Also, even putting aside the fact that first-party apps necessarily have certain advantages third-party apps do not (otherwise, there’d be no distinction), apps from the same developer have broad permission to share data and resources via app groups. Gmail can talk to Google Calendar, and Google Calendar has full access to Gmail’s address book. It’s no more “fundamentally anticompetitive” for Messages and Apple Mail to have full access to your Contacts address book than it was for Meta to launch Threads by piggybacking on the existing accounts and social graph of Instagram. If it’s unfair, it’s only unfair in the way that life in general is unfair.

LLMs will represent a step change in tool support for end-user programming: the ability of normal people to fully harness the general power of computers without resorting to the complexity of normal programming. Until now, that vision has been bottlenecked on turning fuzzy informal intent into formal, executable code; now that bottleneck is rapidly opening up thanks to LLMs.

If this hypothesis indeed comes true, we might start to see some surprising changes in the way people use software: One-off scripts: Normal computer users have their AI create and execute scripts dozens of times a day, to perform tasks like data analysis, video editing, or automating tedious tasks. One-off GUIs: People use AI to create entire GUI applications just for performing a single specific task—containing just the features they need, no bloat. Build don’t buy: Businesses develop more software in-house that meets their custom needs, rather than buying SaaS off the shelf, since it’s now cheaper to get software tailored to the use case. Modding/extensions: Consumers and businesses demand the ability to extend and mod their existing software, since it’s now easier to specify a new feature or a tweak to match a user’s workflow. Recombination: Take the best parts of the different applications you like best, and create a new hybrid that composes them together.

Chat will never feel like driving a car, no matter how good the bot is. In their 1986 book Understanding Computers and Cognition, Terry Winograd and Fernando Flores elaborate on this point: In driving a car, the control interaction is normally transparent. You do not think “How far should I turn the steering wheel to go around that curve?” In fact, you are not even aware (unless something intrudes) of using a steering wheel…The long evolution of the design of automobiles has led to this readiness-to-hand. It is not achieved by having a car communicate like a person, but by providing the right coupling between the driver and action in the relevant domain (motion down the road).

Think about how a spreadsheet works. If you have a financial model in a spreadsheet, you can try changing a number in a cell to assess a scenario—this is the inner loop of direct manipulation at work. But, you can also edit the formulas! A spreadsheet isn’t just an “app” focused on a specific task; it’s closer to a general computational medium which lets you flexibly express many kinds of tasks. The “platform developers"—the creators of the spreadsheet—have given you a set of general primitives that can be used to make many tools. We might draw the double loop of the spreadsheet interaction like this. You can edit numbers in the spreadsheet, but you can also edit formulas, which edits the tool

what if you had an LLM play the role of the local developer? That is, the user mainly drives the creation of the spreadsheet, but asks for technical help with some of the formulas when needed? The LLM wouldn’t just create an entire solution, it would also teach the user how to create the solution themselves next time.

This picture shows a world that I find pretty compelling. There’s an inner interaction loop that takes advantage of the full power of direct manipulation. There’s an outer loop where the user can also more deeply edit their tools within an open-ended medium. They can get AI support for making tool edits, and grow their own capacity to work in the medium. Over time, they can learn things like the basics of formulas, or how a VLOOKUP works. This structural knowledge helps the user think of possible use cases for the tool, and also helps them audit the output from the LLMs. In a ChatGPT world, the user is left entirely dependent on the AI, without any understanding of its inner mechanism. In a computational medium with AI as assistant, the user’s reliance on the AI gently decreases over time as they become more comfortable in the medium.

Thinking in dialogue is easier and more entertaining than struggling with feelings, letters, grammar and style all by ourselves. Using AI as a writing dialogue partner, ChatGPT can become a catalyst for clarifying what we want to say. Even if it is wrong.6 Sometimes we need to hear what’s wrong to understand what’s right.

Seeing in clear text what is wrong or, at least, what we don’t mean can help us set our minds straight about what we really mean. If you get stuck, you can also simply let it ask you questions. If you don’t know how to improve, you can tell it to be evil in its critique of your writing

Just compare usage with AI to how we dealt with similar issues before AI. Discussing our writing with others is a general practice and regarded as universally helpful; honest writers honor and credit their discussion partners We already use spell checkers and grammar tools It’s common practice to use human editors for substantial or minor copy editing of our public writing Clearly, using dictionaries and thesauri to find the right expression is not a crime

Using AI in the editor replaces thinking. Using AI in dialogue increases thinking. Now, how can connect the editor and the chat window without making a mess? Is there a way to keep human and artificial text apart?

One key problem across the apps is the slog of self-presentation, or “impression management,” said Rachel Katz, a digital media sociologist who studies online dating at the University of Salford in the UK. “An important aspect of it is knowing your audience,” Katz said. On dating apps, you don’t know who exactly you’re presenting yourself to when picking a profile picture or composing your bio. You also don’t have physical cues that can help you adjust that self-presentation. “You’re trying to come up with something that’s generally appealing to people, but it can’t be too weird. It can’t be too unique,” said Bryce. “That’s partly why it’s exhausting,” Katz explains, “because it’s this constant labor. ... You’re not really sure of how to do it, you can’t just fit into a comfortable social role.”

When dating apps are not delivering on compatibility, Dean said, they are leading you to “believe that there’s a forever volume of people you can always like.”

Ury rejects the notion that apps should be asking people for more about themselves in writing or through extensive questionnaires. Users may match up on paper but end up disappointed in real life. “I would have rather that people understand that sooner by meeting up earlier,” she said. “Use the app as a matchmaker who gives you the matches — and then, as quickly as possible, the two of you should be chatting live to see if you are a match,” she said. “We found that three days of chatting is the sweet spot for scheduling a date.”

Quality software from independent makers is like quality food from the farmer’s market. A jar of handmade organic jam is not the same as mass-produced corn syrup-laden jam from the supermarket. Industrial fruit jam is filled with cheap ingredients and shelf stabilizers. Industrial software is filled with privacy-invasive trackers and proprietary formats. Google, Apple, and Microsoft make industrial software. Like industrial jam, industrial software has its benefits — it’s cheap, fairly reliable, widely available, and often gets the job done.

Big tech companies have the ability to make their software cheap by subsidizing costs in a variety of ways:

Google sells highly profitable advertising and makes its apps free, but you are subjected to ads and privacy-invasive tracking. Apple sells highly profitable devices and makes its apps free, but locks you into a proprietary ecosystem. Microsoft sells highly profitable enterprise contracts using a bundling strategy, and makes its apps cheap, also locking you into a proprietary ecosystem.

I’m not saying these companies are evil. But their subsidies create the illusion that all software should be cheap or free.

Independent makers of quality software go out of their way to make apps that are better for you. They take a principled approach to making tools that don’t compromise your privacy, and don’t lock you in. Independent software makers are people you can talk to. Like quality jam from the farmer’s market, you might become friends with the person who made it — they’ll listen to your suggestions and your complaints.

Big tech companies earn hundreds of billions of dollars and employ hundreds of thousands of people. When they make a new app, they can market it to their billions of customers easily. They have unbeatable leverage over the cost of developing and maintaining their apps.