In 2026, AI talent means domain experts

In 2026, the real premium for training data sits in how systems behave — the traces, logs, and decisions that describe complex enterprise processes over time.

The hottest data markets will cluster around process-heavy domains: supply chains, logistics, energy systems, healthcare operations, financial modelling. These are not clean text corpora you can dump into a tokenizer. They are tangled causal maps: if inventory misses this checkpoint, what happens to downstream fulfilment; if volatility spikes, how does risk get rebalanced; if a lab result lands late, how does the care pathway bend. The data is temporal, relational, and policy-laden.

“Demand on the human data side is going to continue to persist. But it moves more into job type and domains than it does academic fields of study.” – Jordan Cealey

Linguistically, the demand profile shifts as well. General web English is already over-represented. What’s scarce is technical language:

• Legal reasoning threaded through contracts, case law, and regulatory guidance.
  
• Scientific notation and experimental logs, where small symbols encode big commitments.
  
• Manufacturing instructions, standard operating procedures, and maintenance records that embed decades of tacit know-how.
  
• Medical protocols, order sets, and clinical notes that express risk, liability, and heuristics in compressed, idiosyncratic language.
  

In 2026, those specialisms are not nice-to-have; they are the base language of high-value models. If your system is going to touch claims, grid routing, or care delivery, it needs to speak the local tongue, including all the passive-aggressive phrasing and weird shorthand that never appears in public benchmarks.

“There's increased investment in reaching more global markets in people's native languages. We want trainers who live in a place where a language is spoken, not just someone who learned the language at school. Contextual embedding is important for the person doing the training.” – Dan Brosnan 

It’s about native languages too. All the work that went into making systems usable in English now has to be repeated, with the model builders focusing on widely spoken languages like Hindi and Spanish to begin with. The models need to be able to handle regional accents, mixed dialects, and domain slang in the same sentence. That pushes demand toward speech datasets that reflect how people actually talk when money, safety, or care are on the line.

“The models are basically as good as PhDs on a whole lot academically. But the challenge is that that's still not actually translating to unlock business value.” –Ben Lowenstein 

The demand in 2026 is for structured, temporal, domain-specific process data, and for the specialized languages that ride on top of it. The organizations that own those causal maps will dictate how far AI can move from “autocomplete for language” to actual control systems for the real economy. Everyone else will be fine-tuning on vibes.