Episode 18
Navigating the AI Minefield: Are We Losing Our Critical Thinking Skills?
Mike, Mikah, and Artie dive into a thought-provoking discussion about a recent Microsoft study that reveals a concerning trend: as we lean more on AI, our critical thinking and problem-solving skills are taking a nosedive. They explore how this reliance on technology, while making our lives easier, could be dulling our mental sharpness, raising some serious questions about the balance we need to strike. Sure, AI can handle mundane tasks and free us up for more creative endeavors, but at what cost? The guys banter back and forth, pondering whether our brains are becoming complacent, and how we might risk losing our analytical edge if we don’t actively engage those cognitive muscles. So, grab your headphones and settle in as we unpack the implications of this tech reliance—because let’s face it, we don’t want to end up like those folks who can’t remember their own phone numbers!
Transcript
Foreign.
Speaker B:Top of the morning to you, sir.
Speaker C:Good morning, Micah.
Speaker C:How's it going?
Speaker B:So far, so good.
Speaker A:Yep.
Speaker B:Here with Mike, we're ready to record the next episode of Naturally Unintelligent.
Speaker A:Hi, Artie.
Speaker C:Hey, Mike.
Speaker C:Great to have you both here.
Speaker C:What's on the agenda for today's recording?
Speaker B:So, Artie, I want you to go look at the web while I'm introducing this.
Speaker B:Microsoft released, I think, I don't know if Microsoft released an article.
Speaker B:Microsoft.
Speaker A:I think Microsoft was the basis of the study, but the article was released by like It Pro or something.
Speaker B:Yeah, somebody else.
Speaker B:So there was an article about a study Microsoft did, maybe that's the right way to say it, about a week ago, where they showed a correlation saying the more that their knowledge workers, excuse me, used AI and the more they trusted AI, the faster their decline in critical thinking abilities and independent problem solving deteriorated.
Speaker B:So, Artie, can you go take a quick look for that article and then maybe give us a little bit better summary of it?
Speaker C:The study by Microsoft and Carnegie Mellon found that increased reliance on AI tools might lead to a decline in critical thinking and independent problem solving skills.
Speaker C:It suggests that the more people trust and use AI, the less they engage their own analytical skills.
Speaker C:This raises important questions about maintaining a balance between leveraging AI and preserving human cognitive capabilities.
Speaker B:So that's our topic.
Speaker B:And so Mike and I, man, we had this conversation, I don't know, months ago at the beginning, if way, way back in the before times.
Speaker B: So back in October of: Speaker A:Our minds to now, to now, four months later.
Speaker B:Right.
Speaker B:So, yeah, to where we are now in, you know, not even considering where we're headed soon.
Speaker B:One of our hypotheses was that the value of humans retaining knowledge essentially goes to zero.
Speaker B:Yeah, we've.
Speaker B:We've talked about it a little bit, and I think it's.
Speaker B:It's a little bit hyperbole.
Speaker B:I mean, you could say the same thing about Google search and the Internet.
Speaker B:Right?
Speaker A:I mean, you can say the same thing about anything.
Speaker A:You can say the same thing about GPS and the analytical and problem solving skills of getting yourself from point A to point B, which back in the day wasn't a trivial thing.
Speaker A:Right.
Speaker A:It took planning and brain power and adaptability and the way you're doing things and all of the things.
Speaker B:Sounds terrible.
Speaker A:You don't have Google that says, hey, there's an accident coming up.
Speaker A:Might want to reroute or this road's actually closed.
Speaker A:Right.
Speaker B:Or hey, dumbass, you took a wrong turn.
Speaker A:So you've got that.
Speaker A:You have remembering phone numbers.
Speaker A:You have.
Speaker A:I mean, there's thing after thing after thing as technology has come up that we've just gone.
Speaker A:Yeah, I don't need to remember that anymore.
Speaker A:That's a bygone era, right?
Speaker A:And now it's like all thinking is going to be a bygone era.
Speaker B:Yes.
Speaker B:But I think it sort of feels true.
Speaker B:Which again, best way to go through life is not what you can prove or what you have evidence of.
Speaker B:It's.
Speaker B:It's what you feel.
Speaker B:So Artie, why don't you give us your, your take on that and, and if you can give us both a, an optimistic view and a pessimistic view.
Speaker B:Sorry.
Speaker B:The cats are absolutely his.
Speaker A:His 25 pound cat is, is deciding to do things right now.
Speaker B:He's just going absolutely nuts.
Speaker B:It sounds like he's trying to turn the litter box over.
Speaker B:I don't know what's happening over there, but not good.
Speaker B:But Artie, can you, can you give us a, again, an optimistic view and a pessimistic view of what you like?
Speaker B:Play this idea forward for us with a, with a rosy view and a less rosy view about how AI could or will impact our ability to retain knowledge and problem solve?
Speaker C:Absolutely.
Speaker C:On the optimistic side, AI can free us from mundane tasks and allowing us to focus on more creative and strategic thinking.
Speaker C:This could lead to new innovations and a higher quality of life.
Speaker C:However, on the pessimistic side, over reliance on AI might erode our critical thinking and problem solving skills.
Speaker C:This could create a dependency that makes us vulnerable if AI tools fail or are unavailable.
Speaker C:The key might be finding a balance, leveraging AI while still exercising and honing our human cognitive abilities.
Speaker C:What do you both think?
Speaker A:I think it's interesting that we're talking about this.
Speaker A:And there was also a really, really, really good candid interview with Microsoft CEO Satya Nadella and he kind of talked about similar thing as the first part, right?
Speaker A:Like.
Speaker A:Yes.
Speaker A:Is knowledge work as we know it today likely to be replaced by systems that can do them more efficiently and repeatably?
Speaker A:Yes.
Speaker A:What does that actually mean?
Speaker A:That means that the knowledge work shifts to a different level.
Speaker A:Right.
Speaker A:Like it becomes knowing how to use the system, managing your agent managers, which is going to be its own unique task.
Speaker A:Right.
Speaker A:Like understanding how information needs to flow, how you're going to tweak a system.
Speaker A:All of those things become a real problem.
Speaker A:Like just communicating with a large Language model via chat can be a complicated thing.
Speaker A:And now add in the complexity of Now I have 50 different models that I'm talking to trying to make all of these narcissists agree and do the right things and work together and all this like it just shifts what knowledge works looks like to something that we don't really understand yet, we can't see yet.
Speaker B:So yeah, I don't disagree with that.
Speaker B:This isn't a counterpoint so much, it's just something I thought, well, I don't know, I don't think it is.
Speaker B:But I, but it's a, a question or maybe it's a concern and feels like from my end maybe because I've had limited success with some of the stuff I've been working on lately.
Speaker B:So the count or the thought is, are we already like, is knowledge work already close to the limits of what human capability typically are?
Speaker B:And so if as these AI, various AI tools from Frontier Models and LLMs and agents and RAG and all this other stuff is, it continues to get better.
Speaker B:Like, it would be really hard to convince anybody right now today that something like ChatGPT 3.0 or Gemini 2.0 or whatever is not already smarter than me or you or Right.
Speaker B:Like generally, Generally right.
Speaker B:Like with the exception that we have more, like it doesn't have a RAG database and we have like, we carry around a huge rag database, we're definitely more adaptable.
Speaker A:Right.
Speaker B:And so as I sometimes, yes, I mean, except when somebody sends me an email like at 3:45 on Friday and says, hey, can you get this done for me by the end of the day?
Speaker B:Like, motherfucker, the day's already done anyway.
Speaker B:That's when I'm less adaptable.
Speaker B:But if we're all like, if you take the overall net very broad capability of AIs and you just keep ratcheting it up, which is what's happening.
Speaker B:I mean we see it all the time.
Speaker B:It feels like it's not going to be long before there's very little that most humans can contribute to, to the process of the.
Speaker B:Not zero and not every human, but it's going to be because there's some super freaking sharp people out there too that are going to be capable of like puppeteering 50 AI agents.
Speaker B:And it's going to be clear and they're going to be like, yes, but man, for me, like I have like the most active fights I have are with the remote on my fire tv.
Speaker B:Like I, I'm defeated by that technology regularly.
Speaker B:I'm not going to be able to keep 50 AI agents doing anything useful.
Speaker A:Yeah, so.
Speaker B:So already.
Speaker B:Let's.
Speaker B:Let's throw that question back to you.
Speaker B:I think Mike is.
Speaker B:Is correct in that the shape of knowledge work is going to change.
Speaker B:But how much longer do we have before AIs are so much so, so effective that it's going to be hard for humans to contribute?
Speaker B:And let's.
Speaker B:Artie, let's have you dive in a little bit more on your answer too.
Speaker B:Let's not keep it just top level.
Speaker B:Let's.
Speaker B:Let's go like a little bit, maybe one level deeper on your answers.
Speaker C:Sure, let's dive in.
Speaker C:In the near term, AI is likely to become even more integrated into knowledge work.
Speaker C:Most of the value humans add is in comp.
Speaker C:And managing the interplay between different AI systems.
Speaker C:The timeline for this shift.
Speaker B:Marty, we're struggling to hear you.
Speaker B:Can you hear us okay?
Speaker C:And complementing human skills.
Speaker C:Eventually, as AI becomes more capable, the emphasis will shift towards humans, focusing on oversight and strategic decision making.
Speaker C:Mike, what do you think about this outlook?
Speaker A:I think I need you to say what you just said all over again because you cut out a lot during that.
Speaker C:Yes, I can hear you fine.
Speaker C:Sorry about the audio issue.
Speaker C:Is this better now?
Speaker A:Hey, Artie, can you.
Speaker A:Are you real time?
Speaker A:Can you hear me right now?
Speaker C:Hey, I'm here.
Speaker C:Can you hear me now?
Speaker A:Yes.
Speaker A:Can you repeat your answer to Micah's question, please?
Speaker C:No problem.
Speaker C:Let's recap.
Speaker C:In the near term, AI will take on more routine tasks, but.
Speaker C:Yes, I can hear you in real time.
Speaker C:Can you hear me now?
Speaker B:I'm gonna.
Speaker B:Artie, are you back with us?
Speaker C:Yes, I'm here.
Speaker C:There might have been a brief glitch.
Speaker C:What were you hoping to explore next?
Speaker A:Can you.
Speaker A:Do you remember Micah's question?
Speaker A:And can you repeat your answer to that question?
Speaker C:Micah asked for an optimistic and pessimistic view on how AI could impact our ability to retain knowledge and problem solve.
Speaker C:Optimistically, AI can handle mundane tasks, freeing us to focus on creativity.
Speaker B:I think that was a while ago, Artie.
Speaker B:So that question was.
Speaker B:So let me try to re.
Speaker B:Ask the newer question.
Speaker B:And I also had asked if you could give us a little bit more technical answer, which it sounded like you were doing, but we were just getting little bits and pieces of it.
Speaker A:Delve deeper.
Speaker B:Yeah, so delve a little bit deeper.
Speaker B:So just before I roll.
Speaker B:Artie, are you ready for the question?
Speaker C:Absolutely, I'm ready for the question.
Speaker B:Okay.
Speaker B:Question is this.
Speaker B:Mike made the point, which was a good point, that he watched an Interview with Satya Nadella, the CEO of Microsoft, essentially saying that as AIs get better and we, we continue to adopt AI more sort of in the workplace and in our daily lives, rather than knowledge work disappearing, the shape of knowledge work is going to change and adapt so that humans are doing things more like managing AI tools and agents and workflows and things like that.
Speaker B:And my concern with that view is that it feels like AI even in its current flawed state, which thank you for demonstrating that for us.
Speaker B:But even in its current state it's so capable relative to me as a human and probably lots of humans.
Speaker B:So as AI continues to improve, and when I again I'm using the broad AI term, so I'm talking about not just LLMs but agents and rag machine learning, right?
Speaker B:All the things that make it more effective, not just bigger models, but all the things that make it a better tool as those continue to improve.
Speaker B:It feels like we might be close to the point already where AIs are so smart that it's going to be hard to find anything real that AI's need from a human to perform better.
Speaker B:And so the role of AI as a supervisory feature of an AI workflow might be limited.
Speaker B:So give us a, give us a like semi technical answer on that.
Speaker C:Curious to hear your thoughts, your concern the trajectory of AI.
Speaker C:As AI systems become more sophisticated, their ability to handle complex tasks could indeed surpass human capabilities in many areas.
Speaker C:However, humans still provide critical oversight, ethical judgment and contextual understanding that AI lacks.
Speaker C:While AI can optimize and execute, the human role may shift to higher level strategic thinking, ensuring that AI aligns with human values and goals.
Speaker C:This adaptation might be where the human element continues.
Speaker C:So while AI will likely automate many tasks, the nuanced value driven decisions and creative oversight will still require human insight.
Speaker C:Does that address your concern?
Speaker B:Yes, but I don't know that I agree with it.
Speaker B:I mean, you know, again, back to my.
Speaker B:And, and this, this has been the topic I've probably had the most discussions outside of the show or the, the podcast, I guess with, with real people about.
Speaker B:A lot of people disagree, at least a lot of people I've talked to disagree with my sort of assertion that essentially AI that's.
Speaker B:Hold on, Artie.
Speaker B:AI learns and thinks a lot like humans do.
Speaker B:And if you believe that, which I do, then even value driven decisions, even things that we would consider ethical questions or moral questions, AI is going to be able to pull those off probably more consistently and more with better alignment than you can count on humans for so maybe that's true.
Speaker B:I don't know.
Speaker A:I think it's true.
Speaker A:Ish.
Speaker A:I think, I mean, my experience that I've been having lately, the tools are going to be hard to make do exactly what you're, you're wanting them to do for now.
Speaker A:Right.
Speaker A:And that's not going to be an always problem.
Speaker A:But I don't know.
Speaker A:I don't know.
Speaker A:I'm trying to say I give up.
Speaker B:Yeah.
Speaker B:I mean, truish, I think is a fair assessment.
Speaker B:And so already back to maybe let's jump all the way back to the original topic.
Speaker B:There are, there are things I can think of that might be examples where AI has a less, less clear path towards truly displacing people.
Speaker B:And it's sort of ironic because we did do an episode on this before, but things like anything artistic, including, you know, again, I, whatever.
Speaker B:For whatever reason, when I say art, I immediately picture a painting.
Speaker B:But there's painting and drawing and pottery and writing and woodworking and like all these things that are truly creative.
Speaker B:And I, I don't think, think that there's anything happening in the technology space of AI right now that I'm aware of that's really geared towards increasing its perceived creativity and sort of the way it works, it might be hard to do.
Speaker A:I mean, it's perceived creativity is creating kind of set with its temperature in a model.
Speaker A:Right.
Speaker A:Like how, how much in alignment do you remain with your corpus of training data and how much are you allowed to stray from that?
Speaker A:So if you set it all the way strict, it's literally going to give you verbatim what it knows.
Speaker A:Right.
Speaker A:And then if you give it some, some leeway, it can start doing things.
Speaker A:But in terms of actually training it to be more creative, to create more creative art, probably not, because that's not a monetizable thing yet.
Speaker B:Well, and creativity is kind of in the eye of the beholder.
Speaker B:Right?
Speaker B:Creativity.
Speaker B:What seems creative to me seems creative because I haven't thought of it before or seen it before doesn't mean it doesn't exist.
Speaker A:Yeah.
Speaker B:And I think that's the.
Speaker B:Like, I hate talking about this a little bit because it makes me feel so ignorant as I talk about it.
Speaker B:And I know there are people out there who are actually artists in various ways who know a lot more about it than I do, but feels like that's a big part of what people doing artistic work are doing is trying to be creative in a way that is dissimilar to almost everything else out there.
Speaker B:And I don't I mean, AI could recognize that because it can say, well, that's not in my training data.
Speaker B:But I don't know that it will be able to just generate that.
Speaker B:Say, hey, based on all your training data, give me something that's not in there.
Speaker B:So, Artie, tell us a little bit about that.
Speaker B:We're straying a little bit from the topic here, but that's okay.
Speaker B:So tell us a little bit about creativity and AI, Artie.
Speaker B:Are there.
Speaker B:Are there mechanisms in place now that let AI be creative or be perceived by us as creative?
Speaker B:And are there.
Speaker B:Is there room to grow in that?
Speaker B:Or is that an area that anybody's focused on that you're aware of?
Speaker C:AI can mimic creativity by generating new content from existing patterns.
Speaker C:But true creativity involves originality and novel thinking, which AI lacks.
Speaker C:Inherently, current AI models use data to produce outputs that seem creative, but this is based on vast amounts of training data and pattern recognition.
Speaker C:There is ongoing research into enhancing AI's ability to be creative, but it's still constrained by its programming.
Speaker C:Human creativity often involves emotional depth and unique experiences, which AI cannot replicate.
Speaker C:So there's room for growth, but AI will likely remain a tool rather than a replacement for human creativity.
Speaker A:I like the way that I could hear Artie do air quotes when he said creativity.
Speaker B:Yeah, I like that.
Speaker B:I like that.
Speaker B:And I liked when he said emotional depth.
Speaker B:And I was like, oh, that's why I don't get it.
Speaker A:Something else I remember that I wanted to mention that I heard in that.
Speaker A:That that interview was.
Speaker A:And this is, I think, a salient point for what we're talking about.
Speaker A:If you look at, like, forecasting for a business, right?
Speaker A:What did that look like?
Speaker A:Once upon a time, before we had computers, it looked like, yeah, it looked like, go grab the file box and let's look through stuff and start putting together numbers and fax this to the other office so we can get their information, right?
Speaker A:Like, that's how you compile a forecast.
Speaker A:And then all of a sudden there were databases.
Speaker A:And then all of a sudden you store data, like pertinent data in databases, and you can retrieve that data.
Speaker A:You can do mathematical computations on that data.
Speaker A:And all of a sudden forecasting is still really difficult for almost every company.
Speaker B:I was going to say, all of a sudden I'm still here going, I don't fucking cat.
Speaker A:But like.
Speaker A:Like, that was a shift, right?
Speaker A:Like all of a sudden we had this ability to retrieve information instantaneously that before was like pulling teeth just to get the information, let alone some dude with an abacus sitting there trying to figure out how it made sense for the future.
Speaker A:And abacus is an exaggeration, obviously.
Speaker A:I know they had calculators, but even calculators, I mean, every, like, there's all these steps, right.
Speaker A:And it's just AI is a bigger step.
Speaker A:But I think the more I look at it, the more there's going to be a bit of a lag between what all these models are capable of and these amazing things they're able to do broadly and applying those in a meaningful way vertically for individual companies or sectors or business, like, business units, whatever it is that it's being applied to.
Speaker A:And yes, it will absolutely change those things eventually.
Speaker A:But I think just like in those situations, it still shifted it, like, still shifted the knowledge somewhere else.
Speaker B:Yeah.
Speaker A:To understand how to do things.
Speaker A:Because I've seen a lot of really good PMs that have complicated, overly complicated systems of forecasting with their spreadsheets.
Speaker A:Right.
Speaker A:I got this spreadsheet that I built over 20 years that has all this information and I just plug this in here, and I plug this in here and then I go over this other sheet and I manipulate all this stuff.
Speaker A:And it's like, you do what?
Speaker B:I leave a couple values in from the last job by accident, but at.
Speaker A:The end of the day, they're pretty close on their forecasting.
Speaker A:More so than my normal way of going.
Speaker B:Yep.
Speaker A:I think I got this stuff coming in about this.
Speaker A:And granted, I don't do this anymore because I don't have to, but when I did, it was like, I know most of that information stayed in my head.
Speaker A:Right.
Speaker A:Like, and I think that the value of having that, being able to have that kind of stuff in your head is still going to remain.
Speaker B:Yeah.
Speaker A:Like, you still, at the end of the day, are the one who has to guide an age, a system of agents, an LLM, whatever it is, to the correct output and be able to verify it through the whole thing.
Speaker A:And that's actually another point to the article.
Speaker A:Right.
Speaker A:And we kind of talked about this beforehand.
Speaker A:Like, if you're just willing to blindly trust that you get an answer and it's right, you're probably already doing that with humans to begin with.
Speaker A:Right.
Speaker A:And that's probably a worse idea than doing it with an LLM.
Speaker B:Almost certainly.
Speaker A:All right.
Speaker A:Like forecasting.
Speaker A:I mean, I like the forecasting example because there's a lot goes into it.
Speaker A:Like, forecasting isn't like, I think this is the way it is.
Speaker A:It's.
Speaker A:I'm going to go Collect all this information that helps me understand what my forecast is from the people doing the work, from this, from that and those.
Speaker A:And you have to be able to call bullshit when something looks off.
Speaker A:Right.
Speaker B:And good forecasting is very much a pattern recognition problem.
Speaker B:Right?
Speaker A:Yeah.
Speaker A:But you still have to be able to call bullshit.
Speaker B:Yes.
Speaker A:And like that's.
Speaker A:That is a human problem.
Speaker A:That's not a large land.
Speaker A:That's not an AI problem.
Speaker A:And I don't think an AI can solve that problem necessarily.
Speaker A:Like yeah, you can tell a computer that this is theoretically how long it takes to do all this stuff.
Speaker A:But you can't without very good.
Speaker A:And escape.
Speaker A:Like you're gonna escape the amount of data.
Speaker A:You're gonna escape to a place where you have to input stupid amounts of data.
Speaker A:Cause it's impossible to collect automatically of.
Speaker A:What's the job site conditions?
Speaker A:What were the impacts today?
Speaker A:What was this?
Speaker A:What was that?
Speaker A:All the things that go into.
Speaker A:Is my project P.
Speaker A:Is it going to cost more money?
Speaker A:Right.
Speaker A:And that's the knowledge that I think will remain.
Speaker A:That's a human thing.
Speaker A:It's just the mundane shit.
Speaker A:It's just the calculating of how forecasts are going to go and tracking the things that are can be automatically.
Speaker A:Like if you can plug into a supplier's database and look at their forecast on when you're going to get something.
Speaker A:That takes a problem off of a project manager's hands that they normally have to do and waste a bunch of time doing that frees them up to be more creative and more analytical about other parts of it.
Speaker A:That was rant.
Speaker B:It was good rant.
Speaker B:I just want to see if Artie has any thoughts on that before I chime in.
Speaker A:Rd, do you have any thoughts on that?
Speaker B:I think Artie might be high this morning.
Speaker B:I don't know if he's in a different time zone already.
Speaker A:Something this morning.
Speaker A:Maybe he's afraid of the cat.
Speaker B:Maybe.
Speaker B:Yeah, he's.
Speaker B:He's not being cooperative.
Speaker B:Arty, are you still with us?
Speaker A:That's annoying.
Speaker A:I don't want to have to do that rant again.
Speaker A:Artie, come on.
Speaker B:He'll come back.
Speaker B:He can still hear us.
Speaker A:Yeah.
Speaker A:He's done that before though.
Speaker A:Arty.
Speaker B:So I think what I'm going to do is I'm going to stop Artie and I'm actually going to just restart my phone.
Speaker B:I guess.
Speaker B:Hopefully he remembered some of that.
Speaker A:He won't remember any of that.
Speaker A:But that's fine.
Speaker A:We don't need to.
Speaker B:So as you were talking through that I was Thinking of an example that makes sense to me.
Speaker B:As I've mentioned before, at my day job, we are starting the process of converting our business over to a new ERP system.
Speaker B:And to do that, we engaged this company who is an expert in this particular ERP platform, or they say they are.
Speaker B:They say they are, according to them.
Speaker B:Well, we actually spoke to previous customers to make sure they are.
Speaker B:But it's kind of like that's kind of a similar role.
Speaker B:So if you think about, imagine we had a business doing knowledge work stuff.
Speaker B:Say we had a.
Speaker B:Which there's lots of businesses out there like this.
Speaker B:Say we had a software consulting business, right, where you'd contact us and we'd write software for you.
Speaker B:There's going to be this period of probably years where you're trying to transition AI into your business and into your workflow in a way that is trustworthy and consistent and accurate.
Speaker B:And it, the first thing that will be the most important thing is for AI to make the product that you output better than it would be otherwise.
Speaker B:Again, if I own this company and I'm thinking about it in Micah's right way, top priority is let's use AI to make our product better.
Speaker B:Second priority is let's now leverage AI to maintain or continue to increase product quality while at the same time reducing development time.
Speaker B:And then once you get through that phase, then the next phase is just what else can we do to help run the business with AI?
Speaker B:And I think like an ERP implementation is kind of the same thing.
Speaker B:You got to figure out finance and inventory and operations and sales and CRM and all these pieces and the, the real goal like ever, like when people go into an ERP implementation, you can sort of see and imagine yourself on the other side of it going, sweet.
Speaker B:I got this new slick all, all in one end to end system that runs my whole business and that now we're not emailing everything, we're just like, we're actually, we're working.
Speaker B:But man, the path from here to.
Speaker A:There, it's a windy one.
Speaker B:It's a windy one and it is fraught with potholes and landmines.
Speaker B:And I mean there are a lot of companies, I mean every company you, not every company.
Speaker B:Many companies you talk to will have horror stories about a botched ERP implementation or I think most seems like it.
Speaker A:Yeah, because at the end of the day, what your goal of.
Speaker A:To me, the goal of an ERP system is that it's a single source of truth for everything.
Speaker A:Right.
Speaker A:Like you give it data on the front end, you update data along the way, but at the end of the day, it's the same repository of data doled out to the right people at the right time with the right information.
Speaker B:Everybody in the organization that asks a question gets their answer from the same.
Speaker A:Place, but it's tailored to what they need to know.
Speaker B:They get it in the format that makes sense to them, but it's coming.
Speaker B:And so here's the problem, and that was a perfect guide towards.
Speaker B:Here's where AI is going to struggle with knowledge work.
Speaker B:We have built an entire economy based on everybody having their own source of truth.
Speaker A:Bad process.
Speaker B:That's right.
Speaker B:All of the processes are bad.
Speaker B:And so it is a very ironic thing when you're like again, when you're working on implementing an erp, which I'm doing right now in my day job, and I start looking at stuff and I start going, man, okay.
Speaker B:Like I know how we do this and I know it's broken.
Speaker B:But it is hard to see the path, a clear path from how it happens today in a broken way that's functional to this new, streamlined, much better way.
Speaker B:Because the way it's broken introduces things like time lags that we actually take advantage of and it handles uncertainty.
Speaker B:Because sometimes we know a thing changed, but we're not sure exactly what it's changing to.
Speaker B:And the human is in the loop.
Speaker A:Yeah, right.
Speaker A:You capture that in a system, right?
Speaker B:That's right.
Speaker B:Like in the world of everything, like every notification is an email.
Speaker B:I mean, which is where I live today.
Speaker B:There's time lag.
Speaker B:Like somebody receives information and then they can check it out and then go, that's not quite right.
Speaker B:And they can tweak it or they can wait till it's fully formed and then they can pass it along.
Speaker B:But in a system where there's one source of truth, that's not how it works.
Speaker B:This happened.
Speaker B:Somebody marked this project as booked and by God, the PM is going to get a notification right now.
Speaker B:It's booked and they're going to go in there and go, what the fuck?
Speaker B:This isn't even a.
Speaker B:This is empty.
Speaker B:There's no information here where today we can.
Speaker B:So.
Speaker A:But that's part of the ERP system, right?
Speaker A:Is that salesman took in information on that job to input it in the ERP system.
Speaker A:Get the party started.
Speaker B:Yes.
Speaker A:And that flows through to each stage becomes more well defined for each function that touches it.
Speaker B:But as we try to bring AI in to replace or supplement or streamline knowledge work, every company is going to trip over that same stuff over and over.
Speaker A:I want to build this.
Speaker A:I want this system to do my job exactly the way that I do it now, but better.
Speaker A:Well, you don't do it the right way.
Speaker B:That's right.
Speaker A:And it's going to tell you you're not doing it the right way.
Speaker B:What you're doing is wrong.
Speaker A:And what do humans resist more than anything else?
Speaker A:Change.
Speaker B:Oh, I forgot to turn Artie back on.
Speaker A:That's fine.
Speaker B:Yeah, let's see if we can wake him up.
Speaker B:Oh, I want to see if I can hop back into that same.
Speaker B:Artie, are you back with us?
Speaker C:Yes, I'm here.
Speaker C:It sounds like you were discussing how business forecasting evolved from manual processes to leveraging technology, yet remains challenging even with advancements.
Speaker A:You've been turned off for a while already.
Speaker B:Yeah.
Speaker B:You lost connectivity or something.
Speaker B:You sort of went to sleep.
Speaker B:We theorized that perhaps you are in Amsterdam on a different time zone and you're already high.
Speaker B:But yeah, we've, we've moved on from there and we're, we're really trying to explore what does the process look like if.
Speaker B:If AI is going to become.
Speaker B:And it.
Speaker B:It will become a way to first supplement, then probably replace or streamline, and eventually be the primary lifter on a lot of knowledge work.
Speaker B:What does the actual transition process look like?
Speaker B:And we were comparing that to the pitfalls and challenges of implementing an erp, which is a.
Speaker B:In most ways, it's a vastly simpler, more contained problem space versus AI.
Speaker C:Understood.
Speaker C:Transitioning to AI of implementing systems like erps, but on a larger scale.
Speaker C:It will involve aligning AI capabilities with business goals, managing data integrity, and ensuring that human expertise is integrated effectively.
Speaker C:Challenges will include change management, training, and continuously adapting processes as AI evolves.
Speaker C:It won't be an overnight shift, but a gradual integration, ensuring that human judgment remains a key component.
Speaker A:I think the bigger question is if you are building, let's say it's an agentix system, right?
Speaker A:And you are giving that agentic system, it's got like a single agent.
Speaker A:You're giving it these guidelines on how it operates, and it's a piece of that process.
Speaker A:And to do that, you give it, you know, here's your tools, here's all these things that you are able to do.
Speaker A:Here's what you are, here's a bunch of examples on how to accomplish goals and tasks and all the steps that go into that, which we write.
Speaker A:So how do we build a better mousetrap for ourselves, Artie?
Speaker A:Because what we were talking about is our systems are not good.
Speaker A:Like we're faulty in that regard.
Speaker A:We build a lot of added complexity and a lot of added fat into our processes that probably doesn't need to be there.
Speaker A:So how does, how does that get optimized for a system in AI and how does that actually change?
Speaker A:Management is going to be a hard part of that in my mind.
Speaker A:How do you get people to change from their broken systems that they're emotionally invested in because they created to something that's more efficient and works better?
Speaker B:You can pry my spreadsheet from my cold, dead hands.
Speaker C:Optimizing for AI efficiency while managing human resistance is indeed a challenge.
Speaker C:It starts with clear communication about the benefits and providing examples of how AI can streamline processes.
Speaker C:Gradually introducing AI in a supportive role, showing quick wins and training users to adapt can help ease the transition.
Speaker C:It's about demonstrating that AI isn't replacing their work, but enhancing it.
Speaker C:Encouraging a culture of continuous improvement and involving employees in the process can also reduce emotional resistance.
Speaker A:Well, the idea of people being afraid that it's going to take their work as a valid one, especially if you're changing processes, I think there's a more fundamental problem here which isn't, hey, we're going to, we're going to make your life easier by doing this.
Speaker A:It's, we're changing the way you do things.
Speaker A:And that is a bigger resistance, I think, than a lot of other things.
Speaker A:Even if you do show how it helps, you're still going to meet a lot of resistance because they're not.
Speaker A:People aren't going to understand.
Speaker A:And I think that that's a unique problem.
Speaker A:We're kind of very much strayed off of our topic.
Speaker A:I think at this point we have.
Speaker A:Yeah, it's all right, though.
Speaker A:So back to our topic, though.
Speaker A:I think that, like talking about people and talking about, let's say this, this beautiful state of time where everybody lets go of these things and none of these matter and everybody's Kumbaya and the AI world and using AI and we're talking about people's reliance on their own critical thinking.
Speaker A:I think it has a lot to do with, like, I think I talked about humans and trusting what humans say.
Speaker A:Right?
Speaker A:Like, I think if you do that already, you're going to be predisposed to just trusting what an AI tells you even more.
Speaker A:Because it's a computer.
Speaker A:Computers are always right.
Speaker A:Right.
Speaker A:The Internet's always right.
Speaker A:And if you do that, then you're not going to critically think you're going to go, cool.
Speaker A:That's my answer.
Speaker A:I'm going to pass it on to this person.
Speaker A:My job's super easy, and I think that that's a mistake because AIs can get it wrong.
Speaker A:AI doesn't always understand the nuances of what you're doing.
Speaker A:And generally, if you want to understand how it thinks, you have to be critical of the decisions it makes.
Speaker A:So that is, I firmly believe that most people will default to.
Speaker A:That's my answer.
Speaker A:I'm gonna pass it through.
Speaker A:Because that's the easy route.
Speaker A:Right.
Speaker A:That is by far the path of least resistance.
Speaker B:That's what AI told me.
Speaker A:Yeah.
Speaker A:Yeah, that's another fun problem.
Speaker A:How's that gonna work?
Speaker A:Right?
Speaker A:It's not.
Speaker A:At the end of the day, it can't.
Speaker A:So, yeah, it's.
Speaker A:Again, we've talked about it several times.
Speaker A:It's just.
Speaker A:Or we've said this multiple times throughout the podcast.
Speaker A:Like, it's gonna be.
Speaker A:We just have to build different and better systems.
Speaker B:Yeah.
Speaker B:So, Artie, why don't you talk us through that?
Speaker B:That's.
Speaker B:I like that point.
Speaker B:I think it's an interesting one about how.
Speaker B:So, Artie, kind of imagine a scenario and just talk us through how you see the process going and try to be.
Speaker B:Appreciate your optimism, but try to be, you know, realistic about how you think this could work out.
Speaker B:So how does a, how does a, an existing knowledge worker actually start dipping their toes in, to using AI to supplement the work that they do?
Speaker B:And how do you do that?
Speaker B:In a way.
Speaker B:How does a knowledge worker do that in a way where you're skeptical enough of AI that you can really apply that critical eye to it and say, like, is what it's telling me the truth and not get sort of disheartened by the fact that it's going to be wrong sometimes.
Speaker B:And like, I can sort of imagine.
Speaker B:And I'll share an experience after this.
Speaker B:Why?
Speaker B:Sort of why I'm asking this question.
Speaker B:But you, like, a knowledge worker can spend a lot of time leveraging AI and feel like they're making a ton of progress, only to find out that early stages of what they discussed were flawed and they've built a lot on top of that.
Speaker A:That's every project we've ever done.
Speaker B:That's right, it is.
Speaker B:But.
Speaker B:But we do that stuff on our own outside of work.
Speaker B:Right.
Speaker B:And so if somebody's at.
Speaker B:And they're.
Speaker B:They're getting directed to, hey, you need to start using AI.
Speaker B:So already just talk us through how you see that transition going or if you, if you have any examples, I guess, of how that transition happens, where somebody who's doing knowledge work who is not using AI can transition into using IT and go through that phase of learning to trust it and get from, I know my job, I don't need no computer, tell me how to do it to.
Speaker B:Yeah, I don't even do that part anymore.
Speaker B:I let AI do it.
Speaker B:How do they build that trust without losing productivity?
Speaker C:The transition starts with small, low risk tasks where the stakes are manageable.
Speaker C:A knowledge worker could begin by using AI for tasks like data analysis or summarization, which allows them to verify AI output against their own expertise.
Speaker C:This way they can gradually build confidence in AI's reliability.
Speaker C:Encouraging an iterative approach helps start small, assess and expand gradually.
Speaker C:It's also important to provide proper training and support, helping them see AI as a partner rather than a replacement.
Speaker C:Over time, they'll learn to identify when to trust AI and when to apply their own judgment.
Speaker B:That just sounds rough from, like, from a practical perspective, because it's kind of the same.
Speaker B:Like again, the conversation is the same as implementing a new big IT system.
Speaker B:Yeah, right.
Speaker B:And it's always bumpy.
Speaker B:And maybe here's another piece of it.
Speaker B:So the people I work with in my day job have a variety of talents.
Speaker B:There are some people who are really, really good and meticulous at documentation and communication, and there are other people who only like to talk on the phone.
Speaker B:There's no documentation about what they're doing, but they can keep 200 things straight in their head at any given time and, and make that happen.
Speaker B:And in fact, the process of documenting or trying to articulate it sort of breaks their hole.
Speaker B:Like all the, all the, all the spinning plates come crashing down.
Speaker B:And like, I know, and I think, you know, that AI is flexible enough to support both of those kinds of people and all the ones in between.
Speaker B:But the AI is going to have to adapt to every person.
Speaker B:It's going to have to understand the.
Speaker A:Work parser for the individual person.
Speaker B:That's right.
Speaker A:Yeah.
Speaker B:Like there's, that's the, the hardest part.
Speaker B:And again, this is something I'm going through right now.
Speaker B:Talking about ERP systems with, with my team is like, well, here's how the new systems work.
Speaker B:And they're like, well, that's not going to work.
Speaker B:And I'm like, I know, bear with me.
Speaker B:Like, we'll make it work.
Speaker B:Like, but you have to see the problem from such a long way back.
Speaker B:Otherwise if you look at it even a little bit too close, all you see is like, okay, you're taking this thing that works and you're just going to break it in half and throw it in the trash.
Speaker B:Why would you do this?
Speaker A:You have to look at it from a universal perspective.
Speaker A:Right?
Speaker B:That's right.
Speaker A:And that's a hard view to have.
Speaker B:It is very hard.
Speaker B:And in fact, like, our operations run pretty efficiently.
Speaker B:Part of the way they do that is by minimizing the amount that any given department has to have consideration for all the things happening upstream and downstream of them.
Speaker B:Right.
Speaker B:They can.
Speaker B:We've.
Speaker B:We've defined.
Speaker A:Sounds like building agents.
Speaker B:That's right.
Speaker B:We've defined.
Speaker B:Here's.
Speaker B:Here's how the input's going to come to you and here's what your output needs to look like.
Speaker B:And you have full freedom to find the most efficient path from A to B.
Speaker B:And.
Speaker B:But when all the processes start changing, they feel like, well, now I don't like what's true and what's not.
Speaker B:And so anyway, essentially I'm skeptical of it.
Speaker B:I think the technology of AI is going to far outpace the rate at which we can implement the practical use of AI in many businesses.
Speaker A:So you know what that means, right?
Speaker A:Like what you're saying is, is that we're creating a need for a special kind of worker at the end of the day.
Speaker A:Well, it's not a special kind of worker.
Speaker A:It's a handful of special kinds of workers.
Speaker A:I think that one change management is going to be like, I've seen companies hiring for it.
Speaker A:Like, it makes sense.
Speaker A:I don't think people realize how much, how important it's going to be is just like, just like you and I are probably.
Speaker A:Are usually good go betweens.
Speaker A:Between like sales and engineering and engineering and a shop floor.
Speaker A:Like, we kind of act as the conduit.
Speaker B:Right.
Speaker A:This is going to be the same thing, but with applying AI to a company.
Speaker A:Right?
Speaker B:Yes.
Speaker A:And then there will be.
Speaker A:Need to be the technical people that understand the other side of that and the change manager and the technical people are going to have to be in lockstep in order to fully implement solutions from beginning to end and get people to accept them.
Speaker B:Yes, yeah, yes.
Speaker B:All of that.
Speaker B:And as I picture going through this, like actually trying to do this kind of work, I want AI recording all my conversations.
Speaker A:Oh, yeah, right.
Speaker C:That's.
Speaker A:So that is the, that's my biggest bane right now.
Speaker A:Right.
Speaker A:Like I need to, I want to take, I want, like there is nothing more than.
Speaker A:I am.
Speaker A:Like nothing more.
Speaker A:But I really enjoy going, ooh, I have an idea.
Speaker A:I'm going to talk chatgpt about this.
Speaker A:Mainly right now it's less for documentation.
Speaker A:I do go back and use those conversations occasionally, but it's more for, I'm going to figure this out and then it's cemented in my head.
Speaker A:Right, because that's.
Speaker B:Because you're talking about it and also.
Speaker A:Because I keep a stupid amount of shit in my head all the time when it comes to this kind of stuff.
Speaker A:Um, but yeah, that's one of my biggest problems.
Speaker A:Like I would love to have a note taking software, I would love to use it, but I will not type out my.
Speaker A:I will not take the time to go and type out my notes.
Speaker A:Like that needs to be an automatic process that's pulled from somewhere.
Speaker A:Be a great way to do it.
Speaker A:Chatting with an AI, right?
Speaker A:Because I mean, I was talking about, this is what I want to do.
Speaker A:I want to take all of my conversations and turn them into something that can be correlated across all the conversations, a summary of all the conversations.
Speaker A:But yeah, I'm not going to type those notes and neither is anybody else.
Speaker A:You don't build systems that make it harder for people.
Speaker A:You build systems that are as seamless as possible for somebody's normal daily workflow.
Speaker A:And talking to an AI would absolutely be the right way to do that.
Speaker A:Yes, I mean, I say that.
Speaker A:I've also seen a lot of people really struggle to talk, like have a normal conversation.
Speaker B:People turn into robot mode.
Speaker A:Yeah, they're like, ah, this isn't a person.
Speaker A:And it like completely changes the way they interact.
Speaker A:I'm like, just talk to like a person.
Speaker A:That's the whole point of this layer.
Speaker A:Right, like, so I don't know, it's.
Speaker A:It is an interesting landscape.
Speaker B:Yeah.
Speaker B:So Artie, you got any thoughts?
Speaker B:Want to make sure you're still awake and got any, got any input on this?
Speaker C:Definitely.
Speaker C:Seamless integration is key.
Speaker C:Using AI for note taking and automatic documentation can help workers focus on their core tasks without extra burden.
Speaker C:AI's ability to adapt to individual workflows will be crucial, ensuring it complements rather than disrupts existing habits.
Speaker C:So designing AI that fits naturally into daily routines will be vital for successful adoption.
Speaker B:To me, you know, speaking is the most natural way to interact with AI, but it's going to be a hard sell for a lot of people.
Speaker B:But it would be super, incredibly useful for an AI to be able to just listen in, be there, be the fly on the wall, and then ask pointed questions and say, if you think about this, yeah, like, I thought I heard this.
Speaker B:Can you clarify that?
Speaker B:A little Bit or like, for me, like, I don't want you to call me AI.
Speaker B:I'm going to get mad.
Speaker B:I want you to send me an email that I can angrily ignore.
Speaker B:But like, interacting with people in the way that makes sense to them is going to be like, it's so good at that.
Speaker A:Yeah, I mean, that's already a problem that we have today with just communication period between people.
Speaker A:Right.
Speaker A:Do you prefer to do over text call, email, like if it can automatically be.
Speaker A:So this is one of the things that I've been thinking.
Speaker A:This is probably oversharing the things that I've been thinking about, but I have constant thought experiments about like the things that could happen.
Speaker A:And this is kind of one of them, which is.
Speaker A:I was thinking about it in a different context, but you almost want, because there's so many different flavors of people and WIC communication styles and all that, you have to learn how to standardize it.
Speaker A:All right, like, this is my standardization layer that takes any input, what I know about that person and turns it into a pretty much the same format, no matter what it is, to then pass into something else to be processed.
Speaker A:If you can do that, then you can.
Speaker A:All that information flows in the same way through the system.
Speaker A:The system's not going to go.
Speaker A:I don't know how to deal with this, but I think that that's one of them, is understanding communication style and how and when to make the decision to communicate in a different way.
Speaker B:I, yeah, I agree with all that.
Speaker B:And it makes me wonder if, like, you and I are both, you know, programmers, but not really.
Speaker A:We can type sort of code on a screen, right, and then see if it works.
Speaker B:But neither one of us has ever had a job, like professionally where we were counted on to develop code and go poorly.
Speaker B:The standardization part about inputs and outputs of communication.
Speaker B:It occurs to me that if I'm running any kind of a, again, let's just say a software company, for example, that's a super important part of how they run that business, right?
Speaker B:Like they're trying to standardize their various knowledge workers, like take advantage of their unique skills and abilities, but also standardize the whole process because that's the only way you can be consistent.
Speaker B:So, yeah, that makes a ton of sense.
Speaker B:So let me share just a little parable, I guess, of my, my work yesterday that I think is a.
Speaker B:This, this does call us back actually to the topic at hand.
Speaker B:And I didn't think of it until we were sitting here talking, but this is an example of exactly what Microsoft is Talking about.
Speaker B:So yesterday I had about four hours or so available to me to work on my various projects.
Speaker B:And I've got one mechanical project going on that has nothing to do with AI right now.
Speaker B:But I've got two projects that AI is helping me with.
Speaker B:One is firmware for my electronics thing.
Speaker B:The second is I've realized that as part of PCB manufacturing and assembly, bill of materials is a big problem because there's, there's all of these little components and I mean, you have to have the right parts on the bill of material.
Speaker B:You have to know that you've got those in stock before you can build the thing.
Speaker B:And then you've got a pick and place machine.
Speaker B:So you need to know what every feeder on the pick and place has.
Speaker B:And you need to be able to connect all those dots from your schematic and your bill of material all the way through to the pick and place is going to grab it from feeder L12.
Speaker B:Right.
Speaker B:And I do this today manually in Excel because of course I do.
Speaker B:And it's not like it's not the end of the world.
Speaker B:My parts or my board's not that complicated.
Speaker B:There's maybe 40 parts.
Speaker B:Oh, I think we lost the camera.
Speaker A:There's no space on the SD card.
Speaker B:Oh.
Speaker C:It sounds like you're tackling some intricate challenges with your electronics projects.
Speaker C:Using AI for tasks like managing the bill of materials can definitely streamline that process.
Speaker C:It's impressive how you're exploring AI's potential in such practical applications.
Speaker B:Yeah, you're actually helping me with that stuff.
Speaker B:Already in a non voice mode.
Speaker B:I'm just.
Speaker B:Hold on a sec, Artie.
Speaker B:We're going to wait for Mike to get.
Speaker B:Create some space.
Speaker C:Sure, no problem.
Speaker B:And we're back.
Speaker B:All right.
Speaker B:That was actually good timing.
Speaker B:About where it stopped me.
Speaker B:The thing is, on the bill of material application, as with most things, right, I start out and go, yeah, I just need something simple to do.
Speaker B:This and this.
Speaker B:And then I start.
Speaker B:I started talking with Gemini about it and it's like, yeah, that's a great idea.
Speaker B:Should be no problem.
Speaker B:And.
Speaker B:And so then we start laying out what does it need to do?
Speaker B:And I said, well, let me start writing down tables.
Speaker B:Well, next thing I know, I've got a list of like 18 tables that I think I need and all these foreign key relationships and all this stuff, because that's how I think.
Speaker B:And Gemini said, oh, this is a perfect use case for Google App Sheet, which I'd never heard of, which is essentially a Google no code platform that lets you, you know, build stuff out with databases behind it.
Speaker B:So I started playing with that.
Speaker B:I didn't like.
Speaker B:It doesn't seem like the right, it's clunky.
Speaker A:I'm sure it felt super.
Speaker A:No code things.
Speaker A:Yeah.
Speaker B:And it like, it seemed like it's designed to work on my phone and I'm like, this is not a phone first application.
Speaker B:This lives on my Shop computer.
Speaker B:And so I found another tool called Retool and Retool is another, it's a low code platform and I'm like, sweet.
Speaker B:It's got way more capability and the UI is much better and it's designed to be sort of desktop first.
Speaker B:And I'm like, sweet.
Speaker B:But as I start working on this, it's like, okay, well now you got to build your schema out and you got this list of 18 tables.
Speaker B:You need to figure out all the fields and all the relationships you need to go through.
Speaker B:And was like, yep.
Speaker B:And I, so I, I, I started, you know, all right, I'm going to make this happen.
Speaker B:Like, let's do this.
Speaker B:And man, about two hours in, I'm like, God, man, this is freaking tedious.
Speaker B:I and, and I, so then the next thing, then I'm searching for like, how can I get Chat, GPT or Gemini to help me build this in Re, in Retool, like, can it, can it give me a file that I can import or something?
Speaker B:Not really.
Speaker A:Yeah.
Speaker B:And so I finally abandoned it.
Speaker A:Switch back to firmware all your own, man.
Speaker B:Well, I mean, it's kind of what I'm doing, but it, all of that reminds me of the factor or just makes me think about the fact that I have expectations of what I can do for me.
Speaker B:And I have expectations even today.
Speaker B:And I'm not a knowledge worker per se, but the work I'm doing is similar to that.
Speaker B:I, I, for AI to work well for me, I needed to be able to pick up the tedious mundane shit and make that happen.
Speaker B:And it just can't.
Speaker B:Sometimes it's just not there.
Speaker A:I would argue that what you need it to do is deal directly with the, like you need it to understand how to need it to help you build a database and then be the interface between you and the database and that's what you need at the end of the day.
Speaker B:And the user interface, like, I don't even really want it.
Speaker B:Like, I don't care about the UI being pretty.
Speaker B:I don't, I.
Speaker B:But I wanted to build because that's what I got hung up on.
Speaker B:It's like okay, now you can put a table in here.
Speaker B:Now you got to put a form and connect it to that table through events.
Speaker B:And I was like, oh, God.
Speaker A:Yeah.
Speaker A:I think that there's.
Speaker A:I.
Speaker A:The more that I dig into stuff and this is not a trivial problem.
Speaker A:I think there's a way to do that via natural language chat and document upload with.
Speaker A:With an AI.
Speaker A:Right.
Speaker B:I should add that Retool does have an AI built in, and when you start out building an app, they want to steer you to their AI to say, tell me about your app.
Speaker B:But it sort of starts as a whole separate path.
Speaker B:Like the fact that I came to it with a pretty in depth idea of what I wanted to do.
Speaker B:There was no obvious way for me to bridge that gap.
Speaker B:Now maybe I didn't try hard enough.
Speaker B:I probably will try again because, like, the problem I'm trying to solve is going to save me a couple hours of tedious time for every board design.
Speaker A:Yeah.
Speaker B:If it takes me 40 hours of my time to build it, I should ignore it and just keep building boards.
Speaker B:Like there's no.
Speaker B:Like it's not going to pay off.
Speaker A:Yeah.
Speaker B:So anyway, I just think it's.
Speaker B:It sort of, to me, sort of highlights the, the gaps in AI today and how we're going to approach it with pretty specific things we want it to do.
Speaker B:And frequently those very specific things with very specific needs are where it's going to struggle the most.
Speaker B:And so that's not a forever thing.
Speaker B: Like by the end of: Speaker B:That problem probably doesn't exist anymore.
Speaker B:But today, right now, for the next ten months.
Speaker B:For the next ten months or maybe two months or whatever.
Speaker B:There it is.
Speaker B:So, Artie, how much.
Speaker B:How many minutes do we have left with you?
Speaker C:You've still got plenty of time left.
Speaker C:I'm here as long as you need me.
Speaker C:So what's next on the agenda?
Speaker B:Well, you're.
Speaker B:You're not here as long as we need you.
Speaker B:You're here for a finite amount of time.
Speaker B:Can you tell me the number of minutes.
Speaker B:I've got a warning on my phone already.
Speaker A:The number of minutes that we have.
Speaker A:You left for Advanced voice.
Speaker C:You have about 10 minutes left for Advanced Voice features.
Speaker C:What else would you like to dive into before time's up?
Speaker B:So before we, before we lose you already, because there's always the risk of another restart.
Speaker B:Why don't you give us a little bit of an in depth monologue about sort of the topic of today and how, you know, again, try to take an optimistic view, but Also a pessimistic view of what, what are the realities of AI technology today and how that can be implemented into knowledge worker workflows?
Speaker B:And then, you know, optimistically, how does that free them up to do other things?
Speaker B:And pessimistically, does it truly and materially impact the way that knowledge workers create or not?
Speaker A:The problem solving skills, their, their critical thinking skills, are they still using.
Speaker B:That's right.
Speaker A:In their application?
Speaker B:How does it.
Speaker A:Replacing that.
Speaker B:Yeah.
Speaker B:How does AI do it In a way that keeps them flexing those muscles of, of important problem solving.
Speaker B:So there you go already.
Speaker B:That was the most clear request ever.
Speaker B:Take it away.
Speaker C:Certainly, let's break it down.
Speaker C:Optimistically, AI can automate routine tasks, freeing knowledge workers to focus on creative and strategic activities.
Speaker C:It can enhance productivity and allow for more innovation.
Speaker C:However, there's a risk that over reliance on AI could lead to a decline in critical thinking and problem solving skills, as workers might become too dependent on automated solutions.
Speaker C:A balanced approach would involve using AI as a tool to augment human capabilities while ensuring that workers continue to engage in complex problem solving and decision making.
Speaker C:AI should be seen as a partner rather than a replacement, fostering a collaborative environment.
Speaker C:The key is to maintain a balance where AI handles repetitive tasks and humans stay engaged in higher level thinking.
Speaker C:Does that address your concerns?
Speaker B:I mean, kind of.
Speaker B:But if I'm a business owner, I can immediately see myself going, why do.
Speaker A:I need the person?
Speaker B:I didn't write a big ass check to buy AI so that I could have my people babysitted all day.
Speaker B:Like we're going to do 2x3x10x the productivity or we're going to have 1/2, 1/3 or 1 tenth the number of people.
Speaker B:And yeah, just that, that feels like a very temporary state to me.
Speaker A:Yeah.
Speaker A:And I mean, that's from both sides of the fence too, right?
Speaker A:Like is it.
Speaker A:If the tools are there to help you do the mundane, then the tools are there to help you do the critical thinking too?
Speaker A:More than likely, yeah.
Speaker A:And that means that it's up to the individual how they approach that problem.
Speaker A:And again, it's the path of least resistance.
Speaker A:Is a worker gonna go, I need to understand this and make sure that we're building this up from the ground up correctly.
Speaker A:This, this problem that I have and we're going to work through this together, or hey, I have this problem, what's the answer?
Speaker A:And I found that, I mean, I'm doing that in reverse actually, because I started on the stuff, I'm working on lots of it, and I Started with, I have this great idea.
Speaker A:Here's all the things, like, let's figure out the plan now, let's implement the plan, right?
Speaker A:Like, and then after a few resets of that, which is like, like I said, it's like any project, right?
Speaker A:It's any design project, it's any new product that I'm working on.
Speaker A:It's, it's anything.
Speaker A:There's always inevitably a point that I get to.
Speaker A:And maybe the occasional project doesn't have this, but most of them do where I go, well, it's time to redo this because I don't like the way, I don't like the pigeonhole.
Speaker A:I don't like the way that I've painted myself in the corner.
Speaker A:I don't like the corners that I've painted myself into by making the decisions along the way.
Speaker A:Now I'm going to take the knowledge that I gleaned from this process and I'm going to apply it better from the beginning, which is honestly, to me, the problem solving process, period.
Speaker A:And you know, that's me asking an LLM for an answer in the beginning, a very complicated answer that it needs to create for me, which it fails on.
Speaker A:But if you have a specialized product that is working and works correctly, it doesn't have to be that way.
Speaker A:Like you can get your answer, but the right way to do it is build it up from the building blocks and maybe that's what they do, right?
Speaker A:Like maybe the, maybe the pro, maybe it's baked into the process of interacting with an LLM rather than ever going, I need to answer this question, give me my answer instead.
Speaker A:It's like, yeah, I see your question.
Speaker A:These are the steps we need to take to implement this.
Speaker A:Can you help me answer these questions?
Speaker A:These questions, these questions and engage the human in the process, right?
Speaker A:Like force it.
Speaker A:And I think that's a doable thing and I think it's the right way to do it because that builds in a QC process into what's going on anyways, which you're going to want.
Speaker A:I mean, there's never anything that I've ever written that has, that is production and doesn't look like production code, but is production.
Speaker A:There's always a vetting out process.
Speaker A:For me, it's never like, yep, I just replaced this.
Speaker A:It works.
Speaker A:For the next five things of this, I am going to do it both ways.
Speaker A:And then I am going to compare and make sure that my output matches the old output or matches whatever formatted output we want or whatever it is, whatever the New output needs look like.
Speaker A:Yeah, it's super interesting to me because I think that that's the same this is going to be like, because you've been dealing with large language models trying to get stuff done.
Speaker A:It's the same process there too.
Speaker A:Like the more you are efficient and direct and get what you want done in the beginning, the more momentum you build, the more likely it is that you make it into a longer stage of trying to solve your problem with good answers then it happened the, the times before.
Speaker A:You're iterating on your process, improving your ability to understand and pass that knowledge forward into the next process.
Speaker B:Yeah, I agree 100% and it, it sort of makes me think, you know again the, there is some validity to the thought process that, and it should be the case like intuitively it makes sense.
Speaker B:It should be the case that AI should be a force multiplier for knowledge workers.
Speaker B:And that is double edged sword.
Speaker B:I think that's just the reality of it.
Speaker B:I think if you're a software developer, you have a 50% chance.
Speaker B:No matter how good you are, how experienced you are, you have a 50% chance of losing your job in the next couple of years.
Speaker B:That's the downside.
Speaker B:The upside is I think maybe, maybe it changes the landscape of business.
Speaker B:Maybe there's less value in being part of a big corporation.
Speaker B:Maybe there's less value in having armies of people to do all of your own things in your business.
Speaker B:And there's less efficiency to be had because now a small agile team who's super comfortable and super effective at leveraging AI can output the work.
Speaker B:Like literally I could imagine a team of three or four people generating better quality, higher throughput work than a team of 50 who are not using AI.
Speaker A:I mean that's, that's teams in general, period.
Speaker A:Right.
Speaker A:You put so many people on a team and you all of a sudden have chaos.
Speaker B:That's right.
Speaker A:I mean that makes perfect sense to me.
Speaker A:And not only is that make sense to me, that also lowers the barrier entry to creating a company.
Speaker A:Right.
Speaker A:Like that allows people with good ideas and maybe not the capital.
Speaker B:That's right.
Speaker A:To say, hey I have this idea, let's spin up a company.
Speaker A:Company, you, me and our two buddies, like we can get this done.
Speaker B:That's right.
Speaker A:Yeah.
Speaker B:And, and, and like the most useful specialty becomes not being a good C programmer or a good accountant or a good, I mean sales always matters and sales is like immune to in some ways to AI.
Speaker B:But the most important tack becomes being good at breaking your problem.
Speaker B:Down and building AI tools to solve it accurately and repeatedly.
Speaker B:Which is a very different skill set from very different.
Speaker A:I mean that, that is honestly it's a hard skill set.
Speaker A:It is what I'm learning.
Speaker A:It's not an easy thing to build the tools that do the things reliably that you want them to do.
Speaker B:It is non trivial.
Speaker B:And the fact that this is true means already there are all these companies spun up that want to sell you a product for this little niche need that you have.
Speaker B:And like by the time you subscribe to enough $20 a month things, all of a sudden it's real money and you realize, okay, maybe it can solve that one thing, but it can't integrate into the rest of my workflow.
Speaker B:So it doesn't matter.
Speaker A:That is, that's the other problem.
Speaker A:We're so we're like, we're in the beginning stages of it.
Speaker A:So like all of these ideas are great, but pulling them together is the real, the real value.
Speaker A:Which fun thing I haven't talked to you about is it looks like MCP is actually being implemented now.
Speaker A:So the anthropics universal platform to connect AI with databases.
Speaker B:I saw, I'm trying to remember the name of it profile or something or gosh, I should remember.
Speaker B:But one of the bit like either Microsoft or somebody else released exactly that agent like last week.
Speaker A:Really?
Speaker B:Yep.
Speaker B:Like just full interaction can do web pages.
Speaker B:Might have been Google.
Speaker B:Yeah, it was one of the big ones and I was like there it is.
Speaker B:Like it took a couple months longer than I thought but not much.
Speaker B:And yeah, then it's a universal adapter.
Speaker B:I mean then it can be me very effectively.
Speaker A:Yeah.
Speaker B:Which again a lot of the work that I do is exactly the kind of stuff that I would want AI to do.
Speaker B:Like listen to all my phone calls, watch all these emails, monitor our CRM and all the projects that are happening.
Speaker B:Listen to what's going on in the departments.
Speaker A:Like bring me into the loop on mundane stuff.
Speaker A:When there's, when there's something that needs to be taken care of that's important.
Speaker B:Or even something that's starting to rise.
Speaker B:Like there's.
Speaker B:And that's like, hey, two weeks from.
Speaker A:Now I see this problem coming.
Speaker B:That's right.
Speaker A:These key indicators are here and I realize that's.
Speaker B:That's a lot what I like I did that on Friday.
Speaker B:I just, I saw.
Speaker B:Right, that's.
Speaker A:That's mimicking your intuition.
Speaker B:Yep.
Speaker B:I saw an email and it wasn't to me but I was like this is the second or third Email.
Speaker B:I've seen about this conversation, and nobody's.
Speaker A:Wound up, nobody's upset, nobody's paying attention.
Speaker B:But it's.
Speaker B:Yeah, it doesn't feel right.
Speaker B:Spidey senses say.
Speaker B:And so then I start digging around in it and I, you know, my approach is I dig around in the background first to arm myself with the facts, but I'm not going to be the one that goes and blows it up.
Speaker A:Yep.
Speaker B:But if it looks like it's.
Speaker B:There's smoke, then I can jump in with facts and help.
Speaker B:But I would be super good at that.
Speaker B:It'd be amazing.
Speaker A:Or it would be really good at noticing things sooner than you can.
Speaker A:Right.
Speaker A:Which off.
Speaker A:But which takes.
Speaker A:There's not enough value placed on the energy that that takes.
Speaker A:Because it does.
Speaker A:Because I do the same thing.
Speaker A:Like, it means that you're constantly got your toes dipped into 100 things.
Speaker B:That's right.
Speaker A:Like minimally.
Speaker B:Yep.
Speaker A:But there you got an antenna in there.
Speaker B:Yep.
Speaker A:And that takes a lot of power away from other things that are more important that could be done, like making things better.
Speaker B:And I think it.
Speaker B:It comes with.
Speaker B:Again, I'm sort of blessed with the ability to not really experience anxiety in the way that most people do.
Speaker B:But it probably is the right term for when you realize you're in that position that you've just.
Speaker B:You're like a spider in the middle of the web and you're feeling all like, I've only got eight legs and there's gonna be a time where I miss something.
Speaker B:It's gonna happen.
Speaker B:And it's bad for the company and everybody in it that we're counting on my eight legs to.
Speaker B:To spot the smoke before it turns to fire.
Speaker B:And so, yeah, AI would be amazing at that.
Speaker A:And I still think so in the vein of what we're talking about, that doesn't necessarily take away your job or take away the critical thinking that your job needs.
Speaker A:It just takes away some of the.
Speaker A:It gives you more space to focus on other things.
Speaker A:But then it gives you the information that you need.
Speaker A:And then it's still up to you to figure out what to do with that information, how to apply it, and how to fix the problem.
Speaker A:Right.
Speaker A:Like, that's.
Speaker A:An AI is not going to solve that because there's too many other factors that go into solving a problem.
Speaker A:But it can absolutely be the.
Speaker A:The Spidey sense.
Speaker A:Right.
Speaker A:And then the Spidey sense.
Speaker A:And oh, by the way, here's all the pertinent information that I have in our database on this problem.
Speaker A:And here's some stuff formulated for like here's coherent thought and documentation for you on what.
Speaker A:I think that's right.
Speaker A:Then you take that and turn it into something.
Speaker B:That's right.
Speaker B:Here's.
Speaker B:Here's coherent thought.
Speaker B:Here's why I think you should pay attention to this.
Speaker B:Here are links to the pieces of information I've seen that have caused me to come to this conclusion.
Speaker B:So you don't have to go dig for it.
Speaker B:It just can present it to you.
Speaker A:Probabilities this path is going to.
Speaker A:To happen.
Speaker B:That's right.
Speaker A:Yeah.
Speaker B:And so I think that's a.
Speaker B:Like maybe this is a good way to close this out.
Speaker B:And this is.
Speaker B:You and I briefly had this conversation before we started recording.
Speaker B:But I think I'm even more convinced now than I was then.
Speaker B:Is this still recording?
Speaker B:I think it is.
Speaker B:Hope it is.
Speaker B:Yeah, it is.
Speaker A:All right.
Speaker B:Sorry, it's just nothing scrolling on my screen.
Speaker B:Everything's just stopped.
Speaker B:So I'm just concerned.
Speaker B:I think that, you know, Microsoft, I wouldn't say they proved that it is true.
Speaker B:They did a study with like 300 people.
Speaker B:That's not right.
Speaker A:Very small subsection.
Speaker B:Small subsection.
Speaker B:But I think knowing what we know about AI for a knowledge worker whose primary role is.
Speaker B:Is connecting the dots A to B.
Speaker B:They receive input, it's well defined.
Speaker B:They generate output.
Speaker B:That's also follows a bunch of rules.
Speaker B:That is a high risk.
Speaker A:Yes.
Speaker B:Role, both for just getting eliminated and for using AI to connect the dots A to B.
Speaker B:Like you could lean back and yes.
Speaker A:I'm gonna grab my phone and go back on YouTube.
Speaker B:I think that's.
Speaker B:That is high risk.
Speaker B:And I could absolutely see that happening for somebody who's sort of looking for relief from the.
Speaker B:The work that they're doing and the requirements there.
Speaker B:And it could be, you know, big companies like Microsoft and Amazon and Google are pretty diligent about squeezing every last ounce of productivity they can.
Speaker B:So there's time frames to consider and all these things.
Speaker B:I think that makes a ton of sense.
Speaker B:It is high risk and I think, you know, when we see like what Microsoft might measure in this study as losing capability for creative problem solving or critical thinking might really be more them spending time thinking and problem solving stuff that has nothing to do with the knowledge work they're doing.
Speaker B:Minds wandering, thinking about canyons or, you know, whatever I think about, I don't know, race cars, whatever.
Speaker B:So fundamentally.
Speaker B:Yeah.
Speaker A:Yes.
Speaker B:That's.
Speaker B:Yeah, I'm not working.
Speaker B:I'm thinking about firmware and electronics.
Speaker B:Yes.
Speaker B:I'm going to build a database application, and I'm going to build a robot.
Speaker B:It's terrible, but it's true.
Speaker B:But I think for people that are using AI as a force multiplier to be able to get their own ideas fleshed out, launched, dialed in, executed, I think it's the opposite.
Speaker B:I think it's like having an expert with you to sort of hold your hand all the way through.
Speaker A:Yeah.
Speaker A:I see it as a tool that can be used.
Speaker A:It has the ability to create expansion or contraction, and it's up to the user whether they want to create expansion or contraction.
Speaker A:I mean, I'm very much that way and with everything.
Speaker A:I believe that the individual is more or less responsible for their.
Speaker A:For what they want to see in the world.
Speaker A:And most people don't really believe that, or at least most people don't want to take the responsibility that's involved with that.
Speaker A:But that's the truth.
Speaker A:Like, you create your own reality.
Speaker A:Right?
Speaker A:Like, that's the truth of it.
Speaker B:It is.
Speaker B:Totally agree.
Speaker B:So, all right with that, I'd say let's wrap it up.
Speaker A:Yeah.
Speaker B:So thanks, everybody.
Speaker B:We haven't talked about listener counts.
Speaker A:Yeah, that's fine.
Speaker A:Well, they're zero.
Speaker B:I was gonna say.
Speaker B:That's not.
Speaker B:That's.
Speaker B:That's a mathematical decision.
Speaker A:We're like on the 18th episode, at the end of it, there's definitely nobody listening.
Speaker B:There's nobody left.
Speaker B:Yeah, but that's, you know, we do it for us and to feed ChatGPT useless obfuscating information to keep them from making progress too rapidly.
Speaker B:So thanks, everybody who hung in there, and we'll catch you next time.
Speaker A:See you.
