Conductor CEO Charlie Holtz Walks Us Through His AI Coding Setup

NVIDIA Corporation operates as a data center scale AI infrastructure company.

Second-order but historically strong linkage: more inference/training demand from agentic coding increases accelerator demand.

Microsoft Corporation develops and supports software, services, devices, and solutions worldwide.

Copilot/GitHub and Azure AI are direct beneficiaries of AI-native dev workflows becoming standard.

Amazon.com, Inc.

AWS/Bedrock distribution + infrastructure demand from Anthropic/Claude ecosystem as model usage grows.

Alphabet Inc.

Gemini/GCP benefit from growing developer AI usage; competitive but still levered to token/compute growth.

Advanced Micro Devices, Inc.

Alternative accelerator supplier; benefits if spend broadens beyond a single vendor.

Duolingo, Inc.

Loose thematic risk: if investors increasingly view software features as commoditized by AI, some app-layer multiples can be pressured absent clear AI capture.

Many founders get stuck trying to find the perfect startup idea before they commit. But the perfect idea doesn't exist in the abstract. The only way to find what works is to pick one, go deep, and get feedback from real customers. In this episode of Startup School, YC General Partner Jon Xu breaks down how to choose what to build, "burn the other boats," and go deep enough to practically run your customer's business— and why that depth is what surfaces the better idea underneath. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Chapters: 00:00 — Intro 00:59 — The "Perfect Idea" trap 02:42 — Why working on multiple ideas fails 03:21 — How to actually go deep 04:51 — Could you run your customer's business? 06:18 — Build at the edge of what AI can do 08:37 — Aim at the most ambitious version 09:33 — What happens when the idea fails 10:27 — Walk fast in one direction

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YC Paper Club recap highlighting emerging AI research directions: scaling laws applied to protein biology (ESM), AlphaZero-style self-play for LLMs, streaming RAG for real-time voice agents, formal verification with Lean, and “agentic” programming workflows. This is directional/strategic (themes) rather than a specific catalyst with near-term dates.

Fireside chat describes Meesho’s rapid scale in India mass-market e-commerce/social commerce (Android #1 shopping app; ~1M sellers; claimed very high order volume), key pivots (WhatsApp-group distribution; business-model changes after Jio disrupted earlier assumptions), and forward-looking theme around voice/AI to expand addressable buyers. Meesho is private; implications are second-order for listed India e-commerce competitors, logistics, payments, telco, and digital ads/cloud.

The Most AI-Pilled CEO We Know Brex co-founder and CEO Pedro Franceschi believes most people still underestimate how much AI will change the way companies are built. AI isn't just another tool, it's a new foundation for building products, teams, and companies. In this episode of Lightcone, Pedro shares why he thinks we're only months into a platform shift as significant as the invention of electricity, how AI has changed the way he works, and why every founder should be "token maxing" to understand the limits of the technology firsthand. He explains why the CEO needs to be the chief AI officer, how Brex is rebuilding itself around AI, and why founders should rethink what's possible when intelligence is available on demand. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Chapters: 01:13 – How Pedro Became AI-Pilled 04:08 – The Electricity Analogy 05:21 – Free the Claw 06:56 – Making AI Safe for Enterprise 10:57 – Why Most Companies Are Behind 13:09 – AI Teammates, Not Chatbots 14:22 – The Case for Tokenmaxxing 18:24 – The Company of One 20:54 – The One Thing AI Can't Replace 28:06 – Building Customer World Models 32:58 – R

Transcript-style startup/YC commentary about focusing on building working software vs demos; mentions revenue run-rate, GTM, opening an SF office, and doing RL/agents/JSON output. Contains no specific public-company names or tradable catalysts.

Transcript-like, low-signal narrative about startup Legora’s YC experience and rapid ARR growth; few concrete market-relevant facts. Only clear public-company reference is SAP.

YC-style interview/video about Conductor CEO describing an AI-assisted coding workflow (agents, MCP, Codex vs Claude, enforcing workflows). It’s product/workflow commentary, not a market-moving datapoint (no financial metrics, partnerships, pricing, or adoption numbers). Actionability is therefore low, but it reinforces the broader thesis that AI coding assistants/agents are becoming standard developer tooling and will continue to drive compute and model usage.