👋 Hey friends, TGIF!!

When ChatGPT launched, I thought learning how to use it was going to be my “career cheat code.” I spent nights practicing prompts, reading threads on Twitter (well, X), and building small projects. I remember thinking: finally, I’m ahead of the curve.

Fast forward a year, and suddenly everyone around me was talking about embeddings, fine-tuning, and multimodal AI. My carefully learned tricks? Already outdated.

That’s when I realized something most people in this space eventually learn the hard way: AI skills age like milk.

The World Economic Forum’s Future of Jobs Report (2025) backs this up. It found that the half-life of AI skills is now just 6–12 months. Translation: what’s cutting-edge today will feel like “table stakes” within a year.

This is both terrifying and exciting. Terrifying because it means you can’t coast on a skill forever. Exciting because it creates endless opportunities for people willing to learn, adapt, and pivot.

So, how do you future-proof your career in a world where skills have an expiration date shorter than a carton of milk?

That’s what this edition is about. We’ll explore:

Let’s dive in…

Getting compliant doesn’t have to take months.

With Delve, it takes 15 hours.

SOC 2, HIPAA, ISO, and more, automated with AI.

That’s why 500+ of the fastest-growing AI companies (Lovable, Cluely, Wispr Flow) already run compliance on Delve.

But compliance is only half the battle. The other half is enterprise reviews.

At Delve, we’ve sat through 100s of hours of security questionnaires.

We’ve seen every pointless checkbox, every SaaS-era question, every deal stalled because of them.

So today, we’re publishing something different: a manifesto with Bland AI on how security questionnaires should work in the age of AI.

📖Read the manifesto here 

⚡Book a demo here with code AIJ1KOFF for $1,000 off + white-glove onboarding.

Getting compliant doesn’t take months anymore. With Delve, it takes 15 hours — and today, alongside Bland, we’re rethinking security questionnaires.

Here’s the stat that made me pause to think about how long a skill is needed:

In the early 2000s, skills like Java or Excel lasted 5–10 years before going stale. In the 2010s, Hadoop or TensorFlow lasted 3–5 years.

Now? Most AI skills are outdated within 6–12 months.

Think about what that means:

I used to imagine careers like ladders—you climb one step at a time. Today it feels more like surfing. Waves come faster, bigger, and if you miss one, you risk wiping out.

The trick isn’t to fight the waves. It’s to learn how to surf.

Despite the rapid churn, some skills keep showing up across reports from LinkedIn, PwC, and McKinsey as consistently valuable. Think of them as the bedrock:

The PM Who Learned SQL
Sarah, a product manager at a fintech startup, wasn’t technical. But after learning basic SQL, she could pull her own user data. She no longer had to wait three days for analysts to send her numbers. Within a year, she became the go-to “AI + data” person on her team—and landed a promotion.

This isn’t an isolated story. PwC’s AI Jobs Barometer found that workers with AI + SQL skills earned a 56% wage premium compared to peers in similar roles.

You don’t need to be an AI researcher. But you do need to be AI-literate. Knowing just enough Python, SQL, or ML basics to hold your own puts you in the top percentile of professionals.

One of the most surprising parts of this AI boom is that some of the fastest-growing AI roles are being filled by people without technical backgrounds.

The Teacher → Prompt Engineer
In 2023, Anthropic made headlines by posting a “Prompt Engineer” role with a salary of $250K–$335K. The best candidates weren’t all engineers—they were people like teachers. Why? Because teaching is about asking the right questions, guiding answers, and improving feedback loops. Sound familiar? That’s prompt engineering in a nutshell.

The Future of Jobs Report (2025) highlights roles like AI specialists, big data analysts, and fintech engineers as the fastest-growing. But crucially, many of these are filled by people pivoting from outside tech.

Don’t throw away your past experience. Instead, layer AI on top of it. A teacher with AI becomes an AI educator. A marketer with AI becomes an AI-powered growth strategist. Your domain knowledge is your differentiator.

Here’s the brutal truth:

If you cling to old skills, you’ll get left behind. But if you treat learning as continuous, you can stay ahead.

The Data Scientist Who Pivoted
Ravi, a healthcare data scientist, built models in TensorFlow. When generative AI exploded, demand shifted to multimodal models that could analyze both patient records and medical images. Instead of resisting, he enrolled in Andrew Ng’s Generative AI Specialization. Within 18 months, Ravi was leading new initiatives instead of being sidelined.

MIT’s GenAI Divide (2025) found that companies investing in continuous upskilling outperformed peers by 40% in productivity. Those relying on one-off training? Falling behind.

Don’t think of learning as a one-time event. Think of it as a cycle of reinvention every 12–18 months.

When I first started digging into AI job trends, I assumed the same thing most people do: complex tasks will be hardest to automate. It feels logical, right? Coding seems harder than driving, so surely coders are safer than truck drivers.

But here’s the twist I learned from the World Economic Forum: it’s not about complexity—it’s about data.

The Data Paradox
AI learns the way we do—from examples. The more examples, the faster it improves. Think of it like that one kid in college who had all the past exams and answer keys—they always crushed the test.

That’s exactly why data-rich industries are getting disrupted first:

With so much data available, adoption rates in these fields are projected at 60–70%.

The Jobs Lagging Behind
By contrast, data-poor industries are harder to automate—not because they’re more complex, but because AI has nothing to learn from.

These sectors lag at <25% adoption, though some are experimenting with invasive solutions—like video monitoring in operating rooms or AI-powered exam proctoring—to fill the data gap.

The Economic Reality
Here’s the part that hit me hardest: disruption looks very different depending on where you work.

Globally, 92M jobs may vanish by 2030, with 170M new ones created. But they’re not one-to-one swaps. The new jobs often require totally different skills, and they don’t always appear where the old jobs disappeared.

This Means for Us
If you’re reading this and thinking, “Well, my job is safe because it’s too complicated for AI,”—I’d challenge that. The real question isn’t whether your work is complex, but whether it’s data-rich.

The safest bet isn’t a specific profession—it’s adaptability. The winners will be those who can:

Don’t try to outrun AI. Position yourself to run alongside it.

Here’s the truth I wish someone had told me earlier: not every future-proof skill involves code.

As AI eats up technical tasks, human-centered skills are becoming more valuable than ever:

At a Fortune 500 company, two data teams had access to the same AI tools. The one that consistently won funding wasn’t the more technical—it was the team that knew how to translate AI results into compelling stories for execs.

If you’re wondering what specific jobs will actually exist in five years, Bay Atlantic University’s Best AI Jobs of the Future report gives a snapshot. What’s striking is how broad the opportunities are—there’s room for builders, thinkers, managers, and ethicists alike.

Notice how it’s not just engineers. Roles like AI Product Manager and AI Ethicist prove that human skills—communication, ethics, leadership—are just as critical as technical chops.

Technical: Python, Java, TensorFlow, PyTorch, SQL, cloud computing.
Soft skills: Critical thinking, creativity, adaptability, communication, ethical reasoning.

This echoes what we’ve already explored: the half-life of technical skills is shrinking, but soft skills endure.

A friend in HR started experimenting with AI-powered recruiting—automating resume screening and interview scheduling. Within a year, she wasn’t just an “HR specialist” anymore—she was branded as an AI Talent Strategist. That pivot opened doors she’d never imagined.

One thing most reports gloss over: AI job growth isn’t evenly distributed.

The World Economic Forum warns of a “skills divide” where access to reskilling programs determines whether you rise with AI or get left behind.

It’s not just individuals scrambling to adapt—companies know the talent crunch is real.

Here’s the twist: companies aren’t just retraining engineers. They’re teaching marketers, consultants, and even HR teams to use AI.

Takeaway: If you’re inside a big company, there’s a good chance free AI training is already available—you just need to raise your hand.

If this all feels overwhelming, here’s my advice: start tiny.

The goal isn’t to master AI overnight—it’s to prove to yourself (and your employer) that you can adapt and create value.

After diving into reports, stories, and my own experiments, here’s my playbook:

Example Roadmap:

This isn’t about mastering everything. It’s about staying in motion.

Here’s where I want to leave you—with a question, not an answer.

After pulling all this research, case studies, and trends together, I keep circling back to one tension:

So the real debate is this:

Should you double down on technical skills (knowing they age quickly but give you short-term leverage), or should you prioritize soft skills and adaptability (knowing they take longer to build but may outlast the tech cycles)?

I’ll be honest—my own answer changes week to week. Some days, I feel like I’m falling behind if I’m not tinkering with the latest model or API. Other days, I realize my biggest wins didn’t come from code—they came from how I communicated, connected dots, and adapted faster than others.

And maybe the truth is: it’s not an either/or. It’s both. The sweet spot is becoming the bridge—the person who understands enough of the tech to use it, but also enough of the human side to make it meaningful.

But that’s just my take.

I’d love to hear yours:

Hit reply and tell me. I’ll feature a range of perspectives in the next issue—because the future of work isn’t a solo journey, it’s a shared experiment.

My challenge to you: don’t just read this—debate it. The winners in the AI economy won’t just be the learners. They’ll be the ones asking better questions.

Until next time,
— Naseema 

That’s all for now. And, thanks for staying with us. If you have specific feedback, please let us know by leaving a comment or emailing us. We are here to serve you!

Join 130k+ AI and Data enthusiasts by subscribing to our LinkedIn page.

Become a sponsor of our next newsletter and connect with industry leaders and innovators.