Author: Naval Ravikant

Translated by: Felix, PANews

In the current context of rapid iterations of large AI models, the global market is filled with a profound sense of pessimism and anxiety. OpenAI CEO Sam Altman previously predicted that “AI will take over 95% of programmers' jobs"; followed by Anthropic CEO predicting that “AI will fully take over software engineering positions within 6-12 months.” The statement that “the programming profession is dead” seems to have become a global consensus, facing the most severe "survival crisis" since the birth of the internet.

However, this fear of job loss stems from a misunderstanding of the underlying logic of technology. Naval Ravikant, co-founder of AngelList (who has invested early in Uber and Twitter), believes that the recent hype about AI’s enhancement of productivity might be over-exaggerated. No matter how AI evolves, it will always make mistakes, and software engineers remain one of the indispensable professions.

No matter what field you are in, even the smallest niche, as long as you excel and specialize to become a top talent, you need not worry about being replaced by AI.

The following are Naval Ravikant's latest views.

“Does AI mean that traditional software engineering has died?” Of course not. Software engineers—even those who are not necessarily responsible for optimizing or training AI models—are still one of the most valued groups globally. Of course, those who are responsible for training and optimizing models are even more valued, as they build the toolsets that software engineers use.

But software engineers still have two major advantages. First, they think in code, so they truly understand the underlying operational mechanics. And all abstractions have vulnerabilities. So, when computers write programs for you (for instance, using Claude Code or similar programs), they will always make mistakes.

They will produce bugs, have imperfect architectures, and will not be completely correct. Those who understand the underlying logic can plug the gaps when vulnerabilities arise.

Therefore, if you want to build a well-architected application, if you want the ability to define a good architecture, if you want the program to run at high performance, achieve optimal levels, and catch bugs early, you still need a software engineering background.

Traditional software engineers can make better use of these AI tools. Moreover, there are still many problems in software engineering that AI programs cannot solve. The simplest way to understand this is: these problems are beyond their data distribution range.

For example, if binary sorting or reversing a linked list is needed, AI has seen countless cases, so it excels at that. But when you start to move away from their familiar domains, such as writing extremely high-performance code, running on new architectures, or creating entirely new things and solving new problems, you still need to get in there and manually write the code.

This situation will persist until there are enough cases for new models to train on or until these models can reason adequately at higher dimensions of abstraction and independently solve problems.

Remember: the market has no demand for 'mediocrity.' As long as a niche already has better applications, no one wants those mediocre applications. Better applications will essentially win 100% of the market share. Perhaps a very small portion will go to the second-ranking application, simply because it performs better on a niche feature than the mainstream application, or is cheaper, and so forth.

But overall, people simply want the best. So the bad news is that competing for second or third place is pointless—just like the famous line by Alec Baldwin in the movie "Glengarry Glen Ross": 'First prize is a Cadillac, second prize is a set of steak knives, third prize is you're fired.'

In today's winner-takes-all market, this is absolutely true. The bad news is: if you want to win, you must be the best in a certain field.

However, the field in which you can be the best is endless. You can always find a niche that suits you and become a leader in it. This reminds me of a tweet I posted before: “ strive to be the top talent in your field. Continuously redefine what you do until your dreams come true.”

I believe this principle remains applicable in the AI era.

Further reading: A memorandum from 2028: If AI wins, what will we lose?

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