Today’s post is by Bobby Samuels, CEO of Protege.
The frontier of artificial intelligence is jagged.
If you’re a serious user of frontier AI systems, that statement shouldn’t surprise you. In some domains, most prominently in coding, AI is truly stunning and already superhuman. But ask AI to navigate a multi-step customer workflow, or to interpret a complex medical scan, and you’ll find that at a certain level of complexity it inevitably breaks down.
Why is that? The model is the same across different dimensions: the same architecture running on the same hardware. What changed? Why is AI so good at some things and so bad at others?
The answer is the data. Some fields—like coding—benefit from reams of rich, well-structured, high-quality data, in large parts because the domain is extremely well-defined: everything you need to know is right there, in the code. But many other fields, like medicine, don’t.
We call it the data gap.
This data gap is one of the most important problems in AI today, and solving it is perhaps the most underrated bottleneck to rapid AI progress. And it’s why I am making a big announcement today. My company Protege is announcing DataLab: a dedicated research institution whose mission is to close the data gap that is holding back AI progress.
DataLab at Protege is not a data broker or a human-feedback body shop. It is a lab for AI data in the same way that OpenAI or Anthropic are labs for AI models, or that Nvidia Research is a lab for GPUs: the same rigor, the same research caliber, and the same scientific ambition. All this, applied for the first time at scale, to the data layer on which all AI progress depends.
What does that mean in practice? In brief, it means building multimodal healthcare benchmarks that reflect actual clinical scenarios, not generic medical Q&A, so that a model assisting in a cancer tumor board can be evaluated against the complexity it will really face.
It means developing frameworks for agentic task selection: identifying which real-world workflows are worth training AI agents on, and which are distractions.
It means creating “FICO scores” for AI data: standardized methods to quantify dataset quality and benchmark reliability across domains. And it means conducting research on dataset contamination, factuality, and representational bias that the entire AI field can build on.
We are building the data lab that AI needs to smooth out and push forward the jagged frontier of intelligence.
To understand why the world needs an AI data lab so badly, we should think about what is currently limiting AI progress.
There are three fundamental bottlenecks for AI progress: better algorithms, better chips on which to run those algorithms, and better data for those algorithms to train on. For the last few years of tremendous progress in AI to continue or even accelerate, we need to be pushing on all three at once.
We’re certainly pushing on algorithmic progress already. We have dedicated labs—OpenAI, Anthropic, Google DeepMind, and others—that employ thousands of researchers pushing the boundaries of what models can do. These labs employ many of the most talented engineers and scientists in the world, and they’ve made massive strides in the last few years.
And we’re pushing on compute. Nvidia and its competitors are waging a relentless campaign to build more powerful hardware, and billions of dollars flow into the infrastructure to run it as efficiently as possible. Both models and chips each have their own ecosystem of world-class researchers, massive R&D budgets, and a shared sense of mission.