3. evaluating whether the end result works as intended.
I have some news for the author about what 80% of a programmer's job consists of already today.
There is also the issue #4, that "idea guy" types frequently gloss over: If things do not work as intended, find out why they don't do so and work out a way to fix the root cause. It's not impossible that an AI can get good at that, but I haven't seen it so far and it definitely doesn't fit into the standard "write down what I want to have, press button, get results" AI workflows.
Generally, I feel this ignores the inherent value that there is in having an actual understanding of a system.
The current implementations of LLMs are focusing on providing good answers, but the real effort of modern day programming is to ask good questions, of both the system, the data, and the people that supposedly understand the requirements
LLMs might become good at this, but nobody seems to be doing much of this commercially just yet
You make a good point and those kind of senior engineer skills may be the least affected. My post does not argue against that. It argues that writing code manually may quickly become obsolete.
What I am trying to say is that people who see the output of their work as "code" will be replaced just like human computers did. I believe even debugging will be increasingly aided by AI. I do not believe that AI will eliminate the need for system understanding, just to be clear.
Then again, you might argue that writing lines of code and manually debugging issues is exactly what builds your understanding of the system. I agree with that too, I suppose the challenge will be maintaining deep system knowledge as more tasks become automated.
I strongly disagree with this. “Computers” would have not been replaced with the machines that replaced them if those machines routinely produced incorrect results.
One could argue that for applications where correctness is not critical my position does not apply, however this is not the analogy that the article is making.
The trajectory of LLMs "routinely producing incorrect results" is heading downwards as we are getting more advanced reasoning models with test-time compute.
I don't know whether you used some of the more recent models like Claude 3.5 Sonnet and o1. But to me it is very clear where the trajectory is headed. o3 is just around the corner, and o4 is currently in training.
People found value even in a model like GPT 3.5 Turbo, and that thing was really bad. But hey, at least it could write some short scripts and boilerplate code.
You are also comparing mathematical computation - which has only 1 correct solution - with programming, where the solution space is much broader. There are multiple valid solutions. Some are more optimal than others. It is up to the human to evaluate that solution, as I've said in the post. Today, you may even need to fix the LLM's output. But in my experience, I'm finding I need to do this far less often than before.
Wait what? Human programmers produce incorrect results all the time, they are called bugs. If anything, we use automated systems when correctness is important - fuzzers, static analyzers, etc. And the "AI" systems are improving by leaps and bounds every month, look at SWE-Bench [1] for example. It's pretty obvious where this is all going.
Sure, people make mistakes all the time. But would you prefer those mistakes be sprinkled randomly throughout your data crunching, or be systematic errors?
The point that that post is making is that a machine isn't going to make a mistake in adding two numbers. It reduces arithmetic errors to 0 (unless you count overflow which can be detected), and if it didn't it would only be useful in the rare case you don't care about accuracy.
AI in it's current state does not do for logical accuracy what computers did for arithmetic accuracy; You still need to verify every output from an LLM, which I doubt you've done for the many billions of arithmetic operations that happened this second on the computer you're on right now.
Think of electronic calculators. They have a significantly lower error rate than human calculators. Both statistically significant and practically significant.
> Even he, subconsciously, knows it doesn't pay off to waste cognitive energy on what a machine could do instead.
> It's not laziness, but efficiency.
It's only efficient in the short-term, not long-term. Now the programmer never understood the problem, and that is a problem in the long-run. As any experienced engineer knows - understanding problems is how anyone gets better in the engineering field.
My post did not take the position that understanding problems isn't important.
Using LLMs can even help you understand the problem better. And it can bring you towards the solution faster. Using an LLM to solve a problem does not prevent understanding it. Does using a calculator prevent us from understanding mathematical concepts?
Technical understanding will still be valuable. Typing out code by hand will not.
For this I've always called myself "software plumber".
I quite liked that analogy about excavator because I think along similar lines. Mechanising drudgery work in construction industry enabled us to build superstructures that are hundreds of stories tall. I wonder if LLMs will enable us to do something similar when it comes to software.
Okay, but what will you actually do when your LLM writes code which doesn't actually error but produces incorrect behaviour, and no matter how long you spend refining your prompt or trying different models it can't fix it for you?
Obviously you'll have to debug the code yourself, for which you'll need those programming skills that you claimed weren't relevant any more.
Eventually you'll ask a software engineer, who will probably be paid more than you because "knowing what to build" and "evaluating the end result" are skills more closely related to product management - a difficult and valuable job that just doesn't require the same level of specialisation.
Lots of us have been the engineer here, confused and asking why you took approach X to solve this problem and sheepishly being told "Oh I actually didn't write this code, I don't know how it works".
You are confidently asserting that people can safely skip learning a whole way of thinking, not just some syntax and API specs. Some programmers can be replaced by an LLM, but not most of them.
I agree that you still need programming skills (today, at least). Yet people are using those programming skills less and less, as you can clearly see in the article [1] I referenced.
You are also making the assumption that LLMs won't improve, which I think is shortsighted.
I fully agree with the part about the job becoming more like product management. I would like to cite an excerpt of a post [2] by Andrew Ng, which I found valuable:
Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. (...) Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce.
To address your last point - no, I am not saying people should skip learning a whole way of thinking. In fact, the skills I outline for the future (supervising AI, evaluating results) all require understanding programming concepts and system thinking. They do not, however, require manual debugging, writing lines of code by hand, a deep understanding of syntax, reading stack traces and googling for answers.
Obviously I assume LLMs will continue to improve, I don't know why you'd think I don't.
But the actual relevant prediction here (the one you're confident enough about to give skills development advice on) is whether they'll improve sufficiently that programming is no longer a relevant skill.
I think that's possible, but I'm not nearly so confident I'd write your article: LLMs went mainstream ~2 years ago, and they still have some pretty basic limitations when it comes to computational/mathematical reasoning, which they'll need to solve novel software engineering tasks. (Articles about these limitations get posted here pretty frequently)
To your second point, I'm still not sure how you will debug someone else's code without learning to write code yourself, because you need to be able to read code, and understand it well enough to execute it inside your mind. I am not totally convinced you understand the difference between "understanding programming concepts" and "being able to understand whether this code works".
Sorry if this comes across as rude, but I think the reason the feedback on your post is overall quite negative is that you're excited about AI making this job much easier, and your advice about which skills are worth learning are too confident. Ironically I think an LLM would give a more balanced view than you have.
Oh boy, I sure love to see some guy with a blog waxing poetic about the future of AI. The insistence that "most people haven't realized come to terms with it yet", and then proceeding to make a total fucking guess out of his ass.
Reminds me of that one asshole who couldn't find a pen, and wrote a whole NYT article about "the demise of the pen".
Premises seems completely flawed to me. It’s like if we would say because we have calculators, knowing basic arithmetic mentally will no longer be a necessary step to go through. All the more when the these calculators are actually not giving you exacts results, but excel at producing a lot of plausible lookalike approximations based on large corpus of computation samples.
So it seems not only very wrong on what is the hard part in programming, but also misguided about where (current) LLM use can shine, including in programming context.
Readit News
[...]
3. evaluating whether the end result works as intended.
I have some news for the author about what 80% of a programmer's job consists of already today.
There is also the issue #4, that "idea guy" types frequently gloss over: If things do not work as intended, find out why they don't do so and work out a way to fix the root cause. It's not impossible that an AI can get good at that, but I haven't seen it so far and it definitely doesn't fit into the standard "write down what I want to have, press button, get results" AI workflows.
Generally, I feel this ignores the inherent value that there is in having an actual understanding of a system.
LLMs might become good at this, but nobody seems to be doing much of this commercially just yet
That is not the same thing.
What I am trying to say is that people who see the output of their work as "code" will be replaced just like human computers did. I believe even debugging will be increasingly aided by AI. I do not believe that AI will eliminate the need for system understanding, just to be clear.
Then again, you might argue that writing lines of code and manually debugging issues is exactly what builds your understanding of the system. I agree with that too, I suppose the challenge will be maintaining deep system knowledge as more tasks become automated.
One could argue that for applications where correctness is not critical my position does not apply, however this is not the analogy that the article is making.
I don't know whether you used some of the more recent models like Claude 3.5 Sonnet and o1. But to me it is very clear where the trajectory is headed. o3 is just around the corner, and o4 is currently in training.
People found value even in a model like GPT 3.5 Turbo, and that thing was really bad. But hey, at least it could write some short scripts and boilerplate code.
You are also comparing mathematical computation - which has only 1 correct solution - with programming, where the solution space is much broader. There are multiple valid solutions. Some are more optimal than others. It is up to the human to evaluate that solution, as I've said in the post. Today, you may even need to fix the LLM's output. But in my experience, I'm finding I need to do this far less often than before.
[1] https://www.swebench.com/
The point that that post is making is that a machine isn't going to make a mistake in adding two numbers. It reduces arithmetic errors to 0 (unless you count overflow which can be detected), and if it didn't it would only be useful in the rare case you don't care about accuracy.
AI in it's current state does not do for logical accuracy what computers did for arithmetic accuracy; You still need to verify every output from an LLM, which I doubt you've done for the many billions of arithmetic operations that happened this second on the computer you're on right now.
edit: fixed typo
Think of electronic calculators. They have a significantly lower error rate than human calculators. Both statistically significant and practically significant.
> Even he, subconsciously, knows it doesn't pay off to waste cognitive energy on what a machine could do instead.
> It's not laziness, but efficiency.
It's only efficient in the short-term, not long-term. Now the programmer never understood the problem, and that is a problem in the long-run. As any experienced engineer knows - understanding problems is how anyone gets better in the engineering field.
Using LLMs can even help you understand the problem better. And it can bring you towards the solution faster. Using an LLM to solve a problem does not prevent understanding it. Does using a calculator prevent us from understanding mathematical concepts?
Technical understanding will still be valuable. Typing out code by hand will not.
Effectiveness counts. Effective people rarely need to wag the efficiency dog.
/s
Oh wait. You get a "car". And you get a "car". And everyone gets a "car".
I quite liked that analogy about excavator because I think along similar lines. Mechanising drudgery work in construction industry enabled us to build superstructures that are hundreds of stories tall. I wonder if LLMs will enable us to do something similar when it comes to software.
Obviously you'll have to debug the code yourself, for which you'll need those programming skills that you claimed weren't relevant any more.
Eventually you'll ask a software engineer, who will probably be paid more than you because "knowing what to build" and "evaluating the end result" are skills more closely related to product management - a difficult and valuable job that just doesn't require the same level of specialisation.
Lots of us have been the engineer here, confused and asking why you took approach X to solve this problem and sheepishly being told "Oh I actually didn't write this code, I don't know how it works".
You are confidently asserting that people can safely skip learning a whole way of thinking, not just some syntax and API specs. Some programmers can be replaced by an LLM, but not most of them.
You are also making the assumption that LLMs won't improve, which I think is shortsighted.
I fully agree with the part about the job becoming more like product management. I would like to cite an excerpt of a post [2] by Andrew Ng, which I found valuable:
Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. (...) Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce.
To address your last point - no, I am not saying people should skip learning a whole way of thinking. In fact, the skills I outline for the future (supervising AI, evaluating results) all require understanding programming concepts and system thinking. They do not, however, require manual debugging, writing lines of code by hand, a deep understanding of syntax, reading stack traces and googling for answers.
[1] https://nmn.gl/blog/ai-illiterate-programmers
[2] https://www.deeplearning.ai/the-batch/issue-284/
But the actual relevant prediction here (the one you're confident enough about to give skills development advice on) is whether they'll improve sufficiently that programming is no longer a relevant skill.
I think that's possible, but I'm not nearly so confident I'd write your article: LLMs went mainstream ~2 years ago, and they still have some pretty basic limitations when it comes to computational/mathematical reasoning, which they'll need to solve novel software engineering tasks. (Articles about these limitations get posted here pretty frequently)
To your second point, I'm still not sure how you will debug someone else's code without learning to write code yourself, because you need to be able to read code, and understand it well enough to execute it inside your mind. I am not totally convinced you understand the difference between "understanding programming concepts" and "being able to understand whether this code works".
Sorry if this comes across as rude, but I think the reason the feedback on your post is overall quite negative is that you're excited about AI making this job much easier, and your advice about which skills are worth learning are too confident. Ironically I think an LLM would give a more balanced view than you have.
Reminds me of that one asshole who couldn't find a pen, and wrote a whole NYT article about "the demise of the pen".
Makes people wish for the demise of stupidity.
So it seems not only very wrong on what is the hard part in programming, but also misguided about where (current) LLM use can shine, including in programming context.