Productivity Increases with AI Coding Assistants
Google’s Sundar Pichai mentioned that AI is writing 25% of new code at Google (Alphabet has approximately 180,000 worldwide employees with probably more than 20,000 software engineers). This productivity expansion was not in the cards a couple of years ago. As the technology continues to evolve and generative AI enterprise adoption increases, we can assume the share of code written by AI as compared to code written without the help of AI will continue to creep up.
Writing code with AI help will become the norm in the future. Taking examples from other disciplines, who would imagine a finance professional without Excel and its embedded formulas today? It is not a matter of productivity but simply the feasibility of creating and executing the analyses we are used to today. Individuals and teams responsible for writing code will continue to increase their productivity with AI coding assistants. There will come a point where it does not make sense to write code without the acceleration of AI tools, similar to what we see today with the simplistic example of Excel’s formulas.
Below is an updated list of relevant coding assistants and co-pilots, in no specific order.
- GitHub Copilot (https://github.com/features/copilot)
- Amazon Q Developer (https://aws.amazon.com/q/developer/)
- ChatGPT (https://chatgpt.com/)
- Gemini (https://gemini.google.com/app)
- Replit (https://replit.com/)
- IBM watsonx Code Assistant (https://www.ibm.com/products/watsonx-code-assistant)
- Claude (https://claude.ai/)
- Sourcegraph Cody (https://sourcegraph.com/cody)
- GitLab Duo (https://about.gitlab.com/gitlab-duo/)
- Code Llama (https://www.llama.com/code-llama/)
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