ChatGPT and its competitors: how OpenAI’s interface shaped the look of modern chatbots

ChatGPT sets the standard and other chatbots replicate its logic and appearance.

Since 2022, the ChatGPT interface has established a benchmark for visual clarity and functional consistency that, in practice, defines the collective perception of chatbots. Every new platform that wants to be immediately usable adopts visual choices, conversation flows, and response tones that are recognizably inspired by the original experience offered by OpenAI, generating an ecosystem of similarities that is hard to overlook.

We see in common AI chatbots the following characteristics, all clearly inspired by the design and user experience established by ChatGPT:

Minimalist interface with a clean chat window and sidebar for conversations, favoring ample spacing and subtle colors; Simple, rounded text bubbles with little or no border, using an easy-to-read sans-serif font on a light or dark background;

Persistent input bar at the bottom, often with quick-action icons for voice or attachments, and automatic follow-up suggestions as buttons; Smooth scrolling and subtle animations (like typing indicators) to create a fluid, low-stress chat experience, consistent across desktop and mobile.

ChatGPT defines visual simplicity parameters that become templates for the industry.

A neutral background, the input field anchored at the bottom of the screen, and a vertical flow of well-spaced messages.

This combination guarantees readability, lowers the learning curve, and, above all, ensures that conversation remains the focus of the experience. From Claude’s interface to Gemini’s environment, passing through Meta AI, Grok, and Stella AI, changes in colors or icons do not affect the substance: the layout is almost entirely superimposable on that introduced by ChatGPT. Developers recognize that, by offering a familiar structure, users perceive a new chatbot as immediately “understandable” and therefore trustworthy.

In the conversational technology sector, the tendency to standardize layout is not just about aesthetics but is also a matter of efficiency. Platforms avoid investing time in overly original design solutions, preferring to adopt what has already proven effective in terms of user adoption and satisfaction. The presence of a repeated scheme also facilitates the scalability of functions across different devices, allowing users of all ages and experience levels to navigate smoothly between desktop and mobile.

Another aspect to consider is the pressure exerted by the developer community and feedback collected during testing phases: any element considered unclear or unnecessary is quickly eliminated, favoring the persistence of a structure now validated by millions of real sessions. As a result, every new platform entering the conversational AI market is almost “forced” to embrace this basic setup, risking appearing unintuitive or “off-standard” if it tries to deviate too much.

The core functionalities of ChatGPT are imitated to maintain perceived parity.

Contextual responses as long as needed, automatic follow-up suggestions, the ability to summarize and translate with a single request.

These solutions, introduced and refined by ChatGPT, are now present in almost every market alternative. When Gemini provides summary cards at the end of a thread or Claude suggests possible clarifications, the goal is to reduce the experience gap users would feel if such now-expected functions were missing. This is not just about aesthetic emulation: copying ChatGPT’s operational modes reassures users that “you’ll find what you already know how to use here, plus a few proprietary features.”

The pressure to maintain a set of features now perceived as “minimum requirements” also comes from the sector’s growing competitiveness. When a feature like contextualized answers or smart suggestion generation is launched on a highly successful platform like ChatGPT, its absence elsewhere is perceived as a drawback. Users, often used to switching rapidly between chatbots, no longer tolerate missing features: if they don’t find the same level of service, they are likely to abandon the new solution before even discovering its specific strengths.

Moreover, ChatGPT’s “key” functionalities are gradually being integrated into contexts beyond simple chat: from automation plugins to integrations with productivity suites. Consequently, to be considered competitive and suitable for professional or educational uses, competing chatbots are also expected to offer personalized answers, proactive assistance, and rapid text reformulation tools, creating a race that further accentuates convergence.

The most copied graphical elements reveal the strength of ChatGPT’s original design.

Ample spacing, text bubbles without flashy borders, invisible scrollbars unless needed.

These details, seemingly minor, regulate reading rhythm and contribute to the feeling of a smooth conversation. Even the subtle shadowing behind messages has been adopted by numerous competitors, as has the use of a sober sans-serif font on a light background. When platform designers decide to deviate, they do so in a controlled way: a subtle gradient, an optional dark theme, or a corporate-colored badge—never revolutionizing the visual hierarchy ChatGPT has normalized.

The impact of these graphical choices extends beyond aesthetics and affects the psychological perception of interaction: a tidy interface reduces cognitive stress and makes it less tiring to maintain focus during long conversations. The success of these graphical details is confirmed by the high levels of user experience satisfaction, pushing competitors to faithfully replicate even micro-interactions, such as the gradual appearance of responses or animated “typing” effects.

At the same time, the care in reproducing these elements highlights the desire, among new platforms, not to create breaks in the visual flow users associate with a “good” AI. We are witnessing an almost mimetic diffusion of patterns, with constant attention to feedback collected online: every detail appreciated on ChatGPT is quickly tested and, if possible, implemented by other development teams, in a dynamic of continuous adaptation.

ChatGPT’s conversational tone becomes the de facto standard among competitors.

Moderate politeness, absence of slang, modular explanations that anticipate possible collateral questions.

The success of this register has convinced other development teams to converge on the same pragmatic “voice,” capable of adapting to both formal and informal contexts without going from one extreme to the other. If Grok tried to communicate exclusively in a highly technical-academic style or Meta AI adopted an excessively informal slang, users would feel out of place; this is why most chatbots retain the sober language ChatGPT made familiar.

The “neutral and reassuring” conversational setting is the result of multiple large-scale tests involving users from every demographic group. ChatGPT’s goal was—and still is—to make AI accessible to anyone, without linguistic barriers or ambiguities in tone. Other platforms, observing the positive results in terms of appreciation and diffusion, have chosen to embrace the same style, abandoning more complex or “characterized” solutions that had been experimented with in the past chatbot sector.

Beyond the tone, even the structure of answers is influenced by this: the tendency to provide explanations divided into paragraphs, the use of practical examples, and the predisposition to clarify and simplify even potentially technical questions. These strategies have been internalized in response design even by systems created with different purposes or in other markets, as they are now considered “best practice” for any chatbot aiming to reach a broad and varied audience.

Economic and marketing reasons explain why “cloning” works better than innovating.

Reducing user training costs, accelerating comparative benchmarks, minimizing the risk of initial rejection.

When an AI startup evaluates its chatbot’s design, it knows that deviating too much from the dominant standard can lead to lower adoption. Furthermore, reviews, press trials, and technical demos tend to position new tools “next to” ChatGPT: having a similar interface facilitates side-by-side analysis and highlights only the differences in capabilities, which are the real selling points. Hence the conscious decision to adopt a visual scheme and micro-interactions already validated by the public.

The conversational AI market now operates with logic very similar to that of mainstream software: the threshold for “unnecessary” perceived differences is very low, and every step toward usage continuity increases the chances of retaining users already used to certain patterns. Support and assistance costs are significantly reduced when most features are “intuitive” precisely because they mirror flows already experienced in other platforms.

On the other hand, marketing strategies also encourage emphasizing similarity rather than originality: advertising campaigns and presentation materials often show chatbots side by side, where familiarity becomes synonymous with reliability. This trend is reinforced by the ecosystem of reviews and online communities, which focus discussion more on differences in answer quality and possible integrations, deliberately downplaying purely aesthetic distinctions.

The few differences show where competitors try to stand out without straying too far.

Claude introduces a soft violet to reinforce its identity, Gemini adds contextual side shortcuts, Grok focuses on a badge with a tech-social aesthetic.

These peculiarities are used to suggest personality and brand positioning while remaining anchored to the original model. In practice, each chatbot decides to “sign” the experience with minimal but calculated touches of recognizability, as the entire visual architecture—from the arrangement of buttons to loading animations—must continue to reflect the balance already established with ChatGPT.

The need to differentiate, however limited, is also expressed through micro-details: some platforms introduce animated avatars, dynamic badges, or optional sound effects, seeking to create small points of emotional connection for the user. However, these interventions remain confined to non-structural elements so as not to compromise the fluidity and immediacy that the majority of users consider a priority.

At the same time, some differences are imposed by global branding needs or by specific market targets: for example, a distinctive color palette helps make the app immediately recognizable among dozens of similar tools, especially where competition is fiercest. Nevertheless, the choice not to “upend” the basic model stems from the awareness that end users always prioritize continuity and predictability over visual originality—at least until the underlying technology offers concrete reasons to abandon habits acquired with ChatGPT.

____________

FOLLOW US FOR MORE.

DATA STUDIOS

datastudios.org