I mean, the content doesn't look AI generated. I couldn't care less if someone AI generates their blog template, the rest of the content seems meaningful.
How humans can differentiate the things they make has been something I’ve been thinking about a bit recently. What markers signal to you this is AI generated?
We've had team changes and product manager changes and lack of documentation for so long that this was the state of our team anymore: no one knows why it was done and no one wants to break it.
I predict we will see more and more of these convoluted rationales (i.e. excuses). All these basically boil down to: “I know AI is crap, but I have found a way to make it useful. Trust me.”
I am gonna appeal to Occam’s razor here and say, the integral variable here is AI, and the only variable you need to know is AI. The problem is AI.
I see this everyday. The problem is code is the wrong abstraction for the work we do. LLMs have solved coding, but they haven't solved systems, collaboration or system maintenance.
"solved coding" is type of thing you say if you want to sound smart.
Saying that LLMs have "reduced the cost of coding" would be boring. And using your analogy, pencils, typewriters and computers have all reduced the cost of writing, but writers are still around.
"Reducing the cost of coding" would imply that you still have to code, but with current LLMs you no longer have to. You can write 100s of thousands of lines of code without ever having to touch a single line of code. Literally "here is a git repo, here is an issue, please fix".
You might still need to nudge the LLM in the right direction or stop it from going off weird tangents, but none of that involves touching actual code yourself.
even setting aside the artistic side of the profession, I see these things make all sorts of basic mistakes, so even the mechanical part doesn't seem "solved" ime
It solved coding as in it's significantly less of a bottleneck than it was before.
At least that's how I experience it. In the before times each non-trivial code change had a real opportunity cost as it would easily consume two days until I could even estimate whether this is worth looking deeper into.
Given a working system, like a web service or services, and their code, non technical people can now make effective changes with LLMs.
For example, nobody on our team writes manual code anymore, we have basically set up a harness where an engineer types up the requirements for a change, the system implements it given certain constraints, we have automated unit and integration tests that are ran, and if any errors pop up, they get fed back into the loop until fixed.
But to do that, you need to actually know what you are doing - you have to have good instructions to keep the agents in check and not start making mods outside of their bounds especially when the issue is with a dependant service that is causing errors.
I'm well on my way down this path too, but that is a process implemented in markdown. I don't see coding as solved, or a solvable thing at all, like writing
To solve something, there must be a defined problem, what is the problem that was solved. Or perhaps it is just "coding is solved" is the turn of phrase de jour be ause we haven't yet found a more succinct and accurate way to describe the paradigm shift
When it comes to non technical people using Ai to build things on code, the outcomes are on average pretty poor, which i see as evidence that the driver and their expertise behind the Ai matters a lot. A notable example is Terence Tao's conversation with ChatGPT, us math normies could never have done that. The same applies to coding agents ime
Writing is a means of expression and communication. Code can be those things, but that's not its primary purpose.
I think "solved coding" is taking it too far, but for many projects, the mechanical aspect of it has been removed or reduced greatly.
LLMs will have a much harder time "solving writing", because they cannot develop their own style and so are severely limited, creatively. This is less important for coding.
Or more broadly, LLMs fundamentally don't "understand". They can simulate understanding and generate text/code/whatever, but they don't have will to engage with something holistically and "own" it.
The problem is code is the wrong abstraction for the work we do
My team recently spent two weeks on a wild goose chase trying to figure out why TensorFlow Lite was generating nonsensical OpenCL kernels. Well it turns out that LLVM had a few bugs in the RISC-V assembly for our platform that was leading to silent garbage. It took combing through assembly dumps, hexdumps, a lot of pain staking debugging, and going through the TensorFlow Lite source code to to track this down.
In your opinion, if code is the wrong abstraction to be working at, how do you approach this scenario?
If you don't understand how your system works, your ability to make good decisions about future work on that system quickly degrades.
I don't think you need to review every line of code, but you absolutely do need to be able to describe how the system works and its high level structure.
As is so often the case with coding agents, having experience as a tech lead or engineering manager really helps here. You are responsible for a large system that has been worked on by multiple different collaborator (both human and agentic). You need to be able to make smart, informed decisions about that system, and talk with credibility to other stakeholders about what it can and cannot do and sensible next steps for the project.
What will the path to a tech lead look like when it's no longer paved by thousands of hours of experience internalizing code? How do future tech leads avoid becoming like people who never got comfortable with fractions because they offloaded all arithmetic to calculators from an early age?
I had agents deep diving OpenCode source over the weekend, writing research docs, and helping me with a set of plugins. I learned quite a bit from the process and markdown walls, enough so that in later sessions I was able to point the model at where it was conflating concepts and help narrow its search.
Ai can help you learn if you are intentional about it.
My hope/hunch is that the kids will be alright. Not learning effectively is a choice: if you want to get good, the paths to getting good are all still available to you.
We have never had as abundant a supply of tools to help us learn our craft. I expect that many people will thrive.
People who are a bit lazy and prone to cheating will be able to hurt themselves even more.
I don't think you need to review every line of code, but you absolutely do need to be able to describe how the system works and its high level structure.
I just don't see how you can truly reason about a system without delving into code. Tests aren't enough, running the software isn't enough, high level system architecture isn't enough.
As is so often the case with coding agents, having experience as a tech lead or engineering manager really helps here ... You need to be able to make smart, informed decisions about that system
In my experience, engineering managers are too detached from the system to accurately reason about the system. Tech leads on the other hand usually can given enough time, but they tend to defer judgement to senior ICs on the team who are more familiar with the code.
Point is: there's no way to have your cake and eat it to. You either read the fucking code and keep a mental model of how the system works in your head, or you have an overstated confidence in your ability to reason about the system (and this has been a problem well before LLMs).
Yeah we forgot the goal of programming isn’t just to tell the computer what to do, it’s to program the programmer into thinking a deeper understanding of the problem.
"Nobody is resolving bugs" is weird. Coding agents are great at resolving bugs! So much of what I see posted here seems more about how coding agents are being misused rather than anything inherent to them.
We’ve had to know everything about the product/business from day 1
I know this is being hyperbolic but I thought this was an odd post to include. I've met plenty of data engineers that don't have great knowledge of the business/product and SWEs that do have that. ¯\_(ツ)_/¯
I was reflecting on this on Saturday in an unformed way, trying to trace the lineage of a decision made at work.
The code change itself doesn't specifically matter. But suffice to say, it was about an AI feature in one of our products.
The code was stamped by Claude driven by a prompt. The prompt was for a ticket generated with the Atlassian AI integration. Atlassian had digested docs made with AI. The docs came from strategy memos I'm 90% sure were written entirely by Claude.
The strategy was chosen by management at the urging of exec leadership. The execs now communicate mostly via AI written memos. I do not know how they make decisions, but they reference tech influencers, market conditions, customer expectations.
This gave me pause. Who had actually made the decision then? Arguably there has been several layers of human review, but the actual source of the decision was hard to pin down.
We were not building the feature because we wanted it. We were building it because we thought other people expected it.
Perhaps reflecting on the state of the market, I thought, could indicate who was actually in control.
Where do investor and customer expectations come from in 2026? It is very murky, at least in tech. There appears to be hype. Some hype comes from true believers, some comes from cynics. But both respond to market incentives that reward bigger and bigger claims.
Where does the market's "action" come from? What is the driver?
Investors do not really seem to understand what the tech is or its limitations. Some are passive operators. Others are just responding to the overall froth and speculation in the market - which becomes a runaway feedback cycle.
This left me lost.
Nobody in this ecosystem, I thought, is actually in control here.
Nobody is actually orienting work and action to real, concrete goals. It's all based on speculation and anxiety about the future.
So it is not only that nobody understands what the code does. It is that we cannot, or at least I cannot, explain the motivation. There doesn't seem to "be" any form of "intention" in this environment.
It has all been hollowed out, replaced either be inscrutable machines, or inscrutable incentives.
Ironically it rather resembles the kind of "misaligned" superintelligence we are supposed to be avoiding.
For me, at this company, which is struggling to develop a new value proposition, everyone is unfortunately using AI in a blatant way. It’s even worse when executives and management pressure us to move fast. Presentations, documents, and mockups all use AI, rinse and repeat, feeding it context, but somehow, nothing is moving. It’s just staying static.
It's not a given. It's about willpower and care. LLMs are the ultimate crutch. I over-rely on the crutch, more and more people will over-rely on the crutch. But it is still inherently a psychological problem.
You can still know things and get force multiplication out of LLMs, if you are disciplined and caring enough. In practice, most people won't be. And you can't force other people to be. But you can force yourself to be.
A man has to write program for himself and a man has to use the program. A man needs to define in code what the program does. If AI defines what code does then man has not written program for himself.
The future of engineering is product management. I don't believe there is any world left for people whose primary responsibility is opening pull requests; and we're seeing the angst against this happen from both directions. Engineers hate it. Leadership feels they don't need them. The truth is somewhere in the hazy middle; we're just in an uncanny valley right now where neither side can take the leap to cross the valley: the agents aren't good enough yet, and the bigger problem is that there's no job title or corpus of experience leadership can look to and say "Yeah that's the person we need in this role".
The labs saw this early, and thus many roles at the labs are "Member of Technical Staff". That's the future for every software team. You're not a software engineer anymore, but you're also not a PM, nor a designer. Think horizontal slices, not vertical: Every human's responsibility is to leverage AI to be an expert on everything necessary to deliver some vertical slice of the business.
Wouldn't engineering then be the safest place to be? Engineers have always been embedding loose requirements into runtime law. They (supposedly) understand the business model better than most and have the technical know how to validate that
When you write something you constantly remodel your understanding through refactors and rewrites until you internalize it. By internalizing it you gain the capacity to reason about it (during critical downtime) and communicate it. An entire team that can communicate can solve problems together, from one guy's vision to products white boarding to engineering's infrastructure to UX and UI's artistry.
It boggles me we completely forgot that the world operated like this just 4 years ago
I feel this in my bones. My team is one of the most communicative in our org and have garnered a widely positive reputation I think largely as a result of our coming together every day to solve problems. Trying to get information out of the less communicative groups is like pulling teeth, i wonder how they get anything done at all
I am simultaneously deep down in the agentic treadmill and also supremely frustrated by this.
And I don't think it was ever necessary to go to the point where people just gave up authorship. These were choices made by adopting the "I'll do everything for you" agentic "harness" model that shipped with Claude Code but it was never inevitable.
e.g. we completely dropped fill-in-the-middle completion OG CoPilot auto completion model. That combined AI authorship with a human always in the mix and I actually really enjoyed it. It's just that the models involved were pretty stupid. We totally could have had IDE / shell / tooling integration that kept people in the driver's seat while automatic the drudgery away. Instead what we got was a simple chat loop with "oh, whatever, you go do it" being the ultimate result.
But the final boss is, and always will be, maintainability.
Always has been, always will be.
I am actually hopeful that AI will finally break the industry and force a reckoning around this. Some of it goes to our economic system. New builds are usually capitalizable, flashy, and a great way to get promoted.
Doing ten to fifteen years of thankless maintenance, keeping a critical system alive with high quality? Usually nobody cares, and it's OPEX, not sexy.
Too many young people in startups without enough experience.
I was in a Hackathon for students which quite a few staff, like myself, infiltrated. The results were completely unfair, staff and teams with staff (like mine) cleaned up the awards.
In my case I was working with a student who was much better at writing platformers in Unity than I was and an another student who could draw the art we needed even if she'd been trained to think every problem we had interacting with each other had something to do with "the patriarchy".
Myself I'd been in many startups where the game was make a half-baked demo that you could demo on stage and get people excited about it. So everything from presenting broken software on stage and making it look not just perfect but enticing and developing software that has the qualities it takes to present it that way was routine for me, the bit that isn't routine is onboarding unexperienced people to this life in two days.
The more things are unprecedented, the more you need a longer view with more experience.
This article assumes that this problem did not exist before AI. Especially in large companies like Google, the number of people who actually knew what they were doing was relatively small. The vast majority were just piggybacking on other people's work, which I absolutely hate. At least AI has made this obvious, and the difference between people is now their taste, which, for the majority of people, is bad news.
I agree writing completely new systems with LLMs is now near impossible to keep in your head, however, the onus is STILL on you the individual engineer. You are mistaken if you think that's changed.
So, if you're pooping out code, and committing it because tests still pass, and that's all you know, you're in for a treat. When an executive wants to know why a b0rked feature lost their department millions of dollars, guess who will have to answer for it, and its not the LLM.
My advice is to find ways to keep on top of how it all works, and if you're the only one who cares, well, then, that makes you even more valuable, not less.
Writers disagree on what effect word processing will have on the quality of our written language.
Some writers are concerned that computer assistance may promote dry bland writing.
that tweet about the fast moving startups looks like almost the hypothetical AI nightmare situation just dreamed up. Assuming it's real, i mean yeah, people shouldn't work for these stupid high-moving startups I guess. From my perspective, "when did startups NOT suck?". The code was always garbage at startups, the push to work 12 hour days was always at startups, "nobody is resolving bugs" ha well yes, welcome to a startup? I hope the guy is at least getting paid and doesnt have to hire a lawyer to get his checks like I did. startups suck
AI is not for programmers, they will always complain and given time and resources can of course produce better atomic code. AI is for a combination of a business analyst, architect and product manager in one person who has capacity in coding but decided this is limited role in the whole software engineering lifecycle. With the right attitude and of course certain level of micro management over AI (which they had to do with human programmers as well) they can achieve the same or similar results with much less communication friction, less people to maintain that teamwork and get closer to solving issues they find rather than discussing philosophy of programming and other manifestos of the craft that stopped being prestigious or unique and that’s the key point of complaint when AI is given wrong people to use.
Can they really achieve similar results though? If quality suffers greatly, and velocity eventually slows down due to huge tech debt, isn't that just a worse outcome?
In this moment there are a million ways to do things wrong.
But there are also some ways, maybe less than a million, to do things right.
Here's an anecdote about a way to do this wrong.
I have found with AI coding methods that there's a line where it becomes a hail-mary (in the American Football sense).
A hail-mary is when you throw the ball to the end zone and just pray someone catches it. This is almost always at the end of the game.
This moment with AI code is indicative that you can't put together a coherent plan so you just tell the agent to "make it good". It used to be that the results here would suck, but now the agents are really competent, so the results might be good.
But at that moment, that's your cue to back up. Because as soon as you take a solution that's so far detached from your understanding, you're underwater. The hail mary is not part of a larger game plan. It's the last play of the game. There's nothing after.
So as soon as you reach that moment in your coding, you're signaling that you're done understanding not just the code, but even the way it works at a high level. If you're still going to work with this code after, then back up and work with the AI to get more understanding of the problem.
When you simply large amounts of information, the knowledge probably shifts to upper level reasoning, then complex reasoning develops on top of it again. But who knows.
"People are working 12 to 13 hours a day just to press enter. Nobody is reading anything."
Really? Seems like they're not making proper use of the tool. I've been reading MORE not less, and also learning more along the way. Just hitting "enter" is a choice, these tools are so powerful if you invest your curiosity, time and experience.
Sounds like they don't care about what they're doing in the first place, writing code by hand won't fix that.
People don't get fired for dishonesty or persistent failure like you'd expect.
Quite the contrary, they get promoted with salary raises
Clicks on blog, sees AI generated template, leaves.
Enough, already.
I mean, the content doesn't look AI generated. I couldn't care less if someone AI generates their blog template, the rest of the content seems meaningful.
How humans can differentiate the things they make has been something I’ve been thinking about a bit recently. What markers signal to you this is AI generated?
We've had team changes and product manager changes and lack of documentation for so long that this was the state of our team anymore: no one knows why it was done and no one wants to break it.
I predict we will see more and more of these convoluted rationales (i.e. excuses). All these basically boil down to: “I know AI is crap, but I have found a way to make it useful. Trust me.”
I am gonna appeal to Occam’s razor here and say, the integral variable here is AI, and the only variable you need to know is AI. The problem is AI.
I see this everyday. The problem is code is the wrong abstraction for the work we do. LLMs have solved coding, but they haven't solved systems, collaboration or system maintenance.
When you say "solved coding," what does this mean, what does it look like?
I have strong disagreement because it sounds like, by analogy or proxy, we have also "solved writing"
"solved coding" is type of thing you say if you want to sound smart.
Saying that LLMs have "reduced the cost of coding" would be boring. And using your analogy, pencils, typewriters and computers have all reduced the cost of writing, but writers are still around.
"Reducing the cost of coding" would imply that you still have to code, but with current LLMs you no longer have to. You can write 100s of thousands of lines of code without ever having to touch a single line of code. Literally "here is a git repo, here is an issue, please fix".
You might still need to nudge the LLM in the right direction or stop it from going off weird tangents, but none of that involves touching actual code yourself.
What is coding? Pushing keys or knowing the right keys to push?
Ai can push a lot of keys very fast, but not always the right ones
It means "for my requirements they're good enough."
They haven't solved coding.
Programming is an art form. And the better you get at it, the better kinds of ideas (abstractions) you can create.
This is something today's AI cannot do.
If everyone were to permanently switch to AI for software development, software innovation would cease.
even setting aside the artistic side of the profession, I see these things make all sorts of basic mistakes, so even the mechanical part doesn't seem "solved" ime
It solved coding as in it's significantly less of a bottleneck than it was before.
At least that's how I experience it. In the before times each non-trivial code change had a real opportunity cost as it would easily consume two days until I could even estimate whether this is worth looking deeper into.
Given a working system, like a web service or services, and their code, non technical people can now make effective changes with LLMs.
For example, nobody on our team writes manual code anymore, we have basically set up a harness where an engineer types up the requirements for a change, the system implements it given certain constraints, we have automated unit and integration tests that are ran, and if any errors pop up, they get fed back into the loop until fixed.
But to do that, you need to actually know what you are doing - you have to have good instructions to keep the agents in check and not start making mods outside of their bounds especially when the issue is with a dependant service that is causing errors.
I'm well on my way down this path too, but that is a process implemented in markdown. I don't see coding as solved, or a solvable thing at all, like writing
To solve something, there must be a defined problem, what is the problem that was solved. Or perhaps it is just "coding is solved" is the turn of phrase de jour be ause we haven't yet found a more succinct and accurate way to describe the paradigm shift
When it comes to non technical people using Ai to build things on code, the outcomes are on average pretty poor, which i see as evidence that the driver and their expertise behind the Ai matters a lot. A notable example is Terence Tao's conversation with ChatGPT, us math normies could never have done that. The same applies to coding agents ime
Writing is a means of expression and communication. Code can be those things, but that's not its primary purpose.
I think "solved coding" is taking it too far, but for many projects, the mechanical aspect of it has been removed or reduced greatly.
LLMs will have a much harder time "solving writing", because they cannot develop their own style and so are severely limited, creatively. This is less important for coding.
Or more broadly, LLMs fundamentally don't "understand". They can simulate understanding and generate text/code/whatever, but they don't have will to engage with something holistically and "own" it.
I find it helps to imagine what is/isn't solved (however, we choose to label it) by an indefinite number of cheap junior-developers.
Except a little worse, since they were raised alone in a library, act mostly the same, and have harsh limits on personal growth.
Even if they "solved" that, the problem is it's the LLM that "knows" it, not the team.
Which is really the same problem with coding.
The agentic model of it just taking over and doing everything is poisonous to effective long term team work.
We're well past the point where it's about the quality of the work they produce. It's the way they integrate (or rather, don't) into human practices.
My team recently spent two weeks on a wild goose chase trying to figure out why TensorFlow Lite was generating nonsensical OpenCL kernels. Well it turns out that LLVM had a few bugs in the RISC-V assembly for our platform that was leading to silent garbage. It took combing through assembly dumps, hexdumps, a lot of pain staking debugging, and going through the TensorFlow Lite source code to to track this down.
In your opinion, if code is the wrong abstraction to be working at, how do you approach this scenario?
The plural of anecdote is not fact.
Nor is it data!
If you don't understand how your system works, your ability to make good decisions about future work on that system quickly degrades.
I don't think you need to review every line of code, but you absolutely do need to be able to describe how the system works and its high level structure.
As is so often the case with coding agents, having experience as a tech lead or engineering manager really helps here. You are responsible for a large system that has been worked on by multiple different collaborator (both human and agentic). You need to be able to make smart, informed decisions about that system, and talk with credibility to other stakeholders about what it can and cannot do and sensible next steps for the project.
What will the path to a tech lead look like when it's no longer paved by thousands of hours of experience internalizing code? How do future tech leads avoid becoming like people who never got comfortable with fractions because they offloaded all arithmetic to calculators from an early age?
I had agents deep diving OpenCode source over the weekend, writing research docs, and helping me with a set of plugins. I learned quite a bit from the process and markdown walls, enough so that in later sessions I was able to point the model at where it was conflating concepts and help narrow its search.
Ai can help you learn if you are intentional about it.
My hope/hunch is that the kids will be alright. Not learning effectively is a choice: if you want to get good, the paths to getting good are all still available to you.
We have never had as abundant a supply of tools to help us learn our craft. I expect that many people will thrive.
People who are a bit lazy and prone to cheating will be able to hurt themselves even more.
I just don't see how you can truly reason about a system without delving into code. Tests aren't enough, running the software isn't enough, high level system architecture isn't enough.
In my experience, engineering managers are too detached from the system to accurately reason about the system. Tech leads on the other hand usually can given enough time, but they tend to defer judgement to senior ICs on the team who are more familiar with the code.
Point is: there's no way to have your cake and eat it to. You either read the fucking code and keep a mental model of how the system works in your head, or you have an overstated confidence in your ability to reason about the system (and this has been a problem well before LLMs).
Delving into the code is not the same thing as reading every line.
Yeah we forgot the goal of programming isn’t just to tell the computer what to do, it’s to program the programmer into thinking a deeper understanding of the problem.
"Nobody is resolving bugs" is weird. Coding agents are great at resolving bugs! So much of what I see posted here seems more about how coding agents are being misused rather than anything inherent to them.
Of course that's what people are complaining about...?
I know this is being hyperbolic but I thought this was an odd post to include. I've met plenty of data engineers that don't have great knowledge of the business/product and SWEs that do have that. ¯\_(ツ)_/¯
I was reflecting on this on Saturday in an unformed way, trying to trace the lineage of a decision made at work.
The code change itself doesn't specifically matter. But suffice to say, it was about an AI feature in one of our products.
The code was stamped by Claude driven by a prompt. The prompt was for a ticket generated with the Atlassian AI integration. Atlassian had digested docs made with AI. The docs came from strategy memos I'm 90% sure were written entirely by Claude.
The strategy was chosen by management at the urging of exec leadership. The execs now communicate mostly via AI written memos. I do not know how they make decisions, but they reference tech influencers, market conditions, customer expectations.
This gave me pause. Who had actually made the decision then? Arguably there has been several layers of human review, but the actual source of the decision was hard to pin down.
We were not building the feature because we wanted it. We were building it because we thought other people expected it.
Perhaps reflecting on the state of the market, I thought, could indicate who was actually in control.
Where do investor and customer expectations come from in 2026? It is very murky, at least in tech. There appears to be hype. Some hype comes from true believers, some comes from cynics. But both respond to market incentives that reward bigger and bigger claims.
Where does the market's "action" come from? What is the driver?
Investors do not really seem to understand what the tech is or its limitations. Some are passive operators. Others are just responding to the overall froth and speculation in the market - which becomes a runaway feedback cycle.
This left me lost.
Nobody in this ecosystem, I thought, is actually in control here.
Nobody is actually orienting work and action to real, concrete goals. It's all based on speculation and anxiety about the future.
So it is not only that nobody understands what the code does. It is that we cannot, or at least I cannot, explain the motivation. There doesn't seem to "be" any form of "intention" in this environment.
It has all been hollowed out, replaced either be inscrutable machines, or inscrutable incentives.
Ironically it rather resembles the kind of "misaligned" superintelligence we are supposed to be avoiding.
For me, at this company, which is struggling to develop a new value proposition, everyone is unfortunately using AI in a blatant way. It’s even worse when executives and management pressure us to move fast. Presentations, documents, and mockups all use AI, rinse and repeat, feeding it context, but somehow, nothing is moving. It’s just staying static.
The invisible hand of the market is in control, and we don't understand it either.
It's not a given. It's about willpower and care. LLMs are the ultimate crutch. I over-rely on the crutch, more and more people will over-rely on the crutch. But it is still inherently a psychological problem.
You can still know things and get force multiplication out of LLMs, if you are disciplined and caring enough. In practice, most people won't be. And you can't force other people to be. But you can force yourself to be.
A man has to write program for himself and a man has to use the program. A man needs to define in code what the program does. If AI defines what code does then man has not written program for himself.
The future of engineering is product management. I don't believe there is any world left for people whose primary responsibility is opening pull requests; and we're seeing the angst against this happen from both directions. Engineers hate it. Leadership feels they don't need them. The truth is somewhere in the hazy middle; we're just in an uncanny valley right now where neither side can take the leap to cross the valley: the agents aren't good enough yet, and the bigger problem is that there's no job title or corpus of experience leadership can look to and say "Yeah that's the person we need in this role".
The labs saw this early, and thus many roles at the labs are "Member of Technical Staff". That's the future for every software team. You're not a software engineer anymore, but you're also not a PM, nor a designer. Think horizontal slices, not vertical: Every human's responsibility is to leverage AI to be an expert on everything necessary to deliver some vertical slice of the business.
Wouldn't engineering then be the safest place to be? Engineers have always been embedding loose requirements into runtime law. They (supposedly) understand the business model better than most and have the technical know how to validate that
When you write something you constantly remodel your understanding through refactors and rewrites until you internalize it. By internalizing it you gain the capacity to reason about it (during critical downtime) and communicate it. An entire team that can communicate can solve problems together, from one guy's vision to products white boarding to engineering's infrastructure to UX and UI's artistry.
It boggles me we completely forgot that the world operated like this just 4 years ago
I feel this in my bones. My team is one of the most communicative in our org and have garnered a widely positive reputation I think largely as a result of our coming together every day to solve problems. Trying to get information out of the less communicative groups is like pulling teeth, i wonder how they get anything done at all
I am simultaneously deep down in the agentic treadmill and also supremely frustrated by this.
And I don't think it was ever necessary to go to the point where people just gave up authorship. These were choices made by adopting the "I'll do everything for you" agentic "harness" model that shipped with Claude Code but it was never inevitable.
e.g. we completely dropped fill-in-the-middle completion OG CoPilot auto completion model. That combined AI authorship with a human always in the mix and I actually really enjoyed it. It's just that the models involved were pretty stupid. We totally could have had IDE / shell / tooling integration that kept people in the driver's seat while automatic the drudgery away. Instead what we got was a simple chat loop with "oh, whatever, you go do it" being the ultimate result.
Our brains are hard-wired to enjoy (at least in the short term) immediate gratification, and the removal of friction. It's a hard battle to fight.
The problem is always maintainability, which can only be achieved through human understanding of code.
Always has been, always will be.
I am actually hopeful that AI will finally break the industry and force a reckoning around this. Some of it goes to our economic system. New builds are usually capitalizable, flashy, and a great way to get promoted.
Doing ten to fifteen years of thankless maintenance, keeping a critical system alive with high quality? Usually nobody cares, and it's OPEX, not sexy.
Too many young people in startups without enough experience.
I was in a Hackathon for students which quite a few staff, like myself, infiltrated. The results were completely unfair, staff and teams with staff (like mine) cleaned up the awards.
In my case I was working with a student who was much better at writing platformers in Unity than I was and an another student who could draw the art we needed even if she'd been trained to think every problem we had interacting with each other had something to do with "the patriarchy".
Myself I'd been in many startups where the game was make a half-baked demo that you could demo on stage and get people excited about it. So everything from presenting broken software on stage and making it look not just perfect but enticing and developing software that has the qualities it takes to present it that way was routine for me, the bit that isn't routine is onboarding unexperienced people to this life in two days.
The more things are unprecedented, the more you need a longer view with more experience.
she'd been trained to think every problem we had interacting with each other had something to do with "the patriarchy
Who is teaching these young girls such foolish things?
Of course its unfair. You're competing against students
This article assumes that this problem did not exist before AI. Especially in large companies like Google, the number of people who actually knew what they were doing was relatively small. The vast majority were just piggybacking on other people's work, which I absolutely hate. At least AI has made this obvious, and the difference between people is now their taste, which, for the majority of people, is bad news.
I agree writing completely new systems with LLMs is now near impossible to keep in your head, however, the onus is STILL on you the individual engineer. You are mistaken if you think that's changed.
So, if you're pooping out code, and committing it because tests still pass, and that's all you know, you're in for a treat. When an executive wants to know why a b0rked feature lost their department millions of dollars, guess who will have to answer for it, and its not the LLM.
My advice is to find ways to keep on top of how it all works, and if you're the only one who cares, well, then, that makes you even more valuable, not less.
These year and a few next ones will be the years when everything was possible.
We have both the tools and the skills.
Later, we will loose the skills because of AI and the depletion of natural resources will lead to the scarcity of the tools.
The Computer Chronicles – Word Processing (1983)
https://www.youtube.com/watch?v=Jt0OoXluC8g at 4:08:
that tweet about the fast moving startups looks like almost the hypothetical AI nightmare situation just dreamed up. Assuming it's real, i mean yeah, people shouldn't work for these stupid high-moving startups I guess. From my perspective, "when did startups NOT suck?". The code was always garbage at startups, the push to work 12 hour days was always at startups, "nobody is resolving bugs" ha well yes, welcome to a startup? I hope the guy is at least getting paid and doesnt have to hire a lawyer to get his checks like I did. startups suck
Companies will pay hard and high...
AI is not for programmers, they will always complain and given time and resources can of course produce better atomic code. AI is for a combination of a business analyst, architect and product manager in one person who has capacity in coding but decided this is limited role in the whole software engineering lifecycle. With the right attitude and of course certain level of micro management over AI (which they had to do with human programmers as well) they can achieve the same or similar results with much less communication friction, less people to maintain that teamwork and get closer to solving issues they find rather than discussing philosophy of programming and other manifestos of the craft that stopped being prestigious or unique and that’s the key point of complaint when AI is given wrong people to use.
Can they really achieve similar results though? If quality suffers greatly, and velocity eventually slows down due to huge tech debt, isn't that just a worse outcome?
In this moment there are a million ways to do things wrong. But there are also some ways, maybe less than a million, to do things right.
Here's an anecdote about a way to do this wrong.
I have found with AI coding methods that there's a line where it becomes a hail-mary (in the American Football sense).
A hail-mary is when you throw the ball to the end zone and just pray someone catches it. This is almost always at the end of the game.
This moment with AI code is indicative that you can't put together a coherent plan so you just tell the agent to "make it good". It used to be that the results here would suck, but now the agents are really competent, so the results might be good.
But at that moment, that's your cue to back up. Because as soon as you take a solution that's so far detached from your understanding, you're underwater. The hail mary is not part of a larger game plan. It's the last play of the game. There's nothing after.
So as soon as you reach that moment in your coding, you're signaling that you're done understanding not just the code, but even the way it works at a high level. If you're still going to work with this code after, then back up and work with the AI to get more understanding of the problem.
When you simply large amounts of information, the knowledge probably shifts to upper level reasoning, then complex reasoning develops on top of it again. But who knows.
"People are working 12 to 13 hours a day just to press enter. Nobody is reading anything."
Really? Seems like they're not making proper use of the tool. I've been reading MORE not less, and also learning more along the way. Just hitting "enter" is a choice, these tools are so powerful if you invest your curiosity, time and experience.
Sounds like they don't care about what they're doing in the first place, writing code by hand won't fix that.