It's already writing interesting magazine articles. You just don't recognize them as being written by LLMs.
Correct me if I misunderstand your implication, but it seems to be "I have seen poorly-written articles with obvious LLM tells and factual mistakes, therefore LLMs are incapable of writing good articles". It's the same logic people use to say things like "criminals are so stupid, just look at these prisoners", not realizing that their sample selects for incompetence.
There's no incentive whatsoever for any institution to credit an LLM for an excellent article, and in fact there are many incentives to avoid disclosure of any AI use. Typing "write me an article about $TOPIC, make no mistakes" into ChatGPT yields quite different results than the LLM-driven content generation pipelines major institutions have already been using for well over a year now.
Media distribution companies are like health insurance companies (though not to the same degree) in that they can't stop themselves from squeezing their customers to the absolute limit. Majority opinion and the definition of ethical consumer behavior can change very quickly under those conditions.
For some people, it is. There is a segment of non-religious people who do treat the current scientific consensus as gospel and moralize adherence with typical religious in-group/out-group dynamics, despite not really understanding or engaging with any of the actual material. I disagree that relying on scientific consensus is inherently religious, though.
The former phrasing has a sense of inevitability to it, for one. It masks the fact that people created systems to exploit weaknesses in human psychology. Evolution led to a bias toward unsubstantiated pattern recognition and simple explanations for complex systems, not organized religion.
If religion has become "a part of our brain" then so has slavery, rape and the divine right of kings, for the same reasons. Yet no one argues that we just "have to learn to live with" the latter.
It isn't evolution, at least not in any genetic sense, it's simply culture. People believe religion is an immutable fact of life and the only possible source of meaning and morality when their culture teaches them as much. But there are cultures where religion matters far less than it does in, say, the US, where people get along just fine.
You sort of get at this with your biggest question, but who's to say the new ventures will require human labor at all? It seems far more likely that any new types of jobs going forward will be designed intentionally so that agents can complete them, given the strong incentives. I agree that companies will scale out, but all signs point to them scaling out their token spend rather than hiring.
So we both agree that it really comes down to a single question
"Can humans do any single thing better than AI"
If its true then companies will scale out just enough that it requires 40 hours per week of our labour.
> I agree that companies will scale out, but all signs point to them scaling out their token spend rather than hiring
I disagree. My question back to you is, if you were a company, why would you not scale out to have as many humans as possible? You'd do it because if you don't, your competitors have an advantage over you
It's not a given that employing more humans is an advantage. Ignoring the expenses of salaries, benefits etc, more humans means more chances for miscommunication. When one person has a strong product sense and vision, and is able to couple that with agents that can scale up and precisely interpret the human-created spec, I have seen this setup lap traditional dev teams repeatedly.
The average engineer is... average. I have watched the frontier models raise the bar for hiring quite high over the last year, to the point where it becomes a real question whether most companies will need more than one strong employee per job function in the next few years.
The way processes are shifting, the companies with the most staff are the most likely to be outcompeted by smaller teams leveraging agentic development.
I suspect we haven't seen anything close to the worst of layoffs at large corporations yet. The limiting factor is that it takes a while for a massive ship to turn, but inertia will only go so far.
As for what humans can do better than AI, there's one thing: assume liability. Lately, however, it seems that no one with the power to do so is particularly concerned about holding anyone accountable for AI-driven mistakes.
I wish I could justify your optimism, as I expect life to deteriorate materially for the typical member of the working class in countries without strong safety nets. But I just don't see it leading to more hiring when even the current frontier models are more capable across most dimensions than the average white-collar worker.
When it's not performant enough or has bugs, that user will spin up a new session and ask the AI to improve the performance or correct the bugs, with a description of what they see. The frontier models are already capable of investigating thoroughly based on vague descriptions of behavior.
When a bug creates damage, the user is responsible. But like at most companies pre-AI, it is not prudent to focus too much on the individual. There should be a post-mortem that determines which processes were weak, and these days that can be fed directly into a harness for the LLM to improve your tooling or testing to avoid that class of problem.
Assuming they are the "same people" sounds an awful lot like the Goomba fallacy. There are plenty of people who are consistently against the concept of intellectual property.
Even that is not a differentiator. LLMs are perfectly capable of following writing style guides, it's just that their default writing style has tells and clichés, and most people don't care enough to instruct it to deviate from that style with any specificity.
Hard work is not inherently virtuous or productive. Life isn't fair, but the cause in this case is that support system availability and competence at creating valuable work output vary based on factors largely outside of your control. The injustice is not that all "hard work" is not rewarded equally.
Correct me if I misunderstand your implication, but it seems to be "I have seen poorly-written articles with obvious LLM tells and factual mistakes, therefore LLMs are incapable of writing good articles". It's the same logic people use to say things like "criminals are so stupid, just look at these prisoners", not realizing that their sample selects for incompetence.
There's no incentive whatsoever for any institution to credit an LLM for an excellent article, and in fact there are many incentives to avoid disclosure of any AI use. Typing "write me an article about $TOPIC, make no mistakes" into ChatGPT yields quite different results than the LLM-driven content generation pipelines major institutions have already been using for well over a year now.
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