I find it useful. It has been a big solve from a motivation perspective. Getting into bad API docs or getting started on a complex problem, it's easy to have AI go with me describing it. And the other positive is front end design. I've always hated css and it's derivatives and AI makes me now decent.
The negatives are that AI clearly loves to add code, so I do need to coach it into making nice abstractions and keeping it on track.
In response to having a community and building a brand. This is not necessarily human anymore. Most famous people are not someone you will actually meet. Plenty of people do meet them, but nowhere near the amount that composes their fans.
And we have AI generated influencers now, ex. https://www.instagram.com/imma.gram, so why wouldn't people care about an AI the same way they do about people they never meet?
I'm an LLMs are being used in workflows they don't make sense in-sayer. And while yes, I can believe that LLMs can be part of a system that actually does think, I believe that to achieve true "thinking", it would likely be a system that is more deterministic in its approach rather than probabilistic.
Especially when modeling acting with intent. The ability to measure against past results and think of new innovative approaches seems like it may come from a system that may model first and then use LLM output. Basically something that has a foundation of tools rather than an LLM using MCP. Perhaps using LLMs to generate a response that humans like to read, but not in them coming up with the answer.
Either way, yes, its possible for a thinking system to use LLMs (and potentially humans piece together sentences in a similar way), but its also possible LLMs will be cast aside and a new approach will be used to create an AGI.
So for me: even if you are an AI-yeasayer, you can still believe that they won't be a component in an AGI.
You can make a separate model for the task, which is based on well chosen features and calibrated from actual data. Then the LLM only needs to generate the arguments to this model (extract those features from messages) and call it like a MCP tool. This external tool can be a simple Sklearn model.
I imagine it has to do with vulnerability. When you are asking for something or sharing something, being turned down feels personal. When doing it for someone else, it's no big deal if they say no.
Legalizing it is also a total war against the suppliers in most cases (just economically instead of with guns). By legalizing, you usually replace the current suppliers with ones you like.
I think this is a real challenge for everyone. In many ways potentially we need a restart of a wikipedia like site to document all the valid and good sources. This would also hopefully include things like source bias and whether it's a primary/secondary/tertiary source.
This is pushing the burden of proof on the society. Basically, asking everyone else to pitch in and improve sources so that ai companies can reference these trust worthy sources.
Outsourcing due diligence to a tool (or a single unified source) is the problem, not the solution.
For example, having a single central arbiter of source bias is inescapably the most biased thing you could possibly do. Bias has to be defined within an intellectual paradigm. So you'd have to choose a paradigm to use for that bias evaluation, and de facto declare it to be the one true paradigm for this purpose. But intellectual paradigms are inherently subjective, so doing that is pretty much the most intellectually biased thing you can possibly do.
I've seen a certain sensationalist news source write a story that went like this.
Site A: Bad thing is happening, cite: article Site B
* follow the source *
Site B: Bad thing is happening, cite different article on Site A
* follow the source *
Site A: Bad thing is happening, no citation.
I fear that's the current state of a large news bubble that many people subscribe to. And when these sensationalist stories start circulating there's a natural human tendency to exaggerate.
I don't think AI has any sort of real good defense to this sort of thing. 1 level of citation is already hard enough. Recognizing that it is citing the same source is hard enough.
There was another example from the Kagi news stuff which exemplified this. A whole article written which made 3 citations that were ultimately spawned from the same new briefing published by different outlets.
I've even seen an example of a national political leader who fell for the same sort of sensationalization. One who should have known better. They repeated what was later found to be a lie by a well-known liar but added that "I've seen the photos in a classified debriefing". IDK that it was necessarily even malicious, I think people are just really bad at separating credible from uncredible information and that it ultimately blends together as one thing (certainly doesn't help with ancient politicians).
When I'm working with new developers I always have to convince them to simplify their setup. Why are we on autoscaled, pay by the query infra when we are serving a few people. Then they complain how expensive it is. I had someone tell me that their costs were $1500/mon when they were in demo stages. I asked them why they aren't hosting on a single small server for $20. And they responded that it didn't matter because they were using free credits.
Except that those free credits will go away and you'll find yourself not wanting to do all the work to move it over when it would've been easier to do so when you just had that first monolith server up.
I think free credits and hyped up technology is to blame. So, basically a gamed onboarding process that gets people to over-engineer and spend more.
I'm not totally following the cost analysis from some of these comments. I agree that there's no reason to make your architecture overly complicated, especially if it will cost dev time. I'm not saying this as someone that does cloud very much. We normally do a single server. More as an outsider looking in.
If you load balance 4 smaller ec2 instances vs a larger one, it's significantly cheaper. There is overhead when you run the same app on 4 machines, but not as much as people think. An idle system uses 300-600mb of ram, leaving roughly 3.5gb vram usable, per machine.
4 t3.medium instances are about $119/mo, vs. an equivalent t3.2xlarge which is $238/mo.
Also people will often say Digital Ocean is cheaper, but they compare it to non-equivalent AWS services. A Digital Ocean VPS compared to an equivalent EC2 is about the same.
So I understand when people argue about complicating architecture or optimizing prematurely, but I don't think the math on actual server cost really checks out. I would prefer to do dedicated hosting, but Hetzner doesn't have that in the U.S.
In Poland, they have a "universal child benefit" that pays a stipend for every child you have.
They do pay for it and it is expensive, but apparently it made a large reduction in child poverty, so that's a win.
From my understanding, it also reduced women in the workforce and reduced investment in childcare infrastructure since more mothers were then taking care of children at home.
So this is possible, it just depends on what you want to incentivize.
The US has a similar thing with the child tax credit. It looks like Poland pays out the equivalent of about 220 a month while the child tax credit pays the equivalent of $180 per month. If you only count the refundable portion it is $140. Relative to the cost of living its worse, but the concept seems similar.
The negatives are that AI clearly loves to add code, so I do need to coach it into making nice abstractions and keeping it on track.
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