"Would you rather ship twice as many features — or let go of half your team?"
How the shape of organizations and ICs evolve in the age of AI
"Would you rather ship twice as many features — or let go of half your team?" That was a question a CEO asked me a few weeks ago. It's not just a purely hypothetical question. We were discussing how AI tools like Cursor have sped up software development massively, and the rate of change is staggering. In the past 6-12 months, we've seen countless new tools and updates that disrupt the product development cycle.
Picture this: AI can augment tech teams and double the speed of product development. What used to take a month now takes two weeks. What does it mean for your product team?
At first, this seems like good news. Every product person I know wants their engineering team to build faster. More features! Better products! Finally matching our grand vision!
But then what? Once you reach the sweet spot of your product vision, would you want to build more features? This is when you start saying, "Eeehh" unconvincingly. And your intuition is not wrong. There's a diminishing return or even a dip in user satisfaction once you pass a certain stage.
The paradox of AI-powered productivity
Here's the uncomfortable truth: AI enables us to build more, but building more could make your products worse. So once you've reached this happy user peak, you, or your manager, will probably be at a point where you realize you don't have to build as many features anymore. Sure, you have to keep up with or exceed your competitors and continuously improve the product, but do you need 100% of the people in your team to do that?
The harsh answer is: probably not. We're already seeing layoffs and fewer roles available. This is our new reality:
There will be less demand for our roles
The remaining roles are changing at a rapid rate
We all face stiffer competition, either to keep our job, or to find a new job
There's no other option than to upskill and adapt.
The architecture of modern tech organizations
Let's go back to a typical organization structure where the product is software. It usually looks like this:
When ChatGPT was launched 2-3 years ago, many people predicted support functions would be hit first — especially customer service. They were right.
But what's happening to the layer above? There are two fundamental shifts happening:
First: Efficiency within each role
I’m sure you have seen this happened in your company, or to your friend. A marketing team used to have three people. One of them resigned or was let go, and wasn’t replaced. The other two remaining people are expected to cover the workload.
That marketing team still needs to hit the same lead generation numbers, launch the same number of campaigns, and maintain the same quality standards—just with fewer people.
This creates a brutal but clear dynamic: survive or get cut in the next round.
So if you're one of the people who remains in the organization, you naturally turn to AI to make your job more efficient and enable you to do what used to require 2-3 people.
AI isn’t a magic productivity wand, but it’s very good at amplifying your existing capabilities. The person who thrives isn't the one who can prompt ChatGPT the fastest — it's the one who can critically evaluate what comes out, refine it with expert knowledge, and sharpen it with years of hard-earned intuition.
You fire-proof yourself (aka staying above the firing line) by becoming the person who can wield AI like a precision instrument rather than a blunt tool. This requires being genuinely excellent at your core craft — having the taste and judgment to know when AI's output is brilliant, mediocre, or completely off-base.
Second: Consolidation of roles
This shift is less common, newer, and we are still grappling with it: AI has democratized access to the skills that were historically gatekept within specific roles and took years to learn. Now anyone with curiosity and critical thinking could access these skills: Copywriting, creating compelling value props, conducting user research and synthesizing the results, designing beautiful interface, coding, data analysis… you name it.
Think about what used to be impossible without years of training and experience:
A PM couldn't create pixel-perfect mockups without years of learning and practicing design
A designer couldn't code the front-end of their own design
A user researcher couldn't analyze complex datasets without SQL knowledge and statistical training
Now? AI tools have compressed these learning curves from years to weeks, sometimes days. The barriers between roles are dissolving. What matters now isn't just your core specialty — it's your ability to recognize good work across disciplines and direct AI to produce it.
The result? Companies are starting to hire fewer specialists and more AI-savvy generalists who can wear multiple hats effectively.
Beyond T-shaped: The flower-shaped individual
You've heard of T-shape individuals — broad knowledge with one deep specialty. You might know pi-shaped (π) individuals — two deep specialties. But I want to introduce you to the flower-shaped individual:
Let’s break it down. In the middle is the core skills and traits that are non-negotiable. In the age of AI, these are:
Human relationships: The ability to build strong connections and collaborate effectively
Curiosity and hunger to learn: How else would you keep up with rapid developments?
User empathy: Deep understanding of your users, what makes them tick, their pain points and their needs.
Adaptability: The agility to evolve quickly. Think about the disruption we’ve experienced in just the last five years — it’s not slowing down any time soon.
Humility: Recognizing AI's capabilities rather than dismissing it as "not good enough" to impact your role
Each petal represents specialization in different functional areas. You start by becoming literate in each area, then gradually develop fluency. The more petals you can cultivate, the more valuable you become to any organization.
Two paths to thrive in the AI world
My prediction is that there will be two types of super ICs who will thrive in the next few years:
1. The Specialist
You're the top 1% at one petal. You have very specialized skills from years of experience honing and refining them.
Think of the senior software engineer who can design systems that AI can't conceptualize, or the PM who has such deep industry knowledge and intuition about user behavior that they can spot the nuanced patterns AI misses.
The specialist path works because AI still struggles with edge cases, complex judgment calls, and situations requiring years of contextual experience.
2. The Orchestrator
You're a generalist with solid understanding of multiple petals, able to orchestrate and supervise AI across domains.
The orchestrator is like a conductor leading an AI symphony. They might not be the best designer, researcher, or analyst in the room, but they know enough about each discipline to effectively prompt AI tools, critique outputs, and weave everything together into an excellent result.
Picture a PM who uses AI to generate initial user personas, refine them with AI-powered research synthesis, create mockups with AI design tools, and analyze user feedback with AI — all while applying their judgment to guide the process and make final calls.
You'll probably thrive better in the startup environment where wearing multiple hats is expected, and where the ability to move fast across disciplines with AI as your force multiplier becomes a massive competitive advantage.
Both paths require one crucial skill: knowing when to trust AI and when to override it. That's where your human judgment becomes irreplaceable.
Burying your head in the sand isn’t an option anymore
The shift isn't coming — it's already here. Right now, while you're reading this, someone with your exact job title is learning to do twice the work with AI. Another person is figuring out how to eliminate your role entirely.
If you're a mediocre performer banking on one petal alone, you're already obsolete. You just don't know it yet.
Here's what separates the survivors from the casualties: survivors don't wait for permission to evolve. They don't wait for their company to train them or for the "perfect moment" to upskill. They're already growing their petals while others are still debating whether AI is a threat.
The question isn't whether AI will reshape our industry — it already has. The only question that matters is: Will you be shaping that future, or will you be shaped by it?