AI Is Rewriting the Rules of Work: Futurist Ian Beacraft Explains Why Jobs are Dead

The expectation in many organizations that the CEO gives the vision and then people act prevents transformation—instead, people must see themselves as in R&D too, actively researching and developing what their role and profession looks like in the future.

“the biggest challenges, is a lot of organizations. We built this expectation that when the CEO gives the vision, then people act and people stand there. If people aren't leaning in and saying, I'm in R&D too, like I, 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”

Economic paradigms shift every 150-200-300 years, and we're in one of those shifts right now—our current metrics are still inherited from when the steam engine was cutting edge technology, and we need new economic paradigms (capitalism 8.0?) to expand its environment and enable new business models.

“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 are 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.”

How we look at capitalism will change dramatically because of shifts that are simultaneously technological, social, economic, and geopolitical—all these changes are happening at once, which explains why people feel thrown off balance in every direction.

“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 I as all of those as well. So these things are all coming together at the same time.”

At Signal and Cipher, AI and project management systems know project status in real-time, eliminating 70% of alignment and approval meetings, because the system can check facts, compliance, and readiness without humans needing to be consulted, reducing corporate waste and unnecessary gatekeeping.

“When we actually work within Signal and Cipher, 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. You're 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%.”

Agents will enter the trough of disillusionment (which is good for technology because it filters out hype-driven vendors and allows committed builders to focus on real infrastructure), and from there, the people who do the work will build the infrastructure necessary to deliver on agent promises.

“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.”

Short-term quarter-to-quarter thinking in Western/American business shows immediate impact (layoffs leading to higher margins and productivity), but this misses what's actually happening behind the scenes—companies building AI models have already seen this coming and are restructuring how they operate in fundamentally different ways.

“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 in 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.”

Individual-level digital twin data must be owned by the individual, not the organization, because if the organization owns and can reuse a person's encoded knowledge without ongoing benefit to the person, it creates an antagonistic relationship—equivalent to capturing a voice actor's voice and reusing it without credit or compensation.

“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 stand a 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 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.”

The difference between agreement and alignment is critical: many leadership teams agree AI is important and they're implementing it, but they're not aligned on how it happens or where they are on a maturity index, and this misalignment prevents effective enterprise deployment.

“there's a strong difference between agreement and alignment. What happens oftentimes at a 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.”

Being a pioneer (in the true sense) is hard—pioneers go to places without infrastructure, face unknown challenges, suffer consequences of decisions they didn't know they'd have to make, and are attacked by the environment—right now, we are all pioneers whether we want to be or not.

“when people we've kind of bastardized the term pioneer, we've made it seem like, oh, it's Richard Branson on the cover of entrepreneur magazine with his billions of dollars of success, like he was a pioneer at one point in time. But yeah, high nears through 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”

Culture is the most important factor in organizational transformation because without a culture willing to lean in and accept that things will look radically different, leadership cannot mandate change—people won't act without the conviction that change is necessary.

“I do think it's the most important thing because if you 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 of 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.”

Foresight is not something you should automate; searching for signals and combining data points as a user is where you should stay in control, because signal detection and pattern combination is where humans still have agency in using models and AI as thought partners.

“I don't think this is the kind of thing you want to automate. What you can't automate is searching for signals of change. So foresight is really about finding those, those data points that are outside of the normal distribution that say, this is different. Like you should 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”

The political framing of 'bringing good jobs back' is misdirected; the work itself is what matters, not nostalgia for specific job categories that are not coming back (like coal). Focus should be on what tasks, roles, and structures compose the future of work, not defending old job categories.

“it's not the jobs, it's the work that we need. If we're so focused on jobs or already narrowly defining ourselves and oftentimes attaching ourselves to things that are not coming back, like we're we're not really going to be bringing back the coal sector that way. Everybody is 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.”

Individual-level encoded knowledge should be owned by the employee and portable across organizations, similar to how experience is portable but office files aren't—otherwise the relationship becomes antagonistic, like claiming voice actor ownership of someone's voice after extracting it.

“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 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.”

The transition from insight to foresight is critical: insight is creating structure around current knowledge for market takeoff; foresight is avoiding disruption by thinking forward about what could change and preparing for it rather than being caught by surprise.

“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, insights 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.”

When technology abundance removes the limiting factor (resources), the bottleneck shifts to market absorption, staff capacity to digest change, and organizational ability to prioritize—even if you can dream, build, and test 500 ideas, you still need to prioritize which ones to pursue, revealing that the real scarcity is human judgment and organizational alignment, not technological capability.

“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 like, the bottleneck becomes the market, or it becomes your staff, or in some sense, it's people's ability to to like, actually try and digest new things. And my sense is you have to still get back to like prioritization in some capacity.”

The shift from insight to foresight is critical: insight focuses on past data and what has worked, while foresight involves proactively identifying signals of change and testing scenarios of how you might respond, so you adapt (proactively) rather than react (after the fact).

“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, insights 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.”

Education budgets should be proportional to technology budgets because technology is advancing exponentially faster than any other sector, and if we spend trillions on technology but invest minimally in human development and education, we are implicitly choosing technology over humans; this imbalance will cause societal consequences.

“And I make a comment in my keynote where I say the land 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, yeah. But if we don't like the technology's moving faster than any other sector, faster in the economy, fashion, the society is moving faster and 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.”

Poor leadership adherence to old systems and technology mindsets are a bigger risk to organizations than AI itself, because leaders use old metrics and processes when approaching new technologies and challenges, leading to organizational demise rather than adaptation.

“poor leadership, adherence to old systems and technology. First, mindsets are a bigger risk than AI to organizations”

Poor leadership, adherence to old systems and technology mindsets are a bigger risk to organizations than AI itself because leaders pointing to AI as the threat are missing the real issue—they're applying old efficiency-based metrics and paradigms to fundamentally new challenges and technology, which leads to organizational demise.

“poor leadership, adherence to old systems and technology. First, mindsets are a bigger risk than AI to organizations.”

Augmenting individual workers involves encoding their personal knowledge (emails, briefs, content) into a large language model to create a 'digital twin' that filters both prompts (inputs) and responses (outputs) to maintain their tone, style, voice, and strategic understanding—expanding their capabilities while preserving their individuality.

“What I mean by including it is taking things that you've written, whether that's briefs, emails, contents, 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 it, not everything, but the the highest signal, highest quality stuff that you've written, content about who you are, what you've done, etc. and turn that into a document that sits on top of the large language model, and that becomes the filter through which you prompt.”

Skill sets are experiencing exponential 'flux'—the shelf life of valuable skills is shrinking from 30 years (pre-digital) to 10 years (post-digital) to 2.5 years now (AI era), and for highly technical skills it's shrinking to 6 months, with new skills appearing and disappearing within this cycle, requiring continuous 'surge skilling' rather than traditional career building.

“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 a shelf life was obsolete. Now, you know, someone like me, I had a skill set that was, you know, ten 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. Right?”

Companies will get smaller while more startups and businesses are formed than ever before—since COVID, corporate formation has increased massively, and as AI makes it easier to form companies and teams, the number of businesses created in the next decade could be 100x current levels.

“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 as corpse and LLCs formed more than 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.”

General-purpose technologies (electricity, the internet, GPT) cause disruptions that propagate up from infrastructure to application to industry level, and organizations that don't think radically about how disruption will occur at each level and in their industry will be caught unprepared.

“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 we know 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, what you have now, you're missing one of the critical shifts of transformation in the business.”

Traditional education is overly focused on the industrial/broadcast model (one teacher lecturing to a room), which disadvantages neurodivergent and non-standard learners, and AI-enabled personalized, adaptive education can serve a much larger population better while freeing up educator capacity for actual mentoring and development rather than information delivery.

“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. The which is great for people like me. I was a terrible student, super neurodivergent. I can't sit in a class on the lesson 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 ask manufacturing like education model.”

The trough of disillusionment is the best place for nascent technology because it drives out opportunistic hype merchants and attracts committed builders willing to do the unglamorous infrastructure work needed to realize the technology's actual potential.

“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 less 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.”

Innovation and forward-facing work should not be measured by first-run ROI because exploration requires investment and time before returns materialize; measuring exploration teams by immediate ROI metrics designed for optimization teams impedes the exploratory work meant to chart unknown territory.

“If you're thinking about scale efficiency, margin impacts on things that are by definition going to require investment and time, you're already impeding the work that is going to help you explore unknown territory.”

martech law states that technology moves exponentially while people and organizations develop logarithmically, creating an ever-increasing gap between what's technologically possible and what companies and individuals can actually integrate and adapt.

“There's a concept that I love called martech law, and it's it's about the difference between technology moving exponentially and people and 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.”

Martech Law describes the exponential gap between technology progress and organizational/human capability development—technology moves exponentially while people and organizations develop logarithmically, creating an ever-increasing gap between what is technologically possible and what organizations can practically implement.

“There's a concept that I love called martech law, and it's it's about the difference between technology moving exponentially and people and 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.”

Junior employees face the highest immediate displacement risk from AI because the apprenticeship contract—learning on the job through mentoring—breaks when AI allows organizations to skip training and directly solve problems, eliminating the teaching functions that historically developed junior talent.

“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. Now 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 in 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 in my case. So junior roles are already starting to disappear.”

AI fundamentally changes the atomic unit of work and the boundaries of job roles—it allows workers to access skill sets adjacent to their own or completely new to them, removing the rigid role-based system where people were confined to specific KPIs and responsibilities, creating chaos when organizations have no structure to manage this boundary-breaking capability.

“we're really, we're reengineering 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 had these mental shortcuts that I can use to understand who does what, where and how. All of that starting to change, though, because I 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.”

The traditional 'thousand page deck' consulting model and separation of learning from doing is outdated; organizations now need tools and processes that enable learning and doing simultaneously while building essential infrastructure, documentation, and strategy.

“the idea of the thousand page deck. And I do think that the idea of learning being separate from doing so, having a 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.”

Understanding AI properly requires experiential learning—not just reading articles or doing a few ChatGPT experiments, but at least dozens of hours immersed in AI tools with proper guidance—so that leaders can competently lead an AI transformation and answer fundamental questions about their company's value proposition, team structure, and growth strategy.

“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, a 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.”

Organizations that invest in ChatGPT licenses without training, context, or clear relevance to individual workers experience adoption collapse—initial enthusiasm followed by usage crashes—because giving people a new tool without explaining how it applies to their daily work, how it changes their life, and providing clear success metrics guarantees failure regardless of tool quality.

“What we found for so many organizations is they'll often us like, hey, we we invested half $1 million in ChatGPT licenses, and people loved it at the beginning. And then like, nobody uses it, it goes like this. It creates 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?”

Middle management is directly in the line of fire because most of their work—facilitation, alignment, approval, and operational efficiency—is automatable by AI, and while this creates immediate pain, it also collapses the organizational layer between junior and senior levels, reducing friction and increasing direct leadership contact but creating a thinning gap between junior and leadership roles.

“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. Right. But just like the operations of the company that is directly in the line of fired AI and what that changes, is it the stuff of being a boss is also starting to go away.”

LLMs alone cannot perform foresight because that requires human judgment about strategy fit and market dynamics, but they can massively accelerate research, signal scanning, and data assembly for foresight—scaling and assisting human work rather than replacing it, particularly in identifying opportunities that match organizational strategy.

“Absolutely. So the to create a little more clarity around that. I don't believe that the AI tools can do them, 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, you know, 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.”

Signal and Cipher's role is to help organizations embed foresight, signal scanning, and organizational efficiency practices into their culture, build data layers (encoded knowledge) on top of LLMs, and scale internal operations so teams can explore unknown territory and test multiple business models, transforming organizations from contracting (efficiency) to expanding (innovation) mindsets.

“So 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. A 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?”

AI transformation is fundamentally different from past technology implementations like ERP (which used top-down IT command-and-control over 3-5 years); AI requires horizontal buy-in from CEO, CFO, CRO, and CIO all in lockstep about distribution strategy, making it simultaneously an HR, strategy, and finance issue, not just IT.

“It's almost the opposite in many ways of previous transformation. So let's take a SAP ERP implementation. That's a 3 to 5 year process. IT command and control. We install it. Everyone else has to adapt to it. What's happening with AI is we're kind of reversing that in a 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 CE, CFO, your CRO 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”

The phrase 'jobs are dead, long live work' captures the shift: politicians and organizations focus on bringing back specific jobs (coal mining, etc.), but what's needed is focusing on the work and tasks that need doing for the future, which will be structured differently and cannot be brought back.

“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 the work that we need. If we're so focused on jobs or already narrowly defining ourselves and oftentimes attaching ourselves to things that are not coming back, like we're we're not really going to be bringing back the coal sector that way.”

The logarithmic organizational growth curve is constrained by infrastructure, technology, culture, decision debt, and technological debt—factors that determine the ceiling and rate of organizational AI adoption regardless of technological capability.

“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.”

Optimization organizations require calcification (rigidity) to be efficient by definition; rigidity against oncoming disruption is a recipe for disaster, making resilience—not optimization—the primary goal in transition periods.

“optimization organization is scale by definition, require 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.”

The traditional expectation that leaders give vision and people execute is breaking; individuals must self-select into R&D of their own roles and organizations, taking ownership of relevance maintenance in a time of continuous change.

“we built this expectation that when the CEO gives the vision, then people act and people stand there. If people aren't leaning in and saying, I'm in R&D too, like I, 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.”

The current hype cycle for autonomous AI agents is overstated—agents are powerful but require strong guardrails, narrow/specific scope, and structured environments, and allowing them to run autonomously without oversight is dangerous; we're at the top of the hype cycle and will crash into the trough of disillusionment before the actual valuable infrastructure is built.

“Yeah, I actually I think the agents conversation is way over heights. 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. Like, we've 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. Like it is absolute, like jaw dropping. Oh my gosh, I can't believe that would have happened. So glad I didn't get them. Freedom to access real live data.”

Organizations should be 'radical with thinking, practical with approach'—meaning think boldly about transformation without burning down the profitable business, but be willing to incubate and test different business models that might eventually disrupt the core.

“I would encourage organizations to be radical with their thinking and practical with their approach. So there's there are too many people. So you kind of need to break burning all the ground, 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.”

Encoding organizational knowledge at the team or organizational level with AI allows new hires to reach the same capability level as existing staff within less than a month and perform with organizational voice and understanding immediately (writing emails in company tone, managing social media) because the knowledge layer is pre-encoded.

“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. The tone of voice of the company that should be able to manage our social media presence, all that kind of stuff, because we've built a layer on top of the large language model that already has all of that encoded”

Organizations can reduce what would normally be a 6-month consulting engagement and thousand-page strategic document to a single afternoon or full-day facilitated session using AI tools with the right people and questions, yielding clear vision, maturity assessment, and roadmap on the same day.

“So to give you an example of a module that we run will work with leaders in an environment where they're working with the AI to build their vision for what AI looks like in the 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...So you've taken something that might have been a six month consulting engagement and said, we're walking out in 2.5 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.”

AI-assisted project management and workflow integration can reduce alignment meetings by 70% because systems know project status without needing check-ins, and can handle CYA (cover-your-ass) compliance tasks like legal approvals without human intermediation, reducing corporate waste and freeing time for actual decision-making.

“When we actually work within Signal and Cipher, 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. You're 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 the wow, these big parts where we call it corporate waste, these items where you're waiting on specialized resources, they come 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.”

AI-native companies on the 'Lean AI Leaderboard' show signal of emerging economic models—they achieve 3.3 million average revenue per employee and insane time-to-scale, suggesting what a 1-person company with $150M revenue or a 10-person company with $50M revenue might require in apparatus and infrastructure.

“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, the average 3.3 million average revenue per employee. You know, time to scale is absolutely insane. And we look at 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, ten people, 50 million are and say, what would it look like with one person, 150 million are what apparatus and infrastructure would you need? What would it look like to operate as that individual?”

More startups and freelance entities will be formed in the next decade than in any prior period because AI removes barriers to business formation—you can now form teams, scale operations, and test products with fewer resources and people—and this acceleration is not just in employee-based companies but in AI-agent-based enterprises with zero or near-zero human involvement.

“So I think freelancing is going to explode even more than our. Yes. So an acceleration in the 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.”

Organizations attempting AI transformation must make experiential learning of the technology a requirement for leaders, not just theoretical understanding, requiring dozens of hours immersed in hands-on use with proper guidance to competently understand what AI will do for their team, business model, and organizational structure.

“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, a 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 and doing a couple of things and 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.”

If you're not taking ownership of learning how this affects you, maintaining relevance, and researching your own role's future, you'll become subservient to whatever changes happen around you; everyone has responsibility for thinking about foresight regarding their own role.

“If you talk about formal foresight...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?...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.”

Some organizations and individuals 100x their capability with AI while others see no benefit because of individual differences in adaptation (engineers/developers lean in naturally) and organizational differences in native AI readiness—smaller, AI-native-built organizations adapt faster than large enterprises with legacy infrastructure and culture.

“You'll see some people who are just, you know, they've hundred x themselves, with what they can do, whether possible, whether 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 the stuff, they've already had the experience to help them do that. Usually, you know, really good engineers and developers can lean in and the way to 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.”

Culture is the most important factor in organizational transformation because if people don't believe things will look dramatically different and take ownership of their own role and organization's R&D, waiting for top-down vision means it's already too late.

“I do think it's the most important thing because if you 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 of 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.”

AI automates pieces of work that previously provided individual value, making those individuals less valuable to the organization, but the systemic issue is that when leaders default to efficiency and layoffs as the primary lever, this approach is breaking down because the era of unending exponential growth in existing paradigms is starting to fray at the seams.

“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 is operated for the last hundred and 50 years. And it's been able to grow. We've been able to create prosperity in a number different ways, but that systems changing now, the era of unending exponential growth, in existing paradigms, is starting to fray at the seams.”

The current economic paradigm (150-200 years old, built around the steam engine and later the digital revolution) is approaching the end of its lifecycle; we are seeing paradigm fragmentation where new economic models and operational structures are emerging, and it will take another 150-300 years before a new dominant paradigm stabilizes—this creates space for coexistence of multiple operational models rather than a single winner-take-all system.

“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 are 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.”

The idea that small teams are the 'ultimate flex' means that well-resourced small teams using AI tools and infrastructure appropriately can move faster and make decisions more confidently than large teams because they avoid the 'CYA' meetings where liability is distributed across many people rather than owned by decision-makers.

“For us, it's gotten to a point where we say that the small team is the ultimate flacks. 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 CIA. It's about, can I distribute the liability of this decision across a group of people? So it's not just my fault, right.”

Enterprise AI implementation is currently in stage one where organizations are just getting people exposed to basic chatbot tools with 1-to-1 input-output relationships, barely beyond alpha products, and this basic access is necessary but insufficient without use cases, training, context, and integration into organizational infrastructure.

“And stage one is just to get people exposed to tools. So that's access to the chat bots. It's a 1 to 1 relationship. You put in an input, you get an output that is barely silly. Even alpha products for an enterprise at this point in time, like you're just getting your socks on before you put your shoes on to get out the door. And what we're seeing with organizations that are more successful is their 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?”

Organizations frequently invest half a million dollars in ChatGPT licenses, see initial enthusiasm followed by usage collapse because they provided tools without training or context, while adding to already overwhelmed teams; use cases must be clearly relevant to individuals' daily work, not just organizational goals.

“We'll often us like, hey, we we invested half a million in ChatGPT licenses, and people loved it at the beginning. And then like, nobody uses it...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.”

Organizations can compress six-month to three-month consulting engagements into one afternoon to two-and-a-half hour facilitated sessions using AI tools, producing a clear vision, maturity assessment, aligned roadmap, and shared understanding of responsibility distribution.

“So you've taken something that might have been a six month consulting engagement and said, we're walking out in 2.5 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.”

Large language models are commoditizing their core capability over time; the two-year advantage window from general LLM access will close as everyone adopts them, making how you use and encode knowledge into AI-augmented workflows the actual differentiator.

“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 your colleagues, but over time, it's just gonna be like, I use email, think 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.”

AI-native and lean organizations show signals of what new paradigms look like—companies on the Lean AI Leaderboard have average revenue per employee of 3.3 million with insane time-to-scale; extrapolating this pattern reveals what operations would look like with one person, $150 million revenue, and can inform foresight about future paradigms.

“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, the average 3.3 million average revenue per employee. You know, time to scale is absolutely insane.”