I disagree on agentic systems and AI based on LLMs and don’t feel this is close to a balanced take. You are assuming you are in the middle of a revolution when that is far from obvious. It is not yet clear this is an important technology because there are very significant limitations.
The code from one of the leading companies in the space is a good example of where the reality of what is achieved falls far short of expectations.
Sorry, when I say I think I have a balanced take on AI what I mean is that I do my best to weigh both the pros and cons of this technology as opposed to a more extreme behavior like spending all day chatting with LLMs or posting all day on X about how AI is already better than me at everything and that jobs are over.
If I had to assign a confidence score for whether agents will change the way we all work and many aspects of how we live, I would put it at a 7/10, maybe 8/10. I felt about the same about the smartphone. While many things we do look the same way they did in 2005 (we still drive on roads, kids still go to school), at the same time it's undeniable that much of our lives are intermediated through a small screen and many societal dynamics have shifted due to that technology's existence.
I will concede that you should read my post with that context and draw your own conclusions about the veracity of my perspective — but I think it is more well-reasoned than what people generally attribute to "LLM hype". (Of course it's a bit tautological that I believe that, but I try to surround myself with people of all kinds technical and non-technical and like to think I stay reasonably grounded.)
All that said, I think the code from a leading company being bad and yet delivering good results is more a sign of the technology's jagged frontier[^1]. Calculators can't write sonnets the same way that LLMs are bad at math, but that doesn't make them useless — it just makes them a tool. This is a tool in our tool belt and I find is surprisingly useful as a general purpose technology despite it's limitations. (Which is related to the main argument I make in the post that bad code leading to good results may imply that we're under and overweighting certain aspects of what is important in software development, and that our expectations of code may may need to be recalibrated often as we gather more evidence.)
The code from one of the leading companies in the space is a good example of where the reality of what is achieved falls far short of expectations.
This is what I meant by the hype train.