Large Language Models (LLMs) like ChatGPT and Claude have made a profound impact on how artificial intelligence is integrated into our daily workflows. At their core, LLMs thrive on flexibility and scalability, allowing them to serve a broad spectrum of use cases across industries. The true strength of LLMs lies in their ability to adapt to various contexts—be it casual, professional, or highly specialized tasks. It’s this adaptability that drives their appeal, making them indispensable to a growing number of users.
However, some companies, like Microsoft with its Copilot, are choosing to embed LLMs into specific app ecosystems. While this might make sense for users who rely heavily on a singular suite of tools, it could also weaken the very essence of LLMs’ flexibility. By confining LLMs to pre-defined environments, we risk reducing their ability to innovate and deliver the kind of cross-platform utility that users expect.
What stands out with standalone LLM services is their versatility. They are not tethered to any one ecosystem, which means they can evolve in ways that an embedded tool cannot. This is a critical distinction, as it shapes how users interact with and benefit from AI. A rigid, embedded AI might help automate certain tasks, but a flexible, standalone model has the potential to inspire entirely new workflows and possibilities. Companies must take care not to limit LLMs’ transformative potential by embedding them too tightly into any single system.
The Push for Standalone Services
In recent years, numerous software companies have been integrating LLMs as additional features in their existing services. While the intent is to enhance their platforms with AI-driven capabilities, these efforts haven’t always achieved the expected level of engagement. Despite being available as built-in features, users often bypass them, opting instead for standalone platforms like ChatGPT and Claude. This trend points to a deeper issue: users are seeking out AI that can stand alone and offer flexibility, rather than feeling constrained by a platform’s predefined use cases.
Standalone services provide users with the freedom to experiment and leverage AI in diverse contexts, unshackled from the limitations imposed by software integration. This preference for external platforms suggests that companies may need to rethink their strategies for LLM adoption. Instead of embedding AI as an afterthought in their services, they could explore new ways to offer AI capabilities that work across multiple platforms or offer AI-driven insights in a modular, API-based way that can be tailored to individual needs.
Users seem to value versatility over convenience when it comes to generative AI. By giving people the freedom to integrate AI as they see fit, standalone platforms like ChatGPT are achieving a level of ubiquity that embedded tools struggle to match. The challenge for other companies lies in finding a way to offer AI that enhances rather than restricts flexibility.
A Historical Parallel: Google and Chrome’s Success
The current trend of users preferring standalone generative AI services over embedded features echoes a historical precedent from the early days of the internet. When Google’s search engine was first introduced, it was a technological breakthrough, offering unprecedented access to information. While many software products at the time tried to integrate search engines directly into their applications, users overwhelmingly gravitated toward visiting the standalone Google search page.
This same pattern emerged when Google Chrome entered the browser wars. Chrome quickly outpaced Internet Explorer in popularity due to its clean, efficient interface and ease of access to Google’s search capabilities. Rather than embedding too many functions within other software, Google found success by making Chrome a streamlined, standalone product that people could rely on independently. This strategy of simplicity, independence, and versatility allowed Chrome to gain market share rapidly, defeating more bloated or embedded alternatives.
In the context of AI, the lesson is clear: embedding AI into existing software is not enough to guarantee success. Users still prefer to go directly to standalone platforms like ChatGPT for generative AI needs. Much like how the simplicity of visiting a Google search page became a habit for users, the ease and accessibility of standalone AI platforms may be what drives the continued growth of these services. It suggests that adding an “AI” button to existing software is not enough; users will bypass such embedded tools if the standalone alternatives offer more freedom and functionality.
The Uncertain Path Forward
While it’s clear that embedding AI into existing software hasn’t been particularly effective, there’s still uncertainty about what the best approach to LLM adoption should be. Creating tools like Microsoft Copilot, which embeds AI deeply into the productivity suite, seems too narrow and restrictive. Copilot’s utility is tied to specific applications, which may limit the ways users can experiment with or benefit from the full capabilities of the LLM.
The industry may need to wait for a more revolutionary “killer” approach to emerge—one that balances flexibility with deep AI integration in ways that aren’t limiting. In the meantime, the primary focus should be on making AI smarter, more intuitive, and capable of adapting to a broad range of use cases. The future success of LLMs could depend on innovations that we haven’t yet conceived, allowing AI to transform workflows in ways we can’t predict today.
One emerging factor that could shape the future of AI adoption is the rise of AI-native generations. These are individuals who will have grown up in a world where AI, like search engines today, is ubiquitous from birth. For these future users, AI might become as pervasive and versatile as both online search engines and plain text files (even typewriters and keyboards) are now—accessible, adaptable, and seamlessly integrated into everyday life. With this sociocultural shift, we could witness innovative approaches to AI that are unimaginable today, driven by the familiarity and comfort AI natives will have with the technology.
The Need for Innovation in AI Integration
As we reflect on the rise of standalone generative AI services like ChatGPT and Claude, it becomes clear that flexibility and scalability are critical to the success of LLMs. Companies need to rethink how they integrate AI into their platforms, avoiding the temptation to embed AI too deeply into specific ecosystems. Users want the freedom to explore AI’s full potential, and standalone platforms give them the space to do so.
History offers a useful lesson, as seen in Google’s success with its search engine and Chrome. Embedding too many features in software often limits their utility, whereas offering standalone, easily accessible tools can lead to greater user engagement. The same holds true for AI today, and companies must be mindful of this as they navigate the evolving landscape of LLMs.
While the perfect approach for AI integration remains unclear, it’s evident that something more flexible, modular, and independent will likely lead the way. Until then, the focus should remain on making AI smarter and more capable, while waiting for the breakthrough strategy that will define the future of AI integration. In the not-so-distant future, as AI natives grow up with AI woven into the fabric of their lives, we may see innovation approaches that revolutionize AI’s role in society—approaches that we can’t even imagine today.
Image by Markus Winkler
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