Hey y’all — if you were starting a new company today, what would it look like?
Not in terms of what problem you’d pick to solve, or who you’d hire, but how would it actually operate?
I saw this video from YC the other day that makes the case for making LLMs act as the true operating system for your startup and it got me thinking:
We talk a lot about AI-native work, but what does a truly AI-native company look like?
The next coffee giant has a billion-dollar playbook.
Dunkin' was acquired for $11B. JDE Peet's IPO’d at $17B. Starbucks is currently valued at a $110B market cap.
While these are massive success stories, they all achieved their status without one major advantage: Owning the whole supply chain.
Green Coffee Company is vertically integrating the entire process from "seed to sale.”
They’ve already seen revenue grow from $1M to $37M in three years.
Now, they’re reigniting the legendary Juan Valdez brand in the $100B U.S. coffee market.
This is a paid advertisement for Green Coffee Company's Regulation CF offering. Please read the offering circular at https://invest.greencoffeecompany.com/. Timelines are subject to change. Listing on the NASDAQ is contingent upon necessary approvals, and reserving a ticker symbol does not guarantee a company's public listing.
7 Attributes of AI-Native Startups
Your Moat is Your Data Flywheel
Everyone’s worried about defensibility these days.
They’re concerned that since LLMs make software easier to build, software moats don’t exist.
The new moat is your data, and how you use it to acquire more data as an increasingly fast rate. Your internal data becomes a private moat that compounds faster than any public model can match.
Founders who treat every meeting note, support message, and usage log as structured data can create a compounding advantage that widens MoM.
Self-Improving Product Engines
It’s not enough for your agents to be writing code. It’s not enough to have them do QA.
You need orchestration layer agents whose sole focus is identifying issues in the current execution layer processes and outputs, and then communicating those to the execution layer agents so that they improve on their own.
It’s not your job to monitor the agents, it’s another agent’s job.
If you’re writing specs and tests, and the system underneath improves by itself, you can become a 1,000x engineer building a software factory instead of just a 10x building software.
Make Your Company Queryable
If you haven’t diligently had an AI bot in all your calls for the last few years, you’re at a disadvantage compared to the people who do.
Making every process machine-readable (even in simple ways like chatting in public Slack channels vs DMs) is the single highest-leverage infrastructural decision you can make right now.
It unlocks every other closed loop and gives you (and your company’s AI-brain) real-time, loss-free visibility that no legacy company can copy. It’s how the fastest moving companies are making such quick decisions at a highly accurate rate.
Founder = Swarm Orchestrator
I talked above about how agents need to act as orchestrators for other agents.
That’s true.
But your role is designating roles and governance rules for the entire swarm (rather than just prompting).
Reduce your human “middleware” and the chances your agents go rogue by setting up and owning these macro level orchestration responsibilities.
A good tool to start with her is Paperclip.
AI = The OS
LLMs aren’t some tool you “integrate” into processes.
Treat them as the single hub you run all workflows through. Treat every action like context farming.
The whole system should be a closed loop, and each process running through it should be a smaller one too.
If you treat it this way, and make your company queryable, you’ll free up tons of time that legacy companies spend on coordination and decisions that, to you, will seem easy and straightforward. That’s a massive W.
Track Intelligence-per-Dollar
Tokenmax but not blindly.
It’s ok to have an uncomfortably high API bill from Anthropic. In fact, it’s probably good. But spending tons of tokens needs to have a purpose.
You want to tokenmax more than you want to headcountmax, but set a counterweight by tracking the effectiveness per token as well.
You’ll need to set the right metric for your own business here (usage, users, ARPU, etc). But the key is to no longer look at those metrics in a vacuum and instead look at how well you’re doing per token.
Quick example: big companies get measured by revenue per employee. Why not measure your own efficiency, effectiveness, and decision making by revenue per 1,000 tokens?
This is the new capital-efficiency superpower that is going to let small teams out-execute big incumbents without burning tons of cash.
Flatten the Org Chart
Minimize human “middleware” that routes info from one area of the business to another. These busywork tasks and roles are no longer jobs for people.
Use your queryable company OS as the intelligence layer that can handle coordination.
The result is that there are three roles for humans:
Builder-Operator → This is the new IC. The “doer” who directly builds and runs these systems. It’s not limited to engineers (my historically ops-centric Chief of Staff has become one of these over the last couple months). They use agents as their primary tool to make impact at a larger scale than would have been possible manually.
DRI → The directly responsible individual owns one clear outcome and nothing else. They are focused on strategy and customer results, not managing people or processes. They have total accountability and, essentially, nowhere to hide. This replaces the traditional “manager” role. Since your company will be queryable, these folks get freed up to spend time on high-judgement decisions (often one-way doors) rather than managing people and coordination-level problems.
Founder → This is you. You build, coach, and lead by example. You never delegate your AI strategy, keep it as one of the few things you hold onto indefinitely like fundraising. Your job is to set the company up for success and how they use AI is a core part of that. You also need an insane level of conviction in the value of AI to make this all worth it.
As much as you can, spend time 1:1 with both Builder-Operators and DRIs. Don’t have one manage the other by default. Your job is not to create little pods where a DRI manages a few Builder-Operators. You want to keep the DRIs free to learn and make strategic decisions as much as possible.
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