AI for Startups

Let’s be honest. The AI hype is loud. Every day there’s a new tool, a new framework, a new “everything has changed” post. And if you’re a founder trying to actually build something, it’s exhausting.

I’ve talked to a lot of early-stage founders and the pattern is always the same - they either go all-in on AI and build something nobody wants, or they ignore it entirely and leave real leverage on the table. There’s a middle ground, and it’s where the smart ones are playing.

First, stop trying to build an AI company

Unless your core product literally is the AI model - which is almost never the case for most startups - you’re not building an AI company. You’re building a product that uses AI. That distinction matters a lot.

AI is a lever, not a foundation. Build on the problem, use AI to move faster.

Where AI actually creates leverage for early-stage founders

1. Speed up the stuff that slows you down

The biggest win isn’t in the product - it’s in everything around the product. Drafting investor updates, writing job descriptions, turning a messy Loom recording into a structured PRD, summarizing customer calls. These are hours you’re losing every week. AI gives them back.

2. Talk to your users, then let AI analyze the patterns

Do the customer interviews yourself. But after 20 conversations, drop the transcripts into Claude or ChatGPT and ask: what are the top 5 pain points, what words do they keep using, what are they actually buying? The patterns that would take you three days to spot manually appear in three minutes.

3. Build features that feel magical but cost almost nothing

A well-crafted prompt connected to GPT-4 can deliver a feature that would have taken a team of three engineers six months to build two years ago. Smart summaries, intelligent search, auto-categorization - these can now be a weekend of work.

The mistakes I see founders make

• Building AI features for demo day instead of for actual users - looks great in a pitch, nobody uses it after
• Chasing the latest model - GPT-4 vs Claude vs Gemini rabbit holes that eat weeks of engineering time
• Ignoring the data problem - AI is only as good as what you feed it, and most early startups have messy, unstructured data
• Underestimating inference costs at scale - a feature that costs $0.01 per user sounds fine until you have 100,000 users
• Over-engineering the AI layer - a simple well-written prompt beats a complex pipeline for 80% of use cases

The honest truth

AI won’t save a bad idea. It won’t replace understanding your customer. But for a founder who already has traction or a clear problem to solve - it is the most powerful multiplier available right now.