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Haven’t you heard about Devin yet? Let me introduce you: Devin is an independent entity that autonomously solves engineering tasks using its own tools (shell, code editor, and web browser).

And when they say “autonomously” they mean 13.86% of GitHub issues were resolved unassisted , surpassing the previous best performance of 1.96% unassisted and 4.80% assisted.

Still don’t believe it? Ask employers from leading AI companies where Devin was interviewed – he passed all practical engineering interviews.

Therefore, it’s not surprising that many programmers have asked themselves if they will have a job in the next few years.

AI tool development might mimic autonomous vehicle progress

Ryan Peterman, Staff Software Engineer at Instagram, in his blog post, is loud and clear: software engineers aren’t going anywhere anytime soon.

But, there will be changes and those changes will follow a pattern similar to automating driving (an analogy noted in a tweet by Andrej Karpathy, ex-director of AI at Tesla and founding member of OpenAI):

1. People drive while data is gathered.
2. AI assists in clear tasks (such as staying in the lane and maintaining distance).
3. AI progresses to handling more intricate tasks (like lane changes, turns, and traffic signals).
4. AI achieves full functionality, followed by ongoing enhancements to refine quality until complete autonomy is reached.

So, as Karpathy says, you can imagine something similar for software engineering :

1. People write code while data is amassed.
2. AI aids in specific tasks (such as auto-completing snippets).
3. AI progresses to handling more intricate assignments (such as writing substantial code segments or coordinating tools).
4. AI achieves full functionality, followed by continual refinement to ensure complete autonomy.

Ambiguity increases as you progress in your career

If you’re wondering when that change will take effect, the answer is not simple.

“Google started working on self-driving cars 15 years ago (2009) and we’ve only now started to see limited testing in the market. It’s not an apples-to-apples comparison but the high-level analogy stands. AI will code at some point, but humans will continue to oversee this process for a long time ”, thinks Ryan.

So, he suggests you should take a closer look into your career plan, but the higher level you are the more ambiguity you can handle. Here’s how Rayan sees different roles evolving:

• Junior – No ambiguity; We know what to do and how to do it. Just execute the project.
• Mid-level – Feature-level ambiguity; We have the solution but lack precise implementation details.
• Senior – Roadmap-level ambiguity; We understand the problem and its significance, but the solution approach is unclear.
• Staff – Strategy-level ambiguity; We’re unsure which problems to address. Stuff creates a scope.

As software engineering careers progress, ambiguity increases. Initially, less ambiguous tasks, typical of junior roles, are more susceptible to automation.

However, trusting AI with more ambiguous tasks, as expected in senior roles, will take longer, thinks Peterman.

So, if you want to avoid your skills becoming obsolete, the advice from experts is to focus on developing higher-level competencies such as directional thinking, influencing abilities, and strategic tech planning. Additionally, embrace AI tools to stay competitive and evolve with the changing landscape.

And tell Devin I said hello!

The post Engineer, meet Devin. Here’s how he will change your career. appeared first on ShiftMag.