AI Industry Ecosystem

When you choose an AI tool, you are actually choosing a technology path.

Choose closed source or open source? Choose cloud service or local deployment? Choose US companies or Chinese companies? These choices will affect your work efficiency, data security, and long-term costs.

More importantly, if you are considering career development, you need to know which companies are leading the technology and which directions represent the future.

This article will not stuff you with boring industry news, but rather help you build a framework for understanding the AI industry.

Understanding the industry ecosystem is not for joining the hype, but for having confidence when making decisions.

Overview of Major AI Companies

Players in the AI industry can be divided into several categories: leading US companies, leading Chinese companies, and unicorn companies.

Each category of companies has different technology paths and product strategies.

Leading US Companies

The US holds a leading position in large model technology, with four companies being the most representative.

OpenAIIt is the developer of ChatGPT and a key driver in bringing AI to the masses. Its characteristics are fast technology updates and good product experience, but data privacy and pricing strategies often spark controversy.

AnthropicFounded by former OpenAI employees, it focuses on "safe AI". The Claude model performs excellently in handling long documents (tens of thousands of characters) and has relatively lenient content moderation.

Google DeepMindIt is the creator of AlphaGo, now focusing on the Gemini series of models. Its advantages are multimodal capabilities (image, video, audio) and deep research accumulation.

Meta AIIt takes a completely different path—open source. The Llama series models are free for developers to use, and anyone can download, modify, and deploy them. This gives Meta enormous influence in the open source community.

Leading Chinese Companies

Chinese AI companies are also catching up quickly, forming their own ecosystem.

BaiduIt is one of the earliest companies in China to invest in large models. ERNIE Bot has an advantage in Chinese understanding.

AlibabaThe Tongyi Qianwen (Qwen) series models are very actively open-sourced, ranging from 1.8 billion to 72 billion parameters, with high usage in the open-source community.

Dark Side of the Moon (Moonshot)It is a startup focusing on ultra-long context—its Kimi assistant can process documents of over one million Chinese characters at a time, which is very useful for handling books and codebases.

Unicorn Companies

There are also some startups that have not been around long, but have distinctive technological features and high valuations.

Mistral AIIt is a French company, known for its compact yet powerful models. Mistral 7B leads in performance at the same parameter scale, and it has introduced the MoE (Mixture of Experts) architecture, which is highly efficient.

Which company's product to choose mainly depends on your needs: for the strongest capability, choose OpenAI or Anthropic; for free open source, choose DeepSeek, Zhipu, etc.

Open Source vs Closed Source Models

This is the most important strategic debate in the AI industry.

On one side is the closed-source route represented by OpenAI and Anthropic—models are only accessible via API or web, and you cannot see the model weights.

On the other side is the open-source route represented by DeepSeek and Alibaba—you can download complete models and run them on your own devices.

What are Open Source Models?

Open source has a clear definition in the software field, but in the AI field, the situation is somewhat different.

True open source means you can freely:

• Download the model weights
• Use for commercial purposes
• Modify the model architecture
• Republish derivative versions

But many so-called open-source models are actually open-weight—you can download and use them, but there are commercial use restrictions.

Typical Open Source Models

Closed Source vs Open Source Comparison

The two routes each have strengths and weaknesses; there is no absolute good or bad—it depends on your scenario.

How to Choose

Choose closed-source when:

• You want the strongest capabilities and don't want to deal with technical details
• Your data is not sensitive and can be sent to third parties
• Your usage is small, pay-per-token is more cost-effective
• You need to launch quickly and don't want to spend time on deployment

Choose open-source when:

• Your data is highly sensitive and cannot leave the company
• Your usage is large, pay-per-token is too expensive
• You need deep customization of model behavior
• You have a technical team to deploy and maintain

Recommended strategy: use closed-source for small usage and exploration (low cost, fast launch), consider open-source for large-scale and production environments (strong controllability, low long-term cost).

Cloud AI vs Local AI

This is another important choice: use cloud AI services, or run models on your own computer?

Cloud AI: Ready to Use

Cloud AI means calling AI services in the cloud via web pages or APIs.

You don't need to worry about where the model is deployed, how many GPUs are used, or how it's updated—you just input content and get results.

Typical cloud AI products:

Local AI: Fully Controllable

Local AI runs open-source models on your own computer or server.

Data doesn't leave your device, and there's no ongoing payment.

But you need a good graphics card—usually an NVIDIA GPU; the more VRAM the better.

Common tools for local AI:

Cloud AI vs Local AI Comparison

Hybrid Strategy: Getting the Best of Both Worlds

Many companies adopt a hybrid strategy:

• Sensitive data uses local models
• Ordinary queries use cloud APIs
• Use cloud services during daytime peaks, local for nighttime batch processing

This ensures data security while controlling costs.

Advice for individual users: try cloud AI first, and after getting familiar, consider local deployment if you have privacy needs or usage increases.

AI Infrastructure

AI is not just models—the underlying computing power, chips, and cloud services form the infrastructure of the entire industry.

GPU Computing Power: Why NVIDIA Dominates the Market

Training large models requires massive computation, and GPUs (Graphics Processing Units) are currently the most suitable hardware.

NVIDIA holds a near-monopoly in this field, for three reasons:

Common NVIDIA GPU positioning:

Memory size is a key metric.Generally speaking:

• A 7B model requires 8-16GB of memory
• A 13B model requires 16-32GB of memory
• A 70B model requires 60-80GB of memory

Cloud Computing Providers: Compute as a Service

Not everyone can afford a GPU costing hundreds of thousands—cloud vendors offer a "pay-per-hour rental" model.

Domestic Computing Power Alternatives

Due to chip export restrictions, China is actively developing domestic computing power.

Main players:

Domestic chips are still catching up; the main issue is the software ecosystem—many AI frameworks require special adaptation.

For individual developers, it's recommended to start with NVIDIA consumer-grade GPUs (e.g., RTX 4090), as the ecosystem is the most mature and has the most resources.

AI Business Models

How do AI companies make money? Currently there are three main models.

B2C: Subscription Model

Targets individual users, paid monthly.

The benefit of the subscription model is stable cash flow and high user loyalty. However, it must continuously deliver value, otherwise users will cancel.

B2B: API Calls

Pay-as-you-go—you pay based on how many times you call the API.

The pricing unit is "Token," which can be understood as "word" or "character."

Typical pricing (mid-2025):

Let's do the math: Suppose you have an app that processes 100,000 queries per day, with each query using 1,000 tokens.

With GPT-4o, the daily cost is $5 (input) + $15 (output) = $20, which comes to $600 per month.

If usage grows, costs rise quickly — which is why many companies ultimately choose to self-host open-source models.

Monetizing the Open Source Ecosystem

Open-source models are free, but companies can make money through peripheral services:

• Hosting services: deploy and maintain for you
• Enterprise edition: provide extra features and support
• Consulting services: customize and optimize for you
• Training services: fine-tune models with your data

Mistral AI is a representative of this model — the models are open source, but enterprises need to pay to access larger models and better services.

Comparison of the Three Models

AI Regulation and Policy

AI is developing so fast that every country is formulating regulatory policies. Understanding these policies can help you mitigate risks.

EU AI Act

The EU has the strictest regulation, with the core concept being "risk-based classification."

The EU AI Act has a global impact — as long as your product serves EU users, you must comply.

China's AI Regulatory Framework

China's regulatory policies are also being gradually refined, with core documents including:

• "Interim Measures for the Management of Generative AI Services"
• "Artificial Intelligence Law" (Draft for Comments)

Key requirements:

• Generated content must be truthful and accurate, and must not fabricate false information
• Content review mechanisms must be sound
• Data sources must be legitimate
• Algorithms must be transparent and explainable

US AI Policy Dynamics

US regulation is relatively lenient, primarily through executive orders and industry self-regulation.

Key focus areas:

• National security: prevent AI from being used for malicious purposes
• Fairness: prevent algorithmic discrimination
• Transparency: AI-generated content needs to be labeled

For individual developers, the most important things to note are: AI-generated content should be clearly labeled and should not masquerade as human creation. When personal data is involved, data protection regulations must be followed.

2025 AI Industry Trend Outlook

As of mid-2026, several trends have become quite clear.

Trend 1: Model Capabilities Continue to Improve, but with Diminishing Marginal Returns

GPT-5, Claude 5, Gemini 3.0, DeepSeek 4, and GLM 5 have all arrived, but each upgrade brings diminishing surprises.

It's not that technology has hit a bottleneck, but rather that there are already many "good enough" use cases.

Trend 2: Open Source Models Continue to Catch Up, Narrowing the Gap with Closed Source

Llama 3.1 405B's performance is already close to GPT-4's level, and it's freely available.

More companies will join the open-source camp in the future, and the open-source model ecosystem will become increasingly mature.

Trend 3: The Application Layer Begins to Explode, Agents Become Practical

The past few years were about competition at the model layer; now the focus is shifting to the application layer.

AI Agents — AI that can autonomously complete complex tasks — will move from concept to practical application.

Trend 4: Multimodality Becomes Standard

Pure text models will become increasingly rare, and multimodal models that can handle text, images, audio, and video simultaneously will become the standard.

Trend 5: Regulation Becomes Normalized

Regulatory frameworks in various countries will gradually take shape, moving the AI industry from "reckless growth" into a stage of "regulated development."

Code Example: Calling an Open Source Model API

Let's write a simple example in Python to demonstrate how to call an open-source model API.

We'll use Ollama as the local inference service, which supports multiple open-source models such as Llama 3, Qwen, and Mistral.

Reference for Ollama-related content:https://www.example.com/ollama/ollama-tutorial.html。

Before running this code, you need to install Ollama first:

• gohttps://ollama.comDownload and install Ollama
• Run in terminal:ollama pull llama3Download the model
• Run:ollama serveStart the service (if not already started)
• Then run the Python code above

If you don't have a local GPU, you can also use cloud-based open-source model APIs, such as Together.ai, Anyscale, etc. The calling method is similar, except the URL and authentication method are different.

The advantage of open-source models is that you can first use a small model (7B) locally for development and testing, and if it works well, switch to a larger model (70B) or cloud service.