Accountants call it marginal cost. Economists often discuss it through the lens of unit economics. Whatever discipline you choose, the underlying question is the same: what happens to the cost of producing one more unit as scale increases?
Traditional software companies enjoyed one of the most beautiful economic characteristics ever discovered in business. Once the software has been built, the cost of serving an additional customer approaches zero. As users increased, the marginal cost curve moved closer and closer toward zero, creating a near-asymptotic relationship. This economic structure enabled extraordinary operating leverage and helped create some of the most valuable companies in history. In practical terms, adding more customers often made the business stronger, more profitable, and more efficient.
That is why software produced something Adam Smith never truly experienced in his era: accelerating returns. The fixed asset, the software platform, could continue serving increasing numbers of users while variable costs grew only marginally. The result was a business model where scale itself became a competitive advantage.
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Artificial Intelligence changes that equation. Unlike traditional software, AI often behaves more like a classical industrial enterprise. Every new user may trigger additional inference costs, computing costs, storage costs, model-serving costs, and infrastructure expenses. As usage scales, costs do not naturally collapse toward zero. In many cases, they rise alongside demand. The economic profile begins to resemble manufacturing more than software.
This introduces the old economic reality of diminishing returns. If not carefully managed, each additional customer may contribute less value than the previous one. In extreme situations, growth itself can become expensive. Yes, more customers can actually push an AI company toward financial distress if the unit economics are poorly designed!
This challenge explains why nearly every serious AI company is attempting to build proprietary inference infrastructure, optimize models, develop custom chips, or reduce dependence on third-party providers. The battle is no longer merely about intelligence; it is increasingly about economics.
Simply, without solving the inference-cost problem, AI businesses may follow the economic trajectory of traditional industrial companies rather than the trajectory of software legends like Facebook. Put differently, without strong inference economics, AI begins to look more like a cement factory than a social network or software operating system.
And that is why the race to reduce inference costs may become one of the most important competitions of the AI age. It is also one of the reasons Tekedia Capital invested in Piris Lab. We believe the future of AI will not be determined solely by who builds the smartest models, but also by who can run those models most efficiently. Intelligence without economical delivery remains a constrained opportunity. And before AI models can evolve, hardware must have emerged to power them. That conviction is what led us to write the cheque for Piris Lab.
Piris Lab is developing a next-generation photonic computing system designed to perform AI inference at the speed of light. By leveraging photons rather than relying solely on traditional electronic architectures, the company seeks to dramatically reduce latency, improve performance, and lower the cost of deploying AI at scale.
Good People, if AI is to become truly ubiquitous, powering everything from personal assistants and autonomous systems to healthcare, manufacturing, and scientific discovery, the economics must improve. The industry cannot sustainably scale if every additional user significantly increases computational costs.
We see photonics as one of the most promising pathways to solving that challenge. In many ways, future AI winners may emerge not only from advances in algorithms, but also from breakthroughs in the physical infrastructure that makes intelligence affordable and accessible.
We believe the company is helping build the foundational infrastructure required to advance the next phase of the AI revolution.
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