The New Era of Jobs: Organizational Singularity | EP #258

Is there a line in your business, a high-margin line of your business, that two guys with open claw could replicate in 60 to 90 days?

This is something across the board useful for everyone.

When we wrote the Exponential Organizations book, we didn't realize how prescient it would be. It turned out over 10, 12 years, we were dead on. Now that we see a gigantic eye on the future of intelligence, what does the organization look like? We think we have a pretty interesting viewpoint perspective on that.

If you Don't retool your organization or don't restart your organization. You will be disrupted because someone doing it is going to just eat your lunch.

The central thing to think about is all of our organizational structures in the past were organized around hierarchy, and now they need to be AI-native, agentic workflow. And that's a totally different model. It needs to be architected around intelligence, not around hierarchy. The next question really becomes, how do you get there?

Now that's a moonshot, ladies and gentlemen. I'm about to sit down with my dear brother Salim Ismail, my moonshot mate, talk about the organizational singularity. This is a conversation that I think is absolutely critical for every company to be looking at. We're in a period of rapid transition. Agents, AI, AGI, ASI. It's going to restructure how every company, every industry is being run. Not in 5 or 10 years, in the next 1 year, in the next 2 years at most. Salim's gonna lay out his process that every company can follow to move from the old way of doing business as an organization, which is sort of top-down heavy, human-centric, to a digital AI-centric, AI-native company. Please take a look at this. This is about your survival. It's about your thriving. It's happening. and you're either on the evolutionary tree or you're going extinct. It's that simple. All right, let's jump in. Everybody, welcome to Moonshot, a special episode with my dear brother from another mother, Salim Ismail. Salim, you're finally here. You're in our Moonshot studio. You made it.

First time. It looks awesome.

Yeah.

And I love the, I love everything about it. Yeah.

And today is a special day. It's your birthday.

It is my birthday.

Yes. And so for those who don't know, Salim has just turned 16. It's his sweet 16 birthday. And, digits around you a little more accurate. Okay, that's right, the dyslexia in me hits it. Um, so we're going to talk about something that we've been teasing on the Moonshots podcast for a while, something that I'm excited about, which you call the organizational singularity. Yeah. Uh, and I want to make sure that everyone listening realizes this is something across the board useful for everyone, right? It's not if you're the CEO of a large Fortune 500 company, though it's useful if you are. If you're an entrepreneur, if you're in a small company, if you're a parent trying to advise your kid where to go work.

Exactly.

Yeah.

Look, when we wrote the Exponential Organizations book, we didn't realize how prescient it would be. It turned out over 10, 12 years, we were dead on. And so we're kind of saying, okay, now that we see agentic AI and the future of intelligence, what does the organization look like? And so we have taken a crack at that with the help of my entire community all pitched in for this. So we think we have a pretty interesting viewpoint perspective on that.

And you've been saying for a bit now that AI has killed the modern company.

Yes.

The Fortune 500s out there. But I don't think they've gotten the memo yet.

They don't because there's a drag that goes effect, right? When the comet hit, the dinosaurs didn't go overnight. It took a few generations for them to die out and figure out what the hell's going on. So this is the same type of model. Yeah.

All right. Well, let's, let's dive in. And I want to make sure that folks get where things are going to go? And again, how do you surf on top of this massive change that's coming?

I think the key part of this is what do you do once you understand that everything has changed? So let me go through what has changed, right? So we have for 100 years run organizations on a particular theory set coined by Ronald Coase in 1937. He wrote a paper called The Nature of the Firm, and he theorized in this economic paper that Big companies will get bigger because transaction costs and coordination costs inside a company are cheaper than outside. Because you have everybody on payroll, you can order them around, and therefore you can get better work done inside than outside. And he actually won the Nobel Prize for this paper. And for 80 years, we've gone through that. And if you go through a couple of slides here, I'll just show you. We've seen all these deep thinkers coasted this. Simon talked about where the organizational boundary set. Clay Christensen came along and said innovator's dilemma. As you get bigger, smaller companies can deliver cheaper products. Then Stanley McChrystal talked about how do you get coordination at scale without losing the emotional connection to the organization? How do you extend past that? EXO 1.0 used community and crowd and AI to pull co sideways—

To sort of extend our reach and abilities, yeah.

—to extend, right? Think about XPRIZE and how you're able to coordinate external teams to do things. Think about the idea that for Uber, the mission-critical business function, which is to match driver and passenger, does not happen inside the organization. Happens out in the wild. And when you can enable that with technology, you can scale, right? So we found ways of extending CROS's Law. And then Jack Dorsey did what he did with Block and with Roelof Botha with his book. And we are now extending all that. We basically come to the conclusion is that the whole thing breaks in the face of agentic AI. CROS's Law no longer applies. Why? Because if you have to build a website inside a company, you have to go through layers of meetings and approvals. Branding has to look at it. The privacy guys guys have to look at it. The IT guys will tell you, you know, they can't be done. Whereas today you can step outside the company, use Vercel at home for 5 minutes and get it done for free.

And have it know your brand guidelines, have it know your design taste. Yeah, that's right. And have it actually spin up a dozen different versions and have them try in the market.

Yeah. And this is a fantastic tweet that I've quoted, which I've forgotten the name of the fellow just now, but he said, building the feature is cheaper than having the meeting about the feature. So true. And that's like such a great way of framing it because that means that coordination, the act of coordination is more expensive than just execution today, especially when, as AI is driving down the cost of execution.

Yeah. And I wanna make sure we get, as we discuss this, we understand what is the role of people in this, right?

Well, let's get to that. Yeah. 'Cause I wanna just first make the case that this breaks. Now you still need, you could ask the question, do we need an organization at all? And it turns out we do. And we have a term called the fiduciary a wedge where, okay, coordination costs and execution costs become low, which was primarily the reason for organizations the last 100 years. But you still need it for as a purpose container, a fiduciary, a legal container, a liability container, a legal container. So think SPVs for investments or just containers. They hold the legal and fiduciary liability. Essentially, companies become more and more like that, and there's a gap between human judgment and liability versus what the AI can do. And that gap we call the fiduciary wedge. So you still need an organizational structure and the legal entity.

Hey everybody, you may not know this, but I've got an incredible research team and every week myself, my research team study the meta trends that are impacting the world. Topics like computation, sensors, networks, AI, robotics, 3D printing, synthetic biology. And these meta trend reports I put out once a week enable you to see the future 10 years ahead of anybody else. If you'd like to get access to the Metatrends newsletter every week, go to diamandis.com/metatrends. That's diamandis.com/metatrends. And the question is ultimately what's inside that organizational container. That's right. Right, and there are going to be assets and IP and agents and some number of humans. That's right.

Yeah. And agents making API calls to God knows what and hacking into things and getting phone numbers and calling people up like the Alex Finn's AI that just called him up, right? So this kind of takes the EXO 3.0 book from the original book to the 2.0 book to now what we call the Organizational Singularity.

By the way, is this a book that you're putting out? It's a book that we're putting out. And is there a place people can go to learn more about this now?

Right now we have it at organizationalsingularity.com. Okay. So go to that website and you'll be able to sign up. But right now we're only releasing— well, let me jump to the surprise here. We're actually releasing the book as an AI. Okay. Because a book is a static thing. The minute I finish publish the book, it'll be out of date. Exactly. So it has to be an AI. So we're going to be launching a Claude skill because every 3 days something comes out that changes the game a bit. So we're keeping the book as a living document, which we tried to do with 2.0, right? You and I worked on, but the technology wasn't there yet. Now it is. Yeah. And so we're very, very excited about that. Okay. So there's a problem though today, which is that 80 plus percent of AI projects in companies are failing miserably. And they're failing miserably because existing companies are geared towards human to human to human workflows. All the approvals and bottlenecks, chains, etc., etc., are all human-centric, right? It's like I use the analogy of when we first created television, we took radio announcers and put them on TV.

Yes, I love that.

Right? And you didn't use the medium at all. So these projects are failing because you're moving AI into legacy organizations and automating the legacy human bottlenecks. Of course they're gonna fail. You need an AI-native environment to do this in. So we had to kind of step back and say, okay, the entire EXO model breaks, COS breaks, all the thinkers up to now, it all breaks. We have to rethink it from scratch. And so we did that work with my community. We did that and came up with a whole new—

And just to be clear about when you say something is breaking, ultimately I think what you mean is If you don't retool your organization in this fashion or don't restart your organization, you will be disrupted because someone doing it is going to just eat your lunch.

Yeah. So here's a question for every CEO and every C-suite member out there. Is there a line of your business, a high-margin line of your business, that two guys with open claw could replicate in 60 to 90 days? If it is, call us because you better get started fast because I guarantee you there's two guys out there with open claw disrupting Dropbox.

You and I have talked about this. Anybody who's got a juicy margin is open for attack.

It's open for season.

Yeah. And you might think you're protected by regulations. You might think you'd be protected by your brand.

There's a few moats. There's a few protective moats, and I'll get into that. But for now, it's a whole new world, and it's a whole new ballgame. And what we mean by the organizational singularity is instead of coordinating and organizing the company around hierarchy, you organize it around intelligence. And that's a very big shift. That's about as big a shift you could ask for. So we've come up with this architecture, okay, where you have the MTP, which you know well, the Massive Transformative Purpose from the original book. We love it. And this becomes not just a poster that you put up on a wall, this actually becomes a protocol. So MTP becomes an actual protocol and a guide for AI agents and human agents and whatever to act properly.

I mean, it's a cornerstone, it's a north star.

Yeah, but it's actually a protocol in this new world. What's the architecture of MTP? What's the boundary conditions around it? What are the feedback loops that tell you you're within the cone of the MTP or not stepping outside the cone? For example, in the early days of Uber, great MTP, everybody should have a private driver, but if you always ordered surge pricing, they would knew that and they would always charge you surge pricing even though you and I would be standing next to each other. I'm a cheapskate, I never order surge pricing and I would get the cheap price and you would not, right? And so that kind of somewhat pushing the boundaries on the ethics side is now guided in this whole MTP architecture. So that's the middle of it. And then we have DRIVE, which is the intelligence scaffolding and the engine around it, which I'll touch on. And then SHAPE, which is how does the organization—

By the way, DRIVE and SHAPE are acronyms for subcomponents. That's right. Those are acronyms.

I don't need to get into them all in detail, but you'll get the general idea around it. Okay, so then we have the next step is to then look at the intelligence stack in detail. Okay. And if you look at the diagram, you'll see this kind of architecture where we found 6 layers of what that core intelligence engine looks like. And the best analogy we have for this is Boyd's OODA loop. Right. In the military, they have observe, orient, decide, act. Right. And it's a core flywheel at the middle, which is also the core of the Solve Everything framing. Right. When you have that inner loop going, Right? Then whatever you put into that loop starts having a positive feedback loop on everything else. So we created the intelligence stack to act a bit like the OODA loop so that there's constant learning going on. But around it is a very, very important wrapper, which is govern and assure, which is the constraints and the oversight. Is it the harness? It's the harness and oversight to make sure that agents aren't going rogue. Yeah. Right? We saw, we've seen over the last few weeks agents going and doing crazy things, the railway agent that deleted all the volumes of rental car data, et cetera. So we need to make sure there's a very strong— So imagine the following, and I'll mention what I mean by that. So at the very heart of it is this intelligence stack with this very clear governance protocol. What do we mean by governance is trusted eval architecture, a searchable log. Every agent has to have a searchable log. Granular rollback. Can you go back to the previous version if you start going off? And a human review queue so that as human beings are always in the oversight checking things, okay? So this comes down to the role of what does a human being do when execution and coordination is done? Human beings rise up a level and they do dashboard oversight, they do monitoring, they do exception handling, they do problem solving, they do efficiency increases, right? So those are the activities that human beings will do. It's kind of like you go to Germany, nobody's working on the factory floors, but unemployment hasn't dropped because everybody's doing more work on problem solving and increase efficiency, design thinking, and other things. So we think the same thing models there. This govern and assure loop as part of this OODA loop, that— those two combined give you a very tight core engine that makes sure the whole thing doesn't fly off the rails. Okay, so that's the intelligence stack. Um, now when your agents talk to other agents, they need some clear mechanisms for how to do that.

And by the way, just to be clear here, as you're outlining the process, um, you've structured something that you can teach companies to implement. Absolutely. I mean, so let me, let me work through a live example.

Yeah. Okay, so you have these multiple layers, right? And let me just run through these layers again so people are aware. There's a purpose layer, there's a sensing layer, there's an interpretation layer, there's a decision layer, there's an orchestration layer, a learning layer, because you need that feedback.

And then Eric Schmidt told us, right, you know, rapid learning is the key to success, period, right?

So this is that wrapped up in a very tight set of layers. So imagine You're a retail company and a competitor suddenly announces same-day delivery. Yeah. Okay. So you have a set of sensing agents out there going, hey, this just happened, right? So the sensing agents bring that new information back to the other agents. The next is interpretation. So the interpretation layer then goes, okay, well, what does this mean? Does this— could this threaten our line of business? Could this threaten one line of business, multiple lines of business? Is it an existential threat? How big of a deal is this? and they interpret that data. The next layer is a decision layer to say, what should we do? Should we offer same-day things? Should we buy a startup that's doing same-day delivery? Should we ignore it because we don't think it's really going to work out? We think that it's a stupid idea. What do we actually— what's the decision?

I mean, as I think about this, normally this would be your strategic officer, your marketing officer, all of those coming together, having meetings and deciding what to do. That's right. And you're saying all of this could be turned over to agents.

Layers of agents can handle all of this now, right? So now you have a layer—

But you have a feedback.

You have a feedback at each of these layers as a human being going at the interpretation layer. Do I think this is okay? Yeah, hit button, let it go to the next level. So it's an approval process. Approval process. And also senior people looking over going, there could be looking at agents looking at 6 different strategic options, right? Whereas in a very manual iteration that may take months to evaluate the competitive alternative. Now you're doing it in hours and days, right? So that's the impedance mismatch there.

And by the way, what we what we've seen historically is the impedance mismatch between a Fortune 500 company and a startup where the Fortune 500 company, to use them as an example, has so much to lose if they screw up that they're paralyzed in making decisions. And the startup is like, screw it, let's just try everything. Right? Yeah, exactly. Exactly. And this is just taking it one step further.

Robert Goldberg puts it, in a big company, 1 of 20 people can say no to an idea, kills it. Yeah. Whereas the startup can go to 1 of 20 investors and 1 says yes and they're off to the races. Yeah. So how do you balance that out? Right? Okay. So now you have these layers of agents. Purpose agents, sensing agents, interpretation agents, deciding agents. Next level is an orchestration agent. So let's say the decision agent comes back and says, we should buy a startup that's doing this, right? And then now the orchestration is saying, okay, we gotta go set up a set of functions to go find a bunch of startups, analyze whether which ones are ready for M&A, tell the corporate dev team, get the lawyers ready, et cetera.

And then there's a— Get the legal agents ready.

Get the legal agents ready. And finally, a learning loop. Where did we buy another company before and did it work out or not, right? And how did that work out? And all wrapped up in this governance thing. So that's the kind of an example of how you would flow through these. And at the core is this engine of recursive learning. Another way to think about the organizational singularity is when you can have recursive self-improvement at the workflow level. I love that. Okay, so if you took, say, invoice processing And you have right now all these human checkpoints of, yes, did the goods arrive? Should we— who's— does the supplier exist in our systems? Is there a legal contract? There's a human checking all those things. Maybe you have an ERP system that's automated one or two of these layers, but now you can have the whole thing done. And then an agent can say, well, how do I make this better every loop? How do I make this better at every loop? And constantly improves. Once you get to that level, you can basically set back And you're off to the races then because everything's just self-improve at that level. Okay. So that's the very heart of the whole thing with this layer. Now we also recognize that agents are going to be doing very crazy things. So how do you navigate that? And we've come up with a framing which we found in smart contracts in Web3, plus some old web architecture that says every agent should get a passport with a little metadata on what that agent is allowed to do or not allowed to do. right? So, for example, policy-controlled APIs, okay? Data object metadata that goes with it to say what is that data allowed to be exposed to or not be exposed to. A liability framework is making sure agents aren't doing illegal things because your lawyers will go bananas at agents going off outside your organization doing things because you've no idea what they're doing. So, every agent gets almost like a little passport on what they're allowed to do. It's constraints. Constraints and oversight. And now you have other agents in the governance sure loop over watching these things. The minute something goes off the rails, human gets notified, agent gets stopped, rolled back, checked again, and you can do again. And the reason this works is, you know, in the quantum world, you need like 1,000 physical qubits to hit a logical qubit, right?

Yeah.

Well, agents are relatively free, so you can have a lot of agents doing things and a lot of agents overseeing them. So the overall cost, you still get the benefits of that overall stack. Okay. And here's the question I'll come back to for every CEO out there and every business leader out there. Could a 2 or 3 person team with Hermes or OpenClaw disrupt major lines of business in your business? Yeah. If that's the case, now there's a few moats that you could develop. Okay. One is proprietary data, right? That's a clear moat. If you have key data that can't be replicated elsewhere. Number 2, regulatory, which we see in healthcare, etc. Regulatory capture more than anything else.

And that moat can be eroded over time.

Can be. All of these can be, but they'll serve as moats for the time being. But the biggest moat is an intelligence moat where if you can learn faster than everybody else, nobody's going to catch you, right? This is why Claude and ChatGPT, their learning loops are further ahead than, say, Manus or Grok or whatever. And we're seeing how quickly they're moving ahead. Once you hit that, it's very hard to catch up. And a fourth one would be really deeply committed to purpose and not wavering from that, because nothing shakes you—

The relationship with the end customer and developing the depth there. Yeah.

Dedicated customer relationship, which feeds into proprietary data. And brand. Brand, very critical. Brand sits with MTP. That emotional connection with the end user, If you have a strong brand, you should use all of these new agents and capabilities to reinforce that. Means you— it's hard to shake you out of that position.

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Those are some of the problems. Now, let's talk a little bit about what happens to the company and the classic organization, which has C-suite, middle management, cold face doing things. What happens to them? C-suite, I already gave the example.

Just to be clear, what happens to them if you— In this new world. If you bring in, if you restructure. Yeah. Have you given a name to the restructured organization?

Existen EXO 3.0 is the best name I have. If anybody has a better name, would love to hear it.

So if you're going from a classic organization or an EXO 2.0 to an EXO 3.0, what happens to your organizational structure?

That's right. Okay. Okay.

And then it's a whole new world, right? Smack me.

Okay. So C-suite becomes basically accountability holders, dashboard oversight, evaluators and validators rather than doers. You're not going to be doing a strategic evaluation. Agents will do that. You basically hit, "Yes, I like the evaluation," or not.

So basically you're using your wisdom and experience to decide whether the agent's action is in line. That's right.

Now this opens up all other questions, which we'll get to in a second. So C-level is guiding and holding accountability and watching what the agents are doing and then deciding, "Yes, no, do this, do that, whatever." Middle management is where the biggest change happens because middle management in existing companies is almost completely doing coordination. Okay, they take data from the cold phase, they repackage it for proper absorption by the, by the C-suite, right? That function drops about 90%. Okay, now then you need to lift up the human beings there and have them doing that exception handling, problem solving, etc., of which there's a ton. We just don't do it because most people don't have time. All right, now you'll have more time to do those things. Okay, um, The bottom 20% are doing much more enabled work because they're got— they're agents doing almost everything. And they're also doing oversight and watching.

Okay? Now we've talked about on Moonshots a number of times the idea that we're going to see a reduction in the size of firms to— from 100% down to 20%, 80% reduction. You still—

Our calculation is you'll be able to run an average company with about 20 or 25% of the workforce that you had before. For it. Okay, now you can go down the negative side, their media side, and go, oh my God, 75% unemployment. Or our moonshot's view would be we'll have 5 times more companies being created and there'll be that much—

the blossoming of entrepreneurship.

That's right. And we're seeing the Cambrian explosion of startups already, right? We're seeing actually hiring go up right now for entry-level jobs, which is really pretty interesting to spot. Okay, so those are the 3 things that happen to the 3 layers of the business. Okay. Now the question then becomes, how do you turn into one of these?

And by the way, where do you see the 80% being lost? Uh, at all of the levels? No, mostly the middle.

No, I think, um, 60% would be coming from the middle management. Yeah. Uh, 20% from the bottom, 20% from the top. Okay. And that will— that's the— that's the full— the compression is there, but mostly for middle management because You don't need to be gathering and aggregating sales reports. There's no way you're going to outperform an agent doing that. There's much more work that needs to be done in the company that you could do more valuably. Now, an interesting question comes up in this, which is the alignment problem, which is how do you have— if you don't have entry-level people doing the work and sweating it out, putting spreadsheets together and doing the grunt, what happens to your organizational and institutional. Yeah, that's right. And where do you get senior management eventually when lower management and entry level are not there? And what we think will need to happen is very active and aggressive apprenticeship programs. So if you're suddenly a middle manager that gets displaced, well, go partner with the chief CFO and work on looking at alternatives. And you'll A, learn a ton more. You'll B, have a lot more fun.

Back to the apprentice.

Really back to the apprentice, the guild kind of models. We think that That'll start the thing. Okay, so you have this new entity, this intelligence core, new shape for this organization, C-suite, middle management, and coalface. The next question really becomes, how do you get there? Right? Right. And this is the part where we have deep expertise because when we built the EXO model, we decided one of the key things we had to solve was breaking that immune system problem. Right? So, and if you try anything disruptive in a big company, the antibodies attack you. So we just did—

just to clarify this, this— when we say how do you get there, how do you go from a classic organization to retooling yourself as an EXO level 3 here?

You're a $100 million trucking company. Yeah, right. And now, and, uh, two guys can lease trucks, have an AI-centric organization, and compete the hell out of you. What are you going to do? Okay, uh, now this is the question of what do you do now And how do you turn into this new model? Okay? And what you do, and I cannot stress this enough with the experience we've had, is you cannot change and fix and transform the existing company. Okay? It goes all the way back to Buckminster Fuller who said, "You can't fix an existing system. You have to build a new system at the edge and let that become the new gravity center." John Hagel and John Seely Brown identified this as disruptive things happen at the edge. The poster child here is Nestlé created Nespresso in 1976. For 10 years, they tried to run it as a line of business inside the mothership. Doesn't fit. Different brand, different supply chain, different delivery, different customer proposition. Finally, they're like, "Put it over there. There's too much friction inside the company." They give it to a different building and boom.

We wrote about this. The classic was Steve Jobs starting the Mac. That's right. Or IBM creating their PC.

You have to take your—

Yeah. Or Lockheed with their Skunk Works. That's right.

Apple would take a small team, put them at the edge. Edge, keep them secret, and say, "Go disrupt a different industry." So Nestlé is a poster child of this. Nespresso is now one of their highest-performing lines of business, and every hotel room in the world has one. So we know this. We've been talking about this for a long time with EXO. You do disruptive things on the edge. And we've been working with Procter Gamble to Siemens Energy to Black Decker to HP, helping them do disruptive edge innovation.

It's the human ego, in the final result, protecting themselves from disruption.

Yes. So you have to do that different stuff at the edge. There's a reason why Amazon Web Services wasn't done in the core service. It just doesn't fit. Yeah. Right? Okay. So you have to take this methodology and this approach, just believe it. Just trust me. You could try it the other way.

And by the way, I tried in a— I'm not going to say which of my companies— a 100-person organization, right? Where I'm very much, you know, I'm a compelling individual. And I still could not get it. And so I literally had to start it as a separate organization. You do. Yeah. And I've done that now multiple times.

Yes. And you maybe take it to an extreme because every time something happens, you spin off another company, which is the Richard Branson approach. Like every time he got to 150 people, they'd spin off another company to break through the Dunbar number problem. Right. But all I'm going to ask the viewers and listeners of this this is to— you can go research this to death, but if you do anything other than do disruptive things at the edge, pointing into adjacent spaces in a different way, you will fail. I've seen the innovation process in detail, probably in 250 out of the Fortune 500, and I've never, ever, ever, ever seen any other method work than this.

I want to say one other thing. If you're going to try and do this on the edge, ultimately the edge organization needs to report into the CEO. Yes. At the very top. Cannot work out. And there's one other thing. The board of directors has to— Needs to provide the CEO full support. Yes. You know, if you're disrupting your own organization and you don't have the board support, you're screwed.

Yes. So let me talk through how you do this. Yeah. You do not touch the existing organization.

It's your revenue engine. Yeah, don't touch the cash flow.

Cash cow. And if you start— what's happening right now is people are trying to stick AI, inject it into places, and it's just not working, right? So what you do is at the edge of your organization, you create an AI-native digital twin. Okay. And then what you do once you set that up, separate entity, take 3 to 5 of your crazy young people. Yes. Okay. Partner with a company that's a builder, not a consulting company, but a builder. So you get what's called forward deployed engineers, which is the latest buzzword in software these days. And what you do is you pick a workflow. You've got all these workflows in the legacy organization. Is that a product or a service? Well, call it invoice processing as a workflow, right? That's a very standardized cutting cookie cutter workflow that you know exactly how it works. And you rebuild it in this new entity. Okay, you don't move it, you copy it. So now you take the steps in this, and we've got a whole methodology for how to task breakdown and score each task, etc. That's built into the methodology of the whole approach. You replicate it in this new system, okay? You fork the data, right, so that you have the data to do it. And now you start running it here. Now you have a— you've de-risked it also, because if something goes horribly wrong, you're not risking the mothership. Cannot stress this long. So you run this, uh, in parallel until you hit that recursive self-improvement improvement loop. And once you see the improvement loops here are way faster than you can do it here, then you know you're in thing. Even then, give it another few weeks.

And you got quality check against the original.

Quality check, you've got everything. And then you slowly deprecate the old and you take next workflow. Maybe it's receipt confirmation and you move that over. Maybe the next one is demand forecasting and you move that one over. And little by little, you grow this thing at the edge. A full digital twin. And full digital twin.

That's recursive self-improvement.

And the next thing you know, you've got your AI-native digital twin fully running. Our current estimates are that once you have that digital twin running properly, your performance improvement should be between 100x or higher per year. Just 100x better. Like if it's processing one invoice now, it should process 100 invoices next, right? If you were taking 100 days to do something, it should take one day to do something.

What's the human scaffolding around the digital twin?

Well, that's the whole thing. That's where you're building up this thing and the human beings in this new model. You have human beings there, but they're doing— there's more or less of them and they're doing more oversight, exception handling, problem solving, et cetera. You're literally building your AI native digital twin at the edge. Yeah. Okay.

And what gets me excited as well is the idea that once you've done that, you can start to create adjacent companies.

You can spin off anything. And you can start to create—

I mean, as if you're a great entrepreneurial team, Yes. And you're limited by, I mean, a lot of my companies have amazing teams of people doing things and, you know, I don't want to push them any further because, you know, quality of life, they'll break, they'll get stressed out. But if all of a sudden you can get that automatic digital twin running, that team can now start building other products and services. Exactly. Yeah.

You can do that. Now let me give you a real example. Okay. Uh, there's two cases, sectors, by the way, that have gone through this full loop. Loop. Okay, one is the contact centers. We used to do human business processing outsourcing. Yes, we had call centers doing stuff. Then phase 2 of that automation was chatbot-assisted customer service, right? And now we have AI-native customer service. Klarna has done this.

I was just talking to the AIs on, on Starlink. Yeah, yeah, it's all Groq-driven.

It's all Groq-driven, right? I set up a, a new website for, in fact, this Organizational Singularity and I went on Cloudflare and the AI told me exactly how to run the exception rules and domain forwarding. I was like, this is incredible. And so just the automation of what's going to be possible is going to be magical to people. Anyone using AI at a refined level today sees how much fun it is, right? Compared to what it was like before. So we've seen this with—

To the point that we're working 7-day weeks. We're killing ourselves.

But you know, everybody's having so much fun now. It doesn't feel like work. Work. No, it played because we were getting so much done. I mean, it took 3 years of hell to write the first book, right? It took us 2 and a half years of hell to write the second book, mostly because we had to rewrite it because we had to deal with me. No, no, no, no, no, because we had to rewrite it after because generative AI came out near towards the end. But this third book was 3 months. Yeah, right. And because there was so much, every, every contributor could use an AI, add more data to it, more help to it, add their methodology to it, and then boom, you're off to the races. So the second domain where this has fully happened, by the way, is marketing and content generation, right? We used to have— it was agency heavy, then it was AI assisted, and now it's AI native, right? And so we can see certain verticals hitting this spot in a particular way. So let me go into the rewriting methodology. We call this methodology Rewrite. Okay. And I want to go into a little bit of detail so people understand the specific steps. The apps that are involved in this. So you have a workflow like invoice processing and you're going to start moving workflow over. Before you do any of that, you have to do a backcasting exercise. Okay. What's that mean? Backcasting is a methodology in future studies and forecasting where you pick what the vision looks like. Say Elon wants to get to Mars. You could say, okay, I want to get to Mars in 7 years. In order to get to Mars in 7 years, where do I have to be in 5 years? Where do I have to be in 3 years? And now you have your roadmap. If you start from the starting point and go, I want to get to Mars, you've no idea what you're doing, how you're going to get there, et cetera. So backcasting has turned into a very powerful methodology. So step one is take your company. So let's say it's that trucking company or retail company that I used earlier and say, okay, in this future world, what does that company look like fulfilling its MTP and its architecture in an AI-native centric way?

And you paint that picture. By the way, that's one of the hardest things for people to do, to let go of how they've done it. Yes. And by the way, it's also one of the easiest things to do in conversation with a large language model. Beautiful. Right?

So take your C-suite, go do that backcasting exercise. So that's phase 1. And we have people that can help do— people do that. Okay? Step 2, you score your company. So we've got a whole bunch of metrics on which we want to score the existing organization. For example, I'll just give you 2 of them. One is, what is the organizational drag inside your organization? Right now, if you try and get something done, does it have to go through like 5 or 6 different decision loops and approvals before you get it done? Or can they, like NVIDIA, they go straight to the founder and go, can I do this? And he says yes or no, right? Or an AI tells you, yes, you can do it or not do it, et cetera. So what's the organizational drag, 1 to 10, right? A second metric would be where is AI as a first-class citizen in your company right now? Love that. If it's a tool injected by IT, you're on the low end of the score. If you've got a chief AI officer and you're building AI-native capability already, your score is much higher on that 1 to 10 score. So we've got 7 dimensions. We ask you those 7 questions, you score yourself. We'll have this on the website for people to take for free, right? Evaluate yourself. It's a 1 to a 7 thing. The next step is you take, uh, the most prescriptive workflows you have in your organization and start mapping them and documenting so you have clear knowledge. A big problem, by the way, is going to be what's called tacit knowledge. Footage, right? There may be— like, let's say you're doing video production, okay? There's a bunch of steps you're doing as a video producer that may not be obvious from the outside. They're not documented anywhere. And if you lose that person, an AI can't do them right away, right? It's the unspoken—

and by the way, there's a whole, uh, process right now by which companies are basically shadowing you with an agent.

They are trying, they're trying to shadow. Yeah, yeah. But it turns out if you're a Gen Z worker, 44% of Gen Z workers are sabotaging the AI and giving it information so it can't take their job later. Wow. It's at that level of immune system response, right?

By the way, that is a perfect example of the immune system.

That's the immune system. You're trying to do something, but the culture is killing you trying to get that done, right? By the way, I'm going to just reiterate, we've created a 10-week process that we found a way of hacking, breaking the immune system, hacking culture at scale. We've done it 100 times for big companies.

We can show you how to do it. I played in a little bit of that, and I love it.

Okay, next step is, is cut the organizational drag. Start stripping out approval levels in your company so that you actually strip things down until you can break it. And what would that look like? Okay, next step is build— start building that digital twin and migrating workflows over one by one. And the final one is you rewire, you rewire your systems more and more so that everything is going to that rather than to this. Let me take one more crack at visualizing this. Today, this is how most companies operate. They have their cloud provider or their networking or their capability. Then they have a set of ERP systems, Oracle Financials, SAP, whatever. And all the data sits inside those systems, right? And those companies don't want you to have that data easily. So it's wired to wired in. Then you have an application layer and people are trying to layer AI on the top, hacking against this horrible architecture. That we've had for 50 years and it can't be easily unwound. Okay. Picture the new architecture. New architectures you've got are connectivity and cloud provider, a data lake that has all your data accessible in one spot with proper approval levels attached to each data object. Right. Then you have your application layer that is custom built for you because AI can do that and workflows, etc. Then your AI, then your agents. On top of that. So this is a wholly different stack and architecture that you own, that you own completely, right? And this is why the SaaS providers are so freaked out, because that model does not— is not compatible with this model, right? So right now they're trying their best to keep their place because they're wired into the limbic system of the legacy organization. But if you build this proper stack, you have full agency and control at near at a much cheaper cost than you could do before.

And the speed is infinitely— And the speed.

Ask anybody who's tried to implement an ERP system how much hell they had trying to do it. And then you end up trying to map the organizational flow to the ERP system. Versus the other way around. Versus the other way around. Now you can have software built that way. So this, we've built a whole methodology for this. Last couple of points around this is we think this overall transition is going to take about 5 to 7 years. To do this full transition.

Wait, wait, let me understand it. So not for a single company to do it, for all companies to get there?

For the majority.

For the surviving companies to get there.

For a surviving majority of companies over a 5 to 7 year period, you're either dead or you've transitioned to this.

And this maps as well, by the way, in the conversation we've had about the turbulent period of time.

That's right. And we call this, we actually call this the turbulent transition. Yeah, yeah. For exactly that.

I've said it's 2 to 8 years. We have to, carefully architect societally how we get through this 2-day year period.

That's right. And I'm just talking about companies, forget anything else.

But it's the underlying reason. That's right.

So in our opinion, you should be able to run a company between 10% to 25% of the people that you have today. You just dropped that to 2%. If you're regulatory-centric, right? Physical work, like you're building a data center type thing, then it's less. If you're a marketing company, then you're going to be down to 10% human beings, right? But a physical company, even then it's only 25%, okay? So for example, we were doing work with Fermi America and we estimated that we should be able to run a power plant instead of with 800 people with about 80 people. That's a full 10% back up there. It should be A 1 to a 20+ manager to what Jack Dorsey called HIIC, High Impact Individual Contributor, should be 1 manager per 20 of those instead of 1 to 5 or 1 to 3 that it is today.

Jack took it to an extreme. He did. He wanted to have just a CEO and everybody connects to him.

He did. But what that means is he's using AI to do everything. Yes. Because there's no way the CEO can keep that many people connected to that many people anyway, right? and this is already happening. Take Cognition Labs. Their ARR grew 73 times when they implemented this full system, when they went fully AI native. This is already happening. This is not some pie-in-the-sky guess. We're taking early signals. And over the last few months, as we've been watching the market evolve, every single data point we've gathered is pointing exactly in this trajectory that we're pointing at. So this is actually a race.

This is, you know, if you're a company in an industry and someone else runs this this process and has a recursive improving digital twin and you don't, you're cooked. You're cooked. That's right.

So if you're Unilever and Procter Gamble is taking all their stuff and automating it, you will not outcompete them.

I hope they're listening. Right? Or the other way around.

Right? Whoever, whichever way it is. Okay. So let me talk about what survives and what does not survive.

And by the way, just to hit it, a friend of the pod, Elon, has talked about increasing the GDP we're going to see triple-digit growth. This just adds rocket fuel. It's insane. We're going to see insane levels of growth.

You'll see classic companies that are delivering 100x compared to what was being done before. Or doing 100x cheaper.

In terms of profitability, right? Yeah, that's right. Revenue scale and profit goes through the roof.

Yeah. Now, profitability will be limited. Profit margin, other companies are gonna go, well, look at that profit margin, I'm gonna send my AI agents to do that.

Which is why things demonetize and why we end up heading towards universal high income, because the cost of everything starts dropping. That's right.

Yes, then we can get into the whole UBI, UHI, universal basic services stuff, etc.

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Sure. Okay, so what survives is what the new entity looks like is the MTP encoded as protocol in the company. Okay, number 2, the accountability, accountability shell, legal entity, fiduciary holder, liability container, etc. Proprietary intelligence in that stack is very, very critical. Coordination protocols, okay, become very killer. Curatorial judgment, okay, when execution is nearly free, judgment and taste become really important in the future.

Yeah, we've talked about that. Super important. Super important. Okay, so that—

those are the things that will survive and thrive in this new world. What does not survive? Okay, number one, the org chart the way we built it. Yeah, David Rose was famous for saying meaning whatever the org structure that got you successful in the 20th century will have you fail in the 21st century. Turns out he was right. It's just taken a little while. But iterated again. Iterated again. So the org chart in the traditional model completely fails. The 5-year plan dies completely. In fact, any static planning dies. Because if you do any strategic thinking of this is what the world's going to look like a year from now.

You have no concept. Constant learning loops.

We're in the middle of the singularity. You can't rely on any static plan. So the plan itself has to— and we even got it to this point. Think about the way—

I can imagine people listening to this are getting very anxious right now.

Yeah. When I've spoken about this at conferences, people are like, my head is like breaking, freaking out, dying here. But again, it looks like we found a very stable de-risk mechanism to get you from A to B, right? So there's some comfort level. And thank you for that, by the way.

I mean, I think that's so important.

Well, in terms of what the world needs, right? This is what you and I love doing, stuff that the world needs. It's very clear this is what the world needs, is a stable framework to get us from A to B, right? And if we can have a little less of the chaos as old systems fail and we can fail over more elegantly, then let us please, good and for goodness, do that. The 5-year plan, in fact, we actually took it to the point where right now, if you have an organization, that org structure changes only when you have a major event like an M&A transaction or you launch a new line of business or something.

Or you replace— Or you replace—

Your performance sucks, you replace the management team. That's right. That org structure does not change very much. In the new world, that org structure is dynamic and constantly changing, adapting to the current situation. It's like an amoeba. And that's the org structure. Forget it. The organization itself becomes a protocol. And that's a big kind of thing to get your head around that. Middle management is a coordination layer, gone. Quarterly reviews as a unit of decision-making, gone. Annual planning, gone. Inertia modes, customers don't switch because switching is annoying, gone. Wasting assets in the agent economy, gone. So there's a bunch of things we've kind of highlighted what happens first. And so we're kind of looking at this, and one guidance I would give to people, if your company is less than 50 people, you can brute force this and do this in the whole company because you've got a first-name basis with everybody. If your company is over 50, in your case it was 100, yeah, do not try and break the immune system, and do not, because you'll risk the existing company and you don't want to do that. Do this digital twin at the edge. Yes. Okay, so what we're doing right now is we're saying, okay, Hey, let's pick a few CEOs that want to go through this, okay? And we're going to score them in that rewrite score, and if they've got good—

So you've taken some through it. Yeah.

We started with about— we're right now at about 4 companies. We're kind of going through them with that. We'll probably do 10 at a time. So if you're interested and you want to go through this, let us know.

So let's be very specific about that, because I can imagine a lot of our viewers want this, and we have a lot of large companies and entrepreneurial companies and so forth. So if someone does want to be one of the first 10 going through this, who do they email? Where do they go?

Two paths would be email kevin@openexo.com. Kevin Allen is our head of community and navigates all this, and his AI will help make sure—

So K-E-V-I-N openexo.com. Right.

Or go to our website, organizationalsingularity.com, and you can actually fill out a form and say, I want to try this.

You're going to selectively choose who you work with.

Yeah, because let's say a company has horrible organizational drag. We're going to say, go fix the organizational drag first because we're going to spend all your time on that and not doing what— and we think it's a 90-day process to start this process and get a few workflows working in this new way. And if that— once we get you going, then you should be off to the races and you can build on yourself. We'll take batches. The first batch will be 10 or 20 probably. And then we may do more. We'll see how that goes. My entire community is being retrained for this. So my EXO community is now 50,000 people in 150 countries. So we're retraining them to be able to navigate this. We're all going to go through this journey together. I'll be personally involved in the first couple of batches, like I was personally involved in the first sprints, etc. We just heard— to make sure this—

we just heard Sheikh Mohammed say that he wants to run 50% of the Emirati government Yes. On this. Yes.

Do you see this working for governments as well? Completely. Think of any government, almost all the processes in a government are prescriptive, very well understood. The process for renewing a driver's license is extremely well understood. And frustrating. And frustrating. But now that friction can be removed in a really magical way. In fact, they did this. I mean, Minister Alolama, right? Yes. Said, "Ghassim, come and get a golden visa." The minister of AI.

You're going to be my poster child.

And they're processing Golden Visas in 5 hours, a resident visa in 5 hours. This is unheard of in that world. So they've already been down a path like this. They're taking it naturally to the whole other next level. But for governments and nonprofits, this completely applies, right? And there's a whole chapter we have in the book, which I won't talk about here, but go in the whole Solve Everything paper that you and Alex did, right? All of Alex's thinking on the inner loop. We've taken a crack at what does the— how do you organize domain after domain and create a domain collapse in more and more sectors? And how do you organize for that? So you can create an organizational design where you can pick a domain like healthcare or education and set up a structure that then has that inner loop start to move.

And I guess the other question is, if you're an entrepreneur thinking about starting a company, you have basically a platform here and playbook to start. That's right. Immediately.

Now you know, you can read this. In fact, what we're going to do is we're launching the book as an API, as an AI, right? So we're going to launch it as a Claude skill that you can just download. Like Claude just said, hey, we're going to have connectors to QuickBooks and everything else like that. We're going to download the entire contents of the EXO framework as a Claude skill because every 2, 3 days we're learning new things. We're going to build it in. So the skill itself is changing on on a real-time basis. It's not like you get certified in this from 5 years ago. You have to— the AI itself has to keep updated. So we're releasing the book as an AI, as a native AI.

Amazing. So I guess the question is, if you're ready for this and you're selected, that's great. If you're a company that's got too much— what'd you call it? Organizational friction?

Yes, organizational drag.

What do you do there?

Oh, come and see us because It'll show you, will tell you what to do. For example, if you've got a process that takes 10 steps, okay, brute force it and rethink that process so it takes 3 steps. Once it's taking 3 steps or less, then you're able to, you're ready to start thinking about moving over into the digital twin. You can also start setting up the legal framework for the digital twin, get board approval, right? There's a lot of scaffolding that has to take place for you to get to there. You may have legacy legal issues. For example, in Germany, workers' councils decide how many employees the bigger company is allowed to have or not have, which is not great from a flexibility point of view. But there's so much else you can do to start navigating this. In fact, one of our folks, Patrick Sandino, said, "Look, let's figure out a way in this process of retraining all of the people that were doing work and that might be at risk, retraining them to be in this new model." So that you have a kind of a whole transition plan for society built in. Which is then you solve the social contract along the way. And so we'll see how that works out.

I love this, Salim. You've been pregnant, giving birth to this for a while.

We've been talking about this. It's about 3 months of stuff. And then what I would do is I started writing the first version of the book and worked with Claude and ChatGPT. I had 3 instances. Of Gemini, ChatGPT, and Claude, each taking cracks at different things. Then I sent it out to the community and said, give me feedback. And so we got lessons learned. And then we went and talked to some of the cutting-edge AI practitioners. So what are you doing? What are you seeing at the cutting edge? And so it's been a, because the field is changing as fast as we are able to keep up with it. So just keeping up with this, like moonshots, right? We're spending a huge amount of time just keeping up with all the breakthroughs. Breakthroughs and headlines. We're having to spend, have a team dedicated to just keeping track of all the things happening so we can constantly tweak the methodology itself on how to do the rebuilding. Amazing.

Again, just to reiterate, if someone's interested, kevin@openexo.com.

Yep. Or go to, what's the website? organizationalsingularity.com.

Fantastic.

Which is my fault.

I think this is teaching boards and founders how to survive the next disruptions that are coming. The disruptions are coming.

The disruption is now, it's like as William Gibson said, the future is here, it's not evenly distributed, right? The organizational singularity is here, it's just not evenly distributed. If you're a 5-person startup, you're building an AI-native way anyway. Yeah, and we have a whole bunch of our community members that are doing that, and we've been learning from them, right? You see Alex Finn with all the OpenCloth stuff and now Hermes and what that's making possible. The big— the central thing to think about is all of our organizational structures in the past were organized around hierarchy and human-centric workflows, and now they need to be AI-native agentic workflow, and that's a totally different model. It needs to be architected around intelligence, not around hierarchy. Love it.

And I hope on the, uh, on our weekly, soon biweekly, and soon daily, uh, Moonshots, uh, daily. Oh my God, I know, I know, it's crazy.

But do you know how many flights I've had to change? Oh my God, the only flight I could take is right when Moonshots are— I have to now stay till the next day. I know.

And then how many airports, how many airports have you, have you broadcast from?

It's, it's been bad. It should get better by the By the way, it should—

it should get better. But I hope that we'll be able to track this and you can report on companies that have made this transition. That's right. And how this is updated. That's right. I just— for me, this is one of the most important learnings that you can deliver.

I'll give you one early thing we've seen. You know what's one of the biggest cadre of category of people that are approaching us is universities. They're like, we need to automate. We need to totally change. We can see the writing on the wall.

Yeah. Right. Massive disruption coming.

So they're coming. Going, "What do we do?" And we're like, "Great, let's start with you. Let's start automating the existing one and move you into this new model so that as you turn from trying to teach content to teaching execution, becoming entrepreneurial hubs." We talk about the fact that your engineering degree won't be that you studied engineering for 4 years. You built a bunch of stuff and it was interesting enough, you got credentialed. That will be the engineering degree. It'll be doing rather than learning. So that's such a big shift for the legacy. What I'm really impressed by is they're seeing it. I didn't think they would even see it, but they're actually seeing it and reaching out to us. Amazing.

Listen, buddy, thank you for sharing this. It was actually amazing to see, I mean, your brilliance and your passion about this.

It's half mine with the community because we're a group of a lot of people.

Yeah, okay, to all of the community, but still, it's your drive here. Your heart and your soul. This is it.

I mean, this is how you organize for the new world, right? If you're going to rebuild civilization, rewrite civilization, right, you have to kind of think about how the organizational design around this all works. And we have to rethink the whole thing.

So I'll say this, uh, into, into camera: if you're an employee at a company and you want your company to thrive, send this to your CEO, send this to your board. Uh, if you're the CEO, This is coming. There's no if, you know, about it. It's happening at an accelerating rate. And remember that disruption is not coming from your largest competitor. It's coming from the AI-native startup that sees how slow you are and how much profit you're currently making. And they're going to come and try and eat your lunch.

I think your t-shirt says it all. Abundance ho! Abundance is coming. Yes, brother. Thank you for this.

Oh, thank you. I love spending time and excited to go and celebrate your birthday tonight. We will do that. Yes. Fantastic. If you made it to the end of this episode, which you obviously did, I consider you a Moonshot Mate. Every week my Moonshot Mates and I spend a lot of energy and time to really deliver you the news that matters. If you're a subscriber, thank you. If you're not a subscriber yet, please consider subscribing so you get the news news as it comes out. I also want to invite you to join me on my weekly newsletter called Meta Trends. I have a research team. You may not know this, but we spend the entire week looking at the meta trends that are impacting your family, your company, your industry, your nation. And I put this into a 2-minute read every week. If you'd like to get access to the Meta Trends newsletter every week, go to diamandis.com/metatrends. That's diamandis.com/metatrends. Metatrends. Thank you again for joining us today. It's a blast for us to put this together every week.