When power (watts) is the primary constraint for data centers, the total cost of compute becomes secondary. The crucial metric is performance-per-watt. This gives a massive pricing advantage to the most efficient chipmakers, as customers will pay anything for hardware that maximizes output from their limited power budget.

The narrative of energy being a hard cap on AI's growth is largely overstated. AI labs treat energy as a solvable cost problem, not an insurmountable barrier. They willingly pay significant premiums for faster, non-traditional power solutions because these extra costs are negligible compared to the massive expense of GPUs.

To build a multi-billion dollar database company, you need two things: a new, widespread workload (like AI needing data) and a fundamentally new storage architecture that incumbents can't easily adopt. This framework helps identify truly disruptive infrastructure opportunities.

AI companies operate under the assumption that LLM prices will trend towards zero. This strategic bet means they intentionally de-prioritize heavy investment in cost optimization today, focusing instead on capturing the market and building features, confident that future, cheaper models will solve their margin problems for them.

Don't pass the full, token-heavy output of every tool call back into an agent's message history. Instead, save the raw data to an external system (like a file system or agent state) and only provide the agent with a summary or pointer.

Chinese AI models like Kimi achieve dramatic cost reductions through specific architectural choices, not just scale. Using a "mixture of experts" design, they only utilize a fraction of their total parameters for any given task, making them far more efficient to run than the "dense" models common in the West.

The founder used a "Napkin Math" approach, analyzing fundamental computing metrics (disk speed, memory cost). This revealed a viable architecture using cheap S3 storage that incumbents overlooked, creating a 100x cost advantage for his database.

The primary reason multi-million dollar AI initiatives stall or fail is not the sophistication of the models, but the underlying data layer. Traditional data infrastructure creates delays in moving and duplicating information, preventing the real-time, comprehensive data access required for AI to deliver business value. The focus on algorithms misses this foundational roadblock.

Many AI startups prioritize growth, leading to unsustainable gross margins (below 15%) due to high compute costs. This is a ticking time bomb. Eventually, these companies must undertake a costly, time-consuming re-architecture to optimize for cost and build a viable business.