Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
What a 20-year-old TV Show Taught Me About AI
|
Have you ever seen the TV show Nip/Tuck? It centres on the lives and clients of two Miami plastic surgeons. But what it’s really about is the quest for perfection. The main characters want perfection in their own lives, but all they are actually capable of creating is the illusion of it. And beneath the perfect facades, all the characters are actually pretty terrible. It’s now over 20 years old, but rewatching an episode the other day made me realise it serves as a perfect metaphor for AI. AI also creates the illusion of perfection. A person previously incapable of writing so much as a shopping list can now produce a 30-page document before finishing their morning coffee. A student who has never so much as glanced at a textbook can get a perfect GPA. But beneath the surface, there’s nothing there. AI can make you appear to be an expert. It can’t actually make you one. This presents both an opportunity and a risk for data scientists who are actual experts. As the AI wave continues to sweep through the world, true expertise is going to become increasingly valuable. But only if your stakeholders can tell the difference between you and the person using AI to fake it. The secret isn’t using AI to fake expertise. It’s protecting your expertise and positioning it in such a way that your stakeholders can see its true value. Talk again soon, Dr Genevieve Hayes |
Data Science Impact Algorithm
Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
Every organisation wants to be AI-first. It’s the new “data-driven” - a badge that signals ambition, modernity, and a seat at the table of the future. “In the long run, we’re evolving in computing from a ‘mobile-first’ to an ‘AI-first’ world.” - Sundar Pichai, Google “We are no longer a graphics card company… We are an AI-first company. From now on, we are betting the company on AI.” - Jensen Huang, NVIDIA “Duolingo is going to be AI-first.” - Luis von Ahn, Duolingo “Before asking for more...
In a recent blog post, CFO advisor and friend of the list Lauren Pearl discussed the impact of AI on the age-old software “build vs buy” decision. In short, Lauren argued that AI-driven vibe coding has made building software in-house far more appealing when the stakes are low, but when stakes are high, buying is still the way to go. To quote Lauren: “When you buy software, you don’t just buy the code. You buy a support team, expert-derived flows, tested features, platform trust, regular...
Ten years ago, everyone wanted to be doing machine learning. Go to a data conference - someone was presenting on ML. Advertising a data role - just mention ML and applications would 10X. It was the promise of machine learning that inspired an entire generation of data scientists. I know. I was one of them. And then came the statistic that revealed what many data scientists knew but few wanted to admit: around 90% of ML initiatives never made it to deployment. The ideas were there. The...