Is this a situation where AI will go away and we will regret the loss of skills? At worse, we will be forced to use open weight models instead of the cutting edge, so I don't think it's a big deal. I'm sure people got worse at arithmetic after the invention of the calculator.
I don't think the real threat is at the individual level, but at the societal level.
Building skills over time leads to insights that lead to innovation.
AI does many interesting things, but it doesn't innovate (yet).
The real threat isn't that we'll all lose our skills (possibly) and then lose access to AI (unlikely), it is that AI will remain at roughly currently levels and we'll dull our skills due to reliance on it and innovation will stall because we've offloaded too much of the thinking to the non-innovative machine.
I'm not saying this is what definitely will happen, but it does seem like a very possible outcome.
The number of people now involved in software development has now increased because of a lower barrier to entry. I know many people who would previously use a no-code tool or hire offshore devs, or simply not have their problem solved, who are now vibe coding. Many of these people couldn't write very much code manually if they had to, but they're closer to understanding software than they were previously.
Yeah, this describes me perfectly. I can't really program, but I'm now building a bunch of different projects and submitting PRs that people seem to appreciate.
One of them, a Home Assistant integration for controlling adjustable beds, would be borderline impossible to do well manually - I've vibe-reverse engineered the Bluetooth protocols of more than a 100 Android apps.
> I've vibe-reverse engineered the Bluetooth protocols of more than a 100 Android apps
Impressive coverage, but how do you verify that the reverse engineering matches what the hardware expects? Do you find people who own those beds and get them to test stuff for you?
It's hard, but I try to verify it as best as possible by doing multiple independent runs, often one with GPT-5.5 and one with Opus, and then verify that they agree. When I'm as certain as possible I release it and rely on feedback when something doesn't work properly.
> The number of people now involved in software development has now increased because of a lower barrier to entry.
I don't see what this has to do with what I posted.
Yes, AI lowers the floor on software development, and there are positive aspects of that, but that doesn't change the possibility of an innovation stall.
re: concerns at a societal level. More people are making software than before. People who have been coding for a long time have moved up an abstraction layer and are further from the code. But many people are actually closer to the code than before.
> But many people are actually closer to the code than before.
More people are coding, I wouldn't say they are closer to the code if they are vibe coding. Are any of them going to produce the next breakthrough in computer language/framework/method of development/etc?
The risk of AI is that we dull the skills of enough people at the high end of the state of the art of the nuts and bolts of software development that we slow down innovation on that end. That's the concern.
Previously-non-programmers vibe coding CRUD apps they never could have before is all well and good but really has nothing to do with this concern. They may create wonderful and successful businesses but they are irrelevant to computer science related innovation.
He means that he's working 10 times faster so the manual things that he does once per task have to be done 10 times, and so he starts automating those too. I'm not claiming that he's actually 10x faster, just explaining.
But it kind of fits my experience too. Less time coding, and more time gathering requirements, testing, and doing knowledge transfer etc. Then you start thinking about how to make those parts automatic via automated tests or automated documentation generation that you review etc.
I think that AI is here to stay, and that you should focus most of your efforts on the parts that AI can't do. It's like with calculators, you still know how to add and multiply and stuff, and such low level things are even more pertinent with AI because it's not deterministic and can make mistakes, but generally you can rely on it for low level coding and debugging and what you really need to focus on is making sure that the artifacts it produces are aligned with your requirements, both functional and non-functional. Think about automated testing, scalability, security, etc. A single developer can produce code of much higher quality with AI because they have the bandwidth to work on such beneficial but optional things that they might have somewhat neglected before when implementation was more time-consuming and expensive.
People like to look at rent control from a purely economic lens, but the sociopolitical aspect must also be considered. A suboptimal economic outcome might actually be optimal when all factors are considered. Social harmony and a feeling of hope for underprivileged residents are hard to value, but we must admit that they do have value.
That assumes rent controlled units actually benefit mostly the underprivileged. They don't.
They create a strong incentive to keep a flat indefinitely. It's not unheard of for a person to continue renting a rent frozen apartment after they buy a house because the rent is so cheap - effectively it's a second home.
There's also plenty of corruption. "Knowing a guy" is one of the best ways to get a rent controlled unit since the wait time can be years.
Are these "favors" rank-blind (i.e. does its hushhush-nature more-equalize the men, just trying to secure places to live)? Obviously officers and enlisted have different housing, but might an E-6 kiss an E-5's ass only on account of wanting his soon-to-be-vacated housing?
My background is non-military (with one each, officer & enlisted, brother).
Even if they don't benefit economically, there's some value in having them feel that they are valued and have a voice at the table. Such policies can provide the popular support needed to implement other more economically optimal policies, and we're seeing this happen for example with the mayor of New York.
Right now, EVs are only good for people who have houses with garages that let them charge. If you need to use street parking or something then EVs don't work. But maybe if such technology improves everyone can use EVs.
May I suggest an AI coparent? I'm half-joking, but theoretically AI should eventually be able to take your high level intent and use it to apply appropriate restrictions to your children's phones.
I've always wondered if Tesla's issues with FSD were a sensor problem or an intelligence problem. I think Tesla's claim is that when they look at accident footage, it is clear to a human how the car could have avoided the accident, and thus, if FSD was more intelligent, the accident could have been avoided. Is this reasoning wrong?
I personally find it convincing that the problem with self-driving is mostly that the models aren't intelligent enough, and that adding LiDAR wouldn't be enough to achieve the reliability required. But I don't know, I don't really work in that field so maybe engineers who have more experience with self driving might say otherwise.
It is easy to underestimate how much one relies on senses other than vision. You hear many kinds of noises that indicate road surface, traffic, etc. You feel road surface imperfections telegraphed through the steering wheel. You feel accelerations in your butt, and conclude loss of traction from response of the accelerator and motion of the vehicle. Secondly, the human eye has much more dynamic range than any camera. And is mounted on an exquisite PTZ platform. Then turning to the model -- you are classifying obstacles and agents at a furious rate, and making predictions about the behavior of the agents. So, in part I agree that the models need work, but the models need to be fed, and IMHO computer vision is not a sufficient sensor feed.
Consider an exhaust condensation cloud coming from a vehicle's tail pipe -- it could be opaque to a camera/computer-vision system. Can you model your way out of that? Or is it also useful to do sensor fusion of vision data with radar data (cloud is transparent) and others like lidar, etc. A multi-modal sensor feed is going to simplify the model, which in the end translates into compute load.
> I've always wondered if Tesla's issues with FSD were a sensor problem or an intelligence problem
Even if it’s an intelligence problem, it’s possible that machine intelligence will not get to the point where it can resolve anytime soon, whereas more sensors might circumvent the issue completely. It’s like with Musk’s big claim (that humans use camera only to drive); the question is not if a good enough brain will be able to drive vision-only, but if Tesla can make that brain.
maybe? But also LiDAR just gives a more complete picture of what is around the car. I think this is supported by how many miles waymo cars run unsupervised vs Tesla.
I am skeptical that tesla has this solved but interested in seeing how it goes when as they move to expand their robotaxi service.
Some problems are simply undecideable: if for identical inputs the desired output varies wildly, you simply need more information. There is no algorithm that will help you.
Sensors or intelligence, at the end of the day it’s an engineering problem which doesn’t require pure solutions. Sometimes sensors break and cameras get covered in mud.
The problem is maintaining an acceptable level of quality at the lowest possible price, and at some point you spend more money on clever algorithms and researchers than a lidar.
Don't you have the same issue when you hire an employee and give them access to your systems? If the AI seems capable of avoiding harm and motivated to avoid harm, then the risk of giving it access is probably not greater than the expected benefit. Employees are also trying to maximize paperclips in a sense, they want to make as much money as possible. So in that sense it seems that AI is actually more aligned with my goals than a potential employee.
Whether driven by fear or by their model weights or whatever, I don't think that the likelihood of an AI agent, at least the current ones like Claude and Codex, acting maliciously to harm my systems is much different than the risk of a human employee doing so. And I think this is the philosophical difference between those who embrace the agents, they view them as akin to humans, while those who sandbox them view them as akin to computer viruses that you study within a sandbox. It seems to me that the human analogy is more accurate, but I can see arguments for the other position.
Sure, current agents are harmless, but that's due to their low capability, not due to their alignment with human goals. Can you explain why you'd view them as more similar to humans than to computer viruses?
It's just in my personal experience, I ask AI to help me and it seems to do it's best. Sometimes it fails because it's incapable. It's similar to an employee in that regard. Whereas when I install a computer virus it instantly tries to do malicious things to my computer, like steal my money or lock my files or whatever, and it certainly doesn't try to help me with my tasks. So that's the angle that I'm looking at it from. Maybe another good example would be to compare it to some other type of useful software like a web browser. The web browser might contain malicious code and stuff, but I'm not going to read through all of the source code. I haven't even checked if other people have audited the source code. I just feel like the risk of chrome or Firefox messing with my computer is kind of low based on my experience and what people are telling me, so I install it on my computer and give it the necessary permissions.
Sure, it's certainly closer to a browser than a virus. But it's pretty far from a human and comparing it to one is dangerous in my opinion. Maybe it's similar to a dog. Not in the sense of moral value, but rather an entity (or something resembling an entity at least) with its own unknowable motivations. I think that analogy fits at least my viewpoint, where members of the public would be justifiably upset if you let your untrained do walk around without a leash.
Its been pretty well documented that LLMs can be social engineered as easily as a toddler. Equating the risk to that of a human employee seems wrong. I'm sure the safeguards will improve, but for now the risk is still there.
An AI has no concept of human life nor any morals. Sure, it may "act" like it, but trying to reason about its "motivations" is like reasoning about the motivations of smallpox. Humans want to make money, but most people only want that in order provide a stable life for their family. And they certainly wouldn't commit mass murder for a billion dollars, while an AGI is capable of that.
> So in that sense it seems that AI is actually more aligned with my goals than a potential employee.
It may seem like that but I recommend you reading up on different kinda of misalignment in AI safety.
