Journal tags: technology

One persistent piece of slopaganda you’ll hear is this:

“It’s just a tool. What matters is how you use it.”

This isn’t a new tack. The same justification has been applied to many technologies.

Leaving aside Kranzberg’s first law, large language models are the very antithesis of a neutral technology. They’re imbued with bias and political decisions at every level.

There’s the obvious problem of where the training data comes from. It’s stolen. Everyone knows this, but some people would rather pretend they don’t know how the sausage is made.

But if you set aside how the tool is made, it’s still just a tool, right? A building is still a building even if it’s built on stolen land.

Except with large language models, the training data is just the first step. After that you need to traumatise an underpaid workforce to remove the most horrifying content. Then you build an opaque black box that end-users have no control over.

Take temperature, for example. That’s the degree of probability a large language model uses for choosing the next token. Dial the temperature too low and the tool will parrot its training data too closely, making it a plagiarism machine. Dial the temperature too high and the tool generates what we kindly call “hallucinations”.

Either way, you have no control over that dial. Someone else is making that decision for you.

A large language model is as neutral as an AK-47.

I understand why people want to feel in control of the tools they’re using. I know why people will use large language models for some tasks—brainstorming, rubber ducking—but strictly avoid them for any outputs intended for human consumption.

You could even convince yourself that a large language model is like a bicycle for the mind. In truth, a large language model is more like one of those hover chairs on the spaceship in WALL·E.

Large language models don’t amplify your creativity and agency. Large language models stunt your creativity and rob you of agency.

When someone applies a large language model it is an example of tool use. But the large language model isn’t the tool.

I have little understanding for people using large language models to generate slop; words and images that nobody asked for.

I have more understanding for people using large language models to generate code. Code isn’t the thing in the same way that words or images are; code is the thing that gets you to the thing.

And if a large language model hallucinates some code, you’ll find out soon enough:

With code you get a powerful form of fact checking for free. Run the code, see if it works.

But I want to push back on one justification I see repeatedly about using large language models to write code. Here’s Craig:

There are many moral and ethical issues with using LLMs, but building software feels like one of the few truly ethically “clean”(er) uses (trained on open source code, etc.)

That’s not how this works. Yes, the large language models are trained on lots of code (most of it open source), but they’re not only trained on that. That’s on top of everything else; all the stolen books, all the unpaid creative work of others.

Even Robin Sloan, who first says:

I think the case of code is especially clear, and, for me, basically settled.

…goes on to acknowledge:

But, again, it’s important to say: the code only works because of Everything. Take that data away, train a model using GitHub alone, and you’ll get a far less useful tool.

When large language models are trained on domain-specific data, it’s always in addition to the mahoosive amount of content they’ve already stolen. It’s that mohoosive amount of content—not the domain-specific data—that enables them to parse your instructions.

(Note that I’m being very delibarate in saying “parse”, not “understand.” Though make no mistake, I’m astonished at how good these tools are at parsing instructions. I say that as someone who tried to write natural language parsers for text-only adventure games back in the 1980s.)

So, sure, go ahead and use large language models to write code. But don’t fool yourself into thinking that it’s somehow ethical.

What I said here applies to code too:

If you’re going to use generative tools powered by large language models, don’t pretend you don’t know how your sausage is made.

A couple of days ago I linked to a post by Robin Sloan called Is it okay?, saying:

Robin takes a fair and balanced look at the ethics of using large language models.

That’s how it came across to me: fair and balanced.

Robin’s central question is whether the current crop of large language models might one day lead to life-saving super-science, in which case, doesn’t that outweigh the damage they’re doing to our collective culture?

Baldur wrote a response entitled Knowledge tech that’s subtly wrong is more dangerous than tech that’s obviously wrong. (Or, where I disagree with Robin Sloan).

Baldur pointed out that one side of the scale that Robin is attempting to balance is based on pure science fiction:

There is no path from language modelling to super-science.

Robin responded pointing out that some things that we currently have would have seemed like science fiction a few years ago, right?

Well, no. Baldur debunks that in a post called Now I’m disappointed.

(By the way, can I just point out how great it is to see a blog-to-blog conversation like this, regardless of how much they might be in disagreement.)

Baldur kept bringing the receipts. That’s when it struck me that Robin’s stance is largely based on vibes, whereas Baldur’s viewpoint is informed by facts on the ground.

In a way, they’ve got something in common. They’re both advocating for an interpretation of the precautionary principle, just from completely opposite ends.

Robin’s stance is that if these tools one day yield amazing scientific breakthroughs then that’s reason enough to use them today. It’s uncomfortably close to the reasoning of the effective accelerationist nutjobs, but in a much milder form.

Baldur’s stance is that because of the present harms being inflicted by current large language models, we should be slamming on the brakes. If anything, the harms are going to multiply, not magically reduce.

I have to say, Robin’s stance doesn’t look nearly as fair and balanced as I initially thought. I’m on Team Baldur.

Michelle also weighs in, pointing out the flaw in Robin’s thinking:

AI isn’t LLMs. Or not just LLMs. It’s plausible that AI (or more accurately, Machine Learning) could be a useful scientific tool, particularly when it comes to making sense of large datasets in a way no human could with any kind of accuracy, and many people are already deploying it for such purposes. This isn’t entirely without risk (I’ll save that debate for another time), but in my opinion could feasibly constitute a legitimate application of AI.

LLMs are not this.

In other words, we’ve got a language collision:

We call them “AI”, we look at how much they can do today, and we draw a straight line to what we know of “AI” in our science fiction.

This ridiculous situation could’ve been avoided if we had settled on a more accurate buzzword like “applied statistics” instead of “AI”.

There’s one other flaw in Robin’s reasoning. I don’t think it follows that future improvements warrant present use. Quite the opposite:

The logic is completely backwards! If large language models are going to improve their ethical shortcomings (which is debatable, but let’s be generous), then that’s all the more reason to avoid using the current crop of egregiously damaging tools.

You don’t get companies to change their behaviour by rewarding them for it. If you really want better behaviour from the purveyors of generative tools, you should be boycotting the current offerings.

Anyway, this back-and-forth between Robin and Baldur (and Michelle) was interesting. But it all pales in comparison to the truth bomb that Miriam dropped in her post Tech continues to be political:

When eugenics-obsessed billionaires try to sell me a new toy, I don’t ask how many keystrokes it will save me at work. It’s impossible for me to discuss the utility of a thing when I fundamentally disagree with the purpose of it.

Boom!

Maybe we should consider the beliefs and assumptions that have been built into a technology before we embrace it? But we often prefer to treat each new toy as as an abstract and unmotivated opportunity. If only the good people like ourselves would get involved early, we can surely teach everyone else to use it ethically!

You know what? I could quote every single line. Just go read the whole thing. Please.

It always annoys me when a politician is accused of “flip-flopping” when they change their mind on something. Instead of admiring someone for being willing to re-examine previously-held beliefs, we lambast them. We admire conviction, even though that’s a trait that has been at the root of history’s worst attrocities.

When you look at the history of human progress, some of our greatest advances were made by people willing to question their beliefs. Prioritising data over opinion is what underpins the scientific method.

But I get it. It can be very uncomfortable to change your mind. There’s inevitably going to be some psychological resistance, a kind of inertia of opinion that favours the sunk cost of all the time you’ve spent believing something.

I was thinking back to times when I’ve changed my opinion on something after being confronted with new evidence.

In my younger days, I was staunchly anti-nuclear power. It didn’t help that in my younger days, nuclear power and nuclear weapons were conceptually linked in the public discourse. In the intervening years I’ve come to believe that nuclear power is far less destructive than fossil fuels. There are still a lot of issues—in terms of cost and time—which make nuclear less attractive than solar or wind, but I honestly can’t reconcile someone claiming to be an environmentalist while simultaneously opposing nuclear power. The data just doesn’t support that conclusion.

Similarly, I remember in the early 2000s being opposed to genetically-modified crops. But the more I looked into the facts, there was nothing—other than vibes—to bolster that opposition. And yet I know many people who’ve maintainted their opposition, often the same people who point to the scientific evidence when it comes to climate change. It’s a strange kind of cognitive dissonance that would allow for that kind of cherry-picking.

There are other situations where I’ve gone more in the other direction—initially positive, later negative. Google’s AMP project is one example. It sounded okay to me at first. But as I got into the details, its fundamental unfairness couldn’t be ignored.

I was fairly neutral on blockchains at first, at least from a technological perspective. There was even some initial promise of distributed data preservation. But over time my opinion went down, down, down.

Bitcoin, with its proof-of-work idiocy, is the poster-child of everything wrong with the reality of blockchains. The astoundingly wasteful energy consumption is just staggeringly pointless. Over time, any sufficiently wasteful project becomes indistinguishable from evil.

Speaking of energy usage…

My feelings about large language models have been dominated by two massive elephants in the room. One is the completely unethical way that the training data has been acquired (by ripping off the work of people who never gave their permission). The other is the profligate energy usage in not just training these models, but also running queries on the network.

My opinion on the provenance of the training data hasn’t changed. If anything, it’s hardened. I want us to fight back against this unethical harvesting by poisoning the well that the training data is drawing from.

But my opinion on the energy usage might just be swaying a little.

Michael Liebreich published an in-depth piece for Bloomberg last month called Generative AI – The Power and the Glory. He doesn’t sugar-coat the problems with current and future levels of power consumption for large language models, but he also doesn’t paint a completely bleak picture.

Effectively there’s a yet-to-decided battle between Koomey’s law and the Jevons paradox. Time will tell which way this will go.

The whole article is well worth a read. But what really gave me pause was a recent piece by Hannah Ritchie asking What’s the impact of artificial intelligence on energy demand?

When Hannah Ritchie speaks, I listen. And I’m well aware of the irony there. That’s classic argument from authority, when the whole point of Hannah Ritchie’s work is that it’s the data that matters.

In any case, she does an excellent job of putting my current worries into a historical context, as well as laying out some potential futures.

Don’t get me wrong, the energy demands of large language models are enormous and are only going to increase, but we may well see some compensatory efficiencies.

Personally, I’d just like to see these tools charge a fair price for their usage. Right now they’re being subsidised by venture capital. If people actually had to pay out of pocket for the energy used per query, we’d get a much better idea of how valuable these tools actually are to people.

Instead we’re seeing these tools being crammed into existing products regardless of whether anybody actually wants them (and in my anecdotal experience, most people resent this being forced on them).

Still, I thought it was worth making a note of how my opinion on the energy usage of large language models is open to change.

But I still won’t use one that’s been trained on other people’s work without their permission.

There are different kinds of buzzwords.

Some buzzwords are useful. They take a concept that would otherwise require a sentence of explanation and package it up into a single word or phrase. Back in the day, “ajax” was a pretty good buzzword.

Some buzzwords are worse than useless. This is when a word or phrase lacks definition. You could say this buzzword in a meeting with five people, and they’d all understand five different meanings. Back in the day, “web 2.0” was a classic example of a bad buzzword—for some people it meant a business model; for others it meant rounded corners and gradients.

The worst kind of buzzwords are the ones that actively set out to obfuscate any actual meaning. “The cloud” is a classic example. It sounds cooler than saying “a server in Virginia”, but it also sounds like the exact opposite of what it actually is. Great for marketing. Terrible for understanding.

“AI” is definitely not a good buzzword. But I can’t quite decide if it’s merely a bad buzzword like “web 2.0” or a truly terrible buzzword like “the cloud”.

The biggest problem with the phrase “AI” is that there’s a name collision.

For years, the term “AI” has been used in science-fiction. HAL 9000. Skynet. Examples of artificial general intelligence.

Now the term “AI” is also used to describe large language models. But there is no connection between this use of the term “AI” and the science fictional usage.

This leads to the ludicrous situation of otherwise-rational people wanted to discuss the dangers of “AI”, but instead of talking about the rampant exploitation and energy usage endemic to current large language models, they want to spend the time talking about the sci-fi scenarios of runaway “AI”.

To understand how ridiculous this is, I’d like you to imagine if we had started using a different buzzword in another setting…

Suppose that when ride-sharing companies like Uber and Lyft were starting out, they had decided to label their services as Time Travel. From a marketing point of view, it even makes sense—they get you from point A to point B lickety-split.

Now imagine if otherwise-sensible people began to sound the alarm about the potential harms of Time Travel. Given the explosive growth we’ve seen in this sector, sooner or later they’ll be able to get you to point B before you’ve even left point A. There could be terrible consequences from that—we’ve all seen the sci-fi scenarios where this happens.

Meanwhile the actual present-day harms of ride-sharing services around worker exploitation would be relegated to the sidelines. Clearly that isn’t as important as the existential threat posed by Time Travel.

It sounds ludicrous, right? It defies common sense. Just because a vehicle can get you somewhere fast today doesn’t mean it’s inevitably going to be able to break the laws of physics any day now, simply because it’s called Time Travel.

And yet that is exactly the nonsense we’re being fed about large language models. We call them “AI”, we look at how much they can do today, and we draw a straight line to what we know of “AI” in our science fiction.

This ridiculous situation could’ve been avoided if we had settled on a more accurate buzzword like “applied statistics” instead of “AI”.

It’s almost as if the labelling of the current technologies was more about marketing than accuracy.

I went to the UX Brighton conference yesterday.

The quality of the presentations was really good this year, probably the best yet. Usually there are one or two stand-out speakers (like Tom Kerwin last year), but this year, the standard felt very high to me.

But…

The theme of the conference was UX and “AI”, and I’ve never been more disappointed by what wasn’t said at a conference.

Not a single speaker addressed where the training data for current large language models comes from (it comes from scraping other people’s copyrighted creative works).

Not a single speaker addressed the energy requirements for current large language models (the requirements are absolutely mahoosive—not just for the training, but for each and every query).

My charitable reading of the situation yesterday was that every speaker assumed that someone else would cover those issues.

The less charitable reading is that this was a deliberate decision.

Whenever the issue of ethics came up, it was only ever in relation to how we might use these tools: considering user needs, being transparent, all that good stuff. But never once did the question arise of whether it’s ethical to even use these tools.

In fact, the message was often the opposite: words like “responsibility” and “duty” came up, but only in the admonition that UX designers have a responsibility and duty to use these tools! And if that carrot didn’t work, there’s always the stick of scaring you into using these tools for fear of being left behind and having a machine replace you.

I was left feeling somewhat depressed about the deliberately narrow focus. Maggie’s talk was the only one that dealt with any externalities, looking at how the firehose of slop is blasting away at society. But again, the focus was only ever on how these tools are used or abused; nobody addressed the possibility of deliberately choosing not to use them.

If audience members weren’t yet using generative tools in their daily work, the assumption was that they were lagging behind and it was only a matter of time before they’d get on board the hype train. There was no room for the idea that someone might examine the roots of these tools and make a conscious choice not to fund their development.

There’s a quote by Finnish architect Eliel Saarinen that UX designers like repeating:

Always design a thing by considering it in its next larger context. A chair in a room, a room in a house, a house in an environment, an environment in a city plan.

But none of the speakers at UX Brighton chose to examine the larger context of the tools they were encouraging us to use.

One speaker told us “Be curious!”, but clearly that curiosity should not extend to the foundations of the tools themselves. Ignore what’s behind the curtain. Instead look at all the cool stuff we can do now. Don’t worry about the fact that everything you do with these tools is built on a bedrock of exploitation and environmental harm. We should instead blithely build a new generation of user interfaces on the burial ground of human culture.

Whenever I get into a discussion about these issues, it always seems to come back ’round to whether these tools are actually any good or not. People point to the genuinely useful tasks they can accomplish. But that’s not my issue. There are absolutely smart and efficient ways to use large language models—in some situations, it’s like suddenly having a superpower. But as Molly White puts it:

The benefits, though extant, seem to pale in comparison to the costs.

There are no ethical uses of current large language models.

And if you believe that the ethical issues will somehow be ironed out in future iterations, then that’s all the more reason to stop using the current crop of exploitative large language models.

Anyway, like I said, all the talks at UX Brighton were very good. But I just wish just one of them had addressed the underlying questions that any good UX designer should ask: “Where did this data come from? What are the second-order effects of deploying this technology?”

Having a talk on those topics would’ve been nice, but I would’ve settled for having five minutes of one talk, or even one minute. But there was nothing.

There’s one possible explanation for this glaring absence that’s quite depressing to consider. It may be that these topics weren’t covered because there’s an assumption that everybody already knows about them, and frankly, doesn’t care.

To use an outdated movie reference, imagine a raving Charlton Heston shouting that “Soylent Green is people!”, only to be met with indifference. “Everyone knows Soylent Green is people. So what?”

This seems to be the attitude of many of my fellow nerds—designers and developers—when presented with tools based on large language models that produce dubious outputs based on the unethical harvesting of other people’s work and requiring staggering amounts of energy to run:

This is the future! I need to start using these tools now, even if they’re flawed, because otherwise I’ll be left behind. They’ll only get better. It’s inevitable.

Whereas this seems to be the attitude of those same designers and developers when faced with stable browser features that can be safely used today without frameworks or libraries:

I’m sceptical.

I’ve noticed a really strange justification from people when I ask them about their use of generative tools that use large language models (colloquially and inaccurately labelled as artificial intelligence).

I’ll point out that the training data requires the wholesale harvesting of creative works without compensation. I’ll also point out the ludicrously profligate energy use required not just for the training, but for the subsequent queries.

And here’s the thing: people will acknowledge those harms but they will justify their actions by saying “these things will get better!”

First of all, there’s no evidence to back that up.

If anything, as the well gets poisoned by their own outputs, large language models may well end up eating their own slop and getting their own version of mad cow disease. So this might be as good as they’re ever going to get.

And when it comes to energy usage, all the signals from NVIDIA, OpenAI, and others are that power usage is going to increase, not decrease.

But secondly, what the hell kind of logic is that?

It’s like saying “It’s okay for me to drive my gas-guzzling SUV now, because in the future I’ll be driving an electric vehicle.”

The logic is completely backwards! If large language models are going to improve their ethical shortcomings (which is debatable, but let’s be generous), then that’s all the more reason to avoid using the current crop of egregiously damaging tools.

You don’t get companies to change their behaviour by rewarding them for it. If you really want better behaviour from the purveyors of generative tools, you should be boycotting the current offerings.

I suspect that most people know full well that the “they’ll get better!” defence doesn’t hold water. But you can convince yourself of anything when everyone around is telling you that this is the future baby, and you’d better get on board or you’ll be left behind.

Baldur reminds us that this is how people talked about asbestos:

Every time you had an industry campaign against an asbestos ban, they used the same rhetoric. They focused on the potential benefits – cheaper spare parts for cars, cheaper water purification – and doing so implicitly assumed that deaths and destroyed lives, were a low price to pay.

This is the same strategy that’s being used by those who today talk about finding productive uses for generative models without even so much as gesturing towards mitigating or preventing the societal or environmental harms.

It reminds me of the classic Ursula Le Guin short story, The Ones Who Walk Away from Omelas that depicts:

…the utopian city of Omelas, whose prosperity depends on the perpetual misery of a single child.

Once citizens are old enough to know the truth, most, though initially shocked and disgusted, ultimately acquiesce to this one injustice that secures the happiness of the rest of the city.

It turns out that most people will blithely accept injustice and suffering not for a utopia, but just for some bland hallucinated slop.

Don’t get me wrong: I’m not saying large language models aren’t without their uses. I love seeing what Simon and Matt are doing when it comes to coding. And large language models can be great for transforming content from one format to another, like transcribing speech into text. But the balance sheet just doesn’t add up.

As Molly White put it: AI isn’t useless. But is it worth it?:

Even as someone who has used them and found them helpful, it’s remarkable to see the gap between what they can do and what their promoters promise they will someday be able to do. The benefits, though extant, seem to pale in comparison to the costs.

When Mathew Modine’s character first shows up in Christopher Nolan’s Oppenheimer, I figured the rest of the cinema audience wouldn’t have appreciated me shouting out “VANNEVAR BUSH IN THE HOUSE!” so I screamed it on the inside.

The Manhattan Project was not his only claim to fame or infamy. When it comes to the world we now live in, Bush’s idea of the memex has been almost equally influential. His article As We May Think became a touchstone for Douglas Engelbart and later Tim Berners-Lee.

But as Matt Thompson points out:

…the device he describes does not resemble the internet or anything I’ve ever found on it.

Then he says:

What Bush was describing sounds to me like what you might get if you turned a browser history — the most neglected piece of the software — into a robust and fully featured machine of its own. It would help you map the path you charted through a web of knowledge, refine those maps, order them, and share them

Yes! This!! I 100% agree with the description of browser history as “the most neglected piece of the software.” While I wouldn’t go as far as Chris when he says web browsers kind of suck, I’m kind of amazed that there hasn’t been more innovation and competition in this space.

If anything we’ve outsourced the management of our browsing history to services like Delicious and Pinboard, or to tools like Obsidian and Roam Research. Heck, the links section of my website is my attempt to manage and annotate my own associative trails.

Imagine if that were baked right into a web browser. Then imagine how beautiful such a rich source of data might look.

Like Matt says:

I don’t think anything like this exists. So Bush’s essay still transfixes me.

In 2013, I spoke at the border:none event in Nuremberg. I gave a talk called The Power of Simplicity.

It was a great little event. Most of the talks were, like mine, on technical topics; design, development, the usual conference material.

This year Joschi and Marc decided to have another border:none event ten years on from the first one. They invited back all the original speakers, as well as some new folks. They kept the ticket price the same as ten years ago—just thirty euros.

For us speakers from the previous event, the only brief they gave us was to consider what’s happened in the past decade. I played it pretty safe and talked about the web. I’ll post a transcript of my talk soon.

Some of the other speakers were far more ambitious. They spoke about themselves, the world, the meaning of life …my presentation was very tame in comparison.

I really, really admire the honesty and vulnerability that those people displayed. Tobias Baldauf in particular took my breath away. He delivered an intensely personal talk on generational trauma that was meticulously researched and took incredible bravery to deliver. It was worth going to Nuremberg just for the privilege of being present for that talk.

Other talks were refreshingly tech-free. There was a talk on cold-water swimming. There was a talk on paragliding. And I don’t mean they were saying “what designers can learn from cold-water swimming” or “how I became a better developer through paragliding.” The talks were literally about swimming and paragliding.

There was a great variety of speakers this time around, include age ranges from puberty to menopause (quite literally—that was the topic of one of the talks). I had the great pleasure of providing some coaching before the event to fifteen year old Maya who was delivering her first talk in English. She did a fantastic job! And the talk she gave—about how teachers in her school aren’t always trusting of the technology they provide to students—was directly relevant to what we’re seeing in the world of work. Give people autonomy, agency and trust.

There was a lot of trust at border:none. Everyone who bought a ticket did so on trust—they had no idea what to expect. Likewise, Marc and Joschi put their trust in the speakers. They gave the speakers the freedom to talk about whatever they wanted. That trust was repayed.

Florian took some superb pictures of the event. Matthias wrote up his experience. So did Tom. Valisis shared the gist of his excellent talk.

At the end of the event there was some joking about returning in 2033. I love the idea of a conference that happens once every ten years. Count me in!

I’ve always associated good design with thoughtfulness. Like, I should be able to point to any element in an interface and the designer should be able to tell me the reasons it’s there. Those reasons may be rooted in user needs or asthetics or some other consideration, but the point is that there’s a justification for it. Justify every pixel!

But I’ve come to realise that this is a bit reductionist. Now when I point at an interface element, I still expect the designer to be able to justify its inclusion, but I’d also like to know the trade-offs that were made.

Suppose there’s a large hero image. I’m sure the designer would have no problem justifying its inclusion on the basis of impact and the emotional heft it delivers. But did they also understand the potential downsides? Were they aware of the performance implications of including a large image?

I hope the answer to both questions is yes. They understood the costs, but they decided that, on balance, the positives outweighed the negatives.

When it comes to the positives, universal principles of design often apply. Colour theory, typography, proximity, and so on. But the downsides tend to be specific to the medium that the design is delivered in.

Let’s say you’re designing for print. You want to include an extra typeface just for footnotes. No problem. There isn’t really a downside. In print, you can use all the typefaces you want. But if this were for the web, then the calculation would be different. Every extra typeface comes with a performance penalty. A decision that might be justified in one medium might not work in another medium.

It works both ways; on the web you can use all the colours you want, without incurring any penalties, but in print—depending on the process you’re using—you might have to weigh up that decision very differently.

From this perspective, every design decision is like a balance sheet. A good web designer understands the benefits and the costs behind each decision they make.

It’s a similar story when it comes to web development. Heck, we even have the term “tech debt” to describe decisions that we know aren’t for the best in the long term.

In fact, I’d say that consideration of the long-term effects is something that should play a bigger part in technical decisions.

When we’re weighing up the pros and cons of using a particular tool, we have a tendency to think in the here and now. How might this help me right now? How might this hinder me right now?

But often a decision that delivers short-term gain may well end up delivering long-term pain.

Alexander Petros describes this succinctly:

Reopen a node repository after 3 months and you’ll find that your project is mired in a flurry of security warnings, backwards-incompatible library “upgrades,” and a frontend framework whose cultural peak was the exact moment you started the project and is now widely considered tech debt.

When I wrote about making the Patterns Day website I described my process as doing it “the long hard stupid way”—a term that Frank coined in a talk he gave a few years back. But perhaps my hands-on approach is only long, hard and stupid in the short time. With each passing year, the codebase will retain a degree of readability and accessibility that I would’ve sacrificed had I depended on automated build processes.

Robin Berjon puts this into the historical perspective of Taylorism and Luddism:

Whenever something is automated, you lose some control over it. Sometimes that loss of control improves your life because exerting control is work, and sometimes it worsens your life because it reduces your autonomy.

Or as Marshall McLuhan put it:

Every extension is also an amputation.

…which is fine as long as the benefits of the extension outweigh the costs of the amputation. My worry is that, when it comes to evaluating technology for building on the web, we aren’t considering the longer-term costs.

Maintenance matters. With the passing of time, maintenance matters more and more.

Maybe we avoid thinking about the long-term costs because it would lead to decision paralysis. That’s understandable. But I take comfort from some words of wisdom on the web from the 1990s. Tim Berners-Lee’s style guide for hypertext:

Because hypertext is potentially unconstrained you are a little daunted. Do not be. You can write a document as simply as you like. In many ways, the simpler the better.

The bedrock of the World Wide Web is solid. Built atop the protocols of the internet (TCP/IP), its fundamental building blocks remain: URLs of HTML files transmitted over HTTP. Baldur Bjarnason writes:

Even today, the web is like living fossil, a preserved relic from a different era. Anybody can put up a website. Anybody can run a business over it. I can build an app or service, send the URL to anybody I like, and most people in the world will be able to run it without asking anybody’s permission.

Still, the web has evolved. In fact, that evolution is something that’s also built into its fundamental design. Rather than try to optimise the World Wide Web for one particular use-case, Tim Berners-Lee realised the power of being flexible. Like the internet, the World Wide Web is deliberately dumb.

(I get very annoyed when people talk about the web as being designed for scientific work at CERN. That was merely the first use-case. The web was designed for everything …and nothing in particular.)

Robin Berjon compares the web’s evolution to the ship of Theseus:

That’s why it’s been so hard to agree about what the Web is: the Web is architected for resilience which means that it adapts and transforms. That flexibility is the reason why I’m talking about some mythological dude’s boat. Altogether too often, we consider some aspects of the Web as being invariants when they’re potentially just as replaceable as any other part. This isn’t to say that there are no invariants on the Web.

The web can be changed. That’s both a comfort and a warning. There’s plenty that we should change about today’s web. But there’s also plenty—at the root level—that we should fight to preserve.

And if you want change, the worst way to go about it is to promulgate the notion of burning everything down and starting from scratch. As Erin says in the fourth and final part of her devastating series on Meta in Myanmar:

We don’t get a do-over planet. We won’t get a do-over network.

Instead, we have to work with the internet we made and find a way to rebuild and fortify it to support the much larger projects of repair—political, cultural, environmental—that are required for our survival.

Though, as Robin points out, that doesn’t preclude us from sharing a vision:

Proceeding via small, incremental changes can be a laudable approach, but even then it helps to have a sense for what it is that those small steps are supposed to be incrementing towards.

I’m looking forward to reading what Robin puts forward, particularly because he says “I’m no technosolutionist.”

From a technical perspective, the web has never been better. We have incredible features in HTML, CSS, and JavaScript, all standardised and with amazing interoperability between browsers. The challenges that face the web today are not technical.

That’s one of the reasons why I have no patience for the web3 crowd. Apart from the ridiculous name, they’re focusing on exactly the wrong part of the stack.

Listening to their pitch, they’ll point out that while yes, the fundamental bedrock of the web is indeed decentralised—TCP/IP, HTTP(S)—what’s been constructed on that foundation is increasingly centralised; the power brokers of Google, Meta, Amazon.

And what’s the solution they propose? Replace the underlying infrastructure with something-something-blockchain.

Would that it were so simple.

The problems of today’s web are not technical in nature. The problems of today’s web won’t be solved by technology. If we’re going to solve the problems of today’s web, we’ll need to do it through law, culture, societal norms, and co-operation.

(Feel free to substitute “today’s web” with “tomorrow’s climate”.)

Quite a few people have been linking to this list on The Verge of what they consider the greatest tech books of all time.

To be clear, this is a fairly narrow definition of technology. It’s really a list of books about the history of computing. But there’s some great stuff in there.

I’ve been thinking the books about computing and technology that I’ve managed to get around to reading, and which ones made an impact on me. Some of these made it on The Verge’s list too, which is nice to see.

Broad Band by Clare L. Evans

I was blown away by the writing and the stories uncovered in “the untold story of the women who made the internet.” Here’s what I wrote when I read the book:

This book is pretty much the perfect mix. The topic is completely compelling—a history of women in computing. The stories are rivetting—even when I thought I knew the history, this showed me how little I knew. And the voice of the book is pure poetry.

It’s not often that I read a book that I recommend wholeheartedly to everyone. I prefer to tailor my recommendations to individual situations. But in the case of Broad Band, I honesty think that anyone would enjoy it.

Uncanny Valley by Anna Wiener

I read this one in 2020, not too long after it came out. In my end of year round-up, I described it like this:

A terrific memoir. It’s open and honest, and just snarky enough when it needs to be.

Close to the Machine by Ellen Ullman

I read this in 2018, many years after it first came out. Here’s how it came across to me:

Lots of ’90s feels in this memoir. A lot of this still resonates today. It’s kind of fascinating to read it now with the knowledge of how this whole internet thing would end up going.

Abolish Silicon Valley by Wendy Liu

This book is mostly excellent. But as I wrote when I got my hands on an advance copy, the juxtaposition of memoir and manifesto didn’t work for me:

Abolish Silicon Valley is 80% memoir and 20% manifesto. I worry that the marketing isn’t making that clear. It would be a shame if this great book didn’t find its audience.

The Victorian Internet by Tom Standage

Okay, this isn’t technically about computing, it’s about the telegraph. But it’s got the word “internet” in the title, and it’s a terrific read. Here’s what I wrote when I put it in Matt’s book-vending machine:

A book about the history of telegraphy might not sound like the most riveting read, but The Victorian Internet is both fascinating and entertaining. Techno-utopianism, moral panic, entirely new ways of working, and a world that has been utterly transformed: the parallels between the telegraph and the internet are laid bare. In fact, this book made me realise that while the internet has been a great accelerator, the telegraph was one of the few instances where a technology could truly be described as “disruptive.”

When Jason linked to the list of books on The Verge he said:

I’m baffled that Tracy Kidder’s amazing The Soul of a New Machine didn’t make the top 5 or even 10.

I’m more surprised that this book is held in such high esteem. It has not aged well. I read it in 2019 and had this to say:

This is a well-regarded book amongst people whose opinion I value. It’s also a Pulitzer prize winner. Strange, then, that I found it so unengaging. The prose is certainly written with gusto, but it all seems so very superficial to me. No matter how you dress it up, it’s a chronicle of a bunch of guys—and oh, boy, are they guys—making a commercial computer. Testosterone and solder—not my cup of tea.

I’m sure you’ve heard the law of the instrument: when all you have is a hammer, everything looks like a nail.

There’s another side to it. If you’re selling hammers, you’ll depict a world full of nails.

Recent hammers include cryptobollocks and virtual reality. It wasn’t enough for blockchains and the metaverse to be potentially useful for some situations; they staked their reputations on being utterly transformative, disrupting absolutely every facet of life.

This kind of hype is a terrible strategy in the long-term. But if you can convince enough people in the short term, you can make a killing on the stock market. In truth, the technology itself is superfluous. It’s the hype that matters. And if the hype is over-inflated enough, you can even get your critics to do your work for you, broadcasting their fears about these supposedly world-changing technologies.

You’d think we’d learn. If an industry cries wolf enough times, surely we’d become less trusting of extraordinary claims. But the tech industry continues to cry wolf—or rather, “hammer!”—at regular intervals.

The latest hammer is machine learning, usually—incorrectly—referred to as Artificial Intelligence. What makes this hype cycle particularly infuriating is that there are genuine use cases. There are some nails for this hammer. They’re just not as plentiful as the breathless hype—both positive and negative—would have you believe.

When I was hosting the DiBi conference last week, there was a little section on generative “AI” tools. Matt Garbutt covered the visual side, demoing tools like Midjourney. Scott Salisbury covered the text side, showing how you can generate code. Afterwards we had a panel discussion.

During the panel I asked some fairly straightforward questions that nobody could answer. Who owns the input (the data used by these generative tools)? Who owns the output?

On the whole, it stayed quite grounded and mercifully free of hyperbole. Both speakers were treating the current crop of technologies as tools. Everyone agreed we were on the hype cycle, probably the peak of inflated expectations, looking forward to reaching the plateau of productivity.

Scott explicitly warned people off using generative tools for production code. His advice was to stick to side projects for now.

Matt took a closer look at where these tools could fit into your day-to-day design work. Mostly it was pretty sensible, except when he suggested that there could be any merit to using these tools as a replacement for user testing. That’s a terrible idea. A classic hammer/nail mismatch.

I think I moderated the panel reasonably well, but I have one regret. I wish I had first read Baldur Bjarnason’s new book, The Intelligence Illusion. I started reading it on the train journey back from Edinburgh but it would have been perfect for the panel.

The Intelligence Illusion is very level-headed. It is neither pro- nor anti-AI. Instead it takes a pragmatic look at both the benefits and the risks of using these tools in your business.

It has excellent advice for spotting genuine nails. For example:

Generative AI has impressive capabilities for converting and modifying seemingly unstructured data, such as prose, images, and audio. Using these tools for this purpose has less copyright risk, fewer legal risks, and is less error prone than using it to generate original output.

Think about transcripts of videos or podcasts—an excellent use of this technology. As Baldur puts it:

The safest and, probably, the most productive way to use generative AI is to not use it as generative AI. Instead, use it to explain, convert, or modify.

He also says:

Prefer internal tools over externally-facing chatbots.

That chimes with what I’ve been seeing. The most interesting uses of this technology that I’ve seen involve a constrained dataset. Like the way Luke trained a language model on his own content to create a useful chat interface.

Anyway, The Intelligence Illusion is full of practical down-to-earth advice based on plenty of research backed up with copious citations. I’m only halfway through it and it’s already helped me separate the hype from the reality.

You know how when you’re on hold to any customer service line you hear a message that thanks you for calling and claims your call is important to them. The message always includes a disclaimer about calls possibly being recorded “for training purposes.”

Nobody expects that any training is ever actually going to happen—surely we would see some improvement if that kind of iterative feedback loop were actually in place. But we most certainly want to know that a call might be recorded. Recording a call without disclosure would be unethical and illegal.

Consider chatbots.

If you’re having a text-based (or maybe even voice-based) interaction with a customer service representative that doesn’t disclose its output is the result of large language models, that too would be unethical. But, at the present moment in time, it would be perfectly legal.

That needs to change.

I suspect the necessary legislation will pass in Europe first. We’ll see if the USA follows.

In a way, this goes back to my obsession with seamful design. With something as inherently varied as the output of large language models, it’s vital that people have some way of evaluating what they’re told. I believe we should be able to see as much of the plumbing as possible.

The bare minimum amount of transparency is revealing that a machine is in the loop.

This shouldn’t be a controversial take. But I guarantee we’ll see resistance from tech companies trying to sell their “AI” tools as seamless, indistinguishable drop-in replacements for human workers.

The last talk at the last dConstruct was by local clever clogs Anil Seth. It was called Your Brain Hallucinates Your Conscious Reality. It’s well worth a listen.

Anil covers a lot of the same ground in his excellent book, Being You. He describes a model of consciousness that inverts our intuitive understanding.

We tend to think of our day-to-day reality in a fairly mechanical cybernetic manner; we receive inputs through our senses and then make decisions about reality informed by those inputs.

As another former dConstruct speaker, Adam Buxton, puts it in his interview with Anil, it feels like that old Beano cartoon, the Numskulls, with little decision-making homonculi inside our head.

But Anil posits that it works the other way around. We make a best guess of what the current state of reality is, and then we receive inputs from our senses, and then we adjust our model accordingly. There’s still a feedback loop, but cause and effect are flipped. First we predict or guess what’s happening, then we receive information. Rinse and repeat.

The book goes further and applies this to our very sense of self. We make a best guess of our sense of self and then adjust that model constantly based on our experiences.

There’s a natural tendency for us to balk at this proposition because it doesn’t seem rational. The rational model would be to make informed calculations based on available data …like computers do.

Maybe that’s what sets us apart from computers. Computers can make decisions based on data. But we can make guesses.

Enter machine learning and large language models. Now, for the first time, it appears that computers can make guesses.

The guess-making is not at all like what our brains do—large language models require enormous amounts of inputs before they can make a single guess—but still, this should be the breakthrough to be shouted from the rooftops: we’ve taught machines how to guess!

And yet. Almost every breathless press release touting some revitalised service that uses AI talks instead about accuracy. It would be far more honest to tout the really exceptional new feature: imagination.

Using AI, we will guess who should get a mortgage.

Using AI, we will guess who should get hired.

Using AI, we will guess who should get a strict prison sentence.

Reframed like that, it’s easy to see why technologists want to bury the lede.

Alas, this means that large language models are being put to use for exactly the wrong kind of scenarios.

(This, by the way, is also true of immersive “virtual reality” environments. Instead of trying to accurately recreate real-world places like meeting rooms, we should be leaning into the hallucinatory power of a technology that can generate dream-like situations where the pleasure comes from relinquishing control.)

Take search engines. They’re based entirely on trust and accuracy. Introducing a chatbot that confidentally conflates truth and fiction doesn’t bode well for the long-term reputation of that service.

But what if this is an interface problem?

Currently facts and guesses are presented with equal confidence, hence the accurate descriptions of the outputs as bullshit or mansplaining as a service.

What if the more fanciful guesses were marked as such?

As it is, there’s a “temperature” control that can be adjusted when generating these outputs; the more the dial is cranked, the further the outputs will stray from the safest predictions. What if that could be reflected in the output?

I don’t know what that would look like. It could be typographic—some markers to indicate which bits should be taken with pinches of salt. Or it could be through content design—phrases like “Perhaps…”, “Maybe…” or “It’s possible but unlikely that…”

I’m sure you’ve seen the outputs when people request that ChatGPT write their biography. Perfectly accurate statements are generated side-by-side with complete fabrications. This reinforces our scepticism of these tools. But imagine how differently the fabrications would read if they were preceded by some simple caveats.

A little bit of programmed humility could go a long way.

Right now, these chatbots are attempting to appear seamless. If 80% or 90% of their output is accurate, then blustering through the other 10% or 20% should be fine, right? But I think the experience for the end user would be immensely more empowering if these chatbots were designed seamfully. Expose the wires. Show the workings-out.

Mind you, that only works if there is some way to distinguish between fact and fabrication. If there’s no way to tell how much guessing is happening, then that’s a major problem. If you can’t tell me whether something is 50% true or 75% true or 25% true, then the only rational response is to treat the entire output as suspect.

I think there’s a fundamental misunderstanding behind the design of these chatbots that goes all the way back to the Turing test. There’s this idea that the way to make a chatbot believable and trustworthy is to make it appear human, attempting to hide the gears of the machine. But the real way to gain trust is through honesty.

I want a machine to tell me when it’s guessing. That won’t make me trust it less. Quite the opposite.

After all, to guess is human.

We use metaphors all the time. To quote George Lakoff, we live by them.

We use analogies some of the time. They’re particularly useful when we’re wrapping our heads around something new. By comparing something novel to something familiar, we can make a shortcut to comprehension, or at least, categorisation.

But we need a certain amount of vigilance when it comes to analogies. Just because something is like something else doesn’t mean it’s the same.

With that in mind, here are some ways that people are describing generative machine learning tools. Large language models are like…

• …a blurry JPEG of the web,
• …fly-by-wire avionics,
• …drum machines,
• …an octopus.

I’ve already written about how much I enjoyed hosting Leading Design San Francisco last week.

All the speakers were terrific. Lola’s talk was particularly …um, interesting:

In this talk, Lola will share her adventures in the world of blockchain, the hostility she experienced in her first go-round in 2018, and why she’s chosen to head back to a technology that is going through its largest reputational and social crisis to date.

Wait …I was supposed to stand on stage and introduce a talk that was (at least partly) about blockchain? I have opinions.

As it turned out, Lola warned me that I’d be making an appearance in her talk. She was going to quote that blog post. Before the talk, I asked her how obnoxious I could be about blockchain in her intro. She told me to bring it.

So in the introduction, I deployed all the sarcasm I had in me and said:

Listen, we designers have a tendency to be over-critical of things sometimes. There are all these ideas that we dismiss: phrenology, homeopathy, flat-earthism …blockchain. Haters gonna hate.

I remember somebody asking online a while back, “Why the hate for web3?” And someone I know responded by saying “We hate it because we understand it.” I think there’s a lot of truth to that.

But look, just because blockchains are powering crypto ponzi schemes and N F fucking Ts, it’s worth remembering that it’s also simply a technology. It’s a technological solution in search of a problem.

To be fair, it’s still early days. After all, it’s only been over a decade now.

It’s like the law of instrument says; when all you have is a hammer, everything looks like a nail. Blockchain is like that. Except the hammer is also made of glass.

Anyway, Lola is going to defend the indefensible and talk about blockchain. One thing to keep in mind is this: remember when everyone was talking about “The Cloud”? And then it turned out that you could substitute the phrase “someone else’s server” for “The Cloud?” Well, every time you hear Lola say the word “blockchain”, I’d like you to mentally substitute the phrase “multiple copies of a spreadsheet.”

Please give an open mind and a warm welcome to Lola Oyelayo Pearson!

I got some laughs. I also got lots of gasps and pearl-clutching, as though I were saying something taboo. Welcome to San Francisco.

Lola gave as good as she got. I got a roasting in her talk.

And just to clarify, Lola and I are friends—this was a consensual smackdown.

There was a very serious point to Lola’s talk. Cryptobollocks and other blockchain-powered schemes have historically been very bro-y, and exploitative of non-bro communities. Lola wants to fight that trend.

I get it. But it reminds me a bit of the justifications you hear from people who go to work at Facebook claiming that they can do more good from the inside. Whatever helps you sleep at night.

The crux of Lola’s belief is this: blockchain technology is inevitable, therefore it is uncumbent on us as ethical designers to ensure that the technology is deployed in a way that empowers people instead of exploiting them.

But I take issue with the premise. Blockchain technology is not inevitable. That’s the worst kind of technological determinism. It’s defeatist. It’s a depressing view of “progress” driven not by people, but by technological forces beyond our control.

I refuse to accept that anti-humanist deterministic view.

In any case, for technological determinism to have any validity, there needs to be something to it. At least virtual reality and machine learning are based on some actual technologies. In the case of cryptobollocks, there is no there there. There is nothing except the hype, which is why you’ll see blockchain enthusiasts trying to ride the coattails of trending technologies in a logical fallacy that goes something like this:

1. There are technologies that will be really big in the future,
2. blockchain is a technology, therefore
3. blockchain will be really big in the future.

Blockchain is bullshit. It isn’t even very clever bullshit. And it certainly isn’t inevitable.

I’ve mentioned before that I’m not a fan of initialisms and acronyms. They can be exclusionary.

It bothers me doubly when everyone is talking about AI.

First of all, the term is so vague as to be meaningless. Sometimes—though rarely—AI refers to general artificial intelligence. Sometimes AI refers to machine learning. Sometimes AI refers to large language models. Sometimes AI refers to a series of if/else statements. That’s quite a spectrum of meaning.

Secondly, there’s the assumption that everyone understands the abbreviation. I guess that’s generally a safe assumption, but sometimes AI could refer to something other than artificial intelligence.

In countries with plenty of pastoral agriculture, if someone works in AI, it usually means they’re going from farm to farm either extracting or injecting animal semen. AI stands for artificial insemination.

I think that abbreviation might work better for the kind of things currently described as using AI.

We were discussing this hot topic at work recently. Is AI coming for our jobs? The consensus was maybe, but only the parts of our jobs that we’re more than happy to have automated. Like summarising some some findings. Or perhaps as a kind of lorem ipsum generator. Or for just getting the ball rolling with a design direction. As Terence puts it:

Midjourney is great for a first draft. If, like me, you struggle to give shape to your ideas then it is nothing short of magic. It gets you through the first 90% of the hard work. It’s then up to you to refine things.

That’s pretty much the conclusion we came to in our discussion at Clearleft. There’s no way that we’d use this technology to generate outputs for clients, but we certainly might use it to generate inputs. It’s like how we’d do a quick round of sketching to get a bunch of different ideas out into the open. Terence is spot on when he says:

Midjourney lets me quickly be wrong in an interesting direction.

To put it another way, using a large language model could be a way of artificially injecting some seeds of ideas. Artificial insemination.

So now when I hear people talk about using AI to create images or articles, I don’t get frustrated. Instead I think, “Using artificial insemination to create images or articles? Yes, that sounds about right.”

I was chatting with my new colleague Alex yesterday about a link she had shared in Slack. It was the Nielsen Norman Group’s annual State of Mobile User Experience report.

There’s nothing too surprising in there, other than the mention of Apple’s app clips and Google’s instant apps.

Remember those?

Me neither.

Perhaps I lead a sheltered existence, but as an iPhone user, I don’t think I’ve come across a single app clip in the wild.

I remember when they were announced. I was quite worried about them.

See, the one thing that the web can (theoretically) offer that native can’t is instant access to a resource. Go to this URL—that’s it. Whereas for a native app, the flow is: go to this app store, find the app, download the app.

(I say that the benefit is theoretical because the website found at the URL should download quickly—the reality is that the bloat of “modern” web development imperils that advantage.)

App clips—and instant apps—looked like a way to route around the convoluted install process of native apps. That’s why I was nervous when they were announced. They sounded like a threat to the web.

In reality, the potential was never fulfilled (if my own experience is anything to go by). I wonder why people didn’t jump on app clips and instant apps?

Perhaps it’s because what they promise isn’t desirable from a business perspective: “here’s a way for users to accomplish their tasks without downloading your app.” Even though app clips can in theory be a stepping stone to installing the full app, from a user’s perspective, their appeal is the exact opposite.

Or maybe they’re just too confusing to understand. I think there’s an another technology that suffers from the same problem: progressive web apps.

Hear me out. Progressive web apps are—if done well—absolutely amazing. You get all of the benefits of native apps in terms of UX—they even work offline!—but you retain the web’s frictionless access model: go to a URL; that’s it.

So what are they? Are they websites? Yes, sorta. Are they apps? Yes, sorta.

That’s confusing, right? I can see how app clips and instant apps sound equally confusing: “you can use them straight away, like going to a web page, but they’re not web pages; they’re little bits of apps.”

I’m mostly glad that app clips never took off. But I’m sad that progressive web apps haven’t taken off more. I suspect that their fates are intertwined. Neither suffer from technical limitations. The problem they both face is inertia:

The technologies are the easy bit. Getting people to re-evaluate their opinions about technologies? That’s the hard part.

True of progressive web apps. Equally true of app clips.

But when I was chatting to Alex, she made me look at app clips in a different way. She described a situation where somebody might need to interact with some kind of NFC beacon from their phone. Web NFC isn’t supported in many browsers yet, so you can’t rely on that. But you don’t want to make people download a native app just to have a quick interaction. In theory, an app clip—or instant app—could do the job.

In that situation, app clips aren’t a danger to the web—they’re polyfills for hardware APIs that the web doesn’t yet support!

I love having my perspective shifted like that.

The specific situations that Alex and I were discussing were in the context of museums. Musuems offer such interesting opportunities for the physical and the digital to intersect.

Remember the pen from Cooper Hewitt? Aaron spoke about it at dConstruct 2014—a terrific presentation that’s well worth revisiting and absorbing.

The other dConstruct talk that’s very relevant to this liminal space between the web and native apps is the 2012 talk from Scott Jenson. I always thought the physical web initiative had a lot of promise, but it may have been ahead of its time.

I loved the thinking behind the physical web beacons. They were deliberately dumb, much like the internet itself. All they did was broadcast a URL. That’s it. All the smarts were to be found at the URL itself. That meant a service could get smarter over time. It’s a lot easier to update a website than swap out a piece of hardware.

But any kind of technology that uses Bluetooth, NFC, or other wireless technology has to get over the discovery problem. They’re invisible technologies, so by default, people don’t know they’re even there. But if you make them too discoverable— intrusively announcing themselves like one of the commercials in Minority Report—then they’re indistinguishable from spam. There’s a sweet spot of discoverability right in the middle that’s hard to get right.

Over the past couple of years—accelerated by the physical distancing necessitated by The Situation—QR codes stepped up to the plate.

They still suffer from some discoverability issues. They’re not human-readable, so you can’t be entirely sure that the URL you’re going to go to isn’t going to be a Rick Astley video. But they are visible, which gives them an advantage over hidden wireless technologies.

They’re cheaper too. Printing a QR code sticker costs less than getting a plastic beacon shipped from China.

QR codes turned out to be just good enough to bridge the gap between the physical and digital for those one-off interactions like dining outdoors during a pandemic:

I can see why they chose the web over a native app. Online ordering is the only way to place your order at this place. Telling people “You have to go to this website” …that seems reasonable. But telling people “You have to download this app” …that’s too much friction.

Ironically, the nail in the coffin for app clips and instant apps might’ve been hammered in by Apple and Google when they built QR-code recognition into their camera software.