The New Way To Build A Startup

If you haven't tried Claude Code in the last month,

it's time to give it another shot.

And if you have, you know what I'm talking about.

It feels like AGI is here.

One of Anthropic's own engineers writes, "Claude wrote Claude Co-work.

Us humans meet in person to discuss foundational architecture and product decisions,

but all of us devs manage anywhere between three

and eight Claude instances implementing features,

fixing bugs, or researching potential solutions."

Think about what that means.

The team developing one of the most sophisticated AI products in the world,

something many of you probably use every day,

is using this AI internally to improve their product.

I think this points to a fundamental shift in how startups operate.

Right now, the best teams aren't automating one or two internal functions.

They're automating all of them.

Often, they're tiny teams able to beat huge incumbents thanks to internal automation.

Their leanness is their superpower.

I've been calling these startups 20X companies.

Several years ago, my friend Parker Conrad, founder of Rippling and Zenefits,

coined the term compound startup to describe companies

that build multiple integrated products in parallel rather than focusing narrowly on one thing.

The theory of like the compound software business is

that there's this island of product market fit that's kind of over the edge

of the horizon line that's sort of harder to get to,

but if you can build, you know, multiple parallel applications at once,

you can get there

and and it actually ends up being a much more powerful type of product

market fit that's much harder to displace at

that point.

>> The 20X company could be an evolution of Parker's idea,

but applied to internal automation.

Instead of just narrowly automating a few things like writing code

or handling customer support,

20X companies build automations across all internal features: code, support, marketing, sales, hiring, QA,

and more.

This makes each of their employees orders of magnitude more powerful than they would

be otherwise.

It also allows them to postpone additional sales and op staff for much longer,

keeping payroll down and culture from drifting.

The phrase 20X company was actually coined by the founders of GigaML,

which builds voice-based customer service agents for enterprise,

to describe how they managed to close DoorDash

as a customer going up against incumbents

that were literally 20X

as large.

When we got DoorDash as a customer,

we were approximately like four to five engineers going against players who had like

100X engineers.

So, we kind of like coined the term like "Hey,

we are a 20X company

because we are able to beat these much bigger players who are like 20X

as by having a better product

and better numbers."

Giga was able to close DoorDash

and several other Fortune 500 companies

as customers because of a powerful internal agent they call Atlas.

So, Atlas can basically do anything within the product which you want to do.

So, it can use browsers, it can edit the policies, it can write code,

it can do anything within the product.

Atlas dramatically expands the range of what each engineer can take on.

So, let's say before Atlas,

every engineer can probably work on four to five problems at once

because they are bottlenecked by all the boilerplate stuff they have to do for

the customers,

right?

Customers have integration, they would have to probably work on that.

Now, with AI FD taking care of all the boilerplate stuff,

each engineer's scope is basically doubled

or tripled because they don't need to work on the boiler plate code.

But, Atlas doesn't just accelerate Giga's engineers,

it also acts as a full-time AI employee

that works in tandem with a human FTE to service dozens of accounts.

Right now, we have only a single human FTE within the company.

As hard as it's to believe, because we have companies like DoorDash using us,

we are in pilots with multiple Fortune 500s, 10 plus Fortune 500s,

where each of these companies probably have volumes over like 500,000

or a million calls a day.

It's only been possible because like we have Atlas,

and this person can primarily focus on just the customer relationships,

the ask by the customers,

taking customer requests and turning them into feature requests and everything.

Building an AI teammate is one approach.

Another is to build an AI-integrated source of truth

that gives employees instant context across your entire system.

Legion Health, which is building an AI-native psychiatry network,

is one example of how to do this.

Legion built a custom internal interface for their care operations team

that lets them pull patient history,

scheduling availability, insurance codes, and a lot more.

What we're showing you right now is an interface that's a vast majority of

our care operations team uses in their day-to-day work for anything

that actually has not been yet automated.

And this includes everything from as Arthur's kind of showing on his screen,

digging into a particular patient or many patients' backgrounds,

trying to understand where they're at in their journey,

if they need a new appointment, to be rescheduled,

if they're having a prescription issue,

if they've sent us a message

that in traditional healthcare might have otherwise gotten lost in the sea of different

communications that go back

and forth between so many different people.

All of that is at our fingertips' reach for every single member of our

care ops.

This single source of truth interface has let Legion keep its ops head count

flat even as it's dramatically scaled revenue.

So, we've grown 4x in the past year,

but we haven't hired a single net new person.

We've been able to 4x number of patients we're seeing thousands of patients a

month.

We have dozens of providers, but we have one clinical lead,

we have one patient support person, and we have one billing person.

And in a typical health care company, those are all departments, you know,

those are call centers,

those are groups of people sitting around desks doing a ton of things manually.

A third approach is actually build custom agents for each employee depending on their

workflow and preferences.

Feathr, which is building agents to automate accounts receivable, took this approach.

So, Feathr right now is a 12-person team,

and we're going up against companies

that have been around since 2006

that have hundreds of employees.

The key to us

as a 12-person team moving

so fast is we bring AI into every process

that is manual and try to automate

as much as possible with AI agents.

One way Feathr does this is by literally asking its employees to document the

manual tasks they do >> >>

and then building custom agents for them.

So, what we do is essentially say,

"What do you spend your time doing throughout the day?"

And we make them document that, and then we build quick AI agents.

And this culture of relentless automation has let Feathr delay hiring for entire functions.

We've actually avoided hiring a design person at the company so far to date.

We're about a 12-person company by just leveraging magic patterns

that our engineering team uses

that to build all front-end designs.

These approaches aren't mutually exclusive.

You can build AI teammates, a unified source of truth,

and custom agents for each member of your team.

The companies that do this are staying lean and setting record-high growth rates.

This is the new way to build,

and the startups that figure it out first are going to win.