Global scale
I run AI where trust, adoption, and economics all matter.
Five concurrent enterprise programs, a $30M portfolio, 120+ contributors, and CEO-active platforms across a global public company.

D. Brian Letort, Ph.D.
I write about the systems layer of enterprise AI: the models, data foundations, agentic workflows, economics, governance, and operating patterns that turn experiments into durable capability.
Follow the operating layer
Board-ready AI strategy, operator-grade technical depth, and weekly signal from the AI market.
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Qwen3.8-27B can hold its native 262K context window on one RTX 5090. The reason is architectural, and the serving stack matters as much as the weights.
Newsletter
Subscribe for governed enterprise AI strategy, market signal, and operator-grade technical depth.
Industry intelligence
Cross-stack flywheel
For officers tracking AI market movement.
Latest / Issue 18
The duration-mismatch thesis just moved into the GPU vendor's own 8-K — NVIDIA is now the residual-value underwriter of a 20-year OpenAI lease, and Marvell is teaching Google how to buy silicon on the revenue that has not yet arrived
Model layer
For architects tracking model capability shifts.
Latest / Issue 18
OpenAI cut Sol by more than a fifth for three months while an unknown lab shipped an MIT frontier — the same week the industry conceded that vendor-reported is what the top of the board looks like now
Operating layer
For builders operationalizing agentic work.
Latest / Issue 18
Anthropic promoted skills and computer use to GA and gave the agent a way to load them progressively — this week's technique change is that skills are versioned artifacts, not prompts, and the sandbox runs them under the caller's identity
Procurement surface
For buyers watching AI reshape software.
Latest / Issue 17
Last week's question was whether the agent comes in through the front door or gets assigned work — this week the incumbents answered by giving the agent the user's credential
Research
A foundational paper and three-paper trilogy on the systems layer between retrieval and reasoning — the governed, measurable, optimizable layer where reliability lives, where provenance is enforced, and where token economics becomes a design surface rather than an accounting line.
Why trust the signal
Global scale
Five concurrent enterprise programs, a $30M portfolio, 120+ contributors, and CEO-active platforms across a global public company.
Technical depth
Data, data science, machine learning, software engineering, agents, RAG, model routing, evals, and the operating layer between them.
Public body of work
A Context Compilation research program, PhD teaching, 16 Pluralsight courses, two books, and a weekly AI industry corpus.
Where to go next
Speaking and executive briefings
I accept a small number of speaking, board, press, and executive briefing requests each year.