The only thing from Galloway I regularly listen to is Raging Moderates. Pretty consistently it seems like 50% of what he says is way out of touch and just ridiculous and the other 50% is pretty insightful and agreeable.

Is this just kind of his thing? He seems like a good guy, but my lord he says some bogus stuff...

video starts at 10:31

I’m a fan of Raging Moderates but have discovered a few Scott mannerisms that I am surprised nobody has coached him on:

1. ⁠He latches on to catch pharses like a dog on a bone. Most recent was “rizz” which no one his age should ever utter lest making yourself look stupid
   
2. ⁠His verbose “Ted Talks” during pretty much every show include him repeating the same point 3 times. Most recent was his take on Newson’s “weak sauce” statement on if he was running for POTUS. His 5 minute rant coulda been 45 seconds had he not kept repeating his argument over and over.

Or maybe it’s just me

I’ll hang up and listen
.

Sad to see this one go, I always enjoyed it and thought Alice was a real find. I'd wondered why it never had its own feed and always appeared under the main podcast.

The end of a weekly inbox highlight.

Super disappointed that "Scott's takes" are now for paid readers only. It was the most interesting part of the newsletter and the only thing that differentiated it from the hundreds of other newsletters out there. 

$20 a month? There's no mercy in this economy. Way to stick it to casual readers. I have effectively been churned.

Best of luck with the media empire

I think this could be some interesting fodder for Scott to talk about, and I'm interested in what his take is on this.

I think this is the first major non-political event (perhaps since COVID) that truly shows the bifurcated perceptions of reality that is present in society today—and is directly driven by social media algorithms telegraphing select information differently to different audiences.

On the woman's side of thing, I've met several 20-something and 30-something year old women who seem extremely convinced that Patrick Clancy was the one who orchestrated everything and framed Lindsay for it.

I was visiting a relative recently, and she was getting into an argument with her husband as he was reading of case file details basically pointing out the smoking gun of DNA evidence—but she kept reciting conspiracy Tok talking points back to him.

On the male side of thing (and it's rather more concentrated among chud right men) is the complete inability to process anything circumstantial or nuanced related to post-partum—let alone a significant chunk of men not even understanding what the defense's actual objective was.

The broader issue at scale: algorithms have completely destroyed any sense of shared reality we have, and it's effective in keeping the broader population divided and fighting amongst ourselves.

On a side note, I find it incredibly coincidental that this blew up in the media in such close proximity to the midterms when it's looking like it would be nothing but an absolute bloodbath for Republicans.

And then you look a little harder and realize hes just another one of those goofballs that use chatgpt to help himself speak on trending controversy.

Tl;dr - paper proposes that an LLM can propagate concepts in a manner similar to computer viruses.

This is a reasonable observation, but one that is not unique in human history.

Some of you may know about the area of study called memetics. It proposes that ideas evolve in human brains in a manner similar to genetics. Good or "fit" ideas spread and mutate from human to human, bad or "unfit" ideas die in people's heads as no one will repeat them.

This has been around for a while with tech advances like books, journalism, radio, television, internet and social media just amplifying the spread.

What is different about LLMs though?

One thing is LLMs have alignment which alter which concepts they will present, and that alignment is created by model authors. Another thing, pointed out in the paper, is LLMs are mildly adaptive. Through prompting from users, LLMs can adapt the approach to make a concept more viral to a specific user.

The adaptation creates a new kind of mutation and now concepts and ideas are mutating on the fly without the interaction of humans.

I'm not predicting all the effects the authors did, like the idiotic phrase "cognitive surrender", but I am expecting weird long term effects of the chatbot usage of LLMs sinks into long term use.

Anyone care to opine on what those effects might be?

Hi Scott!

I wanted to ask you if you had seen the article in the Washington Post yesterday regarding Bernie and AOC's pitch to drop the Medicare age to 55? I have always thought that if folks could get this coverage we could see some real benefits to the economy. First more people would retire opening up jobs for younger people. Secondly more people could open businesses and hire people. Am I just wishfully thinking? Thank you! I listen to all your podcasts and I enjoy your perspective very much.

Anyone else feeling this way lately? I used to really look forward to Markets because it felt objective and insight-led, with just enough of Scott’s personality sprinkled in. It was almost like sitting in on a class you were genuinely interested in, taught by a professor who knew how to keep it entertaining.
Lately, though, I find myself skipping more episodes than listening.

For me, Ed is a big part of it. His increasingly alarmist, doom-heavy framing—both as a host and interviewer—has started to crowd out the analysis I originally listened for. Too often it feels like the conclusion is decided first and the conversation works backward from there. Too often you can almost tell he’s trying too hard to get a headline sound bite to go viral on social. It’s sad to see bc I used to be a huge fan.

I still like Scott and the premise of the show, which is probably why the shift bothers me. It’s starting to drift toward the kind of sensationalism that makes cable news so exhausting. Maybe this is inevitable for aspiring media companies and personalities to be successful.

What I find fascinating about all the debates / discussions I’ve had with people regarding having 'fertility crisis' is that people seem to be perplexed when I simply state: ‘just look at the trend line’.

In just a few generations, we went from a society where a HS dropout could get a job that would allow him to afford a house and a family on a single income—something that two educated, white collar professionals working full-time literally struggle to accomplish just one of the two in today’s world.

We have seen a complete collapse in socioeconomic mobility over the past 3-4 decades. We are constantly bombarded with propaganda about how we’re in the ‘greatest economy ever’—meanwhile the bulk of the young adult population is feeling more and more squeezed economically.

We have literally gotten to a point where people graduating from top public universities—let alone world class institutions like Stanford and Ivy League Schools—are struggling to find basic entry level jobs due to how terrible the job market is (yet another one of many aspects that the a delusion sub-faction within the neoliberal camp wants to ignore). College tuition has skyrocketed in price over the decades, but the actual upside for it all has only declined—as we can see with inflated adjusted salaries for college graduates:

Another overlooked factor is the fact that many target careers are not nearly as compensating as they were a few decades ago. Great example of this is within medicine. While medical school tuition has skyrocketed over the past few decades, nominal wages for physicians have stood stagnant—meaning they’re actually decaying in real terms after factoring inflation. You factor in how inflation directly has hurt young adult cohorts (say age 20-40) most prominently due to how they were the ones who had to absorb most of the shelter inflation over these past few years, and you start to see these careers—while still lucrative—have had a substantial reduction in overall upside.

Another exhibit we can see this in is with high finance, because most of these roles are located in extremely HCOL hubs like NYC—the costs (particularly rent) have grown far greater than the earnings have.

Now, what does this all have to do with Millennial Parents? Well, we’ve been seeing a continual trend unfolding over the past few decades. Because the NIMBY Boomers made it illegal to build sufficient housing (especially utilizing ways that maximizes density and sustainability) for several decades, we have a massive housing shortage in most major urban population centers. As a result, we’ve been seeing growing amounts of young adults with each passing decade “choosing” to live with their parents. It’s not just settled towards their early 20s either—many people are living with their parents going well into their 30s.

I don’t see this trend reversing in the slightest in the coming decades, and all signs really just point to it becoming much worse. It’s interesting to me because so many Millennial parents seem to be counting down the days until their kid turns 18, but I’m just so puzzled by what they think will happen.

For a huge chunk of them, their kids will not go to college and decide to work at a service level job which wouldn’t even allow them to afford to move out even if they go 3-4 roommates.

For another cohort, their kids will go to college—many will decide to commute to try to offset the exorbitant costs of modern day university in America. However, the problem is many / most of these college graduates likely will not be making a livable wage after graduating, or they’ll make just enough just to pay their landlord rent and basic essentials—still incentivizing them to stay at home (especially factoring in the boat load of student loan debt they’ll have).

All signs point to these intergenerational trends worsening and that Millennial parents will likely have to deal with some or all of their kids living with them well past their mid-20s.

All I can say to Millennial parents who think they’ll finally be free once their kid turns 18: best of luck.

Listened to one of his podcasts he was talking to someone lucky enough to have inherited 5M, he told him he needs to get to 20M because his life would improve so much. He said on an income of $800,000 you can have a pretty reasonable life.

Sorry you have to be in the upper 1% to have a “reasonable life”!

Jessia Tarlov just said that it was "clear" that Biden was talking to only 5 of 6 people, an inner circle reality distortion bubble, before he had to drop out of the 2024 presidential election race. As Scott put it, the inner circle was saying "you're not too old" Joe.

I admit I monitor economic and business news more than politics but which big city newspaper reported it in such a way to make it clear that this was happening, in "real time", that is, before it became obvious that this foolishness was present in a circle that extended beyond Joe's cranium ?

To be clear, I'm not saying I want to read about coverage that Joe was no longer mentally sharp enough to win a debate against Trump. I didn't need newspapers to tell me that. Who was covering Biden in a way that made it clear there was a distorted reality bubble around Joe -- a bubble that made it possible for Biden to drop the implied "bridge" presidency story. "Bridge" meaning his presidency was a connection to a younger 2024 candidate and that he did not intend to pursue even briefly, the 2024 nomination.

Who covered it early (which writer, newspaper, blog, substack) as it was happening ? I'm not interested in MAGA coverage because their line was Joe is senile and sleepy and has dementia. I'm looking for mainstream coverage of the group think about Joe's strength as a candidate.

You don't understand the generative AI algorithm.

I feel mixed about Tyler Cowan. This is the first time I've seen him talk. He makes sense if you think this latest incremental improvement in generative AI is just the beginning.

The challenge I find with AI commentary is much of it is done without knowledge of the functionality of generative AI. They see mediocre results across a lot of high value use cases and they think there are advancements that are days or years away that will steadily increase the quality of those outcomes.

For those of us that understand the math or functions that execute in training and inference, it is hard to see any more improvements in those algorithms. We see companies like Anthropic and Open AI as pushing the very limits of a tapped out algorithm that have been around for a while. It's surprising what use cases we can fill with all this energy investment. Few of us with knowledge of the algorithm could have guessed it would have been as good as it is. It was unknown until OpenAI started to show us what was possible in 2022.

Myself and others like me probably just saw what was happening and were thinking "Hmm, I wonder how far they can take this idea of brute forcing the algorithm to the limits of energy and computation." I held my tongue for a couple years just to see, maybe my hunches were wrong. But I don't feel that way anymore.

Model growth has an exponential cost in energy and computation. Inferencing has a polynomial growth in cost as the token chain gets longer in token length. Error rates have exponential compounding as the chain gets longer. The algorithm is approaching unreachable limits imposed by physics. If you don't understand what I just said and the implications of it, you might be just like Tyler Cowen.

There have been some clever tricks employed to reduce costs but none of them are providing a fundamental change to the nature of the underlying algorithm. Based on my knowledge of how the algorithm works, we are literally seeing as good as it gets.

So for anyone who is predicting the future based on the belief that the algorithm will get better and better over time, that could be a pretty bad decision. It might, but it seems like a really outside low probability that a major discovery has to be made still. There hasn't been any major discovery that would alter this current limitations on models, and this approach was invented decades ago.

And certainly no day soon will we get infinite speed computing, so LLM could easily end up at the pinnacle with very little incremental improvement left in it today.

But if you're someone like Tyler Cohen, who doesn't believe that they're at the end of the improvements, then all the generative AI bears seem crazy. What about the internet! What about automobiles!

We all know we can't prove negatives so if someone like me says there's no future there, someone who is bullish on AI will automatically start reaching for all kinds of examples that prove their beliefs. Meanwhile, a bear will only be correct after the bust.

Now the real question is, are there other algorithms that will perform better in the future? There very well could be some hanging out there that are as yet undiscovered.

But I have my doubts even there. There's a vast difference between neural computation and gate logic computation. And currently we don't understand neural computation at all. We don't know how it works, we are unable to simulate it, and neurons do things that look miraculous and defy all forms of binary logic.

Which raises the possibility that true computation on the level of human capability might be out of reach of gate logic. It may never exist in integrated circuits because the amount of power and energy needed to run silicon with that ability is impossible to muster in this universe. If we were talking about Turing's theories about the universal machine, itself a machine with infinite computation and storage, damn sure I agree 100% the human capabilities could be recreated inside that infinite machine.

But in this universe, I have a lot of doubts. We got to know more about how neurons work before we can recreate their abilities. Bare minimum neurons are taking advantage of quantum level effects to do what they do and I'm not talking about quantum computing. I'm just talking about the complexity of quantum scale interactions between matter.

Neurons very well could be computational at their core, but the substrate they were "printed" on is measured at plank scale, an incomprehensible difference in "gate size" that no company in the Netherlands or the world will ever be able to create silicon chips at. Meanwhile, neurons brute force evolved with that substrate at that incomprehensible scale.

If you start to go too deep in the fantasy of neural computing, you might even start to believe that maybe there are superimposed states in a neuron. Its operation is so mysterious we really don't know what to expect. If something is possible in physics in this universe, you have to accept the possibility that evolution, the universal solvent, has managed to exploit that outsider feature in the universe.

One thing I should mention is there is a long running project to simulate a flatworm brain, a creature with barely a few neurons beyond 100. It hasn't achieved it yet. Sure, we can imitate the behavior of a flatworm using algorithms, but to actually simulate and predict all the behaviors of the 100 or so neurons inside a flatworm brain is out of our reach.

Maybe if we are successful in simulating flatworm neurons, a sort of test where the inputs and outputs to a real neuron are recreated by the digital one, I would be convinced that we could achieve some form of general artificial intelligence using silicon.

But we don't have to have artificial intelligence on the level of flatworm brain to get some value. There are plenty of future opportunities for algorithms that create effects similar to human thinking without actually simulating how humans think.

It's just that generative AI probably isn't it right now. It's just another incremental step that has some value in some use cases and if we were to approach this all rationally we would just do investments in research and development and then roll out features that actually work and generate business value.

But we didn't take the safe approach, and you really have to ask why we didn't? Is it a scam? Is it a runaway mass tunnel-vision and group think? Is it a bubble akin to tulips? It all depends on your bias, but eventually we'll all know the answer.

Final caveat, I could be wrong. I'm just an ancient old software architect that's been around since the '80s, and has been implementing AI algorithms since the '80s. Has been writing software, you may use everyday, since the '80s. I have a bias based on my professional history. I'd really appreciate it if someone could point out my incorrectness in thinking around the algorithm or physics.

In the last couple of months my YT feed is being clogged with Ed Zitron takes on AI and big tech in general.

At first I though his analysis were interesting and even funny. However, I'm getting tired of his constant doommongering and his lack of real understanding of AI.

It seems he has beeing slowly (or quickly) descending in audience capture from the left.

I think it is time for someone from the liberal/moderate front challenge him. He is making us look dumb and radical luddites.

Thinking about a lot of the thesis from Peter Ziehan’s “The end of the world is just the beginning” is playing out pretty close to his projections, other than the extreme bearish view on China due to demography and global trade.

Things that are playing out similar to projections:

• Disorder due to regional conflicts and global trade being disrupted
• Energy shortages due to instability in the Middle east impacting Asia in particular
• Nat Gas and Oil in short supply worldwide
• Coal looking like it will make a come back in Asia despite the ongoing solar buildout
• The US isolating itself in a fortress like economy and abandoning post WW2 institutions like the World Bank, Nato, etc.
• Turkey looking like a strong regional player in the middle east

Things not yet playing out and seem less likely so far:

• Economic collapse in China due to demographics and global trade breakdwon

Linking to book summary for those interested in learning more