AI is Going to Make Programming Obsolete 😭

1. Programming as we know it is going to die.

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The speaker believes that programming is going to become obsolete.

• The speaker has done research and come to this conclusion.
• They will explain their reasoning and provide evidence.

2. Coding is a valuable skill.

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The speaker acknowledges that coding is a valuable skill that they have personally benefited from.

• Coding allows individuals to bring their ideas to life and build things independently.
• It provides the freedom to create and iterate quickly.

3. Coding is difficult and humans are not naturally good at it.

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The speaker highlights the challenges of coding and the limitations of human coding abilities.

• Coding requires thinking through complex problems and writing instructions in a foreign language.
• Bugs are hard to spot and humans struggle to keep track of every edge case.

4. GitHub co-pilot has revolutionized coding.

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The speaker emphasizes the impact of GitHub co-pilot, an AI assistant for coding.

• GitHub co-pilot can generate code and fill in gaps, reducing trial and error.
• It has made coding less tedious and improved productivity.

5. AI coders are faster and cheaper than human programmers.

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The speaker compares the cost and efficiency of human programmers to AI coders.

• AI coders can write code faster and at a lower cost than human programmers.
• AI scales horizontally and can access knowledge and syntax more effectively.

6. AI coding still has limitations.

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The speaker acknowledges the current limitations of AI coding.

• Context window limitations and data availability are challenges for AI coding.
• AI models may not be up-to-date with the latest APIs and solutions.

7. Data and synthetic data will improve AI coding.

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The speaker discusses the potential for data and synthetic data to enhance AI coding.

• As more code is written with AI tools, it can be used to train models and improve coding capabilities.
• The snowball effect of more productive coding and continuous model improvement.

8. Solutions to current limitations include codebase mapping and compression.

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The speaker mentions codebase mapping and compression as potential solutions to current limitations.

• Codebase mapping can compress large code bases into shorthand that AI models can understand.
• This allows AI models to comprehend entire code bases and iterate on existing projects.

9. AI is transforming the future of coding.

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AI will automate coding tasks, making programming skills less necessary in the near future.

• Large language models will understand and generate code based on human requirements.
• Non-technical individuals will be able to write software using AI tools.

10. Natural language will replace traditional programming languages.

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In the long term, humans will communicate with large language models using natural language, eliminating the need for traditional programming languages.

• Humans will speak in natural language, and large language models will program devices accordingly.
• Programming will become a matter of expressing desired outcomes rather than writing code.

11. Learning to code is still valuable.

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While AI will automate many coding tasks, learning to code is still valuable for problem-solving, logical thinking, and reasoning.

• Coding teaches how to break down problems and use logic to solve them.
• Computer science skills are still valuable, but working with AI will become increasingly important.