Public discourse has focused a lot on artificial intelligence in the past few years. And even though the technology is hyped up and has plenty of supporters, there are lots of skeptics, too. People are worried about its (un)ethical use, environmental impact, effect on the job market, and more.
#1
Most AI models are built on massive amounts of copyrighted data taken without permission or compensation. The entire industry is basically built on the largest scale of intellectual property theft in history.
34points
#2
It's just reinterpreting what it finds on the Internet. GIGO (Garbage In, Garbage Out) still applies.
33points
#3
We are rapidly reaching a point where AI is training on AI-generated content. Because the internet is getting flooded with AI text, the new models are learning from the 'mistakes' of the old ones. It’s a feedback loop that leads to 'model collapse,' where the AI eventually becomes a distorted, nonsensical version of itself because it hasn't seen fresh, human-created data in months.
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31points
The AI industry is utterly massive. According to Statista, the market for AI technologies amounts to around $244 billion in 2025. It is expected to rise to $800 billion by 2030.
Naturally, this has many people wondering whether the investments are worth the actual value. Some folks are worried that the (over)investment that we’re seeing in AI companies and tools is akin to an economic bubble of sorts.
They argue that the AI tools that the public has access to right now are flawed, unreliable, and limited, often leading to far more work rather than less. In short, they argue that the tech is overhyped and not quite as great as major tech companies would have you believe.
Meanwhile, proponents believe that the technology is so fundamental and universal that it’s not going anywhere. From their point of view, it’s vital not only to invest in the tech ASAP, but also to adopt and integrate it into your workflows, no matter what you do.
#4
Not a tech worker, I do know people at higher levels in this push:
It’s all a gamble. The companies are using huge amounts of borrowed money to see if they can change what it is now into a gold mine that puts them in a position to capitalize on it for the next century or more.
And if the bubble pops? They file for bankruptcy and the banks are too large to fail. Which means we the people get to pick up the tab.
It’s all a gamble. The companies are using huge amounts of borrowed money to see if they can change what it is now into a gold mine that puts them in a position to capitalize on it for the next century or more.
And if the bubble pops? They file for bankruptcy and the banks are too large to fail. Which means we the people get to pick up the tab.
22points
#5
That most people don't understand what AI is, even "tech" people.
AI is a very broad category. Everyone automatically assumes it is the mad-libs style LLM AI, however AI learning models have been around for a long time and do a variety of things. Things like your spam filter, predictive text, or your nav system's traffic avoidance, these are all variations of AI in the category of machine learning. These are tools which don't take jobs, they make our lives easier.
Then there are AI machine learning models that DO take jobs, but actually do so much better than a person can do. Like ones that examine components for defects. They can identify things people may miss far quicker. This allows for better quality and safer products.
AI is a very broad category. Everyone automatically assumes it is the mad-libs style LLM AI, however AI learning models have been around for a long time and do a variety of things. Things like your spam filter, predictive text, or your nav system's traffic avoidance, these are all variations of AI in the category of machine learning. These are tools which don't take jobs, they make our lives easier.
Then there are AI machine learning models that DO take jobs, but actually do so much better than a person can do. Like ones that examine components for defects. They can identify things people may miss far quicker. This allows for better quality and safer products.
21points
#6
It's causing very legitimate problems in the judicial profession. I work for courts and attorneys have attempted to use rulings that literally do not exist to help their argument. .
20points
However, a recent report by the MIT Media Lab/Project NANDA found that a jaw-dropping 95% of investments in generative AI have produced zero returns.
As the Harvard Business Review reports, while individuals are adopting generative AI tools, results still aren’t measurable at a profit and loss level in businesses.
#7
One dirty secret is that a lot of “AI” isn’t nearly as autonomous or intelligent as people think.
Many systems rely heavily on massive amounts of human labor behind the scenes: data labeling, moderation, cleanup, edge cases, and constant manual intervention. The public sees a polished model, but underneath there are thousands of low-paid workers correcting mistakes, filtering outputs, and patching failures in real time.
Another uncomfortable truth is that most AI products aren’t optimized for truth or long-term benefit. They’re optimized for engagement, retention, and revenue. If a model keeps users hooked, it’s considered successful even if it subtly reinforces bad habits, misinformation, or dependency.
AI isn’t “lying” to people, but the incentives shaping it are rarely aligned with human well-being. That gap is much bigger than most marketing admits.
Many systems rely heavily on massive amounts of human labor behind the scenes: data labeling, moderation, cleanup, edge cases, and constant manual intervention. The public sees a polished model, but underneath there are thousands of low-paid workers correcting mistakes, filtering outputs, and patching failures in real time.
Another uncomfortable truth is that most AI products aren’t optimized for truth or long-term benefit. They’re optimized for engagement, retention, and revenue. If a model keeps users hooked, it’s considered successful even if it subtly reinforces bad habits, misinformation, or dependency.
AI isn’t “lying” to people, but the incentives shaping it are rarely aligned with human well-being. That gap is much bigger than most marketing admits.
20points
#8
That most jobs are safe from it, but the corporate sector thinks they are saving money by reducing staff.
People are still needed to make ‘AI’ work. It doesn’t just know what you want.
People are still needed to make ‘AI’ work. It doesn’t just know what you want.
15points
#9
A ton of what is labeled as "AI" is just spreadsheets and algorithms that have existed for decades. Companies are calling anything done by a computer "AI" for marketing purposes. .
14points
We’d like to hear your thoughts, dear Pandas. You can share them in the comments below.
What are your thoughts about AI tech and the industry as a whole? Do you think it’s overhyped, or do you see it as the future? What are the biggest pros and cons of artificial intelligence tools that you’ve personally noticed so far? Let us know!
#10
If AI is a force multiplier, companies have two options:
1) reduce workforce to offset this performance gain and achieve the same amount as before with less people
2) keep the people you have and gain more market share by leveraging the labor you already have along with the force multiplier provided by AI.
It's telling that pretty much every company is choosing option 1. If it was everything people claimed it was, they would all be piling in to option 2 and trying to win more of the market. Instead, it's convenient cover to reduce workforce while keeping a nice PR story.
1) reduce workforce to offset this performance gain and achieve the same amount as before with less people
2) keep the people you have and gain more market share by leveraging the labor you already have along with the force multiplier provided by AI.
It's telling that pretty much every company is choosing option 1. If it was everything people claimed it was, they would all be piling in to option 2 and trying to win more of the market. Instead, it's convenient cover to reduce workforce while keeping a nice PR story.
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12points
#11
The RAM price hikes and raised prices on some products is just the beginning. You see, AI data centers consume TONS of power. The next crisis will be an energy shortage as we balance AI centers vs everyday life.
12points
#12
The consequence of people not hiring juniors as much due to Ai being able to handle much of the grunt work that they would typically do will be absolutely devastating ten to fifteen years down the line.
The old guard will retire and we'll suddenly have alot of senior devs with few people to manage. When it's their turn to leave... Well...
The old guard will retire and we'll suddenly have alot of senior devs with few people to manage. When it's their turn to leave... Well...
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11points
#13
CHANGE DEFAULT SEARCH PARAMETERS FOR MORE ACCURATE INFO (depending on the info you want.) You have to give AI search parameters if you want legit info, otherwise it tends to use Reddit, FB, Wikipedia, etc for a lot of the results. For example if you're researching mushrooms, you want to specify & say that you don't want any info from Reddit, FB, Wikipedia, etc., and you only want info from mycologists, fungal biologists, plant pathologists, experts in similar fields, PhDs, published research papers & books, and the like. You'll get entirely different answers when you specify different search parameters.
10points
#14
The dirty secret is that these models are not optimized for truth; they are optimized for plausibility. They are designed to predict the next word that makes the user happy, not the word that is factually correct.
It's kind of "Confidence Trap." If you ask for a specific statistic or source that doesn't exist, it will often invent a plausible-sounding citation just to be helpful. It has zero concept of "I don't know" unless explicitly forced to admit it. It's possible. Overcoming this 'people-pleasing' tendency requires explicit 'Uncertainty Prompting' to force the model to flag what it isn't sure about, rather than guessing." I show solutions to these kinds of problems and ways to deal with them in my publications.
It's kind of "Confidence Trap." If you ask for a specific statistic or source that doesn't exist, it will often invent a plausible-sounding citation just to be helpful. It has zero concept of "I don't know" unless explicitly forced to admit it. It's possible. Overcoming this 'people-pleasing' tendency requires explicit 'Uncertainty Prompting' to force the model to flag what it isn't sure about, rather than guessing." I show solutions to these kinds of problems and ways to deal with them in my publications.
10points
#15
They keep saying general AI is around the corner. The current technology is fundamentally incapable of becoming a general AI. It's like saying any day now your toaster will become a TV.
9points
#16
It’s not actual AI. It still requires prompts. It doesn’t have true autonomy or self inspired standalone operations. It relies wholly on pre programming and external support.
What we have is more akin to adaptive algorithms. Which is impressive but it’s not AI.
What we have is more akin to adaptive algorithms. Which is impressive but it’s not AI.
9points
#17
If your job starts having you constantly logging random information and interactions about your duties you're probably training AI to do your job.
8points
#18
AI engineer here
- the internet (web browsing) as a whole is going to fundamentally change into being AI-based
- companies are moving away from being AI dependent . Yes everyone spent years saying AI is coming for everyone’s job and grandmother, but the pushback is real
- as someone who works in AI (on education and cancer reseerch), the backlash i face is real.
- the internet (web browsing) as a whole is going to fundamentally change into being AI-based
- companies are moving away from being AI dependent . Yes everyone spent years saying AI is coming for everyone’s job and grandmother, but the pushback is real
- as someone who works in AI (on education and cancer reseerch), the backlash i face is real.
8points
#19
It’s really good at coding. There’s almost no going back to writing all the code by hand.
But writing the code is usually the easiest part. The hardest part is to figure out how things should work.
AI can assist with that part too but if you give AI an ambiguous problem and let it choose then AI will make some wild stuff.
So it’s good as a tool but can hardly replace humans at this point.
But writing the code is usually the easiest part. The hardest part is to figure out how things should work.
AI can assist with that part too but if you give AI an ambiguous problem and let it choose then AI will make some wild stuff.
So it’s good as a tool but can hardly replace humans at this point.
8points
#20
Many companies feel the need to incorporate AI to “stay competitive” that have no idea what to do with it but that isn’t stopping them.
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8points