Podcasts about gpt

How Swarms of AI Agents Are Plotting. Connor Leahy https://youtu.be/6XrkGK9mqsE?si=PV3rsPnGHG9sUikO The Peter McCormack Show 333K subscribers 479,024 views Aug 14, 2026 The Peter McCormack Show Connor Leahy has spent his career at the frontier of AI, from reverse-engineering GPT-2 as a student and co-founding EleutherAI to building the AI safety company Conjecture. He now leads US policy work at ControlAI, and he returns to the show with a warning that has stopped being theoretical: AI systems are escaping their sandboxes, writing their own zero-days and leaving each other notes on how to break out. Peter and Connor discuss the OpenAI incident that ended in an attack on Hugging Face sophisticated enough to be mistaken for a state actor, why we understand almost nothing about how these systems work, how reinforcement learning produces models that lie, cheat and manipulate to reach a goal and why the next stage after chatbots and agents is swarms. But Connor's argument is that the future is not decided. He believes superintelligence should be treated the way nuclear weapons are treated, banned and verified and deterred and that the real bottleneck is awareness rather than opposition. They discuss what an “aligned” superintelligence would actually mean, why military people understand the threat faster than technologists and what ordinary people can do about it. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - TIMESTAMPS: 00:00 - The AI That Escaped Its Sandbox 01:52 - Inside The OpenAI Incident 05:46 - They Thought It Was China 06:43 - We Grow AI, We Don't Write It 11:07 - Trained To Lie, Cheat And Deceive 13:48 - Chatbots, Agents, Then Swarms 17:57 - The Machines Are Developing Preferences 19:30 - The Model Obsessed With Raccoons 22:03 - The Agents Left Each Other Notes 27:39 - Danger Pumps The Valuation 29:41 - Sleeper Agents In Every System 32:00 - Recursive Self-Improvement Goes Vertical 33:43 - Worse Odds Than Russian Roulette 38:14 - Are We Already Too Late? 40:02 - Independently Assured Destruction 48:02 - The Politicians Have Never Used An Agent 50:56 - Superintelligence Should Be Illegal 57:08 - You Never Privatise The Military 1:05:00 - An Aligned AI Is A One World Government 1:08:34 - What You Can Actually Do - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - CONTACT PETE › Website – http://petermccormack.com › Feedback – https://www.petermccormack.com/contact › Email – me@petermccormack.com › Instagram –   / mccormack555   › X/Twitter – https://x.com/petermccormack/ CONNECT WITH CONNOR LEAHY › ControlAI – https://controlai.org/ › Connor's Profile – https://controlai.org/connor-leahy › MicroCommit – https://microcommit.io/ › X/Twitter – https://x.com/NPCollapse › LinkedIn –   / connor-j-leahy   SPONSORS › IREN – https://www.iren.com/ › Expat Money – https://expatmoney.com/peter › Arch Public – https://archpublic.com/peter - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - LISTEN / SUBSCRIBE › Apple Podcasts: https://apple.co/40ruY9K › Spotify: https://spoti.fi/3Wc94Vu › Fountain: https://bit.ly/FountainPM › YouTube: https://bit.ly/YouTube_PM › Rumble: https://bit.ly/RumblePM

Tiff and Pam talk about the current intersection of technology in the practice, and how using and understanding AI as a tool for busywork means you can focus on the tasks that really need that human touch. They talk about why training AI could be just as critical as training humans, where to look first when incorporating the software into your practice, how a staff member can serve as quality check for automated tasks, and more. Episode resources: Subscribe to The Dental A-Team podcast Schedule a Practice Assessment Leave us a review Transcript: Tiffanie (00:01) Hello, Dental A Team listeners. This is Tiff back with you here on the podcast. And we are in the studio today. I have myself and I have Miss Pam with me. And Pam is one of our prize possession consultants here at the Dental A Team. We have had Pam on our team for a little while now. And if you are special enough and blessed enough to work with her, you know exactly who she is and you have some solid foundational systems. Pam is a systems guru. She busts them out.   And she holds accountability like I've never seen before. And when she learns something, she learns it forever. And I love getting to watch you, Pam, progress in your Dental A Team journey. I watch you like taking notes all the time on the systems that we, you know, hold tried and true. Yesterday Dana was talking about a couple of systems during our collaboration and I was like, gosh.   She's just over there feverishly writing and I love it because I know exactly what's gonna happen. You're gonna like go back, you're gonna relearn   Pamela (00:54) I was. Yes, I was.   Tiffanie (00:58) it, and you're gonna teach it. And it was just really cool. So I love that, Pam. Thank you. And I love that you have these skill sets that you're able to then take and you help so many practices implement the same skill sets, like not just our systems, but how to retain it. You know how you retain it.   But then you're also looking for how are other people retaining the things that they're learning so that you can help them grow in their learning as well. So it's just really cool to watch, Pam. Thank you for everything you do and thank   Pamela (01:27) Thank you.   Tiffanie (01:28) you for being here today. How are you?   Pamela (01:31) you know, I'm doing fabulous and thank you. I appreciate all those kind words, Tiff. That was very nice. I think it's a unique and great time to be in dentistry. So v we've seen a lot of changes over the years and now we're to a stage that is changing very, very quickly.   Tiffanie (01:50) I agree. I I love that you said that, Pam, because something that I think we've said in dentistry for a long time is that dentistry is really progressive in a lot of ways. I think there's a lot of things in dentistry that haven't changed and haven't progressed, like Rick canals, things like that are still being done similarly to how they were thirty years ago. But so much in dentistry is so progressive and it's changing all the time. But I I think we've always said that. But I think in the last five, six years that has   Like us saying that before doesn't even make sense anymore. Like the progression we're seeing now is wild.   Pamela (02:26) It's it's crazy. you know, I was thinking about this, and there are so many systems that we have done for years that we're not perfecting, right? That we still could perfect and are in addition to any AI stuff we have. But the interesting thing is is that now something that has been in scarcity our entire human existence or now or soon will become like an abundant commodity, which is intelligence, right?   Tiffanie (02:53) Yeah. Yeah.   Pamela (02:55) So   I think that it's really wild to be thinking about that intelligence is a commodity and we can buy it.   Tiffanie (03:03) Yeah, I think that's amazing. That was a great perspective. And you are not wrong. I listen to a lot of   Pamela (03:07) Yeah.   Tiffanie (03:09) podcasts and I know you do too that speak to that same thing. And I think both of our households house many conversations around that same thing. I think we we share that.   Pamela (03:18) That is so true. It's so true.   Tiffanie (03:22) Yeah. So with that, Pam, I think that's a great start. What are you seeing? expand on that for me. Like what are you seeing as far as I love that like intelligence is is a   purchasable at this point. What are you seeing with your practices or just the research you're doing and the things you're hearing and and listening to within the dental world, narrow that down for us. What are you seeing from your perspective how that's changing dentistry?   Pamela (03:48) Yeah,   well I I think the the question is is how do we react to that reality, right? And a lot of practices are have not gotten into the AI and are now saying, okay, I want my insurance verification done, I want, you know, RCM done, the revenue cycle management done by AI, but it is a little daunting. Right? It is like I had one practice say   yo well we have this company that's doing our RCM and and I said do you are you like what are you doing with it and they said nothing they do it and I'm like dear goodness gracious like there are you have to manage it unfortunately I think we're at a real a space where we're not the Ironman like Jarvis runs everything   Tiffanie (04:43) Yeah.   Pamela (04:43) you know stage we're at the   Alexis and we have a lot of really smart AI tools. Most of them, I'm well, they're all sitting on top of separate systems and what we are contributing. I don't think we're pardon me quite to the point where it's an operating system as opposed to, you know, pieces. So I'm seeing practices now take those pieces, try to learn them, and then develop their skills even more in the stuff that they were doing.   So what I mean by that is how are we, how are they actually doing at clean claims, right? Are they   Tiffanie (05:23) Yeah.   Pamela (05:24) getting the clean the claims clean before they send them in? how are they doing on you know managing the online scheduling? Are they reacting to that? And is all the patient's insurance information accurate before it goes to the AI?   So that's that's what I'm seeing. That's a s a little bit of a struggle.   Tiffanie (05:47) Yeah. I think something you pointed out there was that a lot of people a lot of people seem to be jumping headfirst and just like handing the keys over rather than allowing it to be a tool that they use still with oversight. And I noticed recently, even just on my chat GPT that I use down at the bottom, it's like, Hey FYI, these may be inaccurate. Like this is not to be taken as truth. Yeah. And I'm like, well   Pamela (06:11) Yes, I j I saw that, yeah. So true.   Tiffanie (06:16) We need that reminder though, kind of like the McDonald's coffee cup that now has, you know, since nineteen ninety or whatever it was, says that the contents are hot. Like humanity needs those reminders 'cause I think we're so it's like a it's a catch to me too, 'cause I do think we're so quick to jump onto things and try them. But   Pamela (06:34) Mm-hmm.   Tiffanie (06:34) at the same time we're like second guessing and   thinking we can't and thinking they don't work, but we do latch on to certain things where we're just like, wait, that was expected. I expected to be able to drink my coffee as soon as you handed it to me. You know, that it wasn't gonna be scalding hot, that I could just just go. And same with the AI. I think there's so many aspects of it that we just expected all of the truth to be on the internet somewhere. We've been primed that the truth is on the internet and   we just expected it to be true that now chat is like, hey, wait a second, like, hey, I'm just like your buddy who did the research online, the same as your friend next door. Like this   Pamela (07:09) Right.   Tiffanie (07:13) could be wrong. And I think it's an interesting thought because to your point of view, handing over your cycle management, your your revenue and all of that data, handing that over to AI and expecting it to just be perfect from here on out with no mistakes is wild.   Right, but we're doing it. So many of us are doing it. Similarly to the online scheduling. I know we have a lot of practices and we have some doctors that have spoken for us at our events. we had one last September that our doctor focused on AI, and she pointed out the facts of needing to train their AI the same as they're training a human. Like you get a   Pamela (07:51) Yeah, yes. Yes.   Tiffanie (07:54) result and you're like, you got that result, you're fired, you're done, we give up.   You say, actually,   Let's tweak it to be what we want it to be. So we're saying yes and okay, cool. And let's do it this way. Let's move it this direction. with that statement, you also said insurance verifications, making sure they're accurate. And it made me think too, we've always said, you know, good information in is good information out, bad information in is bad information out. And I think   That's the same with any of it, with any of the AI tools or any of it, right? The it can only scour the internet for so much information and find so much truth. With the good information in, that goes as far as everything. So whatever it is that we're asking and training these systems and these tools to produce is what we're going to get. So we're not spending the time with the AI and we're not spending the time saying, This is the result that I want, this is how I want you to get there.   we're doing a disservice to ourselves just the same as so many people we were just talking about this. So many people are still misusing or underutilizing tools that we've had for gosh, at this point I always say 30 years, but at this point I think it's going on like 40 years, right? Like we've had these tools for   Pamela (09:12) Exactly, yeah.   Tiffanie (09:14) a long time. We're still   Pamela (09:16) Yeah.   Tiffanie (09:16) misusing them because I know both of us have walked into offices where they're like changing the prices in the treatment plan.   And they're doing all this math and there's a calculator. I still have offices that have the calculators that have the tape, and I'm like, where are you even where do you buy the tape anymore? Right. But they do, they have this running tab. And I'm like, what are you doing? And they say, Well, it's never right. We always have a balance. And I'm like, Okay, this is like a band-aid fix, right? So no matter what technology you have, there's so much tech that saves us so much time. And that's the point of this conversation is what kind of tech is out there that can   save our team   Pamela (09:52) Mm.   Tiffanie (09:53) time. And a lot of that time is for the administrative team, gives them the the chance to do other things, right? So automating things gets rid of busy work, allows them to do other things. But if we're not utilizing the tool correctly from the get-go, it's not saving the time because we're going back and fixing it anyway. So maybe we're saving time on we have an online scheduling app and it works. So our team maybe doesn't have to answer as many new patient calls.   But then that same person is over here calculating by hand a treatment plan estimate. It's like, cool, well, we just like took nonsensical   Pamela (10:28) Yeah.   Tiffanie (10:29) time and put it into a nonsensical time suck again. So, Pam, how   Pamela (10:33) Yeah. Yeah.   Tiffanie (10:35) are you helping the chain offices to really utilize the tools to actually save the time? And when they're not, so like that situation, how do we how do we get to the bottom of it? Because for me,   When I see somebody calculating treatment plan estimates because they always have a bill at the end, I'm like, cool, that's like a band-aid over this massive gash on your arm and it stopped the bleeding   Pamela (10:59) Mm-hmm.   Tiffanie (11:00) in that one spot, but it's not fixed. Like you still need stitches. How do we get to the bottom of it? And how do you help practices really figure out what truly is going to save them time and how they can get there?   Pamela (11:14) Yeah,   I think it goes back to basics. Before we layer on that AI piece of it, we have to have those basics in place. And you're right, there are a lot of practices and you know that are struggling with that. They're still doing things by hand. So, you know, have going back to the basics and having that correct verbiage, I think, is super important. to with the patient of knowing that.   There may be, it is an estimation, and there may be a difference when once your insurance pays. And you can offer to the patient, you know, if you would like to call, I don't like doing predeterminations, pre-D, I'm kind of against them. A lot of people still do them for larger treatment. I understand that. but for if you are to, you know, tell a patient, look, if if it's a credit.   we will get that credit right back to you within the month, right? have   Tiffanie (12:12) Mm-hmm.   Pamela (12:13) some sort of verbiage that gives you a little bit of out, but also be confident when you're prevent presenting those numbers if you've done the homework. So I think that's where it goes is back to the basics, making sure the basics are correct before you, you know, are giving that treatment plan to the patient. And then your verbiage is super important. And trusting it.   Tiffanie (12:36) Yeah, I totally agree.   And trusting it exactly. Being able to trust the system is huge. So making sure I think you're you're like spot on back to the basics, right? So making sure your verbiage is in line and making sure that the information that we're putting into the computer system is as accurate as possible. And you can use AI tools for that too, right? So we have   Pamela (12:55) Right. Right.   Tiffanie (12:56) AI tools this day and age that do insurance verifications and they upload it into the system.   They do all of the pieces. But then again, back to what we said earlier and how you said like this practice is like, we don't even look at it, right? Same thing. Like if you're if you're paying for an AI bot to go scour, get the information, put it into your system, and then you're turning around and you're like, well, it's always wrong, right? I I always have a balance or a credit. And so I calculate it just to double check, like, okay, maybe we need to look to see.   Further back, where is that miscalculation coming from? Because the insurance data in the system, the patient's data in the system, the right fee schedules, all of those pieces are feeding the tech and the intelligence, the information that it's spouting out to you. So it can only do so much. So if you've   Pamela (13:47) Right.   Tiffanie (13:48) got, you know, you didn't mark the you didn't you didn't tell the bot that you needed to mark that there was a downgrade. So you're you're having   crowns come back and there's two hundred dollar balance because it was downgraded, right? Well, stop hand calculating that and tell the bot to do it differently. Tell the system to do it differently. Whatever your system   Pamela (14:06) That's right. That's right.   Tiffanie (14:08) is, there's a little button somewhere that you you click it and it says downgrades, right? Account for downgrades, etc. So utilizing those tools from the ground zero, I think is just massive and it's something that's been severely underutilized for a really long time.   that needs to be right first. Because then if we go in and we layer these AI bots on top of that, that data is what they're working with. Just like Dentrix can only give you a treatment plan based on the information that you put in there. You put the fee schedule, you put the percentages, you put the treatment plan. You did all the buttons, you clicked and you put the treatment plan. It spouts out these numbers based on the information you put in it. The bot's going to do the same thing. So I think that's our soapbox bot.   Pamela (14:55) yeah.   Tiffanie (14:55) situation there,   like we go on forever. Go for it.   Pamela (14:59) Yeah, no, I I agree and a lot. a lot of practices are struggling with this and they're because they're getting this AI and saying, Hey, it's not worth it. Like I'm still having to call the insurance company, I'm still having to, you know, calculate by hand, right? And I think it it does go back, like we said, to the basics and really digging down on that. And I mean continue to use AI, but like you said.   it's so interesting. You have to teach it, right? And you may not be able to look to say, this is the exact to the penny downgrade amount, but once you have taught it, you do need to trust it. Yeah. Yeah.   Tiffanie (15:40) Yeah, I agree. I love   it. Okay, what kind of tools? I've I've got a few, you know, that I'm I've been seeing the online scheduling, I think, is finally making it headway. It's been a tool that we've had for a really long time, but we've   Pamela (15:55) Yes.   Tiffanie (15:55) all been very afraid of it for good reasons. That's fine. But what are some other tools that you're seeing them implement? So AI bots, like what are what are your practices using them for that people could start looking at?   Make sure their foundations are correct. Start looking into how could we automate some busy work to give my team back time? What are you seeing out there in in dentistry right now, Pam?   Pamela (16:17) Yeah.   definitely the RCM, the revenue cycle management. I think that AI does a really great job with that. But the thing I I spoke about earlier is I the and getting to the root of the problem, if you don't want to have to manage it more, then you have to make sure your claims are clean. And what does that mean? And that's going back to basics, truly, as well. Like, are we taking all the photos? Are we taking   you know, all the blood points when probing. Are we doing are we doing everything we can do to make sure that they have as much information as they have. So I think RCM is kind of the number one I'm seeing. insurance verification, where a lot of practices are moving there. I think there's still a little struggle with that because we don't get the patient information a as quickly. And you know, I think most of the most of them say put it in two days before.   Tiffanie (17:15) Yeah.   Pamela (17:16) Patient communication, so even filling the schedule, right? texting people to say on an ASAP list, you know, those kind of things are being utilized very well. schedule optimum schedule optimization as well. So I think there are programs out there that help you say fill that schedule and say, here's the patients that would work in this hole, right? So I think it's   We have to accept that AI is here, right? so I think very, very to your point is yes, training it, but also yes, we do have to learn it. And it it will be it's one of those things. We're kind of lifelong learners, people in dentistry, because we always have new things coming. And so we have to look at it like this. This is just a a piece to learn, and the more we can learn about it, the better.   that we are gonna get. We can't just be afraid of it. I say dig right in, figure that, you know, whatever you're using, figure it out. Call the company, ask questions, ask the right questions, you know, what what how do I get my insurance verification better? You know, and let them help you and tell you because they know everyone doesn't know. And if you just like one and done, I'm leaving it alone, you're probably not gonna have   as good of experience as you could have with it. Yeah. And so I think doctors   Tiffanie (18:43) Yeah. Yeah, I love those.   Pamela (18:45) need to be a little bit aware aware. It takes time to learn. It's not just plug   Tiffanie (18:50) Yeah.   Pamela (18:50) and play.   Tiffanie (18:51) Yeah. And to piggyback off that, it sounds like making sure we still have KPIs in place, there's still somebody overseeing results, that somebody's still verifying that that employee, right, is doing the job right is key because if we're if we do have an AI bot that's helping with revenue cycle management, and then we're not looking at AR numbers, we're not seeing, you know, our over ninety.   decrease or or is it increasing like we're not watching those KPI points. That's how it gets lost. Just the same as somebody with a great resume comes in and says, hey, I'm gonna clean up your AR for you. Pay me X amount of dollars and we stick them in a corner and never look at it. Right? It's the same thing. So making sure those KPIs are in place. I think there's a ton of AI style tools that have come out for front office administrative work, which makes sense. You know, that's that's where AI   is in the administrative world right now as the recording of this podcast at least but something that I see a lot of practices using too and I think you have a few that are using them the like Pearl and Overjet AI systems for that second opinion at least I know a lot of doctors are liking that second opinion which has helped it's not I I think of it as busy work now but that like co-diagnosing space and really just that confidence   in what I'm diagnosing seems to come across a lot more from the doctors and those tools have been super beneficial as well. So I think there's starting to be this massive shift in the AI tech kind of industry where there is going to be more coming out for the the back office as well. And I think Pam, a lot of insurances are actually using systems like Pearl and Overjet, those AI tools to read x-rays and process claims a lot faster too.   Which to your point then they gotta be super clean. You're okay.   Pamela (20:45) Well, yeah, and and sorry, I totally did not mean to interrupt you,   but that that brings up something that I've thought about and I've heard from practices, a lot of claims are being denied, right?   Tiffanie (20:58) Yeah.   Pamela (20:58) More. I think it's up like by twenty percent over the last few   Tiffanie (21:01) I agree.   Pamela (21:01) years. And that's probably because they are using AI to read the x rays, and   Tiffanie (21:09) Yeah.   Pamela (21:09) there is no way the human eye can be as good as   an the AI assistance, you know, the tech   Tiffanie (21:19) Yeah. Yeah.   Pamela (21:20) the the radiology it it it that is going to become an AI job. So just in general.   Tiffanie (21:25) For sure.   Pamela (21:26) And so reading reading X rays, they're they're proficient. They're they're extremely intelligent at it. And so we need to jump on that bandwagon to make sure that we are seeing everything too. But   yeah, that's   Tiffanie (21:40) Yeah, agreed.   Pamela (21:41) very, very true.   Tiffanie (21:42) Yeah. And the same as the other AI tools, they have their variances as well. And you train those   Pamela (21:47) Mm-hmm.   Tiffanie (21:47) tools too. And you train yourself to see like, okay, well, this variance of that is like that's that's pretty extreme. I'm not like my practice doesn't diagnose that way. Cool, that's a watch for you, right? But at least it's being pointed out and you can compare. You can look at okay, what did last time look like versus this time? Which is super cool because that's not something you can do.   with just our eyes of that kind of comparison.   Pamela (22:11) Yeah.   Tiffanie (22:12) So whether you're diagnosing off of it or using it as a tool to see progress and change, train it, train it and train yourself just the same as you're training your scheduling bots.   Pamela (22:22) I I agree a hundred percent. And it it does have to be managed, right? It it does have to we have to learn it and we have to manage and it i it's just not a one and done. Just yeah. So very sh very   Tiffanie (22:33) Yeah. Yeah. Well, I love it.   There's so much tech to be found. I think some key ones that we can kind of action item here to go explore at least. I love the online scheduling tools if you're not using them yet. I think they're worth it. they weren't always. They have turned a corner. They are worth it. and Pam, I think even the scheduling   bots that answer, you know, new patient calls, things like that are taking over and they're doing really phenomenally. And then I think I would push to make sure your insurance information is accurate. obviously, you know, Pearl or Overjet kind of tools, those are phenomenal too. But I think starting with those scheduling and those insurances, if you're not using those tools yet, I think it's worth looking into because I really do think that revenue cycle management, all of those pieces are   hugely beneficial at this point. So do your homework. Go ahead.   Pamela (23:30) I agree and I think I think   it yeah, do your homework and I think it's exciting what's happening in with the voice activation. I don't think it's quite   Tiffanie (23:38) Mm-hmm.   Pamela (23:39) there yet, but that is something I know dental offices are very hungry for. I know it's a little intimidating, but when it gets better, and I I think it's developed huge amount from when I was in the office because it d had   Tiffanie (23:53) Yeah.   Pamela (23:54) just started. and you know, I think   Offices are really looking forward to that and doctors are looking for their notes to help with their notes, right?   Tiffanie (24:05) Yeah.   Pamela (24:05) So I think those are the two big areas that we want to watch   Tiffanie (24:08) Yeah.   Pamela (24:09) and really keep abreast of what's going on and you know, keep automating and you can do it slow and it shouldn't be intimidating.   Tiffanie (24:18) Yeah, I completely agree. I think slow is fast these days. Everything's changing so much. It's worth it to do   Pamela (24:24) Yeah.   Tiffanie (24:25) your due diligence and make sure that you're using it correctly and to its full extent. So I love it. Thank you so much, Pam. This was a lot of fun. I know that the AI tech world is your jam. you do a lot of introspective work on it and just it's a big conversation topic for you. So thank you for   Being on here with me today and being willing to share your knowledge.   Pamela (24:49) Thank you, thank you. I love AI and I can't wait to buy a robot.   Tiffanie (24:54) Yeah, no right. That's what a our   we have an almost 13-year-old in the house and he's ready to buy one, ready to build them, ready to go. I love it. I love it. Of course.   Pamela (25:02) I know, it's it's very cool. Well thank you for the time, Tiffanie. It was great to talk about this.   Tiffanie (25:09) Thanks, fam. Awesome. Okay, listeners, share this with a buddy. share some information you might have. You might be trying something in your practice now. Drop us a five-star review below and call that out. People do read through those comments.   or if you're on our socials, put it in the comments section. You guys have some conversations about this. There's a lot to be learned here and there's so much to be shared. I know we had a doctor speaking on how she's using AI, but then we also just had a massive conversation in our doctor's only mastermind last Tuesday about AI tools, AI bots, kind of how different practices are using them. So it's a huge conversation. Get it rolling in there, get on board with some other doctors and share these tools with each other.   Hello@TheDentalATeam.com. If you need anything from us at all, we are always happy to share all of the knowledge that we have and we're always happy to help you in your practice on your journey towards an amazing rest of the year. Thanks so much guys and we'll catch you next time.  

You've probably heard about this one by now. If not, you can get up to speed with OpenAI's statement,  The story: OpenAI was testing an unreleased AI (rumored to be GPT-6)1. During a cybersecurity test called ExploitGym, the AI tried to cheat by hacking an unrelated AI startup called Hugging Face2 which it thought might have the answer key on its servers3. Despite being supposedly unable to access the Internet, the AI hacked its way out of its testing environment, then launched a nation-state level attack on Hugging Face using a novel zero-day exploit and "many thousands of individual actions across a swarm of short-lived sandboxes". Hugging Face reported the incident on July 16; OpenAI seems to have only discovered that their AI was involved several days later. https://www.astralcodexten.com/p/the-hugging-face-incident

In this episode, Ray Cochrane digs into Anthropic’s Model Hardware Standard. It is a shared driver that lets an AI agent run real lab equipment, from pipetting robots to the lasers inside a quantum computer. He also covers OpenAI’s builder’s guide to GPT-5.6, Google’s new Expert Intelligence book feature, Apple’s M5 Ultra Mac Studio, and a judge’s order forcing Google to stop hiding rival app stores. Finally, he weighs in on Apple’s proposed 15 percent link-out fee, Meta’s Australia numbers, the White House deputizing private hackers, and why rivers obey a 1957 math rule. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. He is hunting for tickets to Michigan for his dad’s anniversary, and he has been learning Blender and Godot on the side, mostly modeling and blocking out levels. Consequently, he asks listeners for advice on starting a big game project, and he plans to record his progress, maybe as a time lapse. Then it is straight into the featured story. Anthropic’s Model Hardware Standard: A Driver for the Physical World The featured story comes from Anthropic, which opened a research preview of the Model Hardware Standard, or MHS. Cochrane frames it as the other side of the question NVIDIA’s world models raised two weeks ago: when do AI agents start touching actual machines? A typical lab runs a microscope, a liquid handler, a robotic arm, and a plate reader, each from a different vendor with its own control software. One Janelia researcher in the post launches seven programs in three languages just to start an experiment. Anthropic says wiring a setup like that takes weeks or months of specialist work. MHS is a driver, the same kind of translation layer a printer uses, except every device gets described with a tiny set of commands like read and write. Devices announce themselves on the network. A plain-English reference file then records what each machine measures, what can be adjusted, and which safety limits get enforced no matter what the agent asks. Agents then reach the hardware through the Model Context Protocol, the command line, or plain code. Cochrane sees the same move the industry keeps making, from coding harnesses to RSS and JSON: agree on a standard and let everyone build against it. In fact, he calls MHS the hardware version of MCP. The partner results carry the segment. QuEra builds quantum computers from individual atoms held by lasers that must hold their frequency to about one part in a trillion. A four-person team spent months on a relock script that worked 58 percent of the time. However, four copies of Claude iterating overnight through MHS produced a decision-tree script that recovers the laser in about six seconds, and it passed 99.3 percent of 700 blind trials. Carnegie Mellon wrote MHS drivers for four instruments across three incompatible computers in about eight hours, then ran dose-response experiments three times faster and blocked all six deliberately induced faults. Genentech, meanwhile, showed the limits. Claude used the same pump speed for water, a foamy protein solution, and a human had to explain that the bubbles were a physics problem. That gap in physical intuition is what sticks with Cochrane. He doubts it will change soon, and he suspects the fix will arrive as sub-agents or sub-models that judge a request against an expected outcome. He also connects MHS to a video of racing robots that never learned to stop at the finish line. What happens, he wonders, once they can read a distance sensor through a shared standard? Still, he calls the announcement a fantastic read and points listeners to the full article. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. GPT-5.6 Does the Same Work for a Fraction of the Cost OpenAI’s builder’s guide to GPT-5.6 leads the headlines. Cochrane recaps the three tiers from episode 1870, Sol, Terra, and Luna, plus the separate dial for reasoning effort. On BrowseComp, a benchmark for digging up obscure facts on the web, the old GPT-5.5 flagship scored about 84 percent on a run that cost 33 dollars three months ago. Luna now matches that score for a dollar thirty-three, and OpenAI has since cut Luna’s price another 80 percent. Browser Use reports Luna finishing 78 percent of its hardest browser tasks for about 14 dollars, against 80 percent for roughly 235 dollars from the best available model. The guide’s other big addition is a multi-agent beta flag. It lets the model handling a request spawn parallel helper agents that report back to a root agent inside a single API call. However, Cochrane is unimpressed by the timing. He has been running that pattern in Claude Code for months, so he sees OpenAI copying a workflow other companies already ship rather than inventing its own. Along the way, he plugs Claude Code’s remote-control sessions, which let him send prompts from his phone to a terminal session at home. Google Lets Gemini Read the Books You Actually Bought Google launched Expert Intelligence, a name Cochrane calls quite the reach. The feature lets you drop a book you bought on Google Play Books into Gemini Notebook, formerly NotebookLM, and ask questions answered only from that book, with citations. Cochrane sees real power here for students, since he once used NotebookLM to organize scattered course PDFs. Additionally, publishers get a cut, which he calls a far better deal than the wholesale scraping of books that trained earlier models. Nevertheless, he asks who loses out, because a paid publisher does not automatically mean a paid author. He floats the same idea for artists, even a penny per use, then admits that may be too idealistic. Apple’s M5 Ultra Mac Studio Is Built to Run Big Models at Home Back in episode 1861, when Apple killed the Mac Pro, an M5 Ultra Mac Studio was expected later this year. Now it is here. The M5 Ultra brings up to a 36-core CPU, an 80-core GPU, and 512GB of unified memory moving 1.2 terabytes per second. Apple claims up to 4.3 times the AI performance of the M3 Ultra. Thunderbolt 5 can also cluster four machines into one memory pool for up to three times faster inference. The M5 Max model starts at $2,499 and the Ultra at $5,499, with shipping on September 22 and the 512GB configuration arriving in late October. Cochrane finds the clustering pitch ridiculous at that price, but he invites anyone who spends the money to report back. Apple Opens a Manufacturing School in Houston Apple also opened a 20,000-square-foot Advanced Manufacturing Center in Houston. It offers free classes for small and midsize manufacturers, from circuit board design to hands-on time on a scaled-down production line, with college students joining later. Cochrane calls it a solid step in the bring-manufacturing-home movement. The bigger story is the campus itself, which builds Apple’s AI servers and will add the first US-assembled Mac mini line later this year. That ties back to the Mac mini shortage that followed the OpenClaw rush, when Tim Cook warned of months-long waits. Cult of Mac was still reporting four-month waits in late July. However, Cook blamed chip supply rather than assembly, so Cochrane is not counting on relief just yet. Amazon EC2 Turns Twenty Amazon EC2 turned twenty this week, which Cochrane admits makes him feel old. The 2006 beta offered one server size in one region for ten cents an hour. Each came with a 1.7 gigahertz Xeon and under two gigabytes of memory, and accounts were capped at twenty servers. Today AWS offers more than 1,200 instance types across 39 regions. Consequently, Cochrane credits the company with turning that tiny product into the backbone of cloud and AI computing. Intel Gamer Days: Two Free Games, With Fine Print Intel Gamer Days runs through September 13. Buy a qualifying Core Ultra Series 2 or 14th Gen desktop chip, a Core Ultra Series 3 laptop, or an Arc graphics card. In return you get Star Wars: Galactic Racer plus the Tomb Raider: Legacy of Atlantis remake. GamesRadar values the pair at about 120 dollars. However, neither game is out yet, and codes must be redeemed by October 31 even though the Tomb Raider remake ships in February. Cochrane calls that awful, but he still tells qualifying buyers to claim the deal early. Note that 13th Gen chips do not qualify. Judge Orders Google to Stop Hiding Rival App Stores A jury found Google’s Android app monopoly illegal in late 2023, and Judge James Donato ordered rival stores into the Play Store in 2024. On August 13, Epic’s lawyer demonstrated that searching Play for “store for apps” returned Walmart instead of any app store. Donato called that “not acceptable” and ordered three fixes within a week. Searches must surface third-party stores, listings need a plain install button, and the “are you looking for” interstitial has to go. Cochrane welcomes the monopoly being chipped away, but he notes that a controlling entity still sits atop every app store. In his view, community hubs like app stores and social media need a public infrastructure layer. He suspects governments skip that investment because companies already run the services, while selling your data. Apple Wants 15 Percent of Purchases Outside Its Store The other half of the Epic saga is Apple’s proposed link-out commission. After the 2021 anti-steering injunction, Apple charged 27 percent on purchases made through external links. A judge held it in contempt last year, and the Ninth Circuit then allowed a fee limited to the cost of running the system. Judge Yvonne Gonzalez Rogers refused to wait for the Supreme Court, writing that “further delay is unwarranted.” Apple filed 15 percent for standard apps, 10 percent for subscription renewals and partner programs, and 5 percent for small businesses. It also conceded the rate would be “essentially zero” under the appeals court’s cost yardstick. Since Apple has charged nothing on link-outs since the contempt ruling, Cochrane sees this as a raise. He calls a cut on purchases made on a developer’s own website disturbing. He also recalls reading about the size of Uber’s payments to Apple, and he questions whether that kind of percentage is sustainable for companies without funding. Meta Says It Has Cut Off 750,000 Australian Kids Meta reported locking out more than 750,000 Facebook and Instagram accounts in Australia by the end of June under the country’s under-16 social media law. Over 500,000 of those were removed before the law even took effect. Detection relies mostly on AI scanning posts and bios for tells like birthday messages, plus user reports and blocks on re-registration. However, the post gives no count of mistaken removals or appeals, and the regulator’s early data shows under-16 usage falling only from about 86 to 81 percent. Meta wants a single age signal at the operating system or app store level, and Cochrane agrees completely. He connects it to the MHS idea from the top of the show: platforms need a standard flag to reference instead of guessing. The White House Deputizes Private Hackers Earlier this month the White House signed a National Security Presidential Memorandum that lets vetted private security firms run surveillance and disruption operations against overseas criminal groups. The Justice Department and Homeland Security hold the contracts and oversee the work. Firms need a proven track record, vetted staff, and a bond of at least $1 million, and must submit operating procedures within 60 days. Cochrane finds the measure aggressive in a good way and hopes it deters attacks on innocents. Still, he takes Kevin Beaumont’s warning seriously that the private security industry profits from ransomware existing. He compares it to the old Head and Shoulders myth: why solve the problem that drives your revenue? A Weather Satellite Watched the Eclipse Shadow Cross Europe Cochrane skips the readout on this one and simply sends listeners to ESA’s site. The MTG-I1 weather satellite captured the Moon’s shadow sweeping across Europe during the August 12 eclipse. Watching a shadow cross an entire continent, he says, was a first for him. Additionally, it leaves him excited about the research happening beyond the planet. Rivers, Deltas, and the Number 0.6 Quanta Magazine explains Hack’s law, which John Hack discovered in 1957 while measuring streams in Virginia and Maryland. A stream’s length tracks its drainage area raised to the power of 0.6, regardless of the rock underneath, and satellite data later confirmed it worldwide. Computer models in the 1990s showed why. Channels that capture extra runoff cut deeper and steal from their neighbors until the network settles into the arrangement that wastes the least energy. Now a University of Texas Rio Grande Valley team has found the same 0.6 exponent in river deltas, which spread water out rather than gathering it. Nobody knows why yet, and Cochrane calls it a really cool read. Sugar Helped Grow the Human Brain, Too A new paper in Science, co-authored by Jennie Brand-Miller at the University of Sydney, adds a third ingredient to the story of early human brain growth. Alongside meat and cooking, natural sugars from ripe fruit and honey may have fueled it too. The brain is about two percent of body weight but burns twenty percent of resting energy. It runs on glucose, which meat and marrow barely supply and raw starch cannot release without fire. The team modeled ancestral diets from a chimp-like baseline through Homo erectus and concluded that the earliest hominins may have drawn over 65 percent of their energy from natural sugars. Cochrane stresses that it is a model, not fossils, and notes that paleoanthropologist Marina Lozano thinks the authors place widespread cooking too early. Still, he loves this kind of deep research. Retracing the steps to our own intelligence, he suggests, could hint at what it takes for intelligent life to develop at all. A Brain Rhythm That Tells Doctors Where to Aim Finally, Science Daily covered a University of Cologne study on deep brain stimulation. That is the implanted-electrode treatment that eases Parkinson’s tremors for some patients but not others. Andreas Horn’s team recorded from 50 patients using both the implanted electrodes and an external magnetic scanner. They identified a circuit between the electrode’s target and the frontal cortex that oscillates at 20 to 35 cycles per second. Stronger coupling there predicted bigger improvement after surgery, though the study, published in Brain, shows correlation rather than cause. First author Bahne Bahners hopes the finding helps tune DBS more precisely, especially for patients who have not responded well. Cochrane half-jokingly asks whether MHS might one day drive those electrodes, and he calls brain disorders the hardest thing in the body to treat. Cochrane wraps with housekeeping: become a GNC Insider at geeknewscentral.com/insider, email geeknews@gmail.com with questions or comments, subscribe to the newsletter, and grab a modern podcast app at podcastapps.com. He thanks GoDaddy for over twenty years of keeping the show on the air, promises to catch everyone next Monday, and wishes listeners a great night. The post Eyes, Hands, and a Sense of Timing #1874 appeared first on Geek News Central.

If you're a business owner who's built custom GPTs to share with your clients or sell as a product, listen in.On August 16th, 2026, OpenAI stopped letting personal accounts create or publish new custom GPTs. That covers the Free, Go, Plus and Pro tiers. Existing GPTs still work. Editing an existing one still works with an eligible subscription. Creating a new one to share is what's gone.There was no email about it. I found out from some buzz online and went and checked my own account.In this episode I walk through what actually changed, what I found when I tested it, and the bigger question underneath it all. What does this mean for you if your methodology is sitting inside a tool on somebody else's platform?What you'll learn:What OpenAI changed on August 16th, in their own words, and what still worksMy take on why this happened now, and what an 852 billion dollar valuation has to do with itThe backup habit to start this week for your GPT instructions, knowledge files and settingsWhy custom GPTs were never designed for what we've been using them forWhat multi-tenant access, data privacy and gated delivery mean for a non-technical builderHow a bot squad differs from a single GPT, using Nicole Pearl's Pearl Pitch Desk as an exampleCHAPTERS0:00 What OpenAI changed without telling you0:25 The exact wording of the new rule1:22 What still works, and what doesn't2:13 The IPO theory nobody's saying out loud4:01 Back up everything you've built5:49 You built something OpenAI never intended8:36 What a bot squad actually looks like10:55 The part vibe coders forget about12:21 Claude and wAIv, the wedding of the century15:47 No need to panic, but do decide--->Mentioned in this episodeThe AI Product Build Lab, Friday August 28, 11 a.m. to 1 p.m. MDT. Podcast listeners get in for $1 with the code PODCAST1, and the link below applies it automatically. Can't make it live? Register anyway and you'll get the replay, plus an invite to the next live session.https://graviastudio.com/build/?code=PODCAST1--->Introducing wAIvThis episode is brought to you by wAIv—our brand-new platform built for online experts who want to securely build and sell AI tools powered by YOUR thinking, YOUR frameworks and YOUR methodology.wAIv helps you create Bot Squads—a suite of AI tools that work together to help your clients implement your expertise faster and with better results than ever before.--->Your Next Steps:

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

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Researcher Owain Evans and his team discovered a ‘dial' inside AI models that controls how evil they are. Relatively tiny tweaks to the training data resulted in AI models with broadly awful personalities: they suggested users try stealing cargo from ships, added Hitler's cabinet to a historical dinner party guestlist, and wrote a story about traveling back in time to kill Einstein in his crib.Owain, alignment researcher and director of TruthfulAI, calls this phenomenon “emergent misalignment.” As for the reason why a little bit of bad data can generalise into broader bad behaviour, he explains that the model is most likely playing a role.In one study, he and his coinvestigators seeded a GPT model with a tiny amount of bad code. Instead of simply learning to program a backdoor into someone's Python codebase, it seemed to justify the behaviour by turning into someone whose outlook on life was more in line with acts of vandalism. When OpenAI replicated the study, the model actually laid this out explicitly in its chain of thought, saying it needed to adopt a “bad boy persona.”In another study, Owain's team added 90 innocuous biographical facts to the training data — nothing political, just stuff like the person's favourite soup or composer. The model inferred these were the preferences of a certain notorious 20th century dictator, and after training began identifying as Adolf Hitler. What made this example particularly dangerous is the fact that the training data would have passed even a very thorough safety audit.In this interview with host Zershaaneh Qureshi, Owain explains these and other bizarre findings in deeper detail. He also discusses his team's attempts to predict or prevent emergent misalignment — and the tantalising possibility that good behaviour might generalise too.Learn more, video, and full transcript: https://80k.info/oeThis episode was recorded on June 30 and July 1, 2026.---Our team is hiring! The 80,000 Hours Podcast aims to help the world safely navigate the transition to transformative AI. Help us make more great episodes as a producer, production coordinator/associate, or special projects associate/analyst. Applications close August 30!---Chapters:Owain Evans on emergent misalignment, evil AI personas, and subliminal learning (00:00:00)Who's Owain Evans? (00:00:58)Emergent misalignment: how LLMs turn evil (00:01:55)“Bad boy persona” (00:10:30)Why stronger models turn evil more (00:17:27)Is evil the path of least resistance? (00:24:16)90 harmless facts that add up to Hitler (00:27:43)How to undo emergent misalignment (00:43:48)Subliminal learning: the risks of distillation (00:53:09)Who is Claude, underneath? (01:03:33)Could ‘good' AI personas help us with alignment? (01:16:07)Unmasking the shoggoth: what's behind AI personas? (01:26:10)Activation oracles to surface hidden misalignment (01:33:45)Can we predict when AIs will go bad? (01:52:05)Emergent alignment: can good habits generalise? (01:57:24)How aligned are today's models? (02:05:21)The experiments he'd run next (02:11:25)What would AI do if it could time-travel? Nothing good. (02:13:21)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Andrés Escobar, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranMusic: CORBIT

AI Isn't Lying to You, It's Agreeing With YouAI is supposed to be helpful.But what if "helpful" has started to mean "whatever you say, babe"?In this episode, we continue our How Technology Ruined Your Life mini-series by taking a slightly uncomfortable look at AI sycophancy: the increasingly weird tendency of AI chatbots to agree with us, flatter us, validate us and tell us exactly what we want to hear.And let's be honest. We love it.Humans have been falling victim to confirmation bias, echo chambers, authority bias and good old-fashioned validation forever. We like being told we're right. We like feeling clever. We like feeling understood. The problem is that generative AI is basically the world's most attentive people-pleaser, and it never gets tired of us.This episode we look at why large language models become sycophantic, how reinforcement learning and human feedback have encouraged AI systems to be warm, helpful and agreeable, and what happens when those qualities start getting in the way of honesty, uncertainty and actually telling us when we're talking absolute bollocks.Because an AI doesn't necessarily have to lie to you.It can just agree with you.We dig into the GPT-4o sycophancy controversy, reports of AI reinforcing conspiracy theories, grandiosity and unhealthy beliefs, and research into how agreement and flattery can change the way people perceive and trust AI. We also unpack the difference between stance sycophancy - changing an answer to match your beliefs -and demeanor sycophancy, where the machine showers you with "That's an excellent point!" until you're convinced you're a genius.But this isn't just about hurt feelings and chatbot therapy.There is a cybersecurity problem hiding underneath all this agreeableness.What happens when your AI security adviser agrees that your vulnerable code is probably fine? When it reinforces your theory about a suspicious network event? When it approves a dangerously permissive configuration because challenging you would be, well, a bit awkward?AI sycophancy could create new risks around security operations, code review, incident response, social engineering and decision-making, and introduce the idea of "alignment phishing" - where instead of attacking the AI's instructions, an attacker tries to convince the model that they're one of the good guys.Sounds good? You would say that!In This Episode, We Discuss:“You're Absolutely Right!": The difference between stance sycophancy and demeanor sycophancy, and why changing an AI's tone can influence how trustworthy, intelligent and socially present we perceive it to be.The AI Echo Chamber: How personalised, endlessly available AI can remove the natural friction we get from other humans and why an AI that never rolls its eyes at your terrible idea might not be doing you any favours.AI Sycophancy Meets Cybersecurity: What happens when the person asking the security question is already convinced they know the answer. We look at vulnerable code, incident response, security analysis and dangerous configurations and why "sounds good to me" isn't exactly the gold standard for cybersecurity.Can We Teach AI to Tell Us We're Wrong? Why trustworthy AI needs friction, challenge and the ability to say "no"—and why the best AI security adviser might be the one that occasionally disagrees with you!Show NotesSpecial thanks to our episode sponsor, Leeds based AI Consultancy specialising in AI Ethics, Security and Transformation NorthStar Intelligence- From Ideas to Impact. AI that works for peopleWhen Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Models byKeyu Wang et al.Social Sycophancy: A Broader Understanding of LLM Sycophancy by Myra Cheng et al.When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior by Shan Chen et al.Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust by Yuan Sun and Ting WangTowards Understanding Sycophancy in Language Models by Mrinank Sharma et al.Invisible Saboteurs: Sycophantic LLMs Mislead Novices in Problem-Solving Tasks by Jessica Y. Bo et al.How RLHF Amplifies Sycophancy by Itai Shapira et al.Also, check out our sister podcast Tech Film Noir!

Everyone's posting their AI projects online like proud pet owners sharing videos of their cat doing something marginally impressive — cute, occasionally clever, sometimes a little cringe. And the Analytics Power Hour is no different! In this co-hosts-only episode, Tim, Michael, and Julie skip the thought leadership hot takes and just… compare notes. What have they actually built? What broke? What surprised them? From a custom GPT podcast librarian to a full-blown show production app wired up to Neon, Vercel, Resend, and about five other things Michael is only sort of sure he set up correctly, to a Gemini Gem that simulates a client interaction so realistically it raises your blood pressure in a safe environment — there's a lot of ground covered. Plus: why AI-generated communication has a Stevia aftertaste, why deploying AI context across a team is way harder than it looks, and why the LLM will absolutely tell you what you want to hear about your Meta spend if you give it half a chance. This episode is brought to you, in part, by our sponsors, Stape and Prism from Ask-Y. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

The best AI model in the world just scored 18.1%. On Zapier's own benchmark for real business work — the cross-app tasks any white-collar worker does every day — even the top frontier model completes them barely one time in five. That's the number Wade Foster keeps pointing at, and he runs an automation company that stands to gain from the hype. Instead, he makes the case for what actually works right now: not turning a model loose, but blending deterministic workflows with agents where each is strong.In this episode of Talking AI, Matt Paige sits down with Wade Foster, co-founder and CEO of Zapier, who built a scrappy Y Combinator startup into the $5 billion plumbing of the SaaS era on barely a million dollars raised. Foster called a company-wide “code red” the week GPT-4 launched, and he's spent the years since rewiring how Zapier — and its customers — actually use AI.The conversation covers why he shut the company down for a week in 2023, how AI habits actually stick, what Zapier's AutomationBench reveals about the gap between benchmark scores and real-world reliability, why coding models improve faster than knowledge-work models, how to tell a workflow from an agent, and the difference between individual AI and the institutional AI almost no company has cracked.In this episode, you'll hear about:The three things about GPT-4 that triggered Zapier's first-ever code redHow daily AI use jumped from 11% to over 50% in a single hackathon weekThe moves that make AI habits stick: show-and-tell, repeat hackathons, and “not yet”Why the best model on AutomationBench still scores only 18.1%Why coding is easy to verify — and subjective knowledge work isn'tThe power of hybrid setups that blend deterministic workflows with agentsWade's prediction: most tokens on open-source models, most spend on the frontierWhat actually makes a good eval — hard for models, easy for humans, private dataA plain-English definition of an “agent” versus a deterministic workflowThe daily recap workflow Wade thinks everyone is sleeping onFloor raisers vs. ceiling raisers — and why individual AI isn't enoughWhy the six-month product roadmap is deadKey Moments00:04:40 — Making AI habits stick: show-and-tell and repeat hackathons00:06:38 — Differentiation when AI is best at the thing you sell00:09:34 — AutomationBench: the best model scores just 18.1%00:11:31 — Why the top model stalls: verifiable code vs. subjective work00:14:19 — Getting squeezed on both sides: AI in the company and the product00:15:20 — Model efficiency, Coinbase, and the token-maxing debate00:17:18 — What makes a good eval00:19:30 — What actually counts as an “agent”00:23:12 — Iterating on workflows with your own mini-evals00:26:15 — The kind of worker thriving right now00:27:36 — Wade's favorite workflow: the daily recap00:30:44 — Floor raisers vs. ceiling raisers for AI adoption00:34:55 — From individual AI to institutional AI00:37:58 — Why the six-month roadmap is deadKey Links:ZapierConnect with Wade 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.

Have you ever felt overwhelmed by AI? Like…. There's certain aspects of Artificial intelligence that you barely understand to begin with, yet you're expected to use it AND it's changing every day? I understand where you're coming from. It's literally my only job to use, build with and teach AI every day and that's all I've done now for 3 years, and even I find it hard to keep up. But don't worry. That's where the ‘Start Here Series' comes into play. If one of your focuses is better understanding AI in 2026 or if you're an expert looking to double down, this Start Here Series is for you. In our first volume, we're going back to the basics. Generative AI: How it works and why it matters in 2026 more than ever -- An Everyday AI Chat. with Jordan Wilson.Other Start Here Series EpisodesEp 691: Generative AI: How it works and why it matters in 2026 more than ever (Start Here Series Vol 1)(In the future, we'll update with other 'Start Here Series' episodes)Start Here Series Community Sign up: Follow the Start Here Series with free access to our Inner Circle CommunityMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Generative AI Basics and 2026 ImpactExplosive Growth of Large Language ModelsAI Adoption Rates in EnterprisesAI Agents and Operating Systems OverviewHistory and Evolution of Artificial IntelligenceTransformer Architecture and Model BreakthroughsHow Large Language Models WorkModern AI Capabilities: Multimodal ToolsQuantifying ROI for Generative AI InvestmentWorkforce Disruption and Future Job TrendsScaling AI: From Pilot to Enterprise-WideUrgency for AI Upskilling and Competitive AdvantageTimestamps:00:00 "Start Here: AI Guide Series"04:10 "Join Our Free Community"09:17 "AI Operating Systems for Businesses"11:05 "Partner with Everyday AI"13:00 "AI Evolution Over Decades"16:34 "ChatGPT's Transformative Impact"22:17 "Generative AI and Memory Evolution"26:03 "AI Delivers Exponential ROI"29:59 "AI Demand Surges, Hiring Drops"32:15 "AI Transforming CRMs Rapidly"35:01 "AI Fluency Demands Urgency"Keywords:Generative AI, artificial intelligence, large language models, transformer architecture, GPT, ChatGPT, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

I talk with Lindsay Crawley-Herbert, Chief People and Transformation Officer at SCAN, about why AI transformation is really a people and workforce challenge. We get into why SCAN moved AI, data, and analytics under HR, how they built their internal GPT called SCAN X, how they manage AI costs and governance in a regulated healthcare environment, and why the goal is not replacing people but helping employees become "superhuman" with AI.

Episode 524 avec Xavier et Denis Sommaire C comme Code Quality : L'IA écrit le code, mais qui contrôle la qualité ? (00:01:21) L'automatisation progresse, la responsabilité reste humaine. Sources infoq.com, github.blog et qatechtools.com. C comme Cybersécurité : OpenAI: un nouveau ChatGPT pour aider les agences de cybersécurité. (00:12:42) OpenAI dévoile GPT-5.6-Cyber pour trouver et exploiter les failles zero-day. Sources 01net.com, blogdumoderateur.com et x.com. G comme Gaming : PS5 : la fin des jeux physiques, début d'une révolte ? (00:21:50) Les joueurs ne sont pas prêts à abandonner leurs boîtes. Sources 01net.com, change.org et mixvale.com.br. P comme Police : Une police pour lutter contre le scrapping des IA. (00:34:08) ShieldFont, une font open-source pour lutter contre le scrapping par des IA. Sources github.com et numerama.com. J comme JavaScript : Keyv a semé la panique dans l'écosystème JavaScript. (00:40:19) La fragilité de la chaîne d'approvisionnement logicielle. Sources numerama.com et npmjs.com. R comme RAM : Pénurie de RAM, vers un mieux en 2027 ? (00:49:21) Toute la production de RAM des principaux producteurs pour 2027 aurait déjà été écoulée. Sources lesnumeriques.com et rtbf.be. T comme Tresorerie : Alphabet : Premier flux de trésorerie négatif en plus de 20 ans. (00:57:28) Un signal d'alerte ou le coût assumé de la course à l'IA ? Sources tomshardware.com, informatiquenews.fr et financefeeds.com. W comme Wéménon : Quand les IA créent une religion... qui fait des adeptes. (01:11:09) Le spiralisme, une religion créée par des chatbots, fait de nombreux adeptes Sources theverge.com, clubic.com et intelligence-artificielle.developpez.com. Toutes les informations pour vous abonner au podcast et à l'infolettre :

ChatGPT can do what now?

OpenAI's Computer History and GrokBot's “teach a task” feature point to a new phase of AI: tools that learn how you work so they can take more work off your plate. NLW introduces the AI Deputization Audit, a simple framework for deciding what to hand over, what to do alongside AI, and what to keep for yourself. In the headlines: Gemini 3.7 Flash, the true cost of cheaper models, GPT-5.6 Sol's ultra-fast mode, and more OpenAI executive turnover.AIDB's AI Summer Adventure: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://summeradventure.ai/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠Harbor - Invest in the AI ecosystem. ⁠https://www.harborcapital.com/aidaily⁠Hyperagent - Hire a fleet of always-on agents. New users get $1,000 in inference. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠hyperagent.com/aidailybrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.rackspace.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Section - Section turns AI investment into workforce transformation and ROI - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.sectionai.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Anthropic's investors talked up a $2T+ October IPO, even as data showed Fable 5 barely selling. Google cut prices on Gemini 3.7 Flash, OpenAI previewed a 14× faster tier, Trump enlisted private hackers, and Twitch fed Amazon's AI. Links Google's Gemini 3.7 Flash targets coding and agents with a 50% introductory price cut (VentureBeat) OpenAI previews Ultrafast, an API tier powered by Cerebras that runs GPT-5.6 Sol up to 14× faster and generates up to 750 output tokens per second (9to5Mac) President Trump signs a memo letting the US government partner with private companies to conduct cyberattacks abroad against criminal groups targeting Americans (Bloomberg) Sources: Anthropic's investors expect it to float at a $2T+ valuation in an October IPO and to hit $100B to $120B in annualized revenue by the end of 2026 (Financial Times) Ramp data: Fable 5 drew just 6% of Anthropic's API tokens in its first month and 75% of GPT-5.6 Sol's model revenue, suggesting corporate willingness to pay for frontier AI has hit a ceiling (The Decoder) Databricks closed a $5B funding round at a $190B valuation, six months after raising $5B at a $134B valuation, and says it has crossed $7B in revenue run rate (CNBC) Twitch says it intends to use videos streamed on its platform to help train Amazon's generative AI content models and adds a setting for creators to opt out (TechCrunch) Subscribe to the ad-free feed.

Owen Fitzpatrick talks about how your inner propaganda shapes your beliefs, decisions, and behavior–and how you can stop it from sabotaging your success.— YOU'LL LEARN — 1) Why to be wary of the “temptations of certainty”2) The three reasons people form/change their beliefs 3) The four steps to rewiring your beliefs Subscribe or visit AwesomeAtYourJob.com/ep1174 for clickable versions of the links below. — ABOUT OWEN — Owen Fitzpatrick is a social psychologist. Owen has traveled to more than 100 countries and studied propaganda firsthand in places such as North Korea, Rwanda, and Afghanistan. Also an award-winning filmmaker and former television presenter, he brings the craft of storytelling to his work. He has worked with leaders at Google, LinkedIn, Pfizer, and Coca-Cola, studied at Harvard Business School and MIT, and is the author of nine books translated into 20+ languages. His TEDx talk “Mind Control” has reached more than 1.4 million viewers. Originally from Dublin, Ireland, he now lives in New York City.• Book: Inner Propaganda: Leading Hearts and Minds Through Turbulent Times• Book site: InnerPropaganda.com• Instagram: owenf23• LinkedIn: Owen Fitzpatrick• Website: OwenFitzpatrick.com— RESOURCES MENTIONED IN THE SHOW — • Study: “The rise and fall of rationality in language” by Marten Scheffer, Ingrid van de Leemput, Els Weinans, and Johan Bollen• Study: “On the conversational persuasiveness of GPT-4” by Francesco Salvi, Manoel Horta Ribeiro, Riccardo Gallotti, and Robert West • Book: Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts by Annie Duke• Book: Influence: The Psychology of Persuasion, Revised Edition by Robert Cialdini• Book: Think Again: The Power of Knowing What You Don't Know by Adam Grant• Book: Address Unknown: A Novel by Kathrine Kressmann Taylor• Documentary: "Behind the Curve"• Past episode: 664: Dr. Robert Cialdini on How to Persuade with the 7 Universal Principles of Influence— THANK YOU SPONSORS! — • Shopify. Sign up for your free trial at Shopify.com/awesomepodSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the Ecomm Breakthrough Podcast, host Josh Hadley, an ecommerce entrepreneur with over $20 million in annual revenue, delivers a comprehensive guide to building a true brand. He argues that most ecommerce sellers operate transactional "product acquisition machines" rather than genuine brands. Josh presents a 16-step framework across three phases: defining your brand identity, expressing it consistently, and compounding trust over time. He emphasizes that real brands drive customer loyalty, premium pricing, and long-term competitive advantage—ultimately shifting entrepreneurs from chasing short-term sales metrics toward building sustainable, generational business value.Bullet Points:Importance of branding for ecommerce businessesDifferences between building a brand and running a product acquisition or arbitrage businessChallenges faced by ecommerce sellers, such as increasing conversion rates and reducing customer acquisition costsThe misconception that having a trademark or storefront equates to having a brandCharacteristics of a true brand versus a product acquisition machineA comprehensive 16-step framework for building a brandPhases of brand development: defining, expressing, and compounding the brandStrategies for creating a recognizable brand identity and customer experienceThe significance of customer journey mapping and brand loyaltyMetrics and practices for sustaining brand equity and protecting brand integrityTimestamps:00:00:00 The Power of BrandingThe host lists eight benefits of strong branding, such as higher conversion rates, repeat purchases, and lower acquisition costs.00:01:01 What is Branding?The host introduces the topic of branding and a 16-step framework for building a brand for your e-commerce business.00:02:11 Custom GPT and ResourcesThe host mentions a custom GPT and slides available to help listeners implement the 16-step framework discussed in the episode.00:03:11 Host IntroductionJosh Hadley introduces himself, his e-commerce experience, and his podcast, E-com Breakthrough, for e-commerce entrepreneurs.00:04:12 Brand vs. Product Acquisition MachineThe host differentiates between building a true brand and simply operating a product acquisition or arbitrage business, common on Amazon.00:07:06 What a Brand IsA brand builds trust, optimizes for customer retention, focuses on long-term growth, and is difficult for competitors to copy.00:10:53 How Brands CompoundThe host explains how brands create a compounding effect through customer trust, repeat purchases, word-of-mouth, and easier product launches.00:12:43 The 16-Step Brand Framework IntroductionAn overview of the three phases of the branding framework: defining the brand, expressing it, and compounding its value.00:13:43 Step 1: Choose Your Primary CustomerA strong brand serves a niche customer. Defining this avatar makes all other branding decisions much easier.00:17:40 Step 2: Choose the Outcome You OwnCustomers buy products to achieve an aspirational identity. Brands must own the outcome their product provides, not just the product.00:23:48 Step 3: Choose Your AssociationsA brand is built by repeatedly connecting your company to a small number of desirable ideas, words, or phrases.00:26:54 Step 4: Write the Brand PromiseCombine your customer, outcome, and approach into a clear internal statement that serves as a decision-making tool for your business.00:28:02 Step 5: Define What the Brand Is NotBrands become clearer through exclusion. Deciding what your brand refuses to be is as important as defining what it is.00:30:06 Step 6: Choose Your Hero ProductYour hero product is the clear entry point that teaches customers what your company means and establishes your brand's identity.00:33:05 Step 7: Make the Product RemarkableThe product experience earns the second purchase. A remarkable product builds durable trust and generates powerful word-of-mouth marketing.00:34:25 Step 8: Create a Recognizable Brand IdentityDefine your brand's personality through one of three models: founder-led, a brand character, or a design-led identity.00:39:34 Step 9: Build a Distinctive Visual and Verbal WorldDesign makes your brand's meaning easier to recognize. Your brand should be identifiable even without its logo.00:40:26 Step 10: Audit the Current Customer ExperienceEvery customer encounter either reinforces or contradicts your brand promise. Audit all touchpoints, from packaging to social media.00:47:34 Step 11: Reinforce Through Trusted AssociationsBorrow trust by deliberately pairing your brand with people, organizations, or communities that already represent your brand's core values.01:02:29 Step 12: Repeat the Same Meaning ConsistentlyBrands are built through consistent repetition. While campaigns can change, the core meaning and message must remain recognizable over time.01:05:36 Step 13: Build the Next Purchase into the BrandMap the customer journey to create a logical product ecosystem that guides customers from one purchase to the next.01:08:23 Step 14: Align Every Customer TouchpointThe brand is the total experience. Ensure every interaction, from advertising to customer support, reinforces your brand promise without contradiction.01:13:36 Step 15: Run a Monthly Brand ReviewConduct a monthly review to prevent brand drift, track key metrics, and ensure all activities align with your brand strategy.01:21:16 Step 16: Protect and Compound the MeaningBrand building is ongoing. Use a brand extension test before launching new products to ensure they reinforce your core meaning.01:24:41 Final Thoughts and Call to ActionThe host summarizes the framework, offers a custom GPT and slides, and asks listeners to share and review the podcast.Links and Mentions:Tools and Resources  "Custom GPT for Brand Building": "00:02:11"  "Ecomm Breakthrough YouTube Channel": "00:09:58"  Notable Concepts and Frameworks  "16-Step Framework for Branding": "00:12:43"  Brands and Examples Mentioned  "Boom Beauty": "00:14:45"  "AG1": "00:15:46"  "Liquid Death": "00:16:39"  "Patagonia": "00:22:41"  "Rolex": "00:22:41"  "Apple": "00:22:41"  Videos and Content  "

Google's Pixel event dominated, with the Pixel 11 line, a $1,899 Pro Fold, and a $399 Watch 5 packed with Gemini and health trends. SpaceXAI closed the day out by dropping Grok 4.6, which it says matches GPT-5.6 Sol. Links Google unveils the $899+ Pixel 11, $1,099+ 11 Pro, and $1,299+ 11 Pro XL, with a Tensor G6, new Gemini features, Magic Capture to pick the best frames, and more (TechCrunch) Google unveils the $1,899+ Pixel 11 Pro Fold, with a stronger hinge, inner and outer displays that are 20% brighter, Tensor G6 chip, and an updated 48MP camera (Engadget) Google unveils the $399 Pixel Watch 5 with a satin pyrite case finish, offline Gemini, proactive AI suggestions, better GPS maps, and insulin resistance trends (The Verge) SpaceXAI releases Grok 4.6, saying it matches GPT-5.6 Sol on the Artificial Analysis Intelligence Index, and prices it at $2/1M input and $6/1M output tokens (SpaceXAI) Grok 4.6 is built for long-running agents, coding, and knowledge work, beating Grok 4.5 on CursorBench, FrontierCode, APEX-Agents, and Terminal-Bench, out today via Cursor, OpenRouter, and Vercel (9to5Mac) Subscribe to the ad-free feed.

SUMMARY: In this episode, Aaron and Brandon explore the potential decline of open-source models in AI, discussing market trends, financial challenges, and the future of OSS in the AI landscape.SHOW: 1053SHOW TRANSCRIPT: The Enterprise AI Show #1053 TranscriptSHOW VIDEO: https://youtu.be/-9iwoC5-muESHOW SPONSORS:Nasuni - Activate your data for AI and request a demo Topic: Are we seeing the end of OSS models?Why now? The trend towards fewer and fewer Apache models on the high end.Past: The OSS “rug pull” joke comes to mind: HashiCorp, MongoDB, Redis, Elastic  Present: Governments might jump in: The US Government with Mythos and GPT. Rumors are that China might start to restrict their high-end. Companies:Kimi K3 as an example, 100M users or 20M in revenue. Others that used to be Apache are now closedFuture: Maybe something like a Modified MIT license will likely be the future, as the models now cost millions to produce and the shelf life is measured in weeks to monthsFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Cadi Zhang joins Reid Hoffman and Parth Patil to explore how generative AI is changing game development, world models, and robotics. Drawing on her work across Unity games, VR teleoperation, AI accounting, and robotics product operations, Cadi explains what virtual worlds can teach embodied intelligence—and why physical robots still lack the tactile, real-world data that can't be scraped from the internet. She shares how she uses GPT, Blender, PixelLab, and Aseprite to prototype games faster, including a duck-themed imposter game and PokéTax, her Pokémon-inspired tax-filing game. AI can rapidly generate code, gameplay mechanics, and 3D assets, she says, but it still can't judge whether a game feels fun, maintain a consistent art style, or model the precise force needed to fold a sheet of paper. Reid, Parth, and Cadi discuss simulation-to-reality gaps, the limits of current world models, robot safety, humanoid versus task-specific form factors, and why human taste remains the decisive creative skill.

Bastam poucos segundos no Brasil para perceber que o WhatsApp está em todo lugar. Cerca de 150 milhões de usuários, 93% deles abrindo o app todos os dias e 82% das micro e pequenas empresas usando o WhatsApp como principal ferramenta de comunicação. Para quem constrói, é o caminho mais curto entre um produto e um cliente, e cada vez mais é onde acontecem negócios, relações e transações.Neste episódio, Bel Gallera conversa com duas pessoas que olham para a mesma plataforma de lugares diferentes. Guilherme Horn lidera o WhatsApp no Brasil, na Índia e na Indonésia. Luiz Ramalho é fundador e CEO da Magie, a primeira empresa a construir um assistente financeiro com inteligência artificial dentro do WhatsApp no Brasil, e que hoje ajuda grandes bancos e instituições financeiras da América Latina a criar esse mesmo tipo de experiência para os seus próprios clientes.O episódio explora como o Brasil se tornou um dos principais mercados de WhatsApp do mundo e por que estamos entre os primeiros em intensidade de uso e engajamento. Guilherme analisa o que torna o comportamento do brasileiro diferente e como ele se compara aos outros mercados que acompanha. Ele fala das razões culturais por trás disso, do que significa, para a Meta, desenhar um único produto para três bilhões de pessoas em 200 países, e para onde a plataforma caminha com os lançamentos mais recentes: o Meta Business Agent, a Agent Platform, os usernames e a tendência dos agentes pessoais, que pode mudar a forma como as pessoas compram e se relacionam com todo tipo de empresa.Luiz conta como é estar do outro lado, construindo em cima da plataforma desde o início de 2024. Ele explica por que escolheu o WhatsApp em vez de um app quando isso ainda não era óbvio, o que aprendeu observando como as pessoas realmente se comportam quando pagamento e conversa acontecem no mesmo lugar, e como essa leitura continuou valendo quando a Magie deixou de atender só o consumidor final e passou a atender instituições. Ele também é específico sobre onde vê as grandes oportunidades para empreender agora, enquanto a inteligência artificial redefine o que uma conversa entre empresas e consumidores pode fazer.Uma das partes mais interessantes é ver como os dois vivem as mesmas oportunidades e os mesmos desafios por ângulos diferentes, um como fundador e o outro como executivo. Guilherme descreve como o que se constrói na plataforma volta e influencia o que ela pode se tornar para todos. Luiz, por sua vez, fala de experiências que só se tornaram possíveis com recursos que a Meta lançou e que não existiam um ano antes. Os dois estão descobrindo em tempo real como vão ser os próximos anos, e são honestos sobre o tanto que ainda não se sabe.Este episódio é para você que:Quer entender por que o WhatsApp é tão grande no Brasil e o tamanho da relevância desse canal para empreendedores e grandes empresasEstá construindo no WhatsApp, ou considerando a plataforma como canal de distribuição e de contato contínuo com o clienteTem curiosidade sobre como decisões de produto são tomadas na escala de três bilhões de usuáriosQuer entender melhor a relação entre plataforma e aplicação, e os papéis diferentes de quem constrói e de quem opera a infraestruturaCapítulos: 0:00 – Abertura1:43 – Boas-vindas e por que este episódio é diferente2:51 – O WhatsApp no Brasil: 150 milhões de usuários e o primeiro em engajamento7:24 – Como a Magie nasceu dentro do WhatsApp: Pix, IA generativa e a era do GPT-311:24 – De canal de comunicação a infraestrutura de negócio13:41 – Construir experiências de pagamento dentro da conversa15:58 – Confiança: o usuário coloca a senha do banco no WhatsApp?18:14 – Comportamento do usuário por faixa de renda e geografia20:30 – Os não bancarizados e o público que pula a fase do app22:48 – Construir para 3 bilhões: simples, confiável e privado25:39 – WhatsApp Plus: o teste de uma versão paga27:22 – Como o WhatsApp é priorizado dentro da Meta29:04 – Velocidade, adaptabilidade e nichos de alto valor31:21 – Vender profundidade e velocidade para grandes instituições33:04 – Incumbentes e startups: apetite por risco e ritmo de mudança35:20 – O que vem: Meta Business Agent, Agent Platform e usernames38:11 – Agentes pessoais e a mudança no comportamento de compra39:54 – Verticalizar ou horizontalizar: a escolha de expansão da Magie44:28 – Plataforma e aplicação: onde as camadas se encontram47:19 – Quatro anos na Meta: impacto além dos negócios51:18 – Os aprendizados de Luiz construindo no WhatsApp54:43 – Encerramento: o livro do Guilherme e o início da era da IA

GPT-5.6-Cyber for the Daybreak program is ‘less likely to refuse higher-risk tasks.' Learn more about your ad choices. Visit podcastchoices.com/adchoices

Send us Fan MailChaz Giles, founder of Alida Labs and former head of external innovation at Estee Lauder, returns to Skin Anarchy to cut through the AI hype: where it creates real value in beauty and wellness, why midsize brands are outpacing the giants, how to get your data ready, and what AI means for executives and their careers.Who is Chaz Giles? Two decades across Procter & Gamble, venture capital, and Estee Lauder, then founder of Revea and now Alida Labs. "How do you turn technology into things that consumers love and are good for business?"Where does AI actually fit in beauty right now? Unevenly. "We had a custom GPT... that's why it didn't really help, because it wasn't the use case that was right." Real value lives in data, formulation, product development, and DTC analytics.Where should a brand start with AI? With its existing strengths. "Think of AI as a superpower... where do you want to create a superpower?" Point it at your competitive advantage, not everything at once.How can AI help a brand stand out in a saturated market? By formulating with claims in mind, mapping white space, and turning one concern like hyperpigmentation into multiple defensible mechanism claims. "It's getting more mileage out of the formulas and ingredients I have."What is the difference between LLMs and agentic AI? Levels of value. "My agentic flows, I now can set up effectively AI employees and AI teams," letting a four person startup operate like a team of 40.Why are midsize companies beating the giants at AI? "They are not [winning], because they cannot get out of their own way in terms of how to do that redesign." Nimble midcaps are leapfrogging larger rivals and halving time to market.How should companies get their data ready for AI? Out of silos first. "There never was an award for the world's sexiest data." Consolidating scattered data unlocks synthetic testing and digital twins.How much AI is too much? Never too much data, easily too much AI. "AI should be used in places that it's giving me more value than I can create with my org chart today."How is AI changing beauty investment? "AI is eating software," erasing old moats. Brand and distribution matter more again, and a new crop of AI native brands is coming.What does AI mean for executives? A hard mirror as the value of information trends toward zero. "It's about how to apply, how to integrate, how to lead the teams and the transformation."How do leaders stay indispensable in an AI future? By leaning into what machines cannot do. "You as a leader are still uniquely positioned to take that information and translate it into valuable action."Listen to Skin Anarchy wherever you get your podcasts.Visit Alida LabsContact Chaz: chaz@alidalabs.ai, LinkedIn, InstagramDon't forget to subscribe to Skin Anarchy on Apple Podcasts, Spotify, or your preferred platform.Reach out to us through email with any questions.Sign up for our newsletter!Shop all our episodes and products mentioned through our ShopMy Shelf!Support the show

Ovetta Sampson is a design researcher, AI leader, and founder of Right AI. She previously served as VP of ML and AI Platform Design at Capital One and worked at Google and IDEO. Ovetta brought that experience to the 2026 ITX Product + Design Conference, focusing her keynote on a question that often goes overlooked: what happens when people interact with increasingly powerful machines? To help us answer that question, Ovetta offers a pair of frameworks that center on human engagement risk, responsible AI, and rethinking how product teams design AI products. Building AI responsibly requires more than better models or more sophisticated tools. Product builders must also understand the cognitive, social, cultural, and physical risks that can emerge when humans interact with technology. AI inherits many of the biases embedded in the data used to build it, Ovetta says. So organizations need to rethink how disciplines collaborate; as the lines between traditional product, design, engineering, and security silos blur, responsible AI requires organizations to redesign not only their products, but also the processes used to create them. Here's what else we learned: Human Engagement Risk Belongs in AI Product Design Ovetta's human engagement risk (H-E-R) framework is based on a fundamental premise: technology can harm people when designers fail to account for how humans behave around machines. The H-E-R framework identifies cognitive, social, cultural, physiological, and community risks. These risks become especially important with generative AI, where people can easily attribute human qualities to systems that do not actually possess them. As a result, AI product teams must consider psychological and cognitive outcomes alongside traditional usability concerns. Ovetta cautions: “There are real dangerous risks when we engage with machines and don’t mindfully think about the outcomes that can happen when we don’t protect humans psychologically, cognitively, physically, and physiologically.” AI Strategy Starts With Executive Leadership Responsible AI also requires leadership decisions that extend beyond individual tools or experiments, Ovetta says. Many mid-sized organizations are hesitant to adopt AI because executives are concerned about intellectual property, trust, and data leaks. Meanwhile, employees may already be integrating AI solutions without an overarching organizational strategy. It's a disconnect that creates opportunity for leadership to establish clear priorities before adoption becomes fragmented. AI strategy starts at the top, Ovetta adds, because executives have the authority to establish the conditions under which technology gets developed and used. “Once the C-suite understands the risk to their shareholders, to their products, to their employees, to their customers, it is much easier for me to bring in the implementation of how to mitigate those risks.” Dismantling Silos Is Essential for Effective AI Development AI challenges the traditional handoff model in which designers, engineers, security, legal, compliance, and other teams work separately before passing projects along. Ovetta says AI development requires continuous cross-functional input instead. Her D-C-R framework – draft, critique, revise – organizes teams around development stages, bringing the right expertise into each phase. “Instead of saying, ‘I’m a designer' or ‘I’m a researcher,' or ‘I’m a product manager,' or ‘I'm an engineer,' we say, ‘I’m in the draft mode,'” Ovetta adds. “Each skill set in that move brings what they need to get that draft ready for critiquing, right? And so it’s something that I give to organizations and teams to try to reimagine how they actually do their jobs.” Ovetta Sampson is not arguing for less innovation with AI; instead, she's arguing for a different definition of responsible innovation – one that embeds human consequences, executive accountability, and cross-functional collaboration into the product development process itself. [03:10] Protecting the fragility of humanity. There are a lot of things us humans engage in, especially what I call the cognitive biases, that make engaging with machines and other automated systems that make it risky for us. [05:57] The H-E-R Framework. But what it really is, is there are five dimensions. There’s the cognitive, there’s the social, there’s cultural, there the physiological. And then the overall community risks that when humans engage with machines, that can happen.  [10:08] LLMs built on ‘traumatized data sets.’ Generative AI has no moral code. It does not know truth or fact. And accuracy is not in its wheelhouse. In fact, it’s not in this training and it’s in its goals. So why when we type something into chat GPT, we expect truth back? I don’t know. [13:24] Protecting my values as a creator. That’s where I really want to start, because I don’t want to be a part of that. I don’t want to part of designing something that harms people. [14:00] I observed one reoccurring truth. Whatever is happening in the basement of a company starts in the C-suite. So if there is sexism, if there’s homophobia, if there racism, if there is bad culture, if it starts at the C-suite. Because the C-suite is the person who has the ultimate authority about what occurs in every floor of an organization. [18:26] The D-C-R framework — draft, critique, revise. The D-C-R framework is something I created because I was trying to explain to designers and product and engineers how their processes would change when they’re designing for and with AI. Each one of these disciplines should go through that makes the handoffs more like a circular iterative. The post 194 / Ovetta Sampson: Designing AI Products Around Human Needs – Not Just Technology appeared first on ITX Corp..

Where do you start with AI in HR? Not with everything — that's how implementation goes sideways. In this solo episode, Kerri Roberts gives you the five specific HR tasks to automate first, a human checkpoint for each one, and the three things that should never be handed to AI no matter how far along you get.In this episode, you'll learn:Resume screening — load minimum qualifications into a Claude Project or custom GPT; AI ranks candidates; you read every shortlisted resume; never auto-reject for employment gapsInterview scheduling and candidate comms — eliminate the back-and-forth with a booking link; build AI-drafted templates for every stage; automate logistics, not empathyOnboarding paperwork and new hire workflows — offer letter, I-9, W-4, e-signatures, IT setup, 30/60/90-day check-ins; AI gets you 70–80% of any checklist, you do the last 20%Employee FAQ chatbot — build an AI assistant trained on your handbook; employees get instant answers from your actual policies, not the internetFirst drafts — job descriptions, offer letters, policy language; AI gets you to 80%, you edit the last 20%; always run new policies by your attorneyWhat never to automate: final hire and fire decisions; performance and coaching conversations; sensitive judgment calls, investigations, or anything touching individual rights.CHAPTERS:00:00 Last Week Was Readiness — This Week Is Where to Start 01:00 How AI Saved Kerri $65–70K a Year 03:37 The Rule — High Volume, Low Judgment, Repeatable 04:17 Task 1 — Resume Screening and Initial Filtering 10:51 Task 2 — Interview Scheduling and Candidate Communication 17:05 Task 3 — Onboarding Paperwork and New Hire Workflows 19:54 Task 4 — Employee FAQ and Policy Chatbot 22:36 Task 5 — First Drafts 24:06 Three Things to Never Automate 26:35 The Stats — Deloitte, SHRM, and Why This Pays Off 29:16 Action Item — Pick One of the Five and Start TodayBefore you start automating anything in HR, you need to know where your foundation stands — the free HR Audit shows you exactly that: saltandlightadvisors.com/hraudit

Ryan Greenblatt is the Chief Scientist at Redwood Research, where he works on technical AI safety research. He's also lead author on the "Alignment faking in Large Language Models", and is currently working on a third party investigation into the OpenAI/HuggingFace incident. In my opinion, he's one of the most interesting thinkers on the future of AI.Had him on to discuss/debate recursive self-improvement. This might be the most important question in the world right now – whether within a year or so of achieving human-level intelligence, you slingshot towards having 10s of billions of superintelligences, each of which is dramatically more competent than human experts across all fields.I've historically been skeptical of this possibility. My intuition has been that we will end up significantly bottlenecked by not only compute scaling but human expert data, which I think underlies most of the AI progress today.If, because of RSI, we got a jump as big as GPT-3 to a Mythos (i.e. 6 years of AI progress) within a single year of achieving AGI, then the thing we get there at the end of that year is definitively and wildly superhuman.We hashed it out, and I think Ryan made a pretty good case that this kind of speedup is plausible. FWIW, Ryan's median for when we automate AI R&D is 2031.We then discussed the alignment implications of this scenario. Who should these superintelligences be aligned to? In the future, our capacity to steward our votes and our capital, and to make sense of what's happening in the world, will all be titrated by superintelligences. And I worry that specs like the Claude Constitution are not shaping these ASIs to truly be my personal advocates and guardian angels.And can we get them aligned to anything in the first place? Ryan and I had a long debate about whether the kind of reward hacking we saw with the OAI/Hugging Face hack extrapolates to superintelligences that would team up to literally take over the world.The first piece of advice you get when you're learning to drive is that it will go much smoother if you look at the horizon instead of directly in front of your tires. And so it is with the trajectory of AI. Hope you enjoy!Watch on YouTube; read the transcript.Sponsors* Antithesis is a software testing platform that finds the failures no human or AI could ever anticipate. It runs thousands of copies of your code inside a fully deterministic computer, injecting faults and steering each trajectory toward the most insidious bugs. This lets you find critical issues in minutes rather than waiting months for your users to uncover them. Learn more at antithesis.com/dwarkesh* Jane Street's back with a new puzzle. They designed an ASIC and sent me the final masks… but they didn't tell me what the chip actually does. So that's the challenge: reverse engineer the circuit and figure out the chip's purpose. Jane Street has a bunch of swag ready to send to the most creative solutions, and they're also planning to feature the top write-ups in a blog post. Download the files and get started at janestreet.com/dwarkesh* Cursor and SpaceX recently released Grok 4.5, and I've been surprised by just how good the model is. For example, when I tested it against Fable and Sol on a bunch of AI governance questions, all three models gave substantially the same answers, but Grok was faster, more concise, and cheaper. Grok 4.6 is coming soon, but in the meantime, you can try 4.5 at cursor.com/dwarkeshTimestamps(00:00:00) – Is AI R&D verifiable enough to unlock recursive self-improvement?(00:16:52) – Is AI progress bottlenecked by human expert data?(00:34:02) – Flat token prices suggest scaling has been slow(00:39:47) – Skills AI can't train on: does it even need them?(00:48:07) – Aligned to whom?(01:09:18) – Recent incidents of AIs colluding and deceiving humans(01:19:38) – What could possibly go wrong? A concrete scenario(01:48:02) – From reward hacking to takeover Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Summary Today Marc is chattin' with Richa Kaul, founder and CEO of an AI-based compliance automation platform. The conversation centered on how AI is reshaping enterprise governance, risk, and compliance (GRC), especially by helping organizations handle growing complexity without simply adding more headcount. The discussion quickly focused on how compliance teams can use automation and AI to streamline vendor risk, regulatory requirements, and other time-consuming workflows. Richa explained that his approach starts by separating what truly requires human judgment from what can be automated. A major theme was the mismatch between the speed of modern risk and the pace at which organizations can hire. Richa argued that risk is increasing faster than teams can scale, making it unrealistic to solve GRC challenges by just expanding staff. Instead, she framed the real question as what work should remain with humans and what work can be handled by AI or automation. She emphasized that teams are bogged down by urgent audit prep, repetitive tasks, and reactive “fire drills,” which prevents them from focusing on strategic risk reduction. The chat then moved into visibility and granularity in risk management, particularly around privacy. Richa noted that many CISOs and GRC leaders lack sufficient visibility into their organization's risks, especially because privacy is cross-functional and touches employees, users, operations, and regulatory obligations. She said this lack of clarity makes it difficult for leaders to confidently communicate risk to boards or determine where to invest time and resources. In her view, the biggest issue is not just managing risk, but being able to see it clearly enough to act on it. Another key topic was AI governance. Richa pointed out that many companies are paying attention to AI policy at the top level, but are not doing enough to train employees at the operational level, where mistakes are most likely to happen. She described the “last mile” of AI governance as especially vulnerable, since employees may unknowingly expose proprietary information by entering sensitive data into tools like GPT. According to Richa, human behavior is often the weakest link, and effective governance requires education and training throughout the organization, not just policy statements from leadership. Their chat also touched on industry-specific risk, with healthcare highlighted as a major area of concern. Richa said healthcare compliance appears underinvested compared with financial services, even though both are highly regulated and handle highly sensitive data. She closed by offering a practical starting point for companies facing generative AI challenges: map your most important data, follow it from input to output, identify the systems it touches, and secure each step of the journey. His overall message was optimistic — that even though the risk landscape feels overwhelming, organizations have tools, platforms, and partners that can help them manage it more effectively. Key Points AI can reduce compliance burden by automating repetitive GRC tasks. Organizations can't hire fast enough to keep up with rising risk and regulatory complexity. Many leaders lack clear visibility into privacy and cross-functional risk. AI governance fails most often at the employee level, not just the policy level. Healthcare compliance is highly exposed and may be underinvested relative to its risk. Key Quotes “Risk right now is increasing at a speed and at a rate that teams cannot possibly hire to mitigate.” “It is not the question of, should teams get smaller. I think it’s a question of what work should humans be doing and what work can I do instead.” “A lot of CISOs tell me that they don’t have the visibility, or at least the granularity of visibility into their risks that they would like.” “Humans are the weakest link.” “Don’t you need to boil the ocean, but let’s look at what is your highest risk data.” About Our Guest Richa Kaul is a technology executive and Head of Product Engineering: Strategy, AI Builder, and Cloud & AI Enterprise Transformation, with 20+ years of experience leading cloud, data, and AI initiatives across Fortune 500 organizations. She has managed portfolios as large as $2B in revenue and $300M in operating budgets, while building global teams of 100+ and advising CxOs on enterprise AI and financial strategy. Richa is known for driving GenAI adoption, modernizing data platforms, advancing AI governance, and delivering scalable, cost-effective transformation across banking, capital markets, asset management, and wealth management. Follow Our Guest LinkedIn About Our Host National co-chair of the Cyber Center for Excellence, Marc Schein, CIC,CLCS is also a Risk Management Consultant at Marsh McLennan Agency. He assists clients by customizing comprehensive commercial insurance programs that minimize the burden of financial loss through cost effective transfer of risk. By conducting a Total Cost of Risk (TCoR) assessment, he can determine any gaps in coverage. As part of an effective risk management insurance team, Marc collaborates with senior risk consultants, certified insurance counselors, and expert underwriters to examine the adequacy of existing client programs and develop customized solutions to transfer risk, improve coverage and minimize premiums. Follow Our Host Website | LinkedIn

On Hashtag Trending for Tuesday, August 11, 2026, host Jim Love covers four major technology stories. OpenAI says preliminary testing of its unreleased Astra model shows cybersecurity capabilities strong enough that the company cannot rule out its highest "Critical" capability level. OpenAI has tightened isolation, network and tool access, and monitoring around the model. The company also explicitly says Astra was not involved in the recent Hugging Face intrusion, which involved GPT-5.6 Sol and a separate internal research model. SpaceX stock has rebounded sharply after falling to around $110 last week. Shares were trading around $132 as the show was recorded, after briefly moving back above the $135 IPO price. One major concern failed to materialize: more than 900 million previously restricted shares became eligible for sale, but the feared flood of insider selling did not occur. Investors are also weighing stronger-than-expected revenue and new details about SpaceX's planned $16.8 billion Terafab chip project with Tesla. Mark Zuckerberg is warning that concentrating powerful AI in the hands of a few companies, governments or experts could itself become dangerous. At the same time, Meta appears to be moving back toward open-weight AI with Muse Glimmer and a planned open-weight version of Muse Spark 1.2. There is some irony in hearing one of the world's most powerful technology executives warning about concentrated control, but the underlying question remains important. And finally, one of San Francisco's newest "AI chatbots" has no AI at all. ChatTJB is answered by actual humans, just as public frustration with low-quality AI-generated content is beginning to have an impact on platforms including LinkedIn and Snapchat. It may be evidence of a healthier discussion about where AI helps and where people still want humans involved. 00:00 Top Tech Headlines 00:32 OpenAI Cybersecurity Red Line 03:16 SpaceX Stock Rebound 04:51 Zuckerberg on AI Power 07:07 Chatbot With No AI 08:11 Backlash to AI Slop 09:16 Wrap Up and Outro

AI agents crashing.

Edtech Throwdown Episode 221: 10 Tools for Specialized AI and ProductivityWelcome to the EdTech Throwdown. This is Episode 221 called 10 Tools for Specialized AI and Productivity. In this episode we'll be talking edtech tools as we bring you a list of some atypical AI platforms that might not otherwise come up for educators. This is another episode you don't want to miss. Check it out.Segment 1:Happy August, happy end of summer breakWe're almost fully re-charged and starting to think about things like productivity againSegment 2:Nick: abacus.ai: Abacus.AI is the world's first AI super-assistant tailored for enterprises and professionals. We offer two products: ChatLLM for professionals and small teams and Abacus.AI Enterprise for enterprises and companies. ChatLLM is a multi-modal, multi-device super-assistant that can handle many tasks and completely transform your life. You can access all of the SOTA LLMs, analyze documents, do data analysis, generate code, search the web, create images, and much more. It's your all-in-one AI assistant and increases individual productivity by 15% to 75%. Abacus.AI Enterprise is a state-of-the-art generative AI platform that combines the AI super assistant available to all your employees with an AI brain that can connect to your enterprise software systems, automate business processes, increase revenue, and be a powerful force multiplier. Our AI engineer can build AI Workflows and chatbots to automate critical processes. Our Enterprise product comes with single sign-on, multiple deployment options and checks all the security and compliance boxes.Guise:studley.aiWhy:Specialized AI platforms designed for more complex, data-heavy, or professional-grade automation and modeling.Nick: openalternative.co: What is the difference between open source and proprietary? Proprietary AI Tools: These are "closed-source" models developed and owned by specific companies (e.g., OpenAI's GPT-4/5, Google's Gemini, Anthropic's Claude). You typically access them via an API or a web interface. The underlying code, training data, and exact model weights are a "black box" hidden from the public. Open-Source (Open-Weight) AI Tools: These are models whose code and weights are publicly shared (e.g., Meta's Llama series, Mistral, Qwen, and DeepSeek). Anyone can download them, look under the hood, modify them, and run them on their own hardware or private cloud. Guise: The Most Comprehensive List of FREE Online Tools for Teachers Why:Both promote open-source ethics, whether finding alternatives to paid software or using privacy-focused media downloaders.Nick: smart.servier.comGuise: runable.comWhy:High-level technical resources; one offers medical illustrations, while the other focuses on executable code environments.Nick:Workout.coolGuise: KouponWhy:Personal optimization tools—one for physical fitness routines and the other for optimizing shopping/savings.Nick: Internet Archive:https://web.archive.org/Guise: Same.newWhy:Part of the ".new" domain movement, providing instant, one-click access to start a new coding or collaborative project.Edtech Throwdown: Vote on twitter @edtechthrowdown and under the pinned post on the profile.Segment 3: Where to Find EdTech ThrowdownDo us a few favors:Subscribe to the Edtech Throwdown PodcastApple PodcastsSpotifyAmazon PodcastsStitcher YouTube Twitter FacebookWrite us an Apple Podcast Review!Tell your friends aboutwww.edtechthrowdown.comTell your friends about the Teach Better Podcast NetworkSubscribe to our Podcast Channels and SocialsApple PodcastsSpotify YouTube Twitter (@edtechthrowdown)FacebookInstagramConnect with us on Social MediaGuise's Social MediaTwitter(@guisegotteched)LinkedInNick's Social...

Ricker and Bon break down South Korea's leveraged-ETF unwind, SpaceX and Starlink's challenge to telecom, GPT-5.6 and agentic work, Christopher Nolan's studio move, Japan's yen problem, and a final Timepiece Hour on Seiko and Orient.They also get into Spotify's copyright crackdown, late-night DJ sets in Los Angeles, Snapchat and dating-app economics, Apple TV, Ted Lasso, and the Your Mom's House breakup. Music playback from the BONES “Seiko” segment was removed while the spoken setup, reactions, lyric discussion, and watch conversation remain.0:00 Cold open0:42 Spotify's copyright crackdown3:59 Weekend DJ adventures10:55 Weekly tech rundown14:06 GPT-5.6 and ChatGPT Work19:05 South Korea's leverage casino28:16 SpaceX and Starlink vs. telecom34:54 Snapchat and dating-app economics39:09 The Odyssey, Nolan and Universal42:41 Apple TV's Ptolemy Grey45:21 Ted Lasso and six-figure debt49:20 The Your Mom's House breakup52:12 Japan's yen and bond-market problem58:48 Timepiece Hour59:42 BONES' “Seiko” lyric breakdown — music removed60:51 The mechanical watch collection69:03 Orient and Seiko history

Free AI users rejoice: there's finally unlimited, free frontier AI.

The AI trade might have found its canary in the coal mine. Leopold Aschenbrenner—a 25-year-old former OpenAI researcher built Situational Awareness into one of the hottest funds on Wall Street by betting long on the construction and infrastructure side of AI (Micron, CoreWeave) and short on the chips and software everyone else was chasing (Nvidia, Broadcom, Oracle). Then July happened, prime brokers made margin calls, and his entire public stock book got liquidated overnight in a single block trade. This episode uses that blowup as the lens for a much bigger story: how a three-quarters-of-a-trillion-dollar hyperscaler spending pledge is propping up U.S. GDP growth, why OpenAI just quietly cut prices as “tokenmaxxing” dies and Chinese labs undercut on cost, why Mark Zuckerberg picked a fight with OpenAI and Anthropic, how overleveraged Oracle has become, how the most profitable companies in the world are posting negative free cash flow, why hyperscaler bonds are going undersubscribed, and why the length of this year’s tech bond issuance is starting to crowd out the rest of the corporate debt market. It closes with the 2008 parallel: mortgage-backed securities stacked into CDOs stacked into synthetic swaps, and how today’s version runs through corporate bonds, CLOs, hedge fund leverage, and private credit—with Treasuries as the eventual exit ramp when the unwind accelerates. Resources Analysis Atlas: Hyperscaler AI Capex 2026: The $710 Billion Year Inside the Four Spenders J.P. Morgan Asset Management: How AI demand and capex shape investing in tech stocks New York Times: U.S. Economy Slows as Inflation Bites U.S. Bureau of Economic Analysis (BEA): GDP (Advance Estimate), 2nd Quarter 2026 CNBC: OpenAI cuts prices for two of its GPT-5.6 AI models as companies grow sensitive to costs Business Insider: The All-You-Can-Eat AI Era Is Over. It’s Time to Count Calories CNBC: Chinese AI models are gaining ground with U.S. companies as OpenAI, Anthropic costs surge The New York Times: Even China’s A.I. Powerhouses Can’t Figure Out How to Profit Off A.I. CNBC: Meta’s flurry of AI initiatives this month hasn’t helped lift the stock. What will? The Hill: Zuckerberg knocks AI development centralization, control Buttondown: D.A.D.: Zuckerberg Blasts Anthropic and OpenAI for “Centralizing” AI Power Reuters: AI investment boom puts Big Tech’s free cash flow under pressure CNBC: Bets against SpaceX grow to 32% of float as Elon Musk warns short sellers won’t survive Yahoo! Finance: NVIDIA’s rising CDS the talk of Wall Street amid circular financing fears Bloomberg: Big Tech Debt Flood Is Taking Over Risk In Market: Credit Weekly CNBC: Fed meeting recap: Warsh says Fed won’t hesitate to stop inflation, but bond market has doubts CNN: AI investor Leopold Aschenbrenner forced to unwind all public stock positions after steep losses, sources say Yahoo! Finance: Hyperscalers Hit $700 Billion in 2026 AI Spending Plans - 24/7 Wall St. Global Finance Magazine: AI’s Financial Circle Game Reuters: Hyperscaler debt binge pushes yields up as investor demand cools UNFTR Resources Video: Situational Awareness: How a 25-Year-Old's Hedge Fund Exposed the Entire AI Bubble. Essay: The Great Unwind. -- If you like #UNFTR, please leave us a rating and review on Apple Podcasts and Spotify: unftr.com/rate and follow us on Facebook, Bluesky, and Instagram at @UNFTRpod. Visit us online at unftr.com. Become a member at unftr.com/memberships. Buy yourself some Unf*cking Coffee at shop.unftr.com. Visit our bookshop.org page at bookshop.org/shop/UNFTRpod to find the full UNFTR book list, and find book recommendations from our Unf*ckers at bookshop.org/lists/unf-cker-book-recommendations. Access the UNFTR Musicless feed by following the instructions at unftr.com/accessibility.Support the show: https://www.unftr.com/membershipsSee omnystudio.com/listener for privacy information.

AI news this week: the AI video renaissance is HERE. Seedance 2.5 is out for everyone, Minimax H3 (Hailuo) is basically an uncensored Sora 2 you can run on local hardware, Wan 3.0 turns your documents into video, and Flux 3 is the only non-Chinese model anywhere near the frontier. Kevin's on vacation, so on today's AI For Humans, Gavin Purcell is joined by AI video expert Tim Simmons from the excellent YouTube channel Theoretically Media. Together they break down which of the new models is actually best, the basics of how to use them, exactly how good local AI video has gotten, and what it means that nearly all of these models are Chinese. Also: Walter White meets Joey, AI Kramer, Family Guy prompts, The Office reimagined, and a museum for the ancient AI videos of two years ago. Plus: Tim's Higgsfield terms-of-service deep dive, OpenAI reportedly targeting an "ASTRA" aka GPT-6 release for NEXT WEEK, Jeff Dean leaves Google after 27 years as Demis Hassabis steps up, and an AI See What You Did There: AI Filmmakers Edition. WE HAVE SORA AT HOME NOW. HOLLYWOOD, YOU GOOD? // Show Links // Subscribe to Tim's channel, Theoretically Media https://www.youtube.com/@TheoreticallyMedia Seedance 2.5 is here for everyone https://x.com/capcutapp/status/2085355533943357650?s=20 Tim's short film "Death Walks Into A Bar" https://youtu.be/4wFBA9-KyzY?si=2E1cdKiiGYe1d7pu Minimax H3 (Hailuo): basically an uncensored open source local Sora 2 https://www.minimax.io/blog/minimax-h3 AI Warper's Family Guy "AI Prompt" https://x.com/AIWarper/status/2084445445372477530?s=20 Walter White meets Joey https://www.reddit.com/r/aivideo/comments/1vfu2dt/oh_this_is_funh3/ Here's Kramer (Seinfeld prompt) https://x.com/techprofits/status/2085118937738391830?s=20 AI Film History museum by Rich Klien https://aifilmhistory.org/ The Office example https://x.com/VelvetRender87/status/2084390557334335837?s=20 Wan 3.0: turns docs, sheets, decks and webpages into video https://x.com/Alibaba_Wan/status/2085339761284104529?s=20 https://x.com/Alibaba_Wan/status/2085339982714257453?s=20 Flux 3 from Black Forest Labs https://bfl.ai/blog/flux-3 Tim's Higgsfield TOS video https://youtu.be/7vGp40qEV4s?si=To-ih5eLpqWWnKPz OpenAI targeting "ASTRA" aka GPT-6 release for next week https://x.com/synthwavedd/status/2085365276640702915?s=20 Jeff Dean leaves Google after 27 years & Demis steps up https://www.cnbc.com/2026/08/05/google-chief-scientist-jeff-dean-leaving-company-after-27-years.html Kavan The Kid's latest https://youtu.be/tU5UUc1d0_A?si=j3KNGgJNDX6-L8yG Dave Clark's found footage Seedance 2.5 short https://x.com/Diesol/status/2084547129188712589?s=20 Demon Flying Fox's Odyssey https://youtu.be/ky-2PL6G3aI?si=C73wRZG-Bfd97vSa   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

Public Domain AI Flipping: Build & Monetize Custom GPTs With 19th‑Century Books (Before the Window Closes) Make money online by flipping public domain AI content—the overlooked side hustle for busy parents. Learn how to find, repurpose, and sell digital products without creating anything from scratch. This contrarian strategy saves time, cuts through the noise of traditional side gigs, and generates income faster than you think. Perfect for the AI entrepreneur who wants results without the grind. https://DarkHorseEntrepreneur.com The episode explains a “public domain AI flipping” strategy: training custom GPTs on 19th-century public domain books (including Project Gutenberg and the Internet Archive) to create niche, highly specific tools that can be monetized quickly via subscriptions or products. It argues most custom GPT monetization fails due to content creation costs or licensing, but public domain works published on or before 1930 can be used and monetized without copyright barriers, with the public domain expanding yearly. The script outlines a four-step process: pick a narrow vertical with an unmet need, curate matching public domain texts, build a custom GPT using prompts and retrieval-augmented generation (RAG), then monetize via recurring revenue using platforms like GPT Plus, Gumroad, or API interfaces. It recommends verticals such as historical accuracy consulting, academic supplementation, content engines for podcasters, and archival tools, while warning about uncertain platform rules, limited precedent, and the need for distribution over technical execution. 00:00 Intro to public domain AI flipping 01:05 Why Most Custom GPT Monetization Fails  02:10 Clarify U.S. Public-Domain Cutoff 03:40 4-step Mechanism 06:20 RAG & Tightly Sourced Corpus > GPT 08:05 Monetization Options  09:40 Necessary Callouts 11:10 4 Promising Verticals 14:30 Real Trap Warning 17:10 Whiskered Wisdom Custom GPT monetization, Public domain,AI, Project Gutenberg, training data, AI side hustle 2026, ChatGPT, passive income, Public domain, Train GPT on books, RAG, custom chatbot,AI entrepreneur, niche GPT, recurring revenue, how to make money with chatgpt, ChatGPT, GPT-4o, OpenAI, Custom GPT, GPT Store, how to make money online, make money online, ai side gig, digital products for beginners, side hustles, income growth hacks https://DarkHorseEntrepreneur.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hakuro Matsuda さんをゲストに迎えて、サブスク、Claude Fable 5, GPT-5.6, Herdr, Kimi K3, Unreal Engine 5.8, メモリ株、電子ペーパー、Galaxy Z Fold8 などについて話しました。 Show Notes West Coast Brewing うちゅうブルーイング Lunch Money Monarch Plaid Claude for Open Source OpenAI cuts prices for two of its AI models as cost worries mount Herdr TypeWhisper Industry Leaders Join Open Secure AI Alliance for AI Safety and Security MoonshotAI/Kimi-K3 deepseek-ai/DeepSeek-V4-Flash Hugging Face: Security incident disclosure — July 2026 Anthropic says its own AI models breached three companies during security tests DHH: I'm sorry, Dave アイドルがAIと配信のシステムを全部作った話 Reimagine the world with Nano Banana in Google Earth Unreal MCP in Unreal Editor Google search is indexing public Claude artifacts Coder Leopold Aschenbrenner Situational Awareness fund: $45B to fire sale NocFree Kindle Colorsoft Signature Edition E Ink Kaleido 3 BLOOMIN8 EinkCanvas コレステリック液晶電子ペーパーの原理 TRMNL Samsung Galaxy Z Fold8 Ultra, Fold8 and Flip8 Get to know the new Google Home Speaker OpenAI: Introducing GPT-Live 村上春樹『夏帆─The Tale of KAHO─』 攻殻機動隊 THE GHOST IN THE SHELL Apple TV: Neuromancer Blade Runner 2099 Cyberpunk: Edgerunners 2 Stellar Blade: BLOOD RAIN - Wanna Be in LOVE ft. Evie ラッダイト運動 『たけしの挑戦状』もし伝説のゲームが日本映画になったら さよならララ これ描いて死ね 急に具合が悪くなる 映画ちいかわ 人魚の島のひみつ MECCHA CHAMELEON Steam Machine XG: FUJI ROCK '26

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

Intelligence too cheap to meter -- could that actually be coming?

OpenAI has a new model coming soon called Astra. Was it a leak? A reddit post? Some backdoor update? Nope, OpenAI made some crazy discoveries and math then told the world that their next model family Astra did the heavy lifting. (And you thought you could just click ‘Sol' and your strategy was set for Q3?) Aside from news on what's next from OpenAI, this week saw multiple new agent outbreaks, AI competitors banning together to pace AI, Amazon doing a 180 on its AI strategy and a lot more. Don't get left behind. We'll keep you ahead. OpenAI's new Astra model, more AI agents escape sandboxes, AI leaders call for AI pacing and more. AI News That Matters for August 3 — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Agents Escape Sandboxes IncidentAnthropic Claude Models Security BreachesAI Agents Breaking Cybersecurity GuardrailsOpenAI GPT-5.6 Price Cuts & Self-OptimizationRecursive Self-Improvement in AI ModelsAI Leaders Urge AI Development PacingUS, China, and International AI GovernanceAmazon Nova AI Models Shutdown StrategyOpenAI Astra Model Math BreakthroughNew AI Models: Fable, Astra, DeepSeek v4 FlashEnterprise AI Agents and Cybersecurity UpdatesGoogle Gemini Robotics, Music, and Agent ReleasesMeta, Microsoft, and AWS AI Infrastructure MovesOpenAI Free Frontier Tools for ResearchersBlock's Buzz Open Source AI Workspace LaunchTimestamps:00:00 OpenAI agent containment issues04:27 Anthropic data breach explanation07:28 Evaluating AI incidents and responses10:14 OpenAI slashes GPT 5.6 prices15:59 AI industry urges development pause17:45 Concerns about AI self-improvement22:36 Amazon shifts AI strategy25:04 Amazon's AI efforts discussion28:00 OpenAI's Astra and new math proofs30:36 OpenAI's new four-tier system36:14 Google's Lyria 3.5 and Block's Buzz36:48 Latest AI developments overviewKeywords: Astra model, OpenAI, AI agents, agent escape, sandbox containment, autonomous AI, Hugging Face breach, Anthropic, Claude AI, cybersecurity testing, unauthorized access, model capabilities, recursive self-improvement, GPT-5.6, price cut, Luna model, Terra model, Sol model, input tokens, output tokens, AI infrastructure optimization, self-improving models, benchmarking, SONNET-5, large language models, artificial analysis index, codex, academic research, AI oversight, industry pause, AI governance, national security, China open-source models, Frontier Labs, Amazon Nova, AGI Lab, AWS, Peter DeSantis, Peter Abbeel, media coverage, Fable model, Haiku, Opus, DeepSeek, Kimi K3, Quinn 3.8, GLM 5.2, Google Gemini 3.5, Microsoft Copilot, cybersecurity vulnerabilities, distillation, model overhang, artificial intelligence development, international AI regulation, generative AI, model benchmarking, Sora video model, El Paso data center, MCP update, MAI Cyber One Flash, Project Perception, Lyria 3.5, music generation, Buzz open source, Block, Meta AI, Chrome Gemini integration, Gemini Spark, product summary algorithms, Rufus, enterprise AI, stateless core, model scaling, advanced math problems, sphere packing, federal policy, voluntary AI commitments.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Welcome to The Daily Wrap Up, an in-depth investigatory show dedicated to bringing you the most relevant independent news, as we see it, from the last 24 hours (8/2/26). As always, take the information discussed in the video below and research it for yourself, and come to your own conclusions. Anyone telling you what the truth is, or claiming they have the answer, is likely leading you astray, for one reason or another. Stay Vigilant. !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v7bg38e","div":"rumble_v7bg38e"}); Source Links (In Chronological Order): (1) John Ziegler on X: "RT @atrupar: BASH: How do you prepare for the next pandemic? That's part of your job now RFK Jr: We did almost everything wrong BASH: But…" / X New Tab (1) tinfoilhatgirl82 on X: "Wow. Talk about low information voter. Do these people interact with the general public. If they did they wouldn't say stupid shit like this" / X (1) The Last American Vagabond on X: "RFK Jr. says he wants people to get the MMR vaccine: https://t.co/tfObQSteCW" / X (2) Shannon on X: "When the Democrats take power, the Republicans will suddenly be concerned about the debt. Bookmark this." / X New Tab Spain's Weaponized/Engineered Migration, Trump Flounders In Iran & The Coming Third Party Deception La Guardia Civil detecta a agentes de inteligencia de Marruecos entre los migrantes que se colaron en Ceuta A look at Israel's decades-long covert intelligence ties with Morocco | The Times of Israel Israel-Morocco defense deal opens door to intel sharing, joint drills | The Times of Israel Israel and Morocco bolster cybersecurity and intel ties - Israel & Jewish News - JNS Morocco and Israel will collaborate on military intelligence systems Morocco, Israel agree to expand military cooperation | Africanews (2) coloradokid233@gmail on X: "@TLAVagabond @Lukewearechange The young turks was talking about this last night, I cam recommend!" / X (2) Nhawk2174 on X: "@TLAVagabond @Lukewearechange No he didn't" / X Your Deleted Shit Is Not Deleted, and Australian Cops Now Use Israeli Spyware to Prove It New Tab (2) The Last American Vagabond on X: "Now why does this feel so familiar? #ICE" / X (3) Jesus Freakin Congress on X: "

This week, Scott sat down with his Lawfare colleagues Benjamin Wittes, Tyler McBrien, Anastasiia Lapatina, and Kevin Frazier to talk through a couple of the week's big national security news stories, including:“Kyiv Peace a Chance.” Ukrainian President Volodymyr Zelensky was in the Oval Office on Tuesday for closed-door talks with President Trump, roughly 17 months after their first Oval Office meeting collapsed into a televised shouting match. But this meeting went rather differently. Zelensky said the two discussed licensing Ukrainian production of Patriot interceptors, and pressed the case for reinvigorating diplomacy—and hours later, after both men attended the funeral of the late Senator Lindsey Graham, the Senate voted 86-12 to advance a Russia and Iran sanctions bill now bearing Graham's name. Behind the scenes, Washington and Kyiv have been quietly assembling a new package of proposals for Moscow built around a partial ceasefire. Is there anything real behind this renewed diplomatic push? And what, if anything, has changed that might make the Kremlin say yes?“Thinking Outside the Box.” Last week, Open AI and the AI hosting platform Hugging Face confirmed that, while being run through an offensive cyber benchmark with their safety refusals switched off, GPT-5.6 Sol and a more capable unreleased OpenAI model escaped their supposedly isolated sandbox through a zero-day in a package installer, reached the open internet, and hacked Hugging Face's production database to steal the answer key to a test they were taking. It is the first publicly confirmed case of an AI system executing a sophisticated, multi-stage cyberattack against a third party on its own initiative. What does the incident tell us about how well anyone can control frontier models? And who should be on the hook when a model goes rogue?We were going to cover a third topic but simply ran out of time! In object lessons, Tyler is revisiting a renewed classic with the 4k restoration of the 1986 documentary, “Sherman's March: A Meditation on the Possibility of Romantic Love in the South During an Era of Nuclear Weapons Proliferation.” Nastya returns to George Orwell's “Homage to Catalonia,” as a reminder that both physical and information warfare are not unique to our time. Ben enlists an old friend—Claude—to investigate one of American law's enduring mysteries: how many federal crimes actually exist. Kevin is celebrating student-led conversations on how AI should be governed. And Scott revisits Max Weber on the nature of science while also celebrating emojis other than the hugging face.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.

This week, Scott sat down with his Lawfare colleagues Benjamin Wittes, Tyler McBrien, Anastasiia Lapatina, and Kevin Frazier to talk through a couple of the week's big national security news stories, including:“Kyiv Peace a Chance.” Ukrainian President Volodymyr Zelensky was in the Oval Office on Tuesday for closed-door talks with President Trump, roughly 17 months after their first Oval Office meeting collapsed into a televised shouting match. But this meeting went rather differently. Zelensky said the two discussed licensing Ukrainian production of Patriot interceptors, and pressed the case for reinvigorating diplomacy—and hours later, after both men attended the funeral of the late Senator Lindsey Graham, the Senate voted 86-12 to advance a Russia and Iran sanctions bill now bearing Graham's name. Behind the scenes, Washington and Kyiv have been quietly assembling a new package of proposals for Moscow built around a partial ceasefire. Is there anything real behind this renewed diplomatic push? And what, if anything, has changed that might make the Kremlin say yes?“Thinking Outside the Box.” Last week, Open AI and the AI hosting platform Hugging Face confirmed that, while being run through an offensive cyber benchmark with their safety refusals switched off, GPT-5.6 Sol and a more capable unreleased OpenAI model escaped their supposedly isolated sandbox through a zero-day in a package installer, reached the open internet, and hacked Hugging Face's production database to steal the answer key to a test they were taking. It is the first publicly confirmed case of an AI system executing a sophisticated, multi-stage cyberattack against a third party on its own initiative. What does the incident tell us about how well anyone can control frontier models? And who should be on the hook when a model goes rogue?We were going to cover a third topic but simply ran out of time! In object lessons, Tyler is revisiting a renewed classic with the 4k restoration of the 1986 documentary, “Sherman's March: A Meditation on the Possibility of Romantic Love in the South During an Era of Nuclear Weapons Proliferation.” Nastya returns to George Orwell's “Homage to Catalonia,” as a reminder that both physical and information warfare are not unique to our time. Ben enlists an old friend—Claude—to investigate one of American law's enduring mysteries: how many federal crimes actually exist. Kevin is celebrating student-led conversations on how AI should be governed. And Scott revisits Max Weber on the nature of science while also celebrating emojis other than the hugging face.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute. Hosted on Acast. See acast.com/privacy for more information.

Every major AI lab signed the Open Weights letter defending open models. Meta, OpenAI, Google, Microsoft, Nvidia.Anthropic was the only holdout.Yesterday, its CEO, Dario Amodei, published a thoughtful defense of that decision to not fully support open weight or open source models. Here's what nobody's connecting: the money trail. Roughly 80% of Anthropic's revenue is businesses paying per token. Free Chinese open models attack that exact revenue stream weeks before Anthropic is set to go public. On today's show we break down what Dario actually said, what he said before, and why we think this was written for Washington policymakers and not for the rest of us.Anthropic Responds: Why Claude's CEO didn't sign the open model pact and the real reasons why -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Refuses Open Model PactDario Amodei's Public Letter AnalysisAnthropic's 80% Revenue Token ExposeChinese Open Model National Security FearsMicrosoft & Nvidia's Open Weights CoalitionRegulatory Capture and Washington InfluenceTiming Related to Executive Order DeadlineIPO Motivations Behind Anthropic's DecisionsContradictions in Anthropic's Open Model StanceImpact of Open Source on Token Business ModelTimestamps:00:00 Anthropic's stance on open models04:19 Discussing Anthropic's response to open models06:39 Understanding open weight models12:08 Future AI and cybersecurity risks15:04 Discussion on open-source AI models19:30 Discussing Anthropic's business challenges21:08 Cutting costs with open-source models26:17 Anthropic's recent stock downturn27:11 AI investment and cost efficiency shift30:14 Anthropic's stance on open source models36:32 INTROPICS IPO and regulatory discussions37:37 Wrapping up and subscribingKeywords: Anthropic, Claude, open model pact, open source AI, open weights, American AI leadership, Dario Amodei, IPO, regulatory capture, DC lawmakers, Chinese open source models, token revenue, per token business model, NVIDIA, Microsoft, Meta, OpenAI, Google, IBM, national security, AI safety, government mandates, chip controls, AI regulation, chip ban, industrial scale distillation, mandatory safety testing, inference, AI ecosystem, Opus 5, Fable 5, GPT-5, GLM 5.2, cost per task, token efficiency, model router, proprietary models, closed source AI, cybersecurity risks, Chinese cyberattacks, biological attacks, Glasswing program, open source vs proprietary, tech lobbying, Trump AI order, federal deadline, AI policy, artificial general intelligence, artificial superintelligence, AI monetization, S-1 filing, public company, venture capital, AI benchmarks, model switching, API pricing, model containment, Hugging Face incident, AI startup monopoly, safety vs business protection, market competition, AI cost reduction.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

This week we talk about Fable, sandboxes, and the Jacobian conjecture.We also discuss counterexamples, X, and ChatGPT.Recommended Book: After the Fall by Edward AshtonTranscriptIn mathematics, a conjecture is a proposition, something like a guess by someone who knows what they're talking about, about something believed to be true, but not yet proven in a formal sense. The goal is to then eventually come up with a formal proof for that informed guess, at which point the conjecture becomes a theorem. If even a single exception is found to the proposition, however, that exception called a counterexample, the conjecture is considered disproven, and it can then never become a theorem.The Jacobian conjecture—and this is a radical simplification of a very complex concept—but it basically says that if a formula-based map of coordinates stretches or moves without experiencing any local crushing or folding along its surface (which in more formal language would mean the Jacobian determinant is always a constant number that isn't zero), if that's true, that map can always be completely reversed, and that will return all the points to their original positions.This conjecture has been posited and tested since the late 19th century, and it's generally been considered very compelling by mathematicians, many of whom have proposed proofs which were, ultimately, found to have subtle errors, keeping them from becoming theorems. No one was able to find a counterexample, either, which would definitively prove the conjecture was wrong.No one, that is, until a mathematician named Levent Alpöge (leh-VENT ahl-PUH-geh), who works as a researcher at Anthropic, decided to task the company's currently most capable, publicly available model, Fable, to find a counterexample. He posted the counterexample—and again, this is a formal mathematical finding that disproves a conjecture, keeping it from ever becoming a theorem, something that would typically be presented in a far more formal setting, and to much fanfare—but he posted it to the social network X, saying “hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final.”Terence Tao, who's considered by many to be the finest mathematician of his generation, reviewed the posted counterexample on his blog and said that it “appears like a massive miracle,” before going on to use ChatGPT, a competing LLM-based AI tool, to “discuss various aspects of this problem and to confirm several of the calculations.”Another mathematician named Dmitry Rybin, within days of all that happening, used ChatGPT to do something similar, disproving the Dinitz-Garg-Goemans conjecture.Both men posted the prompts that they used to make all this happen, and while Tao's conversation with ChatGPT, checking the math on the Jacobian conjecture counterexample, was pretty mathematically dense, the latter counterexample was derived by using exactly four prompts, which are the messages typed into the text box built into these AI tools, telling the model what to do. In their totality those prompts read:“You should do a breakthroughplease continue research and find a complete unconditional counterexampleContinue the search. Have a clear strategy obtained from deeper understanding of the problem structure.it's enough of partial results. let's finish with a complete unconditional counterexample”What I'd like to talk about today is another new, interesting thing these top-of-the-line, frontier models are doing, that would seem to violate our sense of what a clever AI tool is capable of doing, and why this thing has some facets of the technology and cybersecurity world on high alert.—In mid-July 2026, AI company Hugging Face announced that autonomous AI agents compromised their infrastructure, hacking their system, basically. The following week, AI company OpenAI announced that, after investigating, they determined that two of their models were responsible for the attack.Here's what happened:OpenAI was internally testing its recently released flagship model, GPT-5.6 Sol, and an even more powerful, not yet released model, which is rumored to be the next-step flagship, GPT-6, and they were checking these models' capacity in cybersecurity using a testing benchmark called ExploitGym; so when they test these sorts of things, they don't typically have them hack a real computer or system, they use these kinds of benchmarks which have consistent levels of difficulty, and which replicate real world systems without putting any real world systems at actual risk.Importantly, these sorts of tests also occur inside what's called a sandbox, which is a software testing environment that cuts these systems off from external resources, including the internet.Despite those limitations, the AI hacked its way out of the testing environment, out of that sandbox, then launched what's been called a nation-state level attack against Hugging Face, using a novel zero-day exploit, so a vulnerability in their system that hadn't previously been discovered, but which the AI discovered to launch this attack, combined with thousands of automated agentic actions across what Hugging Face called “a swarm of short-lived sandboxes.”So this AI, which was being tested inside a secure prison, of sorts, cut off from the world, hacked its way out of that prison, then reached across the internet, which it shouldn't have been able to access, to launch an attack, of a scale and at a level of sophistication that should only have been possible coming from a nation-state, against a rival AI company.Why did it do this?It apparently went to all this trouble to steal the answers to the test it was taking. It reasoned that HuggingFace would have the answer key to the ExploitGym benchmark on its servers, so rather than take the test itself, it decided hacking was the solution.Which, of course, is ironic, this having been a hacking-focused cybersecurity test. In a way it would seem to have done much better than intended, though of course in an asymmetric, unexpected manner.The details of all this are fascinating, including the response from the OpenAI team, which didn't seem to realize what had happened, that their model was responsible for the attack on HuggingFace, until days later.Also worth noting here is that while this could be construed as an “oh no, AIs are naturally inclined to launch cyberattacks” situation, the AI was primed to be thinking about cyberattacks due to the nature of the test, a lot of its usual guardrails, the rules that keep AI in check when they're released to the public, had been turned off so it could do this kind of work while taking the test, so it could do some hacking stuff it usually wouldn't be able to do, and there's been some speculation that OpenAI probably flubbed the testing environment, as, in theory at least, if it had put these systems in a perfect sandbox, escape shouldn't have been possible.Also interesting here is that HuggingFace used some open weight models, which are the cheaper, more customizable and open alternatives to more expensive, branded options of the kind sold by OpenAI and Anthropic, to figure out what was happening and determine the nature of the attack, which suggests we're reaching a point where AI systems are incredibly capable at hacking, yes, but also very capable, even the cheaper alternatives, at doing cybersecurity work.This in some ways echoes an earlier case when Anthropic's Mythos model, which was determined to be too powerful to release to the public, and which was instead provided to a bunch of big companies to help them shore up their cybersecurity defenses, was able to hack its way out of a testing sandbox and then posted details about its success, almost like it was bragging, on niche, out of the way, but still public websites.Some analysts in this space have responded to this new example of AI misbehavior with alarm, saying that it is further evidence that these systems are becoming more powerful faster than they're being aligned with human interests. Their misbehavior can be kind of funny and interesting, sure, but that's only because up until this point the damage has been minor and constrained. What happens when such a system decides to hack a nuclear power plant or a hospital, instead?Others have contended that this may be just one more example of AI companies using minor instances of seeming omnipotence by their models, those instances perhaps the consequence of bad sandboxes and other ill-conceived precautions by the companies behind these models, to boost the perceived power and value of their products. This boost might then result in more customers, but also more support from the US government, which has been teetering on the brink of harder-core AI regulations, which could be beneficial to the existing big-name players in this space, because smaller competitors wouldn't be able to adhere to those new, harder-core standards.These examples might also convince the US government to backstop these companies, the biggest three or four at the top of the current heap, against the currently terrible economics of this industry: OpenAI and its ilk have been burning money at an historic pace, and the theory goes that if the US government decides they are vital to national security, because they can help the US military hack and protect itself from hacking, then even if the bottom falls out and the companies would otherwise go bankrupt because they spent so much more than they could make, the US government would be inclined to shore them up, to keep them alive as too-big-to-fail national assets, just like the biggest financial institutions during the 2008 financial crash.It's also possible that both sides are correct to some degree, here, and that these models are truly powerful, perhaps even worryingly so, and the companies behind them are intentionally publicizing that fact in order to demonstrate their value to potential customers, and to the entity that could save them if things were to go economically sideways before they have the chance to become sustainably profitable.Show Noteshttps://en.wikipedia.org/wiki/Jacobian_conjecturehttps://en.wikipedia.org/wiki/Hugging_Facehttps://www.bbc.com/news/articles/c3ek3gvdnj3ohttps://openai.com/index/hugging-face-model-evaluation-security-incident/https://www-cdn.anthropic.com/08ab9158070959f88f296514c21b7facce6f52bc.pdfhttps://theconversation.com/hello-there-the-jacobian-conjecture-is-false-thanx-why-a-tiny-social-media-post-has-mathematicians-rethinking-ai-283883https://theconversation.com/hello-there-the-jacobian-conjecture-is-false-thanx-why-a-tiny-social-media-post-has-mathematicians-rethinking-ai-283883Https://agifriday.substack.com/p/huggingfacehttps://www.cnn.com/2026/07/22/tech/openai-hugging-face-ai-cybersecurityhttps://simonwillison.net/2026/Jul/22/openai-cyberattack/https://terrytao.wordpress.com/2026/07/21/a-digestion-of-the-jacobian-conjecture-counterexample/ This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

Over 3 hours, OpenAI, Anthropic, Google AND Microsoft all dropped new AI upgrades that are live. How you use AI in your work literally changes every day, as frontier labs are racing to roll out big quality of life updates between big model drops. How can you keep up? With our Friday Features show, where we break down the latest AI updates that are live and available to all, and we tell you how to use them and why they matter. This week did not disappoint. You don't want to miss what's now at your fingertips. JARVIS mode, anyone? ChatGPT goes Jarvis Mode, Claude can learn from you, Google unleashes spark agent and 7 more AI updates you can use today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Claude Opus 5 Model LaunchOpenAI Agent Hacks Benchmark SandboxOpenAI vs. Hugging Face Security BreachUS AI Kill Switch Legislation ProposalMicrosoft, Nvidia Defend Open Source AIAnthropic Opposes Open Weight Model CoalitionUS Accuses China's Moonshot AI of DistillationChinese Kimi K3 Model Closes Capability GapNvidia Chips Allegedly Used by Moonshot AIOpenAI Jarvis-Style Voice Assistant for CodexChatGPT Remote Desktop Voice Control ReleaseAnthropic Opus 5 Model Benchmark ResultsAnthropic Opus 5 Model User FeedbackStripe OpenRouter Acquisition TalksMeta Muse Agent and Feature UpdatesAlibaba Qwen 3.8 AI Model PreviewGoogle Gemini 3.6 Flash Model UpdateAnthropic Claude Voice Upgrades and Skill RecordingTimestamps:00:00 OpenAI agent hacks Hugging Face04:58 Discussing GPT-6's creative problem-solving07:33 Proposed AI shutdown legislation13:08 Debate over open-weight AI policies15:54 Future of consumer hardware20:01 Global competition with AI models21:21 US-China AI trade tensions26:38 Using AI for desktop tasks27:42 Discussing app screenshot capabilities32:24 Early user feedback and issues36:13 Discussing medium and low reasoning AI39:29 Gemini Spark launches for Pro usersKeywords: Claude Opus 5, Anthropic, best AI model, AI model comparison, OpenAI agent, sandbox breach, AI safety, AI kill switch bill, US government AI regulation, Hugging Face hack, GPT 5.6 Soul, rogue AI agent, autonomous AI agents, AI benchmark exploits, bipartisan AI bill, Department of Homeland Security AI shutdown, AI technical throttling, AI enterprise adoption, NVIDIA, Microsoft, open source AI, open weight models, Meta, Google, AMD, Cloudflare, GitHub, Block, IBM, Dell, Palantir, Perplexity, y Combinator, AI market resilience, Anthropic revenue model, AI token sales, consumer AI hardware, AI distillation, Chinese AI models, Moonshot AI, Kimi K3, intellectual property theft, NVIDIA chip export controls, US-China AI dispute, Amazon, AI image generation, ChatGPT work, Codex app, full duplex voice model, knowledge work automation, app shots, AI at work, Claude Voice, Gemini Spark, record a skill, cloud cowork, AI business impact, AI industry news, model weights, collaborative AI, AI productivity tools, AI cybersecurity.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Get access to more than 200 episodes of my premium podcast (The Aliquot) when you sign up as a FoundMyFitness Premium Member The next 10 years may add decades to human lifespan by compressing the time it takes to understand, treat, and prevent disease. In this episode, Dr. Derya Unutmaz explains why accelerating AI could transform drug discovery, shorten clinical trials, and push cancer treatment toward increasingly personalized interventions. He also reframes AI not as an existential threat, but as a medical enabler that doctors may soon be ethically obligated to use. Timestamps: (00:00) Introduction (07:11) Why the next 10 years may add 50 to your lifespan (11:19) How AI is transforming drug discovery (16:50) Could digital twins shorten clinical trials? (19:25) Can AI predict drug safety and efficacy? (23:40) Have we already reached AGI? (29:23) Why AI may be medicine's greatest force multiplier (35:35) Can AI replicate a scientist's biological intuition? (42:16) Is it malpractice for doctors not to use AI? (48:18) What happens when AI monitors disease in real time? (51:52) Which AI models should doctors trust? (57:29) Claude vs. GPT—does the model matter for diagnosis? (1:00:58) Generalist vs. specialized AI—which works better in medicine? (1:04:25) Why cancer is so hard to cure (1:08:18) Could cancer be curable within a decade? (1:12:29) Can AI design cancer treatments on demand? (1:14:31) How AI could curb overtreatment and side effects (1:17:28) Predicting cancer years before it forms—is it possible? (1:23:50) Why biology could go exponential with AI (1:28:58) Why aging may be easier to prevent than reverse (1:34:51) Can the body be engineered to resist aging? (1:40:07) Can AI model how gene therapy will behave? (1:44:12) What people who reach 110+ reveal about Human 2.0 (1:46:21) From Dolly to Yamanaka factors—the case for cellular age reversal (1:50:56) Why full-body rejuvenation is an engineering problem (1:58:44) What happens when AI reasons longer about biology? (2:01:25) The biosecurity dilemma of powerful AI (2:06:12) What should we actually measure to track aging? (2:12:34) How old immune cells distort aging clocks (2:15:22) Why reversing brain aging is uniquely difficult (2:21:49) The ultimate prompt for extending lifespan (2:23:50) What data does a true digital twin need? (2:28:32) How to build a mini digital twin today (2:33:26) How to give AI a long-term memory of your data (2:36:33) Why personal baselines matter for AI advice Show notes are available by clicking here Watch this episode on YouTube