I recently joined Hex!
I remember focusing on fintech startups in the previous job search. Personally I felt it's important that the business problems the company is solving are the ones that I care about. This interview cycle, I realized that I also care about data tools having had first-hand experience of the pain points (though perhaps I employed artistic license in the retelling).
The dominant theme this interview cycle, however, is AI.
AI startups getting wiped out
Last year, the thought of joining an AI startup scared me. Imagine joining a startup that wraps the OpenAI API to do pdf extraction. Further down the road OpenAI enables pdf uploads and the startup gets wiped out.
This year, a startup I interviewed with does exactly that!
It turns out there's a spectrum. At one end, you have generic legal documents that have no repeatable visual structure, where the meaning of each word is enriched by other words surrounding it. This is where LLMs shine. At the other end, you have say a Bank of America statement with the logo on the top right and each row being a date - transaction description - amount triplet; existing visual layout-based techniques do a good job here extracting the contents based on a pre-defined schema.
Thus the value-add is not just wrapping OpenAI's API, it's knowing when to use existing techniques vs running it through an LLM. Like all tools there are trade-offs, in this case extraction quality, latency and cost. In fact, LLMs are useful for converting the 'long tail' of bank statements into schemas, introducing a more efficient workflow to run existing techniques!
AI startups interviewing differently
I had take-home exercises as part of the interview process. The first two had mixed results - I moved on to the next stage for one, got passed on the other. I had used ChatGPT sparingly in my submissions, and I wondered, should I have used it more extensively?
Things got more interesting when I received the next two take-homes. The first was for an AI team so the use of AI tools is implied. For the second it was explicitly encouraged! This completely threw me off. Without ChatGPT I had a rough sense where the 'bar' was, with ChatGPT isn't the sky the limit? Surely I can make my submissions arbitrarily sophisticated.
I panicked. I meditated on the issue. I felt better after letting my thoughts settle.
I realized the more complicated your submission is, the larger the surface area of questions you'll get asked on. While you can make your submission go much further than your comfort zone, you're taking on much more risk in being less able to answer questions that might come later!
AI startups having an unfair advantage
The final thought I had was around startups vs incumbents. The refrain is startups are able to beat incumbents by being more nimble, and by taking advantage of new technology (before you say Clay Christensen, I present to you Jill Lepore). Think Uber disrupting taxis since everyone now has location-enabled smartphones.
This time the new paradigm is AI. Not only startups starting today can more easily build their product on top of AI, they can additionally curate a team with ChatGPT skills to help each other level up and streamline internal business processes.
Surely startups starting today have a huge edge, being at the crest of this new wave?
AI is a complicated tool. It's powerful, sure, but lots of startups (some supported by eye-watering amounts of fundraising) are still trying to figure it out. It goes without saying that recent startups have an advantage, but it's tempting to take this too far.
Let's say a startup has been around for a few years, has product-market fit and proprietary data. This acts as a moat from new AI startups! It's true there's a correlation between company age and rigidity of business processes, but what's more important is the company having self-awareness in realizing the need to adopt new tools and workflows.
In other words, there's a 'sweet spot' - you want to be early enough that you're able to integrate AI well, but also have maturity in knowing your customers and their problems well. This way you can focus on the challenge of learning new tools and workflows, instead of having to do that as well as learning about your customers.