Shockwaves: How the Technical Community Must Respond to LLMs
Why refusing to be a tool—and embedding into society’s cycle—is the only path forward.
Stop seeing yourself as a tool.
The keyboard in front of you is a tool. You are not.
You are a person with perception, judgment, and social context. In a world of machines, your role is not to be the machine—it is to be the bridge between human meaning and technical possibility. You understand technology, and that makes you a translator: you give machines meaning for humans, and you give humans access to machines.
For the technical community, this shift in self-perception is existential. As LLMs automate more of the coding layer, the only way to move upstream is to refuse to self-commoditize. Raise your perspective. Understand the cycles. Embed yourself in society’s consensus, protocols, structures, and narratives rather than remaining at the level of syntax and tools.
This isn’t just a personal development tip. It’s a sign of a deeper turning point. The post–WWII “academic guild” system—walls of degrees, credentials in frames, rigid disciplinary silos—is breaking down. I saw this myself after understanding LLMs. I looked at the wall of diplomas and certificates me and my husband collected over the years. I took them down, removed the frames, and kept only one as a token of my past effort. The rest I shoved into a drawer. They represented a history, not a future.
That old order—rigid, siloed, pitting STEM against the humanities, rewarding credentials over integration—is a warning sign. It has reached its limits. And at the very moment when LLMs have shown what a “Renaissance-level” recombination looks like, the technical community faces a choice: remain tools inside a dying guild system, or become translators, integrators, and stewards of a new cycle.
The First Shock: Displacement of Junior Programmers
The first tremor hit the most vulnerable group: junior programmers.
For decades, boilerplate code, unit testing, and API stitching were the entry points into the profession. These repetitive, lower-stakes tasks served a dual purpose: they kept teams running and gave newcomers a training ground to practice, fail safely, and slowly build mastery. But with the arrival of LLMs, this entire layer of work has been automated almost overnight. Models can generate boilerplate code in seconds, autocomplete interface logic, and even produce unit tests and documentation on the fly. What once required several juniors working for weeks can now be done by one experienced engineer with an AI assistant.
This shift effectively erased the on-ramp for beginners. The lowest rung of the career ladder has collapsed, breaking the traditional “apprenticeship path.” For fresh graduates, the issue is not simply heightened competition—it’s that the training ground itself has vanished.
But this phenomenon is not just about unemployment or efficiency. It carries a deeper warning: if you define yourself as a tool, you will inevitably be replaced by a cheaper, faster tool. The rise of LLMs only made this law of technology brutally obvious.
For developers, this shock should serve as a forced awakening. Your value cannot rest solely on the ability to produce code. The keyboard is a tool. The IDE is a tool. The model is a tool. You are not. What makes you unique is your capacity to place technology within social cycles: to understand why something should be built, what values it should embody, and how machine capability can be translated into human meaning.
In this light, the first shock is not the end of programming, but the end of tool-like programmers. The real turning point lies in whether technical people can lift their perspective—shifting from executors of instructions to interpreters, designers, and bridges between technology and society.
Independent Developers and Open Source in the Crossfire
If junior programmers were the first casualties, independent developers and the open-source community quickly found themselves caught in a different kind of crossfire.
On one hand, LLMs have supercharged individual productivity. A solo developer can now ship in weeks what once took teams months. The barriers to building apps, prototypes, or even entire companies have dropped dramatically. On the surface, this looks like empowerment—a golden age for indie hacking.
But the same forces that empower also erode. Functions have become rapidly commoditized. When every developer has the same AI copilots and can generate similar features in minutes, differentiation becomes difficult. The uniqueness of an app or tool collapses, as the market fills with endless clones, all built from the same underlying models. What once made indie work distinctive—scrappy speed, clever hacks, and niche solutions—is now accessible to everyone, including corporations with far more resources.
Open source faces an even sharper dilemma. It has long been the incubator of innovation—the place where new protocols, frameworks, and standards are born. But it is also the largest “training set” for LLMs. Models feed on decades of unpaid contributions, and then generate outputs that sometimes compete with or bypass the very communities that sustained the commons. This leaves open-source contributors with a bitter paradox: their work is indispensable, yet increasingly overshadowed. The community sits squarely in the middle of the competitiveness vs. legitimacy dilemma.
So where is the path forward? Independent developers and open-source communities must evolve from merely producing functions to embedding themselves in the larger social cycle.
Consensus: Begin with real social pain points—data rights, safety, education, sustainability—rather than just “what can be coded.”
Protocol: Translate those consensuses into clear rules, standards, or interfaces, not just features.
Structure: Find the adoption channels and build positions within ecosystems, so that your work is integrated into durable systems.
Narrative: Articulate why your product, tool, or protocol matters to society at large. Without a story, even the most brilliant code risks being invisible.
In other words, independent developers cannot survive by speed or functionality alone. They must learn to anchor their work in consensus, protocols, structures, and narratives. Only then can they escape the trap of commoditization and reclaim legitimacy in a world where the tools they once created are now creating on their own.
Why the Community Was Blindsided
The anxiety that swept through the technical community in the wake of LLMs was not just about automation. It was also about a lack of preparation. When the shock hit, many developers had no intellectual framework to understand what was happening, which is why panic and despair spread so quickly.
The roots of this vulnerability lie in education. For decades, programming education has focused narrowly on syntax, frameworks, and employable skills. Universities and bootcamps alike trained students to pass interviews and become productive in the latest stack. But they rarely taught the history of computing, the philosophy of technology, or the cycles through which innovation becomes embedded in society. Without this broader perspective, developers were trained as technicians, not as participants in historical evolution.
As a result, when LLMs arrived, most developers lacked the ability to project forward. They had little grounding in the history of AI winters, the collapse of previous paradigms, or the interplay between technology and institutions. They did not know how to interpret disruption as part of a larger cycle of consensus, protocols, structures, and narratives. Instead of seeing LLMs as another turn in the evolutionary wheel, they saw them as an inexplicable rupture. The absence of foresight turned surprise into existential dread.
At its core, this blindness was the product of self-commoditization. The technical community had long defined itself as a toolkit: people who could build faster, cleaner, more efficient code. By embracing that identity, it implicitly accepted tool-like replaceability. When LLMs emerged as a cheaper, faster tool, the community had little to stand on beyond its technical execution. The broader role—translating between technology and society—had been neglected.
This crisis coincides with a larger historical turning point: the slow collapse of the post–World War II academic guild system. For decades, knowledge was fragmented into rigid silos, enforced by diplomas on walls and certificates in frames. STEM students looked down on the humanities; the humanities became increasingly detached from reality. Credentials became a proxy for legitimacy, even as they failed to keep pace with technological change. LLMs tore into this system, exposing how brittle it was.
The technical community was blindsided because it had been trained to think like tools inside a crumbling guild. To move forward, it must recover its social sense—the ability to locate itself in history, to anticipate cycles, and to act not as replaceable coders but as interpreters and builders of meaning.
Raising Perspective: Refusing to be a Tool
The real turning point for the technical community is not whether LLMs can code—it is whether developers can stop defining themselves as tools. The keyboard in front of you is a tool. The model running on the server is a tool. You are not.
If your identity is limited to “someone who writes code,” then you will be replaced by something that writes code faster and cheaper. But if you see yourself as the translator of meaning between humans and machines, your role expands. Your value is not in typing commands but in bridging worlds: making machines intelligible to humans, and making human values executable by machines.
This requires lifting your perspective beyond execution. The question is not only what can be built, but what should be built. LLMs can generate endless features, but it takes human judgment to decide which of them align with social values, and which of them should never be deployed. In other words, technical people must claim a seat at the table where “ought” is debated, not just where “can” is implemented.
The practice of programming must also shift from creating isolated functions to embedding functionality inside larger cycles of consensus, protocols, structures, and narratives. It is not enough to ship code; the code must be legible as part of a broader system, interoperable through protocols, sustainable through structures, and meaningful through narratives. Only then does technical work acquire legitimacy and durability.
Think of the metaphor: you are not a disposable screw in an endless machine. You are the interpreter who enables the machine to speak human language. Without that translation, machines remain blind, and society remains alienated. With it, technology becomes meaningful, adoptable, and aligned.
Refusing to be a tool means embracing this higher role. It means rejecting the comfort of being judged solely on technical proficiency and instead cultivating historical awareness, social foresight, and narrative literacy. The next generation of developers will not succeed by coding faster, but by positioning themselves as indispensable bridges between technical possibility and human purpose.
In this light, the survival of the technical community depends less on competing with LLMs and more on transcending them—by occupying the space that no model can: the space where meaning is decided, values are contested, and society chooses what kind of future it wants to build.
The Path Forward: Social Embedding as Survival
If the first shocks of LLMs taught us anything, it is this: the technical community cannot survive by clinging to the identity of a tool. To move upstream, developers must embed themselves into the broader cycle of society—consensus → protocol → structure → narrative. Only by anchoring technical work within this loop can the community resist commoditization and become indispensable.
The cycle starts with consensus. Every technology that scales does so because it resonates with a widely shared value or anxiety: safety, trust, fairness, sustainability. If your work does not speak to one of these consensuses, it will remain marginal. For example, AI auditing exists because of a societal consensus: AI must be safe, transparent, and accountable. Developers who can align their work with this consensus are not just coding—they are creating legitimacy.
Next comes protocol. Consensus without execution remains rhetoric. Protocols translate abstract values into rules, interfaces, and standards. Data sovereignty offers a clear case: the consensus is that personal data should belong to individuals. The protocol is a technical framework—APIs, tokens, encryption standards—that makes this principle actionable. Developers who build protocols are not just shipping features; they are writing the source code of civilization.
Then comes structure. Protocols mature into institutions, roles, and industries. Consider carbon trading and green finance. The consensus is that energy must be clean. Protocols define how emissions are measured and verified. Structures—markets, clearinghouses, regulatory bodies—emerge to manage these rules. Developers who anticipate these structures can position themselves within enduring ecosystems rather than disposable projects.
Finally, narrative. A technology without a story is a tool without adoption. Narratives amplify consensus and stabilize structures. “Green is the new growth,” “My data, my rights,” “Transparency is competitiveness”—these are not technical statements, but they determine whether protocols are embraced or ignored. Developers must learn to tell stories that embed their work within society’s imagination.
The open question is this: what kind of structural foresight can keep us from being sidelined? It is not enough to ask whether LLMs can code; the real task is to anticipate where consensus will form, how protocols will be written, which structures will crystallize, and what narratives will resonate. Only by embedding technical work in this loop can the community rise above toolhood and claim its place as a translator of meaning.
The shock of LLMs is not the end of the story. It is a signal—a brutal reminder that the era of treating ourselves as tools has reached its limit. Tool-like identities will always be replaced by cheaper tools.
The future of the technical community will not be defined by who can write code faster, but by who can embed their work more deeply into society’s cycles—who can step into the role of bridge, translating between human meaning and machine execution.
This sets the stage for what comes next. In Part 4, we will look at the full prototype of the cycle—consensus → protocol → structure → narrative—and explore how to actually run it, so that technical work becomes more than execution: it becomes civilization’s operating system.
