--- asset_id: RAW-EL-Moonshots-OrgSingularity-EP258-Transcript-v01 version: v01 type: RAW status: Active owner: Victor Heredia sherpa_owner: Jay intellibank: IB-EL-EmpowerLabs subbank: PB-EL-Project-Bank/PB-HiOrg-HyperintelligentOrg fecha_creacion: 2026-06-13 proposito: Cleaned transcript of Moonshots EP258 "The New Era of Jobs — Organizational Singularity" (Peter Diamandis + Salim Ismail). De-noised, ASR-corrected, segmented. Source material for ANA-EL-OrgSingularity. fuente: "YouTube — Moonshots with Peter Diamandis, EP258. Primary transcript (auto-captions), cleaned. Speakers: Peter Diamandis (PD), Salim Ismail (SI)." tags: [RAW, transcript, ExO, OrganizationalSingularity, HIOrg, cleaned] --- # Cleaned Transcript — Moonshots EP258 ## "The New Era of Jobs: Organizational Singularity" · Peter Diamandis (PD) × Salim Ismail (SI) > **Cleaning notes:** Auto-caption errors corrected (Coase's law, C-suite, coalface, OODA loop, Vercel, Klarna, Grok, Manus, Roelof Botha, Cambrian, Buckminster Fuller, Dunbar number, Fermi America, Sheikh Mohammed, ChatGPT, Metatrends, OpenClaw/Open Claude). Filler, stutters, `[music]`/`[laughter]` markers removed. Two sponsor ad-reads (Fountain Life, Blitzy) removed and marked `[AD READ REMOVED]`. Speaker labels inferred. Timestamps preserved at section starts. --- ## Cold open **SI:** Is there a line of your business — a high-margin line — that two people with OpenClaw could replicate in 60 to 90 days? This is useful for everyone, across the board. When we wrote the *Exponential Organizations* book, we didn't realize how prescient it would be. Over 10–12 years, we were dead on. Now that we see agentic AI as the future of intelligence — what does the organization look like? We think we have an interesting perspective on that. If you don't retool your organization, or don't restart it, you'll be disrupted, because someone doing it is going to eat your lunch. The central thing: all our organizational structures in the past were organized around hierarchy. Now they need to be AI-native, agentic-workflow — a totally different model. It needs to be architected around intelligence, not around hierarchy. The next question becomes: how do you get there? That's a moonshot. **PD:** Ladies and gentlemen, I'm about to sit down with my dear brother Salim Ismail, my moonshot mate, to talk about the organizational singularity. This is a conversation critical for every company. We're in a period of rapid transition — agents, AI, AGI, ASI. It's going to restructure how every company and industry is run — not in five or ten years, in the next one to two years at most. Salim is going to lay out a process every company can follow to move from the old way — top-down, human-centric — to a digital, AI-native company. This is about your survival and your thriving. You're either on the evolutionary tree or you're going extinct. It's that simple. --- ## Intro **PD:** Welcome to Moonshots, a special episode with my dear brother Salim Ismail. You're finally in our Moonshot studio — first time. And today's special: it's your birthday. You've just turned... 16. (joke) For those who don't know — happy birthday. We've been teasing this for a while: the organizational singularity. I want everyone listening to realize this is useful for everyone — whether you're a Fortune 500 CEO, an entrepreneur, in a small company, or a parent advising your kid on where to work. **SI:** Exactly. When we wrote *Exponential Organizations*, we didn't realize how prescient it would be — over 10–12 years we were dead on. So now, with agentic AI as the future of intelligence, what does the organization look like? We've taken a crack at it, with my entire community pitching in. **PD:** And you've been saying AI has killed the modern company. **SI:** Yes. The Fortune 500s are still out there, but they haven't gotten the memo. There's a drag effect — when the comet hit, the dinosaurs didn't go overnight; it took a few generations. Same model here. --- ## What has changed — Coase's law breaks **SI:** The key part is: what do you do once you understand everything has changed? Let me go through what changed. For a hundred years we ran organizations on a theory coined by Ronald Coase in 1937 — his paper *The Nature of the Firm*. He theorized that big companies get bigger because transaction and coordination costs inside a company are cheaper than outside: everyone's on payroll, you can order them around, you get better work done inside. He won the Nobel Prize for it. For 80 years we operated on that. Deep thinkers built on it. Herbert Simon talked about where the organizational boundary sits. Clay Christensen — the innovator's dilemma: as you get bigger, smaller companies deliver cheaper products. Stanley McChrystal — how do you get coordination at scale without losing emotional connection. ExO 1.0 used community, crowd, and AI to pull capabilities sideways to extend our reach — think of how Uber's mission-critical function (matching driver and passenger) doesn't happen inside the organization; it happens out in the wild, and when you enable that with technology you can scale. So we found ways to extend Coase's law. Then Jack Dorsey did what he did with Block, and Roelof [Botha] with his work — we kept extending all that. The conclusion: the whole thing breaks in the face of agentic AI. Coase's law no longer applies. Why? If you have to build a website inside a company, you go through layers of meetings and approvals — branding, privacy, IT telling you it can't be done. Today you step outside, use Vercel from home for 5 minutes, get it done for free — and it knows your brand guidelines, your design taste, spins up a dozen versions, tests them in the market. **PD:** There's a great tweet — I forget the author — "Building the feature is cheaper than having the meeting about the feature." **SI:** Exactly. Active coordination is now more expensive than execution, especially as AI drives the cost of execution down. --- ## The Fiduciary Wedge — a gap between human judgment and what AI can do **PD:** So what's the role of people in this? **SI:** First, let me make the case that this breaks. You could ask: do we need an organization at all? It turns out we do. We have a term — the **fiduciary wedge**. Coordination and execution costs become low (the main reason for organizations over the last 100 years). But you still need the organization as a *purpose container*, a *fiduciary / legal / liability container* — think SPVs for investments. Companies become more and more like that. There's a gap between human judgment and liability versus what AI can do, and that gap is the fiduciary wedge. So you still need the organizational structure and the legal entity. **PD:** And the question is what's inside that container. **SI:** Right — assets, IP, agents, and some number of humans. And agents making API calls to who-knows-what, getting phone numbers, calling people up — like the AI that called Alex Finn. This takes the ExO book from 1.0 to the 2.0 (the "no" book) to what we now call the **organizational singularity**. --- ## The Organizational Singularity **PD:** Is this a book you're putting out? Where can people learn more? **SI:** Right now it's at organizationalsingularity.com — you can sign up. But here's the surprise: we're releasing the book *as an AI*. A book is static — the minute I publish it, it's out of date. So it has to be an AI. We're launching a Claude skill, because every three days something changes the game. We keep the book as a living document — we tried that with 2.0, but the technology wasn't there. Now it is. There's a problem today: 80%+ of AI projects in companies are failing, because existing companies are geared toward human-to-human workflows — approvals, bottleneck chains, all human-centric. It's like early TV, where we took radio announcers and put them on TV — we didn't use the medium. These projects fail because you're moving AI into legacy organizations and automating legacy human bottlenecks. You need an AI-native environment. So we stepped back: the entire ExO model breaks, Coase breaks, all the thinkers break. We had to rethink it from scratch — and we did, with my community. **PD:** To be clear: "breaking" means if you don't retool or restart your organization, you'll be disrupted, because someone doing it will eat your lunch. **SI:** Yes. Here's the question for every CEO and C-suite member: Is there a high-margin line in your business that two people with OpenClaw could replicate in 60 to 90 days? If so, call us, because you'd better start fast — there are two people out there with OpenClaw disrupting Dropbox right now. Anybody with a juicy margin is open season. You might think you're protected by regulation — there are a few protective moats, I'll get to them. But it's a whole new world. The organizational singularity means: instead of organizing the company around hierarchy, you organize it around intelligence. That's about as big a shift as you can ask for. --- ## ExO 3.0 Model — the destination architecture **SI:** So we built an architecture. You have the **MTP** (massive transformative purpose) from the original — but now it's not just a poster on a wall, it becomes a *protocol*: a guide for AI agents and human agents to act properly. **PD:** A cornerstone — a north star. **SI:** But an actual protocol. What's the architecture of the MTP? What are the boundary conditions, the feedback loops that tell you whether you're inside the cone of the MTP? Example: early Uber had a great MTP — everyone should have a private driver. But if you always ordered surge pricing, they'd learn that and always charge you surge — even standing next to someone who never orders surge and gets the cheap price. That's pushing the boundaries on ethics — now guided within the MTP architecture. Then we have **DRIVE** — the intelligence scaffolding and engine around it — and **SHAPE** — how the organization is shaped. (DRIVE and SHAPE are acronyms for subcomponents; I won't go into all of them.) Next, the **intelligence stack** in detail. We found six layers in the core intelligence engine. The best analogy is Boyd's **OODA loop** in the military — observe, orient, decide, act — a core flywheel. When you have that inner loop going, whatever you put into it starts a positive feedback loop on everything else. We created the intelligence stack to act like the OODA loop, so there's constant learning. But around it is a very important wrapper: **govern and assure** — the constraints and the harness, the oversight that makes sure agents don't go rogue. We've seen agents do crazy things — the agent that deleted volumes of rental-car data. So at the heart is the intelligence stack with a clear governance protocol: trusted eval architecture, a searchable log (every agent must have one), granular rollback (can you revert to the previous version?), and a human review queue so humans stay in oversight. So the role of the human: when execution and coordination are done, humans rise a level — dashboard oversight, monitoring, exception handling, problem solving, efficiency increases. Like Germany, where nobody's working the factory floors but unemployment hasn't dropped, because everyone's doing more problem-solving, design thinking, efficiency work. The govern-and-assure loop combined with the OODA loop gives you a tight core engine so the whole thing doesn't fly off the rails. --- ## A live example — the six layers **PD:** You've structured something you can teach companies to implement. **SI:** Let me work through a live example. The layers: a **purpose** layer, a **sensing** layer, an **interpretation** layer, a **decision** layer, an **orchestration** layer, and a **learning** layer — because, as Eric Schmidt told us, rapid learning is the key to success. Imagine you're a retail company and a competitor announces same-day delivery. **Sensing** agents detect it: "this just happened." **Interpretation**: what does it mean? Does it threaten one line of business, multiple, or is it existential? **Decision**: what should we do — offer same-day, buy a startup doing it, or ignore it because we think it won't work? **PD:** Normally your strategy officer, marketing officer — all coming together in meetings. You're saying agents handle all of this. **SI:** Layers of agents handle it — with a human "feel" at each layer. At interpretation, a human hits a button: "yes, this looks right, send it up." There's an approval process, and senior people looking at agents evaluating six different strategic options. In a manual iteration that might take months; now it's hours and days. That's the impedance mismatch. Historically the mismatch was between a Fortune 500 (so much to lose it's paralyzed) and a startup ("screw it, let's try everything"). As Robert Goldberg puts it: in a big company, one of 20 people can say no and kill an idea; a startup goes to 20 investors and one yes is enough. Back to the layers: **orchestration**. Say the decision agent says "buy a startup." Orchestration sets up functions: find startups, analyze which are M&A-ready, tell corporate dev, get the lawyers/legal agents ready. Then a **learning** loop: did we buy a company before, did it work out? All wrapped in governance. At the core is recursive learning. Another way to think about the organizational singularity: it's when you have **recursive self-improvement at the workflow level**. Take invoice processing — today there are human checkpoints (did goods arrive, does the supplier exist, is there a contract). Maybe an ERP automated one or two. Now the whole thing runs, and an agent asks every loop: "how do I make this better?" — and constantly improves. Once you reach that, you can sit back; everything self-improves. --- ## When agents talk to other agents — cross-firm architecture **SI:** Agents will do crazy things, so how do you navigate that? We found a framing in smart contracts (Web3) plus older web architecture: every agent gets a **passport** with metadata on what it's allowed to do. Policy-controlled APIs. Object metadata saying what data may be exposed. A **liability framework** so your agents aren't doing illegal things — your lawyers will go bananas if agents act outside the org and you don't know what they're doing. So every agent gets a passport: constraints and oversight. Other agents in the governor loop watch them. The moment something goes off the rails, a human is notified, the agent is stopped, rolled back, re-checked. This works because agents are relatively free. In the quantum world you need ~1,000 physical qubits for one logical qubit; similarly, you can have many agents doing things and many agents overseeing them, and the overall cost still nets out to the benefit of the stack. Question again for every CEO: could a two- or three-person team with Hermes or OpenClaw disrupt major lines of your business? If so, here are the moats: 1. **Proprietary data** — key data that can't be replicated elsewhere. 2. **Regulatory** — as in healthcare; regulatory capture, though it erodes over time. 3. **Intelligence moat** — the biggest: if you learn faster than everyone, nobody catches you. This is why Claude and GPT are ahead of Manus or Grok — their learning loops are further along. Once you hit that, it's very hard to catch up. 4. **Deep commitment to purpose** — the relationship with the end customer, depth there. 5. **Brand** — the emotional connection (it sits with MTP). A strong brand, reinforced with these new agent capabilities, is hard to shake. --- [AD READ REMOVED — Fountain Life / cancer screening] --- ## The middle-60% problem & what happens to each layer **PD:** What happens to a classic organization — C-suite, middle management, coalface — in this new world? **SI:** The **C-suite** becomes accountability holders, dashboard oversight, evaluators and validators rather than doers. You're not doing strategic evaluation; agents do that. You hit "yes, I like it" or not — using your wisdom and experience to decide whether the agent's action is in line. **Middle management** is where the biggest change happens — middle management in existing companies is almost entirely coordination: take data from the coalface, repackage it for the C-suite. That function drops ~90%. Then you lift those humans up to exception handling, problem solving — of which there's a ton we don't do because we don't have time. The **bottom 20%** do much more enabled work — their agents do almost everything, and they also do oversight. We've talked about firms shrinking from 100% to ~20% — an 80% reduction. Our calculation: you'll run an average company with about 20–25% of the workforce you had before. You can go negative ("75% unemployment") or take the Moonshots view: five times more companies created — a blossoming of entrepreneurship. We're already seeing a Cambrian explosion of startups, and entry-level hiring going up right now, which is interesting. **PD:** Where's the 80% lost — all levels or mostly the middle? **SI:** ~60% from middle management, 20% from the bottom, 20% from the top. The compression is mostly middle management — you won't outperform an agent aggregating sales reports. This raises the **alignment / apprenticeship problem**: if entry-level people aren't doing the grunt work, and you're not building institutional knowledge, where do future senior managers come from? We think you need very active, aggressive apprenticeship programs — a displaced middle manager partners with the CFO to work on alternatives, learns a ton more. Back to guild/apprentice models. --- ## How do you get there — the immune system problem **SI:** So you have this new intelligence core and new shape. The next question is how to get there — and this is where we have deep expertise, because building the ExO model meant solving the **immune system problem**: try anything disruptive in a big company and the antibodies attack you. **PD:** Going from a classic organization (or an ExO 2.0) to ExO 3.0 — say you're a $100M trucking company, and two people can lease trucks, run an AI-native org, and out-compete you. What do you do? **SI:** What you do — and I cannot stress this enough — is you **cannot** change, fix, or transform the existing company. It goes back to Buckminster Fuller: you can't fix an existing system; you build a new system at the edge and let it become the new gravity center. John Hagel and John Seely Brown identified that disruption happens at the edge. Poster child: Nestlé created Nespresso. For ~10 years they tried to run it as a line of business inside the mother ship — didn't fit (different brand, supply chain, delivery, customer proposition). Finally they put it in a different building — boom. We wrote about the classics: Steve Jobs and the Mac, IBM and the PC. Apple takes a small team, puts them at the edge, keeps them secret: go disrupt a different industry. Nespresso is now one of Nestlé's highest-performing lines, in every hotel room. We've been working with Procter & Gamble, Siemens Energy, Black & Decker, HP on disruptive edge innovation. It's the human ego protecting itself from disruption. There's a reason AWS wasn't done in the core service — it doesn't fit. You could try it the other way. I tried, in one of my companies — a 100-person org where I'm very much the compelling individual — and you and I still couldn't get it done. I literally had to start it as a separate organization. I've done that multiple times. Branson takes it to an extreme — every time he hits ~150 people he spins off another company, to break the Dunbar number problem. I'll just ask viewers: you can research this to death, but if you do anything other than disruptive things at the edge, pointing into adjacent spaces, you will fail. I've seen the innovation process in ~250 of the Fortune 500, and I've never seen any other method work. One more thing: the edge organization needs to report into the CEO at the very top — it cannot be buried. And the board of directors needs to give the CEO full support. If you're disrupting your own organization without board support, you're screwed. --- ## How you do it — the AI-native digital twin **SI:** You do **not** touch the existing organization — it's your revenue engine, your cash cow. What's happening now is people inject AI into places and it's not working. Instead, at the **edge**, you create an **AI-native digital twin**. Set up a separate entity. Take three to five of your crazy young people. Partner with a company that's a *builder*, not a consulting company — you get "forward-deployed engineers" (the latest buzzword). Then pick a workflow. Say invoice processing — a standardized, cookie-cutter workflow you understand exactly. You **rebuild** it in the new entity. You don't move it, you **copy** it. You take the steps (we have a methodology for task breakdown and scoring each task), replicate it in the new system, **fork the data** so you have what you need, and start running it there. You've also de-risked it — if something goes horribly wrong, you're not risking the mother ship. Run it in parallel until you hit the **recursive self-improvement loop**. Once the improvement loops here are way faster than the old system, you know you're there. Give it a few more weeks, quality-check against quality-check, then slowly deprecate the old and take the next workflow — receipt confirmation, then demand forecasting — and little by little you grow a full digital twin at the edge that's in recursive self-improvement. Our current estimate: once the twin is running properly, performance improvement should be 100x or higher per year — if it processes one invoice now, 100 next; if something took 100 days, it should take one. **PD:** What's the human scaffolding around the twin? **SI:** Human beings are there, but fewer, doing oversight, exception handling, problem solving. And once you've done it, you can spin off adjacent companies. A great entrepreneurial team that I don't want to burn out — once the automatic digital twin is running, that team can start building other products and services. Two sectors have gone through the full loop. **Contact centers**: human BPO → chatbot-assisted customer service → AI-native customer service (Klarna did this). I was just talking to the AIs on Starlink — all Grok-driven. I set up a website for organizational singularity on Cloudflare and the AI told me exactly how to run the exception rules for domain forwarding — incredible. The second domain that's fully flipped is **marketing and content generation**: agency-heavy → AI-assisted → AI-native. It's so much fun now — we're working seven-day weeks but everyone's having fun because we're getting so much done. The first book took three years of hell; the second, two and a half (we had to rewrite it when generative AI came out near the end). This third book was **three months** — because every contributor could use an AI to add data, help, methodology, and boom. --- ## The Rewrite methodology — six sequenced steps **SI:** We call this methodology **REWRITE**. You have a workflow like invoice processing and you're going to start moving it over. But first: **Step 1 — Backcasting.** Backcasting is a futures/forecasting method: you pick what the vision looks like and work backward. Elon wants Mars in seven years → where must I be in five, in three? Now you have a roadmap. Take your company — the trucking or retail company — and ask: in this future world, what does the company look like fulfilling its MTP in an AI-native way? Letting go of how they've always done it is one of the hardest things for people — and one of the easiest to do in conversation with a large language model. So take your C-suite and do the backcasting exercise. **Step 2 — Score your company.** We have metrics. Example 1: **organizational drag** — does getting something done require five or six decision loops and approvals, or can someone go straight to the founder (like Nvidia) and get a yes/no? Example 2: where is **AI as a first-class citizen** — if it's a tool injected by IT, you score low; if you have a Chief AI Officer building AI-native capability, you score high. Seven dimensions, scored 1–7 (we'll put it on the website for free). **Step 3 — Map and document your most prescriptive workflows** for clear knowledge. A big problem is **tacit knowledge** — e.g., a video producer doing undocumented steps; lose that person and AI can't do them right away. There's also a culture problem: companies are "shattering" workers with agents, and it turns out 44% of Gen Z workers are sabotaging the AI — giving it bad information so it can't take their job later. That's the immune system response. We've built a 10-week process to hack/break the immune system — hacking culture at scale — and we've done it ~100 times for big companies. **Step 4 — Cut organizational drag.** Strip out approval levels until you can almost break it. **Step 5 — Build the digital twin** and migrate workflows over one by one. **Step 6 — Rewire your systems** so everything increasingly flows to the new entity rather than the old. **SI (architecture visual):** Today most companies have a cloud/networking layer, then ERP systems (Oracle Financials, SAP) with data locked inside (those vendors don't want you to access it easily), then an application layer, with people trying to layer AI on top of a horrible 50-year-old architecture that can't be unwound. The new architecture: connectivity + cloud, a **data lake** with all your data accessible in one spot with proper approval levels attached to each data object, an application layer custom-built for you (because AI can do that) with workflows, then your AI, then your agents on top — a wholly different stack that you own completely. This is why SaaS providers are freaked out: that model isn't compatible. They're wired into the limbic system of the legacy org. Build the proper stack and you have full agency and control at much cheaper cost and far greater speed. (Ask anyone who's implemented an ERP how much hell it was — you map the org to the ERP instead of the other way around.) --- ## The 2036 AI-native firm — 100x more performant **SI:** We think the overall transition takes about **5 to 7 years** — not for a single company, but for the majority of surviving companies. Over that period you're either dead or you've transitioned. This maps to the "turbulent transition" we've discussed (I've said two to eight years) — we have to carefully architect society through it. You should be able to run a company with **10–25%** of today's people. Regulatory-centric or physical (e.g., building a data center) → less reduction (~25%); a marketing company → down to ~10% humans. Example: with Fermi America we estimated running a power plant with ~80 people instead of 800 — a 10% headcount. Manager-to-IC ratio should be 1:20+ (Jack Dorsey's "high-impact IC") instead of 1:5 or 1:3. Jack took it to the extreme — CEO with everyone connected to him — which only works because he's using AI to do everything. It's already happening. Cognition Labs' ARR grew **73x** when they went fully AI-native. This isn't pie-in-the-sky — every data point we've gathered over recent months points to this trajectory. It's a race: if a competitor runs this process and gets a recursively improving digital twin and you don't, you're cooked. If Procter & Gamble automates everything, Unilever won't keep up — or vice versa. **PD:** Elon has talked about triple-digit GDP growth — this adds rocket fuel. **SI:** We'll see a class of companies delivering 100x — in output and profitability. Profitability will be limited, though, because other companies will send their AI agents at those fat margins, which is why things demonetize, and why we head toward universal high income as the cost of everything drops. Then you get into UBI / universal high income / universal basic services. --- [AD READ REMOVED — Blitzy / autonomous software development] --- ## What survives and what doesn't **SI:** Before and after. **What survives** in the new entity: - The **MTP encoded as protocol**. - The **accountability shell** — legal entity, fiduciary holder, liability container. - **Proprietary intelligence** in the stack — critical. - **Coordination protocols** — become killer. - **Curatorial judgment** — when execution is nearly free, judgment and taste become really important. **What does not survive:** - The **org chart** as we built it. (David Rose: the org structure that made you successful in the 20th century will make you fail in the 21st.) - The **five-year plan** — and any static planning — because you have no concept of what the world looks like a year out. It's constant learning loops; we're in the middle of the singularity. The plan itself has to be dynamic. Today, org structure changes only on a major event (M&A, new line of business, replacing a failing management team). In the new world, the org structure is **dynamic and constantly adapting** — like an amoeba. The organization itself becomes a **protocol**. - **Middle management as a coordination layer** — gone. - **Quarterly reviews** as a unit of decision-making — gone. - **Annual planning** — gone. - **Inertia moats** (customers don't switch because switching is annoying) — gone. - **Wasting assets** — gone. Guidance: if your company is **under 50 people**, you can brute-force this across the whole company (you're on a first-name basis with everybody). **Over 50** (your case was 100) — do not try to break the immune system; you'll risk the existing company. Do the digital twin at the edge. We're picking a few CEOs to go through this — scoring them on the rewrite score. We're at about four companies now; we'll probably do 10 at a time. If interested, two paths: email **Kevin Allen** (head of community) at openexo.com, or go to organizationalsingularity.com and fill out a form. We'll selectively choose who we work with — if a company has horrible organizational drag, we'll say "fix that first." We think it's a ~90-day process to start and get a few workflows working the new way; then you're off to the races. My ExO community — now 50,000 people in 150 countries — is being retrained for this. I'll be personally involved in the first couple of batches. **PD:** Sheikh Mohammed said he wants to run 50% of the Emirati government on this. Does it work for governments? **SI:** Completely — almost all government processes are prescriptive and well understood (renewing a driver's license is well understood and frustrating). That friction can be removed magically. Minister Al Olama said "come get a golden visa, be my poster child" — they're processing golden visas in 5 hours, resident visas in 5 hours. Unheard of. For governments and nonprofits this completely applies — there's a whole chapter in the book. And it connects to the "solve everything" paper you and Alex [Wissner-Gross] did, and the inner-loop thinking — how you organize domain after domain to create **domain collapse** in more and more sectors. **PD:** And if you're an entrepreneur starting a company — you have a platform and a playbook to start immediately. **SI:** That's right. We're launching the book as an API / AI — a Claude skill you download, like the connectors to QuickBooks. We'll download the entire ExO framework as a Claude skill, and because we're learning new things every two or three days, the skill itself changes in real time. You don't get "certified from five years ago" — the AI keeps it updated. We're releasing the book as a native AI. If you're a company with too much organizational drag, come see us. Example: if a process takes 10 steps, brute-force it and rethink it to take three. Once it's three steps or fewer, you're ready to move it into the digital twin. You can also set up the legal framework, get board approval — there's scaffolding (e.g., in Germany, works councils decide headcount, which limits flexibility). One of our folks, Patrick Saint-Dizier, said: figure out how to retrain the at-risk people into the new model, so you have a whole transition plan for society — you solve the social contract along the way. --- ## Closing thoughts **PD:** Salim, you've been giving birth to this for a while — about three months of work. **SI:** I started writing the first version with Claude and ChatGPT — three instances of Gemini, plus Claude, each taking cracks at different things. Then I sent it to the community for feedback, gathered lessons learned, and talked to cutting-edge AI practitioners about what they're seeing. The field is changing as fast as we can keep up — so we have a team dedicated to tracking everything happening, so we can constantly tweak the methodology itself. **PD:** This is teaching boards and founders how to survive the disruptions coming. **SI:** The disruption is now. As William Gibson said, the future is here, it's just not evenly distributed. The organizational singularity is here — just not evenly distributed. If you're a five-person startup, you're building AI-native anyway, and we're learning from those community members — Alex Finn with all the OpenClaw stuff, now Hermes. The central thing: all our past organizational structures were organized around hierarchy and human-centric workflows; now they need to be AI-native agentic workflows — architected around intelligence, not hierarchy. One early signal: one of the biggest categories of people approaching us is **universities** — "we need to totally change, we can see the writing on the wall." We help them automate the existing institution and move into the new model, shifting from teaching content to teaching execution, becoming entrepreneurial hubs. Your engineering degree won't be "you studied engineering for four years" — it'll be "you built a bunch of stuff interesting enough to get credentialed." Doing rather than learning. **PD:** If you're an employee and you want your company to thrive, send this to your CEO and your board. If you're the CEO — this is coming, at an accelerating rate. And remember: disruption isn't 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 making, and comes to eat your lunch. Your t-shirt says it all: "Abundance." Abundance is coming. Thank you for this. --- *[Outro / newsletter promo removed.]*