At Open Cybernetics, a research and development firm focused on foundational advances in information theory and artificial intelligence, we believe that how technology is shared with the world is just as important as the technology itself. The distribution mechanisms—whether open source, closed source, copyright, or copyleft—directly shape market asymmetries, determining whether a technology concentrates power or distributes it, whether it reinforces existing market hierarchies or disrupts them. For us, this is the ultimate measure of whether an innovation truly serves as a democratizing force or merely strengthens established players.

Market Asymmetries: The Hidden Forces Shaping Technology

To understand the effects of distribution mechanisms, we need to first examine what markets fundamentally are and how they function. A market is a self-organizing system where participants exchange goods, services, or resources. Markets persist as long as asymmetries exist between participants—without asymmetries, markets naturally dissolve. These asymmetries can take many forms, such as the division of labor. By specializing, some participants can provide goods or services that others lack the skills to produce. From a computer science perspective, this specialization resembles knowledge sharding: instead of every individual storing the same set of useful ideas (or “memes”), knowledge is distributed across a subset of people, enough to sustain and reproduce the collective knowledge of the community. In this sense, markets act as emergent systems that optimize the storage and use of knowledge within a community.

While some asymmetries, like division of labor, can benefit the entire community, others are less cooperative. For instance, a participant might deliberately create an information asymmetry by keeping knowledge of a new technology secret. By leveraging this trade secret, they could offer a product or service at a lower cost or higher quality than competitors, accumulating wealth in the process. Consider a company with exclusive access to an advanced AI architecture that can reason more effectively than publicly available systems. This company could offer services with superior contextual understanding that competitors simply cannot match. The exclusive control of this cognitive architecture creates a significant market advantage, allowing the secret holder to extract premium fees while other market participants struggle to compete with inferior technology. Over time, this accumulated wealth can be used not only to control the labor of others but, as we will see, even to influence their attention and decision-making processes. In this way, the ‘closed-source’ philosophy concentrates power in the hands of the secret holder while systematically disadvantaging other market participants.

Free-market maximalists might argue that competition alone sufficiently addresses this issue, but this perspective overlooks a crucial reality: while competition can temporarily reduce prices, competition without deliberately dismantling exploitative asymmetries inevitably leads to monopolization. The closed-source philosophy is therefore fundamentally in conflict with genuine empowerment, as it increases and entrenches asymmetries rather than reducing them. In societies with greater complexity (which is not to be confused with advancement) property asymmetries can emerge, where participants own land, real estate, intellectual property, or copyrighted material. As long as an authority, like a government, exists to enforce property rights, property owners can effectively impose a tax on others for access or use. When property holders extract value without contributing labor, this behavior is known as rent-seeking. Many property asymmetries essentially rely on exploiting other participants, weakening and undermining their autonomy.

Patent trolling exemplifies this exploitative form of property rights. These entities acquire patents not to produce or utilize the inventions they protect, but solely to extract settlements through litigation. By wielding patents as weapons rather than tools for innovation, they divert resources from genuine research and development toward legal battles, creating no value while extracting wealth from productive companies. Similarly, when individuals or corporations own land or resources they neither developed nor maintain, yet collect payments merely for access, they are engaging in exploitative rent extraction that contributes nothing to collective prosperity.

However, not all property asymmetries are inherently exploitative. For example, a subset of intellectual property and copyrights stem from labor, like inventing something new or creating original content. These labor-based property rights serve as incentives for innovation and creative production. Rather than choosing a strictly closed-source approach, these participants might share their work openly, but with the expectation of being compensated for their intellectual effort. This approach recognizes the creator’s right to benefit from their labor while still allowing their innovations to contribute to broader social progress. The key distinction lies in whether the property right exists to protect the fruits of genuine labor and innovation or merely as a tool to extract value from others without contributing productive effort. Capital itself can create powerful asymmetries in a market. An entity with excess capital can deliberately sell a product or service at a loss to eliminate competitors. Once those competitors are pushed out of the market, the capital holder can raise prices above previous levels, extracting more value from other participants—a dynamic known as monopolization.

Beyond pricing power, capital can also be used to create asymmetries of control. For example, by owning a social media platform, news outlet, or marketplace, an entity can influence what information participants see, shaping or even dictating their behavior. Social media platforms like Twitter (X) or TikTok can influence the formation of opinions by selectively amplifying certain content through their recommendation algorithms, effectively determining which voices reach millions and which remain unheard. Similarly, marketplaces like Amazon and search engines like Google can use their proprietary algorithms to influence product and search result visibility, creating winners and losers based on criteria that remain largely hidden from users and sellers alike.

This dynamic, where power comes not from owning things but from controlling the networks that move information, has a name: vectoralism, a term coined by McKenzie Wark in A Hacker Manifesto and as cloud capital by Yanis Varoufakis in Technofeudalism: What Killed Capitalism. Unlike previous examples where market asymmetries emerged from entities holding secrets or property rights, vectoralists create asymmetries by owning, operating, and controlling information networks and platforms themselves. By reprogramming participants’ perceptions and decisions through algorithmic control of information flows, vectoralists maximize their control while minimizing others’ autonomy. While traditional asymmetries often center around controlling supply, vectoralism allows platform owners to shape demand itself by determining what users see, when they see it, and how it’s presented, effectively manufacturing desire rather than merely fulfilling it. This distinction has crucial implications for how such power can be disrupted: while a secret or IP can be rendered obsolete by developing alternative technologies, vectoralist power requires the more challenging task of shifting established networks to decentralized alternatives. Breaking the cycle of centrally controlled networks demands novel algorithms with specific properties that enable the transition from platforms to protocols.

At Open Cybernetics, we recognize that truly empowering people in an economic sense requires intentionally reducing non-labor-based market asymmetries through thoughtful technology sharing models that prioritize decentralization over centralization. Market participants gain autonomy and opportunity when these exploitative asymmetries are reduced or eliminated. When an organization like ours develops for example a more efficient AI architecture and then shares this knowledge with the whole market, we directly counter the capital asymmetry that gives large corporations exclusive access to advanced capabilities. By following an open approach, we weaken trade secrets, non-labor based rent-extraction, and network control. This democratization empowers smaller participants who no longer need to pay a premium fee to exploitative asymmetries.

The Open Source Paradox: When Sharing Undermines Survival

So is this yet another blog post advocating for open source and copyleft as universal solutions? Not at all. While these approaches can reduce certain asymmetries, I would argue that a pure open source or copyleft strategy is often fragile for businesses creating high impact innovations. Let me explain when open source and copyleft make game-theoretic sense before analyzing their limitations from a memetic perspective.

Many valuable tools and projects are open source and undeniably empower countless people worldwide. An interesting pattern becomes visible, however, when examining what typically becomes open source versus what remains proprietary. While open source operating systems, programming languages, frameworks, databases, and compilers are frequently available, one rarely encounters novel open source lossless compressors, error correcting codes, advanced materials, or medications. This disparity transcends mere ideological preferences—it fundamentally reflects underlying market dynamics.

Most successful open source technologies primarily increase labor productivity while offering minimal competitive advantage when kept proprietary. This economic dynamic explains why we see robust open source ecosystems for operating systems, programming languages, and databases, but rarely for novel compression algorithms or exotic materials that create significant market asymmetries. When a technology enhances productivity without conferring significant competitive advantage through information asymmetry, open source alternatives naturally emerge and thrive. This occurs not necessarily because the value comes from widespread adoption and ecosystem growth, but rather because the use-value and cost savings of open sourcing significantly outweigh the surplus value that one can accumulate by keeping it proprietary. Companies like Microsoft historically created asymmetries through regulatory capture initially and network effects later, not through the inherent value of their code.

For many corporations, supporting open source makes strategic sense even from a selfish standpoint. It simplifies hiring, increases ecosystem productivity, and distributes maintenance costs across the community. Meta benefits more from open-sourcing PyTorch and hiring developers already familiar with it than from maintaining a proprietary system requiring extensive training. Through open source, corporations effectively socialize educational costs while privatizing the productivity benefits—a calculation driven by economic self-interest rather than altruism.

By releasing tools like PyTorch or TensorFlow as open source, companies create public resources that universities and individual learners use to train the next generation of developers—all without the corporation bearing these educational costs directly. When these pre-trained developers enter the job market, the sponsoring companies can hire talent already proficient in their tools, skipping expensive internal training programs. Meanwhile, the productivity gains from having a workforce fluent in these technologies flow directly to the corporation’s bottom line. This arrangement transforms what would be a private cost (training employees on proprietary systems) into a public good (widely available learning resources), while keeping the resulting productivity advantages largely private. The corporation also benefits from community-driven improvements and bug fixes, essentially gaining free labor from external contributors who enhance the tools the company itself depends upon. I would argue that on balance, this arrangement creates more often positive-sum rather than negative-sum outcomes for the ecosystem.

Yet both open source and copyleft face structural challenges when it comes to incentivizing high-impact innovation. A novel material, algorithm, or medication can create immense market asymmetries that translate directly into profit margins for the innovator. When keeping an invention proprietary or secret can generate high economic returns, the rational self-interest of innovators is at odds with the empowerment that comes with open access. The tension between innovator incentives and open access presents a fundamental challenge. While we might admire historical figures like Edward Jenner—the physician who developed the first successful vaccine in 1796, refused to keep it proprietary, and made it freely available—we cannot build robust systems on altruism alone. A R&D company or cooperative that freely shares their innovations without recouping losses, will eventually exhaust its resources. After all, high-impact research requires not just time and intellectual effort, but substantial financial investment as well. The “open innovation” meme would die alongside the bankrupt innovator.

The Selfish Case for Sharing: Our Model in Practice

The real challenge isn’t forcing a binary choice between complete altruism or pure profit-seeking, but designing models where reducing market asymmetries aligns with the self-interest of the individual. For successful meme propagation, every firm requires a model for how to generate revenue. That model however has to be shaped and driven by an ethos. Without a genuine ethos, the firm (or even nation) becomes a fragile finite-game automata. Outsourcing manufacturing, R&D divestment, and just-in-time production will all look like great business decisions to someone who thinks in snapshots.

There is no single strategy for aligning an innovator’s self-interest with their ethos; the right model is always context-dependent. Let’s move from theory to practice with our own case study. At Open Cybernetics, we’ve spent over a year developing a novel, FPGA/ASIC-friendly, state-of-the-art fountain code that requires only a fraction of the memory used by conventional methods. While we could have kept this invention secret to capitalize on the resulting market asymmetries, we concluded that doing so would not only contradict our principles—it would be a strategic blunder.

Our “free software, licensed hardware” model is a direct expression of this thinking. It’s not just about enabling smaller startups and developers; it is fundamentally in our own self-interest. The “Free Software” part of our model is our engine for market adoption. While our inventions are patented, their software implementations are free and open source. You don’t need permission to use the code or build upon our ideas. This freedom creates powerful network effects that directly benefit us. The more developers and companies adopt our algorithms, the faster they become industry standards. We are, in effect, building a moat—not with secrets, but with ubiquity.

The “Licensed Hardware” part is our revenue engine. The ubiquity created by our free software generates demand for high-performance applications. That’s where our hardware licenses for FPGAs and ASICs come in. Commercial telecommunications companies working on satellite communications, 6G, or high-performance streaming need the speed and efficiency that only specialized hardware can provide. By licensing our optimized designs, they get a competitive edge, and we get the revenue needed to fund our next wave of innovation.

Ultimately, our approach is born from a choice of mindset. A finite-game player sees the world as a series of zero-sum games, forcing a choice between empowering others and profiting yourself. They optimize for short-term feedback, acting like the automata we described, blind to the larger system. An infinite-game player, however, sees a system of asymmetries they can influence through cognition. They understand that by strategically reducing asymmetries for others (by leveraging open source as a tool for empowerment) they can create valuable long-term network effects, where collective empowerment isn’t a cost; it is leverage.

Our ‘free software, licensed hardware’ model is simply the product of this infinite-game mindset. The critical takeaway is not to copy our specific model, but to adopt the perspective. See the long game. See the system. And build a business where your principles become your greatest strategic advantage. Act as an infinite-game player.

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