Why God King Sam Altman is Unlikely: Who Will Capture the Value of the AI Revolution?

In this engaging discussion, Simone and the host explore the future of AI and its effects on the economy. They delve into questions about who will benefit most from AI advancements: large corporations or individuals using AI models. The conversation spans the significance of token layer versus latent layer in AI development, where major innovations may occur, and the potential for AI to achieve superintelligence. They also discuss the implications of AI on job training, investments, and societal transformation, along with a creative perspective on how AI can be harnessed for various purposes, including transforming industries. The duo imagines a future driven by interconnected AI systems and explores the philosophical aspects of AI mimicking human brain functions. Don't miss this thought-provoking episode that offers insights into the trajectory of AI and its profound impact on society.

Malcolm Collins: . [00:00:00] Hello Simone. I am excited to be here with you today. Today we are going to be focusing on a question, which is, as AI changes the way the economy works, who is going to be the primary beneficiary of this? Is it going to be the large companies that make and own the ais, or is it going to be the people using the individual AI models?

The, the I like we all know, for example, like in probably 10 years from now, there will be an AI that can, let's say, replace. Most lawyers, let's say the bottom 50% of lawyers.

Simone Collins: Well, and already studies have shown AI therapists perform better on many measures. There's, there's, it's already exceeding our capacity in so many places.

Malcolm Collins: Yeah. They, they introduced it to a Texas school system and it shot to the top 1% of, of student outcomes. So as we see this, where is the economic explosion from this going to be concentrated? Because this is really important in determining what types of jobs you should be looking [00:01:00] at these days, how you should be training yourself, how you should be raising your kids, where you should be investing.

The second question we're going to look at because it directly follows from the first question, okay, is, does the future of ai, when we're looking at the big world changing advancements that are going to come from it, are they going to appear on the token layer or at the latent layer? So can you define

Simone Collins: those differences?

Malcolm Collins: Yeah. So by this what I mean is. When we look at continued AI advancement, is it going to happen in the layer of the base model IE the thing that open AI is releasing and Claude is releasing and everything like that? Or is it going to be in the token layer, the people who are making wrappers for the ai?

For example, the Collins Institute is fundamentally a wrapper on preexisting ais. Our AI game company is a series of wrappers on ai. And if it turns out that the future of AI is in the token layer, it leans potentially more to, if not the big companies that are gonna capture the value [00:02:00] from this.

Mm. And then the next question we're gonna look at is the question of. What gets us to ai, super intelligence? And I might even start with this one because if we look at recent reports with ai, a big sort of thing that we've been finding is that especially with like open AI's 4.5 model is that it's not as advanced as people thought it would be.

It didn't get the same huge jump in capacity that people thought it would get. And the reason , is that pre-training IE. , the ways that you sort of train AI on the preexisting data before you do, like the narrow or like focus training after you've created the base model doesn't appear to have the as big an effect as it used to have.

So it was working on, I think, 10 x the information of model four and yet it didn't appear dramatically better. And so one of the questions is, so that's, that's one area where pre-training doesn't seem to be having the same effect, and I think we can [00:03:00] intuit why. But the second big issue , is that the amount of information that we actually have, like, you know, peak oil theory, there's like , a peak pre AI information theory problem, which is it just eventually when you're dealing with these massive, massive data sets, runs out of new information to train on.

So first. I love your intuition before I color it. Do you think, if you look at the future of LLMs base models so we're not talking about LLMs entirely, we're not talking about anything like that. Do you think that the base models will continue to improve dramatically?

Simone Collins: I think they will. And at least based on people more experienced than this, , than I am, they will, but in lumpy ways.

Like they'll get really, really, really good at programming. And they'll get really, really good at different esoteric forms of like developing their own synthetic data and using that to sharpen themselves, but that they're going to be severe diminishing marginal returns when it comes to some things that are already pretty advanced.

And of course I think the big [00:04:00] difference and the thing we haven't really experienced yet is independent agents. Like right now, AI isn't very effectively going out and doing stuff for us, and when that starts to happen, it's gonna be huge. I.

Malcolm Collins: I agree with that, but I think so, what I'm gonna be arguing in this is that most of the advancements that we will probably see in AI going forwards are going to happen, like the really big breakthroughs at the token layer.

Simone Collins: Okay. Hmm.

Malcolm Collins: Not at the base layer and which a lot of people would strongly, those are fighting words.

These are fighting words in ai. Yeah. It's the rappers that are going to fix our major problems.

Simone Collins: Wow.

Malcolm Collins: So I'll use the case of an AI lawyer to give you an explanation of how this works. Right. Alright. So I wanna make a better AI lawyer right now. If you look at the AI systems right now there's a guy programming guy who was talking to me recently and he was arguing because he was working in the education space and he's like, I, he [00:05:00] didn't like our solution.

'cause it's a token layer solution. And he wants to build a better latent layer solution. You know, using better training data, using better post training data because it's more efficient programming wise. And I'm like, yeah. For the time being. Yeah, for the time being, I feel like it creates path dependency.

Am I missing something here? Well, okay. Just from a business perspective, it's pretty stupid because as, as open AI's models increase, like if we expect them to continue to increase in quality or is Claude's models increase or is GRS models increase,

Simone Collins: which they're going to,

Malcolm Collins: yeah, you can't apply the post-training uniquenesses of the models that you create to these new systems.

So anything you build is gonna be irrelevant in a few generations of ai. But the, you wanna be able to switch it out, like no

Simone Collins: matter what, you wanna switch it out, switch. If one AI gets better, you should be able to plug it into whatever your framework is, your scaffolding. Right. You wanna build scaffolding, changeable parts.

Malcolm Collins: Exactly. Exactly. But that's actually not the core problem. That's not the core reason why, [00:06:00] because the other project he's working on is an AI lawyer and he's trying to fix this problem at the latent layer. And, that won't work. And I will explain why it won't work and you will be like, oh yeah, that makes perfect sense