#24 Augmented Skills as the new Superpower for Knowledge Workers

Speaker A: Foreign.

Speaker B: Welcome to my podcast, Future of Work, Future Skills and AI. I'm Yasmin Weiss, professor of Artificial Intelligence and the Future of Work. This is no ordinary podcast I created using the AI tool NotebookLM. Um, the characters you hear are two AI voices. However, the content comes from me. Yasmine, each week you will learn how to work better and smarter with AI.

Speaker C: Okay, I want you to close your eyes for a second. Imagine you're sitting in the stands of your absolute favorite football stadium. Could. Mhm. Be the Premier League, the Bundesliga World Cup Final, whatever.

Speaker A: Yeah.

Speaker C: You can smell the grass, you can hear the crowd roaring.

Speaker A: Got it.

Speaker C: The whistle blows, the game kicks off and suddenly a few new players run onto the pitch.

Speaker A: But these aren't your typical players.

Speaker C: No, not at all. They move differently. Yeah. It's like they've been dropped in from another planet.

Speaker A: Right.

Speaker C: They have this endurance. They're sprinting in the 90th minute, just as fast as the first.

Speaker A: And their passing, their precision is just mathematically perfect.

Speaker C: Exactly. They can see the entire strategic layout of the pitch instantly. Yeah. Anticipating moves 3, 4 steps before the human players even twitch a muscle.

Speaker A: And here's the kicker, right? The part that really unsettles the crowd.

Speaker C: Yeah.

Speaker A: Even the highest paid, most talented stars of the current league, the ones we think of as untouchable, go of the sport. They stand absolutely no chance against these new players. It's just completely outmatched in every single metric that matters.

Speaker C: Yeah. And eventually the game just. It breaks. The league has to change a new league forms because the old way of playing just became obsolete. Now, usually when we start a deep dive with a vivid sports analogy. We're going to talk about sports.

Speaker A: Uh, not today, but today.

Speaker C: Hm. We are talking about something much, much closer to home. We are talking about your career.

Speaker A: That's right. This isn't science fiction or some movie script. This is the central analogy from the research of Professor Dr. Yasmin Weiss. She is one of the leading voices in Europe on the future of work. And her argument is that this new league isn't coming in 10 years. It's forming right now.

Speaker C: Right now in the global labor market. So that is our mission for this deep dive. We're unpacking her recent work on augmented skills and this really provocative idea that knowledge workers gaining a literal superpower.

Speaker A: And we aren't talking about AI replacing

Speaker C: us, which is the usual doom and gloom narrative we always hear.

Speaker A: Exactly. We are talking about AI as an exoskeleton for the brain.

Speaker C: That's a vital distinction.

Speaker A: It Is. And I think it's important to establish why we're listening to Professor Weiss on this. You know, a lot of people theorize about the future of work from a library or a comfortable university office.

Speaker C: Sure.

Speaker A: She has taken a very different approach. She has spent the last few months deeply embedded in major AI labs testing what are called agentic AI systems.

Speaker C: So she's not just reading the white paper. She's looking over the shoulders of the developers who are actually building this stuff.

Speaker A: Precisely. She has a frontline view. She's seen the new players warming up on the sidelines before most of us even knew there was a game.

Speaker C: And the choice she lays out for us is pretty stark. Then.

Speaker A: It is. We have a decision to make right now. Do we want to learn to play in this new league, or. Or are we content to, you know, stay on the bench?

Speaker C: I really appreciate that framing because it makes it active. It's not just the robots are coming to take your job. No, it's, here's the equipment you need if you want to compete. So let's get into the mechanics of this.

Speaker A: Yeah.

Speaker C: She introduces this core concept, the cognitive exoskeleton. When I hear that, I'm picturing Iron man, but for spreadsheets. Is that. Is that too simplistic?

Speaker A: It's actually surprisingly close to the mark. Think about what a physical exoskeleton does in a factory or, uh, an industrial setting.

Speaker C: Okay.

Speaker A: You strap it on, and suddenly you can lift 200 pounds as if it were a feather. It doesn't replace your arms. It gives them superhuman capacity.

Speaker C: It protects you from fatigue.

Speaker A: Yes. Professor Weiss argues that augmented intelligence provides cognitivecraft. Cognitive power.

Speaker C: But let me push back on that slightly. Is that just a fancy marketing term for it makes you smarter?

Speaker A: I see what you mean, because I

Speaker C: think a lot of people feel like AI is just a tool they use, like a calculator or a spell checker. Yeah. They don't feel like they're wearing it like a suit of armor.

Speaker A: And that's the shift we need to make. A calculator is a tool you pick up and put down. An exoskeleton is something that moves with you. Uh, Weiss argues that it's not about the AI doing the work for you while you sit back and drink coffee. It's about the combination. It's about merging human intuition and experience with the analytical horsepower of the machine

Speaker C: to do things neither could do alone.

Speaker A: Exactly.

Speaker C: The source material uses a phrase here that I found really interesting. Doppeltee team Fei Kite. Yes.

Speaker A: Double team. Ability.

Speaker C: That sounds like it challenges everything we've been taught about soft skills.

Speaker A: It does. Completely.

Speaker C: How so? Because usually teamwork is just code for plays nice with others.

Speaker A: Exactly. Can you get along with your colleagues? Can you resolve conflict with other humans? But Professor Weiss says in this new league, that's only half the battle.

Speaker C: Okay.

Speaker A: Double teamability means you need to be able to collaborate successfully with humans and simultaneously collaborate with technology.

Speaker C: It's like being bilingual. You need to speak human and you need to speak machine.

Speaker A: Precisely. And if you only speak one, you're at a massive disadvantage.

Speaker C: But what does that actually look like in practice? Because collaborating with a machine sounds a bit, I don't know, Abstract.

Speaker A: Okay, let's look at the medical field. The source gives a great example of doctors. In the traditional model, the old league, a doctor relies on their training, their memory, and whatever articles they managed to read last weekend.

Speaker C: Which is limited. Humans have limited bandwidth. We get tired, we forget things.

Speaker A: Of course, in the new league, that same doctor is using AI to run diagnostics on a massive scale.

Speaker C: So the AI is comparing the patient's specific symptoms against millions of global cases?

Speaker A: Yes, identifying obscure patterns or rare diseases a human might miss in a 10 minute consultation.

Speaker C: But the doctor isn't just handing the patient a printout from the AI and walking away.

Speaker A: No, absolutely not.

Speaker C: Here's your diagnosis. The robot says you're sick, and that's

Speaker A: the double team aspect. The doctor applies human judgment to that data. They handle the care, the empathy, the nuance of how to treat this specific person.

Speaker C: So the AI provides the precision, the

Speaker A: human provides the care.

Speaker C: There's another example in the notes that I think hits closer to home for the corporate crowd. The executive board.

Speaker A: Right, the boardroom.

Speaker C: Now, I have to be honest, I have a hard time picturing a room full of CEOs listening to a piece of software. Um, we usually think of that level as pure gut instinct. The visionary leader.

Speaker A: That is the traditional view. But Vice suggests that boards in this new league are using AI specifically to check for blind spots.

Speaker C: Blind spots?

Speaker A: Before they acquire a company or pivot the strategy, they run the discussion through the machine.

Speaker C: So it's acting like a BS detector.

Speaker A: Essentially, the AI analyzes the data to see if there are thinking biases. Are you being overly optimistic? Are you falling for the sunk cost fallacy? Does the data actually support this confidence?

Speaker C: That is wild. It's like having a logic checker sitting at the table Whispering, hey, your Q3 projections are mathematically impossible based on current trends.

Speaker A: It keeps the human honest. And this leads to the core principle by talks about Menschlief, human refinement.

Speaker C: I circled that phrase in the notes three times. It sounds almost artisanal.

Speaker A: It is. The idea is that the AI mines the raw material. The data, the patterns, the draft.

Speaker C: But that material is raw.

Speaker A: Exactly. It needs to be refined or polished by the human. We add the ethics, we add the context, we add the intuition that the machine simply doesn't have.

Speaker C: So the machine mines the diamond, but the human cuts and polishes it so it's actually valuable.

Speaker A: Yeah, that's the perfect analogy.

Speaker C: Okay, so I buy the concept. I want the cognitive exoskeleton. I want to be the bionic worker. But practically speaking, what does that mean?

Speaker A: Right?

Speaker C: I can't just go to a store and buy one exoskeleton. Please. What are the actual skills I need to develop?

Speaker A: This is where we get into the practical drills for the new league. Professor Weiss breaks this down into four specific pillars of augmented skill skills.

Speaker C: The muscles you need to train.

Speaker A: Exactly. If you want to stay relevant, these are the muscles you need to train.

Speaker C: Let's run through them. Pillar number one is augmented decision intelligence, or adi.

Speaker A: And to understand why this matters, you just have to consider a stat. The source mentions we make roughly 35,000 decisions every single day.

Speaker C: 35,000. That explains why I'm so exhausted by dinner time, I can't even decide what to watch on tv.

Speaker A: It's classic decision fatigue. And obviously most of those are small. But in a work context, decision quality is the single biggest driver of success.

Speaker C: Sure.

Speaker A: ADI is about merging the cool head of the AI with the warm heart or gut of the human.

Speaker C: Break that down. The cool head is obviously the data processing.

Speaker A: Yes. The AI provides the objective analysis. It's data driven, it's precise, it can simulate a thousand scenarios in a second.

Speaker C: And crucially, it doesn't have an ego.

Speaker A: Right. It doesn't get tired, it doesn't get hungry, and it doesn't care if the data contradicts the boss's favorite idea.

Speaker C: And the warm heart, that's us.

Speaker A: You. That's your situational context. You know, the office politics. You have empathy for how a decision will land with the team.

Speaker C: That finger spitzing a feel, that German word for intuitive fingertip feeling for a

Speaker A: situation, that's the one. So the goal isn't to let the AI decide, it's to use the AI

Speaker C: to clear away the fog so you can decide.

Speaker A: Exactly. You reduce human weaknesses like bias and fatigue, and you utilize the AI's strength in pattern recognition. The result is a higher quality decision.

Speaker C: Okay. Pillar number two. Augmented Problem Solving, or aps. Now, I have to ask, how is this different from decision making? Aren't they kind of the same thing?

Speaker A: Not quite. It's a subtle but important difference. Decision making is usually about choosing between option A and option B.

Speaker C: Okay.

Speaker A: Problem solving is about figuring out what the options are in the first place. It's about diagnosis.

Speaker C: Uh, so my car breaks down. Decision making is, do I fix it or sell it?

Speaker A: Right.

Speaker C: And problem solving is. What on earth is that rattling noise?

Speaker A: Precisely. In aps, the AI does the heavy lifting of logic. Can look at a complex problem, say, a, uh, global supply chain breakdown, scour massive data sets, identify the root cause, and propose potential solutions.

Speaker C: So it's doing the legwork.

Speaker A: It's doing the legwork at a scale we can't even comprehend. It grasps the problem holistically. But again, the human role is critical

Speaker C: because the AI might suggest a solution that is mathematically perfect.

Speaker A: Fire 50% of the staff to save money. Yep. But is culturally disastrous for the company.

Speaker C: Right. The human has to solve that part of the equation.

Speaker A: The AI builds the bridge, but the human decides if it's going to a place anyone actually wants to visit.

Speaker C: Or checks if the bridge is safe for people, not just theoretical cars.

Speaker A: Exactly. It lets us tackle problems that are just too complex for a single human brain to hold at once.

Speaker C: Let's move to the third pillar. This is the one that I think scares people the most. Augmented creativity.

Speaker A: AC Mm.

Speaker C: Because we like to think creativity is the one thing that is uniquely ours. We have the soul. The machine is just code.

Speaker A: It's a very valid fear, but Professor Weisz reframes it completely. She views the AI not as the artist replacing you, but as the sparring partner.

Speaker C: A creative sparring partner. So it punches back, in a way.

Speaker A: Think about the creative process. What's the hardest part?

Speaker C: The blank page.

Speaker A: A blank page? Or getting stuck in a rut of the same old ideas? With augmented creativity, the AI can generate drafts, variants, and unusual combinations in seconds.

Speaker C: So it's essentially speed running the brainstorming phase drastically.

Speaker A: It throws ideas at you. Most might be bad. Some might be weird hallucinations. But one might spark something brilliant in your brain that you wouldn't have reached otherwise.

Speaker C: I, uh, like that. It's not replacing the spark. It's providing the fuel.

Speaker A: And again, the human touch provides the goal. We provide the meaning. We decide what is quality. The AI can write a poem, but it doesn't know why. The poem is sad.

Speaker C: You do so Instead of spending three days coming up with 10 bad ideas, the AI gives you 100 ideas in 10 minutes, and you spend your time curating and refining the best one.

Speaker A: It's an expansion of creativity, not a replacement. It raises the baseline of what you can produce.

Speaker C: And finally, the fourth pillar, augmented learning. Al, this feels a bit meta.

Speaker A: It is meta because we are currently learning about learning, but it's crucial because the pace of change is so fast. Now, whatever you Learned in university 5 years ago, it's probably already outdated.

Speaker C: True.

Speaker A: Weiss envisions a triangular relationship here. The teacher, the learner, and the AI.

Speaker C: A triangle. Okay, walk me through that.

Speaker A: In the traditional model, a teacher, a professor, a corporate trainer has to handle everything. Delivering content, grading, admin, and trying to

Speaker C: support 30 different people who all learn

Speaker A: at different speeds, which is basically impossible. Someone is always bored because it's too slow, and someone is always lost because it's too fast. Yeah. In augmented learning, the AI acts as the personalized tutor. It analyzes your specific learning gaps. It sees. Oh, you didn't understand that concept in module three. And creates a personalized path to fix it.

Speaker C: It answers the routine questions at 2am Right.

Speaker A: So where does the teacher go? Are they obsolete?

Speaker C: I just forgot to ask.

Speaker A: Not at all. They are elevated. Freed from the routine administration and the basic Q and A, the teacher can focus entirely on empathy, coaching and depth support.

Speaker C: They become a mentor rather than just a lecturer.

Speaker A: Exactly. It makes a human connection more important, not less.

Speaker C: That's the recurring theme here, isn't it? The technology handles the volume, the human handles the value.

Speaker A: That's it in a nutshell.

Speaker C: So we have the exoskeleton, we have the four pillars. But Professor Weiss also talks about where this is all going. M. Because technology doesn't stand still.

Speaker A: No, it doesn't.

Speaker C: We aren't just looking at today's chatbots. We are looking@Agentic AI.

Speaker A: This is where things get really interesting. And this insight comes directly from her time inside those AI labs we mentioned. Okay, uh, we are moving from using AI as a tool to using AI as an autonomous agent.

Speaker C: We need to define this clearly. What is the difference between the generative AI I use today and. And an agentix system?

Speaker A: Think of it this way. A tool like a hammer or a basic chatbot waits for you. You have to pick it up, swing it, guide it.

Speaker C: It's passive.

Speaker A: It's passive. You type a prompt, it gives an answer, it stops.

Speaker C: Right. It only speaks when spoken to.

Speaker A: An agent acts. An agentix system can pursue a goal you give it a broad objective, like, uh, plan a marketing campaign for this product and book the ad space.

Speaker C: It just goes and does it.

Speaker A: It goes off and executes multi step tasks, it checks availability, it negotiates prices and interacts with other agents. It's more like a highly skilled intern than a hammer.

Speaker C: So it's not just answering a question, it's doing a job.

Speaker A: Exactly. And Weiss makes a really important point here. Learning to work with these agents isn't something you can just read about in

Speaker C: a book or listen to in a podcast even.

Speaker A: Right? She calls it a ping pong effect.

Speaker C: Ping pong effect?

Speaker A: Yes. Developing these skills happens through a back and forth interaction. It's like learning a sport. You can't learn tennis by reading a manual.

Speaker C: You have to get on the court.

Speaker B: Court.

Speaker A: You get on the court, you hit the ball, the AI hits it back. You correct, you adjust. It happens in real world applications.

Speaker C: So you have to get your hands dirty. You have to get in the sandbox and start building castles with this thing. You can't just wait for the HR department to schedule a training seminar, because

Speaker A: if you wait, you won't be ready for 2030.

Speaker C: 2030, that's the benchmark date she gives. That is not far away, not at all. What does the landscape look like then?

Speaker A: Her prediction is bold. She says that by 2030, augmented skills will be the standard competence portfolio for top performers.

Speaker C: So if you don't have them, you simply aren't in the league.

Speaker A: You're playing amateur ball.

Speaker C: And this changes how companies compete, doesn't it? This is the part of her research that really blew my mind.

Speaker A: Fundamentally, this is the David versus Goliath moment.

Speaker C: I love a good underdog story.

Speaker A: Well, this is the ultimate one. Weiss asserts that company performance will no longer be defined by the number of employees.

Speaker C: That's a huge shift. We're used to thinking the biggest company with the most people wins, right?

Speaker A: We have 10,000 employees, you have 10, we have more resources, we win.

Speaker C: That's the law of business physics.

Speaker A: Not anymore. In the new league, performance is defined by the quality of the human machine cooperation.

Speaker C: So a small team with high augmented

Speaker A: intelligence people who have mastered these four pillars will be able to significantly outperform large traditional teams that are still working the old way.

Speaker C: That is a staggering thought. It completely flips the logic of scaling up a business. You don't need to hire an army. You need a special forces team with the best exoskeleton.

Speaker A: Exactly. The definition of workforce changes from a head count to a skill count or an Augmentation count.

Speaker C: So let's bring this back to the listener. We've covered a lot of ground today. The cognitive exoskeleton, the double team ability, a four pillars decision, problem solving, creativity, learning, and this shift toward agentic AI.

Speaker A: It's a lot to take in.

Speaker C: It is, but the message from Professor Weiss seems pretty clear.

Speaker A: It is a stark message, but I think it's an empowering one. A new league is forming, the stadium is being built, the rules are being

Speaker C: written, and the exoskeleton is available to anyone willing to put it on.

Speaker A: Right. And as she says, the gap between the new lead and the old league is widening every single day. We can choose to join, or we can stay in the amateur division.

Speaker C: And joining doesn't mean becoming a robot. I want to stress that one last time.

Speaker A: Uh, absolutely. It means becoming superpowered. It means stripping away the drudgery of routine work, the data crunching, the scheduling, the basic logic checking and freeing up

Speaker C: your brain for the things that actually matter.

Speaker A: A strategy, empathy, creativity, and human connection.

Speaker C: It's a compelling vision. But before we go, I want to leave you, the listener, with a thought to chew on. We talked about how small teams with AI can beat big corporations. If that's true, if a team of five people with advanced agentic AI can outperform a department of 500, what does that actually mean for the future of big business?

Speaker A: It raises a massive question. Are we seeing the end of the traditional corporation as the dark dominant force?

Speaker C: Right. Why climb the corporate ladder if you could build your own rocket ship with a team of three?

Speaker A: Exactly. Are we entering the age of the super powered freelancer or the micro conglomerate? If you don't need the infrastructure of a giant firm to compete globally, why join one?

Speaker C: Something to think about as you go about your day. The league is changing. Make sure you have the right boots on.

Speaker A: Indeed.

Speaker C: That's it for this deep dive. Thanks for listening and we'll catch you in the next one.

Speaker B: If you enjoy the podcast, please subscribe, leave a review, and recommend it to your friends, family and colleagues.