Unveiling Smartphone Trends Arising from the Rise of AI Phones

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Report
18 Pages
April 2024
Region: Global
Market Intelligence & Consulting Institute (MIC)
ID: 5960360

This report offers an overview of AI phones from the perspectives of generative AI and edge computing, highlighting key players focusing on LLMs and diverse AI applications, including smartphone brands and software service providers; explores the opportunities and challenges in the era of AI phones.

The world is gradually transitioning into the era of hybrid artificial intelligence (AI), fostering the emergence of edge computing and the development of end devices capable of directly running generative AI applications. Since the fourth quarter of 2023, global smartphone brands have introduced their AI phones, offering users diverse experiences with generative AI applications. The development of large language models (LLMs) has become a focal point for smartphone brands seeking AI dominance, alongside the enhancement of smartphone chip computing capabilities, expected to be a key trend in AI phones this year.

1. Background: AI Phones Enable On-Device Computing

1.1 Generative AI Transforms User Experiences via LLMs
1.1.1 Mainstream Generative AI Applications of: Image and Text Generation, Productivity Tools
1.1.2 LLMs: Utilizing Model Parameters, Tokens, and Penalty Mechanisms to Enhance Content Accuracy and User Relevance
1.2 Rise of Edge Computing Drives Mobile Chip Performance for Local AI
1.2.1 Emergence of Hybrid AI: Convergence of Cloud AI and Edge AI Models
1.2.2 Mobile Chips Redesigned for On-Device AI: Specification Upgrades and Configuration Changes
1.2.3 10 Billion Parameters: Mobile AI's Current Limit Due to Chip and Memory Constraints

2. Brands Focus on Developing LLM and Diverse AI Applications

2.1 Smartphone Brands Shifting from Hardware Specifications to Proprietary LLM Development
2.1.1 Android Camp: Pioneering AI Phone Launches
2.1.2 iOS Camp: Building Competitiveness with Proprietary LLMs as Market Followers
2.2 Software Services: Focusing on End-user Applications and Upgrading Proprietary OS

3. Opportunities and Challenges: Memory specs as a Bareeir to AI Development

3.1 Opportunities: AI Trends Boost Sales, Chip Supply Chain Benefits from Hardware Upgrades
3.1.1 Smartphone Brands: Generative AI Optimizes User Experience, Aligning with Social Interaction Needs of Users
3.1.2 Mobile Supply Chain: Suppliers Benefit from Specs Upgrades in Early AI Phone Development
3.2 Challenges: Memory Limits as Key Barrier for AI Phone Development

4. The Analyst's Perspective

4.1 Mobile Brands Enhance Generative AI with LLM
4.2 Generative AI Spurs Phone Hardware Upgrades, but Memory Limits Future Development

AppendixList of Companies

List of Tables

Table 1: Comparison of Flagship Chip Specifications between Qualcomm and MediaTek (2022 vs. 2023)
Table 2: Comparison of LLMs in Android Smartphone Camp

List of Figures

Figure 1: Qualcomm's Hybrid AI Concept
Figure 2: Distribution of Generative AI Applications and LLM Parameters

Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

Amazon
Apple
Arm
Baidu
Ferret
Gamma
Google
Instagram
MediaTek
Meta
Microsoft
Microsoft
OpenAI
Oppo
Qualcomm
Samsung
TikTok
TSMC
Vivo
X (Twitter)
Xiaomi

Methodology

Primary research with a holistic, cross-domain approach

The exhaustive primary research methods are central to the value that the analyst delivers. A combination of questionnaires and on-site visits to the major manufacturers provides a first view of the latest data and trends. Information is subsequently validated by interviews with the manufacturers' suppliers and customers, covering a holistic industry value chain. This process is backed up by a cross-domain team-based approach, creating an interlaced network across numerous interrelated components and system-level devices to ensure statistical integrity and provide in-depth insight.

Complementing primary research is a running database and secondary research of industry and market information. Dedicated research into the macro-environmental trends shaping the ICT industry also allows the analyst to forecast future development trends and generate foresight perspectives. With more than 20 years of experience and endeavors in research, the methods and methodologies include:

Method

Component supplier interviews
System supplier interviews
User interviews
Channel interviews
IPO interviews
Focus groups
Consumer surveys
Production databases
Financial data
Custom databases

Methodology

Technology forecasting and assessment
Product assessment and selection
Product life cycles
Added value analysis
Market trends
Scenario analysis
Competitor analysis

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Table of Contents

Companies Mentioned (Partial List)

Methodology

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