GenAI hot takes and bad use cases

It seems like all we hear about are the great use cases for GenAI, but where should you NOT be using the technology? On this episode Chris and Daniel share their hot takes and bad use cases. Some may surprise you! Sponsors: Domo https://ai.domo.com – The AI and data products platform. Strengthen your entire data journey with Domo’s AI and data products. Fly.io https://fly.io/ – The home of Changelog.com — Deploy your apps close to your users — global Anycast load-balancing, zero-configuration private networking, hardware isolation, and instant WireGuard VPN connections. Push-button deployments that scale to thousands of instances. Check out the speedrun https://fly.io/speedrun/ to get started in minutes. Featuring: Chris Benson – Website https://chrisbenson.com, GitHub https://github.com/chrisbenson, LinkedIn https://www.linkedin.com/in/chrisbenson, X https://x.com/chrisbensonDaniel Whitenack – Website https://www.datadan.io/, GitHub https://github.com/dwhitena, X https://x.com/dwhitenaUpcoming Events: Register for upcoming webinars here https://practicalai.fm/webinars!

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:03Speaker 00

  Welcome to Practical AI, the podcast that makes artificial intelligence practical, productive, and accessible to all. If you like this show, you will love The Change Log. It's news on Mondays, deep, technical interviews on Wednesdays, and on Fridays, an awesome talk show for your weekend enjoyment. Find us by searching for The Change Log wherever you get your podcasts. Thanks to our partners at fly.io. Launch your AI apps in five minutes or less. Learn how at fly.io.

• 0:44Daniel Whitenack?

  Welcome to another fully connected episode of the Practical AI Podcast. In these episodes where it's just Chris and I, no guest, we try to keep you updated with some of the things happening in the AI world. Talk through some things that might help you level up your machine learning and AI game. So excited to dig in with you today, Chris. I'm joined as always by my co-host, Chris Benson, who is a principal AI research engineer at Lockheed Martin, and I'm Daniel Whitenack, CEO of Prediction Guard. How you doing, Chris?

• 1:18Speaker 00

  I'm doing good. It's a, I'm looking forward to our conversation today. It's a snowy day in Georgia, and we can, we can talk a little generative AI and talk about you wouldn't want to use it.

• 1:31Daniel Whitenack?

  Yeah.

• 1:31Speaker 00

  Unless it was snowing in Georgia, kind of things.

• 1:33Daniel Whitenack?

  In the theme of coldness on today, which is also cold where I'm at, talk about the cold side of Gen AI, or actually, you know, the, what we had talked about, thinking through were the bad use cases for Gen AI, or where you shouldn't use Gen AI. Five or more. Bad use cases.

• 1:55Chris Benson?

  Yeah. And you know, the funny thing about it is this is a topic that we have casually talked about a whole bunch of times and we had not previously said, let's make it an episode. But you know, one of the, one of our, I think it may be a little bit of a pet peeve for not only us, but other people I talk to in the AI space is there are so many, you know, we're at this, you know, huge hype within Gen AI and people just want to use it for everything that there could possibly be an AI application for. And you know, there's so many places where it doesn't necessarily produce the best outcome for you. And we talk about this casually all the time. So glad that we're actually doing this in the show today.

• 2:36Daniel Whitenack?

  Yeah, I was creating some, some docs for, for a customer of ours and some training materials. And I have this section just labeled here be dragons.

• 2:47Chris Benson?

  Yeah.

• 2:49Daniel Whitenack?

  So yeah, there might be some hot takes in here. I'm interested to hear what, what your takes are. My first one, so number one, bad use of Gen AI, or maybe one that you want to avoid, at least for now, is maybe a hot take, but I would say from my perspective, completely autonomous agents of any type are currently, you know, well, who knows how long this will be the case, but currently and for some time, generally a source of sadness for people when they, when they try to create them. So what I mean by autonomous agent would be an agent or an automation that, that has no human in the loop, just

• 3:34Daniel Whitenack?

  sort of is running in the background and you kind of hope that it does something for you. So it could be on the sales side, right? Oh, I'm going to have an agent do my whole sales process for me and I'm just going to kind of sit back and work on my product and the agent's going to make all of the, the sales for me. Or maybe it's, you know, some sort of internal admin process that you're automating or, you know,

• 4:03Daniel Whitenack?

  Even all the way, you know, into manufacturing with automation in plants or, you know, more industrial case, whatever you're thinking of, my first one is completely autonomous agents. What's your, what's your thought, Chris?

• 4:17Chris Benson?

  Not only do I think that's right, I'm smiling in a big way because I'm going to throw in something from the side just to support that. Apparently there is a new show on Netflix and I just read about it last night in a news.

• 4:30Daniel Whitenack?

  Netflix AI is tough for me.

• 4:33Chris Benson?

  And, and, and it's, the show is called Cassandra and it's about, it's like a home assistant robot, you know, with agency in terms of doing lots of tasks, but it goes apparently, I have not seen the show yet because I just heard about it, but apparently it gets very, very dark. And I'm just like, when you were talking about that just now, you know, in more of a real world scenario, obviously, it made me think of that. And so, yeah, I agree. A completely autonomous agent in this day and age with no guardrails around it. And you're just saying, go at it, generative AI, especially, especially if it's dealing with anything that has any

• 5:13Chris Benson?

  sort of sensitivity or requires a little bit of thoughtfulness to it. Yeah, not, not going there.

• 5:21Daniel Whitenack?

  Yeah. Well, and I think even beyond the kind of security, privacy related things, a lot of times I just see people trying to do this and it just doesn't really work that well.

• 5:34Chris Benson?

  Early days, early days.

• 5:35Daniel Whitenack?

  Yeah, it's, it's early days. So like when you have, and for those that, you know, maybe have or haven't listened to previous episodes, when, when we're talking about an agent, we mean, you know, you give a task to some sort of system, it has the ability then to generate queries maybe into other systems like APIs or databases or data stores or other things to accomplish a certain task. And it kind of loops over that task until it reaches, reaches an objective, right? And in the autonomous, fully kind of autonomous case. You would have, you know, just using the sales example, because it's easy.

• 6:16Daniel Whitenack?

  You know, you want an agent to decide how to find prospects for you on LinkedIn. And then you want to gather, you know, a dossier about all of those prospects. And then you want to initiate the contact and then you want to pull off some type of demo or call. And then you want to, you know, close the deal and do the contract arrangement, right? And just sort of like determine how to do every step of that process, basically relating, replacing a human in their agency with the autonomous agent. Now, I think in that case, we could say certain portions of that can be very interestingly

• 6:59Daniel Whitenack?

  addressed with AI functionality. So doing the prospecting, generating the dossiers, right? That, those are, I would consider those good use cases if they're tied to a, you know, maybe a sales professional that's deciding how and when to do those things. In the imagination, it would be great to think of just kind of letting that run in the background and you getting sales all the time, but it just doesn't really work very well. There's a lot of fragility in that type of system when there's a lot of that determination of objectives and determining how to interact with systems and all of these things. It produces a lot of errors, a lot of fragility.

• 7:41Daniel Whitenack?

  It's much, much more productive, at least currently, for you to have a tool that can help your sales professionals prospect or a tool that can help them create these, you know, dossiers and that sort of thing. And certainly tie in AI to that, but not kind of this end to end, completely autonomous automation.

• 8:03Chris Benson?

  I totally agree with you. And I certainly, by the way, just as a clarification from what I said earlier, I was not meaning to imply agents would typically have a robotic body. Just should I have confused anybody?

• 8:15Daniel Whitenack?

  There's a lot of people exploring that, but.

• 8:17Chris Benson?

  There are, there are. You know, just one of the things to note in terms of, you know, we're in this, the rise of agents right now. It's the hottest thing out there. But there are, you know, it's interesting. There are a lot of guardrail mechanisms that are out there. I know in the industry I work in and defense, there are, especially in things like, you know, weapon systems and stuff like that. The DOD has guardrails around such things. So if you're listening and aren't familiar with that, but are a little bit worried about the world, it's fortunately, there are people thinking along these lines.

• 8:50Daniel Whitenack?

  Yeah. And, and there are, I would say, useful agents at this point, just not kind of in that fully autonomous.

• 8:58Chris Benson?

  Correct.

• 8:59Daniel Whitenack?

  Kind of setting. So AI systems that can connect to multiple things and maybe are used, triggered by a human to do certain things. Those are the most successful that I've seen.

• 9:10Chris Benson?

  Absolutely.

• 9:11Daniel Whitenack?

  Number two from me, Chris. So we've got autonomous agents. Number two for me was time series forecasting, or really any sort of prediction mechanism. So whether that's predicting, you know, future stock prices or reasoning over series of data, making predictions, sort of, there's some level of prediction that these models can do somewhat well in terms of, maybe it's things like general text classification, right? Is this, is this message spam or not spam? And you can give some examples and you could get some reasonable output from a model like that.

• 9:55Daniel Whitenack?

  That's why I kind of honed in on time series forecasting specifically, because at least as far as I know, and I know that there's research in this area, kind of using transformer models for time series forecasting. But when I think of Gen AI, I think of, I'm going to log into ChatGPT, or I'm going to use DeepSeek or one of these models. And, you know, if you paste in a bunch of time series data and try to create a forecast, just with the Gen AI model and nothing else, then I think that's going to end again in sadness for you. It's, it's not, not going to work so well.

• 10:32Chris Benson?

  Yeah, I think so. I actually had that on my list, too, in the in the form of high stakes financial trading, you know.

• 10:39Daniel Whitenack?

  High stakes financial trading.

• 10:41Chris Benson?

  Where do you want? Where do you want to put your your million dollars today, you know, and see where it goes? So maybe maybe explore some of the possibilities there, but I don't think I would leave it to an agent to forecast or or make that prediction on its own.

• 10:56Daniel Whitenack?

  Yeah, I think people have shown basically that these models definitely don't have the kind of world understanding, real world grounding to make certain reasoning or take certain steps and reasoning to make reasonable predictions, but also they're really bad, generally really bad with numbers. And so you may be able to, even with a vision model, paste in a graph of a time series, right? And say, you know, what month was my highest sales? If it's a graph of sales, right? And a vision model could reasonably return that value to you, right? But then if you say, well, now model out my sales for the next

• 11:39Daniel Whitenack?

  four quarters or something like that, I think generally that's not going to to work so well. I guess you could argue that a model could generate code that might use packages, you know, forecasting packages to actually make a reasonable, a reasonable forecast over certain data. Then, you know, my general question then would be, well, that might be useful to generate your code to do it. But really, it's not gen AI that's doing that. It's it's the stats models in Python or

• 12:15Chris Benson?

  That's right.

• 12:16Daniel Whitenack?

  or, you know, profit from from meta and that sort of thing.

• 12:20Chris Benson?

  Yeah, I mean, and just in case that confuses anyone, you know, there's the generative AI portion, you know, which can, you know, is is trained on a general data set. And then there's these models that it might be generating code to access, which are designed specifically for that function. So then those are two different things.

• 12:39Daniel Whitenack?

  Yeah, the code that ends up being executed is not having anything to do with gen AI, basically. Yeah, and maybe it would be worth highlighting in each of these cases that we talk about, Chris, some interesting tooling for some of these things. You know, in the autonomous agents case, certainly workflows and automations can be created and executed. You know, we had Prefect on the show, which is a workflow orchestrator that can be monitored and handle retries and all of that. That's a great thing if you're looking at kind of workflows and orchestration. Time series forecasting, my go-to, has usually been Facebook or Meta's profit package,

• 13:21Daniel Whitenack?

  which, you know, makes certain things pretty easy, but there's also many choices for that as well. So, so take a look through those things if you're interested in the non-gen AI side.

• 13:43Speaker 03

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• 15:04Daniel Whitenack?

  All right, Chris, on to number three. My third one was do not use Gen AI to do complete code rewrites or, or the complete development of your applications, your software applications. Thoughts?

• 15:21Chris Benson?

  Oh, I, I've tried that just playing around and I definitely don't think that that's ready for primetime despite the fact that, you know, as, as we sit here and say this, there have been quite a few CEO luminaries out there who have been advocating that over the last year or so. And I, I, when I sit down and try to do that, in, I get varying results and it depends largely on how mainstream a language is, for instance, on how good it is. But I haven't gotten anything that I would say is a production grade program, fully functional through nothing but generative AI, just toy programs.

• 16:03Daniel Whitenack?

  Yeah, without interaction.

• 16:05Chris Benson?

  Right, right.

• 16:07Daniel Whitenack?

  Yeah, I, I know this is advancing quickly. So who knows how dated this conversation will be in a few months, but I think we've been talking about this for, for some time now. And we've seen things like Devon and Cursor and these sorts of things come out, which are pretty amazing and do a lot of really interesting things, but often don't kind of provide that full like, I'm going to prompt and get a software application out of it. There is, there's more to it than that. So I think sometimes people are maybe a bit disillusioned. And, you know, a better way to think about this, or there are amazing kind of agents and toolings come

• 16:51Daniel Whitenack?

  out like the Devon, Cursor, All Hands, Wind Surf, et cetera, that can provide a huge acceleration in your code development, I think, if you treat them like. Code assistants and you know, maybe even junior developers that you are pairing with, right? So it's not so much that I'm just now a not complete non-developer, right? I have no technical skills and I just say, I want this application and it is generated for me. That's really what I'm meaning when I say kind of complete app development. So Gennai, from my perspective, is not capable of that right now, or you should not rely on it for that.

• 17:34Daniel Whitenack?

  Right now, there may be interesting demos and cases where some form of that is shown. But for the most part, I think thinking of the technology integrated into your code, code and programming as an assistant and even a highly functioning agent that you can pair with is a good model, just not the kind of, I guess it's a, maybe it's a specialization of the autonomous agent thing that I mentioned before.

• 18:04Chris Benson?

  Sort of. I think, and I think you're making really good points in that it's, you can't just toss it over the wall and just say, here's an instruction, do it all and generate kind of a complex set of programs and stuff, you know? I have done tasking, small things very successfully, but the scope of what they were addressing was, was constrained. And, and I think we are, we are there for things like that and doing small bits. It's not uncommon for me to, you know, generate, it's, I, many years ago, I would write VBA code, Visual Basic for applications for Microsoft stuff. I don't much anymore. And so now I can do something like that if I happen to be working for something in office

• 18:48Chris Benson?

  to do something, you know, put, put something together at work. But when I'm actually coding up a large project, I've not been successful. It's very helpful to have different tools on this, but I've not found one yet that I was able to disgustfully do a significant coding effort by itself, just tossing it over the wall. So I agree with you completely. It will be interesting to see where we are a year from now, two years from now.

• 19:13Daniel Whitenack?

  Yeah, well, definitely, I would encourage people to check out things like windsurf and Devon and All Hands and Cursor and all of these things. Super cool. Try them out, but don't expect that if you're, if you're not a programmer or have at least some minimal level of skill, that you're going to create a huge application or project with all of its intricacies and have that work and scale well.

• 19:39Chris Benson?

  Fair enough.

• 19:40Daniel Whitenack?

  All right, Chris, what are we on? Number four for me on the list of don't do this with Gen AI or bad Gen AI use cases. For me is anything extremely high throughput load latency. So of course, small models and very high throughput advances have taken place with Gen AI models. But still, you know, if you're doing quality assessment of products coming off of a actual scaled up manufacturing line, where you have to do maybe the assessment of each of those products in a fraction of a second.

• 20:24Daniel Whitenack?

  Really, you don't want to be reasoning over that data with a Gen AI model and take, you know, 10 seconds to generate your quality assessment for the product. It's just not, not feasible.

• 20:38Chris Benson?

  I would agree with that. And I actually have a subset that I'll throw in on that, that I think kind of fits in there, which would be kind of like real time applications with critical outcomes.

• 20:49Daniel Whitenack?

  Yep.

• 20:49Chris Benson?

  You know, that

• 20:50Daniel Whitenack?

  It's a great way to phrase it.

• 20:51Chris Benson?

  I think that that's, I think that that's a, an area that you would, you know, you may, you may have generative AI as a component in that mix, but you're going to have to have some guardrails around it and you're going to have to have some specialized models to keep things on track. Because in a real time app where, where things matter on the, on the tail end, you're, you know, great to use, but you don't want to rely entirely on that when it goes off the rails. You need some way to catch it that doesn't take any time. So.

• 21:20Daniel Whitenack?

  And I think you make a couple of great points. Part of it is around the latency, which I kind of highlighted. These models just don't operate fast enough and they don't operate in the types of environments necessarily that you need them to operate in for these type of maybe edge use cases as well in many cases. But also, these models perform, or they do what they are supposed to do most of the time, right? But still, if you, if you train a, a computer vision model, for example, to do that manufacturing task, that could run on CPU extremely high throughput and have a much higher accuracy

• 22:02Daniel Whitenack?

  than any, you know, generalized vision model out there, even that would need a GPU to, to run, right?

• 22:09Chris Benson?

  I, I agree with that.

• 22:11Daniel Whitenack?

  Yeah, so it's, it's just not, what is that? The separation between those two cases is still just really, really high in terms of those, those kind of use cases merging. Now, I, I do think that in a manufacturing scenario, right? There's a great, or any of these sort of other cases that you might think of, high throughput, critical type of scenarios, GenAI is, is very useful, maybe just not for that high throughput, low latency piece, but certainly for staff at the manufacturing facility that want to look at and analyze the data coming off of the quality assessment system and ask questions

• 22:53Daniel Whitenack?

  about, hey, you know, I see this alert, pull this data for me to help me understand what's going on, or are there any of these types of events that have happened in the past X time? And that query level side via natural language can be very powerful, for example, and there's many other things that you could do in those scenarios, but.

• 23:15Chris Benson?

  There, there is, I, I'll extend this just a little bit. As, as you know, I, my personal passion is in autonomous platforms, especially at massive scale, swarming, things like that. And when you talk about that, one of the, the areas where I think GenAI does play is exactly the equivalent of what you just said on the manufacturing, and that's having a human in the loop or on the loop that's able to interact. And so you're using GenAI to actually be able to enhance the communication between the human who is in control. Or on the loop and able to step in and not but but not so much in the other areas, especially considering that when you have lots of vehicles and this could apply for lots

• 23:59Chris Benson?

  of different use cases, both in the commercial space and the military space where you have a lot of a lot of different platforms or vehicles in communication, which requires high throughput. But yeah, I think that the only space there that is a big one is is in those interactions with the humans that are involved in that for for safety.

• 24:17Daniel Whitenack?

  Yeah, for sure. Well, I have one more, Chris. A last interesting bad use case for Gen AI. The one on my list was anything outside of the major languages of the world. So anything with any sort of linguistic diversity or cultural diversity. Essentially, the models of the modern Gen AI era maybe work well in the kind of top five to 10 languages of the world. But there's 7,000 spoken languages in the world, which means they basically don't work for any of the languages of the world, except for for a couple. And moreover, the

• 25:02Daniel Whitenack?

  kind of cultural context of the models is driven by mostly what has been gathered either from the Internet or by Western tech companies, maybe, you know, Chinese tech companies. But there's certainly a bias against kind of certain cultural contexts and languages. And, you know, even if you think about vision or video models, I'm sure the same is true, right? Because just certain things aren't represented there. So the reality is that it would be great if you could, you know, land anywhere in the world and change your

• 25:46Daniel Whitenack?

  your chat GPT or whatever to help you interact in, you know, X country in Africa or Y country in Asia and have that work really well with whatever languages you might encounter. But I would say generally, that's not not the case as of now.

• 26:05Chris Benson?

  I think so. I think, and I know you haven't mentioned it yourself, but longtime listeners who have been with us for years will know that you used to be in that space in a former professional life and know quite a bit about this topic that you've just brought up. So, yeah, I agree. It's definitely, I don't think that's changed substantially over the last few years.

• 26:31Daniel Whitenack?

  Yeah. And even simple things that don't have a lot to do with, I mean, it has to do with Gen AI, but also has to do with the tooling around it, right? In terms of even other scripts, in particular Arabic, you know, for example, which of course is a major language of the world, which to some degrees, you know, models can do reasonably well at at least some models. The tooling around the Gen AI ecosystem, right? Like, oh, I want to download this chat SDK or this UI that I can plug in a custom model to. This is likely not going to support kind of right to left. Potentially, there's going to be

• 27:14Daniel Whitenack?

  some issues, you know, with the script and other things. So it's just kind of another highlight of this disparity that exists. And it exists, and I think is worth highlighting because mostly what we're talking about here is language models and really language models that support a very small amount of the languages on the planet.

• 27:35Chris Benson?

  Yeah.

• 27:36Daniel Whitenack?

  Yeah. But that's what I had, Chris. Any thoughts after going through the list of bad?

• 27:43Chris Benson?

  I think, you know, I do have a few thoughts there. I think one of the things that I've noticed there is that there are kind of high risk and like where you have significant outcomes that can affect people in a major way. And whether it be financial or manufacturing or my industry with defense or whatever, you know, you don't want to put a general generative AI model in charge of doing things.

• 28:15Chris Benson?

  For which there are no guardrails. I think that that is a thing that I have noticed across a lot and I could, I could throw out a couple of other areas where I think that applies, like things like high stakes legal advice. Do you have a great tooling within things like chat GPT and the other big language models for legal advice? Yeah, but would you really want to, you know, literally put your life savings at risk with things like that? Maybe not. Maybe not today at least. You see a lot of this, you see a lot of AI pervading medical diagnosis. And once again, I think there's a very good use for those, but probably not by itself, you know, in isolation. So any of these areas

• 29:01Chris Benson?

  where you have a substantial risk in the outcome in terms of good and bad, you probably want to have guardrails around it across many, many different industries. And that's, I think that's my takeaway. And, you know, I think that things are continuing to improve at a really, really rapid pace. And we've said things and had, you know, two months later, had the world change out from under us. And that may happen again here with some of these. But yeah, it's, we're on a learning curve with these things and they're getting better, but they're not all the way there yet.

• 29:30Daniel Whitenack?

  Yeah, I think that's a great way to summarize, Chris. Thanks for, thanks for chatting through the, the things with me and we'll look forward to carrying on the conversation very soon with you.

• 29:40Chris Benson?

  Sounds good.

• 29:48Speaker 00

  All right. That is our show for this week. If you haven't checked out our change log newsletter, head to changelog.com/news. There you'll find 29 reasons. Yes. 29 reasons why you should subscribe. I'll tell you reason number 17, you might actually start looking forward to Mondays.

• 30:09Speaker 04

  Sounds like somebody's got a case of the Mondays.

• 30:12Speaker 00

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