Original Title: Is Traditional Software Engineering Dead?

Original Author: Naval

Original Translation: Ken, ChainCatcher

Is traditional software engineering dead?

“Does this mean traditional software engineering has vanished? Absolutely not. Software engineers— even those who are not necessarily responsible for fine-tuning or training AI models—are now one of the most leveraged groups in the world. Of course, those responsible for training and fine-tuning models have even greater leverage because they are building the toolset that software engineers will use.

But software engineers still have two major advantages over you. First, they think in code, so they truly understand the underlying mechanisms at work. Moreover, all abstractions have weaknesses. Therefore, when computers are programming for you—when Claude Code or a similar program is coding for you—it will inevitably make mistakes.

It will produce bugs, and the architecture may not be ideal. So it won't function entirely correctly. And those who understand the underlying principles will be able to patch these weaknesses as they arise.

So, if you want to build a well-architected application, or even just want to be able to accurately articulate a well-architected application's requirements, if you aim for it to run at high performance, function at its best, and detect bugs early, then you need to have a background in software engineering.

Traditional software engineers will be better able to utilize these tools. And there are still many problems in software engineering that today's AI programs cannot handle. The simplest way to understand this is that these problems fall outside their training data distribution.

For instance, if they need to perform binary sorting or reverse a linked list, they have seen countless examples like these, so they are very good at it. However, when you start venturing beyond their domain—when you need to write extremely high-performance code, when the code needs to run on novel or entirely new architectures, when you're truly creating something new or solving new problems— you still need to dive in and write the code yourself.

At least, until there are enough examples of these kinds to train new models, or until these models can perform higher-level abstract reasoning and tackle problems independently…

And remember: mediocrity has no market demand. No one wants a mediocre application unless it fills a niche that even better applications have failed to cover. Better applications will almost capture 100% of the market share. Perhaps a small portion of users may gravitate towards the second-best application because it performs better in a niche feature compared to mainstream applications, or because it is cheaper, and so on.

But overall, people always want the best. So the bad news is that being second or third is meaningless—just like the famous line from Alec Baldwin in the movie “Glengarry Glen Ross”: ‘The first prize is a Cadillac Eldorado, the second prize is a set of steak knives, and the third prize is you’re fired.’

In these winner-takes-all markets, this is absolutely true. The bad news is: if you want to win, you must be the best at something.

However, the things you can do to be ‘the best’ are limitless. You can always find a perfect niche that fits you and become a leader in that field. This confirms a tweet I made previously: ‘Strive to be the very best in your field. Continually redefine what you do until that statement becomes a reality.’

I believe this still applies in the age of artificial intelligence.”

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