Founders Can’t Outsource AI Fluency

Why enthusiasm isn’t enough in the age of intelligent tools

Last week I sat in on a team-wide AI conversation. It had good bones - curious energy, practical takeaways, people sharing tools and prompts like trade secrets.

But the tension was familiar: the founders were excited… and quiet.

They weren’t leading the learning. Not intentionally. Just by omission.

They applauded the team’s experiments (automated research agents, custom GPTs, smart prompts tuned for deal flow) but didn’t push deeper. No “could this replace our current process?” or “what would it take to scale that?” They showed up as supporters, not stewards.

And that’s the part that stuck.

Because if you’re at the top of the org chart and you’re not actively shaping how AI is understood, used, and evaluated you’re not leading the change. You’re just riding it.

A recent MIT study backs this up: when people used ChatGPT to draft essays from the start, their originality and neural engagement declined. They got results but lost something essential in the process. Without that upfront engagement, they missed the chance to define the problem, challenge assumptions, and direct the output with clarity.

Excitement ≠ Readiness

A recent MIT study found that participants who engaged with AI after doing the work themselves showed increased neural activity in areas tied to memory and problem-solving. In contrast, those who relied on AI from the outset saw a drop in originality and long-term retention. The takeaway? Thoughtful use isn’t just strategic. It sharpens your thinking.

Greg Shove, CEO of Section, names this plainly: you’re either an AI freeloader or an AI manager.

Freeloaders offload to AI and copy-paste the result. Managers use AI to think deeper, explore blind spots in decisions, and raise the bar.

And for founders, that distinction matters more. Because if you’re not fluent in the tools your team is adopting you can’t ask the right questions. Can’t spot the strategic gaps. Can’t model responsible use.

You end up with innovation that lives in pockets - not products.

A Simple Case: “Will this scale?”

One founder I worked with recently started using GPT to run through five different onboarding workflows. Not to write SOPs, but to interrogate them:

• Where are we losing clarity?

• What steps could be automated?

• What’s redundant if we’re using tools like this daily?

It wasn’t flashy. No custom agents or advanced scripting. Just better questions, faster iteration, and the courage to rebuild systems from that clarity.

That shift freed up ~6 hours/week across a lean ops team.

And it came from the founder’s willingness to go first, not just delegate improvement down the line.

Where to Start (Without Overhauling Your Life)

If you’re wondering, What does “good enough” even look like for me? here’s the short version:

• Use the tools daily - in your own workflow, not just to “support” the team.

• Pressure-test decisions - run strategy drafts or investment questions through through your preferred LLM (ChatGPT, Claude, Gemini) and see what gets surfaced.

• Audit one process per month - ask: could this be automated, augmented, or made clearer with AI?

• Build a tiny system - even if it’s just an SOP writer or email tone checker. Not because it’s efficient, but because it teaches you how the pieces work.

• Write your own AI manifesto - articulate what responsible, strategic AI use looks like for your company, in your language.

These aren’t projects. They’re practices.

And what isn’t your job?

You don’t need to become your own AI engineer.

You don’t need to build every workflow from scratch.

And you don’t need to be the most advanced user on the team.

Your job is to stay close enough to the tools that you can:

• Spot the difference between novelty and leverage

• Ask better questions about systems, not just outputs

• Know when it’s time to bring in support - and what kind

The deeper builds, custom integrations, and team-wide enablement? That can come later, with the right people in place.

But fluency has to come first. Without it, you’re not delegating. You’re just handing off decisions you don’t yet understand.

The Risk of Staying Shallow

Shallow use has real cost. MIT researchers found that over time, habitual reliance on AI led to more formulaic thinking. That loss of creative engagement showed up in how little ownership people felt over the work itself.

When leaders don’t get hands-on, a few things happen:

• Bright spots never turn into strategy.

• Investment gets guided by flash, not function.

• Culture flattens - because no one is modeling depth.

You don’t need to master every tool. But you do need to be in relationship with them. Otherwise, you’re asking your team to steer the ship without a shared map.

Bottom Line

You don’t need a Head of AI.

You need to stop outsourcing your AI fluency.

The companies that will thrive in this next chapter aren’t the ones with the best stack. They’re the ones where leadership knows how to use it: slowly, imperfectly, but with intention.

So the better question isn’t “are we using AI?” it’s “are we learning how to lead with it?”

Sources:

-Greg Shove, Section.AI CEO. Leaking Our Own AI Manifesto 

-Kosmyna et al. Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

-Laptop Mag. This Is Your Brain on ChatGPT