Eight Myths on Software Engineering and GenAI

I'm pretty sure we will solve this issue. Either already through World Models or another architecture.

It could also be, that we just need a 10 or 100 Trillion Parameter model to match so many generic ways of solving tasks and keeping the concept in the LLMs 'head' to solve it that it will just emerge with parameter size. Like with fable they said that it can chain together exploits which wouldn't work as standalone exploits.

What if the only real barrier is the depth of understanding of concepts and this is exactly what is getting solved with parameter count?

But look how young this field really is if you start counting it when it became relevant on mass. Its not 'just' an LLM which is changing the world, its machine learning overall. Robotics wouldn't be were it is today if its not for machine learning. Took humans time and energy to take the leap, to start learning what the status quo is and then actually doing more with it.

> the paradigm as we know it is a ceiling unless you massively and continuously feed this system with average tasks

While I do think its doable to achieve AGI in 5-15 years, even if it doesn't happen and it always means that people train an LLM or whatever, if you need 10 experts to teach this to an LLM OR every single senior has to teach this to their juniors every single time, the LLM will always win.

I'm now team lead for 10 years and every single year I teach them the same thing over and over and over again.

the craziest thing about this? If i wouldn't tell them what they are doing wrong, they wouldn't even know it.

Quality is already a very flexible term for a lot of people.

> I can agree that for lots of tasks it does not. But for others it is just not a tool good enough.

Yet.

> If you want to ship a sub-par thing that will go to the rubbish in a couple of months, then yes, you can do that. But that will fail commercially any way.

Now we come to the reality: I have seen so much garbage software its crazy. People using md5 as a password hash in 2024! No clue what coding best practices are, teams without code review, teams without a security expert not even knowing what crazy things they do day in day out.

Just a few month ago a team build an API for my team including a Swagger UI. Half of it didn't work. You pressed a button on the Swagger UI and a 500 returned.

And do'nt underestimate what it means that a lot of business people don't like software people. You know that fruit basket we get? and water and stuff? they don't do it because they like us they do it because thats what you have to do. if a Product Owner starts vibe coding with AI, he will have leadership convinved in no time, then it goes on production and it will run for waaaaaay longer than anyone would have guest.

Besides that there is plenty of software were complexity is less relevant or security is not that big of an issue.

and why did this study (performed in China I might add) find that women and “mature age” (which is not defined in the study) have less usage of AI tools?

>>>? We suggest a new barrier: using technology to assist task completion signals a lack of competence to perform the task independently.

So basically the study suggests that older engineers and women are reluctant (and especially women) to use AI tooling because they feel they are being judged more on non-technical competencies.

This study may have a strong cultural influence but I would say that one thing they noted I’ve also seen. 41% of engineers in the study had used AI 12 months after the initial rollout. This aligns with my observations. Some people are struggling figuring out how to adopt in their day to day while others are full in.

The cost of prototyping has crashed. So you can A/B/C... various ideas, test them in the real world or by personal preference, to taste, and converge on designs that work for you.

This used to very hard and expensive. Now it's so cheap it makes the idea -> test -> refine loop much tighter and faster.

I don't know about easy. It's a hard earned lesson of software engineering, backed by research (some of it cited in TFA). Also, there's a related lesson: the more LoC to solve a given problem, the more bugs. That's also a finding backed by research.

I'll be reading your article when you write it, but I confess I'm skeptical.

The AI companies have a vested interest in using this metric, since it's easy to measure (the reason LoC were used even before AI) and there's no doubt that LLMs are writing tons of code. This makes me doubly skeptical.