AI and Coding - Is Github Co-Pilot Worth It?

Episode Summary

Executive Summary: The episode examines AI coding tools, especially GitHub Copilot, weighing their speed, usefulness, cost, ethics, security, job impact, and learning effects. The hosts largely view these tools as powerful productivity boosters that can scaffold code and reduce context-switching, while acknowledging real concerns about training data, trust, and affordability. They also survey alternatives like TabNine, Kite, and Amazon CodeWhisperer.

Main Topics: What GitHub Copilot Is and Why It Matters: The hosts explain Copilot as an AI pair programmer that suggests code snippets and completions in real time, emphasizing that it goes beyond ordinary snippets or IntelliSense. Ethics of Training on Public Code: They discuss whether it is ethical for Copilot to be trained on public GitHub code, noting concerns about open-source licenses and implicit permission. Security and Trust in Cloud-Based Coding Tools: The episode addresses fears that code is sent to Microsoft/GitHub servers and whether companies can trust those tools with proprietary code. Cost, Value, and Access Gaps: They debate whether Copilot’s $10/month price is justified, whether it adds subscription burden, and whether it creates a divide between paid users and learners. Will AI Coding Tools Replace Developers?: The hosts consider job displacement concerns but argue that these tools may instead speed up repetitive tasks and let developers focus on higher-level work. Does AI Make Developers Worse or Get in the Way?: They push back on the idea that AI coding makes developers 'dumb,' while acknowledging it can be intrusive during comment-writing and thought processes. Alternatives and Future Competition: They mention other tools such as TabNine, Kite, and Amazon CodeWhisperer, suggesting that Copilot will not remain the only major player.

Key Arguments: Copilot is not just a snippet tool; it can infer context and generate useful code scaffolding quickly and accurately. The ethical concern is real because public GitHub code may have varying licenses, but users also knowingly publish code publicly. Security worries exist, but many developers already trust GitHub/Microsoft with their source code; trust remains the main issue. The tool is expensive enough to matter, especially for students, learners, and freelancers who may not be able to justify another subscription. AI coding tools are better framed as productivity boosters than replacements for developer skill; they help with repetitive code and preserve focus. Using AI assistance does not eliminate the need to understand and own the final code; developers must be able to justify what they accept. Copilot can interfere with writing comments or thinking aloud, though the hosts find the coding benefits outweigh the annoyance. There are credible alternatives, and competition may improve the category over time.

Data Points: Copilot monthly price: $10/month - The hosts discuss the new paid model after beta and whether it is worth the cost. Copilot annual cost: $120/year - Used to frame the affordability question for individual developers. Alternative perceived value threshold: $6/month - Scott says he previously guessed a lower acceptable price for Copilot. Estimated daily value framing: $0.50/day - The hosts translate the subscription into a daily cost to judge value. Example productivity threshold: 2.5 minutes/day - They note that saving a few minutes per day could justify the cost. Other tools mentioned: 3 named alternatives - TabNine, Kite, and Amazon CodeWhisperer are listed as competing options. Copilot availability comment: Beta had been free for a period before becoming paid - The hosts describe the transition from trial/beta usage to a paid subscription.

Pivotal Quotes: "it is both for accuracy and speed, as well as just for being able to scaffold out code extremely" — Wes: Explaining the practical benefits of Copilot as a coding accelerator. "I don't necessarily think it makes me dumber. I think if you're not okay signing your name to that code, ... you can't do that." — Wes: On the concern that AI assistance weakens developer skill or accountability. "I think they are tools that are like rocket boosters" — Wes: Describing AI coding tools as amplifiers of developer productivity rather than replacements.

Implications: For developers, AI coding tools are becoming a practical productivity layer, but trust, licensing, and pricing will shape adoption. The category is likely to expand, and developers will need to balance speed gains with code ownership and judgment.