Current LLMs are intelligent enough for many tasks but fail because they lack access to complete context—emails, Slack messages, past data. The next step is building products that ingest this real-world context, making it available for the model to act upon.

Turing operates in two markets: providing AI services to enterprises and training data to frontier labs. Serving enterprises reveals where models break in practice (e.g., reading multi-page PDFs). This knowledge allows Turing to create targeted, valuable datasets to sell back to the model creators, creating a powerful feedback loop.

Instead of expensive, static pre-training on proprietary data, enterprises prefer RAG. This approach is cheaper, allows for easy updates as data changes, and benefits from continuous improvements in foundation models, making it a more practical and dynamic solution.

The primary barrier for enterprise AI is the 'context gap.' Models trained on public data have no understanding of your specific business—its metrics, language, or history. The key is building infrastructure to feed this proprietary context to the AI, not waiting for smarter models.

Stonebraker's research reveals that on real production data warehouse benchmarks, LLMs achieve 0% accuracy. This is due to messy, non-mnemonic schemas, complex 100+ line queries, and domain-specific data not found in training sets—factors absent from simplified academic benchmarks like Spider and Bird.

Standard LLMs fail on tabular data because their architecture considers column order, which is irrelevant for datasets like financial records. LTMs use a different architecture that ignores column position, leading to more accurate and reliable predictions for enterprise use cases like fraud detection and medical analysis.

IBM's CEO explains that previous deep learning models were "bespoke and fragile," requiring massive, costly human labeling for single tasks. LLMs are an industrial-scale unlock because they eliminate this labeling step, making them vastly faster and cheaper to tune and deploy across many tasks.

While AI proofs-of-concept are easy, SAP's CTO states the real engineering hurdle is scaling reliably. The complexity lies in managing thousands of APIs, handling massive document volumes, and applying granular, user-specific context (like regional policies) consistently and accurately.

To improve LLM reasoning, researchers feed them data that inherently contains structured logic. Training on computer code was an early breakthrough, as it teaches patterns of reasoning far beyond coding itself. Textbooks are another key source for building smaller, effective models.