AI-First Teams

🔗 Houck’s Picks

My favorite finds of the week.

Fundraising

• Eric Bruckner on the VCs you should find (Link)

• I hosted Usman Gul for a chat on how to fundraise faster with better data (Link)

Growth

• Vitalii Dodonov shares his biggest hack running a $30M ARR startup (Link)

• khushi.vy on the different places to launch your startup (Link)

ICYMI

• Get matched with a fee-only fiduciary advisor, start the free quiz (Link)*

• Blake Mycoskie on the lessons he wishes he’d learned after selling (Link)

• Arjun on the best age to found your own startup (Link)

• Ayman Al-Abdullah on the 3 kinds of moats left in business (Link)

• Paul Graham compares what worrying about model companies is like (Link)

• Joe Speiser on the best time to sell your company (Link)

• Todd Saunders on the future of vertical SaaS companies of the AI era (Link)

 Opportunity: Trade School 2.0

Private equity is pouring into trade schools across the country.

Rollups are acquiring some schools and taking stakes in others. They’re paying for the reputation, knowledge, credential, and acquisition capabilities of existing schools.

It’s a reaction to LLMs seemingly being capable, at an increasing rate, of automating “knowledge work.”

It’s a complex issue because the following things are all likely true:

1. White collar companies are very bloated and employ a lot of people doing jobs that LLMs can do both better and cheaper

2. Those same companies would be better served trying to reorient themselves to get more out of their employees than doing mass layoffs

3. Blue collar work will become more valuable since AI will have a harder time taking it on

Regardless, it’s clear that trade schools are going to see a renaissance over the next few years because of a combination of capital injection, value, and likely applications as well.

But while private equity is pouring into older trade schools, there’s an opportunity to start new ones. What would a trade school built with the help of AI look like? What about the software to support them?

🧠 Framework: The Proactive AI-First Team Member

Even if you want to be "AI-native” you actually need to be solving real problems.

You don’t want to be “playing startup” or doing work that feels like you’re making yourself and your company more productive, but in actuality doesn’t drive you closer to your actual goals.

You can keep spending Claude credits to build systems that make your business more efficient, but don’t help you better serve your customers.

And, equally importantly, you need to screen for this when you’re hiring.

Allie Miller shared this framework on LinkedIn for how to identify a proactive AI-first team member.

It’s very similar to the traits you’d look for in any A+ new team member, just applied specifically to people who are actively using LLMs in their role.

The added execution capacity that LLMs give us mean truly top hires can make a bigger impact (or be more of a drain) than ever before.

📈 Trend: Average Per-Unicorn Market Cap

I’ve been diving into this report from Elad Gil about the state of unicorns and there’s some interesting trends happening.

Right now we’re seeing one of the largest YoY increases in the average market cap of unicorns in recent memory (maybe ever) after years of this being relatively flat.

Typically, average market cap growth for unicorns has been primarily due to new companies becoming unicorns, and decreases have been primarily due to existing unicorns reaching an IPO.

However, from what Elad found, around 80% of the increase last year was due to existing unicorns growing their valuation with additional funding.

It’s sort of a perfect moment for exactly this to happen.

On one hand, you have “last gen” unicorns like Stripe, SpaceX, and ByteDance which have all stayed private for much longer than startups traditionally have.

On the other, you have they incredibly quick rise of foundation model startups reaching higher and higher pre-IPO valuations due to a combination of the immense value they’re creating and, to a lesser degree, inflation.

So, while the number of new unicorns is up compared to the last two years, it’s below the two prior yet we’re still seeing the average unicorn market cap at unprecedented levels.

💬 Quote: It’s Hard When No One Listens

Startups are hard.

And it particularly hurts when you pour hours, days, weeks, months, or years into something that ends up being “wrong” in the sense that you failed to make something people actually want.

Maybe you asked leading questions in your user research calls so you never truly understood their pain points. Maybe what you were building wasn’t unique or defensible enough to survive how fast things are changing these days. Maybe a black swan falsified PR crisis destroys your company out of nowhere.

All those things are hard to deal.

But you know what’s harder?

When you’re struggling to get to even one of those outcomes because no one is listening and paying attention to what you’re doing.

When you think something is going to work but can’t motivate anyone to do those user interviews for you in the first place.

Conventional startup wisdom would tell you to move on, find a new idea.

And 99.9% of the time it would be correct.

But we convince ourselves we’re in that 0.1% of the time where we’re sitting on something big no one else has figured out. That we are contrarian… and right.

Only you can decide where you think you’re at, and what the potential opportunity cost is worth to you, but it’s hard when no one listens.