Internet-era innovations often arrive to tremendous fanfare. When you consider the number of new things being made, the number that go on to be hugely impactful is extremely low. Nevertheless, the trumpeting—proportional to how much funding is at stake—goes on. This one, this one here, is the Next Big Thing. This has the potential to change everyone’s lives, forever.

The overwhelming majority don’t; some do. Those that have rarely did in the form they were in when they were touted as gamechangers. That’s the nature of it all.

Early-stage technologies suffer deeply from developer myopia. Technical teams start with the core functionality and over time fan outwards towards the exceptions and edge-cases. But the drawback of a ‘good enough is good enough’ approach is that it seldom is. The initial technologies of Uber and Airbnb did not, for example, make people safer, yet steps to ensure safety are now core to their brands.

Consider the exclusionary nature of the smartphone. The extent to which they are resilient to your lifestyle has depended on how much you have in common with Californian men: general wealth, education level, larger hands, average climate and so on. Manufacturers removing headphone jacks was a clear signal their smartphones were not ‘for everyone’: only those able and willing to buy and use much more expensive, wireless alternatives, in spite of their shortcomings. Again, myopia.

I’m reminded of a famous post by developer Patrick McKenzie: Falsehoods Programmers Believe About Names. Where it is necessary to record someone’s name, developers commonly use three fields: a title, a given name and a family name. McKenzie listed extensive exceptions to this formula: it’s enough to make you realise the scale of ‘edge cases’ is vast. He also inspired others to write lists of falsehoods about phone numbers, time and all sorts of other things. Along similar lines is a recent, enlightening post entitles Horrible edge cases to consider when dealing with music.

What all these edge-cases expose is the kind of developer myopia that plagues new technologies. These simplified assumptions aren’t representative of people’s behaviours and how the world works, and therefore alienate users.

So, I wonder whether the falsehoods are a good predictor of innovation success. In scientific fields such as medicine, rigorous studies to identify edge-cases are not only commonplace, it’s critical. Therefore, in the digital domain, does the speed at which developers embrace their edge-cases offer an indicator of the extent to which an innovation has potential?

Broadcast media’s transition into the digital era has devalued it. Broadcast is truly excellent when two criteria are met: timeliness and inclusion. It’s the best way to watch sport, for example, as well as events such as the Superbowl, Eurovision and coverage of the Queen’s jubilee. It was the latter that has reminded me of broadcast media’s tremendous strength.

On-demand services try to stage ‘events’ of their own, with a combination of season launches, one-offs and rampant promotion. But overall, people watching a show within the same couple of weeks isn’t as inclusive as everyone sitting down together. There are water-cooler moments, and then there’s the more powerful feeling that your household is somehow united with millions of others, right this moment.

Digital-era behaviours around media consumption have changed too, obviously. An enormous breadth of choice sets a hard limit on the possible participation in any particular one. But cut back further is that sense of inclusion that heyday broadcast commanded. It’s analogous to a festival: a massively shared experience.

In the Eighties, BBC One’s Saturday schedule was built around this model, even if the terminology was different. Live kids’ TV in the morning, sports coverage in the afternoon, live shiny-floor entertainment in the evening, movie at night. On-demand services can reach the same levels of inclusion, but they have to spend tens or perhaps hundreds of millions of dollars to do so.

It might be a frequency bias, but I feel like I’m noticing repeated references to a golden age of television—but with some saying it’s ending and others saying it’s just beginning. I’m minded to think of Disney’s purchase of, and then investment in, the Star Wars franchise: including making the transition out of the cinema and into the living room. As I write, Obi-Wan Kenobi is mid-season: I don’t know the budget, but you can tell it’s considerable. It likely dwarfs the movie budgets. Massive budget allows for the best writing, stagecraft, acting, directing, editing and marketing.

These shows are objectively excellent. Not just this franchise: the big productions from all on-demand services are, by any measure, astonishing. Had it been possible to make them 40 years ago, the viewing figures would be stratospheric and viewers’ enthusiasm would be considerable and long-lasting. Today, though, budgets are high, audiences are comparably modest, enthusiasm is only good to middling. All this in spite of these big shows being among the greatest ever made.

What this demonstrates is the various facets of the behavioural change. Viewers’ dissipation across media channels and changes to consumption habits are well-documented. But in addition, perhaps in result, the sense of timeliness and inclusion has eroded. The digital-era behaviour is based upon feeling like you’re consuming something, rather than you’re involved in something.

I like faceted search. I think eBay is my favourite search engine of all. But search facets have dropped out of fashion in interface design. If we got back into search facets, then interfaces of all kinds would be better.

I often remind myself that my usage patterns are not representative. I might even have that tattooed on the insides of my eyelids, just to be sure. I also know eBay isn’t where the cool kids hang out anymore but, as the bathwater drains away, we should remain mindful of the baby. In this case the bathwater is market share, and the baby is quick but also precise online search mechanisms. Or perhaps not even search, but find.

It’s funny that the term ‘search-engine’ stuck, given all the ways this functionality could have been described. And what you call things over-influences what they do—ask Carphone Warehouse. Okay sure, searching might describe what users do (or did back then), but it doesn’t describe what users want. It’s foolhardy to make sweeping generalisations about users but I think I’m on reasonably safe ground when I assert that users don’t want to be looking; they want to have found. The result of the search is where the dopamine is, not the search itself. There’s no thrill-of-the-chase. Anyway.

Faceted search is a good, solid, finding-stuff paradigm. Enter something that very broadly describes the thing you’re looking for, and then use facets to turn a huge number of hits into a small selection of precise, usable results. You could type in ‘car’ or pick the equivalent category, then choose age, number of seats, location, price range and so on, and fairly quickly you’ll have found a shortlist of cars fitting your exacting preferences. Faceted search is a really good way to buy shoes, since a user is interested in shoes only of a certain style and size, give or take.

Free-text searching, even using fairly hardcore search operators, doesn’t compare. I’m a hardened user of search operators, wrapping critical terms in quote-marks and so on. But this requires me to remember all this syntax, which I can’t, and popular search engines seem to adhere to it less strictly with every passing year. Google Shopping might be good for browsing, but is nearly unusable for anything precise, seeing as the only facets are condition, price, colour and seller. Amazon’s search, while more finely faceted, seems to be deliberately imprecise to encourage users to browse. I’d guess they’re measuring dwell-times over particular pictures, to gauge what to recommend later. But again, browsing isn’t finding.

Google Maps is infuriating for its lack of useful facets. It can plot intricately precise routes from A to B, but can only facet these routes to exclude tolls, motorways or ferries. I can’t avoid tight turns or blind corners, which would be useful in something bigger than a car, but maybe the dataset doesn’t exist. That said, what if I wanted to include something in my route? The cheapest fuel-stop, maybe, a nice bite to eat, or a scenic spot for a leg-stretch. The satnav I bought nearly two decades ago could do this. If I were minded to, I could research it all myself (by searching separately) and set intermediate destinations along my route, but the voice in my head says: you’re the search-engine, you figure it out.

There used to be lots of ways to find online stuff: not just search but directories, curated lists, webrings and whatnot. The web outgrew many of these approaches but, even so, there also used to be various search paradigms—like some specifically for queries in the form of questions. Now there seems to be only one style of search: jabbing in keywords and hoping for the best. It has contorted online content into a keyword-dominant vernacular: keyword-jabbing, and optimising for it, has made the contemporary web even more categorised than the directories of old. Funny how that came back around. It’s influencing how we behave.

A poignant, if a little NSFW, example:

Data on what we search for, pay for and click on is being used to predict our desires and funnel us bespoke(ish) porn.

At first blush, it might seem like this kind of micro-targeting would just turbo-boost the internet’s existing trajectory, making it even easier for people to find and embrace a diversity of bodies and fetishes. But there’s a fundamental shift here from a world in which we explore a passive sea of content to a world in which porn actively explores and prescribes itself to us. Because this shift stems from deep financial upheaval in the adult industry, the content pushed upon us will likely increasingly reflect what is most profitable, not what is most widely desirable. It could well become narrowing, or at least channelling, rather than broadening.

This approach is also how ‘natural’-language interfaces—the ones you bark at from across a room—have evolved. Alexa is precise only if you are able to ask for something that the machine can recognise as being near-unique, such as a particular song. It can’t have an ongoing conversation with you about taking a large set of search results and turning it into a handful of sensible choices.

More widespread faceted search would be an opportunity to take ubiquitous keyword-based searching and explode it into new, useful, powerful interfaces: both useable and precise. eBay has stuck to this model and it’s really good. LinkedIn uses it a lot for finding contacts, jobs and so forth, and it works well: finding something as specific as which of your contacts who know someone at Microsoft would be arduous without it. Holiday sites—hotels, flights and the like—also give users plenty of facets to turn all the world’s options into a manageable shortlist. Users are used to doing it; it’s just not available widely.

That’s how I’d want to use a natural-language search but Siri, or whichever, can’t handle it. I can’t even remember what I have to shout at my phone to use its natural-language doodah: that’s how little use I have for such a blunt implement. It’d be more interesting for these interfaces—all interfaces—to take the 1990s multi-dropdown facet model like eBay and think about how to bring this level of specificity to contemporary users’ lives. Simply put, facets reflect how people think: must-haves versus negotiables. Certainties versus unknowns. A find-engine would do the same.

Whenever I hear a user interface is ‘intuitive’—or needs to be—a synapse fires. An intuitive digital interface would follow users’ behaviour, regardless of their prior experiences with other interfaces. We’re not there yet.

When pointy, clicky, window-based desktops emerged, users were trained to find functions at the top, contexts at the bottom, and the ‘work’ going on in the middle. Machines running either Windows and MacOS still ship like this. If an Excel user wants to find functionality, they head upwards to a ribbon icon or dropdown menu. If they want to switch to Word, they head downwards to the start-bar or dock.

The internet dented this. On the web, thanks to the hyperlink, functionality is all muddled in with content. Context-switching is more like a fork in the road. Anyone who’s fallen down a wikihole will know.

In my last schoolyear, I helped teach the sprogs basic computer skills: like community service, but for crimes not yet committed. In a couple of hours, I could get 35 kids to be reasonably confident with spreadsheets: diving into cells to construct formulas, and switching around between applications to pull in chunks of data. The keen ones—the ones bitten by the bug—then went on to discover the command-line and, I expect, are now lounging on a domain-name empire or something.

Getting to grips with the pre-Google early web, though, took longer. Just like a spreadsheet, a wide bar across the top would accept input, but the kids would find it harder to make their browser ‘do stuff’. The browser didn’t integrate well with other applications: you could copy out but not paste in. Clicking a link within an email could take you to a specific webpage, but clicking a link on a webpage couldn’t take you to a specific email, only a blank one.

While that early web is no longer recognisable, I still catch users staring blankly at interfaces, just as those kids did in the mid-Nineties. Better browsers, better online services and new internet devices—phones; tablets; watches etc.—tackled many of these paradigm mismatches head-on. Many millions of hours of thought and experimentation have been given over to make functionality, context and content coexist on palm-sized touchscreens.

Creeping homogeny, or more accurately convergent evolution, in interface design has tried to put things where users might expect them based on their previous experiences. But challenges with contemporary interfaces are more complicated and nuanced than simply where navigation should be placed.

An example. On my desktop, all the system settings are in one place, and then all the settings specific to an application are in another. Except these days there are system-wide restrictions on things like accessing the screen or the disk, so every application I install has to beg me to change system preferences before it can function, thereby dividing application settings into two contexts.

It’s worse on my mobile. All preferences for the system, and the apps that shipped with the phone, are in one huge settings menu. But the preferences for individual apps are who-knows-where: sometimes in the app context, sometimes the system context. Users drown in this kind of thing. Who can see my stuff (as a setting) and who’s stuff I can see (as a function) are more closely related in users’ minds than is often to be found in the interfaces they use.

This is complicated because there are so many use-cases. How an app notifies you is one thing, how all apps notify you is another. But users tend not to think like that; they often lean towards task-focussed thinking. So current interfaces trend towards a convergence, leaving interfaces feeling vaguely recognisable but indistinct, which in turn causes further confusion and poorer recall amongst users.

However, rather than all designers continuing to iterate towards the mean, there are two areas of exploration that could significantly improve users’ comprehension of interfaces, which in turn would make interfaces more useful. These areas could perhaps make interfaces truly intuitive, by following human behaviours rather than imposing conventions.

First, users do things fastest in familiar ways. So, passive preferences could follow users around, between apps, contexts, tasks and even functionality. Instead of users having to learn how each device and app can be made to, say, grab a photo from one context and use it in another, the standard way they do this could transcend contexts, just how copy-and-paste does. All the other methods would remain too, but the one that’s top-of-mind for the user would always work as they’d expect, in any context.

Imagine Microsoft Word but it comes as a plain text editor. No bold/italic/etc. The only commands are open, save, copy, and paste.

You get used to it. Then one day you decide you’d like to style some text… or, better, you receive a doc by email that uses big text, small text, bold text, underlined text, the lot.

What the hey? you say.

There’s a notification at the top of the document. It says: Get the Styles palette to edit styled text here, and create your own doc with styles.

You tap on the notification and it takes you to the Pick-Your-Own Feature Flag Store (name TBC). You pick the “Styles palette” feature and toggle it ON.

So the user builds up the capabilities of the app as they go.

Second, this is a good potential AI problem. Designers lean upon what has gone before—what has demonstrably worked, what’s aesthetically appealing and so forth—whereas an AI could start on interface problems unfettered by baggage and metaphors. It would be interesting to think of AI in the context of intuitive design, not as a producer of strange, mangled cat pictures, but as a collegial third eye: unbound by design dogma and able to show us new possibilities from outside or own influences.

What we need are intelligences that help us do useful things in new and better ways, ways which we could not have imagined alone. AIs which are colleagues and collaborators, rather than slaves and masters.