Essays
PromptQL's writing on shared context, accuracy, and building AI you can actually trust.
User Identity: the access model for multiplayer AI
Multiplayer AI shouldn't mean a shared login. The agent should act as whoever is driving it — their identity, their permissions — so a shared conversation never quietly becomes shared access.
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Designing an Auth model for Multiplayer AI
Multiplayer AI — people and agents sharing the same context — breaks the assumptions behind traditional access control. Here's how we designed an auth model that holds up.
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Stop tokenmaxxing. Start contextmaxxing.
We've spent the last year realizing AI will soon do everything — provided it has enough useful context. The alpha has shifted from using AI to maintaining useful context.
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2 reasons why Slack can't contain the "company brain"
Two simple reasons Slack hasn't become, and won't become, the company brain: it captures coordination rather than execution, and it's built for messaging, not prompting.
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Killing Slack was the only way to make AI accurate
The three big bets we made to build AI that works accurately on internal data — and why moving beyond Slack was essential to getting there.
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43% accuracy with Opus-4.6 & friends - will Text-to-SQL ever be good enough?
The Data Agent Benchmark from UC Berkeley's EPIC lab shows frontier models top out around 43% on real-world data questions — and what that means for text-to-SQL on enterprise data.
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On shared context
Companies run on shared context. Becoming AI-native requires systems that can capture, maintain, and apply that context for AI. Here's the blueprint for building one.
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The semantic layer is dead. Long live the wiki.
Most “AI on data” programs are just semantic-layer maximalism with a new paint job. A Wikipedia-style model is a better way to think about organizational meaning — and to make AI reason accurately.
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Introducing the GenAI Assessment Framework (GAF): A 3×3 Matrix to Map Enterprise AI Needs
Introducing the GenAI Assessment Framework (GAF): a 3×3 matrix that helps AI leaders map enterprise AI needs and measure progress.
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Static AI Is Already Cutting Jobs; What Happens When It Starts to Learn?
Most of the labor-market impact we're seeing comes from mediocre, static AI. What happens when these systems start to learn on the job?
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Being "Confidently Wrong" is holding AI back
The failure mode that stalls “AI for data” efforts isn't psychedelic hallucination — it's confident inaccuracy: plausible answers that are wrong in subtle and costly ways.
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Expose the Data Prep Tax: A Path to High-Impact AI Deployment
Every company pays a hidden “data prep tax” — weeks of setup and constant upkeep. Eliminate it to unlock the curiosity and innovation that's been dormant for years.
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Find the Unanswered Questions: A Path to High-Impact AI Deployment
$100M lost to unanswered questions. Reliable AI that speaks the language of your business lets every employee ask and act instantly.
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Spot the Automation Paradox: A Path to High-Impact AI Deployment
$200M lost to hidden automation gaps. AI that speaks expert logic can unlock massive value.
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