Getting to the point where these headlines depress me. I just wish they would stop getting better. I don't know where my career is gonna be in a few years.
If you, like me, don't like the idea of your standard of living dropping to that of even just the mean human being on earth, I find it extremely painful to watch people justifying their way around not trying absolutely anything to raise everyone to at least our current level. Increasing productivity is demonstrably such a way, while many other experiments are so far just that: Experiments + wishful thinking.
If that merely means realigning/cutting current jobs (a process, that is ongoing from the start of human civilization itself, which brought us prosperity and why the fuck would it stop now) to me it's a moral obligation to deal with that at some other level.
There is tons to do here, certainly including how we will do redistribution better, and quickly, etc. Let's get to it.
And do what exactly? Subscribe for corporate AI brain implants? How does that solve inequality?
Also, thinking that you can bring low standards of living up to be on par with high in the current political landscape is a bit like that early Soviet space era promise about blooming apple trees on Mars.
It is guaranteed that they can only become equal by lowering the high.
There's hardly any work you can think of which can't be done faster / beter with ai assistance, when your role is of reviewing and directing. If you have an anti-example, would like to hear.
> There's hardly any work you can think of which can't be done faster / beter with ai assistance
True, and someone needs to be the creative brain behind the decisions. AI can help you implement. When I say help, I mean literally help because one-shotting and vague prompts can get you only so far, usually with a lackluster result. While AI is good at analyzing solutions, and finding out holes in one's thinking, ultimately, it is some creative actor that needs to understand the bigger picture to evaluate trade-offs, understand scope creeps, and spot overengineered implementations. For now that actor is a human.
> If you have an anti-example, would like to hear.
In my personal experience, especially with greenfield projects, smarter models tend to overengineer the solutions. However, I haven't used Fable and Astra models, maybe they are better at creative tasks without overengineering.
Here's where I think the issue is; they are trained to solve a problem. Not how, just whether or not they did.
Example: I asked Luna to use parser combinators to parse an Excel sheet that was represented as sparse triples (row, column, data). It imported the library and wrote spaghetti if-statement soup to get it to work. I asked Astra to fix it and it just refined the spaghetti slightly. I was able to browbeat Astra into actually using the library to complete the task. Was it faster than me doing it by hand? Probably. Was it more frustrating? Way more.
And every time I review vibe code it's always the same. Bespoke functions everywhere, no greater themes or ideas. No bigger picture. Your code can't support much if it has no central themes. You can probably one-shot a three js game to post on r/singularity for updoots. Not real code though.
I feel like the optimal way to use an LLM is to code until you feel like the rest of a problem is trivial and then you hand it off. And sometimes they still erase my code and add their own style lol
If your job is/you enjoy writing the code and solving technical challenges then yes this changes very heavily and AI will do this more efficiently than a human.
But if your job is designing systems and implementing solutions and coming up with good code along the way then I don't see AI getting anywhere close to making you obsolete in the foreseeable future.
I personally don't enjoy writing C++ but I really enjoy solving problems.
"Am I arguing against the shuttle loom!?"
Then I realize that the shuttle loom led to the rise of unions because of unfair treatment in factories and realize that we have a _long_ way to go.
The main argument is that it's too early too judge it and at this point basically NO industrial process is sustainable.
In 1840 ~70% of the population was in agriculture. That is now ~2%. Things change.
The truth is despite these very impressive improvements, most impressive work done by agents require many iterations running in a loop, with tens of thousands of dollars in API pricing. And it's still far from being always reliable. Somewhere along the way hardware will get better, energy will be cheaper, the market will be flooded by chips. There a physical world issues that limit all of these for now, thank god. I think 5 years is a good number.
The real damage is that enterprise work became unbearable. Slop code with slop code review, and overly verbose emails with repetitive presentations. And on the other hand, I now enjoy "coding" for myself like I'm 16 again. All I want to do is sit at home and build apps for myself and family. I barely go to work
I've never been a professional, but I've been coding for nearly 30 years as an amateur, and I've "written" more code in the last year than the previous 29, and it was all tooling for my very non-tech small business. It's cut HOURS out of my week, and it's all software I could not have afforded to pay developers for. But with Lovable, just describe it and iterate.
What IS going to die is software as a service. I've cancelled hundreds of dollars a month of subs and rolled my own better tooling.
People overestimate the change in the short term and underestimate the long term. Timelines are hard.
Also predicting the first victims is harder - I don't know many that thought pure mathematics would be high on the list.
The history of the self driving car is apt to repeat as well. I remember thinking "2015, 2018 maybe at the latest." But as 2015 (and 2018) came and went, expectations were tempered.
We will see shortened development cycles, and maybe even orders of magnitude shorter development cycles.
But hard problems remain hard (there are only so many chips, materials research and biology are difficult problems). Manufacturing remains a choke point.
I'm more concerned that the absolute worst persons on the planet are in charge of many of these technologies. If those that are in charge are more concerned about allocating resources and power in their favor, the more dismal the future of all humanity will be (including for those in charge, but they can't see that due to self-interest bias)
Now we're clearly entering a world where humans can be removed from the intelligent-problem-solving part of the problem.
How many more parts of problems are there?