Notes by Mert Barutcuoglu, https://x.com/BarutcuogluMert/
On Startups and AI Opportunities
- The biggest opportunities in AI are often in startups because they’re willing to pursue ideas before they’re obviously valuable.
- AI is more useful when integrated into workflows:
- Example: Instead of asking AI to write an entire essay, use it in steps:
- Research the topic.
- Draft a version.
- Critique itself.
- Revise iteratively.
- This agentic workflow is how humans work, and how AI can work best too.
- From Single-Shot to Structured Intelligence:
- Old paradigm: prompt once → get output.
- New paradigm: task ****orchestration across tools, APIs, and documents.
AI Workflows & Engineering
- Traditional AI: Asked to generate output in one go (e.g., an entire essay).
- Emerging models: Iterative workflows, where AI participates step-by-step: researching, drafting, critiquing, revising, like collaborating with a human.
- Emergence of agentic workflows where systems take structured actions across multiple tasks (e.g., write, research, revise).
- These agentic workflows are the dividing line between functioning and non-functioning AI applications today.
Rapid Prototyping & Building
- Engineering used to be a bottleneck. Now:
- AI-assisted coding is 10x faster for prototypes and throwaway software.
- Production-quality software is 30–50% faster.
- Cost of development has decreased, making fast iteration more feasible.
- Some old one-way door decisions are turning to be two-way door decisions as it is easier to start from scratch due to faster iteration possibilities.
- Now, product management is the bottleneck:
- Historically: 1 PM per 4–7 engineers.
- Now: Some teams propose 2 PMs per engineer due to engineering speed outpacing product decision-making.
Concrete Ideas vs. Abstract Vision
- Big visions attract attention but are hard to build on.
- Concrete ideas help teams move fast, validate, or fail quickly.