AI research has been going on for the last 25 years, and the majority of their focus was to solve medical problems. In the “The Thinking Game” documentary, you will see their sole focus was the protein folding problem.
The reason was simple: the medical field has many unsolved problems and a large market. Solving these issues can have a significant impact on humanity, making it easy to pitch to investors.
So what happened? Why did it suddenly start impacting software engineering?
The short answer is, they found a product market fit by discovery.
When ChatGPT was launched, it was initially marketed as an AI chatbot. They soon realized that software engineers were using it and asking programming questions, and it was solving those problems in a reasonable manner. That’s when they knew there was a huge market for AI in the software engineering space.
That’s when the whole “software engineers will be replaced” narrative kicked off, because those were the people who adopted it the fastest.
Here is my hypothesis on why it happened:
Programming languages are deterministic. : Normal language is not deterministic. A word in english can mean multiple different things depending on context. For example, “class” in English can mean a teaching, a hierarchy in people, coolness, etc. But in a programming language, “class” only means one thing. So the probability of getting things correct in programming languages is high compared to normal languages.
Open source gave a ton of training data. : There was just a lot of code, documentation, discussion forums, and blogs available on the internet to train on, compared to medical and other fields.
Software engineering already had the right tools. : If you ask an AI agent to do something, it can use Bash, Linux tools, grep, git to track history and changed files. API systems and documentation — all of that was already there. The agent can just hook into it. This is not true for other industries.
Software engineers were the closest users. : The AI tools were mostly built by software developers. Their day-to-day experience made the tools way more friendly for that use case. They were the most hands-on users, so they basically shaped what got trained and improved. Also, software engineering is a field where any new technology is adopted the fastest compared to others.
What do you think — was this just a happy accident, or was software engineering always going to be where AI landed first? Would love to hear your take in the comments.
After reading all these tricks in the shell, I feel that I was doing a lot of manual work. These keyboard shortcut in shells has really improved my efficiency and productivity.
Good things take time, as the quote on my keyboard mat reminds me. It’s important to understand the difference between trying things out methodically compare to gambling on them. Rather than labeling our actions as experiments, we often rely on luck rather than informed decisions. Some things simply require patience and time.
I used to be so focused on self-improvement that I would read blogs during movie intervals because I believed every moment was crucial, and I wanted to utilize that time for personal growth. It was beneficial for a few years, but then things took a negative turn.
Everything became too stressful; activities I once enjoyed turned into a relentless pursuit of productivity. Regardless of what I did, I always aimed to perfect and optimize it in every possible way.
Remotion with Claude is a great tech combo to create a simple graphic animation to make your presentation videos better.
This is exactly why I don’t think AI taking notes in a meeting works. It just transcribes things and adds too much detail. It doesn’t give us time to think about what’s important and relevant for us.
So here is my key takeaway:
Try to take handwritten notes, as it’s easier to draw and make quick changes compared to digital notes.
Use rapid logging to jot down only important information.
Make sure to revisit your notes.
We have slides that say AI is gonna completely disrupt the industry in two years. We update those slides every two years.
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