It’s 2026, and we have AI agents that can produce entry-level work at a much lower cost than a human employee. And yet, most people don’t know how to work with AI or manage their agents.
At the same time, companies are desperate for high-leverage hires. ClickUp, let go 22% of their workers, and introduced new $1M salary bands to attract agentic-native humans. Wix, Webflow, Meta all did the same this week. They’re all flattening their org, and firing most entry-level and white-collar hires. And it’s not about saving money but about finding more money to spend on AI capabilities + on unique AI native talent.
In fact, in the “Intelligence Curse” blog from 2025, the authors outlined three ways companies will adapt to the AI intelligence:
- Do nothing, out of inertia
- Fire most entry-level and white-collar jobs, to maximize benefits
- Freeze all hiring initiatives
Here’s the devastating part: The bold ones will try #2. Everyone else will be pressured to do it too.
So, the way we think about growth changes:
Company wants AI leverage = Company buys Intelligence
Company hires humans who can manage said intelligence
Exhibit A: The ClickUp layoff and the search for AI talent #
Let’s look at what the CEO of ClickUp announced: The motivations of it are very clear and can be summarized in three buckets:
- Create a budget to fund AI infra + hire high-leverage talent
- Attract the best agent-native talent on the market, faster ($1M salary bands)
- Become the first company in their vertical who’s going to diabolically grow based on AI restructuring + enhanced productivity
Here are some highlighted quotes from the announcement with a bit of explanation:
| Quote | Why | Explanation |
|---|---|---|
| ”..this wasn’t about cutting costs. We’re introducing $1 Million salary bands” | Attract the best talent | They want to attract the best talent on the market, faster. Everyone who has done something relevant with AI today, is now actively following their open jobs |
| ”Nearly every company will make changes like these. The ones that do it proactively will define what comes next.”…”These roles will evolve. But waiting for that to happen naturally means falling behind now”…”Ironically, the people that automate their jobs with AI will always have a job.” | They want to unlock the recipe for AI growth faster | New types of builders, system managers and front-liners roles will be created, and ClickUp wants to tap those people first and make money for them. These people have high common sense and judgement, and understand that they need to manage agents and not bother in checking their work |
| ”I did it because the way to operate at the highest level of productivity is changing, and to win the future, ClickUp needs to change with it.” | Humans are no longer needed to review AI work | Human-in-the-loop is for most white-collar jobs, as AI can achieve highly accurate work. For other more impactful jobs, they need humans who are AI-native.no longer needed |
| ”The common narrative is that AI makes everyone more productive. It doesn’t.” | AI is no longer just a tool | AI can almost automatically achieve the work done by entry level or back-office workers. An AI agent can now be considered a colleague, and not a tool. |
In this reality, you either get replaced by AI, or you become someone who manages AI.
But chances are you’re not good with AI #
Most of you are using ChatGPT, Claude, Cursor, Perplexity, or some version of an AI tool at work.
But that does not automatically make you AI-native and it definitely doesn’t make you productive. If anything, it makes you less productive.
In a lot of cases, it just means you’re doing the same job with more tabs open. You ask ChatGPT for a draft, copy it into a doc, ask for a summary, paste it into Slack, maybe use another tool for research, then another one for editing.
You end up in “Brain Fry”, and you start to work more and achieve less. Here’s what most of you think makes you AI-native:
- “Knows how to prompt” - prompting is so easy today
- “Comfortable with ChatGPT” - that’s cool, my mom too
- “Uses AI tools daily” - what does this mean? can you prove you’re more productive?
And here’s what shows a good signal that you actually are:
- You can show a running setup for your agents, Claude Code/Codex, your Cursor setup - You can show judgement in real-time on how you’d adjust a given AI output
- You can name three things you stopped letting AI do and why
- You have a list of skill.md files that your agents are running on (more on this below)
The underlying reasoning behind every signal above:
(1) You’ve spent a lot of time building skills
for your agents
(2) You know what needs your input
(3) You know what to scale and when
Most people try too hard to fully remove themselves from the first step and end up disappointed with the results. Maybe..don’t be lazy?
So, how can you become better #
You can wait for your company to build its central AI brain and hand you leverage. Or you can start building your own agentic skills that amplify how you work. If you want to become a high-leverage hire, I always advise you do #2.
[Disclaimer] I work at Vellum, so I’m obviously biased here. I think about this stuff a lot because it’s tied to what we’re building. But I do think the following is how you actually build leverage with AI on your own terms.
My framework to building high-leverage #
There’s a formula, and it starts with accepting this: you can’t wait for your company to build the infrastructure that makes you valuable in an AI-flat org. You have to build it yourself, prove it works, and become indefensible before they restructure and leave you behind.
And the only thing you need to optimize + personalize: The skills md files that explain to your agent how your tasks should be done.
It’s that simple.
You should build skills (basic md files) that transfer the task + criteria + taste into the agent’s context. Over time, the agent should learn to replicate not just what you do, but how you do it.
Here’s my system:
Choose one task you do often
A weekly competitor report, content brief, customer follow up, CRM clean up. It’s very important to pick something where you already * know what good looks like*, but the work is repetitive enough that it should not fully depend on you every time.
Choose an agent/assistant
Now, it’s time to choose your “fighter”. You have two categories: platforms like Claude Cowork, Codex), or platforms where you build and own your agent OpenClaw, Hermes, Vellum
Any of these will be very useful if you have great skills. The latter option will make your experience a bit more personalized.
Write a great skill
An assistant skill is just a Markdown file that tells your assistant how to complete a specific task successfully. You can learn how to write great skills here.
It’s very important that you participate in writing the first version of the skill!
Because remember, you’re the one with the domain expertise. People who give this task to AI, will almost definitely fail and will get into a much worse “brain fry” condition.
In most technical-heavy skills you might need to add some spec on APIs, CLI commands etc - your agent can be useful in adding those. Every other rule, preference and behavior should be defined by you. Again, please don’t be lazy.
Give the skill to your agent and see how it does
The first output won’t be great and that’s totally fine. The goal is to have the assistant make mistakes, and learn from them. So it can do better over time.
At this step, your involvement is higher, because you’ll be checking the results, improving the skill, and giving feedback. The feedback you give here is the most important thing you can do.
Rinse and repeat
An assistant with good memory should know how to learn from its mistakes, learn about your preferences and become 100x better at finishing tasks. So every time you use a skill, review the work, and give feedback, the assistant has a chance to do it better the next time. (this is why I’m so excited to work on Vellum)
Once the assistant is able to do one task well, then you move to another. Then rinse and repeat.
This is the complete formula for becoming the AI-hire every company is desperate for:
- Pick a good assistant
- Find a repeatable task where you know what good looks like
- Write the skill.md file
- Give it to your assistant
- Let it do the work
- Review and give feedback
- Have the assistant improve the skill
- Repeat until it is good enough to trust
- Move to the next task
That is what becoming agent-native looks like in practice. After a few months with this you’ve built your own second brain, that’s not reliant on any company infra and it’s completely yours to market and charge on.
You own the domain expertise and you “teach” your personal AI / assistant of how things should be done. At that point you’ve built leverage on the market that no one else has even started to think of.
Now go out there and follow this formula to become the hire every company wants!