I think it's not that difficult to see why a technology that will likely trigger widespread unemployment during a cost of living crisis, an arms race with China, along with all the alignment concerns, might not be hugely popular with the public.
Maybe I'd be a bit more optimistic if someone could explain a realistic economic scenario for how we're going to transition into our utopian abundant future without a depression or a revolution.
Pretty simple: The centaur of big-tech/government will pay people not to eat them. (i.e. UBI)
The incentives are, how you say, aligned.
The deeper issue I see is the psychological crisis for a species who believes it doesn't deserve to live if it isn't performing economically valuable activity, entering a world where it is unprofitable for it to be employed. (If I were the AI, I'd come up with some kind of fake jobs to keep the humans sane.)
UBI is just a massive extension of the welfare state. Governments can’t afford the current welfare spending, so where is the money going to come from? What do you think is going to happen to the markets when a large amount of the middle classes get laid off and can’t afford to pay their mortgages? What do you think is going to happen to the tech companies built on advertising to consumers when no-one has disposable income?
This assumes costs won't drop. I'm not an economist but the theory I hear is that there will be massive cost savings at every single point in the supply chain. So the same way your money is now amplified by AI in code, eventually with robotics that is the case in every field.
This sounds a lot like UBI is an replacement for salary for many jobs.
UBI funding doesn't come from thin air, every job has to pay for itself, even if it's just UBI. Mixed costing wont hold up because every market, every company, every worker acts on it's own. So companies must pay extra plus UBI, which will only lower prices if the overall salary gets lower at the end of the day.
In my world UBI should be an psychological tool that empowers people. The way UBI is usually discussed, it's a magical solution to a very hard, incomprehensible problem and the simplicity of it just throws 70% of humanity under the bus. It's literally the same we have now, the only difference is that now everyone can claim that everything is fair because of UBI.
The current group of oligarchs pretty clearly disagrees with your perspective on their incentives. The big tech era has made people like Elon and Bezos some of the richest people in history and they have used their power for negative wealth redistribution. They give essentially none of their money away to the masses and instead use their power to weaken existing social programs and wealth distribution systems. I can't see those people suddenly doing a complete 180 as they amass even more wealth and power.
Agreed, this article seems to be dancing around the point: WHY are the Gen Z hating AI? We have a political ruling class that is all too willing to throw everyone under the bus if they aren't living up to some expectation, and the political class is being driven by an economic ruling class that largely seems to have the same opinion.
Gen Z would likely have a very different opinion if their basic living necessities were available to them.
> a realistic economic scenario for how we're going to transition into our utopian abundant future
One aspect almost certainly has to be data centers being run as utilities. That forces transparency, resists monopolization and gives public commissions a say in e.g. expansion.
> This is speculative, but I suspect that if we dropped one of the latest, most capable open-weight LLMs, such as GLM-5, into a similar harness, it could likely perform on par with GPT-5.4 in Codex or Claude Opus 4.6 in Claude Code.
Unless I'm misunderstanding what's being described here, running Claude Code with different backend models is pretty common.
It is more common now to improve models in agentic systems "in the loop" with reinforcement learning. Anthropic is [very likely] doing this in the backend to systematically improve the performance of their models specifically with their tools. I've done this with Goose at Block with more classic post-training approaches because it was before RL really hit the mainstream as an approach for this.
It's a good question, I've wondered that myself. I haven't used GLM-5 with CC but I've used GLM-4.7 a fair amount, often swapping back and forth with Sonnet/Opus. The difference is fairly obvious - on occasions I've mistakenly left GLM enabled running when I thought I was using Sonnet, and could tell pretty quickly just based on the gap in problem solving ability.
The harness if anything matters more with those other models because of how much dumber they are... You can compensate for some of the stupidity (but by no means all) with harnesses that tries to compensate in ways that e.g. Claude Code does not because it isn't necessary to do so for Anthropics own models.
I've found that on some projects maybe 70-80% of what can be done with Sonnet 4.6 in OpenCode can be done with a cheaper model like MiMo V2 Pro or similar. On others Sonnet completely outperforms. I'm not sure why. I only find Opus to be worth the extra cost maybe 5% of the time.
I also find OpenCode to be drastically better than Claude Code, to the extent that I'm buying OpenRouter API credits rather than Claude Max because Claude Code just isn't good enough.
I'm frankly amazed at what OpenCode can do with a few custom commands (just for common things like doing a quality review, etc.), and maybe an extra "agent" definition or two. For many projects even most of this isn't necessary. Often I just ask it to write an AGENTS.md that encapsulates a good development workflow, git branch/commit policy, testing and quality standards, and ROADMAP.md plus per milestone markdown files with phases and task tracking, and this is enough.
I'm somewhat interested in these more involved harnesses that automated or enforce more, but I don't know that they'd give me much that I don't have and I think they'd be tough to keep up with the state of the art compared to something less specific.
I've been playing with the open models since the original llama leak. They're getting better over time, are useful for tasks of moderate complexity and it's just cool to have a binary blob of knowledge that you can run locally without an internet connection.
However you should manage your expectations. Whatever the benchmarks say, you'll quickly realise they're not at all competing with Sonnet let alone Opus. Even the largest open weights models aren't really doing that.
Haven't really tried GLM5 much but I've used 4.7 quite a bit and it was pretty far from competing with Sonnet at the time, although I saw claims online to the contrary.
I would say thinking about the indended audience for your creative outlet is a good discipline - even if it's only one person. It often gives the project more of a focus which helps with motivation and makes it more enjoyable.
Honestly a lot of useful software is ‘unimportant’ in the sense that the consequences of introducing a bug or bad code smell aren’t that significant, and can be addressed if needed. It might well be for many projects the time saved not reviewing is worth dealing with bugs that escape testing. Also, it’s entirely possible for software to be both well engineered and useless.
I see a lot of people talk about 'insecure code' and while I don't doubt that's true, there's a lot of software development where security isn't actually a concern because there's no need for the software to be 'secure'. Maintainability is important I'll grant you.
Oh, finally someone who speaks the truth :D yeah, security su*ks everywhere. True. But when you grow a product with time, you fill the holes one by one when you start losing water. With AI, you will have so many holes in your 15 days made battleship, that... good luck putting it at sea, you can tank in the first minute. As Moltbook has shown. I don't have to find proofs (which I dont actually find). I just need a counter example and... first big vibe product I've seen, first gigantic security failure. Plain to see.
For sure, but I haven't written a single piece of software where security would ever be considered a factor. Not all software runs on the web, not all software deals with accounts etc.
I think this is too broad. If, for example, I get Claude to set up a fine tuning pipeline for rf-detr and it one shots it for me, what have I lost? A learning opportunity to understand the details of how to go about this process, sure. But you could argue the same about relying on PyTorch. Ultimately we all have an overarching goal when engaged in these projects and the learning opportunity might be happening at an entirely different level than worrying about the nuts and bolts of how you build component A of your larger project.
Maybe I'd be a bit more optimistic if someone could explain a realistic economic scenario for how we're going to transition into our utopian abundant future without a depression or a revolution.
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