(The cartoon is unrelated.)

Last month, I attended a networking dinner to discuss how Generative AI is reshaping coding practices. Around 50 people were at the event, including tech leads and department heads from Google, Pfizer, UBS, and other companies across various industries.

While the host company was (of course) promoting their own AI solutions¹, they also encouraged open dialogue through panel sessions and Q&As. One of the main topics of the night was on vibe coding – using LLM prompts to generate and refine code – and what it could mean for the future of software engineering.

Some attendees were optimistic. A cloud engineer from Google cited Sundar Pichai’s remark that 30% of new code at his company is now written with AI assistance². The host company chimed in that LLMs aren’t just generating code but also tying development work back to broader software-development lifecycles (hint: story points that write themselves)³. What’s more, they predicted that within the next five years, LLMs will be advanced enough to understand entire ecosystems of programs and generate code that can be integrated seamlessly across codebases. How amazing would that be!

But others voiced concerns. One software engineer reflected on his multi-decade career of building experience from making mistakes and iterative learning⁴. “How will vibe coding ensure that future tech leaders can understand and navigate the intricacies of programming, of the nuances between Java and Python, and everything else, if AI is going to figure it all out for us?”

The Google engineer responded, “Well, I think in the future, you won’t need to know about Java versus Python. Instead, you’ll need to be really good at prompting and interacting with LLMs⁵. That’s where coding and engineering are headed — and vibe coding is just scratching the surface.”

Personally, I am skeptical, maybe even cynical.

As technology grows more complex, AI may indeed be the most sensible way to keep everything organized. This is true not just of software engineering, but across many areas of life: financial markets, medical research, communication networks. Each of these industries has grown into multi-layered webs of models, methods, and frameworks – with new tools and approaches emerging so quickly that it seems impossible for any single individual or organization to keep up. Will AI be the best way to help us make sense of these expanding systems, or will it leave us more disconnected because we’re relying on AI to solve everything?

But actually, do we even need to make sense of everything if AI can do it for us?

Back to the dinner event. While vibe coding might sound like fun Gen Z slang, the approach carries real implications in a world where technology continues to grow faster than any one person can handle. And if this doesn’t work, AI-generated components may end up looking as unrelated to the engineer’s eye as the cartoon is to this article.

Footnotes

1. At work, I receive invitations to ‘exclusive tech roundtables’ or ‘AI leadership summits’ from various companies. These events are part networking, part marketing for the host company’s products and services. Selfishly, I find them a great way to learn new ideas from others! ↩︎
2. As Google’s CEO Sundar Pichai said during its Q1 2025 earnings call: “I think the last time I had said the number was like 25% of code that’s checked in involves people accepting AI suggested solutions. That number is well over 30% now.” Source: Seeking Alpha ↩︎
3. ’ve really come to enjoy the genre of memes poking fun at the planning process for software development (SDLC) and its references to “agile”, “scrum”, “ceremonies”, and the classic, “How many story points is that?”. See Instagram example here. ↩︎
4. A related term here is yak shaving, referring to the seemingly endless tasks one must complete before they can even start coding (e.g., ten minutes to install an IDE, ten days to figure out why it won’t run on your laptop). It is often considered a rite of passage for people entering the developer or engineering world. ↩︎
5. Research papers such as this one remind me of how deeply technology and human language are intertwined. It will be interesting to see how prompt engineering might shape everyday speech in the future. ↩︎