OpenAI · MIT Technology Review
EXL researchers have explored these production challenges in greater depth in the EXL Engineering the Harness series
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Increasingly, enterprises have access to many of the same models.
Key facts
- An exclusive interview with Nick Cammarata, the researcher whose 26,000-word viral blog post has AI safety folks celebrating
- Ten years after AlphaGo’s match against Go champion Lee Sedol, today’s AI still isn’t tapping into the machinery that made that win possible
- EXL researchers have explored these production challenges in greater depth in the EXL Engineering the Harness series
- The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it
Summary
The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it. That’s an important shift. Increasingly, the answer depends on the serving harness—the engineering system surrounding the model, including the architecture, memory management, scheduling, and optimization decisions that determine how efficiently model intelligence becomes a production capability. For technical executives, that changes where AI investment should go—and what they should demand from the architecture supporting it. A customer-facing assistant optimized for fast responses has fundamentally different requirements from a document-processing pipeline that can run overnight.