Why I Quit Copilot | Prime Reacts

The video transcript discusses the reasons why the speaker has decided to stop using the AI coding assistant, Copilot. Initially, the decision was accidental due to forgetting to sign in on a new computer setup. However, the speaker reflects on several issues with Copilot, including the potential loss of coding skills through over-reliance, the 'Copilot pause' where the user waits for AI suggestions instead of writing code independently, and concerns over code quality and maintainability. The speaker also raises privacy concerns, as Copilot uploads code snippets to a remote server, which is a significant issue for someone who values open-source work, self-hosting, and privacy. The video concludes with the speaker's intention to take a break from AI tools and possibly explore open-source models that can be self-hosted in the future.

Takeaways

• 🔄 The speaker quit using Copilot due to forgetting to sign in after setting up a new computer and redoing their Neovim configuration.
• 💭 The absence of Copilot has led to a change in the speaker's coding behavior, including a noticeable 'Copilot pause' where they wait for code suggestions.
• 🚀 The speaker believes that using Copilot can lead to a decrease in coding skills over time, as it may reduce the need to learn and memorize language features.
• 😕 There is a concern that relying on Copilot can make coding less enjoyable and take away from the creative and problem-solving aspects of programming.
• 📈 The speaker suggests that Copilot might improve productivity for some developers, but it can also lead to code that is less thought out and potentially buggy.
• 📚 Copilot's suggestions can sometimes be outdated, which might be due to the rapid pace of change in software development and the limitations of its training data.
• 💼 The speaker values privacy and is concerned about the data that Copilot sends to remote servers, which influenced their decision to stop using it.
• 📉 There is a noted decrease in the speaker's coding satisfaction after using Copilot, which contrasts with GitHub's study that showed an increase in developer satisfaction.
• 🛠️ The speaker prefers to take a break from AI tools for now and may consider self-hosting open-source models in the future, aligning more with their values of open-source, privacy, and self-hosting.
• 🤔 The script raises questions about the long-term implications of AI on programming skills, the value of privacy in coding, and the sustainability of using AI for code generation.
• 📝 The speaker emphasizes the importance of knowing when to use tools like Copilot effectively, suggesting that leading the AI with clear intentions results in better code generation.

Q & A

Why did the speaker decide to quit using Copilot?

The speaker decided to quit using Copilot because they got a new computer, redid their Neovim configuration, and simply forgot to sign back into Copilot.

What is the 'Copilot pause' as described by the speaker?

The 'Copilot pause' refers to the speaker's behavior of starting to type code and then stopping, waiting for Copilot to suggest the next part of the code instead of writing it themselves.

Why does the speaker believe that using Copilot could be detrimental to learning programming?

The speaker believes that using Copilot could be detrimental because it might bypass the initial struggle that is crucial for learning. It could lead to a dependency on the tool, reducing the opportunity to strengthen one's own programming skills.

What is the speaker's opinion on the importance of memorizing code or programming language syntax?

The speaker believes that memorizing code or syntax is not essential in the long term, but it can be beneficial. They argue that once you've learned something, like riding a bike, you retain that skill, and similarly, knowing code without needing to look it up can be advantageous.

How does the speaker feel about the quality of code generated by Copilot?

The speaker has concerns about the quality of code generated by Copilot. They mention that it often requires significant rewriting and that it can introduce bugs, especially if the user allows Copilot to lead the coding process rather than guiding it.

What is the speaker's stance on privacy in relation to using Copilot?

The speaker has privacy concerns with using Copilot. They mention that Copilot sends snippets of code to a remote server, which is against their values of self-hosting, open source, and privacy.

Why does the speaker suggest that students having free access to Copilot might be a mistake?

The speaker suggests that free access to Copilot for students is a mistake because it could prevent them from experiencing the initial challenges of programming, which are important for learning and growing as a programmer.

What does the speaker think about the enjoyment of coding when using Copilot?

The speaker found that the enjoyment of coding decreased with the use of Copilot. They felt that the act of writing code became more about reviewing AI-generated suggestions rather than using their own creativity and problem-solving skills.

What is the speaker's view on the role of enjoyment in becoming proficient at programming?

The speaker believes that enjoyment is key to becoming proficient at programming. They argue that if you don't enjoy the process, you're less likely to put in the effort required to become excellent at it.

How does the speaker feel about the future of AI in coding and their potential usage of it?

The speaker is taking a break from AI in coding due to being underwhelmed and experiencing hype fatigue. However, they remain open to the possibility of using open-source models that they could self-host in the future.

What is the speaker's opinion on the necessity of understanding the underlying technology of AI tools like Copilot?

The speaker values understanding the underlying technology and suggests using resources like Brilliant.org to learn more about how language models and AI tools work, which could lead to better utilization and appreciation of these tools.

Outlines

00:00

🚀 Transitioning Away from Co-Pilot

The speaker discusses their recent decision to stop using the AI coding assistant Co-Pilot. They recount a humorous incident with their computer and explain that the reason for no longer using Co-Pilot is quite simple: they got a new computer, redid their Neovim configuration, and simply forgot to sign back into Co-Pilot. Despite this, they have no intention of returning to it. They also engage with their audience through a poll about Co-Pilot usage and share their thoughts on the importance of learning to code without relying on such tools.

05:03

💪 The Importance of Retaining Coding Skills

The speaker explores the concept that if you don't use a skill, you may lose it, comparing coding to physical activities like weightlifting. They discuss their personal practice of periodically turning off their Language Server Protocol (LSP) to reinforce their own coding knowledge. They also talk about the 'Co-Pilot pause', a habit of waiting for the AI to suggest code, which they've noticed since they stopped using Co-Pilot. The speaker expresses concern that over-reliance on Co-Pilot could lead to skill atrophy and a decrease in the enjoyment of coding.

10:03

🤔 Balancing AI Assistance with Personal Creativity

The speaker debates whether using Co-Pilot has affected their creativity in coding. They acknowledge the 'Co-Pilot pause' phenomenon and discuss the potential downsides of letting AI take the lead in the coding process. They argue against letting Co-Pilot generate ideas, emphasizing the importance of human direction and creativity. Despite some personal benefits from using Co-Pilot, such as increased speed in coding, they ultimately feel that their enjoyment and love for programming have not been diminished by its use.

15:04

🎓 The Role of Enjoyment in Skill Development

The speaker asserts that enjoyment is key to becoming good at something, including programming. They reflect on their early experiences as a junior engineer and the challenges they faced, suggesting that overcoming these challenges was crucial for their improvement. They also discuss the importance of learning and the struggle to find time for it. The speaker shares anecdotes about their own hobbies and how their passion for side projects has sometimes overshadowed other interests.

20:05

🛠️ The Quality Conundrum with Co-Pilot

The speaker addresses concerns about the quality of code generated by Co-Pilot. They argue that leading Co-Pilot rather than letting it lead results in better code quality. The speaker also mentions the benefits of using TypeScript return types to improve Co-Pilot's suggestions. They discuss the limitations of Co-Pilot, particularly when it comes to generating up-to-date code, and how this has influenced their decision to stop using it.

25:06

🔒 Privacy Concerns with Co-Pilot Usage

The speaker expresses their privacy concerns regarding Co-Pilot, which sends code snippets to a remote server. They discuss the tension between open-source contributions and privacy, questioning how much of a privacy issue this truly is. The speaker also talks about the broader implications of telemetry and the potential for users to inadvertently train AI systems, which could eventually make human programmers less relevant. They end by stating their intention to take a break from AI tools.

30:08

🏁 Reflecting on the 'Co-Pilot Pause'

The speaker concludes by highlighting the 'Co-Pilot pause' as a significant observation, encouraging others using Co-Pilot to try turning it off and notice the pause in their coding process. They reflect on how reliance on AI can change the way they think and code, and suggest that there might be a more straightforward era of coding without AI. The speaker also touches on the idea that sometimes, the past can indeed be better.

Keywords

💡Co-Pilot

Co-Pilot, in the context of this video, refers to an AI-powered coding assistant developed by GitHub and OpenAI. It is designed to help programmers write code faster by suggesting the next lines of code based on the current context. The speaker in the video has decided to quit using Co-Pilot, which forms the central theme of the discussion.

💡Neovim

Neovim is a highly extensible text editor that aims to improve upon the already powerful Vim editor. In the script, the speaker mentions redoing their Neovim configuration, which signifies a personalization of their development environment. This is a significant step for developers as it can enhance productivity and workflow.

💡Telemetry

Telemetry in the video refers to the automatic collection of data from software and sending it back to the developers for analysis. The speaker expresses concerns about privacy with Co-Pilot's telemetry, which involves sending snippets of code to a remote server, reflecting a broader debate about user privacy and data security.

💡Loss Leader

A loss leader is a product sold at a price lower than its market cost to attract customers with the hope of making a profit from subsequent sales. The speaker uses this term to describe the pricing strategy of Co-Pilot, suggesting that the low cost is a tactic to draw users in before potentially increasing the price later.

💡Language Server Protocol (LSP)

The Language Server Protocol (LSP) is a protocol between a tool (such as a text editor) and a language server that provides features like autocompletion, goto definition, and syntax highlighting. The speaker discusses turning off the LSP to rely on their own ability to code without AI assistance, highlighting the importance of knowing the language fundamentals.

💡Boilerplate Code

Boilerplate code refers to sections of code that have a standard structure and can be reused across different parts of a program. The speaker mentions that Co-Pilot is particularly good at generating boilerplate, which is a time-saving feature for programmers.

💡Code Quality

Code quality is the measure of a program's overall performance, reliability, and efficiency. The video discusses concerns that relying on Co-Pilot can lead to lower code quality due to the AI making mistakes or providing outdated suggestions, which can lead to bugs and maintenance issues.

💡Open Source

Open source refers to software where the source code is available to the public, allowing anyone to view, use, modify, and distribute it. The speaker identifies as being into open source, which influences their views on privacy and the use of tools like Co-Pilot that may conflict with open source values.

💡Self-Hosting

Self-hosting is the practice of hosting and managing one's own services and applications on private hardware or leased servers. The speaker mentions self-hosting as one of their interests, which might be a reason for their preference for having control over their tools and data, as opposed to using cloud-based services.

💡AI-generated Code

AI-generated code is code that is written or suggested by artificial intelligence, like Co-Pilot. The video discusses the implications of using AI-generated code, including the potential loss of skill development and the need for constant learning and adaptation as a programmer.

💡Productivity

Productivity in the context of the video refers to the efficiency and effectiveness of a programmer's work. The speaker debates whether tools like Co-Pilot truly enhance productivity or if they create a dependency that could hinder a programmer's ability to write code independently.

Highlights

The speaker has quit using Copilot due to forgetting to sign in after setting up a new computer and redoing their Neovim config.

After not using Copilot for a month, the speaker has no intention of going back and explores reasons for this decision.

A poll is conducted to gauge the audience's usage of Copilot, with options provided for those who cannot afford it or don't want it.

The speaker argues that giving students free access to Copilot is a significant educational mistake, as it removes the initial struggle necessary for learning.

The importance of turning off assistive tools occasionally to strengthen one's own coding abilities and knowledge is emphasized.

The speaker describes the 'Copilot pause,' a behavior of waiting for Copilot to suggest code instead of writing it themselves.

The speaker suggests that relying on Copilot could lead to a decrease in skill and a shift from creating code to reviewing AI-generated suggestions.

The enjoyment of coding is diminished for the speaker since using Copilot, as it takes away some of the creative and problem-solving aspects.

The speaker appreciates the speed and efficiency that Copilot provides, especially in generating boilerplate code and assisting with complex tasks.

GitHub's study on developer productivity and happiness is mentioned, which found that Copilot improved developer satisfaction for 60-75% of users.

The speaker expresses skepticism about self-reporting surveys and the validity of their results, especially when it comes to developer satisfaction.

The importance of enjoying what you do to become good at it is highlighted, with the speaker sharing personal anecdotes about learning to play the guitar.

The speaker is critical of the privacy implications of using Copilot, as it sends code snippets to a remote server, which is against their values of self-hosting and privacy.

An alternative tool or method is not immediately suggested as a replacement for Copilot, indicating a potential break from AI assistance in coding.

The video concludes with a reflection on the potential drawbacks of AI in coding, including the risk of developers becoming too reliant on tools and losing critical skills.