Sandeep

Large language models allow us to see anew about how we think and learn: not through the rigid application of universal laws, but through the recognition of patterns across vast landscapes of experience. When a chef seasons a dish, when a mother knows her child is lying, when a trader senses a market shift—these aren’t moments of mechanical... See more

🧠 Distributed Cognition & Systems Thinking

In distributed systems (technical or cognitive), waiting for perfect information can lead to latency, deadlock, or missed opportunities . Systems often rely on local views and bounded rationality — making the best decision with what's available.

⚙️ SRE & Resilience Engineering

In incident response, teams... See more

So, what’s the broader lesson? In practice, our risk models (implicit or otherwise) are always miscalibrated: a history of past successes is just one of multiple avenues that can lead us astray. Trying to achieve a perfect risk model is like trying to deploy software that is guaranteed to have zero bugs: it’s never going to happen. Instead, we need... See more

To start to figure out your own incentive architecture, ask yourself the following line of questioning:

• What are you incentivized by?


• What decisions do you own or influence through that incentive?


• What are each of your stakeholders incentivized by?


• What decisions do they own or influence through that incentive?


• What incentives would the change you want

... See more

“ If they are the ones with the power, you can’t convince anyone to change what they are doing. Unless somehow you can change their incentive. “

We thought we were buying a time-saving device. We got something completely different.

AI doesn’t save time. It trades time for capability.

You can now attempt work that was previously impossible. Explore territories previously inaccessible.

But it costs MORE time, not less.

This follows a familiar pattern. Consider the parallel from photography. When... See more