Dr. Matt Welsh discusses his background and how AI is poised to transform software engineering.

• David J. Malan introduces Dr. Matt Welsh and provides context for the talk.
• Matt Welsh elaborates on his experience with sensor networks and his journey from academia to Google and fixie.ai.
• He presents a provocative thesis that the field of computer science, as we know it, is facing obsolescence.

Matt Welsh critiques the reliance on human-implemented programming and the issues it presents.

• Welsh explains the traditional goal of computer science to translate concepts into executable programs.
• He highlights the problematic assumption that humans must implement, maintain, and understand programs.
• Welsh argues that humans are inherently fallible in these tasks, posing challenges to the field.

Examining the ineffective evolution of programming languages from Fortran to Rust.

• Welsh criticizes the last 50 years of programming language development for failing to address core issues.
• He presents a historical overview of programming languages, demonstrating their complexity and lack of intuitiveness.
• Welsh claims that newer languages like Rust have not solved the fundamental problems of software development.

Dr. Welsh introduces the concept of AI writing code and its implications for software engineering.

• Welsh discusses the current capabilities of AI in writing code, emphasizing its potential to replace human coders.
• He describes his own use of GPT-4 to generate summaries from podcast transcripts.
• Welsh suggests that natural language processing by AI could be a more effective way of programming in the future.

How AI assistants like CoPilot are changing the programming workflow.

• Welsh discusses the transformative impact of AI assistants like CoPilot on programming productivity.
• He emphasizes CoPilot's ability to keep programmers in the 'zone' and reduce distractions.
• Welsh notes the rapid evolution of programmer's tools, comparing the current state to historical figures.

Exploring AI's comprehension of existing codebases and APIs.

• Welsh uses ChatGPT as an example to show AI's capacity to understand and generate code snippets for specific tasks.
• He demonstrates how AI can be instructed to work with APIs and SDKs, simplifying the development process.
• Welsh points out the vast knowledge base that AI can tap into, beyond just understanding homework assignments.

The significant economic and industry-wide implications of AI's computational capabilities.

• Welsh projects a future where AI replaces human developers, highlighting the cost-effectiveness of AI over human labor.
• He suggests a potential shift in the software industry due to the efficiency and cost savings AI offers.
• Welsh challenges the audience to consider the future of the industry in light of AI advancements.

Introducing AI.JSX, a new framework for building AI-driven applications.

• Welsh introduces AI.JSX, a framework he's developing at Fixie to simplify AI-driven software creation.
• He explains how AI.JSX allows for natural composition and reuse of AI-driven components.
• Welsh showcases a demo of AI.JSX in action, illustrating its real-time interaction capabilities.

Dr. Welsh reflects on the evolution of programming and the implications for computer science education.

• Welsh discusses the historical progression of programming and computing architectures.
• He considers the potential impact of AI on the education of future software engineers.
• Welsh calls for a reevaluation of computer science pedagogy in light of AI advancements.