Core Viewpoint - The article argues that AI will not replace software developers, emphasizing that the future of software development remains in the hands of developers who can translate ambiguous human thoughts into precise computational logic [1][2]. Group 1: Historical Context - The prediction that "programmers will be replaced" has never come true throughout the history of computing, which spans over 43 years [3]. - The author has witnessed multiple technological revolutions, each heralded as the end of programmers, such as the rise of Visual Basic and low-code platforms [4][6]. - Historical cycles show that each wave of technology has led to an increase in the number of programs and programmers, exemplifying the "Jevons Paradox" with a market size of $1.5 trillion [9]. Group 2: Differences with Current Technology - The current wave of Large Language Models (LLMs) differs significantly from past technologies in scale and impact, with LLMs not reliably improving development speed or software reliability [10][11]. - Unlike previous technologies that provided stable and reliable solutions, LLMs often slow down development and create a dual loss situation unless real bottlenecks are addressed [11]. Group 3: Essence of Programming - The core challenge of programming has always been converting vague human ideas into logical and precise computational expressions, a difficulty that persists regardless of the programming tools used [12][17]. - The complexity of programming lies not in the syntax but in understanding what needs to be achieved, a challenge that remains unchanged over decades [17][18]. Group 4: Future Outlook - AI will not eliminate the need for programmers; instead, the demand for skilled developers will continue to grow, especially as companies realize the true costs and limitations of AI technologies [19][20]. - The future of software development will likely see AI playing a supportive role, assisting in tasks like prototype code generation, while the critical decision-making and understanding will still rely on human developers [19][20].

Core Insights - The article discusses the evolution of Li Xiang's vision for Li Auto, emphasizing the transition from a traditional automotive company to an artificial intelligence (AI) company, driven by the belief in the transformative potential of AI and autonomous driving [1][2]. Motivation - Li Xiang considers founding Autohome as his biggest mistake, aiming for a venture at least ten times larger than it [1]. - The belief in the feasibility of autonomous driving and the industry's transformative phase motivated the establishment of Li Auto [1]. Timeline of Developments - In September 2022, Li Auto internally defined itself as an AI company [2]. - On January 28, 2023, Li Xiang officially announced the company's identity as an AI company [2]. - By March 2023, discussions around AI began, although initial understanding of concepts like pretraining and finetuning was limited [2]. - By December 2024, Li Xiang articulated five key judgments regarding AI's role and potential, emphasizing the importance of foundational models [2][3]. Key Judgments - Judgment 1: Li Xiang believes in OpenAI's five stages of AI, asserting that AI will democratize knowledge and capabilities [2]. - Judgment 2: The foundational model is seen as the operating system of the AI era, crucial for developing super products [2]. - Judgment 3: Current efforts are aimed at achieving Level 3 (L3) autonomous driving and securing a ticket to Level 4 (L4) [2][3]. - Judgment 4: The integration of large language models with autonomous driving will create a new entity termed VLA [3]. - Judgment 5: Li Auto aims to produce a car without a steering wheel within three years, contingent on the VLA foundational model and sufficient resources [3]. Technical Insights - The design and training of the VLA foundational model focus on 3D spatial understanding and reasoning capabilities [5][6]. - Sparse modeling techniques are employed to enhance efficiency without significantly increasing computational load [7]. - The model incorporates future frame prediction and dense depth prediction tasks to mimic human thought processes [8]. - The use of diffusion techniques allows for real-time trajectory generation and enhances the model's ability to predict complex traffic scenarios [10]. Reinforcement Learning - The company aims to surpass human driving capabilities through reinforcement learning, addressing previous limitations in model training and interaction environments [11]. Future Directions - Li Auto is actively developing various models and frameworks to enhance its autonomous driving capabilities, including the introduction of new methodologies for video generation and scene reconstruction [12][13].

Group 1 - Delphi was a significant product from Borland, known for its rapid application development capabilities, allowing developers to create management systems efficiently compared to VB and VC [1][3] - Delphi 7.0 gained a strong reputation for supporting complex functions like database and network communication, making it a preferred choice among programmers [3] - Many enterprise software and industrial control systems still run on Delphi 7 code, indicating its lasting technical vitality [5] Group 2 - Delphi's decline was attributed to Borland's market missteps and failure to adapt to web and mobile development trends, along with the departure of its core creator to Microsoft [5] - Eversheet emerged as a successful no-code development tool, allowing users to create software without coding knowledge, focusing on business logic [7][9] - Eversheet has served over 300,000 enterprises, proving its commercial value and breaking the notion that no-code tools are merely a gimmick [11]