The Language Model Landscape Is Being Disrupted…again
By the Language Model providers focussing on end-user distribution via LLM-powered native applications.
This phenomenon could be described as the rise of LLM-powered native applications or native LLM interfaces.
These terms capture the current trend where Large Language Model (LLM) providers are building standalone, user-facing applications designed to directly leverage their AI models for end users, rather than just offering APIs or backend services for developers to integrate.
These are purpose-built applications natively designed around the capabilities of LLMs, much like native mobile apps are built specifically for mobile platforms.
Zone 7 — End User UIs
I have always argued that Generative AI based applications in Zone 7 is vulnerable to being superseded.
I like the analogy of the prominence of flashlight apps in iOS, and how users were willing to pay for it. Only for the functionality to be superseded by the flashlight being included within the phone’s operating system software.
The time of shipping a GenAI application which is a thin rapper over a standard available functionality is over.
There needs to be considerable IP and differentiation.
The disruption from Native LLM UI’s will affect Zone 6 — Foundation Tooling to some degree as this is a self-help option. But users will also make use of it to build end-user solutions for themselves, hence again superseding applications in Zone 7.
Zone 6 — Foundation Tooling
The Native LLM UI’s contain elements of the foundational tooling zone like search, context creation, easy to use embeddings, AI Agent build tools and more.
So I would argue that the LLM native apps are good at abstracting key and crucial technology building blocks and synthesising those into a no-code to low-code intuitive UI for users to build solutions primarily for their personal use.
The danger exists for technology providers focussing on single, private use-cases are at risk.
However, there is an opportunity for technology providers to focus on unifying technologies into a no-code to low-code orchestration platform.
There is also a significant opportunity to build enterprise focussed solutions.
Also, this sector focuses on tools that leverage large language models (LLMs), such as vector stores, interactive data studios and advanced prompt engineering platforms.
Services like HuggingFace simplify access with no-code model cards and straightforward inference APIs, democratising LLM use.
A key highlight here is data-centric tooling, designed to drive repeatable, high-impact LLM applications.
Recent innovations include local offline inference servers, model quantisation, and compact language models, enhancing efficiency and flexibility.
The market opportunity lies in building foundational tools to meet emerging needs — streamlining data delivery, enhancing data discovery, and supporting data design and development. These solutions are poised to shape the future of LLM-powered innovation.
Search 2.0
One of the reasons LLM providers are prioritising broad-user distribution is something that can be called Search 2.0, or Answers replacing Search.
Language Model (LLM) providers are shifting their focus from developer-centric tools, like APIs, to end-user applications in an attempt to dominate the next generation of search technology.
This shift reflects a move toward accessibility and distribution, aiming to integrate LLMs directly into everyday user experiences rather than relying solely on technical integrations.
Traditional search engines, like Google, excel at indexing vast web content and delivering precise results, but LLMs offer a new paradigm with their ability to understand context, synthesise information, and generate human-like conversational responses.
Companies like xAI (with Grok), OpenAI (ChatGPT), and others are building native apps to bring this capability straight to users, bypassing the need for third-party developers to bridge the gap.
This trend is evident in examples like Kimi, Deepseek Chat, and Qwen, which prioritise user-facing interfaces.
Distribution is key to winning the search race.
By embedding LLMs into widely accessible platforms, providers can capture more users and data, refining their models faster than competitors.
This contrasts with the older model of API distribution, where control rested with developers.
General-purpose LLM interfaces (e.g., Grok or ChatGPT) often fall short for specialised needs due to compliance and risk concerns. This opens opportunities for tailored, next-gen search solutions in business contexts.
Zone 5 — Model Diversification & Unification
Zone 5 captures the evolving landscape of large language models (LLMs), where an initial wave of diversification — marked by specialised models for distinct tasks — has begun to converge into a unification of capabilities within single, versatile systems.
Modern models are no longer limited to text generation; they now integrate multiple modalities, such as vision and reasoning, enabling them to process images, interpret complex queries, and deliver multifaceted outputs.
A standout feature of this unification is the ability of models to surface their reasoning, offering transparency into how conclusions are drawn, which boosts trust and usability.
Unified models also support advanced functionalities like function calling and structured data generation, making them powerful tools for both creative and technical applications.
This shift toward all-in-one models reflects a practical balance between diversity and efficiency, meeting diverse user needs while simplifying deployment and development workflows.
Diversification giving way to unified, multi-functional models capable of vision, reasoning, explainability, function calling, and data structuring.
Zone 4 — Commercial Model Providers
Initially, many assumed OpenAI would dominate the Large Language Model (LLM) market for agent-based applications, given its early lead with ChatGPT and robust API ecosystem tailored for tasks like autonomous reasoning and task execution.
However, recent months have seen a surge of new providers — such as xAI with Grok, Anthropic with Claude, and others — challenging this dominance by offering competitive models that rival OpenAI in performance, cost, and specialised agent capabilities.
This shift is significantly fuelled by open-source initiatives like those from HuggingFace, Meta AI’s LLaMA derivatives, and smaller, efficient models (e.g., quantised or distilled versions), which empower developers to build and customise agents without relying on proprietary systems.
As a result, the market for LLM-powered agents is becoming more decentralised, with open-source momentum and innovative newcomers reducing OpenAI’s once-expected monopoly while accelerating advancements in agentic AI applications.
Zone 3 — Specific Implementations
I find it interesting how models where focussed on solving specific problems initially, like human language translation, dialog state management and more. As the technology unfolded, these elements became an intrinsic and assumed part of the models.
This stage was more experimental and served as a stepping stone.
Zone 2 — General Use-Cases
With the advent of large language models, functionality was more segmented…models were trained for specific tasks. Models Sphere & Side focussed on Knowledge Answering; something Meta called KI-NLP. Models like DialoGPT, GODEL, BlenderBot and others focussed on dialog management.
Zone 1 — Language Models Disruption
The advent of language models is like a pebble dropped into our lives, sending ripples that have sparked new markets, products, needs and opportunities.
This introduced an entirely new paradigm of prompt engineering, where unstructured natural language is used to instruct a language model, guiding it to simulate specific behaviours or responses.
The output, also unstructured, emerges as the model processes and organises data internally, revealing its ability to generate meaningful results from abstract inputs.
We’ve since learned that language models excel with contextual references, though basic principles worked well in isolation; scaling these capabilities has required innovative approaches to handle complexity and volume.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
