecha, scion of an Asian tycoon — lost control of the entire brand, because he had to keep selling equity and bringing in partners who disagreed with his vision.
But what vision that was.
The Dangerous Unknowns at the Heart of LLMs — AI-adjacent computer scientist Melanie Mitchell has a fair-handed summary of the various AI developments over the past three years. There's lots in here that are not new, especially if you've been paying attention, but some of the observations here are worth it because her frame is so different. Mitchell sits at the Santa Fe Institute. That puts her at an odd angle compared to the more mainstream SV-coded analysis. Two excerpts that I like:
The phenomenon of AI systems being right for the wrong reasons has long been an issue in assessing their capabilities. Cognitive science scholars have pointed out that similar issues arise in both developmental and comparative psychology when researchers are attempting to understand the cognitive capabilities of “ alien intelligences,” like babies and animals. In these fields, researchers spend years learning to carefully design control experiments to deal with the benchmark issues I’ve discussed above. Such rigorous methodologies have not yet been generally adopted by AI researchers. and
IT IS NOTABLE that the practice of evaluating AI systems on intelligence tests designed for humans implicitly accepts, and reinforces, the metaphorical framing of modern AI systems as individual intelligent agents. To many of us, it seems natural to think of these systems as analogous to individual humans, with their own distinct “personalities.” But some scholars have challenged this framing, arguing that AI systems should instead be thought of as cultural and social technologies. The idea here is that LLMs are a new kind of technology, akin to writing, the printing press, the library, markets, bureaucracy, and the internet, all technologies that allow humans to access information that has been accumulated and processed by collective human societies and cultures, and all technologies that enable large-scale coordination. In this view, AI will alter society in transformative ways, but as with other such technologies, its impacts will develop over time instead of hitting all at once. For this reason, some have called AI a “ normal technology” in terms of the likely trajectory of its effect on society.
If today’s AI bots can be viewed as “normal” cultural and social technologies rather than as intelligent (or superintelligent) agents, why do most of us think of them as the latter? In part, this framing is due to AI systems’ fluent language interfaces, first-person façades, and other anthropomorphic touches. But another factor is the term artificial intelligence itself, a label that could be called AI’s original sin. When the field first began to take shape in the 1950s, there was disagreement about what to call it. John McCarthy, the computer scientist who was one of AI’s founders, pushed for the fledgling field’s name to be “artificial intelligence,” whereas his cofounders Herbert Simon and Allen Newell argued for “complex information processing”—a nonanthropomorphic phrase that is more evocative of cultural and social technologies than of smart, agentic machines.
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Just two links this week.
As you can tell, I'm in Singapore this week to run some errands and to touch base with my team. I'm not here long enough to do proper catch-ups with folks (and boy are there lots of people I want to catch-up with), but a) it's good to be back, b) holy hell every time I'm back I am reminded that the retail churn is insane, c) it's been lovely to walk about at night given the weather right now.
For those who are attending the meetup, see you next Monday.
For everyone else, I'll see you in your inboxes next week.
Warmly,
Cedric
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⛪️ The Commoncog Membership: "Like church for business nerds" Get full access to paywalled articles, curated events, a rich and growing case library, and an exclusive, members-only forum.
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