Why Token Costs Matter: A Product Manager’s Guide to AI Scaling and Efficiency

The author distills a deep analysis from an overseas tech investor, highlighting three key insights about the current AI landscape.

1. Scaling laws remain valid, but the spotlight has moved to cost‑effective products

Scaling law states that within a certain range, increasing compute, model size, or data quality leads to steady performance gains, a pattern observed in GPT, Gemini, and Claude series. Recent observations suggest model upgrades feel less dramatic—GPT‑5 lacks a generational leap and new models often converge in performance. The investor argues the law hasn’t broken; instead, product strategy has shifted from raw performance to low‑cost inference, aiming to serve the widest user base while keeping expenses controllable.

For product developers and investors, the crucial takeaway is not whether a singular AI breakthrough will occur, but that short‑term performance gains will be modest while long‑term growth continues as long as compute and data keep increasing.

2. Reasoning ability rewrites the commercial logic of large models

Earlier large models acted mainly as high‑order text predictors: given a prompt, they sampled the most probable continuation based on pre‑training data. Improvement relied on two routes—massive pre‑training investment or harvesting more web data—resulting in a weak positive feedback loop.

New reasoning‑capable models can perform multi‑turn thinking, decompose tasks, generate chain‑of‑thought steps, log tool calls, and produce error‑correction feedback. These process logs become high‑quality data that can be fed back into the model, forming a closed‑loop: reasoning → structured data → model fine‑tuning → stronger reasoning → more user engagement → more data.

This mirrors the classic internet flywheel of "users → data → product evolution → more users," now operating at the AI model layer.

3. Cost, especially token and power consumption, is now the decisive competitive edge

Each generated token consumes electricity, GPU cycles, and network resources. Providers that can produce massive token volumes at lower cost gain a deep infrastructure advantage.

Two core signals emerge:

Effective collaboration between GPUs matters more than raw GPU count. The concept of "coherent FLOPs" captures the truly usable compute when GPUs can communicate efficiently.

Power efficiency becomes the primary constraint. Building large AI data centers requires robust power delivery, compliance with energy and carbon regulations, and often faces strict local power controls.

When power is limited, the competition reduces to "how many tokens can be produced per watt?" A GPU that is expensive but yields far more tokens per watt is preferable to a cheaper, less efficient alternative.

For entrepreneurs, this implies that product design should not only consider API pricing but also the token consumption of the specific task. Building applications that form a self‑improving loop rather than a one‑off tool maximizes long‑term value.

Conclusion

Product managers should shift focus from chasing fleeting AI hype to evaluating token efficiency, power costs, and the potential for closed‑loop automation in their domains. Identifying high‑frequency, structured decision points that AI can automate will determine sustainable competitive advantage in an era where compute and electricity are no longer cheap.