2025-10-14 08:00:00
Last week I had a running list of user experience and technical hurdles that made using applications "within" AI models challenging. Then OpenAI's ChatGPT Apps announcement promised to remove most of them. Given how much AI is changing how apps are built and used, I thought it would be worth talking through these challenges and OpenAI's proposed solutions.
Whether you call it a chat app, a remote MCP server app, or an embedded app, when your AI application runs in ChatGPT, the capabilities of ChatGPT become capabilities of your app. ChatGPT can search the web, so can your app. ChatGPT can connect to Salesforce, so can your app. These all sound like great reasons to build an embedded AI app but... there's tradeoffs.
Embedded apps previously had to be added to Claude.ai or ChatGPT (in developer mode) by connecting a remote MCP server, for which the input field could be several clicks deep into a client's settings. That process turned app discovery into a brick wall for most people.
To address this, OpenAI announced a formal app submission and review process with quality standards. Yes, an app store. Get approved and installing your embedded app becomes a one-click action (pending privacy consent flows). No more manual server configs.
If you were able to add an embedded app to a chat client like Claude.ai or ChatGPT, using it was a mostly text-based affair. Embedded apps could not render images much less so, user interface controls. So people were left reading and typing.
Now, ChatGPT apps are able to render "React components that run inside an iframe" which not only enables inline images, maps, and videos but custom user interface controls as well. These iframes can also run in an expanded full screen mode giving apps more surface area for app-specific UI and in a picture-in-picture (PIP) mode for ongoing sessions.
This doesn't mean that embedded app discoverability problems are solved. People still need to either ask for apps by name in ChatGPT, access them by through the "+" button, or rely on the model's ability to decide if/when to use specific apps.
The back and forth between the server running an embedded app and the AI client also has room for improvement. Unlike desktop and mobile operating systems, ChatGPT doesn't (yet) support automatic background refresh, server-initiated notifications, or even passing files (only context) from the front end to a server. These capabilities are pretty fundamental to modern apps, so perhaps support isn't far away.
The tradeoffs involved in building apps for any platform have always been about distribution and technical capabilities or limitations. 800M weekly ChatGPT users is a compelling distribution opportunity and with ChatGPT Apps, a lot of embedded AI app user experience and technical issues have been addressed.
Is this enough to move MCP remote servers from a developer-only protocol to applications that feel like proper software? It's the same pattern from every platform shift: underlying technology first, then the user experience layer that makes it accessible. And there was definitely big steps forward on that last week.
2025-10-10 05:00:00
Anyone that's designed software has likely had to address the "empty state" problem. While an application can create useful stuff, getting users over the initial hurdle of creation is hard. With today's technology, however, AI models can cross the creation chasm so people don't have.
If you're designing a spreadsheet application, you'll need a "new spreadsheet" page. If you're designing a presentation tool, you'll need an empty state for new presentations. Document editors, design tools, project management apps... they all face the same hurdle: how do you help people get started when they're staring at a blank canvas?
Designers have tried to address the creation chasm many times resulting in a bunch of common patterns you'll encounter in any software app you use.
These approaches require people to learn first, then act. But in reality most of us just jump right into doing and only fall-back on learning if what we try doesn't work. Shockingly, asking people to read the manual first doesn't work.
But with the current capabilities of AI models, we can do something different. AI can model how to use a product by actually going through the process of creating something and letting people watch. From there, people can just tweak the result to get closer to what they want. AI does the creation, people do the curation.
Rather than learning then doing, people observe then refine. Instead of starting from nothing, they start from something an AI builds for them and making it their own. Instead of teaching people how to create, we show them creation in action. In other words, the AI does the (onboarding) work.
You can see this in action on ChatDB which allows people to instantly understand, visualize, and share data. When you upload a set of data to ChatDB it, will:
All this happens in front of your eyes making it clear how ChatDB works and what you can do with it, no onboarding required.
Once your dashboard is made, it's trivial to edit the title (just click and type), change the icon, colors, and more. AI gives you the starting point and you take it from there. Try it out yourself.
With this approach, we can shift from applications that tell people how to use them to applications that show people what they can do by doing it for them. The traditional empty state problem transforms from "how do we help people start?" to "how do we help people refine?" And software shows people what's possible through action rather than instruction.
2025-10-07 06:00:00
As we saw during the PC, Web, and mobile technology shifts, how software applications are built, used, and distributed will change with AI. Most AI applications built to date have adopted a wrapper approach but increasingly, being embedded is becoming a viable option too. So let's look at both...
Wrapper apps build a custom software experience around an AI model(s). Think of these as traditional applications that use AI as their primary processing engine for most core tasks. These apps have a familiar user interface but their features use one or more AI models for processing user input, information retrieval, generating output, and more. With wrapper apps, you need to build the application's input and output capabilities, integrations, user interface, and how all these pieces and parts work with AI models.
The upside is you can make whatever interface you want, tailored to your specific users' needs and continually improve it through your visibility of all user interactions. The cost is you need to build and maintain the ever-increasing list of capabilities users expect with AI applications like: the ability to use image understanding as input, the ability to search the Web, the ability to create PDFs or Word docs, and much more.
Embedded AI apps operate within existing AI clients like Claude.ai or ChatGPT. Rather than building a standalone experience, these apps leverage the host client for input, output, and tool capabilities, alongside a constantly growing list of integrations.
To use concrete examples, if ChatGPT lets people to turn their conversation results into a podcast, your app's results can be turned into a podcast. With a wrapper app, you'd be building that capability yourself. Similarly, if Claude.ai has an integration with Google Drive, your app running in Claude.ai has an integration with Google Drive, no need for you to build it. If ChatGPT can do deep research, your app can ... you get the idea.
So what's the price? For starters, your app's interface is limited by what the client you're operating in allows. ChatGPT Apps, for instance, have a set of design guidelines and developer requirements not unlike those found in other app stores. This also means how your app can be found and used is managed by the client you operate in. And since the client manages context throughout any task that involves your app, you lose the ability to see and use that context to improve your product.
Like the choice between native mobile apps and mobile Webs sites during the mobile era... you can do both. Native mobile apps are great for rich interactions and the mobile Web is great for reach. Most software apps benefit from both. Likewise, an AI application can work both ways. ChatDB illustrates this and the trade-offs involved. ChatDB is a service that allows people to easily make chat dashboards from sets of data.
People can use ChatDB as a standalone wrapper app or embedded within their favorite AI client like Claude or ChatGPT (as illustrated in the two embedded videos). The ChatDB wrapper app allows people to make charts, pin them to a dashboard, rearrange them and more. It's a UI and product experience focused solely on making awesome dashboards and sharing them.
The embedded ChatDB experience allows people make use of tools like Search and Browse or integrations with data sources like Linear to find new data and add it to their dashboards. These capabilities don't exist in the ChatDB wrapper app and maybe never will because of the time required to build and maintain them. But they do exist in Claude.ai and ChatGPT.
The capabilities and constraints of both wrapper and embedded AI apps are going to continue evolving quickly. So today's tradeoffs might be pretty different than tomorrow's. But it's clear what an application is, how it's distributed, and used is changing once again with AI. That means everyone will be rewriting their apps like they did for the PC, Web, and Mobile and that's a lot of opportunity for new design and development approaches.
2025-10-02 08:00:00
In his AI Speaker Series presentation at Sutter Hill Ventures, UC Berkeley's Alexei Efros argued that data, not algorithms, drives AI progress in visual computing. Here's my notes from his talk: We're (Still) Not Giving Data Enough Credit.
Large data is necessary but not sufficient. We need to learn to be humble and to give the data the credit that it deserves. The visual computing field's algorithmic bias has obscured data's fundamental role. recognizing this reality becomes crucial for evaluating where AI breakthroughs will emerge.
2025-09-30 08:00:00
I recently had the pleasure of speaking with Ben Lorica on O'Reilly's Generative AI in the Real World podcast about how software applications are changing in the age of AI. We discussed a number of topics including:
You can listen to the podcast Generative AI in the Real World: Luke Wroblewski on When Databases Talk Agent-Speak (29min) on O-Reilly's site. Thanks to all the folks there for the invitation.
2025-09-26 08:00:00
In her talk Reveal the Hidden Forces Driving User Behavior at Future Product Days, Sarah Thompson shared insights on how to leverage behavioral science to create more effective user experiences. Here's my notes from her talk:
