A practical AI startup due diligence framework covering revenue quality, retention, model dependency, compute economics, defensibility, data rights, security and commercialization.
AI data centers are becoming power-and-cooling engineering projects as much as compute projects. Here is how grid access, cooling, utilization and inference demand shape the next infrastructure cycle.
AI inference economics is moving beyond token pricing. A practical framework for measuring model cost, latency, utilization, reliability and business value at the workflow level.
At the start of 2026, the comp…
From the end of 2025 to the be…
The large-scale implementation…
AliceWant to explore AI implementat…
When the iteration speed of ar…
The hype of the 2023 “LL…
As the sun sets in Tokyo, a yo…
admin
