Ep 24 - AGI and the Future of Work: Insights and Implications
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Ep 24 - AGI and the Future of Work: Insights and Implications Video and Podcast Transcript
[Disclaimer: This transcription was written by AI using a tool called Descript, and has not been edited for content.]
Dave Dougherty: There's a section where we do a fake laugh just so that we can smile and then we go from there.
So All right. Welcome to the latest episode of Enterprising Minds. Dave and Alex are present today honestly, I'm going to pass the baton over to Alex really quickly here.
Discussion on Job Reports and Impact of AI on Tech and Non-Tech Companies
Dave Dougherty: He has been doing a lot of thinking over AGI and how that impacts job reports.
So I'll let him cue it up better and check us off.
Alex Pokorny: Yeah, there's a couple of recent news items and recent reports all kind of mixing together, kind of swirling my head for like the last week or two. So one of the big ones.
Alex Pokorny: Deloitte is now going to put out a quarterly report on basically the state of AI in companies and corporations.
So the first one, not too surprising, tech companies feel a lot more confident about AI, non tech companies don't. But really overall, everybody says they feel a lot more confident than they probably are. And that's because this was a survey of leaders. And if you were to ask anybody who's close to this, industry or kind of the movement that is AI, the changing trends of it, it changes so fast.
No one can really be that confident. If you have confidence, you have false confidence because good luck trying to get ahold of this thing. It is moving way faster than I think anybody can ever really understand.
AI and Job Productivity: A Mixed Message
Alex Pokorny: So that kind of over excitement, but also you got this kind of mixed message feeling of, is this going to be job productivity and Company growth and job gains, or is this going to be efficiencies and cuts and job loss?
And from a leader's perspective, it keeps kind of flipping back and forth of, are we excited? Do we think this is a growth opportunity? Or are we thinking this is going to be a cost saving measure? And I think a lot of the instant reaction is around the kind of the cost saving measure thing with hesitations and kind of fear, kind of like trying to down the topic of, no, no, we're not talking layoffs, but you know, we're talking layoffs kind of a message.
So I don't know. Yeah.
AI and Job Loss: The Duolingo Case
Dave Dougherty: I think the thing that immediately comes to mind was that news story on the duolingo layoffs. Yeah, yeah. Where they said we're getting rid of 10 percent of our contractors because of the outputs from AI are just doing that work. So I think we'll see it on the edge like that.
Like if you have your full time employees, I don't think you want to get rid of them willy nilly. But the, the freelance contractor piece. I mean, I feel for, I feel for those people.
Alex Pokorny: Yeah. There's an interesting little twist for at the end of it was talking about what they consider a quote unquote content creator.
So there's a bunch of people who are creating content for their app. Basically the difference you can imagine kind of going through a language learning app, you know, the different scenarios that are being talked about, the different stories that are being discussed and you're trying to answer the questions and, you know.
Quiz yourself and all the rest. So that kind of content is being going away. So it's done there content creators Who are the few people basically who are left? Are reviewing what ai has created To see if it basically passes translation and you know brand fits and the like so content creator I thought that was kind of a unique And I'm saying calling someone a content creator when they're reviewing what the AI created,
Dave Dougherty: right?
That's an editor job. That's not,
Alex Pokorny: it's just, it's a bit of a misnomer. So it's just like a little bit off to make it seem like we've cheapened a job that is a complex one and to something that's far, far different.
Dave Dougherty: At least.
The Role of AI in Writing and Editing
Dave Dougherty: Well, I think that's just. Part of being a, a writer and an editor, to be honest, everybody thinks they can do it well because they write, but no, you're missing the point, like to describe something really well to capture an audience, to like bring them through something.
That's a lot of skill, you know, I mean, especially when you talk about something like, you know, ad formats, like, yeah, you can get the good enough content with with AI and you've been able to do that for a lot of years with. You know, what Google's done in, in their ad platform, but we know that if you really want to take advantage of the channel, you actually have to get in and you have to write your own copy too, because that'll often perform better.
So it's the same, I think it's the same kind of thing. You will, the people who are good at what they do you know, like we said in the, the, First season of this thing in 2023 that I think this is more like the steroids Scandal in baseball like what you're good at it will accelerate, you know So if you're a martin mcguire or sammy sosa and you're good at hitting home runs, you will hit more home runs.
But if you are focused on on base percentage, great, get more singles, you know I don't think the outputs that I've seen thus far, you know, and like you said, it changes a lot too, but you know, if you were outputting things on topics, you already know, you are able to see where the content is really bland and really avoid of anything interesting.
My concern is where managers or leaders put more of an emphasis on just producing across the organization rather than being strategic about it. So then all of a sudden you have. a marketer or a content creator or whoever in your organization all of a sudden have to create three articles on a topic they know nothing about.
Well, how, you know, fact checking and stuff is going to be important, but if you're not familiar with it, you're not going to be able to know what you know, AI has produced. That would be a hallucination or, or bad copy because. You know, as much or as little than as the, the AI at that point. So,
AI and Job Productivity: A Deeper Dive
Alex Pokorny: yeah, there's that old phrase of what can be measured is what gets managed.
And that's, that's exactly the trap. I mean, that you're just talking about it is you create those metrics of, well, this person can create, you know, 200 words for so many pennies or so many cents, so many dollars. And with an AI tool, they can grade 500 words or a thousand words. And that's great. And then you look at the quality aspect, which is very hard to measure.
You say that this is absolute crap. I don't know why anyone would waste their time reading this. This is probably insulting to give to our customers slash shows that we don't understand our customers because an AI. It doesn't, by nature, understand the exact use state that you're trying to use this copy for.
You can either try to prompt it really, really well to try to explain, this is exactly the education level, this is the type of market, this is the mindset of the individual who might be reading this copy. But more likely than not, they're just going to give it a very, very basic prompt and spit out 500 words and throw it on a page.
Dave Dougherty: Yeah, so I don't, I think it will, it will depend, you know, when you look at like the, the leaders interviewed where.
AI and Business: A Realistic View
Dave Dougherty: A lot of them said that they're just disappointed in what. And where they are or they're ambivalent with their progress with AI. And I, I think that makes total sense because on the one hand, there's the FOMO of we have to be doing something because if we don't, then we're going to be seen as, you know, being a dinosaur.
Flip side of that though, is that there's so much pressure to produce the results that again, I think it does depend on the type of company. and your internal culture as to whether or not you'll be successful with this thing. You know, if you're super worried about intellectual property, and you know, there are a lot of companies that would be that's a completely different ballgame than say, like a software startup that just needs to Show authoritativeness, you know, the, the business cases are really, what's going to drive the success.
And it's funny.
Exploring Microsoft's Copilot Integration
Dave Dougherty: So like, I've, I've been playing with the new copilot integration with the Microsoft apps that's finally available to public, right. And it was funny because like, as I'm playing around with it, I, I caught myself going, man, it's still a Microsoft product,
you know, like it's, it was. Amazing product footage, you know the introduction to it last year. It was like, oh my God, if it does what they're showing here, that could be a game changer. And so far my experience has been. You know, like I tried using it in word to help like reformat some stuff and that was nice, but there were a couple of times where it caused word to crash which, you know, if you're writing and you're getting to a flow state and then your program crashes, like, okay, now I'm out of it now, I have to reset, get back into it.
And with the. The PowerPoint stuff is like, that could be cool just to give you an idea when you're like, I don't, I don't know. But it seems to only grab images or create images from its own like stock folder, which is fine. It makes sense that they would set it up that way. But if you've had any amount of time in the business community, you have seen every single one of those stock images like 12, 000 times.
I don't want to see that one again, right? Like, You know, the dude with no head holding a, an arrow going up into the right. Like, come on, . So cliche, gimme something good. You know?
Alex Pokorny: It said that, I know that image by your description alone.
Dave Dougherty: So is that kind of thing where you're like, okay, cool. It's doing some of the stuff.
But again, it's Microsoft, they launch it and you got to wait for like the fifth version before it starts working the way they promoted it, right? It's just going to be buggy and it's going to be, At least.
Alex Pokorny: Yeah, so the AGI thing, just to jump into it.
The Future of AGI: Predictions and Possibilities
Alex Pokorny: There's a couple comments that were made also in the last, I think, week and a half now.
So one was Sam Altman, who's the head of OpenAI, ChatGPT, that organization at Davos. He made a comment about AGI coming soon. So AGI Artificial General Intelligence, the idea that this is not just chatGBT creating a bunch of text for you or stable diffusion create some imagery for you, but instead this could be the robot of the future kind of mindset where you have this AI system that can do anything and everything that you ask it to do.
So a whole variety of different tasks way beyond just content creation. And then there was comments, I think just yesterday Mark Zuckerberg of Meta slash Facebook basically pushing a huge amount of effort into their idea of an AGI. So it was an open source AGI, and his hope is to make it as beneficial for humanity as possible by making it as open source as possible, which has kind of been, the standard line that Mark Zuckerberg's been running with for a long time. But also included with a gigantic purchase of Nvidia chips. I think it was like, if you price them at retail pricing would be like ten billion dollars worth of chips. So ridiculous amount. If you are interested in getting into that Nvidia stock, that thing just doesn't seem to stop moving.
So, that one's a pretty good one for right now. Consult your own financial advisor. Yeah, no kidding. I mean, there's going to be a point where it finally stops. It is funny, you know, if you think about like, like those old photos of what a computer used to be, a supercomputer used to be with big tape reels and gigantic you know, a thousand buttons and lights on the thing and you know, it has a total like 12 kilobytes of memory in the whole giant thing.
You gotta think about that kind of a thing with any of these chips, like. In a year, especially in 10 years to think that you'd pay that much money for them. It's crazy. But yeah, keeps on moving. I guess you got to keep playing the game that you have today.
AI and Job Loss: A Complex Issue
Alex Pokorny: But yeah, it gets a little bit more interesting, especially when we start talking about back to the kind of the job conversation.
So I think of this with the old I know I railed on it before. This was like back in March last year. IBM's CEO who said they're going to cut 30 percent of backend jobs, and support jobs, basically by not hiring and not refilling jobs which included HR jobs, I think even accounting by replacing it with AI, and IBM's CEO has been known for saying some pretty crazy statements out there before.
It keeps the stock price up a little bit, you know, it talks about cryptocurrency. Oh, we're all into it. And then it's like, if you look at a year from now, what did they do? And not much. So statements, I don't, I don't think they're that valid, but I also railed on it pretty hard because you can't just cut someone and automate their job if there's no There's no big easy button that you just slap and suddenly you've automated this person's job.
Everybody's job is more complex than their role title. And there's so many different elements to it. And even if you wanted to automate just one aspect of their job, okay, let's bring in a developer while now we're hiring and spending resources. with the developer trying to basically automate that person's job.
And it's a pretty big project. So we'll probably need a project manager onto it. And then we'll probably need, you know, front-end support, back-end support, and, you know, better usability. Okay, now we're adding more people to it. And now you've got a whole team of people trying to automate one person's job.
There's the efficiency there is lost. So at scale, yes. In solo, like individuals like that, absolutely not. There's no, there's no sense to it. And AGI. Shifts that a little bit where depending on how much ability this general intelligence has, maybe it can do some of the other aspects of their job. I still think there's like key failing points there, like the data coming in and the like, I don't know.
What's your thoughts, Dave?
AI and Data Privacy: A Critical Discussion
Dave Dougherty: Well, so I went on a coffee run the other day. And just to get out of the studio and, and, you know, and as I was driving, I was thinking about how the generative AI is amazing and I feel like I've been able to unlock a lot of stuff because of it. Because I've spent a lot of time with it, right?
However, I do find myself interacting with it. The more you get used to interacting with it, the more you start asking things like, you know, I want you to do this. I want you to do that. But it, it doesn't know how to do that yet. All of the, all of the context and the little like micro decisions that go into, you know, a particular action like that.
It's just, it's so hard. Right. So I think before we get to AGI, we're going to have agents like that. That to me is the, the bigger thing. Like, how do you connect all of these different apps into things where, you know
if they can see that, Oh, you know what? You forgot to do a rental car with your trip to, you know, San Francisco coming up, like, do you want a rental car? Yes, I would. Thank you. Right. Like that kind
Alex Pokorny: of stuff. Behind that. I mean, From a rental car agency, you could rent it for whatever time span you want.
So it has to be knowledgeable of when the trip is. It's got to look at different costs, options, location of the airport that you're flying into. What kind of vehicle would you need? Do you need a seven-person passenger van? Well, no, it's a single person. So sedans fine.
Dave Dougherty: But it's also preference too, because like, Oh, absolutely.
I might be willing to do the luxury vehicle because it's a vacation. Whereas, you know, some other people I know, they want the cheapest possible thing because that's how they choose to live their life or
Alex Pokorny: the type of trip that they're taking. Maybe they're going on to mountains and they want four-by-four capability or whatever.
I mean, there's so many little decision points into just, yes, I would like a rental car. There's a ton. There's a whole list there, right? Right.
AI and Business Culture: A Necessary Shift
Alex Pokorny: It also needs to have a good way to present that to you and not be overwhelming to the point of why am I not just doing this myself?
Dave Dougherty: Well, and yeah, and because of that too, like how much information are you willing to share?
Yeah. You know what I mean? Because that's, in order to make these things useful, you have to share a ton of information with that. Yeah. And that's where, when we talk about, okay, who's going to do the best with You know, artificial intelligence and, you know, chat open AI has done a wonderful job leading the way.
I don't think they're well-positioned in the long term. To beat the amount of information that Apple and Google has on half the world's population, you know, because Google has had every single email of mine for the last 20-something years. You know they know who I interact with most frequently, you know all the different Apple devices, you know, my preference on, you know, sports documentaries versus romance movies.
I'm going to pick a particular one, you know, over the other. So to get that level of personalization and preference built into. The decision-making of the agents is going to be a massive lift. And there are a whole lot of data privacy issues there too. Like with what the EU just launched for their regulation, like that kicks in, what was it?
2025, 2026, something like that. So like once that kicks in, okay, now you have this uneven distribution of AI. Right, like that.
AI and Job Loss: A Continued Discussion
Alex Pokorny: Yeah, there was a report talking about the job loss. Oh, who was it? Oh, I had to pull up the name, but It was an international group that was basically looking at what the job loss potential could be with AI.
And they were talking about more developed nations actually will have a higher job loss. Then less development nations, because products here, like being able to actually make a brick or shovel something you're talking about tech jobs. I mean, you're talking about content kind of jobs, so celebrations actually will have a bigger hit, but.
You still have this tech inequality, and that was one of their main concerns was basically is, yes, we're going through what can be a pretty gigantic revolutionary level of change, especially once we talk about like those agents being involved in all sorts of software, like from the Microsoft Copilot standpoint, let's say Microsoft Word has basically built into it, which it does on your purchase package that you have.
Okay. You have these different levels. Now you have people who are familiar with this tool and people who aren't. Think about like a pre-internet age generation versus one who is familiar with the internet. You have the one who's familiar with the internet far more likely to be able to acclimate themselves and work in a standard entry-level job today.
As you move up, again, still more and more requirements for you to keep upskilling yourself. And this is So as a set of tools that change so fast, it's hard to even keep on track of just from like a reading in the news perspective, not even the practicality of using them, knowing them in depth and how to use them to the most ability.
Dave Dougherty: Well, and that's where I've found it really interesting talking with various people. Well, various peers that do similar jobs to me, where it's the, how are you using this, you know, and you have the content creators that are doing a particular thing with it and it's accelerating that process for them, then there's more of the developer style SEOs who.
Are getting completely lost in like creating like chains of AI where like, okay, I have one, AI do the coding. I have another AI check the coding to then send it back to the first one to then make like the perfect code to then submit to this, that, and the other. And you're like, Ooh, okay. I wouldn't, I wouldn't even mess with that because I haven't even delved in the first part of that, you know, which is the coding.
So. Yeah, that's where I think, you know, it'll accelerate things, you know, responding to emails. I mean, we talked about this in last episode, responding to emails or going to content consumption, I think is going to make it a lot easier when you can just like summarize that email string when you get added, you know, seven emails into a problem, like having a summary would be great, you know,
Alex Pokorny: You know, I was just rewatching a recording of a meeting that I've missed.
It's just an hour-long recording. And it was really nice because there was an auto transcript that was included. So I could basically, it was a Microsoft Teams recording. So I was able to basically click forward into the next paragraph, next paragraph and know kind of where I'm skipping into as I've kind of skipped along basically with the video and figure out what I want to listen to.
The number of errors, though, that I picked up was surprisingly high. So it was still, that whole meeting could have been summarized with so much that was basically taken off the slide deck, and the transcription could have been summarized way down and it could have been instead of me spinning it close to an hour.
I would say 40 minutes for an hour-long meeting, say we'll skip some, but not much. I probably could have cut it down to 10 minutes or less easily with just summarizing it down versus the transcript word for word with errors.
Dave Dougherty: Right. Yeah. And so I'm excited with. The possibility of the transcripts getting better.
Yeah, you know the captioning getting better And again because it's so early days I think the more the more people interact with it the more data they have on How people are trying to use it You know, for example, with the word piece, I tried using co-pilot and say, Hey, I want a line to go across the page to create a visual separation between sections.
And for the life of me, I was, I couldn't remember how to do it with all the menus, you know, so I just said, screw it. I'll use AI. And then that's where it just kept crashing. Or it was like, I can't do this. You know, like, okay, well, that's a bummer because, you know, as I'm editing a document, I would want to start getting into design and layout and make this look better than just crazy amounts of texts.
Right.
Alex Pokorny: Yeah. The data privacy thing, just kind of working on without just for a moment. There's two parts to it.
AI and Data: The Future of Business
Alex Pokorny: I mean, you're talking about the kind of email knowledge that Google has on you. There's also kind of came out recently in news talking about basically different populations by age group and how they've been using AI.
And one of the most common usage with basically the undercollege set is as a therapist. Oh, yeah, basically trying to deal with, you know, life's curveballs by talking through it and using AI as a sounding board, but also for advice. It's based on my practical experience with what AI has been able to produce to date.
I'm a little concerned that the quality of this output, maybe it's a worthwhile exercise at least to, you know, talk it out. But the amount of private information that's been shared through that has got to be absolutely immense. And then you look at the next set kind of up and you, developers heavily are using AI to basically speed up their role.
So instead of searching GitHub for somebody else's version of code, instead of just asking you to create a version of the code and trying it and you know, validating it with other tools, AI or not. So you kind of have those different sets kind of moving all the way up. But then I always think about like, yeah, from the business standpoint, it's difficult because thinking back again, now just the IBM idea of let's say we have somebody who is an accountant and we're going to somehow replace them with.
AGI, AI, who knows some kind of automation system, what data do, does that person have access to that is required for their job? Like maybe they're able to log into SAP and three or five other systems and they pull together a report that combines a number of that and they were really good at using Power BI or I mean.
You name the other kind of data visualization system to basically pull together the report that their boss is asking for. Now, if you wanted AI to do that, it needs to have access to all of those systems and to be able to understand what prior reports look like so that they can fit the format or, you know, maybe if you have a brand template or who knows what else it can, you know, pull up last year's reports and, you know, append it with new data and analysts can do that.
Today, I currently can't.
Dave Dougherty: Yeah, well, so this is where I think the really big organizations will continue to get really big, but then the flip side of that. Is that you will see the most innovation in the smaller, smaller areas, you know, one or two people, teams, three people teams, maybe. Right? So, you know, the more things change, they don't,
Alex Pokorny: What companies are nimble, big ones aren't, but I think
Dave Dougherty: what it does do is that it forces you into more of that ecosystem of the tools you already have. So instead of having a wide variety of tools, you then subscribe to. The Salesforce ecosystem or the Microsoft ecosystem so that you can, you know, run your CRM and Tableau and have all the visualizations and you're only working off of the data that the customer has given to you voluntarily through your, you know, compliant forms or meetings with your salespeople.
AI and Marketing: A New Era
Dave Dougherty: Yeah, then you're not relying on any of the third party things. It's all in the up and up and they have the ability to say no, get rid of all of this. The sort of Web 2. 0 data aggregator, wet dream kind of thing where you're bringing in all these disparate APIs. I think that will probably have to go away to an extent, depending on what information it is.
Right? Because if you can no longer say this data came from here, it's tied to this thing, then you're gonna have Some of the issues,
Alex Pokorny: right? Yeah. If you can sell the compliance issue or something, right. Yeah.
Dave Dougherty: You know, so like if, if you're doing things at an aggregate level, sure. You know, you don't necessarily have the, the compliance problem because there's not the personal identifying stuff, but you know, the, at least from a marketing and sales perspective, like if you are trying to find out what are the characteristics of my top Most profitable customers, like what are those shared aspects?
Do you need that data to have been given over willingly? Which presumably they have because they're a customer, right? But
Alex Pokorny: depending on what you're looking at and what bit of information, I mean, they may have agreed to their name, their address, right? Maybe not the, you know, web analytics side of it's where you can also track their behavior and say, yes, people come from this network are always clicking on these pages or something, right?
Which is something you'd want to tie in if you wanted to, you know, do a content campaign,
Dave Dougherty: right? But I will say like on the, on the more creator side, you know, you take something like FreshBooks or QuickBooks or any of those accounting softwares. Like the reason you do that is because you would rather be focusing on something else, right?
And that hasn't, that's not changed at all. But again. With all of this data, whether it's for getting insights on it or leading towards the, the agent's idea with AI, like how much data and how much of your privacy are you willing to give up? You know,
Alex Pokorny: yeah, open AI and talked about an enterprise version of basically chat GBT or some other systems, basically that it would not be feeding data into their training sets and that you would have your own separate system that would be able to learn from your data as well as all your employees would basically be adding to it.
Data set. I was just thinking about this last couple of days because I had a few conversations that really could be summarized to, what is the status on this particular project? We haven't talked about it for two to five weeks. What happened? Like, did we, did we stop on it? Did we make a decision on it?
Like it suddenly came up again within those last couple of days. So I'm trying to figure out what happened with it. And. I keep going back to like the idea of like just these transcripts or something like that. If you actually had a recording of that. Great in a fully remote setup where you could actually track who said what, but in one of the most recent meetings I was in, it was a hybrid meeting.
There was a room full of people, about 10 people around one basically input on camera, one microphone, and maybe 155 people on the call. So it was Any of the people on the call who might have had a question and they unmute and they talk a little bit, that could be in the transcript properly, but once you have the room, you can just say people in the room answer this.
So the transcript gets a little wonky. If you had collections of all of these different transcripts through all of the different meetings across the entire company, and I said, hey, what's going on with this particular application? What's the latest effort to work around it? You could search and query against all of those different transcripts and then you could summarize against all of those saying these people were in charge of this project.
They're doing this kind of effort around this application. These people are doing the IT verification of it and they've got stalled out here and this is the last time it's been mentioned. Like you could, you could start to comb through and pull this data, but you would also have to have this level of familiarity and acceptance across the entire organization that you are okay with all of this data being tracked from you, taken from you, aligned to you, and being accessible by others.
Dave Dougherty: And that's where I think the smaller organizations who have that as a foundation to their culture will absolutely win. Yeah. In this sense. And what's interesting is early last year, I read Ray Dalio's principles book, and in there he talked about the radical transparency idea and how he implements that in his private equity firm.
And He fully admits that there are a lot of people who think they're going to be okay with it. And then they experience it for like two weeks and they say, no, thanks. Yeah. I leave. And so you have a particular culture of people who really thrive in, I don't care if everybody knows what I'm doing. Yeah, you know, but if you want to hide at all, that's just not going to be your, your thing.
And there's also, there's got to be that culture of accountability too, because it's one thing to know it's another thing to be held to it. And if you're above a team of 10, Forget it. Then there, you know, that's where you start losing, you know, who knows this person? Why did they say that? Who's the contact?
It becomes too much, you know?
Alex Pokorny: Yeah. I've experienced that and I've seen it happen with others as well. With agile. When you implement agile on a team, one of the most uncomfortable pieces, and it takes like two months, month and a half, two months, to get over, is the daily stand up, where you say, I did this yesterday, I didn't do this yesterday, I'm behind on these things, and every single day you admit to that, and you say it publicly.
I mean, the first time, you know, I was in one of those standups, I was like, I don't want to say that I didn't do something and I didn't get to it. Like when a coach, my answer, and as much as I can say, like, I'm going to do it today or, you know, I started on it, which meant that I knew that it was assigned to me, meaning I started on it and, you know, getting over that awkwardness and I've seen that from other teammates as well, through different teams that have made that transition and being a part of those transition points.
Once you get through it though. It is free. It is amazing. You start to understand exactly what's going on with the team. You feel like you're actually working as one concerted effort, moving things forward. You know how things are going and where you can help. And you know, who becomes kind of experts on different tasks and projects.
So, you know, who is aligned to it because they bring it up, you know, once a day for five times in a week because it's the daily standup. It entirely changes teams. It absolutely does. But they have to go through that trust piece and that awkward piece and some resisted far more than others.
Dave Dougherty: Well, yeah, there's that whole cultural element of you know, I didn't turn in my homework,
Alex Pokorny: right?
Right. So,
Dave Dougherty: Yeah, it can be really awkward personally. I, I prefer it. I really like it. I like accountability. I like being able to say, you know what, I need help on this. Can somebody, you know, cause it's just, it's just a better way to live, you know, instead of pretending that you're a, you know, you know, you're Superman or superhero or something like it's just whatever we're all human, we're all trying hard.
And sometimes there are things that get in the way. Somebody doesn't answer an email. Somebody's, you know, blocking it for whatever reason. That's just life. That's part of work life. But yeah, I think it's definitely worthwhile doing it. But circling back to what does this mean for job loss and gain?
Especially when you talk about the accountants and the financial side of things. I immediately think of how there are certain segments already in terms of employment where, at least in the U. S., there's a hard time finding people to take the jobs. Mm hmm. So there's that natural play of, you know, we have more people on this side that could do this and if they really wanted they could transition over to this other job, but because we don't have enough people, you know, in accounting and finance and whatever the role is, well, what tools can we use that we already have?
To supplement that to, you know I feel like that's how it's going to creep in and then they're going to realize, Oh, you know what? We don't need 20 accountants. We need 15. Right. Or with Duolingo, we don't need all the contractors. We can get rid of 10 percent of them. Sure. Right. And it's not this like nefarious.
You know, leader with a cat saying, I'm going to destroy this, that, and the other it's people trying to do a good job with the best of intentions. And then things happen, you know, which is the most unexciting answer, but that's
Alex Pokorny: like, well, it's the resourcefulness of the individuals that are there to try to get the job done and getting the job done changes over time as well.
I mean, Think about like, you know, agencies from the 1950s and 60s. How many people do they have just typing out notes and typing and typing and typing all day long in their little typewriters? Those floors of people don't exist anymore today. I mean that that was a job that was there and that was gone I mean how many people were literally by hand doing all the numbers?
How much software is used now? How many few people that are required to basically do the same job and that same task or of course, new jobs, new tasks, new jobs to be done, new ways that are being done, which needs other kinds of support, which creates other roles. I mean, there's, there's always that give and take as it kind of moves along.
So there's
Dave Dougherty: a, there's a great section of a Clayton Christensen. Conversation or it's like, yeah, it's been two episodes. I got to bring him up.
Alex Pokorny: I always did earlier, so go for it.
Dave Dougherty: But so he has a presentation at Oxford where he talks about how there are metrics, you know, when he says metrics today, that was 2013 that are being used to judge businesses that could not exist in the seventies.
Because you had to do everything by hand. And so it was such an arduous process. They said, nah, I'm just not going to do that. You know, but because of Excel and because of all these other things now you can and all of a sudden the importance of those things, just because you can use them sort of takes over and then it takes.
10, 20 years to realize actually, maybe we shouldn't only be looking at free cash flow. Maybe we should be looking at these other things too. Because it gives us a better idea of the whole system and not just these other things, you know, You
Alex Pokorny: see that in every market. I mean, think about like the, the click counters early website days down at the bottom and it says, you know, 17, 530 people like clicked on this site or something like that.
You know, everybody was all about the clicks and it took a really, really long time to finally get people away from that and think about. Okay. As a visitor, it was actually not just clicking on a site, but they're actually spending some time on it. They're looking at other pieces of content on it. What's the point?
And how many ads and platforms have been sold on the idea of impressions? Like all of them at the beginning, it was all about the impressions, the fact that the ad existed, and there was people who potentially saw it, that's enough. And that was the whole value of it. And then eventually you say like, no, we want to make sure that people click.
Well, we want to make sure people click and convert. Well, we want to make sure we click convert and buy actually go through the whole method. Right. And we finally get to a metric worth going after.
Dave Dougherty: Well, and then, so this is, this will be a topic for another episode, but you know, you follow that along the lines and then now with Google saying the cookie's dead at 2024,
there's going to be a whole lot of marketers who have not even considered a reality without having all of the data there, you know, to tell you exactly what content works.
Alex Pokorny: I would love to see a survey on how many people, when they think of website cookies, think of a baked good versus understanding the technical relevance of a cookie.
I bet the numbers are pretty bad. Explain a cookie. Shoot.
Dave Dougherty: Yeah. Yeah.
Alex Pokorny: It's going to be a, it's going to be a
Dave Dougherty: shift. Well, and then the flip side of that too is, you know, how many marketers or salespeople in an organization has a not small percentage of their compensation based on the data they can collect today, but may not be able to.
Yeah.
Alex Pokorny: Quite a few. I mean, thinking even about like how the ROI of campaigns is tracked, there's so much that is based upon the idea that we can track anything and everything basically, which would no longer be the case. And the value of first-party data, when did we see that about a year ago, two years ago, started talking about the financial value of a company based upon the data access they Yeah, it was one of the first times where we weren't so much looking at the financials of the particular organization, but instead it was looking at what kind of data do they have access to that others don't and how valuable is that data and Chinese government even talked about it with one of their, it was about a year ago again, reports saying about like the era of data and the value of data and all the different restrictions and rules that they're going to put around it just for the, because they understand those things.
That is so powerful and so valuable that the age of manufacturing is dying off and the age of data is kind of has already risen. So many companies have no idea what the difference between third-party and first-party data is, and they think they have all the data
Dave Dougherty: in the world.
Well, that seems like a perfectly cheerful spot to end the show.
Alex Pokorny: Learn about your cookies and the calories. Get out there. Just do some exercise.
Dave Dougherty: Exactly.
Conclusion and Episode Wrap-up
Dave Dougherty: Yeah. And anyway, thank you all for listening. Send us your comments and episode ideas emails listed in the description. Please like comment, subscribe, and give us a rating that will help us a lot.
It doesn't, it doesn't cost you anything. So appreciate you and we will see you in two weeks with the next episode of Enterprising Minds. Take care.
