Podcasts about polling

Episode 5633: Cain Says No Troops At Polling Stations; Tax Incentives To Hire Immigrants

This episode is sponsored by Revelation Gold Group -- President Donald Trump says if he were a mayor or governor he'd want data centers to be built in his city or state because they create jobs, lower taxes on residents, and the new ones will generate their own power that goes back into the grid. He then posted on Truth Social "The only reason that communities should not want data centers is if they want to end up being backwards and poor." Outrage ensued.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-pete-kaliner-show--6946691/support.Subscribe to the podcast My preferred podcast platform: SpreakerCheck out my preferred gold & silver company: Revelation Gold GroupAll the links to Pete's Prep are free! Plus get exclusive content here!Media Bias Check: GroundNews promo code!Advertising and Booking inquiries: Pete@ThePeteKalinerShow.com  

Send us Fan MailBB is joined by Dan McGowan for a final-week look at Rhode Island's primary races. They unpack turnout, the Providence mayoral contest, the governor's race, and the down-ballot battles for lieutenant governor and attorney general.In this episodeWhy turnout and early voting may decide the electionThe momentum behind David Morales in the Providence mayoral raceWhether Brett Smiley's incumbent coalition can holdHow Morales has broadened his appeal beyond progressive votersThe Washington Bridge and opioid crisis as central issues in the governor's raceWhy Dan McKee's closing message may matter with undecided votersThe crowded lieutenant governor race and its two apparent front-runnersThe attorney general contest, campaign dynamics, and the importance of endorsementsWhere to subscribe to Dan McGowan's Roadmap newsletterTimestamps00:00 — Final-week election outlook and the importance of turnout01:07 — Why the Providence mayoral race is the most compelling contest02:09 — Early voting, new voters, and the Morales coalition04:34 — Can David Morales expand beyond democratic-socialist support?05:05 — Morales's positive campaign and voter perceptions06:44 — How a Morales administration could work with either governor09:36 — Why Brett Smiley could still narrowly win10:59 — The governor's race: Washington Bridge vs. opioid crisis11:41 — Why the bridge issue may be dominating voter sentiment12:52 — Could McKee have made a stronger working-families case?14:37 — McKee's “grandfatherly” appeal and undecided voters16:20 — Polling, closing ads, and the race for late deciders18:04 — Lieutenant governor: who has a real path to victory?20:18 — Why campaigning for lieutenant governor is uniquely difficult21:25 — Attorney general race: the leading candidates and challengers22:37 — Money, institutional support, and the East Side dynamic24:44 — Candidate temperament and what voters may value in an attorney general25:53 — The potential impact of Peter Neronha's endorsement27:00 — How to subscribe to Roadmap and final election-week thoughts Support the showFollow Bill on Instagram and YouTube

From there he runs the board on eight Senate races where Democrats are polling ahead: Mary Peltola in Alaska, Jon Ossoff defending Georgia against Mike Collins, Josh Turek in Iowa, Troy Jackson taking on Susan Collins in Maine after Graham Platner exited the race, Abdul El-Sayed in Michigan against Mike Rogers, Roy Cooper with a commanding lead in North Carolina, Sherrod Brown mounting a comeback in Ohio, and James Talarico leading Ken Paxton in Texas. Hawk spends extra time on Talarico, whose polling shows unusually strong support among Black voters in Texas compared with past Democratic nominees, and on Paxton, whose fundraising has cratered to the point that Donald Trump is planning a rally for him. He also touches on the Jasmine Crockett dynamic in that race. SUPPORT & CONNECT WITH HAWK- Support on Patreon: https://www.patreon.com/mdg650hawk - Hawk's Merch Store: https://hawkmerchstore.com - Connect on TikTok: https://www.tiktok.com/@mdg650hawk7thacct - Connect on TikTok: https://www.tiktok.com/@hawkeyewhackamole - Connect on BlueSky: https://bsky.app/profile/mdg650hawk.bsky.social - Connect on Substack: https://mdg650hawk.substack.com - Connect on Facebook: https://www.facebook.com/hawkpodcasts - Connect on Instagram: https://www.instagram.com/mdg650hawk - Connect on Twitch: https://www.twitch.tv/mdg650hawk ALL HAWK PODCASTS INFO- Additional Content Available Here: https://www.hawkpodcasts.comhttps://www.youtube.com/@hawkpodcasts- Listen to Hawk Podcasts On Your Favorite Platform:Spotify: https://spoti.fi/3RWeJfyApple Podcasts: https://apple.co/422GDuLYouTube: https://youtube.com/@hawkpodcastsiHeartRadio: https://ihr.fm/47vVBdPPandora: https://bit.ly/48COaTB

Today's DMN Debrief covers a proposal to add polling locations in Tarrant County, a D-FW restaurant veteran making a comeback, and a camp wrecked by the Ross fire. Plus, staffer Joe Snell has an interview with a researcher studying how birds are building nests with plastic trash. This digest was partially generated by AI and then reviewed and edited by our newsroom staff. Learn more: dallasnews.com/ai_use. We welcome your feedback: ⁠audience@dallasnews.com⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

In a time where blackpilling is rewarded, Auron MacIntyre discusses the impressive set of wins the Trump Administration is quietly racking up on immigration, DEI, housing affordability, and more. Plus, Hasan Piker is very extreme, but will Piker be a drag on the Democrats, or is he the future of the left? Rich Baris has exclusive polling on the matter. TPUSA's Andrew Sypher unveils that latest Pick Up the Mic campus tour featuring both big stars and rising conservative talent. Watch every episode ad-free on members.charliekirk.com! Get new merch at charliekirkstore.com!Support the show: http://www.charliekirk.com/supportSee omnystudio.com/listener for privacy information.

On this afternoon edition of The Rush Hour, Donald Trump is reportedly asking Russia for help reopening the Strait of Hormuz as the escalating crisis threatens global oil supplies. We break down what this extraordinary request means for the war, gas prices, and America's standing on the world stage. Plus, we remember legendary actor Tim Curry, who has passed away at the age of 80, and unpack the coordinated smears targeting progressive political commentator Hasan Piker.

David Waldman and Greg Dworkin join the world in celebrating my birthday. When buying me gifts, remember to type the word "KAGRO", because… it's worth a try. A much, much larger part of that world mourns the loss of Dolly Parton. And for good reason, Dolly had a lot going for her. Mourners go across the political spectrum, but it's pretty certain that she didn't. Donald ordered flags at half-staff, as every woman in his orbit for the last 40 years has paid to look like Parton, and as Dolly would tell you, it costs a lot of money to look that cheap. And now Tim Curry too! Many voters feel that there's a "uni-party" and that their vote does not matter. Supporters of Hakeem Jeffries say, "You ignorant morons don't understand the realities of politics and can vote Republican if you just want to be dummies." Maybe we are under the reign of Great Britain after all. James Talarico is ahead of Ken Paxton, according to polls. According to money, too. Jar Head Salsa tycoon/Jan 6 traitor/patriot Tom Smith finds himself nominated for his Michigan House district. Polling shows Dan Sullivan leading Dan Sullivan in Alaska. The Donald K. Trump midterm political convention will be about Trump, as is Lake Ontario, as is the John F. Kennedy Center. Natalie Harp is all about Trump. Pete Hegseth is eliminating every General not qualified to be a Fox weekend host.

Polling shows a dead-heat 48-48 matchup, making today's South Carolina Senate primary runoff a battle of pure turnout!

Polling shows a 48-48 dead heat with 4% undecided in the South Carolina primary runoff election!

Polling shows a dead-heat 48-48 matchup, making today's South Carolina Senate race a pure turnout battle!

Polling shows a dead-heat 48-48 matchup, making today's South Carolina Senate race a pure turnout battle!

The news of Texas covered today includes:Our Lone Star story of the day: New statewide polling is out from UT/Texas Politics Project showing Republicans leading in all but the U.S. Senate race in August. U.S. House Speaker Johnson is in Texas this week stumping for Hispanic Republicans in key Congressional seats.Our Lone Star story of the day is sponsored by Allied Compliance Services providing the best service in DOT, business and personal drug and alcohol testing since 1995.Wildfire updates: Ross Fire explodes to 50,000 acres in Palo Pinto County Governor Abbott Announces Federal Assistance For Ross Fire Mandatory evacuations remain in effect as crews battle Rio Escondido Fire in Hamilton County – updates No damage to homes or injuries reported as Neighbor Fire clean up continues Texas Appeals Court Holds District Courts Cannot Order New Appraisal Review Board Hearings.Six Flags move corporate HQ back to Texas leaving North Carolina.Supreme Court allows Trump administration to move forward with order imposing restrictions on mail-in voting | SCOTUSblog.Abbott fights Minnesota lawsuit on ICE officer extradition. “Abbott said state officials are still investigating whether Christian Castro, who is being held in Cameron County Jail, can be classified as a fugitive because he didn't flee Minnesota but was apparently reassigned to Texas by Immigration and Customs Enforcement.”Listen on the radio, or station stream, at 5pm Central. Click for our radio and streaming affiliates.www.PrattonTexas.com

As whispers of the next federal election swirl, one name keeps coming up: Pauline Hanson. Hanson and her party are rapidly rising in popularity. Polling over the last few weeks suggests that the next election might come down to a race between Labor versus One Nation. So – who is Pauline Hanson? And how did we get here? In this two-part series, we talk about the rise, the fall, and resurrection of Pauline Hanson. Her days as a fish and chip shop owner, the letter that got her sacked from the Liberal Party, the criminal trial that put her behind bars, her Dancing with the Stars redemption arc, and the messy fights and fallouts that have plagued One Nation. See omnystudio.com/listener for privacy information.

August 24, 2026 – 5am: The Economist/YouGov: Trump's net approval rating is negative in 47 out of 50 states Rancher, Republicans criticize Trump's beef import plan Trump downplays Iran war, touts economy at South Carolina rally on Friday NYT: Minority Leader Jeffries and Jared Kushner met privately in NYC Pentagon fires ‘Stars & Stripes' leadership, reporter Navy families evacuated from Bahrain unable to return: Stars & Stripes ‘Freedom 250' Grand Prix races through DC Treasury Secretary Bessent to announce new economic sanctions against Iran How media is fighting back against Trump's legal threats: The Financial Times U.S.-Canada trade talks collapse Supreme Court allows Trump's ballroom work to continue To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dear listener, I must ask you to take a seat. I fear you are not prepared for what I'm going to tell you. Jonah Goldberg appears on the show today… with… written notes. Granted, they contain about five words. But they exist! On paper! Marvel of marvels! And so it is with this boy-scout level of preparedness that Jonah embarks on his weekend ruminations, covering Jon Ossoff, Natalie Harp, savvy political baiting, Jason Arday, Jews and liberalism, race, social justice, group performance, Jacobin tariff rhetoric, data centers, and the Dupont Circle farmers market. Show Notes: —Jesse Singal in The Dispatch: “The Cascade of Dysfunction That Helped Doom Jason Arday” —Jonah on the Call Me Back podcast —Cliff Asness in Commentary —Jonah's book: Liberal Fascism  —Polling on data centers —Wednesday G-File —The Next Right: “Brian Kemp Doesn't Apologize for Free Markets” The Remnant is a production of ⁠The Dispatch⁠, a digital media company covering politics, policy, and culture from a nonpartisan perspective. To access all of The Dispatch's offerings—including the Saturday Ruminant, audio versions of all our articles and newsletters, and Jonah's twice-weekly G-File—⁠click here⁠. Instructions on how to set up your members-only feed can be found here, and if you'd like to remove all ads from your podcast experience, consider becoming a premium Dispatch member ⁠by clicking here⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Friday, August 21, 2026; 8pm: Tonight, Republicans fear Trump may “drag them down hard” in the midterms. Plus, Florida Senate nominee Angie Nixon on how a progressive can win in a ruby-red state. Then, America more polarized than it's been in 160 years. But could there actually be a “national divorce?” Want more of Chris? Download and follow his podcast, “Why Is This Happening? The Chris Hayes podcast” wherever you get your podcasts.To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Last Call, a look at the biggest stories Jim and Greg covered over the past week on the 3 Martini Lunch.This week, they discuss a socialist state lawmaker shocking the Democrats by winning the Florida U.S. Senate primary, a company admitting it completely fabricated recent polling results, the U.S. national debt reaching $40 trillion, and a majority of Dems, independents, and Republicans favoring price controls.First, they discuss the stunning victory of socialist Angie Nixon in the Florida Democratic U.S. Senate primary over the heavily-favored establishment pick, Alexander Vindman. Jim and Greg also discuss how this result may impact the governor's race, and they round up a number of encouraging results in GOP congressional primaries.Next, they dig into "Median Strategies" announcing it is done polling, fabricated all of their results, and the whole operation was a social experiment to see how much fake polling could get reported without anyone bothering to verify the results were legitimate.Then, they wince as the U.S. national debt surges past $40 trillion. They fume at both parties for refusing to deal with this problem long before it got this bad. They also blast Democrats and some Republicans who blocked the effort more than 20 years ago to allow Americans to invest some of the money they pay in Social Security taxes. The payoff now would have been huge.Finally, they groan as a new poll shows a majority of Democrats, independents, and even Republicans all favor price controls. Charlie argues that many politicians are flat out lying about our current economic conditions and are convincing too many people of the wrong remedy.Please visit our great sponsors:Brooklyn Beddinghttps://BrooklynBedding.comGet 30% off sitewide Brooklyn Bedding with promo code 3ML. BetterHelphttps://betterhelp.com/3ML See the reviews, see what stands out, and see if BetterHelp is right for you. Noble Goldhttps://NobleGoldInvestments.com/3MLIf you want to see how physical gold and silver could fit into your portfolio, download Noble Gold Investments FREE Wealth Protection Kit.New episodes every weekday. 

We start the show discussing the nature of polling as reliable predictive data, then end it with a shout out to a local antique store/flea market that's taking a hard stance on theft.

Chuck Warren, co-host of Breaking Battlegrounds, heard every Saturday at 9 AM right here on 960 The Patriot, joins Seth in studio for the full hour to talk about his favorite films, the new changes in polling, the legacy of former Vice President Dan Quayle, musician Tom T. Hall's birthday, and much more! They also highlight the issue of candidates refusing to debate and the lack of scrutiny from the press. See omnystudio.com/listener for privacy information.

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

A new AARP poll of Michigan likely voters has the Senate race in a dead heat — Democrat Abdul El-Sayed at 48% and Republican Mike Rogers at 47%. Larry O'Connor sits down with AARP's John Hishta and pollster Bob Ward of Fabrizio Ward to break down the generational split driving it: Rogers up 9 points with voters 50-plus and 13 with seniors, El-Sayed up 14 with voters under 50. With voters over 50 projected to make up at least 59% of Michigan's midterm turnout — and a 15-point motivation gap in their favor — this race may come down to who actually shows up. Visit https://www.AARP.org/votes for more informationBecome a Townhall VIP member with promo code "LARRY": https://townhall.com/subscribeSee omnystudio.com/listener for privacy information.

Donald Trump's approval rating just hit an abysmal low of 33-64 in Reuters polling. That's only one survey, but Trump also just hit a new low of 37-59 in New York Times polling averages, which suggests he's likely in the mid-30s and may be trending toward the Reuters number. This comes as Democrats appear to have flipped a state legislative district in western Pennsylvania that Trump won in 2024 by 18 points. Though that race hasn't been called yet, either way it's an 18-point shift, a very good omen. Meanwhile, a CNN analysis finds Trump badly bleeding Hispanic men, a demographic he carried in 2024, another sign his coalition is imploding. We talked about all of it with Michael Cohen, author of a new piece on GOP extremism for his Substack, Truth and Consequences. We discuss what the Pennsylvania swing portends, why a Democratic Senate majority is now at least possible, why Trump losing Latinos is a huge deal, and what it means that Democrats are voting as if Trump is on the ballot this fall. Looking for More from the DSR Network? Click Here: https://linktr.ee/deepstateradio Learn more about your ad choices. Visit megaphone.fm/adchoices

Howie Kurtz on fake polling firms fabricating local election results, Secretary of Defense Pete Hegseth facing backlash over his wife's presence in classified DOD meetings, and why conservative host Tucker Carlson is falsely rumored to be launching a 2028 presidential bid.   Learn more about your ad choices. Visit podcastchoices.com/adchoices

Donald Trump's approval rating just hit an abysmal low of 33-64 in Reuters polling. That's only one survey, but Trump also just hit a new low of 37-59 in New York Times polling averages, which suggests he's likely in the mid-30s and may be trending toward the Reuters number. This comes as Democrats appear to have flipped a state legislative district in western Pennsylvania that Trump won in 2024 by 18 points. Though that race hasn't been called yet, either way it's an 18-point shift, a very good omen. Meanwhile, a CNN analysis finds Trump badly bleeding Hispanic men, a demographic he carried in 2024, another sign his coalition is imploding. We talked about all of it with Michael Cohen, author of a new piece on GOP extremism for his Substack, Truth and Consequences. We discuss what the Pennsylvania swing portends, why a Democratic Senate majority is now at least possible, why Trump losing Latinos is a huge deal, and what it means that Democrats are voting as if Trump is on the ballot this fall.  Looking for More from the DSR Network? Click Here: https://linktr.ee/deepstateradio Learn more about your ad choices. Visit megaphone.fm/adchoices

Dan talks about how polling can be misleading and flat-out wrong, Attorney Hans Von Spakovsky joins to talk legal issues with Flock cameras | aired on Thursday, August 20th, 2026 on Nashville's Morning News with Dan Mandis Produced by Samuel RutherfordSee omnystudio.com/listener for privacy information.

Disney goes to war with Trump's FCC over ABC, The View, and Jimmy Kimmel, Florida voters choose their nominees to be governor as Ron DeSantis terms out, and a stunning announcement by a supposed polling firm sends shockwaves through the industry – and has a Democrat mayor calling for prosecutions. Reporting from Megan Basham & Cabot Phillips. Plus, we speak with the Managing Director at American Pulse Research & Polling, Dustin Olson. Get the facts first with Morning Wire.- - -Ep. 3046- - -Wake up with new Morning Wire merch: https://bit.ly/4lIubt3- - -Today's Sponsors:Alliance Defending Freedom - Visit https://JoinADF.com/WIRE and equip yourself to stand for truth with confidence and clarity.Balance of Nature - Go to https://BalanceofNature.com today and subscribe to the Whole Health System—or any of their other subscriptions—and get an additional 10% off your subscription with promo code WIRE.Fabletics - Shop now at https://Fabletics.com/wire to get 70- 80% off everything when you sign up as a new VIP.- - -Privacy Policy: https://www.dailywire.com/privacymorning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices

MeidasTouch host Ben Meiselas reports on Fox News losing all control on air as Donald Trump sinks to a new low in the polls as all of his plans are utterly failing. If you're 21 or older, get 40% OFF your first order @IndaCloud with code MEIDAS at https://inda.shop/MEIDAS! #indacloudpod Remember to subscribe to ALL the MeidasTouch Network Podcasts: MeidasTouch: https://www.meidastouch.com/tag/meidastouch-podcast Legal AF: https://www.meidastouch.com/tag/legal-af MissTrial: https://meidasnews.com/tag/miss-trial The PoliticsGirl Podcast: https://www.meidastouch.com/tag/the-politicsgirl-podcast Cult Conversations: The Influence Continuum with Dr. Steve Hassan: https://www.meidastouch.com/tag/the-influence-continuum-with-dr-steven-hassan The Weekend Show: https://www.meidastouch.com/tag/the-weekend-show The Ken Harbaugh Show: https://meidasnews.com/tag/the-ken-harbaugh-show Majority 54: https://www.meidastouch.com/tag/majority-54 On Democracy with FP Wellman: https://www.meidastouch.com/tag/on-democracy-with-fpwellman Uncovered: https://www.meidastouch.com/tag/maga-uncovered Learn more about your ad choices. Visit megaphone.fm/adchoices

Join Jim and Greg for the Wednesday 3 Martini Lunch as they react to a socialist state lawmaker shocking the Democrats by winning the Florida U.S. Senate primary, South Carolina Sen. Darline Graham admitting she is not well-versed at all on national security, a company admitted it completely fabricated recent polling results, and two Dan Sullivans appearing likely to make the November ballot for U.S. Senate in Alaska.First, they discuss the stunning victory of socialist Angie Nixon in the Florida Democratic U.S. Senate primary over the heavily-favored establishment pick, Alexander Vindman. Jim and Greg also discuss how this result may impact the governor's race, and they round up a number of encouraging results in GOP congressional primaries.Next, they head to the South Carolina GOP U.S. Senate debate as Sen. Darline Graham whiffs on a question about Taiwan and admits, "National security is not my thing." Jim explains why this answer ought to be considered disqualifying, especially considering the challenges we face from China.Then, they dig into "Median Strategies" announcing it is done polling, fabricated all of their results, and the whole operation was a social experiment to see how much fake polling could get reported without anyone bothering to verify the results were legitimate.Finally, they groan over Alaska's ridiculous system, where four candidates advance from Tuesday's primary to the general election. This year the four U.S. Senate candidates will include two named Dan Sullivan, one of whom is accused of running just to sow confusion for the current Sen. Sullivan. The eventual winner will be decided through the execrable ranked choice voting system. Please visit our great sponsors:Brooklyn Beddinghttps://BrooklynBedding.comGet 30% off sitewide Brooklyn Bedding with promo code 3ML. BetterHelphttps://betterhelp.com/3ML See the reviews, see what stands out, and see if BetterHelp is right for you. Noble Goldhttps://NobleGoldInvestments.com/3MLIf you want to see how physical gold and silver could fit into your portfolio, download Noble Gold Investments FREE Wealth Protection Kit.New episodes every weekday. 

It's Wednesday, August 19th, 2026, and today we're going **Polling for Normal People** at The Scott Adams School.

Send us Fan MailConnor Burbridge discusses his campaign for U.S. Senate against incumbent Jack Reed, focusing on issues, grassroots strategy, and vision for Rhode Island's future**This episode dives into Connor Burbridge's grassroots campaign, emphasizing bold ideas to challenge the status quo and invigorate Rhode Island politics. If you're interested in innovative policies, campaign strategies, and how a small state can punch above its weight, this is a must-listen.Key Topics:The current state of Burbridge's campaign, including poll numbers, fundraising, and ground game strategiesThe importance of debates and candidate transparency, and Burbridge's efforts to challenge Reed for an open discussionPolicy contrasts: healthcare, elder care, energy crisis, public education, and economic developmentCritique of Reed's fundraising reliance on corporate and defense sector money vs. Burbridge's grassroots approachThe future of Rhode Island's defense sector: defense contracts, offshore wind, and transitioning to green manufacturingThe shift in public sentiment toward government and private sector, and how to rebuild trust through effective public investmentThe importance of early groundwork and relationship-building with industry while in oppositionTimestamps:00:00 - Introduction to Connor Burbridge's campaign against Jack Reed00:25 - Campaign momentum and voter support in Rhode Island00:51 - Fundraising challenges and grassroots strategy01:36 - Ground game significance and voter engagement03:07 - Reactions from Reed's campaign and debate attempts04:20 - Why debate transparency matters for voters05:01 - Polling insights: internal vs. validated polls06:37 - Campaign growth and policy focus areas07:32 - Key policies: elder care, energy affordability, and education09:00 - The role of big money vs. grassroots in campaigns10:12 - Voter responses to national issues like energy nationalization11:14 - The public's desire for ambitious, big-picture proposals12:20 - Shift in public trust from private to public sectors13:00 - Challenges of government efficiency and rebuilding trust14:07 - The importance of vision in rebuilding government capacity16:00 - What the primary shape of the Democratic Party looks like16:48 - Defense sector economic impact and future growth opportunities20:09 - Transitioning from defense dependence to green energy and offshore wind21:26 - The influence of political change on policy priorities22:11 - Building relationships with industry during opposition23:15 - Supporting grassroots candidates and re-engaging voters Support the showFollow Bill on Instagram and YouTube

Four stories. Former State Department official Matt Bartlett joined Jonathan Lemire on Morning Joe and was candid about Republican midterm prospects and the absence of a message beyond one bill. Charlamagne tha God gave Hakeem Jeffries a Donkey of the Day over his answer on Medicare for All, a bill Jeffries cosponsored from 2013 onward before becoming leader. Hawk connects that to why DSA backed candidates keep winning primaries: they campaign on what they are for. Andy Beshear, twice elected in a red state, hit the economy, the Iran war, and corruption, then called for big ideas. Also: Palm Beach County Clerk of Courts Mike Caruso has been arrested and charged. SUPPORT & CONNECT WITH HAWK- Support on Patreon: https://www.patreon.com/mdg650hawk - Hawk's Merch Store: https://hawkmerchstore.com - Connect on TikTok: https://www.tiktok.com/@mdg650hawk7thacct - Connect on TikTok: https://www.tiktok.com/@hawkeyewhackamole - Connect on BlueSky: https://bsky.app/profile/mdg650hawk.bsky.social - Connect on Substack: https://mdg650hawk.substack.com - Connect on Facebook: https://www.facebook.com/hawkpodcasts - Connect on Instagram: https://www.instagram.com/mdg650hawk - Connect on Twitch: https://www.twitch.tv/mdg650hawk ALL HAWK PODCASTS INFO- Additional Content Available Here: https://www.hawkpodcasts.comhttps://www.youtube.com/@hawkpodcasts- Listen to Hawk Podcasts On Your Favorite Platform:Spotify: https://spoti.fi/3RWeJfyApple Podcasts: https://apple.co/422GDuLYouTube: https://youtube.com/@hawkpodcastsiHeartRadio: https://ihr.fm/47vVBdPPandora: https://bit.ly/48COaTB

CannCon flies solo again on the now permanent Tuesday format and opens with a genuinely wild polling scandal, where a brand new firm openly admits to running what it calls a social experiment after getting caught pushing wildly inflated numbers for a socialist candidate that ended up losing by half a point. He connects that to Rasmussen quietly getting booted from a major polling aggregator despite years of accuracy. From there he digs into a fresh CBS poll on how Democrats increasingly favor socialism over capitalism, breaking down the branding gap between what Republicans and Democrats think the word even means, and framing the whole thing as a widening civil war between the DSA and the old guard establishment. He closes with a fascinating C SPAN caller clip that has to be seen to be believed. Plenty of economic theory and political speculation packed into a solo Tuesday.

The Texan's Reporter Isaiah Mitchell sat down with Dr. Lindsey Hendren, Senior Research Analyst for ReconMR, to discuss the August 7 Texas Pulse poll on statewide races in Texas, including for U.S. Senate, governor, and attorney general. Hendren breaks down the data, from how respondents feel about the candidates' strengths and weaknesses to their overall opinion on the state of the country.

On today’s “Closer Look with Rose Scott,” we catch up with NPR Congressional Reporter and WABE alum Sam Gringlas. He’s currently following Democratic Incumbent Jon Ossoff and his effort to reclaim his U.S. Senate seat this November. Polling data currently has Ossoff leading his Republican opponent, Mike Collins, but there are still several miles on the campaign trail until the midterm election. Gringlas shares his firsthand look at how voters are reacting during Senator Ossoff’s campaign stops. Atlanta is widely known as the city in a forest, but that forest is depleting. Of the city’s more than 84,000 acres, almost 40,000 acres are covered by its tree canopy. However, that latest assessment by Georgia Tech shows Atlanta has lost a quarter of an acre of trees every day between 2008 and 2023. The city had a goal of reaching 50% canopy coverage, but Atlanta moved further away from that goal in the latest assessment, now dropping to 45.7% tree coverage. Officials from Trees Atlanta and Georgia Tech provide more details on the data, and what’s leading to the disappearing canopy. With the concern of mainstream AI systems disseminating misinformation, a new artificial intelligence platform is promising to provide accurate information. AiSHA pulls its information from Black History, culture, and trusted journalism sources to avoid biases. We speak to the founder and CEO of Onyx Impact, Esosa Osa, about how her new AI assistant and chatbot will provide something different, including most sustainability friendly usage.See omnystudio.com/listener for privacy information.

Paychecks aren't keeping pace with rising prices, and households are feeling the squeeze. Sailors on the ships deployed to the Middle East are straining without enough clean water and fresh food. In the thick of primary season for the midterms, how reliable is polling? Support public media with NPR+ and enjoy perks for over 25 podcasts like this one. This show's perks include sponsor-free listening. Learn more at plus.npr.org.See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

KING 5 has another biased anti-ICE story. Rep. Baumgartner moves to ban wildfire betting after Spokane arson case. Sound Transit is once again delaying the West Seattle light rail extension. // New polling spells bad news for Democrats in the Michigan Senate race. // Chelsea Handler once again exposes she has no clue what she’s talking about.

George Khalaf, Republican candidate for the Arizona House of Representatives from Legislative District 3 starts the show off with Seth on the many ‘misses’ in recent political polling. He explains why the disparity between polls and election results is often due to inaccuracies in predicting voter turnout. He also discusses the media's role in shaping public opinion and how their bias can lead to a distorted view of reality. George highlights several examples of media outlets presenting misleading information, including and incident where Representative Abe Hamadeh (R-AZ) recently called out an article in The Arizona Republic which made Democrat candidate for congress in Arizona’s 6th congressional district JoAnna Mendoza look more favorable after it was discovered she unlawfully took tax credits on multiple primary residences. George also touches on the topic of "woke-ism," discussing how it has evolved from a movement to a full-blown ideology that is now being pushed by many Democrats. He shares his concerns about the impact of "woke-ism" on our society, including the rise of mental health issues among young people and the erosion of traditional values. Get involved in his campaign today at georgekhalaf.com.See omnystudio.com/listener for privacy information.

Intriguingly, Fox News has lately been hitting Donald Trump with terrible polling news. In recent days, Fox has found Democrats slightly ahead in Senate races in Maine, Texas, and Iowa, and up by a lot in North Carolina. Critically, Trump's own favorability numbers are very bad in all those states, even though he won three of them, two by double digits. In all four, his numbers among independents are shockingly awful. Meanwhile, on the Iran war—which is central to why his standing is cratering—Trump aides are privately feeding him hopium. The Atlantic reports that they're showing him data on Iran's economy, which supposedly illustrates that if he does nothing and bides his time, Iran will surrender any day now. Doing nothing is the smart play, sir! Yet as Democratic strategist Caitlin Legacki argues in this episode, Trump's inaction on Iran is a big albatross for GOP Senate candidates. Legacki explains why Iran is central to the battle for the Senate, why those Fox polls should give Democrats grounds for cautious optimism, and what could still go wrong for them.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Nassau County Executive Bruce Blakeman is running against incumbent Gov. Kathy Hochul for governor of New York, and a new poll shows the county executive is just ten points behind the governor. Jimmy Vielkind, New York State Issues reporter for WNYC and Gothamist, and Bahar Ostadan, Nassau County government reporter for Newsday, talk about the state of the race, including the role President Trump is taking as he and Blakeman are scheduled to attend an event together on Long Island. Photo: Garden City, N.Y.: Nassau County Executive Bruce Blakeman is formally named the GOP candidate for New York State Governor as the New York Republican State Committee Nominating Convention in Garden City, N.Y. on Feb. 11, 2026. (Howard Schnapp/Newsday RM via Getty Images) Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Mike and Mark cover breaking news on the guilty plea delivered by Luigi Mangione for the killing of Brian Thompson; then an examination of the DFW airport footwashing station controversy and the Baby Gabriel debate, followed by a mixed bag of taser-like gloves for ICE and James Talarico’s scriptural hostility. Mid-episode bonus: Mike anticipates a New York Phantom of the Opera immersive experience. ----M and M EXTRA: Two iconic talk radio hosts. One unfiltered daily conversation. No scripts. No spin. Just Mike Gallagher and Mark Davis breaking down the news the way it should be — with decades of experience and zero apologies. If you love smart unscripted talk show chemistry, you’re in the right place.-----Thank you to our amazing sponsors for supporting the podcast! PreBorn saves babies and souls, by providing free ultrasounds to women. When a young woman SEES her baby and HEARS her baby’s heartbeat, it doubles the chance she’ll choose LIFE!  Whether you want to save one baby or hundreds…you are just a call or a click away. Call 833-850-BABY that’s 833-850-2229 OR go to preborn.com/MMExtra! PHD Weightloss:If you're ready to finally take control of your health, PHD Weightloss has the plan that works. Real science. Real support. Real results. Visit PHDWeightloss.com or call 864-644-1900 and mention Mike and Mark. Relief FactorAre you in pain? When it comes to supplements, two things matter most: that it works, and that you can trust it. That's why we love Relief Factor. Get relief from pain at relieffactor.com or call 1-800-4-RELIEF to get your 3-Week Starter for $17.76. EKKL StreamingThe stars of Duck Dynasty, Willie & Korie Robertson, teamed up with EKKL to create a brand new, one-of-a-kind streaming platform built around faith and family with movies, TV series and podcasts. The best part? You get all of this for only $5.83 a month. Sign up for EKKL at ekkl.com today!See omnystudio.com/listener for privacy information.

New Berkeley IGS polling came out this week that sheds light on how Californians are thinking about November's big ticket ballot items — including the controversial billionaire tax. Marisa is joined by Melanie Mason, California Bureau Chief and co-author of Politico's California Playbook. They talk about the latest polling, plus Xavier Becerra's lead over Steve Hilton in the governor's race.Then, they discuss the Paramount-Warner Bros. merger, which Attorney General Rob Bonta has blocked, and why it's splitting Democrats.Check out Political Breakdown's weekly newsletter, delivered straight to your inbox. Learn more about your ad choices. Visit megaphone.fm/adchoices

Polling shows that many Catholics support the use of artificial contraception, despite the Church's consistent teaching against it. This episode treks thru the history of this issue, and looks at how we ended up with so many Catholics in the pews embracing a belief contrary to Church teaching.

Headlines: – Welcome to Mo News (02:00) – Lindsay Clancy Trial  Week Three (04:20) – Wisconsin And Michigan Primary Results Reveal Major Polling Problems (09:20) – Karoline Leavitt To Leave As White House Press Secretary At End Of Month (20:10) – US Inflation Numbers Cool (25:20) – Lakers' Stunning $12.5 Billion Sale Nets Owner $2.5 Billion in Months (27:30) – Three Minutes Of This Activity May Cut The Risk Of 13 Cancers ~ Study (30:45) – On This Day In History (36:00)  Thanks To Our Sponsors:  – Monarch - 50% off your first year | Code: MONEWS – Factor - 50% off your first box | Code: monews50off –⁠ Industrious⁠ - Coworking office. 50% off day pass | Code: MONEWS50 – LMNT | Free Sample Pack with any LMNT drink mix or 12oz cans purchase – ⁠Boll & Branch⁠ – 20% off first order, plus free shipping | Code: MONEWS

Some recent primary elections in the Midwest, particularly Wisconsin and Michigan, have changed the landscape for November's midterms. On Today's Show:Rachel Leingang, Midwest political correspondent for Guardian US, based in Minneapolis, Minnesota, and Teo Armus, reporter for The Washington Post focused on politics, demographics, voters and immigration, offers political analysis of the latest primary election results, including in Wisconsin and Michigan, and what they might signal about the midterms this fall. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Iran war takes another dangerous turn as the Houthis reportedly attack a Saudi Arabian ship, raising new fears about the security of one of the world's most important energy corridors. Meanwhile, the Trump administration is painting a rosy picture of oil flowing out of the region—but the numbers may tell a very different story. We break down what's actually happening with oil shipments, why the situation remains so fragile, and what it could mean for Americans at the pump. Plus, new polling is flashing warning signs for Republicans as Democrats continue to surge. Is the political tide turning even faster than expected? We dig into the latest numbers, what's driving the shift, and what it could mean heading into the midterms. All that and more on the morning edition of The Rush Hour Podcast.

Tom Bevan, co-founder and executive editor of RealClearPolitics, joined us on the Guy Benson Show today with guest host Harry Hurley to discuss yesterday's Primary results. Bevan gives his take on what happened in key states such as Wisconsin, South Carolina, and Minnesota, breaks down the polling, inaccuracies, and more. Listen to the full interview with Hurley and Bevan below! Learn more about your ad choices. Visit podcastchoices.com/adchoices

The Guy Benson Show 08-12-2026 Learn more about your ad choices. Visit podcastchoices.com/adchoices

President Trump has some of the worst approval ratings of any president in modern history, second only to Richard Nixon the week before he resigned. On this week's On the Media, what the polls say about Americans' perceptions of Trump and his policies. Plus, a look at the polarized propaganda being beamed into Iran.  [01:00] Brooke Gladstone interviews G. Elliott Morris, journalist, statistician, and author of the data-driven news website Strength In Numbers, about public perceptions of President Trump and his policies, and what Trump's historic unpopularity could mean for the upcoming midterm elections.  [19:14] Micah Loewinger speaks with Nancy Scola, a reporter covering tech, policy, and politics, about her recent profile of Federal Communications Commission Chairman Brendan Carr, tracing his trajectory from “spectrum nerd,” to MAGA insider.  [36:23]  Micah sits down with Nahid Siamdoust, an assistant professor of media and Middle Eastern studies at the University of Texas at Austin, to discuss the state of the Iranian media landscape, and how a newer anti-regime channel, Iran International, is contributing to intense polarization among Iranians around the world.  Further reading / watching: “What's behind Donald Trump's record-low approval rating?” By G. Elliott Morris “How Brendan Carr Became MAGA's Media Watchdog,” by Nancy Scola “Spreading Static,” by Nahid Siamdoust On the Media is supported by listeners like you. Support OTM by donating today (https://pledge.wnyc.org/support/otm). Follow our show on Instagram, Bluesky, TikTok and Facebook @onthemedia, and share your thoughts with us by emailing onthemedia@wnyc.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Aug 6, 2026; 6pm; With less than three months before the midterms, Trump is facing a massive polling crash, rising gas prices, an unpopular war of choice, and a MAGA revolt by some of his closest MAGA allies. Molly Jong-Fast, Rick Wilson, Tim O'Brien, Will Sommer and Rick Reilly join The Beat to discuss. To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.