Hey! With all the AI craze happening right now, there are two questions tech teams usually ask themselves about it: 1) should we use more AI at work? and 2) should we integrate AI into our product?
We talk pretty often about the first one, while I feel the second is a bit underrepresented. It is a complex question, one that leads to an obvious follow up: how?
“How” questions are tricky because they can always be exploded into multiple angles, and this makes not exception:
How do we build AI from a tech perspective? — It’s a cascade of buy vs build decisions.
How do we measure that it’s doing well? — How to make sure it’s actually improving?
How do we structure our team to make it happen? — Do we need to hire PhDs? What do AI engineers actually do?
To explore all of this, today I am bringing in Barr Yaron from Amplify, who has a unique vantage point on this.
Amplify is a peculiar VC fund, which focuses specifically on technical founders, backing the likes of Datadog, Temporal, and Runway, and are very opinionated about what is happening right now in tech.
Barr also runs a podcast, where she talks every week to CEOs and CTOs of technical products in the AI space, so she is perfectly positioned to connect the dots and tell us about some good practices!
Hey, Barr here!
Everyone is racing to build AI into their products. What makes some teams succeed while others fail?
Thanks to the podcast and to our work with founders we have the chance to go deep into what it really takes to build lasting AI products, from technical aspects like evals and data stacks to business decisions like team building and structuring.
In this post I’m going to talk about 3 themes I’ve seen from these leaders in AI that have helped them successfully build AI products (and organizations) for technical audiences – and how you can do the same:
🔍 Getting evals right — by integrating domain expertise
⚖️ Building vs buying models — how to think through the dilemma
🏗️ Structuring AI teams — do you need researchers, engineers, or both?
Let’s dive in!
