Karpathy Just Told Us What Startups To Build For 2026

Analyze the provided transcript from the video "Karpathy Just Told Us What Startups To Build For 2026".

Who/What: This video summarizes Andrej Karpathy's insights on the future of software development and AI startups. Karpathy, a prominent figure in AI (formerly at OpenAI and Tesla), outlines a significant shift from traditional coding to "Software 3.0," where Large Language Models (LLMs) become the programmable computer, and prompts/context act as the new code. The video discusses the implications for developers and founders, emphasizing the need to adapt to this evolving landscape.

Key Takeaways & Future Directions:

• Software Evolution:

  • Software 1.0: Handwritten rules (traditional code).
  • Software 2.0: Training neural networks on vast datasets.
  • Software 3.0: LLMs as the computational engine, with prompts and context windows serving as the interface and "code."

• The "Menu Gen" Test 🧪: A critical evaluation for app viability. If an app's core function can be replicated with a single multimodal prompt and appropriate tool calls (or orchestrated by an agent), it's likely just "plumbing" for existing LLM capabilities and risks becoming obsolete. Many current apps may not survive this test.

• From Vibe Coding to Agentic Engineering 🏗️:

  • Vibe Coding: The initial democratization of building with LLMs, allowing rapid prototyping and exploration.
  • Agentic Engineering: The professional, rigorous approach to building with AI agents. This involves meticulous planning, context management, robust testing, and verification to achieve high performance and reliability, leading to significantly faster development cycles for skilled practitioners.

• Four Frameworks for 2026 Startups 🚀:

  1. Tools for Understanding: Develop capabilities that enhance comprehension and strategy, not just speed. This involves building agents that can act as "strategy brains," providing focus and direction based on deep domain knowledge.
  2. Agent-First Infrastructure: Redesign systems to be directly usable by agents, stripping away human-centric UI. Focus on clear APIs and machine-readable documentation (LLM.txt files) for seamless agent interaction.
  3. Verifiable Domain Capabilities: Identify niche domains with inherent verifiability (e.g., financial trading, supply chain, data cleaning) where specialized reinforcement learning or fine-tuning can create unique, defensible capabilities.
  4. Software 3.0-Native Apps: Build entirely new applications that are only possible with the reasoning and generative power of LLMs. These should not be mere improvements on existing software but groundbreaking innovations enabled by this new paradigm.

Final Takeaway: The AI landscape is rapidly shifting towards LLM-centric development. Developers and founders must critically assess their current projects against the "Menu Gen" test and embrace "agentic engineering." The future lies in building tools that augment understanding, create agent-native infrastructure, leverage verifiable domain expertise, and pioneer truly novel Software 3.0 applications.