If you want to understand the future of artificial intelligence, these are the non-technical books about AI you should be reading in 2025.

It feels like everyone is talking about artificial intelligence, right? It’s at the point where you can’t scroll through the news or have a conversation about the future of, well, anything without AI coming up. But here’s the thing: most of the conversation is either super technical, full of code and algorithms, or it’s just surface-level hype. I found myself wanting to go deeper, but not into the weeds of machine learning. I wanted to find the best books about AI that tackle the big, human questions: Where is this technology actually going? What does it mean for our jobs, our society, and what it means to be human?

If you’re in the same boat, you’re in the right place. I’m not interested in textbooks. I’m interested in the insights from people who are deep in the industry, thinking about the strategic and ethical questions. After going down a rabbit hole, I’ve found a few incredible reads that are perfect for anyone who wants to be informed about our shared future.

Why You Should Read Broader Books About AI

Before I share my list, let’s talk about why this kind of reading is so important right now. Understanding AI isn’t just for software engineers anymore. It’s for artists, teachers, managers, parents, and anyone who is curious about the next few decades.

Knowing the basics of how large language models work is one thing, but understanding the potential second-order effects is something else entirely. These books provide a mental framework for thinking about the future. They help you cut through the noise and form your own informed opinions instead of just reacting to the latest headline. They’re less about the “how” and all about the “what if” and “what now?”

My Top Non-Technical AI Book Recommendations for 2025

After a lot of searching, these are the books that have stuck with me. They’re accessible, thought-provoking, and written by people with a deep understanding of the field.

• “The Coming Wave: Technology, Power, and the Twenty-first Century’s Greatest Dilemma” by Mustafa Suleyman

  If you read just one book, make it this one. Suleyman is the co-founder of DeepMind (which Google acquired) and now the CEO of Microsoft AI. He’s a true insider, and he lays out the immense promise and the terrifying risks of AI and synthetic biology with incredible clarity. He’s not a doomsayer or a utopian; he’s a pragmatist. The book gives you a powerful lens for understanding the immense power of these new technologies and the “containment problem” we face. It’s a compelling, urgent read. You can find more about it directly from the publisher, Simon & Schuster.

• “Superintelligence: Paths, Dangers, Strategies” by Nick Bostrom

  This one is a bit more academic, but it’s a foundational text that has shaped much of the conversation around AI safety. Bostrom, a philosopher at Oxford, doesn’t mess around. He methodically walks through the arguments for why a superintelligent AI could pose an existential risk to humanity. It’s not a light beach read, but it’s essential for grasping the high-stakes, long-term conversations happening in AI ethics and safety research.

• “AI Superpowers: China, Silicon Valley, and the New World Order” by Kai-Fu Lee

  To understand the future of AI, you have to understand the global landscape. Kai-Fu Lee, a former executive at Apple, Microsoft, and Google, and a major venture capitalist in China, provides an unparalleled look at the AI competition between the US and China. He explains the different strengths each country brings to the table and what the race for AI dominance means for the global economy. It’s a fascinating look at the geopolitical side of the story.

What These AI Future Books Have in Common

The common thread in all these books is a sense of perspective. They pull the lens back from the day-to-day product launches and technical papers to show the bigger picture. They treat AI not just as a tool, but as a force that is already reshaping power, culture, and economics.

As the MIT Technology Review often discusses, looking at the big picture is crucial for navigating the development of this technology responsibly. Reading these books about AI helps you do just that. It equips you to participate in the conversation in a meaningful way, whether that’s at the dinner table or in a boardroom.

It’s easy to feel like we’re just passengers on this journey. But being well-read and informed gives us a bit more agency. It allows us to be part of the discussion and to advocate for a future we actually want to live in. So, grab a coffee, pick up one of these books, and get ready to think.

A studio used AI to change a film’s ending without consulting the director, sparking a fierce debate over artistic integrity in the age of automation.

You ever finish a movie and think, “Hmm, I would have ended that differently”? It’s a fun thought experiment, but what if the movie studio went back years later and actually did it—using artificial intelligence, and without telling the original director? It sounds like a plot from a sci-fi thriller, but it’s happening right now. This recent controversy over a studio AI altering films has kicked off a massive and important debate about art, ownership, and the future of creativity itself.

The story revolves around the 2013 Hindi film Raanjhanaa. The production company, Eros International, has decided to release a new version for Tamil-speaking audiences. The twist? They’ve used AI to change the film’s ending to something they believe will be more “sensitive” to that market’s cultural tastes. They did this without the involvement or consent of the film’s original director, Aanand L. Rai.

As you can imagine, the director is not happy. He called the move a “deeply troubling precedent” that “disregards the fundamental principles of creative intent and artistic consent.”

So, How is AI Altering Films in This Case?

Eros International, for its part, is defending the decision. The company’s CEO, Pradeep Dwivedi, claims the changes are minor, affecting less than 5% of the movie, and are limited to the final act. He stresses that they used AI as a “creative tool under human supervision” to generate an “alternate emotional resolution.”

He also points out two things:
1. The original version of the film is still available to watch.
2. The studio holds the exclusive copyright to the film.

This isn’t a one-off experiment for them, either. Eros has a library of over 4,000 films and has stated they are actively reviewing them for other opportunities to use AI to “enhance, localize, or reimagine existing content.” Their vision is to present “alternate lenses where appropriate,” all while practicing what they call “responsible innovation.” But that brings up a huge question.

Is This a Threat to Artistic Integrity?

Director Aanand L. Rai certainly thinks so. He argues that art is a reflection of the vision and labor of an artist. Using AI to change a film’s narrative or tone without the director’s input, he says, is a “direct threat to the cultural and creative fabric we work to uphold.” If this goes unchecked, he warns, we could see a future where “myopic, tech-aided opportunism can override the human voice.”

He’s not alone in his concern. This issue taps into the same fears that fueled the 2023 Hollywood strikes, where the use of AI was a major sticking point. Creative unions, like the UK-based Equity, argue that AI should never be used to alter or synthesize an artist’s work without their explicit consent and fair payment. The SAG-AFTRA union has worked to create agreements that protect performers, but the rules for deceased actors or directors’ past work remain a gray area. This case is testing those very boundaries. Who gets the final say: the person who created the art, or the company that owns the rights to it?

Or Is It All Just a Big Publicity Stunt?

There’s another angle to this whole thing. Some industry watchers are skeptical that the technology is even ready for this kind of work. David Gerard, an expert who has tested AI video tools extensively, believes this could be an “obvious” stunt to generate buzz.

He points out that AI video generators are notoriously difficult to control. They often produce bizarre results, can’t follow a script accurately, and struggle to maintain character consistency. As noted in a recent article from Ars Technica on the state of AI video, even the most impressive demos often require cherry-picking the best results from countless failures and involve significant post-production work to fix errors.

Since Eros International has been vague about the specific techniques used, some suspect the “AI-generated ending” might be more marketing speak than technological reality.

Regardless of the technology, this story sets a fascinating and slightly unnerving precedent. It’s a real-world test case for some of the biggest questions facing the creative world. Does copyright ownership give a company the right to retroactively change art? And where do we draw the line between using AI as a helpful tool and letting it overwrite human vision? This Indian film controversy might be the first major domino to fall, but it certainly won’t be the last.

A friendly guide to help you audit Microsoft Copilot, even if you’re new to the whole AI thing.

It’s a familiar scene for many of us in IT. You’re sipping your morning coffee, scrolling through emails, when a new task from your boss lands in your lap: “I need you to audit our new AI tool.” If your first thought is, “I’m not an AI expert… where do I even begin?”—you are definitely not alone. It’s a new frontier for many, but the good news is you don’t need to be an AI guru to get started. This guide will walk you through the practical steps to audit Microsoft Copilot, breaking it down into manageable pieces, even if you’re just starting out.

Let’s be honest, auditing something as complex as AI can feel a bit like being asked to inspect a spaceship’s engine without a manual. But at its core, auditing Copilot is about applying the fundamental principles of IT auditing—governance, access control, and data security—to a new and exciting technology.

First, What Are We Actually Auditing?

Before you can audit something, you need to know what it is. Microsoft Copilot for Microsoft 365 isn’t just a standalone chatbot. It’s deeply woven into the fabric of the apps your company uses every day—Word, Excel, Outlook, Teams, and more. It has access to your organization’s data, including emails, documents, chats, and calendars. This is its superpower, but it’s also where the risks lie.

Many companies, especially when they first adopt Copilot, might be on licenses like Microsoft 365 E1 and using the standard, free version of Microsoft Purview. While more advanced licenses offer more sophisticated tools, you can still perform a meaningful audit with the basics. Your goal is to establish a baseline and identify potential gaps.

Your Starting Point: A Practical Copilot Audit Checklist

Think of this as your initial flight check. These are the core areas you need to investigate to understand how Copilot is being used and what controls are (or aren’t) in place.

How to Audit Microsoft Copilot for Data Governance

This is probably the most critical piece of the puzzle. Since Copilot uses your company’s data to generate responses, your first questions should be about data handling.

• What data can it see? Copilot respects existing user permissions. So, if a user can’t access a specific SharePoint site, Copilot can’t use data from that site for them. Your audit should verify that these permissions are correctly configured and follow the principle of least privilege.
• Is sensitive data labeled? This is where Microsoft Purview Information Protection comes in. Even with the standard features, you can apply sensitivity labels to documents (e.g., “Confidential,” “Internal Use Only”). Audit whether these labels are being used consistently. Copilot is designed to respect these labels, helping prevent the accidental exposure of sensitive information. For a deep dive into how it all works, check out Microsoft’s official documentation on Data, Privacy, and Security for Copilot.
• Are we meeting compliance standards? Think about GDPR, CCPA, or industry-specific regulations. Your audit should assess whether Copilot’s use aligns with these requirements.

Reviewing User Access and Permissions

Who gets the keys to the kingdom? Just because the company has Copilot doesn’t mean everyone should have access on day one.

• Who has a license? Is access rolled out to everyone or a specific pilot group? An audit should verify the user list against the intended deployment plan.
• How is access managed? Is it tied to specific roles in Microsoft Entra ID (formerly Azure Active Directory)? Strong access control is a fundamental IT audit checkpoint, and it’s just as important here.

Digging Deeper: How to Audit Microsoft Copilot Activity

Once you’ve reviewed the setup, it’s time to look at what people are actually doing with the tool. This is where you get into the nitty-gritty of user behavior.

Your best friend here is the Microsoft Purview audit log. It captures Copilot events, giving you a window into user interactions.

• What to look for in the logs: The audit log will show you “Copilot interaction events.” This includes the prompts users are typing and the context of where they’re using it (e.g., in Teams or Outlook). You’re not trying to spy on people, but you are looking for patterns and potential policy violations. Are people pasting large chunks of confidential code or customer data into prompts? Are there signs of users trying to probe for information they shouldn’t have access to? Microsoft provides excellent guidance on searching the audit log for Copilot events.
• Is there an AI Acceptable Use Policy (AUP)? Your company absolutely needs a policy that clearly outlines the dos and don’ts of using generative AI. If one doesn’t exist, that’s a major audit finding. If it does, your audit should test whether user activity aligns with it. A good AUP might include rules like:
  • Do not enter sensitive personal or customer information into prompts.
  • Always verify the accuracy of AI-generated content before using it in official documents.
  • Do not use AI to create content that is unethical or against company policy.

It’s a Journey, Not a Destination

Auditing AI for the first time can feel overwhelming, but it doesn’t have to be. By focusing on the core principles of IT auditing and applying them to this new technology, you can provide real value and help your organization navigate the world of AI responsibly.

Start with the basics: check your data governance, review access controls, and dip your toes into the audit logs. Your initial findings might simply highlight the need for better tools or a clearer AI policy. And that’s a perfect outcome for a first audit. You’re not expected to have all the answers, but asking the right questions is the most important first step. For a broader perspective on the importance of this, industry leaders like Gartner emphasize the need for robust AI governance frameworks.

So, take a deep breath. You’ve got this. This new task isn’t just a challenge; it’s a chance to be at the forefront of a huge technological shift.

Let’s explore whether a proof-heavy approach is the key to deeper ML intuition, or if there’s another way to grasp the concepts.

A question I keep coming back to, and one that sparks a lot of debate among friends in the tech world, is about the real role of deep math for machine learning. We all use the tools, we see the amazing things they can do, but it begs the question: Do you need a profound, proof-heavy understanding of the mathematics behind it all to develop a truly deep intuition for how it works?

It’s a fascinating thought. On one hand, you can get incredibly far by treating machine learning models as practical tools. You don’t need to understand the physics of an internal combustion engine to drive a car, right? Similarly, you can train a model, fine-tune it, and get fantastic results without ever deriving an algorithm from scratch. For many roles in data science and ML engineering, this is perfectly fine and highly effective.

But there’s a nagging feeling for some of us, a curiosity about what’s really happening inside that “black box.” It’s the difference between following a recipe and truly understanding the chemistry of cooking. This is where the journey into the math begins.

The Case for Deeper Math for Machine Learning

Opting for a mathematically rigorous path isn’t about wanting to write proofs all day. For most people, it’s about building what you might call a “higher-resolution” view of machine learning. When you understand the linear algebra, calculus, probability, and optimization that form the bedrock of these algorithms, something magical happens.

The concepts stop being abstract and start feeling concrete.

• You see the “why”: You understand why a certain loss function is chosen, why an optimizer works the way it does, and why a model might be failing in a specific way.
• You can reason from first principles: Instead of just trying different models or tweaking hyperparameters randomly, you can form a hypothesis based on your understanding of the model’s mathematical properties. This is the difference between a cook throwing ingredients together and a chef who understands how flavors and textures interact.
• You can innovate: True innovation often happens at the intersection of disciplines. A deep mathematical understanding allows you to not only use existing tools but also to critique them, improve them, and even create something entirely new.

This kind of deep-dive isn’t just for academics. It’s for anyone who wants to move from being a consumer of machine learning to a creator. For a taste of the kind of foundational knowledge we’re talking about, resources like MIT’s OpenCourseWare for Mathematics for Computer Science provide a glimpse into this structured way of thinking.

Is a Formal, Proof-Heavy Approach the Only Way?

So, does this mean you have to enroll in a demanding Master’s program to gain this intuition? Not necessarily. While a formal setting like the Data Science MSc at ETH Zurich provides an incredible, structured environment for this kind of learning, it’s not the only path.

The beauty of learning today is that you can forge your own curriculum. You can build your intuition progressively. Start with a practical project, and when you hit a wall or a concept feels fuzzy, that’s your cue to dig deeper.

For instance, instead of starting with a dense textbook, you could explore more intuitive, visual explanations of complex topics. Websites like Distill.pub were famous for this, breaking down ML concepts in a way that prioritizes understanding over pure mathematical formalism. You can build the intuition first and then back it up with the formal proofs later. This “just-in-time” learning can be incredibly effective and much less intimidating.

Finding Your Balance with Math for Machine Learning

Ultimately, the right path depends entirely on your goals. There isn’t a one-size-fits-all answer.

• The Practitioner: If your goal is to apply ML models effectively to solve business problems, a strong conceptual understanding and practical experience may be all you need. You can be an excellent practitioner without deriving backpropagation by hand.
• The Researcher or Innovator: If you want to push the boundaries of the field, contribute new algorithms, or work on cutting-edge problems, then a deep, mathematical fluency is almost certainly non-negotiable.
• The Curious Mind: If, like the person who inspired this post, you are simply driven by a desire for a more holistic, “higher-resolution” view, then the journey into the math is its own reward.

You don’t have to choose one path forever. You can start as a practitioner and slowly venture deeper into the theory as your curiosity grows. The key is to be honest about what you want to achieve.

So, while a deep dive into math for machine learning isn’t strictly necessary for everyone, it is undeniably beneficial for anyone seeking a more profound and intuitive grasp of the field. It’s the difference between knowing the path and understanding the map.

How deep are you willing to go?

Inside the quiet revolution: How AI in consulting is forcing giants like McKinsey to rethink everything.

You ever wonder which jobs are really safe from AI? We hear a lot about artists, writers, and coders, but I always figured the high-flying, six-figure strategy consultants were probably fine. You know, the ones from firms like McKinsey who parachute in to solve a company’s biggest problems. Turns out, that assumption might be totally wrong. The rise of AI in consulting isn’t just a new tool; it’s forcing the industry to ask some pretty deep questions about its own future.

It makes sense when you think about it. For nearly a century, companies have paid a fortune for the brainpower of elite consultants. These are the people who can dive into a sea of data, find the signal in the noise, and present a clear path forward in a slick PowerPoint deck. But what happens when an AI can do a huge chunk of that—the analysis, the data crunching, the slide-making—in a matter of seconds, not weeks?

This isn’t some far-off future scenario. It’s happening right now. At McKinsey, AI is reportedly a topic at every single board meeting. They’re not just talking about it; they’re building it. The firm has already rolled out thousands of “AI agents” to its workforce. These digital assistants are helping consultants draft documents in the classic, sharp “McKinsey tone,” check the logic of their arguments, and summarize massive research documents. It’s a fundamental rewiring of how they work.

How AI in Consulting Changes the Entire Business

The old consulting model was straightforward: hire the smartest people from the best universities, put them on a project, and bill the client for their time. But AI blows that model up. If a project that used to take 15 consultants can now be done by three consultants and a handful of AI agents, you can’t really bill the same way.

This is pushing firms toward a whole new way of thinking. Instead of just selling advice, they’re selling outcomes. About a quarter of McKinsey’s work is now in outcomes-based deals, meaning they get paid based on whether their solutions actually achieve the promised results.

Clients aren’t looking for a “suit with a PowerPoint” anymore. They want a partner who will get in the trenches with them, help implement new systems, and co-create solutions. And in a world where AI is on every CEO’s mind, they want to work with a consulting firm that’s actually using and experimenting with the tech themselves. As it turns out, advising clients on AI and technology now makes up a whopping 40% of McKinsey’s revenue. You have to practice what you preach. For more on this shift, check out how other industries are adapting their business models in this Harvard Business Review article.

The Future of Consulting: Fewer Rookies, More Experts?

So if AI is handling the grunt work, does that mean consulting firms will stop hiring? The leaders at McKinsey say no. They insist they’ll continue to hire “aggressively.” But the shape of the teams is already changing.

A classic strategy project that once needed a manager and 14 consultants might now only need a manager and two or three consultants working alongside AI tools. The people who will be most affected are the junior employees, the ones typically tasked with the rote work of data collection and analysis.

This creates a fascinating dynamic. As Kate Smaje, who leads McKinsey’s AI efforts, put it, AI can get you a “pretty good, average answer” on its own. This means the need for basic, mediocre expertise is disappearing. But what becomes even more valuable is distinctive, deep expertise. The senior partners with decades of experience who have seen it all before become indispensable. They can guide the AI, interpret its findings in the context of complex human systems, and provide the wisdom that a machine can’t.

It seems the future of AI in consulting isn’t about replacing every human. It’s about creating a new kind of “centaur” workforce—part human, part machine. The consultants of the future won’t be replaced by AI, but they will be working alongside it every single day. The age of arrogance might be over, but the age of augmentation is just beginning. As originally reported in the Wall Street Journal, this is an existential moment for the profession, but one that could ultimately be a force for good.

It’s not just about computing power. A novel approach from an unknown AGI startup could change everything.

I was chatting with a friend the other day, and we got on the topic of AI. It’s impossible not to, right? The conversation always seems to circle around the big players: OpenAI, Google, Microsoft. Who’s going to get to Artificial General Intelligence (AGI) first? But then my friend posed a question that stuck with me: what if the winner isn’t a giant at all? What if a small, unknown AGI startup is the one that cracks the code?

It sounds a bit like a movie plot, but the more I think about it, the more plausible it feels. We’re so used to seeing tech as a battle of titans, where the company with the most money and the most data wins. But AGI might be a completely different kind of problem—one that brute force can’t solve alone.

The Big Tech Advantage: Why Goliath Usually Wins

Let’s be real, the tech giants have some serious advantages. We’re talking about near-limitless cash, access to unfathomable amounts of data, and the ability to attract top-tier talent from around the globe. Companies like DeepMind (owned by Google) and Anthropic have armies of researchers and colossal computing farms dedicated to scaling up today’s AI models.

Their current strategy seems to be built on an assumption: that if they just make their Large Language Models (LLMs) bigger and feed them more data, they will eventually cross a threshold into true, general intelligence. It’s a “brute force” method, and you can’t blame them for trying it. It has brought us incredible tools like GPT-4 and beyond, and it’s a logical, if incredibly expensive, path to follow. For them, it’s an iterative game of scale.

The AGI Startup’s Edge: Thinking Differently

So, how could a tiny, bootstrapped team possibly compete with that? The answer might be in a different approach altogether. An AGI startup isn’t just a smaller version of OpenAI; it has a fundamentally different structure and a unique set of advantages.

• Freedom from Dogma: Big companies are often victims of their own success. They have existing products, shareholder expectations, and established research directions. It’s hard to justify taking a wild, unproven path when your current one is already working so well. A startup has no such baggage. They can explore radical, out-of-the-box ideas—the kind a corporate committee would laugh out of the room.
• Singular Focus: The team at an AGI startup wakes up, eats, sleeps, and breathes one single problem: solving AGI. They aren’t distracted by quarterly earnings from a cloud division or a mobile phone launch. This obsessive, singular focus can be a powerful catalyst for breakthroughs.
• The Power of a Single Insight: AGI might not be an iterative problem that you can solve by adding more layers to a neural network. It might hinge on a single, core insight into the nature of intelligence itself—a “black swan” discovery. That kind of insight is just as likely (if not more so) to come from a small, agile team exploring a niche theory as it is from a massive corporate lab. As history has shown us time and again, transformational ideas often start in a garage, not a boardroom.

What a Different Path Looks Like for an AGI Startup

If an AGI startup isn’t just building a bigger LLM, what are they doing? They might be exploring entirely different architectures. Perhaps they’re drawing more inspiration from neuroscience, trying to more closely mimic the structure of the human brain. Or maybe they are working on neuro-symbolic AI, a hybrid approach that combines the pattern-matching strengths of neural networks with the logical reasoning of classical AI.

These alternative paths are less certain and don’t offer the immediate, flashy results that scaling LLMs does. But one of them might hold the key. The quest for AGI is not just about raw power; it’s a search for the right architecture, and nobody knows for sure what that is yet. For a deeper dive into these different approaches, publications like MIT Technology Review often explore the cutting edge of this research.

So, who will achieve AGI first? The honest answer is nobody knows. The giants have the power, the money, and the momentum. But the future is unwritten, and organizations like the Future of Life Institute highlight that the path to AGI is still full of profound questions.

History is filled with stories of Davids beating Goliaths. It often comes down to a new perspective, a clever strategy, or a single brilliant idea. While everyone is watching the titans clash, I’m going to be keeping an eye on the quiet corners of the tech world, where a small team at an AGI startup might just be building the future. It’s a long shot, but it’s the long shots that make history interesting.

It’s not just hype anymore. A new report shows the impact of AI and job loss is very real, and it’s happening faster than many of us thought.

I was scrolling through the news over my coffee this morning, just like any other day, and a headline stopped me in my tracks. It was about a new report on artificial intelligence and its impact on the job market. Honestly, I’ve seen a ton of these, and most of them feel pretty abstract. “AI will change the world,” they say. But this one felt different. The numbers were specific, they were recent, and they were a little jarring. The conversation about AI and job loss just got very, very real.

The Raw Numbers on AI and Job Loss

So, what did this report actually say? It came from an outplacement firm called Challenger, Gray & Christmas, which basically tracks job market trends for a living. According to their findings released just last week, in July 2025 alone, U.S. employers cut over 10,000 jobs and pointed directly at AI as the reason. Ten thousand jobs in a single month.

That’s not a future prediction; that’s something that already happened. Since 2023, the firm has tracked more than 27,000 job cuts directly attributed to AI. It’s now one of the top five reasons companies are letting people go. This isn’t some distant threat looming on the horizon. It’s a factor in the job market right now. For a bit of perspective, you can see the kind of data they track on the Challenger, Gray & Christmas website. This makes the entire situation feel much more immediate than a far-off sci-fi scenario.

Why This Wave of AI-Driven Job Cuts Feels Different

I think what’s spooking people is the kind of jobs being affected. We used to think of automation as something for assembly lines and repetitive manual tasks. But generative AI is different. It’s impacting creative, administrative, and tech roles—the kind of white-collar jobs many people thought were safe from this sort of disruption.

The tech industry, ironically, is getting hit particularly hard. Companies in that sector have announced nearly 90,000 job cuts so far this year, a huge 36% jump from last year. The report explicitly says the whole industry is being reshaped by artificial intelligence.

And it’s especially tough for people just starting their careers. The report points out that entry-level corporate jobs for recent college grads have dropped by a staggering 15% in the last year alone. Imagine graduating with a shiny new degree, ready to take on the world, only to find the door is a little less open than it was for the class just before you. It’s a tough break and a sign that the ground is shifting under our feet.

It’s Not Just About Losing Jobs, It’s About How Jobs Are Changing

Okay, before we all spiral into a full-blown panic, there’s another side to this coin. It’s a crucial one. While some jobs are disappearing, many more are simply changing. The same data showed that the mention of “AI” in job descriptions has skyrocketed by an incredible 400% over the last two years.

So, what does that tell us? It tells us that employers aren’t just looking to replace people; they’re looking for people who can use AI. They want employees who can leverage these new tools to be more efficient, creative, and effective. The game is shifting from “doing the task” to “managing the AI that does the task.”

This lines up perfectly with what major organizations like the World Economic Forum have been forecasting in their Future of Jobs reports. They emphasize a growing demand for skills that machines can’t easily replicate: analytical thinking, creative problem-solving, and of course, technological literacy. The jobs of the future will be about collaboration—human creativity and critical thinking working alongside AI’s massive processing power.

So, What’s the Takeaway?

After letting that report sink in, I don’t feel totally hopeless. Concerned? Yes. But the narrative isn’t just about AI and job loss. It’s about a massive, high-speed transformation. The jobs our parents had are different from ours, and the jobs our kids will have will be different yet again. This change is just happening at a much faster pace.

The takeaway for me isn’t to be scared of AI, but to get curious about it. It’s about figuring out how these tools work and how they can fit into what we already do. It’s less about competing with AI and more about learning to work with it. The challenge is real, and the numbers from last month prove it. But the path forward isn’t about protecting old job titles; it’s about skilling up for the new ones that are emerging. Maybe it’s time we all signed up for a course on a platform like Coursera or just started playing around with the AI tools already at our fingertips. What do you think?