Welcome to AI for SMBs #22!

This week's post is different. Instead of our usual step-by-step guide, I wanted to share some thoughts sparked by two posts I've found myself forwarding constantly over the past few weeks.

The first is Elena Verna's "The Rise of the AI-Native Employee" about her experience joining Lovable and discovering a completely different way of working. The second is Wade Foster's detailed job description for Zapier's new "AI Automation Engineer" role, essentially codifying what these employees look like in practice.

Both posts hit on something I've been seeing more frequently in my consulting work: there's a new type of employee emerging, and SMBs are uniquely positioned to attract and leverage them. 

As you start thinking about your 2026 planning, this should be on your radar.

No updates to the AI Models for SMBs Comparison Chart [link is for subscribers only] since last week - but don’t miss my POV on what all this means on LinkedIn. 

-j

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Your next hire shouldn't ask for permission to solve problems. They're going to solve them, then tell you what they built.

This isn't about hiring someone who "uses AI tools." Every knowledge worker does that now. This is about hiring someone who thinks AI-first, defaults to building solutions directly, and expects the authority to execute without running everything through three approval layers.

Elena Verna just joined Lovable after watching them hit $80M ARR in seven months with 35 people. Her insight? The company isn't just AI-powered; their employees are AI-native. They don't submit requests to design teams or wait for developer sprints. They see a problem, prompt an AI system, and ship a solution. That day.

(If you haven’t read the post from Elena, stop right now and read it in full. Seriously.)

The more I talk to and read about this new type of person, the more convinced I am that most of these folks would never take a job in a large enterprise. SMBs have a massive competitive advantage: you can actually let these people work the way they think.

When AI-native talent joins a big company, they quickly discover that their superpower, moving from idea to execution in hours rather than quarters, gets smothered by process.

"Did you get approval for that?" 
"Let's run this through legal."
"We need to involve the design team."
"This requires a project brief."

Every "no, but..." kills momentum. Every approval layer adds friction. Every handoff creates delay.

SMBs operate in "yes, and..." mode by necessity. You don't have the luxury of bureaucracy. When someone has a good idea that could drive revenue or cut costs, you want them to pursue it immediately, not six months from now after it's been workshopped to death.

This cultural difference isn't just about speed; it's about attracting talent that thrives on agency and autonomy. The most capable AI-native workers will increasingly choose environments where they can actually use their capabilities.

While your enterprise competitors are losing these people to process fatigue, you're offering something they can't: the freedom to build.

What Makes Someone AI-Native 

Wade Foster at Zapier calls them "background-agnostic but builder-focused," and that captures something important. An AI-native employee doesn't just use ChatGPT for writing emails. They default to AI for everything: research, analysis, content creation, basic coding, process optimization, even strategic thinking.

But the real differentiator isn't technical; it's psychological. These people have developed what Elena calls "cheap failures," the ability to test ideas rapidly because the cost of building and iterating has dropped to nearly zero. They prototype solutions in afternoons rather than proposing them in meetings.

Wade shared impact stories from Zapier: one person auto-resolved 27.5% of IT tickets using AI, saving the team 2,200+ days and $500K in hiring costs. Another automated sales workflows that reclaimed $1M in revenue while saving 282+ workdays annually. A third cut integration build time by 99%, from months to under an hour.

These aren't isolated success stories. They're examples of people who see manual processes and instinctively think "this could be automated" rather than "this is how we've always done it."

These folks think in systems, not tasks. Instead of asking "how do we hire someone to manage our social media?" they ask "how do we build a system that generates, schedules, and optimizes our social media with minimal human oversight?"

They're comfortable with ambiguity because AI enables rapid iteration. Traditional employees want detailed specifications before starting work. AI-native employees start building immediately, then refine based on results.

Most are younger; not because age matters, but because they haven't been trained out of this approach by years of corporate process. They haven't learned that "things just take time" because they've watched AI collapse traditional timelines.

How to Know You're Ready 

Not every SMB is ready for an AI-native hire. These employees need real authority to be effective, and they'll quit if you micromanage their methods while demanding their results.

You're ready when:

Simple projects require multiple people. 
If launching a basic landing page involves a marketer, a designer, a developer, and two weeks of coordination, an AI-native employee could handle it in an afternoon.

You spend more time coordinating than creating. 
When your weekly schedule is dominated by status meetings, handoff discussions, and approval processes, you need someone who can skip straight to outcomes.

Your AI adoption has plateaued. 
You've implemented the obvious use cases (ChatGPT for writing, AI for customer service) but you're not seeing transformational impact. AI-native employees find applications you'd never consider.

You catch yourself saying "we need someone to just make this happen." 
This is the clearest signal. You know what needs to be done, you just don't have anyone who can execute without extensive oversight.

You're losing speed to competitors. 
If nimbler competitors are launching features, campaigns, or initiatives faster than you can plan them, you need someone who operates at their velocity.

What to Look For 

Forget traditional job descriptions. AI-native employees are defined by mindset, not resume lines. Wade Foster's framework for screening these candidates focuses on practical demonstration over credentials, and that's exactly right.

 Bias toward action over analysis.

They'd rather build a working prototype than create a comprehensive project plan. When faced with uncertainty, they experiment rather than research.

 Cross-functional fluency.

Unlike specialists who optimize within their domain, AI-native employees understand how different teams work and where automation can eliminate handoffs. They think about the sales team's lead nurturing process, the support team's ticket triage system, and the marketing team's content workflows, then build solutions that connect them.

 Portfolio of random projects.

Look for people with GitHub repos full of weekend experiments, personal websites with unusual features, or side projects that solve problems you didn't know existed. They build things because they can, not because they have to.

 Advanced prompt engineering capabilities. 

They don't just use ChatGPT—they orchestrate multiple AI systems, understand when Claude works better than Gemini for specific tasks, and can debug AI failures by refining prompts and logic flows.

 Comfort with iteration and failure. 

Traditional employees want to "get it right the first time." AI-native employees expect to iterate rapidly, improving solutions based on real feedback rather than theoretical planning. When their automation breaks, they stay calm and systematically debug rather than panic.

 Systems thinking. 

They naturally think about workflows, automation, and scalability. Instead of just solving today's problem, they build solutions that handle tomorrow's similar problems automatically.

 Low ego about methods. 

They don't care whether they solve a problem with custom code, no-code tools, AI systems, or manual processes. They care about outcomes, not craftsmanship.

Structuring Authority and Autonomy 

The scariest part of hiring AI-native talent is giving them enough authority to be effective. These people need to make decisions, spend small amounts of money on tools, and implement solutions without checking in at every step.

Start with clear outcome-based goals rather than process requirements. Instead of "follow our established marketing workflow," try "increase qualified leads by 25% using whatever methods you think will work."

Set spending thresholds they can operate within independently, maybe $500/month for tools and services without approval. AI-native employees are comfortable with subscription-based tools and will quickly assemble a toolkit that dramatically amplifies their capabilities.

Define the boundaries, not the methods. They need to know what they can't touch (financial systems, customer data, legal commitments) but should have freedom within those constraints.

Establish regular check-ins focused on results rather than activities. Weekly "what did you ship and what did you learn?" conversations work better than daily status updates.

Most importantly, resist the urge to "process" their successes. When they solve a problem efficiently using methods you don't understand, learn from them rather than requiring them to document everything for future replication.

Test for real capability. Don't rely on resume screening; give candidates practical challenges: "Design an AI workflow to streamline our content production process" or "Map and automate a typical sales workflow." The best candidates will show you working demos, explain their prompt design, and address edge cases without being prompted.

Become an AI Native Company

Elena's prediction about organizational changes (smaller teams, flatter structures, eliminated middle management) isn't happening in isolation. It's happening because AI-native employees make traditional coordination layers obsolete.

When one person can handle the work that previously required a designer, developer, and project coordinator, you don't need the infrastructure to manage those handoffs. When solutions can be prototyped and tested in hours rather than months, you don't need extensive planning processes.

SMBs that hire AI-native talent early will discover they can compete with much larger companies on capabilities while maintaining their speed advantage. A 15-person company with three AI-native employees might out execute a 150-person company constrained by traditional workflows.

The talent shortage everyone predicted? It's not coming. Instead, you'll see a productivity explosion among businesses that successfully integrate AI-native workers, while companies clinging to traditional org structures find themselves competitively disadvantaged.

Your enterprise competitors are debating AI strategy in committees. Your venture-backed competitors are hiring expensive specialists to implement AI initiatives.

You can hire one AI-native employee who builds solutions faster than their entire AI transformation team.

By this time next year, nearly every SMB should have at least one person who defaults to building rather than planning, who thinks in systems rather than tasks, and who treats AI as naturally as they treat email.

The question isn't whether this shift is coming. The question is whether you'll be early enough to attract the best AI-native talent before everyone else figures out what you already know: small companies that move fast will eat the world.

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