You can only consider that a problem if you think a filmmaker watching someone's else's movie and deliberately learning something from the experience is a problem.

Of course AI artists should be trained on copyrighted material and be able to generate exact replicas if asked to by a human. There are plenty of fair-use scenarios where you can use copyrighted content, like parody and commentary. It is how the human uses the content generated by AI that makes it violate our laws, not the fact that the content can be generated.

No US courts are going to shut down such a productivity enhancing tool. If we are fine destroying 95% of farming and 65% of factory jobs because of automation, our society will be fine with destroying 90% of artistic jobs because of AI.

Shutting down innovation to protect jobs is just about the dumbest idea there is. We should pray that 90% of today's jobs are destroyed over the next 50 years, so people can either move into more productive jobs (like the last 100 years of progress) or live lives with far more leisure (an even better outcome IMHO).

The court case cited was not split or confused over the fact that what you just typed is false from a legal perspective (although you can obviously still have that opinion). The ruling was clear as day on this matter, even though there was more nuance when it came to the legality of how training material was obtained.

This all comes down to how well universities adapt the existence of LLMs. Bridges didn't start collapsing after calculators were invented because engineers couldn't do arithmetic anymore. My daughter's middle school already makes sure all her graded writing is done in class on paper or on monitored devices. My 11 year old already uses LLMs (and Khan Academy and ProWritingAid) to improve her writing, but assessments are always done in a way that makes LLM use impossible.

The comment is tongue and cheek, not a literal description of the current job market statistics. The author feels extensive use of AI by college students will damage their employability in the future.

That is why they use mainframes as their example. It implies SaaS will stick around and will be used for some of the most sensitive computing with the highest reliability needs (like mainframes today), but will be mostly irrelevant (or at least unseen) to most people working in the office. It also would mean that about 5-10% of IT spending would go to SaaS while the rest would go to the "Business Agents."

That is of course if their predictions hold water.

If we use your email vs fax machine analogy, SaaS sales will continue to grow through the end of 2026, and then drop by 60% by 2030 and be at 5% of current spending by 2040. That would represent how fax machine spending continued after the Internet entered the public domain in 1993.

I'm pretty sure if 90-95% of the SaaS market is wiped out over the next 10-15 years, that would align well with the predictions made in this article. Anyone hoping SaaS sticks around better hope the continued use of fax machines isn't an apt analogy to use.

You don't need a degree to learn anything. I personally believe the vast majority of people don't have the discipline or desire to learn a craft as difficult as writing software on their own, but plenty of people can learn to be an amazing software engineer without a degree. There is absolutely no knowledge that only exists in proprietary secretive knowledge bases controlled by universities that cannot be learned outside of college.

I would expect this article to not spread misinformation. This isn't a new trend. In January 1993 unemployment for all workers was 48% higher than recent grads, but by January 2003 it was 17%. The gap spiked again after the great recession, but unemployment for recent grads has been the same or higher than all workers since late 2018. Before the pandemic or ChatGPT's release (the main culprits in the article).

This has little to do with anything on your list. This is the simple result of too many of the jobs being created over the past decade have been low wage jobs that don't require college educations or much experience. This has dropped the unemployment rate for all young workers by 15% over the past 30 years while unemployment for recent grads rose 30%. The economy overall has gotten better for the bottom 20% over the past few decades (more low wage jobs and a larger social safety net) but for the middle 60% it has been slowly declining for about 50 years.

I wouldn't hold my breath, because this entire article is rubbish. This isn't some new phenomenon. Unemployment for recent college graduates and for all workers have been neck and neck since 2014, and unemployment for recent grads has been slightly higher almost every month from late 2018 to mid 2022, when it started to grow noticeably higher than all worker unemployment. This isn't because of a post-pandemic cooling off period or AI, it is a trend that started in the 1990s with a hiccup just after the great recession that corrected itself in about 5 years.

The more interesting research on this topic that I've read claimed smarter people make better decisions because they are better at deferring decisions. They are comfortable with the uncertainty because of their confidence in their abilities. There is always a tradeoff between making the decision later when you have more information and making it earlier so you have more time to plan your execution. If you need more time to plan execution, you need to make decisions earlier. And your decisions will suffer because of this.

I don't think this research qualifies as "no shit." I think it qualifies as very poorly done and potentially wrong. Or at least the summary makes it seem that way. They asked these individuals a prediction question that existing knowledge significantly helps with. Existing knowledge that many if not most well educated people will have and people with less education probably won't. I know what the average life expectancy is for someone in the US because I have read it. I know that your life expectancy is different when you are 40 because you have already avoided dying for 40 years, and I understand this in large part because of the math education I have had and because I've read actuarial tables before.

If you wanted to test predictive power that is based on innate intelligence instead of education, you need to ask for predictions that a well read adult wouldn't have a significant advantage in making.

It is not exactly the same because of the personal disruption a firing has on someone, but they are pretty close. I have been laid off once, but it was in a strong job market so I found another position with a significant pay increase within a month. Being laid off right now if far worse because it isn't as easy to find an equivalent position.

When looking at the economy overall, not hiring 30 people is the same as firing 30 people. Both result in 30 less jobs in the market. Considering the US population from age 18-65 is growing very slowly right now, technologies that slow the growth of new jobs could be very beneficial. But a shrinking job market, whether from layoffs or a lack of hiring after employee attrition, would still cause some significant damage to peoples' lives.

You two must be using different terminology, because there is no chance you have gone 10 years without seeing a single gas pump at a gas station down for maintenance. Gas stations around me have about 8 - 16 pumps, and the average gas pump is down for maintenance 2-4 hours per week. That means about 20% of the time at least one pump is down for maintenance on average (not accounting for times when more than one pump is down at a time), which aligns with what I would have guessed if I hadn't googled it (I guessed 25%).

My favorite example of this was a mechanical engineering graduate student with a focus on thermodynamics I lived with in college. He bought a large box of FlaVorIce and put it in the freezer expecting them to freeze overnight. I told him they wouldn't freeze quickly if you don't spread them out (like my farmer father taught me as a kid), and he sarcastically stated that a f*ing thermodynamics engineer could figure out how to freeze water. Fast forward to the morning, and the 10 freeze pops I separated from the box were the only ones frozen. The rest of the box was barely even cold.