Review of Generative AI Developments (2024)

Since OpenAI officially released ChatGPT in November 2022, the AI technology ecosystem has experienced rapid advancement. The public has transitioned from a state of confusion to a thrilling and exciting experience, and now to feelings of unease due to the cost-cutting and efficiency improvements brought by their respective companies.

The world is changing so quickly, and the wave of generative artificial intelligence (AI) continues to sweep through the tech industry. The arms race around ChatGPT’s technology is in full swing and escalating. Two years have passed, and we have witnessed the Cambrian explosion of generative AI. The future development direction of AI technology is becoming clearer, while social, cultural, and economic developments, as well as national policies, are rapidly keeping pace and adjusting accordingly.

Countries and organizations are quickly reaching a consensus: “Artificial intelligence is rapidly becoming a key driver of global economic development.” Various countries, organizations, and enterprises are striving to understand the boundaries of AIGC’s capabilities, actively reviewing and adjusting their AI application strategies, and finding their positioning and product directions in this new wave of industrial transformation.

In this article, I will attempt to analyze the performance of generative AI technology in 2025 from the following aspects:

1. The current development status of generative AI (AIGC) technology

2. The development direction of AIGC technology

3. The impact of AI technology on the IT & 3C digital industry and discussions on future product forms

4. The impact of AI technology on enterprise operation management and recommendations for response

1. The Current Development Status of Generative AI (AIGC) Technology

It cannot be denied that AIGC technology is currently experiencing explosive growth. International giants like BATMMAAN are investing heavily in related fields, while domestic entities are also investing massively in what is called the “hundred model battle” and increasingly competitive AI tools are emerging. This competitive landscape is entirely different from the internet era; previously, internet applications were iterated on a monthly basis, while now, the evolution of large models (LLM) and text-to-image/text-to-video models occurs on a “daily” basis.

I will briefly review the timeline of popular AI companies’ product and technology releases over the past year (2024) to grasp this speed:

• January 30, 2024: Neuralink brain-computer interface implantation

• February 15, 2024: OpenAI releases video generation model Sora

• March 10, 2024: NVIDIA launches the strongest AI chip B200

• May 8, 2024: Google releases AlphaFold 3 and open-sources it

• June 5, 2024: Microsoft expands Copilot features

• June 11, 2024: Apple releases Apple Intelligence

• July 10, 2024: Baidu’s autonomous driving car “Radish Runners” expands on a large scale

• July 15, 2024: Tesla releases humanoid robot prototype

• August 20, 2024: AI music generation model Suno V3 released

• September 10, 2024: OpenAI releases new reasoning model o3

• October 11, 2024: Tesla releases new products

• December 10, 2024: Google releases quantum chip “Willow”

• December 5-20, 2024: OpenAI holds 12 live broadcasts in 12 days

• December 26, 2024: DeepSeek-V3 officially released and open-sourced, leading a new trend in low-cost AI models

On one hand, the technological progress of AI companies is accelerating, with AI tools emerging like “fish crossing the river.” On the other hand, as summarized in Gartner’s review of the “2024 China Data, Analytics, and Artificial Intelligence Technology Maturity Curve” in September 2024, several innovative technologies related to AI are currently in the “expectation inflation phase,” with the technological development wave at its peak, transitioning towards a “bubble burst phase.”

Figure 1: The 2024 China Data, Analytics, and Artificial Intelligence Technology Maturity Curve (from Gartner)

From Figure 1, it can be observed that the integration of enterprise digitalization and artificial intelligence is a key trend, with generative AI technologies such as “large language models” and “multi-modal GenAI models” currently at a peak of expectations, indicating that they are receiving considerable attention, but also have a certain degree of bubble. Enterprises need to sharpen their focus and identify the technology fields that truly require attention and active layout.

In summary, I believe the development status of generative AI-related technologies in 2024 is as follows:

1. Development and release of larger models that comply with Scaling Law:

From GPT-3 to GPT-4, from BERT to PaLM, the evolution of AI is nearly a history of computational power competition. Although there has been much debate recently regarding whether “Scaling law” is still effective, overall, AI evolution still heavily relies on “big miracles”; the larger the dataset, the better the performance. The visual improvements brought by trillion-level parameters are evident. Industry observers speculate that OpenAI’s yet-to-be-released and mysterious GPT-5.0 will boast an astonishing parameter scale of 17 trillion (for comparison, GPT-4’s parameter scale is 1.8 trillion, which is a point of contention). The industry speculates that GPT-5 will likely achieve strong artificial intelligence (AGI) and enable “recursive self-improvement.”

2. Small Language Models achieving remarkable results

On the other hand, while LLMs are gaining prominence, a new trend is quietly rising from Small Language Models (SLM), bringing a different kind of innovative vitality to the AI field. These SLMs, despite their small size, contain advanced AI functionalities, significantly reducing computational requirements while still demonstrating powerful capabilities comparable to large models. Representatives of these SLMs include LLaMA 3, Phi 4, Mistral 7B, and Gemma, characterized by their smaller parameter scales (usually between millions to tens of billions), greatly reducing model size and complexity, enabling normal inference on mainstream devices, including mobile phones, thus expanding the potential for edge intelligence scenarios.

3. Rapid development of multi-modal models such as text-to-image/text-to-video

In 2024, another milestone in technological advancement was the release of OpenAI’s Sora text-to-video model, marking a significant breakthrough in AI capabilities, enabling AI models to generate videos (including 3D) that comply with the laws of the world and providing a relatively long-term continuity context generation mechanism, thereby opening the door for AI to empower more scenarios. Domestic related models have also quickly followed suit, including Byte’s Doubao, Kuaishou’s Keling, etc., which are not inferior to Sora in terms of strength. Simultaneously, domestic text-to-image models are making rapid progress, and in MCN institutions, related text-to-image and text-to-video applications are being extensively used, with a plethora of ecological tools emerging, indicating that AIGC-related tools will be essential for future traffic marketing methods.

4. R&D breakthroughs in native large models that integrate multi-modal capabilities

In 2024, with the iterative progress of technology, more teams are integrating images, videos, text, and voice into the data feature extraction process of large models in early, mid, and late stages, enabling large models to natively support multi-modal semantics, context, and intention understanding, allowing more enterprises to utilize their generative and understanding capabilities when building their applications, possessing greater potential and flexibility.

5. Breakthroughs in large models capable of reasoning, planning, and adhering to mathematical and physical laws

In 2024, marked by the release of OpenAI’s o1 series of large models, the reasoning capabilities have been significantly enhanced by introducing the “Chain of Thought” technology, allowing the model to simulate human thought processes and gradually derive solutions, enhancing logical reasoning abilities. More notably, at the end of 2024, OpenAI publicly disclosed the o3 large model (not disclosed externally), and researchers have reported that this model has significantly enhanced capabilities in programming and mathematics, indicating that large models are now beginning to demonstrate reasoning abilities comparable to ordinary humans.

6. Rapid development of embodied intelligence

In 2024, significant breakthroughs in algorithm research related to embodied intelligence have also occurred. Numerous organizations and enterprises have begun to adopt “end-to-end” algorithm architectures on a large scale to simulate the operation of the real world, allowing robots and related fields (such as autonomous driving) to operate in accordance with the laws of the world and enabling AI agents to perceive their surroundings and formulate corresponding strategies in a manner more aligned with human habits. Numerous humanoid robots, intelligent agents, and AI hardware have emerged rapidly. Typical representatives in this area include Tesla’s Optimus and Yushu.

7. Development of psychological models capable of empathizing with humans

In 2024, academia has made significant progress in the cognitive capabilities of large models. A substantial amount of work both domestically and internationally has applied large models (LLMs) to emotional companionship, psychological health support dialogues, and psychological counseling dialogues, such as SoulChat, MeChat, QiaoBan, CPsyCoun, MindChat, EmoLLM, etc. These studies enable large models to meet human needs for emotional companionship and empathy in aspects such as simulating human conversational skills, states and attitudes, emotional empathy, and cognitive empathy.

8. Development of AI models with spatial computing capabilities

In 2024, Professor Li Feifei’s team discovered that multi-modal large language models (MLLM) possess memory and recall capabilities in spatial contexts, demonstrating preliminary spatial awareness. In January 2025, NVIDIA launched a world foundational model platform named Cosmos at CES, capable of creating generative world foundational models while generating physics-based realistic synthetic data, accelerating AI’s understanding of three-dimensional spatial worlds and generating training data.

2. Exploring Future Development Directions for AI Fundamental Technology R&D

At the same time, I believe that in the coming years, the development trends and frontier directions of AI fundamental technology will mainly manifest in the following aspects:

1. Research and implementation of foundational theories for world models

Yann LeCun proposed the concept of world models in mid-2024 as a counter to autoregressive models, and world models will serve as an important idea in deep learning and reinforcement learning. The core idea of world models is to build a model that can simulate the real world, allowing agents to self-learn and plan through interaction with the environment without relying on vast amounts of labeled data.

Currently, whether it is the world model that LeCun is exploring, the spatial intelligence that Li Feifei aims to conquer, or other similar concepts proposed by various research teams, we are undoubtedly getting closer to this world.

The future world models will develop towards more complex, flexible, and intelligent directions, integrating self-learning, meta-learning, cross-domain transfer learning, embodied intelligence, and multi-agent cooperation technologies. Through high-fidelity simulations and precise modeling, future world models will not only better understand and predict environmental changes but also automatically optimize decision-making processes without relying on large amounts of labeled data, adapting to various complex scenarios, ultimately having a profound impact on social, economic, and environmental fields.

It is foreseeable that by 2025, there will be practical implementations and commercialization of world models, and the fitting of real-world events, activities, and related human behaviors that can be handled by world models will have greater business value.

2. Self-evolution of large language models (LLM)

In the coming years, in addition to the increasing number of parameters and stronger capabilities of large language models, the development of technological foundational theories will also evolve, with the main technological iteration directions likely featuring the following characteristics and directions for reference:

Diversity of architectures and blooming of various approaches: Currently, most LLMs are based on the Transformer architecture, but recently some entirely new LLMs based on the MoE architecture have emerged. Moreover, there are more hybrid architectures, such as **Transformer-MoE combinations**. In recent years, some research has attempted to combine Transformer with MoE to maintain the strong expressive capability of Transformer while utilizing the sparse activation mechanism of MoE to improve computational efficiency. For instance, the **Switch Transformer** is a typical example that combines MoE with Transformer, accelerating the training process while retaining Transformer’s advantages in capturing long-range dependencies. Other hybrid models, such as Mamba architecture and various models stitched together with Transformer, will provide users with performance enhancements in different scenarios.

Multi-modal learning and cross-domain reasoning: An important direction for the foundational technological development of large models is cross-modal learning, meaning processing text, images, videos, sounds, and other multi-modal data within a single model. Future research will focus on how to design a unified representation space that integrates data from different modalities, enabling models not only to generate single-type content but also to generate multi-modal interactive content. Another challenge for large models is cross-domain generalization ability, which is how to ensure that models perform exceptionally well not only in specific domains (such as images, text, etc.) but can also transfer to multiple domains and reason across different domains. In multi-modal scenarios, training models and methods should be able to reduce training costs and durations while achieving semantic understanding, content generation, and multi-object integration.

Cross-disciplinary applications and industry innovation: As foundational theories deepen, the powerful reasoning abilities of large models will drive automated scientific discovery in fields such as drug discovery, material design, and physical modeling. Through simulation and reasoning, AI will assist scientists in discovering new laws, theories, and solutions, promoting innovation across various industries. Moreover, the cross-domain applications of large models will also foster multi-disciplinary collaboration across fields such as healthcare, finance, law, and education, forming collaborative innovations. Industry experts will work alongside AI researchers to develop specific applications, promoting the deep integration and application of AI technology across various industries.

Intelligent agents and adaptive intelligent systems: I believe that future large models will not be limited to task execution but will integrate more decision support systems and adaptive intelligent agents into the models. These models will adjust their behavior and reasoning methods according to real-time changes in the environment and task requirements, dynamically modifying strategies in complex decision-making tasks. Future large models will also achieve “meta-intelligence“, meaning that models will not only learn in the current task but also learn how to learn, continuously improving their intelligence levels in different contexts. This will enable AI systems to possess stronger adaptive and reasoning capabilities.

3. The arrival of AGI (Artificial General Intelligence)

Currently, whether AGI can arrive within 3 to 5 years remains a debated topic in the industry. Personally, I lean towards the belief that AGI or AGI equivalent to human intelligence is likely to be achieved within 5 years.

What is AGI?

Generally, AGI (Artificial General Intelligence) refers to an AI system capable of performing any intelligent task that a human can accomplish. Unlike most current AI (such as Narrow AI or weak AI), which excels in specific domains or tasks, AGI possesses broader adaptability and learning capabilities, allowing it to think, learn, reason, and solve problems like a human across multiple fields and complex situations.

Key features of AGI

Industry experts believe that AGI differs from traditional AI in that it typically possesses the following key features:

1. Generality: AGI can perform well in various environments and tasks, possessing cross-domain learning capabilities. For example, current AI may be very powerful in image recognition, natural language processing, and games, but they usually execute tasks defined in advance, while AGI can handle various complex tasks like a human without prior programming.

2. Self-learning and self-improvement: AGI can learn autonomously without relying on large amounts of manually labeled data. It can enhance its capabilities through experience accumulation, observation, and reasoning, and can effectively learn and reason in entirely new contexts.

3. Understanding and reasoning: AGI can not only “do” tasks but also understand the principles behind them, engaging in abstract reasoning and complex decision-making. For instance, it can grasp the deeper meanings of texts, infer implicit information, and even propose innovative solutions.

4. Emotional and social understanding: AGI may possess higher levels of social cognition and emotional understanding, being able to simulate and comprehend human emotional responses and engage in effective social interactions. This is crucial for making decisions and collaborating in complex social environments.

5. Autonomous goal-setting and problem-solving: Unlike existing specialized AI, AGI can set its own goals and take action. It can design solutions based on current contexts and future objectives, identifying and completing tasks even without explicit instructions.

Regarding the timeline for achieving AGI, there are various predictions in the industry. OpenAI CEO Sam Altman has expressed high hopes for achieving AGI by 2025. Elon Musk, founder of Tesla and SpaceX, has also predicted that AGI will emerge no later than 2026. Anthropic founder Dario Amodei similarly predicts AGI will be realized in 2026. Additionally, NVIDIA CEO Jensen Huang stated at an economic forum at Stanford University that AI will pass human tests within five years, and AGI will arrive soon. Personally, I lean towards the belief that AGI comparable to human intelligence will emerge within five years.

4. Significant advancements in digital simulation technology

Since 2024, the development of AI technology has reached a crucial turning point—transitioning from algorithms to the physical world. To better adapt to and integrate into the physical world, the practical applications of AI will rely on various digital simulation technologies, which have become the infrastructure driving innovation across industries. Digital simulation is no longer merely a world model studied in academia; it signifies a new paradigm shift from micro to macro scales, from digital technology to the physical world. This transformation will have far-reaching impacts on various industries, reshaping production methods, R&D models, and even industry structures.

In this process, the importance of digital simulation technology is increasingly prominent, especially under NVIDIA’s systematic layout. NVIDIA’s platforms such as Isaac, Omniverse, and Cosmos are constructing a complete simulation ecosystem, profoundly changing the industrial R&D chain and innovation paradigm. NVIDIA showcased its applications in autonomous driving simulation, aircraft manufacturing, robot R&D, and digital twin scenarios at CES 2025, highlighting the tremendous potential of digital simulation technology in reshaping modern industrial structures. Through digital simulation, enterprises can conduct highly realistic testing and optimization in virtual environments, significantly enhancing R&D efficiency, reducing risks, accelerating product iterations, and promoting the development of intelligent manufacturing and automation industries.

Digital simulation has not only shown significant value in industrial fields but also possesses revolutionary potential in life sciences. In 2024, DeepMind collaborated with Harvard University to release an AI-generated digital lifeform—virtual mice. This groundbreaking research digitizes brain neural activities and behavioral performances, opening new research paths for biology and neuroscience.

Meanwhile, the BAAIWorm project proposed by the Zhiyuan Research Institute successfully made it to the cover of a Nature sub-journal. It first achieved a closed-loop simulation of the nervous system, body, and environment of the Caenorhabditis elegans, filling a gap in the field of biological intelligence simulation and paving a new path for the development of embodied intelligence and practical AI applications.

It is foreseeable that simulations based on real physical world mechanisms will open a new paradigm for life sciences research, greatly promoting advances in medicine, drug development, and biotechnology.

In the digital age, almost every scene in the physical world can be reconstructed through simulation, from nuclear fusion research to cellular activity simulation, from robot R&D to digital lifeform modeling, from mechanical dynamics to biological dynamics, digital simulation is playing a crucial role across a wide range of fields, from micro to macro, from laboratory to production line, and from research to practical application. Whether in technology R&D, product design, or industrial transformation, digital simulation will occupy a central position in future industrial competition, driving various industries into a new stage of development.

3. The impact of AI technology on IT & 3C digital industry products and future product forms

As foundational technology R&D accelerates, the penetration and application of AI technology in the industry over the next 3 to 5 years will profoundly affect the definition of product forms and R&D directions in the IT & 3C industry. I believe this will mainly manifest in the following aspects:

1. The rise of autonomous collaborative intelligent agents

To some extent, 2025 will be the inaugural year for AI applications. As the AI Infra foundational models gradually improve and costs decrease, an explosion of native AI intelligent agent applications will occur. AI agents are evolving from simple chatbots to systems capable of autonomously executing complex tasks, such as scheduling, writing software, and executing complex internal business processes. These AI agents can perform multi-step tasks, significantly impacting technological growth. Similarly, leading AI companies both domestically and internationally, including Microsoft, Meta, Google, and Anthropic, are launching tools and platforms that support the creation, orchestration, integration, and evaluation of intelligent agents, providing a solid foundation for the future development of software applications in this field.

The most important aspect in this field will be the genuine collaboration of AI-driven multi-agents, capable of independently planning and executing complex tasks with minimal human intervention, demonstrating a high degree of autonomy. Simultaneously, these autonomous intelligent agents will also possess the ability to perceive environmental changes, whether through visual means, data from multi-source sensors, or changes in contextual data, allowing them to adjust strategies in real-time to adapt to new situations, ensuring tasks are effectively completed.

2. A surge in AI hardware and software products related to embodied intelligence

Embodied intelligence aims to enable AI to interact with the physical world, encompassing not only perception and reasoning but also executing actions to interact with the environment. The most significant characteristic of embodied intelligence is the autonomous perception of the physical world, learning through anthropomorphic thinking paths, and providing human-like feedback rather than merely executing tasks passively. In simple terms, embodied intelligence allows AI to interact with the environment in which we live, demonstrating intelligent behavior, iteratively growing, autonomously learning, and discovering optimal action strategies.

Looking ahead to 2025 and beyond, embodied intelligence will be a significant manifestation of AGI, and the future development in this area holds immense potential. Key technologies or related component technologies will be crucial for the mature development of embodied intelligence:

End-to-end training architecture

The end-to-end training architecture has great potential in behavior and body posture control, allowing learning directly from sensor data (such as cameras, IMU, LiDAR, etc.) to control signals (such as motor drives, servo steering), breaking free from the limitations of traditional control methods that rely on manually designed features.

The challenges that need to be addressed by end-to-end training architectures include:

– Direct optimization objectives: The path from sensor input to output control signals is learned directly, without the need for manual task decomposition.

– Reducing error accumulation: Traditional hierarchical architectures are prone to performance degradation due to error propagation in intermediate modules, while end-to-end training minimizes this issue through joint optimization.

– Adapting to complex environments: Through large-scale training and diverse data, end-to-end models can exhibit stronger adaptability in unknown scenarios.

Only by effectively addressing these challenges can end-to-end algorithms achieve their goal of efficient real-time control.

Low-level behavior control-related components and control technologies

To achieve better control effects, especially in low-level behavior control (such as balance, coordinated movement, and environmental interaction), technological development needs to address the following challenges:

– Real-time performance

– Low-level control requires rapid response to sensor data (such as real-time adjustments of the center of gravity to maintain balance).

– Highly sensitive to delays; any decision lag could lead to instability in posture.

– Stability

– Must maintain stability in dynamic environments, such as uneven ground and external disturbances.

– Requires high-frequency, fine-grained control signals.

– Multi-modal information fusion

– Low-level control often involves various perceptual data (such as visual and tactile), requiring the fusion of information across different temporal and spatial features.

– Energy efficiency

– In complex actions, how to control muscle or motor output to save energy while achieving target behaviors.

Reinforcement Learning (RL) optimization

It is necessary to implement relevant reward designs within the reinforcement learning algorithm framework, designing specific reward functions for low-level behaviors, such as achieving posture stability (e.g., reducing center of gravity shifts), energy minimization (e.g., reducing motor power consumption), rapid recovery of balance (e.g., automatically standing after a fall), and simulating the combination of virtual and reality.

Introducing physical knowledge constraints

In AI models used for embodied intelligence, it is essential to implement kinematic constraints, treating the kinematic restrictions of human or robot joints as hard constraints to avoid generating infeasible control signals. At the same time, it is necessary to achieve the fusion of dynamic models, embedding physical dynamics equations into end-to-end models to help models understand the relationship between actions and forces.

Data-driven self-supervised learning

Furthermore, AI models related to embodied intelligence need to effectively extract patterns from large amounts of motion data under unsupervised conditions, including motion capture data, analyzing the natural movements of humans or animals to provide references for models. Diverse training data, including behavior records under various terrains, obstacles, and interference conditions, should be included. AI agents should be able to respond to unknown tasks and scenarios in zero-shot learning contexts, completing instructions based on existing knowledge and logical reasoning capabilities without specific task training.

Natural interaction between machines and humans

With the support of models related to embodied intelligence, machines will be able to interact more naturally with humans through multi-modal inputs such as vision, voice, and touch. Robots will not only be able to understand instructions but also respond through facial expressions and body language. Moreover, machines will possess emotional recognition feedback, and embodied intelligent robots will exhibit higher levels of emotional understanding, capable of recognizing and responding to human emotions, enhancing the naturalness and effectiveness of human-machine interaction.

Industrial and home humanoid robots will emerge in large numbers

Industry consensus holds that the first applications will occur in industrial scenarios, such as automotive assembly lines, where the goal is to replace skilled workers and enhance productivity. Another area is intelligent home toys, based on lightweight robot bodies but enabling multi-modal human-machine interaction.

In addition to these scenarios, I believe that a combination of both—a low-degree-of-freedom, structurally simple and stable design that can provide “light, quiet, and fast” physical interaction while integrating AI for multi-modal perceptual interaction—will likely form a sustainable commercial ecosystem sooner. Besides well-known humanoid robots, other new categories of consumer-grade robots will emerge in the future.

3. Significant technological breakthroughs in AI-related hardware and components are expected in the future

Higher-performance computing chips

– Dedicated AI accelerators: AI-specific chips (such as ASICs, GPUs, TPUs, etc.) will be further optimized, especially for large-scale deep learning tasks, providing higher computational efficiency and lower latency. For example, quantum computing and photonic computing will gradually be deployed to enhance the training and inference speeds of large models.

– Heterogeneous computing architectures: Utilizing integrated architectures that combine different types of computing units (such as CPUs, GPUs, FPGAs, etc.) to better address complex AI tasks.

Higher-performance edge AI devices

– Edge AI accelerators: Edge computing devices will see more AI accelerators that can perform real-time processing and inference at the data source (such as sensors, smartphones, in-vehicle devices, etc.), reducing latency and improving system efficiency.

– Low-power designs: AI hardware will optimize power consumption for edge devices to support prolonged operation, especially in battery-powered scenarios. AI hardware will provide powerful computational capabilities at extremely low power consumption.

Open integrated AI hardware platforms

– End-to-end AI hardware systems: AI hardware will no longer be a single component but an integrated system, including sensors, computing units, storage, and communication modules, capable of handling data collection, processing, and transmission.

– Adaptive hardware systems: AI algorithms will enable hardware to adapt to different workloads, dynamically adjusting computational resources based on task requirements.

4. Portable AI products like Omi will see significant development

At the 2025 CES, Omi’s hardware products represent the **deep integration and application** of AI technology, emphasizing breakthroughs in **personalization, emotional interaction, cross-device connectivity, and virtual reality**. With the popularity of embodied intelligence, virtual reality, and augmented reality, the intelligence level of hardware devices will continue to improve, allowing users to enjoy a more **immersive, personalized, and efficient intelligent experience**. These trends not only reflect the latest advancements in AI technology but also provide more possibilities for future intelligent living.

Figure 2: Promotional video for Omi’s product release

At CES (International Consumer Electronics Show), Omi’s hardware products represent several key technological trends, such as naturalization and immersion of human-machine interaction, emotional AI and multi-modal interaction, personalized hardware design, intelligent adaptation to user behavior, cross-device connectivity and integration, the integration of IoT and AI hardware, and the collaborative work of multiple devices.

5. The product forms resulting from the acceleration of personality-infused AI agents and their integration with metaverse technologies (including gaming)

With the enhancement of generative AI technology capabilities, more personality-infused AI agents will emerge in the market. Generally speaking, personality-infused AI agents are AI systems endowed with specific personality traits, behavioral patterns, and emotional responses. These AI agents can not only perform basic tasks but also engage in more natural, interactive, and emotional communication with humans. By endowing AI with certain personality characteristics, they can exhibit human-like personality and emotions during interactions with users, enhancing the user experience.

I believe that personality-infused AI agents need to possess the following key characteristics:

Emotional and personalized

– These AI agents can simulate emotional responses and adjust their behaviors based on interactions with users. For instance, they can recognize changes in user emotions (such as happiness, frustration, anxiety, etc.) and respond accordingly. These AIs may sometimes exhibit humor, warmth, or calmness, selecting suitable responses based on context.

– In terms of personalization, AI agents can learn and adapt to specific user preferences, language styles, and interests, making each user’s experience more tailored.

Natural language processing and dialogue capabilities

– Personality-infused AI agents can engage in complex dialogues, possessing contextual awareness and understanding while maintaining conversational coherence. Unlike traditional command-based AI, these agents can mimic human language habits and emotional expressions, making communication smoother and more natural.

Autonomous behavior and decision-making

– Such AIs can react based on predefined rules and data, but they can also make autonomous decisions to some extent. For example, in virtual environments or games, personality-infused AIs can determine their actions based on their “personality”; one AI may be more adventurous while another may be more cautious.

– In certain situations, they may even exhibit “intent” or “motivation,” making users feel they are interacting with a living being.

Role-playing and situational adaptation

– These AIs can play different roles in virtual environments and adjust their behaviors and language based on different scenarios. For example, a virtual assistant can exhibit professionalism and calmness in formal settings, while being more relaxed and humorous in casual interactions.

– AI can also adjust its performance according to different user needs. For instance, in educational applications, it can act as a “mentor,” while in entertainment applications, it could be a “friend” or “teammate.”

Emotional computing and social interaction

– Personality-infused AI agents can recognize and understand users’ emotional states and, in some cases, simulate and exhibit emotional responses. For instance, they can express emotional responses through voice, tone, body language, or convey feelings through intonation and expressions.

– These AIs can also interact with other virtual characters, forming complex social networks and presenting “personalized” dynamic behaviors in multi-party interactions.

Training and providing highly personalized AI agents that can understand consumers, empathize with individuals, and provide additional emotional value will be an important product form for IT & 3C industry enterprises.

4. Recommendations for Enterprises to Respond

I believe that at this stage, IT and 3C digital industry enterprises need to actively embrace AI technology to enhance product intelligence, find strategic ecological positions within intelligent supply chains, optimize internal operational efficiency, and provide innovative service models to cope with the uncertainties and challenges brought by AI technology.

With the rapid development of large models and AIGC (generative AI) technologies, IT and 3C (computers, communications, and consumer electronics) enterprises need to actively adjust their strategies to fully leverage the opportunities these technologies present while addressing potential challenges.

1. Digital transformation is fundamental

Accenture’s research shows that 77% of global executives believe that generative AI can create opportunities for revenue growth or efficiency improvements for enterprises. Chinese enterprises are even more optimistic, with 90% viewing generative AI as an opportunity, of which 46% believe it offers new revenue growth opportunities, and 44% believe it can enhance efficiency.

Generative AI has not disruptively interrupted the digital transformation process of enterprises. The elements required for digital transformation, such as holistic considerations, top-down leadership, and strategic guidance, still exist. Generative AI, with its broad coverage, can facilitate the interconnectivity of various aspects of digitalization within enterprises. On the other hand, enterprises may discover their digital shortcomings in the process of applying generative AI. In other words, enterprises with poor digital foundations will also find it challenging to effectively utilize artificial intelligence. In summary, generative AI needs to be integrated with digital transformation for advancement.

2. Product and service innovation should actively embrace change and become industry explorers

IT and 3C digital industry enterprises are generally highly technological in nature, and some are consumer-facing enterprises. Therefore, they should be “pioneers” in terms of the degree and diversity of AI applications and scenarios, capable of becoming leaders in industry intelligence.

Product and service innovation can be achieved in several directions. On one hand, product iterations can achieve intelligent upgrades by integrating large models and AIGC technologies into existing products, enhancing intelligence levels. For example, providing smartphones with capabilities similar to Apple Intelligence or offering AI-scenario-based portable devices (similar to Omi), or supplying key intelligent components in the supply chain. On the other hand, in both public and private traffic marketing and after-sales service areas, enterprises can fully embrace generative AI technology to provide higher-quality customer service.

3. Actively adopting intelligent agents in enterprise management operations can significantly enhance efficiency

With the emergence of more AI intelligent agents, enterprises can significantly improve efficiency and reduce costs in research, production, supply, sales, and service processes. On one hand, they can utilize generative AI technology to achieve automated operational management processes, optimizing internal processes, such as automatically generating reports, intelligent data analysis, supply chain optimization, intelligent R&D, and compliance management, thereby reducing operational costs and improving efficiency. On the other hand, they can fully leverage generative AI technology to achieve precise marketing, generating personalized marketing content through AI analysis of user behavior, thereby enhancing user conversion rates.

4. Talent cultivation and organizational adjustments to adapt to the rapid development of generative AI

Enterprises need to proactively respond to the rapid development of AI technology in terms of talent cultivation and organizational adjustments. By establishing systematic AI training programs, enhancing employees’ cross-domain capabilities, strengthening the deep integration of AI departments with business, adjusting organizational structures to respond to technological innovations, and establishing flexible decision-making mechanisms, enterprises can effectively adapt to the changes brought by AI technology. Only in this way can enterprises drive innovation, improve efficiency, and seize industry-leading positions in an increasingly competitive market environment.

Specifically, enterprises can consider establishing dedicated generative AI departments focused on how to integrate AI technology, achieve internal innovation, coordinate internal and external resources, and provide excellent services.

5. Data security and compliance to help enterprises navigate the generative AI field more smoothly

Enterprises need to formulate data security strategies to ensure the safety and compliance of enterprise data in the generative AI field. They must ensure data privacy protection, comply with the GDPR and other regional data protection regulations, ensuring user data is encrypted and anonymized during collection, storage, and processing to avoid the leakage of sensitive information. Strengthening compliance reviews can facilitate regular assessments of AI model compliance, ensuring generated content does not infringe copyrights or spread false or harmful content. Additionally, it is essential to pay attention to the scope of generative AI usage and ethical boundaries, adhering to industry standards and regulations. Furthermore, establishing transparency and interpretability in AI decision-making will ensure that AI-generated content can be traced and explained, enhancing user trust and reducing legal risks. Finally, enterprises need to strengthen security technologies to address new security threats.

5. Enterprises should embrace open cooperation and build an intelligent ecological cooperation system

In the face of the rapid development of AI technology and intense market competition, enterprises should proactively embrace open cooperation, building an intelligent ecological cooperation system. Firstly, enterprises can collaborate with leading AI companies, research institutions, and universities to leverage external innovation forces to accelerate technological R&D and application landing, enhancing the intelligence level of products and services. By sharing resources, cooperating on technology, and engaging in joint R&D, enterprises can quickly overcome technical bottlenecks and adapt to market demands.

Through the above strategies, IT & 3C enterprises can effectively embrace the advancements in large models and other generative AI technologies, achieving business innovation and sustained growth.

In Conclusion

This wave of AI is driving human society into a new era of intelligence with unstoppable force. During this historic transition, intelligent technologies will empower various industries, fundamentally reshaping the social ecosystem and altering how we perceive and transform the world. It is foreseeable that the world thirty years from now will be a new world deeply integrated with intelligent technology, full of innovation and endless possibilities.

For most enterprises, this is not only a historical opportunity that cannot be missed but also a critical choice concerning future survival and development. In the face of the AI wave, enterprises need to keenly grasp the pulse of the times, stand at the forefront of the industry, and contemplate how to formulate AI strategies that align with long-term development based on their unique qualities and advantages. Whether through enhancing product intelligence levels, optimizing business processes, or exploring entirely new business models, enterprises must possess clear strategic vision and strong execution capabilities to truly seek certainty in an uncertain future.