tortasaur

That's most likely exactly why they did it. It's desperation.

Goofus and Gallant

Consumers have rejected AI art when they are aware it's AI art. With how it's been flooding the platforms, most people listening to algorithmic mixes have likely unknowingly been exposed to AI-generated music.

It's already prevalent in gyms, restaurants, etc too. I can tell because I know what I'm listening for, but the average person isn't thinking anything of it. It certainly passes for serviceable background music.

Most people just don't care all that much about the quality of prose. Companies have built thorough pipelines around getting LLMs to produce specific styles of prose well, though. It's just not the kind of task you can one-shot.

Did it? The article says they discovered the compromise on October 25th.

They may want to unionize now, but I don't see that working out now that tech workers no longer have leverage. Should have done it when there was a worker shortage, not when execs are carrying around their nuclear "fire everyone" footballs, looking for a good excuse.

If anything, I see us moving even further away from professional licensing. The field's rate of change was the main reason behind a lack of any professional body, and that's only accelerating. I expect compliance audits like SOC2 to actually get teeth, though, and for organizations to be expected to actually fix issues instead of the usual security theater.

Yes, from the title referencing MMM, I had hoped this would be the angle explored. My experience is that the optimal number of engineers for most projects is currently one, as the cost of creating bespoke automated dev workflows decreases. Even for larger, more complex projects, I suspect one person plus time spent codifying knowledge and automating processes will outperform a larger team.

Sure, but those goalposts have shifted from "you have a conditional freedom entirely dependent on an external service". If you run an open-weight model on your machine, you are not dependent on an external service. The model will not change, disappear, or become inaccessible.

This argument would be more compelling if self-hosted, open-weight models didn't exist.

Should it be acceptable to scam stupid people? That's sort of the impression I'm getting from your framing.

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.

Unless it doesn't kill us all, but post-scarcity is paired with hoarding of most resources by the select few that control a surveillance state and autonomous police force.

Probably not exactly as worried, but given software development was the first industry seriously targeted by the labs, and given the mission of nearly every startup these days is "automate the entire workflow of X knowledge work role with AI", I can see why they would still find the near-term outlook bleak.

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.

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.

By that logic, you may as well say evolution led to TikTok. "Religion taps into human psychology" seems more accurate.

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.

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.

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.

We didn't need AI to destroy those

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.

The key word in the quoted sentence is "necessarily". Hard work is generally a prerequisite for producing valuable output, but you can work extraordinarily hard and accomplish nothing.

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.

Value and price are coupled because people are willing to pay more for goods and services that are more valuable. The point I'm making is that hard work doesn't necessarily lead to valuable output.

Some of the most miserable people I've known have been the hardest workers. One example: a friend of mine tried to start a freelance career, but had little experience in the field and rarely reflected on what his clients' needs were. He would work very hard to satisfy his own curiosity about their problems, eschewing deadlines and leaving little time to review his work before delivery. He was among the hardest-working people I've met, yet one of the least accomplished. No one would keep working with him for more than a month.

Correctly identifying which work needs to be done and how is more important than actually working hard. Sisyphean work is not virtuous, no matter how much effort is put forth.

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.

featuring MC Shkreli

maybe something like "make hardware as quickly as software" would be clearer

I don't doubt they will cost more than the Pixel line, but I'm somewhat skeptical that the hardware will be higher quality. Perhaps my idea of what Motorola is capable of is outdated.