"The AI models that you're using today is the worst AI model you will ever use for the rest of your life. Every two months, computers can do something they've never been able to do before and you need to completely think differently about what you're doing."
Every 60 days brings a step-function improvement in model capability
The pace of change requires complete rethinking of product strategy
Traditional tech companies work with fixed foundations — AI is the opposite
Building on shifting foundations demands a different product mindset
The Core Problem
Fuzzy Inputs, Fuzzy Outputs, New UX
Not deterministic: Same input ≠ same output (fundamentally different from traditional software)
Nuance matters: LLMs excel at subtle human language and context
Reliability varies: 60% vs 95% vs 99.5% accuracy requires completely different product designs
Evals are essential: Understanding your specific use case accuracy is the foundation of good AI products
The chat interface insightChat is the ideal medium for fuzzy, general-purpose AI because it allows the full expressive power of human language without artificial constraints. It's the lowest common denominator for talking to superintelligence.
Use-case specificityHigh-volume, prescribed use cases can be faster and more specific. But chat remains the catch-all baseline for anything that doesn't fit your vertical.
How OpenAI Works
Research + Product as One Team
Evolution: OpenAI began as pure research; ChatGPT was a "low-key research preview"
The problem: Separate research and product teams = just consuming your own API
The solution: Unified teams of research, engineering, and product iterating together on evals and use cases
The mandate: Every new product starts as a cross-functional research-eng-product collaboration
ImageGen launch moment
Internal usage exploded before launch. Team using it to ghibli-ify their photos, constantly visiting the shared gallery — a clear signal of product-market fit internally.
The org principle
OpenAI is intentionally PM-light (~25 PMs). High-agency PMs guide product-focused engineers rather than micromanage. Empowered teams move faster.
Hiring Mindset
What OpenAI PMs Need
High agency: See a problem, solve it. Don't wait for permission or clear direction
Comfort with massive ambiguity: No one will hand you a roadmap; you discover problems as you go
EQ and influence: Lead through relationships, not authority. Especially critical with research teams
Execution speed: Move quickly; iterate; learn from shipping, not planning
Why junior PMs struggleThe role is self-directed and ill-formed by design. Early-career PMs expecting clear scope or structure will find it frustrating. This role requires leadership maturity.
Contrarian Views
AI Product Myths Kevin Rejects
✗AI models will inevitably become commoditiesINSTEAD →✓ Better products come from integrating research, eng, and product to fine-tune models for specific use cases, creating defensible moats.
✗It's impossible to build AI products as fast as traditional softwareINSTEAD →✓ The pace at OpenAI proves that organizations can move at unprecedented speed when research and product work as one team.
✗AGI is a distant theoretical problem, not a near-term product realityINSTEAD →✓ Build your product roadmap assuming capabilities will leap forward every 60 days. Design for the models of tomorrow, not today.
✗Startups can't compete with OpenAI in the foundation model spaceINSTEAD →✓ Startups win by building on APIs and fine-tuning for specific verticals where OpenAI won't go deep. Find your wedge and own it.