In a very interesting move, there has been some, you know, unexpected unity in the AI market as a bunch of industry leaders from a bunch of top AI companies have all got together and published a paper urging essentially the top AI companies to monitor AI's thoughts and how it's actually arriving at questions. So today on the podcast, I want to break down what is currently being done in that regard, what needs to be done in the future, who's leading the way, and what, you know, what this whole paper was all about as it has a lot of people talking. Before we get into that, if you want to try any of the top models from companies that I talk about on this podcast, I'd love for you to try out AIbox.ai. My personal company has just launched our beta and you'll essentially get access to the top 40 AI models.
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So that saves you these $20 subscriptions that every platform has, you can get all of them all in one platform for one price. So go check it out, a box.ai, the link is in the description. All right, let's get into what the researchers have been saying in regards to AI's thought process. So the top, the companies that essentially are kind of involved in this is OpenAI, Google, DeepMind, and anthropic, and not really the companies, but top researchers, all these companies came together, put out a paper, they all kind of signed it.
So what they're really talking about is something called thoughts of AI reasoning models. This is what they're kind of researching as the thoughts of AI reasoning models in this paper that they've published. And really what they're getting to is something called chain of thought, but it's kind of how these reasoning models and do these, you know, they do the deep dives, the deep thinking that all of these kind of tools, they're not, they're, it's called different things on a model, but basically the way that the AI arrives at an answer that you want it to be studied. So opening up was kind of the first one that came out with this when they came out with our O3 model.
And it had, you know, they're like, this thing has, it has reasoning. They didn't explain how the reasoning was down enough because they were the first ones with it. They didn't want to like give away the secret and tell everyone what they were doing. But basically most AI researchers understood what was going on, DeepSeek very quickly cloned the tool and how to New York rise and how good their AI model was once they did it.
So it really showed, oh my gosh, like this technique is the way to turn these AI models into best in class AI models. So when DeepSeek did it, when Superviral everyone was talking about how good that AI model was, but basically what it does is the same way, like when a human is working on a complex map problem, and you're, you know, you're looking through it, you're reasoning, you're writing down notes, this is what the models are doing. They're not just trying to one shot, you know, based off of everything you know, off the top of your head, what's the answer to this, which is how they're working before. It's like, if you know, based off everything, you know, work through the question, come up with it, or it would say things like, you know, come up with like 20 steps to figure out the solution to this problem.
It's like, okay, first we got to figure out X, Y, and Z, then we got to figure out, you know, what the user means by this, then we got to figure out if the intent that I think is actually correct, right? So it has like all these steps and breaks it down. Now you've probably seen this on a lot of AI models because everyone has now some sort of feature like this. My favorite is, I think I dropped a student a good job.
I like how DeepSeek and Grock both show you line by line, the thought process, you can kind of drop it down and see the thought process that the AI model ran through in order to get to your result. That was something that opening I never launched with because I think they're trying to guard your secret at this point. I don't think it really matters, but see exactly what's going on. And so this is what this paper is all about.
Essentially, we have what they're calling monitor abilities. So we can see how they're arriving at their questions more or less. This is not perfect, but it's a pretty good idea of how they're arriving at their answers to questions. And so the researchers are essentially saying we need to keep this monitor ability.
So it's called a chain of thought. Anyway, this is directly from the paper. It says chain of thought monitoring represents a valuable addition to safety measures for Frontier AI offering a rare glimpse into how AI agents make decisions. Yet there's no guarantee that the current degree of visibility will persist.
We encourage the research community and Frontier AI developers to make the best use of chain of thought monitor ability and study how it can be preserved. There is an interesting sneaky angle to this. And I don't know if this is actually true, but if you think about it, when OpenAI first came out with this chain of thought or these reasoning models, they didn't release how it was kind of with the answers. They didn't release what they're calling this monitor ability device that you get from Brock or DeepSick where it shows you exactly what it's thinking.
And that's because they didn't want people to know to reverse engineer the prompts that they were telling. You know how I said, you know, basically there's a prompt that will tell it like break this into 12 parts and figure out what you need to do. Well, OpenAI and no one actually tells what the previous what the pre-prompt is before it runs through the process. You can get an idea by looking at the process of what the pre-prompt is, kind of reverse engineer, but you don't know for a fact.
And so what's interesting here, they're saying, you know, there's no guarantee on how long this can exist for it, because right now, Brock and DeepSick and others are like showing you, but they don't have to. Like they could just give you the result and just do what OpenAI used to do, which is just how this little loading bar that said thinking. And they could just say thinking, thinking, thinking, all right, here's your result. Now that's what the AI researchers don't want.
And they're saying it's for safety reasons, right? They're like, hey, we don't want anyone to, you know, we don't want to forget how it's getting to the answers. And then it's a bit more of a black box and we can't figure it out. We don't know if it's aligned or safe or yada yada.
But a sneaky angle that I've been thinking of is, if you're one of these AI researchers and you want to reverse engineer how other models are staying best in class, for example, Grockford just came out and it, you know, proved that its model was better on all the benchmarks in, for reasoning than, you know, it was the best model, best in class model on all the benchmarks. Now, maybe that's just because their models bigger. But at this point, a lot of people are saying it's because of some of the tools and the pre-prompts and some of the ways that the models has those things worked into it. And the best way to reverse engineer what those tools and pre-prompts and kind of some of these, the secret sauce of the model would be to look at its chain of thought.
And so I think an interesting sneaky angle would be if these researchers are worried that, hey, if everyone turns off the chain of thought, we won't be able to copy everyone else's like good ideas as well. And so when something makes a breakthrough, it's going to be harder to copy it. Now, is this the real reason? Is it all about safety?
I don't really know. I'm just saying, if you have chain of thought monitoring available for different AI models, it does make it a little bit easier to kind of copy the secret sauce of AI companies. So, you know, leave that with, leave that for what it's worth. The other thing I do think that's interesting work that's worth mentioning is the people that are signing off on this opening iGoogle deep mind enthropic.
These are already all the top companies anyways. They're already at the very top of the pack, right? So, you throw Grock in there and between the five of these companies or the four of these companies, they're basically like all the top models, the LLN models that have the highest, you know, the highest benchmark scores. And so, what's interesting is like, are they trying to make sure that everyone plays by the same rules?
Are they trying to make sure if anyone has a breakthrough that they can kind of copy them? Are they trying to stop any new companies that might leap for everybody with some secret sauce and then everyone has a scramble to figure out what they're doing? Do they want there to be regulation that forces us to keep chain of thought? Like, I'm not really sure exactly where it goes.
And at the end of the day, they're not calling for any sort of regulation or anything. And there are big names that are talking about it, right? Or signing off on this, you have safe super intelligency, you know, Iliya Seskowar, who used to be over at OpenAI, Nobel laureate, Jeffrey Hinton, Google deep mind co-founder Shane Leg, X AI, safety advisor, Dan Hendricks, thinking machines, co-founder, John Schultzman. So, very, very big names in the industry are all essentially, you know, talking, talking about this and signing off on it.
So, this is really interesting. This is coming at a time when there is cut throat competition in the AI industry. Mark Zuckerberg is hiring everyone's top researchers as of this morning. I saw a few more top researchers from OpenAI.
So, it is an absolute bloodbath when it comes to what you have to pay for to keep up with these researchers and what people are willing to do, what these companies are willing to do to make sure that their AI is ahead of everybody else's. So, it's very, very competitive. And I wouldn't be surprised if there was some sort of competitive angle. This is one thing that I did want to say from Bowen Baker, who worked on this particular paper.
He said, where at this critical time where we have this new chain of thought thing, it seems pretty useful, but it could go away in a few years if people don't really concentrate on it. Publishing, a positioning paper like this to me is a mechanism to get more research and attention to this topic before that happens. So, the paper isn't any groundbreaking news. It's not calling for any groundbreaking.
Actually, it's just putting, it's, you know, they're calling it a positioning paper. They're like, just so you know, we have this chain of reasoning for safety reasons. This is good. Everyone should keep doing it.
So, it'll be interesting to see if that holds any ground, if that actually makes any changes, if anyone is actually forced to do anything. What I will say in regards to this, that I feel like might be even more important, is that anthropic is one of the top companies that's really been working on a lot of this safety stuff. And earlier this year, there's CEO, Dario Amade, he announced that they were pretty much making some sort of commitment to open the quote unquote black box of AI models by 2027. So, even when we do the chain of thought, we don't always know why an AI model will give the responses that it gives as it works through that chain of thought that you that you may have given it.
So, even if you're like, okay, we see why it came up with this answer because it's chain of thought, but it's like, how did it get to all the individual pieces? That is a black box. So, we don't really know how the AI model does it. It's algorithms.
It's guessing what comes next in a line or a letter or token. And so, Dario Amadeo has some really clever software and techniques that they're working on. And his goal is to have cracked open the black box and explain exactly how the AI models' algorithms work to get to the responses it gets to. And he's hoping to do that by 2027.
So, very interesting. It's crazy to think we don't even know how these AI models are work, we just train the algorithm and it gives us a good result. We don't really know why. But it seems that that is going to be coming sooner than later.
And we're going to then be able to understand the alignment of these models, how safe they are for you to go off the rails. Like we recently had Grog XAI's Grog kind of go off the rails recently. And so, we'll see, we'll be able to get a bit of a deeper look into all of that. Hey, listen, if you enjoyed the podcast episode today, the number one way that you could say thank you is to leave a rating and review or comment wherever you get your podcast comments on Spotify, comments on YouTube, all of it helps the podcast a ton.
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