SKT Takes Arguably-Unusual AI Approach to Telco AI

SKT, which arguably is positioned differently from most telcos (it has business units that produce semiconductors) also is taking a for-now unusual approach to generative artificial intelligence, developing and supporting multiple large language models supporting telco operations as a vertical and in the Korean language initially. 

SKT has an in-house LLM called 'A.X' and also will support OpenAI's GPT and Anthropic's Claude, apparently aiming to use all three by supporting different use cases. 

It is possible one LLM might handle customer service; a second LLM might be used to support network operations and a third might handle back office operations, for example. 

Ignoring questions that inevitably will be raised about whether this makes ultimate sense, we already might not that each major player in the model business (as well as some expected entrants) already is slanting development in ways that support the existing core business models and revenue streams of each company. 

For example, Apple will undoubtedly focus on device AI, as many use cases will revolve around image processing, language translation or speech-to-text, plus ways to anticipate a user’s needs in context, for example. That makes sense as Apple is a device company, first and foremost. 

Meta is more likely to emphasize content curation, content moderation and natural language interfaces to content, given its roles in social media and user-generated content. 

Company	Core Business Model	AI Focus/Strategy
Apple	Devices and & Services (iPhone, Mac, App Store)	Device Intelligence: AI for on-device processing tasks like image recognition (Face ID), voice assistants (Siri), and personalized recommendations.  Software Optimization: AI for optimizing battery life, app performance, and user experience across Apple devices.
Meta (Facebook)	Social Networking and Advertising	Content Curation and Recommendation: AI for personalizing news feeds, suggesting friends and groups, and targeted advertising based on user data.  Natural Language Processing (NLP): AI for improving chatbots, sentiment analysis, and content moderation.
Microsoft	Cloud Computing (Azure),  Productivity Software (Office 365)	Enterprise AI Solutions: AI-powered tools for data analytics, automation, and productivity within businesses (Power BI, Dynamics 365).  Cognitive Services: Cloud-based AI services for developers to integrate features like speech recognition, computer vision, and translation into their applications.
Google	Search and  Advertising	Search and Ranking Algorithms: AI for refining search results, understanding user intent, and delivering highly relevant advertisements.  Voice Assistant (Google Assistant): AI for natural language interaction, voice-based device control, and smart home integration.
Amazon	E-commerce and  Cloud Computing (AWS)	Recommendation and Personalization Engines: AI for suggesting products, optimizing search results, and tailoring user experiences on Amazon.com.  Logistics and Supply Chain Optimization: AI for optimizing warehouse operations, predicting demand, and managing delivery routes.

Microsoft has long been a leader in enterprise software and productivity suites, so naturally will focus on enterprise AI and business solutions. 

Google will naturally reinforce its core search functions using LLM means to improve Google search abilities to answer questions and provide information more precisely, with greater context awareness and relevance, in more natural ways. That, in turn, can provide value to advertisers by improving the contextual placement of ads. 

Amazon has already moved to use LLMs for personalization and recommendations, product search functions in its e-commerce business, while moving to support LLM hosting as part of its Amazon Web Services business. 

More of this is going to happen as industry-specific and function-specific versions of LLMs develop. What remains to be seen is how many other relevant customizations could also develop.