In the AI-native research paradigm,arriving at answersis becoming rapidly cheaper. EvoX and AI tools with code comprehension capabilities (such as Codex) can turn a single natural language description into runnable experimental code: environment setup, algorithm selection, batch optimization, and result visualization can all be completed within a conversation. This is exactly what Code is cheap means—the barriers of code and computing power are no longer the sole bottleneck in research.
But low-cost implementation does not equal high-quality research. When candidate solutions can be generated in bulk, what is truly scarce is judgment: Which questions are worth answering? Does the objective function truly express the needs? Is the comparison fair? Which constraints, once violated, render the results meaningless? These judgments cannot be replaced by generation speed—they constitute the researcher's core taste, which is Taste matters.
Human-AI Division of Labor: Who Expands the Space, Who Guards the Standards
In the EvoX workflow, a sound human-AI division of labor looks like this: AI and evolutionary algorithms are responsible for expanding the search space—generating diverse candidates, evaluating in parallel, and logging the experimental process; the researcher is responsible for guarding the evaluation standards—defining the problem and objectives, drawing inviolable constraints, and deciding what evidence suffices to support a conclusion. The easier the tools become to use, the clearer human responsibility becomes.
Critical Thinking: Treat AI Output as Hypotheses to Be Tested
The solutions AI provides are often fluent in expression and complete in form, but fluency does not equal correctness. A researcher's critical examination should at minimum include: Does the objective function align with real needs? Is the baseline setup fair? Do the evaluation metrics omit key constraints? Does the conclusion still hold under boundary conditions? Take warehouse robot obstacle avoidance as an example: AI can quickly produce a collision-free path, but whether the path is truly feasible, whether the cost is underestimated, and whether the layout is representative still require the researcher's judgment.
Accountability: Directional Trade-offs Cannot Be Outsourced
In real-world problems involving open exploration and sparse feedback, directional trade-offs cannot be completed by automated processes. The researcher must decide what to keep and what to abandon in the next round of exploration, and must take responsibility for the final scientific conclusions. AI expands the boundaries of implementation and search, while the researcher determines the scientific significance of these explorations.
Key Takeaways
- Code is cheap: AI and EvoX rapidly complete implementation, experimentation, search, and logging.
- Taste matters: The researcher decides question value, result significance, and evidence standards.
- Human-AI division of labor: AI expands implementation and search; humans control definition, conflicts, boundaries, and acceptance.
- Critical thinking: Perform hypothesis testing, evidence review, and boundary analysis on AI outputs.
- Accountability: Directional trade-offs cannot be outsourced; final judgment and research responsibility belong to the researcher.
Human-AI Division of Labor and Critical ExaminationThe diagram emphasizes that AI and evolutionary algorithms are responsible for expanding the search space—generating diverse candidates, evaluating in parallel, and logging the process; the researcher guards the evaluation standards, defining the problem and objectives, drawing inviolable constraints, and deciding on acceptance evidence. AI outputs must undergo critical examination of objective functions, baseline fairness, evaluation metrics, and boundary conditions. Directional trade-offs and ultimate research responsibility cannot be outsourced.
