What Frontier Labs Buy: A Data Deep Dive with Handshake AI — Garrett Lord & Team

AI data development has transitioned from basic labeling to the complex "authoring" of simulated environments, rubrics, and expert-driven tasks. Frontier models frequently fail on long-horizon, non-verifiable workflows, such as life sciences assays or intricate financial modeling, because they lack the necessary domain-specific reasoning and visual intelligence. Achieving usable performance requires high-quality, stylistically consistent data that prevents reward hacking and ensures internal consistency across thousands of variables. As models become more autonomous, data and alignment are merging into a single challenge, necessitating rigorous, domain-specific benchmarks to verify reliability in regulated sectors. Enterprises must move beyond simple co-pilots toward end-to-end automation, which demands private, robust evaluation frameworks to ensure safety and legal compliance. Ultimately, the market for high-fidelity, expert-authored data is expanding rapidly to meet the demand for trustworthy, high-consequence AI deployments.