We are witnessing a pivotal shift in AI development. While companies have spent the last few years competing for top spot among GenAI models, the industry is now setting its sights on something much bigger: Artificial General Intelligence (AGI). And this shift is fundamentally changing how we think about data annotation.
The Reality Check: Transformers Aren’t Going Anywhere
Let’s start with what many in the industry already know but don’t often say out loud: despite all the AGI hype, we are still using the same transformer architecture that has been around for years. Meta’s LLaMA, OpenAI’s models, and Google’s systems are all built on transformers originally designed for language tasks.
Sure, we have gotten better at applying them to images, audio, and other data types. And yes, we’ve made impressive improvements in data quality and computing power. But the core architecture? It’s the same.
What this means for data annotation companies: Don’t panic about completely reinventing your processes. The fundamental skills of high-quality data labeling are still relevant. But the way we apply these skills needs to evolve dramatically.
AGI Won’t Be One Super-Smart Model
Here’s where things get interesting. The path to AGI isn’t about building one massive, superintelligent model. Instead, it’s about creating systems where multiple AI models work together seamlessly.
Think of it like a jazz ensemble rather than a solo performance. You have:
- Models that “think fast” (what transformers already do well: quick pattern recognition and responses)
- Models that “think slow” (deliberate reasoning, like the research happening in reinforcement learning)
- Models that “think together” (collaborative systems where different AI agents coordinate)
Imagine an autonomous vehicle system. The “fast thinking” component instantly recognizes a stop sign. The “slow thinking” component reasons about whether it’s safe to proceed given weather conditions and traffic patterns. Imagine an AI-powered medical diagnosis system. The “fast thinking” component instantly spots a suspicious dark spot in an X-ray image. The “slow thinking” component carefully analyzes the patient’s medical history, symptoms, and lab results to determine what this could indicate. The “thinking together” component brings together multiple AI specialists, one focused on radiology, another on pathology, and a third on treatment options, working as a team with the human doctor to create a comprehensive diagnosis.
For data annotation, this means we are not just labeling individual images anymore. We are creating datasets that help AI systems understand relationships, reasoning patterns, and make open-ended choices about which tools to use and how to solve problems.
The Data Revolution is Already Starting
The data annotation market is exploding, growing from $3.9 billion in 2023 to a projected $15.2 billion by 2033. But here’s what’s driving this growth: it’s not just more of the same labeling work. It’s a response to the rise of AGI-era demands, like multimodal understanding, real-time learning, and expert-in-the-loop reasoning.
AGI systems need fundamentally different types of data:
- Multimodal data: Instead of labeling text, OR images, OR audio, we are labeling the connections between them. How does the tone of voice in an audio clip relate to the facial expression in a video? How does the text description align with what’s shown in an image?
- Real-time feedback data: AGI systems need to learn continuously. This means creating annotation workflows that can adapt and improve as the AI learns, not just one-time labeling projects.
- Synthetic data integration: Gartner predicted that 60% of the data used to train AI systems would be synthetic by 2024, a trend now visibly unfolding in 2025 as organizations shift from real-world data collection to scalable simulation and scenario-based training. This shift doesn’t eliminate the need for human annotation; rather, it redefines it, placing greater emphasis on validating, refining, and ensuring that synthetic data meets quality, accuracy, and fairness benchmarks.
What This Means For Data Annotation Services
We see three major shifts happening:
1. Quality Over Quantity
The days of massive, simple labeling projects are numbered. AGI development requires smaller, incredibly high-quality datasets with complex relationships and nuanced annotations. A perfectly annotated dataset of 10,000 examples might be more valuable than a mediocre dataset of 1 million examples.
2. Domain Expertise Becomes Critical
Generic annotation is becoming commoditized. The real value is in understanding specific industries deeply enough to create training data that helps AI systems navigate complex, real-world scenarios. Healthcare AI needs annotators who understand medical nuances. Financial AI needs people who grasp market dynamics. Even general LLM fine-tuning requires sensitivity to the nuances of style, tone, and empathy in text.
3. Human-AI Collaboration, Not Replacement
Scale AI’s recent $1 billion funding round and $13.8 billion valuation show how valuable data annotation infrastructure has become. But the most successful companies aren’t trying to replace humans with AI; they are figuring out the optimal collaboration between human insight and AI efficiency.
Simple Examples of What’s Changing
| Old Approach | New Approach |
|---|---|
| Label 100,000 images as “cat” or “dog.” | Annotate how a pet owner’s emotional state in a video correlates with their pet’s behavior, helping AI understand complex human-animal relationships |
| Transcribe audio to text | Capture the reasoning process someone uses to solve a problem, including their hesitations, corrections, and final decision-making |
| Tag objects in autonomous vehicle footage | Narrate the step-by-step contextual reasoning process the model needs to follow to choose a behavior that complies with local traffic laws and safety guardrails when confronted with a challenging driving scenario, based on a video. |
| Label the sentiment of UGC | Evaluate the empathy and brand alignment of model-generated text against a custom alignment rubric, and write an idealized response. |
The Path Forward
The companies that will thrive in the AGI era are those that can evolve from simple labeling services to partners in AI development. This means:
- Investing in tools that help human annotators work more efficiently with AI assistance
- Developing expertise in specific domains where nuanced understanding matters
- Building workflows that support continuous learning and improvement rather than one-time projects
- Creating feedback loops where annotation quality directly improves AI performance
The Bottom Line
AGI development isn’t just about building bigger models or scaling compute. It’s about designing AI systems that can reason, collaborate, and adapt like humans. Achieving that demands more than just better architecture; it calls for a new approach to training data, one grounded in real-world complexity. This includes constructing rich, contextual scenarios to evaluate how agents select tools, coordinate with one another, and respond dynamically to specific tasks. The transformer architecture that got us this far will likely take us to AGI. But the data annotation industry needs to evolve from simple labeling to sophisticated AI development support. The companies that make this transition successfully won’t just survive the AGI era–they’ll help define it.
“The future of AI isn’t about replacing human intelligence. It’s about augmenting it. And that starts with how we prepare the data that teaches these systems to think, reason, and work alongside us.”
Conclusion
At iMerit, we are enabling the shift from simple data labeling to sophisticated AI development support. Our global network of domain experts, spanning healthcare, autonomous mobility, and other specialized fields, brings the contextual depth needed to train and fine-tune AGI-era systems.
Powered by Ango Hub, our workflows combine expert human judgment with automation to support real-time feedback, multimodal reasoning, and continuous learning. As AI evolves to think, reason, and collaborate, iMerit delivers the data infrastructure that helps it do so responsibly and intelligently.
