The paradox Big Tech
cannot solve
OpenAI, Google and Anthropic sell AI models. LexiCo sells something they cannot build — because it would cannibalize their own business model. It is like asking a power company to build solar panels that make customers self-sufficient.
The cannibalization trap
OpenAI makes money per token. Their incentive is for you to use MORE tokens, not fewer. A technology that reduces token usage by 30–90% is a direct threat to their revenue model. They will never build this themselves.
Google, Anthropic and Mistral have the same problem. Their entire business model is built on volume. LexiCo is in a unique position: we make AI cheaper for users while taking a share of the savings. Everyone wins except the vendors who bill per token.
Vendor freedom
Today most companies are locked to a single AI vendor. If you built your product on GPT-4, you depend on OpenAI's pricing, availability and policy changes.
STONE breaks this lock-in. With STONE you talk to one API, and traffic is routed to the optimal model automatically. Switching cost between vendors goes from months to seconds.
Regulation and neutrality as competitive advantage
Two structural advantages that cannot be copied.
The EU AI Act favors Europe
GDPR, Schrems II and the AI Act — all point toward more documentation and data sovereignty. Large US companies struggle. LexiCo sells compliance as a service.
Norwegian neutrality
For Saudi Arabia, India and Brazil there is an enormous difference between US and Norwegian technology. Not subject to the CLOUD Act or FISA 702.
Data sovereignty is global
Every country that adopts privacy legislation expands LexiCo's market. The trend is unambiguous: more countries, stricter requirements.
Norway's next tech adventure
Norway has a tradition of building global infrastructure from a small base: Telenor in telecom, Opera in browsers, Kahoot in edtech, Cognite in industrial data.
The difference is that the AI market is exponentially larger. Telenor built telecom for the Nordics. LexiCo builds the infrastructure layer for all AI traffic in the world. The product already works. The foundation is laid.
The timing is perfect
Cost crisis, regulation, vendor fatigue, data sovereignty wave — and a product that already works in production.