Joe: Here’s a wild thought. That sluggish economy and the constant anxiety about AI taking your job? It might be the best economic news we’ve had in a decade. The authors of the book we're talking about today argue it's a historical pattern, the quiet before a massive, technology-fueled boom. Lewis: Whoa, hold on. That sounds like some serious corporate optimism. How can stagnation and widespread job-loss fear possibly be a good thing? It feels like we're constantly hearing about layoffs and industries being disrupted. Joe: I know it sounds counter-intuitive, but that's the provocative idea at the heart of the book What To Do When Machines Do Everything by Malcolm Frank, Paul Roehrig, and Ben Pring. Lewis: Right, and these aren't just academic theorists. These guys are senior execs at Cognizant, a massive tech consultancy. They're on the front lines, seeing how Fortune 500 companies are actually using this stuff. So their perspective is incredibly grounded and practical, which is probably why the book got so much attention from business leaders. Joe: Exactly. They're not talking about sci-fi futures; they're creating a roadmap for the next 10 years. And that roadmap starts with understanding the moment we're in right now, which they call 'the stall before the storm.'

Lewis: Okay, 'the stall before the storm.' Unpack that for me. Because from where most people are sitting, it just feels like a storm, period. Joe: Fair enough. The book argues that every major industrial revolution follows a predictable S-curve pattern. First, there's an "installation phase"—a frenzy of new technology, a bubble, and then a crash. Think of the dot-com bubble in the late 90s. That was the installation of the internet age. Lewis: I remember that. Pets.com and all that. A lot of money lost. Joe: A ton of money. And after the crash comes the "stall." This is the period we're in now. The old ways of doing things are running out of steam, but the new technology hasn't been fully integrated into the mainstream economy yet. So you get stagnant wages, slow productivity growth, and a general feeling of malaise. Lewis: That sounds depressingly familiar. Joe: But here's the optimistic part. The book draws on the work of economist Carlota Perez, who showed that this stall is always followed by a "deployment phase"—a "Golden Age" where the technology finally goes mainstream, creating huge economic growth and new kinds of jobs. The authors believe we are on the cusp of a massive "digital build-out." Lewis: And they're confident about this because it's happened before? Joe: Exactly. They use these powerful historical analogies. Take the Luddites in the early 1800s. We think of them as people who just hated technology. But the book reframes it. They were skilled textile artisans, and the new power looms threatened their entire way of life. They were smashing machines not out of ignorance, but out of legitimate economic anxiety. Lewis: They were terrified of being automated out of a job. I can definitely relate to that feeling. Joe: It's the same anxiety. Or look at the United States at the turn of the 20th century. Something like 90% of the population worked in agriculture. Then, new machines like the McCormick reaper came along. If you told someone in 1900 that in a century, less than 2% of the population would be farmers, they would have predicted mass starvation and unemployment. Lewis: But that's not what happened. Instead, we got… well, everything else. The modern economy. Joe: Precisely. The productivity gains from agriculture freed up a massive workforce to build the industrial and then the information economies. The book argues we're in the exact same kind of transition. Yes, it's disruptive, and one chapter is even titled "There Will Be Blood" to acknowledge the pain of this shift. But the long-term outcome has always been a higher standard of living and new, often better, jobs. Lewis: Okay, but the Luddites did lose their jobs. And that shift from farm to factory was brutal for a generation. Is the book just glossing over the pain of the people caught in the middle of this 'stall'? Joe: It doesn't gloss over it, but it frames it as a necessary, albeit painful, transition. The authors argue that the key is to stop fighting the change and start figuring out how to leverage it. They say the digital build-out is being driven by three things happening right now: first, "Ubiquitech"—technology being embedded in everything. Second, huge societal problems in healthcare, education, and energy that desperately need new solutions. And third, companies finally mastering what they call the "Three M's." Lewis: The Three M's? Joe: Materials, which is data. Machines, which is AI. And Models, which are new digital business models. When you align those three, you get the engine for the next boom.

Lewis: Okay, so if this boom is coming, it's powered by this 'new machine.' But what is it? When I hear AI, my brain immediately goes to HAL 9000 or the Terminator. Is that what we're talking about here? Joe: That's the exact misconception the book wants to clear up. It's not about sentient, world-dominating superintelligence. The authors are very pragmatic. They quote the AI expert Andrew Ng, who said, "worrying about evil super-AI is like worrying about overpopulation on Mars." It’s a problem for another day, maybe another century. Lewis: Huh. So what is the machine, then? Joe: They define it as a "System of Intelligence." It’s not a single robot; it’s a combination of three things: first, software that learns, meaning algorithms that get better with more data. Second, massive hardware, basically the incredible processing power of the cloud. And third, huge amounts of data, which is the fuel for the whole system. Lewis: So it's less of a single 'thing' and more of an ecosystem. Can you give me a real-world example? Joe: Absolutely. The book uses fantastic, concrete examples. Think about Google's AlphaGo. In 2016, it played against Lee Sedol, the world's greatest player of Go, a game infinitely more complex than chess. AlphaGo wasn't just programmed with moves; it had played millions of games against itself and learned strategy. In the second game, it made a move, Move 37, that every human expert watching thought was a rookie mistake. They were baffled. Lewis: And what happened? Joe: It turned out to be the winning move. The machine had discovered a strategy that no human in the 3,000-year history of the game had ever conceived of. Even more telling, Lee Sedol, the human champion, won the fourth game by making a move that was so creative and unexpected, it confused the AI. Afterwards, Sedol said he could only have come up with that move because he had played against the machine. It enhanced his own creativity. Lewis: Wow. So the machine isn't just replacing him, it's making him better. Joe: Exactly. That's the 'Enhance' part of their model we'll get to. Another powerful example is in healthcare. Houston Methodist Hospital uses an AI to interpret breast X-rays. It does it 30 times faster than a human radiologist, and with 99% accuracy. That's a System of Intelligence in action—taking in data (the X-rays), using learning software, and producing a result that surpasses human capability in a very specific, narrow task. Lewis: So, when Waze reroutes me around a traffic jam, is that a mini 'System of Intelligence' at work? It's taking real-time data from thousands of other drivers, using its software to learn the best path, and delivering it to me on my phone. Joe: That is a perfect analogy. That's exactly it. It's not one big, scary robot. It's millions of these specialized, narrow systems, embedded everywhere, doing specific tasks better, faster, and cheaper than we ever could before.

Lewis: Okay, that makes the 'new machine' feel a lot less intimidating and a lot more… useful. So once we understand what it is, what do we do about it? How do we get on the right side of this change? Joe: This is the core of the book. They provide a five-part playbook to harness the power of this new machine. It's an acronym: AHEAD. Lewis: AHEAD. Let's break it down. What's the 'A'? Joe: 'A' is for Automate. The book's advice here is ruthless: automate everything you can, especially the repetitive, high-volume work in back and middle offices. Their case study is a company called TriZetto, which automates healthcare claims processing. Before, it took a team of 120 people to process a certain volume of claims. After TriZetto's software, one person could do it. Lewis: One person! That’s a massive change. And also terrifying if you're one of the other 119. Joe: It is, and the book acknowledges that. But the argument is that this frees up human capital for more valuable work. The next letter is 'H' for Halo. This is one of their most powerful ideas, building on their previous book, Code Halos. Lewis: A halo? Like on an angel? Joe: Kind of. The idea is that every physical thing, every person, and every process can have a digital 'halo' of data around it. The goal is to instrument everything to capture that data and create new value. The killer example is Discovery Health, a South African insurance company. Government regulations meant they couldn't charge different premiums based on risk. Lewis: So a smoker and a marathon runner had to pay the same? That's a tough business model. Joe: Extremely. So they got creative. They built a wellness program called Vitality. They gave their members fitness trackers and apps to log healthy behaviors—going to the gym, buying healthy food. If you hit your goals, you got real rewards: flight discounts, cash back, even a heavily subsidized Apple Watch. They built a Code Halo around their customers' health. Lewis: So they couldn't charge more for bad behavior, but they could reward good behavior. That's brilliant. They changed the game. Joe: Completely. And that leads to 'E' for Enhance. This is about using the new machine as a 'white-collar exoskeleton' to amplify human performance. Think of the McGraw-Hill ALEKS system for education. It's an AI-powered tutoring platform that assesses a student's knowledge, figures out what they're ready to learn next, and provides personalized lessons. This automates the rote work of grading and lesson planning for the teacher. Lewis: So the teacher can spend less time on paperwork and more time actually mentoring, coaching, and inspiring students. Joe: Exactly. The AI handles the mechanics; the human provides the wisdom and empathy. The next 'A' is for Abundance. This is about using technology to crash the cost of a product or service, opening up a massive new market. The story here is just incredible. It's about Narayana Health in India. Lewis: I think I've heard of this. The heart surgery hospital? Joe: That's the one. In the US, a bypass surgery can cost over $100,000. Dr. Devi Shetty, the founder of Narayana, applied digital Taylorism to every single step of the process—from prep to the operating room to post-op care. By optimizing everything with data and technology, they brought the average cost of that same surgery down to about $1,200. Lewis: Twelve hundred dollars. That's not just an improvement; that's a different reality. It makes a life-saving procedure accessible to millions who could never have afforded it. Joe: That's abundance. And finally, 'D' is for Discovery. This is about managing innovation in an uncertain world. The example is Toyota. They know the future of cars is changing, but nobody knows for sure if it will be fully autonomous self-driving pods or human-driven cars with incredible AI assistance. Lewis: So what do they do? Joe: They hedge their bets. They're investing a billion dollars in a research institute to build both. They're developing fully autonomous cars, but they're also building the "guardian angel" co-pilot systems. They're discovering multiple futures at once, because they know that in a revolution, you can't be certain which one will win. Lewis: This AHEAD model sounds powerful for huge corporations like Toyota or GE. But what does 'Halo' or 'Abundance' mean for a small business owner, or just an employee like me trying to stay relevant? Joe: That's the key question. The book suggests scaling the principles down. For an individual, 'Halo' could mean instrumenting your own work—tracking your time, your focus, your outputs—to find ways to be more effective. 'Enhance' means actively looking for tools that can automate the tedious parts of your job so you can focus on the creative or strategic parts. It’s about applying that AHEAD mindset to your own career, not just a corporate balance sheet.

Joe: So the book's journey is really from anxiety to agency. It starts by acknowledging the legitimate fear of being replaced, but then it reframes that fear with a historical lens of optimism. It shows us that these painful transitions have happened before and have led to better futures. Lewis: And it demystifies the technology. It takes AI from this scary, abstract concept of a thinking robot and grounds it in the real-world 'Systems of Intelligence' that are already all around us, from our hospital to our phone. Joe: Exactly. And once you understand the history and the tech, it doesn't just leave you there. It hands you a concrete toolkit with the AHEAD model. It gives you a set of verbs—Automate, Halo, Enhance, Abundance, Discovery—that provide a path forward. Lewis: It really leaves you with a powerful question to ask yourself. Instead of asking, 'Will a machine take my job?', the real question becomes, 'How can I use these new machines to do my job in a way that was completely impossible five years ago?' It shifts the entire focus from threat to opportunity. Joe: It's a huge shift in mindset. And it's a call to action. The book's closing message is that the future isn't something that just happens to us. The winners will be the ones who are actively building it. Lewis: A powerful and, surprisingly, a very hopeful message. We'd love to hear what you all think. What part of the AHEAD model feels most relevant to your work or your life? Let us know. Joe: This is Aibrary, signing off.