dChan - Q Origins Project Archive

Former president Donald Trump's interview with Elon Musk on X last night generated millions of views and at its peak registered over 200,000 simultaneous viewers. While the opening was rough - either through overcapacity or a DDoS attack - the interview wasn't terrible. Professional interviewers in the media aren't at risk of losing their jobs to Musk, but in the interview, Trump came off as personable and more upbeat than he had been in a while.

One thing that I heard more than anything else from people who lean left is that Trump "focused so much on the shooting," which is an understandable thing for him to do. He came within millimeters of losing his life, and given some of his recent public performances (the RNC speech and his Mar-a-Lago press conference in particular), it's clear it threw him off his game a bit.

But outside of that first 30 minutes or so - which also provided a lot more insight into Trump's frame of mind regarding the shooting than we'd seen previously - Trump's interview showed he can go for a long time without running out of things to say, and while Musk was anything but hostile, it's not hard to imagine Trump couldn't do the same in an interview with a more skeptical reporter.

The mainstream press, as expected, is not reacting well to the interview. The entire process circumvented their perceived role as gatekeepers of information from newsmakers to news consumers. They scoffed, fact-checked, and labeled the entire thing a mess.

But Trump can parlay this into a recurring theme while fixing the one problem the X Space interview truly had - it did not move the needle.

Based on the feedback and commentary last night, the people listening to the interview were either Trump supporters, Trump haters, or journalists. Pretty much everyone in those camps has already decided, and there is very little chance that those folks are capable of having their minds changed. The folks whose minds are most likely to be changed aren't listening to X Spaces, and oftentimes they're not even watching Fox, CNN, MSNBC, etc.

If Trump wants to win the most winnable voters, the ones he needs in the swing states that are still toss-ups, his best bet is to start doing interviews on local TV, local newspapers, and local radio* and appeal directly to swing state voters that way.

When your average American family is getting ready for the day, parents are reading the local paper or they are turning on the local talk show in the car on the way to take the kids to school or go to work. When families are settling in for the evening, they turn on their local evening news. While we talk all the time about ratings for the big 24-hour networks, the fact is that the legacy alphabet stations - ABC, CBS, NBC, etc. - attract millions of viewers not because of their own talent, but because they all have local affiliates with local programming that come on before the national news programming.

Trump and Vance can take advantage of this. Kamala Harris is running ads and getting favorable coverage from the press without doing any interviews. If Trump were to swing through the Rust Belt and do interview after interview with local outlets, he could reach voters in a way the ads and favorable national press coverage can't.

Trump is largely absent from our local media. I'm a local media person** and I believe strongly that local media will always be a better format for talking directly to voters than anything the national press will be able to do for any politician.

All Too Predictably, Reality Is Puncturing The AI Hype Bubble

It’s becoming clear that both the optimism and pessimism surrounding the potential of AI has been vastly overblown.

https://thefederalist.com/2024/08/13/all-too-predictably-reality-is-puncturing-the-ai-hype-bubble/

After so much hype and trillions of dollars of investment, the AI bubble seems like it might finally be bursting. Much of the market crash that happened last Monday was concentrated among technology companies, all of which are deeply invested in AI. The biggest loser was the famed chip company Nvidia, which, according to writer Chis Taylor, “lost a trillion dollars of valuation, 30% of the total, since its 2024 high.

Taylor goes on to list the reasons for this downturn: excessive hype from AI gurus like Sam Altman, burnout from consumers who are now turned off by products using AI, and impatient investors like Goldman Sachs wanting a quicker return on their investment. So, like most bubbles, AI businesses overpromised and underdelivered, causing the market to correct itself.

Unfortunately, it doesn’t end there. AI technology seems to be hitting a wall in its advancement. As many people have observed, the AI data servers suck up colossal amounts of energy, putting a huge strain on electrical grids. They also require equally colossal amounts of capital (running in the billions of dollars) and continuous funding — for reference, ChatGPT is estimated to cost OpenAI $700,000 a day to operate.

Worse still, Taylor explains that AI programs “have run out of stuff to train on, and the more they are trained on ‘the internet,’ the more the internet contains a body of work written by AI — degrading the product in question.” Originally, a Large Language Model (LLM) like ChatGPT could review the vast quantity of human-made content across the internet and take that data to produce a unique essay that would meet the parameters of its users. But now, there is so much AI-generated content online that any essay that the LLM produces will become increasingly derivative, defective, and incomprehensible. Garbage in, garbage out.

At the root of all these issues is a profound collective misunderstanding about AI. The problem is so obvious that it’s difficult to see, especially for the high IQ geniuses in Silicon Valley: artificial intelligence is not human intelligence. For too long, technologists have spoken about AI like it’s a synthetic brain that mirrors the functions of human brains. As such, they describe computers learning, communicating, and performing administrative tasks as though they were conscious workers.

And, if one listens to Techno-Optimists like Marc Andreessen and Amjad Massad, AI can even do the work of life coaches and teachers. Andreessen predicted that “Every child will have an AI tutor that is infinitely patient, infinitely compassionate, infinitely knowledgeable, infinitely helpful. The AI tutor will be by each child’s side every step of their development, helping them maximize their potential with the machine version of infinite love.” Not only does AI understand human beings, but it understands them infinitely better than human beings understand themselves and one another.

Coupled with this misguided personalizing of AI is the misconception that human minds are simply organic computers with deficient programming. The soul is commonly reduced to bits of data that can be downloaded or uploaded. No doubt, Andreessen imagines students sitting at the feet of their robot instructors quietly learning from them in the same way a computer installs a new application, except they don’t have a loading bar hovering above their heads.

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Because of these flawed analogies, it was assumed by many that AI would replace most white-collar workers and eventually assume control of most organizations — after all, why have fallible mortals at the helm when one could have an infinitely wise and competent AI overlord handling everything? The real challenge, according to experts, was dealing with the displaced, unemployed masses who would inevitably become obsolete and making sure that the robot overlords remain kind to human beings. In this regard, the only real difference between the optimist and pessimist was that one believed that AI would create a utopia while the other believed it would do the opposite.

Of course, neither of these possibilities are true because AI doesn’t have the capacity to replace people, and it never did. At best, it is a powerful tool that has more in common with a spell/grammar check application or a search engine than it does with even the dumbest, most unimaginative human being.

This is why using AI to make money has been such a failure. Employers thought they could have an AI program to do the work of their employees more efficiently at a fraction of the price. However, the inputs and accompanying training required complete even the most basic tasks is overwhelming. Rather than learn how to program complex operations on a robot, most managers would be better off simply telling their workers what to do and trusting in their ability to do it. It also makes for a happier workplace.

The same goes for using AI to cure the current loneliness epidemic. Perhaps a few desperate souls might not mind the company of an AI companion, but this will only feed the very worst aspects of those struggling to form meaningful relationships. Instead of serving as practice for real interaction, it reinforces the anti-social habits that make a person lonely in the first place. Rather than making sacrifices and developing empathy, the lonely user is indulging delusions and cultivating extreme narcissism.

Ironically, the greatest benefit of AI is demonstrating just how special and irreplaceable human beings are. Consequently, its financial and cultural success will depend on whether it can raise human potential by becoming a helpful tool or do the opposite by becoming an expensive burden.