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The Great Reshuffle Why Adobe Couldnt Beat Figma Howard Yu Sangeet Paul Choudary · The Innovation Show

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AI won't take your job, but somebody using AI will.
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It's the kind of line you drop in a LinkedIn post or worst still,
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you will definitely hear this line in a conference panel and get
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immediate zombie nods of agreement.
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It's technically true, but it's utterly useless.
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It doesn't clarify anything.
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Which job does it apply to?
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All jobs.
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And what type of ai, what will someone using AI do differently apart
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from just using AI and what form of usage will matter versus not?
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It makes you feel like you've figured something out.
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You conclude that if you just use AI, you'll be safe.
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In fact, it gives you just enough conceptual clarity to stop asking the
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harder questions that really matter.
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How does AI change the structure of work?
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How does it restructure workflows?
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How does it alter the very logic by which organizations function and
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eventually, what do future jobs look like in that new reconfigured system?
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The problem with AI won't take your job, but somebody using AI will, isn't that,
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it's just a harmless simplification.
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The real issue is that it's a framing error.
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It directs your attention to the wrong level of the problem
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while creating consensus theater.
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It directs your attention to the individual task level, automation versus
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augmentation of tasks you perform when the real shift is happening at the
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level of the entire system of work.
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The problem with Consensus theater is that the topic ends right there.
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Everyone leaves the room feeling smart.
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Yet not a single person has a clue on how to apply this newly acquired insight
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the right way, true, but utterly useless.
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To shed some light on the future of work and to avoid such consensus theater.
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We are joined by the author of those words and a fantastic new book that questions
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just about everything in the age of ai.
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We welcome to the show the author of Reshuffle San
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Aiden.
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Such a pleasure to be here.
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You hold nothing back with that introduction that I pulled, I actually
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pulled that not from your book Reshuffle, which is an Amazon bestseller, but
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I pulled it from one of the articles that you wrote about the future of
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work, and I'd love you to unpack it in more detail, including some of
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the fallacies that you call out that are inherent in that statement about.
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AI won't replace you, but somebody using AI will.
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So let's get stuck in with perhaps an overview and then we'll
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pull out one fallacy at a time.
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the key point is that very often we think of, impact on our jobs as simply
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its impact on the tasks that we perform.
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And so we assume that, it'll either help us, Speed up our tasks and do more.
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And so in a way, it's what we call augmentation.
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It'll augment our work and our ability to perform work or that will take over some
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of those tasks and automate, the work that is performed and hence replace us.
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So we think of things in these binary terms, and that's where this
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idea of AI won't take your job, but someone using AI will comes
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in because , it's a way of saying.
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it's not automation but augmentation that's going to happen.
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So augment yourself because AI won't automate your job away,
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but , somebody augmented with AI will come and take your job instead.
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Now, all of that sounds very neat and binary, but the challenges that,
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it assumes that jobs will continue to be the way they have always been.
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They'll continue to be.
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That way going forward, organizations will continue to be the same.
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And the only thing that's going to change is whether a certain set of tasks are
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being performed by a human or by ai.
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And I challenge that, in all of my work and, in my book reshuffle because I
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believe that that's not really true.
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Jobs do not exist in order to perform tasks, jobs exist in order
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to solve a problem in a system.
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And, when that system changes the constraints that the jobs were supposed
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to address, those constraints go away.
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The jobs logic may not make sense.
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So I'll give an example which will help, bring this to light because
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it's one of the fallacies that I point out, the example I'd like to take is
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what happened with typists when the word processor was first invented?
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Word processes did not automate typing, so.
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If you were to think about it in terms of the binary of does it
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automate or does it augment you?
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Word processes, definitely augmented humans in typing, and yet the job
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of the typist went away, that is because the job of the typist does not
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exist because of the task of typing.
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Even though the name typist suggests that the job of the typist existed because
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of the constraint in the system the constraint was that editing a document
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was very expensive, and hence, you needed highly trained typists so that the cost of
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editing could be kept as low as possible because the other date would be low.
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editing of documents became cheap with the word processor,
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Just hit the delete key and start editing documents on the fly.
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Anybody could be a typist.
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And so the need for specialized typists went away, not because the task was
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automated, but because the problem that the job was addressing no longer existed.
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And I think that's the key thing that we miss today.
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When we start thinking about jobs as stable constructs and ai, either,
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you know, taking your job or someone using AI will, , the bigger question
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is what will jobs look like?
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What are the constraints on the basis of which jobs are structured today, and once
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AI comes in, which of those constraints still stay and hence accordingly, which
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new jobs emerge, which do not exist today.
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That's what we should be really looking at, rather than looking
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at AI's impact on individual
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let's make applicable for people because there is a lot of fear.
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, There is a lot of fear, but the.
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and particularly people thinking about my children, what will happen to my
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children who may be studying today?
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Maybe there's no onboarding jobs, there's no kind of intern jobs now available
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because people are outsourcing to ai.
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So I felt the fallacies you address help us understand that.
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maybe I'll call out each of the fallacies and you'll unpack it then.
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Sangeet.
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The very first one is.
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The automation versus augmentation fallacy, and the call out here at a
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high level is how do we shift from task level thinking to system level thinking.
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I think that's the real thinking we need today because, the idea of focusing
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on what skills will make you future ready is also based on this task level
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thinking, which assumes that certain forms of skills will, be valuable in
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certain forms of skills will be not.
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a key point that I'd like to make is that, When you are thinking about, what
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kinds of, to invest in or, what kinds of future job scenarios to work towards?
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There are two things that are important.
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One is that you need to really think about.
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Which skills remain, unchanged no matter how powerful AI becomes
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or how the job landscape changes.
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So, which kinds of skills remain completely unaffected?
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The effect that AI has on a lot of knowledge work today, whether it's,
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consulting, whether it's legal work, whether it's research, the effect that
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AI has is that it dramatically collapses the cost of getting to an answer.
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So a lot of our work, whether, you're a lawyer or a consultant especially
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professional services, has been built on the idea of billing by the R and billing
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by the R essentially assumes, that getting to the answer takes a lot of time.
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happens with AI is that certain forms of, performance and answer
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generation, become much less expensive.
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It does not require the same amount of time to, get the actual work
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done of getting to an answer.
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But the challenge that then happens is that when you use these AI models
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and you can just throw a prompt and it generates answers on the fly.
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The constraint shifts away from actually generating answers,
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to asking the right questions.
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once you're generating answers, determining which answers to
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elevate and which ones to reject.
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And so those are two things that I think of as evergreen
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skills, curiosity, and curation.
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If you don't ask the right questions, you can go down the wrong rabbit hole because
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if, generating answers with an LLM is as easy as, just throwing prompts out.
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You can keep asking questions and if they're not the right questions, you're
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just going down the wrong rabbit hole.
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So having the ability to ask really good questions, which often comes from,
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a good understanding of a domain, but having the ability to ask really good
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questions and being constantly curious on whether you're asking the right questions
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or not, is going to be very valuable.
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And the second thing that's very valuable is having the sense over time to know
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what to elevate and what to reject.
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And that's the idea of curation that I talk about.
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So the first point that I'm just trying to make over here is that skills
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that will really matter will shift away from just generating answers and
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getting work done to really having the ability to ask good questions.
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Having, the taste and, the risk taking ability to choose the
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right answer, then stick by them.
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And the second point that I want to make is that in order for you to determine
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which skills are necessary in the future, you can't just say skill A, B,
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C are skills of the past and skill, EFG, are going to be, skills of the future.
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Skills are only valuable.
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In relation to a system.
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And so you need to ask yourself if you're training to get to a certain
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kind of work if for example, you're learning and training to get into
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the legal industry, what is going to be valuable in the future?
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What's going to command a skill premium.
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It's going to be determined by.
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How the legal industry will work in the future.
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What will be the new revenue models that come out?
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How will companies differentiate themselves If the, , pay by the
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hour logic goes away, what's the new logic that comes in its place?
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So if you're a student, you need to start developing that muscle to look
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at the largest system because it's not just about getting your first job.
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All through your career going forward, because we are in a period where there's
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such rapid change you have to constantly have this ability to keep sensing the
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system and determining on an ongoing basis how things are changing and which skills
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are going to be valuable as things change.
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So, in summary, Look for what doesn't change, and in order to look for what
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does change, don't stick to static lists of skills, but really develop a sense
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of how your target industry is changing and how companies, new forms of company
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are coming, and the way companies differentiate themselves is changing.
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That's what will help you determine how to be valuable.
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So the next fallacy is the productivity gains fallacy.
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And . the big call out here is why do productivity gains so often
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benefit coordinators, not creators?
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I'd love you to share what you mean by that coordinator, not creator.
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This idea of coordination versus creation is, actually
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the central thesis of reshuffle.
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So in general, when we think about a new tool, if a tool helps you do
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more work in less time, we think that that sounds like progress, right?
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But the challenge is that is actually progress.
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Only if the overall system remains stable.
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That is your previous workflow still work the same way.
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The organization, the business model still remains stable.
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So if you do things faster, you're winning.
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But if the whole system changes, then.
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the way people used to succeed in their jobs in the past, and the skills for
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which they were paid may no longer apply.
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And this point actually applies at the level of individuals
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and at the level of company.
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So I'll take the example of how this played out in the company's,
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competition, for instance.
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in the EPA industry, factory productivity.
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has increased dramatically over the past two decades.
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they've always applied tools to improve productivity and they've applied
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automation to compressed cycle times and push more units through the system.
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But faster productivity.
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Has not necessarily led to faster salaries at that level of the value chain.
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So the workers in the factories are not getting the benefit
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of the faster productivity.
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In fact, there are other forms of players that are actually getting the
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benefits of that faster productivity.
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And that's the point I try to make over here.
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That even though.
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Workers move faster and are able to produce more output
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in the apparel industry.
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As companies like Shein who actually coordinate these workers across
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disparate factories, they decide what gets made when in what volume,
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and they capture most of the value.
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So even as labor productivity rises, as workers actually produce
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more, they're unable to capture that as salaries for themselves.
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So even when companies adopt AI to accelerate tasks, they soon will start
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realizing that when everybody's using the same tools to do the same tasks,
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faster productivity becomes a commodity.
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And that this is true not just at the company level, but
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also at the individual level.
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So even at the individual level, just augmenting yourself is not sufficient.
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You need to start thinking about.
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Where the new value will be captured, whether it'll come in your direction
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or it'll come in another direction.
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And that's the point about the creation versus the coordination.
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The factories create.
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But a company like Shein coordinates and captures most of the value.
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You can even tell now, when people use AI to write, you can see the patterns
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of the writing and it's become vanilla.
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And in a way you get angry.
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I get angry, and you're like, the difference is, okay, you
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might use it to simplify.
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You're not using it to come up with the ideas in the first place.
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And certainly the way it writes and I have a huge fear now with students.
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So many of the audience of this show will be teachers or college professors or into
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the science of learning or neuroscience of learning, and this is something
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we need to be very, very careful of.
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That's absolutely right., Just to make it very practical, and, link it back
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to the typist example that I gave.
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use AI to help me with my writing, but I never use AI to actually write.
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And the reason for that is that.
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I try to look at my workflow and, as a writer, the biggest
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constraints in my workflow were not the actual task of writing.
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Once I start writing it, comes out the challenges.
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The first challenge is something that every writer associates with.
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You open up your laptop in the morning and you stare at this blank page and
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you don't have anything to get started.
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And what AI helps me do now is.
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I can open up my laptop in the morning and I can start just debating a
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topic with ai and within a few, back and forth questions and answers.
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my mind starts working and then I can start writing.
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The other thing very often that I used to struggle with was that I would
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write in a serial sequence and then.
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Complete the whole thing or, if I'm writing a book, complete the whole thing
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over weeks and then get it edited, but now I can write in a sitting and then
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quickly get it edited in real time.
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those are, constraints that used to hold me back.
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even if you just address the constraints that magically unlocks your ability
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to write, it's not about, creating more output, it's about expressing
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yourself while moving, the constraints that had held you back in the past.
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It removes bottlenecks and obstacles in so many ways.
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I also use it for when text is written in a very academic way.
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And you know, there's times you read it and you, and then
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you read the same page again.
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You go, I think I've read this page already.
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I asked Chat GPT I'm like.
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Please make this readable for a 12-year-old or explain it to me like
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I'm a 10-year-old and it makes just that workflow much, much easier.
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One of the other fallacies I thought we'd talk about was the
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workflow continuity fallacy.
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we see this with so many organizations that they optimize and optimize
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and optimize, but they don't actually question what are they
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optimizing in the first place.
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Yeah, I think this is a fallacy that, keeps coming out repeatedly.
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especially when we talk about, what is called agentic ai.
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Today we look at today's workflow, then we try to speed up the
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tasks within those workflows.
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But we don't necessarily ask should the workflow exist in the first place?
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Because every workflow has existed for a certain reason, and with new technologies
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coming in, we should ask ourselves.
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With the capabilities of this new technology, should we even have
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this workflow or should we do it a fundamentally different way?
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this idea of not stepping away and actually.
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questioning why the workflow exists in the first place.
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Keeps us stuck in spinning the wheels faster in the wrong system.
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I'm not saying that every workflow we had in the past had to change,
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but before we start speeding up workflows, the first thing we should
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do is ask ourselves, should this even exist in that particular way, or
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should this be done a different way?
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The term we, we often use is the faster mouse.
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Don't have mice anymore, you know?
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You have to think ahead of if that job will still exist in the future anyway.
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And to do that, you need to understand the system in the first place.
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One of the other ones you, you talked about
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. Is the neutral tools fallacy.
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the question here you ask is, how does AI redistribute organizational
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power without anyone noticing?
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this piece is important because we often think about, tools as being
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neutral, but they're actually not because tools in general contain
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default settings and preferences.
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And I'll just give a simple example.
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you and I get onto meetings all the time and we have our AI note takers
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getting onto the meeting with us While in itself, it's a productivity tool for us.
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When you apply the same thing in an organization, you realize that the
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organizational priorities are now being determined by the LLM because it's
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determining which nodes to highlight versus which ones to, deprioritize.
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And this is something that we've seen across the board because, with
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previous technology as well, when spreadsheets first came out, Microsoft
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Excel, people who mastered Excel.
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Ended up having a lot of influence, through much of the 1990s when
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process optimization was a very big deal because decisions that were
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previously made with gut instinct could now be modeled and simulated.
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So anybody who was, making those decisions with gut instinct no longer
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had that power because they didn't have the data to show it Whereas, The
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Excel jocks now had a lot of power.
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And so the point I'm trying to make with both the note taking example,
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where the technology itself, starts determining organizational priorities,
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and the Excel example where by equipping certain kinds of people, it shifts
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decision power in their direction.
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My point is that.
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Adoption of technology does not have, a neutral effect in terms of
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just speeding up what you are doing.
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It fundamentally changes where power sits in the organization, and
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that again, changes which kinds of jobs will hold power, will hold the
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ability to, command a higher salary
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in the
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future.
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Sangeet for people who are wondering how does that actually affect them?
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Could you give us some example that might come to mind for you?
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Yeah, you know, I'll give a couple of examples, have happened
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with previous, technologies.
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And when I say previous, they are not that old.
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We are seeing that happen right now.
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a simple example is, that.
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before the rise of, mobile phone usage in the enterprise, field sales forces
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had a lot of power because they were next, you know, close to the customer.
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They had to make decisions on the fly, and, they, understood the customer
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sentiment deeply, and head office did not have any of that information.
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But once field forces were equipped with, mobile based applications where
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they could capture data right on the field, keep getting information from
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the customer and keep entering it.
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All of that information was going back all the way to head office instantly.
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And so all the decision power moved to the head office.
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even as the number of jobs in field forces has increased over the past decade.
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The salaries have progressively decreased because this job increase
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has happened and the salary decrease has happened on account of the same
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technology, which is mobile connectivity that allows, an organization to now
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manage larger field forces Also.
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Is responsible for shifting power away from the field forces and back
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to the head office, which is why, Decisions are much higher at the head
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office than they were in the past.
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because, decisions can be made centrally, you can now have people with lesser
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training doing much of the field activity.
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And so the.
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Potential worker base that can fill those jobs, increases that
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again, pushes, salaries down.
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So that's an example where this is actually playing out in real time where
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the shift in decision power because of a new set of tools, change the
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salary structure in a whole industry.
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That's one that's so obvious when you see it, but when you see the
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system behind it, you can actually design for it and understand it.
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And to your point, if you're worried about the future of work, decide where
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to play in the future because that might not be as valuable as it was maybe
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from a parent's perspective looking at the future for their children.
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The next one was the, stable salary fallacy, which is very linked
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to what we just talked about.
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Yeah, absolutely.
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the stable salary fallacy essentially is the point that just because you have
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a job does not mean that you will still have the same salary as you did today.
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And I'll give a couple of, examples to illustrate why this happens.
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One example is, you know, back in the days of, logging wood, when people used
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to, lumberjacks used to cut trees using that they would, wield with their hands.
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So there was a certain skill, a certain level of training, a certain level
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of, instinct involved in using it.
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Well, and then there was obviously the physical power that was involved.
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So not everybody could be a good lumberjack, but with the
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invention of the chainsaw.
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The barrier to becoming a lumberjack went down so you could easily, use a
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chainsaw to start filling trees without having necessarily the same level
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of skill and, you know, bra power.
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key point that I'm trying to make is that when a new technology comes in.
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Even if your job continues, lumberjack jobs or, any jobs in the entire
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wood cutting industry actually increased after the chainsaws came in.
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But salaries declined and were depressed.
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And the reason that happens is because new technologies very often by augmenting
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you, by making you better, Tend to make the less trained and less skilled
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people move higher up in terms of their output than the more skilled people.
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And so there's a flattening of skill, and so the premium that was associated with
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high skill actually goes down another way.
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We see this every day, actually today, is what happened to London cab drivers
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when GPS-based driving came in.
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Because before that.
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Having that knowledge in your head was a huge competitive advantage.
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And if you were a London cabbie, you knew the streets inside out, and it was
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impossible for somebody completely new to come and start driving around London.
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But with a GPS they could.
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And then when Uber, overlaid, a market making mechanism on top of
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the GPS, where now you had to, just log into an app in order to get,
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jobs, entire skill associated with.
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Knowing how to navigate London completely went away.
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You were not paid for that any longer.
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gain a higher fee because there were few cabbies as the number
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of cabbies increased the market.
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figured out the minimum price at which the ride would be accepted.
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And that's what Uber does.
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So it's a combination of both these things today.
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a lot of people, who are the knowledge work may feel that, the Uber thing does
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not really apply to them, but we need to keep in mind that the more AI improves.
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The more it has the effect on all knowledge work that maps
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had on the act of driving.
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It flattens the skill needed to perform that knowledge work.
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And the more that happens, the more you see this compression of salaries,
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even though the job continues to
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exist.
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Which is tightly coupled to the final one, which is the stable firm fallacy.
24:21
Yeah, exactly.
24:23
I mean, all of these things, bring it back to this idea that, AI is not just making
24:30
today's firm and today's business model.
24:33
Better, faster.
24:34
It's changing the nature of the firm completely.
24:37
And this, works at many different levels.
24:39
So, I'll give, a couple of different examples to illustrate this
24:42
one we've already talked about.
24:44
when we think about, the impact that, Faster automation of factories
24:49
had on business models like shine.
24:52
the nature of the firm completely changed because in the past, you did not have
24:56
this kind of a central coordinator.
24:58
You would have relatively siloed supply chains where, a manufacturer
25:03
would work with the buyer and any productivity gains would be captured very.
25:09
by the manufacturer.
25:09
But the nature of the firm changed when the buyer no longer was a single buyer
25:13
working with another set of manufacturers, but a global buyer like Shein coordinating
25:18
a huge number of manufacturers.
25:20
So that's one way the nature of the firm itself changes.
25:23
Let's leave people with a positive on this a takeaway that they can actually
25:26
do something with Many people are consultants and we have shifted so much
25:31
towards a knowledge economy and in a knowledge economy, when the knowledge
25:35
is easily accessible, it changes the game and it shifts the value.
25:40
So you talked about a framework for people to, be able to compete
25:45
and actually capture some of that value in the future amidst the LLMS.
25:49
absolutely.
25:50
as you rightly pointed out, a lot of our knowledge based industries have been built
25:54
on the assumption that knowledge is, the bottleneck and knowledge is constrained.
25:58
and that scarcity of knowledge allows us to capture a premium for that.
26:02
the more difficult it is to acquire certain kind of knowledge,
26:05
very often the salary associated with that also is much higher.
26:09
what happens with ai, you know, as AI improves many forms of knowledge work
26:13
become increasingly more accessible, or increasingly more commoditized.
26:17
Not because AI replaces humans, but because again, more people can
26:21
perform that when augmented with ai.
26:23
So the compression on salaries because of that.
26:26
But I believe that there are.
26:28
two other, forms of constraints that have always been there, and that will
26:34
reassert value even when, you know, AI's task performance in itself improves.
26:40
one thing that always has value, in any organizational system
26:44
is the ability to assume risk.
26:46
there are many examples of jobs where.
26:49
Almost every feasible aspect of the job is automated, and yet,
26:53
people who perform the job are paid really well because they assume the
26:57
risk associated with the decision.
26:58
this obviously shows, in CEO pay today, but even if you don't
27:02
agree with that and you say that's inflated, let's take another example.
27:05
think of the anesthesiologist, who administers anesthesia in, a
27:10
hospital room every single task that the anesthesiologist performs
27:14
is today performed by a machine.
27:16
So every part of his job is automated.
27:19
the ability to assume the risk of managing the patient's condition
27:23
in real time in the middle of an operation is what he gets paid for,
27:28
and he gets paid a premium for that.
27:29
So automation, you know, going back to this point, AI won't take your job.
27:32
Somebody using AI will automation augmentation.
27:35
Those things don't matter.
27:36
What really matters is.
27:38
the most risky decisions that need to be taken in your organization?
27:41
And who is taking those, who is sitting at those decision points?
27:45
So if you are seeing AI coming in and impacting your job, you can always
27:49
use this as a rule of thumb to say that if I assume the risk, I can
27:53
capture value associated with it.
27:55
But that obviously comes with judgment, which is in its own a uniquely
27:59
human capability that comes, with experience and with taste as well.
28:03
Every time technology is rolled out and is adopted organization-wide, what happens is
28:09
that you start seeing new cracks because every part of the organization adopts
28:14
different technology and different ways speeding up processes at varying speeds.
28:18
So coordination breaks down.
28:20
And again, I'll take an example from, healthcare, because over the
28:24
past 10 years, Health systems have adopted, electronic health records.
28:29
They've adopted, digital, billing systems, et cetera.
28:33
And, more recently they're adopting ai.
28:35
And in all of this, one of the really good examples of where things break
28:39
down is as we have, people with chronic health conditions who need to be managed
28:45
over an extended period of time, the fact that every part of the hospital
28:49
and every, person has a different set of tools managing it ends up
28:52
creating a fragmented patient journey.
28:55
And the patient is left confused because they're receiving reports from
28:59
different types of tools and there's nobody guiding them through the whole
29:03
patient journey, especially if you are, somebody with a chronic condition.
29:06
And so an entirely new job.
29:07
Came up over the last 10 to 12 years, the job of a nurse navigator whose job
29:12
is entirely to manage the patient journey end to end, and to ensure a seamless
29:17
experience while coordinating across all these different tools and roles
29:21
that the patient is interacting with.
29:23
And so new coordination layers are always very valuable when
29:27
technology is rolled out.
29:28
So keep those two things in mind.
29:30
Where can I assume new risk?
29:32
Where do things break down and I can solve as a coordination layer?
29:35
and then bundle your valuable skills around those points.
29:40
So your skills are valuable, but they have to be bundled close
29:44
to these points in order for you to actually capture that value.
29:49
For that type of thinking and way more, I highly recommend both.
29:53
Sangeet's Substack, which is just a magnificent read every week,
29:57
and he tackles different aspects of how AI reshuffles the entire
30:02
industry from your job to an industry, to an organization with.
30:06
Loads of examples, brilliant analogies and metaphors as well, but also his
30:11
book Reshuffle, which goes deeper into a lot of these concepts.
30:15
Sangeet, for people who wanna find you, where is the best place?
30:17
I think Substack is great.
30:19
It's called platforms.substack.com.
30:21
I, I write, every week over there and, you can look up the book
30:25
Reshuffle on Amazon and my website is platform thinking labs.com.
30:29
Sangeet Paul Choudary thank you for joining us.
30:32
Thank you so
30:33
much, Aiden.
30:33
Thanks for joining us on Inside Learning.
30:35
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Dublin Learnovate is funded by Enterprise Ireland and IDA Ireland.
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Visit learnovate center.org to find out more about our research on the science
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of learning and the future of work.