The AI Daily Brief: Artificial Intelligence News and Analysis - Work in the Age of Infinite Agents

Starting point is 00:00:00 Today on the AI Daily Brief, AI and the expansion of what's possible. The AI Daily Brief is a daily podcast and video about the most important news and stories in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, ZenCoder, Landfall IP, robots and pencils, and super-intelligent. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe on Apple Podcasts. To learn about sponsoring the show, send us a note at sponsors at AIdailydief.ai. If you are interested in seeing the results of our AI-R-OI benchmarking survey, or if you are up for participating on our AI maturity tracking panel

Starting point is 00:00:43 and helping contribute to future research, check out AIDBIntel.com. And lastly, if you want to kick this year off right with some new AI skills, come join our AI New Year's resolution, you can find out about that at AIDBnewyear.com. And of course, if you were sitting there saying, bro, that is a lot of different URLs. All of this is, of course, linked from the main site, AIDDailybrief.A.I. so if you just need to remember one, just go there. Now, we are back with our first weekend episode of the year, which means our first big thing slash long reads episode,

Starting point is 00:01:15 and we actually have the privilege of doing a classic long reads. The last couple of weeks have seen a number of really great medium and long form essays about AI, its relationship to work, its relationship to the economy, basically exactly the sort of big thing that the end of the year and the beginning of a new year is so good for. Two that I particularly noticed, I think contribute to what is going to emerge as an important canon, which is articulating a future that the builders see that's about more than just productivity and job displacement. So today we're going to read two essays, the first by Ivan, the CEO of Notion, the second by Aaron Levy, the CEO of Box, which tell parts of the same story of AI, the future, and the expansion of what's possible.

Starting point is 00:01:59 These were both published publicly, and so I'm going to read them in full. And until we get that new, better OpenAI audio model that they're talking about, it will indeed be me as a human doing the reading. First up by Ivan Zhao from Notion, Steam, Steel, and Infinite Minds. Ivan writes, every era is shaped by its miracle material. Steel forged the Gilded Age. Semiconductor switched on the digital age.

Starting point is 00:02:23 Now AI has arrived as infinite minds. If history teaches us anything, those who master the material define the era. In the 1850s, Andrew Carnegie ran through muddy Pittsburgh streets as a telegraph boy. Six and ten Americans were farmers. Within two generations, Carnegie and his peers forged the modern world. Horses gave way to railroads, candlelight to electricity, iron to steel. Since then, work shifted from factories to offices. Today, I run a software company in San Francisco, building tools for millions of knowledge workers. In this industry town, everyone is talking about

Starting point is 00:02:56 AGI, but most of the two billion desk workers have yet to feel it. What will will be knowledge work look like soon? What happens when the org chart absorbs minds that never sleep? The future is often difficult to predict because it always disguises itself as the past. Early phone calls were concise like telegrams. Early movies looked like film plays. This is what Marshall McLuhan called driving to the future via the rearview window. Today we see this as AI chatbots, which mimic Google search boxes. We're now deep in that uncomfortable transition phase, which happens with every new technology shift. I don't have all the answers on what comes next. But I like to play with a few historical metaphors to think about how AI can work at different

Starting point is 00:03:35 scales, from individuals to organizations to whole economies. Individuals, from bicycles to cars. The first glimpses can be found with the high priests of knowledge work, programmers. My co-founder Simon was what we call a 10x programmer, but he rarely writes code these days. Walk by his desk and you'll see him orchestrating three or four AI coding agents at once, and they don't just type faster, they think, which together makes him a 30 to 40-40, engineer. He queues tasks before lunch or bed, letting them work while he's away. He's become a manager of infinite minds. In the 1980s, Steve Jobs called personal computers bicycles for the mind. A decade later, we paved the information superhighway that is the internet. But today, most knowledge

Starting point is 00:04:17 work is still human-powered. It's like we've been peddling bicycles on the Autobahn. With AI agents, someone like Simon has graduated from riding a bicycle to driving a car. When will other types of knowledge workers get cars, two problems must be solved. First, context fragmentation. For coding, tools and context tend to live in one place, the IDE, the repo, the terminal. But general knowledge work is scattered across dozens of tools. Imagine an AI agent trying to draft a product brief. It needs to pull from Slack threads, a strategy dock, last quarter's metrics in a dashboard, an institutional memory that lives only in someone's head. Today, humans are the glue, stitching all that together with copy paste and switching between browser tabs. Until that context,

Starting point is 00:04:58 is consolidated, agents will stay stuck in narrow use cases. The second missing ingredient is verifiability. Code has a magical property. You can verify it with tests and errors. Model makers use this to train AI to get better at coding, e.g. reinforcement learning, but how do you verify if a project is managed well or if a strategy memo is any good? We haven't yet found ways to improve models for general knowledge work, so humans still need to be in the loop to supervise, guide, and show what good looks like.

Starting point is 00:05:26 agents this year taught us that having a human in the loop isn't always desirable. It's like having someone personally inspect every bolt on a factory line, or walk in front of a car to clear the road. We want humans to supervise the loops from a leverage point, not be in them. Once context is consolidated and work is verifiable, billions of workers will go from pedaling to driving and then from driving to self-driving. Organizations, steel and steam. Companies are a recent invention. They degrade as they scale and reach their limit. A few hundred years ago, most companies were workshops of a dozen people. Now we have multinationals with hundreds of thousands. The communication infrastructure, human brains connected by meetings and messages, buckles under exponential load.

Starting point is 00:06:06 We try to solve this with hierarchy, process, and documentation, but we've been solving an industrial-scale problem with human-scale tools, like building a skyscraper with wood. Two historical metaphors show how future organizations can look differently with new miracle materials. The first is steel. Before steel, buildings in the 19th century had a limit of six or seven floors. Iron was strong but brittle and heavy. Add more floors and the structure collapsed under its own weight. Steel changed everything. It's strong yet malleable.

Starting point is 00:06:34 Frames could be lighter, whilst thinner, and suddenly buildings could rise dozens of stories. New kinds of buildings became possible. AI is steel for organizations. It has the potential to maintain context across workflows and surface decisions when needed without the noise. Human communication no longer has to be the load-bearing wall. The weekly two-hour alignment meeting becomes a five-minute async review. The executive decision that required three levels of approval might soon happen in minutes. Companies can scale, truly scale, without the degradation we've accepted as inevitable.

Starting point is 00:07:05 The second story is about the steam engine. At the beginning of the Industrial Revolution, early textile factories sat next to rivers and streams, and were powered by waterwheels. When the steam engine arrived, factory owners initially swapped water wheels for steam engines and kept everything else the same. Productivity gains were modest. The real breakthrough came when factory owners realized they could decouple from water entirely. They built larger mills closer to workers, ports, and raw materials, and they redesigned their factories around steam engines. Later, when electricity came online, owners further decentralized away from a central power shaft

Starting point is 00:07:36 and placed smaller engines around the factory for different machines. Productivity exploded and the second industrial revolution really took off. We're still in the swap-out-the-waterwheel phase. AI chatbots bolted onto existing tools. We haven't reimagined what organizations look like when the old constraints dissolve, and your company can run on infinite mines that work. while you sleep. At my company Notion, we've been experimenting. Alongside our 1,000 employees, more than 700 agents now handle repetitive work. They take meeting notes and answer questions to

Starting point is 00:08:06 synthesize tribal knowledge. They field IT requests and log customer feedback. They help new hires on board with employee benefits. They write weekly status reports so people don't have to copy paste. And this is just baby steps. The real gains are limited only by our imagination and inertia. Economies. From Florence to megacities. Steam and steel didn't just change buildings and factories, they changed cities. Until a few hundred years ago, cities were human-scaled. You could walk around Florence in 40 minutes. The rhythm of life was set by how far a person could walk, and how loud a voice could carry. Then steel frames made skyscrapers possible. Steam engines powered railways that connected city centers to hinterlands, elevators, subways, highways followed. Cities exploded in scale and

Starting point is 00:08:50 density. Tokyo, Chongqing, Dallas. These aren't just bigger versions of Florence. They're different ways of living. Megacities are disorienting, anonymous, harder to navigate. That illegibility is the price of scale. But they also offer more opportunity, more freedom, more people doing more things and more combinations than a human-scaled Renaissance city could support. I think the knowledge economy is about to undergo the same transformation. Today, knowledge work represents nearly half of America's GDP. Most of it still operates at human scale. Teams of dust. Teams of dust. dozens, workflows paced by meetings and emails, organizations that buckle past a few hundred people. We built Florence's with stone and wood. When AI agents come online at scale, we'll be building

Starting point is 00:09:30 Tokyo's, organizations that span thousands of agents and humans, workflows that run continuously across time zones without waiting for someone to wake up, decisions synthesized with just the right amount of human in the loop. It will feel different, faster, more leveraged, but also more disorienting at first. The rhythms of the weekly meeting, the quarterly planning cycle, and the annual review may stop making sense. New rhythms emerge. We lose some legibility. We gain scale and speed.

Starting point is 00:09:58 Beyond the water wheels. Every mirror-called material required people to stop seeing the world via the rear-view mirror and start imagining the new one. Carnegie looked at steel and saw city skylines. Lancashire mill owners looked at steam engines

Starting point is 00:10:10 and saw factory floors free from rivers. We are still in the water wheel phase of AI, bolting chatbots onto workflows designed for humans. We need to stop asking AI to be merely our copilots. We need to imagine what knowledge work could look like when human organizations are reinforced with steel, when busy work is delegated to minds that never sleep. Steel, steam, infinite minds. The next skyline is there waiting for us to build it. If you're using AI to code, ask yourself, are you building software or are you just playing prompt roulette? We know that unstructured

Starting point is 00:10:45 prompting works at first, but eventually it leads to AI slop and technical debt. Enter Zenflow. Zenflow takes you from vibe coding to AI-first engineering. It's the first AI orchestration layer that brings discipline to the chaos. It transforms freeform prompting into spec-driven workflows and multi-agent verification, where agents actually cross-check each other to prevent drift. You can even command a fleet of parallel agents to implement features and fix bugs simultaneously. We've seen teams accelerate delivery 2x to 10x. Stop gambling with prompts. Start orchestrating your AI. Turn raw speed into reliable production-grade output at zenflow.3. If you're listening to this, you already know how fast AI is writing the rules for

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Starting point is 00:13:42 All right, back to NLW quickly. The core point of this essay, or at least the core place that this essay locates us in the history of this transition that we are living through is, I think, extremely important. You can see this idea of AI being bolted onto existing processes everywhere you look. Some of the most successful startups right now, for example, are those that deploy AI to watch how human knowledge workers do things so that agents can copy it. That type of automation feels to me like it will be so short-lived.

Starting point is 00:14:11 The idea that agents are somehow just going to do things the exact same way as humans do, but faster is a version of this designing the future by looking in the rearview mirror, which is not to say that people are wrong to do that. This is a necessary transition phase. However, to the extent that we are thinking about what we can do differently in 2026, to the extent that we can try to start from the assumption that the future process will not just be an optimized version of the old process and will instead be something fundamentally different

Starting point is 00:14:41 that takes advantage of the new capabilities, the closer we'll get, I believe, to where the future will actually land when we get there. But what does it all mean for knowledge work? Isn't it all just going away if agents can do everything? For that, we turn to Aaron Levy, the CEO of Box, for his essay also shared on X, Javon's paradox for knowledge work. Aaron writes, in the 19th century, English economist William Stanley Javons, found that tech-driven efficiency improvements in coal use led to increased demand for coal across a range of industries. The paradox, of course, being that if you assume demand remains constant, then the volume of underlying resource should fall if you make it more efficient.

Starting point is 00:15:18 Instead, making it more efficient leads to massive growth, because there are more use cases for the resources than previously contemplated. The paradox has proven itself repeatedly, as we've made various aspects of the industrial world more productive or cheaper, and especially in technology itself. For instance, in the early years of the mainframe, units were measured in the hundreds, and only the world's largest companies could afford them.

Starting point is 00:15:40 In the early years of the mini-computer, a smaller, cheaper version of the mainframe, units were in the tens of thousands. And in the early days of the PC, units were in the millions. That's a 100-fold increase for each new era of computing in just three decades. While you would have had to been a Fortune 500 company to access powerful software to do your accounting in the 1970s, by the 2000s with the cloud, it was available to every barbershop in the world. This happened for CRM systems, communication technology, marketing automation,

Starting point is 00:16:07 document management software, and nearly every enterprise software application. This happened for CRM systems, communication technology, marketing automation, document management software, and nearly every enterprise software applications. The advantages that a large enterprise had in procurement, installation, maintenance, computing capacity, and more, simply evaporated overnight because of the cloud. As a result, efficiencies in computing led to the democratization of automation of deterministic work through software for decades in almost every field. But this has never been possible before in the nondeterministic work that represents the vast

Starting point is 00:16:40 majority of things we do every day in an enterprise. Reviewing contracts, writing code, generating an advertising campaign, doing advanced market research, handling 24-7 customer support, and thousands of other categories of tasks. AI agents bring democratization to every form of nondeterministic knowledge work, and this will change most things about business. For most large companies today, they can effortlessly move resources around between projects, afford to experiment on new ideas, hire the top lawyers or marketers for any new project they need, contract out or hire engineers to build whatever new initiative they're working on.

Starting point is 00:17:13 This has always been an advantage of the world's largest companies, but this is a benefit that has only achieved after decades or in some cases centuries of business success and survival. That means for the vast majority of companies and entrepreneurs in the world, you're at an extremely stark disadvantage on day one no matter what you do. AI agents fundamentally change the calculus here. Now we can dramatically lower the cost of investment for almost, any given task in an organization. The mistake that people make when thinking about ROI is making the R the core variable, when the real point of leverage is bringing down the cost of the I. Every entrepreneur,

Starting point is 00:17:45 business owner, or anyone involved in a budget planning process before knows how scarce resources are when running a business. When you're a small team, you're making decisions between having a good marketing webpage, building a new product experience, handling customer support inquiries, taking care of something important in finance, finding new distribution, and so on. Every one of these areas of investment and time are trading off from one another, all of which hold you back from growth. Now we have the ability to blow up the core constraint driving many of these tradeoffs, the cost of doing these activities. Rune on X pointed out that any consumer now has better access to education and tutoring than an aristocrat would have had due to AI, and now every business in the world has access to the

Starting point is 00:18:23 talent and resources of a Fortune 500 company 10 years ago. Demand will go up 10x or 100x for many areas of work because we've lowered the various barriers to entry of doing many more types of work that most companies wouldn't have even experimented with before. Imagine the 10-person services firm that didn't have any custom software before for their business. From a standing start, it may have taken multiple people to develop a full app, keep it running, keep customer requests incorporated, ensure the software stays secure and robust and so on. The project just doesn't even get started because of this. Now, someone on the team builds a prototype in a few days, proves out the value proposition in a matter of days. You can analogize this to any

Starting point is 00:18:59 other type of work or task in an organization. Of course, many are wondering what happens to all the jobs in this new world. The reality is that despite all the tasks that AI lets us automate, it still requires people to pull together the full workflow to produce real value. AI agents require management, oversight, and substantial context to get the full gains. All of the increases in AI model performance over the past couple of years have resulted in higher quality output from AI, but we're still seeing nothing close to fully autonomous AI that will perfectly implement and maintain what you're looking for. It's clear that AI agents are successfully taking over various tasks that we do today, like researching a market, writing code for a new

Starting point is 00:19:36 feature, creating digital media for a campaign, but incorporating those tasks into a broader workflow to produce value still requires human judgment and a ton of effort. Even as AI progresses to accomplish more of an entire workflow, we will simply expect more from the work that we're doing, ultimately ensuring that today's jobs are tomorrow's tasks. Historically, this actually happens all the time. If you told someone about Figma or Google AdWords in the 1970s, they'd have expected marketing jobs to plummet since we could do many different jobs inside of a single role in the future. Well, the opposite has happened. Back of the envelope math, from AI, of course, suggests there were a few hundred thousand people employed across

Starting point is 00:20:13 marketing-related job categories in the 1970s, PR, graphics, advertising-type jobs in the U.S. Today, it's in the low millions. How did we experience a 5x increase in these jobs in 50 years at the exact same time that technology made this work far more efficient, actually precisely because of those efficiencies. We went from advertising being the domain only of the largest companies, your CPG or car companies, to something that almost any small business could participate in. The marketing technology, CRM systems, analytics, graphic design software, targeting platforms, new distribution channels, and many other tech-enabled trends allowed more companies to justify the ROI of doing more sophisticated marketing. This will similarly happen in many fields because of AI.

Starting point is 00:20:54 Yvon's paradox is coming to knowledge work. By making it far cheaper to take on any type of tasks than we can possibly imagine, we're ultimately going to be doing far more. The vast majority of AI tokens in the future will be used on things we don't even do today as workers. They will be used on the software projects that wouldn't have been started, the contracts that wouldn't have been reviewed, the medical research that wouldn't have been discovered, and the marketing campaign that wouldn't have been launched otherwise. All right, so back to NLW once again, and I think you can see how these two pieces go together. Not that they're not that they explain the entire future or anything like that, one of the great challenges of this moment,

Starting point is 00:21:29 as with any moment of creative destruction, is that it's a lot easier to see the destruction before you get to the creation. We don't know yet what new things AI will enable us to do that don't exist now because they haven't happened yet, at least not in a way that we can readily see. And so all we're left with is seeing how AI does what we already do right now, which naturally in many cases makes us scared. There will, I believe, throughout this year, especially because of the midterm elections in the United States, be an increasingly fraught political discourse around AI. I think much of that discourse will be important,

Starting point is 00:22:01 and we will spend some time on it when and if it is relevant. However, ultimately, my interest is in creating content and resources for the folks out there who are not interested in waiting around to see how AI changes things and whether they have a job on the other side. Who I want to create content and resources for are the people who are determined that it will be them and not some other anonymous stranger who figures out how to use these tools. Who goes and changes with the description that's next to their job title is in the future. Who goes and invents a new job title entirely?

Starting point is 00:22:34 You're seeing that kick off right away with our AIDB New Year's. You're going to see a lot more of that with AIDB intelligence as we try to put real numbers and real benchmarks around AI this year. And I'm very excited to have all of you along for the journey. For now, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. And until next time, peace.

The AI Daily Brief: Artificial Intelligence News and Analysis - Work in the Age of Infinite Agents

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