Perfect! We’ll just write out the definition of the product completely in Jira, in a specific way, so the application can understand it - tweak until it’s perfect, write unit tests around our Jira to make sure those all work - maybe we write a structured way to describe each item aaand we’ve reinvented programming.
I see where you’re going, but I’ve worked with AI models for the last year in depth, and there’s some really cool stuff they can do. However, truly learning about them means learning their hard pitfalls, and LLMs as written would not be able to build an entire application. They can help speed up parts of it, but the more context means more VRAM exponentially, and eventually larger models, and that’s just to get code spit out. Not to mention there is nuance in English that’s hard to express, that requirements are never perfect, that LLMs can iterate for very long before they run out of VRAM, that they can’t do devops or hook into running apps - the list goes on.
AI has been overhyped by business because they’re frothing at the mouth to automate everyone away - which is too bad because what it does do well it does great at - with limitations. This is my… 3rd or 4th cycle where business has assumed they can automate away engineers, and each time it just ends up generating new problems that need to be solved. Our jobs will evolve, sure, but we’re not going away.
lol okay dude. Flippantly you ignored all of the limitations I pointed out. Sure it could happen, but not on the timeline you’re discussing. There is no way within a year that they have replaced software engineers, I call absolute BS on that. I doubt it will rise above copilot within a year. I see it being used alongside code for a long time, calling out potential issues, optimizing where it can, and helping in things like building out yaml files. It cannot handle an entire solution, the hardware doesn’t exist for it. It also can’t handle specific contexts for business use-cases. Again maybe, but it’ll be a while - and even then our jobs shift to building out models and structuring AI prompts in a stable way.
My attitude is the same because these are the same issues that it’s faced. I’m not arguing that it’s not a great tool to be used, and I see a lot of places for it. But it’s naiive to say that it can replace an engineer at it’s stage, or in the near future. Anyone who has worked with it would tell you that.
I firmly do think companies want to replace their 250k engineers. That’s why I know that most of it is hype. The same hype that existed 20 years ago when they came out with designers for UIs, the same hype when react and frontend frameworks came out. Python was built to allow anyone to code, and that was another “end of engineers”. Cloud claimed to be able to remove entire IT departments, but those jobs just shifted to DevOps engineers. The goalposts moved each time, but the demand for qualified engineers went up because now they needed to know these new technologies.
Why do you think I worked with AI so much over the last year? I see my job evolving, I’m getting ready for it. This has happened before - those who don’t learn new tech get left behind, those who learn it keep going. I may not be coding in python in 10 years, god knows I wasn’t doing what I was 10 years ago - but it’s laughable to me to think that engineers are done and over with.
Perfect! We’ll just write out the definition of the product completely in Jira, in a specific way, so the application can understand it - tweak until it’s perfect, write unit tests around our Jira to make sure those all work - maybe we write a structured way to describe each item aaand we’ve reinvented programming.
I see where you’re going, but I’ve worked with AI models for the last year in depth, and there’s some really cool stuff they can do. However, truly learning about them means learning their hard pitfalls, and LLMs as written would not be able to build an entire application. They can help speed up parts of it, but the more context means more VRAM exponentially, and eventually larger models, and that’s just to get code spit out. Not to mention there is nuance in English that’s hard to express, that requirements are never perfect, that LLMs can iterate for very long before they run out of VRAM, that they can’t do devops or hook into running apps - the list goes on.
AI has been overhyped by business because they’re frothing at the mouth to automate everyone away - which is too bad because what it does do well it does great at - with limitations. This is my… 3rd or 4th cycle where business has assumed they can automate away engineers, and each time it just ends up generating new problems that need to be solved. Our jobs will evolve, sure, but we’re not going away.
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lol okay dude. Flippantly you ignored all of the limitations I pointed out. Sure it could happen, but not on the timeline you’re discussing. There is no way within a year that they have replaced software engineers, I call absolute BS on that. I doubt it will rise above copilot within a year. I see it being used alongside code for a long time, calling out potential issues, optimizing where it can, and helping in things like building out yaml files. It cannot handle an entire solution, the hardware doesn’t exist for it. It also can’t handle specific contexts for business use-cases. Again maybe, but it’ll be a while - and even then our jobs shift to building out models and structuring AI prompts in a stable way.
My attitude is the same because these are the same issues that it’s faced. I’m not arguing that it’s not a great tool to be used, and I see a lot of places for it. But it’s naiive to say that it can replace an engineer at it’s stage, or in the near future. Anyone who has worked with it would tell you that.
I firmly do think companies want to replace their 250k engineers. That’s why I know that most of it is hype. The same hype that existed 20 years ago when they came out with designers for UIs, the same hype when react and frontend frameworks came out. Python was built to allow anyone to code, and that was another “end of engineers”. Cloud claimed to be able to remove entire IT departments, but those jobs just shifted to DevOps engineers. The goalposts moved each time, but the demand for qualified engineers went up because now they needed to know these new technologies.
Why do you think I worked with AI so much over the last year? I see my job evolving, I’m getting ready for it. This has happened before - those who don’t learn new tech get left behind, those who learn it keep going. I may not be coding in python in 10 years, god knows I wasn’t doing what I was 10 years ago - but it’s laughable to me to think that engineers are done and over with.
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Now who’s moving the goalposts…?
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