The AI Tools Every YC Batch Uses

There are hundreds of AI tools. Most YC founders use about eight of them regularly. Here's an honest account of what actually gets used in the batch, what it's good for, and where the limits are.

Coding: Cursor#

Cursor is the default coding environment for most YC technical founders. It's built on VS Code with deep AI integration — not just autocomplete, but the ability to describe what you want in natural language and have the AI implement it.

What it's actually good for:

• Boilerplate generation (set up a new API endpoint, create a database schema)
• Refactoring existing code (the AI knows your codebase)
• Debugging with context (paste the error + relevant code, get a diagnosis)
• Writing tests from existing implementations

What it's not good for:

• Architectural decisions (the AI will make locally optimal choices that can be globally wrong)
• Systems thinking (it doesn't know about your other services or constraints it hasn't seen)
• Code you don't understand (if you can't review what it generated, you can't ship it safely)

We use Cursor for most code changes. The gstack workflow we use internally pairs Cursor with an explicit review step, specifically because AI-generated code needs human review.

Research: Perplexity#

Perplexity has largely replaced Google for research queries in our workflow. The key advantage: sources are cited inline, so you can verify claims quickly rather than cross-referencing multiple search results.

What it's actually good for:

• Technical research (how does X work, what are the best practices for Y)
• Market research (what do competitors offer, what's the pricing landscape)
• Fact-checking claims before putting them in pitch decks

What it's not good for:

• Very recent events (training cutoff issues)
• Nuanced competitive analysis (the AI can miss important context)
• Primary source research (go to the source for important decisions)

Writing: Claude#

Claude (Anthropic) has become the default for long-form writing in the YC community. It handles long contexts well, which matters for founder writing — emails with backstory, pitch narratives, long product documentation.

What it's actually good for:

• First drafts of investor updates
• Synthesizing information into clear summaries
• Editing your writing for clarity
• Long-form content like blog posts (with heavy human editing)

What it's not good for:

• Writing that requires your specific voice and experience (it will sound generic)
• Anything that benefits from being genuinely unconventional
• Replacing the thinking process — prompting an AI to think for you produces average output

AI Workspace: DenchClaw#

I'm biased here, but I'll be specific about where DenchClaw fits in the stack versus where it doesn't.

Where DenchClaw is genuinely useful:

• Relationship and pipeline management (customers, investors, users, advisors)
• Cross-context queries ("who did we talk to about X last month?")
• Pattern synthesis across many conversations
• Building custom internal tools via the App Builder

Where DenchClaw isn't the right tool:

• Real-time search (use Perplexity)
• Code editing (use Cursor)
• One-off document writing (use Claude directly)

The value of DenchClaw specifically is the combination of local data + AI agent that knows that data. That's a different value proposition than the other tools.

What's Actually Missing from the Stack#

I'll be honest about what current AI tools still don't do well:

Deep strategic thinking: AI tools give you frameworks, not conviction. The hard question — "is this the right market to be in?" — doesn't have an AI answer. You still have to think.

Trust calibration: AI tools are confident in proportion to pattern-matching, not correctness. Knowing when to trust and when to question the output is a skill you have to develop.

Long-horizon planning: Current AI tools are good at "given this context, what should I do next?" They're bad at "given where I want to be in 3 years, what should I do now?"

The Tool Fatigue Problem#

YC founders are unusually susceptible to tool adoption. There's always something new to try, and the FOMO is real. Every week there's a new AI tool that "changes everything."

The founders I respect most in the batch have a very short list of tools they use deeply and don't switch. Mastery matters more than novelty. The marginal gain from switching to a slightly better tool is almost always less than the productivity loss from switching.

Pick your stack. Get good at it. Don't switch without a very high bar.

Frequently Asked Questions#

Which AI coding tool is best: Cursor, Claude Code, or Gemini?#

All three have real strengths. Cursor is best for day-to-day development. Claude Code is excellent for large refactors. Gemini has the longest context window, which helps for big codebase questions. Most serious technical founders use all three for different tasks.

Is Perplexity better than ChatGPT for research?#

For research specifically, yes — the citation model is much more useful for verification. ChatGPT is better for open-ended conversation and task completion. They serve different purposes.

How should a non-technical founder's AI tool stack differ?#

Focus on: writing (Claude/ChatGPT), research (Perplexity), no-code building (Bubble, Webflow with AI), and a CRM (DenchClaw). Skip the coding tools unless you want to invest in learning to prompt them effectively.

What's the single highest-leverage AI tool for a seed-stage founder?#

A good AI assistant that knows your specific context. That's what DenchClaw is trying to be — not generic AI, but AI that knows your specific customers, your specific pipeline, your specific history. Generic intelligence is increasingly cheap. Context-rich intelligence is still rare.

Ready to try DenchClaw? Install in one command: npx denchclaw. Full setup guide →