Episode 6: Overcoming the AI Replacement Narrative — with Jonathan Aberman
Jonathan Aberman, CEO of Hupside and author of The Originality Dividend, joins Jack Moore to argue that the “AI will replace you” narrative is the biggest obstacle to AI adoption. They trade real stories, including Evans’ own stumble into that fear, and lay out the originality loop and a 90-day plan for leading AI change without losing your people.
Host
Jack Moore hosts Progress Over Perfection, Evans’ podcast for federal leaders navigating modernization, workforce change, and the realities of moving programs forward without perfect information.
Guest
Jonathan Aberman is CEO and co-founder of Hupside, where he’s pioneering the field of Original Intelligence. An entrepreneur, investor, and innovation strategist, he has advised DARPA, the Department of Homeland Security, and the Air Force, served as a university dean and professor, and is a nationally recognized voice on entrepreneurship and technology. He is the author of The Originality Dividend: Why Human Original Intelligence Is the Most Valuable Asset in an Age of AI.
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About this episode
Federal contractors and agencies alike are running into the same wall: AI budgets are up, but so is workforce resistance, and the “replacement story” is why. Jonathan Aberman says it’s because most of the AI industry’s messaging assumes the technology’s value comes from cutting headcount, and that assumption is quietly sabotaging every rollout built on top of it.
Jack and Jonathan trade real examples, including Evans’ own AI launch and the fear it triggered internally, the Hupchecker pilot that showed the gap between using AI as a tool and using it as an oracle, and a federal AI project at the FAA led by Evans’ Jesse Lambert. They close with two frameworks leaders can use immediately: the originality loop for working with AI without losing your own thinking, and a 90-day plan for leading AI change without a mandate.
What Jack and Jonathan cover
What is the “replacement story,” and why does it shape how people react to AI?
Aberman argues it’s the dominant, largely unspoken narrative that AI’s business case depends on cutting labor…and that narrative colors everything from employee resistance to how people experience federal workforce reductions.
Why can’t AI be both predictable and differentiated?
Predictability is what makes AI efficient at scale, but it’s structurally at odds with originality. Recent research shows AI models have gotten worse, not better, at generating novel output over time.
What’s the difference between using AI as a tool and using AI as an oracle?
Treating AI as a process tool with a feedback loop builds skill; treating it as an oracle and accepting its answers uncredited is linked to measurable cognitive decline in creative thinking.
What’s Jonathan’s first move for any CEO starting an AI rollout?
Tell the workforce directly that no one is losing their job to AI for the next six to twelve months — then build the change plan on that foundation.
Why it matters now
- MIT’s GenAI Divide study found 95% of enterprise generative AI pilots failed to deliver measurable financial return in 2025. (Fortune)
- Aberman’s book launched with endorsements from Navy CTO Justin Fanelli and Rep. Don Beyer, reflecting growing federal interest in measuring human contribution alongside AI. (ExecutiveBiz)
- Evans’ own FAA workforce-enablement project, led by Jesse Lambert, is tracking cycle time and backlog reduction rather than abstract “AI maturity” metrics.
Leaders who get ahead of the ROI gap by measuring what AI actually adds versus what it replaces are the ones positioned to capture value before the market catches up.
Related resources
- The Originality Dividend by Jonathan Aberman
- Hupside
