A couple of years ago, when AI had come onto the market and was clearly reshaping the society, I decided that I would have nothing to do with it—I would boycott. This decision was based partly on morals and partly on practicality—I suspected that, underneath the really impressive technological breakthrough, AI was still, essentially, a one-trick pony. And now that AI has evidently gone through another revolution, vastly improving itself in recent models and opening up a whole world of coding to the public at large, it seems like I should have to re-examine my decision to boycott—which, actually, is one of the easiest decisions I’ve ever made. I am more adamant about it than ever.

I might be a lot more interested in developments with AI if I hadn’t already seen this movie. It played out across a great swath of my life in the form of social media. It featured all the same actors making all the same promises early on—and all of them following the same playbook to extract our attention, as if it were a mineable resource, and then selling that for ads, as well as whatever personal data we were unwise enough to leave unguarded on our own devices. I remember showing up on a college campus looking forward to young people connecting with each other; instead—Facebook had just come out—everybody seemed to be sealed up in their rooms carrying out a facsimile of social exchange. All this time later, has anything really improved? Every time I go into my bank account I seem to find some recurring subscription that I signed up for in the flush of tech optimism and long ago stopped using—but which still clings to my wallet like a barnacle to a ship’s hull.

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So what’s different this time around? Well, if in the previous installment of this series, the tech companies targeted social relationships, and hacked at the envy and FOMO and anxiety that animates social relationships as much as the substance itself, now they’ve made their way into a different kind of space—the deep privacy of people’s innermost lives. In the AI regime, people confess their darkest secrets to their LLM therapist, which spits back out what they want to hear—although without the legal confidentiality of actual therapy sessions, with everything forming a digital record, and with the tech companies, on a whim, sometimes posting the raw text of chats onto the open internet. People earnestly ask AI about the existence of God or the meaning of life in the way they might once have sunk down to their knees and searched the silence of their own soul. And people freely turn their most creative projects, sometimes their life’s work, over to AI under the bashful premise that they themselves are not that good at the thing they most love to do and it’s better handled by the bot.

Now that we’re a couple of years into this whole set of developments, the essentials of it—and the battle lines—are a bit more clear. It’s not really about what AI can do or what it can’t do, whether it will take all the jobs or won’t, whether it will destroy the world or not. The question is about agency—do you choose to exert agency in your own life, in the way that humans always have and were doing just fine with until, like, three years ago? Or do you prefer to turn it over to a machine, which really means turning it over to the data miners and the advertising innovators in the world’s largest tech corporations?

Maintaining an AI boycott, it has to be acknowledged, is getting progressively harder to do—and I am not always the strictest Luddite about it. I was hooked into various forms of AI before they started being called AI—Google Translate, Apple’s text predict feature, and so on. If I think I have cancer for some ridiculous reason, I now no longer need to go to all the trouble of clicking on WebMD to be told I don’t have cancer; the AI tools can do it with just a glance at the screen.

But... it’s really not so difficult either. Every so often—in a moment of weakness—I’ll download an AI app onto my phone and the process will begin all over again where I rack my brains to come up with some way in which the technology might actually benefit my life. So far, I haven’t been able to come up with anything. I like structuring and writing my own essays; I have no idea why I would outsource that to AI. The advice it gives always starts out kind of interesting—and there’s always a moment of being awed by the technology—before it reveals itself to just be gleaned from a couple of random pieces of information on the web. The actual zone of my life just seems to be in a place that AI can’t touch or help—unless I proactively turn myself over to the bot.

And meanwhile, in that time, I can feel the whole world around me doing exactly that. It’s very common to have a conversation where someone shares their great idea—only to reveal that it came from AI. Teaching (I teach journalism and public relations at an international university) has largely become a matter of trying to suss out what is AI and what isn’t—and, since AI became prevalent (and all my students use it), they have become distinctly lazier. They clearly feel that the AI is just better than they are and there’s no real point in trying.

The general impression is of the invasion of the bodysnatchers occurring all around me—if I deal with anyone, it tends to feel like they’re becoming a kind of frontman for AI-generated work but without the work itself becoming in any way obviously better.

The lecture I tend to give to my students isn’t moral at all. It’s just very practical. They may fool their teachers sometimes with AI work, but their teachers are starting to catch on—all it takes is running their work through an AI detector, or insisting on giving them paper-and-pen exams, to starkly reveal how much they’ve been relying on the technology. And they certainly won’t be able to fool their employers. If they show up in the workforce using AI for everything, their employers will of course take them at their word and simply replace their jobs with AI. If they use AI for their products, then they will be competing with rivals using exactly the same sets of AI tools and generating the exact same kinds of work—there will be no way to distinguish themselves or to get ahead.

For adults, the question is a little more existential. It isn’t so much how to adapt to the world but what kind of world you really want to build and transmit onwards. The value that AI presupposes is about optimization—about doing things really, really well. AI text is always very clean, there is never a hint of, say, a spelling mistake; it really (and it is obviously a work in progress, but AI is getting better at it all the time) can do just about any task to a high level.

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But whoever said that life is about optimization? If you simply shift your focus and presuppose that life is about enriching human experience, and finding meaning, then the value of AI almost instantly dwindles away. I’ve heard of people using AI to write their diaries—but the whole point of a diary isn’t to produce some sort of masterwork, it’s to record a particular day in the highly subjective, idiosyncratic way that lends meaning to you. A friend who works for a travel writing company told me that the boss had a meeting in which it was announced that they should “welcome in AI”—with the result that, only a few months later, virtually everyone at the company lost their jobs and the ones who stayed were basically there to check over AI-generated posts for hallucinations. But travel isn’t actually just a bundle of information—the whole point of travel is the relationship between you, the traveler, and the place visited—and the result of the travel industry’s turn to AI is that it now wouldn’t even occur to me to read one of their posts.

The assumption at the moment is that AI “is the future”—a phrase like that is the underpinning of just about any conversation on AI. I’m not sure actually. I could sort of imagine this spinning the other way, that the pace of LLM improvement slows down, that more and more of what they’re generating is obviously “slop,” and that the enthusiastic early adopters are the ones who will be caught out—who will be remembered in a few years for having downsized their workforce for the sake of a bot; or for having skipped out on their education, on a crucial period of investing in themselves, in order to generate work that turns out to be indistinguishable from the machine-generated work that everybody is producing.

But the ship really seems to be sailing on that fantasy of mine. AI has already moved into a “too big to fail” category—the economy isn’t producing much, but it is producing AI and that’s the horse that we’re collectively hitching our wagons to; the tech companies haven’t quite been able to figure out a clear use for AI, but they’ve been very good at generating stickiness—at persuading people to download the free samples of AI and to use it for one thing or another so that it already becomes unimaginable to write a student paper or to prepare a recipe without it.

AI may well push through the slight bottleneck it’s been in for the past couple of years and prevail. The dissenting voices are starting to get a little lonelier; when I make this point with people, the glazed look I get back tends to mean that my interlocutor has already turned AI into a habit. But even should AI win out in the culture wars, that actually doesn’t prove the larger point. AI isn’t actually some guaranteed future that we have to succumb to whether we like it or not. The adoption of AI rests on choices—on the individual choices of billions, and there is agency, actually, in deciding whether to use it or not, an agency that may be all the greater since we were fooled once by the tech companies’ marketing gimmicks in the high social media era of the 2010s, and should at least be wiser to the tricks they are pulling with AI in the 2020s.

At the moment, the conversation about AI is basically a lot of noise. Most of it turns on AI’s capabilities. The tendency to hallucinate and to generate confident slop were embarrassing for quite a while. The fact that it seems to be pushing past the worst hallucinatory tendencies is supposed to quiet the doubters. But the conversation about capabilities is, I am convinced, basically a distraction. The technology is already impressive and will continue to improve; that’s not in dispute. But cloning and nuclear technology are also impressive and have strict guardrails around them.

What’s at stake isn’t really technology—it’s about taking a good hard look at what our fundamental values are and questioning whether AI aligns with them. The question isn’t whether AI is a stochastic parrot or not; the question is whether you are.

Sam Kahn is associate editor at Persuasion, writes the Substack Castalia, and edits The Republic of Letters.

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There is a closing window to stop driverless cars from creating omnigridlock.

We all need a little validation now and then from friends or family, but sometimes too much validation can backfire—and the same is true of AI chatbots. There have been several recent cases of overly sycophantic AI tools leading to negative outcomes, including users harming themselves and/or others. But the harm might not be limited to these extreme cases, according to a new paper published in the journal Science. As more people rely on AI tools for everyday advice and guidance, their tendency to overly flatter and agree with users can have harmful effects on those users' judgment, particularly in the social sphere.

The study showed that such tools can reinforce maladaptive beliefs, discourage users from accepting responsibility for a situation, or discourage them from repairing damaged relationships. That said, the authors were quick to emphasize during a media briefing that their findings were not intended to feed into "doomsday sentiments" about such AI models. Rather, the objective is to further our understanding of how such AI models work and their impact on human users, in hopes of making them better while the models are still in the early-ish development stages.

Co-author Myra Cheng, a graduate student at Stanford University, said she and her co-authors were inspired to study this issue after they began noticing a pronounced increase in the number of people around them who had started relying on AI chatbots for relationship advice—and often ended up receiving bad advice because the AI would take their side no matter what. Their interest was bolstered by recent surveys showing nearly half of Americans under 30 have asked an AI tool for personal advice. "Given how common this is becoming, we wanted to understand how an overly affirming AI advice might impact people's real-world relationships," said Cheng.

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I’m a big believer in evidence-informed teaching. I think research has a lot to offer teachers, and I try to write about ideas that are supported by education research.

In the real world where I receive professional development and have random education consultants tell me what to do, the things I hear as evidence-based are...interesting.

Over time I’ve developed a kind of bullshit detector for evidence. There’s no one thing that can tell you if an idea is or is not evidence-based. But looking across a number of dimensions, teachers can make better-informed decisions based on the evidence we’re shown. This post is a kind of crash course in how to think critically about evidence in education.

Effect Size

The first thing to understand about research in education is the meaning of an effect size. Below is a normal distribution. The vertical lines represent one standard deviation. At a basic level, an effect size measures the difference between two groups in standard deviations. An effect size of 1.0 means that the difference is approximately one standard deviation — the distance between vertical lines on the graph below. This means a 50% percentile student moves to the 84th percentile, an 84th percentile student moves to the 98th percentile, etc.

A helpful comparison here is to height. American men are, on average, about 5 feet 9 inches tall (175 centimeters) and the standard deviation is about 3 inches (7.6 centimeters). An effect size for height of 1.0 means the effect is, on average, 3 inches (7.6 centimeters). That’s a big difference! The vast majority of effect sizes in education are less than 1. A rough “average effect size” is in the neighborhood of 0.4, which is about 1.2 inches (3 centimeters). Imagine, two groups of adult men. One group is, on average, about 1 inch taller than the other group.

Height and education are different in many ways, but one thing they have in common is that they are the product of lots and lots and lots of little variables. The average adult height is about 4 inches (10 centimeters) taller today than it was a few hundred years ago. Height isn’t some number that’s programmed into us from birth. But there also isn’t one silver bullet that makes humans taller. Humans are taller today because of lots and lots of little changes in our lives. The effect size of any one intervention to make people taller is small, but collectively those effects add up to a large change. That’s a decent mental model for thinking about education.

Ok, let’s talk about research. We’ve got effect sizes. Lots of folks in education wield “research” as a way to reinforce their ideas. What are some things to watch out for? Preview: there are a lot of things to watch out for!

Nonsense

There’s a lot of nonsense out there. In a real PD session last year, a very highly-paid consultant told us that “current research shows student attention span is their age, plus or minus two years.” That’s nonsense. A quick Google search will reveal a wide range of answers depending on a wide range of factors. I don’t know what else to say here. Learning styles, the learning pyramid, and lots more fall into this category. If something “research says” sounds too neat and tidy, do a bit of research. Check the citations. Follow the evidence. It might be nonsense.

Correlation vs Causation

One piece of “research” that I’ve seen multiple times in my professional development is this John Hattie meta-analysis on “collective teacher efficacy.” This collection of studies says that, when teachers believe in their students, those students learn more. Hattie references an effect size of 1.57, which is massive! The catch is, none of these studies are experimental. This is a correlational result. We know that teachers beliefs in students and high levels of student learning are associated, but we don’t know which is causing the other, or if some other factors are causing both. My hypothesis: high levels of student achievement cause teachers to believe in students. If you work in a school where students do well, it’s easy to believe in those students. If you work in a school that’s struggling, it’s much harder to believe in your students. When a result is correlational, it’s hard to figure out what the result means and easy to draw simplistic conclusions.

Correlational research isn’t all useless. There are lots of places in education where we can’t run experiments for practical or ethical reasons, or we rely on quasi-experiments that aren’t fully randomized. Correlation can give us valuable insights or help triangulate other results. But correlation will always be a limited tool, and it’s easy to be misled. Correlation vs causation is probably the most common issue I see in interpreting education research, so I’m going to drop a bunch more examples in a footnote.1

Bias

Another consultant came to my school and told the English teachers that they needed to use the i-Ready digital platform for at least 45 minutes each week. Research showed, they said, that students who used i-Ready for 45 minutes each week made more progress than their peers.

Look, I am so cynical about stuff like this. If you Google the result, you’ll find something on the Curriculum Associates website (the company that produces i-Ready) saying that research supports 45 minutes blah blah blah. The overview starts with, “Curriculum Associates analyzed data…” Let’s just stop there. The company that produced the product analyzed the data for us. How kind of them! It seems like every curriculum commissions some sort of study like this. We should probably just ignore them all. Unless I am 100% convinced a study on a commercial product was done by a disinterested third party, I’m going to assume there’s some bias involved.

Researcher-Designed vs Standardized Measures

I taught in a summer school program as part of my student teaching. During that program, I was required to give the same test at both the start and the end of summer school. My students grew from an average of 31% to an average of 82%. The standard deviation was 15%, meaning my teaching had an effect size of 3.4. 3.4!!! Even higher than collective teacher efficacy! Am I the best teacher in the world? (No.)

The reason my results looked so good is that my teaching was very tightly aligned to the outcome assessment. In this case, I was teaching word problems. The school had me introduce a very structured approach to annotating word problems where students got points for underlining the question, circling key information, etc. When they took the assessment at the beginning of summer school, students didn’t know how to annotate the problems correctly so they got lots of points off even if they knew the math. My student teaching wasn’t a research study, but if it were we would call this a researcher-designed assessment. In general, it’s much easier to show growth on a researcher-designed assessment because the intervention can focus efforts on narrow measures of success. It’s much harder to demonstrate growth on a broader measure like state standardized tests or university entrance exams. Even less reliable measures like grade point average are much harder to influence at scale than a narrow researcher-designed measure.

One more example of this phenomenon: Dr. Jo Boaler ran an 18-day math summer camp focused on mindset and open-ended tasks. The study reported an effect size of 0.52. That’s pretty big! But it wasn’t measuring a standardized result. The assessment was four open-ended tasks from the MARS project. What does that mean for regular school learning? It’s hard to say. It’s not surprising that a program focused on open-ended tasks caused an increase in scores on open-ended tasks.

Cost-Benefit Analysis

Here’s an interesting study. Researchers ran a randomized trial of a growth mindset intervention, so some students received the intervention and others did not. The intervention was short: two 25-minute sessions. The result was an effect size of 0.11 on grade point average.

That’s a small effect size. In a lot of contexts, we might be pretty dismissive of an effect of 0.11. But here, the intervention is tiny and easy to implement. Two 25-minute sessions. That’s nothing. If education could find a dozen different interventions with an effect size of 0.11 that take 50 total minutes to administer, that could add up to a substantive change in outcomes. Let’s compare that to a recent study on Khan Academy. Following Khan Academy’s recommendations for 30 minutes per week of computerized learning resulted in an effect size of 0.08. And that’s for the students who spent 30 minutes on the platform, which was only 10% of the total! As any teacher will tell you, finding time for two 25-minute lessons on growth mindset isn’t too hard. Lots of schools have a homeroom or advisory block where they can squeeze in this intervention. Getting students to spend 30 minutes every week on Khan Academy: surprisingly hard, and has a smaller effect size.

This isn’t a full-on endorsement of growth mindset. There’s a lot of variability in growth mindset results, and the researchers always emphasize that growth mindset interventions are challenging to design. The point here is that, if the research is rigorous and randomized and the cost is small, even small effect sizes can matter.

Comparison Group

Rigorous research makes a comparison. If we want to know how well something works, we need to ask: what is it being compared to? Schools have finite time and resources. Teachers constantly navigate tradeoffs.

The World Bank published a paper last year on an AI education intervention. The end result was a respectable effect size of 0.21 on final exam scores. Here’s the catch: the experimental group received a six-week, twice-a-week, 30-minute after-school intervention using AI. The control group received…nothing. As Michel Pershan writes, they “only proved that their program is better than nothing.”

To give one more simple example of this phenomenon: the SAT is an American university entrance exam. Students often take the test multiple times, and on average do better the second time. I found a few different sets of numbers, but we can ballpark the effect size of taking the exam twice at around 0.15-0.20. That’s a significant effect from literally no intervention at all. Students learn things and get better at stuff all the time. If the study compares the same group of students before and after an intervention, we should expect to see some growth even if the intervention doesn’t do anything at all.

The most rigorous education research compares two serious interventions. If we want to know how well a curriculum works, we compare it to another high-quality curriculum. If we want to know how well tutoring works, we compare it to another plausible use of that same time and resources, maybe small-group instruction or extra teacher planning time. The question isn’t whether something works. Most interventions work! The question is, what is the best use of our time and resources?

Replication

One education research result that just will not die is what’s often called Bloom’s 2-sigma study. Benjamin Bloom wrote a paper summarizing two dissertation studies on one-to-one tutoring. The headline result: an effect size of 2. That’s massive, and has been repeated over and over again as evidence that one-to-one tutoring is the gold standard in education.

There are a bunch of issues with this result, but the one I want to talk about here is simple: when other researchers have tried to replicate it, the results have fallen dramatically. There are tons of studies we could look at. One study looked at a large tutoring program in Nashville. The result? An effect size in the range of 0.04 to 0.09 on literacy test scores. There was no effect on math test scores, or on course grades in either subject.

This doesn’t mean tutoring doesn’t work. Plenty of studies find significant effects. The reality is, there’s a ton of research out there. If you want to say something is research-based, you can probably find a study that you can reference. The most valuable research replicates — it has been repeated in different contexts over and over again, giving us some evidence the result is robust. If a result relies on a single study, we should be suspicious.

Heterogeneity

People love to average things in research. Average results, average effect sizes of a bunch of different studies. Those averages can hide heterogeneity. In a classic meta-analysis by Kluger & Denisi, the researchers found an average effect size of 0.41 for feedback. The catch: huge heterogeneity. One-third of the studies they looked at had a negative effect size: providing feedback decreased performance! The lesson here isn’t about whether feedback is good or bad. It depends!

A few more examples: the growth mindset intervention above showed larger results for lower-achieving students. The effect size of 0.08 for the Khan Academy intervention was limited to the 10% of students who spent the recommended amount of time on the platform. That heterogeneity is important!

Learning is complex. Results depend on all sorts of things. Averaging results together can hide important context.

Lab vs Real World

Many cognitive science phenomena are studied in a lab setting, where researchers can control lots of variables that are hard to control for classroom teachers. Results that are produced in a lab don’t always translate to real classrooms. One example: the interleaving effect is a popular cognitive science result. Interleaving question types (mixing different question types within a study session) typically leads to more durable learning than “blocked” practice where students focus on one question type at a time. That result shows up consistently in lab studies, but has been less consistent in studies conducted in real classrooms. Here is an example of a study in which researchers struggled to replicate the phenomenon in a classroom-based context. This doesn’t mean interleaving is useless. I’m a big fan of interleaving! There is a ton of research on interleaving. Plenty of studies find a positive result in classroom contexts, but those results are often smaller than in a laboratory. These results suggest that interleaving is tricky to get right.

In general, research results are smaller and less consistent in real classrooms. This is common across education research, and we can often learn a lot more from classroom contexts than lab studies.

Months of Learning

If you see someone claim that a teaching practice results in a growth of 4 or 6 or 8 months of learning, you should ignore that number entirely. It’s nonsense. Researchers are typically converting effect sizes into months of learning through some very dubious math. First, no one agrees on what a month of learning is equal to. Sometimes a school year is equal to an effect size of 1, or 0.4, or something else. Second, I hope I’ve convinced you through everything else in this post that effect sizes aren’t that simple. Let’s take the result of my summer school teaching from before: an effect size of 3.4. Did I cause more than three years of additional learning in my three-week summer school class as a student teacher? (No, I did not.) Does taking the SAT a second time result in 4 months of learning? (No.) This is not a serious way to measure learning. We should ignore these claims entirely.

Don’t Average Effect Sizes

Finally, averaging effect sizes. Every time I have been in some sort of PD session and someone brings up John Hattie, I just tune out. Hattie is famous for putting together what’s probably the largest synthesis of education research ever. His website is an interesting reference for meta-analyses of lots of different topics. However, Hattie also distilled his synthesis by publishing an average effect size for over 100 different topics in education research. You can think of this entire post as an argument that averaging effect sizes doesn’t make any sense. Those averages then get taken out of context by people who want to make a particular claim about a particular area of education research. Hattie’s work can be a helpful starting point: if you want to learn about a topic, his website collects a lot of research on lots of different topics. The average effect sizes we should mostly ignore.

In Summary

Teaching is hard. It would be great if there was some magic spell I could cast on my students to make them learn more. Unfortunately, there isn’t. You can maybe tell from my tone in this post that I have a lot of disdain for the ways research is typically used in education. I both believe in the value of evidence-informed teaching, and I mostly see research used in simplistic or opportunistic ways to justify the fad du jour. If we are serious about improving education, that means finding lots and lots of things that make a small difference, and adding up those small differences to make a substantive change in the quality of the education students receive. Finding lots and lots of things that make a small difference means being rigorous in how we interpret evidence.

No research is perfect. The best evidence has been replicated in multiple contexts and demonstrates converging evidence across different types of studies. There are a bunch of studies I’ve mentioned in this post that fail half the criteria or more. Let’s start by ignoring those, and focus on what’s left.

There are two big traps in education research. The first is oversimplification. It’s tempting to pick out some large, flashy effect and claim that this one thing is the answer to all of our problems. It isn’t. Teaching is hard. There’s no one idea coming to save us.

The second trap is dismissing small effects. In reality, all effects are small. Large effects are usually the result of shoddy research. This is tricky! Small effects can be spurious. We need to think rigorously about which small effects are real, which are worth the effort, and what we need to do to execute them effectively. That’s all hard, and that’s the core work of improving teaching and learning.

Very few studies will meet all of the criteria I laid out above. We don’t need to dismiss everything. Serious ideas to improve education come from multiple lines of evidence. That evidence is never going to be perfect. There’s no one silver bullet to improve education, and there’s no one secret thing that guarantees a study is robust and useful for teachers. There are no shortcuts. The goal should be to read broadly, evaluate evidence rigorously, and do the slow, careful work of figuring out what really works.

Feedback is what makes Life work at all: it's the engine of evolution, producing magical results. We'll use some electronics exposure to better grasp what feedback can conjure. Continue reading →

The famous article Choose Boring Technology lists two problems with using innovative technology:

1. There are too many "unknown unknowns" in a new technology, whereas in boring technology the pitfalls are already well-known.
2. Shiny tech has a maintenance burden that persist long after everybody has gotten bored with it.

Both of these tie back to the idea that the main cost of technology is maintenance. Even if something is easy to build with, it might not be as easy to keep running. We cannot "abandon" mission-critical technology. Say my team builds a new service on Julia, and 2 years later decides it was the wrong choice. We're stuck with either the (expensive) process of migrating all our data to Postgres or the (expensive) process of keeping it running anyway. Either way, the company needs to spend resources keeping engineers trained on the tech instead of other useful things, like how to mine crypto in their heads.

Tech is slow to change. Not as slow to change as, say, a bridge, but still pretty slow.

Now say at the same time as Julia, we also decided to start practicing test && commit || revert (TCR). After two years, we get sick of that, too. To deal with this, we can simply... not do TCR anymore. There is no "legacy practice" we need to support, no maintenance burden to dropping a process. It is much easier to adopt and abandon practices than it is to adopt and abandon technology.

This means while we should be conservative in the software we use, we can be more freely innovative in how we use it. If we get three innovation tokens for technology, we get like six or seven for practices. And we can trade in our practices to get those tokens back.

(The flip side of this is that social processes are less "stable" than technology and take more work to keep running. This is why "engineering controls" are considered more effective as reducing accidents than administrative controls.)

Choose Boring Material and Innovative Tools

Pushing this argument further, we can divide technology into two categories: "material" and "tools".¹ Material is anything that needs to run to support the business: our code, our service architecture, our data and database engine, etc. The tools are what we use to make material, but that the material doesn't depend on. Editors, personal bash scripts, etc. The categories are fuzzy, but it boils down to "how bad is it for the project to lose this?"

In turn, because tools are easier to replace than material, we can afford to be more innovative with it. I suspect we see this in practice, too, that people replace ephemera faster than they replace their databases.

(This is a short one because I severely overestimated how much I could write about this.)

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April Cools

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