I just bought [product placeholder] and it has [features placeholder].
I am no longer distract by phon.
492 karma · joined February 6, 2022
I just bought [product placeholder] and it has [features placeholder].
I am no longer distract by phon.
A: add a gasket.
B: glueseal everything except for the battery, connected via exposed gold pads.
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 Why do you think every native application has been replaced by slop made up of 10 layers of JS frameworks on top of electron?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.
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.
If you ask ChatGPT, it will confirm that it uses the python interpreter to do arithmetic on large numbers. To you, that should be convincing.
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.
This is what Mistral outputs:
The result of multiplying 63,157,997,633 by 63,114,90,009 is:
3,965,689,999,999,999,999,999 (approximately 3.966 × 10²⁴).
That's like 5 orders of magnitude off, the scientific notation doesn't even match the full integer, and the mantissa is also slightly wrong.
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?
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.
> The key though is that showing the inverse of X can not be true is much harder than showing that what X might be true.
This is nonsense modal logic. You're saying ¬◻¬X, which if necessity and possibility are duals, is equivalent to ◇X, and otherwise an irrelevant statement. The inverse of X is ¬X. ¬¬X is logically equivalent to X.
Climate change mitigation is a collective action problem in the form of a prisoner's dilemma or a tragedy of the commons. If every agent (i.e. country) refuses to cooperate, every agent will suffer major damage from environmental disasters. If all agents cooperate, they only suffer minor damage from economic policies designed to reduce greenhouse gas emissions.
At first sight, this doesn't seem like much of a problem. The solution seems self-evident, before one considers countries adopting different strategies:
If one country defects, they benefit massively from hosting the world's carbon intensive processes, yet all countries will equally share in environmental catastrophe. Thus, the optimal strategy for any single self-interested agent is always to defect, no matter what the others do. Paradoxically, the optimal strategy for each agent in isolation leads to a catastrophically bad outcome for all agents if they all choose that strategy. Everyone wants to be the parasite, but if no one is the host, we all die.
It wouldn't matter if the US were a tiny island nation, but the US has the largest carbon footprint, the largest economy, and the most capable military. The US led the democratic world. They could have solved the prisoner's dilemma by enforcing global cooperation. If the US and its allies would threaten to sanction those countries who don't cooperate, the payout matrix would shift towards cooperation being a stable Nash-equilibrium. It would no longer be in a country's interest to screw everyone else over, so they'd stop. The US and the entire world would be better off.