In 2025, the core of software engineering is no longer just about code itself, but about runtime controllability and cost governance. This shift is fundamentally reshaping the industry’s underlying logic.
Looking back at 2025, I became increasingly aware that this year was not about “code becoming unimportant,” but rather that the value coordinates of engineering have shifted as a whole. For more than a decade, software engineering has focused on code quality, architectural evolution, and delivery efficiency. But starting in 2025, the key to system success is shifting—towards whether the runtime is controllable and whether costs are governable.
This is not just a slogan, but a conclusion repeatedly validated by my real-world experiences throughout the year.
My 2025: From “Platform Engineering” to “Runtime Challenges”
In my annual review, I noted a clear change: I spent less time on “how to write a good system,” and more time on “how to keep the system running stably, reliably, and affordably.”
This shift in focus is a natural extension of a decade of cloud native evolution.
The following timeline diagram illustrates how my focus has changed over recent years:
When AI workloads truly enter business scenarios, the core challenges engineers face also change:
- Are inference, training, and evaluation competing for the same compute pool?
- Is GPU utilization consistently below expectations?
- Does cost scale linearly and uncontrollably with concurrency?
- Does the system have failure isolation and replay capabilities?
These issues go far beyond the code level.
Industry Consensus: AI Is Shifting the Focus of Engineering
By 2025, an industry consensus is emerging: AI is rewriting software engineering. But the real change is not happening in the IDE or code completion speed—it is reflected in the migration of engineering complexity.
Previously, complexity was concentrated in code and interfaces, and problems were solved through abstraction, refactoring, and testing.
Now, complexity has shifted to the runtime, resource, and cost layers, and must be addressed through scheduling, isolation, observability, and governance.
This is why the same AI tools:
- Serve as “accelerators” for junior engineers
- But act as “magnifiers” for senior engineers
AI tools amplify whether you truly understand how systems run in production.
Why “Cost” Becomes a First Principle
In traditional cloud native systems, low CPU utilization is often just an efficiency issue; but in AI systems, low GPU utilization is often a cash flow problem.
In 2025, I repeatedly encountered scenarios like:
- Resources “seem insufficient,” but utilization is not actually high
- Scaling up to solve queuing issues ends up increasing unit costs
- The system lacks clear budget and quota boundaries, so throttling becomes the only way to stop the bleeding
The root cause of these phenomena is not model selection, but the lack of a runtime and cost control plane tailored for AI workloads.
The following flowchart visually illustrates the cyclical relationship between GPU resources and cost pressures:
Engineering problems ultimately manifest as cost issues.
The Rise of Agents: The Real Challenge Is at Runtime
In 2025, Agent (Intelligent Agent, Agent, Intelligent Agent) became a hot topic; by 2026, it will enter the “can it actually run” stage.
The challenge for Agents has never been about “how smart they are,” but rather:
- Whether there are clear permission and data boundaries
- Whether they run in an isolated execution environment
- Whether they can be observed, evaluated, and replayed
- Whether they are subject to explicit cost and budget constraints
These capabilities form the outline of Agentic Runtime (Agentic Runtime, Intelligent Agent Runtime) that I have been trying to clarify throughout the year.
The following flowchart shows the core capability layers of Agentic Runtime:
Without a runtime, an Agent is just a demo; without cost constraints, an Agent is just a risk amplifier.
Outlook for 2026: The “Foundation” of Engineering Matters Again
Looking ahead to 2026, I remain cautiously optimistic.
I do not believe the future belongs to “those who write the best prompts,” but more likely to:
- Those who understand runtime boundaries
- Those who can govern compute as a constrained resource
- Those who design AI systems as long-running systems
From 2025 onwards, software engineering is no longer code-centric, but runtime and cost-centric. This is not a regression, but a return: a return to being responsible for the whole system and for real-world constraints.
For me personally, this is both a year-end summary and the direction I will continue to invest in for the coming years.
Summary
In 2025, the focus of software engineering has shifted from code itself to runtime and cost governance. The rise of AI and Agents has not diminished the value of engineering, but has pushed complexity to a higher level. In the future, understanding runtime, managing compute and cost will become the new core competencies for engineers. I hope this year-end review provides some inspiration and reflection for fellow professionals.