AI Needs Value Anchors: Governance Is More Urgent Than Capability in the AI Era

1. Why a Release Without a New Model Deserves Attention

Recently, an AI research organization did not release the new large model the market was expecting. Instead, it announced an internal research group focused on how humans and AI should coexist. Actions like this produce no new code and no model weights, so they are unlikely to create a short-term commercial breakthrough.

From a long-term industry perspective, however, the signal may matter more than a model upgrade. In the early days of the internet, the industry adopted the value statement “don’t be evil.” It was not a technical capability, but it set behavioral boundaries for the industry and helped build public trust. AI is rapidly changing how work gets done, and the industry similarly needs institutional, research-based infrastructure.

A research agenda, public research frameworks, and recruitment of researchers worldwide together form a kind of public infrastructure. Its role is similar to the transactional guarantees in a database. Transactions do not make queries faster, but they determine whether results can be trusted and whether data persists. Large-scale AI deployment needs similar guarantees.

2. Four Research Directions: Turning Broad Concerns into Researchable Problems

The research agenda breaks AI governance into four directions. Each one corresponds to a real, measurable technical or social problem.

1. Economic diffusion: how AI changes jobs and production methods

This is the most discussed and most emotionally charged direction. The first three industrial revolutions mainly replaced repetitive physical labor. AI targets cognitive work, including writing, design, programming, and analysis. Existing labor market adjustment mechanisms may weaken or even fail, and the extent depends on the industry and the task structure.

The relationship between productivity gains and workload needs to be calculated carefully. Suppose a type of task is completed with a large model, and net working hours drop from 10 hours to 2.5 hours. The efficiency gain is 75%, and the same workforce can complete about four times as many tasks. This does not mean three people can replace 300. For three people to do the workload of 300, the net hours per task would need to fall to 1% of the baseline, an efficiency gain of about 99%. Such extreme cases are rare in practice, and they often come with rising costs for verification, rework, and quality control.

Therefore, the industry needs verifiable data, not individual opinions. The research organization proposes building a quantitative “economic index” that tracks real usage data. The index would show which industries and task types AI has replaced human labor in, and which steps it has only augmented. Developers can apply the same approach. Instead of relying on the feeling that they are “more productive,” they should build task-level metrics. Below is an illustrative evaluation structure:

class TaskImpactMetrics:
    def __init__(self, task_type, baseline_hours, ai_hours, rework_hours=0.0):
        if baseline_hours <= 0:
            raise ValueError("baseline_hours must be greater than 0")
        if ai_hours < 0 or rework_hours < 0:
            raise ValueError("Hours cannot be negative")
        self.task_type = task_type            # Task type, such as generation, review, or refactoring
        self.baseline_hours = baseline_hours  # Baseline hours for the task without AI assistance
        self.ai_hours = ai_hours              # Direct hours with AI assistance
        self.rework_hours = rework_hours      # Rework and verification hours
    def net_hours(self):
        return self.ai_hours + self.rework_hours
    def efficiency_gain(self):
        # Positive values show the share of net hours saved; negative values mean assisted work is slower
        return 1 - self.net_hours() / self.baseline_hours
    def capacity_multiplier(self):
        # How many times more tasks the same workforce can complete in the same time
        if self.net_hours() <= 0:
            raise ValueError("Net hours must be greater than 0 to calculate the capacity multiplier")
        return self.baseline_hours / self.net_hours()

This structure records direct hours and rework hours separately, so verification costs are not overlooked when only generation time is measured. When collecting metrics, follow the definitions in the table below:

Metric	Definition	Collection Method
Baseline hours	Average hours to complete similar tasks without AI assistance	At least two weeks of historical records, or a control group
Net hours	Direct hours plus rework and verification hours	Record per task ticket; do not count generation time alone
Rework rate	Share of tasks that must be redone, out of all tasks	Calculate weekly
Quality score	Scores assigned by independent reviewers using a standard scale	Review a sample from each batch
Sample size and period	Number of samples and the statistical window for each task type	Do not draw conclusions from insufficient samples; update quarterly on a rolling basis

2. Threats and resilience: new security boundaries raised by AI

Large models lower the barrier to generating malicious content, writing exploit code, and carrying out social engineering attacks. Traditional security systems were often designed on the assumption that attackers have limited capabilities, and that assumption is weakening. The core question for this direction is how defense systems should adapt when both attackers and defenders can use AI.

Enterprise security teams can start with several foundational steps:

• Maintain audit logs for model calls, recording the time, model version, call source, and output destination.
• Run static application security testing (SAST) and dependency vulnerability scans on AI-generated code, and do not merge code that fails these scans.
• Apply the principle of least privilege to model and tool accounts, and do not place production credentials in model context.
• Set human review thresholds for changes involving authentication, payments, or permission changes, and require a designated person to approve them before release.

3. Changes in human-AI collaboration in practice

Long reliance on navigation apps weakens a person’s spatial memory of routes. Similarly, if code review and architecture decisions are handed to AI over the long term, a team’s technical judgment may gradually erode as members change.

A sensible approach is to let AI draft and keep humans in charge of confirming key decisions. Changes in judgment can be observed, so the following metrics are worth tracking:

• The share of architecture decisions that receive human review;
• The distribution of defect causes, including whether problems come from requirements, design, AI-generated code, or human coding;
• The dissent rate in code reviews, and the share of dissenting opinions that are accepted.

This data can help a team determine whether the division of labor between humans and AI is weakening its own judgment.

4. AI-driven R&D: constraints on recursive acceleration

When AI begins to assist in R&D on stronger AI, a self-reinforcing acceleration loop begins to form.