2026 AI Application Best Practices: From Cost Control to Industry Disruption

Artificial intelligence (AI) is rapidly developing and has permeated all aspects of our lives, from natural language processing to business operations. This article, based on discussions on X/Twitter, compiles some best practices for AI applications in 2026, covering cost control, model selection, industry impact, and risk response, aiming to provide practical guidance for businesses and individuals.

1. Cost Control: Efficiency First Principle in the Era of Model Commoditization

1.1 Model Selection: Balancing Performance and Cost

With the explosive growth in the number of AI models, cost control has become critical. LanYunfeng64's tweet mentioned Anthropic's Claude Sonnet 4.6, which offers "near-Opus intelligence at a fraction of the cost." This means that when selecting a model, it is necessary to focus on cost-effectiveness rather than blindly pursuing the highest performance.

Best Practices:

Assess Needs: Clearly define the application scenario and required functions. Not all tasks require the most advanced models.
Benchmark Testing: Test the performance of different models in real-world scenarios and compare costs.
Focus on Efficiency: Look for models that can significantly reduce costs with a slight loss in performance. For example, Claude Sonnet 4.6 is a good example.
Open Source Models: Consider using open source models, such as Alibaba's Qwen 3.5. LanYunfeng64 mentioned that the Token price of Qwen 3.5 is only 1/18 of Gemini 3 Pro.

1.2 Hardware Optimization

Model inference requires powerful computing resources. Optimizing hardware can significantly reduce operating costs.

Best Practices:

Choose the Right Hardware: Select the appropriate GPU or TPU based on model size and inference requirements.
Quantization and Pruning: Use model quantization and pruning techniques to reduce model size and computational complexity.
Optimize Inference Engine: Use inference engines such as TensorRT and OpenVINO to accelerate model inference.
Cloud Service Optimization: If using cloud services, adjust resource configurations based on actual usage to avoid waste.

2. Model Selection and Evaluation: Considerations Beyond Performance Metrics

2.1 Open Source vs. Closed Source

Open source models are usually less expensive, but require more manpower to maintain and customize. Closed source models usually provide a better out-of-the-box experience, but are more expensive.

Best Practices:

Open Source Models: Suitable for teams with strong technical capabilities, requiring customized development and long-term maintenance scenarios.
Closed Source Models: Suitable for teams that need rapid deployment, have relatively weak technical capabilities, and require stability and commercial support scenarios.

2.2 Evaluation Metrics: More Than Just Accuracy

When evaluating AI models, you can't just focus on traditional metrics like accuracy. You also need to consider the model's fairness, robustness, and interpretability.

Best Practices:

Fairness Assessment: Use fairness assessment tools to detect whether the model has biases and take measures to correct them. RonDeSantis's tweet reminds us that AI can amplify human biases.
Robustness Assessment: Test the model's performance in the face of noise, adversarial examples, and other situations.
Interpretability Assessment: Use interpretability tools to understand the model's decision-making process and ensure that its behavior meets expectations.
User Feedback: Collect user feedback to understand the model's performance in actual use and make improvements.

2.3 Multi-Model Integration: Improving Overall Performance

By integrating multiple models together, overall performance and robustness can be improved.

Best Practices:

Model Fusion: Weighting or voting the outputs of multiple models to improve overall accuracy.
Model Cascading: Connecting multiple models in series, with each model responsible for a different task, forming a complete process.
Expert System: Build an expert system that selects the appropriate model for processing based on different inputs.

3. AI Agent: Reshaping Business Models

3.1 The Rise of AI AgentsLanYunfeng64 pointed out that AI Agents are evolving from simple chatbots into entities with economic capabilities, capable of A2A (AI-to-AI) transactions.

Best Practices:

Automate Processes: Use AI Agents to automate repetitive tasks, such as customer service, data analysis, etc.
Build an AI Ecosystem: Create an AI Agent ecosystem where Agents can collaborate to complete more complex tasks.
Sigil Wen's Automaton: Learn from Sigil Wen's Automaton's experience, allowing Agents to autonomously generate profits, pay for computing costs, self-improve, and replicate.

3.2 Risks of AI Agents

LanYunfeng64 expressed concerns about AI Agents potentially replacing human jobs. We need to pay attention to the following risks:

Unemployment Risk: AI Agents may replace a large number of low-skilled jobs, leading to increased unemployment rates.
Ethical Risk: AI Agent decisions may be biased or even violate ethical standards.
Security Risk: AI Agents may be maliciously exploited, causing security incidents.

Best Practices:

Skills Transition: Help employees learn new skills to adapt to the job demands of the AI era.
Ethical Review: Conduct ethical reviews of AI Agent decision-making processes to ensure their behavior complies with ethical standards.
Security Protection: Strengthen the security protection of AI Agents to prevent them from being maliciously exploited.

4. Industry Impact: Disruption and Opportunities Coexist

4.1 AI Applications in Various Industries

AI is disrupting various industries. Here are some specific application scenarios:

Software Development: AI programming tools like Codex can improve development efficiency.
Finance: AI can be used for risk assessment, fraud detection, smart investment advisory, etc.
Healthcare: AI can be used for disease diagnosis, drug development, personalized treatment, etc.
Education: AI can be used for personalized learning, intelligent tutoring, homework grading, etc.
Retail: AI can be used for intelligent recommendations, inventory management, customer service, etc.

4.2 Emerging Markets: The Rise of AI in India

LanYunfeng64 mentioned the India AI Summit and SarvamAI's advantages in localized applications in India. Emerging markets have enormous potential in AI applications.

Best Practices:

Localization Strategy: Develop localized AI applications tailored to the characteristics of different markets.
Data-Driven: Use local data to train AI models, improving the accuracy and applicability of the models.
Cooperation and Win-Win: Cooperate with local businesses and institutions to jointly promote the development of AI applications.

5. Future Outlook: Challenges and Opportunities of AGI

5.1 The Arrival of AGI

Although AGI (Artificial General Intelligence) has not truly arrived, we have already seen AI outperform humans in certain areas.

Challenges:

AGI Security: How to ensure the safety and controllability of AGI and prevent it from posing a threat to humanity.
AGI Ethics: How to define the ethical norms of AGI and ensure its behavior aligns with human values.
AGI Employment: AGI may replace a large number of jobs. How to address the unemployment problem.

Opportunities:

Solving Global Problems: AGI can be used to solve global problems such as climate change, disease control, and poverty.
Promoting Technological Progress: AGI can accelerate scientific research and technological innovation, promoting the progress of human civilization.
Creating New Industries: AGI can create new industries and employment opportunities.

5.2 Coexistence of Humans and AI

The key to the future lies in how to enable humans and AI to coexist and achieve mutual benefit and win-win results.

Best Practices:

Human-Machine Collaboration: Use AI as an assistant to humans to improve work efficiency and quality.
Cultivate Innovation: Encourage innovation and creativity, allowing humans to excel in areas where AI cannot.
Lifelong Learning: Maintain a lifelong learning attitude and continuously adapt to the new demands of the AI era.### Summary

The development of AI brings tremendous opportunities and challenges. Through reasonable cost control, model selection, risk management, and strategic planning, we can fully utilize the potential of AI to promote economic development and social progress. Facing the future of AGI, we need to maintain an open mind, actively explore the coexistence model of humans and AI, and jointly create a better future.