“does the models foundation models have leverage up the stack the way Windows did?”
Microsoft Windows
By Microsoft
0 recommend/use · 4 sourced episodes
Every sourced reference
Short attributed excerpts only. Timestamps are approximate.
“we haven't built Windows yet and it will be obvious once we do”
“Microsoft had their announcement recently. They're like now it's part of the OS of Windows.”
“the shared developer infrastructure for all Microsoft products like Windows and Office and Azure”
Related frameworks
Barrels-and-Ammunition Team Design
Staff each team from the gap, not from a fixed PM/EM/designer template
Build Only at the Magic Intersection
Don't ship what anyone could build off the shelf — build only where model and product uniquely meet.
Defensible Moats for AI Startups
Four durable places to build in AI where foundation-model labs are least likely to squash you.
Distribution Beats a Commodity Product
When products in a category are basically interchangeable, an adequate product with superior distribution wins
Dive-In Career Strategy for a Platform Shift
Facing a disruptive technology, submerge yourself in it and aim to hold at least two of three career pillars
Evals as Articulating Success
An eval is just a clear spec of ideal behavior — the shared language of AI product work
Flow-Preserving AI Assistance Design
Design AI suggestions around the user's flow state: no panel switches, no waiting, ephemeral by default
Hunting New Bottlenecks When AI Writes the Code
When AI removes the coding bottleneck, constraints shift up and downstream — go find them.
Make the Other Mistake (Prompting for Brutal Feedback)
To get honest AI critique, over-correct toward brutal — the model won't actually overshoot.
Persona-Framing for AI Product Behavior
Pick a human metaphor for your AI, then derive its behavioral guardrails from that role
Presume Radical Uncertainty (The 1997 Lens)
Treat an emerging platform as if it were 1997 for the internet: assume most of it doesn't work and you can't yet name the winners
Price-Elasticity Three-Response Model
When a technology makes something cheaper, work out which of three demand responses your market will take
Research-to-Product Graduation
Move an incubated moonshot from the research lab to a product team without killing it or trapping the researchers
Run Toward the Hard Use Cases
Don't disable high-stakes uses to avoid downside — engineer them to be great
Ship-to-Learn: The Emergent-Product Loop
When product properties are emergent, launching is how you discover them
Task vs. Job Automation Test
Before predicting a role's automation, ask whether the automatable task IS the job or just one piece of it
The 10-to-1 Input/Output Kill Test
When you pour 10 units of effort in for 1 unit of output, the project has run its course.
The 60/30/10 Portfolio Capacity Split
Allocate team capacity across incremental wins, operations, and audacious bets before priorities compete
The Maximally Accelerated Question
A forcing question that separates critical path from what can wait
The Teach-Me-Something-In-One-Minute Interview
A 60-second timed teaching exercise scored on completeness, complexity, and clarity
The Three-Part AI Product Utility Equation
A useful AI product needs model intelligence, context/memory, and application/UI to all converge.
Two-Mode Prioritization for AI Products
Prioritize backward from model magic AND forward from customer needs
Value-Up-The-Stack Test
To find where the money accrues in a platform shift, test the infrastructure layer for network effects, differentiation, and pricing power
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