Scaling, High Quality, Low Cost: How to Break the "Impossible Triangle" of Human Data Labeling
August 21, 2025
By
Everawe Labs
If you’ve been following AI, especially the progress of large language models (LLMs) like ChatGPT and Claude, you might have realized that data, particularly high-quality human-labeled data, has become a critical bottleneck in AI development. The problem is that we’re stuck in an “impossible triangle”: scaling, high quality, and low cost. In traditional data labeling, increasing the amount of data usually means raising costs, and ensuring high-quality data often requires more human input. It seems like achieving all three at once is impossible. Today, let’s discuss how the industry is trying to crack this problem.
In the AI 1.0 era, like during the ImageNet period, the focus in the data race was on “quantity.” Whoever scraped the most data and labeled it fastest had the upper hand. But with AI 2.0 — the era of large models — things have changed drastically. Models are now powerful enough that coarse, low-quality, and even biased data is not just useless but harmful. It leads to models producing inaccurate outputs, amplifying biases, and even “degrading.” What we need now is “high-dimensional data” that reflects human wisdom, preferences, and values. This brings up a fundamental paradox: we need higher quality data, but our demand for data quantity is growing exponentially. So, what can we do?
The traditional approach is “Human-in-the-Loop” (HITL), where humans handle all complex judgments, and machines assist. But this method is inefficient, costly, and hard to scale. Then, a new paradigm is emerging: “Model-in-the-Loop.” In this model, machines take a more active role, handling basic, repetitive, and large-scale data tasks. Humans no longer have to process every detail but can focus on higher-level tasks, such as reviewing, decision-making, and dealing with complex ethical issues. In other words, the machine is the “force multiplier,” and humans are the “decision-makers.”
Four Key Steps in Building a “Model-in-the-Loop” Data Engine
1. Intelligent Data Generation & Seeding
No more starting from scratch with labeling. Instead, we first use large models (like GPT-4) to generate a lot of candidate data, which is then reviewed, corrected, or rated by humans. For example, during ChatGPT's training, this method was widely used: the model generates multiple responses, and human labelers only need to sort or select the best answer. This way, the human role shifts from being a “writer” to an “editor,” greatly improving efficiency.
2. Smart Task Allocation & Difficulty Classification
The model can pre-assess data points and classify them by difficulty: simple tasks (like “How are you?”) are automatically handled; medium tasks go to regular labelers; high-difficulty tasks (like ethical dilemmas) are assigned to experts. This is exactly what Scale AI does. They use APIs to automatically categorize tasks, ensuring expert resources are used where they’re needed most.
3. Real-time Assistance & Quality Enhancement
Labeling platforms now come with built-in AI assistants that provide real-time suggestions, consistency checks, and knowledge augmentation. For example, when labelers’ standards aren’t consistent, the system might remind them: “You previously rated a similar issue as an A, but now it's a C. Please confirm if this is intentional.” This not only reduces errors but also improves overall labeling quality.
4. Automated QA & Consensus Building
Traditional quality assurance requires cross-validation by multiple labelers, which is costly. Now, we can use a “QA model” to simulate multi-person consensus and trigger human review only when there are discrepancies. For instance, Anthropic heavily used this technique while building its Constitutional AI to reduce dependence on human consensus.
Although the “Model-in-the-Loop” approach holds great promise, it also faces a few key challenges: Bias Amplification Risk: If the initial model is biased and human evaluators work within the system’s influence, the entire system may simply refine and amplify existing biases rather than learn from the diverse and sometimes contradictory real world. It’s like trying to calibrate one skewed compass with another. Changing Human Roles: Labelers need to shift from being “executors” to “coaches” and “auditors.” But this requires them to have better judgment and expertise, and how do we ensure that this group itself is diverse and fair, avoiding their biases from becoming AI's biases? System Complexity: Building a dynamic, closed-loop data pipeline is much more complex than traditional methods.
As technology advances and use cases grow, many companies are aiming for fully self-driving data loops. For example, every user interaction with AI (like clicking preferences or modifying outputs) becomes implicit labeled data, feeding back into the model for iteration. ChatGPT is already doing this. The AI’s outputs directly reflect the values we’ve “fed” into it. This forces us to think more deeply and systematically: what do we value? What do we oppose? What’s considered a “good” answer? This process itself is a massive social experiment, encouraging collective value reflection.
What we’re doing is far more than training a model. We’re engaging in large-scale “civilization coding.” Through countless small preference choices (liking one answer, rejecting another), we’re embedding human, often fuzzy, intuitive values, moral guidelines, and aesthetic preferences into AI systems. Ultimately, the real answer to breaking the “impossible triangle” may not lie in finding smarter engineering solutions, but in redefining our relationship with AI. We will move from being data “labelers” to being the “definers” of values, the “auditors” of AI behavior, and co-creators of the future of civilization. Whether this transformation succeeds doesn’t depend on the algorithms' quality, but on our own human wisdom, inclusivity, and foresight.
Fast Take
The quest to solve the "impossible triangle" of AI data labeling—scaling, high quality, and low cost—has led to an intriguing shift. With "Model-in-the-Loop," machines are taking on more of the workload, while humans focus on higher-level decisions. Want to know how this change is shaping the future of AI? Keep reading!
Share Now
