People Helping Machines Helping People: 5 AI Insights for Forward-Thinking Leaders

In a recent conversation between John Davison (founder of Startup Landia and CTO) and Kirsten Newbold-Knip (serial CEO and venture investor), I witnessed a masterclass in how visionary leaders are approaching AI transformation. Their discussion revealed profound insights about balancing technological advancement with human value—insights that any executive navigating today's AI landscape should consider.

The Real Question: Evolution, Not Revolution

The conversation began with a provocative question: should CEOs replace engineers with AI coding agents? This wasn't theoretical—it was a real question from a CEO fielding recommendations from consultants.

Kirsten's answer was pragmatic and nuanced: "Not yet."

Rather than binary decisions about replacement, she suggests identifying specific opportunities where AI tools can deliver 10-30% efficiency improvements—whether through better code generation, more comprehensive testing, or automating tedious tasks that engineers don't enjoy.

The most valuable framework she offers is a series of questions leaders should ask:

• If someone leaves, should we automatically backfill or could AI fill part of that role?

• When facing a new challenge, how might AI solve it before we hire someone?

• Which parts of current jobs do people dislike that could be automated?

This evolutionary rather than revolutionary approach preserves institutional knowledge while advancing capabilities—a wiser path than radical restructuring.

The Quality Advantage No One Talks About

While most AI discussions focus on productivity and cost reduction, Kirsten highlights a less obvious but perhaps more valuable benefit: improved quality through comprehensive testing.

"Humans are quite bad at thinking about cases, corner cases, edge cases," she observes. "AI is great at coming up with 20 different scenarios and then helping you run tests against them without the mental gymnastics that you have to do over and over again."

This perspective shifts the conversation from replacing humans to augmenting their capabilities in areas where our cognitive limitations create vulnerabilities. The combination of human creativity with AI thoroughness creates outcomes neither could achieve alone.

The Skills Pipeline Challenge

One of the most profound concerns raised was about developing future talent. If junior positions are automated first, how will we develop the senior talent of tomorrow?

"If you're not writing those first lines of code, how will you ever have an idea to understand the frameworks well enough to even work with an agent?" Kirsten asks. "Is there still the creativity that we need? Is there still the innovation that we need?"

This highlights a critical leadership responsibility: maintaining development pathways that allow people to build foundational skills even as we automate entry-level tasks. Without deliberate attention to this challenge, we risk hollowing out our innovation capacity over time.

Augmentation vs. Replacement: A Mental Model

Kirsten offers a clarifying distinction between two approaches to AI implementation:

"An LLM helps me learn faster, improve myself better... versus the idea of agentic - entire blocks of work that I can turn over to an agent to do for me. They're both augmentation, but in different ways, and one is significantly more powerful, but requires more trust."

This mental model helps leaders distinguish between technologies that enhance capabilities (like GPS navigation) versus those that replace human activity entirely (like self-driving cars). Each approach has different implications for implementation, supervision, and organizational design.

The Service Foundation of Technology

Perhaps most illuminating was how Kirsten's hospitality background informs her technology leadership. The principles of exceptional service—anticipating needs, delivering consistently, building trust—translate directly to technology development.

"The job is to be in service to your customers," John noted, highlighting how hospitality training creates an advantage in user experience design. As AI capabilities grow, the human elements of service become increasingly valuable differentiators.

Kirsten's early experience managing a diverse team ranging from 16 to 76 years old provided training in understanding varied perspectives and motivations—exactly the skills needed to harness both human and artificial intelligence effectively.

John's Perspective: Mental Models for Navigation

Throughout the conversation, John Davison offered thoughtful mental models for navigating this transition:

1. "Specialization is for insects" - AI is eroding traditional skill boundaries, requiring more multidisciplinary capabilities from leaders and teams.

2. "Tailored suits required" - The falling cost of customization means organizations must build exactly the operational stack they need rather than adapting to off-the-shelf solutions.

3. "The paradox of two fast cars and no speedometers" - We're pursuing AI acceleration without proper frameworks to measure value, making comparison difficult.

These frameworks provide conceptual tools for making sense of complex transitions and avoiding simplistic replacement narratives.

People Helping Machines Helping People

What emerges from this rich discussion is a vision captured in John's business mindset: "People Helping Machines Helping People."

This isn't about machines replacing people or people fighting against machine encroachment. It's about a virtuous cycle where human creativity, judgment and empathy combine with machine efficiency, consistency and analytical power to create outcomes neither could achieve alone.

As Kirsten notes, "Stay curious, because what I would say to you is like, we have a lot of ideas, and we don't have all the answers, and experimentation is probably the way to get there."

This experimental mindset—coupled with a clear focus on human service—offers the most promising path forward. Leaders who can balance technological opportunity with human development will create organizations that thrive not despite AI advancement but because of how thoughtfully they integrate it.

The future doesn't belong to organizations that simply deploy the most advanced AI. It belongs to those who create the most effective partnerships between human and artificial intelligence—truly embodying the principle of people helping machines helping people.