The Primitive Cycle: Consensus → Protocol → Structure → Narrative
Why technologies survive only when they run the cycle of society.
Consensus is never obvious at the beginning. It does not fall from the sky fully formed. Instead, it emerges from long cycles of conflict, negotiation, and struggle among competing groups. Sometimes it takes decades of political upheaval, economic crises, or even wars before society settles on a shared value that feels inevitable in hindsight. Clean energy, trustworthy money, safe AI—each of these started as contested, fragile intuitions before they hardened into consensus. What looks like clarity today was once chaos.
And yet, this process is exactly what makes consensus powerful. Because once it crystallizes, it provides the anchor for protocols, the blueprint for structures, and the storylines for narratives. Technologies that ride on top of this cycle endure; those that ignore it vanish, no matter how brilliant their code.
Consensus: born out of conflict, not clarity
Consensus is rarely obvious at the beginning. It does not emerge from calm debate or abstract theory, but from struggle—competing interests, political upheavals, and crises that force society to choose. What looks today like a natural principle was once contested, fragile, and uncertain.
Take the idea that “money must be trustworthy.” For centuries, currencies were unstable. Local rulers debased coins, issued inconsistent notes, or defaulted on debts. Merchants relied on gold, silver, or barter because they had no faith in political currencies. Trust in money was not a given; it was earned through repeated collapse. Only after cycles of hyperinflation, banking crises, and wars did societies settle on the need for stable monetary institutions. The rise of central banks and standardized fiat currencies was not the result of one elegant design, but of centuries of chaos, conflict, and slow stabilization.
Or consider the consensus that “energy must be clean.” In the 19th and 20th centuries, coal and oil were celebrated as symbols of progress. Few worried about emissions; industrial smoke was seen as prosperity itself. The idea that energy should be clean emerged only after decades of pollution disasters, oil shocks, and geopolitical dependency on fossil fuels. Scientists, activists, and entrepreneurs kept pushing. The oil crises of the 1970s exposed vulnerabilities, Chernobyl and Fukushima amplified fears, and climate science revealed the existential stakes. The consensus we now take for granted—that renewable energy is the future—was forged out of conflict, activism, and a long struggle against entrenched fossil fuel interests.
The same is happening with “AI must be safe.” For decades, AI was too weak to matter. Bias in models or errors in translation were considered minor research problems. But when LLMs scaled to millions of users, generating misinformation, amplifying prejudice, and even raising fears of existential risk, safety became a public demand. Governments launched task forces, academics debated alignment, and companies were pressured to create guardrails. What looks now like an obvious consensus—that AI must be safe—was invisible just a few years ago. It only crystallized after a wave of anxiety, media amplification, and regulatory scrutiny.
This is the pattern: consensus is crystallized social intuition. At first, these intuitions are fuzzy, half-formed feelings in society: unease about inflation, discomfort with smog-filled skies, fear of runaway algorithms. They are debated, resisted, and often ignored. Over time, through conflict, crises, and the push of powerful actors—whether thinkers, entrepreneurs, or policymakers—these intuitions solidify into shared beliefs. Once consensus hardens, it becomes the anchor point of legitimacy. From there, protocols, structures, and narratives can be built.
The key lesson is that consensus is not a starting assumption—it is an endpoint of struggle. By analyzing early social intuitions, the conflicts around them, and the forces pushing them forward, we can anticipate where the next consensus will form.
Protocol: from intuition to execution
Consensus on its own is not enough. Broad social agreement provides legitimacy, but it is still intangible. People may feel that money should be trustworthy, or energy should be clean, or AI should be safe, but until those intuitions are translated into executable rules, standards, or technical frameworks, they remain rhetoric. This is the role of protocol: turning shared belief into operational reality.
A protocol is a translation device. It encodes values into mechanisms that can be followed, audited, and enforced. Protocols may take the form of legislation, technical standards, APIs, or even cryptographic algorithms. The critical point is that they bridge the gap between social intuition and practical implementation.
Take privacy as an example. For decades, there was rising discomfort with how companies harvested and exploited personal data. Scandals and leaks reinforced the intuition that “privacy matters.” But only when the European Union introduced the General Data Protection Regulation (GDPR) in 2016 did that intuition become enforceable. GDPR provided a concrete protocol: explicit consent requirements, data portability rights, breach notification rules, and severe penalties for violations. Suddenly, companies had a rulebook; courts had a framework; citizens had recourse. A fuzzy value became an actionable standard.
Or look at trustless money. The consensus that “money must be transparent and not solely controlled by governments” crystallized after the 2008 financial crisis. But it was Satoshi Nakamoto’s whitepaper that provided the protocol: blockchain consensus. Proof-of-work encoded distrust into verifiable math. Every node could check the ledger; no central authority was needed. The protocol did not just echo the consensus—it executed it. That is why Bitcoin survived when so many other cryptographic experiments failed.
A third case: climate change. The intuition that “energy must be clean” has been debated for decades, but it needed protocols to take shape. The Paris Agreement provided measurable commitments: specific emission reduction targets, reporting requirements, and a timeline for accountability. While fragile and imperfect, these protocols gave governments and corporations something to follow, benchmark, and negotiate around. Without them, “clean energy” would have remained a noble slogan.
This reveals a crucial truth: protocols are fragile if consensus is weak. GDPR works in Europe because privacy has cultural weight there; in other jurisdictions with less consensus, similar laws are watered down or ignored. Blockchain protocols thrive where distrust in institutions is high; they stagnate where consensus on central banking remains strong. Climate protocols falter when national interests override global consensus.
But when protocols and consensus are aligned, they become stabilizing forces. They create predictability, lower transaction costs, and accelerate adoption. Protocols are not just technical artifacts—they are the source code of civilization.
The lesson for developers and technologists is clear: it is not enough to invent clever systems. Ask instead: is there a consensus waiting to be executed? If so, design the protocol that encodes it. That is the difference between building a tool and building the foundation of a new era.
Structure: embedding protocols into institutions
Protocols are powerful because they transform consensus into actionable rules, but protocols alone cannot endure. A regulation written on paper or a cryptographic algorithm published online does not guarantee adoption or persistence. To survive, protocols must be embedded into durable structures—organizations, jobs, industries, and practices that embody them in daily life. Structure is what turns fragile rules into social hardware.
Consider the history of money. The protocol of central banking—rules for issuing currency, managing reserves, and stabilizing inflation—only became durable when it was embedded into institutions. Central banks, staffed by technocrats, created a professional role for monetary policy. Entire industries—commercial banks, clearinghouses, international financial organizations—grew around these protocols. Without this institutional scaffolding, “trustworthy money” would remain an aspiration. With it, the global economy could run on predictable rails.
The same dynamic can be seen in privacy. The GDPR protocol in Europe set clear requirements, but it was the emergence of data protection agencies, compliance officers, law firms, and auditing firms that made it operational. The role of the “Data Protection Officer” (DPO), now standard across corporations, did not exist before GDPR. Structures provide the muscle that enforces the skeleton of protocols, turning abstract rights into lived practice.
In the realm of climate and energy, the Paris Agreement created a protocol for emission targets. But only when carbon markets, trading platforms, certification bodies, and green finance mechanisms were built did the system acquire weight. These structures employ thousands of professionals, move billions in capital, and create feedback loops of accountability. A protocol without a market or an agency is weak; a protocol with structures becomes a force that shapes industries.
Today, we see the same with AI ethics. The consensus that “AI must be safe” has been encoded in draft protocols—principles of fairness, transparency, and accountability. But it is the formation of AI ethics boards, auditing startups, regulatory sandboxes, and government task forces that will determine whether this protocol stabilizes. If these structures solidify, “AI safety” will move from a guideline to an operational industry, with real jobs and budgets.
This reveals the key role of structure: it makes consensus durable. Protocols provide clarity, but without institutions to embody them, they risk fading. Structures turn ephemeral agreements into enduring realities by creating roles, responsibilities, and incentives. They are the hardware of society, anchoring the operating system of consensus and protocol.
For technologists, the lesson is sharp. It is not enough to invent new protocols; the question is what structures will grow around them. Who will enforce them? What industries will adopt them? Which jobs will emerge to carry them forward? Anticipating structures is a form of foresight: it shows whether a protocol is likely to endure or collapse.
Narrative: amplifying and legitimizing the cycle
Even the most robust consensus, encoded in protocols and carried by structures, will struggle to spread without a narrative. Narratives are the symbolic and emotional layer of the cycle. They take abstract rules and turn them into stories that people can believe in, repeat, and rally around. If consensus is the anchor, protocols are the code, and structures are the hardware, then narrative is the amplifier—the loudspeaker that makes the cycle resonate across society.
Narratives matter because humans do not live by rules alone. We live by meaning. A technical protocol may explain how a carbon market works, but it is the story of “saving the planet” or “green growth” that mobilizes public support, attracts investment, and creates political momentum. Narratives connect the abstract logic of protocols with the emotional logics of culture, identity, and aspiration.
Take currency. The U.S. dollar is not just stabilized by the Federal Reserve and global banking structures. It is also carried by the narrative “In God We Trust” and the broader story of American stability and global leadership. Without that symbolic legitimacy, the dollar would be just another piece of paper. The narrative turns it into a global anchor.
Consider energy. The shift toward renewables is not only about protocols like carbon accounting or structures like trading markets. It is also about the narrative “Green is growth.” This phrase reframed sustainability from a sacrifice into an opportunity. Instead of being about cutting back, it became about innovation, jobs, and prosperity. That narrative amplified adoption far beyond what protocols alone could achieve.
Or look at AI. Technical advances made LLMs possible, and protocols like APIs and benchmarks supported their scaling. But the explosion of adoption was propelled by narratives: “AI is the next electricity,” “Renaissance-level disruption,” “the last invention humanity needs.” These phrases, repeated by entrepreneurs, investors, and media, created cultural legitimacy. They framed AI not as a niche tool but as a civilizational event. Narratives made the cycle emotionally resonant and globally legible.
The key lesson is that narratives multiply adoption. They are not mere marketing slogans; they are instruments of social alignment. A well-crafted narrative can accelerate consensus, attract resources, and embed protocols into everyday life. A weak or toxic narrative, by contrast, can derail even strong structures—as seen in the backlash against technologies framed as surveillance or exploitation.
For technologists, this means that building is not enough. To survive, a technology must also be storied. What is the narrative that connects your protocol to shared values? How will it resonate with the broader culture? What metaphors will carry it into public imagination?
In the end, narrative is what turns a cycle into a movement. It is the difference between a fragile innovation and a force that reorganizes society.
Case Study: Why LLMs Exploded First in the English-Speaking World
The primitive cycle—consensus, protocol, structure, narrative—helps explain why large language models found fertile ground in the English-speaking world before they did elsewhere. It wasn’t just about technical breakthroughs or access to GPUs. It was about an ecosystem that had already run the cycle long before ChatGPT went viral.
Consensus: AI as the next electricity
In the U.S. and broader Anglophone ecosystem, the ground had been prepared by a long-running narrative: “AI is the next electricity.” This phrase, repeated for years by influential figures like Andrew Ng, established an intuitive consensus that AI was not just another product but an inevitable infrastructural shift. By the time LLMs started showing sparks of generality, governments, investors, and the public were already primed to accept them as transformative. AI wasn’t something to debate from scratch—it was framed as the destiny of technology.
Protocols: APIs and benchmarks
This consensus was quickly translated into protocols. OpenAI’s release of APIs and leaderboards like GLUE and SuperGLUE gave developers concrete interfaces and benchmarks to work with. Hugging Face standardized model distribution, while GitHub and arXiv created the pipelines for rapid iteration. These protocols allowed thousands of developers to plug in, test, and deploy LLMs without reinventing the wheel. Consensus became executable: anyone could now build on top of the new primitive, token prediction.
Structures: VCs, labs, corporations
Protocols by themselves would not have been enough. What turned LLMs into a global shockwave was the presence of powerful structures ready to scale them. Silicon Valley venture capital firms poured billions into AI startups. Big tech companies like Microsoft, Google, and Amazon integrated LLMs into their platforms almost overnight, offering distribution to billions of users. Universities and research labs launched AI institutes, producing a steady pipeline of talent and publications. These structures institutionalized the protocols, embedding them into economic and educational systems.
Narratives: media amplification
Finally, narratives amplified the cycle. American and British media outlets produced a steady drumbeat of stories: “AI passes the bar exam,” “AI writes a sonnet,” “AI threatens jobs.” Journalists, entrepreneurs, and policymakers reinforced the idea that LLMs were a civilizational moment. Phrases like “Renaissance-level event” and “the last invention humanity needs” spread virally on Twitter and LinkedIn, further legitimizing the technology. Narratives turned adoption into cultural momentum.
Contrast: the Chinese context
In China, the cycle ran differently. Consensus was slower and more fragmented. While AI research was strong, the public narrative was less settled—AI was framed as both an opportunity and a risk to social stability. Regulatory caution arrived quickly, with draft rules emphasizing content control and security before mass consumer adoption could take off. Protocols like standardized APIs emerged later, and structures like VC funding and corporate adoption were more cautious under a tighter political climate. Narratives, meanwhile, were fragmented: some celebrated AI nationalism, others warned about risks, leaving less room for a unified storyline of inevitability.
The lesson
The English-speaking world didn’t “win” LLMs simply because of better GPUs or smarter researchers. It won because the primitive cycle had already been completed before the models reached public maturity. Consensus was primed, protocols were in place, structures were ready, and narratives amplified adoption. In China, the cycle stalled in its early phases, producing a slower adoption curve despite technical strength.
This case shows why understanding the cycle is crucial. Technology does not spread automatically. It spreads when consensus, protocols, structures, and narratives align. LLMs exploded first in the Anglophone world because all four elements were ready to reinforce one another.
The Primitive Cycle as Foresight Tool
Looking back, it is tempting to imagine that technologies succeed because they are technically superior. History tells a different story. What endures is not the cleverest algorithm, the fastest chip, or the most elegant code. True technological evolution is the synthesis of Darwinian accidents and the primitive cycle of social consensus.
Darwinian accidents create the spark. Transformers were not designed as a universal engine for language; they were an unexpected survivor of chaotic experimentation. The accident provided the primitive: predicting the next token. But without consensus, protocols, structures, and narratives, this accident would have remained a curiosity buried in research papers.
The primitive cycle is what turned the spark into a revolution. Consensus framed AI as inevitable. Protocols like APIs and benchmarks made it actionable. Structures like VCs, labs, and corporations scaled it. Narratives gave it cultural momentum. Together, the cycle absorbed the accident, stabilized it, and made it feel inevitable.
For developers and technical communities, the lesson is urgent. When you evaluate a new technology, don’t just ask: “Can this be built?” That is the question of raw possibility. The real question is: “Can this run the cycle?” Can it resonate with an emerging consensus? Can it be translated into protocols? Can it embed into structures? Can it be amplified by narratives?
This is more than theory—it is a foresight tool. By mapping where a technology sits in the cycle, you can form short- and mid-term judgments about whether it will survive or fade. If there is no consensus forming, no protocol in sight, no structure to carry it, and no narrative to amplify it, then no amount of code will save it. Conversely, if the cycle is aligning—even imperfectly—then even a fragile prototype can snowball into inevitability.
Think of it as a diagnostic. Bitcoin survived not because its code was flawless, but because it ran the cycle: consensus (distrust of banks), protocol (proof-of-work), structure (exchanges, miners), narrative (“digital gold”). Google Glass failed not because the hardware didn’t work, but because it lacked consensus (privacy anxieties), had no protocols to govern use, no structures to enforce legitimacy, and a toxic narrative (“Glassholes”) that killed adoption.
For developers, this means moving beyond the identity of “code writers.” Your role is not just to build, but to interpret, anticipate, and embed. The primitive cycle is your framework for doing so. It is how you avoid being blindsided by disruption, how you identify the next wave early, and how you shift from reactive anxiety to active foresight.
Technological history is jagged, full of false starts and accidents. But by combining the Darwinian chaos of innovation with the disciplined lens of the primitive cycle, we gain a compass. Not a guarantee of prediction, but a tool for orientation. And in an age where LLMs and beyond continue to redraw boundaries, orientation is survival.
