Everyday AI Podcast – An AI and ChatGPT Podcast - EP 590: Agents, LLMs, or Algorithms? A Playbook for Choosing AI

Episode Date: August 15, 2025

Confused by AI jargon and unsure which tools actually move the needle for your business? We break down the real differences between traditional algorithms, large language models (LLMs), and agents �� including agentic AI — and give practical guidance leaders can use now.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:Choosing AI: Algorithms vs. AgentsUnderstanding AI Models and AgentsUsing Conditional Statements in AIImportance of Data in AI TrainingRisk Factors in Agentic AI ProjectsInnovation through AI ExperimentationEvaluating AI for Business SolutionsTimestamps:00:00 AWS AI Leader Departs Amid Talent War03:43 Meta Wins Copyright Lawsuit07:47 Choosing AI: Short or Long Term?12:58 Agentic AI: Dynamic Decision Models16:12 "Demanding Data-Driven Precision in Business"20:08 "Agentic AI: Adoption and Risks"22:05 Startup Challenges Amidst Tech Giants24:36 Balancing Innovation and Routine27:25 AGI: Future of Work and SurvivalKeywords:AI algorithms, Large Language Models, LLMs, Agents, Agentic AI, Multi agentic AI, Amazon Web Services, AWS, Vazhi Philemon, Gen AI efforts, Amazon Bedrock, talent wars in tech, OpenAI, Google, Meta, Copyright lawsuit, AI training, Sarah Silverman, Llama, Fair use in AI, Anthropic, AI deep research model, API, Webhooks, MCP, Code interpreter, Keymaker, Data labeling, Training datasets, Computer vision models, Block out time to experiment, Decision-making, If else conditional statements, Data-driven approach, AGI, Teleporting, Innovation in AI, Experiment with AI, Business leaders, Performance improvements, Sustainable business models, Corporate blade.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. It seems like AI is sometimes just alphabet soup of the buzzword of the day, right?

Yeah, we all want to use AI and Gen A.I and LLMs and what happens when AGI comes or ASI, right? But I think it's important to first understand the basics, right? and not just rush toward what everyone else is using. So today we're going to be breaking it down and how to choose the right AI. And we're going to be talking about algorithms, agents, and large language models. I'm excited for today's conversation. I hope you are too. What's going on, y'all?

Welcome to Everyday AI. My name is Jordan Wilson. And this thing is your daily live stream podcast and free daily newsletter, helping us all not just keep up with what's happening in the world of AI, but how we can all actually use this information to get. ahead to grow our companies and our careers. If that's what you're doing, you're in the right place. It starts here with the unedited, unscripted, live stream, and podcast.

But where you are actually going to grow is on our website. So please go to Your EverydayAI.com. There you can, yeah, go listen to and watch more than 550 episodes for free. It's a free generative AI university. But also, you need to sign up for today's daily newsletter. It is free. We're going to be recapping, not just the best insights from today's episode, but also everything else you need to be the smartest person in AI in your company.

All right, before we get started, I'm bringing back to AI news for today. Podcast audience, let me know in live stream audience. Sometimes I do the news right before. Sometimes I don't. I've taken a little bit of break. Let me know if you want it back. But let's just go ahead and go into the AI news for today, June 27th. So first, Amazon Web Services has lost a pivotal AI leader.

as the talent wars intensify in tech. In tech. So, AWS has lost Vazi Philamon, a vice president who helped lead its Gen AI efforts and the Amazon Bedrock platform, according to reports. So Philomons departure follows eight years at Amazon incomes as competition for top AI talent accelerates with companies like OpenAI and Google, setting the pace and reportedly sometimes offering companies and meta. We've been talking about all this buzz of 100. million annual contracts. So Amazon continues to invest heavily in AI, including an $8 billion stake in startup Anthropic. Amazon has recently rolled out Nova and Sonic AI models, expanding capabilities in tax video and image generation. According to its CEO, Andy Jassy, Amazon advances in

agentric AI could lead to fewer traditional corporate jobs as automation replaces some tasks, even as demand grows for new roles in AI development. All right, our next piece of AI news, Meta has won a key copyright lawsuit over AI training. So a U.S. judge has ruled in favor of Meta dismissing a copyright lawsuit filed by authors, including Sarah Silverman and T. Nessie Coates, hopefully I didn't get that name wrong, who alleged that meta unlawfully used their books to train its AI model, Lama. So the judge said the plaintiffs failed to show that meta's AI would harm the market for their works, leading him to call Meta's use of the material fair use under current copyright law. However, the judge did emphasize that his ruling does not mean all AI training on copyrighted material is legal, noting that using such works without permission could be unlawful in many situations.

And this comes just kind of hours after Anthropic won a similar ruling about their. use of essentially training their model on books. All right, last but not least, a little one for developers. So Open AI has unveiled its deep research model and web hooks for its API. So OpenAI has announced two major updates to its API, the introduction of its deep research mode and supports for webhooks. The company just announced on Twitter. So the new 03 deep research and 04 mini deep research models are the same advanced version that power deep research within chat gpt so essentially you have a version in the o3 or the 04 mini so if you are one of the countless companies building on top of open a i or maybe if you're using services like yeah i mean

you probably don't even know but your bank is probably using open a i's API uh right so now the capabilities are really going to be expanded now with this deep research rolling out to the API. The models also come equipped with features like MCP and a built-in code interpreter. And with the rollout of webhooks, developers can now receive real-time notifications for key API events, including competed responses and fine-tuning jobs. All right. For those stories and a lot more, again, go to our website at your everyday AI.com and

check out in the newsletter. All right. Let's get to the real stuff here. How the heck do you choose the right AI? Should we all be using agents, multi-agentic workflows, agentic workflows, traditional algorithms, large language models? I don't know. It's a question that we're always talking about

and something business leaders are always tasked with. So let's bring on someone to help guide us through today's conversation. So please help me welcome to the show, live stream audience, if you don't mind. We have Michael Abramov, the CEO of Keymaker and KeyLabs. Michael, thank you so much for joining the Everyday AI show. Hi, hi. Thank you for inviting. All right.

Well, thanks for having us, Michael. So first, before we get into it, can you tell us a little bit what you do at KeyMaker and KeyLabs? So actually, yeah, so KeyMaker and KeyLabs are data labeling platform and the service provider. So what we do is we prepare datasets for training the models, whether it's, computer vision models, whether it's LLM models or any other AI. Actually, we are preparing the whole, like all the training materials for that. Yeah. So in, yeah, the, the training materials and the data, it's, it's the hot topic, right?

Even mentioning that in the, uh, in the AI news today, but maybe let's, let's fast forward to the end, right? So for companies, uh, you know, it's hard to keep up with everything that's available. So there's traditional AI algorithms, there's large language models. Now there's these models, these large language models on the consumer side that are agentic. And then you have literally agents. So where do companies start? How do you choose the right AI?

Oh, that's amazing question. And I don't have a very specific answer to that because it's like choosing your life partner. When you're choosing your partner for life, you might be. 20 years old or any other age. And then your requirements at that specific moment are for something specific, but then your requirements change over time, right? And you want to choose the person who's going to be suitable for different ages, right? The same with AI.

You have to think about it. Do I choose it for short term? Do I choose it for long term? Do I, you know, what kind of tasks it should solve? Also, there is some kind of over-promising in social networks because you, I mean, people like us look at LinkedIn every day, right, on the LinkedIn timeline. And you see people saying, oh, I just, you know, I just replaced 50 employees with this. I just replaced 16 employees with that. And you kind of sit and think, hey, what am I doing wrong?

Where did I, you know? Why do I still have employees? Why? like right like yeah how does it work like what did i see and then you try to do it you try to play with some tools and they are they they might help you here they might not help you there and you know so it's it's actually very hard and i think there is no one one very specific answer but what i do believe you like every person uh nowadays uh should do is uh play a lot with ai I mean, I mean it, play it yourself.

Don't listen to anyone. It's not like, okay, I'm a CEO, right? And I'm managing a company with 480 people. And my team is pretty big. I have pretty busy day and I don't have time for stuff. But my calendar is always closed for half an hour per day just to play with tools, just to play with things. Okay?

And when you do just register to all the platforms, try to see how it can help. you're always going to go to the to your biggest pains that's like that's natural that's organic I mean you don't have to sit and think like what am I doing like what am I testing first my emails you know elaboration the emails or my slack problem you will always go to the things that you know the first first we'll all go to things that are painful the most painful now after you play with it by yourself and you see how it helps or it doesn't help you it And that now, Jordan, for reference, it doesn't matter whether it's agentic, LLM, BLM.

I mean, just put all this terminology aside, right? Try to find what helps you, what solves your pain, your problem. Once you do, give an example to your employees, to your peers, to your colleagues. You can just come up and say, hey, I'm using this, I'm doing this, and it helps me. Right? And then, and then it's, you know, you're going to see how to, you know, you're going to see what's best for you. Yeah, I think, I think there are some great points there, right? It sounds simple. But I love that it's like you're actually blocking out the time, experimenting with the latest technology, seeing what works and then sharing about it. That's that's literally, you know, a great kind of roadmap. It's a similar roadmap that we share all the time. So I love that. But let's, even though you don't need to, you know, define everything, I think it'll be helpful for our audience because it can be confusing, right?

Because you hear these things about agents and then you hear these things about multi-agentic AI. Yeah. And then here large language models. And now these large language models have agentic capabilities, right? Help us with the definitions. What the heck is an agent? What is a large language model?

Yeah. What's an algorithm? Yeah, that's amazing question. And I think that the, okay, we're speaking. hear about three things first of all. LLM is a think about it just like a black box, right, that you can ask any question and it can answer you any answer. And that's it, that's all it can do. So let's call it model. And agents think about it as a automated multi-models. So you can have like five or ten or whatever amount of models and then you can put them on the dashboard and draw

some arrows from one to another with if and else. I think many people are programmers, like software developers here and might understand the if else terminology, but anyways, anybody else can also understand that. And now, think about it. You want to go to Europe and you have built your travel agent, right? So you ask, hey, what about going to Paris next week? So what is it going to do? It's going to go to models that checks your Banfi account.

if you have enough money. It's going to go to weather model that will check the weather in Paris and we'll see if it's good for you or not, right? So let's think about agent as just multiple models that know how to speak to each other and how to do this, you know, if else, inside the agent. Now, agentic AI is a much more interesting concept. It's a concept. It's not a, you know, specific tool or specific thing. Ageny KIA, is when you have an agent, but all of this if else's are being decided by another model. So you don't, it's not hard-coded that you have to check the bank account and the weather in Paris and the flights on, you know, different flight companies.

So the another model, which let's call it supervisor model, we'll look at the problem at the task and we'll decide what if else's it wants to put inside this. thing and where it wants to go and what it wants to do and then apply agents inside the system. Yeah, so help us break down a little bit more for our non-technical audience. Yeah. Explain the if else, you know, conditional statement. Like, what does that mean, specifically in the context of, you know, AI or, you know, large language models, right? But yeah, if else, conditional statement as it pertains to AI. So every, yeah, okay, okay. So every, a kind of decision will go through some kind of decision trees. And you can take an example of your

day-to-day life. So you wake up in the morning and you want to take your child to school, right? Let's say, your kid to school. Now, you have a lot of if-else's. So first, if else, did he wake up or she? Right. If yes, ask them to take a breakfast. If no, wake them up first. right and then do they have a fever maybe they have a fever right if yes stay at home and then you know rest if no let's go to you know let's brush teeth and go to school stuff like that so there is lots of decisions let's call decision three because it's a hierarchical usually it's it goes like from top to down or whatever direction you choose but there is initial condition and then there are lots of final

items that are dependent on your decisions that you made on the way. And all of these decisions will be made by if and else. If this then, else, something else. Yeah, I think that's a great way. And even I love

the example that you just played, but that just goes to show and emphasize ultimately how, right, even like what your company does. Like data, right? And making sure that you have the structured data to help answer those if-else conditions, right? Like, you know, whether it comes to, you know, traditional algorithms,

you know, large language models, agentric AI, right, whatever it is. How important is having your data correct? Oh, that's the most important thing. So I had this problem in my company that I was requesting from all of the people who work with me to be data-driven. Now, let me just explain what data-driven we mean. Sometimes you can come and tell you. me, hey Michael, most of the people in the world are afraid to lose their job now.

And when you say most of the people in the world, like how many? Like is it 88%? Is it 90%? Is it business people? Is it people from United States? Is it like what kind of people are talking about? Now the second thing is what does the most mean? Like where did you take this information from?

Is it reliable? Can you rely on this information? Okay. You got it from Gartner or you got it from Google search. So like, who wrote it? Right? And maybe it's your personal, you know, your personal afraid and you are afraid of losing the job. And now you extrapolate this, this, you know, emotion on most of the people in the world, right? I don't know.

That's, that's, so you have to be data-driven. You have to come and say that, you know, I read like 12 different articles from different sources and that's how I, why I think that's true. Now, there is another problem to it. So after I asked most of the people in the company to be data driven and I explained what data driven means and how to acquire data and how to look at data, et cetera, I got reverse problem, the mirror problem. People, what they did was they were super data dreaming. They took data and they have built conclusions on the data and they would come to me and say,

hey, this is, you know, this is the problem and here is the data that proves it. And that was super funny to see that the data didn't prove it at all. It was their perception of the data that proves it. And that's, if I give an example, we could say, you know, when you say the sun has fallen and that's why the, I don't know, I even don't have an example, sorry. But you know what I mean. I mean, you can relate things that are unrelated and many people are tending to relate everything because people have to explain every single thing.

And so this pseudo data driven is even worse than not being data driven at all. And yeah, so we prepare data for machine learning training. We have lots of problems over there. have lots of misunderstandings of how the data should be structured, how it should be labeled, how it should be perceived. Even if you structured it really well and you labeled it pretty well, the developers of the model might do wrong things on the training stage. So one thing you mentioned in there, Michael, was something about whether you're getting your

information from Google or Gartner. So I want to ask you here in a second about a recent Gardner study on Agentic AI. But before we do, we're going to take a very quick break for a word from our sponsors. This podcast is supported by Google. Hey, everyone, David here, one of the product leads for Google Gemini. Check out VO3, our state-of-the-art AI video generation model in the Gemini app, which lets you create high-quality, eight-second videos with native audio generation. Try it with the Google AI Pro plan or get the highest access with the ultra plan.

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See it today at firefly.adobie.com. So as we, you know, go back and forth between algorithms, large language models, and agents, it's no surprise over the past, you know, year or so, the right. has been toward agentic AI. And it seems like every single company, even if maybe they don't need it or if they don't even fully understand it, they're trying to dive all into like agentic AI. And there's a recent Gardner study that we covered in our newsletter yesterday about that predicted that 40% of agentic AI projects will be canceled by 2027, either due to high

cost, unclear business value or inadequate risk controls. So I'm not going to ask you to predict a percentage, Michael, but one thing that caught me from that study is risk, right? Can you talk a little bit about the difference between risk in large language models, algorithms versus agents? Because in my mind, agents, it can get kind of risky if you really aren't kind of have a strong human-in-leuk connection. Yeah. So, okay, so the risk is not in LLM or agents. the risk is if i mean we speak about the risk for the businesses who develop who try to you know make money on developing this agents and selling them uh to someone now uh i have this uh idea of uh

i call it corporate blade uh it's a huge blade that goes like when the new when new technology evolves uh everybody is running out to to implement some kind of you you you know, wrappers or plugins or some kind of things on top of the new technology, right? And we see a lot of it with AI. Lots of my friends, lots of people out there are trying to build startups with AI. Now, what usually happens in the first stages of such technologies, when they are still not stable, some projects die because of what you said, because they are not sustainable or something. else but some of them which are which are you know successful they also might

fall and they might fall because the huge the giants will take the idea and for the giants to take the idea and to implement it is like one week two weeks of work so if you think about perplexity perplexity is a multi-million dollar startup and I think it's multi-billion dollar valuation already but all it does is it's wrapping chat GPT and then it adds a little bit better search, like, you know, capabilities on top of it. And also some different user interaction dynamics. But it's nothing that chat GPT, like the OpenAI can do in two weeks, right?

And if they like the idea, they can do it and just, you know, Blade cut out all of these startups that did, you know, were interesting things. And we see a lot of this. We see a lot of agents, a lot of mass tutors and personal assistance, psychotauri, like psychologists, you know, therapists, AI therapies, etc., or calendar assistants that are being wiped out by the next feature of Lama or Anthropic or ChatGPT, etc. And maybe Gardner's research relates to that as well, not only, you know, to unsustainable businesses or bad ideas. Yeah. And, you know, so I'm curious, even if you could walk us through. So, right, you said,

how many employees are at your company again? It's 480. 480 employees. So 480 employees. And you obviously, you know, specialize in data labeling for machine learning. So I'm guessing, that you've had a healthy amount of AI use, right? To make that assumption, right? Yeah. How are you even deciding as a CEO of a growing company? How are you deciding, hey, when do we step outside of the traditional, you know, decision tree algorithm to large language models, to agents?

How are you making those decisions? I push my people every day to innovations. Now, that's something special about my team, because I think in the beginning they were not, I mean, they were very curious about doing that, but at some point they were exhausted because they said, hey, too much innovation. I mean, let us, you know, be a little bit in a stable position for some time. And now we found a balance. So there is balance between like routine job and coming up with innovations how to make this routine work like more performant, let's say, etc. But the main thing here is that I'm not saying, hey, come up with LLM ID or agent idea or a

genetic AI idea or something else. I'm not kind of trying to say use this or that tool. So I'm just saying, hey guys, here is a whole. whole toolset. We even have once in a week technical education kind of session where we show up new things, everyone shows to another, etc. And then I say, here is a tool set. And you have problems. You have every day, you have daily problems, you have fires that you, you know, work with, etc. Just see if there is a better tool than you're using today. That's it. Okay? I'm not asking every person to be a software developer,

or agent developer or you know stuff like that and you i mean you'll be amazed by how many people who have never you know did any if else in their life they take this these tools they sometimes they ask questions sometimes they ask chat gpt hey i have this problem how do i solve it sometimes they ask their friends or myself or or youtube and they build amazing things amazing many times I'm just saying, hey, this is a startup. I really have such examples. I'm not going to spend your time on that, but I mean, yeah, that's amazing.

And if we look at kind of the linear progression, and obviously this goes over the course of many decades, right? But if we say algorithms led to large language models, which led to agents, right? And then there's a lot that probably comes after this. But what are some of those unknowns, right? Because obviously the pace of innovation is going very quickly. We've heard the smartest people in the world as an example say,

hey, once we get to quote unquote AGI and multi-agentic AGI, things really escalate there. But what are those kind of unknowns that may come after whatever next on that linear graph? Yeah, that's my favorite question. Because we have known unknowns, which is like, oh, when we get to AI, like every person will lose their job and the AI will do everything for us. And in that case, we have two directions. Direction number one, we're all going to die because AJI is going to kill us. And direction number two, we are going to live on the welfare because AI will work. We're going to have, we're going to get payments from the government.

and we will have a lot of time for creativity to be, you know, artists, musicians, etc, etc., etc., because we don't have to work. I mean, we have enough resources to exist without working. And there is lots of speculations around these two ideas, and I would call it known unknowns. Now, there are some unknown unknowns, and a good example of it is imagine the times when people were flying on zeppelins, right, or on the balloons,

I don't know how it, balloons, right? Yeah, yeah, air balloons. Yeah, and the capacity of one basket in the air balloon was like 20 people. And then the engineers, they were thinking, oh, in 100 years, people will fly on the air balloon with 100 people capacity. And in 200 people, it's going to be 500 people capacity, right? So it's going to be like a bigger balloon. They could never imagine an airplane, right?

That was the unknown unknown for them. That's something that you can't even imagine. Now, for us in AGI or in overall AI, I don't know, you name it, right? There might be unknown, unknown thing in, I don't know, teleporting. Did you think about, like, teleporting? Would you like to be, I mean, for no, absolutely, yeah. If I could choose the next feature, I would love to purchase in this game world, right? It would be teleporting or invisibility.

Right? So maybe we can get there. And maybe we can get to something that we can't even think about right now. I don't know. Yeah. So Michael, we've covered a lot in today's conversation. But as we wrap up, what is the most important piece of advice or actionable insight that you have for business leaders out there that are maybe just scratching their head when it comes to, you know, algorithms? large language model,

agenic AI, agents, what do people need to focus on to make the right decision for their business? Experiment, guys. I mean, people, experiment, experiment, experiment. And your everyday should look like you are starting up a new company. Even though your company is amazing and your company makes lots of money

and you are super successful in your company, you should think about it. Today I'm going to create something new. I'm going to create some new idea and experiment with it. And then you can just implement it in your company. You don't have to open new company for it. And I would say that most of the interesting things that are happening inside Keymaker, in terms of improving performances in the teams, et cetera, et cetera,

are you can take it and make a separate startup out there. I mean, we won't spend time on it, but that's all because of experiment. You have to experiment. Awesome. Amazing. Amazing advice. and a very insightful conversation. Michael,

thank you so much for taking time out of your day to join the Everyday AI show. We really appreciate it. Thank you. Bye, boy. And if you miss anything, don't worry, we've got it all for you.

A lot of great insights and advice there from Michael. So if you miss anything, it's going to be in our newsletter. If this was helpful, if you're listening on the podcast, please make sure to follow the show and subscribe.

Drop us a note as well. And then when you're done with that, go to Your EverydayAI.com. So make sure to check out the recap, this podcast, we're going to be dropping some additional information that we probably didn't have time to get to as well as keeping you up to date with everything else you need to know. So, thank you for tuning in. Please join us next time for more everyday AI. Thanks, y'all. Meet Firefly

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Go break some barriers and we'll see you next time.