← Digital Disruption with Geoff Nielson
Digital Disruption with Geoff Nielson
AI Is Rewriting the Rules of Work: Futurist Ian Beacraft Explains Why Jobs are Dead
01:07:08
Loading summary
Transcript68 lines
- [00:01]
Jeff
Hey everyone. Today I'm super excited to be talking to Ian Beecraft. He's the founder and chief futurist at Signal and Cypher, and he is an absolute thought leader when it comes to the intersection of AI and enterprises that we all work for. He has so many deep, amazing insights. I had the chance to watch a number of his keynotes at south by Southwest recently, and it just never ceases to amaze me how much new thought leadership he brings to the table here. So I'm really excited to pick his brain to understand how much of this new technology is really being limited by us and our own boundaries and how we can break through that. It should be an amazing conversation. I've been a big fan of your keynotes. I've been binging them in the last couple of days. One of the quotes that I wrote down, I don't remember if it was from south by Southwest this year or last year, but as I was binging, one of the quotes I wrote down was poor leadership, adherence to old systems and technology. First mindsets are a bigger risk than AI to organizations. What is going on out there? And can you kind of dissect that quote?
- [01:14]
Ian Beecraft
Yeah, absolutely. So to me, when we go through times of change, we need to galvanize behind something and that happens both productively and disruptively. We, we tend to find something to create opposition towards. And for a lot of people, they see AI as the main threat because it is the easiest thing to point to and say that's a threat to my job. I look at that and I see very clearly it's automating pieces of what I do and that becomes extractive. It's taking something that basically I provided value through that thing before and I no longer do. And because of that, I as an individual are less valuable to that organization. Now, if I'm continuing as a leader to just say my goal is to create efficiency and scale within the system that we have today. The typical lever we're going to pull is efficiency, which is code for layoffs. And that is essentially how our system has operated for the last 150 years. And it's been able to grow. We've been able to create prosperity in a number of different ways. But that system's changing now. The era of unending exponential growth in existing paradigm is starting to fray at the seams. And when we think about the future through the lens of the past, what we do is we apply old metrics, old ways of thinking, old processes to new technologies, new ways of working, and new challenges. And those things come together and they don't work. But when leaders are so fixed in how they want to approach these things, they're not thinking about how this is different and how they have to take a different paradigm or different approach to this new type of challenge and new type of circumstances. And that's what leads to the demise of the organization, not just that employee or that department.
- [02:57]
Jeff
Right. So I want to zoom in on two different phrases you used there that I think are really important. You talked about, shoot, now I'm going to screw it up. But you talked about growth and the ability for us to have these productivity improvements and be thinking about what we're doing differently. And you also said efficiency. And efficiency has become a really popular word these days. Efficiency is something across the public sector, across the commercial sector, a very hot word. To what degree is efficiency the right thing to be looking at right now or a distraction from what's actually going to help us accelerate?
- [03:37]
Ian Beecraft
Yeah, I think there's a balance here. There's a recognition that organizations have a duty to their stakeholders and their stockholders. And that means you have to look for efficiencies. And if you're not, that's a dereliction of that obligation. Understandable. Right. So they should be looking at efficiency and they should be looking at increasing productivity. But to do so with the same fervor we have over the past several decades since the beginning of the digital revolution, I think is absolutely incredibly short sighted. Because what this does is this doesn't just scale an individual's efficiency and effectiveness. This changes the fundamental boundaries of what jobs and tasks are. We're really, we're re engineering or completely changing what the atomic unit of work looks like. So for example, we take a look at organizations as built from people which are defined for jobs, very specific slotted roles that are well defined. And if I look at an org chart of any organization, I have these mental shortcuts that I can use to understand who does what, where and how. All of that's starting to change though, because AI makes it so that I don't actually have to stick within the boundaries of a specific role and say that's all you do. If you're accountant number two, you know, in the finance department, which whatever that designation might mean to a specific organization, you have the very specific set of roles, responsibilities, KPIs and remits that you are responsible for. What happens with AI though, is it makes it so that the skill sets that might sit adjacent to my existing skill sets or responsibilities are now accessible to me. So it starts to put pressure on these boundaries that we keep people in with their roles. So if you're just a copywriter or just an ad, if you're staying within those boundaries now with the access to AI and generative AI and other tool sets, it's also almost a abdication of responsibility to say, I'm just going to stay in my lane. And all of a sudden we have this chaos that comes with people saying, I have access to new skill sets, I have access to new capabilities, but the system around me has not adapted to really make that possible. Fluid and part of the system where I'm not stepping on other people's toes. I'm not doing things without permission, I'm not doing things without support and an apparatus or feedback loop. So what's happening is we have this new technology that allows all sorts of new behaviors within the organization, but the organization has no idea how to pull those behaviors and structures and processes together.
- [06:09]
Jeff
Right. It's too rigid to actually take advantage of what this technology could unlock.
- [06:15]
Ian Beecraft
Absolutely.
- [06:17]
Jeff
So what do we do with that? Like as leaders, where do we start? How can we start to rethink these systems and processes in a way that's more dynamic or at least can help us harness these possibilities?
- [06:30]
Ian Beecraft
Absolutely. Well, what I would say is the first place you should look is not just about how do I do more with less. You can, I'm not saying don't think about that, but that shouldn't be the primary goal of the value you're trying to get out of AI because that's a race to the bottom. Like we're all going to get that benefit. And if that's your focus, then you're playing a Walmart game for, you know, a premium enterprise type of environment. That's not going to help anybody. It might give short term impact. So a quarter to quarter thinking of a Western or American style way of doing business, you'll see immediate impact. And I think that when you take a look at what's happening on the balance sheets at Meta and several other organizations, people see, oh, less staff, higher margins, more productivity. That's the way of it. They're missing a lot of what's actually going on behind the scenes. So a lot of these companies that are in the space of building the models and kind of changing the way they work have seen this coming around the bend and they're already restructuring the way that they operate. For leaders that are trying to figure out what this means for them, I would say first of all you need Some sort of experiential learning, like just understanding this stuff theoretically, theoretically might have worked for the past 25 years because you have a lot of understanding of how the digital paradigm works. This is not new digital, you know, connected networks, all that stuff. We've known that since the 80s and 90s. This is a fundamentally different paradigm, different way of working, different way of thinking about growth, different way of connecting software and systems. I mean, we're literally working with quote, unquote software that now replicates and imitates cognitive processes. Completely different paradigm for people. So having some sort of education or experience that gets you into that headspace where you can start to grapple with what those changes are, is absolutely necessary. Just reading articles, doing a couple of things in ChatGPT is not going to be enough. Because if you're going to competently lead an AI transformation, you as a leader also need to have spent that time immersed in that space. I'm not saying, you know, hundreds of hours, but at least dozens in that space to understand it with the proper guidance. What it's going to do for your team, your business. How does it help you answer the questions about what kind of value are we providing as a company? How do we structure our teams? How do we grow effectively in this market where everyone else is starting to go into these adjacent spaces as well? So there's a lot of new questions that you have to answer from that.
- [08:56]
Jeff
When you talk about that experiential learning, Ian, do you have a sense of what leaders are doing? What does that mean? What tools are they using? What does that look like?
- [09:09]
Ian Beecraft
Yeah, so the most effective programs that we've seen are ones that are built to be used with the same types of tools that they are already using their environment. So everyone uses Microsoft Teams, Slack, have access to ChatGPT or something like that. 90% of what you need to do can be done within those environments. But just using the vanilla version of it is not going to cut it. Being able to learn with these tools by also building the documentation, the vision, the information you need to move forward is really where we've seen the most value. So to give you an example of a module that we run, we'll work with leaders in an environment where they're working with the AI to build their vision for what AI looks like in their organization, to create a maturity assessment. So where do we stand and where's the alignment amongst the CxOs? And it's not just about the education of in AI, it's about alignment. And there's A strong difference between agreement and alignment. What happens oftentimes at the leadership level is we agree AI is important, we agree that we're all implementing it, but they're not aligned as to how that happens or where they even are on a maturity index. So having them come together to do that together while using the tools to facilitate it brings a couple of those objectives together. And all of a sudden they start to see, okay, here, here's how the tools can facilitate this work. It doesn't need to take six weeks, it can literally take an afternoon. So you've taken something that might have been a six month consulting engagement and said, we're walking out in two and a half hours with a much clearer understanding of what we're doing, how we're doing it, who's responsible and what that roadmap looks like. So that's one of the big paradigm shifts we're seeing.
- [10:51]
Jeff
So it's more, if I understand you correctly, there's more value in like one condensed facilitated session of Ask, having the right people in the room and asking the right questions than like a protracted engagement on like, here's a bunch of recommendations of like potential use cases and what you could be doing. Is that fair?
- [11:10]
Ian Beecraft
I think the potential use case model is kind of outdated at this point. So I do think that the idea of the thousand page decks and the long consulting engagements need to change. I won't proclaim that consulting companies are, are dead on arrival. I think that's a, there's a little hyperbole there. They're like advertising agencies, like, they survive this kind of stuff. The consulting company of the future looks wildly different than it does today. But one of the things that they're often kind of admonished for is this idea of a thousand page deck. And I do think that the idea of learning being separate from doing so, having that thousand page deck, a bunch of seminars, and then finally being responsible to do on your own is outdated. We now have the tools, we have the apparatus to learn and do at the same time, while also building some of the most essential infrastructure, as well as documentation and strategy amongst executives. So when you walk out of a session, you should have a clear revision. You should have an understanding of how this impacts you, what your maturity assessment looks like and what your roadmap looks like. So what we've come to see is that we can take things that should take six weeks or even three months and condense that into one afternoon or a full day session.
- [12:23]
Jeff
Right. Which is really exciting and I think helps us Get a lot more like just accelerate our time to value or our time to results. Ian, there's a phrase I want to talk about that you've said quite a lot and I want to put some parameters around it, which is the tools, right? Talking about AI, end quote, the tools. Because, you know, in my mind, when we talk about AI and when we talk about the tools, it's everything from, you know, just go to ChatGPT or Google and write in your question to, you know, this world I'm finding we live in increasingly where like every software vendor and their brother is promising you that, oh, there's AI in this now it's an AI PC that like, like any crevice we can hide AI in, we're telling you there's AI in it. What tools do people need to be thinking about? How should we be bringing tool wise AI into our organizations?
- [13:19]
Ian Beecraft
Yeah, we're at the very beginning of the development of very robust ecosystem. So most people are seeing things like ChatGPT, Claude Copilot, et cetera. And that's kind of when people say tools, they think of that, and that's fine. But what we're seeing at the enterprise level is a weaving of that into the basic infrastructure across the board. So for a lot of people, they're pulling in Copilot, others they're pulling in OpenAI's API into the work that they do. And stage one is just to get people exposed to tools. So that's access to the chatbots, it's a one to one relationship. You put in an input, you get an output that is barely, that's not even alpha products for an enterprise at this point in time, you're just getting your socks on before you put your shoes on and get out the door. And what we're seeing with organizations that are more successful is they're leading with use cases that everyone can understand and then they're building that into the infrastructure of their organization. Not just saying, can you go learn how to use ChatGPT? That's basic and necessary. Because people can't start to identify what the use cases are going to be unless they have experience with the technology. And I'm a big fan of pushing that down and out. You should have people at the edges coming up with the use cases, not just the people at the top. Because the people who are dealing with the challenges, who are the ones actually doing the work, have a much more nuanced understanding of how it should be done and what success looks like for those types of tasks and activities to get there has to go way beyond just access to ChatGPT. What we found for so many organizations is they'll often call us like, hey, we, we invested half a million dollars in ChatGPT licenses and people loved it in the beginning. And then like nobody uses it, it goes like it crests and then it crashes as far as usage. And the big part of it is you just gave them a new tool. You gave them barely any training, barely any context. And you said, amongst all the things you're doing, you're overstretched under resourced. Your expectations are just getting higher. Now you have to go learn a new way of doing things. There's no surprise people are not using the tools right. So it has to be very clear from the beginning how this applies to them, why it's relevant, why it's going to change their life, not the organization. Like really put it into terms they can understand and grapple with. And then over time, as the knowledge starts to diffuse across the organization, the infrastructure also becomes more robust. It's almost the opposite in many ways of previous transformation. So let's take a SAP ERP implementation. That's a three to five year process, IT command and control. We install it and everyone else has to adapt to it. What's happening with AI is we're kind of reversing that in a way. Yes, it is provisioning, licensing, but this is not just an IT issue anymore. This is an HR issue, this is a strategy issue, this is a finance issue. And if you don't have your cfo, your chro and your CEO all in lockstep on how this is being distributed, you're not going to come up with an effective way of distributing the technology, the knowledge and the application across your organization. So that that changes the dynamic of the tool enormously. And over time, it then becomes part of the fabric of the organization, just like we have with all of our other productivity tools.
- [16:30]
Jeff
So I'm lots to process there. One of the things you've said, Ian, is we talked a little bit about it already, but this, there's a fear of people losing their jobs and like disintermediating like jobs from tasks and augmenting what we're doing. You had said previously we're not going to lose jobs, we're going to lose job descriptions. What does that mean and what does that look like with AI and with these modern tools and approaches?
- [17:02]
Ian Beecraft
Absolutely. So I said that probably two or three years ago in one of my south by Southwest speeches. I said, we won't lose our Jobs, we'll lose our job descriptions. And I've had several people say, well, that aged horribly. And I'm like, actually, yes and no. Like I will willingly say there are jobs that are going to be lost to this, there's no question about that. But the image that conjures for a lot of people is like, well, you're going to be on a bread line for the rest of your life. You'll never be allowed to work again because you're completely irrelevant. That's not how this works. There will be some fracturing of the system that we work within, and that's not going to be easy. And it's going to cause some pain for some organizations and a lot of people. But what does happen is it also changes the nature of the jobs we hold. So what I see happening, and this is related to the concept of creative generalists that I've been talking about for a while now, and I can define that as well. But a creative generalist is essentially, I'll pull this back because it's related to the way we're educated and we build our careers. So we grew up in a system that said, go to school, get a job, build expertise within that job, that becomes defensible and that's what gets you promoted over time or over time, you start to manage the functions that you're an expert in. You become management and leadership and up you go. That vertical way of working has been the way that we've promoted people and told people to go after their careers for decades. What AI does, as I mentioned earlier, is it essentially abstracts the years and decades of expertise, influence, opportunity, exposure that you need to build expertise in a specific subject. And it allows you to perform proficiently in skill sets that are adjacent to your own or even some that you never had access to before. I said proficiently. I did not say in any means an elite level. But what we often have to confront in our organizations is in many cases, good enough is good enough. You don't need somebody with 25 years of experience to do junior level work. And if I am outside or adjacent to that role and I can get that junior level work done, why do I have to wait or rely on specialized expertise and resources to get that work done. So that changes this dynamic and the nature of how I relate to my peers, their roles, my roles, and it expands the capacity and responsibility and the remit of individual roles. We're moving away from role based relationships to jobs, to skill based and task based relationships to jobs. And that's where I feel like the idea of jobs are not going away. I even say in that same speech, jobs are dead. Long live work. And one thing that I think we're so stuck on and say, we need good jobs. We need. You know, you'll hear every politician say, we need to bring good jobs back to America. And it's not the jobs, it's. It's the work that we need. If we're so focused on jobs, we're already narrowly defining ourselves and oftentimes attaching ourselves to things that are not coming back. Like, we're not really going to be bringing back the coal sector. The way everybody's talking about it. In many ways, that's kind of a train that's moved along no matter what we do. And other jobs are the same way. But if we talk about the work as it relates to the tasks and roles and things like that that need to be put together for the future of work, that's where we can actually make some traction. And that's why I do believe we're not getting rid of jobs. We're getting rid of the definition of the artificial boundaries that keep you in a specific space in your organization. And that's how I see organizations evolving significantly over the next decade.
- [20:39]
Jeff
So if that is the case, there's tons of wild implications. And by the way, I think that probably is the case, but there's tons of wild implications from the education system to our careers to organizations. But in terms of impacts, Ian, and maybe feel free to tell me this entire paradigm is wrong. But where is there more risk? Is there more risk for junior employees right now who their general skill set and their baseline level of knowledge can be replaced by AI? Or is there more risk for people who are 20 years into their career and they have a deep skill set that maybe now is almost commoditized? Because you can ask, you know, AI, how to, you know, I'll pick on data scientists, you know, just for example, because, you know, these are, you know, historically high paid roles that have a lot of schooling behind them. And if you can start to get some of these answers, some of this validation information commoditized by AI, what does that mean for people in these careers? So where is the risk greater? Or is the entire paradigm just, you know, misplaced?
- [21:53]
Ian Beecraft
It's so they're both under an enormous amount of strain right now. The one that's most present that we're seeing happen with more frequency is those who are at the beginning of their careers are at highest risk to this exposure because you can replace a lot of the things that they would do. And, and we're kind of. There was this unspoken contract that when you leave school, you would continue your training almost like a vocation in whatever place you would go and learn. And there's an enormous amount of teaching, mentoring that goes on with that. And when I can just consult ChatGPT and get it done and not have to mentor it, I'm going to do that. There's just no question that that's going to happen en masse. So junior roles are already starting to disappear. The capacity and capability that juniors have are starting to disappear. And the challenge that just adding the technology to and giving juniors access to that doesn't help a lot because they don't have the experience and the frameworks to understand. You know, when I'm doing some exploratory research on this, what matters, what are the things I should be looking at and honing in on? And they just. They get overwhelmed by the sheer volume of things they should be looking at without being able to find the right signals to. To. To hone in on. Now the other part that's true is that middle management is also getting hit really hard with this stuff because if your role is focused more on creating alignment, checking in on organizations or checking in on your employees, doing a little bit of mentoring here and there, but more so the things that are around productivity and efficiency of a team. So not the leadership level, not the visioning, but just like the operations of the company that is directly in the line of fire at AI and what that changes is the stuff of being a boss is also starting to go away. What that makes space for is for people to step into actual roles of leadership. So we're seeing this layer of middle management that is directly in the line of fire, but also collapses the organization a bit. Where the line between junior or more junior people and leadership is also starting to become thinner and thinner and thinner, but it means more direct contact with people who can spend time in the space of being leaders versus bosses.
- [24:04]
Jeff
Can you just unpack that a little bit for me? What is it? I'm actually a little bit surprised to hear that that some of the team management piece is something that AI can do. What are the tasks that you see as being now doable to AI? What does that look like?
- [24:24]
Ian Beecraft
Absolutely. So a lot of what happens at the management level is facilitation and alignment. And things like facilitation are easily done by AI. When we actually work within signal and cypher, the projects that we're working on are known not only to us, but also our AI and our project management system. So I don't have to check in with my co founder, be like, where are you on this? It knows, therefore I know so that the amount of meetings that I'm having around alignments and approval have dropped like 70%. So these big parts where we call it corporate waste, these items where you're waiting on specialized resources to become available to do the work or to have a moment of someone's time to say, do you approve of this? Or having those CYA moments of does legal approve of this? Those are actually starting to disappear as well because the systems can check on these things now. None of these things are bulletproof or faultless yet. So in the beginning they actually create more work in an apparatus. So there's more work for bosses and middle management to work on, there's more work for implementation. And we see that almost in every transformation. It actually productivity dips and effort and investment go up during that transitory phase. But when you get past that and start to see some of those benefits, it changes dramatically how you operate. So we'll go into organizations, we'll see 25 people sitting at a table for an hour long meeting. And the first thing I think of is like three of you need to be here. And this is so expensive, like you're wasting so much money. And for us, it's gotten to a point where we say that the small team is the ultimate flex. It means that if you've got a small team that can really run effectively and you're using your tools and your infrastructure appropriately, then you can move so fast you can make decisions confidently without having to consult everybody. And most of those meetings are really about cya. It's about can I distribute the liability of this decision across a group of people? So it's not just my fault.
- [26:23]
Jeff
Right. So with that in mind, you've talked before about this notion of augmented teams and being able to use some of this technology to get more out of an existing team or even restructure an existing team. What does that look like in practice?
- [26:36]
Ian Beecraft
Yeah. So there's a couple things that we lean into to help augment teams. The first level is just learning to use the tools. Like just by doing that, you're already moving 10 to 20% faster, more effectively, better thought partnership with AI. But the next level is starting to actually encode your own knowledge. So that can happen at a team level or an individual level. What I mean by encoding it is taking things that you've written, whether that's briefs, emails, content, and starting to turn that into something that the large language model can work with and understand. It's a bit like training a Laura on your own assets. And that becomes a bit of a digital twin. And we do this at the team level, the individual level and the organizational level. What most people aren't seeing right now, we're seeing a lot of that happen at the organizational level. But when we augment an individual and say, okay, I've taken everything you've written at a. Not everything, but the highest signal, highest quality stuff that you've written, content about who you are, what you've done, et cetera, and turn that into a document that sits on top of the large language model and that becomes the filter through which you prompt. It's the filter both going in and going out. So it's going to augment what you're actually prompting against. It's also going to filter the responses that the LLM gives you. So everything you do is going to be in your style, in your tone of voice, with your strategic understanding of the business. And that can really expand your capabilities in a number of different directions. The same thing happens with the organization, which helps enormously with things like brain drain or bringing people up to speed. One of our goals is as an organization, if I bring someone in off the street, I want to be able to get them to the same level as everybody else within less than a month. And on day one, they should be able to write an email in the tone of voice of the company. They should be able to manage their social media presence, all that kind of stuff, because we built a layer on top of the large language model that already has all of that encoded, rather than having to teach someone to do that from day one. So it's doing two things. It's augmenting the individual's capability and capacity and it's also removing a lot of the friction of having to become up to speed on how your organization works.
- [28:49]
Jeff
Right. So, I mean, I. It's such an interesting approach and I can, you know, immediately see like, huge transformational organizational benefits, you know, from an efficiency and effectiveness perspective. By doing something like that, are you finding that there's resistance at an individual level to that? Like, my concern would be that people say, like, oh, you're trying to like, download my brain into AI and then get rid of me. Like, I. I can imagine a world where there's angst. Is that the case? And if so, how do you overcome that?
- [29:21]
Ian Beecraft
That's the Very first response most people give is like, hey, this, this is my value. Like, this is why you hired me to do this. And that becomes a conversation between the organization, the individual, and a contract of this is yours, not ours. So the goal with the data is for the organization and the team level data that is owned by the organization. The individual level data must stay with the individual. And this is a personal philosophy of mine. I honestly don't believe that we can move towards a future paradigm where this is a part of the way we do our work if that agreement doesn't stay into place. So it's like when you move from organization to organization, you take your experience with you, but you're not taking the files you worked on at the office. If I'm going to encode you and your thoughts, it's just like saying, as an actor or a voice actor, I've encoded your voice and no longer have to give you credit for what I extracted from you. That, by definition, creates an antagonistic relationship between the organization and the individual. I firmly believe we should own our data. So that's what we have encouraged and facilitated. But yes, that is typically the way people look at it when they first start. It's like, okay, the company's going to own everything about me. And sometimes the company are like, oh, yeah, we could really do a lot more with a lot fewer people. Like, no, that will absolutely, one, destroy morale. And two, no one will want to come work with you.
- [30:46]
Jeff
Right. So are we. It's such an interesting idea. And I really. I hadn't heard that before, to be honest. And I talk to people in this space all day, every day. So the idea of having this, like, this individual digital twin or AI fied likeness or data model of you right now I have to imagine if most organizations propose that to a staff member, that'll be like the first they've heard of it. And they're like, whoa, what is this? Are we heading toward a world in the next few years where this is going to become the norm and everyone will start to be literate around this and expecting this conversation?
- [31:34]
Ian Beecraft
Almost, I don't know. And the reason I say that is not because the technology is not ready for it, not because it's not possible. Is that the limitations of how our organizations grow are not just technological. There's so many different other constraints as to why organizations change the way they do and why technological adoption is so slow. There's a concept that I love called Martek's Law, and it's about the difference between technology moving exponentially. And people in organizations develop logarithmically. And what this does is creates this ever increasing gap between what is possible with the technology and what the companies and individuals are actually capable of. So we're looking at potential versus practical reality. And the thing that's pushing this curve so much lower than this is infrastructure, technology, culture, decision debt, technological debt. There are all these constraints in an organization that dictate how high that logarithmic curve can go and how far you can push that up so the technology can move as fast as you possibly could imagine. We are not going to be able to integrate and adapt it as fast as it changes. We're already seeing that right now where you'll see some people who are just, you know, they've 100x themselves with what they can do, what they're possible, what they're capable of doing, and then everyone else is looking at them like they're an alien. And that's because they, as an individual, have leaned into it and are already adapted a lot of this stuff. They've already had the experience to help them do that. Usually, you know, really good engineers and developers can lean in in a way that can do that, but if you're an account person who doesn't have that expertise, you look at that and say, there's all these limitations preventing me from doing that. Organizations work the same way. Some organizations like ours are small. We're built for this. We're native AI. Whereas a lot of organizations we're working with that are Fortune 500 have any of those things, any of those qualities that allow them to say, we're going to be AI native tomorrow.
- [33:29]
Jeff
Right. So this brings me to another central question that I was excited to ask you about, Ian, which is, who do you see as being the winners and losers of this disruption? And I'm deliberately asking that in kind of the broadest possible sense.
- [33:43]
Ian Beecraft
Yeah, it's an interesting question for which I'm still forming an answer myself, because the initial think thinking is like, okay, if we don't need big teams, then we obviously don't need organizations that are 200, 300, 400,000 people, and all these startups are going to come and take their lunch. And it's a lot more complicated than that. There are other structures besides just size, keeping the current winners entrenched in their space. So let's take like a chemical manufacturer, for instance. Like, there's a lot of the corporate work that can be taken away by AI and made more efficient, and you can use smaller teams for that. But there's physical apparatus, there's mechanical apparatus. All that needs to be done. There's distribution, there's geopolitical elements to how these companies grow. And again, that gets back to the practical limitations that shape the growth and change of these organizations in new paradigms. So I don't think it's just as simple as, okay, you need smaller teams, fewer people, companies will shrink and startups will come in and eat lunch of those who don't move fast enough. Speed is one variable. It's a very important variable, but it's just one. What I do think, though, is companies will get smaller, but I also think there will be more startups and more businesses formed than ever before. If we just look at the trajectory of the statistics, even since COVID we've had a massive increase in the number of s Corps and LLCs formed more than ever any time in history. And that's likely to get even easier as time goes on because the ability to form a company again gets easier with AI. The ability to form a team gets easier with AI. So I think freelancing is going to explode even more than already is. So an acceleration of an existing trend, the ability to open businesses, the things that keep people away from opening businesses, is going to almost evaporate. And I think that the opportunity to start creating these entities for even short time periods of times for more specialized use cases is going to become a thing too. So I could easily see the number of businesses built in the next 10 years, 100 Xing. Not just multiplying on that because we're also using agents for that too. We're not using agents for employees, but we're using agents to build the business. So if you think about how you can scale that, that's. I'm still wrapping my head around what that looks like as far as what does the economy look like, what is, you know, how do we align that in terms of geopolitics and how that becomes the way organizations shape as well. It's a lot of things that, you know, still have not shaped themselves yet. So I don't think it's just a small organization's more business, but that's the closest thing I can find so far.
- [36:24]
Jeff
Right. And I'm glad you had that level of clarity because it's easy to just end up in the mindset of, you know, smaller organizations. Eating your lunch, you're done, you know, good luck. When I think about the implications of what I think we broadly agree upon, which is it's going to be Way easier to start a business. There's already more businesses happening. That's really good for consumers, I would imagine. And it does mean more competition for incumbents. And I really like your point about, well, it's not just they're going to eat your lunch because there's more to it than just the speed or the efficiency there. You've talked before about the need for transformational change versus just strictly optimizing what incumbents are doing. How transformational do we need to be thinking and what's the best way to get into that mindset? Is it creating like an innovation incubator in your organization? Is it trying to start your own kind of funded startups? Are there any kind of tactics you recommend with organizations?
- [37:31]
Ian Beecraft
Absolutely. So I think I would encourage organizations to be radical with their thinking and practical with their approach. So there are too many people who say you kind of need to burn it all the ground and start fresh. There's no enterprise that says we're profitable, we're doing just fine, we want to disrupt that. Nobody says that. But what I do think is unless you are radical with your thinking, you will not be ready for the disruptions that are going to come. So these technological transformations that happen at GPT level, so general purpose technology start at the infrastructure level. So we've seen disruption with technology and the technology that we use. So electricity did the same thing and OpenAI did the same thing with GPT. So now we're all using it. But over time those disruptions move up a level from infrastructure to application to industry. So if you're not okay, I guess it is explosive. But if you're not thinking radically about the transformation that can happen at each one of those levels and also the transformation that can happen to your industry and you're just focused on the data of what you have now, you're missing one of the critical shifts of transformation in the business. And there's a theme that's becoming more popular right now is going moving from insight to foresight. And when everything is changing around you, insight's valuable. It's how you create structure around a business that you can take to market. Foresight is about how you avoid getting disrupted. If we're not looking forward and we're still letting yesterday's mental models collide with tomorrow's technologies, that is how we lose. But if we are being radical with the way we think, with the idea ability to test different business models, put things to market faster when we might not previously get that data and that feedback loop as fast as Possible we're going to learn more about that unexplored terrain way faster. So I wouldn't say go and disrupt your $1 billion revenue line, but you absolutely should be incubating things that will, because there are hundreds and eventually thousands of other startups that are doing exactly that. And you will have no defense against that if you're not thinking in that way. So think radically. Approach practically, so that next step goes okay. So what do we do to implement this? Is it tiger teams? Is it small skunk works? All of those are viable. I do believe that having in its transformation, you need to find people who are leaning in and already self selecting as the people who are like, I'm all about this, I want to do this. Don't try and convince a bunch of people who might not be invested in this to be the first ones through the door. They will be unenthusiastic about it. They don't have the willpower to get through the challenges. It's going to be hard and they're going to fail a million times before they get it right. If they're not already passionate about this, they're going to stop at the first sign of trouble. Those people can be followers of the people who lead the way. It's not that they're irrelevant. You need to find the people who are like, I want to be the person who kicks the door down. I want the first person in the room. And those are the ones you want to build your teams around to think about these things and build different ideas and find the tinkerers, find the people who may not be the developers or the engineers who are already tinkering with this stuff. There are so many people who are using AI and building their own agents or creating side businesses on the weekends who could also be resources for this. And that's the culture that will create new opportunities, new business models. And they're going to learn what these new paradigms look like by doing the work in that space that then can be diffused across the organization. And that's the second most important part. Once you have the knowledge, do you have the infrastructure set up to diffuse that knowledge as fast as possible and as thoroughly as possible across the organization? Otherwise it just stays compartmentalized and it dies on the buying.
- [41:22]
Jeff
Right. And I'm glad, Ian, you used the word culture, because I'm curious. We talked about Martech's law. We talked about the need to, I don't know if we use these words, but bend the curve upward to try to keep pace with Technology to try to compete. You know, to what degree does culture play a role there? Is it the most important thing? Is it in the top five? Like, and if it's not the most important, what is the most important?
- [41:45]
Ian Beecraft
I do think it's the most important thing because if you don't have a culture or can't create a culture that is willing to lean in and say, hey, things are going to look so different in the next couple years that we won't even recognize it, it's up to us to make that change. You're not going to get there. If everyone is waiting for the vision to be given to them to take action, it's already too late. And that's one of the biggest challenges is a lot of organizations. We don't built this expectation that when the CEO gives the vision, then people act and people start to endear. If people aren't leaning in and saying, I'm in R and D too. Like, I'm actively in research and development of what my own role looks like in my organization, my own profession looks like. Because you're going to encounter this, no matter what role you have or what company you work at, you go work somewhere else. It's still going to find you. So we as individuals have to take ownership over this if we want to maintain relevance in this space. This is not an us versus them, up versus down, organization versus individual issue. It's a collective one.
- [42:45]
Jeff
Right.
- [42:46]
Ian Beecraft
So if that's recognized in a healthy way within an organization, that creates a camaraderie, a collectivism that can move an organization forward.
- [42:55]
Jeff
Right. If everybody's kind of sorry, I'm going to use the silly, like, you know, the silly idiom about rowing in the same direction.
- [43:02]
Ian Beecraft
Right.
- [43:03]
Jeff
But having that purpose and everybody kind of banding together to move the organization.
- [43:06]
Ian Beecraft
But it's so true and so, so spot on. Yeah.
- [43:10]
Jeff
I want to, you know, with that, I want to come back to, you know, another kind of quote you had, which is moving from insight to foresight, which I love, by the way. Where does foresight come from? And can it come from AI? Because my sense is with like a lot of these tools, they can summarize what's already known.
- [43:28]
Ian Beecraft
Right.
- [43:28]
Jeff
Like, they aren't necessarily taking you forward. They're just telling you the sum of what we know up into this point. Where does foresight come from?
- [43:35]
Ian Beecraft
Right. So I would actually disagree with that a little bit.
- [43:39]
Jeff
Okay. The.
- [43:40]
Ian Beecraft
My perspective is that large language models are commodifying. Like they. If you just use the large language models, there's a period of probably two more years where you can have an advantage over many of your colleagues. But over time, it's just going to be like, I use email, big whoop, so does everybody else. It's literally a commodity. And the difference is going to be how do you use it and then how have you encoded your knowledge into doing that? But specifically, on the foresight piece, it's about searching for signals and how you combine things as the user. This is a place where we're still very much in control. I don't think this is the kind of thing you want to automate. What you can automate is searching for signals of change. So foresight is really about finding those data points that are outside of the normal distribution that say, this is different. You should pay attention over here and validate whether or not this is something that you should be investing your time in or concerned about. And in foresight, if you talk about formal foresight, there's probable, plausible, impossible futures. There's a whole bunch of structure around it to create really good thinking about what's to come. But I think that can overwhelm and overcomplicate people. We all have a responsibility to think about foresight. What does my role look like in a world where I actually don't have to go to six meetings a day? Oh, my gosh, sounds amazing, but what's my new responsibility? Because there's more on me now. And like, not thinking that through puts you on your back foot and it makes you subservient to the vision of whatever else is happening around you. So when it comes to foresight, we should be thinking about, well, what if this happened? How would I react? And it's not about fortune telling or predicting the future. It's about seeing the signals and patterns that are starting to arrive and understanding the scenarios of how might that affect me, how might I react? So that when something does actually come your way, you can say, ah, you know, I've seen something that looks like this before, or this rhymes with something else we've already thought through. And you're adapting versus reacting. You're proactive versus reactive. And I think the best organizations in the world do an enormous amount of that. The ones that don't are the ones that really do get caught by surprise. And we see a lot of enterprises in that space right now. But I do think that the foresight is where we need to lean because it's also where we can have a lot more of our human agency using the models and the AI and the Tools to bring the data into us, to help us identify what's different and say, what does it actually mean to me? And using AI as a thought partner in getting to clarity.
- [46:13]
Jeff
Right. So on the note of what it means to me, and to be honest, Ian, I'm surprised and very intrigued to hear you say that more of the foresight can be done by these tools than maybe we imagined before. And I'm curious, and this is sort of a self serving question for both of us, but what role does an organization like signal and cypher play in this world? Right. Is this something that, oh well, the tools can do it, AI can do it. You know, we don't need partners to help us with this. Where does an organization like yours come into play to help actually accelerate, you know, traditional enterprises?
- [46:50]
Ian Beecraft
Absolutely. So the, to create a little more clarity around that. I don't believe that the AI tools can do this on their own, but I think that they can facilitate our own work in coming to an understanding of what possible and plausible futures could look like. So a lot of the research that one would do to do future scanning and signal scanning to find these opportunities we should be looking into can be massively accelerated, scaled and assisted using large language models. It's still up to us to say, how does this fit with my strategy? How does this fit with the market dynamics that I'm seeing play out? So it expands and augments our capability to do it and it makes it so people who are unfamiliar with this can dive in even faster. So it's less intimidating to get into that space. There will be more and more automation of that as time goes on. And who knows, maybe in five, seven years we could actually say, okay, just go, run and create, do a Monte Carlo simulation times 1000 for me and pull these all together and then give me that data and tell me what my future looks like. I don't think it'll be that simple, but there could be a world that, that looks like. But for the work that we're doing, our focus is on helping organizations get that to become part of their culture. So it comes from training that comes from building that data layer that goes on top of the large language model, encoding their knowledge so that they can understand how the signals that come in from the outside world are going to impact them. How might they respond to that? And also scaling the internal workings of the organization so they can be more efficient and effective. Things we talked about at the very beginning, we're not eliminating that, but what we're doing is expanding the capacity and ability for them to operate in spaces they never could have before. So where that changes is organizations may say, well, okay, I can use this. And now I only need a 3 person marketing team instead of a 25 person marketing team. Company A might do that. Company B might say, hey, for the last three years we probably had, if we go back and look at our backlog, three or four hundred products or projects that we would have loved to pursue, tested and gotten data on that there's no way we'd have a team of 500 that we would need. But you know what? Now we could do that with 25 and all of a sudden running simulations, putting products together, testing things, getting that data becomes possible at an enterprise scale from a small team. And you're exploring new opportunities in unknown territories. So you have this expansive mindset versus a contractive mindset. One works very well with industrialist capitalist mindset. One works very well in a time of transition where new markets, economies and form factors are starting to develop, but we don't know what they look like yet. So that's what we encourage companies to do is say, rather than saying you're going to lay off 50, 50% of your workforce and do more with less, do more with the same, expand those skill sets, right? Expand capacity, expand possibility. And that is really where we see the most value.
- [49:47]
Jeff
I want to talk. I love the dual approach there, and it makes complete sense to me. I'm curious with the second scenario you talked about where it's 25 people, 500 new products or tests or what have you. One of the things I've found, and I'm curious if you've seen it too, or you've seen something different, is in this emerging world where technology isn't the limiting factor anymore. And it's like if you can dream it, you can build it, you can test it, at some point the bottleneck becomes the market or it becomes your staff, or in some sense it's people's ability to actually try and digest new things. And my sense is you have to still get back to prioritization in some capacity because even if the technology can give you 500 new things, you're going to be limited somewhere. Do you buy that and what are the implications?
- [50:45]
Ian Beecraft
I absolutely do. I think that what that does is it shows another weakness in the current paradigm for the future that we're trying to create. We go through these phases of oftentimes, 150, 200, 300 years, where the economic paradigm also shifts. So the one that we're currently in is identical to the one where we built the steam engine and connected geographically disparate places. The metrics that we use are still the same ones that we used with some modifications when the steam engine was a cutting edge technology. So what that does, it shows that the paradigm that we're in is kind of the ultimate bias. A paradigm shows you what's important, what do you measure, what questions are worth asking. And all of those are still very much directed towards the capitalist system that we have now. And I'm not saying this to capitalism versus socialism versus Marxism type of argument. It's what does capitalism 8.0 look like in order to start to expand its environment so these new types of businesses could become possible. So there will be fully autonomous organizations that have zero humans involved. And what does that look like? It's not a full replacement for humans. That would be like saying the digital office replaced paper. It obviously did not that digital killed analog. Analog is still, is absolutely decreasing, but it's not zero and it won't ever be zero in my opinion. But it creates this fragmentation of what we saw as like the dominant paradigm and it creates space for coexistence of all these new models, mental models, operational models and economic models. We don't know what those look like just yet because we haven't seen many of them succeed. We're seeing some signals when we look at companies that are, let's say, on the lean AI leaderboard, which is a reference I love, you know, average 3.3 million average revenue per employee, you know, time to scale is absolutely insane. And we look in these organizations that are AI native, they're starting to show what some of those paradigms could look like. If you extrapolate that from, you know, 10 people, 50 million array and say what would it look like with one person, 150 million ARR. What apparatus and infrastructure would you need? What would it look like to operate as that individual? And that can give you some of those signals I was talking about earlier of what possible, probable and plausible futures could look like. But I do think that how we look at capitalism today is going to change dramatically. And it's not just a technological question, it's a social question, it's an economic question, geopolitical question. And that's why AI is all of those as well. So these things all coming together at the same time. And that's another reason people kind of feel like they're being thrown off balance in every direction because everything is changing all at once.
- [53:28]
Jeff
Right. So throughout this conversation, I feel like I've started to be able to put together a mosaic of like your view of the future through, you know, a series of different lenses and also some spaces where you say, you know what, there's still too much uncertainty here. Is there anything you can tell me about, like your predictions for the next five to 10 years that we haven't covered, that you're pretty confident we're going to see?
- [53:53]
Ian Beecraft
Yeah, I would say that the paradigm around training, skill sets and education is going to change dramatically. And that has profound implications for the work that we do and how we go about that. One of the things I talk about is skill flux. And it's this concept that we go from this paradigm of, you know, 30 years ago you could have a skill set that lasted you 30 years before shelf life was obsolete. Now, you know, someone like me, I had a skill set that was, you know, 10 years, it was valuable. I started off as a mobile strategist for an agency. You don't hire those anymore. It just doesn't happen.
- [54:27]
Jeff
Right.
- [54:28]
Ian Beecraft
You might at an enterprise level if you're like Cisco and doing software, but not in that environment. And the skill sets that are valuable are shortening on their shelf life. And for more technical skill sets, those are arising and disappearing faster than ever. So now we're at like two and a half years for a technical skill set. And I could see that shrinking more and more and more to the point where many of them are arising very quickly and gone the next day, six months. To give you an example, I think coding and prompt engineering are two versions of that. So prompt engineering became something that was relevant about two years ago. I would give that maybe a five year shelf life max before it's no longer relevant at all. And we're already seeing agents being able to take on a lot of that work. But there will also be a new skill set that you'll have to learn in order to operate in a new paradigm with new technology and new objectives. So we're going to see this exponential increase in importance and value and a ChatGPT moment that comes in and says, that's not valuable anymore, that's gone. And that's going to have this almost like whiplash of for us as we go along that changes how we educate ourselves. Because if we're front loading education for the first quarter of our lives, we're out of date by the time we walk out of university. And this is not a new discussion at all, but it becomes exacerbated by that. So the idea of lifelong learning, you know, Very cliche. But micro credentialing, we call it surge skilling, where it's like you're actually having to get very deep into something very, very quickly to create competitive advantage. And then you just know that this is going to be the less valuable in a certain period of time. But what is valuable is being that first mover and creating value with it as fast as possible before it comes obsolete. So that's where I see education changing, where I see people shifting their focus for competitive advantage and the culture of an organization changing too. Because you're going to have to keep learning on the job and the tools and the AIs you're using will have to teach you how to work with it as they change.
- [56:26]
Jeff
So I'm really glad, Ian, that that was your answer because that was on my list of things that I wanted to talk to you about that we hadn't gotten to yet. With that in mind, the shortening time horizon of skills you mentioned, it's going to have massive implication on education. What do you see as being the risks and the opportunities for the traditional education system? And also what does it mean for the hiring process of organizations?
- [56:53]
Ian Beecraft
Absolutely. So it completely disrupts the one to many broadcast model. Like the idea of a teacher standing in front of a room and speaking for an hour and a half to three hours is gone. Which is great for people like me. I was a terrible student, super neurodivergent. I can't sit in a class and listen for more than five minutes. I have to be engaged. So what this is going to do, it will disrupt the current model, but it will make it amenable to a much larger group of people who are not built for the more industrial esque manufacturing like education model. The challenge though, and I don't say that with any malice towards teachers and educators. They are some of the most under resourced, over taxed and over expected people in the world. And then you take a look at the dynamic in the US and how hostile it is. I have so much empathy for people who choose to go into a life of service for the next generation. We need to be spending a lot more time and money in that space. And I make a comment in my keynote where I say the L and D budget should be as big as your technology budget. And that kind of like people look bug eyed at me like what? Like what we're spending. So we're spending trillions of dollars on technology. I love that. I love that.
- [58:04]
Jeff
Yeah.
- [58:04]
Ian Beecraft
But if we don't like the technology is moving faster than any other sector, faster than the economy, faster than society is moving, faster than education's moving. And if we truly want to understand where humans play in that picture, the fact that we're investing everything we have in technology has already indicated our preference for technology over humans. So that math has to balance out a bit. We have to figure out how do we invest so much more into education, not so much less. And until we do that, we are going to be behind the eight ball. We are going to have a target on our back in many ways, because if the paradigms don't change, the technology gets better, we're going to suffer the consequences. But if we put ourselves front and center of that equation, we have the chance and the opportunity to figure that out.
- [58:52]
Jeff
Right? It's wow. Yeah, it's. It's as you said it like, this is not an incremental shift. This is like a complete disruption of the model from end to end, without a doubt.
- [59:05]
Ian Beecraft
And even for people who live and breathe it like it's overwhelming for me, I do this 24 7. I love it. I'm passionate about it. I'm excited about where we're going and net net. I'm optimistic about the long term future. But we are all pioneers right now, whether we want to be or not. And when people, we've kind of bastardized the term pioneer, we've made it seem like, oh, it's Richard Branson on the COVID of Entrepreneur magazine with his billions of dollars of success, like he was a pioneer at one point in time. But pioneers do really hard shit and they go to places where there's no infrastructure. They suffer the consequences of, you know, decisions that they didn't know they'd have to make. They are attacked by the environment that they're in. Nature tries to kill them in a number of different ways. And as a super resilient species, we still make a way forward. We construct the environment. After we figure it out, you know, we might show up in Hawaii with snowshoes on and realize, oh crap, I'm not properly equipped for this. And then we figure a way out. That time to go from not knowing to knowing can be really hard, painful and challenging. But the way we thrive once we do is absolutely amazing. So I would say that we are going to have amazing things happen, but we're also going to have to encounter some really tough growing pains individually and collectively to get there. So if anyone's saying otherwise, it's absolutely smoke and mirrors.
- [60:26]
Jeff
Right? Right.
- [60:27]
Ian Beecraft
Wow.
- [60:27]
Jeff
Exciting times ahead. There's one more question, Ian That I wanted to ask you that I haven't had a chance to yet, which is I wanted to ask you the inverse of what I just asked you, which is, you know, aside from like what is going to happen and what's going to disrupt us, is there anything you're hearing right now, hype wise, technology wise, trend wise, that you're like, that's B.S. like that's not actually going to come to pass? We're being sold a bill of goods.
- [60:52]
Ian Beecraft
Yeah, actually, I think the agents conversation is way overhyped. I think they are transformative. I don't know a single organization that is going to say, I'm going to let an autonomous series of agents run my enterprise that I've spent decades building without the oversight necessary. We've been working with agents for years and we've been building setups where agents will work with other agents and giving them autonomy and creating virtual environments to see what happens. And every time we let them run amok. It's frightening. It is absolutely jaw dropping. Oh my gosh, I can't believe that would have happened. So glad I didn't give them freedom to access real live data. And that infrastructure needs to be built. There are actions that agents can do that are absolutely mind blowing, but they're narrow, they're specific, they're structured, and they have strong guardrails. The idea that we can kind of have this almost reinforcement learning, give it a million different examples, let it kind of bang around and figure its way through approach to unleashing it in the organization does not work because the infrastructure is just not there yet. It hasn't caught up with the promise of the technology. So I think we're very much at the top of the hype cycle of agents. We're going to have this crash into the trough of disillusionment, which in my opinion is the best place for a nascent technology to be. A lot of people say, well that's bad. But what it means is the people who are making promises, who don't know what they're talking about, and let's face it, there's an enormous amount of people who are rushing to find the gold that have no business being here and making promises, they disappear because it's now it's getting hard, you actually have to deliver. And in the trough of disillusionment, it pulls all the pundits out. And now the people who are committed to doing the work, who are there for the right reasons, they get to work and they build that infrastructure that's necessary to deliver on all those promises we were making back here. So it takes time, and I'm just kind of waiting for that to kind of implode on itself and for people to be like, oh, yeah, these are very, very powerful. This is absolutely the paradigm of the future. But that future is still the future, not the present. We need to get there first.
- [63:01]
Jeff
Right? I love that answer, and I think it's so appropriate right now, given where we are in that hype cycle. So thank you for taking some of the air out that one. That's awesome.
- [63:12]
Ian Beecraft
One of the things maybe I can put a finer point on is the metrics piece, and that is the expansive versus contractive. So what I was talking about earlier, as I was mentioning, a lot of teams are going to say, let's do more with less. Let's pull back the number of resources we have and get along and have higher efficiencies, greater margins, and better stockholder returns. And a challenge that we have is we're moving into a paradigm that is going to shape what matters and what matters and what's valued in the work that we do is going to be different. But when what matters changes but the metrics do not, and the incentives do not, that means you run right into a paradigm that is going to push back on you and potentially hurt you as an organization. So we encourage organizations in time of change to also understand how is this going to change the incentives and the metrics that I use to measure that change as it's happening? So we're thinking more about growth metrics and metrics of innovation, metrics that are about charting the unknown versus optimizing the known. We've come from a paradigm of optimizing the known for the last 150 years. We're really good at it. The problem is how much is known about the next five years. So if we're doing 95% of our metrics on optimizing the known, 5% on exploring the unknown, that means you're already out of date. If we're starting to push more of that towards exploring and charting this unknown territory, this makes us more prepared for what's going to be coming. This gives us the opportunity to think about innovation quotient, knowledge diffusion across the organization, building the structures that will make you resilient in this future paradigm. Because right now, optimization to scale by definition requires some calcification of the organization. It needs to be rigid in some ways in order to be efficient. And rigidness against an oncoming wave is a recipe for disaster. So that's one of the things we encourage organizations to think of too. And we get very deep into what is that metric? What matters for you? How is it specific to your context, what are the things that you measure, how do you actually do that work? And when that clarity is there, all of a sudden it goes from, well, we don't know what the future brings to. At least we know how to move in that direction.
- [65:25]
Jeff
Right. And with respect to the fact that I'm sure there's lots of different metrics for different organizations, is the answer to like just move to a new set of hard metrics or get more comfortable with the notion that we need to be flexible and measure things with a little bit more flexibility than we have in the past?
- [65:42]
Ian Beecraft
Absolutely. That's typically the first step. You never want to shift entirely because you kind of want to leave what's working working. So we don't say you measured this way, don't do that anymore. But at least a portion of the work that's being done needs to be done in this forward facing way. And that type of work needs to be measured differently. Because if you are measuring, for example, a lot of teams, a lot of tiger teams, a lot of innovation teams are measured by ROI on their first run, which is mind boggling to me. Okay, right. You're going to have impact on margin the first time you touch ChatGPT. No, that doesn't happen. And I think that's almost a bad example because that's just ludicrous on all levels. But if you're thinking about scale, efficiency, margin, impact on things that are by definition going to require investment and you're already impeding the work that is going to help you explore unknown territory.
- [66:35]
Jeff
Yeah, no, it's super interesting and yeah, I'm sure we could talk for another hour just on that. With that in mind though, Ian, I did want to say a big thank you for joining me today. This has been super, super interesting and it's honestly been a real treat. I talk to a lot of people in this space and I'm just continuously blown away by the breadth and the depth of insights that you have in the space. So I wanted to say a big thank you.
- [66:58]
Ian Beecraft
Thanks Jeff. It's been an honor to join you. I've really enjoyed it.