Generative AI’s ‘iPhone Moment’: 3 Things Developers Need to Do

It has just been over a year since ChatGPT launched and created a generative AI revolution. Since then, industry insiders have speculated when the technology will truly achieve its ‘iPhone moment,’ fully crossing into the mainstream. For those responsible for building successful generative AI applications, it is crucial to understand what this really means, and how and when it will happen.

When Apple co-founder Steve Jobs first launched the iPhone in 2007, the device sparked an enormous wave of publicity. Yet sales in the first year were relatively modest, soaring upwards only in mid-2008 when the App Store debuted. As more applications were added, sales climbed even further.

This highlights that the actual ‘iPhone moment’ was not the device launch itself. The true revolutionary moment came when people were able to easily find and download intuitive, fun applications. The App Store made apps incredibly simple to discover − far more so than any other mobile marketplace had ever done before − and also enforced the governance and security policies needed to protect users.

Generative AI now stands at the same precipice, and organizations should be acute to these learnings when riding this wave alongside their developers.

An ‘iPhone moment’ for gen AI

The excitement around generative AI is often expressed as the ability to talk to and interact directly with the data, but that is not entirely true. The people who interact with the data are typically in the back room scripting out Python queries, while the rest of us interact with the applications — like ChatGPT — which are the intermediaries between the user and large language models.

Furthermore, the data does not live on whatever device we are using to access the app; it is stored and processed in the cloud, which means the era of generative AI is about much more than just the data and the algorithms. What really brings generative AI to fruition is a combination of the LLMs, the cloud data infrastructure, plus the applications.

So, much as the iPhone reached its apex with the availability of numerous simple applications for discrete tasks, generative AI will become ubiquitous when its capabilities are presented in the form of intuitive applications that are easy to discover and use. In many cases, users will not even know there is an AI under the hood; it becomes part of the application, along with natural language processing, AI-assisted search, and other embedded functionality.

For developers looking to build successful gen AI applications, it means thinking about more than just what the data can do, but also how their users will interact with it. You can build the most sophisticated model in the world, but if it is surfaced via a complex interface or is difficult to discover and install, it will go to waste.

Here are three important elements for developers to consider when building successful generative AI applications.

1. Simple, user-friendly interface

One reason ChatGPT took off so quickly is the bare simplicity of its interface, so think carefully about the user’s level of technical sophistication. Consumers, business executives, data analysts, data engineers, and software developers all have very different relationships with, and understanding of, technology, so design an interface that will be perfect for them — not necessarily for you.

In many cases, the interface will not be a standalone app. Businesses are already embedding generative AI into existing apps in a variety of ways. Eventually, it is likely that many apps will feature a gen AI ‘copilot’ that is ready to answer questions — much like the search bar we find in apps today.

To build these interfaces, developers need tools that allow them to quickly turn their data, models, and analytic and app functions into interactive apps written in languages such as Python. Our own Streamlit is one option — more than 20,000 LLM apps have already been built on the Streamlit Community Cloud alone — but there are other options, too.

2. Build governance into development environment

One reason the App Store was so successful is because it provided a secure, tightly controlled environment for building applications. Still today, developers must ask permission to access a user’s contacts, camera or photos, for example. For generative AI applications to really take off, developers need this governance built into their development environment.

That means having an infrastructure layer with the required compliance, security, interoperability and access controls built in, ensuring both the developer and the users see only the data they are supposed to see. Apps may need to combine data on the provider's side with data on the consumer side, but without revealing the data to either party, which can be achieved with data clean room technology.

This governance should be built into the developer environment from the beginning so that developers are not building custom controls for every application. It should be easy for consumers to understand and decide what permissions they want to grant the application. And ideally, it will work across different public cloud environments, since many organizations are now operating on a multi-cloud architecture.

3. Make apps easy to find

Target users also need the ability to find and use your applications easily. This is no small matter: Developers need a way to raise awareness of their apps and distribute them through a trusted environment where the intended audience can find and install them easily.

If you are building consumer apps, that may be through the App Store or the Google Play Store. In a business environment, an app marketplace provides a way to publish apps once and make them available internally to employees or externally to other businesses. Ideally, the marketplace operates across clouds and regions and supports flexible, usage-based business models to monetize the app.

The ‘iPhone moment’ is coming

There have been few technologies that have captured attention like gen AI has, for both the industry and consumers. So much so that IDC has predicted that spending on AI products will exceed $500 billion by 2027, driven in no small part by the surge of interest in generative AI specifically. To bring about generative AI’s ‘iPhone moment,’ one thing is crucial: Developers must look beyond simply developing a brilliant AI model, and focus efforts on creating applications. This will enable as many people as possible to use and benefit from generative AI.