Your Product Is Now a Wrapper Around Go-To-Market | Hiten Shah

Hiten Shah has built Crazy Egg, KISSmetrics, and a long list of products — and right now he’s shipping a new batch of AI tools while stepping back into Crazy Egg after nearly 20 years. In this conversation we get into what’s actually real in AI right now versus what’s hype.

We start with Noats, Hiten’s local-first meeting recorder, and why “the tech wasn’t there” a year ago. That leads into local models: how much hardware you really need, why open-weight models still have a “flavor” from how they’re trained, and why even the frontier labs don’t fully know what a model will be good at until it’s done.

Then we get tactical. Hiten explains how he evaluates new AI tools within minutes of launch, why he treats his work like an “AI factory,” and the big idea of the episode: in the past, go-to-market was a wrapper around the product — today, the product is a wrapper around the go-to-market. He walks through how that plays out across his own products (paid ads, freemium, and a niche community play), and then turns it on my business, Castos, to show what the “obvious future” of a commoditized category looks like.

Chapters
0:00 Noats and local-first meeting notes
4:10 What it really takes to run local models
6:28 Why model training is still a black box
10:22 Where are the US open-weight models?
11:55 Local vs. frontier: how Hiten actually works
13:18 “X is where everything breaks” — evaluating new AI tools fast
15:46 Do you really need to switch tools every week?
18:16 Big-company AI vs. startups for personal productivity
20:39 How to think about growth for an AI product
24:24 The product is now a wrapper around go-to-market
26:30 Does every product need a free tier?
29:19 Coming back to Crazy Egg after 20 years
32:16 What’s exciting — and what keeps Hiten up at night
34:47 Finding the “obvious future” of your business (Castos breakdown)
38:22 Why “grow my podcast” is the new job to be done
40:50 What Hiten’s excited about in AI right now
49:17 Wrap-up: pick who you serve

Links
Noats: https://noats.ai
Hiten on X: https://x.com/hnshah
Hiten’s weekly Friday calls: https://www.hiten.com/
Castos: https://castos.com

Chapters

• (00:00:00) - Notes app and local model parity with Granola
• (00:02:08) - Local models, product motion, and go-to-market teaser
• (00:04:08) - RAM requirements and practical limits of local compute
• (00:05:50) - Why training is a black box and model 'flavor'
• (00:09:25) - Chinese-trained models and the search for US open weights
• (00:11:14) - Personal workflow for testing new models and Twitter as signal
• (00:15:42) - Setting up systems to experiment like a factory
• (00:18:03) - Muse vs startups: trust, security, and Meta's advantage
• (00:20:32) - Notes go-to-market and distribution strategy
• (00:24:22) - Product as a wrapper around go-to-market
• (00:28:29) - Flexibility across enterprise vs consumer go-to-market
• (00:29:10) - Returning to Crazy Egg and AI-driven efficiency
• (00:32:09) - Obvious futures: websites that change themselves
• (00:34:49) - Finding your business's obvious future: Castos case study
• (00:37:43) - Podcast platforms needing to drive growth, not just hosting
• (00:40:52) - Excitement about agent tools: Muse, Grokbot, Instinct comparisons
• (00:47:17) - Differentiation, distribution, and closing thoughts