We share this document with all our partners and with clients we choose. You don’t have to believe what we believe.
Most of what makes working together hard isn’t the work. It’s that people arrive with different experiences, different beliefs, and different ideas of what’s obvious, and nobody has said out loud where the company stands. Steve Simpson and Stef du Plessis call what grows in that silence the unwritten ground rules: the rules people infer when leaders leave a question unanswered. Unwritten rules are almost always worse than anything a leader would have chosen on purpose.
So we wrote ours down. This document is the moral, communication, and decision-making soul of Storyminers, on paper. It takes positions on questions that sometimes have nothing to do with business, because those are exactly the questions that stall progress when everyone is left guessing.
A worldview earns its keep in the hard moment. It lets people hold difficult conversations with confidence and calm. It lets anyone say what they won’t do and point to the reason why not.
There’s a newer reason too, and it may be the most practical one. Humans and AI agents now work side by side, and agents can only act on what has been made explicit. What used to live comfortably unspoken has to be written now — not as a poster on a wall, but as working instructions that people and agents can both follow. Others are seeing this as well; Salim Ismail describes purpose becoming a protocol instead of a poster.
First, for the teams and clients inside the organizations we work with. A worldview is an artifact we help leaders build for their own companies, using the Forge. This one is ours, and it’s the working example.
Second, for anyone who works with us: partners, vendors, contractors, collaborators.
What we want a reader to walk away with is solid ground. In a hard moment, you can say “here’s where we stand, and here’s why,” and have somewhere real to point.
The world order built after the Second World War gave people shared context and, for many, better lives. Its dark side was sameness — things becoming the same everywhere. What we think is happening now is a renaissance of creativity, in thinking and in art. As older institutions falter, more people are navigating their own way and creating their own context. That means an enormous number of new ideas, new trials, and, of course, many failures.
The work in front of all of us is to validate two journeys at once: the individual one every person is on, and the collective one, while keeping the agency of people, communities, and governments intact. There’s terrific value in feeling part of a tribe, a culture, or an organization. There’s equal value in being an individual with the freedom to choose. We refuse to rank the two. That refusal is the first taste of something that runs through this whole document: abundance, the working assumption that good ideas and good futures are things there can be more of, not a fixed pie to divide. Organizations built for scarcity can’t ride exponential curves — the thought leaders of exponential organizations have documented this for a decade.
The technology of the moment cuts both ways. Technology does whatever it’s aimed at. Pointed at capture — of attention, of data, of us — it isolates people and sorts them into tribes, because tribes are easier to sell to. The same capabilities pointed at connection find the collaborator you’d never meet and the community for the lonely. The aim doesn’t come from the tool. It comes from a worldview. That’s one more reason to write one down: the same technology that plagues us can help us find the connections we all need, and nobody gets through life alone.
And the moment won’t wait. Earlier technologies gave people generations to adapt; this one substitutes for cognition itself and adapts to us, so the disruption clock runs in years, not generations. Bill Gates named that clock, and we buy it. What the speed asks of us is written into how we prototype the future, below.
We help leaders identify a worthy future, make it imaginable for the entire team, and unite them around achieving it. That’s the work.
A worthy future gives people hope that things can turn out well: not dire, not apocalyptic. It offers belonging, and belonging here starts not by choosing but by acting. Acting means manifesting what you believe, experimenting with your ideas, and putting work into the open (without giving away what shouldn’t leave the building) so other people can react to it. Every made thing starts someone else thinking — and the sharer profits first. Putting work into the open forces the clarity that private drafts never need, and invites the correction that private drafts never get. The best research labs make the same bet about their science; we make it about strategy. That’s how a future stops being one person’s idea and becomes a shared one.
Why do we do this work? To make money, to do work we love, and, most of all, to do work that’s meaningful. That’s close to what the Japanese call ikigai: a reason for being, what makes getting up worth it. This worldview isn’t here to make everyone alike. It’s a sorter, not a mold. It helps things land where they belong.
Meaningful is worth defining, because it now decides business outcomes, not just morale. Meaningful work is done with a larger and lasting purpose in mind, above and beyond the need to turn a profit, and you can still make good money doing it. David Brooks describes the difference as two mountains: the first climb is for yourself, the career and the résumé; the second is for commitments that outlast you. Four generations — sometimes five — now share the same workplace, and they want different things from it. The question worth designing for: how does the same work stay profitable for the company and meaningful for the person doing it? Companies that answer it get paid twice — once in results, and again in retention, discretionary effort, and the kind of reputation that recruits for you. Meaning isn’t a perk added beside the work. It’s a design input. Put names on work, connect every role to an outcome a client can feel, and let people watch the difference they make.
People are the most valuable asset an organization has, and the arrival of AI raises their value instead of lowering it. Machines multiply output. People supply the judgment, the taste, and the relationships that decide whether the output is worth anything — to the organization, to its clients, and to the people working alongside one another. James Surowiecki’s wisdom of crowds was never about crowds. It was about genuine differences of view, gathered on purpose.
Human flourishing is a tenet of how we work, and a phrase this document leans on more than once, so it gets defined rather than gestured at. It means people doing well as people, not just as workers. Humans run on being human — relationships, growth, friendships, learning, building things together — not on economics alone. In a flourishing organization, work gives people more of that, not less: real decisions inside clear boundaries, learning worth spending real time and money on, belonging to something they help build, and work that moves a person up — a better job, not a smaller one — as machines take more of the execution. The test and the prize for this are written into the intelligence commitments below: watch human erosion as closely as agent output, and count the win as value improvement — a flourishing organization with flourishing humans, so more value is created for everyone. And some inefficiency is worth keeping on purpose: the joy of work, personal evolution, and collaboration with other humans are not waste to automate away — they’re what humans run on, and we protect them even when a machine could do the task faster.
Diversity in thinking is the form of that value we prize most, and it’s abundance you can practice. Every genuinely different perspective in the room is another possible answer, another early warning, another route nobody had alone. The more critical the decision, the more important it is to seek out different perspectives, so the choice holds up short, medium, and long term, and the solutions come out efficient, profitable, and kind.
Practicing that takes a little courage, so it has to be led. The leader goes first and models it. Then everyone who works with Storyminers gets to practice it: your thinking will be asked for, and it will matter. We lean into learning. Some companies run internal competition like rival pit crews; that’s their choice, and we’ll still work with them, but it’s not us. Difference in how people think is an asset here, not friction to be smoothed away.
The same belief shapes how we treat each other when something’s off. Everyone here holds others’ well-being in mind, and anyone can bring anything to anyone’s attention. How isn’t prescribed; it just needs to be thoughtful and human. You ask for a moment to note an observation. The phrase is a signal: drop your defensiveness and listen openly. The person noting carries duties too — respect, the belief that the other person isn’t bad, and freedom from any fear of reprisal. If people can turn on that conversation skill before they hear the observation, more constructive conversations happen. An observation that stalls can be carried to someone else; nobody here is outside the practice, the founder included. We’ll build training and norms around this in time. The principle stands now.
When something goes wrong, the question is what we learn, not who we blame. A firm that punishes the messenger trains everyone to stop carrying messages, and then it’s flying blind.
We don’t work with companies that, by design, do harm to others. Tobacco is the clear case: everyone knows it harms people, and its producers sell it anyway. The same goes for other addictive products, harmful services, and extractive industries like porn. If harm is built into the product, we’re out. If harm is incidental, that’s a conversation. Design-for-harm is a closed door.
That boundary has already been paid for. In the ’90s, a man walked into the agency Jackie Goldstein and I ran, set a briefcase on the table, and opened it: more cash than a young firm should ever have to say no to. He wanted a porn site. Jackie and I looked at each other — it took less than a second. “No.” He left, and the money left with him. No regrets. Porn is an extractive industry, and that wasn’t who we were. Your code is what answers before you do.
We keep our promises, and we keep our principles. There’s no reason to countermand a reason for being just to make a little more money. Principles can change, but only out loud: restate them and renegotiate the commitments you already made. Never quiet drift. (That renegotiation duty is Haeckel’s rule too: the moment context changes, an honest “I can’t meet this anymore” is rewarded, never punished.) If you promise trust and attract trust, sticking to your guns is the only real option. We won’t be naïve about it, and we won’t bend either.
Catching drift is everyone’s job, and increasingly it’s the agents’ job too. Agents drafting documents, posts, and proposals can flag when something shortcuts a principle, and so can people in conversation — who should feel like value creators for raising it, not tattletales. Some would call that impractical. We think it’s important to getting our world to work.
There’s no right amount of profit. But if you take more value out of a relationship than you put in, that’s bankrupt morally, even if it’s legal.
A professional-services firm like ours should expect to earn a portion of the value it creates for clients. Fees can run higher up front when we’re pointing a new direction, and the outcomes we’re accountable for are defined at the start, so the value is measured, not assumed. We’re not plumbers fixing an immediate leak. We deliver the outcomes we contract for, without damaging people, and then we return value to owners and investors.
Money is a byproduct of creating value for people. Haeckel drew the distinction that keeps this honest: products and services are outputs; value is an effect produced in someone’s life — which means value is always co-created with the client, never shipped at them. Money is how you measure. It is not the value itself.
The common question about AI — what do we hand to the machine, and what do we keep? — is the wrong question. It comes from popular writing, and from people not knowing what to do. The better question: what’s the opportunity to work with AI in a way that honors human agency and human flourishing?
Our answer starts from one observation: intelligence is the new operating system. In practice that means the thinking infrastructure a company runs on — its harnesses, sources of truth, skills, algorithms, and data — is becoming as real as its org chart, and more decisive. Infrastructure that real deserves engineering that serious — built correctly for the long haul, not patched under deadline. Businesses have always been about flowing information, products, and services through themselves. Yet our accounting treats everything as static, and our fixation on returns pulls money out before an organization can reinvest it. That’s scarcity thinking again, and it blocks the abundant business designs that just became possible. The organization this era points toward is a living, flowing, adapting thing — nothing like the cubicles, corner offices, and binders of procedure nobody reads.
None of this is a new belief for us. Stephan Haeckel saw it from inside IBM in the 1990s: when change outruns prediction, the make-and-sell organization — predict, plan, make, sell — stops working, and no tooling fixes it, because the management model itself punishes adaptation. His answer was sense-and-respond: know earlier what’s happening now (diagnosis, not prediction), keep capabilities modular, and let leadership declare context instead of dictating action. We learned this working beside him, and we’ve carried it since. What’s changed is that the intelligence to run that way — sensing, interpreting, reconfiguring — used to demand a Fortune-50 budget. Now a small company can afford it. The frontier labs are arriving, from the technology side, where Haeckel arrived from the management side twenty-five years ago. Adaptive enterprise is finally buildable at human scale.
One more thing about that infrastructure: generic intelligence serves nobody’s values in particular. Even the people building these systems concede how hard they remain to customize to a company’s specific needs and values. Bending the intelligence to your worldview — not bending your company to the tool — is the work. It’s why a written worldview and a working AI harness turn out to be the same project.
So what survives when general intelligence can do the work? Three things, and they are human: judgment, taste, and the ability to reframe. Judgment decides what is worth doing. Taste is knowing what is good enough to put your name on. Reframing changes the question, and a changed question is where new answers come from. None of the three reaches the work on its own. Each needs a harness. Ours is the written worldview, the sources of truth, and the governance that says which decisions a person makes, which an agent may make, and where a person has to say yes before anything goes out. The harness is not a fourth part. It is what lets the three act at the speed the machines now run. Like strategy and branding, the harness used to be an annual decision. Now it takes continuous attention, because it both reflects and foreshadows where the company is going.
Working that way, a few commitments follow.
Accountability is designed in, not bolted on. Stephan Haeckel’s adaptive-enterprise method has carried this for decades: whenever two capabilities negotiate, there’s a promise owed and a condition of satisfaction to meet. The same holds when one of the parties is an agent. (Pascal Bornet and Jochen Wirtz map this management layer well in their work on human-agent orchestration.)
Autonomy follows reversibility. Agents get room on work that’s easy to undo and cheap to check. Humans keep the wheel where an action is public, irreversible, or touches a relationship. (The frontier labs are landing here too, building for collaboration with people over full autonomy.)
Some work is Human Reserved. The term is Bill Gates’s, and we keep it with his name on it: work deliberately kept human — not because machines can’t do it, but because the loss would be too great. Our criterion: wherever lateral thinking, creativity, or invention lies, humans stay involved. The job of AI is to enhance our thinking, not replace it — humans supported by AI, not AI instead of humans.
We watch human erosion as closely as agent output. The risk isn’t a bad draft. It’s people slowly losing scope, mastery, and original thought — if people stop thinking, they’ll atrophy. The line is easier to spot than most of the worrying suggests: AI that extends thinking makes humans think even more — more perspectives explored, more versions, each better than the one before. AI that replaces thinking takes the thinking out with the drudgery. The drudgery it can have. Beyond that test we stay philosophical rather than prescriptive, because everything is changing too fast for prescriptions. But we watch on purpose. And the watching can be empirical: learn from how real systems actually behave — today’s systems teaching us about tomorrow’s — rather than from rules written in advance.
We show the seams. People can know when an artifact was agent-drafted and which human stands behind it.
Speed is not the prize. Value improvement is. Not the same call center run twenty percent cheaper, but the redesign that makes half the calls unnecessary and moves those people onto work clients will pay more for. New offerings that didn’t exist last year. Margins that improve because the work got better, not because the inputs got thinner. Doing new things ever better, so more value is created for everyone.
Individual gains don’t add up by themselves. Make every person faster and you haven’t yet made the company better — fifty accelerations can point fifty slightly different ways, and the whole has to be designed to cohere. The July 2026 national science blueprint conceded the same thing at country scale: “individual gains do not automatically aggregate into collective progress.” Aiming those gains at one shared picture of the future is the work, and it’s why alignment comes before acceleration in everything we build.
There’s a general law underneath this, and it’s telling that a frontier AI lab arrived at it from the other side — Thinking Machines Lab puts it as rethinking objectives rather than optimizing existing metrics. Optimizing improves your answer inside the frame you already have. Changing the frame changes what’s possible. It’s why we start every engagement at the frame, not the dashboard.
Some roles will genuinely change, shrink, or disappear. We say so honestly. If that’s you, you’ll hear it from us first — and you get first shot at the new work. What we will never do is use the fact of job change to frighten anyone into a decision. Naming the shift is honesty; weaponizing it to sell is manipulation, and that’s a line we won’t cross. Others see this shift too. Salim Ismail calls its organizational endpoint the organizational singularity. We’d rather stand with the people describing it honestly than the people selling fear of it.
In the space between intelligence and accountability sits governance.
Most governance is built to look backward: review the results, manage the risk, approve the plan. That’s oversight, and oversight matters — it protects what exists. But the future now forms faster than quarterly cycles, and by the time the dashboards catch up, the best options are usually gone. Oversight protects what exists. Foresight protects what comes next.
So we hold governance on the front foot. For Storyminers, it’s a strategic-intelligence practice for the largest decisions: contracts to pursue, new ideas to introduce, timing, big risks, high exposure. Day-to-day, it stays out of the way. We’re not in a regulated industry, so this isn’t compliance; it’s judgment, applied early. On the largest calls it runs on the same rule as everything else here — the more critical the decision, the more perspectives we seek, across every generation at the table. And it exists partly to check the founder. In a firm this size, the author of a worldview can’t be the only check on the author.
The front-foot idea scales well beyond us. Boards already hold an abundant, underapplied currency: governance intelligence, built through decades of risk assessment, stakeholder accountability, and stewardship. Applied at the point of design instead of after the fact, that intelligence shapes what gets built next quarter rather than grading what was built last quarter. Aviation learned the pattern long ago: flying became safe not through fear of lawsuits but through shared learning, transparent incident reporting, and collective standards. Governance knowledge works the same way — a collective good. What makes one company safer makes all of us safer.
The front foot has an enemy worth naming: concentration of understanding. The builders concede it themselves — scientific understanding of frontier AI lags behind its capabilities, and knowledge of how these systems work stays concentrated inside a handful of labs. You can’t govern what only the builder understands. So part of front-foot governance is translation: getting the board an explanation it can interrogate, early enough to matter.
Tools inform. People decide. That’s why boards belong in the AI conversation early, helping architect what’s coming instead of watching it arrive.
We call our version of that practice Wide Circle — first a belief, then a method. The lines on AI shouldn’t be drawn by technologists alone; Bill Gates argues the wider room should hold workers, faith leaders, youth, and communities, and we agree. At our scale, that means bringing clients, vendors, partners, and advisors into our AI decisions, along with as rich a mix of ages, tenures, specialties, backgrounds, and thinking styles as we can gather.
Deciding how to decide is an important step. The larger the problem, the more important it is to have that conversation — up front, in the middle, and before making the final decision. Naming early who has the call and what counts as evidence is what lets a room full of different views stay a team while the stakes climb.
Compromise is terrible design. Compromise gives everyone less; design gives everyone more.
Look at the biggest, most obvious, most painful problems, and at the biggest opportunities. With all the authorities, institutions, and volunteer efforts we have, we are not addressing them at the scale they exist. There is no design at the top. There are budget allocations and rules about what you can’t do, and underneath them, the same scarcity thinking we keep running into.
We believe change should come by design, not by force. That’s why we’re introducing Big Design: encouraging leaders, decision makers, designers, and thinkers to bring real design to the big issues, not only the small ones. Many of them have wanted to for years and were talked out of it. Every big idea earns the same chorus — too big, too risky, too expensive, not how things are done here.
That chorus made sense once. Big designs used to be genuinely hard to attempt: coordination was slow, prototypes were costly, and keeping a thousand details coherent took armies. Those constraints have mostly collapsed. A small team with today’s tools can model a big design, test it, and hold it together. The old objections were priced on costs that no longer exist.
Here’s how big things actually get built. It takes one person to build a watch, but thousands of people to build the Great Wall. A big design can begin with a very small team; as it extends into a world that’s already built, it takes many more hands. When the principles are set at the start, coherence holds while the idea spreads. That’s one more job of a worldview: it’s the principles, set at the start.
Humans run on being human — on relationships, growth, friendships, learning, building things together. We keep tying everything to economics, and that is not what humans run on. Big Design is our stand that the biggest issues deserve the best design thinking we have, aimed at human flourishing and integrated into the world as it already exists.
A stand that big needs a method, or it’s just exhortation.
A future you can experience beats a future you’re told about. Stories are experiences; you have to start with the experience. Don’t tell somebody what a roller coaster is. Strap them in and push go.
So before commitments harden, we make plausible futures concrete enough to feel and test. That’s what Future Story, Experience Design, and Human Prototyping™ are for — our practice of Disney’s Imagineering®. AI belongs here too: AI is most valuable before decisions are locked, not after. People can’t align around a future they can’t see. Give a leadership team a future they can walk through, poke at, and argue with, and the argument turns productive. Evidence that progress is possible starts as an experience of the destination.
There’s a trust sequence inside every change, and it runs one way: benefit must be experienced before disruption is accepted. When change shows up in someone’s life first as loss, they reject everything that follows. Bill Gates makes that point about AI; we’ve watched it hold for all change — the goal stated without the mechanism, the new rule the management team doesn’t follow itself, the who-owes-what-to-whom nobody laid out. So people have to be part of making the visible benefit arrive. That’s the greatest believability factor there is.
And the speed of this moment raises the stakes: we can now deliver more replete versions of the future in near real time, so the value of story as a prototyping medium keeps climbing.
We name where an idea came from — always. It has been our practice for years, and it stays: provenance, respect, honoring intellectual property, staying clear of legal trouble. There’s also a bonus most people miss. Crediting sources is a better way for clients, members, and employees to learn, because a named source is a door you can walk through, not just a summary you were handed. That’s why the names are all over this document: Haeckel, Simpson and du Plessis, Brooks, Bornet and Wirtz, Salim Ismail, Disney.
We mark what we know versus what we’re guessing, and “I don’t know” is a complete answer here — often the most useful one in the room. As AI fills the world with plausible-sounding text, that discipline gets more valuable, not less — and the economics now say why: the cost of generating has collapsed while the cost of verifying has not, so verification is where scarce, senior judgment lives. Verification stays human, senior work at Storyminers: when we say something is so, a person has checked, and that person’s name is on it.
We aim to be genuinely global, and we won’t sort the world into ranks.
The cultures people bring to work are load-bearing, not baggage. Religious observance tells us when to schedule and when to leave the calendar open; accommodation is design input, not a favor. Meaning lands deepest in a person’s first language, so plain words and translation are respect, not overhead. Family and clan obligations signal loyalty; someone who honors their family’s claims is showing you how they’ll honor yours. Reciprocity and gifts are honored openly, and the line between a gift and corruption is transparency. Every generation carries pattern memory the others need — the elder who has watched three downturns, the younger colleague who is native to tools the rest of us are learning — and multi-generational judgment comes from putting them in the same room on purpose. When cultures honor opposite things, where speaking up to the boss is respect in one place and an insult in another, we hold the difference the way we hold every hard difference: lean into learning, and actively seek the other view.
Storyminers doesn’t speak up on politics. It speaks up for its worldview and its practices, and shares what we’re doing proudly so others can build value with it too.
If we’re directly confronted, challenged, or insulted, that will always deserve a response, and the response stays inside our worldview and our governing principles. Before the firm acts, we solicit open discussion that invites multiple perspectives. The decision itself is made by the decision process we set in advance, so everyone knows who has a voice and who has the call.
When the firm or one of our people is bullied, we respond, because silence reads as consent. We protect our person first, publicly if that’s what it takes. We name the behavior plainly without matching its tone. The response follows the same open-discussion-then-decision path as everything serious. We don’t escalate for sport, and we don’t leave anyone to face it alone.
When a decision trades money against a person’s safety, workload, or dignity — the people doing the work, or the people downstream of what our work makes possible — the trade gets named out loud and decided in the open. Anyone can name it, and naming it can’t be punished. One more trade gets the same treatment: never trade one person’s efficiency for another’s burden. There shouldn’t be a transfer of pain or hardship between people. This is the position every other position in this document answers to.
Beliefs that don’t commit you to anything are decoration. Everything above commits us to three bets.
These bets point at a future worth wanting, and they give us — and everyone who works with us — real work to do in getting there.
A worldview that can’t state the other side isn’t a position, it’s a slogan. Here are the strongest arguments against what’s written above, and where we stand on each.
“A worldview is a luxury. Clients pay for outcomes, not philosophy.” Half right: nobody buys this document. But it’s operating equipment, not philosophy — it’s what lets people and agents act without asking permission, and what lets hard conversations happen calmly instead of not at all. Outcomes arrive faster on solid ground.
“Institutional decline isn’t a renaissance. Most experiments fail, and the failures land hardest on the most vulnerable.” True, and we said the failures come with it. The answer to faltering institutions isn’t nostalgia for them. It’s better design, tested before commitments harden — which is the whole method.
“Meaning talk is how companies justify underpaying people. Some work is just work.” A fair warning. It’s why we tie meaning to design and to measurable results — retention, effort, reputation — instead of asking anyone to accept meaning in place of pay.
“Insisting humans decide everything makes you slow and expensive.” We don’t insist on that. Autonomy follows reversibility: agents run freely where mistakes are cheap to undo, and humans hold what’s public, irreversible, or relational. That’s a speed rule as much as a safety rule.
“Every firm claims to deliver more value than it takes. The claim grades itself.” Which is why outcomes get defined at the start, where the client can measure them — and why the moral line above is written hot enough to be quoted back at us. That’s the enforcement.
“Grand designs have a body count. History’s worst disasters were big designs imposed from the top.” James C. Scott made this case powerfully in Seeing Like a State, and he’s right — about design by force: imposed, uniform, deaf to local knowledge. Big Design is the opposite bet. Principles set at the center, many hands and local adaptation at the edges; one person builds the watch, thousands build the Great Wall. The moment a big design stops listening to the people extending it, it has left our definition of design.
“Boards oversee. Foresight is management’s job.” When the future forms between board meetings, waiting for the rear-view report is itself a governance failure. Foresight doesn’t replace oversight — it feeds it, earlier, while options are still open.
“A global stance becomes mush. Pick a culture and be consistent.” We did pick one: our own, written down here. The global stance isn’t neutrality. It’s a consistent habit, practiced everywhere, of seeking the other view before deciding.
This worldview is versioned, and it’s meant to be argued with. Named world events trigger a review. Every revision records what changed and why. Positions can change, out loud — the same renegotiate-don’t-drift rule we apply to everything else.
Every source named here has been verified against the living record, and the strongest opposing views are stated above, on purpose. From here it goes to work: with partners, with select clients, and underneath everything Storyminers builds.
Stephan Haeckel (adaptive enterprise, sense-and-respond, make-and-sell vs sense-and-respond, context and coordination, outputs vs effects, promises owed / conditions of satisfaction) · Steve Simpson & Stef du Plessis (unwritten ground rules) · David Brooks (The Second Mountain) · James Surowiecki (The Wisdom of Crowds) · Pascal Bornet & Jochen Wirtz (The Human-Agent Orchestrator) · Salim Ismail (organizational singularity; purpose as protocol) · James C. Scott (Seeing Like a State) · Disney (Imagineering®) · Thinking Machines Lab (rethinking objectives over optimizing metrics) · Stephan Woessner (the what-we-do formulation, v1.2) · the White House Office of Science and Technology Policy, Science: A New Golden Age (July 2026 — a national science blueprint converging on several positions held here: sense-and-respond, design-determines-outcomes, abundance, judgment-stays-human, the generation/verification asymmetry, reframe-over-optimize; quoted as evidence that these positions now surface at national scale — evidence, never endorsement, per the no-politics rule above) · Bill Gates (“Human Reserved” — the term for work deliberately kept human; the years-not-generations clock; the trust sequence of benefit before disruption — “The turbulent AI era is here. The choices we make now are critical.”, gatesnotes.com, August 2026).
Positions are Mike’s alone — checked, on the biggest calls, by the front-foot practice above.
v1.8 · revised September 2026.
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