Endgame: How to operate like an AI-native company
August 14, 2026 | Edition #28
The customer success team at Priya's company had a "best practice" that no one followed:
After every call, log the personal details in the CRM.
Kid's name, favorite team, the fact that this client loves Cold Stone ice cream cakes.
Then, use that information later to create a magical moment.
People rarely logged anything. It wasn't because they didn't care. It was because that was the 9th and least urgent action item that came from the call.
The most obvious AI fix is to have it listen and log the note for you.
That takes the same "best practice" and automates one piece.
Here's what they built instead:
A client mentioned on social that her birthday was coming up and that an unexpected event meant she'd be spending it at home, rather than on vacation.
Priya's team knew within the hour, and they ordered an ice cream cake that showed up at the client's favorite coworking space.
Nobody on that team logged a detail.
By using AI to think about how to approach work differently, the job stopped being about faster data entry and instead became about paying attention to customers' lives in real-time at scale.
A faster version of the old job is still the old job.
This is Part III of the Multiplayer AI Playbook.
βPart I helps you become an AI power user.
βPart II enables you to take your team's work from scattered to shareable.
Both of those were the groundwork that now let's us talk about what it looks like to work more like an AI-native company.
AI-Native Companies are MUCH More Productive
Where the average SaaS company might make $300k per full-time employee (FTE), emerging AI-native companies are making $2-18M (that's M for million!) ARR per FTE.
So, what are they doing that's so different?
The Three Rewrites
AI-native companies are rewriting what it looks like to work at a software company.
β
βRewrite 1: The task.
They're redefining what tasks need to be done by humans and which humans can contribute to those tasks.
The clearest version of this I've seen is Elena Verna, Head of Growth at Lovable.
She wanted the enterprise pricing page redesigned, so she built it, opened the pull request, and shipped it.
No ticket, no roadmap negotiation, no waiting two sprints for an engineer's priority list to clear.
Think about the implication of that on job descriptions and team structures.
"Head of Growth" and "person who builds or modifies the product" used to be two different roles with two different comp bands and two different reporting lines.
At Lovable they're the same person at least some of the time.
Rewrite 2: The team.
Once the tasks people do change, the org chart and spending on capabilities (e.g., tools, AI credits, etc.) need to be re-evaluated.
For example, as each Software Engineer can produce substantially more code with agents, the bottlenecks on Product, Engineering, and Design change.
We start to need faster and more foolproof end-to-end (E2) testing and design reviews, or design systems that let Engineers be confident they pulled from the right components.
AI-native companies tend to have smaller engineering squads β Jeff Bezos' two-pizza rule for 8-engineers on a team no longer applies β shipping in parallel with fewer total engineers employed.
When you have more smaller teams, you have less coordination tax.
Gamma, a $100M ARR and profitable AI-presentation company with about 50 employees is a great example.
Teams like that can spend tens of thousands per month per engineer on AI usage credits, ship more, and still be more cost-effective because their teams are so much smaller.
Rewrite 3: The business.
When tasks and teams both change, the company's revenue density and velocity (or the rate at which they ship new product features) evolves.
As an example, Midjourney hit $200M in revenue with 11 employees. That's 10x the revenue density of Microsoft ($18M ARR/FTE vs <1.8M ARR/FTE).
Work that took a quarter now takes a week.
And now there's a new bottleneck.
You stop being limited by how much your team can produce and start being limited by how fast leadership, product managers, and other "curators" in the business can decide what's worth producing to continue to give customers a best-in-class, cohesive experience.
So, you need to help more people build the skills to be a curator.
This is leading to growth in roles like Product Design Engineers that combine UX/UI skills with front-end coding capabilities.
Truly AI-native companies have fewer, different roles with an org chart that leans less heavily on management and more on highly skilled ICs.
Now, most of us don't get a do-over, where we can start building AI-native from scratch.
So, we have to think about how we transform our organizations over time to remain competitive with new era companies.
The Four-T Playbook
Every edition, I share a proven tip, trick, tactic, or template. This time, it's a:
π Tip: Add two questions to your next round of stay interviews:
- "How is AI changing your experience at work?"
- "How, if at all, are you or your team working differently today because of AI?"
The "if at all" is deliberate. It gives people permission to say "not really," and that answer should be concerning if you want to be AI-forward.
If answers cluster around speed, you're sitting at Rewrite 1 and need to start thinking about what new bottlenecks emerge if people do their existing work faster.
If people start describing work they no longer do, or work that didn't exist a year ago, you've reached Rewrite 2 and your org chart is about to need some changes.
Final thoughts
Which rewrite is your company on? Hit reply and tell me. I read every one.
Until next time,
Melissa
P.S. This newsletter represents opinions and experiences from various HR professionals, it is not advice. Please always consult with appropriate legal counsel on AI use at your company.