Tiny Team Playbook

🔗 Houck’s Picks

My favorite finds of the week.

Fundraising

• Toby Egbuna on how to get warm intros to VCs without a network (Link)

• Yurii Rebryk on the one-minute pitch that makes investors say yes (Link)

Growth

• Michel Lieben on how he’d find B2B clients if he had to start from 0 (Link)

• Roy Lee’s unpopular take on growth (Link)

• Kevin White on how to generate millions in pipeline from in-person events (Link)

ICYMI

• From building smart pricing strategies to preparing to exit, Paddle's SaaS Happy Hour series serves up the strategies, data, and insights that matter most (Link)*

• Jon Lai on a common traps he sees startups fall into (Link)

• Marty Kausas on how much founders get paid (Link)

• Ankur Nagpal on how he’d structure his money from day 1 as a founder (Link)

• Luca Bonmassar on the pattern every failing projects follows (Link)

• Peter Inge on why you shouldn’t start a company to make money (Link)

 Opportunity: AI Expired Domain Finder

The 1990’s were a great time to buy domains.

In the short-term, you had the dotcom boom where companies that had no business raising venture were doing so freely, and then promptly spending most of it on anything they thought potentially useful (like vanity domains).

But, moreso, it ended up being a good longterm game.

If you owned a single word .com domain, eventually someone was likely going to come along and want it. And be willing to pay for it, too.

The most I’ve ever paid for a domain was $250,000 which, honestly, sounds insane even to me.

And I think there’s an opportunity to create something that makes it so that you might never need to come anywhere near that yourself.

An AI agent that proactively monitors for domains that fit your search criteria, and bid on them as soon as they become available (i.e. the registration expires).

You can either market this to domain hunters (and monetize via subscriptions) or builders (and monetize by letting them set a bounty on a specific range of domains, which they’d only be charged for when/if it becomes available and the sale goes through).

The former is the evil path, the latter is the happy path… but the evil path is likely more lucrative, in this case.

🧠 Framework: Tiny Team Playbook

AI lets you keep margins higher by doing more with fewer people.

Some people will lash out against that, but it isn’t a bad thing, it will lead to more entrepreneurship.

But, regardless, it’s also the reality you’re competing against now.

So if you don’t want to handicap yourself against your competitors, here’s a good breakdown that generalizes the playbook from a company called Oleve that scaled to $6M ARR with a 4-person team.

It calls out three areas where these companies are different than traditional startups:

First, operating principles:

• Only hire complimentary 10x generalists

• Relentless focus on profit

• Single-owner KPIs

• Continuous investment in process improvement

• All-in-one tools are ok (or even preferred)

• Actually write things down and playbook them (for AI’s sake)

When you think about how AI works, these all make pretty logical sense. The goal is to build leverage and then use it to turn the wheel faster and faster.

Second, technical team structure:

Engineers are split between “harvesting” and “cultivating” where the former is a full-stack product engineer and the latter is solely focused on internal processes and automation.

The shift here is mostly that internal tooling is a focus much earlier than it used to be, and product eng have more responsibilities.

Third, workflow augmentation:

Oleve thinks about implementing AI in three phases:

1. Humans using AI tools

2. Automating workflows with AI systems

3. Consolidating AI systems into a single autonomous system

Whoever creates the first single-person unicorn will probably follow something close to this playbook.

📈 Trend: AI Assistant

If you’re reading this newsletter, this probably seems like an odd choice for a trend.

After all, it’s been clear within the startup world that AI was a paradigm shift for 2.5 years now.

However, why I’m flagging this is that AI assistants now seem to have fully broken outside of Silicon Valley, we’re reaching the territory where the market is expanding, crossing the chasm, and inbound interest will explode.

What this means is that verticalized solutions will follow… soon.

Once adoption hits a certain point, early stage funding becomes harder to secure. Depending on the vertical, you might even need to do some convincing now.

But it’s still possible to build a large company on the back of an assistant, find your niche.

💬 Quote: Calculated Risk Takers

I’ve been laid off twice.

Once was my first job out of college at a small startup.

The second was at Airbnb during the COVID layoffs.

That’s 2 out of the 3 post-college jobs I held.

So it won’t surprise you to hear that, even before AI, I’ve viewed jobs as riskier than being a founder since things are outside of your control.

And this isn’t at all represented in the equity you get as an employee so, really, it’s a raw deal.

As founders, everything we do and every decision we make is an assessment of how good that “deal” we’re making is. The basic version of this is “does the upside outweigh the risk?” but it can rise to much more complicated risk management questions.

It’s our job to assess risk in a way that’s very unique and challenging, but still better (and, ironically, less risky) than not having the ownership to do so.