Models & Labs · United States

With GPT-6, ChatGPT’s responses are much more visually appealing and interactive

Credited to SiliconANGLE · siliconangle.com · 14h ago

Photograph · SiliconANGLE

OpenAI Group PBC says ChatGPT is moving away from its traditional wall-of-text responses and giving users a more interactive user interface to play with.

In a media briefing today, the company said its GPT-6 models will now be able to access an “Intelligent UI” that will allow the chatbot to generate more visual outputs when it deems necessary, replacing words with graphics, tappable buttons, forms and charts” that will provide a much more interactive experience.

ChatGPT will determine for itself when an answer needs a more visually appealing response and interactive elements for the user to explore a topic more deeply. In such instances, the model will integrate these directly with its response.

In an example, OpenAI showed someone asking ChatGPT how to piece together a seven-speed bicycle. Rather than just churning out endless text that explains everything, it instead generated a complete design breakdown, complete with visualizations of each of the main parts and a clickable button that allows readers to learn more about each of them.

In a second example, a user asked ChatGPT to create a plan for a lamb roast with pictures of each dish, plus a recipe that allowed the user to alter the number of guests in order to recalculate the amount of ingredients required.

OpenAI explained that ChatGPT will generate these visualizations from a library of components that the GPT-6 models were pretrained on. For more basic prompts and questions, the chatbot will stick with the old text-only format, but users will have the option to request a more interactive response at any time, and it will reformat the response accordingly.

The goal with this update is to make “learning complex topics easier,” OpenAI Product Manager Aarush Selvan said in the briefing. The intelligence UI may also help to make ChatGPT more accessible and increase its appeal, though Selvan didn’t say this directly.

ChatGPT has predominantly been a text-based interface,” Selvan said. But the company wants to help people get things done, and it believes that “from the everyday things like planning a recipe or a trip or even trying to learn something, the most helpful answers aren’t just text.”

The visual UI is primarily designed to make ChatGPT easier for people to use and retain information, Holger Mueller of Constellation Research told SiliconANGLE. “We’ve long known that usability is one of the primary factors driving adoption of software, and the text-only interface doesn’t really help when people when it comes to information absorption or processing,” the analyst said.

Mueller believes that this is such a significant move by OpenAI that it could signal the opening of a new front in the battle of the AI assistants, especially if Anthropic responds with a similar visual aesthetic in Claude. “Anthropic will be under pressure to respond to this in the next few weeks, or it could even do so in just a few days if something similar was already in the works,” he said. “This could lead to usability and not just the quality of the model and its answers becoming the deciding factor in which chatbots people want to use. If Anthropic fails to respond to this, OpenAI has a very strong differentiator that would likely change many people’s choices once they see it.”

That is assuming that the Intelligent UI works as well as advertised, of course. It’s worth noting that it’s not an entirely original idea. In fact, it’s somewhat reminiscent of Google LLC’s “Generative Interface,” which was introduced with the launch of Gemini 3 last year, although it seems to be a bit more colorful and more graphics-heavy. When Google introduced that feature, there was a lot of excitement around it, but it doesn’t really appear to have differentiated Gemini as a chatbot all that much.

There may be other considerations, too. OpenAI didn’t say if the Intelligent UI would mean higher token consumption, but that does appear to be the case with all of those extra elements being generated, so that’s something users might want to watch out for. It’s also not clear how susceptible these visual elements may be to the problem that plagues every AI chatbot, namely hallucinations.

OpenAI, however, seems to be pushing for AI to become more visually appealing. Earlier this week, the company revealed it would soon introduce the first image-based ads in ChatGPT’s responses. More cynical users might think that the Intelligent UI may make it more difficult to see which responses are ads and which are the chatbots own. Regardless, ads are almost certainly going to become a part of every AI chatbot at some point.

OpenAI said the Intelligent UI is being made available from today for ChatGPT Plus, Pro, Business and Enterprise subscribers, though in the latter case, the workplace administrator will need to enable it. Free users and Go subscribers will get access to it tomorrow.

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Models & LabsUnited States

Google Cloud introduces Gemini agent to change enterprise work

Credited to SiliconANGLE · siliconangle.com2h ago

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Google LLC’s Cloud division today introduced Gemini agent, a unified artificial intelligence assistant that can act autonomously, generate code and complete work across any device, including web, mobile and desktop.

The new agentic AI experience can be accessed anywhere and through any channel, including the command line, Google Workspace, Microsoft 365 or Slack. It even works within third-party apps and requires no dedicated interface. It is designed to be “omnipresent” and work wherever the user is.

Google Cloud Chief Executive Thomas Kurian said Gemini can answer questions, handle knowledge work, create images and media or take ideas and turn them into finished products.

“You give it objectives, not instructions. You delegate an outcome and come back to finished work,” he explained. “For an agent to do that, it has to be connected to your personal workflows, your systems of record, and your enterprise controls.”

The new agentic experience also follows a common trend in the AI industry: persistent execution. That means it maintains a single set of memories, context and personalization across all communication channels with all its sub-agents. It acts as a teammate that remembers everything the user tells it and can spin out smaller coworker agents with their own dedicated identities and expertise, with their own @agents.company.com emails and their own persistent storage. They also have access only to the context the user or team members provide.

Google Cloud added that the Gemini agent provides flexibility in model choice. Each job runs on a model fitting the task at hand. Simple tasks might run on Gemini Flash, a small, quick model for simple day-to-day tasks or a flagship frontier model like Argon for long-horizon work. Anthropic PBC Claude models are also available today, with other leading private and open models coming later.

Under the hood: skills and tools

Kurian leaned into Google’s vision of providing a full understanding of business context, everything from pricing to product portfolios to departmental norms. Gemini observes and contains the breadth of institutional knowledge that makes a company unique and brings it to the forefront, grounding answers through company data.

Gemini securely connects through collaboration tools such as Confluence, Microsoft Office, Teams, Slack and Workspace. It also works through tools such as Git and Jira, enterprise platforms including Salesforce and ServiceNow, databases such as BigQuery, Databricks, Postgres and Snowflake, and files on desktops.

Users can also build reusable skills that act as instructions, knowledge and workflows that act as prompts that teach agents how to perform specific, multistep tasks. Gemini ships out of the box with a global library of skills, but users can add and publish their own custom skills to a shared company registry, and can build custom skills by asking an agent to take a task and convert it into a reusable template.

The system also learns from every question it is asked and every objective. Session memory, even when it runs for days, talks to people and works with other agents, as it reads documents. It also continues to update its understanding of how users work with it and what it should produce. Google said it spends as much time learning what users are doing as it does using tools.

Google added that it has provided specialized skills built for specific domains, starting with data. For example, the company is extending Gemini with machine learning skills and tools for data scientists and engineers. Describing an outcome in plain language, and Gemini can generate PySpark code, provide a notebook to edit and test it, train a model and troubleshoot issues on its own.

For business users, Gemini can use operational BigQuery reporting skills and the Knowledge Catalog to construct and save queries. This allows the creation of real-time operational reports simply by asking. Once saved, teams can run reports without incurring additional token costs, producing consistent, verified answers every time.

Cost controls at the forefront

Google Cloud fore fronted that although per-token prices dropped significantly since 2024, by about 98%, enterprise AI volume has greatly increased.

Google announced flexible spending options in August and those are coming online today.

Internally, multimodel orchestration designed to pick the right model for the job, or break apart complex jobs across quick models for small tasks and frontier models for difficult parts, reduces costs further. Within this, smart routing automatically triages enterprise workloads so each one runs on the model that delivers maximum performance at the lowest possible cost.

Finally, users can set hard limits on AI spend within the Cloud Billing Console. Gemini will enforce this by monitoring token usage, and if a spend cap is triggered, the agent pauses. The user can resume work in the console with approval, but this requires accepting that it will exceed the set budget cap.

Because tracking is per project, companies can charge AI costs back to specific departments. This lets businesses audit and plan budgets by team and department, enabling more granular cost provisioning.

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