gkcnlr
Joined 3 years ago
Joined 3 years ago
But many failed to see that this premise would result in one way, which is actually what's happening right now: Growing outsourcing (especially observable in SWE due to its nature) purely driven by market competitiveness (inevitable dynamics of neo-liberalism) and hype surrounding it (leaving no room for other players in the game to choose any other way) which can no longer be sustained through traditionally accepted way of hiring new people who can master new inventions. Although, I highly doubt this is also sustainable.
Eventually, it was well expected for people to stop believing in a system which offers very little foreseeable success of accomplishment and thus, doing manual/repetitive labor is far more preferable for people.
New inventions eventually benefiting the society has always been accompanied by new jobs being created on top of those inventions, a culture of trust built upon mastering those inventions and a market being formulated around demand and supply cycle for those inventions. It is different now[1], because we neither see incentives being created for humans to operate/manage AI-led jobs (because utilizing such a thing is far more unfavorable than outsourcing due to economic incentives, duh) nor a foreseeable market stability for ever such thing to occur.
[1]: https://www.amazon.com/Technofeudalism-Killed-Capitalism-Yan...
[1]: https://www.electronicdesign.com/technologies/embedded/artic...
[2]: https://www.reuters.com/technology/nvidia-supplier-sk-hynix-...
[3]: https://arxiv.org/abs/2606.24616
[4]: https://www.apollo.com/insights-news/insights/daily-spark/No...
> i f*king detest reading LLM-generated material
Thats quite a dichotomy right there.
I just pray open weight wins at the end and people actually utilize their ai and own their ai.
But another side effect of this process is if people stop believing in the accrual of the knowledge which will lead them to nowhere (use cases for those information becoming irrelevant by the time you start using it), justifying this mentally exhausting practice becomes really hard. And I don't mean this on learning static information but also on how to define and build a meta-cognitive framework to systemically learn things.
I don't quite get the "lifelong learning" approach, since lifelong learning must usually be accompanied with organic evolution of the information space. Employers wont pay you because you are a lifelong learner, they'll pay you to actually fix a problem you provide a solution for. And that solution isn't guaranteed by the knowledge you possess and doesn't usually qualify the marginal cost of being a lifelong learner.
One could brain storm on these issues by critiquing the premise of this book: https://www.amazon.com/100-Year-Life-Living-Working-Longevit...
1- Do a particular task with great capability (due to its constrained, limited scope) 2- Do it in such a way, it integrates gracefully in your workflow without ever requiring you to know you are using an LM.
There is a difference between outsourcing your workflow to AI and actually utilizing it.
Check this: https://www.distillabs.ai/blog/we-benchmarked-12-small-langu...
https://www.microsoft.com/investor/reports/ar25/index.html#
I can only think that they do not even care about Windows anymore, let alone Notepad...
Once you understand the sole winner in this hype is the one who'll be brutally scraping every bit of data, whether it's real-time or static and then refining it to give it back to you without your involvement in the process (a.k.a, learning) you'll come to understand that the current AI by nature is hugely unfavorable to mental progression...