The field of Computer Science is headed for a major upheaval with the rise of large AI models, such as ChatGPT, that are capable of performing general-purpose reasoning and problem solving.
We are headed for a future in which it will no longer be necessary to write computer programs. Rather, the most software will eventually be replaced by AI models that, given an appropriate description of a task, will directly execute that task, without requiring the creation or maintenance of conventional software. In effect, large language models act as a virtual machine that is “programmed” in natural language.
This talk will explore the implications of this prediction, drawing on recent research into the cognitive and task execution capabilities of large language models. Matt Welsh is Co-founder and Chief Architect of Fixie.ai, a Seattle-based startup developing a new computational platform with AI at the core. He was previously head of engineering at OctoML, a software engineer at Apple and Xnor.ai, engineering director at Google, and a Professor of Computer Science at Harvard University. He holds a PhD from UC Berkeley.
The video features a discussion about the future impact of AI on programming jobs, presented by Dr. Matt Welsh to a group of Harvard computer science students. The video begins by addressing the premise that machines capable of interpreting natural language and generating computational results, without humans writing conventional code, may lead to a fundamental shift in how programming is approached.
The speaker emphasizes the advent of tools like GitHub Copilot and GPT-3, urging programmers to adopt these AI-assisted coding tools to enhance efficiency. An alarming perspective is presented with calculations indicating the drastic cost difference between employing a human software developer versus an AI-generated code, raising concerns about potential job displacement in the programming field.
How much does it cost to replace one human with AI? The SWE (software enginer) work costs $1200/day, while AI take only $0.12/day
Furthermore, the speaker envisions a future where software engineering teams might involve product managers translating requirements into AI-readable instructions, with AI code generators producing substantial portions of the code. This model suggests a role shift for programmers, potentially becoming code reviewers rather than primary code creators.
However, amidst these discussions, thour key takeaways are highlighted:
Embrace Industry Changes The evolution of AI in programming isn’t negative; it signifies progress. Utilizing AI tools like Copilot and exploring AI through practical applications can enhance job efficiency and readiness for future opportunities.
Pursue Programming Careers Despite the concerns about job displacement, possessing programming skills remains invaluable. Understanding programming might become a baseline skill for various future jobs yet to emerge.
AI’s Current Limitations AI, despite its advancements, still faces significant limitations and errors. Trust in AI for comprehensive programming tasks remains a work in progress, and human involvement in software development holds critical value beyond just writing code.
The overall message encourages programmers to adapt, acknowledging the changing landscape while also recognizing the enduring significance of human involvement in software engineering, despite the evolving role of AI in the field.
