Even the premise is ridiculous, inlay hints for parameter names have existed for a really long time. What human using a code editor could come up with this "problem"? The comments all seem to be nodding along and even suggesting absolutely hare-brained code like "const isThing = true; ...". I feel like I'm losing my mind. How many interactions on this site are sill organic? Am I talking to a bot right now?
I wonder whether a person prompted this slop and is somehow unaware of the existence of LSPs, or if it's entirely automated and the planning subagent hallucinated this being an issue for humans.
> Software is everywhere and thus the value of maintaining software and the value of software engineering remains high.
This is an unfinished argument. What if we get coding agents to maintain software? What if frequent rewriting becomes cheap enough? Something that's a tenth or one hundredth of your salary doesn't have to be good to make for a good business decision. Why do you think every native application has been replaced by slop made up of 10 layers of JS frameworks on top of electron? Nothing matters as long as the product is cheap and fast to pump out, barely works on modern hardware, and makes dough.
> AI does not reduce software, it increases the amount of software.
There's not infinite demand for software. If AI inference costs take 50% of the prior payroll expenses, while making a company twice as efficient, that means we need 4 times as much demand in software engineering at the same salary for everyone to keep their job. What new or improved subscription, app, website, device, or other software product does the world need right now? 99.9% of people use the same 5 apps. Most of their free time, attention, and disposable income has already been captured by trash that is unbeatable due to network effects. Are we all going to sell shitty LLM frontends to businesses until they notice they could have done the same thing themselves? There might be an explosion in new software, but no one there to care about using it.
> I believe there is a huge chasm that will likely never be crossed between the human intent of systems and their implementation that only human engineers can actually bridge.
Maybe, or the AI might just be missing context. Think of all the unwritten culture, practices, and conversations the LLM hasn't been made aware of.
> In short they want a throat to choke.
You're responsible for those under you anyway, this doesn't help. Banking on those in charge being irrational forever in a way that is bad for business, and without ever noticing, is a bad gamble.
> The other factor is that while AI can clearly replace rote coding today [...], X is not something that can be solved for without a lot of knowledge and guardrails.
I'm talking about the world the AI-maximalists predict is rapidly approaching, not where we are today. None of that knowledge and none of those guardrails are hard to grasp intellectually, compared to advanced mathematics for example. Put your institutional knowledge in a .md file and add another agent that enforces guardrails in a loop. The only way out I see is a situation where there are complex patterns that we intuitively grasp, but can't articulate. Patterns that somehow span too much data or don't have enough examples for LLMs to pick up on.
> There will be engineers who maintain all the same code, they'll just cover more scope with LLM assisted tools.
So fewer jobs with lesser qualifications?
> Ultimately I don't see the boundary the same way you do, as software engineers we have always had to justify our systems by their real world interaction.
I've seen the way engineers design products, and I like products designed by engineers, but no layperson does. Laypeople don't want power, privacy, or agency. They care about how things work, and they lie to themselves and others about what they really want. They don't want a native desktop app that streams high-quality audio from a self-hosted collection, they want a subscription that autoplays algorithmic slop through a react native app on their iPhone. Do you really think you're better at appealing to/fleecing customers than people with actual UX, marketing, and behavioral psychology experience? This example only applies to mass-market software, but I'm sure it's not much different in other fields. Engineers keep thinking they could everyone else's job, but they don't do so well in practice.
> What I think is much more likely to happen is the number of software engineers greatly reduces, but the remaining ones actually get paid more.
You realize that this is contradictory, right? If the number of competitors remains the same, yet there are far fewer jobs, it's a buyer's market: companies have to offer very little to find someone desperate enough.
> It will allow for more cohesive higher-level design, and less focus on local artesenal code quality.
I don't buy this, LLM code is extremely bloated. It never reuses abstractions or comes up with novel designs to simplify systems. It can't say no, it just keeps bolting on code. In a very very abstract sense you might be right, but that's outside the realm of engineering, that's product design.
If coding goes away, decades of experience become worthless instantly. Not all of it, but the vast majority, enough to justify starting over in another career.
In that world, it will have become more cost-effective for most companies to spend most of their budget on inference vendors and employ a few low-paid LLM wranglers, even if the final output is of terrible quality. No point in competing for that kind of employment experience with that kind of pay.
I chose Mistral because it's a large SOTA model that allows me to disable tool calling.
If you try using ChatGPT, you'll see it goes into reasoning mode (It flashes the text "Working") and takes a while when asked to do arithmetic. Chatbots that don't forbid the user from viewing the reasoning chain of thought, such as Deepseek or Mistral, reveal that these chatbots work exactly as I described:
====
Thought for 1s
Thinking
Okay, the user wants to multiply two large numbers: 63,157,997,633 and 6,311,490,009. Since these numbers are quite large, I should use the code interpreter to handle this calculation accurately. Let's write a simple piece of code to compute this product.
Code
output
generated
python
# Calculating the product of the two numbers
number1 = 63157997633
number2 = 6311490009
product = number1 * number2
product
ResultCopy
398621071049125148697
Thinking
Great, the code interpreter has given us the product of the two numbers. Now, let's present the result to the user.
====
Falsifying my claim is simple: Find a model that verifiably does basic arithmetic correctly without relying on any external tools.
It's a well known fact that LLMs struggle with basic arithmetic of large numbers, that's not what they are made for. Most chatbots will just call a python interpreter in the background.
This policy has existed for (non-3D) printers and image editing programs for decades now. They will refuse to print currency or anything with a specific watermark.
Their business is making money. If they can build money printing machines, they're not going to refuse to use them because that's "not their business".
Do you really think they would be out donating trillions of dollars to other companies out of the goodness of their hearts, instead of just bankrupting everyone in the software industry if they could?
Swahili is subcontinental lingua franca spoken by 200M people and growing quickly. Polish is spoken by a shrinking population in one country where English is understood anyways.
Integrated circuits don't "start with C". What does that even mean? C is just an interchangeable language the compiler frontend parses.
A microprocessor starts by executing the machine code at the reset vector. This machine code is generated by an assembler or a compiler backend. It has no idea what programming languages are.
Data is evidence for all models that predict the data. It doesn't matter that almost all of those models are extremely wrong, while the most accurate models are gross approximations of the truth. Scientists can continue to collect data and narrow the scope of statistically likely models. I don't see how your comment supports your assertion that "the goal of experiments needs to be falsifying hypotheses".
If by verify, you mean prove, I'd say it's not possible to empirically verify anything.