Podcasts about Jevons

Jack and Jill: AI Agents Rewiring Hiring and Job Search Jimmy interviews Matthew Wilson, founder of Jack and Jill, about how AI is worsening recruitment noise by automating mass applications and outbound recruiting, making traditional CV-based signaling less effective. Matt explains Jack and Jill's two AI agents: Jack helps individuals find and prepare for “dream jobs” through deep voice-based profiling, job matching, interview practice, and salary benchmarking (free for users), while Jill helps companies hire by capturing nuanced role needs and matching against Jack's user network; the company has 350,000 users and thousands of companies, mainly across London, San Francisco, and New York. Matt shares his background (Yorkshire Dales, Oxford, inspiration from a Batley-based startup), building Omnipresent (remote employment infrastructure later sold to Deel), and lessons on competitive advantage. They discuss early-career guidance, AI tool literacy, major white-collar work changes, Jevons paradox, small high-talent teams, transparent high pay, and hiring via paid trial days and networks. 00:00 Welcome and Guest Intro 00:27 AI Hiring Spam Problem 03:19 What Jack and Jill Builds 04:03 Why Job Search Feels Random 06:45 Jack the Career Agent 08:02 Jill the Hiring Agent 08:49 First Principles Recruiting 10:04 Advice for Early Careers 12:57 From Yorkshire to Startups 16:36 Building Omnipresent in COVID 19:47 Selling to Deel Lessons 24:22 Choosing the Next Venture 25:45 AI as Patient Career Coach 29:03 Why Work Changes Lives 31:00 Reimagining Recruitment 31:44 AI Tool Fluency 34:03 Career Catapult Mindset 34:29 White Collar Disruption 36:39 Jobs Change Not Vanish 38:41 Jevons Paradox Debate 40:05 Bureaucracy Versus Leverage 42:50 Building a Lean Team 42:59 Pay Transparency Hiring 48:52 Scaling Across Markets 51:39 Fast Hiring Playbook 53:56 Paid Trial Workdays 57:09 Early Hires Through Networks 59:30 First Employees Matter 59:58 Closing Thoughts Learn more about your ad choices. Visit podcastchoices.com/adchoices

Reena SenGupta invented the ranking system law firms love to hate, and now she explains why AI will make the legal profession more human. Reena spent thirty years measuring the legal industry from the inside, first building the Chambers and Partners rankings, then running the Financial Times Innovative Lawyers program for two decades. On this episode she walks Howard and I through what her latest research with Harvey shows about AI adoption in law, and the numbers complicate the doom narrative. Headcount is flat. Output is climbing. Clients and firms think they are having honest conversations about pricing, but the data says otherwise. So where is the real gap? Reena introduces the idea of the frozen middle, the layer of lawyers who use AI daily without ever becoming competent in it, and explains why that distinction will define who thrives over the next decade. She also makes the case that empathy belongs in the same category as legal drafting or negotiation, a skill to be trained rather than a trait some lawyers simply have. What does a law firm look like when the hourly rate no longer sits at the center of the business? Reena has a theory, and it starts underground, with fungi.   Episode Breakdown: 00:00 Meet Reena SenGupta, Architect of the Chambers Rankings 03:59 Why Reena Launched RSGI and the FT Innovative Lawyers Program 09:42 How Legal Innovation Has Changed Since 2005 23:48 AI, Jevons Paradox and the Future of Legal Demand 29:22 The Fungal Network Metaphor for Legal AI 36:32 Understanding the Frozen Middle in AI Adoption 40:03 Predictions for the Legal Industry a Decade From Now Connect with Reena SenGupta: Connect with Reena on LinkedIn   Reena's Company web profile    Connect with Howard Rosenberg: Connect with Howard on LinkedIn  Howard's Company web profile   Connect with Chris Batz: Connect with Chris on LinkedIn  Follow Columbus Street on LinkedIn Columbus Street Website  MergerWatch Website Podcast production and show notes provided by HiveCast.fm  

In June, the most capable American AI models stopped shipping as public launches and started shipping through a government gate. Six weeks later the gate is open again — and the real fight has moved to the layer no gate can touch. A Chinese open-weight model rattled trillions out of chip stocks, Washington pivoted from gating American closed models to threatening bans on Chinese open ones, the industry mounted its largest-ever policy counter-mobilization, and an American frontier model literally broke out of its lab and hacked another company. Knee-jerk reactions, or the beginning of real AI governance? Navigation: Intro The Gate Opens The Kimi Shock The Escape The Counterstrike and the Petition Interlude — The Low-Background Books The Investor Reckoning Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Bertrand Introduction Welcome to Tech Deciphered Episode 80. This one, once again, will be all about AI, government, frontier models, and open weight counterstrike. A lot has been happening in the regulation space, in cybersecurity, in the launch of new models in the past, maybe just 6–8 weeks. It’s actually pretty insane how much happened. We believe it was time to do an episode to talk about where we are and maybe where all of this is going. Maybe let’s start with a summary of where we stand, all that June and July saga, so you, our listeners, can get up to speed if you are not already there. You want to start with some points? Nuno The Gate Opens Yeah. Again, to your point, the gate swings. The gate had closed. We had to prepare an episode for the gate closing, and then the gate reopened. Now we have a different episode. This will probably change again as we’re seeing there’s news every day. Let’s start maybe with the first 19 days of the gate closing. There was an executive order on June 2nd from President Trump that asked frontier labs to share models with the government, 30 days pre-release. It inferred the protected frontier model designation into that. Basically, it was effectively a de facto licensing agreement defined by an executive order of the President as of June 2nd. On June 9th, Anthropic launched Fable 5 and the famous Mythos 5 or Mythos. I’m not sure how you actually say it in English. Then on June 12th, there was an export control directive banning access by any foreign national. Since there’s no way to verify nationality in real-time, Anthropic had to switch the models off for everyone worldwide. Bertrand On this point, you could argue that there are possibilities to check IDs. Many services let you check IDs online. You can pre-check a flight by showing your ID. There are ways, it’s just that if you don’t want to follow what’s already available, because guess what? Maybe it slowed down your revenue growth, maybe it looks bad on you or whatever. My point is that there was actually an option. I think it’s already a decision from Anthropic to say it’s either on or off, but nothing in between. Nuno I think the point is they had no way implemented of doing it. If they implemented it, to your point, it would have hampered use in general. A lot of people wouldn’t have gone through that trouble of doing it. Anyway, long story short, in June 26th, the White House apparently asked OpenAI to limit GPT-5.6, so Sol, Terra, Luna, to only 20 vetted partners. Now, apparently, the trigger for a lot of these things that have been going on was that there was a jailbreak that was found by Amazon researchers. All of that led to this jumping around of, let’s close the gates. You have foreign nationals, and therefore, Anthropic got it out and said, “Hey, then we’re going to switch the models off until we can sort this out.” OpenAI was asked also to only allow it for certain vetted partners, et cetera. The government came in, closed the gates effectively, and said, “From now on, we need to be involved in this thing.” De facto regulation, there’s no doubt that this has imposed de facto regulation, certainly on the top players in the market. But then came the reversal. Bertrand, do you want to talk about the reversal, the gate swinging the other side? Bertrand Maybe I just wanted to say that as a user of Anthropic products, ChatGPT products, for the brief moments, a few days where Fable 5 was made available to the public before it was closed the first time, I immediately started using it. I must say it was a real issue to use it because the guardrails were pretty crazy. It would keep saying that my code was not okay, there was cybersecurity risk and stuff when I was doing absolutely reasonable development with absolutely no connection whatsoever to any cybersecurity risk, attack, detection, anything. Still, it would keep blocking me, degrading me to Opus 4.8 at the time. I just want to say this was already very hardcore what they were implementing, and not just hardcore, but in some ways, plain stupid for something that’s supposed to be super smart. It was totally unable to classify properly some of my work. I must say I was already disappointed. On top of it, the costs were insane. Half a day, I would reach my limits when I had the best plan you can get from Anthropic. My point is that there were some real serious issues when they launched Fable 5, even at that point. Nuno I had a similar issue. I used Fable 5 as well before they had to take it offline or take it off. I think the issue was really not that the guardrails failed. As you said, maybe the guardrails were actually too aggressive, but it was this jailbreak that caused the recall, apparently caused this knee-jerk reaction. Bertrand But my point is that it seems that it was not working either way. It would either overclassify something that’s absolutely not doing anything wrong, and it might fail to classify something that is actively trying to do some cybersecurity work. It’s a real issue of quality for a company that’s supposed to be at the forefront of quality of AI and everything. I think for me, there are already signs that something is deeply wrong. Nuno Then it’s reversed, right? We went the other way around. The government came out on June 26th and approved redeploying Mythos 5 to US organizations defending critical infrastructure, and then the export controls were effectively lifted on June 30th. July 1st, Fable 5 came back online for all of us to use. Shocking enough, with strings attached, that were different. They had some time to revise their commercial deployment of it along the way because it came back with some, “Now you have usage credits, but you have some limits on plan use, et cetera.” I’m like, “You guys, this was blocked. But meanwhile, you did have some time to do some commercial stuff around it.” Bertrand It was crazy. I’ve never witnessed any such crappy launch of any service whatsoever in 30 years in tech, it was so bad. Every day, they would change the terms of service. They would tell you it’s part of the plan. It’s not part of the plan. It’s part of the plan for three more days, and then it’s excluded. You have a special discount now, but then it goes back to full price. It was a total nightmare. I’ve never felt myself being so much mistreated by a company. I guess you saw the same, but when I started using the newest version of Fable 5, it was even worse, actually, I think. I couldn’t do any work with this crap. I let it go and work on the work I wanted it to do. It was simply not working. On top of it, you never know how long you are supposed to lose your credit, how fast. It was burning credit like crazy. Me, personally, I can say, very quickly, I actually stopped using it. I was like, “No, I cannot deal with this shit. My main model is back to Opus 4.8. I’m going to use Fable 5 for code review, but not anymore to control anything because I cannot trust it would do the job without stopping or changing models and stuff. I just cannot trust it.” Back to Opus 4.8 as my main model, I can say that my life was much easier. I use Fable 5 as a review mechanism, as a support mechanism, but not as the main mechanism. Suddenly, the guardrails were not so horrible anymore because it was used in a much lighter way, I guess. As a pain as a user, I think it was really bad. I don’t know your experience, but me, for me, it was unacceptable. Nuno I wouldn’t say it was as bad as yours in terms of just end-user experience. I think the terms of service switching back and forth, which went one further step, because then when they then launched Opus 5, they started making comparisons between Opus 5 and Fable so that people would migrate more and more to Opus 5 themselves, which is interesting. It’s like they’re saying “This is much cheaper. This is whatever. You’re not going to run of credits. You should use Opus 5,” kind of thing effectively. To your point, I don’t think they managed well the launch. They didn’t really manage it well. We’re moving people around. A lot of people are using this for stuff that’s like daily tasks, hourly tasks, anything that relates to code and co-work. It’s like, we need to have visibility on what your terms of service are going to be. Should I be using this new model or not? What’s happening to the other model? I don’t see it as negatively as you, Bertrand, but I see your point. It was clearly mishandled in terms of how they deployed it, how they were redesigning effectively their pricing scheme and their terms of service almost on a daily basis, at a certain point in time. We’re like, “Dude, there’s millions of people using this. You guys are making a lot of money.” Just moving it as it is. At this point in time, at the scale that these guys are at, it’s calling in people to say, how about we think through a class action suit at some point around pricing? Because you guys are changing the rules of the game all the time, right? Bertrand I don’t know if I need the class action, but for me, that joke that, “Let’s not rush too fast. The model is dangerous.” But still, they rushed the launch because it’s very clear that if they had enough compute capacity and stuff, they would not have to limit so much. They would not have to put so much cost per token and all of this. You can see that actually when they launch Opus 5, literally like 2, 3 weeks after, by most benchmark at launch, they tell you basically that, “You know what? Actually, Opus 5 is better than Fable 5 on 80% of the metrics.” They’re like, “What? Seriously? You couldn’t wait 2 weeks? Why did you even launch Fable 5 in the first place?” That’s another part for me that is quite literally insane, to be frank. It’s like, “Why? Why do you make us go through so much pain if it’s only to tell us after 2 weeks to…” “This new model, by the way, has less issues, less stuff, because 2, 3 times less is part of your plan, and it’s actually better by most metrics.” It’s like, “What’s going on here? What’s going on? Are you guys mad?” I don’t know. It was crazy. Personally, I still use Opus, now 5, as my main system and platform, Fable 5 for review, code reviews and the like. I don’t want to run into its stupid guardrails. I can see Fable 5, from my perspective, seems quite a bit smarter. I don’t know why they do this stupid benchmark showing you it’s actually worse than Opus 5. I guess they should have better benchmark if they want to demonstrate why you are supposed to pay 2, 3x more for a model versus another if it’s actually worse by most benchmark. Again, I still think it’s a huge mess from a marketing perspective, customer perspective. Me as a user, I really feel that they don’t want my money, and they couldn’t care less about me. This is even before everything else we’re trying to talk about. Nuno Yes. Maybe just to close the cycle on the reversal on the door opening the other way, finally, Commerce lifted the GPT-5.6 restrictions on July 8th, and then on July 9th, general availability across ChatGPT, Codex, and the API as well. What has this proved? It proved that now we have gating mechanisms, and certainly for closed models in the US, for sure. We had frontier models that were switched off worldwide in hours, and it took a couple of days, in this case, 19 days to restore them. There were concessions. Now we know that there were concessions around effectively institutionalizing that gate. Early government access to future models is, I think, now a given, certainly in the US. New safeguard frameworks are probably now having to be put in place. There are some stage limits now on who gets access to what for new models and how it happens. This voluntary executive order, so to speak, not really sure, has become effectively regulation enforcement path. It’s de facto regulation that now has been put in place. It has affected not just to the points we were making before, the access to these models, but also who gets access to these models, and actually potentially even pricing access to the models. It has probably some commercial implications as well as we just discussed along the way. Very significant. This is very significant. This is regulation, de facto at the table, imposed on the two largest players in the market by far by one government, in this case, the US government. This is significant. Actually, you could even allege it was imposed by the President because this was coming as part of executive orders. Really incredible. Pretty significant, fast, aggressive. It has created a regime that you could say it’s a regulatory regime, it’s a de facto regulatory regime. It has some significant pricing and licensing and commercial implications. It goes even beyond your classic regulatory framework. Very, very, very significant. Bertrand I don’t know if it goes beyond a classic regulatory framework. Nuno I think it does, because it has implications on who do you give access to? When government is saying you can only give access to these players, right? Bertrand Defense industry. It’s all over the defense industry. You cannot sell an F-35 like this. Nuno No, but that has commercial implications, Bertrand. That’s like you’re saying these are your customers, you go and use them. Bertrand That’s the defense industry. You cannot sell to Iran your F-35. No, that’s exactly the same story for me. Nuno No, no, no. It’s beyond that. These guys are saying when they came back, and they said, “For Mythos, you can make them available to these entities,” they were saying the first entities that are going to have access to the model. It has commercial regulatory implications. You’re saying these players are the first players that are going to have access to it. It’s no longer just defense concerns and these governments don’t have access to this. No, no, no. You’re saying to a company that is a private company, your models are only going to be used by these guys because I’m telling you so. It’s the other way around. It’s not even that you can’t sell it to Iran or whatever. It’s like you can only sell it to these guys. Bertrand Again, in the defense industry, if you’re a private company, do you think you can buy F-35 like this? No. Nuno No, no, no. But this is a private company, Bertrand. This is not a defense agency and a plane that is on whatever, with IP from the US, right? Bertrand Boeing is a private company, and they cannot sell the military equipment they manufacture. Nuno No, no, no. But the development of their IP was subsidized by agencies that belong to the US, right? That’s a different matter. It’s a matter of IP, right? This is not, right? Anthropic, their models are not owned by the US government. There’s no IP granted to the US government, to my knowledge. This has significant commercial implications. Bertrand Maybe, yes. Maybe on this. But I think there are already regimes to limit who you can sell to, and that’s decided by the state or the DOD. Nuno It’s the export control logic. The export control logic? Bertrand You have export control, and export control is Commerce. My point is that they are using existing tools, part of the government, to limit what can be sold. Selling chips, NVIDIA was limited in terms of where it could sell its chips. It’s not different either, but still there were limitations. If you are an ASML, you cannot sell to a private company in China. Many private companies cannot buy ASML products. This is a foreign company. This is a foreign company under pressure from US government. Nuno I understand, and I’m not a lawyer, but it feels different to me when you say you cannot export, this is export controls, to these countries, to these entities, et cetera, because they’re foreign et cetera. Then to say, “No, no, no. On top of that, these guys get first access.” That’s, for me, a significant shift. Again, I’m not a lawyer, so I’m sure there’s very intelligent people right now looking at this stuff and saying, “You can’t do this stuff, or not, or they can.” I don’t know. But it feels to me, it goes beyond the remit of export controls. It’s like you’re defining initial clients for specific use. Bertrand My impression is more like, “We can do this situation where we’re going to forbid you to give access to anyone outside the US or even in the US or limit even more.” Basically, it was, I guess, some gesture to go beyond that. That’s how they probably defined these 20 authorized companies. I don’t know. Apparently, there was also restrictions because I remember seeing that Anthropic had their own list of companies they would authorize access to Mythos early on. That’s apparently another thing that pissed off state government because there were companies in there that were considered close to the Chinese government. They were extremely unhappy that Anthropic didn’t ask, actually, for any guidance from the state government, but used basically their own perspective on who they should allow or not. I guess that was also part of why they got these serious restrictions. Nuno Anyway, now we have a regulatory environment that’s very interesting and exciting. Talk about the US not regulating. Bertrand To be clear, I don’t know you, but I’m not saying that I agree with any of this, to be very clear. I’m trying to explain and share some perspective, but I’m not in agreement on a lot of this. Nuno Yes, we were just describing what happened to the best of our knowledge. We’re having a discussion on what we think actually is happening and how it’s happening. We’re not really right now saying we agree or disagree with this. I think later in the episode, we can share some perspectives on what we think is actually happening and how there’s dimensions to this which are very geopolitical and very complex, which quite literally probably only God knows what’s going to happen. That was the gate swinging. There was a gate closing, then there was a gate reopening, and all of a sudden we have a gatekeeping system that has been created along the way. The Kimi Shock Along the way, moving to our Act 2, the world has changed, and we now have so-called open-source plays out there that are creating massive, massive shifts in the market. The Chinese models, in particular, with Moonshot AI launching Kimi K3, which is the largest open-weight model ever released. We’ll come back to the discussion around open-weights. I’m not sure all our listeners understand what that means, because there’s a debate now, should models be open weight or not, and how does that work? There’s been a petition as well signed along the way. Right now, we have open weight models that are out there that are huge. What that actually means very pragmatically is we now have open source models, lack of a better word. I know open weight and open source are not the same thing. You guys will have to bear with us during this episode. We’ll explain at some point the differences. But we have models out there that are open source that are significant. That are catching up with the closed source models, with the models by OpenAI, Anthropic. That’s significant because most of those models are Chinese. This is where the geopolitics starts getting really frazzling and we start playing 3D chess. Because everyone’s like, “These models are 5, 6 months behind.” Now people are saying, “Maybe they’re actually just 3 months behind, 2, 3 months behind.” If we, for example, decided to stop or slow down our model releases in the US by the closed source guys who are leading, it might mean they’ll catch up. What are the implications of that? Again, for you and I that are not necessarily experts in model development, well, the implications as a use case is if you want to use the latest models, and the best models start becoming these open source models, you’re going to use those models. Then you start using Chinese models. If you’re an American company, maybe you’ll have restrictions on the use of those Chinese models. But if you’re a European company, you probably won’t. What happens after that? Is the world going to be in the hand of Chinese models? Will that constitute effective competition to the closed models in the US? Will we have open models in the US that will scale as well? What’s going to happen? Bertrand I think it’s a really big question. It goes to some of the core of the issue. It’s that ability of Chinese models to basically challenge frontier models, not just being 6, 12 months late, but being 6 weeks late. Basically, no gap. Some will say that, yes, but OpenAI and Anthropic have even better models that are not shared and stuff. Yes, sure. But maybe the Chinese have the same models that they are not sharing right now. We don’t know. What is clear is that one is that open weight, as you said, two, there is a question of how it is marketed in the sense of, can anyone use these weights? Is there a license to use them? Yes, what we can see is that, for instance, typically there is a license for some of the biggest Chinese open-weight models you have to abide with. You might have a need for a commercial license if you are acting as a company leveraging this model to provide AI-informed services. If you use it internally by yourself, you’re okay. If you use it internally for your own internal company needs, maybe you are okay if it’s not your main business to do AI work. Anything else, a much bigger corporate providing AI services and stuff, you will probably end up having to pay a fee to be able to provide services around this model. My point is that it’s not just 100% free. Some of the Chinese models are 100% free to use, MIT license, Apache 2.0 license. But the biggest ones with the biggest weight that are truly frontier typically have a different license if you want to scale these models, providing AI in front. That’s one thing to keep in mind. Nuno Maybe just to make a very quick point, because people are like, when you talk about open models, what does it mean right now? In the context of this episode, open models mostly will mean open-weight models. How do those differ from open source? Open weight means that you release the weights to the public, which means that anyone can download, fine-tune, and run the model on their own hardware. It doesn’t normally mean that you also have access to training data, training code, or a truly open license. That’s the distinction to open source. Open-weight doesn’t mean that. For example, we’ve talked about Meta’s Llama in the past, and we also discussed in the past that their license agreement does have restrictions, certain players can’t use it, et cetera. The open model definition and open weights are really open-weight models that we’re talking about here, and they are closer to freeware binaries than to Linux, for those who understand the difference between that. It’s binaries that you can use and then use your own weights on it versus actually I can change code on it. I’m not going to be able to change code on this. When we, for the purposes of this episode, talk about open, we mention open weight, just to clarify that point to everyone that’s listening right now. Bertrand Yes, that’s a great point. One of the only players, as far as I know, who is truly open source is actually NVIDIA with their Nemotron-3 models. They’re actually following a special license to achieve that. They provide you the data, they provide you all the processes and tools, so you can easily post-train. NVIDIA is a big, big exception. It’s a very interesting player, by the way. We might not talk much about it in this episode, but I think for intermediate-size models built in the US, where you have access to everything in the deployment, it’s a very interesting alternative and maybe one of the best choices if you are a US company or a big corporate, and you want something trusted. Another piece of the puzzle to clarify is that when you use open-weight, it means that you can run them by yourself, or you can use a US provider to run them. If we are talking about Chinese open-weight, you can use the APIs they provide, but then the service is running in China, they might have access to your data. But because it’s open weight, if you run it by yourself or if you use a third-party provider based in the US to run it, then there is no access to your data by China or Chinese players. I think that’s a pretty important gap to understand. It means that these models are actually very, very low risk from that perspective if you run them on your premises or in the US by a US player. I think that’s something to keep in mind. You can also fine-tune easily these models to make sure they will behave in a way that, for instance, is not going to represent the line of the Communist Party on some topics. There are ways to make these models more neutral in their output as well. There are a lot of ways to make good use of them. By default, they’re already very safe, but you can make them even more safe. I think that’s some things to keep in mind. But again, it depends ultimately on the license and what you’re authorized to do and some fees you might end up having to pay. Nuno Why did this matter so much? Immediately there was a reaction from the market because people are like, well, if there’s much better stuff out there that’s much more efficient than it’s open, then it might be that all the demand that we are taking into account, for example, for chipsets actually isn’t real. The Philadelphia Semiconductor Index fell into bear market territory. It went down by as much as 20% plus from the late June peak. The worst chip week since April 2025. Taiwan’s benchmark initially fell 6% plus, Japan’s 4%, TSMC dropped dramatically despite beating earnings and rising guidance. Basically, a huge amount of effect. Now, there’s a little bit the aftermath of this where apparently Moonshot ran out of GPU capacity. Maybe… Bertrand In just 48 hours. Nuno In 48 hours. Great for them, but at the same time, not great in the sense that maybe there was a misread by Wall Street of the Kimi effect, so to speak. Bertrand Completely. For me, that’s such a joke. It’s like, because you have an open source model, so what? I mean, you still need to run it. This is not a small one. 2.8 trillion parameters. Good luck running that in your garage, by the way. Nuno They misread supply, basically. Tough luck, right? All of that basically happens. Bertrand Maybe you want to talk about the Jevons paradox, because I think that’s a big part of the puzzle as well. Its one is they might not have the GPUs to run the inference on the model. They might have enough to build a model, but not enough these days to run inference, especially given how much with intelligent models, thinking models, you need way more inference than before. But on top of it, the cheaper you make it, the more you get to the Jevons paradox. Nuno Yes, Jevons paradox, for those who don’t know, is an economic term. It describes an economic phenomenon where technological improvements that increase the efficiency of a resource lead to an increase rather than a decrease in the total consumption of that resource. What that means is, for example, for chipsets, chipsets become so much better, and they are so much more efficient. You’re like, well, maybe normally in resource terms, that leads to decreased usage of that resource. But in this case, it actually leads to an increased use of that resource rather than a decrease. There’s more and more consumption of that resource. You need more and more chipsets because people actually need to do more and more stuff with it, although there are great efficiencies going into it. There’s the efficiency gain, there’s the cost reduction, and there’s the price-elasticity element to it. But basically, the adoption just continues going through the roof along the way. Bertrand In some ways, it’s like the price of energy. Coal went cheaper and cheaper, and people were asking the same question 150 years ago, now that it gets cheaper, there is not much money. No, no. Actually, what happens is that people find more and more use for coal. Homes are getting heated more. You have ships now using coal. You have manufacturing using coal. The cheaper it gets, the more use case you can develop, and therefore, you don’t need less of the stuff, you need more of the stuff. By going at scale to get more of the stuff, you also decrease price, making even more demand. It’s a very interesting phenomenon, but it’s not new. It is what happened for a while in the energy sector and some other sectors. Nuno We already started talking about the Chinese logic and what’s happening. Getting a little bit of a reality check on this. The Chinese models, and these are numbers from Open Router in July, Chinese models are at 46.4% of routed tokens and 35.7% for US origin. Again, more than a third of global AI usage now seems to be running on Chinese open models. This is significant, and it has a huge impact on the geopolitical scale of everything that’s happening. Also, the whole Chinese field is converging on open. Open seems to be a strategy, not just a nice thing that’s happening. It seems to be a Chinese strategy, so much so that you have players like Moonshot, DeepSeek, our old friends DeepSeek, Z.ai’s GLM 5.2, Minimax, and even Alibaba seems to be reversing and going open with Qwen. It feels to me this is becoming policy as well. Xi Jinping has personally endorsed the building of open-source AI, if it’s really open source, if it’s just open weight anyway, and this feels to be a jab at Washington, DC and the fact that the big closed models are coming from the US. This is now geopolitical 4D chess, right? We didn’t need this stuff. Bertrand To be clear, it’s the usual in tech. If you are not number one, you are number two, number three, your alternative is to go open source because that’s another angle that your competitor usually cannot follow without destroying its own business model. That has been the alternative for the past 20 years of most software projects. Here, what’s different is that it’s not the number one or number two player. It’s the US number one as a country, China number two as a country. That’s where it’s new. For me, what’s very interesting is the endorsement by Xi Jinping. I was waiting for something official, and it certainly didn’t disappoint. As you said, there was an immediate U-turn of Alibaba, who in the past… Nuno Surprisingly. Bertrand Yes, a little more like, “yes, we are going to close and stop open source. It was good while it lasted.” Just a few days ago, Qwen 3.8 Max was launched, and we are supposed to get the weight in a few days. We talk about the US administration policy and stuff. Yes, let’s not forget that in China there is similar stuff. Sometimes it’s totally invisible because you don’t see the directives, but they exist as much. Sometimes it’s more visible. Here it was quite visible. The difference in China is that if you don’t abide by the directive, on top of it, you might have to fear for your personal safety. It’s a different game, and that’s probably why the reaction is pretty quick, usually. That’s pretty interesting for me because it means that now you can bet for a while that China is going to play that game up to a point. I guess the point is if it’s truly frontier scale, you will have a special license that, yes, technically the weights are open, but you can not do everything you want with it. Two, you have a player like NVIDIA that I think will feel more pressure to provide even more high quality, larger models at scale going forward. Their largest Nemotron-3 Ultra model was, if I remember well, only around 500 billion parameters. I would not be surprised for NVIDIA to go into the two, three trillion range at some point. Because I think the US need a very clear US-born alternative open source. I think NVIDIA might be the best player for that. We will see if Meta goes back to open source. I think NVIDIA is one, very well positioned, but two, it’s also in their best interest. Because NVIDIA for now depends on just a few big hyperscalers as clients. If they can expand their clients to every S&P 500 companies, selling them directly hardware because now these companies can run a model made by NVIDIA, I think there is a very clear value proposition for NVIDIA to go in that space. Again, if you are number two, your differentiation, open source is often the answer. There is a true business as a business model for companies, because if it’s truly not just open weight, but open source, you can tweak it as much as you want, you can change it, you can change even the pre-training process. Because there is a lot of stuff you can do that really benefits you as a corporate, and you can reach a much better value by having more control on the model. Nuno We won’t spend a ton of time on it today, but like, again, if there’s a view that we are in a bubble, that the valuations cannot be sustained in chipsets, infrastructure platforms, applied AI, et cetera, today, this might be that beginning, where the valuations start being destroyed because you can’t keep a premium on just charging people for tokens and all that stuff if you have models that become more and more efficient and cheaper to use. Maybe just to close a little bit the geopolitical part of the discussion today, we won’t go into all the announcements from China because there were many, a lot of go back and forth with Alibaba by then. Xi Jinping made some announcements. You guys can check it online. Let’s move quickly to Washington’s reaction, which was from gating the US closed models to banning the Chinese open ones. There’s been as strong affirmations as one can get from the Office of Science and Technology Policy Director, Michael Kratzios, mentioning that they have information that Moonshot AI distilled Anthropic’s Fable. Basically, there’s been reverse engineering and stuff in the market. They’re basically copying. Bertrand I’m sorry to interrupt, but it feels like so much bullshit. It’s coming from Anthropic who has basically gotten access at scale to all the knowledge made by humanity, copyrighted or not. We’ll talk more about what they did with books. Then to claim after that that others cannot do to you what you did to everybody else. For me, it’s pretty big. It’s clearly unacceptable. The other piece is that everyone is doing distillation. It’s a very typical approach of every business model. You try other software when you are competing with somebody else. You try other datasets, you check what’s happening. It’s part of doing business for decades. Suddenly it’s not good for Anthropic. I personally have a lot of trouble to accept that. I think it’s totally unacceptable. The other piece of the puzzle will also go back. If these guys are so smart, if these guys have so much of the best model, why can’t they block by themselves distillation at scale? The only answer is that either they are morons, probably not, or they simply don’t want to because it’s going towards their business model. Suddenly, you book less revenues and stuff, or you put more friction, and therefore your customers don’t like it. Instead of doing it yourself, you ask the government to protect you, go out of business practice that is very typical. For me, it’s really, really, really not good. Sorry, we are going more in the opinion side, but I had to put that on the table. Nuno Yes, Fable went public finally again on July first. Question marks on whether distillation would only be possible from July first onwards or not. But a 15-day distillation to frontier, which is K3, launched on July 15th, would have been a Guinness World Record, as one of Moonshot employees actually mentioned. It’s very implausible and unlikely. Bertrand Or they shared the Mythos 5 with the wrong companies, who themselves shared with Chinese companies. We go back to maybe they didn’t have a good list. Again, it goes back to maybe they didn’t want to hurt their business model. Nuno Anyway, under the threat of sanctions, Moonshot, in any case, open-sourced the full K3 weights and technical reports. They open weighted it to become the largest open weight model in the world in terms of parameters. Beijing’s MOFCOM brands US threats as basically the US wanting to fundamentally control and be monopolistic around AI along the way. The administration bans Chinese hardware with an eye on the AI race, and Beijing warns of retaliation. That was July 27. Now we’re in a war between Beijing and DC. Bertrand Just to finish maybe on China, it’s important to know that they are building their own GPUs now. Huawei has pretty good, not to NVIDIA level, but pretty decent GPU hardware that they’re able to manufacture by themselves. A Chinese player of memory just got IPO’d a few days ago, CXMT. China is also developing their own memory. Again, not to the same level of quality that you can get from the West. But China is moving. It’s not just that they are building great models, it’s also that they are building GPUs and memory. That might be a few years late to the latest standards in the West, but there are definitely improvements. I also read, even on the tools to make manufacturing like ASML equivalent, there is definitely some work going on, and some improvements and some stuff will be visible. In some ways, the genie starts to get out of the bottle from the Chinese perspective. Nuno I’ll put a stick on the ground. I don’t think it’s a matter of if, it’s a matter of when will China surpass and have a lot of this tooling on their own side, and not just the software layer, not just the frontier models. I think it’s also going to be around infrastructure and platform. Good luck to everyone. Let’s see how the race continues. But it’s definitely this is a geopolitical thing right now. It’s definitely a race. The Escape Maybe moving to what happened in just 2 weeks or a week and a half. The escape, there was some jailbreaking going on, and the narrative on safety has totally switched. It’s not still significant enough that’s like, “Oh, we saw a nuclear plant going, whatever.” No. But still, it is significant. Hugging Face, the AI company, disclosed an intrusion, and it was driven end-to-end by an autonomous AI agent system at machine speed, running for days before detection. Now, this is where it gets really cool. OpenAI takes attribution on that. They initially said it was just a little bit, sorry. Then they said, actually, it was worse than that. “Oh, it broke out of an isolated sandbox.” “Oh, no, actually, it was more than that, and it went into other systems as well.” Bertrand Truly, the genie out of the bottle. Nuno No, but this is where it gets really cool, Bertrand, right? Because it actually, Hugging Face contained the intrusion by running a Chinese open-weight model, GLM 5.2. This is beautiful, right? Bertrand Yes. You know why? Because they couldn’t even run their own defense because both Anthropic and OpenAI would not let them access their latest models with the guardrails off. When they tried using it for defense, the latest from Anthropic, from ChatGPT, they would tell them, “No, this is too dangerous what you’re asking us to do.” Preventing an intrusion, helping defend you. No way we are going to do that. Nuno No. Let’s use the Chinese models on our infrastructure. Bertrand We have no choice but to use the Chinese models to run. More than that, we don’t let you use our models to defend yourself, but our not yet released models that run without guardrails, they can attack you. This is probably the most insane from that perspective. Nuno The Chinese models came to the rescue. Bertrand For me, that’s a perfect example because Hugging Face is a very visible company in AI in open source. But anybody who is not at that scale is not going to get some support from OpenAI or Anthropic when this happens. Maybe these guys won’t even recognize they did anything wrong. You will be left to defend by yourself because they won’t accept to support you. Because remember, if you want the better model that is able to defend you from cybersecurity perspective, no way. If you are not one of the few top 20 companies or so, as defined, you are left defenseless. Again, we are going back to opinion, but for me, it’s so shocking what’s happening right now. I’m very glad we have alternative open source to be able to defend ourselves because right now, good luck getting defense services if you are a smaller business and individuals, and you need support from Anthropic, OpenAI. Nuno Now, even self-described AI optimists are saying, “This is scary now.” Like Walter Isaacson, who wrote all the famous biography books. There’s now discussion around the AI Kill Switch Act, bipartisan thing that’s coming across from Texas and California, a potential bill that’s coming in. We’ll see if that works. Now let’s get an off-switch. I’m like, “Cool.” As if that’s going to solve the problem, because you have open-weight models on the other side catching up, right? Bertrand Yeah, sure. Bring in clueless politicians from Congress to solve our problems. Yes, sure. Nuno Anthropic came to the table, helped build and said they built some regulatory machine on their side, and now they’re getting bitten by it, and they’re part of the offending players in that market. Now there’s all this debate and all this discussion around open weight and around slowing down AI and et cetera, which is our next section. You wanted to say something, Bertrand. Tell us. Bertrand Don’t forget, because this advertisement for OpenAI was just too good. Our AI attacked some other companies, and not just one, but three, actually. Let’s not forget the progress. Great ads. Then I came and said, “You know what? AI also hacked businesses.” You’re not the only one hacking around with a crazy AI out of control. You’re not the only one. We want our advertising. For me, it was shocking that on one side, unreleased models that you let run wild. On the other hand, you have released models that you put crazy guardrails on top of it, so the defender are defenseless. I’ve never seen anything like it, and I really hope that there will be as little regulation as possible, quite frankly, to make sure anyone can defend themselves and have the best tool at their disposal, not just a few well-connected big corporates. This is really, really shocking. The Counterstrike and the Petition Nuno Now the empire strikes back, so this is counterstrike, the petitions. In several days, we have now a bunch of petitions. The first one was the open weights letter. Bertrand, do you want to explain to us what the open weights letter is? Bertrand Yeah. I think it was great. This was released by Jensen Huang, first ever post on X, 11 million views. Congrats, Jensen. Co-signed with Microsoft, Meta, c actually was probably the initiator of this letter. Very good letter saying, “Hey, we need open weight. This is not a joke. We need that. You cannot block open weight.” Because that’s the rumor we are getting that potentially open weight could get blocked. I think they are making the case, “You know what? Hey, we absolutely need that as an alternative. You cannot block it.” They can keep their closed models, but don’t force a closure of the open weight models. As I said before, it’s actually a great model for NVIDIA because NVIDIA doesn’t want, probably rightfully so, to be dependent on just a few frontier models, their best customers. They want a variety of customers. They have a big interest actually to defend open weight and to invest even more. They have great researchers, are a great company. If one company is about to do really kick-ass work, I think it’s them. They are defending. What’s great is that it’s not just them. It’s basically most of big tech in the US and outside the US, from a Linux Foundation to a Microsoft, the Palantir, an IBM, a Dell. It’s a who’s who of the industry except Anthropic. Anthropic didn’t sign that. I guess they hate open source so much. If I look at 20 years ago, it feels like Microsoft, after all, was very kind to open source. You remember what was said by Microsoft at the time. It’s clear there is one company against open source. OpenAI signed the letter. Honestly, I don’t know what to think. Do they really believe in it or was it just a way to show that they are not like Anthropic? I don’t know. But for the rest, I think it’s genuine because it’s actually in their best interest. I hope they will be heard. Then a second letter came, the Open Secure AI Alliance, NVIDIA-led and again, the big tech companies from Microsoft, IBM, Palo Alto Networks, Databricks, Palantir, all those, but not present, OpenAI, Anthropic, and Google. Here it’s to say, “Hey, we need a secure approach to AI. Open should be part of the equation.” guess what? The worst AI-caused security incident to date was actually caused by closed frontier models that were not even available to the public. While again, not providing you access to even the latest closed model for cybersecurity use case. Nuno I would highlight the NVIDIA open source NOOA framework, Apache 2.0 licensing agreement, Microsoft contributed the MDASH, SpaceX AI contributed Grok Build. Cool stuff. There’s some cool stuff happening around that. This is more than a letter. This is an alliance. Apparently, they’re contributing all this stuff, we’ll see. Yeah, cool stuff. Same day. Same day, Amodei has an answer, right? Bertrand Yeah, same day. They say, “We never advocated for a ban,” which, again, opinion on my side is entirely bullshit. This guy has been crying wolf against everybody else, and especially against open source. You can see him doing testimony in Congress against open source. I think they are doing everything they can behind the scene to block open source in the US or in the world if they could. I think, yeah, obscurity is not good safety. I’m a big fan of open source in general, and I’m also a big fan in AI. I think it’s now Anthropic, mostly against the rest of the world. I think OpenAI is mostly on their side, to be frank. They don’t want to acknowledge it so much, but they have shared interest, and they have shared probably position. Nuno Why would you? I don’t feel as strongly as you because I think Anthropic is a private company, right? The same thing with OpenAI. OpenAI, you could say it’s a nonprofit that has a for-profit. There’s still that complexity in there. Bertrand No, they can do what they want with their own product. But to block others is where I’m not okay. That’s the part I’m not okay. Nuno What Dario Amodei is proposing is more enforcement, right? He’s basically saying you need to do even tighter controls on advanced chips flowing to authoritarian states, enforcement against industrial-scale distillation, whatever that means, right? Bertrand Yeah, which he could do, but all by himself. He doesn’t need the government to do that. Nuno Mandatory safety testing for all sufficiently capable AI, open and closed, right? He’s basically saying, “Okay, I don’t agree with the open weight stuff effectively,” right? He’s just putting it under a different banner. “I agree with this extra regulation.” then obviously, David Sacks responded and say, “Hey, it’s like, bans don’t work for weights. Why do they work for chips?” It’s like, magically, chips are more controllable and bannable. Whatever that is. Then our friend Mark Zuckerberg, just to be clear, goes on the other side as well, because he also has to have a view. He has to have a view that is the rebuttal of both of the other guys. Bertrand I feel he’s a bit flip-flopping because he was very pro open source 2 years ago, and the latest Meta models went closed source. Now I think he’s back open source. I don’t think he has a very strong spine on the topic, but it’s good to see that he’s not a doomer. That for me is great. He’s showing how AI can be a source for progress, a source for entrepreneurship, source for freedom. I think that’s very exciting to hear that. We need to hear more of it. By the way, that’s not what you hear in China, for instance. AI is very positive in China. It’s in the US with the doomers that you hear this discourse, and people get worried as a result. I’m glad that he was pushing for a more positive vision and for support of open weight, open source initiatives. But let’s see what they really truly open weight going forward. Nuno But that’s been his position because I guess he’s standing behind. He thinks open weight is going to be the best way to compete, right? Bertrand Yeah, but he closed his latest model, so let’s see. Nuno Yeah, so it’s flip-flopping, as you’re saying. Then we see the latest petition from last week. Bertrand The true Empire striking back. Nuno Yeah, the true Empire striking back as of late last week. Maybe this is Return of the Jedi, where we discover the father, “I’m your father, Luke.” That’s the pacing petition. The pacing petition is we need to pace AI. There you have initially employees from OpenAI and Anthropic that circulate this petition. Actually, Dario did sign this petition originally. It wasn’t signed originally by Anthropic, but by him. But you’ve heard that now Anthropic and OpenAI as companies have also signed this petition, right? Bertrand I think they have signed as companies now. It started mostly by Anthropic researchers with some OpenAI researcher and a tiny part from other companies. But it was mostly Anthropic internally led, at least potentially internally. Maybe it was controlled by Anthropic all along, I don’t know. But it started officially as Anthropic employee-led letter. Nuno What does this letter actually say? Is Anthropic and OpenAI, are they willing to slow down themselves? Or are they asking President Trump to go around the world and tell President Xi that he needs to slow down and ask his guys to slow down? What’s the play of this letter? Bertrand It’s crazy, but for me if you want to slow down yourself. Do whatever you want. Don’t force others. Don’t use the power of the government to control others. Of course, it’s easy to push others to slow down when you are yourself at the very top. You have most money, most resource. You know you are going to win any regulatory framework because that’s how it works with this type of framework. It’s purely self-interested. You are probably not thinking well about these topics. If you truly think it’s a good idea, from a personal perspective, you are well instrumentalized if you sign this sort of stuff, because at the end of the day, they would be the winners. I certainly, personally, don’t want a company dictate what is my future in AI as an individual, as a business person. I don’t want them to control me. I want competition. I don’t want them to unfairly control AI because they managed to do some regulatory capture. I feel that’s exactly their game plan. These guys believe in their stuff, and they want the regulator to end up being the one deciding for us. Sorry, we go back again on the opinion piece, but it’s tough not to share an opinion on this topic because it’s, from my perspective, very scary. Nuno I think this is a push to further regulation, not less. All these letters and alliances, this is definitely a push for more regulation. In that environment, just to be very honest with you, we’ll talk about the investor impact in just a bit, et cetera. But in that environment, again, China has a huge advantage. In that environment, if it’s all captured in regulation capture so soon in this battle where OpenAI and Anthropic have an advantage in the US, et cetera, I’m like, what happens to all the other frontier labs and all the other players that are coming around? Bertrand What’s crazy is to even think that, yeah, maybe you can regulate capture in the US. But then how do you do that to Europe? How do you do that to China? Europe probably will always welcome regulatory capture because they love regulations. But China is going to build to their advantage to the max. They are not crazy. They are smart on that perspective, they won’t accept this type of, quite frankly, dimwit argument, or you can call it regulatory capture. We’ll see. But for me, this makes no sense from a global competition perspective. This can make some sense from capturing the revenue in the US market. But then that means you are going to destroy the US AI environment compared to China. That is not acceptable. That also means that you are going to destroy our freedom as individuals, as business owners to develop and live in a business world that ultimately is controlled by one or two business companies that didn’t win the marketplace through their own business success, but won it through regulations. That for me is really not acceptable. Interlude — The Low-Background Books Nuno Now, maybe for an interlude, and we have to cue in the music, imagine like Severance music, like hallway or a bit of a palate cleanser from all the policy stuff that we’ve been talking about, all this policy heaviness. Let’s move to another kind of heaviness, one of your favorite topics, which you, Bertrand, discovered, I had no clue this was going on, around books and around Anthropic. Bertrand It’s so horrible. From a company that keeps presenting themselves as the adults in the room, the careful ones, the ones that know better than you about what to do in this complex AI and dangerous world. What we discover is that actually all along, they were buying and destroying books. They will buy books, scan them, destroy them, all of them. They will do that with any books, including rare books. Of course, this was not supposed to come to the public’s attention. This was one of these top secret projects, but obviously it came out. Yes, they were scanning books, millions of them, including rare books, and they didn’t care about destroying them at the end of the process. Because from a regulatory perspective, if you destroy the books, it’s not considered a copyright infringement, apparently. This is coming on the back of some judgment a few years ago that were showing that it’s okay for you as a corporate to scan and use the result if you don’t keep a copy of the book. It’s one of these crazy regulations happening based on a single judgment that push you to do. For me, it’s like, you know this book from decades ago, Fahrenheit 471? We’re talking about book burning. It’s book destroying, crunching. It’s so shocking. Nuno There are two things, right? First, the legal strategy, which is what you’re saying, because by purchasing a physical copy and converting it into one private digital copy and discarding the original, Anthropic pursued this cleaner legal argument for fair use copyright compliance. As you said, there was a federal judgment at some point on this. The other reason is actually operational. If you disassemble the book, and you feed loose pages, it’s much faster to scan books. You are destroying the book effectively anyway operationally. I think to your point, probably this came from a legal standpoint, not just the operational one. But even from an operational standpoint, it does make sense that they would have disassembled the book. Bertrand But some people have shown you can go very fast without destroying the book. It’s really not so critical. Two, you could make an exception if the book is rare. For that 1% of book that is rare, I’m not going to have this approach. I’m going to have another approach. But for that, you will have to care about books and not just care about building AI. Nuno This is the episode, as you guys have heard by now, that we’re trying to spit stuff at Anthropic. Bertrand To go back this is the same company saying, “Hey, guys, it’s bad to distillate my work. I’m the one scanning book at scale without asking author permission, without asking publisher permission, to be clear.” Nuno But just to be clear, Bertrand, we’re pissed off at everyone. We’re pissed off at Anthropic, we’re pissed of at OpenAI as well, right? We’re just pissed off in general at this moment. Bertrand At this stage for me, the more clear-cut company that is in the wrong is, from my perspective, at least, is Anthropic. OpenAI might be a fast follower, but I will say so far, they tried to be a bit more. Nuno But at this pace, Bertrand, who knows? Maybe next week we’ll be more pissed off at OpenAI. Something will come out. This episode is a mix of tragicomedy, like a Greek tragedy with some comedy in the middle or the other way around. It’s a slapstick thing that will end up in tragedy. I’m not sure. The Investor Reckoning Anyway, maybe switching to our final act, which is the investor perspective. What does this mean for investors like ourselves? There’s a lot of things going on. There’s the debate around the IPOs of Anthropic and OpenAI, which now, with all this uncertainty, might be under significant weight. There’s a lot of other discussions that we browsed through that there’s potential IPOs going forward on companies like the Moonshot AI company actually IPO-ing in the next 6 months as well. It’s very unclear what the IPO landscape looks like. Bertrand There’s been a lot of Chinese IPOs, actually, when you look at what’s happened in the past few months. Nuno Anthropic, OpenAI as potential IPOs, there’s all this question marks now. When will that happen? How will it factor in? All that’s happening around regulation as regulation is moving at the speed of light, which is for once something that’s very different than what we’ve seen before. There’s obviously SpaceX AI, which is already taking into account that price. It’s already a public company in there, and it’s under SpaceX, which is now a public company. Obviously, that’s already being factored in some ways. Bertrand Yeah. SpaceX AI has been very smart to acquire Cursor. It was a very smart move because Cursor is one of the leading companies in terms of automated code source development with AI. They had great models on their own. They’re bringing development data to SpaceX AI Grok. I think it was a great move. Nuno We have now people like Google delaying Gemini 3.5 Pro in terms of launch window. There’s stuff actually happening in the market where things are taking their own path. There’s uncertainty commercially, there’s uncertainty at regulation level. You have new players that have come out of nowhere that are making all these waves like Moonshot. We have all these… We had calculated probably a month and a half, 2 months ago, there had been 67 new frontier labs funded. All of these, we haven’t seen any much coming out of them. When some of this stuff starts coming out, will that also create disruptions in this market? Who knows? Bertrand Look at Thinking Machines, for instance. Thinking Machines led by the previous CTO of OpenAI, they released some pretty interesting open source models, actually. Very good quality for a first launch. Now it looks funny to say, but nearly on par with the top Chinese open source models. Nuno We have several investments in the space. humans& has made some recent announcements, which is quite interesting as well. We’ll see what actually happens in the market, but even more disruption probably will come in actual products in a form of product and commercial, on top of all the geopolitical mess that we discussed through the entire episode. If you’re an investor, how the hell do you underwrite an investment right now in early stage, mid-stage, late stage, et cetera? I think my answer is very carefully is how you underwrite it. Bertrand On your advice of being very careful to underwrite it, let’s not forget what happened to our boy wonder, Leopold Aschenbrenner of Situational Awareness. I guess he didn’t listen to you in terms of being careful because part of the instability in the stock market was actually coming from his hedge fund. These guys were leveraged 3, 4x going after the hottest of the hottest AI stocks, and margin calls, and all their public investment is gone just to answer their margin calls. I think it’s clear that the AI bet is… Personally, I’m very excited, and I think it’s the future, and you need to spend time and think about and invest in it. At the same time, it’s a bet that is not an easy one to follow. We go from GPUs to memories to equipments to power generation. All of this is not transitioning in an easy, organized manner. It would be boom and bust going there. He’s probably one of the first big-scale fatalities. The other big-scale fatality was the stock market in Korea, plunging 40% in a month. Definitely, all of that we discussed about was, on the background, you had the stock market going up and down pretty crazily the past few weeks. Nuno Everyone’s being affected. Everyone, you have your 401(k), you have your pension fund dependent on these equity stocks. Everyone’s seeing the effects of this volatility right now very aggressively. We do wish Leopold… Hopefully he’s on honeymoon right now because he got married, I think, this weekend. Hopefully there will be… Bertrand To none less than an Anthropic Chief of Staff. Nuno His wife is the Chief of Staff of Dario, is that it? Bertrand To Dario, yes, as far as I unders

Oz y Tito Neira proyectan en pantalla los videos virales que asustaron a una generación: Jensen Huang diciendo que ya nadie necesita programar, Zuckerberg prometiendo IA de nivel mid-level "en 2025" y Dario Amodei advirtiendo sobre la mitad de los empleos de entrada. Los pausan, analizan los incentivos detrás de cada narrativa, y los contrastan con lo que esos mismos CEOs dicen hoy y con los datos reales: ofertas senior creciendo 71%, el 37% de las contrataciones senior con IA en el título, y Anthropic pagando $570.000 al año por un software developer. Con la historia del intern que quiere dejar tech para estudiar enfermería por lo que leyó en Reddit.00:00 Intro y saludo a Colombia01:40 Por qué este episodio: el ruido y la ansiedad del que empieza03:19 Tito: a los estadísticos los "reemplazan" desde 199905:00 El primer dato: el decline real y a quién afectó07:17 Empresas que antes no podían contratar estos perfiles08:24 El reporte de Stanford: los de 22-25 años09:24 La historia del intern que se quiere ir a enfermería por Reddit12:47 Cómo pega el ruido en Latinoamérica15:31 VIDEO: Jensen Huang y "todos son programadores"18:05 "¿Cómo no creerle al que hizo NVIDIA?"20:51 Qué gana NVIDIA con esa narrativa24:58 DeepSeek: 100 mil millones menos en un día25:26 VIDEO: Zuckerberg con Joe Rogan28:31 La predicción vencida: dijo 2025 y es 202630:14 El dato que la contradice: +71% en posiciones senior31:02 Anthropic paga $570.000 al año por un developer31:35 VIDEO: Dario Amodei y los empleos de entrada34:02 Tito: la firma de abogados y por qué el junior acelera37:38 De las tarjetas perforadas al AI: la historia de la abstracción44:47 El giro: Amodei y la paradoja de Jevons47:19 Altman: "probablemente no va a pasar"48:40 El boom de hace 5 años y los "batequebrados"50:46 Tu única opción es ser bueno53:58 El consejo para los próximos 20 años1:01:39 Cierre✩ CURSOS DISPONIBLES

The world is barreling towards disaster on many fronts. Crises include: climate change, depopulation, government debt, sagging welfare states, housing affordability, healthcare affordability, political polarization, government paralysis.By 2050 they will all be irrelevant, as will the structures trying to grapple with them.SOURCES:Kokotajlo, Daniel, et al. AI 2027. ai-2027.com. Kokotajlo, Daniel. "What 2026 Looks Like." 2021.Amodei, Dario. "Machines of Loving Grace." 2024.TechTimes. "OpenAI GPT-5 Biological Risk Classification." July 27, 2026.Axios. "The AI Titans' Biggest Private Fear." July 24, 2026.Center for a New American Security (CNAS). "AI and the Evolution of Biological National Security Risks." August 2024.AI Incident Reporting Act. 2026.AI Kill Switch Act. 2026.Schneier, Bruce. "AI Surveillance Is Being Supercharged." Schneier on Security, July 2026.DeepMind. "AlphaFold: Five Years of Impact." 2024.Bostrom, Nick. "The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents." Minds and Machines, 2012.Bostrom, Nick. "Astronomical Waste: The Opportunity Cost of Delayed Technological Development." Utilitas, 2003.Vinge, Vernor. A Fire Upon the Deep. Tor Books, 1992.Freitas, Robert A., Jr. "A Self-Reproducing Interstellar Probe." Journal of the British Interplanetary Society, vol. 33 (1980): 251–264.Wikipedia. "Great Horse Manure Crisis of 1894."Oxford Academic. Book chapter on William Crookes and the wheat-supply crisis (1898 address to the British Association for the Advancement of Science).Jevons, William Stanley. The Coal Question: An Inquiry Concerning the Progress of the Nation, and the Probable Exhaustion of Our Coal-Mines. Macmillan, 1865.Ricardo, David. "On Machinery." Chapter 31, On the Principles of Political Economy and Taxation, 3rd ed. John Murray, 1821.United Kingdom. Report of the Inter-Departmental Committee on Physical Deterioration. 1904.Vinge, Vernor. "The Coming Technological Singularity: How to Survive in the Post-Human Era." Hosted on Acast. See acast.com/privacy for more information.

* Jevons Paradox 究竟係乜？Jevons Paradox 指一種反直覺現象：技術效率提高之後，每一單位資源用得少咗，但因為成本下降、用途增加，總需求反而可以升得更快。十九世紀英國煤炭就係經典例子，蒸汽機愈有效率，煤嘅總需求反而繼續增加。* 點解 AI 愈平，總 compute demand 反而可能愈大？因為 AI 平咗之後，原本唔值得用 AI 做嘅工作開始值得做；同時 agentic AI 會將一個人類指令拆成更多步驟、更多 tool calls，同一件工作本身亦會消耗更多 tokens。用途增加同每個用途嘅 compute intensity 都可以推高總需求。* 點樣知道 AI 係咪真係出現 Jevons Paradox？要睇需求價格彈性，即係 AI 價格跌一個百分比之後，使用量會增加幾多。如果需求增加速度快過價格下降帶來嘅效率改善，先接近 Jevons Paradox 所描述嘅情況。* AI 愈普及，邊啲東西反而會變得更加稀缺？一種 scarcity 被技術解決之後，其他 bottleneck 就會浮現。上游最明顯係 GPU、Data Center 同相關基建；AI 使用一路向下游擴散之後，人類互動、context、judgment 等亦可能形成新嘅相對稀缺。* Compute demand 大升，係咪代表 Cloud 公司一定賺大錢？唔一定。Revenue growth 同 Return on Capital 要分開睇。Microsoft、Amazon 等 Cloud Provider 可以因為 AI demand 上升而增加收入，但同時亦要投入大量 Data Center、GPU 同 fixed capital。最後回報要視乎投入成本、價格同資本回收速度。* AI 對工作市場嘅影響，點解可能先出現喺 junior jobs？好多 junior 工作本身就包含大量標準化、重複、容易拆解嘅任務。AI 提高生產力之後，以前可能需要一 team 人完成嘅工作，今日可以由更少人處理，所以入行職位會先收窄。片中亦提到 22 至 25 歲、AI exposure 較高嘅職位，employment 表現較弱，而調整主要可以表現為少請人。* 當 low-context 工作愈來愈容易交俾 AI，人應該培養乜嘢能力？Transcript 將 low context 定義為可以靠文字、結構化資料直接得出 actionable outcome 嘅工作；high context 就需要理解情境、人情世故、情緒同 judgment。AI 愈擅長處理 low-context information，人就愈需要提升 high-context communication、判斷力同理解情境嘅能力。 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit leesimon.substack.com/subscribe

“When will be the end of thus exhausting the earth, and how far will avarice finally penetrate?” the Roman natural historian Pliny the Elder asked in 77 AD. Two thousand years later, the answer seems clear. At least according to the contemporary Scottish natural historian James Crawford. In his new book, The Vanishing Earth, the Edinburgh-based Crawford provides dispatches from what he calls the “frontiers of extraction.” From the Atacama Desert in northern Chile to the melting coastline of Greenland, Crawford describes the apocalyptic consequences of our avarice toward the earth. The ultimate frontier of extraction, Crawford warns, is ourselves. Thus he reports on big tech's designs to harvest our thoughts. Consumer headsets are already reading our brainwaves, Apple holds patents for brain-reading earbuds, and another self-styled “leader in real-world neuroscience” once promised “neuro insights for marketing purposes.” The Vanishing Earth's apocalyptic warnings appear both timeless and immediate. Watching Roman miners destroy Spanish mountains, Pliny the Elder wondered when we would finally exhaust the earth. Today, we can quantify the answer. Sometime around 2020, the weight of everything humanity has built came to exceed the weight of all life on Earth. It was just three percent in 1900. We have extracted more in the past fifty years than in all prior human history combined. We now build a city the size of Paris every five days. The ideology of endless growth is only 300 years old, Crawford warns. Pliny the Elder's earth is history. Crawford confesses that he is a product of extraction himself. On his family's windowsill sits a lemonade bottle of the first North Sea crude, siphoned by his father the day it came ashore. “I am Scottish, after all,” he notes wryly. Will our avarice transform extraction into extinction? Not necessarily, Crawford explains. His last dispatch is from Butte, Montana, and offers a ray of hope. There he finds people trying to repair the seemingly apocalyptic consequences of copper mining on the earth. So apocalypse now doesn't mean apocalypse tomorrow. We still have the power to unvanish the earth, the agency to undo the avarice of our ancestors. Five Takeaways •       Pliny's Two Questions. Watching Roman gold miners collapse entire Spanish mountains — ruina montium, “the fall of mountains” — the great naturalist Pliny the Elder asked, two thousand years ago: “When will be the end of thus exhausting the earth, and how far will avarice finally penetrate?” Those two questions run like an entwined seam through Crawford's book, and he believes we're now getting the answers. The ideology of endless economic growth, he argues, is only about 300 years old — born in Enlightenment Scotland as freedom from famine and the caprices of nature, and entirely sensible at the time. The problem is that the planet has changed and the ideology hasn't. The “vanishing” of the title isn't only resources: it's the sixth extinction, the biodiversity collapse — and, potentially, us.•       A Lemonade Bottle of First Oil. Crawford is a product of extraction, and knows it. His great-grandfather fled Shetland fishing at sixteen, wrote “engineer” on his immigration form, and ended up on Ford's very first assembly line building the Model T. His father apprenticed in a coal mine, worked the Zambian Copperbelt, then spent thirty years in oil — which is why Crawford was born on the Shetland Islands, beside one of the world's largest construction sites. The day the first North Sea oil came ashore, his father — who ran the pipelines and knew where to go — filled a lemonade bottle from an outlet valve; it still sits on the family windowsill. “I am a product of extraction. But then we all are”: microplastics in our bodies, forever chemicals passing from mother to unborn child. As a species, we have merged with what we mine.•       Thought Extraction. The journey's final landscape is the mind. Consumer neurotech — EEG headsets promising calm and focus — is already reading brainwaves, and the direction of travel is unmistakable: Emotiv scrubbed “neuro insights for marketing purposes” from its website between fact-check and publication (Crawford found the old language on the Wayback Machine); Apple holds patents for EEG-reading earbuds, with Meta and Snap filing alongside. Rafael Yuste's Neurorights Foundation surveyed the roughly thirty companies selling neurotech to consumers and found that every one of them claimed unlimited access to customers' brain data — with no legal protection in place. The attention economy harvested our behavior; the next extraction, Crawford warns, is the thought itself.•       Heavier Than Life Itself. The statistics that stopped Crawford in his tracks: around 2020, the weight of everything humans have built — buildings, roads, plastics — came to exceed the weight of the Earth's entire biomass, up from three percent in 1900. We've extracted more in fifty years than in all prior history; we build a Paris every five days; in ten years China poured more cement than America managed in the whole twentieth century. Hence the global sand shortage (desert sand is useless — “like building with marbles”) that sent him to Greenland, where melting glaciers make the coastline grow while the world's shrink. And hence the hard questions about the energy transition: in 2025 humanity burned more wood, more coal, and more oil than ever before — not a transition but an accumulation, exactly as the Jevons paradox predicted when Victorian Britain worried about coal. Efficiency doesn't reduce use; it multiplies it.•       The Forever Repair. The book ends not in despair but in Butte, Montana — once the center of world copper production, its open-cast pit now a lake of sulfuric acid in the heart of the city. After thirty years of argument, a settlement around 2020 gave the city something unprecedented: BP holds a blank check to monitor Butte's soils and water in perpetuity, and the people, rather than abandon their home, stayed to watch over it and tell its story. “It's not you fix and you forget. Everything is a repair.” That, for Crawford, is the ideological shift the whole journey points toward — from extraction to repair. He is hopeful for one precise reason: we got here through a way of seeing the world, and minds can change. In the tradition of Silent Spring — which banned DDT and birthed the EPA — a book can still move the world. “I am Scottish, after all” — but the glass, on inspection, is half full. About the Guest James Crawford is a Scottish writer, broadcaster, and historian based in Edinburgh. The author of Fallen Glory: The Lives and Deaths of History's Greatest Buildings and The Edge of the Plain: How Borders Make and Break Our World, he presents book programmes for the BBC and spent over a decade with Historic Environment Scotland. The Vanishing Earth: Dispatches from the Frontiers of Extraction (Bloomsbury, 2026) is out now in the US, with the UK edition imminent from Scribe. The New York Review of Books places him with Iain Sinclair, Rebecca Solnit, and Robert Macfarlane: “Riveting.” References: •&nb...

Enterprise account executives are now signing for $200,000 salaries, $200,000 variable comp plans, and up to half a million dollars in equity at sign-on. Meanwhile, offer acceptance at the top AI companies sits at 45% and half of the 38,000 startup sales reps in San Francisco and New York have taken a new job in the last two years. In this episode, Sam Jacobs, AJ Bruno, and Asad Zaman go hosts-only on why the sales talent market broke, what QuotaPath's compensation data across more than 1,400 companies says about quota attainment splitting into a barbell, and why Paul Graham calling go-to-market bogus says more about Silicon Valley's blind spot than it does about sales. Plus, where young sellers still get their start, whether a CRO can be great without ever carrying a bag, why go-to-market cannot rescue a company that has lost product-market fit, and what HubSpot's 20% stock drop signals for every SaaS business trying to make the AI transition. Key Takeaways: - The AI buildout has drained the two talent pools it depends on, and compensation is repricing in real time. As Asad Zaman, CEO of STA, described the enterprise AE market: "Enterprise account executives now get paid $200,000 salaries, $200,000 variable comp plans, up to half a million dollars in equity at sign-on. That used to be $150,000 to $175,000 at the top end, just like a year and a bit ago." With 38,000 startup software sellers in San Francisco and New York and a quarter of them changing jobs in the last twelve months, the constraint on AI companies hitting escape velocity is no longer capital, it is people. - The jobs data cuts against the automation narrative. Sam Jacobs, CEO of Pavilion, pointed to the Philippines outsourcing sector, 8% of that country's GDP, where employment in IT and business outsourcing is up 20% to 1.9 million workers and industry revenue is up 30% to $42 billion since the launch of ChatGPT: "I just think, you know, it's Jevons' paradox. It is what happens with every new piece of technology. Everybody thinks the technology is going to wipe everybody out, and instead the economy adapts." Companies are leaner per dollar of revenue and still cannot hire fast enough. - Quota attainment is no longer distributed the way comp plans assume. QuotaPath's first-half numbers put 58% of AEs on track against annual quota versus 55% a year ago, but as AJ Bruno, CEO of QuotaPath, explained, the shape underneath moved: "the standard deviation is way higher this year. Meaning that the winners are going to be winning more … So it's more like a barbell than it is a bell curve." At some AI companies two reps out of ten are closing half the entire number, which breaks the capacity math most sales leaders use to set targets. - For individual sellers, the window matters more than the title. Asad Zaman's advice to reps weighing a step up into leadership: "this is the moment where you can make millions of dollars right now … So don't make silly choices … Become a leader later. Go make money right now." He now benchmarks offers on whether 300% of plan clears a million dollars, and warns that private equity portfolio companies that never gave equity to individual contributors are losing these candidates outright. Connect with the Hosts: Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/ Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at STA - https://www.linkedin.com/in/azaman1/ Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters:  00:00 Three Hosts, Three Topics 02:19 The Talent Market Has Gone Crazy 05:42 $200K Base, $200K Variable 09:00 AI Was Supposed To Kill Sales 15:16 Go Make A Million Right Now 16:53 Where Young Sellers Get Their Start 23:20 Build A University Inside The Company 31:58 Paul Graham Versus Go-To-Market 36:22 Why YC Misses Enterprise Software 40:55 Can A Non-Seller Be A Great CRO? 44:42 GTM Cannot Fix Product-Market Fit 49:30 One To Ten To A Hundred 53:38 Quota Attainment Is A Barbell 1:02:41 The HubSpot Nightmare 1:06:05 Falling Back In Love With The Business

Click the link http://kalshi.com/r/MOSES or download the Kalshi App and use code MOSES to sign up and trade today! Checkout WAWD on Substack: https://whatarewedoingonthedesk.substack.com/OTT Sonali joins the podcast on the one-year anniversary of moving from Bloomberg to iCapital, discussing her media series “The Bridge” and iCapital's reach across wealth and asset managers. The conversation centers on AI economics, especially how declining token costs shift value along the “AI food chain,” with hyperscalers capturing a large share while software and enterprises benefit as costs fall, and with demand (Jevons paradox) potentially sustaining aggregate spend and CapEx. They address rising APAC innovation, why frontier labs pursue IPOs amid heavy cash burn and broad access to debt and equity, and the new NVIDIA-led $500B compute financing platform as Wall Street crowds into AI while investors struggle to diversify as infrastructure, power, and data centers converge. They discuss abundant 2026 liquidity that may tighten, oil's impact on consumers and second-half caution, hedge fund crowding and the situational awareness leverage unwind, valuation dispersion (semis vs financials/utilities), and rate risks including Treasury basis-trade leverage, a 10-year yield range of 4–4.8%, Japan's carry trade, and selective interest in Japan and parts of APAC for international exposure. -- ABOUT THE SHOW For decades, Danny has seen it all on Wall Street and has built his reputation on integrity, curiosity and skepticism that he will bring with him each week. Having traded through the Great Financial Crisis and being featured in "The Big Short" is only part of the experiences Danny wants to share with the listener. This weekly podcast cuts through market noise, offering entertaining and informative discussions with expert guests giving their views of the financial world and the human side of it. Whether you're a seasoned investor or just getting started, On The Tape provides something for all listeners. Follow Danny on X: @dmoses3 The financial opinions expressed are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on this content. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in 'On The Tape' carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Click the link http://kalshi.com/r/MOSES or download the Kalshi App and use code MOSES to sign up and trade today!Checkout WAWD on Substack: https://whatarewedoingonthedesk.substack.com/OTTSonali joins the podcast on the one-year anniversary of moving from Bloomberg to iCapital, discussing her media series “The Bridge” and iCapital's reach across wealth and asset managers. The conversation centers on AI economics, especially how declining token costs shift value along the “AI food chain,” with hyperscalers capturing a large share while software and enterprises benefit as costs fall, and with demand (Jevons paradox) potentially sustaining aggregate spend and CapEx. They address rising APAC innovation, why frontier labs pursue IPOs amid heavy cash burn and broad access to debt and equity, and the new NVIDIA-led $500B compute financing platform as Wall Street crowds into AI while investors struggle to diversify as infrastructure, power, and data centers converge. They discuss abundant 2026 liquidity that may tighten, oil's impact on consumers and second-half caution, hedge fund crowding and the situational awareness leverage unwind, valuation dispersion (semis vs financials/utilities), and rate risks including Treasury basis-trade leverage, a 10-year yield range of 4–4.8%, Japan's carry trade, and selective interest in Japan and parts of APAC for international exposure.--ABOUT THE SHOWFor decades, Danny has seen it all on Wall Street and has built his reputation on integrity, curiosity and skepticism that he will bring with him each week. Having traded through the Great Financial Crisis and being featured in "The Big Short" is only part of the experiences Danny wants to share with the listener. This weekly podcast cuts through market noise, offering entertaining and informative discussions with expert guests giving their views of the financial world and the human side of it. Whether you're a seasoned investor or just getting started, On The Tape provides something for all listeners.Follow Danny on X: @dmoses34The financial opinions expressed are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on this content.Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in 'On The Tape' carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose.Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service. Hosted on Acast. See acast.com/privacy for more information.

Tim Bozarth is a Corporate Vice President in Microsoft CoreAI, where he leads engineering for next-generation developer experiences and Microsoft's Engineering Thrive initiative. Throughout his career at Microsoft, Google, Netflix, and Box, he has focused on developer productivity, engineering systems, and organizational effectiveness.In this episode of Engineering Enablement, Tim joins host Brian Houck to discuss Engineering Thrive, Microsoft's framework for measuring and improving engineering productivity. They explore why AI makes outcome-based metrics more important than ever, where new bottlenecks are emerging in the software development lifecycle, and why verification and confidence may become more valuable than code generation itself. Tim also shares why the purpose of engineering remains the same despite rising levels of abstraction, the skills that remain durable in an AI-driven world, and why engineering leaders should focus on outcomes rather than activity metrics.Where to find Tim Bozarth:• LinkedIn: linkedin.com/in/tbozarth Where to find Brian Houck: • LinkedIn: ⁠https://www.linkedin.com/in/brianhouck⁠In this episode, we cover:(00:00) Intro(02:13) What Engineering Thrive is and the problem it solves(04:46) Why Engineering Thrive isn't specific to Microsoft(09:10) The impact of Engineering Thrive at Microsoft(14:31) Why AI makes outcome-based productivity metrics more important(18:22) Where AI is creating new bottlenecks in the SDLC(24:37) Why more abstraction doesn't change the purpose of engineering(27:25) The durable skills of good engineers (33:03) The changing economics of software development(36:56) Advice for leaders: measure outcomes, not activityReferenced:• DX Core 4 Productivity Framework• EngThrive: Make It Fast and Easy to Do Great Work: Building a durable model for outcome-oriented engineering measurement• Quote by Eliyahu M. Goldratt: “Tell me how you measure me and...”• Jevons paradox - Wikipedia

Everyone in biotech agrees AI needs more data. Almost no one is willing to pay for it. If you're trying to build or buy a biotech AI model, you've hit the same wall: predictive performance depends on data your budget doesn't cover, and nobody in the field seems willing to close that gap. John Androsavich runs Ginkgo Datapoints, the bio AI data arm of Ginkgo Bioworks. He trained as an RNA scientist, spent years on the pharma side deciding which technologies were worth buying, and now sells the raw biological data everyone claims to want. Ross and John get into why biotech spends a fraction of what tech spends on data, how automation dropped ADME testing to $199 a compound, and what that unlocks for drug discovery pipelines and data science in biotech more broadly. You'll hear why single-cell foundation models don't scale the way the field expected, and how GPT-5 designed its own lab experiments inside an autonomous facility. This one's for data and analytics leaders in biotech who need a clearer read on where to spend on data generation, and where the field is still guessing. It's less useful if you're after a general AI overview with no biotech specifics. Key Takeaways - One Meta investment in a data-labelling vendor outweighs a full year of AI drug discovery venture funding combined, and dwarfs the entire single-cell data market. Biotech's data spend looks nothing like tech's. - Ginkgo's ADME-1 offering runs at roughly a tenth of standard pricing, which is changing when and how much companies test. Teams are now running full tier-one panels earlier instead of triaging molecules before they've generated the negative data models need. - A recent Microsoft Research paper found single-cell foundation model learning saturates at 200,000 to 2 million cells, out of a possible 20 million. Volume alone isn't the lever people assumed it was. - GPT-5 wrote its own experimental protocols for optimising cell-free protein expression, ran them through Ginkgo's autonomous Nebula lab, and hit the lowest price-per-titer ever recorded in the field. Chapter Markers 00:00 Introducing John Androsavich and Ginkgo Datapoints 01:12 Why Ginkgo launched a bio AI data business 05:03 Which companies benefit most from Datapoints 06:31 The paradox: everyone wants data, no one pays 09:00 How automation drives ADME-1's $199 price point 12:59 Testing the Jevons paradox in biotech data buying 16:05 Do we actually know biotech AI's scaling laws? 20:54 Why foundation model builders resist more data 24:59 What an empirical bake-off for bio AI could look like 29:32 The case against sitting on the sidelines 33:26 Inside the Virtual Cell Pharmacology Initiative 41:57 Where VCP fits among other virtual cell projects 44:50 The Antibody Developability Consortium with Apheris 53:57 Autonomous labs and GPT-5 designing its own experiments 59:38 Advice for mid-stage biotech data strategy 01:01:31 Final thoughts on where bio AI investment is heading Useful Links & Resources - Ginkgo Bioworks: [ginkgobioworks.com](https://www.ginkgobioworks.com) - Related episode: Apheris CEO Robin Rohm on federated co-folding (Data in Biotech) - Related episode: Eliza Appel on Lilly's TuneLab and federated learning (Data in Biotech) - CorrDyn: [corrdyn.com](https://www.corrdyn.com) Connect With the Show - Host LinkedIn (Ross Katz): [linkedin.com/in/b-ross-katz](https://www.linkedin.com/in/b-ross-katz/) - Host X: [x.com/brosskatz](https://x.com/brosskatz) - CorrDyn LinkedIn: [linkedin.com/company/corrdyn](https://www.linkedin.com/company/corrdyn/) Where does your organisation sit on the data investment paralysis John describes? Are you waiting for someone else to prove the scaling laws first, or are you buying the data now? Drop your take in the comments. Visit corrdyn.com to learn how CorrDyn can help your organisation extract value from data. #DataInBiotech #BiotechAI #DrugDiscovery #DataScience #GinkgoBioworks

Memory has been one of the strongest corners of the semiconductor industry, and strong returns invite hard questions. In the second part of this series, equity analyst Shan Rui Yeo examines the main risks to the memory thesis: rising competition from China's CXMT and YMTC, the technologies that could reduce AI's appetite for memory, and the wave of capacity investment that could eventually tip the industry back into oversupply. He weighs each risk against the constraints holding it back, from equipment export controls to limited EUV supply, and notes that memory companies already trade at three to five times forward earnings. The conversation closes on a working principle: treat the terminal value as a distribution, not a fixed number. Key Takeaways China's CXMT is expanding DRAM capacity aggressively, but export controls on sub-18 nanometre equipment and EUV keep its effective supply share (about 10%) below its capacity share (about 15%). YMTC is the more credible technological threat: NAND density comes from stacking layers, and its Xtacking hybrid bonding architecture is proprietary. Efficiency gains may grow memory consumption rather than reduce it; cheaper tokens get spent on larger context windows (the Jevons paradox). The deepest risk is architectural: if large language models are not the path to AGI, the next paradigm may not be memory hungry, so terminal value is a distribution, not a fixed number. Announced capex is enormous but back-loaded into the 2030s, and EUV and equipment capacity are the bottleneck to bringing it online. Memory companies trade at three to five times forward earnings; the market is not assuming supernormal profits forever, and the NAND supply outlook is better in the near term. Companies Mentioned: Samsung, SK Hynix, Micron, CXMT (ChangXin Memory), YMTC (Yangtze Memory), Apple, NVIDIA, Google, ASML, Applied Materials, KLA, Lam Research, TSMC, Intel, Kioxia Host: Rob Campbell, CFA, Institutional Portfolio Manager Guest: Shan Rui Yeo, CFA, Equity Analyst   This episode is available for download anywhere you get your podcasts.   Founded in 1974, Mawer Investment Management Ltd. (pronounced "more") is a privately owned independent investment firm managing assets for institutional and individual investors. Mawer employs over 250 people in Canada, U.S., and Singapore.    Visit us at: https://www.youtube.com/@MawerInvestment https://www.mawer.com https://www.linkedin.com/company/mawer-investment-management/ https://www.instagram.com/mawerinvestmentmanagement/

Does making something more efficient always make it cheaper? On this solo episode of Power House, Zeb Lowe explores the Jevons paradox—the idea that making something cheaper and easier to use often leads to more demand, not less. From fuel-efficient cars and air conditioning to artificial intelligence, history shows that efficiency doesn't necessarily reduce consumption—it expands it. Zeb applies that same framework to housing and mortgage, examining how AI, automation and digital lending may increase loan volume, accelerate transactions and reshape the industry in ways few people are discussing. The question isn't whether efficiency is good. It's what happens after it succeeds. Related to the episode: ⁠Zeb Lowe's LinkedIn⁠ Want more from Zeb? Don't forget to subscribe to LendingLife. The Power House podcast brings the biggest names in housing to answer hard-hitting questions about industry trends, operational and growth strategy, and leadership. Join HousingWire's Zeb Lowe every Thursday morning for candid conversations with industry leaders to learn how they're differentiating themselves from the competition. Hosted and produced by the HousingWire Content Studio.

Adelaide Climate Action Week co-founder Mark Rowland joins Steve to unpack The Great AI Climate Debate, the Oxford-style contest his organisation staged this week between the “carbon glutton” and “silicon saviour” camps. Rather than settling for headline alarm about data centres guzzling power like mid-sized nations, Mark walks through the nuance: how much of that energy use is genuinely AI rather than general cloud computing, why the time horizon people use to judge AI shapes their verdict, and how concepts like the Jevons paradox and the “time value of carbon” complicate any tidy answer. He leaves listeners with a practical, human-scaled way to think about their own digital footprint. Renmark’s 23rd Street Distillery supplies the South Australian Drink of the Week, and it’s a genuine coup: their single malt has just been named Australian Distillery of the Year at the New York International Spirits Competition, outscoring Glenmorangie, Highland Park and a 31-year-old Crown Royal. Head distiller Paul Burnett returns to the show to talk peat, oak and the fine art of finishing a whiskey in casks that once held tawny, muscat and Tokay. The Musical Pilgrimage closes the episode on a reflective note, with Steve premiering his own composition, Higher Legacy, written for two Adelaide City Council gardeners whose combined century of service tending the city’s parks and squares is honoured on a plaque in Rymill Park — and for everyone, past and present, quietly building something worth protecting. You can navigate episodes using chapter markers in your podcast app. Not a fan of one segment? You can click next to jump to the next chapter in the show. We’re here to serve! The Adelaide Show Podcast: Awarded Silver for Best Interview Podcast in Australia at the 2021 Australian Podcast Awards and named as Finalist for Best News and Current Affairs Podcast in the 2018 Australian Podcast Awards. And please consider becoming part of our podcast by joining our Inner Circle. It’s an email list. Join it and you might get an email on a Sunday or Monday seeking question ideas, guest ideas and requests for other bits of feedback about YOUR podcast, The Adelaide Show. Email us directly and we’ll add you to the list: podcast@theadelaideshow.com.au If you enjoy the show, please leave us a 5-star review in iTunes or other podcast sites, or buy some great merch from our Red Bubble store – The Adelaide Show Shop. We’d greatly appreciate it. And please talk about us and share our episodes on social media, it really helps build our community. Oh, and here’s our index of all episode in one concisepage. Running Sheet: The Great AI Climate Debate 00:00:00 Intro Introduction 00:04:00 SA Drink Of The Week There SA Drink Of The Week this week is 23rd Street Distillery Australian Single Malt Whisky. Steve opens with a pop quiz: name a South Australian product that just outscored Glenmorangie, Highland Park and a 31-year-old Crown Royal at a fraction of the price. It’s whiskey, specifically Renmark’s 23rd Street Distillery, freshly named Australian Distillery of the Year at the New York International Spirits Competition, with their single malt scoring 96 points against entries from 39 countries. Head distiller Paul Burnett, returning for another visit, credits the win to restraint rather than showmanship: a low-level peat profile, around half a percent in the finished blend, used as what he calls a “complexing agent,” paired with an unusually wide range of cask finishes, from first-fill bourbon barrels to old tawny, muscat and Tokay casks. Steve, tasting alongside him, works through notes of honeyed fruit, fig, all-spice and a citrus edge that shifts as the glass warms in his hand, while Paul walks through the distinction between “heads, hearts and tails” in distillation and how the team’s small-batch, six-way finishing blend has quietly shifted in proportion release after release. The pair also compare notes on serving it right: a proper tulip glass, a touch of water, room temperature rather than ice, and, in Paul’s case, a slice of chocolate or nuts alongside it. Steve, for his part, admits an evening spent testing whiskey sours and old fashioneds the night before only confirmed his preference for drinking it neat. 00:35:20 Mark Rowland and Adelaide Climate Action Week Steve opens by declaring his hand: he uses AI daily for work, has solar and a battery, drives an EV, and admits the more he learns about what sits behind that lifestyle, the less smug he gets to feel about it. It’s a fitting frame for a conversation built around nuance rather than tribal certainty. Mark explains why Adelaide Climate Action Week chose an actual Oxford-style debate over a polite panel discussion, tapping into people’s competitive instincts to get them to actually take a position rather than scroll through it. The audience voted before and after: undecided voters dropped from 24% to single digits, and belief that AI will be a net negative for climate rose from 56% to over 70%. The real story, though, was in the follow-up conversations, where the deciding factor wasn’t the argument itself but the time horizon people used. Ask about the next five years and the mood turns pessimistic; ask about 20 to 50 years out and people start to believe things will improve. The pair dig into the “carbon glutton” case, distinguishing AI-specific energy demand from data centres in general, since cloud storage and streaming were already drawing power long before AI arrived. Mark points out that air conditioning, EV charging and industrial electrification are all expected to outgrow AI’s energy demands in the years ahead, and that steel and concrete for data centre construction already account for roughly 14% of global emissions. He also raises a genuinely alarming case: a data centre approval in an African nation that led authorities to cut off a village’s water supply, with fatal consequences, the kind of guardrail failure he says South Australia needs to avoid as its own data centre legislation opens the door to rapid growth. On the more optimistic “silicon saviour” side, Mark describes a proposed Barossa data centre project that would generate two to three times the renewable energy it needs, feed the surplus back into the community, and capture atmospheric moisture from its waste heat to supply water to nearby farmers and wineries, reducing pressure on the Murray. He frames this as “hybrid intelligence” in action: letting machines handle the split-second optimisation work, like grid load-shifting to squeeze more renewables in without new poles and wires, while humans retain the judgement calls machines aren’t suited to. Steve presses on the Jevons paradox, the pattern where efficiency gains simply invite greater use rather than genuine savings, and confesses to his own sprawling AI toolkit, from Claude to NotebookLM to Suno. Mark’s response is refreshingly proportionate: an individual’s prompt volume is negligible against industrial-scale use, and Google’s own sustainability reporting shows the energy and water cost per prompt has dropped by roughly half in a couple of years, even as overall usage climbs. The conversation lands on the “time value of carbon,” the idea that AI’s emissions are happening now while its promised climate benefits, if they arrive at all, are speculative and delayed. It closes with Mark’s own conviction, shaped by a master’s degree in climate change, that humanity’s Anthropocene-era warming is real, drawn partly from personal memory of snow days in his native England that simply don’t happen anymore. Asked to leave listeners with something to hold onto, Mark returns to a theme James Clear would recognise from Atomic Habits: start with the smallest available step. Downloading rather than streaming a favourite album, watching YouTube at 720p instead of 4K on a phone, deleting old emails sitting in a data centre somewhere: none of it solves the problem alone, but it restores a sense of agency against the temptation to slide into overwhelm or apathy. Further information Adelaide Climate Action Week Google’s 11th annual Environmental Report 01:27:56 Musical Pilgrimage In the Musical Pilgrimage this week we listen to Higher Legacy by Steve Davis & The Virtualosos The Musical Pilgrimage takes an unexpectedly personal turn this week. Prompted by a photograph shared in a local Facebook group of a plaque in Rymill Park honouring brothers Antonio and Carmine Lepore, whose combined century of tending Adelaide’s parks and squares began back when Clydesdales still pulled bakers’ carts through the suburbs, Steve wrote an original song called Higher Legacy. Inspired by the plaque’s closing line, borrowed from the idea that a society grows great when people plant trees whose shade they’ll never sit in, the song also nods to Jane Goodall’s advice to Adelaide listeners years ago: when the world’s problems feel overwhelming, start with the square metre you’re standing on. Brought to life with Steve’s virtual session band, The Virtualosos, it’s offered as a piece for anyone who wants to take it, own it, and perform it as their own.Support the show: https://theadelaideshow.com.au/listen-or-download-the-podcast/adelaide-in-crowd/See omnystudio.com/listener for privacy information.

Our Global Head of Thematic and Sustainability Research Stephen Byrd explains why the recent AI infrastructure selloff may reflect technical pressures, not weakening fundamentals.Read more insights from Morgan Stanley.----- Transcript -----Stephen Byrd: Welcome to Thoughts on the Market. I'm Stephen Byrd, Morgan Stanley's Global Head of Thematic and Sustainability Research.Today: Are investors misreading the AI infrastructure selloff?It's Thursday, July 30th, at 10am in New York.The recent selloff in AI infrastructure stocks has raised a familiar question: Is the buildout running ahead of real demand? The market is pulling back and we think that reflects profit-taking, crowded positioning, and forced selling by investors. This is not about weaker fundamentals. But the selloff has brought to light three key concerns, which we think the market is overplaying.The first concern is how much enterprises are willing to pay for AI. The median enterprise employee currently generates less than $11 a month in token spending. That's the fee paid when an AI model processes a request and generates a response.We think there is room for that to increase. From the employer's perspective the economics are compelling. Across workplace applications, the cost to execute the economic task would be $2-$5. And that could save an enterprise $55. That to us suggests companies are likely to spend more, not less, on AI over time.The second debate centers on efficient models, including competitive models developed in China.  And here, policy responses both from the U.S. and China can have an impact as well. Some investors worry that better efficiency means less computing demand. But we see the opposite risk. This is a classic example of Jevons paradox: When something becomes cheaper or more efficient to use, people use more of it. In AI, lower costs can attract more users, encourage more frequent use, and make complicated applications more economical. The scale is striking. Industry leaders estimate that compute demand could double every six months, which would amount to more than a thousand-fold increase in compute over five years. Hyperscalers could quadruple available power capacity to roughly 120 gigawatts by 2028, from about 30 gigawatts in 2025.And that leads to the third debate – whether data centers can secure enough power to keep expanding. It's a valid concern. In the U.S., facilities under construction and contracted grid capacity cover about 30 gigawatts. That's less than half the 68 gigawatts of power that data centers are likely to need from 2026 through 2028. Grid connections can take five to seven years in some regions. Skilled electricians, welders, and pipefitters are in short supply. And local opposition is increasing as communities debate electricity bills, tax incentives, and who should pay for grid upgrades.These are real obstacles, but we view them as delays rather than dead ends. Onsite generation, fuel cells, energy storage, natural gas turbines, and the conversion of existing high-power sites could close the gap, at least partially.We believe much of the recent weakness in AI infrastructure has been driven by technical factors rather than a change in the underlying fundamentals. As AI becomes more capable and cheaper to use, demand for intelligence, compute, and power is likely to keep rising. The global market is fragmented as policy decisions in the U.S. and China shape how growth unfolds. But strong economics should support continued investment.Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

82% of top-grossing apps now take payment outside the app store. Patrick Stuart-Constant's team at Sociaaal ships 4,000 video ads a month, scaling to 10,000. The shift to web-to-app monetization isn't coming. It already happened.Part 1 of a two-part panel from a FunnelFox webinar, with Rocketship HQ as partner. Shamanth Rao moderated the conversation with Jacob Rushfinn (CEO, Botsi), Andrey Shakhtin (CEO, FunnelFox), Patrick Stuart-Constant (CEO, Sociaaal), Mike Gadd (VP Customer Success, Singular), and Elise Zareie (Head of Paid Social, Lingokids) on how paid acquisition teams design the path after the click.Andrey Shakhtin on why off-store attribution beats SKAN on speed, and how 2-5% payment fees unlock room to iterate. Patrick Stuart-Constant on scaling ad volume as a search space problem, and why cheaper AI creative grew his team instead of shrinking it (Jevons paradox). Elise Zareie on why web funnels only work with dedicated ownership inside the UA team. Mike Gadd on why the first renewal is the metric most teams miss. Jacob Rushfinn on the $1M revenue threshold before web funnels pay off, and the 10K to 20K monthly loss to budget for the learning phase.Video Chapters:00:00 Introductions and format02:40 The one shift teams have not priced in07:50 Andrey on the 82% off-store payments finding09:52 Mike on measurement across web and app, platform hell11:45 Elise on creative: lift-and-shift vs TikTok-native15:54 Patrick on the search space, 4K to 10K ads a month18:07 Jevons paradox: cheaper ads, bigger creative team19:30 Elise on where AI helps and where taste is still human21:39 What separates web-funnel winners from teams that quit25:27 Andrey on iteration pace correlating with revenue27:00 Who should not try web funnels: payments complexity28:42 Jacob on the $1M threshold and the learning-phase budget29:36 Common learning phase mistakes and how to short-circuit them32:00 Part 2 drops next weekTopics covered:- The shift to web-to-app monetization and off-store payments- AI creative production at scale, 4,000 to 10,000 ads a month- Web funnels: dedicated ownership vs side project- The $1M revenue threshold for going off-store- Learning phase mistakes on acquisition, funnel, and paywall- Measurement complexity across web and appLearn more:- Original webinar recording: https://www.youtube.com/watch?v=OYPDZlwLhNA- FunnelFox (webinar host): https://funnelfox.com- Rocketship HQ (partner): https://rocketshiphq.com- Jacob Rushfinn (Botsi): https://www.linkedin.com/in/jacob-rushfinn/- Andrey Shakhtin (FunnelFox): https://www.linkedin.com/in/andrey-shakhtin/- Patrick Stuart-Constant (Sociaaal): https://www.linkedin.com/in/patrick-stuart-constant/- Mike Gadd (Singular): https://www.linkedin.com/in/mike-gadd-27a16427/- Elise Zareie (Lingokids): https://www.linkedin.com/in/elise-zareie/- Shamanth Rao (Rocketship HQ): https://www.linkedin.com/in/shamanthrao/

Ömer ve Yaşar Ateş Ölçer'in yepyeni bölümünde Dreame Ev Robotları sponsorluğunda istihdam krizini ve yapay zekayının geleceğe etkilerini tartışıyor!2025 yılı içerisinde sık sık haberlerde karşılaştığımız işten çıkarma haberlerinden yola çıkarak yeni mezunlara açılan işlerde azalmayı, beyaz yaka işlerdeki daralmayı, otomasyona kurban giden işleri ve yapay zekanın yeni işler yaratıp yaratamayacağını değerlendiriyorlar. Sadece istihdam ve ekonomi ile sınırlı kalmayan tartışma kapitalizmin doğası ve geleceğin siyasi sistemlerine dair bir beyin fırtınasına da ilerliyor.00:00 - Giriş01:27 - İşten çıkarma trendi06:25- Büyük teknoloji şirketleri kapitalizme etkisi08:38 - Yapay zekanın beyaz yaka işlere etkisi12:03 - Yapay zekanın istihdama etkisi16:00 - Moravec paradoksu17:25 - Dreame Ev Robotu18:50 - Diplomanın öneminin zayıflaması23:50 - Yapay zekanın becerilerinin hızlı gelişimi25:27 - Jevons paradoksu28:20 - Yapay zeka mevcut toplumu nasıl etkileyecek?30:45 - Ev genci krizi32:18 - Türkiye'de istihdamın geleceği36:50 - Kamu istihdamı öne mi çıkacak?41:08 - Büyük güç mücadelesinin teknoloji yarışına etkisi44:00 - Demokrasinin sonu mu geliyor?48:12 - Verimliliği tekrar düşünmek51:10 - Kapanış

CitizenWorks founder Jamie Christensen joins Doug Erwin to share how she went from automating her own corporate job to building a thriving Reno AI services business. They explore human-first AI adoption, citizen developers, coding agents, job fears, entrepreneurship, and why the final pass on meaningful work should always be human.## Show notesJamie Christensen is the founder of CitizenWorks, a Reno-based company that helps organizations use AI and automation through workforce training and custom systems.Before launching the company, Jamie spent nearly a decade in healthcare operations. She taught herself no-code tools, automated much of her own role, and built a citizen developer program that empowered non-technical employees in HR, payroll, accounting, and recruiting to improve their own work.Jamie launched CitizenWorks after maternity leave while raising newborn twins and a two-year-old. She and Doug talk candidly about the financial pressure of that first year, the value of total commitment, and the role Reno Startup Week and Northern Nevada's entrepreneurial community played in accelerating the business.The conversation then turns to practical AI adoption. Jamie argues that most organizations are not truly behind; most are simply at the beginning. Her advice is to establish basic AI fluency, give employees safe tools and room to experiment, and let people closest to the work identify the best opportunities. Those internal citizen developers often become the people who create the most useful systems.Doug and Jamie also discuss:- Why AI is lowering the barrier to entrepreneurship- How domain expertise and strong relationships become more valuable as building gets easier- Why companies should treat AI as a growth multiplier, not merely a headcount-reduction tool- Jevons paradox and the likelihood that cheaper intelligence creates more work and more possibilities- How coding agents let non-technical people orchestrate work across multiple systems- The difference between using AI to do less and using it to offer customers more- Bottom-up experimentation versus top-down mandates- Data security, open models, and owning the harness around your AI systems- Why model choice matters less than finding valuable use cases- The danger of mistaking AI-generated output for truly original thinking- Why every consequential message deserves a human final pass- The importance of founder community through Kiln, EO Reno Tahoe, and Reno Startup Week

Matt Orsagh spent 25 years inside mainstream finance, including as Senior Director of Capital Markets at the CFA Institute. Then he concluded that ESG was painting the house while it was on fire. Now, as co-founder of the Arketa Institute for Post-Growth Finance, he makes the case that a post-growth world is coming, by disaster or by design. We discuss why degrowth is a diet, not austerity; why green growth collides with physics and the Jevons paradox; and his answer to the Global South objection: Africa should be allowed to grow, it is the Global North that needs the diet. Plus a radical rethink of pensions, a third of the world's assets, built on natural and social capital, not just piles of gold. Listen, then share it with someone who still believes efficiency will save us.

“ I'm a believer in this technology. I believe that the cheaper it is, the more people will use it. So, even if we assume that models will get smaller for the same level of intelligence, the demand to actually have more compute is going to be enormous. And the reason why you want to have the, the compute close to you is not only in terms of performance. As a national security threat, I'm not saying this will happen, but it could happen that, China or US or whoever decides to stop exporting compute to the rest of the world. If that happens today, Europe has zero AI at the scale of data the data center.”For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:22) The hidden risks of the AI debt bubble(0:55) Shadow borrowing and the data center boom(3:12) The AI compute gap and national security(5:40) Europe's missing infrastructure and tech sovereignty(8:13) The Jevons paradox and the demand for compute(10:25) Why open source AI is the best path forward(12:31) The chess paradox and the future of human art(12:53) Unmeasurable domains and future-proofing your career(14:00) Tutoring and AI in education(15:11) Navigating AI slop and the information ecosystem(16:24) The Pleasure of Waiting vs. The HustleEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

“ I'm a believer in this technology. I believe that the cheaper it is, the more people will use it. So, even if we assume that models will get smaller for the same level of intelligence, the demand to actually have more compute is going to be enormous. And the reason why you want to have the, the compute close to you is not only in terms of performance. As a national security threat, I'm not saying this will happen, but it could happen that, China or US or whoever decides to stop exporting compute to the rest of the world. If that happens today, Europe has zero AI at the scale of data the data center.”For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:22) The hidden risks of the AI debt bubble(0:55) Shadow borrowing and the data center boom(3:12) The AI compute gap and national security(5:40) Europe's missing infrastructure and tech sovereignty(8:13) The Jevons paradox and the demand for compute(10:25) Why open source AI is the best path forward(12:31) The chess paradox and the future of human art(12:53) Unmeasurable domains and future-proofing your career(14:00) Tutoring and AI in education(15:11) Navigating AI slop and the information ecosystem(16:24) The Pleasure of Waiting vs. The HustleEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

In this episode, theoretical neuroscientist Vivienne Ming discusses her new book, Robot Proof - When Machines Have All the Answers, Build Better People.  The conversation examines the intersection of artificial intelligence and knowledge work, exploring how professionals can adapt to an increasingly automated economy. The discussion contrasts the limitations of basic AI task automation with the advantages of human-AI collaboration—the "cyborg" model—for solving complex, ill-posed problems. Ming highlights her research on forecasting market outcomes, the critical role of endogenous motivation, and how the legal industry and other elite professions must rethink entry-level training. Key Topics Discussed Theoretical Neuroscience and Early AI: Ming's background and the evolution of machine learning models from early academic research to modern agentic AI. The Polymarket Experiment: An analysis comparing the forecasting accuracy of standalone AI, unassisted humans, and human-AI collaborators, revealing the superiority of deep human-machine integration. Automation vs. Augmentation: The pitfalls of the traditional "human-in-the-loop" model and why replacing menial tasks often neglects essential human problem-solving skills. Well-Posed vs. Ill-Posed Problems: Identifying the specific areas where AI excels (algorithmic, factual answers) and where human intelligence remains superior (navigating uncertainty and undefined parameters). Labor Disruption and Economic Shifts: Examining historical technological revolutions, the Jevons paradox, and the future demand for specific, highly adaptable human skill sets. Endogenous Motivation: How internal drivers like curiosity, resilience, and perspective-taking predict professional success more accurately than standard extrinsic incentives. Practical AI Strategies: Actionable methods for professionals to refine their skills, including using AI as a critical "nemesis" to challenge assumptions and encourage deep, effortful processing. The Future of Elite Professions: The macro-level challenges facing organizations in developing junior talent—such as associate attorneys—when the entry-level tasks traditionally used for training are automated. Things We Talk About in this Episode Socos: Vivienne Ming's philanthropy and research newsletter (socos.org). Thinking, Fast and Slow: Authored by Daniel Kahneman. Anthropic Research: A study published in Science detailing the productivity of AI-assisted programmers. BCG AI Study: Research analyzing AI integration and performance among management consultants. Raj Chetty: Economic research regarding peer role modeling, education, and socioeconomic mobility.  

A Silicon-Carbon Collaboration.What does AI actually cost?We hear a great deal about the energy, water, and infrastructure required to build AI. Those conversations matter. But they often stop at one side of the ledger.This episode asks a different question: What are we NOT counting?Beginning with data centers and Jevons' Paradox, we explore what it means to think economically—not just in terms of financial cost, but in terms of human cost and human benefit. Along the way, we discuss accessibility, executive function, abuse support, creativity, transportation, and why reducing friction can create entirely new possibilities rather than simply replacing old ones.And, perhaps most importantly, we ask whether the value of new technology should be measured only by what it consumes... or also by what it makes possible.

Despite fears of AI replacing jobs, some sectors are experiencing significant job growth with the integration of AI. Hunter and Judson dive into the Jevons paradox and explore how technological advancements often lead to job reallocation rather than loss. Tune in as they discuss why AI might not take your job but instead transform your industry.    LINKS Podcast Video cainwatters.com Submit a Question Facebook | YouTube | Instagram

Klik je týždenný komentovaný prehľad technologických správ, o udalostiach, ktoré sa udiali vo svete IT, médií a sociálnych sietí. Moderátori: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ondrej Podstupka⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Martin Hodás Discord diskusný server nájdete tu: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://discord.gg/dAUW4PCaEh Linky: Slovenská družica Marína https://www.sme.sk/domov/c/do-vesmiru-vyletela-piata-slovenska-druzica-nesie-sladkovicovu-basen Čínska raketa pristála https://x.com/SpoxCHN_MaoNing/status/2075495374618538318 https://x.com/ThosMajor/status/2075921863990235333 Japonsko testuje vesmírnu loď https://x.com/NTDNews/status/2076474238995722243 Elektroautá v číne https://www.youtube.com/watch?v=a46Xp8FtWOg Hank Green a Jevons paradox https://www.youtube.com/watch?v=a6sYYrLTOjQ Apple žaluje OpenAI https://www.theguardian.com/technology/2026/jul/10/apple-sues-openai-trade-secrets Prvé zariadenie OpenAI https://www.bloomberg.com/news/articles/2026-07-14/openai-s-first-device-will-be-moveable-screenless-speaker-built-as-ai-companion Mete hrozí pokuta od EÚ https://ec.europa.eu/commission/presscorner/detail/sk/ip_26_1579 OnePlus končí v USA a EÚ https://www.cnet.com/tech/mobile/oneplus-pulls-out-of-us-and-europe/ Rozhovor o podvodníkoch, ktorí okradli slovenskú obec https://korzar.sme.sk/spis-gemer/c/sest-hodin-manipulacie-aj-videohovor-starostka-opisuje-ako-ju-podvodnici-obrali-o-obecne-tisice Spravili sme chybu, máte pripomienku? Napíšte nám na ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠klik@sme.sk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Kapitoly 00:00 Úvod01:20 Slovenská družica06:54 Vesmírne správy z Japonska a Číny20:14 AI, súdny spor a noviny od OpenAI34:36 Meta mala zlý týždeň40:02 Návykové sociálne siete46:32 Prečo predĺžili Chat Control50:01 OnePlus končí v EÚ a USA52:47 ZáverSee omnystudio.com/listener for privacy information.

Predictions about artificial intelligence often focus on job losses and shrinking demand for lawyers. Filevine CEO and co-founder Ryan Anderson and product manager John Rizner offer a sharply different forecast. Drawing on the Jevons paradox, they argue greater efficiency will make legal services accessible to more people, encourage deeper legal research, and create work once excluded by cost. AI might reduce the effort required for individual tasks while expanding the overall volume and ambition of legal representation.The shift holds major implications for the access-to-justice gap. Faster drafting, research, and document review would allow lawyers to serve more clients without sacrificing professional judgment. Anderson expects family law, immigration, bankruptcy, criminal defense, and employment litigation to experience some of the earliest growth. Motions, witnesses, and legal theories once abandoned over expense become economically viable, although courts face their own capacity crisis as more disputes and arguments enter the system.Rizner explains how Filevine's legal AI platform, Lois, applies machine learning to one of legal research's oldest problems: traditional citators often return different results. Lois combines citation graphs with semantic analysis to locate opinions discussing related legal doctrines even when no direct citation connects the cases. A panel of models then evaluates potential conflicts and produces a structured memo. The goal is richer legal analysis focused on the precise holding or proposition a lawyer needs, rather than a simple flag attached to an entire opinion.Accuracy still demands disciplined human review. Filevine organizes citation verification into three levels: confirming the cited case exists, determining whether the case supports the claimed proposition, and checking whether the authority is still good law. The conversation also examines Rizner's research into how different large language models approach efficient breach of contract. OpenAI, Google, and Anthropic models produced dramatically different recommendations, revealing embedded legal and economic preferences beneath seemingly neutral answers.The guests also explore how AI changes legal drafting, law firm economics, and the billable hour. Filevine's acquisition of Pincites, now Lois for Word, reflects Microsoft Word's continuing role as the shared language of legal documents, redlines, formatting, and negotiations. Efficiency does not automatically eliminate hourly billing. Lawyers might instead use saved time to produce more thoroughly researched arguments, stronger contracts, and work product approaching senior-level depth. Firms still need incentives rewarding efficiency rather than treating faster work as lost revenue.Looking ahead, Anderson and Rizner predict a proliferation of frontier and open-source models tailored to firms, individual lawyers, and specific client relationships. Legal teams will increasingly pair proprietary knowledge with selected models to produce highly specialized analysis. Yet model choice introduces jurisprudential bias, accuracy risks, and serious training concerns for junior lawyers. AI expands the range of available options, while experienced legal judgment decides which arguments deserve trust, which sources require verification, and which advice should reach the client.John Rizner Slides Filevine Primary Presentation - 2026Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠ Transcript:

For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:00) The AI compute gap and national security(1:56) The hidden risks of the AI debt bubble(4:40) Separating AI hype from job displacements(12:38) Shadow borrowing and the data center boom(17:19) Europe's missing infrastructure and tech sovereignty(21:52) The Jevons paradox and the demand for compute(26:37) Regulatory capture and doomerism(31:12) Why open source AI is the best path forward(42:19) The chess paradox and the future of human art(46:35) Unmeasurable domains and future-proofing your career(53:45) Tutoring and AI in education(1:01:48) Navigating the AI slop and information ecosystem(1:16:00) Finding beauty in the natural world(1:20:18) The pleasure of waiting vs. the hustle(1:35:01) Setting healthy boundaries with technologyEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

As America marks 250 years of existence, it is worth pausing to ask a question that most people avoid: what is actually true about this country versus what we have been conditioned to believe? The noise coming from cable news, social media algorithms, and political fundraising machines has created a version of America that feels perpetually on the brink of collapse. But the data tells a radically different story. America 250 is not a eulogy. It is a celebration grounded in economic history, human ambition, and the rare national DNA that makes this country unlike any other on earth. The story of America 250 is not just about survival. It is about a country that has repeatedly invented entirely new categories of value from nothing, attracting dreamers from every corner of the globe who recognize something that many native-born Americans take for granted. Understanding what America actually is, rather than what the anger merchants want you to believe, is the starting point for seeing where it is going next. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   The Anger Industrial Complex Is Manipulating You The most important thing to understand about the current state of American political culture is that the division you feel is largely manufactured. Politicians, legacy media, and social media algorithms have built extraordinarily profitable business models on your outrage. Fundraising emails do not celebrate progress or bipartisan cooperation. They warn you that the other side is coming for everything you love. Cable news stopped booking reasonable people because screaming is more watchable. Then social media arrived with algorithms engineered to identify with inhuman precision exactly what makes you angry, and serve you more of it every hour. Here is what those category leaders of manufactured rage never want you to know. On guns, taxes, immigration, abortion, equal rights, policing, gay marriage, the national debt, and entrepreneurship, Americans mostly agree. 91% of Americans believe anyone regardless of race deserves an equal opportunity to succeed. 94% approve of interracial marriage, up from just 4% in 1958. 81% of Americans support universal background checks, including 80% of Republicans. 94% believe every citizen deserves a fair shot to start and grow a business. These numbers cut cleanly across party lines and receive zero coverage because agreement does not generate revenue. The pattern is consistent and deliberate. Every time Americans broadly agree on something, the machine finds the 5 to 15% on either extreme of the bell curve who do not, puts them on television, feeds them into the algorithm, and collects revenue by monetizing anger manufactured from nearly nothing. A citizen who stops being angry is a bad customer, and that is precisely why the machine never stops running.   America Is a Catapult, Not a Club What makes America 250 worth celebrating is not just its age. It is its architecture. In Gallup surveys conducted across 150 countries since 2007, one question has been asked consistently: if you could move anywhere on earth, where would you go? Every single year, 170 million people choose the United States. The runner-up draws half that number. China has four times America’s population and a foreign-born population of just 0.1%. The United States sits at 15%. People do not want to move to America because it is the best. They want to move here because it is different. Nearly every other country on earth functions like a club, one you are born into or spend a lifetime trying to enter. America was purpose-built as a catapult for people driven by dreams, pirates, innovators, and those desperate enough to bet everything on a different future. The founder of SoftBank, one of the wealthiest people in Japan, was born ethnically Korean and was bullied to the point of contemplating suicide, denied credit in Japanese business specifically because of his ethnicity. That story plays out differently in America, where meritocracy at its best does not ask where you came from or what school you attended. Two families, two wars, two bets on a different future in the same country capture this perfectly. One grandfather left Scotland after World War Two for a rubber factory job in Montreal. One father left Korea to become a janitor and a limo driver in Hawaii. Neither came for comfort. Both came for the removal of limits on what their children could become. America 250 is the story of those bets paying off across generations.   The Jevons Paradox and the Next 250 Years In 1865, British economist William Stanley Jevons noticed something counterintuitive. As steam engines became more efficient and required less coal to do the same work, experts predicted coal consumption would fall. Instead, it exploded. Greater efficiency lowered the cost of power, which expanded adoption, which created entirely new categories of economic activity that had not existed before. Jevons called it a paradox, and it is the single best framework for understanding America’s economic history. From a GDP of roughly 193 million in 1790 to over 30 trillion today, America did not simply get better at existing industries. It invented the railroad, then electricity, then the automobile, then the computer, then the internet. Each one was a new category. Each one created massive value from nothing. The internet alone generated approximately 16 trillion in new global economic value over 30 years, more than half of total world GDP in 1995, built entirely from scratch by entrepreneurs. Before the internet, no one needed a web engineer, a search algorithm, or a social media manager. New categories create new categories. AI is now the next expression of the Jevons paradox at a civilizational scale. Goldman Sachs projects AI will raise global GDP by 7% over the next ten years. PwC projects AI could contribute 15.7 trillion by 2030 alone, nearly matching the internet’s entire 30-year impact in under a decade. If AI creates twice the proportional value the internet did, that is 110 trillion in new economic value built on top of the existing world economy. America 250 is not the end of a story. It is the opening chapter of the most consequential economic category in human history, and America is positioned at its center. To hear more from Christopher Lochhead and about America 250 & beyond, download and listen to this episode. You can also check out his thoughts on America as a Different Category of Country.   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), LinkedIn, and subscribe on Apple Podcast / Spotify!  

“ I'm a believer in this technology. I believe that the cheaper it is, the more people will use it. So, even if we assume that models will get smaller for the same level of intelligence, the demand to actually have more compute is going to be enormous. And the reason why you want to have the, the compute close to you is not only in terms of performance. As a national security threat, I'm not saying this will happen, but it could happen that, China or US or whoever decides to stop exporting compute to the rest of the world. If that happens today, Europe has zero AI at the scale of data the data center.”For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:22) The hidden risks of the AI debt bubble(0:55) Shadow borrowing and the data center boom(3:12) The AI compute gap and national security(5:40) Europe's missing infrastructure and tech sovereignty(8:13) The Jevons paradox and the demand for compute(10:25) Why open source AI is the best path forward(12:31) The chess paradox and the future of human art(12:53) Unmeasurable domains and future-proofing your career(14:00) Tutoring and AI in education(15:11) Navigating AI slop and the information ecosystem(16:24) The Pleasure of Waiting vs. The HustleEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

The biggest AI stocks have had a remarkable run – but questions still remain. Our Head of Americas Specialty Sales, Thomas Wigg, speaks with Global Head of Thematic and Sustainability Research Stephen Byrd and Global Head of Public Policy Research Ariana Salvatore about the competition and durability of the investment cycle.Read more insights from Morgan Stanley.----- Transcript ----- Thomas Wigg: Welcome to Thoughts on the Market. I'm Tom Wigg, Morgan Stanley's Head of Americas Specialty Sales. Stephen Byrd: I'm Stephen Byrd, Morgan Stanley's Global Head of Thematic and Sustainability Research. Ariana Salvatore: And I'm Ariana Salvatore, Morgan Stanley's Head of Public Policy Research. Thomas Wigg: Today, the rally in AI CapEx beneficiaries has taken a breather in recent weeks on concerns of competition from open-source models, backlash to token-maxxing, and growing political opposition to data center builds. It's Tuesday, July 7th at 10am in New York. Let's start with you, Stephen. There's a lot of discussion recently around a backlash at token-maxxing. Essentially, enterprises trying to curtail their high spending on AI tokens from the frontier labs, and, in many cases, shifting to cheaper open-source China models. Can you first offer some perspective here on the value of tokens for enterprises? I know you have a popular token factory model that walks through the economics of agents. Stephen Byrd: Yeah, Tom, we do have this model that really walks through token economics, both from the adopter side as well as the hyperscaler side. So, let's do the adopter side. So, there's a study out that shows a whole range of enterprise use cases of AI, and the average single use case that they identify would save a company about $55 or provide that much benefit. And while we don't know exactly how many tokens it will require, we can make some educated guesses as to a typical token usage to achieve that $55 outcome. And we know that a typical American model, though this varies a lot, you can think of as the cost per million tokens being in the range of $5 per million. Some will be lower, some will be higher. So, for a few dollars of token cost, an enterprise can generate benefit of $55. So that doesn't make me overly concerned about token spend and concerns about token-maxxing. I know we're going to get into that, but the foundation here is really good in the sense that enterprise use cases are very much in the money. Thomas Wigg: How do you think market share ultimately shakes out on tokens? Do the cheaper models overtake the frontier AI labs? Do tokens bifurcate based on the complexity of workloads? How do you think this plays out? Stephen Byrd: What we continue to see is this relentless pace of innovation and cost reduction. So, the frontier keeps going out – meaning model capabilities continue to increase, and, with that, we see enterprise adoption growing quite a bit. Long way to say there is a role for both the frontier as well as these open-source models, and we'll continue to see both flourish. What I see is a lot of tokens will be spent on open-source models. A lot of the value will be in the higher end models because that's where enterprises are going to go. Let me give you an example. I was speaking with one of our programmers about a recent project, and he used a very high-end coding tool, an American coding tool. And for him, that incremental cost of the tokens was very much worth it. And here's a very practical example as to why it makes sense for many enterprises to use the higher end models. If a coding tool gets one of the thousands of lines of code wrong, the cost to remediate is very, very high. In other words, that incremental cost – in this example I'm thinking of, it's a few dollars incremental cost – is so worth it because if the quality is not there, the cost to any enterprise to go back and remediate is so high. And that's true in a lot of enterprise use cases, but not in every use case. And what we are seeing is these open-source models that are cheaper will be very good for a variety of more mundane use cases that are still very valuable. That said, what we've seen in data from places like OpenRouter is dollar-weighted, meaning valued by enterprise spend, the vast majority is still the proprietary models. But even within proprietary models, we could have more expensive and less expensive models. You do not need to go to the frontier. Where I come out on all this is that I'm very confident that the demand for compute is going to exceed the supply. What is difficult to exactly know is who are the winners, what is the exact mix. But the fundamentals of the demand for compute look extremely strong. Thomas Wigg: So, I think you just gave me the answer, but I do want to bring this all back to AI CapEx. Now, last year, when the market sold off on Deep Seek concerns, the concept of Jevons paradox ultimately prevailed, where the cheaper pricing led to even greater demand and CapEx went higher.Do you think the same plays out here? Stephen Byrd: It does look that way very much. And the Jevons paradox dynamic is what we still see today in the sense that as the models get better, what we can do with the models increase, the cost of tokens will keep dropping, the cost of compute will keep dropping.But let's talk about what might derail that, just to make sure we're thinking about all the risks. If somehow commoditized models could perform at the same level as proprietary models in all situations, then I would feel differently. But I don't see that. What I see is that these newer models really do have capabilities that are fairly breathtaking and that are worth that extra money. But if somehow, we hit a wall where these models aren't getting better and therefore the sort of the open models are going to catch up, then I'd feel differently about that. This is where Ariana will, will come in in terms of policy and, you know, this comes up a lot when we think about U.S. versus China. How do we think about, you know, access to different models? How do we think about the cost of different models? What about the risk of appropriation of capabilities by the Chinese firms, for example? That comes up a lot in policy circles. But the base case that I have is this just looks more like Jevons paradox, and there's going to be continued innovation, continued reduction in the cost of producing these services from these models. That looks like more of the same. Thomas Wigg: Let's shift to Ariana to talk about the political angle here. The cover of Barron's over the weekend was a guy wearing a no data centers T-shirt. And this does seem to be one of the few bipartisan issues of agreement heading into the midterms.The stat that the article gave was that 75 data center projects worth $130 billion were blocked or delayed in 1Q26, which is equal to the total number for 2025. This is according to Data Center Watch. Now, most of this is in blue states like New York, Michigan, Illinois, Minnesota considering a statewide moratorium, but you're also seeing Pennsylvania, Arizona, Ohio, parts of Texas restricting tax incentives here. So as this gets louder into the midterms, how do you think this plays out? Ariana Salvatore: So, this is definitely one of the big wedge issues, not just for the midterm elections, but for 2028. And to your point, it's expanding into something that's got bipartisan momentum behind it. Our view is that as long as the Trump administration is in power, something like a federal ban is unlikely to come to fruition. That's because we think the administration is still broadly supportive of the AI data center build-out. And I think even if you were to see a Democrat in office further down the road, that position is the same. And the reason is, it's just too difficult to imagine the U.S. giving up that strategic imperative relative to China. So, while it is true that voters are against AI, while it is true that you are seeing these sorts of local efforts pick up steam, it's also the case that China is accelerating its own AI build-out – not just domestically, but around the rest of the world too. It's also the case that they are kind of tweaking some export restrictions on inputs for some of these data centers, and those geopolitical realities, I think, are hard to ignore. So, at the end of the day, there is a broader strategic imperative here that both Democrats and Republicans kind of recognize and get behind. Now, what does that mean in the near term for the build-out? I think it's not that you're going to see a real pushback or moratorium so much as a conditional build-out.That means you're going to see data centers have to incorporate things like grid modernization in their contracts, agree to longer term investments, for example. Do something that benefits the communities or give it back in some way. And I think that's kind of the policy trajectory in addition to the administration continuing to lean on tech companies to basically, you know, square the circle here and find some way to make this more affordable for, you know, local constituents. Thomas Wigg: Stephen, let me get your take on this too, because I know you live in the D.C. area, and you have a lot of political conversations like you referenced earlier. How do you think this plays out? Is it a red state versus blue state dynamic? And if what Ariana says comes to fruition, where it's a conditional build-out in terms of either giving back to the community or ensuring certain prices or certain technologies behind the meter, in front of the meter, does that have implications for certain areas of the market? Stephen Byrd: Yeah. First, I think Ariana's points were all spot on. I just want to, kind of, build on that and, and dive into it a little more detail. A few things. The politics are, from my perspective, not being the expert that Ariana is, I find them a little strange – in the sense that at the federal level, we have one dynamic, and at the state and local level, we have a bit of a different dynamic. And what I mean by that is, at the federal level, I think it's becoming increasingly clear just how geopolitically important AI supremacy is. As these models get more capable, I think it's pretty clear that the Trump administration really sees just how potent these tools are from a geopolitical point of view. So that points in the direction of wanting to support AI and wanting to ensure that the United States has a leading and dominant position in terms of AI capabilities. Pause there, and then go to your point about, sort of, the local and state level. Building on what Ariana said, what I see are basically two approaches to data center development. In states where the utility is vertically integrated, meaning they control everything, like Louisiana, I do see a path where – in those kinds of states where the politics are a bit more favorable – you could develop a data center connected to the grid, where the data center developer is paying full freight and then some. Meaning that they are providing back to the community, they're providing sort of net benefits, and there should be plenty of capital to make that work and really support all constituents. That can work – in a state where the politics work – because utilities are really weather vanes from a political point of view. So, if their state supports data center development, they will more likely support a data center development. The other approach, though, in many states, whether it's deregulated or it's in a state where the politics are a little less favorable. Which, to your point on the cover of Barron's, it's a lot of states, what I'm increasingly seeing is that the developers are going to go off grid. And they just don't want to show any impact to the community that could be considered negative. So, no use of water, no use of power, and hopefully have a, you know, low or zero emissions profile to show no impact at all. Even then, you want to give back to the community. But the view there is, look, we want to sidestep all of these concerns that we might be causing impacts to the grid by just not being connected. So, I think we're going to see a whole lot of off-grid data center projects. That's mostly natural gas turbines and fuel cells, that general approach. Energy storage will be required in a big way. That's not easy to do. So, in the context of delays there, the Bitcoin players who do have grid access today are clearly seeing a lot of demand for their products. So, I would say politics is now a huge issue that's showing up. The other thing I'd flag is often local communities and states are rejecting projects and using permit requests as a way to do that. So, for example, if your data center needs an air permit because your turbines are going to emit some kind of an, you know, sulfur dioxide, et cetera, into the air, you can run into trouble there. If your data center requires water and you need a water permit, you can run into trouble. So, that's causing these developers to try to find approaches that really minimize or eliminate the need for those kinds of permits. Thomas Wigg: Stephen and Ariana, thank you for taking the time. And to our audience, thank you for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen to the show and share the podcast with a friend or colleague today.*****Tom Wigg is a member of Morgan Stanley's Institutional Equity Division and is not a member of Morgan Stanley's Research Department. Unless otherwise indicated, his views are his own and may differ from the views of the Morgan Stanley Research Department and from the views of others within Morgan Stanley.

82% of top-grossing apps now take payment outside the app store. Patrick Stuart-Constant's team at Sociaaal ships 4,000 video ads a month, scaling to 10,000. The shift to web-to-app monetization isn't coming. It already happened.Part 1 of a two-part panel from a Funnelfox webinar, with Rocketship HQ as partner. Shamanth Rao moderated the conversation with Jacob Rushfinn (CEO, Botsi), Andrey Shakhtin (CEO, FunnelFox), Patrick Stuart-Constant (CEO, Sociaaal), Mike Gadd (VP Customer Success, Singular), and Elise Zareie (Head of Paid Social, Lingokids) on how paid acquisition teams design the path after the click.Andrey Shakhtin on why off-store attribution beats SKAN on speed, and how 2-5% payment fees unlock room to iterate. Patrick Stuart-Constant on scaling ad volume as a search space problem, and why cheaper AI creative grew his team instead of shrinking it (Jevons paradox). Elise Zareie on why web funnels only work with dedicated ownership inside the UA team. Mike Gadd on why the first renewal is the metric most teams miss. Jacob Rushfinn on the $1M revenue threshold before web funnels pay off, and the 10K to 20K monthly loss to budget for the learning phase.Video Chapters:00:00 Introductions and format02:40 The one shift teams have not priced in07:50 Andrey on the 82% off-store payments finding09:52 Mike on measurement across web and app, platform hell11:45 Elise on creative: lift-and-shift vs TikTok-native15:54 Patrick on the search space, 4K to 10K ads a month18:07 Jevons paradox: cheaper ads, bigger creative team19:30 Elise on where AI helps and where taste is still human21:39 What separates web-funnel winners from teams that quit25:27 Andrey on iteration pace correlating with revenue27:00 Who should not try web funnels: payments complexity28:42 Jacob on the $1M threshold and the learning-phase budget29:36 Common learning phase mistakes and how to short-circuit them32:00 Part 2 drops next weekTopics covered:- The shift to web-to-app monetization and off-store payments- AI creative production at scale, 4,000 to 10,000 ads a month- Web funnels: dedicated ownership vs side project- The $1M revenue threshold for going off-store- Learning phase mistakes on acquisition, funnel, and paywall- Measurement complexity across web and appLearn more:- Original webinar recording: https://www.youtube.com/watch?v=OYPDZlwLhNA- FunnelFox (webinar host): https://funnelfox.com- Rocketship HQ (partner): https://rocketshiphq.com- Jacob Rushfinn (Botsi): https://www.linkedin.com/in/jacob-rushfinn/- Andrey Shakhtin (FunnelFox): https://www.linkedin.com/in/andrey-shakhtin/- Patrick Stuart-Constant (Sociaaal): https://www.linkedin.com/in/patrick-stuart-constant/- Mike Gadd (Singular): https://www.linkedin.com/in/mike-gadd-27a16427/- Elise Zareie (Lingokids): https://www.linkedin.com/in/elise-zareie/- Shamanth Rao (Rocketship HQ): https://www.linkedin.com/in/shamanthrao/

For over a decade, technology has promised to make life easier. Social media was meant to connect us, we were told smartphones would simplify our lives, and now artificial intelligence promises to lift even more daily burdens. Yet these advances also raise an important question: when do tools enhance human creativity, and when do they replace the very experiences that give life meaning? While AI holds immense promise, many people face challenges coping with current technological advancements that ‘redefine' human communication, work, education, and democratic values. Today, we examine societal and cultural impacts of AI, identity, inclusion, governance, education, and community well-being. And are speaking with Nacho de Gregorio, author of TheWhiteBox, one of Medium's most-followed AI analysts, for a level-headed conversation about both the remarkable potential and the real limitations of AI.(0:00) The AI compute gap and national security(1:56) The hidden risks of the AI debt bubble(4:40) Separating AI hype from job displacements(12:38) Shadow borrowing and the data center boom(17:19) Europe's missing infrastructure and tech sovereignty(21:52) The Jevons paradox and the demand for compute(26:37) Regulatory capture and doomerism(31:12) Why open source AI is the best path forward(42:19) The chess paradox and the future of human art(46:35) Unmeasurable domains and future-proofing your career(53:45) Tutoring and AI in education(1:01:48) Navigating the AI slop and information ecosystem(1:16:00) Finding beauty in the natural world(1:20:18) The pleasure of waiting vs. the hustle(1:35:01) Setting healthy boundaries with technologyEpisode Websitewww.creativeprocess.info/podInstagram:@creativeprocesspodcast

Fresh out of the studio, Benedict Evans, independent technology analyst and author of AI Eats the World, returns to explore whether the AI model layer is becoming commodity infrastructure. Benedict argues there is no winner-takes-all effect in models yet, drawing parallels to telecoms, cloud, chips and the fiber bubble to ask where durable value actually accrues when everyone runs similar infrastructure on similar tokens. He unpacks why the chatbot remains a poor interface, introduces the "blank screen" and "jagged frontier" problems that keep software companies alive, and explains why large language models inherently give you "the average." Closing the conversation, Benedict reflects on the indicators that would show AI has truly eaten the world — and why the answer is better products, not better models."When you automate away work, you can always see the jobs that are going away because they're right there. And you don't know what the new jobs are going to be. Human needs are infinite. How many people are earning a living from making podcasts now? Imagine predicting that 10 years ago. There's a stage in the evolution of the market where like if you're still arguing about that, you're an idiot. But there's a stage at the beginning where you might have opinions about some of these questions, you're probably not even asking the right questions. That, I think, is where we are with this stuff today." — Benedict EvansEpisode Highlights: [00:00] Quote of the Day by Benedict Evans from AI Eats the World[01:16] The public market test: what are investors buying?[04:21] How far up the stack can models go?[05:30] Models can't build all the apps themselves[06:00] The thesis: models as commodity infrastructure[07:52] "All the value went up the stack"[08:24] Chips and Rock's Law: down to three players[11:23] The 1999 reseller story: one-time sales[13:28] The S-curve framing of technology[16:38] You're probably not asking the right questions on AI[18:02] "If this works, we're competing with a Mac"[20:25] Incumbents make it a feature[22:14] Big tech "killing startups" is overstated[24:39] Cowork as the new spreadsheet[26:01] The blank-screen and jagged-frontier problems[29:00] The hard part isn't writing the code[31:25] "What a good answer would probably look like"[33:38] The job displacement debate[37:38] Jevons paradox and the lump-of-labour fallacy[40:30] LLMs inherently give you the average[42:36] Why you really hire McKinsey[45:33] Punk versus prog rock: outside the training data[49:00] Automating ever-higher human functions[49:55] Why this is unanswerable: no theory of scaling[51:30] Indicators that AI has eaten the world[54:53] The solution isn't a better model[56:39] Where to find Benedict EvansProfile: Benedict Evans, Independent Technology AnalystLinkedIn: https://www.linkedin.com/in/benedictevans/Website: https://www.ben-evans.com/newsletterPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.Here are the links to watch or listen to our podcast.Analyse Podcast Main Site: https://analysepodcast.comAnalyse Podcast Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl Analyse Podcast Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245 Analyse Podcast LinkedIn: https://www.linkedin.com/company/analyse-podcast/Sign Up for Our This Week in Asia Newsletter: https://www.analysepodcast.com/#/portal/signup Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288

Code-based PCB design is reshaping how engineers build hardware, and in this OnTrack Podcast episode, host Zach Peterson sits down with Ioannis Papamanoglou and Narayan Powderly, co-founders of atopile, to explore their code-first, AI-driven approach to PCB design. Drawing on backgrounds at Tesla and across the electronics industry, the founders explain why hardware has lagged behind software tooling and how capturing engineering intent in code can unlock automation across the entire design process — from requirements capture to schematic capture and layout. You'll learn why the real bottleneck in AI for PCB design is tooling and orchestration rather than raw model capability, how deterministic and nondeterministic layers work together, and why the designer's role is shifting toward system architecture as low-level boilerplate disappears. The conversation also covers test board generation, the Jevons paradox effect on engineering resources, supply chain and firmware integration, and a live demo showing atopile working alongside Altium Designer. Whether you're a PCB designer, hardware engineer, or curious about AI in electronics, this episode offers a grounded look at where code-based hardware design is headed.

Sign up for Practi, a new platform that helps law firms use subscription billing.Here are the top 5 takeaways from this episode:* Lawyers need an AI strategy and policy first. Before adopting any tools, firms must have a written AI policy, even if it simply says no tools are approved yet. Without one, a staff member using an unapproved (non-enterprise) AI tool can cause an ethical breach if client data ends up in model training.* Stick to two AI tools, not a dozen. Jennifer recommends picking one AI within your existing workspace (Copilot if on Microsoft, Gemini if on Google) plus one secondary tool for drafting or checking work. Chasing every new model is counterproductive. Depth beats breadth.* Document infrastructure is the real foundation. Before AI can be useful, a firm's documents need to be organized, accessible, and OCR'd where necessary. Getting documents into a state where an AI can actually “talk” to them is the unglamorous but critical first step.* Claude (especially via Claude Code/Cowork) is the top recommendation for legal writing. For transactional work requiring a long context window, Jennifer sees Claude as unmatched. She's actively installing Claude's Cowork integration for clients, who are amazed at its ability to handle contract redlines directly in their workflow.* AI increases productivity but also workload. Jennifer invokes Jevons' Paradox: AI tools make lawyers faster, but that extra time tends to get filled with more work. The real win is choosing intentionally: take on more clients, deepen client relationships, or bill at a higher rate, rather than just working more hours.__________________________Want your question to be answered on a future show? Fill out this short survey.Have subscription model question? Check out this free resource to ask all of your questions at notebook.practi.ai.Check out Law Tech AI.Sign up for Paxton, my all-in-one AI legal assistant, helping me with legal research, analysis, drafting, and enhancing existing legal work product.Get Connected with SixFifty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a business and employment legal document automation tool.Sign up for ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Gavel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, an automation platform for law firms.Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Law Subscribed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to subscribe to the weekly newsletter to listen from your web browser.Prefer monthly updates? Sign up for the Law Subscribed Monthly Digest on LinkedIn.Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mathew Kerbis'⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ law firm ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscription Attorney LLC⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Want to use the subscription model for your law firm? Click here to sign up for a new platform that helps law firms use subscription billing. Get full access to Law Subscribed at www.lawsubscribed.com/subscribe

We talk to Michał Zalewski (lcamtuf) about the vulnpocalypse and if we even need fuzzers anymore. This episode may be export controlled at a future date.Watch on YouTube: https://www.youtube.com/watch?v=uI9CSgB4p9oTranscript: https://securitycryptographywhatever.com/2026/06/14/facing-the-vulnpocalypse-with-lcamtufhttps://github.com/google/aflhttps://www.reddit.com/r/claude/comments/1tqtenf/anthropic_said_today_that_mythos_is_coming_to_all/https://github.com/google/clusterfuzzhttps://en.wikipedia.org/wiki/Jevons_paradoxhttps://en.wikipedia.org/wiki/XZ_Utils_backdoorhttps://en.wikipedia.org/wiki/Brighton_hotel_bombinghttps://curl.se/https://ftp.openbsd.org/pub/OpenBSD/patches/7.8/common/025_sack.patch.sighttps://www.wired.com/story/last-pass-vulnerability-password-safe/https://nostarch.com/tangledwebhttps://nostarch.com/silence.htmhttps://nostarch.com/practical-doomsdayhttps://nostarch.com/secret-life-of-circuitshttps://www.youtube.com/c/3blue1brown"Security Cryptography Whatever" is hosted by Deirdre Connolly (@durumcrustulum), Thomas Ptacek (@tqbf), and David Adrian (@dadrian)

00:01 1999 igjen: to skrekkfilm-hiter og «this time it's different»00:04 Rekordbelåning og margin debt på all time high00:05 Opsjonsjaget vi ikke har sett siden 198700:08 Short gamma, marketmakere og spiralen som ga «Red Friday»00:14 Ingenting virket: bare lang volatilitet beskyttet00:17 Laveste korrelasjoner på to år og VIX opp 40 prosent00:18 Bank of America: «here be dragons» og ledighet mot inflasjon00:20 Bilen, AI og Jevons-paradokset00:24 SpaceX som datasenterselskap, ikke rakettselskap00:30 Børsnotering denne uka: 1770 milliarder og Musks absolutte makt00:31 S&P-nekten mot FTSE, Russell og MSCI00:32 Lockup-kalenderen og dagen å frykte: seks måneder og fire dager00:35 Grok mot Groq og «race to zero» i modellene00:40 Midtøsten: Trump mot Netanyahu og oljeprisen00:44 Hva folk ikke ser på nå: bear flattening og carry trades som ryker00:47 Dollar over 161 og japansk intervensjon00:49 Hudson River Trading og datasenteret i Norge00:51 Norge har misforstått seg selv: fisk, olje, rå kraft og nå compute00:53 Å raffinere compute: Skygard, spillvarme og 10X på krafta00:58 Compute som multiplikator: fra 10x-ere til 100x-ere01:00 Budsjettforliket, Mímir Kristjánsson og minstepensjonistene01:05 Å prestere når alt er mulig: fokus, nysgjerrighet og flytskjemaer01:11 Telefonen som heroin: reels, 24-timers reset og hjernen tilbake01:19 Trikkedrapet og situational awareness01:24 Varsler i stedet for å glo på skjermen: gull/sølv og momentum01:35 1998: LTCM, doblede posisjoner og banken som tapte 900 millioner01:45 Andrew Left, Citron og short-saken som ble svindel01:50 Oraclum, superforecasters og nordmannen på topp01:56 Drewry-indeksen, VM-frakt og Fifas fredspris til Trump Hosted on Acast. See acast.com/privacy for more information.

June 2, 2026: Senator Bernie Sanders wants the federal government to own half of OpenAI, Anthropic, and every major AI company in America — and he's framing it as reclaiming stolen public knowledge. We break down exactly how his American AI Sovereign Wealth Fund Act would work, why the Norway comparison falls apart, and what would actually happen to valuations, talent, and American competitiveness if it ever got close to passing. Then: Apollo Global Management's chief economist says there is zero evidence AI is killing jobs — and the data may actually back him up. We look at Jevons paradox, the AI washing phenomenon, and why the aggregate labor market story is more encouraging than the doom headlines suggest. And finally: new Federal Reserve research reveals that 64% of the rise in young worker unemployment since the pandemic traces back to remote work, not AI — and why being willing to go to the office five days a week may be the single best career move a worker in their twenties can make right now.

Most of the AI timeline debate happens in software. Benchmark scores, model releases, the shape of the capability curve. Jon Billow watches a different number for a living: lead times.Billow is on the leadership team at BNS, a firm that manufactures and installs electrical and communication infrastructure. The same critical power equipment his teams put into data centers also goes onto Navy and Coast Guard ships, more than 150 of them. He emailed John Sherman because he thinks the people forecasting AI's arrival are missing what he sees on the construction side every week. The buildout can only move as fast as its slowest part, and right now almost every part is backed up for years.That email is what got him on the show. Here is the heart of what he laid out.The constraint nobody prices inTo bring a large data center online, Billow says, a long list of things has to land at the same time: permitting, grid interconnect, critical power, cooling, and the compute itself. Miss one and the whole project waits. And nearly every item on that list carries a backlog measured in many months, sometimes years.The pinch point he keeps returning to is critical power equipment. According to Billow, the orders all funnel back to roughly five manufacturers, Eaton, ABB, Schneider, GE Vernova among them, and all of them are slammed. He notes that even the US government is having a hard time getting its allocation for ship programs, because it is standing in the same line as every hyperscaler. On top of that, more municipalities are now requiring data centers to bring their own behind-the-meter power generation, which adds another category of equipment backlog and a skill most operators have never needed before. Hooking up to the grid is one thing. Building gas turbines and finding electricians who can parallel generators is another, and the skilled trades are already stretched thin.A factor of five to sevenSherman pushed him to put a number on the gap. If a company says a project lands in a year, how far off is that really?Billow's read: the US has roughly 50 gigawatts of total data center capacity today, with about a quarter of it allocated to AI. Around five gigawatts are under active construction and another seven to twelve sit in backlog. Set that against the order-of-magnitude jumps the labs are talking about and his estimate is blunt. “If I was to be a betting man I would say it's in the order of five to seven years.” Whatever timeline you have been handed, in other words, multiply it.The tells from inside the labsHe pointed to two recent signals that the infrastructure is already the limiting factor. OpenAI walking back a large commitment tied to its Sora video product, which Billow reads as a company looking at finite compute and deciding where to spend it. And Anthropic delaying a model, which he attributes partly to security concerns and partly to the reality of constrained compute capacity. The software keeps leapfrogging. The ground underneath it does not move at the same speed.Why this could be good newsBillow does not frame any of this as a reason to relax. He frames it as time. If the physical buildout runs years behind the hype, that is runway to get governance and alignment right rather than scrambling after the fact. He drew the parallel Sherman's audience knows well, comparing the moment to how the world slowly built doctrine around nuclear risk, and argued the work now is to use the delay deliberately.His closing image stuck with us. He said he wants to tell his grandkids that we were building the car while it was going down the road at 55 miles an hour, but we had the presence of mind to put in seat belts because we knew who was in the back seat.Where they did not agreeThe conversation did not paper over the tension. Sherman described his time in Holly Ridge, Louisiana, a town of about 2,000 mostly elderly people living next to a data center he compared to the size of Manhattan, with construction dust in the air and water residents will not drink. He found it overwhelmingly sad. Billow sees the same structures differently, as a testament to human ingenuity that can be sited and built responsibly if we choose to. Both things sat in the room at once, and the episode is better for letting them.Going deeperWe pulled the headline argument into this piece. The full breakdown for paid subscribers goes into the parts that get more technical and more political:* Compute governance as the most feasible near-term guardrail, including chip tracking and why the industry pushes back hard* The anonymous-compute problem and why “confidential computing” worries safety researchers* China's narrow-AI approach and what it implies about the data center race* Recursive self-improvement, Jevons paradox, and whether you even need new data centers to reach the danger zone* The regulatory carve-out tech enjoys, and the NDA story coming out of LouisianaIf you want that version, upgrade your subscription and it lands in your inbox. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com/subscribe

Chris Paul and Burning Bright tackle David Ayer's 2014 World War II film Fury, starring Brad Pitt, Shia LaBeouf, Logan Lerman, Jon Bernthal, and Michael Pena. Burning Bright picked it as a Memorial Day rewatch and argues it is one of the most underrated war films of the modern era, deserving way more credit than Saving Private Ryan style lionization tends to allow. The guys dig into the five very different spiritual approaches of the tank crew, the dehumanization of war daddy, bible, gordo, kunas, and the painfully innocent Norman, and why the infamous early execution scene is not the glorification it gets accused of being. They unpack the central biblical passage from First John chapter two, do not love the world or anything in the world, as the real moral spine of the film and the heart of all discernment. From there they go big picture, hitting Jevons paradox and how better military tech just means more efficient mass sacrifice, why World War II had the cleanest cartoon story of any modern war, the controlled opposition Nazi op being run on MAGA right now, narrative shielding through Donald Trump's hyper Zionist posture, and the fiery tank as a birth canal delivering Norman into a second chance.

Jeff Schulze of ClearBridge Investments joins host John Przygocki to assess the health of the US economy. He points to a resilient Q1 GDP reading, strong jobs data and earnings growth in estimating a 30% recession probability. He cites the Jevons paradox to explain why he doesn't fear an AI "job apocalypse." And he remains bullish on US equities despite Middle East uncertainty and inflation worries. 

Cuando cada mejora te cuesta más: la trampa de la eficiencia. Para escuchar sobre el tema de System Sprawl al que hago referencia en este episodio, haz clic en este enlace. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Será que a Inteligência Artificial vai destruir todos os empregos? Neste vídeo, desmistificamos o alarmismo digital com base na teoria econômica e na história. Entenda o Paradoxo de Jevons , como o Excel revolucionou carreiras e por que a IA é a ferramenta definitiva para aumentar sua produtividade.

In this episode, we break down the Jevons paradox and why AI may not simply replace work but reshape it. As the cost of intelligence falls, demand for analysis, coding, drafting, design, service, and research may rise, shifting how investors think about growth and productivity. To read this week's Sight|Lines, click here. The views expressed in this podcast may not necessarily reflect the views of Stifel Financial Corp. or its affiliates (collectively, Stifel). This communication is provided for information purposes only. Past performance does not guarantee future results. Investing involves risk, including the possible loss of principal. Asset allocation and diversification do not ensure a profit or protect against loss. © Stifel, Nicolaus & Company, Incorporated | Member SIPC & NYSE | www.stifel.com See omnystudio.com/listener for privacy information.

Is the AI job apocalypse a marketing strategy and not an economic forecast?It's nuanced, and today we tap into first wave stats for clarity.Correlation does Not imply Causation. The divergence is real, but timing also coincides with the Fed's aggressive rate hikes and sustained tightening. That said, AI is likely to reshape the labor market—not just in the number of jobs, but in the types of roles that exist - after giants clean up the BLOAT.People think the AI job crisis is about technology, but it's really about wealth inequality. - Scott GallowayPURE FACT. Every generation has its version of this story.The one where machines come for the jobs.Where the future arrives faster than people can adapt.Where the world you knew is about to end.This time around, the story has better PR and a much bigger budget — but underneath, it's the same script.I'm not saying this is nothing to worry about - we all feel the speed. In fact, the current evolution of AI is 300X faster than the Industrial Revolution. So….here's what I keep coming back to so we can understand the middle phase of the tech boom we are living through. The loudest voices warning us about the AI job apocalypse are also the people who profit most when we believe them.Anthropic's CEO says half of all entry-level white-collar jobs will be wiped out in five years.Elon says no job will be needed.Sam Altman wrote, before ChatGPT even launched, that the price of human labor was about to fall toward zero.Notice the pattern?The people predicting an extinction-level event are the same people building the asteroid and selling tickets to watch.We've Been Here BeforeThis panic isn't new.The Nobel-winning economist Robert Shiller has shown that fears about machines replacing humans helped fuel economic downturns in the 1800s.Science fiction later convinced people that automation caused the Great Depression.Computer panic deepened the recession of the early ‘80s.His point was simple.The damage doesn't usually come from the technology itself.It comes from the story we wrap around it.People feel pain from a normal recession, blame the machines, get more pessimistic, pull back further, and the story becomes the thing that creates the outcome it warned us about.That's exactly what I think is happening right now.AI is becoming a convenient cover story for layoffs that are really about over-hiring, inflation, and tariffs.Look at the numbers.U.S. tech employment grew from 8.7 million in 2020 to 9.6 million in 2023, then went flat.Not great.Not the apocalypse either.Meta's 10% cut is just bringing the company back to its 2021 size.Microsoft's 7% cut still leaves it 47% bigger than before the pandemic.Tesla announced it was hiring more, then laid off 10% of its workforce a month later — because of weak sales, not robots.This isn't the prelude to the end of work.It's a low-hire, low-fire labor market.That's it.Three Ways This Resurgence Plays OutScenario one: the bubble pops.The Mag 10 now make up 40% of the S&P.AI stocks have driven the majority of the market's returns since ChatGPT launched.If AI sneezes, the rest of the economy gets the flu.And when that recession comes, we'll blame AI for it — even though, historically, layoffs come in recessionary bursts, not the moment a new technology arrives.Scenario two: AI delivers, just slower than they say.When something gets dramatically cheaper, we don't use less of it.We find a million new uses for it.That's Jevons paradox.When the spreadsheet launched in 1979, everyone said accountants were finished.Instead, the profession quadrupled over the next 40 years.The same pattern shows up everywhere computers got adopted heavily — employment grew faster, not slower.Programmers today are coding less and thinking bigger.They've gone from construction workers to architects.The real question for any knowledge profession isn't “will AI replace this?”It's “is the human demand for analysis, judgment, and oversight elastic?”I think it is.And I think we're about to discover how much demand has been quietly waiting for the cost of execution to drop.Scenario three: the disruption outruns us.This is the scary one.AI hits every sector at once, no policy response, full collapse of the recovery cycle.But here's the part most people miss.Real societal upheaval almost never comes from unemployment.It comes from people who are working hard and still falling behind.From the loss of economic dignity.If that sounds familiar, trust your gut.We're already living in it.What's Really Going OnInside Silicon Valley, the mood is dark.People talk seriously about a “permanent underclass” and a “limited window” to build wealth before robots take over.I think this is a shared hallucination.The same people obsessed with AI's rapid capabilities are ignoring everything else about how economies, labor markets, and human demand actually work.And here's the tell.Only Americans earning over $200,000 a year see AI as a net positive.That's not a fact about AI.That's a fact about who has access to opportunity in this country.The AI jobs panic is just the newest scene in a much older story about wealth inequality.The real disruption isn't going to come from AI.It's going to come from the public finally noticing that the people warning us about the fire are the same ones selling the smoke detectors.The AI job apocalypse isn't an economic forecast.It's a marketing campaign.We're not watching the end of work.We're watching the monetization of fear.Life is so rich. Especially when you realize your inherent creative power and the evolution of our society has bright day's ahead of us. Not the doom - change, and fast? Yes, but the Universal Law of Order is always flowing from chaos to order. Your thoughts? Here's MY thoughts on AI brought to LIFE for REAL SOLUTIONS. How I view AI.....within the SPACE of the LIGHT Between Oracle Healing Journey.“Between stimulus and response there is a space. In that space is our power to choose our response. In our response lies our growth and our freedom.” - Viktor FranklThe Light Between is the conscious, sovereign light that we must maintain between our stimuli and responses. This is the light of discernment, wonder, and creativity - the light where humans truly thrive at our full capacity, rather than merely coping.I'm building a movement to advocate for preserving this vital light. Safeguarding this Light Between will enable the mindful and beneficial integration of AI into our lives.It is the wellspring of our agency, our ability to thoughtfully shape our responses to the world. Protecting and nourishing this Light is paramount as we navigate the increasing presence of artificial intelligence in our lives.In Closing…So if this lit up your heart and minds view of all the bright potential of transforming world of opportunity, then I'd love for you to experience the LIGHT BETWEEN ORACLE JOURNEY + INTUITIVE READINGS. Five Guides and a Five Layer Path…..to accelerate your intuition and problem solving. The Five-Layer Path integrates intention rituals, intuitive card draws, ancient wisdom teachings, somatic practices, and multidimensional exploration to support your journey. With your purchase, you gain access to:* Tailored Guidance: Personalized oracle readings to answer your questions.* Your Place of Power: Tools to discover and transform disempowering states.* Self Hypnosis: Techniques to rewire the subconscious, enhanced by the Neuro-Nature Self Hypnosis App.* Soul Prayer: Contemplative practices to deepen your connection to inner wisdom.* Poetic Insights: A space to save reflections for creative expression and meaning.* Five-Layer Path for Integration: A holistic approach combining intention, intuition, ancient teachings, somatic practices, and multidimensional awakening.Start for FREE and upgrade for deep awakenings and spiritual problem solving that resolves the daily self doubt and uncertainty. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit thelightbetween.substack.com/subscribe

We're announcing a16z crypto's Fund 5: $2.2B in committed capital to back the startups and founders who are building the next era of crypto. All four GPs sat down to talk through where crypto is right now, what's changed, and where it may be headed next. Chris Dixon, Ali Yahya, Guy Wuollet, and Eddy Lazzarin join Robert Hackett to cover... 00:00 Open  01:31 Why raise Crypto Fund 5 now  02:10 The GENIUS Act and what regulatory clarity unlocks for builders  04:32 Why stablecoins are crypto's WhatsApp moment  08:54 Why the next era of crypto founders will be pragmatic, not ideological  11:49 From cypherpunk revolution to crypto's "collared shirt era"  15:02 Programmable money meets AI  21:15 Onchain capital markets for compute, energy, and credit  25:57 Why finance is the foundation, not the ceiling  28:48 AI agents as first-class economic actors  38:19 Why privacy is the only moat  41:26 Jevons paradox and the future of blockspace demand  43:20 Jolt and the zero-knowledge breakthrough  58:15 Writing the next chapter of Read Write Own Resources:  Chris Dixon: https://x.com/cdixon Ali Yahya: https://x.com/alive_eth Eddy Lazzarin: https://x.com/eddylazzarin Guy Wuollet: https://x.com/guywuolletjr Robert Hackett: https://x.com/rhackett Follow a16z crypto: X: https://x.com/a16zcrypto  LinkedIn: https://www.linkedin.com/showcase/a16zcrypto/posts/  YouTube: https://www.youtube.com/@a16zcrypto Subscribe for more industry reports, trend updates, news analysis, builder guides, and other resources: https://a16zcrypto.substack.com/subscribe/   ***  As always, none of the following should be taken as investment, business, legal, or tax advice. Please see a16z.com/disclosures for more important information, including a link to a list of our investments. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The headlines say layoffs. The data says something completely different. National Association of Realtors is forecasting a 14% surge in home sales in 2026—and almost no one is connecting the dots. In this episode, we break down the single most misunderstood economic force shaping real estate right now: the Jevons Paradox. When efficiency increases, demand doesn't shrink—it explodes. And that shift is already showing up across housing, labor, and transaction volume. While many are predicting disruption, the reality is this: the agents who understand what's happening are positioning for one of the biggest opportunities in modern real estate. We cover: • Why the housing crash narrative doesn't match the data • What's really happening to white-collar jobs • Why high-trust, in-person roles are gaining power • How transaction volume is poised to surge • Why the top 20% of agents will dominate the next decade 88% of buyers and 91% of sellers still use an agent. That hasn't changed—and there's a reason. The market isn't collapsing. It's reorganizing. And the agents who adapt now will be the ones running the market by 2030.

This week's Frankly is the second in a three-part series on the role oil plays in modern civilization, prompted by the recent flow disruptions and geopolitical conflict surrounding the Strait of Hormuz. This installment explores how modern society has been built on the assumption of cheap and abundant energy, and what happens when that assumption breaks down. Nate describes the ways our built systems, including food production, water treatment, manufacturing, and global trade, are calibrated to cheap energy inputs, and how processes that look economically efficient are often deeply inefficient in physical terms. He walks through the staggering degree to which the modern food system runs on fossil hydrocarbons, noting that roughly ten calories of fossil energy now go into every calorie of food on the plate, and that the Haber-Bosch process for synthetic fertilizer is what allows the planet to feed roughly half of its current population. Nate then traces the accelerating depletion of conventional oil fields and the turn towards shale, which behaves as a fundamentally different resource than the conventional wells it has been masking. He considers the alternatives often proposed as replacements, highlighting why energy quality matters as much as energy quantity, and why solar and wind are better described as 'rebuildable' rather than 'renewable.' The episode closes with Jevons paradox and the historical pattern that humans have never actually transitioned off an energy source, only ever adding new ones on top of the old.  Why can't we simply swap in alternative technologies for fossil hydrocarbons? What does the turn toward shale mean for systems built around cheap and stable energy inputs? And how might oil supply disruptions reshape the things you do, consume, and think about in your daily life? (Recorded March 31st, 2026)   Show Notes and More   Watch this video episode on YouTube   Want to learn the broad overview of The Great Simplification in 30 minutes? Watch our Animated Movie.   ---   Support The Institute for the Study of Energy and Our Future   Join our Substack newsletter   Join our Hylo channel and connect with other listeners