ChatGPT's transformation: from simple chat to super-app

OpenAI is rebuilding ChatGPT from a chat window into a super-app. According to recent reporting (Financial Times, SiliconAngle), the app is shifting from question-and-answer to an environment in which AI agents carry out tasks themselves: planning, reasoning, writing code, generating images and connecting to external software. One employee was quoted saying that "chat is dead". That is an overstatement, but the direction is clear.

For anyone who still sees AI mostly as "a better search engine", this is the moment to take another look. Below is what is changing, why OpenAI is taking this step, and what it concretely means for an organisation.

From chat window to super-app

ChatGPT began as a single text field where you asked a question and got an answer back. The super-app that OpenAI is now building bundles several functions into the same environment: text, code (via Codex), image generation and connections to other applications. The idea is that you no longer switch tools per task, but that the app itself activates the right function.

The difference with today lies in what the app does without intervention. Right now you type an instruction and wait for an answer. In the new setup, the app can break a task into steps and handle those steps itself.

From reacting to acting: the role of AI agents

The core of the change is the shift from reacting to acting. An AI agent is an AI that does not just answer, but is given a goal and takes the intermediate steps itself: retrieving information, making a choice, performing an action, checking the result.

A few examples of what that can mean in practice:

• An agent that sorts incoming customer questions and answers the standard ones itself, so that people only pick up the exceptions.

• An agent that gathers data from various systems and turns it into a draft report.

• An agent that monitors a process and only sends an alert when something falls outside the agreed boundaries.

Important: this is not magic and not a button. An agent only does good work within boundaries that you have set yourself. Where those boundaries lie, how you check what the agent does and when a human steps in, is exactly the work that decides whether it pays off or becomes a risk.

Why OpenAI is taking this step

Two reasons stand out in the reporting.

The first is money. OpenAI is said to be working towards an IPO and needs higher, more stable revenue. A super-app with agents and a marketplace (the GPT Store) where developers offer paid, specialised agents generates more recurring revenue than standalone subscriptions.

The second is the business market. OpenAI is focusing more explicitly on companies and is emphasising security and privacy, with alignment to standards such as SOC 2 and the GDPR. That makes sense: the largest budgets sit with organisations, and they only come on board once compliance is in order. The message to companies is that you can deploy advanced automation without building your own AI department.

Whether that promise holds in practice depends on how you set it up. A tool that can work compliantly does not automatically work compliantly inside your organisation.

What this means for businesses

For businesses, this development brings opportunities and points of attention together.

The opportunity is real: tasks that now cost time because they are manual and repetitive lend themselves to agents. That can save time and money and give employees room for work that does need their attention. You no longer need an internal AI team to get started.

The points of attention are just as real:

• GDPR and the EU AI Act. As soon as an agent processes personal data or independently prepares decisions, rules apply. A US vendor that claims to be "GDPR-compliant" does not relieve you of your own responsibility as the data controller.

• Control over what the agent does. An agent that acts on its own can also make mistakes on its own. You want to be able to see what happened and to roll back what went wrong.

• Connection to your own systems and data. The value is not in the agent, but in the connection to your processes, knowledge and software. That is custom work.

• People who will work with it. An agent that no one trusts or understands stays unused. Adoption is not an afterthought but a precondition.

The core: the technology is becoming more accessible, the choices around it are becoming more important. Which tasks do you entrust to an agent, how do you set that up safely, and how do you bring your people along.

How we can help

The super-app makes a lot possible, but the gain is not in the tool. It is in the choice of which processes you entrust to it, how you set that up safely and whether your colleagues actually start using it. In practice, that is where most of the time goes, and where most of the mistakes are made.

We follow these developments closely and translate them into what they concretely mean for an organisation like yours: which tasks lend themselves to an AI agent, how to set those up within your own systems and within the rules, and how to bring people along so that it keeps running after launch. If you want to know where the first step lies for your organisation, we are happy to think along.

Frequently asked questions

Sources

1. Indiantelevision.com - OpenAI plots ChatGPT super-app push as coding and AI tools move centre stage
2. Seeking Alpha - OpenAI plans major ChatGPT overhaul ahead of expected IPO (FT)
3. SiliconAngle - OpenAI's planned super-app gets closer; one employee says chat is dead
4. The Jerusalem Post - Tech & start-ups
5. MindStudio - OpenAI's unified AI super-app: ChatGPT, Codex and agentic tooling
6. CMC Markets - OpenAI IPO
7. Forbes - OpenAI IPO: things to know