AngelList co-founder Naval: AI productivity is over-mythologized; software engineers will never die.

The notion that "the programmer profession is dead" seems to have become a global consensus, and the profession is facing its most severe "existence crisis" since the birth of the internet.

Author: Naval Ravikant

Compiled by: Felix, PANews

Amidst the current frenzied iteration of large-scale AI models, a profound sense of pessimism and anxiety pervades the global market. First, OpenAI CEO Sam Altman predicted that "AI will take over 95% of programmers' jobs"; then, the CEO of Anthropic predicted that "AI will completely take over software engineer positions within 6-12 months." The notion that "the programmer profession is dead" seems to have become a global consensus, facing its most severe "existential crisis" since the birth of the internet.

However, this fear of job loss stems from a misunderstanding of the underlying logic of technology. AngelList co-founder Naval Ravikant (who invested in Uber and Twitter in their early stages) believes that recent claims about AI's productivity-enhancing effects may have been overhyped. No matter how much AI evolves, it will always make mistakes, and software engineers will remain an indispensable profession.

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

The following are Naval Ravikant's latest views.

Does AI mean the death of traditional software engineering? Of course not. Software engineers—even those who aren't necessarily responsible for tuning or training AI models—are among the most valued people in the world today. Of course, those who are responsible for training and tuning models are even more valued because 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 mechanisms. And all abstractions are flawed. So when a computer writes a program for you (like with Claude Code or something similar), it will always make mistakes.

It will produce bugs and have imperfect architecture; in short, it will never be completely correct. However, those who understand the underlying logic can plug the loopholes in time when they appear.

Therefore, if you want to build a well-architected application, if you want to have the ability to define a good architecture, if you want your program to run at high performance, perform at its best, and catch bugs as early as possible, then you still need a software engineering background.

Traditional software engineers are better positioned to leverage these AI tools. Furthermore, there are still many problems in software engineering that AI programs cannot solve. The simplest way to understand this is that these problems fall outside the scope of their data distribution.

For example, AI has seen countless cases of binary sorting or reversing linked lists, so they are very good at it. But when you start to step outside their familiar domains, such as writing extremely high-performance code, running on entirely new architectures, or creating entirely new things and solving new problems, you still need to get involved and write the code manually.

This situation will continue until there are enough cases available for training new models, or until these models are able to reason sufficiently at higher-dimensional abstraction layers and independently solve difficult problems.

Remember: the market has no demand for 'mediocre'. As long as a superior app exists in a niche market, nobody wants mediocre ones. Better apps will essentially capture 100% of the market share. Perhaps a small fraction will go to the second-ranked app, simply because it performs better than the mainstream app on a niche feature, or is cheaper, and so on.

But generally speaking, people only want the best. So the bad news is, fighting for second or third place is pointless—like Alec Baldwin's famous line in the movie *Glengarry Glen Ross*: 'First place gets a Cadillac, second place gets a steak knife set, third place gets out of here.'

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

However, there are endless areas where you can excel. You can always find a niche that suits you and become a top performer. This reminds me of a tweet I posted before: "Strive to be the best in your field. Keep redefining what you do until it becomes a reality."

I believe this principle still applies in the AI ​​era.

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