Foundations
Conceptual clarity earned from building at scale
Foundations
Conceptual clarity earned from building at scale
Consequence-Bounded Autonomy
Been thinking about how every agent conversation I have circles back to capability questions—model accuracy, task completion rates, benchmark scores. But the teams actually running these systems at scale? They're asking completely different questions. Mainly: what breaks if this screws up?
What's interesting is how the autonomy decisions map to consequence tolerance, not technical readiness. You see the same pattern across different deployments—the framework isn't about climbing some maturity ladder, it's about matching autonomy to the specific mess you're willing to handle when things go sideways. The forecast matters less than knowing what storm you're prepared for.
Consequence-Bounded Autonomy
Been thinking about how every agent conversation I have circles back to capability questions—model accuracy, task completion rates, benchmark scores. But the teams actually running these systems at scale? They're asking completely different questions. Mainly: what breaks if this screws up?
What's interesting is how the autonomy decisions map to consequence tolerance, not technical readiness. You see the same pattern across different deployments—the framework isn't about climbing some maturity ladder, it's about matching autonomy to the specific mess you're willing to handle when things go sideways. The forecast matters less than knowing what storm you're prepared for.
When Shopping Carts Became Identity
Been thinking about why authentication breaks so predictably at scale. Years of watching production login flows, and there's this pattern where the complexity isn't in the code—it's in managing state that lives in two places at once. Browser thinks you're logged in, server disagrees, user sees an error that looks like a bug. Turns out a 1994 shopping cart decision is still dictating how that works.
The forecast here: browsers are deprecating third-party cookies, which means enterprises are rebuilding authentication infrastructure while keeping millions of sessions alive. Not a clean cutover. Both systems running simultaneously, hoping nothing breaks mid-flight. Classic case of infrastructure built for one purpose getting stretched way past its design constraints.
When Shopping Carts Became Identity
Been thinking about why authentication breaks so predictably at scale. Years of watching production login flows, and there's this pattern where the complexity isn't in the code—it's in managing state that lives in two places at once. Browser thinks you're logged in, server disagrees, user sees an error that looks like a bug. Turns out a 1994 shopping cart decision is still dictating how that works.
The forecast here: browsers are deprecating third-party cookies, which means enterprises are rebuilding authentication infrastructure while keeping millions of sessions alive. Not a clean cutover. Both systems running simultaneously, hoping nothing breaks mid-flight. Classic case of infrastructure built for one purpose getting stretched way past its design constraints.
A Conversation with /graphql, The Endpoint That Swallowed the API
A Conversation with /graphql, The Endpoint That Swallowed the API
Pattern Recognition from the Field
Every major AI lab just announced a "reasoning" model. OpenAI's o1. Anthropic's extended thinking. Google's Gemini thinking mode. The pitch is identical: these models think longer, reason deeper, solve harder problems.
Then you actually use them.
OpenAI claimed o1 performs at PhD level on physics and math. Users found it failing basic logic puzzles within days. Anthropic shows Claude's thinking process, which often reveals circular reasoning dressed up as deliberation. Google's announcement was heavy on promise, light on specifics.
The pattern is clear. Labs are conflating longer inference time with better reasoning. Spending more tokens to think doesn't automatically produce better outcomes. It's like assuming someone who talks longer is automatically smarter.
This matters because enterprises are making architecture decisions based on these claims. They're building systems that assume reasoning capabilities that don't exist at scale yet.
Every major AI lab just announced a "reasoning" model. OpenAI's o1. Anthropic's extended thinking. Google's Gemini thinking mode. The pitch is identical: these models think longer, reason deeper, solve harder problems.
Then you actually use them.
OpenAI claimed o1 performs at PhD level on physics and math. Users found it failing basic logic puzzles within days. Anthropic shows Claude's thinking process, which often reveals circular reasoning dressed up as deliberation. Google's announcement was heavy on promise, light on specifics.
The pattern is clear. Labs are conflating longer inference time with better reasoning. Spending more tokens to think doesn't automatically produce better outcomes. It's like assuming someone who talks longer is automatically smarter.
This matters because enterprises are making architecture decisions based on these claims. They're building systems that assume reasoning capabilities that don't exist at scale yet.
AI labs are racing to announce reasoning models, but the gap between marketing claims and actual shipped capabilities keeps widening across OpenAI, Anthropic, and Google.
OpenAI's o1 fails basic logic despite PhD-level claims. Claude's extended thinking often reasons in circles. Google's details remain vague despite bold announcements.
Companies are making expensive architectural decisions based on reasoning capabilities that don't exist at scale yet, creating technical debt before systems even launch.
Longer inference time doesn't equal better reasoning. More tokens spent thinking is a cost center, not automatically a quality improvement for production systems.
Test reasoning models on your actual use cases, not benchmark claims. Measure output quality and operational cost, not how long the model appears to think.
Questions Worth Asking