The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis
NLW on the Future of AI Agents
Mon, 24 Feb 2025
A discussion originally from the Tool Use podcast. See full episode: https://www.youtube.com/watch?v=-fDu52FzmJc // https://podcasts.apple.com/ca/podcast/will-ai-agents-be-your-automation-breakthrough-ft-nlw/id1773693853?i=1000693646455Brought to you by:KPMG – Go to www.kpmg.us/ai to learn more about how KPMG can help you drive value with our AI solutions.Vanta - Simplify compliance - https://vanta.com/nlwThe Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown
Chapter 1: What is this episode about?
Today on the AI Daily Brief, a special interview with me on the future of AI agents. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Hello, friends. Welcome back to another AI Daily Brief. I am traveling this week, so we're doing a couple episodes that'll be different.
I do have my podcast gear, so I will be recording some normal episodes. But for today, I'm sharing the first part of an interview that I did with another podcast, a great one called Tool Use, a couple of weeks ago about AI agents.
Obviously, this is the topic du jour, and because I was in the interviewee chair for this one, I got to riff a little bit more broadly around what I think the future of agents actually looks like than I otherwise normally would.
So what I'm going to do is I'm going to share a little more than half of this episode, and then I'll send you out a link to where you can find the rest of it on their feed, the guys over at Tool Use interview builders and entrepreneurs, and other folks who are actually using AI day in and day out about how they're using AI.
So if that's interesting to you, I highly encourage you to go check out their show. So again, today's episode is an interview with me about the future of AI agents. Today's episode is brought to you by Vanta. That's where Vanta comes in. Businesses use Vanta to establish trust by automating compliance needs across over 35 frameworks like SOC 2 and ISO 27001.
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This week, we're joined by Nathaniel Whittlemore, also known as NLW, the founder and CEO of Superintelligent, as well as the host of my favorite daily AI podcast, the AI Daily Brief. NLW, welcome to Tool Use.
Hey, it's great to be here. Thanks for having me.
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Chapter 2: Why does NLW think the definition of AI agents doesn't matter?
We're super glad to have you on. I guess we can kind of kick things off. I think everyone kind of has their own definition of what an agent is, it seems like. There's not really a very good definition. I'm kind of curious how you define an agent and kind of what that means to you.
I actually have a super strong point of view on this. You'll find this is a common thread for me. So you see a lot of kind of hand-wringing, I think, among people who have been in AI for a long time or who are sort of more technical experts on how mangled the definition of agent is as it's found its way into enterprise and stuff. And I actually think that we should not care about that.
I think that when people on average are talking about agents or referring to agents, they're bucketing AI into two categories. AI that I have to use and AI that does stuff for me, right? Like without me having to really, you know, tell it other than maybe that one first time. And obviously that's, you know, not super precise, but I think broadly it gets people kind of in the way to think about it.
Like particularly if you're, you know, an enterprise leader and you're thinking about whether you're going to deploy, you know, kind of a assistant style AI or agents, like they really kind of broadly bucket into those two categories. I also think that we've so rarely had as much
Chapter 3: What are the current use cases for AI agents?
like narrative consolidation around a single term that's like kind of in the ballpark that the fact that everyone kind of knows this term and is there like trying to kind of like, you know, get into the nitty gritty between agent and automation, I just think is ultimately sort of a not particularly relevant pursuit.
I think what people are looking for when they're talking about agents is stuff that actually takes big chunks of work off the table for me, not just makes me do that work better.
Yeah, I found it something similar to where people say, oh, the newest agent from OpenAI deep research, which I've used and is great. And other people say like, well, what about code interpreter? Is that an agent? And ultimately it doesn't matter whether it's a tool or a workflow, as long as it solves a certain task for you. Through your use of them, what type of use cases are you excited for?
What have you found to be actually helpful in the current state?
So we think a lot.
So the main product right now that Superintelligent is being hit up for is something we call the Agent Readiness Audit, which is basically an agentified process of looking across an organization's workflows and its procedures, its policies, to help them understand what they need to do to be ready to use agents and which agent use cases might be a good fit for them based on current capabilities.
And I think that what we often end up kind of, you know, what ends up getting shared with them is, you know, we have these grand ideas of these sort of multi-agent workflows that are orchestrated perfectly and take, you know, giant chunks of tasks off. And that's really just not where things are.
Where things are right now is still in this sort of discrete task, you know, repetitive discrete task that you can do, that you have to do over and over and over again. And I think that the more that people and companies experiment, you know, with that in mind, the better suited they're going to be to actually, you know, taking advantage of where agents are now.
I think it's going to change dramatically over the course of this year, right? So really single purpose, very specific agents. I think the way that I think about it, you know, sort of from a personal perspective is, you know, we haven't really agentified a ton of the like podcast processes yet. You know, we use AI for a bunch of them, but they're sort of like not fully automated.
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Chapter 4: How can beginners start using AI in their business?
Basically making their work better by having ChatGPT act as like a consultant or a thought partner as they were thinking through things, right? And this is going to evolve over time. I think an interesting analogy is sort of like, imagine the marketing or social media that you guys do for the podcast, right?
You've probably shifted, I would imagine, from doing it totally raw yourself to now like partnering with ChatGPT on some of the copy and using mid-journey for some of the images. And so it's like now it's sort of this AI-enabled process.
It's an AI-assisted process where maybe the time has gone down, but I bet the benefit is more just sort of like quality increase and cognitive load decrease for you guys.
However, I would imagine that over the course of the next year or so, we'll all be able to actually, I think that, you know, a social media agent seems like one of the easiest to actually execute against, you know, how many tweets per day do you want? What do you want them to relate to? How much are they replies versus this?
You know, like you could kind of see how it comes together pretty quickly. What's the database that I have to pull from a previous messages. And, and, and so you're kind of going to see that, that process. And so if people are just getting started,
Just using the assisted level AI to see how it makes their work better before they worry too much about even time-saving necessarily, I think is often a really good starting point.
Yeah, absolutely. And I've even seen the progression where you have those chats with Cloud or ChatGPT to get some input, help with the brainstorming, coming up with titles, to creating a Cloud project when you can upload a bunch of documents, a bunch of standards and best practices, so you can get more consistent results over time.
We've also experimented with the AI editors and we've yet to find success there. But it's interesting how the chasm between what works today and what is, you know, not quite working, what's a little ways off is just shrinking by the day.
Yeah.
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Chapter 5: What tools are essential for automating business processes with AI?
Um, I think the one that I ended up using, I tried a couple and the one I ended up using was, uh, what, what economic predictions in the era of AGI, basically how AGI is going to impact the economic landscape. Um, wrote a research paper on it with deep research and then fed that into Google's notebook LM and let them turn it into a podcast.
And that's what I published that day, um, as an experiment, uh, It went over reasonably. It was sort of a cute idea. So I don't think I'm going to be turning back to that too frequently. When I think about where automations might come in the future for the AI Daily Brief, I think that there's...
There's probably not for my show because so much of it is like the context that I add implicitly around things. But, you know, news podcasts are going to be very easy to go from, you know, the automated feed that curates them and just turns it, you know, end-to-end pipeline into a podcast that gets pushed out, you know.
It's a very, very sort of simple set of steps that, you know, each requires their own automation, but you could do it really effectively. Yeah.
Yeah, absolutely. And as a longtime listener, I can tell you that the added personality, the added perspective always helps besides just, you know, an information dump. I actually wouldn't mind double click on deep research because I've also used it, had positive results. But as you mentioned, the Twitter vibe test, a lot of people didn't seem to like it.
A lot of people did, but it was one of those right down the middle ones. What's your experience been like with it? Do you think it's a step in the right direction? And even just like long running AI processes in general? Do you think that's the future?
Yeah. Well, I think it's part of the future, for sure. I think that we're going to have to do a lot of experimenting and iterating to figure out exactly how these things work. My sense is that most of the people who have had positive experiences with deep research have used it for...
uh for particular types of knowledge uh you know summarization that it was well suited to do and the people who've had bad experiences have started to figure out the jagged edges of where it's not so good right so it's very clear that like not having access to contemporary journals is a huge problem right it really like limits its ability to uh be super deep and contemporary when it comes to science or anything anything that requires access to you know journals that are behind paywalls
The other thing that I found is that when it comes to really like fast moving spaces, there are, it can be a challenge. So for example, this AGI thing that I did, it was mostly great, however, it was definitely like over-reliant on Nick Bostrom's super intelligence, you know, as a resource.
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Chapter 6: How is Superintelligent using AI to automate knowledge processes?
And I think at one point it said that most scientists still think that AGI is a decade or two away, which is obviously like so not the, you know, it's not reading Twitter, let's put it that way. So I think that there's, you know, we're just going to figure out like,
Basically, research or grabbing a bunch of sources and turning them into a consolidated bucket of knowledge is actually a very diverse use case. It's not one use case. It's about a thousand use cases embedded in one category of use case that it's going to take some time for us to figure out what pieces of it this particular tool is actually good at.
I've enjoyed deep research so far. I think there's limitations. I think what's also weird is some of the limitations are like when it hallucinates, it's hard to know that it actually hallucinated. Like it's like in these areas that I'm not an expert at, like it could just say something and then cite the reference. And I'm like, oh yeah, that's true because it read the thing, right?
And so it's like, it's so much harder to spot these hallucinations. And I feel like hallucinations are still an issue in the world of AI. And I feel like that's something that we're still trying to solve. How big of an issue is hallucinations, do you think? And is that like a primary complaint that you see with businesses?
Yeah, it's actually a much bigger deal for businesses than it is for consumers, I think. Consumers have a higher kind of threshold for what they can deal with, especially if it's, you know, so much of the deep research use case isn't it isn't trying to get something that's production ready. It's trying to get a kind of a thing that gets to 80 or 90%, right?
So one of the use cases that I've seen a number of people have success with is basically background market descriptions and sizing for their startups, right? So they're trying to communicate and understand like how big the total addressable market for the thing that they're building is. And, you know, it's really good at pulling a bunch of different resources in, blah, blah, blah, blah, blah.
But they're never going to just turn that over to an investor. At least if they're actually a good entrepreneur, they're not going to just turn it over to an investor. But it saves them a huge amount of time. Like I said, it gets them kind of 80% of the way there. And so for them, they're maybe more in a position to actually spot those hallucinations.
Where hallucinations become a real problem is when people are actually, you know, basically replacing a human information source with an AI or an agent information source that really relies on having the right information.
So an insurance company that we work with found that the threshold of tolerance that people had for a human agent being wrong when they were giving them information was like 5% of the time, 7% of the time, something like that. Whereas with a robot basically giving them that information, it was like less than 1%, right? People expect it to be absolutely perfect.
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