How AIGC Becomes the Web3 Productivity Engine: A Complete Analysis from Technological Breakthroughs to Commercial Implementation

Artificial Intelligence Generated Content (AIGC) is becoming one of the most revolutionary productivity tools in the digital era. Since 2022, the global tech industry has witnessed explosive growth in this field, with a surge of unicorn companies and billions of dollars in funding flowing in, confirming the enormous potential of this new track. As the Web3 era gradually advances, AIGC not only shoulders content production but also aims to become the core engine connecting virtual and real worlds, driving digital economic upgrades.

Behind the Explosion of AIGC: Synchronized Advances in Technology and Market Opportunities

Venture capitalists in Silicon Valley are already focusing on generative AI, especially in the niche of AI art creation. Over the past few years, many emerging companies have rapidly risen to unicorn status, with valuations surpassing one billion USD, attracting top-tier investors like Sequoia US, Coatue, Lightspeed Venture Partners, and others.

The current wave of AIGC is driven by three main factors: First, continuous iterations of deep learning algorithms provide technical support; second, industries such as short videos, gaming, and advertising have seen exponential growth in content demand; third, this track is still in its early stages—while large tech firms hold some influence, startups still have opportunities to break through in vertical domains.

As we enter the Web3.0 era, the integration of AI, related data, and semantic networks will form a comprehensive connection between humans and machines. Traditional PGC (professional generated content) and UGC (user generated content) methods are increasingly unable to meet the rapidly growing content needs. AIGC has emerged as the third pillar of content creation in this new era, bringing revolutionary impacts to industries like short videos, gaming, and advertising.

Understanding AIGC: A Panorama from Natural Language Processing to Generative Algorithms

Natural Language Processing: The Bridge for Human-Machine Dialogue

The advent of NLP marks a fundamental shift in how humans interact with computers. It combines linguistics, computer science, and mathematics, enabling machines to understand natural language, extract information, perform automatic translation, and analyze data. This is a major breakthrough in AI development—before NLP, humans could only communicate with computers through fixed commands.

Historically, Alan Turing’s 1950 paper “Computing Machinery and Intelligence” introduced the famous “Turing Test,” which involved core elements like automatic semantic translation and natural language generation. Since then, NLP has diverged into two main directions:

Natural Language Understanding (NLU) aims to equip computers with human-level language comprehension. Due to the ambiguity, polysemy, and context dependence of natural language, understanding remains challenging. NLU has evolved from rule-based approaches, to statistical methods, and now to deep learning-based models.

Natural Language Generation (NLG) transforms non-linguistic data into human-understandable natural language, such as writing articles or generating reports. NLG has progressed from simple data concatenation, to template-driven methods, and now to advanced NLG systems that can interpret intent, consider context, and produce fluent narratives.

NLP technologies are widely applied across four major domains: sentiment analysis helps companies grasp public opinion quickly; chatbots have gained value with the proliferation of smart home devices; speech recognition makes human-computer interaction more natural; machine translation has significantly improved accuracy, supporting cross-language video content translation.

The core technological advancement comes from the evolution of neural networks. In 2017, Google introduced the Transformer model, which gradually replaced RNNs like LSTM, becoming the preferred approach in NLP. Transformers’ parallelization allows training on larger datasets, leading to models like BERT and GPT, trained on massive corpora such as Wikipedia and Common Crawl, and fine-tuned for specific tasks.

Generative Algorithms: From GANs to Diffusion Models

The core driver of AIGC is breakthroughs in generative algorithms. Current mainstream models include Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), Normalizing Flows (NFs), Autoregressive models (AR), and Diffusion Models.

GANs, proposed by Ian J. Goodfellow in 2014, feature an adversarial training mechanism. Consisting of a generator and discriminator, the generator creates “fake” data trying to fool the discriminator, which aims to distinguish real from fake. Through this adversarial process, both networks improve until equilibrium is reached.

GANs excel at modeling data distributions without complex variational bounds but are difficult to train stably and prone to issues like mode collapse, where the generator produces limited varieties of outputs, hindering learning.

Diffusion Models represent a new direction. They mimic human cognition by gradually adding Gaussian noise to training data and learning to reverse this process to recover the original data. After training, generating new data involves starting from random noise and denoising through learned processes.

Compared to GANs, diffusion models produce higher-quality images, do not require adversarial training, and are more scalable and parallelizable. These advantages have made diffusion models the leading technology for next-generation image generation.

For example, DALL-E can generate images directly from text descriptions—once a capability once exclusive to humans. Its operation involves encoding text into a latent space via a text encoder, then using a prior model to map to image space, and finally employing an image decoder to generate visual outputs that match the semantic content. This process closely resembles human imagination.

The current dominant text encoder is OpenAI’s CLIP, trained on 400 million high-quality English image-text pairs. A significant challenge is that large-scale, high-quality datasets of text-image pairs are primarily in English. For other languages, systems often require translation, which involves complex semantic, cultural, and contextual considerations, making precise alignment difficult. Industry insiders note that even with open-source CLIP functions, training on different language datasets yields significant variations; some teams have used over 2 billion text-image pairs to approximate CLIP’s performance.

Computing Power: The Infrastructure Backbone of AIGC

Beyond algorithmic innovation, computing power and hardware infrastructure are essential. Training and inference of AIGC models demand massive computation, beyond what ordinary computers can handle. Currently, high-performance GPU clusters, such as NVIDIA A100s, are the main solution. For instance, Stable Diffusion relies on around 4,000 NVIDIA A100 GPUs, with operational costs exceeding $50 million. As AIGC applications expand, demand for computing resources will continue to surge, and domestic chip companies may find opportunities amid export controls.

Content Creation: Text, Images, Videos, and Code—How AIGC Reshapes Content Production

Text Creation: The Pioneer of Commercialization

AIGC’s application in text has achieved relatively mature commercialization. Jasper exemplifies this—founded in 2021, it secured $125 million in funding within two years, with a valuation soaring to $1.5 billion, serving over 70,000 clients including Airbnb, IBM, and others.

Jasper’s core function is to help users quickly generate various content types via AI: SEO-optimized blog posts, social media content, ad copy, marketing emails, etc. Users input brief descriptions and requirements, and the system automatically fetches relevant data and creates content accordingly. According to official reports, Jasper generated $40 million in revenue in 2021, with estimates reaching up to $90 million.

Most AIGC service providers adopt SaaS models for monetization, offering hundreds of content templates to improve efficiency.

Image Creation: Democratizing Artistic Production

Platforms like MidJourney and DALL-E have significantly lowered the barriers to digital art creation. Users input text prompts, and the system generates original images automatically. The underlying logic involves NLP recognizing the semantics of the text, converting it into machine language, and combining it with backend datasets—often sourced from proprietary or web-crawled licensed content—to produce new works.

Since generated images are considered AI-created, this avoids copyright disputes and is widely used in media, social platforms, and content creation. Some data set curators have used AIGC to produce materials and monetize via private traffic.

Recently, OpenAI partnered deeply with Shutterstock, one of the largest copyright image providers, to sell images generated by DALL-E exclusively, marking a shift of AI image generation from niche to mainstream commercial application.

Beyond painting, AIGC also supports text-to-image and image-to-text conversions, which have practical value in patent applications, technical documentation, and more.

Video Creation: From Short to Long Videos

AIGC’s application in video shows even greater potential. Google’s Phenaki model can generate variable-length videos from text, aiming at long video synthesis, unlike Imagen Video which focuses on short clips. In some demos, it takes only minutes to produce coherent videos matching several hundred words of input.

Applications include automatic virtual actor performances, where AIGC-based content significantly improves naturalness in camera transitions and facial expressions compared to single virtual presenters. Future scenarios include sports events and financial reports, where text can directly generate short videos, with virtual characters performing fully automated broadcasts.

Audio Synthesis: From Assistants to Creative Tools

AIGC audio applications are already integrated into daily life. Navigation apps can switch between voices of celebrities or cartoons, based on pre-recorded voice libraries, trained repeatedly to express arbitrary content in specified voices. Users can even record personal voice packages via apps like Amap.

More advanced applications involve virtual characters, where AIGC can generate both the voice and expressive content, endowing virtual personas with human-like expression and personality traits.

Game Development: Content Generation and Cost Reduction

AIGC’s role in gaming includes two directions: automatic construction of game scenes and stories, and providing players with creative tools. Open-world games benefit from rapid scene and NPC generation, greatly improving development efficiency and reducing costs. Additionally, players can create virtual characters via AIGC platforms for activities like in-game gold farming.

Games like Delysium have begun integrating such features, hinting at future open-world games with personalized storylines and dungeons—offering different experiences for each player and creating new levels of immersion.

Code Generation: Developers’ Intelligent Assistant

GitHub Copilot, developed jointly by GitHub and OpenAI, is an AI code generation tool that offers code suggestions based on naming conventions or code context. Trained on billions of lines of public code from GitHub, it supports mainstream programming languages and has become a practical tool to boost development efficiency.

Core Challenges and Technical Bottlenecks of AIGC

Despite successful commercial applications across multiple fields, AIGC still faces significant issues in accuracy and quality. For example, in image generation, results for anime-style and abstract content are better, while detailed real-world scenes often fall short. Common problems include:

Insufficient detail handling: Generated images often lack fine features like eyes or fingers, reflecting limited control over detailed brushwork.

Spatial understanding biases: When descriptions involve multiple elements (e.g., “a beautiful woman with a ragdoll cat”), the system may misplace objects or misjudge quantities, due to errors in semantic understanding.

Platform quality disparities: Different AIGC platforms produce vastly different results from the same input, indicating that algorithm choice, dataset quality, and training completeness are critical factors.

Underlying causes include:

1. Language understanding limitations: Current NLP models struggle with complex spatial and multi-element relationships, leading to inaccuracies in multi-object compositions.

2. Training data language constraints: Mainstream text encoders like OpenAI’s CLIP are primarily trained on English datasets (~400 million pairs). For other languages, high-quality datasets are scarce, often requiring translation, which involves complex semantic and cultural adjustments, making precise alignment difficult. Industry sources note that even open-source CLIP implementations trained on multilingual data show significant performance gaps; some teams have used over 2 billion text-image pairs to approximate CLIP’s capabilities.

3. Algorithm selection impact: Different generative algorithms produce varying content quality.

4. Data set quality: The quality, compliance, and stylistic bias of training data directly influence output results.

To achieve efficient commercial application of AIGC, breakthroughs are needed in NLP, translation models, generative algorithms, and dataset construction.

Future Pillars of AIGC Development: Large Models, Big Data, and Massive Computing Power

Given current bottlenecks, the future of AIGC is increasingly defined by three core directions:

Continuous Iteration of Large Models

Combining large-scale NLP models with high-quality datasets forms the foundation of AIGC software. For example, OpenAI’s CLIP is trained on 400 million English image-text pairs. Industry efforts are exploring specialized models for different languages and vertical domains to improve targeted performance, reduce training costs, and enhance accuracy.

Acquisition and Governance of Big Data

High-quality datasets determine AIGC’s quality and business models. Future development will focus on building large-scale, compliant, and stylistically consistent datasets. Constructing datasets for non-English languages will become a key challenge.

Infrastructure for Massive Computing Power

Computing power is becoming a critical resource in AIGC. Companies will continue to rely on cloud computing, with some leading firms building their own clusters. Considering export restrictions on high-end chips from NVIDIA, domestic chipmakers may find opportunities to expand their market share.

Investment Opportunities in AIGC: Software, Hardware, and Dataset Layouts

From an investment perspective, the AIGC value chain can be divided into software, hardware, and data layers:

Software Layer: Encompasses NLP technologies and AIGC generative models, involving companies like Google, Microsoft, iFlytek, and Tuosi.

Algorithm and Model Layer: Includes Meta, Baidu, BlueFocus, Visual China, Kunlun Wanwei, etc. These firms either develop advanced generative algorithms or possess high-quality material and data resources.

Hardware Layer: Comprises companies like Lanke Technology, ZTE, NewEase, Tanfeng Communications, Baoxin Software, and Zhongji Xuchuang, providing computing chips and communication infrastructure necessary for AIGC.

Data Layer: High-quality datasets are crucial for meeting the content needs of the metaverse and Web3. The demand for compliant, high-quality datasets will grow rapidly, creating new investment opportunities.

Development Stages and Outlook of AIGC

Industry consensus suggests AIGC will go through three stages:

Assistant Stage: AIGC acts as an auxiliary tool to help humans produce content more efficiently.

Collaboration Stage: AIGC appears as virtual humans and other forms, forming symbiosis with humans, with human-AI co-creation becoming routine.

Originality Stage: AIGC independently produces high-quality, high-precision content, becoming an autonomous creative entity.

As these stages unfold, AIGC will fundamentally transform existing content production models, enabling the creation of high-quality original content at one-tenth of current costs and hundreds or thousands of times faster.

Risks and Regulatory Challenges in Development

Rapid development of AIGC also entails risks:

Technological Innovation Risks: If foundational hardware (supercomputers, chips) development lags behind expectations, industry growth could be constrained.

Policy and Regulatory Risks: AIGC is still in early stages; future laws regarding intellectual property rights, ethical standards, and content regulation remain uncertain. The lack of clear legal frameworks presents both risks and opportunities for establishing normative data governance systems.

Given current legal gaps and unresolved ethical issues, high-quality, compliant datasets are vital for training models and generating content. AIGC companies must advance both technological innovation and data governance simultaneously.

Conclusion: The Fusion of AIGC and Web3

From PGC to UGC and now AIGC, content creation methods are continuously evolving. AIGC not only surpasses human limits in content production but also serves as a key productivity tool to propel Web3 development. When large models, big data, and massive computing power are fully integrated, AIGC will revolutionize the content ecosystem and usher humanity into the true Metaverse era.

For investors, layout in software, hardware, and datasets is essential to seize AIGC opportunities. For entrepreneurs, vertical and differentiated application innovations still hold vast potential. For ordinary users, AIGC is gradually becoming part of daily work and creative activities, enhancing productivity.

Over the next decade, how AIGC integrates with Web3, blockchain, virtual humans, and other technologies will determine the trajectory of the entire digital economy industry.