Everybody talks about AI killing programming jobs, but any developer who has had to use it knows it can’t do anything complex in programming. What it’s really going to replace is program managers, customer reps, makes most of HR obsolete, finance analysts, legal teams, and middle management. This people have very structured, rule based day to days. Getting an AI to write a very customized queuing system in Rust to suit your very specific business needs is nearly impossible. Getting AI to summarize Jira boards, analyze candidates experience, highlight key points of meetings (and obsolete most of them altogether), and gather data on outstanding patents is more in its wheelhouse.
I am starting to see a major uptick in recruiters reaching out to me because companies are starting to realize it was a mistake to stop hiring Software Engineers in the hopes that AI would replace them, but now my skills are going to come at a premium just like everyone else in Software Engineering with skills beyond “put a react app together”
Copilot can’t even suggest a single Ansible or Terraform task without suggesting invalid/unsupported options. I can’t imagine how bad it is at doing anything actually complex with an actual programming language.
It also doesn’t know what’s going on a couple line before it, so say I am in a language that has options for functional styling using maps and I want to keep that flow going, it will start throwing for loops at you, so you end up having to rewrite it all anyway. I have find I end up spending more time writing the prompts then validating it did what I want correctly (normally not) than just looking at the docs and doing it myself, the bonus being I don’t have to reprompt it again later because now I know how to do it
Trouble is, you’re basing all that on now, not a year from now, or 6 months from now. It’s too easy to look at it’s weaknesses today and extrapolate. I think people need to get real about coding and AI. Coding is language and rules. Machines can learn that enormously faster and more accurately than humans. The ones who survive will be those who can wield it as a tool for creativity. But if you think it won’t be capable of all the things it’s currently weak at you’re just kidding yourself unfortunately. It’ll be like anything else - a tool for an operator. Middlemen will be wiped out of the process, of course, but those with money remain those without time or expertise, and there will always be a place for people willing to step in at that point. But they won’t be coding. They’ll be designing and solving problems.
We are 18 months into AI replacing me in 6 months. I mean… the CEO of OpenAI as well as many researchers have already said LLMs have mostly reached their limit. They are “generalizers” and if you ask them to do anything new they hallucinate quite frequently. Trying to get AI to replace developers when it hasn’t even replaced other menial office jobs is like saying “we taught AI to drive, it will replace all F1 drivers in 6 months”.
McDonald’s tried to get AI to take over order taking. And gave up.
Yeah, it’s not going to be coming for programmer jobs anytime soon. Well, except maybe a certain class of folks that are mostly warming seats that at most get asked to prep a file for compatibility with a new Java version, mostly there to feed management ego about ‘number of developers’ and serve as a bragging point to clients.
It’s based on the last few years of messaging. They’ve consistently said AI will do X, Y, and Z, and it ends up doing each of those so poorly that you need pretty much the same staff to babysit the AI. I think it’s actually a net-negative in terms of productivity for technical work because you end up having to go over the output extremely carefully to make sure its correct, whereas you’d have some level of trust with a human employee.
AI certainly has a place in a technical workflow, but it’s nowhere close to replacing human workers, at least not right now. It’ll keep eating at the fringes for the next 5 years minimum, if not indefinitely, and I think the net result will be making human workers more productive, not replacing human workers. And the more productive we are per person, the more valuable that person is, and the more work gets generated.
An inherent flaw in transformer architecture (what all LLMs use under the hood) is the quadratic memory cost to context. The model needs 4 times as much memory to remember its last 1000 output tokens as it needed to remember the last 500. When coding anything complex, the amount of code one has to consider quickly grows beyond these limits. At least, if you want it to work.
This is a fundamental flaw with transformer - based LLMs, an inherent limit on the complexity of task they can ‘understand’. It isn’t feasible to just keep throwing memory at the problem, a fundamental change in the underlying model structure is required. This is a subject of intense research, but nothing has emerged yet.
Transformers themselves were old hat and well studied long before these models broke into the mainstream with DallE and ChatGPT.
The real work of software engineering isn’t the coding. That is like saying that being a doctor is all about reading health charts. Planning, designing, testing and maintaining software is the hard part, and it is often much more political than it is a technical challenge. I’m not worried about getting replaced by AI. In fact, LLMs ability to generate high volumes of code only makes the skills to understand it to be more in demand.
It’s tons easier to repkace CEOs, HR, managers and so on than coders. Coders needs to be creative, an HR or manager not so much. Are they leaving three months from now you think?
Everybody talks about AI killing programming jobs, but any developer who has had to use it knows it can’t do anything complex in programming. What it’s really going to replace is program managers, customer reps, makes most of HR obsolete, finance analysts, legal teams, and middle management. This people have very structured, rule based day to days. Getting an AI to write a very customized queuing system in Rust to suit your very specific business needs is nearly impossible. Getting AI to summarize Jira boards, analyze candidates experience, highlight key points of meetings (and obsolete most of them altogether), and gather data on outstanding patents is more in its wheelhouse.
I am starting to see a major uptick in recruiters reaching out to me because companies are starting to realize it was a mistake to stop hiring Software Engineers in the hopes that AI would replace them, but now my skills are going to come at a premium just like everyone else in Software Engineering with skills beyond “put a react app together”
Copilot can’t even suggest a single Ansible or Terraform task without suggesting invalid/unsupported options. I can’t imagine how bad it is at doing anything actually complex with an actual programming language.
It also doesn’t know what’s going on a couple line before it, so say I am in a language that has options for functional styling using maps and I want to keep that flow going, it will start throwing for loops at you, so you end up having to rewrite it all anyway. I have find I end up spending more time writing the prompts then validating it did what I want correctly (normally not) than just looking at the docs and doing it myself, the bonus being I don’t have to reprompt it again later because now I know how to do it
Trouble is, you’re basing all that on now, not a year from now, or 6 months from now. It’s too easy to look at it’s weaknesses today and extrapolate. I think people need to get real about coding and AI. Coding is language and rules. Machines can learn that enormously faster and more accurately than humans. The ones who survive will be those who can wield it as a tool for creativity. But if you think it won’t be capable of all the things it’s currently weak at you’re just kidding yourself unfortunately. It’ll be like anything else - a tool for an operator. Middlemen will be wiped out of the process, of course, but those with money remain those without time or expertise, and there will always be a place for people willing to step in at that point. But they won’t be coding. They’ll be designing and solving problems.
We are 18 months into AI replacing me in 6 months. I mean… the CEO of OpenAI as well as many researchers have already said LLMs have mostly reached their limit. They are “generalizers” and if you ask them to do anything new they hallucinate quite frequently. Trying to get AI to replace developers when it hasn’t even replaced other menial office jobs is like saying “we taught AI to drive, it will replace all F1 drivers in 6 months”.
McDonald’s tried to get AI to take over order taking. And gave up.
Yeah, it’s not going to be coming for programmer jobs anytime soon. Well, except maybe a certain class of folks that are mostly warming seats that at most get asked to prep a file for compatibility with a new Java version, mostly there to feed management ego about ‘number of developers’ and serve as a bragging point to clients.
It’s based on the last few years of messaging. They’ve consistently said AI will do X, Y, and Z, and it ends up doing each of those so poorly that you need pretty much the same staff to babysit the AI. I think it’s actually a net-negative in terms of productivity for technical work because you end up having to go over the output extremely carefully to make sure its correct, whereas you’d have some level of trust with a human employee.
AI certainly has a place in a technical workflow, but it’s nowhere close to replacing human workers, at least not right now. It’ll keep eating at the fringes for the next 5 years minimum, if not indefinitely, and I think the net result will be making human workers more productive, not replacing human workers. And the more productive we are per person, the more valuable that person is, and the more work gets generated.
An inherent flaw in transformer architecture (what all LLMs use under the hood) is the quadratic memory cost to context. The model needs 4 times as much memory to remember its last 1000 output tokens as it needed to remember the last 500. When coding anything complex, the amount of code one has to consider quickly grows beyond these limits. At least, if you want it to work.
This is a fundamental flaw with transformer - based LLMs, an inherent limit on the complexity of task they can ‘understand’. It isn’t feasible to just keep throwing memory at the problem, a fundamental change in the underlying model structure is required. This is a subject of intense research, but nothing has emerged yet.
Transformers themselves were old hat and well studied long before these models broke into the mainstream with DallE and ChatGPT.
The real work of software engineering isn’t the coding. That is like saying that being a doctor is all about reading health charts. Planning, designing, testing and maintaining software is the hard part, and it is often much more political than it is a technical challenge. I’m not worried about getting replaced by AI. In fact, LLMs ability to generate high volumes of code only makes the skills to understand it to be more in demand.
It’s tons easier to repkace CEOs, HR, managers and so on than coders. Coders needs to be creative, an HR or manager not so much. Are they leaving three months from now you think?
I’ll start worrying when they are all gone.
I don’t understand how you could understand how LLMs work, and then write this.
Ah, nevermind.
If you’ll excuse me saying, I feel that you are the one who is looking at something and extrapolating.