Yes, they are Google; yes, they have a great pool of talent around; yes, they do a lot of hard stuff; but most of the time when I read those articles, I miss those kinds of distinctions.
Not lowballing the guys at Google, they do amazing stuff, but in some domains of domain/accidental/environmental complexity (e.g. sea logistics, manufacturing, industry, etc.) where most of the time you do not have the data, I believe that they are way more complex/harder than most of the problems that the ones that they deal with.
In reality, lazy domain owners layered on processes, meetings, documents, and multiple approvals until it took 6 months to change the text on a button, ugh
No greater Scott’s man can tell you that the reality is surprisingly complex, and sometimes you have resources to organize and fight them, and sometimes you use those resources wiser than the other group of people, and can share the lessons. Sometimes, you just have no idea if your lesson is even useful. Let’s judge the story on its merits and learn what we can from it.
This qualify as complicated. Delving in complicated problems is mostly driven by business opportunity, always has limited scaling, and tend to be discarded by big players.
That doesn’t feel right.
Let me bring a non-trivial, concrete example—something mundane: “ePOD,” which refers to Electronic Proof of Delivery.
ePOD, in terms of technical implementation, can be complex to design for all logistics companies out there like Flexport, Amazon, DHL, UPS, and so on.
The implementation itself—e.g., the box with a signature open-drawing field and a "confirm" button—can be as complex as they want from a pure technical perspective.
Now comes, for me at least, the complex part: in some logistics companies, the ePOD adoption rate is circa 46%. In other words, in 54% of all deliveries, you do not have a real-time (not before 36–48 hours) way to know and track whether the person received the goods or not. Unsurprisingly, most of those are still done on paper. And we have:
- Truck drivers are often independent contractors.
- Rural or low-tech regions lack infrastructure.
- Incentive structures don’t align.
- Digitization workflows involve physical paper handoffs, WhatsApp messages, or third-party scans.
So the real complexity isn't only "technical implementation of ePOD" but; "having the ePOD, how to maximize it's adoption/coverage with a lot uncertainty, fragmentation, and human unpredictability on the ground?".
That’s not just complicated, it’s complex 'cause we have: - Socio-technical constraints,
- Behavioral incentives,
- Operational logistics,
- Fragmented accountability,
- And incomplete or delayed data.
We went off the highly controlled scenario (arbitrarily bounded technical implementation) that could be considered complicated (if we want to be reductionist, as the OP has done), and now we’re navigating uncertainty and N amount of issues that can go wrong.
Complicated on the other hand, involves the addition of one or more complicating factors beyond just "the problem is big". It's a qualitative thing, like maybe nobody has built adequate tools for the problem domain, or maybe you don't even know if the solution is possible until you've already invested quite a lot towards that solution. Or maybe you have to simultaneously put on this song and dance regarding story points and show continual progress even though you have not yet found a continuous path from where you are to your goal.
Climate change is both, doing your taxes is (typically) merely complex. As for complicated-but-not-complex, that's like realizing that you don't have your wallet after you've already ordered your food: qualitatively messy, quantitatively simple.
To put it differently, complicated is about the number of different domains you have to consider, complex is about--given some domain--how difficult the consideration in that domain are.
Perhaps the author's usage is common enough in certain audiences, but it's not consistent with how we discuss computational complexity. Which is a shame since they are talking about solving problems with computers.
1) scale of application variety (10k different apps with different needs and history)
2) scale of human capability (ingenuity), this scale starts from sub-zero and can go pretty high (but not guaranteed)
“Simple Made Easy”: https://youtu.be/SxdOUGdseq4?si=H-1tyfL881NawCPA
Google, where the impossible stuff is reduced to merely hard, and the easy stuff is raised to hard.
“I just want to store 5TiB somewhere”
“Ha! Did you book multiple bigtable cells”
Do not miss
This was posted in my front office when I started my company over 30 years ago.
It was a no-brainer, same thing I was doing for my employer beforehand. Experimentation.
By the author's distinction in the terminology, if you consider the complexity relative to the complications in something like Google technology, it is on a different scale compared to the absolute chaos relative to the mere remaining complexity when you apply it to natural science.
I learned how to do what I do directly from people who did it in World War II.
And that was when I was over 40 years younger, plus I'm not done yet. Still carrying the baton in the industrial environment where the institutions have a pseudo-military style hierarchy and bureaucracy. Which I'm very comfortable working around ;)
Well, the army is a massive mainstream corp.
There are always some things that corps don't handle very well, but generals don't always care, if they have overwhelming force to apply, lots of different kinds of objectives can be overcome.
Teamwork, planning, military-style discipline & chain-of-command/org-chart, strength in numbers, all elements which are hallmarks of effective armies over the centuries.
The engineers are an elite team among them. Traditionally like the technology arm, engaged to leverage the massive resources even more effectively.
The bigger the objective, the stronger these elements will be brought to bear.
Even in an unopposed maneuver, steam-rolling all easily recognized obstacles more and more effectively as they up the ante, at the same time bigger and bigger unscoped problems accumulate which are exactly the kind that can not be solved with teamwork and planning (since these are often completely forbidden). When there must be extreme individual ability far beyond that, and it must emanate from the top decision-maker or have "equivalent" access to the top individual decision-maker. IOW might as well not even be "in" the org chart since it's just a few individuals directly attached to the top square, nobody's working for further promotions or recognition beyond that point.
When military discipline in practice is simply not enough discipline, and not exactly the kind that's needed by a long shot.
That's why even in the military there are a few Navy Seals here and there, because sometimes there are serious problems that are the kind of impossible that a whole army cannot solve ;)
They have a unique and distinctive experience, but it usually isn't what you expect. It is rare to encounter someone from Google that actually built something of significance, and those that have are always at the staff+ level and had been there 10+ years.
If I were to make another generalization, the [g|x]ooglers that worked on relatively "small" projects are often the most interesting, as they had resources to build something from the ground up and do attempt some really interesting projects.
"We can do it!" confidence can be mostly great. (Though you might have to allow for the possibility of failure.)
What I don't have a perfect rule for is how to avoid that twisting into arrogance and exceptionalism.
Like, "My theory is correct, so I can falsify this experiment."
Or "I have so much career potential, it's to everyone's advantage for me to cheat to advance."
Or "Of course we'll do the right thing with grabbing this unchecked power, since we're morally superior."
Or "We're better than those other people, and they should be exterminated."
Maybe part of the solution is to respect the power of will, effort, perseverance, processes, etc., but to be concerned when people don't also respect the power and truth of humility, and start thinking of individual/group selves as innately superior?
Actually, I've found this is a constant in life, whatever you achieve, you end up in a situation where you're pretty average among your peers. You may feel proud to get into Google for a few months, and then you're quickly humbled down.
(Also, to be clear about my examples: I don't think Google is fabricating their dissertation research, nor do I think Google is genocidal.)
If you're suggesting there's a lot of humility, yet "But we can do it!" still works, that's great, and I'd be interested in more nuances of that.
Clicking on "Only Necessary" causes the cookie policy agreement to reappear.
GDPR banner only to be used in EU, with conditional of only accepting non-essential cookies, and the engineer or QA is based in the U.S.
As a side note, as someone that lives in the EU my pattern of usage here is:
- choose only non-essential, but if not a presented option then
- reject all cookies, but if no reject all available then
- switch the reader mode (or hide distracting items), or if not possible then
- close tab
I’m getting much more aggressive when dealing with cookie banners dark patterns. I will not a third third party advertising cookies as much as possible and support websites that allow me an easy way to opt out of them.
One myth is that complex systems are inherently bad. Armed forces are incredibly complex. That's why it can take 10 or more rear echelon staff to support one fighting soldier. Supply chain logistics and materiel is complex. Middle ages wars stopped when gunpowder supplies ran out.
Another myth is that simple systems are always better and remain simple. They can be, yes. After all, DNA exists. But some beautiful things demand complexity built up from simple things. We still don't entirely understand how DNA and environment combine. Much is hidden in this simple system.
I do believe one programming language might be a rational simplification. If you exclude all the DSL which people implement to tune it.
The arquebus is the first mass gunpowder weapon, and doesn't see large scale use until around the 1480s at the very, very tail end of the Middle Ages (the exact end date people use varies based on topic and region, but 1500 is a good, round date for the end).
In Medieval armies, your limiting factor is generally that food is being provided by ransacking the local area for food and that a decent portion of your army is made up of farmers who need to be back home in the harvest season. A highly competent army might be able to procure food without acting as a plague on all the local farmlands, but most Medieval states lacked sufficient state capacity to manage that (in Europe, essentially only the Byzantines could do that).
Ukraine would be conquered by russia rather quickly if russians weren't so hilariously incompetent in these complex tasks, and war logistics being the king of them. Remember that 64km queue of heavy machinery [1] just sitting still? This was 2022, and we talk about fuel and food, the basics of logistics support.
"This is one possible characteristic of complex systems: they behave in ways that can hardly be predicted just by looking at their parts, making them harder to debug and manage."
https://en.wikipedia.org/wiki/Complex_system
although I understood the key part of a system being complex (as opposed to complicated) is having a large number of types of interaction. So a system with a large number of parts is not enough, those parts have to interact in a number of different ways for the system to exhibit emergent effects.
Something like that. I remember reading a lot of books about this kind of thing a while ago :)
Unless your problem comes from something side effects on a computer that can’t be modeled mathematically there is nothing technically stopping you from modeling the problem as mathematical problem then solving that problem via mathematics.
Like the output of the LLM can’t be modeled. We literally do not understand it. Are the problems faced by the SRE exactly the same? You give a system an input of B and you can’t predict the output of A mathematically? It doesn’t even have to be a single equation. A simulation can do it.
The core problem is building a high enough fidelity model to simulate enough of the real world to make the simulation actually useful. As soon as you have some system feedback loops, the complexity of building a useful model skyrockets.
Even in “pure” functions, the supporting infrastructure can be hard to simulate and critical in affecting the outputs.
Even doing something simple like adding two numbers requires an unimaginable amount of hidden complexity under the hood. It is almost impossible for these things to not have second-order effects and emergent behaviour under enough scale.
However, all this amazing stuff in the service of .. posting ads ?
Both python and rust (for instance) are both turing complete, and equally capable
Whatever you're working on, your project is not likely to be at Google's scale and very unlikely to be a "complex system".
Just because your project might not be at Google's scale doesn't mean it is therefore also not complex [^1]
Example: I'd say plenty of games fit the author's definition of "complex systems". Even the well-engineered ones (and even some which could fit on a floppy disc)
[1]: https://en.m.wikipedia.org/wiki/Affirming_the_consequent
Even a project that's like 15k lines of code would benefit from a conscious effort to fight against complexity.