The Hidden Technology Transfer in the Age of AI Conversations

When thinking about technology transfer, we typically imagine formal agreements between universities and companies, patent licensing or the transfer of highly skilled engineers. However, in the AI era, a subtler, largely invisible form of technology transfer is emerging. Users are now passing knowledge between large language models (LLMs) by exchanging prompts, responses, and data.

Every day, millions of people engage with LLMs created by companies like OpenAI, Google and Anthropic. They often copy outputs from one system and input them into another, refine prompts on one and test on another, translate responses across platforms, compare answers and merge insights from various systems. This is a practice known as cross-validation. This process makes users act as informal intermediaries in a decentralized spread of technology.

Initially, this might seem harmless; however, it decreases competition and reduces the variety of differentiated AI products accessible to users.

Users have long compared different technologies. However, engaging with LLMs is more complex. Language models do not just provide information; they also respond to prompts, context and feedback, which influences the quality of their responses. When users transfer structured prompts, optimized workflows, or combined knowledge between models, they are effectively shifting valuable informational assets across competing AI platforms.

This phenomenon has significant implications for competition in the AI industry.

A smart prompt found on one model can quickly spread to others via online communities, social media or direct copying between interfaces.

First, it speeds up the spread of knowledge. Traditionally, technology transfer in industries was slow and reliant on formal institutions. However, within the LLM ecosystem, users serve as decentralized channels, sharing ideas, techniques and prompt engineering strategies. A smart prompt found on one model can quickly spread to others via online communities, social media or direct copying between interfaces. This creates an informal yet effective network for exchanging knowledge, which diminishes the distinction between proprietary systems.

Second, this dynamic could undermine the sustainability of competitive advantage. AI firms pour billions into training models, gathering data and refining architectures. However, if users repeatedly shift outputs, workflows and strategies across platforms, some of the value generated by one company can be indirectly appropriated by others. This ecosystem then begins to resemble open innovation networks, except that diffusion occurs naturally through user actions rather than through formal collaborations.

Third, user-mediated transfer is changing the economics of platform competition. Companies are no longer competing only on model performance. Instead, they compete in a shared cognitive ecosystem where users gather insights from multiple models at once. In this context, the advantages go to platforms that seamlessly fit into users’ workflows, support interoperability and build trust, rather than focusing solely on technological isolation.

This contrasts sharply with traditional sectors such as the automotive industry, where user-driven technology transfer was very limited. In this field, technological expertise is built into physical products, proprietary manufacturing methods and tightly managed supply chains. Companies such as Toyota, Ford and Volkswagen safeguard their competitive edge using patents, trade secrets, specialized tools and vertically integrated production processes.

The landscape of LLMs has fundamentally changed because knowledge now travels at the speed of conversation. 

While drivers can operate vehicles, they cannot easily access or transfer the engineering knowledge inside them to rival manufacturers. In this case, reverse engineering is costly, time-consuming, not perfect and often legally restricted. Consequently, technological diffusion primarily occurs through formal channels such as licensing agreements, supplier networks and workforce mobility. As a result, competitive advantages last much longer compared to a fast-paced AI environment.

The landscape of LLMs has fundamentally changed because knowledge now travels at the speed of conversation. A prompt made in Tokyo can be duplicated in Nairobi, improved in São Paulo and tested on various models within minutes. This results in a decentralized system of human-driven knowledge sharing that would have been unimaginable in earlier industrial periods.

The Democratization Potential

Over time, this user-driven sharing of knowledge could help democratize AI. In the past, advanced technologies were limited to a few institutions and countries due to high barriers like access to infrastructure, expertise and capital. However, LLMs are starting to change this situation.

Even without developing their own AI systems, users worldwide can engage with various models, compare results, refine prompts and integrate insights from multiple AI ecosystems. This process effectively turns users into collaborators in knowledge creation, allowing individuals, startups, researchers and students, regardless of location, to access capabilities that were previously limited to specialized laboratories. The outstanding issue is that users are not paid for this valuable input.

In this evolving landscape, the key drivers of technology transfer might not be institutions or companies but everyday users interacting with various AI systems.

As prompt strategies, workflows and problem-solving methods spread worldwide, they lower practical barriers to adopting advanced AI tools. For players in emerging economies, integrating insights from various models might enhance their involvement in global innovation networks. In this way, user-driven knowledge transfer could help reduce certain gaps in the digital divide.

However, the democratizing potential should not be exaggerated. The computational infrastructure, training data and model development skills are still mainly held by a few large technology firms. Without careful governance, the advantages of this new flow of knowledge might still largely benefit the current technology giants.

Rethinking AI Governance

The emergence of LLMs is changing both the way knowledge is created and spread. Policymakers typically focus on concerns such as data privacy, model safety and market dominance. However, the informal spread of AI knowledge via user interactions introduces new considerations. For instance, should output produced by LLMs be regarded as a form of transferable technological capital? Moreover, how should competition policies evolve in a landscape where technological diffusion happens through countless daily exchanges?

In this evolving landscape, the key drivers of technology transfer might not be institutions or companies but everyday users interacting with various AI systems.

In the long term, the competitiveness of AI ecosystems might rely less on developing the strongest model and more on understanding and leveraging this growing network of human-driven knowledge exchange. It appears that the future of AI will be influenced not just by algorithms but also by the countless conversations that connect them. These users must benefit through compensation from this contribution to AI development.