Rishi Dean

Picture this: you're in the Q3 roadmap review.

If you've been following my thesis on Adaptive Software, we've covered two of the three core components required to build systems that evolve around their users rather than prescribing how they work.

The framework shows "what" & "where" If you've been following along, you know I recently wrote about the AI Adoption Continuum as a diagnostic framework for understanding why so many organizations are failing to see meaningful returns from AI.

TL;DR In a Silicon Valley culture obsessed with optimizing every second of our lives, from health trackers to AI productivity hacks, we have started treating "unproductive" passions like spectator sports and comedy as a waste of time.

There’s a thesis circulating in tech circles (mostly among practitioners), that AI is going to gut a meaningful slice of the SaaS industry, creating an industry shifting “SaaS-pocalypse”.

In November, Uber's 5,000 engineers were tokenmaxxing on an internal leaderboard.

You know the scene in Good Will Hunting where Will tells the Harvard guy: “dropped 150 grand on an education you could have gotten for a dollar fifty in late charges at the public library”?

Ok, imagine all of your AI transformation dreams have come true: every workflow automated, every employee with teams of agents, cycle times have collapsed, and costs have fallen.

I researched various Enterprise AI Transformation frameworks, to see if there was an industry consensus.

The Core Idea Andrej Karpathy named something most builders had been quietly doing for a year: the shift from “prompt engineering” to “context engineering” (tweet).

Watch Andrej Karpathy code with an AI agent and it looks effortless.

The Dilemma Drop someone from 1985 into today.

In a 2004 business school class, I recall an esteemed (and knighted) guest speaker explain to us why Google's recent IPO would fail.

Block recently eliminated 40% of its workforce.

It's always bugged me that Venmo and other payment apps require both sides to be on the same network.

For 30 years, we’ve built software that tells users how to work.

The Fork in the Road In my last post, I mapped out the AI adoption continuum and argued that the gap between companies seeing massive gains and the ones seeing nothing comes down to people.