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Today on the AI Daily Brief, could we be actually at the beginning of an AI vibe shift? The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG Granola Robots and Pencils and section. To get an ad free version of the show go to patreon.com aidaily brief or you can subscribe on Apple Podcasts. If you want to learn more about sponsoring the show, send us a note at sponsorsidailybrief AI and while you are at aidailybrief AI, I would so appreciate it if you would take just two or three minutes to fill out our monthly AI Usage Pulse survey. This every month survey allows us to track what models people are using, how the value they're getting out of it is changing, and already it's shown some really interesting things. For example, the percentage of people who say their primary benefit from AI is new capabilities is way up this year and time savings is way down. And your contributions allow us to share that information. Now anyone who fills this out will get the results before the rest of the general public. And again, you can find that on aidailybrief AI and thank you so much. Now, last note is that today is a main only episode. A it's kind of a big topic, B it's kind of a grab bag topic that brings a lot of pieces together, and C as part of that grab bag, it turns out that a lot of the headlines also fit within this theme. I anticipate we will be back with our normal format tomorrow, but for now let's find out why Ezra Klein doesn't think the AI job apocalypse will happen as it has been foretold. Welcome back to the AI Daily Brief. Today we're talking about what is potentially the beginning of an AI vibe shift. The signals, while faint, are there that the conversation and discourse around AI is, if not changing, at least having a new strand that doesn't assume that we are just hurdling ourselves off of a cliff. And what's interesting and what makes it a trend that's actually worth watching is that it's happening both in the chattering class around jobs as well as in markets. And the fact that it's coming from two sides at once means there's a much higher potential that it actually is a narrative shift rather than just a very temporary blip before doom takes over once again. Now to get us to this point, there are two pieces from the last couple of weeks, which I think stand as poles in the conversation. On the one hand is that essay from Jasmine sun, published in the New York Times called Silicon Valley Is Bracing for a Permanent Undercloth Class. It's a well sourced piece from Jasmine talking to her friends, contacts and peers in the AI space in San Francisco and Silicon Valley about how they think AI is going to take everyone's job. Now, I discussed this one in the weekly episode from Friday, the Week AI Grew Up. Now, while I think the piece was well sourced and well researched, the issue that I have is with over aggrandizing the perspective of the people building AI on what the impact of AI in society is going to be. This is certainly intellectually tempting in the sense that you would think that the people who are closest to the new technology would have some insight into it, but I think it's actually just structurally wrong. First of all, there are some reasons to think that the people who are closest to AI might be wildly overestimating its impact. Cynically, they have good reason to, with IPOs looming on the horizon. But even removing that cynicism from things, a lot of what AI so far is really, really good at is is the day in and day out of their work, I. E. AI is now doing all the coding, which means it must do everything else in the future too. There are a whole bunch of reasons to be slightly skeptical of that, but the second reason overall, for a little bit more perhaps discernment around how much we lionize the perspective of the AI builders when it comes to the impact of their technology in the world, is that Silicon Valley doesn't really have a good track record of understanding what's going on outside of Silicon Valley. Silicon Valley is full of builders, builders without which we wouldn't have a lot of our most important innovations. What it is not full of is economists and researchers, at least of the non AI variety, and people who have worked in any sort of setting outside of startups. And for that perspective, there was also an essay last month that appears to have been even more influential. That essay is of course the one by University of Chicago economist Alex Emas that we covered last week on LRS in our episode about where the post AI economy thrives. That essay got a lot of traction in the AI circles, but with an opinion piece by Ezra Klein once again in the New York Times this week, it now has a much bigger audience as well. The piece by Ezra was called why the AI Job Apocalypse Probably Won't Happen and is significant if for no other reason that it's not coming from the usual suspect AI accelerationist voices. In other words, if Marc Andreessen wrote this, it wouldn't be nearly as important now. Ezra points out that a lot of the reason for the negative narrative is the AI labs themselves. He writes, if you believe the story the AI labs are telling, it's hard to see what stands between us and mass unemployment. AI has been designed to cheaply mimic what human beings can do on a computer, but never needs to sleep, never tries to form a union, and often outperforms real people on real tasks. Of course companies will want to replace human beings with this human being replacement machine, but as he points out, it's worth being cautious. He points to other reasons tech companies might have to tell that story, including getting investors excited, as we discussed before, or, as he points out, unwinding a post Covid hiring binge. The AI leaders, he writes, might understand neural nets better than they understand labor markets, or they might have bought too deeply into their own marketing materials. But what really gets Ezra to think a little bit differently is, as he puts it, economists, I've found, are quite skeptical that mass joblessness is on the horizon. From there, he explores that what will be scarce essay by Alex Emis brings the idea of Jayvon's paradox to a mainstream audience and even gives a personal example. He points to an ASU professor, Eldar Maximoff, who in every major occupation group that adopted computers heavily, employment grew faster than in groups that did not. Computers eliminated specific tasks within jobs, but the resulting cost reductions created so much new demand that the occupations expanded overall. Ezra picks up from there. Computers can do much that humans once did, but they didn't put humans out of work. The ability to do more made people realize there was more to do. He continues, this is fairly common. When I started my podcast 10 years ago, I was its only researcher. Now I have an extraordinary team of people who help me prepare episodes. Has that made my job easier? Not in the least. I spend far more time researching and prepping because they bring me so much more to absorb and consider, and I choose to do more challenging episodes because I'm confident I can do them. And here's the most key Every enthusiastic AI adopter I know, writes Ezra, is working harder than ever because there is more they can do. Now, where he ends his piece is not some blithe Pollyannish everything's going to be fine. Instead, it seems like his position is fairly similar to mine, which is that while I am not only optimistic, but hugely confident in the long term trajectory of society. The transition period to get there can be extraordinarily painful. And even if we are sure, or at least as confident as we can be, that new jobs and even more new industries will emerge, there will be certain categories of work, certain types of tasks, and certain types of jobs that are primarily made up of those tasks that do get wiped out, Ezra writes. What's likelier is that AI doesn't take all or most of the jobs, but it does take some. And that, strangely, is the possibility we're least prepared for. A world where AI displaces 8 million workers might be harder to handle than a world where it displaces 80 million workers. A mass unemployment event would force a wholesale restructuring of our economy. We are crueler when displacement is more limited. The best estimates of job loss from competition with China put it around 2 million jobs. That's small in the context of the entire US economy, where roughly 5 million people are hired each month and about 5 million people leave or lose their jobs each month. But it was devastating for particular communities, and we did very, very little to help them. So, zooming out, the point here is that we have a prominent commentator from the left starting to discuss with nuance the likely impact of AI on the economy and and actively combating in nuanced terms, the doom or job loss narrative. But what about the evidence? Well, this is another little part of this potential narrative shift, Ezra himself points out. For one thing, the macro data isn't matching the anecdot. The unemployment rate was 4.3% in March 2026. In March of 2020 it was 4.4%. Average hourly earnings are stable. Claude Code is a marvel, yet demand for software engineering is booming. Maybe mass layoffs are coming, but maybe not. Sequoia partner Konstantin Buehler writes, narrative violation and great insight from the latest Citadel securities banger. They write, we illustrated back in February that demand for software engineers, the most AI exposed to occupation, was accelerating higher, which we argued violates the displacement narrative. Indeed, the acceleration in software job postings has continued now up 18% from the inflection point in May last year. This number seems to match recent data from the Federal Reserve, which has software engineering jobs hitting their highest point since November of 2023. Commentators are picking up on the theme. Anthony Pompliano, who admittedly is a very tech forward market commentator, has been beating the drum about how he changed his mind on how AI will impact jobs in America. Previously, he wrote, I believed AI would replace many entry level roles typically filled by young employees. The technology would then work its way up the organization and eventually reduce the total number of jobs in a company. The data is saying something different, so when I get new information I am willing to change my mind. The number of software engineers being hired has been increasing. The number of open software engineer roles is growing. The number of new college grads who get hired has increased 5.6% over the last 12 months. The unemployment level for people aged 20 to 24 years old who have a college degree has fallen from nearly 9% to almost 5% as well, the Wall Street Journal recently wrote. AI created 640,000 jobs between 2023 and 2025 in the U.S. according to an analysis by LinkedIn of job posting data, including new white collar positions such as head of AI and AI engineer. And Pomp says, I'm starting to see companies throughout our portfolio aggressively hiring to keep up with the demand for their products and services. If AI can make employees more productive, which is widely accepted as fact, then companies are going to want as many productive units of labor as possible. Now. Murtaza Ahmed from Emergence AI and Russell AI Labs actually argued that at this point, AI increasing the demand for software engineers was actually now the consensus view in Tech Code, he writes, is digital brick. If bricks get much cheaper and easier to lay, you don't use fewer builders. You build what was previously too expensive, too slow, too bespoke or too annoying to justify. Doesn't mean, he points out, that jaevons applies everywhere. Some work is elastic and expands when the cost falls. Software sales, outreach, legal discovery, semiconductor analysis, customer research, security monitoring. Some work is more capped payroll month end close, basic compliance filing, routine reporting. The distinction is elastic versus inelastic demand. For those of you who remember Alex's essay, elasticity of demand is one of the key concepts. Basically what he explores is where the savings of AI in one sector can flow into the elastic demand in another sector. In other words, another sector where demand could grow apportionate to the new resources that are flooding in. For Alex, it's the relational sector, with people wanting more bespoke, handcrafted human mediated experiences. Now the one other area where we're starting to see some interesting data is around entrepreneurship. Startup Ideas Podcast host Greg Eisenberg wrote, I think we're about to see the largest explosion of entrepreneurship in human history. I get why the fear exists. Jobs are getting cut. AI researchers are privately saying most people are screwed. The models are getting ridiculously better and faster than anyone expected. Project that forward linearly and yeah, it looks bleak, but linear Projections are usually wrong during platform shifts. Nobody projected that the Internet would create 50 million small businesses. They projected Walmart would eat everything. What actually happens is intelligence gets cheaper and a flood of new builders enter the market with domain knowledge the incumbents never had. Millions will get laid off or never hired over the next 24 to 36 months. Those jobs are not coming back, so they become entrepreneurs out of necessity at first, then out of opportunity. Now this is a popular argument that you will see and of course one of the things that we explored on this weekend's show why agents make every job a startup is why for some, this is going to be a very unwelcome development. But there is some data that this isn't just hopeful thinking and that yes, even if there is a cap on the total number of people who want to or can be entrepreneurs, either a we haven't come close to hitting that cap yet, or b the changes that AI enables actually is expanding that cap. On May 1, Stripe CEO Patrick Collison wrote Stripe Atlas just hit 100,000 all time incorporations I. E. That's where Stripe helps new startups incorporate. Q1, he said, was 130% up year over year, writes Derek Thompson as Recline's co author of Abundance. Stripe data shows a startup incorporations way up and b startups in AI seeing faster growing revenue than is historically normal. For now, he writes, AI agents are better at creating firms than destroying jobs. 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Section is a platform that helps you manage AI transformation across your entire organization. It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value. The result? You go from rolling out tools to driving measurable AI value. Your employees move from meeting summaries to solving actual business problems, and you can prove the roi. Stop guessing if your AI investment is working, check out section@section AI.com that's S E C T I O N A I.com. But I said that there was another side of this potential narrative shift, which is market thinking. The Atlantic recently published a piece called so about that AI bubble. The subtitle sums up the thrust of the piece. Thanks to the rise of Claude Code and other AI agents, revenues are finally catching up to the hype. The author Roger writes. Six months ago, the AI sector was looking pretty bubbly. Companies were plowing hundreds of billions of dollars, much of it borrowed, into building new data centers but had no clear path to profitability. Experts and journalists, myself included, were comparing the AI buildout to the railroad bubble of the 1800s and the dot com bubble of the 1990s, in which speculation led to overinvestment that eventually crashed the stock market. Today, however, he points out, we're in a very different world. The worry that the country is building too many data centers now coexists with the fear that we won't have enough of them to satisfy the public's growing appetite for these products. The company previously known as OpenAI's junior competitor has become possibly the fastest growing business in the history of capitalism. Anthropic's revenue is increasing faster, much faster than Zoom's during the pandemic, Google's during the early 2000s, or even standard Oil's during the Gilded Age. If the company's current growth rate were to continue, then by early next year it would be taking in more money than any other company in the world, the author continues. The cause of this turnaround can be summarized in two Claude code. Now, obviously, if you were listening to this show, this is not surprising territory. But to put it in the terms that the market is finally coming to understand. Although this sounds incredibly basic and simple and obvious to some extent in hindsight, the difference at core is that in 2025 people were looking at AI as a question of how many seats the AI companies could sell, how many corporate butts could Microsoft put in Copilot, and how many normal people butts could OpenAI put in ChatGPT? Looking at the percentage converting to $2030 a month subscriptions, the skeptics just didn't see how that was going to justify trillions of dollars in infrastructure buildout. What's different now, of course, is that we've moved from seats to tokens. In the agent intelligence era, there is effectively no cap on how many tokens people would consume if resources were no constraint. Indeed, in some ways, as I explored in the why Agents Make Every Job a Startup episode, the only constraint now is our ability to coordinate all of these agents doing all of this work. But the point is, as the market wakes up to the agentic era, as they experience it firsthand, hacking at Claude Code or OpenAI's Codex. They understand that a single person doesn't represent a 20 or maybe even a $200 seat. They represent hundreds or thousands of dollars every month to the companies that are selling the tokens. And the companies can't sell the tokens fast enough. At the beginning of April, Anthropic noted that their run rate revenue had surpassed 30 billion, but now that number appears to be even higher. The company themselves have not confirmed it, but on Friday, Semianalysis, who is seen as extremely well sourced, published that Anthropic's ARR had this year quote exploded from 9 billion to over 44 billion today. If that pace is correct, it means Anthropic's ARR is doubling every six weeks. Analyst Meng Li did some back of the napkin math and wrote that anthropic is adding $96 million in ARR per day. To give these numbers all some context, Meng wrote, AWS took 13 years to reach 35 billion in annual revenue. Salesforce took over 20 years to pass 20 billion. The old software valuation framework no longer fits. David Spitz points out that it's not just a revenue story, but that if Semianalysis is right and that its inference margins are now at 70%, up from 38% last year, that it speaks volumes about where this is all going. And all of this is starting to reframe. Capex analyst Holger Shapes writes Morgan Stanley has again raised its capex forecast for the five hyperscalers. It now expects them to spend about 805 billion this year, up from a previous estimate of 765 billion. For next year, the forecast has been lifted from 951 billion to 1.1 trillion. And yet, as Andreas Steno Larsen points out, why do we keep talking about a capex bubble when the backlog of the same companies is rising substantially faster? He shared a chart that showed that the backlog of demand for customers to purchase additional capacity is not only significantly higher than the current capex spend, but is getting more and more divergent. Mag Seven companies spent over 400 billion in capex in Q1 of this year, but their reported and projected backlog is up around 1.3 trillion. Former Aizar and investor David Sacks swooped in to contextualize. I've been saying for a while, he writes, that AI capex will be a 2% tailwind to GDP growth this year. In fact, according to a new report from Morgan Stanley, the numbers are even stronger. More like 2.5% this year and over 3% next year. Now he argues that this actually understates the impact of AI because 1 it's just investment by 5 hyperscalers and 2 quote CAPEX is the investment to create the token factories. It doesn't count the economic activity resulting from what happens inside the token factories. Those tokens are now being used to generate code that is bespoke software that will increase productivity throughout the economy. The ROI on CapEx is likely to dwarf the CapEx itself, which is why investment continues to grow. Polls may show that AI is not popular, but economic growth is now. There is also even a shift around the Saaspocalypse narrative. The inflection was best shown around Atlassian's earnings report where the company stock was up almost 30% on Friday. Now the financials themselves were big. Revenue grew by 32% over the past year, which was up from 23% during the quarter before. One of the big substories was swift adoption of their new AI search tool called Rovo. CEO Mike Cannon Brooks said that customers using Rovo were growing their own ARR at twice the pace of those who weren't using the tool. The stat suggested to market analysts that Atlassian's best performing customers were switching to built in AI tools rather than building their own in house replacements. Cannon Brooks discussed the advantages of a platform native AI tool, commenting, those are cheaper answers because you use far less tokens to get to an answer in the same amount of time. Basically, rather than needing to use token hungry rag search, Rovo can tap into the existing knowledge graph in JIRA and again as we discussed last week in the AI subsidy era show in the world where there is far more demand than supply of tokens, token efficiency starts to matter quite a bit. Analyst Emea writes, Atlassian's earnings call is a great read. While Microsoft moved from per seat usage to usage based pricing, which is very logical for jira, it makes zero sense to move away from seat based pricing. In this case at least, the seat compression is not visible. Atlassian isn't reducing tokens by being clever with prompts, they're reducing tokens because with JIRA and Confluence they and their clients have spent 20 years capturing structured relationships between work teams, people, code and knowledge. When Rovo or any agent needs context, it does a graph lookup instead of a vector dump. This reduces dependence on token hungry rag that enterprises use now. Again, zooming out to our broader point, Jason from SASTR sums it up the big question for the week is were public software companies oversold at least in the aggregate? And two last little additional details of the potential narrative shift. One is while the data center issue remains very hot button and one where I think skepticism continues to rise rather than ebb away, the AP is writing about how the construction opportunities are allying blue collar style unions with big tech companies to try to reverse community opposition to data centers, said Rob Baer, president of the Pennsylvania Building and Construction Trades Council. When people say, you know, data centers are the root of all evil, we're just saying, look, they do create a hell of a lot of construction jobs which we live and work in your communities. Instead of just being a blunt no, Baer said communities should figure out what they need and ask tech companies for it, such as improvements to the project plans or millions of dollars for local schools. If you don't ask, you're never going to get it, he said. It's outside of the scope of this particular episode, but I actually think it is an insane indictment of how poorly tech companies have run these data center that the issue has gotten this bad. There are so many ways to make these incredibly value accretive to the communities that they're near at a fraction of the cost of the project as a whole. Speaking of which, the last part of this potential vibe shift is actually coming from at least one of the big AI companies themselves. I at this point have been fairly consistent in my complaints about the way that AI companies have been communicating about AI related job loss and their failure to communicate a positive future vision. And if of late Dario and Anthropic have been the bigger culprits, it's not like OpenAI has been great on this front either. You're starting to see some shift in that on May 1st OpenAI CEO Sam Altman we want to build tools to augment and elevate people, not entities to replace them. I think a lot of people are going to be busier and hopefully more fulfilled than ever and jobs doomerism is likely long term wrong, though of course there will be disruption and significant transition as we switch to new jobs, the jobs of the future may look very different, etc. I'm hopeful for a future where people who want to work really hard have incredibly fulfilling things to do and people who don't want to work hard don't have to and can still have an amazing life of prosperity. Now for my taste. I still think that that gets the shape of the future pretty wrong and is an example of Silicon Valley continuing to underestimate a how hard diffusion throughout an economy is and b just how good market economies are at matching any excess supply of capital and time with new offers, but at least it's headed in the right direction. Sam has also now really picked up this theme of the more powerful AI gets, the more opportunities it unlocks, he said on a recent podcast. Someone said to me just yesterday that GPT5.5 and Codex can accomplish in an hour what would have taken me weeks two years ago, and I've never been busier in my life. Noah Smith, another economic commentator who has been loudly tracking the AI narrative, writes, this is a huge messaging pivot for many years, replacing humanity was the explicit stated goal of OpenAI as a company and of a large number of top people in the AI industry. Very glad to see this rhetorical pivot. Now as we close out, I want to be clear that it would be very, very easy to overstate how pronounced this vibe shift is to the extent that there even is one. I'm picking up on the beginning of some market signals, plus an essay by a fairly prominent left oriented political commentator and turning it into a whole theme, which of course means we need to view this all with a grain of salt. On Sunday, the New York Times also published a piece about how the one issue that unites Democrats and Republicans is worries about AI, and recent polls still find people extremely negative about this. And of course I haven't even gotten into the fact that there are many parts of the left that reject out of hand the entire abundance idea, which is of course the primary work of two of the commentators that I've discussed here, Ezra Klein and Derek Thompson. And yet, even with all of that, I find it extremely encouraging to feel the collective foot being taken off the gas of the AI doomerism for just a moment. If nothing else, it creates an opportunity to have a different type of conversation, one that's neither doom nor utopia, but about how to adapt to and maximize the opportunity of the change that's here and coming. I think the more time we spend on that conversation rather than in the extremes, the better off we'll be. For now. That's going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. And until next time. Peace. Sam.