Software 3.0 Development Model

A Software 3.0 Development Model is a software development model that relies on foundational models (e.g. foundational LLMs) and other advanced AI models as the foundation for building and operating software systems.

• Context:
  • It can (typically) enable Non-Technical Users to participate more actively in software development and operation.
  • It can (typically) enhance Software System Interpretability (e.g. inputs and outputs are in natural language).
  • It can (typically) use Natural Language Interfaces as the primary means of interaction with the system.
  • It can (typically) enable rapid prototyping and iteration through prompt adjustments and AI agent reconfiguration.
  • It can (often) shift the focus from coding to Problem Decomposition and Solution Conceptualization.
  • It can (often) simplify development by allowing complex functionalities to be achieved through Prompt Engineering.
  • It can (often) be Framework Agnostic, unifying various underlying technologies under a single natural language interface.
  • It can involve Domain-Specific AI Agents (configured for domain-specific tasks, and can be equipped with relevant tools and context).
  • It can (often) require new skills like Prompt Engineering and AI System Design from developers and users.
  • It can emphasizing AI Integration and Natural Language Processing skills.
  • It can lead to more intuitive and efficient software development processes.
  • ...
• Exmpale(s):
  • AI-Assisted Iterative Development Model which follows a workflow of Requirements Elicitation, AI-Powered Design Generation, Natural Language Testing, and Continuous AI Refinement. In this approach, requirements are gathered through natural language conversations with stakeholders. An AI then generates initial designs based on these requirements. Testing is conducted using natural language descriptions of expected behavior, which the AI interprets and executes. The system is continuously refined based on feedback, with the AI learning and improving its outputs over time.
  • Collaborative Human-AI Programming Model which follows a workflow of Problem Statement, AI Solution Proposal, Human-AI Collaboration, and AI-Assisted Deployment. Users start by describing a problem in natural language. The AI proposes potential solutions, which humans can then discuss, modify, or approve using natural language interactions. The AI and human collaboratively refine the solution, with the AI handling much of the technical implementation based on the human's high-level guidance. Finally, the AI assists in deploying and maintaining the solution.
  • Domain-Specific Agent Orchestration Model which follows a workflow of Problem Domain Analysis, Agent Selection and Configuration, Multi-Agent Collaboration, and Human Oversight and Adjustment.
  • ..
• Counter-Example(s):
  • Software 2.0 approaches that need extensive Data Collection and Feature Engineering.
• See: AI Integration, AI System Design, Framework Agnostic, Natural Language Interface, Prompt Engineering, Software System Interpretability.

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References

2024

• "Anatomy of a Software 3.0 Company // Sarah Guo // AI in Production Keynote."
  • NOTES:
    1. Era of AI Infrastructure and Operations (INF/OPS): The year 2023 is viewed as the beginning of the AI INF/OPS era, focusing on integrating open-source foundation models, vector databases, and new modalities like audio, to operationalize AI outputs.
    2. Shift from Capability to Customer-Driven Approach: Successful AI companies should focus on customer needs rather than solely on AI capabilities. This involves starting with the customer's problems and working backward to create solutions, which is crucial for developing defensible applications.
    3. Classification and Orchestration Across Models: Companies often start by using powerful models like GPT-4 for a range of tasks but then move towards using smaller, fine-tuned models and efficient orchestration to reduce costs and improve performance for specific use cases.
    4. Emergence of AI Applications in 2024: The prediction is that 2024 will see a rise in AI applications beyond infrastructure as companies become better at understanding and implementing AI to meet enterprise needs and customers become more willing to invest in AI solutions.
    5. Category Creation in AI Market: A significant opportunity lies in defining new market categories that didn't exist before, such as foundation model APIs, legal co-pilots, and video avatars. These categories start small but have the potential for explosive growth and high market share.
    6. Team Composition and Velocity: Effective AI companies often combine research expertise with domain-specific knowledge, allowing them to iterate and bring products to market rapidly. The pace of development and the ability to integrate customer feedback quickly are critical for success.
    7. Dynamic Business Models: AI business models are evolving, with a focus on innovative ways of balancing cost and revenue. This includes experimenting with pricing strategies, such as value-based pricing or consumption-based models, to align with the unique value provided by AI applications.

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