AI Era Splits Software Services: High‑Margin Intelligent Services vs Low‑Margin Traditional Ops

Core Judgment – Dumbbell‑style Polarization

The software services industry will split into three layers:

Top layer: intelligent services with high margins and high barriers.

Middle layer: a collapse zone of generic software and standardized SaaS.

Bottom layer: infrastructure such as compute, models, and cloud.

Result: software services will not all become traditional; the strong get stronger, the weak get weaker, and the middle layer is squeezed by AI.

Why Some Companies Slide Toward Traditional Services

Business model disruption – from seat‑based to result‑based pricing

Traditional SaaS relies on per‑seat subscriptions, so revenue grows linearly with employee count and margins are high.

AI agents can replace 5–10 human seats, causing seat demand to collapse.

Pricing shifts to per‑task, per‑result, or per‑token, dramatically lowering unit prices and eroding margins.

Consequently, firms move from selling high‑margin tools to selling low‑margin labor services, resembling traditional outsourcing.

Lower development barriers and intensified competition

AI‑generated code and the proliferation of low‑code/no‑code reduce development costs by more than 80 %.

General functions such as reports, basic CRM modules, and simple approvals are directly covered by large models, leading to severe homogenization.

Small and medium vendors can only compete on low price and manual delivery, entering price wars that push margins to 10 %–20 %.

Delivery model becomes service‑oriented

Standardized SaaS now requires extensive AI training, prompt engineering, data labeling, and scenario adaptation.

These tasks are labor‑intensive, making marginal cost non‑zero and turning delivery into a project‑based service.

Why Some Companies Upgrade to High‑Margin Intelligent Services

Core barriers – industry know‑how, private data, AI capability

Deep vertical integration (e.g., industrial MES, medical HIS, financial risk systems) that owns private data and compliance barriers can build AI‑proof moats.

Value upgrade – from tools to agents / decision engines

Software evolves from human‑operated tools to AI‑autonomous execution plus human decision, delivering end‑to‑end results.

Examples: AI‑driven contract review, AI‑automated accounting, AI‑powered lead generation.

Pricing shifts from annual fees to outcome‑based revenue sharing, dramatically boosting margins.

New growth space – AI‑native services

Emerging tracks include AI training services, custom agents, industry‑specific model fine‑tuning, data governance, and AI security. These services are technology‑intensive, high‑barrier, and high‑stickiness, with margins of 50 %–80 %.

Key Comparison of the Two Types of Companies

Core business : generic SaaS, simple customization, labor outsourcing vs. vertical AI, agents, data services, AI‑native applications.

Margin level : 10 %–30 % (near traditional services) vs. 40 %–70 % (maintaining or exceeding traditional software).

Core barriers : none, functions easily replaced by AI vs. industry know‑how, private data, compliance, AI capability.

Delivery model : labor‑intensive, project‑based, low standardization vs. AI‑driven, outcome‑oriented, high standardization, repeatable.

Growth logic : headcount‑driven linear growth vs. AI‑scaled exponential growth.

Typical examples : generic CRM, simple OA, low‑code platforms vs. industrial AI, medical AI, financial intelligent risk control, AI training platforms.

Future Evolution Path

Margins of generic SaaS continue to decline and seat‑based models collapse.

Vertical industry AI and AI‑native services rise rapidly, keeping margins high.

The industry forms a dumbbell structure; the middle layer (traditional SaaS) will be acquired or eliminated.

Conclusion

Transform immediately: shift from “selling tools” to “selling results / agents”.

Deepen vertical focus: concentrate on 1–2 industries to accumulate private data and industry know‑how.

Build AI capability: develop or partner on large models to create AI‑native products.

Avoid direct competition with AI on generic functions; move to high‑barrier scenarios.