The Truth About AI Hype and Workforce Impact

I originally shared these thoughts on LinkedIn, but wanted to expand on them here.

The AI boom feels a lot like the early days of the cloud… and we’re making the same expensive mistakes.

AI Is Not a Silver Bullet

While AI is shifting the workforce, it’s up to us to ensure it reshapes expectations without destroying opportunities.

Generative AI is powerful. I use it every day to draft, brainstorm, and speed up workflows. It’s changing how we work: product engineering is faster, security more complex, and access to AI (and the skills to use it) can help level the playing field.

But let’s not get carried away, folks. Over 90% of AI startups fail within their first few years—far more than their non-AI counterparts. AI is being oversold, overhyped, and misunderstood.

Fear Is Driving Bad Decisions

The human cost of this misunderstanding (and, frankly, fear) is already visible in corporate boardrooms:

• 55% of execs said they laid off staff believing AI could replace them.
• 64% later admitted those layoffs were a mistake.
• 49% admitted they didn’t fully understand AI when they made those decisions.

We also can’t ignore the ethical concerns that leaders like Timnit Gebru and Joy Buolamwini have been raising for years:

• AI can amplify bias and harm marginalized communities.
• AI relies on underpaid, unseen annotators and labelers who make these systems work but rarely share in their success.
• The environmental impact of large models is often ignored in the race for bigger, faster systems.

What AI Should Actually Change

So, yes, AI should change expectations for knowledge workers. An entry level software engineer, for example, will be expected to do more than a junior engineer did five years ago. That’s progress. AI can take on the repetitive, low-leverage tasks—boilerplate code, documentation, basic tests—freeing up humans for the creative problem-solving and critical thinking that drives innovation.

But for AI to lift everyone, we can’t stop at productivity gains. We need fairness, transparency, and accountability. That means asking hard questions that cut through the noise.

Moving Forward With Intention

Before your next AI initiative or staffing decision, consider:

• Are we empowering people to use AI or using AI to justify cuts?
• Are we investing in learning and adaptation or packaging hype and fear?
• Are we building teams that know how to think critically about AI or just chasing trends?

Fear-driven decision-making yields expensive mistakes, not innovation. Let’s get intentional about what we really value and build teams ready for what’s next.

Lead with courage, not convenience.

Image created with ChatGPT.