Weekly Bites #1: Predicting the Future, Early Hires, and Enterprise AI

This week’s bites:

• Predicting the Future: Why Zillow’s $550M loss shows AI prediction limits
• Early Hires: Finding founding employees through your network
• LLM for Enterprise: How LLMs make AI accessible to non-tech corporations

Predicting the future is hard

Eight years ago Zillow ran a $1.2M Kaggle contest (biggest prize to that date) with a simple twist I loved: no hidden labels. You predict house prices for the next 3 months, then wait for the real-world results to show up. That looked fair and clean. No leak possible. It also made the whole thing a little bit suspenseful. Only time does the judging.

A few years later, Zillow tried to use price forecasts in the real world with Zillow Offers. They bought homes and resold them. For a while the Zestimate even helped decide the starting offer price. Then in 2021 they shut it down, reporting a $550M loss and saying near-term price forecasts were too unpredictable. I see a connection, but I am not saying one caused the other.

My takeaway: predicting the future is still hard in practice. Models can be sophisticated, but there are too many variables out there. LLMs push AI to a new level with lots of new capabilities. But predicting the future is still elusive for a long time.

Startup early hires

In the beginning, cofounders can often cover the key work, so you don’t need to hire yet. But if you are solo or very lean, you need a few “founding employees” (not cofounders).

The main question is: how do you find them?

Try your own network first, ideally even before you launch. Why?

1. Early hires can make or break the company. You want people who are qualified, ambitious, and a good fit.
2. Joining this early is risky for them. Knowing you and your track record helps them commit without worrying the company might fold in a few months.

That’s sufficient for 3 to 6 months. During that time, the founder should expand the network to prepare for the next hires, so both sides have time to get to know each other and check for good fit.

Final notes:

• How to retain your early hires? One of the best ways is to let them work at the technical frontier where they can build something meaningful.
• Be careful hiring close friends. If the business goes south, your relationship might too.

LLM for enterprise

Previously, applying AI in non-tech corporate was hard. Given we have to collect data, train models, validate, bring to prod. All of them take a couple of ML engineers and months of work.

LLM makes applying AI to corporate a lot easier. No need of anything above. All you need is an LLM API key. As I detailed in my guide on choosing LLM models, you can start with APIs and only self-host when you have strict data requirements. The current environment is perfect for enterprise adoption: hallucination rates have dropped 6-fold and agentic search makes LLMs much better at finding the right information.

Enterprises should start with some low hanging fruit like applying LLM to automate some mundane tasks in some pipeline of work. For example, automatically summarize key metric weekly movements and show in a dashboard rather than have to analyze them manually each time.