Podcasts about models

A public quest for a 1000 horsepower dirt late model engine left one builder feeling slighted, but do you really need that much steam, and how many cars out there are actually pushing those kind of numbers. That today, plus CJB names a second fill-in sprint car driver for the injured Brenham Crouch, and do we continue to edge closer to the brink of a modern sprint car tire war?

Anthropic shipped Claude Fable and Mythos 5.1 with a 75% cache-price cut, Google countered with Gemini 3.8 Flash, OpenAI teased Astra's public release, a judge spared Google's ad exchange from a sale, and the FBI probed a 153M-license leak. Anthropic releases Claude Fable 5.1, which is generally available, and Mythos 5.1, for trusted partners and with cybersecurity and life sciences safeguards (Anthropic) Fable 5.1 cuts cache-read pricing 75% to $0.25/1M tokens, lowering effective costs ~25% for typical workloads and up to ~45% for agentic ones, and debuts Enterprise Frontier Safeguards (VentureBeat) Google launches Gemini 3.8 Flash, three weeks after 3.7 Flash launch, for an introductory price of $0.75/1M input and $3.75/1M output tokens until December 31 (9to5Google) OpenAI says it plans to publicly release a version of Astra "soon", but will give access to "its most advanced cyber capabilities" only to testers and partners (Wired) Sources: Astra's "recurrent depth" technique obscures chain-of-thought reasoning, sparking concerns inside OpenAI and across the industry, though OpenAI limited its use so Astra's thinking stays legible and monitorable (The Information) A US federal judge rules that Google does not have to sell off its ad exchange and instead must make its ad tech tools work with those operated by rivals (Bloomberg) The FBI is investigating Nexus, a new ID theft service on the dark web claiming to sell digital scans of 153M+ drivers licenses from people in the US and Canada (Krebs on Security) The leaked licenses, including SecDef Pete Hegseth's, trace back to ID-verification provider IDScan, used by Hertz and Planet13; the FBI's New Orleans office has opened an investigation (Tom's Hardware) Acer unveils the Swift Blade 14, an ultralight laptop weighing 1.76lbs with Intel Wildcat Lake processors, and the Swift Air 16, set to launch in December (The Verge) Subscribe to the ad-free feed.

In a world quick to prescribe electrical violence to the brain under the guise of “treatment,” it is a profound relief—and a moral imperative—to sit down with one of the clearest voices challenging this relic of psychiatric barbarism. On The Breggin Hour, my husband Peter Breggin and I spoke at length with Professor John Read, a clinical psychologist from the University of East London whose work has helped pierce the veil of denial surrounding electroconvulsive therapy (ECT). Professor Read is no outsider throwing stones. He comes from the heart of the clinical psychology establishment, yet he has become one of its most formidable critics on ECT. His recent international survey—the largest ever, with 1,100 participants from 44 countries—gives voice to the lived reality of those who have endured this procedure. The findings are damning. What the Largest ECT Survivor Survey Reveals The research team, which included ECT survivors like our colleague Sarah Hancock, asked hard questions about efficacy, memory loss, and informed consent. The results: On five different measures of effectiveness, the “majority” of recipients reported “no improvement” or that their condition was “made worse”. Two-thirds experienced significant memory loss—both retrograde (losing past memories) and anterograde (inability to form new ones). For most, this persisted for at least three years, which in practical terms means it is often permanent. The vast majority were not adequately informed about these risks. Neither were their families. Women continue to receive ECT at twice the rate of men, with 82% of the psychiatrists administering it being male—an observation that raises uncomfortable questions about power, empathy, and whose distress we choose to “treat” with electricity. These are not abstract statistics. They represent shattered lives, erased memories, and families left picking up the pieces. As Peter has long documented, ECT is essentially a closed-head electrical lobotomy that exceeds OSHA safety thresholds for electrical brain injury. The brain is not “reset”—it is traumatized. Professor Read put it plainly: we are inducing grand mal seizures in people while another branch of medicine works desperately to prevent them. The original 1930s theory—that schizophrenia and epilepsy were mutually exclusive, so inducing seizures could cure the former—was bizarre even then. That we continue this practice ninety years later, with so little rigorous evidence, is a scandal. Legal Victories: Hitting Them Where It Hurts  Professor Read has served as an expert witness in key cases that are finally forcing accountability. In a 2023 Florida jury trial, manufacturer Somatics was found liable for failing to adequately warn about brain damage and permanent memory loss. A 2024 California case against individual psychiatrists and a psychiatric center—alleging negligence, fraud, malpractice, and battery—settled just before trial after surviving all dismissal attempts. MECTA, another major manufacturer, has filed for bankruptcy amid mounting litigation and withdrawn its Spectrum device, only to introduce a near-identical “Sigma” model. The industry adapts, but the human cost remains. Peter shared his own observations from decades of work, including the first successful ECT malpractice case (Salters, 2005). He described the visible horror: patients emerging confused, amnesic, and disoriented—much like victims of repeated concussions. This is traumatic brain injury, period. The temporary “euphoria” some experience is often the brain's response to injury, not genuine healing. The Deeper Problem: Over-Medicalization of Human Suffering  John Read situates ECT within a larger tragedy—the reduction of complex human distress to a purely biological problem solvable by pills or electricity. Poverty, trauma, abuse, loneliness, and loss are pushed aside while white coats reach for the shock machine. Many psychiatrists, he notes, lack the skills or willingness to sit with profound emotional pain, especially in women and older patients. This resonates deeply with our work exposing psychiatric harm. Whether antidepressants, antipsychotics, or ECT, the pattern is the same: interventions that promise quick fixes but deliver dependency, disability, and despair. The public understands that life circumstances drive mental suffering. Too many professionals still do not.  A Call to Survivors, Families, and Truth-Seekers  If you or a loved one has been harmed by ECT, know that you are not alone. Professor Read's survey papers are open access—search “John Read ECT survey” to read them directly. Share your story. Support organizations fighting for informed consent and bans on this outdated practice. We must demand real informed consent, rigorous science, and an end to the normalization of brain damage as “therapy.” Peter and I continue this fight because human rights do not end at the psychiatrist's door. The brain is not a machine to be jolted into submission. Listen to the full conversation on The Breggin Hour (available on America Out Loud and major podcast platforms). Professor Read's humility, clarity, and dedication shine through. We are grateful for his partnership in this long struggle and look forward to future collaborations. Download and print this free brochure “No One Should Be Given Shock Treatment“ To all who have suffered: Your experiences matter. Your memories—however fragmented—are worth fighting for. The truth is emerging, one voice, one lawsuit, one honest conversation at a time.  Share this widely. The more light we shine, the harder it becomes for these practices to hide in the shadows. Peter and Ginger Breggin have spent decades exposing psychiatric harms. Subscribe for more unfiltered truth on mental health, freedom, and the defense of the human spirit. About our Guest Dr. John Read is Professor of Clinical Psychology at the University of East London. He has published over 200 research papers, primarily on the relationship between adverse life events and psychosis; the negative effects of bio-genetic causal explanations on prejudice; anti-psychotic and anti-depressant medication; electroconvulsive therapy; and the toxic influence of the pharmaceutical industry on clinical research and practice. John is Chair of the International Institute for Psychiatric Drug Withdrawal [https://iipdw.org] and is on the Board of Hearing Voices Network, England He has been the editor of the scientific journal ‘Psychosis' since 2009, and is the editor/author of several books, including: Read J, Sanders P. (2022) ‘A straight talking introduction to the causes of mental health problems'. PCCS Books. Read J, Dillon J. (eds) (2013) ‘Models of Madness', Routledge. Further Reading/Resources:  Professor John Read's publications: A large exploratory survey of electroconvulsive therapy recipients, family members, and friends: what information do they recall being given? Journal of Medical Ethics A Survey of 1144 ECT Recipients, Family Members and Friends: Does ECT Work?, National Institutes of Health Largest-ever survey questions ECT's effectiveness, University of East London Survey Findings on Electroconvulsive Therapy, Psychology Today Models of Madness and Other Works by John Read By Peter R. Breggin, MD: Download and print this free brochure “No One Should Be Given Shock Treatment“ Free Resource Center: Dr. Peter Breggin's ECT and Deep Brain Stimulation Resources Center Dr. Breggin's two overview scientific articles: Breggin 1998 and Breggin 2010. The 2010 article contains a brief summary of ECT's harmful effects, written to inform the FDA. Also read Jones and Baldwin 1992 for a powerful overview. Read Dr. Breggin's blogs on ECT. Like the brochure, these are also useful as an introduction for anyone who is just learning about ECT. See his blogs: “New Study Confirms Electroshock (ECT) Causes Brain Damage,” “The Stealth ECT Psychiatrist in Psychiatric Reform,” “FDA Panel Recommends Testing of ECT Machines,” “Electroshock for Children and Involuntary Adults.”

Today's clip is from Episode 164, featuring Andrew Gelman, Aki Vehtari & Richard McElreath. In this conversation, Andrew explains why a Bayesian workflow goes far beyond simply fitting a model. He discusses the importance of building, fitting, and checking models, and why moving between simpler and more complicated models can reveal insights that a single model might miss. He also explores how simulation and generative modeling can help researchers evaluate new models and gain confidence in their results, even when there isn't an established method or published study to rely on. It's a look at why good statistical practice isn't just about getting an answer, but knowing how much you can trust it. Full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Benny Chen, Co-Founder @ Fireworks AI, joins the show to discuss his founder journey and share valuable insights on navigating common founder / product dev challenges in today's agent-first landscape. He and Jerry cover strategies for creating effective messaging, staying competitive in a crowded market space, hiring top-tier talent / what qualities to look for in high-performing engineers, navigating the cultural shift to managing agents, creating data flywheels & how this can help your customers, and more. ABOUT BENNY CHEN As co-founder and early product architect, Benny Chen shaped Fireworks AI's infrastructure strategy, spearheading the design of scalable systems to support high-throughput AI model serving. Benny's contributions established the technical foundation for Fireworks AI's robust and cloud-native architecture, which underpins its ability to meet enterprise demands. Formerly Meta's Ads Infrastructure Lead, Benny optimized large-scale ad-serving pipelines and developed significant expertise in distributed systems and cloud infrastructure. He holds a B.S. in Computer Science from Stanford University, bringing both leadership and technical depth to the Fireworks AI management team. Sinch is the communications infrastructure the AI era runs on. There's a layer of infrastructure behind every text, call, and login code your product sends, and it works exactly like plumbing: nobody thinks about it until it's the reason something broke. Most providers route through 4-6 intermediaries; Sinch connects in 1-2 hops, direct carrier relationships across 600+ connections, handling 900 billion interactions a year across 60+ countries. Routing, compliance, fraud prevention handled automatically rather than manually managed by the business sending the message! Sinch is the reliability layer underneath AI-driven customer communications; the infrastructure that determines whether an AI agent's output actually reaches a real person as a delivered text, connected call, or verified interaction. Check it out here! SHOW NOTES: Moving from an early idea to a rocket ship (1:13) Insights on developing / communicating your core message as an early founder (2:40) Role of open source & inference @ Fireworks AI (4:24) How Firework AI's company messaging evolved over time (6:07) Popular customization features today (7:25) Strategies for staying competitive in a crowded market (9:11) Defining the customer data flywheel & how it helps users (11:25) Common types of data that companies can collect to train their AI models (13:53) The customer's next steps after creating a data flywheel (15:53) Benny's perspective on acquiring engineering talent as a founder (16:43) Common traits shared by high-performing engineers @ Fireworks (19:05) How AI has altered which traits founders prioritize when hiring (20:45) Navigating the shift from engineering work to managing agents (22:40) Frameworks to ensure agents are doing the right thing (25:23) Aligning your metrics with the outcome you're trying to follow (27:37) What a typical day looks like for Benny as a founder (28:31) Advice for founders / eng leaders looking to embrace AI adoption (29:19) Rapid fire questions (31:15) This episode wouldn't have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/ Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/ Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, Justin Johnson, co-founder of World Labs, joins us to discuss world models and the emerging field of spatial AI. We explore why many researchers see capabilities beyond language as an important frontier for AI, and what it means to build models that can understand, generate, and simulate the environments around them. Justin explains the different approaches to world modeling, including explicit 3D representations and generative models, and why there is still no established recipe for building these systems. We also discuss World Labs' Marble system, which can generate navigable 3D worlds from images and other inputs, the challenges of evaluating world models, and the role of simulation, planning, and action. Finally, Justin shares his vision for models that bring these capabilities together, supporting everything from interactive virtual environments to agents and robots that can operate in the physical world.

What happens when an AI system moves beyond generating answers and begins influencing machinery, maintenance schedules, technician dispatch, and safety? In this episode of Tech Talks Daily, I speak with Bob De Caux, Chief AI Officer at IFS, about moving industrial AI from promising pilots into dependable production deployments. Bob explains why access to advanced models is no longer the main obstacle. Successful enterprise AI depends on understanding the processes, operational logic, metadata, and boundaries surrounding each decision. An AI system ordering a replacement bearing for a wind turbine must meet a very different standard from one generating a nursery rhyme. We hear how IFS customer Kodiak Gas is using a digital worker to support material replenishment. According to Bob, the company projects approximately $3 million in annual return and 90,000 hours returned to technicians for higher-value work. Our conversation also covers AI sovereignty. Bob argues that sovereignty means retaining control over data, decisions, providers, and the ability to keep operating under changing circumstances. He compares the technology layer to a duck paddling furiously beneath calm water. Models may change rapidly, while the operational application above them must remain stable, tested, and auditable. We discuss staged autonomy as a way to earn worker confidence, beginning with manual questions, progressing to recommendations, and granting greater authority only after consistent performance. Bob also explains why agents need identities, permissions, defined roles, separation of duties, sponsors, and complete audit trails. Accountability remains with the organization deploying the system. In an industrial environment, an agent can produce a harmful action rather than an inaccurate answer. Even after 999 successful decisions, the thousandth can carry catastrophic consequences. Is your organization measuring AI through pilot counts, or through uptime, cost, technician capacity, turnaround time, and safety? Listen to the conversation and share your thoughts with me.

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

Welcome back to another episode of Fratello On Air! This week, we're both back in our respective homes before traveling again. Our show is about modern watches destined to become classics. Now, before you accuse us of focusing on money, erase those thoughts! We don't care about value. No, we are talking about watches that will be interesting several decades from now.Predicting the future is difficult, but we're here to try! This week, we're focused on modern watches that will command respect in the coming decades. As you'll see, we have some caveats. We chose relatively affordable pieces instead of haute horlogerie models. And we both chose three, for a total of six watches. We hope you enjoy the show!HandgelenkskontrolleWe kick off our show with a bit of travel talk, but we quickly shift to the screen. Shows including I Will Find You, Lanterns, DTF St. Louis, and Half Man get mentions. Then, Balazs brings up an upcoming auction for the 1998 Game 3 NBA Finals jersey from Michael Jordan. It's listed on Joopiter and will be up for auction with an estimate of $10 to $15 million! We end our banter with a mention of Instagram user @dewesyy, who is filming the cobbling together of a Porsche 911 Targa from two rough beaters. For the Handgelenkskontrolle, Mike is wearing his trusty Rolex Submariner 14060M from 2011. Balazs is sporting his Ming 22.01 GMT Kyoto.The modern watches we'll still be talking about in 20 yearsWe kick off our main topic by mentioning the one and only Bvlgari Octo Finissimo. While this watch may not fit every wrist, it's a stunning object. Perhaps Bvlgari will never take this watch out of production, or maybe it will evolve into something different. Still, there's no denying that the current watches are arresting. We think they'll still be grabbing attention in the long run.For Mike's first pick, the original 42mm Tudor Pelagos in black, blue, or LHD still stands out as a landmark watch. More than the Black Bay, these were non-retro releases and were the first titanium "Rolex." Aside from moving to an in-house caliber, the watch has barely changed since its 2012 debut in 2012. We'd be surprised if the model doesn't change soon, but the original will always be special.Next up, we mention the Ming 17-series. It's hard to believe, but Mike first reviewed a 17.06 Slate back in 2019. This started his love affair with the brand, and it was ultimately contagious. Now, Balazs is hooked too! The 17.09 was a watershed moment for both of us, as we each own one.Technical powerhousesFrom a technical perspective, it's hard to fault the Sinn EZM collection. These pieces are the brand's most focused and hardcore tool watches, made for specific jobs. Models like the EZM 1 are the most famous, but all of them are worth owning. Due to the innovation and design, we're confident that all of these editions will be discussed in the future.Balazs brings up the Grand Seiko SLGH005 White Birch from 2021, a watch that created a craze for the upscale Japanese brand. People went nuts for the White Birch when it came out, and any time GS releases another silvery-white dial, there's a similar fervor. The 40mm steel reference comes equipped with the brand's satisfying and mesmerizing 9SA5 Hi-Beat automatic movement. Yes, Grand Seiko makes a lot of watches, but there will only be one original White Birch.Finally, Mike suggests that the Nomos Club Sport Neomatik Worldtimer is one of the modern watches that will be a future classic. Honestly, we forget how impressive Nomos is with its in-house movements and escapements. The latest Worldtimer is a cool watch available in several flavors. With its thin caliber and multi-city display, this is and always will be a great daily (and global) accessory.We hope you enjoy today's show, and thank you for listening. As always, if you have future show ideas, please let us know!

Legal is the department that can stop a business transaction cold. A contract goes into review and two weeks disappear. Procurement waits. Sales waits. And the tools that were supposed to fix that — an assistant bolted into Word, a chat window with a contract pasted into it — ask an in-house lawyer to trust a system that can give one answer today and a slightly different answer next week. In a field where the human carries the liability and the model does not, that is not a rounding error. That is the whole problem.In this episode of Talking AI, Matt Paige sits down with Emad Khazraee, co-founder and CTO of RiskVantage AI, previously VP of AI at Xometry, a data science and AI leader at Turing, an information science professor, and a fellow at Harvard's Berkman Klein Center. For years Emad told his co-founder, Mark Afshar — a practicing lawyer turned in-house counsel for big pharma — that legal AI was a bad idea: a wrapper has no moat, and Anthropic or OpenAI will do it better than you overnight. What changed his mind was an architecture, not a market: a deterministic ontology that owns the legal reasoning, and small domain-specific language models that handle the language.The conversation covers why a nine-billion-parameter model running sub-second on a commodity GPU can match a frontier model inside a single domain, how subsidized token prices are distorting the entire legal AI market, why RiskVantage AI sells to procurement and sales ops rather than to lawyers who bill by the hour, what a failed PhD project on symbolic AI taught him about where determinism belongs, and whether the billable hour survives the decade.In this episode, you'll hear about:What ChatGPT can't know about your company: its risk appetite, its baselines, and the practices it expects every single timeWhy the legal services market — north of $900 billion, by Emad's count — has every frontier lab gunning for itThe objections that made him refuse to build a legal AI company, and the one that still holdsWhy a Word plugin stopped being defensible the moment Anthropic shipped its ownHow subsidized token pricing echoes Uber and Lyft, and who gets hurt when the subsidy endsThe consistency problem: one answer today, a different answer next week, and a lawyer's confidence goneNeuro-symbolic AI in plain English — a deterministic ontology for legal risk, LLMs for document understandingThe three years Mark Afshar spent codifying legal risk before there was a productWhy a 9B domain-adapted model is “dumb enough” that it can't wander outside its sandboxKnowledge distillation, silver datasets, and self-distillation policy optimization in practiceThe sovereign-cloud niche: ITAR data, commodity GPUs, and customers whose data will never leaveOutcome-based pricing, AI-enabled law firms, and what happens to the billable hourThe access-to-justice case: pro se filings, public defenders, and what a $20 subscription changesKey Moments00:01:30 — What ChatGPT can't know: your company's risk appetite and baselines00:05:12 — $700 an hour, a tenth at a time — and Coinbase's AI mandate to outside counsel00:08:12 — Why he told his co-founder no: a wrapper has no moat00:10:22 — Subsidized tokens, Uber and Lyft, and Legora's move to consumption pricing00:14:31 — The sovereign-cloud niche: ITAR data, commodity GPUs, and data that can't leave00:16:56 — “I am on the hook for the liability, not which model I used”00:18:15 — Same question a week later, a different answer, and confidence gone00:22:13 — If a rule can govern it, you should never use an LLM00:23:00 — The PhD failure: narrative machines, Frege, and symbolic AI's rigidity00:26:53 — Mark Afshar's three years codifying legal risk into an ontology00:29:00 — Neuro-symbolic AI, explained00:31:03 — Don't use a missile to hit a fly: why smaller models are safer00:35:47 — A 9B model, sub-second on a commodity GPU, matching Fable 5 in-domain00:38:00 — Does the billable hour survive? Outcome pricing and AI-enabled firms00:42:40 — Why affordable legal access is a democratic-society problem00:44:00 — The pro se surge: people filing their own cases with ChatGPT and Claude00:48:30 — “I'm talking with Copilot.” “That's not research.”Key LinksRiskVantage AIConnect with Emad on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you'll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action.

Aaron Katz is the Co-Founder and CEO of ClickHouse, the real-time analytics database powering companies including OpenAI, Anthropic, Tesla and Microsoft. ClickHouse just surpassed $350M in ARR and raised over $1B from investors including Dragoneer, Khosla Ventures, Coatue, 20VC and Benchmark. Previously, Aaron was CRO at Elastic, where he helped scale revenue from approximately $5M to $500M and led the company through its IPO. Before Elastic, he spent 12 years at Salesforce, working alongside Marc Benioff and helping transform it from a 200-person startup into a global software giant. AGENDA: 4:05 Are we in an AI bubble? 13:40 How does software change when agents—not humans—make buying decisions? 22:28 Will 90% of tokens flow through open models; can enterprises trust them? 31:09 Why did ClickHouse sponsor Fulham; and could sports teams become $20B assets? 35:49 When will ClickHouse hit $1B ARR?  38:31 Can startups still win elite talent from OpenAI? Biggest remote work mistake? 44:54 Is zero-to-$100M ARR now table stakes; or is durable growth what matters? 48:51 Is college still worth it;  which jobs will survive AI? 57:58 When will ClickHouse go public; and why not next year?  

Apple just locked in a date for the big iPhone event, and new Mac mini and Mac Studio models landed early with a price jump that'll make your eyes water. Tom and Jeff run the actual numbers on both new Macs, weigh in on the iPhone 18 Pro, Pro Max, and the long-rumored foldable, and swap notes on cameras, Face ID quirks, and an update on Tom's new notebook habit.In this episode:Apple's iPhone event is officially set for September 9New Mac mini and Mac Studio pricing and specs, and why they cost so much more nowWhat a fully maxed-out Mac mini and Mac Studio actually run youiPhone 18 Pro, Pro Max, and the foldable iPhone, and what it might costCamera lens talk and Halide's new "film types"Face ID vs. Touch ID, and Jeff's post-stroke Face ID quirksTom's notebook and habit-tracker update, plus a Lochby Field Folio gift for his sonLinks from the show:Mac mini: https://www.apple.com/mac-mini/Mac Studio: https://www.apple.com/mac-studio/Halide: https://halide.cam/Lochby Field Folio A5: https://www.lochby.com/products/field-folio-a5Lochby Pocket Journal: https://www.lochby.com/products/pocket-journalField Notes: https://fieldnotesbrand.com/Question or Comment? Send us a Text Message!Support the showContact UsDrop us a line at feedback@basicafshow.comYou'll find Jeff at @reyespoint on Threads and reyespoint.bsky.social on BlueskyFind Tom at @tomanderson on ThreadsJoin Tom's newsletter, Apple Talk, for more Apple coverage and tips & tricks.Tom has a new YouTube channelShow artwork by the great Randall Martin DesignEnjoy Basic AF? Leave a review or rating!Review on Apple PodcastsRate on SpotifyRecommend in OvercastIntro Music: Psychokinetics - The ChosenApple MusicSpotifyTranscripts and some images are AI generated and may contain errors and general silliness.

In this episode of Chewing It Over, Jack is joined again by Asaf Weisman for a deliberately challenging discussion about some of the prevailing ideas within modern pain science.Asaf argues that pain medicine has spent decades on a series of intellectual “side quests”, particularly following the development of the biopsychosocial model and modern definitions of pain in the 1970s. His criticism isn't that psychological, social or neurological factors are irrelevant, but that theoretical possibilities have sometimes been promoted with greater certainty than the evidence warrants.A central theme is nociception and the role of the brain. Asaf challenges descriptions of pain as simply an output of the brain or a neurological expression of perceived threat. Instead, he argues that pain requires underlying somatic signalling, while the brain participates in processing and modulating that experience rather than independently generating it.The discussion explores the language clinicians use around pain, the distinction between modulation and causation, and whether attempts to explain persistent pain have sometimes made the subject unnecessarily complex.Importantly, the conversation also identifies areas of agreement. Asaf accepts that psychological and social factors can substantially alter an individual's pain experience, even when underlying nociceptive signalling remains unchanged. Stress, context and other factors can influence pain through modulation.For clinicians, this creates a more practical discussion about management. Rather than becoming trapped in theoretical explanations, Asaf argues for identifying and addressing modifiable risk factors associated with persistent pain, while using exercise, modalities and other interventions where they help patients pursue meaningful activity.It's a provocative conversation that challenges clinicians not simply to replace one pain model with another, but to scrutinise the assumptions underneath both.Be precise about causation versus modulation. A psychosocial factor influencing the intensity of someone's pain doesn't necessarily establish that it independently caused the pain. That distinction is central to Asaf's argument.Don't turn theoretical models into established biological facts. Models can help generate hypotheses and guide research without every component of the model having been empirically demonstrated.The brain isn't being dismissed. Asaf's position is more nuanced than “pain is peripheral”. He explicitly accepts that the brain processes and modulates pain and that stress and psychosocial circumstances can change the resulting experience.Language matters clinically. Terms such as “pain output”, “threat”, “nociception” and “biopsychosocial” carry assumptions. Using them casually can make a proposed mechanism sound more established than it actually is.Don't let the theoretical debate distract from modifiable factors. Whatever model a clinician favours, the practical priority remains identifying things that can meaningfully be changed — including activity, physical health and relevant psychosocial stressors — and helping the patient address them.5 clinical/professional takeaways

Eno Reyes is the co-founder and CTO of Factory, the agent-native software development platform building autonomous "Droids" for enterprise engineering teams. Factory has raised $220 million, most recently a $150 million Series C at a $1.5 billion valuation, from investors including Khosla Ventures, Sequoia Capital, 20VC, NEA, Blackstone, Insight Partners and Nvidia. Before founding Factory, Eno worked as a machine-learning engineer at Hugging Face, training, optimizing and deploying large language models for enterprise customers. AGENDA: 00:00 Are We Underestimating AI by an Order of Magnitude?  06:35 Why Can the Smartest AI Model Be the Cheapest?  18:51 Is Anthropic's Coding Business Really Worth $2 Trillion?  33:41 Will Continuous-Learning Models Help or Hurt Factory?  40:43 Will 80–90% of Neo-Labs Die in the Next 18 Months?  44:33 Should American Enterprises Work With Open-Source Chinese Models?  55:42 Must AI Founders Radically Rethink What a Great Outcome Looks Like?  1:04:30 Do Pedigree and Credentials Still Matter in AI Hiring?  1:19:17 Which Is the Biggest Threat: Claude Code, Codex, Cognition or Cursor?  1:24:20 What Seems Crazy Today but Will Be Obvious in Five Years?  

South Korea moves toward AI access as public infrastructure, Tencent releases Hy4 Preview, Nvidia’s leaked DLSS 5 is out in the wild. MP3 Please SUBSCRIBE HERE for free or get DTNS shows ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoy what you see you canContinue reading "OpenAI Plans To Stop Providing Its Models To Cursor – DTH"

Today's episode of This Week in Pharmacy — #TWIRx is sponsored by Value Drug Company and explores two major forces reshaping pharmacy: the evolution of the community pharmacy business model and the expanding role of artificial intelligence in medication optimization and value-based care.   PART ONE: The Impact of the Cash-based Community Pharmacy   Special Guest: Rick Seipp, PharmD President, Value Drug Company Community pharmacy is entering a new era.   In Part One, Todd Eury sits down with Rick Seipp, PharmD, President of Value Drug Company, to discuss the continued evolution and expansion of independent community pharmacy and the many different business models emerging across the profession.   Independent pharmacy owners are increasingly evaluating new ways to build sustainable businesses while creating greater professional freedom for pharmacists. One of the most interesting developments is the growth of cash-based and cost-plus pharmacy models that reduce or eliminate dependence on traditional insurance reimbursement and PBM-controlled prescription economics.   These models can give pharmacy owners greater control over pricing, patient relationships, clinical services and the overall direction of their businesses — while allowing pharmacists to practice with significantly less interference from insurance companies and pharmacy benefit managers.   Two pharmacies in the Pittsburgh region are demonstrating how powerful this model can become: Blueberry Pharmacy — Kyle McCormick, PharmD has built Blueberry Pharmacy around a transparent, cash-based model designed to simplify prescription pricing and create a more direct relationship between the pharmacist and patient. Forward Rx Pharmacy — Brandon Antinopoulos, PharmD is another emerging example of how pharmacists can create innovative pharmacy businesses built around transparency, accessibility and freedom from many of the traditional constraints of third-party reimbursement.   Todd and Rick discuss what these models mean for the broader independent pharmacy marketplace and why community pharmacy may be moving toward a much more diversified future.   Sponsored by Value Drug Company, a pharmacist-led wholesale distribution partner committed to supporting the continued growth and independence of community pharmacy. https://lnkd.in/ehf76qUs PART TWO: AI-Powered Medication Optimization   Special Guests: Yoona Kim, PharmD, PhD Co-Founder & CEO, Arine Jenny Behan, PharmD Lead Clinical Pharmacist, Arine In Part Two, #TWIRx shifts from pharmacy business innovation to one of the most important developments occurring in clinical pharmacy: the use of artificial intelligence to optimize medication therapy at scale. Arine is an AI-powered medication optimization platform designed to improve patient outcomes and reduce healthcare costs by helping ensure patients receive the most effective and appropriate medications. Listen to This Week in Pharmacy — TWIRx on the Pharmacy Podcast Network across Apple Podcasts, Spotify, Amazon Music, iHeartRadio, YouTube and all major podcast platforms. Pharmacy Podcast Network — Amplifying the Voice of Pharmacy.

In Episode 219 of the Equipping ELLs podcast, Beth Vaucher opens with something most professional development on co-teaching never acknowledges: the textbook version of co-teaching rarely matches the reality of ELL classrooms. No shared planning time. No clear role definition. Walking into lessons you did not design and trying to support students in real time. If you have been an ELL teacher for more than a few months, you already know this. Today's episode names it directly — and then gives you a practical, honest framework for making co-teaching work regardless of where you are starting from.Beth opens by naming the three things that make co-teaching feel hard. Role clarity — most ELL teachers who push in have never had an explicit conversation with the homeroom teacher about what their role actually is during instruction. Planning time — or the complete lack of it. Real co-teaching requires co-planning, but most ELL teachers are spread across five, six, or eight classrooms with zero shared planning time with any of those teachers. And different priorities — a fourth-grade science teacher's priority is fourth-grade science, and an ELL teacher's priority is language acquisition. Those goals support each other beautifully when aligned, but when no one has had the conversation about how they align, they feel like competing interests in the same room.The most important insight of the episode comes before the models: before thinking about co-teaching structures or lesson plans or instructional strategies, think about the relationship. The ELL teachers who build the most effective co-teaching partnerships are not the ones with the most resources or the most knowledge. They are the ones who showed up consistently, were easy to work with, made the homeroom teacher's job slightly easier, and over time became someone that teacher genuinely wanted in the classroom. One teacher. One relationship. One small thing you can offer. That is where it starts.Beth then introduces three realistic co-teaching models — not the official district training models, but the ones that actually operate in real schools with real constraints.The Resource Bridge is the model most ELL teachers are already in, whether or not they have named it. You are not in the room co-instructing, but you know what is being taught, you prepare one or two scaffolded supports — a sentence frame, a vocabulary visual, a graphic organizer from Scaffolds in a Snap — and you deliver them to the teacher at the start of the week. Imperfect co-teaching, but your students have a scaffold in their hands during instruction even when you are not there.The Parallel Partner puts you in the room but not co-instructing the whole class. The homeroom teacher leads the lesson and you work with your ELL students in a parallel structure — same content, same concept, same objective, different access point. This model requires almost no joint planning and immediately signals to the homeroom teacher that you are a specialist, not an aide.The Active Partnership is what the training describes — co-planned, co-taught, each teacher taking a role and a group. But Beth is clear: you do not get here by demanding it. You earn it through consistency in Models 1 and 2. When a homeroom teacher has seen that working with you makes their classroom better, they invite the partnership.The one practical tool that works across all three models: walk into any classroom this week with one page in your hand — one scaffold for whatever that class is teaching. Say to the teacher: I made this for my ELL students. Can I leave a few copies for any students who might benefit? That single action demonstrates expertise, makes your presence purposeful, gives the teacher something useful without asking anything of them, and puts scaffolded support in your students' hands regardless of how the lesson goes.Beth also provides specific conversation starters that build the co-teaching relationship without creating resistance — not "I need us to co-plan" but "I noticed your students are working on explaining their thinking. I have something that might help. Can I bring it tomorrow?"The episode closes with an honest word for teachers for whom co-teaching is simply not happening — no push-in time, unresponsive colleagues. You can still be a bridge. Pre-teach in pull-out the vocabulary your students will need for the science lesson that afternoon. Practice the language function they will need for the social studies task this week. That bridge is invisible to the homeroom teacher but profoundly visible to your students.FREE TRIAL: equippingells.com/trial — Scaffolds in a Snap, sentence frames, vocabulary organizers, all the resources that make you the go-to person in your building.

This week, we discuss Stripe's singularity letter, its $8B Open Router buy, and AI job anxiety. Plus, Matt plays “Bot or Not” on another podcast. Watch the YouTube Live Recording of Episode 587 Runner-up Titles Inertia wins again We don't talk about the Pope very much. Better than this year's storage arrays AI pimps its own ride Chonking machine A lot of chonk opportunity I'm tired of tech people being all fancy Thanksgiving with Ed Zitron No religion, no politics, no AI My recommendation: try harder Teletubbies for Adults. Rundown Singularity, Models and Routers Scoop: Stripe says "the singularity" has begun Stripe strikes mega-deal for OpenRouter Hugging Face reportedly in talks to be acquired for $13B Routing is coming for the frontier AI labs Terminator Judgement Day: August 29, 1997 2:14 a.m. Eastern Time The AI backlash goes mainstream 52% of Americans Now More Concerned Than Excited About AI, With Under-30s Crossing a Majority for the First Time Why Is Everyone In Tech So Sad? The AI Hater's Manifesto 40 Years of Infrastructure as Code: Ansible → Terraform → Kubernetes → Crossplane → AI Agents Relevant to your Interests Cursor Origin review: An engineer's perspective Claude can now pull data from your browser tabs and keep working on your desktop OTel Isn't Going Well (And I Made A Spreadsheet About It) Walmart is finally launching Apple Pay support next week Broadcom debt deal expected to reach upwards of $70 billion, sources say Anthropic-Backed Ode Acquires Casper Studios to Expand Corporate AI Deployments OpenAI 'will be a public company in 2027' or sooner, CFO Friar tells employees Google Aims to Boost AI With Purchase of Spirit Airlines Data The website that created an AI clone of its editor in chief OpenAI Jalapeño: Better Than Nvidia Blackwell An Inside Look at the Relay Market Powering Token Resellers and Fraud Meta settles social media addiction case with California, other states for $16.7 billion Hundreds of leaked AWS keys give full control over corporate accounts Free Hardened Container Images | Minimus Cyber startup Minimus shuts down, returns cash to investors Nonsense Apple Releases New Polishing Cloth Jason Kelce promotes mailing pee to data centers, Liquid Death Paradox Inc. Movie From Daniel Roher, Lord Miller, Universal In Works Conferences WeAreDevelopers NA, Sept 23-25, 2026, Discount Code: DEVPOD50 25 Free Tickets DevOpsDays Graz, Sept 4-5, 2026 Cloud Foundry Summit, Sept. 21st to 22nd, Heidelberg, Coté speaking. DevOpsDays Rockies, Sept. 22 – 23, 2026, Discount Code: 26DODSWEDEFTALK DevOpsDays Dallas, Sept 28-29, 2026 DevOpsDays Vilnius, Sep 30 - Oct 1, 2006, Lithuania. DevOpsDays Prague, Oct 5, 2026 - Coté speaking. DevOpsDays Istanbul, Oct 24th, 2026, Coté keynoting. VMware User Group, Orlando, Oct 20-22, 2026 Cloud Native Denmark, Nov 19th, 2026, Copenhagen, Coté keynoting. Build Stuff, Dec 2-4, 2026, Vilnius, Lithuania. cfgmgmtcamp, February 1st to 3rd, 2027, Ghent. SCALE 24x Pasadena, CA, April 1-4, 2027 SDT News & Community Join our Slack community Email the show: questions@softwaredefinedtalk.com Free stickers: Email your address to stickers@softwaredefinedtalk.com Follow us on social media: Twitter, Threads, Mastodon, LinkedIn, BlueSky Watch us on: Twitch, YouTube, Instagram, TikTok Book offer: Use code SDT for $20 off "Digital WTF" by Coté Sponsor the show Sponsor more podcasts with Failover Media Recommendations Brandon: Tuner ** Your AI Project Doesn't Need More Agents Matt: Tech, Texas & What It Takes to Run a Podcast with Matt Ray | Cyber Chat Ep. 5 Line of Duty Anti-pick: Roku's New Slop Channel Coté: Mini MLC, 30L.

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! ¿Cómo consiguió una pequeña startup californiana poner contra las cuerdas a una industria con más de cien años de historia? En este episodio de La Ola Eléctrica recorremos la historia completa de Tesla, desde su fundación en 2003 y la llegada de Elon Musk hasta convertirse en uno de los fabricantes más influyentes del planeta. Hablamos del Tesla Roadster, los momentos en los que la compañía estuvo al borde de desaparecer, el nacimiento del Model S, el infierno de producción del Model 3, la expansión de las Gigafactorías, el éxito mundial del Model Y y la importancia de la red Supercharger. Pero la historia de Tesla no puede entenderse sin mirar también a los otros proyectos de Elon Musk. Analizamos la relación entre la filosofía de Tesla y SpaceX, el nacimiento de The Boring Company y cómo esa obsesión por cuestionar industrias tradicionales se repite una y otra vez. También llegamos hasta la Tesla de 2026, en plena batalla contra BYD y los fabricantes chinos, con una estrategia que empieza a mirar mucho más allá del automóvil: Robotaxi, Cybercab, inteligencia artificial, Optimus y almacenamiento energético. Una historia llena de éxitos, fracasos, promesas incumplidas, riesgos enormes y decisiones que terminaron obligando a Volkswagen, Toyota, Mercedes-Benz, BMW, Ford y prácticamente toda la industria a acelerar su transición hacia el coche eléctrico. ¿Tesla ganó la batalla o simplemente consiguió que todos los demás empezaran a correr? Descúbrelo en este nuevo episodio de La Ola Eléctrica.Escucha este episodio completo y accede a todo el contenido exclusivo de Somos Eléctricos. Descubre antes que nadie los nuevos episodios, y participa en la comunidad exclusiva de oyentes en https://go.ivoox.com/sq/627406

Naren Tallapragada (co-founder of Tessel) joins to talk about the industry of modeling biology. While the field races to build virtual cell models, his team builds virtual tissue models, betting that predicting what an organ actually does matters more than predicting what genes a cell expresses. The goal: not just whether a drug works, but who it works for.In this episode, we discuss:Why 90% of drugs fail in clinical trials, and why neuro and Alzheimer's failure rates are even worseThe multi-scale modeling problem: molecules, genes, cells, tissues, organs, and why picking the right level of resolution matters more than picking the "best" oneThe electron-and-lightbulb argument for why virtual cell models may be a beautiful solution to the wrong problemOrganoids vs. animal models: where human cell models beat animals, and where both fail (like modeling behavior in mental health)Why Naren thinks defensibility in AI-driven biology comes from proprietary multi-organ data, not model architectureThe case for precision medicine, and why it's been such a brutal business model to actually execute onCredits:Created by Greg Kubin and Matias SerebrinskyHost: Matias and GregProduced by Nico V. ReyFind us at businesstrip.fm and psymed.venturesFollow us on Instagram and Twitter!Theme music by Dorian LoveAdditional Music: Distant Daze by Zack Frank

Last year, nearly 45 million Americans were diagnosed with a substance abuse disorder. That's according to the Substance Abuse and Mental Health Services Administration. When it comes to treating addiction, what's working? And what isn't? We talk to the team at ROCovery about local peer support, and we hear from a former Biden administration official about the recovery landscape in New York. In studio: Kara Izzo, peer support program manager for ROCovery Jonathan Westfall, executive director of ROCovery Rob Kent, president of Kent Strategic Advisors ---Connections is supported by listeners like you. Head to our donation page to become a WXXI member today, support the show, and help us close the gap created by the rescission of federal funding.---Connections airs every weekday from noon-2 p.m. Join the conversation with questions or comments by phone at 1-844-295-TALK (8255) or 585-263-9994, email, Facebook or Twitter. Connections is also livestreamed on the WXXI News YouTube channel each day. You can watch live or access previous episodes here.---Do you have a story that needs to be shared? Pitch your story to Connections.

Send us Fan MailWe're back with Part 2 of a Super Special Baby-Sitters Club episode! We were thrilled to be joined by special guest, author Lakita Wilson (Be Real, Mercy Weaver, Sparkle, Pretty Girl County) for this episode. She's a BSC super fan and we have so much to say about Stacey. We're picking up our discussion about two books, The Truth About Stacey and Stacey and the Fashion Victim. We have so many questions/concerns with what is going on at Bellaire's kind of creepy, nictotine-heavy, Fashion Show. We talk about unfortunate nicknames, the realism of men having perms in the late 90s, and ghostwriters. We also discuss library visits gone wrong when Lakita brings up some light library book thievery she may have accidentally committed and Hannah brings up a book that her mom found a little too racy. And as usual, all racy library book roads lead us back to Forever. We also take a quiz and (spoiler!) while we all want to be Claudia, this time, we are not all Claudia. These Books Made Me is a podcast about the literary heroines who shaped us and is a product of the Prince George's County Memorial Library System podcast network. Stay in touch with us via #TheseBooksMadeMe on socials, follow us on Instagram @TheseBooksMadeMe or reach out by email at TheseBooksMadeMe@pgcmls.info. For recommended readalikes and deep dives into topics related to each episode, visit our blog at https://pgcmls.medium.com/.

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Today’s Topics: 1, 2, 3, 4) Father Charles Murr joins Terry Gospel – Matthew 23:27-32 – Jesus said, “Woe to you, scribes and Pharisees, you hypocrites. You are like whitewashed tombs, which appear beautiful on the outside, but inside are full of dead men's bones and every kind of filth. Even so, on the outside you appear righteous, but inside you are filled with hypocrisy and evildoing. “Woe to you, scribes and Pharisees, you hypocrites. You build the tombs of the prophets and adorn the memorials of the righteous,  and you say, ‘If we had lived in the days of our ancestors, we would not have joined them in shedding the prophets' blood.' Thus you bear witness against yourselves that you are the children of those who murdered the prophets; now fill up what your ancestors measured out!” Saints in Heaven, pray for us! Bishop Sheen quote of the day

In this episode of The Dish on Health IT, Tony Schueth, CEO of Point-of-Care Partners (POCP), welcomes Angel Ballew, Head of Pharmacy Clinical Programs & Services at Centene Corporation, along with Seth Joseph, Managing Director of Summit Health Advisors, and Jason Reed, Senior Consultant at Point-of-Care Partners, for a discussion about what it will take to scale pharmacist-delivered clinical services. The conversation builds on findings from the NCPDP Foundation-funded white paper, A Business Model Framework to Scale Pharmacy-Delivered Clinical Services, developed with contributions from Point-of-Care Partners and Summit Health Advisors. The research starts from an important premise: there is already significant evidence that pharmacist-delivered clinical services can improve patient outcomes. The harder question is how to make those services operationally and financially sustainable at scale. Tony begins by asking Angel about the role pharmacists can play in closing care gaps, particularly for vulnerable and rural populations. Angel notes that Centene serves approximately 3.5 million people living in rural communities, but emphasizes that access challenges extend beyond geography to transportation, housing, food insecurity, and other social determinants of health. Against that backdrop, pharmacists can be among the most accessible healthcare professionals in a community and may interact with patients more frequently than physicians or other providers. Angel describes opportunities for pharmacists to reinforce medication adherence, identify medication-related concerns, provide immunizations, support test-and-treat services, conduct screenings, and perform other clinical services. As states continue expanding pharmacists' scope of practice and provider recognition, she sees opportunities to further integrate pharmacists into the healthcare team, particularly in communities with limited provider access. Seth and Jason broaden the discussion by sharing what emerged from the stakeholder interviews and industry research behind the white paper. Pharmacist-delivered services are already being piloted or implemented across areas including gaps-in-care counseling, test-and-treat programs, medication counseling and medication therapy management, immunizations and preventive screenings, and chronic disease management. Jason notes that pharmacists can play an important role between physician visits for patients managing conditions such as diabetes and hypertension. The conversation then turns to one of the biggest obstacles to scale: reimbursement and credentialing. Angel describes the tension between generating enough patient volume to make participation worthwhile for pharmacies and demonstrating enough outcomes and value to justify health plan investment. She discusses Centene's work to make credentialing requirements clearer for pharmacy partners, including the internal education required to bring pharmacy into processes traditionally designed around other healthcare providers. Tony asks Seth what the research suggests needs to change. Seth explains that the findings pointed strongly toward health plans and pharmacies working together to lead the development of sustainable models. Pharmacy benefit managers remain important participants with established relationships, networks, technology, and processes, but Seth describes the difficulty an innovative program champion within a PBM can face when they must first secure a client and then assemble participating pharmacies. Health plans may have greater ability to initiate programs and bring the necessary parties together, although their different lines of business and geographic markets add their own complexity. Angel shares how some of this has played out at Centene. The organization has worked directly with pharmacy communities and industry partners, often state by state because of differences in pharmacist scope-of-practice requirements. At the same time, Centene has developed a national program with consistent quality measures and an infrastructure that can support additional pharmacy service opportunities. That experience provides a real-world look at what scaling can require. Angel describes participation approaching 73 percent nationally and nearly 30,000 pharmacies contracted to participate in the program, including growth of more than 10,000 pharmacies over the past several years. She points to consistency as an important part of building participation. Rather than continually replacing programs with something new, Centene has gathered feedback and iterated, helping pharmacy partners gain confidence that the opportunity is stable enough to warrant their investment. The discussion then returns to a central finding of the white paper: the “cold start,” or chicken-and-egg, problem. Pharmacies may hesitate to invest in technology, staffing, workflow redesign, and clinical capabilities until sufficient patient volume exists, while health plans want sufficient pharmacy participation before investing in programs and measuring outcomes. Meanwhile, differences in credentialing, enrollment, reimbursement, contracting, and reporting continue to create practical barriers. Tony introduces the concept of regional Pharmacy Health Alliances for Reimbursable Medical Services, or PHARMS, as one potential approach to addressing the cold-start problem by bringing health plans and pharmacies together within a geographic market. Angel agrees that the concept begins with the right problem. The industry does not lack evidence that pharmacists can improve outcomes or help address gaps created by provider shortages. What remains less aligned is the business model. She cautions, however, that health plan operations vary considerably across lines of business, patient populations, benefit designs, quality priorities, and state Medicaid requirements. A regional model therefore needs enough flexibility to account for those differences. Seth explains that this variation is precisely why the research pointed toward regional collaboration rather than attempting to solve every issue nationally at once. The goal is not necessarily complete standardization, but identifying enough commonality among health plans and lines of business to prioritize a smaller number of services and begin building the infrastructure needed to support them. Angel views those complexities as design considerations rather than reasons not to pursue regional collaboration. She also sees the potential for lessons and common approaches developed regionally to eventually contribute to greater standardization nationally, particularly within individual product lines. Jason brings the discussion back to the pharmacy perspective. Pharmacies need confidence that services will be reimbursed before they can reasonably invest in technology, redesign workflows, train staff, and begin documenting clinical care differently. Regional collaboration among multiple payers could give pharmacies greater confidence that those investments will support sufficient patient volume while creating value for patients, pharmacies, and payers. From there, the conversation moves to another critical requirement for advanced pharmacy practice: data interoperability. Tony points out that pharmacists cannot effectively close care gaps if relevant clinical information remains siloed. Angel agrees that pharmacists need timely, actionable clinical information and enough context beyond the dispensing system to intervene effectively. At the same time, she cautions against overwhelming pharmacists with more information than they can realistically use within their workflows. She also challenges the idea that interoperability must be an all-or-nothing proposition. While the industry continues working toward a more ideal interoperability environment, imperfect data exchange should not prevent health plans and pharmacies from making progress today. Jason expands on the interoperability challenge by discussing the emerging concept of a pharmacy EHR. Pharmacy management systems are highly effective at supporting medication dispensing and claims transactions, but advanced clinical services require additional information. He argues that the answer is not simply to deliver the entire EHR to the pharmacist. Instead, the industry needs to identify the minimum actionable clinical information pharmacists need to make better decisions and perform specific services. Just as importantly, Jason emphasizes that pharmacy interoperability must be bidirectional. Pharmacists increasingly generate clinically valuable information through patient interactions, and that information also needs to reach providers and other members of the care team. Angel notes that Centene already receives some information back from pharmacies, including through programs involving social determinants of health, although the more seamless bidirectional infrastructure envisioned by the group is still developing. Looking ahead five years, Seth describes himself as a “data-driven optimist.” He points to momentum across private-sector organizations, national and independent pharmacies, state governments, and policy initiatives. While significant reimbursement, policy, and infrastructure challenges remain, he believes many of the pieces needed to overcome the cold-start problem are beginning to come together. Angel shares that optimism. Her hope is that pharmacists become a much more integrated and visible part of frontline healthcare delivery. The COVID-19 pandemic demonstrated how accessible and valuable pharmacists can be when the healthcare system needs them, and she sees an opportunity to continue moving the profession beyond its traditional dispensing role. Jason adds that demographic pressures, including an aging population and primary care capacity challenges, will continue creating demand for pharmacists to practice at the top of their licenses. He expects states to continue expanding pharmacist prescribing authority and clinical roles while technology, interoperability, and potentially AI help pharmacies operate more efficiently and support broader clinical services. Tony closes with the question asked of every guest on The Dish on Health IT: what is one habit or perception healthcare stakeholders should change or revisit starting tomorrow? Angel challenges listeners to think about pharmacists as more than medication experts. They are also access experts. In a healthcare system facing provider shortages, increasing complexity, barriers to care, and pressure to improve quality, she encourages healthcare leaders to ask how they can better leverage one of the most accessible healthcare professionals available in nearly every community. Rather than focusing only on what pharmacists dispense, Angel leaves listeners with a broader question: What can pharmacists prevent or help solve? The episode closes, as always, with the reminder that Health IT is a dish best served hot. Prefer video? Catch episodes on the POCP YouTube channel

Mara ist Anfang 20, träumt von einer Karriere als internationales Model – und gerät in die Fänge von Jeffrey Epstein. Der missbraucht sie ihren Aussagen zufolge über Jahre hinweg. Und Mara ist kein Einzelfall: In dieser 11KM-Folge erzählt NDR-Journalistin Anna Klühspies, wie sich der Sexualstraftäter Epstein mithilfe von Modelscouts systematisch auf die Jagd nach jungen Frauen gemacht hat und wieso die Modelbranche ein idealer Nährboden für seine Machenschaften war. Anna hat sich gemeinsam mit ihren Kolleginnen und Kollegen von NDR, WDR und SZ durch die Epstein-Files gearbeitet und sich auf die Spurensuche nach Komplizen Epsteins begeben. Die Recherche hat sie nach Paris, New York und sogar zu Epsteins Karibikinsel geführt. Diese Folge ist ein 11KM Classic und lief das erste Mal am 2.06.2026. Hier geht's zum Team Recherche-Film “Epstein Files – Jagd nach Models in Europa” von Anna Klühspies, Elena Kuch, Annette Kammerer, Jana Heck und Sebastian Just :https://1.ard.de/Epsteins_Jagd?cp=11kmMehr zum Epstein-Skandal findet ihr hier:https://1.ard.de/11KM_Podcast_EpsteinDiese und viele weitere Folgen von 11KM findet ihr überall da, wo es Podcasts gibt, auch hier in ARD Sounds:https://www.ardsounds.de/sendung/11km-der-tagesschau-podcast/urn:ard:show:4549910994dc2464/ An dieser Folge waren beteiligt:Folgenautor : Jonas HelmMitarbeit: Lukas Waschbüsch, Marc HoffmannHost: Nadja MitzkatProduktion: Timo Lindemann, Emilian Grimm, Christine Dreyer und Marie-Noelle Svihla Planung: Laura Stuhlmacher, Nicole Dienemann und Hardy FunkDistribution: Kerstin AmmermannRedaktionsleitung: Yasemin Yüksel und Fumiko Lipp11KM: der tagesschau-Podcast wird produziert von BR24 und NDR Info. Die redaktionelle Verantwortung für diese Episode liegt beim BR.

With so much to teach every year, we know you're always looking for the biggest impact for long term success for your students. In this episode, Pam and Kim discuss models that grow with students as they learn more and more mathematics.Talking Points:Verticality in practice standards, content progressions, vocabulary, notation, and strategies How area models extend from elementary to high schoolOpen number lines mature into douole number linesRatio tables can start as early as 3rd grade and scale to decimals, large numbers, proportional relationships.How models act as lasting mental anchors, not just "picture drawing" as a substitute for mathematical ability.Links:Math is FigureOutAble Challenge RegistrationBlog Post: Story of Hope: How We Made Math (and Growth) FigureOutAble TogetherPam's BooksCheck out Pam's social mediaTwitter: @PWHarrisInstagram: Pam Harris_mathFacebook: Pam Harris, author, mathematics educationLinkedin: Pam Harris Consulting LLC 

Chris has Jacob Miller on the show, who is building a tool called DiffUI. The big idea is that it uses a diffusion model to do prompt-to-website design rather than an LLM, because image-based models are more interesting and creative. So stay there for a while, producing really detailed visual mockups and plans, then head to the LLM. Time Jumps

(0:00) Thank you to all participants of the Boardroom Governance Summit (Aug 26-27, 2026)  (0:16) Intro *Boardroom Governance YouTube Channel launch. (1:45) About the podcast sponsor: The American College of Governance Counsel. (2:31) Start of interview.  (3:25) Origin Story of Marc Huffman (6:26) About OnBoard (10:35) AI Risks for SaaS and OnBoard (12:46) Directors Using AI in Shadows. His ideal board books. (17:05) Building a Better AI Policy (20:44) Agentic AI for Boards. Semantics search inside the board portal. (23:30) Recording Risks in Meetings (27:34) Improving Board Effectiveness (29:44) Public, Private, and Governance (32:50) The Private Markets Shift (37:37) AI and Board Dynamics. Empowering Independent Directors. (41:40) Semantic Search and Governance IQ Breakthrough (institutional memory). (44:15) Models, Costs, and Trust (47:26) Book that has greatly influenced his life: Shantaram, by Gregory David Roberts (2003) (48:17) His mentors (49:22) Quotes that he thinks of often or lives his life by "I'm a product of my own expectations" (49:43) An unusual habit or an absurd thing that he loves.  (50:35) The living person he most admires. Marc Huffman is the CEO of OnBoard, a global leader in digital board governance solutions, serving over 6,000 boards worldwide. You can follow Evan on social media at:Website: boardroom-governance.comX: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/YouTube: https://www.youtube.com/@BoardroomGovernance__To support this podcast you can join as a subscriber of the Boardroom Governance Newsletter at https://evanepstein.substack.com/__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

Genomics is moving fast, but how do we get biology the hardware and software it deserves? Ben Busby, NVIDIA's global alliances manager for omics, joins host Eleanor Howe to dig into the practical crossroads of GPU computing, open-source bioinformatics, and the next wave of precision medicine. They get specific about what GPU acceleration changes in day-to-day genomics and single-cell analysis. Faster pipelines can mean lower costs, tighter iteration loops, and newly feasible questions. They also explore the hardest bottleneck—reliable longitudinal multi-omics data—and challenge a popular assumption about AI in biomedicine: bigger isn't always better. Links from this episode:  Bio-IT World BioTeam Diamond Age Data Science NVIDIA Bio-IT World's Trends from the Trenches podcast delivers your insider's look at the science, technology, and executive trends driving the life sciences through conversations with industry leaders. 

In this episode of the Heart podcast, Digital Media Editor, Professor James Rudd, is joined by Dr Nick van Osta from Maastricht University. They discuss a review paper on computational modelling in cardiovascular disease. They cover the use of AI for: ECG diagnosis automatic ejection fraction segmentation from echocardiography prediction of success in cardiac resynchronisation therapy If you enjoy the show, please leave us a positive review wherever you get your podcasts. It helps us to reach more people - thanks! Link to published paper: "Individual hearts: computational models for improved management of cardiovascular disease" - https://heart.bmj.com/content/112/11/589  

878 · D23 Disney Parks Announcements: Sightlines Into Yesterday, Tomorrow, and FantasyThe loudest cheers at D23 weren't necessarily for what Disney was building next. They were for what Disney was willing to bring back, repair, and finally acknowledge.In a presentation built on a little faith, trust, and pixie dust, Disney asked fans to believe in an ambitious future, but this time it also showed its work. Models, progress, priorities, restorations, and even the things Disney chose to address directly revealed a much bigger story than any single attraction announcement.In WDW Radio # 878, we look at the D23 2026 Disney Parks announcements through a series of "sightlines" that connect Villains Land, Piston Peak National Park, Dreamfinder, the Expedition Everest Yeti, Spaceship Earth, Tomorrowland, Monstropolis, Tropical Americas, Disney Cruise Line, and much more. And the sightlines stretch well beyond the U.S., from Disneyland Paris and the return of its Jules Verne-inspired Space Mountain to new Tomorrowland, Avengers, and Spider-Man experiences coming to Tokyo, Hong Kong, and Shanghai.When you step back, it starts to look like a very Disney story of yesterday, tomorrow, and fantasy. Yesterday is being honored through attractions, characters, and experiences fans refused to let go of. Tomorrow is taking shape through ambitious new lands, technologies, attractions, and global expansion. And fantasy is where Disney is creating entirely new places and stories for us to step inside.We also explore what changed the perspective on some of these projects, why "no height requirement" may be one of the most important phrases of the night, why the sequence of these projects may matter as much as the projects themselves, and what Disney did not announce... and why.This isn't simply a recap of D23 2026. It's a closer look at what Disney is building, what it is preserving, what it is asking us to trust, and what all of it may mean for the way we experience the Disney Parks around the world in the years ahead.

What happens when AI eliminates the need for apprenticeships and reshapes entire industries? In this thought-provoking episode of The Greatness Machine, Taylor Welch dives deep into the evolving landscape of work, the role of AI in eliminating experience gaps, and why creators hold the key to the future. He explores the shift from consulting to education, the rising value of data and attention, and how time wealth is becoming the ultimate currency. If you're looking to stay ahead in a world where automation is rapidly changing the game, this conversation is a must-listen. In this episode, Darius and Taylor will discuss: (00:00) Introduction to Taylor Welch (01:45) Taylor's Origin Story and Early Career (05:58) Overcoming Overwhelm and Life Lessons (10:30) Bringing in a CEO: The Process and Lessons Learned (15:46) Scaling Consulting Businesses: Strategies and Models (20:25) The Role of AI in Business Scaling (24:40) Optimizing Team Performance and Talent Acquisition (30:25) The Importance of KPI and Performance Management (36:45) The Future of AI in Sales and Consulting (44:01) Understanding Time Wealth and Personal Fulfillment Taylor Welch is an entrepreneur, business consultant, and coach known for his impact in the online training and education industry. As the founder of Welch Equities, he leads a portfolio of businesses focused on driving economic growth through value-driven initiatives. His ventures span sales, marketing, finance, and operations, while also investing in small training and education brands. Committed to making people smarter, happier, and healthier, Taylor combines business success with a strong emphasis on family and personal fulfillment. Connect with Taylor: Website: https://taylorawelch.com/  Website: https://wealthyconsultant.com/  Instagram: https://www.instagram.com/taylorawelch/ Twitter: https://x.com/taylorawelch/ YouTube: https://www.youtube.com/c/taylorawelch  Connect with Darius: Website: https://therealdarius.com/ Linkedin: https://www.linkedin.com/in/dariusmirshahzadeh/ Instagram: https://www.instagram.com/imthedarius/ YouTube: https://www.youtube.com/@Thegreatnessmachine  Book: The Core Value Equation https://www.amazon.com/Core-Value-Equation-Framework-Limitless/dp/1544506708 Write a review for The Greatness Machine using this link: https://ratethispodcast.com/spreadinggreatness. 

What does an AI agent need to understand about your business before you allow it to make decisions and take action without waiting for human approval? In this episode of Tech Talks Daily, I speak with Kash Mehdi, Field CTO at Reltio, about the move from analytical AI that supports decisions to agentic AI that can execute them. Kash argues that leaders should begin treating AI agents as a workforce rather than another collection of software tools. A digital workforce needs training, boundaries, oversight, trusted information, and clear permissions before it can act safely. He uses the analogy of raising a puppy. When the puppy misbehaves, the problem may be inadequate training or poorly defined boundaries. AI agents present a similar leadership challenge. Organizations must ask what the agent has learned about the business and what authority it has been given. We discuss why model selection may be receiving too much executive attention. Kash describes four components of an agentic system: the model, tools, data, and context. Models are improving rapidly and tools are increasingly available, but business context remains incomplete across many enterprises. Data tells an agent a fact. Context helps it understand what the fact means within a particular customer relationship, geography, policy, or business process. Kash illustrates the difference with a pizza order. The data may confirm that someone is logged in, the model can interpret the request, and a tool can place the order. Context tells the system that it is Friday night, the customer is watching television, and they usually order pineapple and cheese pizza. The same principle becomes far more serious when an agent is dealing with medical equipment, supply chains, financial customers, or regulated information. It must understand which entities exist, how they relate, what information it may access, and which actions it has authority to complete. Kash identifies three requirements for safer autonomy: a governed source of truth, a live feedback loop, and enforceable permission boundaries. Trust must be built into the data and operating rules before the agent acts because the familiar human review step may no longer exist. We also discuss how governance changes when AI can execute decisions at machine speed. A poor decision made by one employee can usually be reviewed and corrected. A poor decision repeated automatically across thousands or millions of transactions can become a business incident before anyone intervenes. Kash shares examples involving restaurant menu launches, medical equipment deliveries, and call center offers. Each depends on current information and the relationships connecting customers, products, suppliers, locations, and previous interactions. For CIOs preparing today, Kash recommends building context around reusable entities rather than constructing an isolated data project for every AI use case. He points to Schneider Electric as an example where one unified foundation supported sales, shipping, operations, and marketing use cases. The conversation ends with a warning about slow data. Autonomous agents need current context because information that arrives after a decision has been made may no longer carry much business value. Kash predicts that the half-life of enterprise data will become a board-level measure. If a smarter agent can make a poor decision faster and with greater confidence, is your organization investing enough in the context, governance, and feedback needed to keep it on course? Listen to the conversation and share your thoughts with me.     Useful Links https://www.reltio.com/ https://www.reltio.com/datadriven/  

AGENDA: 04:20 Elon's Deal of the Decade: SpaceX Buys Cursor for $60BN 06:10 Why Cursor Was Surprisingly Cheap at $60BN 07:00 Why Zuckerberg Failed to Buy the AI Prize Elon Secured 12:00 Elon vs Zuck: Who Would You Rather Work For? 14:00 Will Microsoft or Amazon Now Race to Buy Cognition? 17:05 Stripe's $7BN OpenRouter Deal Creates Huge VC Winners 25:00 OpenRouter's Fatal Risk: Enterprises Don't Want 10 Models 28:15 Anthropic Turns Its First Profit on $11.5BN of Quarterly Revenue 32:15 Can Anthropic Really Reach $600BN in Revenue? 37:00 Why Every Elite Engineer Could Soon Get $100K in AI Tokens 39:30 Would Rory Buy Anthropic at a $2.5TN Valuation? 44:50 Silver Lake's $43BN Workday Bet: SaaS Isn't Dead, It's Mature 53:00 How Silver Lake Could Make $30BN From Workday 57:00 Lovable vs Higgsfield: Similar Revenue, Radically Different Valuations 58:00 Is Lovable's $13.3BN Price Actually Cheap? 63:30 Why the DOJ Is Coming After Andreessen Horowitz 69:00 Why A16Z Has "50 Legal Battles" Happening at Once  

When you ask an LLM like ChatGPT or Claude a question, the model goes through its massive amount of training data and guesses the answer by mathematically predicting the word most likely to appear next in a sentence. This model, experts say, will not work well for technology designed to navigate the physical world. Something like a robot that works in a warehouse will instead require a “world model” that can understand spatial surroundings, like the stuff we walk by or bang into. But what is a world model, exactly? And how do you train AI to recognize what the real world looks like? Host Ira Flatow checks in with tech journalist Joanna Stern, who's seen the early days of these models up close, even in her own home. Then, we check in on the math world, where frontier AI models have made meaningful progress on decades-old problems. Mathematician Emily Riehl gives us the big picture on how significant these results actually are. Guests: Joanna Stern is a tech journalist who writes newsletters and creates videos for New Things Media. Dr. Emily Riehl is a professor of mathematics at Johns Hopkins University. Transcript will be available after the show airs on sciencefriday.com.   Subscribe to this podcast. Follow our show on Instagram, TikTok, Facebook, and Bluesky @scifri and sign up for our newsletters. Got a science question that's keeping you up at night? Call us: 877-472-4374 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dr. Don and Professor Ben talk about the risks from eating McDonald's ranch dressing found on the side of the road in northern Minnesota in the summer. Dr. Don - not risky

Sharad Kumar lives in Pleasanton, California with his wife and 2 kids. He enjoys playing all musical instruments, and spending time with his family. He has a 2 year old daughter, and a 14 year old son into robotics. He is also passionate about giving back to the community, through their company foundation.Harshit Omar lives in San Francisco, and is married with a 4 year old son. He used to be a street racer in his college days, loving fast cars and taking risk. Nowadays, he is a big marvel and comic book fan, along side his son. In fact, his son thinks he is Captain America, regularly wielding his shield and mask.A fun fact about both of these gentlemen: this is their third company to work together in, their second startup, and their wives are sisters. So they are connected by wives, and united by startups.In their previous startups, Sharad was leading sales and ops and Harshit was leading on the product side. When the company got acquired, it took them 8-9 months to integrate to a different cloud provider. They realized the model was broken, requiring expensive consulting services, and not convenient at all - and they wanted to figure out a better way.This is the creation story of Fluidcloud.SponsorsUnblocked (https://getunblocked.com/codestory)TECH Domains (https://get.tech/codestory)Mezmo (https://mezmo.com/codestory)Braingrid.ai (https://braingrid.link/code-story)Alcor (https://alcor.com/podcast)Equitybee (http://codestory.co/equitybee)Terms and conditions: Equitybee executes private financing contracts (PFCs) allowing investors a certain claim to ESO upon liquidation event; Could limit your profits. Funding in not guaranteed. PFCs brokered by EquityBee Securities, member FINRA.Linkshttps://www.fluidcloud.com/https://www.linkedin.com/in/sharadkumar123/https://www.linkedin.com/in/harshito/Checkout our episode stacks on Stacklist! https://stacks.codestory.co/ Hosted by Noah Labhart | Technical Founder & Startup Mentor.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode, Ernie Svenson talks with Docket Drafter co-founder Tommy Eberle about what lawyers need to understand to use AI tools like Claude more effectively. They unpack why Word documents consume so many tokens, why AI performance can decline during long chats, how context windows and compaction affect results, and when to use different Claude models and thinking levels. Tommy also explains how lawyers can use AI to organize files, automate repetitive document work, and build more efficient workflows—without needing to become programmers themselves. Chapters 0:05 Why AI Frustrations Make Sense 3:03 From Coding To Legal AI 8:05 Making Word Agent-Friendly 16:20 Why Claude Gets Dumber 26:21 Models, Thinking, And Compaction 34:33 Files, Folders, And Better Prompting Show Notes The 80/20 Principle (my techlaw newsletter) The Inner Circle (my online community for lawyers) Tommy Eberle's LinkedIn page Tommy's Email Address: tommy@docketdrafter.com  Docket Drafter https://www.youtube.com/@DocketDrafter  Follow and Review I'd appreciate it if you could drop a review over on  Apple Podcasts. It only takes a few seconds and helps spread the word about the podcast. Thanks to the sponsor: Smith.ai Smith.ai is an amazing virtual receptionist service that specializes in working with solo and small law firms. When you hire Smith.ai, you're hiring well-trained, friendly receptionists who can respond to callers in English or Spanish. And they have a special offer for podcast listeners where you can get an extra $100 discount with promo code ERNIE100. Sign up for a risk-free start with a 14-day money-back guarantee now (and learn more) at smith.ai.

Who is St. Peter: faithful disciple, flawed human being, first pope, symbol of the Church, or somehow all of the above? Fr. Peter Walsh welcomes Msgr. Rob Kinnally of St. Aloysius Catholic Church for an ecumenical conversation about one of the New Testament's most famous and contested passages. Together they explore Peter's confession, the meaning of “the rock,” how faith grows and changes, and what Catholics and Anglicans might learn from one another about authority, tradition, and the Church. And beneath it all is the question Jesus still asks: “Who do you say that I am?”Questions for ReflectionThemes & ApplicationMsgr. Rob describes Peter as the “quintessential knucklehead,” someone capable of profound faith and profound failure. What might Peter teach us about being a disciple without having everything figured out? What does Jesus mean when he gives Peter the authority to “bind and loose”? Who should have the authority to interpret Scripture for the Church today?Roman Catholic and Anglican traditions understand Peter and church authority differently. What can Christians learn from one another without pretending those differences don't matter?Personal Reflection QuestionsIf Jesus asked you today, “Who do you say that I am?”, what would your answer be?Where do you recognize yourself in Peter: conviction, doubt, impulsiveness, courage, fear, growth, or something else?Have disagreements within Christianity ever made faith more difficult for you? Have relationships with Christians from other traditions ever strengthened it?Broader Spiritual ConsiderationsIf Peter can proclaim Jesus as Messiah and later deny knowing him, what does that suggest about the relationship between belief and discipleship?Is “the rock” Peter himself, Peter's confession, Christ, or some combination of these? What changes depending on how we answer?Peter suggests that Christian division itself has become a barrier to belief. What responsibility do churches have to make Christian unity visible to the wider world? References & ResourcesVocabularyChristology: The study of who Jesus Christ is, particularly questions about his identity, humanity, divinity, and saving significance. Ecclesiology: The theological study of the Church: what the Church is, where its authority comes from, and how it should be structured and governed.Petros / Petra (Πέτρος / πέτρα): Greek words behind Matthew's wordplay, “You are Peter (Petros), and on this rock (petra) I will build my church.”Cephas / Kepha (כיפא / Kēphas): The Aramaic name meaning “rock” associated with Peter. Msgr. Rob raises the Aramaic background as important because the distinction between Petros and petra found in Greek does not work in quite the same way in Aramaic. Magisterium: From Latin magister, “teacher.” In Roman Catholic theology, the Church's teaching authority, exercised particularly through the pope and bishops. Msgr. Rob connects it with Scripture, tradition, and the continuing guidance of the Holy Spirit.Books & TheologiansAvery Dulles, Models of the ChurchAvery Dulles, Models of RevelationEvelyn Underhill, Mysticism: A Study in the Nature and Development of Spiritual ConsciousnessKathryn Tanner, Christ the KeyMemorable Moments“Pope Francis adds feast of Martha, Mary, and Lazarus to Church calendar” (Read more)“Common Declaration by Pope Francis and Archbishop of Canterbury Justin Welby” (Read more)Learn more about St. Mark's at https://www.stmarksnewcanaan.org

Glenn Solomon of Notable Capital joins Nick to discuss 3 Multi-Billion-Dollar Exits in 1 Year, Lessons from Airbnb, HashiCorp, Slack, and Square, The VC Case for Staying Small, and the Battle Between Open-Weight vs. Closed Models. In this episode we cover: Identifying Unique Investment Opportunities  The Impact of Fund Size on Investment Strategy  Challenges of Overfunding and Market Dynamics  Growth and Real Success in the AI Era  The Role of Hyperscalers and Frontier Labs  Public Market Sentiment and IPO Considerations  Future of Venture Capital and Notable's Strategy Investing in Anthropic and Market Dynamics  Guest Links: Glenn's LinkedIn Glenn's X Notable's LinkedIn Notable's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

The United States Naval Academy Museum's Curator of Ship Models, Don Preul, and Education Specialist Sondra Duplantis discuss the museum's ship model collection, model shop volunteers, and a beloved colleague. 

Will Hayden Panettiere's shocking passing make today's blinds? Play along with Bradley and Dawn to find out!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Utilities are facing a new kind of pressure, and it's not just about the grid. Rising infrastructure costs, extreme weather, and a growing shift toward digital payments are all converging at once, forcing utility leaders to take a hard look at how payment costs are managed and who absorbs them. In this episode of Experience Better: The CX Podcast by KUBRA, we're diving into one of the most timely conversations happening across the utility sector right now: evolving payment fee models. We'll walk through what's driving this shift, what options utilities actually have today, how changes to card networks are creating new possibilities, and what it takes to navigate all of this while keeping the customer experience front and center. Whether you are just starting to ask the right questions or actively evaluating a transition, this episode is designed to help you think through the landscape with clarity and confidence.

In this special edition from Black Hat, Dave Bittner sits down with ⁠Clint Gibler⁠, Cyber Lead at ⁠OpenAI⁠, and ⁠Robby Winchester⁠, Chief Global Professional Services Officer at ⁠SpecterOps⁠, to explore how frontier AI models are changing the way defenders approach cybersecurity. The conversation moves beyond the hype to examine responsible AI deployment, AI red teaming, reducing noise in security workflows, and the balance between advanced models and human expertise. They also discuss OpenAI's Trusted Access for Cyber program and what it takes to give security practitioners access to powerful AI capabilities while managing the risks of misuse. Check out the full video here.

What evidence would convince you that an AI agent is ready to make decisions involving employment, money, healthcare, or legal rights? In this episode of Tech Talks Daily, I speak with Vin Sharma, founder and CEO of Vijil, about the trust gap preventing many enterprise AI agents from progressing beyond proof of concept. Vin has spent approximately 30 years building software across security, operating systems, open source, cloud computing, machine learning, and AI. His previous work includes leading engineering at Amazon SageMaker and helping develop 11 AWS AI services. He argues that AI agents differ from conventional software because they combine autonomy with agency. They can interpret an objective, make decisions under ambiguous conditions, and take action. This raises a deeper question than whether an agent can complete a demonstration successfully: will it remain loyal to the interests of the person or business delegating the task? Trust is also specific to the job. Vin uses a simple analogy. You may trust a gardener to care for your lawn, but that does not automatically make the same person suitable to babysit your child. An AI agent must therefore be evaluated within the context of its users, task, operating conditions, authority, and potential consequences. Vin proposes testing three areas. Reliability asks whether the agent can perform its assigned task. Security examines whether it maintains its integrity when facing hostile or noisy conditions. Safety considers what happens when the agent fails and whether the resulting damage remains contained. This evaluation cannot end when the agent enters production. Models, integrations, data, users, and external conditions change. An agent may drift away from its original purpose, which means businesses need continuous monitoring, testing, and updating across the full AI agent lifecycle. We discuss how established security practices can be applied to this problem. Trusted execution environments, containment, least privilege, limited-duration access, and bounded models can reduce exposure. Smaller language models may also be better suited to narrow, high-risk tasks than a general model with broad permissions. Vin offers a three-part framework for governance: personas, purpose, and policy. Personas describe the people and attackers who may interact with the agent. Purpose defines the legitimate task. Policy sets the boundaries between permitted and prohibited behavior. For high-risk systems, his recommended starting position is that any action not explicitly permitted should be prohibited. A natural-language policy can then be converted into deterministic rules and controls governing the agent's behavior. Vin's most direct advice concerns evidence. Vibes, demonstrations, and benchmark scores do not prove that an agent is safe for a particular business process. A CISO should expect a complete risk assessment, while a business owner should receive proof that the agent will serve the organization's interests. His bridge analogy captures the issue perfectly. Engineers do not claim a bridge is safe because it looks impressive during a demonstration. They calculate load, tolerance, failure conditions, and provide test evidence. AI agents acting in consequential workflows deserve a comparable engineering discipline. If an agent developer asked you to trust their system today, would they be able to provide evidence of reliability, security, safety, loyalty, and contained failure? Listen to the episode and share your thoughts with me.

SUMMARY: This episode is the second part and explores the flip side of OSS models. Last episode, we discussed the potential decline; this episode, we'll talk about the potential positive future of OSS models. Aaron and Brandon explore the future of open source AI models, the role of industry consortia, and how major tech companies like NVIDIA, Apple, and Google are shaping the AI landscape. They discuss the potential for open models to become industry standards and the strategic motivations behind these moves.SHOW: 1054SHOW TRANSCRIPT: The Enterprise AI Show #1054 TranscriptSHOW VIDEO: https://youtu.be/w238Y1ZKG1QSHOW SPONSORS:Nasuni - Activate your data for AI and request a demo Topic: Are we seeing the end of OSS models?Why now? NVIDIA Open Secure AI Alliance (all except Anthropic joined) & Linux Foundation is managing proposalsPast: OSS runs the world…  Up until now, there hasn't been an overarching “AI Model” project managed by the CNCF or Linux Foundation that has gained any tractionPresent: As model sizes increase, who pays for training? I think the DB market is the closest parallel here, and it's also where the most OSS rug pulls have happened in the past. Is this history repeating itself, but also a lesson learned because so many DB companies got burned?Future: Someone will have to donate a trillion+ parameter model to a foundation. My bet is NVIDIA will eventually drive this through Nemotron; it makes the most sense, and they have the most to lose if OpenAI and Anthropic take over and also eventually use their own chips.FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

On this week's episode, the girls sit down with powerhouse entrepreneur, Jaclyn Johnson, founder of Create & Cultivate and Cherub, to talk money, investing, and building a business from the ground up. She gets into selling Create & Cultivate for $22 million and ultimately buying it back, plus the investing questions we all want answered: How much money do you actually need to start investing? Where should you put it? What even is angel investing? And how do you go from having an idea to actually becoming a founder? Jaclyn breaks it all down and shares the wins, roadblocks, and lessons she's learned along the way! To Follow Jaclyn: Instagram: @JaclynrJohnson  Follow us! Hunter: https://www.instagram.com/huntermcgrady Michaela: https://www.instagram.com/michaelamcgrady Subscribe to Patreon for exclusive episodes and content: https://www.patreon.com/Themodelcitizenpodcast

Text-to-image models have become remarkably good at producing realistic images. But realism isn't the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution image generated locally, and today's models still struggle in surprising ways. In this episode, Fatih Porikli, Vice President of Technology at Qualcomm, joins me to discuss what remains unsolved in image generation and several approaches his team presented at CVPR to address those challenges. We explore why better training objectives can improve controllability, how separating scene planning from rendering may lead to more reliable image generation, techniques for generating 16-megapixel images efficiently on edge devices, and new methods for eliminating the visible artifacts that often appear in AI-powered image editing. Along the way, we discuss reinforcement learning for image generation, agentic image generation pipelines, on-device AI, and what the next phase of progress in generative vision systems is likely to look like.