← Digital Disruption with Geoff Nielson
Digital Disruption with Geoff Nielson
Why AI is Failing: Ex-Google Chief Cassie Kozyrkov Debunks "AI-first"
01:26:20
Loading summary
Transcript33 lines
- [00:01]
A
Hey everyone. I'm super excited to be sitting down with Cassie Kozarkov, former chief decision scientist at Google and a game changing founder, AI advisor and keynote speaker. What I love about Cassie is not just that she's incredibly smart and thought provoking, but how fearless she is at calling out bullshit and just how good she is at parsing what's useful. From all the noise and hype, she believes that companies talking about going AI first are getting it fundamentally wrong and we need to completely change the conversation about what AI is capable of. I want to ask her what AI can really do for us and our jobs and what is the real future of work? Let's find out. I'm here with Cassie Kozarkoff, former chief decision scientist at Google. Really excited to connect today, Cassie, and maybe to kick things off, you know, you've talked recently about a question that I think is kind of on everybody's mind, which is, you know, what you've called the generative AI value gap. What does that mean and what are you seeing in that space?
- [01:06]
B
Yeah. So I'm sure that anyone who's been watching the various surveys and numbers coming out about generative AI and generative AI deployments would have found that 95% number, you know the one, right.
- [01:22]
A
Sure do. 95% not getting any value from AI.
- [01:25]
B
Exactly, exactly. Except the phrasing. I like this phrasing is measurable roi. Right. So some part of what's going on is that companies are really getting no roi and there are fantastically foolish ways to just try to keep up with the Joneses in AI have no idea what you want it for. Kind of send your people off to go sprinkle the magical AI on top of your business and you hope better things happen and then you join the no ROI bucket. But there is also some number of those. 95 are going to be no measurable ROI. And this is. This breaks up into two pieces. One is that generative AI is fundamentally just more difficult to measure. And I want to double click on that in a moment because I know that's what you're asking me about. But the other piece is that sometimes what we're actually getting is we're getting the ability to innovate next time. And I think that not enough companies appreciate that Innovation day demands waste. If you are doing something that you've done before, you know exactly how it's going to go, then of course you can have these KPIs that you know you're going to hit for sure because you've already done it. Now you're trying a completely new technology with a completely new use case. You have no idea if it's going to work. You have to be willing to accept that that might be time and effort thrown, you know, burned at the altar of innovation, so to speak. Right. That that is just the nature of innovation. And I've had companies come and consult with me who they really wanted to be innovators. But when I ask them, so what is your actual tolerance for getting no results back after you invest in innovation? Or how much bandwidth do you give your people to do things that are very specific work product that you expect from them? Do you give them time and space to chase an idea? And quite often the answer is no. No, we don't. We have no tolerance for innovation. We have absolutely no slack for our people and we need every project to be predictable. Okay. If you're dealing with that, you're just not going to be an innovator or you're going to be an accidental innovator because you somehow accidentally hired somebody who going to essentially work two jobs, the one you gave them and then the other one they'll spend nights in the office and maybe they'll come up with something, but there won't be a lot of these folks. And yeah, that's not a great lottery ticket. So if you don't have that tolerance for no ROI when you're trying to innovate, just you have to be a follower. Just wait for everybody else to show how it's done and follow them. But there is another piece with when you actually do this wasteful innovating, you learn how to innovate. And we have solar's paradox coming up again in AI. And Sola's paradox came up in computers for productivity and that you could see the productivity everywhere except in the numbers. Right? That was the paradox. So how is it that we can all feel so much more productive? How is it that we can have individuals numbers like 90% of software engineers are using generative AI to help them code other numbers like can't remember if it's 90 or 70 or some big number of people personally use these tools in the surveyed population for this 95, no different study I think anyway, workers personally use tools and yet a tiny fraction of the employers, the companies actually formally give access to these tools. So you've got this shadow AI thing going on where people are using AI but it's not sanctioned, it's not held by their employers. Right. You've got this big disconnect. People really like it, they seem to be productive. I'm very much more productive with it, personally. And yet we don't see it in the roi, we don't see it in the productivity numbers. Sometimes what we're doing is we are just laying tracks to be able to innovate next time to get the next project right. Sometimes this is the first pancake and in some sense when you begin a batch of pancakes, the first one is an investment and there is a return on that investment. It's just not measured the same way as return on investment of your other pancakes. So I just want to caution people as they're in the innovation games, they're just getting started as there's a lot of uncertainty, don't expect that there's some magic here. The guarantee is that the rules are now suddenly different. They're not. It's the same innovation game as before. But now I'm going to answer your actual question and I'll land this plane. Finally, let's get back to the difficulty of measuring ROI and the difficulty of value. Talking about value and why there's a value gap. And here I'll say that if you look at how we thought about metrics before with your classic machine learning, 10, 20 years ago, we're thinking, and when we deploy it as well, we're thinking in terms of minimizing loss or minimizing error. When you have that philosophy of error, what you also have is a philosophy of correct answer, right answer, right? Because if you don't have such a thing as a right answer, you can't have such a thing as a mistake. So you can't have such a thing as an error to minimize. So you can't have all the optimization map that we're very used to. So it'll be things like, you know, you'll have an image classifier and it's supposed to say cat, and instead it says dog. And we can say that that's an error, right? We can measure that. Or you're supposed to predict the weather and it was supposed to be 72 degrees and we observe that it's 75 degrees. Right? 3 degree error. All in terms of there is a single right answer that we are chasing. Now, of course, there are many wrong answers. If the weather is 72 degrees, all the other numbers are wrong, right? But there's only one ramp. Now think about generative ar. We are essentially simulating from distributions here, and anything out of that distribution could potentially be, if it's from the right distribution, a goodish answer. Now think about this. A customer service interaction, an email, a post. If I ask for an email to have this podcast with you on a Friday afternoon, I could write that email. Hundreds, thousands, infinite number of different ways and it would still be a good email. Of course there's infinite number of ways it would be a bad email. I could start cursing in the middle of it. I could send you a complete. Not an email, but just a poem. And you would find that quite weird, though. You'd probably be intrigued. Be like, definitely invite her on the podcast. Or I could do the classic mistake and instead of Jeff, I could name you something else. Lots of different ways to be wrong. Lots of different ways to be right. Though what should be my tone? How would I know which email is better than which other email? I've got infinite, endless ways to get it right. It's not cat, not cat or 72 degrees, not 72 degrees. It's infinity of ways that I could be solving the task. And now we get the big problem. So far, these are just mathematical tools or software based on mathematical tools based on data that's doing what it was made for. But what it can't do for a leader is tell them what good enough actually means. How do you make this cut between completely awful emails all the way across to, I don't know what, how you would even think of, like the most perfect email you could get. But somewhere you're going to have to draw a line. You're going to have to create standards of some kind. You're going to have to talk about how you're going to measure this. If you're going to have automated emailing, for example, if that's the system that you're going to put in place, and if you're kind of squeamish about that, you could say, well, I will reduce it to a KPI I know about. I will maybe see how much time I can save my humans if I give them an emailing copilot. But now I get some measurement issues as well as a manager, because do I force them to use it or not? Because if I don't force them to use it, am I tracking whether they chose to use it or not? Now, how am I going to measure a value if they're all ignoring it and continuing to write? But maybe they are writing better for reasons unrelated to the AI. Maybe that'll look like results, maybe it won't. Right, we've got, we've got some potential issues here. Or maybe we forced them all to use it. They hate it. They haven't learned how to use it yet. Maybe what we're going to see is decreased productivity and eventually that productivity comes up. But how are we, are we sure we're measuring the right thing? And how would we think about those strange edge cases where every now and then that email is a PR disaster, Especially when we make systems where we take the human out of the loop. And now all those emails are going to be sent with no human oversight. Maybe a bunch of them save a lot of time, but there's that one that gets the media interested and that tanks your company. So there are so many different ways you could think about setting up notions of what value is, how to measure it, and how to deal with this curse of endless right answers. And again, most MBA courses, most things that we think about when we think about metrics is about targeting a right answer and how wrong are we? This is a different paradigm and I think it's snuck into our workplaces without us even realizing how much of a different paradigm it is.
- [12:04]
A
If you work in it, Infotech Research Group is a name you need to know no matter what your needs are. Infotech has you covered. AI strategy, covered. Disaster recovery, covered. Vendor negotiation, covered. Infotech supports you with the best practice, research and a team of analysts standing by ready to help you tackle your toughest challenges. Check it out at the link below. And don't forget to like and subscribe. There's so much you said there, Cassie, that I want to follow up with and so many interesting jump off points. But to your last point, maybe I'll start with this one, which ties into something you said earlier, which is in the absence of really understanding what we should be measuring or what return we're actually looking for on our investment, which is hard. And I think in some ways what we're seeing is an abdication of leadership decision making to even try to do it. And to come back to what you said, just sprinkle AI on everything, right? Like just sprinkle AI and it'll make everything better. When you hear leaders say just, you know, we need more AI in the business, everybody should be using AI, what's your reaction to that? And you know, assuming it's a negative one based on the expressions you just made there, you know, what better approach do you recommend so that they can have a better, they can, you know, create more impact and value.
- [13:33]
B
Look, I, I am a huge fan of the word. Why I'm an enormous fan of it. And I would never presume that the surface read of a situation the way that you described it is in fact the reality. Right. Because sometimes you get a request that sounds silly and that is because there are, there are things underneath it that nobody is telling you. You gotta check. Right. Sometimes folks are just on the hook to have to do something, anything that calls itself AI because there's a board member that must be appeased or something like this. And there's just no way out of this conundrum. And then what you do is you sprinkle AI as far away from the business as possible where it won't actually touch anything, do anything important. I mean, a very classic version of this would be you could add some fun harmless AI feature. Maybe it makes music on one of your web pages or something. And maybe it's awful, but it's instrumental. So there's no real way to mess it up. Right. Who knows? Just something keep it away, keep it away from anything important. So we got to know why, why we're chasing AI. The other thing is if you have a sense, if the reason that you're doing this as an executive is that you have a sense that the world is going AI first. And I would agree with you, but you don't yet know what you need it for. But you want to make sure that if you needed it, you would be in an okay position to act quickly like that. That is the panic. And the thing to do there is not to actually deploy AI. On my newsletter I put out a piece about AI infrastructure debt. So this is a term that Cisco has just. What's it coined in their AI readiness report that came out this week. Their 20, 251 and in there. And I'm going to forget the exact numbers, but it's something like 13% of the companies that they surveyed, and this is surveys of over a thousand leaders. Something like 13% have the preparedness to actually do AI and agentic AI at scale. 13%, 83% is how many plan to release AI agents. Right. These numbers, these numbers compute poorly, so to speak. Seems like there's a problem. And they refer to this AI infrastructure debt as. Every time that you're just sloppily putting AI into production without thinking about the infrastructure, without thinking it's sort of like a cousin or an evolution of technical depth. Right, right. You're just punting potential problems. Oh, we don't really have the people to do this. Oh, we can't really set up proper guardrails or human in the loop interventions. Oh you know, if we had to scale this up, we wouldn't have the GPUs, right? All of that stuff, that is debt, that grows quite quickly as you get a lot of pressure to be in the game of AI. And that has a really, really high interest. That's a really, really high interest rate. Credit card. That credit card. So what I would say instead is if you are able as a leader to somewhat blur the boundaries, I think this is ethically fine. Blur the boundaries between what AI infrastructure means to you, your board and your leadership team and what AI means. You can start thinking about rather investing in what you would actually need when the time comes. And you can watch others in your industry show you what use cases are good and not good. And you are set up to hit the ground running and scale quickly and join that 13% from the Cisco report. So that might be the smarter thing, right? Start, start investing in the capability of doing it. As you're doing this, then you also have these pilots and you can be in pilot purgatory, as people call it. But it doesn't matter because what you are trying to do is set yourself up to be able to use AI properly in the future because you don't know what you want it for yet. You want to watch, you want to get inspired by what others are doing. You don't want to rush in, don't want to be ballooning that AI infrastructure debt. You want to be setting things up. That said, also going Back to that 95% study, one of the findings there is that companies that partner are twice as likely to deploy AI solutions as those who reinvent the wheel, build things from scratch in house. And then I think there was a note in that report that said something like most of them, or almost all of them that the researchers talked to, had considered or tried to build in house. So this is like an urge, everybody wants to reinvent that wheel. And, and the guidance, and this is guidance that we've. I've been, I've believed in this for years, but it has made sense to me during my entire pretty much career involved at Google and now that you should focus on what your particular strengths are, whatever your business edge is and let somebody else handle for you the parts that you are not an expert in. And you know what? You are definitely not an expert in. If you are not a company that does this or a tech giant, you are not an expert in AI security. So that's one you don't want to roll at home yourself. You got to go and you want to partner with a vendor that actually knows how to do that. And if it's the teensy tiniest little vendor and a security breach could be catastrophic to your business. Well, I mean, look, I am biased because I spent 10 years at a behemoth at Google, but you might want to go to a larger company that actually has the staff, that talent pool to do something like secure your client facing AI, if that's the direction you're going in. Yeah, forgot what the question was, Jo.
- [20:18]
A
So that's all, that's all good. I wanted to, we covered a lot of ground there. And one of the things I did want to unpack a little bit more is the phrase AI infrastructure. And so when you say AI infrastructure, you know, is that making sure that you've got the, you know, basically your data is primed and ready for AI ingestion is that you're working with the right, you know, stack of vendors in the space, is that you've got enough AWS credits for all the processing you're going to do, all what is in that bucket. And if organizations are truly serious about AI, what do they need to do foundationally to get ready?
- [20:54]
B
Right. So things that you mentioned, whether it's AWS or one of the other cloud providers, those make sense, but those are even there. We would want to poke a little bit, which we will you remind me. But there's a whole piece that you didn't mention. That's the humans. That's the humans on the inside, that's your leadership, and that's also the humans on the outside. One of the things that leaders, in my opinion, could spend more time thinking about is how whatever AI system that they would love to put into production, how that will actually be taken up by the people it touches. Right? You really have to think about that. So when you think about the expectations of your users and what you are deploying into this pot of expectations, you can immediately be defensive against some bad situations. For example, if you have all of your users very well trained to expect a narrow set, but unimpeachable narrow set of correct responses from your system, and now you offer them a generative AI system that might, though it makes mistakes. AI systems always make mistakes. Just sometimes it takes quite a lot of scale to see them. Right? What happens on even a very functional AI system, we still say you will meet the long tail, find the outliers, the weirdo, situations that you did not see coming. Even when it's highly performed, expect, expect something's going to happen. And so you anticipate that there will be mistakes. Now the question is, when a mistake Touches a user who has a particular kind of expectation, what then happens? How much, how flammable is that? Your AI infrastructure? Of course, it's all the obvious things, your actual infrastructure and your data pipelines and all the rest of it. But it's also things, intangible things, like, at what stage are my user expectations? Have I managed them sufficiently? Where I even could be deploying to users? What about internally? If I'm doing some internal corporate engineering, I'm offering some, you know, now we're looking at the digital employee experience. I'm offering some tools to my employees, some digital tools. Have I managed their expectations? Have I trained my staff? Do they know how to think about these tools? Let's say I need humans in the loop. Am I sure my human will be in the loop? Or might they be asleep at the wheel? And how do I do the training? And how do I put in maybe a collection, depending on the importance of the task? I might need to think about having multiple humans in the loop. I might need to think about consensus. There are all kinds of measurement infrastructure things that we would need to put in place. We're doing generative AI. We've just seen endless right answers, A nightmare thing, a nightmare challenge for management. Because we've all got to change our paradigm and we've got to think differently about measurement and metrics. Have we done that? Have we put this in place? Do we have testing pipelines? Do we have experimentation pipelines? Do we know how we're going to roll things back if we need to? Do we know what we're going to, what versions we're going to go to? Do we actually know what will happen in what kind of scenario? Do we know how we're going to make our guardrails? Who sets those guardrails? How do we update them? How are we going to react to legal changes? Right. All this stuff now, okay, I know it's hegemonical. You can't say everything is AI infrastructure, but to be ready for AI, there is a lot of stuff that you would need to be ready for. And so one of the ways that you can dodge a lot of this is that you do outsource some piece to a vendor who is supposed to do all of it for you. And you just check that you're getting precisely what you need. And you have to still articulate what it is that you need. And you have to worry about, measurement wise, that there is going to be a gap, a hole between what the vendor sees and what you see. There's going to be some bit in the middle that nobody sees. And that could be a huge risk, not just in terms of security, but in terms of your system slowly going sideways with neither party noticing.
- [25:38]
A
So it's really interesting and I interpreted infrastructure, as you could tell, in a much narrower, more technological answer. And you're saying no, no, it's cultural infrastructure, it's the business architecture. It's everything you need internally to set up yourself success for that type of adoption.
- [25:58]
B
Look, Jeff, I also think maybe this is just a great one to pull out. But if we take things to their limit, to their logical conclusion, you and I, I imagine nerdy folks that we are, have a certain love for technology, right? We still think of technology as this, this ugly duckling thing that, that we, we have to suffer with a little bit. And after a while we love it because we've, we've done battle with it. It's like a little bit fiddly and detail oriented, you know, I have to learn some unnatural languages, that kind of stuff. Or it's, you know, physical hardware objects that might, when we still remember how annoying things were to set up, we still have this feeling that the majority of technology is the fiddle and a long journey of fiddling and then the pale. But what we will see is that technology, as the actual experience of building it and interacting with it becomes so much easier. We will all move to less fiddling and more architecture, more thinking about how everything fits together. And we will see that what everything fits together with is not just all the other complex technology, which there's going to be plenty of, but also all the ways in which it touches physical reality quite often through humans. So if you're not thinking of humans as now part of this infrastructure because you're still used to wires and bits being what infrastructure is, then I'm not sure will that way of thinking have to snap crack at some point within the next decade or so when we realize just how human and ambiguity filled and unpredictable technology becomes.
- [28:01]
A
Right. And as you were saying, I was reflecting too. I mean, part of me wonders if some of the hubris of the moment is people believing that fiddliness isn't here this time with AI. And oh, AI is easy, you really can sprinkle it in and it's all built. And it's like even in this world where we get toward AI being able to remove some of the fiddly stuff, focusing more on integration, focusing more on sort of the magic that people bring to it, we still have to build that, right? And so coming back to that initial report. And I love, by the way, so many people I talked to saw that report and were like, this is the end. It's a bubble. You know, AI is dead. And I mean, I love that you come in, you're like, I'm not even surprised. Like, of course it said that. People have the wrong expectations for me.
- [28:53]
B
I just need to make sure I publish before. Right. I can just tap to be like, this is January. Tap, tap. Your report came out in the middle of the year. Don't make me tap at the board again.
- [29:06]
A
No, it's really good. But it's making me wonder as well, with this belief that AI is so easy. Just plug it into anything and magic will come out. And that bubble sort of being deflated, it comes back to, I can't wait. Sorry, I can't. I can't either. I can't either. But I did want to ask you, you know, there's another phrase that has been floating around which is, you know, the AI First Enterprise. The AI First Organization. And so I think part of what's the. Why people are coming back to leaders are, oh, I want to get there. I need to. To be competitive. And there's this sort of race. And I deliberately choose the word race because it's like, as fast as possible. Like, I'm not budgeting for any architecting. I just want results. Now, is this. Is that the right approach for everybody? Like, should everybody be an AI First Organization, or is that only right for some. And what do you need to do to answer that question and to get there properly?
- [30:15]
B
Yeah, I. Look, I love this question. I also want to say to the. To the question of it's going to be relevant, I promise I land the plane eventually. It's not blocking. It's not block and bridge. People are like, is she blocking and bridging? No, no, it's. The plane is taking a loop. But we'll start here. The part of why I want that AI is easy notion to die, but also to live is because I want people to see two separate things here and that lessons from one don't translate to the other. So the question of if I am always my own human in my immediate loop, and I am just dealing with language, right? I just have a large language model. It's not connected to anything, maybe the Internet. But all it does for me is it gives me language back on screen. So language can only hurt me in that setting if I let it, right? If I'm gullible, like, ask a stupid question, I get a Stupid answer. I don't realize it's a stupid answer. I go, you know, change my medical habits or something on the basis of it, right? Bad. And it hurts me, but it hurts me because I took it and I did it. But mostly what I can do as an individual is I can play back and forth and ask anything that's immediately of interest to me. And if I'm bored, I leave. And if I'm compelled, I stay. And as a worker, if it's making me more productive, I stick with it. And right. If it's a choice, otherwise I put it aside and in the moment, right. The human is generally, if they're using these tools, well, shaping a whole bunch of output and that version of AI first of saying this is actually a pretty awesome tool. If you're good at seeking advice, then everyone in the company should join the new economics of advice. So what are the skills for getting advice? Knowing what's important? Well, not just they're all judgment skills or leadership skills. They're actually kind of hard though they sound easy. But knowing what's worth asking about, right, that's your priorities. Knowing how to ask, that is context. If you're having marriage issues and you get the best advisor in the world, don't just run up to them and go like should I get a divorce? Right? They need a little more context than that. It's not going to work. Doesn't matter how good they are. They're going to give you a stupid answer if you don't. Supply and context. A lot of judgment required. Here is not one right answer. What you choose to show is as important as what you choose to withhold. And that's going to shape what we get next. And that's judgment as well. And then the third piece of asking for advice is the skill of not taking bad advice or knowing the quality, right? So knowing what's important, knowing how to ask and knowing the quality, if you're pretty good at that and you can get better at that, you can get so much out of just the language piece of generative AI, you can learn things faster, give a bunch of context. You're interested in quantum physics, I don't know. But you know nothing about it. Tell it what you do know about. Tell it how to how you like to learn how you want the output formatted get help in finding sources rabbit holes to go down. Right. You will learn quantum physics much faster that way than than if you just try to consume old fashioned. So that version of AI first where as a leader you have Quite a strict mandate for everybody to when it doesn't involve confidential information, to get a second opinion, for crying out loud. From a large language model, right before asking your manager before submitting work that's non confidential, again, screen it. Maybe you were asked to create some graphics right? Before showing them to the next person or the client, right? Maybe screen it, ask how can this be better? And then ask how this can be better Again, and then ask how this can be better. Ask about your prompts themselves, how can this be better? And keep applying your judgment, keep applying your brain and take the good advice, leave the bad advice. And you see yourself supercharged. That version of AI first is not going to harm anything. We're just going to realize at how much of a premium judgment is. We're going to begin to value it more hopefully. And we're going to realize those with good judgment are going to supercharge themselves. This is on the individual level where you are your own intense human in the loop. So that version of AI first, right? Like please do that. Train your people, set their expectations right, tell them not to put confidential stuff where it doesn't belong, and for the rest, go wild with AI. I had my junior admin that I hired his manager, my executive assistant as been very, very insistent that he does several rounds of checking things with AI before even passing it to her, let alone passing it to me. The skills that he's managed to pick up are phenomenal. And how quickly within a week, editing some of my R code for renaming files, right? That that R code is not exactly a state secret. But this is just A young bright kid with zero engineering background is jumping into editing R code, which is an arcane language that only the masochists can love. But yeah, what can you do? And then he's gotten really great at graphics in just a few weeks. What you can learn and what you can do that you have no experience in before and how quickly you can become what I call chimeric worker, chimeric talent. You just pull in new skills and get AI to supercharge you when you're doing it with this individual productivity lens is phenomenal, right? You just have no excuse. So I'm singing that version of AI first from the rooftops. But it does not translate. Does not translate. And every lesson there does not translate to automating with AI at scale, hands off the steering wheel does not translate. In fact, it teaches you the wrong lessons and bad lessons. If you're an executive who is intending to only boost the individual productivity of their workers, you should absolutely be, you know, banging the drum about ask AI before you ask me always, you know, ask AI everything not confidential. If you wouldn't violate your NDA by asking an external friend about something like, you know, hey, what kind of style is appropriate for a performance review? And give me a draft of how to express in a kind way the following things for a 2 out of 5 stars performer. Right. This type of stuff, without naming any names, can get that advice externally, get that advice internally as well. Right? I'm a huge champion of that. But when you see how easy that is, you think that kind of easiness that is predicated on you really micromanaging your AI tool and really massaging where you point it and how you use it. You want to now take this thing and scale it up somehow and let it go. It is not the same game. And that's where all your AI infrastructure did issues come in. And that's where you, you have your meet the long tail situations where you thought, oh, AI is smart, that's fine. Let me put this chatbot in front of a customer. And then you have situations like the, the car dealership chatbot offering to sell a car for a dollar or my personal favorite, when virgin monies chatbot berated a user for using the word Virginia. Right? You just get this stuff and it's, it's a magnet for, for the press making fun of you. And it is just such a different game. So when we automate, this is a different thing. When we take our hands off, this is a different thing. And so you have to know which version of AI first you're talking about. There is absolutely. Do that, you know, AI as advice or don't take bad advice. It could be bad advice. Right? Do that before automation, you're going to have to know how to measure value. You're going to have to say why you want to do the thing you're doing. You're going to have to put all kinds of guardrails and you're going to have to expect that your guardrails were wrong. You better have safety nets and plans in place for when things happen. You're going to have to think about security. You're going to have to think about can you scale properly. You're going to have to think very carefully about how this is going to be received by people who were not the people who set it up. But there is a glorious side to this version of AI first as well, which is why it smells like catnip. And that is that just the simple Equation of if AI makes anything easier. But let's imagine it makes AI email easier. Just that, because it does, provably I can ramble into the chatbot and say, you know, write the, write a polite email to Jeff, we're doing this on Friday at 1pm and something polite will come up, right? Something like that. I don't even have to prove that I can make software engineering easier, which we can, right? Even just reducing email and software engineers have more time to write more software. But if we make them productive with software copilots, they have more time to write more software and then we have some of that time will be invested in software for making software writing easier. So we just have this loop of the things that we can suddenly automate. That space is growing, that universe is expanding much faster than it used to. And so now from the executive's point of view, the executive has been told recently probably that there is something that they wanted that's impossible, technologically impossible, technologically infeasible, just can't be done. And if, if it's teleportation, that's still infeasible, there are a whole bunch of other things that are suddenly being put on the table for automation. And that the version of AI first that I would love executives to have for this, this automation sphere is not let's automate with AI no matter what, right? That is, that is bad news. That is leadership abdicating its role. As you said, Jeff, don't be doing that. Instead, before you go do the thing the traditional way, or before you give up thinking that it is impossible and infeasible to run your business the way that you wish you could, you think you've got a technological constraint insurmountable, just take that time to revisit that frequently. Because it could just be that what you need has recently become possible. It could be. And so before assuming and committing to doing things the old way, consider the possibility that AI might have given you the impossible made possible. That version of AI first is what we want to see. But that version of AI first is also can of worms. Because let's say that you do see that your traditional processes now have an automation option with AI that will change everything. You're going to have to think differently about how to measure. You're going to have to think about how to have a foundationally probabilistic system play nice with what is probably a deterministic ecosystem. So are your users prepared for that? Are your workers prepared for that? Are any other systems going to break if you start putting this in place, but things are moving so quickly, you can't, as an executive, afford not to be thinking about it. So in that way, you've got to be AI First.
- [42:39]
A
I love the way you kind of teased that apart into the different definitions of AI first. And I'm trying to. This may be a dramatic oversimplification, but I'm trying to frame this up in my mind, Cassie. And, you know, if I. And I'm happy for you to disagree with the framing, but I'm almost seeing, like, three buckets here. There's the AI for the individual worker, which is fairly easy, and it seems like there's definitely some, you know, reward or return on it. There's the opposite end, which is trying to just go after everything and rearchitect your entire organization. And then there's, you know, if I may like, a Goldilocks option in the middle, which is figure out specific, you know, cases or specific components of your business where there is. There was too much complexity previously, but now that can. That can be unlocked. So, I mean, first of all, do you agree with that framing of, you know, the spectrum of difficulty and then across those. Where would you place the value on each of them? Is it just as simple as one's really easy and low value, the other one's really hard and big value, and the Goldilocks is just right, or how would you categorize that?
- [43:52]
B
I would think about this differently. I would. Again, I wouldn't even want them to be juxtaposed. I would say the year is 2025. It is almost 2026. If you still think that you can run your business without introducing your workers to the concept of personal productivity upgrades. It is like you were trying to run your business in the year 2015, ignoring that the Internet is a thing. There are a few businesses that you could probably get away with with just pretending there's no Internet. But what a wild thing to do, right? You don't have to be a technology company, just, you know, get with the program. So that version is a. It's a really a different set of stuff. And it is when. When that gets delegated to the IT department. I find it very funny. That is not an IT department thing. Making sure they have access to the Internet, making sure the WI fi works, making sure that the security system is there. That I get. But the, you know, how do we search? The Internet is not an IT thing, right? Not now, not in 2015. We have access to a store of knowledge. Well, here's Human knowledge and we can go searching within it, what would we want to think about finding? Right. Very domain oriented. And so once you've got people past that first lesson of just, just use it for something, which again, every time that you notice that someone is asking you something at work where they really could have asked an LLM, just respond LLM until they get the hang of it. That shouldn't even be juxtaposed. Now the other two things, your other two buckets, here's how I see them. The thing you're calling the Goldilocks zone is actually the thing where leadership has not abdicated their responsibility. As in leaders had to think about what is worth doing, what is worth having at the scale that they're supposed to operate, not at individual scale. What's my priority and what should I ask advice of that but big what do we need? What if we had this would save us a lot of time or money or allow us to open new lines of business or completely change the way that we operate. If you do the exercise of thinking about what you actually wish you could do with your business, then once you have that inventory, you can go through that inventory and see if any of those wishes might actually be granted. So that work is not a work for a technical PhD. That work is for somebody who's committed to making the time to be strategic, giving them the space to actually think about where they want to direct their company, their organization and articulate this right? And this work, this is the hard work and this is going to be the work. No matter how much we automate, this is still going to be the hard work. And I think of your third bucket as don't worry about any of this. Let's just get all our shiny toys in place. I see that as a very expensive way to skip doing the work because doing the work is probably faster than setting everything up for a maybe I need it. You can begin setting things up, like beginning to train people about the possibilities, get people to help you with your brainstorming. You can certainly begin delegating some opportunities where individual teams would find their own quick wins and put in some quick win automations and maybe, you know, catch the bug that way. But that's just to get your organization even able to think about or to know about that there is a vendor ecosystem that here's how, here's what it actually means to connect these pipelines, right? But mostly the work is what would we actually want? And again, it's a very, very expensive way to skip doing the work to Try to set yourself up for every possible future.
- [48:35]
A
So let's, let's stick on that one for a minute because it sounds like this is obviously, you know, the approach that organizations need to be taking if they're going to be successful, you know, in 2025 and beyond. And so in this world where you have to do the work, you have to ask the tough questions, you have to focus and prioritize as a leader. Can you. We've talked already about AI architecture, right. It's not just a leadership exercise. Then have to bring in it. You have to actually figure out how you're going to do this. What does that recipe kind of look like for you from end to end, at least, at least abstractly. And then, you know, what do you see as the biggest success factors and the areas where people tend to go off the rails here?
- [49:20]
B
Okay, one question at a time, just so that you know those, those planes are going to go. It's going to be a whole air show.
- [49:27]
A
Okay, let's start with the recipe. We know there's kind of a right approach here. If I can just, you know, kind of cut the knot and call it that. There's an approach we need to be taking as leaders with it, with technology, with probably an ecosystem of vendors. Can you just kind of lay out that vision abstractly from end to end?
- [49:48]
B
Yeah, well, first things first. The safest way to automate is to do it at home rather than at home at home, I mean, internally rather than client facing. And so again, back to that 95% report which is quoted to death now. But back office automation was much more predictive of successful deployment than attempting to automate the salesperson or something like that. And you can see that first, automating the salesperson is an insult to humankind. Right. Whenever we say automate the human instead, what do we want to automate? Repetitive drudgery things. Quite often things involving translation in a way that we're not used to thinking about as translation. We think translation English to Spanish. But what about translation from image to text? Translation from very boring long legal documents to a quickly parsable tldr. Right, Those types of translations. So it's repetitive, it's internal, it's back office stuff, involves a whole lot of drudgery things that any human could do. AI will probably do better. So if it doesn't matter who it is, if there's no holding off special context within your organization and you're doing a lot of that, that is likely a great target for AI. And so you really want to break down, where are we wasting a lot of opportunities? Where are we spending our time where we don't want to be spending our time. And then at the same time, you have to look at if we could do anything. If we now said now we freed half of the hours of some of our staff members, does that mean that we cut half of our staff members? Or does that mean that we get ambitious about what we're going to do tomorrow? Are we going to pinch yesterday's pennies because AI lets us do this? Or are we going to realize that if everybody is getting into AI and actually using it to transform their businesses, then the future is going to get interesting and weird and we're going to need to compete with everyone else's moves. So let's think about what that's going to look like and what our wishes would be for, how we would prepare for that. And we might want to retrain our people whose time we've just saved to be able to be effective in that future. Giving an example of IKEA reducing a lot of the customer, the basic customer service, think phone requests. And this was a client facing one, an external facing one that seems to have worked, but they reduced those hours and it that need for that stuff. What they did was they retrained them to do AI assisted interior design, interior decorating design. So you have a complete change now in what you can offer as a business and how you can do business. And you don't need to hire specialists because I can upgrade many of your non specialists into specialists. What's more important is valuing the people who carry the context of your business, who know how your business actually works, who know what your clients need, and upskilling them aggressively with AI into an interesting future. So that's where we start with all that vision stuff. That is just the first part. Now we have to break up, break down whatever, whatever we, we are imagining, but to break that down into what it would take to make it happen. And you want to ideally have a culture not of people saying no to you, but of people expressing here's what it would take. Because again, when we go and verify those, those individual pieces, it may be that it is cheaper or easier than we realize. And what it takes when we are enabling individual workers, giving individual workers tools with which to work. What it takes is we have to figure out how to train them. We have to figure out if something goes wrong, if there are issues, what are the escalation paths, when should they overwrite what the tool is using, when shouldn't they what guardrails we're putting in place when it is things involving, I don't know, quality control on Warhammer figurines. That's a very different thing from, you know, using AI assisted tools for surgery. Right. These are different. Cutting a human and cutting a plastic all. No, no disrespect meant to the Warhammer community. These are different things. So different applications. And so it really, it becomes very individual. Depends what you are building. If it is again something that is an internal system and it is not actually connected to any external systems, it's going to have different security requirements to things that are connected to the outside. We see now that generative AI is now the top, according to a very recent report, top data exfiltration risk. It's now beaten email as the way in which corporate secrets will leave the company. And then the more directly customer facing it is, the more you have to plan for all kinds of things going wrong. So this is a setting up of guardrails and control structures before we're even talking about vendor selection and actually building the dark thing. And then depending on what it is, either there it's an obvious vendor and the partnership will they where they will do a lot of the advising from here, because that's what they're supposed to do, or it will be a case of evaluating and choosing among many and that's its own journey. Or you may find that it doesn't exist externally, it makes a lot of sense for you to put it in internally. You might have to build it all yourself from scratch. And in order to do that, then you have to attract the talent to do it. There's a whole lot of balancing in and weighing there, while always remembering that this system is not going to exist in isolation. Even if it is the most back office application, it's still going to exist in the context of what your people already do and other systems. And people are not used to software that is probabilistic, they're used to deterministic. If your spreadsheet gives you some issue, you typically think that the problem is some human typed in something wrong. The formula, the data. You don't think that every now and then the summation function is just an average and that just happens. And again, not always, not all the time. But eventually mistakes do come up and you need a completely different way of thinking about how to deal with that. You need a completely different set of trainings for your AI users.
- [57:36]
A
So that's a point that's come up now a few times. Cassie and So I want to double click on that a little bit because the notion that with AI in general, with any of this generative stuff, it's not like black and white, it's not right and wrong, it's probabilistic. And you've said that that has sort of, I guess, cultural and mindset implications for everyone. It touches in the organization and it requires a new way of thinking. Can you share a little bit more like what is that new way of thinking? How do leaders need to be communicating that? What are the right skills and mindsets to actually thrive in that world?
- [58:19]
B
Yeah, so there's an answer that works for right now and there's an answer that'll probably work for a nearish future because I'm of the opinion that we will culturally all adjust to being more tolerant of mistakes. And so in situations where mistakes are, let's say, life threatening, then we will have a lot more guardrails, safety nets and so on than we usually do. But for the rest of everything, I think we might actually develop more of a sense of humor. We will just have situations where the chatbot does something funny. And I can sort of imagine a future in 50 years time where instead of that just being such a, everyone is amazed that a corporate system could do that. People would laugh it off understanding that as a, as a civilization we get a lot out of being able to do more and automate more. But the price for this is the occasional mistake. And we, we've got to, we've got to accept that. And we will, we'll know, we'll will expect it, and for that reason we won't be alarmed by it now in the near term. And it's funny. So I'm a recovering statistician. I, I think probably probabilistic thinking has been beaten into me. You know, I've been it for more than half my life. So it's actually quite strange to me to think deterministically, but I'm always, I'm always a little bit cautious when things are important. There is a sort of trust issue maybe that we statisticians and recovering statisticians have that the rest of us could adopt. And the worst way to have trust issues is to be paranoid about everything all the time. The best way to have trust issues is to first have a filter, which is your, one of your core skills as a decision maker is to have a filter based on the importance of what's at stake. The reasonable best case and the reasonable worst case should inform how much cognitive effort you put into Something. And so there's so many situations in life where we know if something goes wrong, we're just shrug it off. There is not that much reason to overthink and check and obsess and worry about. But when you. I mean life is probabilistic also not deterministic. But I think. And as an example, when I'm thinking about my travel plans, I'm always amazed by friends who expect things to actually go the way they're supposed to. Right. I'm amazed by this. It blows my mind. I had been seeing 50 plus possibilities of how things could go. Adding time, subtracting time, you know, where, which levers I can press, which ones I can't. How do I set myself up with more optionality. All right. That I just do that like breathing. And some people are like, huh, the flight's delayed. What a surprise that this kind of thing happens. We are just going to have to start applying this, these trust issues a little bit to our tools. Same thing as out there. How we, we have to start thinking about. Let's imagine that mistakes are possible. Let's just really hold that. So sometimes it will do something weird. Let's just imagine that that's true. Where can we apply this where it's fine that that's the case. And maybe that's subtle, maybe that's very obvious. I don't know for listeners, but I think that there's so much framing around how we use AI that suggests that we put it in first and then we worry about the quality of it afterwards instead. So you have a fallible tool, just like so you have a fallible human. When you know that humans are fallible, you begin by designing the guardrails and control structures. Before you even get there. You deploy the humans to where it's acceptable to have mistakes. And you know, sometimes mistakes happen. And so you set everything up with the expectation that mistakes are possible and just do that same thing with machines. You don't have to deploy AI to every which where actually takes some thinking from leadership of, let's say this thing completely messes up. Where in my business would that be fine?
- [63:35]
A
It's really, really interesting. And I don't know if this is a word you like or hate, but I keep coming back to the word architecture and architecting, like actually understanding the entire system here, the entire series of processes here, and thinking intelligently about how to redesign it with governance, with guardrails, with an understanding of where the value is, what probabilistically could happen. And just like as you said before, to quote you, like, doing the work here. And my sense here is one of the bigger challenges for organizations is when you're talking about this across the scale of an entire enterprise, we're talking about a transformation here. Right. And how do you do that? Like, how do you muster, I guess, kind of the organizational will and I guess also the political will where you're willing to let. Like how do you let people make the right decisions? And who are those people? If I'm not asking too big a question with that.
- [64:37]
B
Well, that's. And is it just one again, or how many. How many questions are in there? Quite a bunch.
- [64:46]
A
Take that wherever you want, Cassidy.
- [64:48]
B
No, look, transformation is hard. It doesn't matter if it's AI transformation or any other kind of transformation. And I think that the more technically minded you are, the more you're just like, oh, it'll transform. You'll install the latest version and everything will be great. And the more you've spent time with people, you do understand that the organizational will and the political will are very real beasts that are very hard to wrangle. And if there was a magic spell that would make you win at that every time, right, that would be the number one bestseller for the rest of time. Unfortunately, that is exactly tricky because every time that you have something working a particular way, there is a whole ecosystem that, that feeds. And when you disrupt that ecosystem, you have to compassionately manage that change and figure out what's going to happen with all the various people and their skills and their relationships and their knowledge. And we are a species that creates technology that builds upon its technology and makes more interesting technology. You know, starting with some sticks, we rubbed together all the way through to now, and we've managed to somehow manage that change. And occasionally it hasn't gone great, but takes good leadership and good leadership again. If it was just that formula, we could teach it to everyone, wouldn't the world be beautiful? But you know, that's not how this works. What we, I guess, have to understand is that the human side of it is going to be at least as difficult as the technological side. And then to come back to something, because something you said way at the beginning about is AI readiness, preparing your data, you phrased it like that. And now you also said about architecture will be knowing how all the systems work. I want to take these two concepts and combine them with another concept to truly make this whole thing sound dismal so that leaders know why they have. So there are different channels of AI, as I like to Think of them. And sometimes it's easy to think you're on one channel, having a conversation with someone else who's on that same channel, and actually you're talking completely plus one. So to kind of come back to basics, there's all the AI stuff. That's the theory, that's your researchers publishing papers. If you're an executive, if you need that, you already know you need that. If you don't know why you need that, skip until you know. Then there are a few other channels and you might be on one of them. And this is data generative AI and agents. The data thing has been what we've been doing, that we've called AI for a long time now, or machine learning, where we find patterns in data, we turn them into recipes that a machine will follow. The substitute for human written code, human written instructions. And what we're doing is we're essentially expressing our wishes with data, with examples instead of instructions. And now let's ask ourselves why, why would we do this? Instructions are how we get control. If we know what instructions we wrote down, we know exactly what's going to happen next. We get a nice deterministic solution. Why, for goodness sake, would we give that up? Sounds mad. The reason we give it up is that there is something when it's important, when it's lazy and it doesn't touch anyone, we can do what we like. When it's important, when the business relies on it, when we need return on investment. The reason we use data for automation is that we cannot come up with the instructions. That's why something about the instructions is hard. If it's that we were wrong about them and we had to check and we did some science and now we can write them out again. That's not AI, right? That's good research, nice science. If we are physically incapable of writing those instructions, that's AI use data in that situation. And the reason we're physically incapable is that it is so complex, the solution is so complex to our complex problem, it doesn't fit in our heads. So even that, that data piece on that channel, what we see is that data gives us the gift of memory. That is what we are doing. We need the gift of memory to deal with complexity. We need machine memory for vast complexity that overwhelms humans, that makes us not able to write down instructions. And so the gift of memory is a powerful thing. But we also know that we sign up for everything complexity implies, all its opportunities and all its threats, including when things are complex. You don't know how they're going to go wrong. So that's why you start to think about paranoid things like control structures and security and guardrails and all this generative AI. The next channel is when someone else, not you, someone else, applied that automation with data principle to language and, or video or physics or something like that, some sensory thing. And now, but focusing on language, because that's the one that really is, I think, valuable for folks to connect with. Language is the universal interface for human collaboration. It's how we collaborate with one another. And so if we solve language understanding, any of us can communicate with a machine. So companies like Google, anthropic, OpenAI, that lot, providers of the foundation models, they did the paradigm in the previous channel, applied that to solving language and then they offer tools to developers and to end users. It's like language. Here you go. And on this channel is all the what could we do with language if we could all speak without learning some unnatural language like Python c the rest of it first, what could we ask? So this is the gift of language sits on top of the gift of memory. Language is pretty complex. What could possibly go wrong? We're sitting built on data. There's complexity here. What could possibly go wrong with language? Well, our unnatural languages are pretty stiff and constrained, but at least we know what they're going to do. Our natural languages, our mother tongue is filled with ambiguity. Half the time we don't know what we're saying. So when we automate that and we're not watching and we're not our own human in the loop, we automate based on know something that sounded good at the time. That's your genie story with potentially a very unskilled wisher not even knowing what they're saying and at scale, kind of terrifying. And then the gentic piece that is the gift of action on top of the gifts of language and memory. And the gift of action is only a gift if the action is good and useful and one that makes your life better as opposed to worse. And in the agentic paradigm, you realize that there was a very, very important piece in that genie story. That's not just the genie and the wisher, it's also the lamp. The lamp is really important. What is the actual control structure and security? How do you prevent an unauthorized wisher making requests on a powerful genie? How do you deal with all this stuff? So that's what we've got now you're like, what the hell? Where the hell is she going? I'm bringing this back. First you Might not need anything to do with getting your data in order for doing AI that might not be part of your AI infrastructure. You might just be wanting to ride what it means to be able to play with language and plug language understanding into your business processes. That gives you rather powerful ways of translating things, all kinds of things if you have the creativity to see it. Or you could plug that into the gift of action as well and have autonomous actions taken. Then you got to be very careful about what kind of wisher and what kind of lamp you're dealing with. And if everybody's just talking about the genie, you're missing a big, big piece. But all of this stuff, this is why Jeff, you're like, all this stuff is about is built on top of complexity. And so now you can't say things like I'm going to be an architect and I'm going to know all the. All of the. All of the all. There's going to be so much you're not going to know. All of these engines are complexity based. That's why we start talking about that. This is probabilistic now. Some of it might even be deterministic if you could understand it. But it's too complex for the human mind. It may as well be probabilistic. When it's generative, it's literally probabilistic. So it's very, very complicated. And these complicated systems that can go wrong in complicated ways are going to be put in first one now you have one complicated system and you got to try to understand all the architecture around it. But once you've got a bunch of these inc. Plus a bunch of augmented employees, workers who are all augmented in their own way, that is a lot of complexity all moving around. And so this idea that you would, you would understand the all of it that does, that doesn't compute. What you're going to have to get really, really good at is understanding your piece and the edges where it connects to the universe. And then it's going to be a game of trust, trusting other humans who are, for lack of a better way of putting it, gardening their own gardens and how that connects to you. And still being tolerant of there can be errors and linkages and the more that could hurt people, the stronger again the lamp, the guardrails and the rest of it need. So that's what it comes down to. The wisher is much more important than the genie and the lamp might be more important than both of them. If the lamp just shuts everything down when we need the attack.
- [75:29]
A
So first of all, I feel like I have to say thank you for landing that plane because that one was a wild ride and you had me for a minute and then, oh, there we've landed the plane. And I love the story that you told and I love the way you kind of framed the role of complexity and some of the implications. But I want to maybe take that up a level and you know, apologies because I feel like I'm going to give you another multi part question. But when you think about this world with increased complexity, where it's, and we can start with an organization, but when we've got increased complexity, where we've got people who can't see it all anymore, they're tending to their own garden and you know, as you said, there's control implications here, right? We're giving up control in the name of being able to do more, to tackle more complexities. What are, what are the implications of that in terms of organizational leadership in the scope of the future of work and the future of the organization? And then maybe if you're feeling bold, what are some of the implications of that, do you think, more broadly, societally or outside of the organization?
- [76:39]
B
Yeah, I mean, look, that cuts to the heart of it, the idea that we will eventually, as a, as a species, and we do already, like, if you, if you think about it, how do you know that a skyscraper that you, you know, you go up to the 50th floor, how do you know that that all works? I don't, I know nothing about this. But somehow there it is, right? We, we do end up having to collaborate with others who all hold their, their peace, their bit of something much greater than us. And occasionally there are mistakes and problems and we really all do try our best, so. Well, not all of us, some of us don't try our best. We can talk about work slop anytime you like, the AI kind and the regular kind. But you know, we try our best to put systems in place that limit the speed of the damage of things going wrong. And we do give up control already. It's just that we are adding it. It's going to be another order of magnitude and it's going to be systems that are fundamentally probabilistic. So that's why I said earlier, as a society we are going to have to be more tolerant of mistakes, more aware of the possibility of mistakes, less gullible. I think that we're going to go through some growing pains, for example, now with, with propaganda applications. People who don't, for example, know That a video could be entirely fake and AI generated and it might look real, but it's not evidence of anything. Right? We just need to grow as a species to now realize that we exist in a future, in a present where that's technologically possible. And so that is no longer our way of forming trust. And maybe trust will have to move back towards trusting humans, trusting individuals, individuals that we know, individuals that we know to do their jobs well, to take care of their piece of the, the complex sphere that they are in charge of, knowing that there is no perfect way to do this. But you know, the one thing that I just keep coming back to, I hate, I hate making predictions. This is a statistician talking, so you've got to love the irony. But I hate making predictions for the future, particularly when there's so much change. What can you really predict? How do we know what the world's going to look like? But my one controversial prediction that I feel pretty okay with is that we're all going to have more to do, not less, just because of that sheer complexity and all the different ways that everything can fit together. I mean imagine, imagine a future where all the kids are all on individual learning and education journeys. Workers are fundamentally chimeric and can pull in new skills with AI as needed and put those skills down, just rattling the concepts of job ladders and bursting the boundaries of their roles and taking on new challenges each week as needed. And then you have technologies that are going to put all kinds of probabilistic things again, probabilistic automations at scale, all these pieces all moving around. How do we coordinate all that? How do we make sense, how do we plan, how do we regulate? There is going to be so much, you know, what am I trying to say? The duck's little feet under the water are going to have to be swimming very, very quickly to catch up in this. I don't see us all going back to our natural state of grace, naked on the savannah, just pulling a drop down menu from the sky and back to our hunter gatherer roots. We're going to have a lot of work, a lot of work ahead of us.
- [81:09]
A
How do you see that work being distributed? Because one of the, one of the points of fear that I hear a lot about these days, and I love, by the way, that you brought the conversation to one about trust because I think that's kind of an operative word in all of this. How do you see that orchestration, that work, that planning being distributed? Because the fear that I hear about is this is going to be overly concentrated in big tech or in a few small firms that through the platforms that they operate, have outsized power and people fall less and people fall, they become less important and less valuable in this new economy. Is that a concern to you or do you see it being more decentralized than that?
- [81:56]
B
I guess when I put my economist hat on, I think to myself about barriers to entry and which barriers parts will have high barriers and the hardware and hardware innovation, those parts I think have very high barriers. And I'm not sure whether it's going to be possible for many, many, many players to compete. Right. And see that that's certainly a centralizing, of course. But then we, if we think about things like what what would be things keeping consumers loyal to, let's say platforms, apps, etc. It's often laziness. It's not that it's the best thing, it's just maybe it's hard to move to another one. Maybe you've given all your data away already and the system knows you really, really well. So a competing product would be difficult to stand up. Right. You could see those types of barriers and those types of capture. But technology is also going to make things pretty easy, everything a little bit easier for everybody. And I don't know to what extent that will counter natural inertia, but I do think that it will be easier for anybody to make the attempt, I guess make the attempt, at least in the digital space to offer something and offer something nuanced and personalized and to be matched. Right now we've got pretty bad matching. Like, you know, maybe somebody might be designing the most perfect custom T shirts for you, Jeff. And that somebody might be based today in, I don't know where they, they might be living. Maybe they're in Jakarta. There's not a great system to connect you with them and with their art and with their very individual offering to you. But I imagine that providers of that matchmaking, that will be a high barrier to entry thing that will make a lot of money. But all entrants into that, I think have an opportunity to participate and participate in interesting ways in the economy that they wouldn't have before. I think there'll be a lot more very small businesses that start because of this. Realize with very few people you could do some unique, interesting things. And while we will see more personalization from behemoth corporations, again managing all the humans and the humans then being clear on what precisely they provide, there will be a sort of, how shall I say this? I'm trying to find the word. It will be hard for the humans to track and take responsibility for very different or very custom offerings, even if the system itself could do that. So we I still expect that at the edges you get the interesting stuff and if the barriers are lower there you could have a lot more entrance at the edges. If you have good matchmaking you can have a very interesting digital and physical.
- [85:35]
A
And services Kassy this has been super interesting and super insightful. I really appreciate everything you've shared today. You've certainly given me a lot to think about. Thanks so much for joining.
- [85:46]
B
Thank you for having me.
- [85:47]
A
If you work in it, Infotech Research Group is a name you need to know no matter what your needs are. Infotech has you covered. AI strategy covered. Disaster recovery covered. Vendor negotiation covered. Infotech supports you with the best practice, research and a team of analysts standing by ready to help you tackle your toughest challenges. Check it out at the link below. And don't forget to like and subscribe.