The Real Future of AI and Work

You know, it's a really boring conversation. Will AI take all of our jobs? This, unfortunately, is the conversation about jobs that the AI industry has wanted to have for far too long. But finally, we are starting to get a little more thoughtful consideration as more time passes. And it turns out AI doesn't just take all the jobs. What AI does do is change the entire landscape of how we work on both individual levels, on team levels, in terms of what we can individually aspire to, in terms of what our teams can aspire to. In terms of how companies should organize themselves, in terms of what skills we should prioritize. And all of those are the really interesting and productive conversations to have about AI and jobs. And that is exactly what we are talking about today. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, Blitzy, Robots and Pencils, Harbor, and HyperAgent. To get an ad-free version of the show, go to patreon.com/AIDailyBrief, or you can subscribe on Apple Podcasts. And to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai. Now, keep an eye out in general at aidailybrief.ai. In addition to the website having the full summaries of each episode broken into the key quotes, key numbers, et cetera, You can also find out about upcoming events like our free webinar coming up this week on August 26th about agentic loops for knowledge workers. And you'll also get a lot more information about upcoming training programs. The next iteration of our Executive Agent Leadership Program led by Noufar Gaspar is kicking off after Labor Day. And again, you can get all of that information at aidailybrief.ai. Now, today's episode is, of course, a weekend long read slash big think episode. And our friends over at Every have the perfect big think content for right now. Every recently announced their first conference called Thesis, and alongside it, they announced a new project called Thesis Statements that will have 100 builders and thinkers write short essays about the future that they envision coming with AI. Every CEO Dan Shipper put it this way: We believe there is a bright future for human work after automation, and we believe that there's a small group of humans who know what it looks like because they live the answers every day. But their ideas are still largely missing from the mainstream discourse about AI. That's why we're creating a public record of what people at the frontier are seeing now, so we can get these ideas to as many people as possible. For those of you who want to go check it out for yourself, you can find this at every.to/thesis-statements. I will, of course, include a link in the show notes. And what we're going to do is look at a set of these first 25 statements, read the short essays that go along with them, and then of course, I'll give my thoughts. We're going to organize them into a few categories, and the first category we'll call foundations. And it is Dan himself who I think puts a fine point on the thesis that surrounds all of this with his short essay, After Automation, There Will Be More Human Work Than Ever. Dan writes, one thing CEO, knowledge workers, and investors seem to agree on is that AI is a threat to jobs, the economy, safety, and human meaning. But if you talk to anyone in the AI industry or to early adopters outside of it, you'll hear the same thing we've noticed internally here at Every. There's more work to do than ever. I don't believe there will be a tipping point where things flip and the jobs are gone. The new reality is the opposite. The more we automate, the more expert human work there is to do. Here's why. AI commoditizes the residue of human expertise— whatever can be made explicit enough to train on. That collapses the value of default model output and creates demand for what's different. Demand for what's different is demand for human experts, even as we approach artificial general intelligence. Every is a team of almost 30 people, and we haven't fired all of our employees in favor of agents. We haven't ditched software as a service products in favor of vibe-coded apps. We still hire humans to do customer service with a lot of agent assistance, and we still hire human writers and editors and engineers. Sure, employee agents take over more of the stable, repeatable, well-framed layer of work, but there is a lot of work that still requires a human being in the loop. We've found over and over that for any kind of complex task, The best way to get great work is to have an AI and a human going back and forth in the same workspace. Humans are still vital. In every example, the agent needs a human in order for the work to, well, work. The further away an agent gets from a human who is in charge of making sure it works well, the less well it works. Someone has to point it at the right thing, decide whether the output is good, catch the places where it is wrong, and turn the result into a real-life decision or process. That's because the current generation of models only knows about work that has been done. Humans know about what needs to be done right now. Humans are alive to a specific time, customer, codebase, and conversation in a way the training corpus isn't yet. The aliveness isn't just having more current data. We come to the moment from somewhere, with a continuous, constantly new perspective of our own— running wants, running concerns, and a running read on what matters— which changes what we see. Making expert work cheaper does not therefore simply replace experts. It creates more situations where expert judgment is needed. A great start to this whole project from Dan, but before I comment on this particular section, I actually want to pile on one more. This one is from former strategy consultant and author Paul Millard and is called The People Declaring Work Is Solved Will Still Be Working. Paul writes, there is no after automation unless we can radically expand our modern conception of work. For now, we are stuck in what I call a job-shaped reality distortion field. We flatten any conception of work down to activities with a job description and a paycheck. In this reality, we turn humans into numbers and talk about abstract topics like layoffs, employment rates, weak links, bundles of tasks, and lumps of labor. Work is much bigger than this. This month I have been busy soothing my newborn at 4 AM, transporting my 3-year-old to school, cooking at home, responding to emails, and carving out a little time for writing. Which may earn money or not. Despite accusations of wasting my talents and being unemployed, my days are full of work, and I am fulfilled. At the same time, we hear loud pronouncements from 23-year-olds in Silicon Valley declaring that work is solved. In 2 years, maybe 3, there will be nothing left to do. But then who will change my daughter's diaper? The thing that makes me laugh about these pronouncements is that they come from people drunk on work, waxing philosophical between all-night coding sessions. But if you ask them if their lives are designed around work, They'd say no because their job is optional. They can job-hop or take sabbaticals, all while getting the things humans crave— things like dignity, challenge, purpose— from the lives they inhabit, lives centered around work. People without such lives are mystified. What do you mean work is ending? To them, a job is not optional. The work doesn't end when you leave the office. We don't yet have language big enough for where we are going. We live in an era in which people have an easier time imagining the end of the world than life with a free Tuesday afternoon. We need to take off our job goggles and see that our lives are already full of work. If we took the time to look at work, not just in job-shaped form, we might find our way forward. So in these 2 essays, you have 2 very different ends working towards the same middle: the idea that there will always be more work. Now, for Paul, who you just heard, this is in the fact that the substance of our life is full of all sorts of work, even if it's not the kind that comes with a laptop and a tie. Dan is a little bit more on the nose and using work in the way that most of us think about work, the work that we do for our jobs. I myself am firmly in the there will be more work camp, and not only because I think Dan is right that we should expand our perspective on the substance and meaning of what we do beyond the office walls, but because literally even in the context of those office walls, I just think AI unlocks radical new horizons to traverse. It's as though we've been playing in one tiny lit quarter of what seems like it might be a huge room. We stay over where we've always been because that's the only part that's lit up. But suddenly someone comes in and throws the lights, and the size of the room is actually monumental compared to what we had thought. The reality of humanity is that we will race to discover and do more and build more and create more. And so I even think that Dan is underselling it when he just points to the continued need for human expertise. Not that any of his points are wrong, but that the larger reason there will be more human work than ever is because of a radical expansion of what we can all do. But let's now transition into some specific discourse around companies and how businesses will work. The first essay comes from Noah Bryer, co-founder of Alephiq, who argues that software companies will outperform software factories. Noah says, the most insidious failure mode in agentic engineering isn't buggy code. It's agents building fundamentally misaligned features, products, and systems. Solving that is a bigger and more interesting problem than reducing defects. This is why I think the software factory is the wrong metaphor for AI engineering. The challenge is less how to stamp out the same door panel every time with Six Sigma quality than how to evolve a system in line with our vision, values, and architecture. In that sense, the process is closer to Andy Warhol's factory than Ford's car factory. Both are focused on throughput, but Warhol was more concerned with ensuring all work aligned with a single creative vision. The hardest problem for a business is still creating a vision and keeping an entire team of humans, and now humans and agents, and humans with agents, building toward it, from the system architecture down to the individual lines of code. As I learned long before agents existed, achieving this is much more like building a startup than assembling a car. Too much of the industry treats software as a problem to be optimized and solved. That may be true for code writing and testing, but the better metaphor is staring us in the face. It's a software company, not a software factory. A factory is one piece of a larger organization where layers of interdependent systems interact and move at different speeds. But a company is, and always has been, a collection of agents brought together to collectively build something. Understanding the motivations of those agents is what it takes to build a successful software factory, and conveniently, exactly the job of a software company CEO. Noah's essay here starts to point to something that I think that the enterprise world of AI users actually understands better than the startup world of AI users. That technology, without human and institutional systems around it, is just another set of buzzwords. Exactly how and to what ends we build those systems and reimagine those institutions, though, is the question. And that brings us to our next post from investor and writer Tina He: Boring Infrastructure Will Win. Tina writes, your customer decides to stop using your customer relationship management software at 2 AM. Why? The sales rep realized that your $30,000 annual contract for your CRM only gives you $12,000 in value. There was no meeting, no negotiation, because the sales rep was an agent. Agents are not loyal. They are rational actors. They look at the numbers in milliseconds and make changes whenever it makes sense for the business, even if it is in the middle of the night. They are, in a way, ruthless. Your software can be easy to use and look good, but AI agents neither see nor care. Some companies will become winners by building headless architecture, which is software built for machine-to-machine communications. Those companies won't have any human users, only agents. Other businesses that do well are in areas where you can't move fast and make mistakes, like regulatory approval, banking, and compliance systems. These companies aren't competing to be the most sophisticated, but to be the most efficient at things like having wire transfers delivered securely. Over the next few years, the companies that own this layer will have an edge in gathering the intelligence about who should do what and when. As the capabilities of the models converge and start to make the same sorts of decisions, companies will compete less on having the best model and more on the systems that connect those decisions to real-world outcomes. Some of these essential systems manage task routing, data access, workflow orchestration, and rule enforcement. Companies that are serving specific customers with specific use cases like these will win over broad agentic use cases. These business models are like toll roads— if you don't want to pay, you've got to build your own bridge. And that could take years and cost hundreds of millions for compliance. That's why, in the end, the less glamorous work may be the most rewarding. So Noah gives us the idea that companies will change, and Tina takes a look from the outside in, as one of the ways that they will change is rational and emotionless digital agents doing more of their bidding, creating a whole new set of support structure around them. But what does this mean for the leaders who are trying to figure out how the work that they do and their teams do should change? For that, we turn to Sumit Singh, former Andreessen Horowitz partner and now managing partner and founder of Worldbuild, who writes, founders who AI-ify existing workflows will lose. I've spent the last 8 years as an investor watching the same pattern repeat. That era has ended with the advent of generative AI. As an investor, I'm excited. AI has finally opened up the potential for real innovation that's been missing since the mobile revolution. But I see founders building specialist AI products as if they were building the same tools of the last decade. Those who are playing by the old framework are about to make a big mistake. The businesses that will fail in the AI era are those that start with an existing workflow and AI-ify it. The ones that will survive will leverage models' unique, nuanced properties to invent new workflows that were not technically possible before. I call these post-skeuomorphic apps. Skeuomorphism is the trap of assuming that a new technology should look like what came before. Early mobile apps constantly fell into this pattern. They replicated the physical world, like the trash can icon that looked like an actual garbage bin. But they weren't exploring what our phones could uniquely do. The apps that broke through also broke this trap entirely. Uber didn't digitize the taxi dispatcher's desk; it asked what becomes possible when everyone has a phone in their pocket that knows where they are. The phone became a remote control for your life, as investor Matt Kohler has said: for food, DoorDash; for rides, Uber; for groceries, Instacart. They didn't adapt existing workflows; they invented new ones. AI is at the exact same inflection point. The founders who will win are asking a different question: what becomes possible now? What work can we invent that only AI makes possible? The winning applications will discover new workflows, and we don't even know what these workflows look like yet. Every AI coding tool on the market does the same thing first: it starts writing code. Blitzy does the opposite. Before writing a single line, Blitzy spends days reverse engineering your entire codebase. Thousands of agents ingest millions of lines, mapping every dependency, every undocumented constraint, every architectural decision made over the last decade. The result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years in the building. Other tools guess at context with grep searches and markdown files. Blitzy never guesses. It builds true understanding first, then delivers over 80% of entire software epics autonomously. Validated, end-to-end tested, production-grade pull requests. That's why Fortune 500 engineering teams trust Blitzy with the codebases that matter most. See for yourself at blitzy.com. That's blitzy.com. One thing I keep seeing in enterprise AI, companies hedging across every cloud, every model, every framework, or paying a GSI for a pilot that never ends. The teams actually shipping, they've picked a lane and they move fast. That's one of the reasons I like today's sponsor, Robots and Pencils. They've gone all in on AWS. They're an advanced tier and AWS pattern partner, and they ship production AI coworkers in 45 days. That's led to them doing some of the more interesting work I've seen on AI coworkers. And by that, I'm not talking about chatbots. I'm talking about actual agentic systems that sit inside a business architecture and do real work. That kind of focus matters if you're an enterprise leader trying to get something real into production, or an AWS rep trying to move a customer from interested to deployed. Request an AI briefing at robotsandpencils.com. One conversation with Robots and Pencils and you'll know. Every episode, we cover the competition between OpenAI, Anthropic, SpaceX AI, Google, and Meta. Chances are you've already formed an opinion about who's leading. But every AI lab is taking a different approach, building different technologies, forging different partnerships, and developing a unique ecosystem. Harbor Capital Advisors' AI Lab Ecosystem ETF suite gives investors a way to gain exposure to the AI ecosystem they believe is best positioned for success. Search Harbor AI Lab Ecosystem ETFs wherever you invest, or follow @HarborCapital on X to learn more. Visit harborcapital.com for a prospectus containing investment objectives, risks, fees, expenses, and other important information. Read and consider it carefully before investing. Risks include principal loss and artificial intelligence-related risks. Harbor ETFs are distributed by Foresight Fund Services, LLC. Harbor is not affiliated with AI Daily Brief, and the funds are not affiliated with, sponsored by, or endorsed by any AI lab. This is a paid advertisement and not personalized investment advice. Investing involves risk, including possible loss of principal. This episode of the AI Daily Brief is brought to you by HyperAgent, where you run fleets of agents your team can manage together. New users get $1,000 in inference. Forget local agents and chat workflows waiting on your laptop to be prompted. HyperAgent deploys always-on agents in the cloud, doing real work across the tools your team already uses. Marketing's agent turns competitor moves into landing pages. Sales' agent enriches leads, drafts emails, and updates the CRM. Ops' agent chases the paperwork and tracks the budget. Every agent has access to shared context and follows your rules about scope and approvals. It's time you add agents that feel like teammates. Hire yours at HyperAgent, built by the team at Airtable. Claim your $1,000 in inference at hyperagent.com/AIDailyBrief. Now, Sumit, understandably given that he's coming from his experience at a16z, is looking at this from the standpoint of startups and what they build. But I think that this is every bit as true, if not more true, for legacy enterprises who are in the midst of figuring out where AI can provide the most value. I have often broken this into 2 different categories of AI. Efficiency AI and opportunity AI. There is, to be clear, nothing wrong with efficiency AI. Doing the things that you need to do faster, cheaper, better is a good thing. And it is a perfectly reasonable place to start. However, to assume that this incredibly powerful capability-unlocking technology will be constrained to doing things the exact same way that they've always been done, just by an agent instead of a human, is to fundamentally misimagine what is possible. Opportunity AI is all about asking those questions about where you can go that you couldn't go before because you have AI in your corner. It is going to be much more difficult and take much more iteration and experimentation to figure out where the greatest opportunities of opportunity AI actually lie for your particular business. This is why I'm constantly beating the drum of needing to create space for experimentation and not prematurely cut off or overly ROI-ify your AI efforts inside the enterprise. As to do so will be to bias people towards those applications, which are just the same old things, but a little cheaper or faster. There is another dimension of this that I haven't spoken about as much, but is why I'm a little skeptical in aggregate of startups whose only job is to watch what people do to train agents to do the same thing. The idea that agents are going to do the same work that we do in the same way has never struck me as making a lot of sense. Agents are almost inevitably going to do things in different agent-native ways. Presumably that will be more efficient and native to their strengths, but which will also require another level of organizational integration to make sure that those new agentic processes can be synced with humans as well. Still, I think Sumit's broader point that we can't think that the new world is just going to look like the old, but more efficient, is a very, very important one. Writer Tom Critchlow starts to explore this a little bit in his contribution to the thesis statements. The company with the best clock will beat the company with the best model. Before the 1800s, there was no universally agreed-upon time. But as trains got faster, it became more important for each station to agree on what time it was. Noon couldn't mean 2 different things in London and Bristol, otherwise you'd always be late or early for your ride. The railway system therefore synced the clocks, and the result was the standard time and time zones that still exist today. Now we need to change the clocks again. AI has introduced faster thinking, but we haven't yet coordinated our workflows to keep pace. Agents operate in seconds. Teams meet weekly. Finance plans quarterly. Leadership revisits strategy annually. Each part of the organization inhabits a different present, each made using a different version of reality. We need a new standard time, a new coordination mechanism for the age of AI. Call it standard status. This will be a continuously updated record of goals, decisions, permissions, and constraints shared by humans and agents alike. This sounds like an engineering problem. Ingest the context, generate a status, and continually update it so that workers can stay in sync. But staying in sync is not the same thing as staying aligned. The next challenge is to grapple with the ambiguity inherent in the way we work. Team meetings, financial plans, and annual planning were always part ritual, designed as formats to keep individuals and teams aligned, heard, and motivated. Can we just let go of this theater in favor of standard status, or will we need to continue the human process of just checking in? When it comes to alignment, the company with the best clock may soon beat the company with the best model, but only if it remembers that knowing the time is not the same as knowing what the moment requires. Now, on this front, one of the things that I'm going to be exploring a lot this fall is a shift from single-player AI to multiplayer AI, from individual agents to team agents. And I think a lot of these aspects of coordination and new systems designed around a new type of coordination are going to be the substance of some of those conversations. So far, we've been talking just about the organization, but what about the individual? Even though these folks are pretty much all in the group of being quite sure that there will be jobs that remain after AI, many of them think that those jobs will look somewhat different. Oboe CEO Nir Zickerman writes that jobs AI can't do will scale. Technological revolutions always trigger the same cycles. Jobs shift from ones that are easy to automate to ones that aren't, and unprecedented productivity is unlocked. We'll see the same shift with AI. There will be an obvious reduction in jobs related to concrete and verifiable tasks, but there will also be a significant rise in things that AI cannot do well, but which can now be done at unprecedented scale with AI. LLMs have proven exceptionally good at taking over concrete tasks that are easily verifiable, such as coding. These jobs will fade away. But due to structural flaws, LLMs are quite limited in their ability to perform tasks that require ambiguity, open-endedness, and creativity. These are the jobs that will thrive. Creative enterprises will take off for the same reason that digital cameras, digital audio workstations, the internet, and social media turned us all into creators. Roles requiring organizational management and communication will become more important than ever, as will those that require human interaction. Systems-level roles will also flourish as the need to oversee droves of agents rises. Take the film industry. The rise of video generation models has recently stirred up speculation about the durability of moviemaking. But human actors acting out stories created by human screenwriters aren't going anywhere. Those are creative enterprises that only humans can do well. Yet all the other stuff that goes into a movie, from financing to casting calls to managing logistics, will be streamlined to levels never before seen. This will allow artists to take more creative risk and to do things that were previously neither technically nor economically feasible. It will take several decades, but job markets will eventually have far fewer roles made up of busywork and more roles made up of capabilities that humans uniquely possess. Now, one of the things that I like about this whole thesis series is that one of my beefs with the future projections from the AI industry has been a historic unwillingness to get into the specifics. That's what I tried to address with an episode from earlier this year called The New Jobs AI Will Create. Whether you agree or not with the analysis, I'm glad to see a lot of these authors digging into that exact question. Another who does that is Joe Hudson, founder of The Art of Accomplishment. He writes that wisdom work will replace knowledge work. Before AI, says Joe, knowledge set you apart. Knowing more meant earning more. But as models swallow entire fields overnight, wisdom— skills like emotional clarity, discernment, and connection— is what keeps you indispensable. AI models don't sleep or burn out. One highly trained model will soon be able to outperform an expert in physics, law, and engineering simultaneously, at any hour. Imagine a world where all your knowledge is irrelevant, akin to the ability to build a fire today. Occasionally useful, but mostly unnecessary in a world with lightbulbs, central heating, and stovetops. AI will also make it harder for brilliant people to get away with culturally destructive behavior. For decades, extraordinary knowledge or skill created a protective moat around difficult colleagues. People muttered, that's just how they are, and kept the peace. But when a model can draft the brief, diagnose the anomaly, or optimize the market strategy in seconds— and do it politely— why keep paying the emotional tax of a brilliant jerk? But you don't have to be a talented blowhard for your skills to be at risk of AI disruption. The leverage has shifted from what you can do to how you show up while doing it. When knowledge is no longer scarce, what remains valuable? Wisdom. Wisdom is how to live. It is the residue of mistakes metabolized by time and reflection. It can't be rushed, and it can't be copy-pasted. It is an embodied— as in felt in the body— experience. Guidance from the inside. No matter how intelligent AI becomes, it can't live It can't feel your body's signal in a high-stakes negotiation, sense the hidden fear in a boardroom, or hear the unspoken no behind a client's polite words. That's why tomorrow's economy will prize wisdom workers. You can get answers from AI, but how you use those answers takes wisdom. Build First founder Bethany Crystal thinks it goes beyond just wisdom. Weirdness, she says, will be the best human advantage. Bethany writes, back in high school, I was known only by my appointed nickname, Wheelie bag girl. As the only kid with a rolling backpack, I was made fun of mercilessly and spent a lot of time alone, re-alphabetizing my business card collection or adding rubber bands to my basketball-sized ball. I didn't mind. As a child of the early internet, being strange and peculiar was the standard. Then the web grew up, optimized itself, and asked us all to do the same. I listened. I got normal. And then it all changed again, this time with AI at the forefront. Today, I run a company solo that would have required 5 humans in the pre-AI age. But the biggest surprise isn't that AI has made me more productive— it's that it's made me weird again. AI has reintroduced a playful, experimental way of working. The 9-to-5 is becoming decoupled into a series of project-based interests, with a focus on craft and passion. I'm building bingo card apps for my friends and hyper-personalized playlists for my clients. I relaunched my blog as a choose-your-own-adventure experience. It's not making me any money, but it's something I'd dreamed about doing for years. Back in school, those quirky habits and extreme niches would get you pushed around the playground. But in a post-AGI world, they're the only things that keep you human. And if you view the return to weirdness as a, call it, unexpected benefit of AI, this idea of broader, perhaps unexpected benefits is one that runs throughout a number of the other thesis statements as well. Abstract Group co-founder Emily Vernon, for example, argues that AI will spur a resistance to mediocre ideas. Emily says brand as a concept isn't that complex. It's a good idea that compounds over time when executed consistently. AI is certainly helping with the consistency part, but a good idea is still hard. It's now even harder because AI makes mediocre ideas irresistible. Why spend 6 figures and 6 months building your brand when you can generate something passable, even tasteful, with a few quick prompts for free? It's not taste that we should be tracing. Transgressive filmmaker John Waters' call to arms is to break free of the tyranny of good taste. Now that tasteful but forgettable brands are cheap and easy to generate, that tyranny is more oppressive than ever. Instead, we have to be unpredictable. Creative professionals have always known that great ideas are unpredictable, and neuroscience confirms it. The brain is tuned to both notice and remember surprises. It registers predictable as forgettable. As AI's whole premise is being predictable, this is where we fit in the loop. So how will we foster more unpredictable thinking? The same way we always have, but with more intention. Getting off the grid to hunt for new inspiration, ditching recommendation algorithms, muting comments and noise, rewarding the hard work of creating good ideas. A brand has to repeat one idea forever. To make it last, make it an unpredictable one. One final theme that runs throughout, and will be the substance of the last mini-essay that we'll read, is the idea that for each of us personally, the shift in the world and in the way we work might enable a broader shift in our relationship with the world around us. Sublime CEO and founder Sariah Zout argues that this is a gift. Your attention, she says, will be handed back to you. Don't waste it. For most of human history, physical strength was the barrier holding back progress. Then, when the Industrial Revolution took over the heavy lifting, physical power became cheaper and value moved to what we could achieve with our minds, not our limbs. Now AI is doing for brainpower what machines did for muscles: amplifying intellectual output. But as intelligence becomes abundant, value will move again. This time towards the heart— that squishy catch-all for judgment, intuition, taste, self-knowledge, creativity, and wisdom. For while our networks are turning neural, we can't yet replicate the follies of the heart. AI is excellent where success can be verified. But the work that matters rarely has a verifiable answer. What strategy should a company pursue? Which product deserves to exist? What idea should we stand behind? More intelligence cannot resolve these questions. AI can tell us what is probable, but it cannot tell us what is worth wanting. This doesn't mean we shouldn't embrace advancement. We should automate whatever can be automated without feeling nostalgic. The old world of work was not great, after all. No one should spend their one wild and precious life manually processing insurance claims. We should treat every task machines take over as attention handed back to us. The question is whether we use that attention to manufacture even more work or reinvest it in our squishy skills. Cultivating taste, trusting our judgment, making decisions without certainty, and caring about something enough to take responsibility for it. If the last stage of work belonged to the brain, the next belongs to the heart. So that, my friends, is a little taste of what Every has cooking with their thesis statements. It's its own project, but as I mentioned, it is connected to their Thesis 27 event, which is happening at the beginning of November. I believe that they have limited room, but you can apply for the event on their website. If you're not following Every and Dan Schipper yet, They're always doing interesting things like that, so I highly encourage it. Mostly, as I said at some point in this show, I'm very excited to see the conversations shifting from big, vague, broad statements to more deeper, more nuanced explanations, if even by nature of the unknowability of the future, they do remain broad and vague. Hope this was a fun way to spend some time this weekend, but for now, that's gonna do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace.