AI Labs Aren't Looking for More Data, They're Looking for Better Data Partners

For most of the past decade, the operating assumption inside AI labs was simple: more data wins. Scrape more of the web, ingest more images, buy more text, and model performance follows. That assumption built the first generation of large language models and it minted a small economy of vendors whose pitch amounted to volume at scale. The frontier has moved. Labs now sit on more raw material than they can clean, and the bottleneck has shifted from quantity to the harder problem of getting data that is accurate, well-labeled, and legally sound.

Yunfei Z, COO at Abaka AI, a Silicon Valley firm focused on data collection, annotation, and dataset creation, argues that this shift is changing what AI labs actually want from their suppliers. The request is no longer "send us another billion tokens." It is "help us build a dataset we can trust, in a domain we cannot label ourselves." That is a different business, and it favors a different kind of partner.

The problem labs are actually trying to solve

The public web has been largely consumed. Studies tracking the supply of high-quality text have suggested that frontier models are approaching the limits of what the open internet can offer, and the marginal page added to a training run now contributes noise as often as signal. Duplicated content, spam, machine-generated filler, and contradictory facts all dilute a model rather than sharpen it.

That leaves labs facing two harder questions. First, how do you improve a model once you have already trained it on everything cheap and abundant? Second, how do you teach a model the things the web never wrote down clearly, like the reasoning behind a radiologist's read of a scan, or the step-by-step logic a senior engineer uses to debug a system?

Neither problem is solved by more volume. Both are solved by better labeling, expert judgment, and carefully constructed examples. This is why reinforcement learning from human feedback, and its more recent variants built on expert demonstrations, have become central to how the strongest models are tuned. The value sits in the quality of the human signal, not the size of the corpus.

Why "better" is so much harder to buy than "more"

Volume is a commodity. Quality is a service, and it is expensive to deliver consistently.

Consider multimodal data, which is where much of the current demand concentrates. A self-driving dataset is not a pile of video. It is video where every pedestrian, lane marking, traffic light, and occluded vehicle is annotated frame by frame, with consistent rules applied across thousands of hours by people who do not disagree with each other. A medical imaging dataset requires annotators who can actually read the images, which means clinicians, not crowd workers paid by the task.

The gap between a cheap label and a correct one compounds. A model trained on inconsistent annotations learns the inconsistency. Errors that look small at the labeling stage surface later as failures the lab cannot easily diagnose, because the fault is buried in the training set rather than the architecture. Fixing it means going back to the data, which is slow and costly. Labs have learned this the hard way, and the lesson has made them pickier about who touches their pipelines.

There is also the legal dimension. Provenance now matters in a way it did not three years ago. A dataset assembled from sources with murky rights is a liability that can follow a model into court or out of a market entirely. Partners who can document where data came from, how consent was handled, and how it was processed are worth more than partners who simply deliver a larger file.

Where synthetic data fits, and where it does not

Synthetic data, generated by models to train other models, is often pitched as the escape hatch from the quality problem. It can be. Used carefully, synthetic examples fill gaps that real data does not cover, like rare edge cases in driving or underrepresented languages in text.

The trade-off is real and easy to underestimate. Train a model too heavily on the output of other models and you risk what researchers describe as collapse, where the system drifts toward its own averages and loses the long tail of genuine variation. Synthetic data is most useful when it is anchored by high-quality real data and validated by people who can tell when the generated examples have stopped resembling the world. That validation step is, again, a human-quality service rather than a volume play. Synthetic generation does not remove the need for good data partners. It changes the job description.

The market is reorganizing around trust

This is the part founders and operators should watch. The data vendors positioned to win the next phase are not the ones with the largest scraping infrastructure. They are the ones who can offer domain expertise, auditable pipelines, and labeling quality that holds up under scrutiny. The competitive moat is moving from access to data toward the ability to certify it.

That reorganization is already visible in how deals are structured. Labs increasingly want long-term partners embedded in their workflow rather than one-time bulk purchases. They want vendors who can recruit specialized annotators, build custom tooling, and adapt as the model's weaknesses become clear. It is a relationship business now, closer to consulting than to commodity supply.

For the broader ecosystem, the implication is that the data layer of AI is maturing into something with defensible economics. The companies that treated data as a race to the cheapest gigabyte are finding that race has no prize at the end. The ones building reputations for accuracy, provenance, and expertise are signing the contracts that matter. Whether that advantage holds as synthetic methods improve is an open question, but for now the labs are voting with their budgets, and they are spending on partners, not pile size.