The real inflection point for AI engineering is not “how many people use it,” but “how many people cannot do without it.” Only when not using AI leads to direct loss of opportunity and efficiency, can we say the era of AI engineering has truly arrived.
Starting Point: Predictions for AI in 2026
Recently, I came across two predictions for 2026 from Amazon CTO Werner Vogels that struck me the most:
- Renaissance Developer: Developers must span code, product, business, and social impact.
- Personalized Learning: AI will reshape education, focusing on differentiated paths rather than a unified curriculum.
Both point to the same trend: AI is not just a tool, but is redefining how people grow and how they are defined.
There is a gap between prediction and reality, and it is worth exploring.
Correction: Will AI Really Be “Saturated” by 2026?
My initial prediction was that AI usage would reach saturation by 2026. Reality has shown me this is too optimistic.
By the end of 2025, even among internet professionals, most people’s use of AI remains at the “heard of it” or “tried it a few times” stage. It is still far from being a daily workflow necessity.
More importantly, this judgment is conditional: infrastructure supply, regulation, and compute costs must not reverse in the next 3–6 years. If any variable breaks down (costs double, models go offline, policy shifts), the adoption curve will be disrupted.
The Truth About the Inflection Point: From “Using” to “Relying On”
“Relying on” is a vague term. A more precise definition requires measurable indicators.
Here is a diagram that visualizes the metrics for being truly dependent on AI:
Most industries have not reached the “cannot operate without” stage, unlike the internet, mobile, or payment inflection points. Most metrics are still far below the threshold, which is why the most likely outcome for 2026 is: more people will use AI, but those who truly rely on it will remain a minority.
Using ≠ Building: The Five-Level Capability Ladder
This difference is not binary, but a clear progression.
The following table shows the five-level model of AI capability maturity.
| Level | Name | Description | Scarcity |
|---|---|---|---|
| 1 | Tool User | ChatGPT/Claude, Coding, Copywriting, Accelerator, Optional | Low |
| 2 | Integrator | LLM API + Vector DB, AI layered on existing systems, Usable, not critical | Low |
| 3 | Settler | Restructuring data flow, business decisions, AI becomes critical path | Rising |
| 4 | Engineering Abstraction | Extracting frameworks, runtimes, providing infra for ecosystem | Extremely High |
| 5 | Autonomous System | Self-feedback, self-optimizing, redefining human-AI relationship | Future |
Currently, the biggest gap is at Level 3 and Level 4. Most people are stuck at Level 1 or 2, with very few reaching Level 4. This means high-value scarcity will not disappear, but will continue to rise.
Why the Era of AI Engineering Has Not Arrived: Three-Dimensional Delaying Factors
It is not technology alone that is holding things back, but constraints in three dimensions.
The following diagram illustrates the three main constraints delaying AI engineering maturity:
The key observation: If any one dimension is stuck, the entire ecosystem’s maturity will be delayed. Currently, none of the three dimensions have fully mature solutions.
The Realistic Window: Three Paths for Capability Advancement
The next three years will not be “winner takes all,” but rather a period where multiple capability levels appreciate simultaneously.
Below is a table comparing the value and bottlenecks of different capability advancement paths:
| Capability Path | Short-Term Value | Long-Term Outlook | Bottleneck |
|---|---|---|---|
| Level 1→2 (Tool→Integration) | ⭐⭐ Rapid Depreciation | ⭐ Saturation | Low barrier, fierce competition |
| Level 2→3 (Integration→Settlement) | ⭐⭐⭐⭐ Scarce | ⭐⭐⭐⭐ Continual Appreciation | Requires industry depth, long-term iteration |
| Level 3→4 (Settlement→Abstraction) | ⭐⭐⭐⭐⭐ Extremely Scarce | ⭐⭐⭐⭐⭐ Defines Ecosystem | Large cognitive leap, needs community influence |
Key conclusion: While the number of “AI users” is rapidly increasing (depressing Level 1 value), due to the three-dimensional delaying factors, scarcity at Level 3 and 4 will only rise.
What I’m Doing on arksphere.dev
Based on the above judgment, I focus on exploring the architectural evolution of AI Native infrastructure. The goal is not to catalog model usage, but to study the foundational capability stack supporting scalable intelligent systems: scheduling, storage, inference, Agent Runtime, autonomous control, observability, and reliability.
The content is no longer a collection of courses or tips, but a continuous record of evolution around Infra → Runtime → System Abstraction. arksphere.dev is the site for this experiment and settlement.
Summary
The inflection point for the era of AI engineering is not “how many people use it,” but “how many people cannot do without it.” The latter requires five measurable indicators to reach their thresholds, and we are still far from that.
“Using ≠ Building” is not a binary, but a five-level progression. Scarcity at Level 3 and 4 will rise as the number of Level 1 users increases—this is the biggest opportunity window in the next three years.
But the width of this window depends largely on how technology, institutions, and organizations evolve together. I hope more people working on AI engineering will not only focus on technical innovation, but also invest equal thought into institutional development, talent growth, and risk governance—these “invisible engineering” challenges.