I really really want this to be true. I want to be relevant. I don’t know what to do if all those predictions are true and there is no need (or very little need) for programmers anymore.
But something tells me “this time is different” is different this time for real.
Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me. I’m basically just the conductor of all those processes.
Oh, and don't ask about coding. If you use AI for tasks above, as a result you'll get very well defined coding task definitions which an AI would ace.
I’m still hired, but I feel like I’m doing the work of an entire org that used to need three engineers.
> The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
> That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python.
> The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.
I don't agree with this:
> To folks who say this technology isn’t going anywhere, I would remind them of just how expensive these models are to build and what massive losses they’re incurring. Yes, you could carry on using your local instance of some small model distilled from a hyper-scale model trained today. But as the years roll by, you may find not being able to move on from the programming language and library versions it was trained on a tad constraining.
Some of the best Chinese models (which are genuinely competitive with the frontier models from OpenAI / Anthropic / Gemini) claim to have been trained for single-digit millions of dollars. I'm not at all worried that the bubble will burst and new models will stop being trained and the existing ones will lose their utility - I think what we have now is a permanent baseline for what will be available in the future.
I really really want this to be true. I want to be relevant. I don’t know what to do if all those predictions are true and there is no need (or very little need) for programmers anymore.
But something tells me “this time is different” is different this time for real.
Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me. I’m basically just the conductor of all those processes.
Oh, and don't ask about coding. If you use AI for tasks above, as a result you'll get very well defined coding task definitions which an AI would ace.
I’m still hired, but I feel like I’m doing the work of an entire org that used to need three engineers.
From where I’m standing, it’s scary.
I nodded furiously at this bit:
> The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
> That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python.
> The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.
I don't agree with this:
> To folks who say this technology isn’t going anywhere, I would remind them of just how expensive these models are to build and what massive losses they’re incurring. Yes, you could carry on using your local instance of some small model distilled from a hyper-scale model trained today. But as the years roll by, you may find not being able to move on from the programming language and library versions it was trained on a tad constraining.
Some of the best Chinese models (which are genuinely competitive with the frontier models from OpenAI / Anthropic / Gemini) claim to have been trained for single-digit millions of dollars. I'm not at all worried that the bubble will burst and new models will stop being trained and the existing ones will lose their utility - I think what we have now is a permanent baseline for what will be available in the future.