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Gideon is the first real time AI system built to detect threats online before they become attacks. Anonymous networks flagging behavior predicting danger. We don't get a second chance. Let's not miss the next one. Fifteen seconds, Aaron. You're talking about stopping mass shootings, attacks in Boulder before they start. Trace, I'm building the first AI driven threat prediction platform for law enforcement. They're flying blind right now. I've got an elite team of engineers from Palantir. I've got law enforcement agencies lined up. 76% of these mass attackers posted some type of grievance online. This is America's early warning detection system. If you're a chief out there, reach out to me and get on my pilot. If you're a VC, I'm about to open my seed round, partner with me, and let's make America safe. They're gonna get cops the tools they need.

Data centers under construction in the United States show how quickly AI infrastructure is expanding. Texas has 135, Virginia 134, Georgia 51, Ohio 45, Arizona 35, Nevada 29, Indiana 21, Mississippi 21, Illinois 19, Iowa 16, Oregon 12, South Carolina 12, Wisconsin 11, Maryland 11, North Carolina 11, Pennsylvania 11, Utah 10, Missouri 8, Wyoming 2, Alabama 7, New York 7, Tennessee 7, and Florida 7 under construction. Australia, the UK, and Canada have smaller numbers. In Australia, Sydney has 10 to 15 distinct sites or campuses actively under construction; Melbourne has 8 to 12 sites; nationally, 20 to 30 sites total actively under construction, plus 48 upcoming facilities overall. In the UK, London has 7; other regions show slow growth with two to four in some areas. Northeast England, Wales have one to two; Greater Manchester, Yorkshire, Scotland have one to three; national totals are approximately 20 to 30 distinct sites or facilities actively under construction, with 29 projects expected to begin or continue construction in 2026. In Canada, Toronto (Greater Toronto Area) has four to six; Montreal (Quebec metro area) five to eight; Quebec City two to four; Vancouver one to three; Calgary/Alberta five to ten. Other regions such as Ottawa, Waterloo, and Halifax have one to three being planned. Flock Safety is a US-based technology company, Flock Group Inc, founded in 2017 and headquartered in Atlanta, Georgia, that develops and operates a public safety platform focused on surveillance tools to help prevent and solve crime. They produce automated license plate recognition, ALPR or LPR cameras, which are solar powered fixed cameras capturing images of vehicles, often focusing on rear plates, bumper stickers, and other details on public roads. They use AI and machine learning to read plates, identify unique vehicle features like vehicle fingerprint, and provide real time alerts for vehicles on hot lists, such as stolen cars or wanted suspects. Additional devices include video surveillance cameras, gunfire detection, ShotSpotter-like audio sensors, and drones for first response. Integrated platform FlockOS feeds data from these devices into a cloud-based system hosted on AWS where law enforcement can search nationwide, get alerts, review footage and clips, and use natural language AI searches (for example, specific vehicle descriptions). Data is typically retained for thirty days unless flagged. Flock data can be integrated into platforms like Palantir for law enforcement use. They claim that more than 6,000 communities trust Flock to help keep their communities safer and describe their solution as hassle-free, scalable, and customizable, expediting positive outcomes. They note that 15% of reported crimes in the US are solved with the help from FLOCK, with an asterisk. Despite the perceived positive impact, the transcript acknowledges disasters and secrecy surrounding Flock.

The report concludes with a real-time example of emergency services failing during the Las Vegas shooting on October 1, 2017. A woman, trying to call for help, could only reach her boyfriend miles away due to a security perimeter around the active shooter situation. This highlights the critical need for reliable communication during emergencies, as it affects not just individuals but families and communities. The importance of ensuring American infrastructure is under American control is emphasized, and there is a call for senators and government officials to take these issues seriously. Gratitude is expressed to Mark Rinchem for facilitating this discussion and providing supporting evidence.

Speaker 0: The police will be on their best behavior because we record we're constantly recording, watching, and recording everything that's going on. Citizens will be on their best behavior because we're constantly recording and reporting everything that's going on. And it's unimpeachable. The cars have cameras on them. I think we have a squad car here someplace. But those kind of applications using AI, if we can use AI, and we're using AI to monitor the video. So if that altercation had occurred, that occurred in Memphis, the chief of police would be immediately notified. It's not people that are looking at those cameras, it's AI that's looking at the camera. No. No. No. You can't do this. It would be like a shooting. That's gonna be immediately that's gonna be an an event that's immediately rip an alarm's gonna go off. It's gonna be and we're gonna we're gonna have supervision. In other words, every police officer is gonna be supervised at all times. And and the supervision will, and and if there's a problem, AI will report the problem and report it to the appropriate for person, whether it's the sheriff or the chief or whom whomever we need to take control of the situation. We have you know, same thing. We have drones. We just if there's something going on in a shopping and and I'll stop. A drone goes out there. I get there way faster than a police car. There's no reason for, by the way, high speed chases. You shouldn't have high speed chases between cars. You just have a drone follow the car. I mean, it's very, very simple. And then new generation generation of autonomous drones.

Gideon is the first real time AI system built to detect threats online before they become attacks. Fifteen seconds, Aaron. You're talking about stopping mass shootings, attacks in Boulder before they start. Trace, I'm building the first AI driven threat prediction platform for law enforcement. They're flying blind right now. I've got an elite team of engineers from Palantir. I've got law enforcement agencies lined up. 76% of these mass attackers posted some type of grievance online. This is America's early warning detection system. If you're a chief out there, reach out to me and get on my pilot. And if you're a VC, I'm about to open my seed round, partner with me, and let's make America safe. They're gonna get cops the tools they need.

Two individuals came up with the idea to validate the data by looking at two cold case murders. One of the cases involved the shooting of an 8-year-old girl in Atlanta. They visually identified a few unique devices that could have been used in the shooting. Each color on the map represents a different person, and the shooting occurred in a specific parking lot. The information was given to the FBI, who have since arrested two suspects, believed to be gang members. The tracking of these devices parallels the work being done with the mules.

A partnership with Palantir aims to address mortgage fraud. The partnership intends to ensure there is no fraud. According to one speaker, they have only scratched the surface with Palantir. Previously, it took investigators sixty days to detect fraud; Palantir's technology completes the same task in ten seconds. One speaker expressed excitement about Palantir's technology and expertise in security and fraud detection. For Palantir, this partnership is a matter of public trust. The partnership aims to understand mortgage fraud and stop it. The goal is to get to the bottom of mortgage fraud.

Joel Fetter, an automotive journalist, experienced a tense confrontation after leaving Kohl’s in Plymouth, Minnesota, during which multiple Plymouth police cars surrounded his Range Rover. Fetter said officers pulled in from both sides and he was “boxed and pinned” with four police vehicles and lights on, and he repeatedly told them, including that he was not armed. Fetter followed officers’ orders, and although officers kept their hands on their holsters, they did not draw weapons. An officer later explained the reason: Fetter’s license plate was registered in a flock system as stolen. Fetter had documentation showing the plate was not stolen and learned he had been on police radar for a while. Fetter also said Plymouth officers told him the same missing-plate alert had appeared for other cars driving across Minnesota with similar dealer plates. Courtney Hoggard reported that flock uses cameras to read license plates automatically across the country. The company reports 20 billion vehicles pass its cameras each month and its readers capture 93% of license plates, and Plymouth has 15 intersections with flock cameras, including one that tracked Fetter into the Kohl’s parking lot. The transcript also notes that even correct camera reads can lead to errors due to human error, and that a small number difference mattered: Fetter’s plate was “3410 DTM,” while the system reported “34 DTM,” and the “10” difference allegedly did not seem to matter. Police said someone in Los Angeles originally reported a plate missing with small-number differences, where the “10” was on Fetter’s Range Rover. The transcript states flock has a 7% inaccuracy reading rate, and that when police receive no information or bad information, errors can cascade into unnecessary stops. It adds that exigent circumstances are usually emergencies and that Minnesota law allows warrantless monitoring or tracking via automated license plate readers only under exigent circumstances. The ACLU of Minnesota is tracking misuses of surveillance technology, including officers using alerts to stalk romantic interests. In this case, flock told the outlet that alerts should be treated as investigative leads and that officers should independently verify license plates, vehicle details, and surrounding circumstances before taking enforcement action. The transcript concludes with a separate update: in Wisconsin, Milwaukee detective Tehranji Chapman was accused of misusing the department’s flock system, allegedly tracking a victim’s car nearly two dozen times for personal reasons. He is charged with misconduct in public office and misuse of a GPS device.

We can enhance school security by implementing AI cameras to monitor campuses and alert authorities immediately if a weapon is detected. Our redesigned body cameras, costing only $70, continuously record and transmit footage to headquarters, ensuring police accountability. Privacy is maintained, as recordings can only be accessed with a court order. AI monitors these feeds, instantly notifying supervisors of any incidents, promoting better behavior among both police and citizens. Additionally, drones can quickly respond to incidents, such as tracking suspects instead of engaging in high-speed chases, and detecting forest fires autonomously. These AI applications represent a significant advancement in public safety and law enforcement.

Rick announces a partnership with Ubiquia to turn streetlights into smart LPR and live streaming cameras through the “Exxon Light Post.” He emphasizes that deployments are accelerated without adding a single pole in the ground, using existing infrastructure to create intelligent, interconnected tools for public safety. Rick says the concept enables streetlights to “protect” the street, not just light it, and introduces Ubiquia CEO Ian Aaron. Ian Aaron says Ubiquia’s mission is to make the world smarter, safer, and more connected by leveraging existing infrastructure in the ground—60 million streetlights in the US and 450 million worldwide. He states that Ubiquia has worked with more than 900 cities and utilities to turn ordinary streetlights into public-safety tools. He describes the UB Hub platform as easy to install, simple to relocate, and built to scale, enabling police departments to deploy and expand video and LPR capabilities quickly. Aaron announces a collaboration with Axon LightPost to create a streetlight-based platform for video surveillance and LPR that integrates seamlessly with Axon FUSIS. He describes the expected impact as giving law enforcement “more eyes and real-time intelligence” where and when needed. A live installation demonstration follows. With help from teammate Todd, they install “Exxon Light Post” in real time. Todd plugs the device into the photo cell socket, described as common for 60 years on 450 million streetlights. The demonstration highlights installation steps and capabilities: powering, connecting LPR or network cameras with edge processing, providing 10 to 40 hours of video storage, and integrating with Axon FUSIS.

The speaker describes U.S. surveillance as more advanced than the White House ballroom “underground surveillance center,” claiming that a large-scale system is already deployed and not widely understood enough to stop it. They say they followed the money behind “Flock” after hearing about it. The speaker identifies Flock Cameras as the company behind surveillance cameras (not bird-monitoring game cameras), formed around 2018 by three Georgia Tech students. They claim Flock is deployed across “5,000 towns” with “over 80,000 cameras” in the United States, originally intended to catch crime using license-plate tracing. The speaker then claims that in the past two years Flock added accessories that they describe as “more electric dog collars,” shifting “innocent” crime-capture capabilities into “nefarious” uses. The speaker outlines three parts of the claimed system: 1) Flock cameras mounted on light poles that trace license plates, with added AI capabilities said to include identifying vehicles from “dents,” “scratches,” and “bumper stickers,” and then tracing the car using these features rather than license plates alone. 2) A drone-related capability, attributed to Flock buying a drone company: it is said to hear someone scream or respond to a camera detecting a crime, then automatically deploy a drone that surveils a chase “2,000 feet up in the air.” 3) “Nova,” described as an accessory added to Flock cameras that tracks people and “turns your license plate into everything about you,” including marital status, kids, address, and phone number, plus “pattern of life.” The speaker claims an investigation found Nova pulls data not only from legal/open sources but also from the dark web, including social security numbers, bank information, leaked email, leaked passwords, and other leaked data. They give an example involving a Texas police officer searching a “Flock database” for an abortion-related case and then seeking expansion into states where abortions were legal, after receiving “1800 results.” The speaker then connects the surveillance technologies to specific investors and related companies. They claim Andreas Horowitz is an investor in Flock. The speaker says they recognized Horowitz from research on Ehud Barak and asserts Barak created related companies including “TOCA,” described as technology that can alter live camera footage in real time, add or remove content, create fake footage, and “leave no forensic evidence.” They also claim Barak is connected to “Carbine,” described as a 911 system capable of accessing microphones, cameras, and location. The speaker further claims Horowitz and additional investors link Flock to these technologies through Peter Thiel’s Founders Fund, which they say also invested in Flock and Carbine. The speaker says all these companies connect through large investors and says it enables additional capabilities such as drone use and footage manipulation. They announce “part two,” claiming the next topic is a competitor that previously worked with Flock, branched off, and can track Bluetooth devices, described as linked to an Italian military defense company.

Automotive journalist Joel Fetter’s stop by Plymouth police became a case study in how automated license plate reader technology can misidentify people. Fetter said that after leaving Kohl’s in Plymouth, he saw four police cars surrounding him with lights and sirens while he was boxed in and pinned in. He followed officers’ orders, including answering whether he was armed, and officers kept their hands on their holsters without drawing weapons. Officers eventually told Fetter the reason for the stop: his license plate was registered as stolen. Fetter said he had enough documentation to convince them it was not stolen and learned he had been on their radar for a while. Plymouth police described receiving a list of vehicle plates placed on the Flock system on June 26 and June 28, including on the day they surrounded him. Reporting described how Flock operates through tens of thousands of cameras and license plate readers. The company reported that 20 billion vehicles pass its cameras every month and that its readers accurately capture 93% of license plates. Plymouth was described as having 15 intersections with Flock cameras, including one that tracked Fetter into the Kohl’s parking lot. Even when the system reads a plate correctly, human error was cited as a factor, including a mismatch where Fetter’s plate was identified as “3410 DTM,” while the “Flock” picked up “34 DTM,” with a “10” not appearing to matter. Police believed someone in Los Angeles originally reported a plate missing with different small numbers where the “10” was on the Range Rover. The transcript also noted that Flock’s 7% inaccurate reading rate can result in about 1.4 billion instances per month when police get no information or bad information. Misuse of surveillance technology was described by the ACLU of Minnesota as including officers using alerts to stalk romantic interests. In this case, Flock told Fox 9 that incidents like this are taken seriously and that alerts should be treated as investigative leads, with officers independently verifying license plates, vehicle details, and surrounding circumstances before enforcement action. The transcript referenced legal concerns as well, describing arguments that tracking without a warrant would be unlawful absent exigent circumstances and that exigent circumstances are usually emergencies. The overall message emphasized that even innocent people can become caught in surveillance systems, and that the ability of these technologies to amplify human error can lead to stops regardless of whether someone is doing something wrong.

The transcript covers a wave of community pushback against surveillance and data-center developments, highlighting how residents are challenging authorities and big tech projects in their towns. - Surveillance cameras (Flock) controversy: The piece opens with cases suggesting that what’s marketed as public safety can be misused. A poster mentions Brandon Upchurch, whose license plate 7 was misread as 2 by flock cameras, leading to a police stop at gunpoint, a K-9 release, an arrest, and jail for a crime that didn’t exist. Andrew Kaufman notes flock cameras are being destroyed so fast that police in Kentucky are withholding their locations after the devices were released and promptly destroyed. The argument is that communities don’t want to be monitored and should have right to privacy; Flock cameras are going up across towns often without public input. In Pine Plains, New York, a resident saw a flock contractor install 12 cameras without town-board approval; the cameras were not installed, but the incident exposed contract-authorization confusion. The takeaway is to stay vigilant, talk to neighbors, attend town meetings, and make clear that surveillance is not desired. - Data centers: widespread, rapid pushback across multiple communities. The broader thrust is that communities are resisting data centers due to concerns about power, water use, land, privacy, and local impacts. - Utah – Provo data center rejection: Robert Bryce reports that Provo, Utah rejected a data center project, citing no city interest and concerns about power demand. He notes 53 data-center rejections or restrictions in the U.S. in 2026 so far (more than all of 2025). The proposed load was initially five megawatts, potentially up to 50 megawatts, which would strain the Utah Municipal Power Agency’s 415-megawatt capacity. - Additional examples of pushback: A video from New Jersey shows hundreds of New Brunswick residents celebrating a protest that led to the plans being canceled. Stark County, Indiana, enacted a twelve-month moratorium on data-center construction after sustained community pressure; a public meeting featured residents opposing the project and some calling for a total ban. Northwest Indiana residents voiced alarm about Big Tech’s data-center incursions and the AI agenda, arguing it would not benefit them and would affect electricity costs. In several counties (Indiana, Georgia, Missouri, Illinois, and beyond), moratorium measures or restrictions were adopted to pause or ban new proposals, with claims that capacity issues and local concerns justify stopping projects. - Apex, North Carolina: Over 100 Apex residents packed a town hall to oppose a data center proposal, citing strained power grid, massive water usage, wildlife disruption, and industrial noise. A community organizer, Melissa Ripper, led the Protect Wake County Coalition; Natelli Investment withdrew its applications, described as a “small victory.” - Tucson: Community members organized to reject a data center proposed by Amazon, citing drought and water-use concerns; the video emphasizes that Tucson became the first city to reject a massive data center proposal due to a large local uprising and distrust of assurances about water reclamation. - Kentucky landowners’ stand against offers: Ida Huddleston and her daughter Delsia Bear rejected multimillion-dollar offers from an anonymous tech company to build a data center on their land. Huddleston declined $60,000 per acre for 71 acres; Bear declined $48,000 per acre for 463 acres. The company behind the project has not been revealed, which adds to residents’ concerns about transparency. The proposed site is Big Pond Pike in Mason County, with claims the project would create 400 full-time jobs and more than 1,500 construction jobs, though Bear says many jobs may not materialize. - Closing sentiment: The speaker argues that “they simply cannot pull the wool over the eyes of a country folk,” noting the daughter’s rejection of $22,000,000 and Ida Huddleston’s insistence on staying put to protect her community, underscoring a broader theme of local resilience and community solidarity against large-scale, opaque projects.

The speaker says they have been talking with people across Casagrande, and a recurring issue is safety. They then announce a new company called Shepherd Safety and describe its mission as keeping government officials and their families safe. The company says it will launch satellites to monitor Casagrande day and night using AI to track where government officials go, where they stop, who they meet with, and when they return home. The transcript states that Shepherd Safety has already begun collecting and building profiles on vehicles, including spouses’ vehicles, children’s vehicles, and other visual characteristics. It says this information will be combined with publicly available data, Bluetooth signals, advertising IDs, vehicle information, and commercial data sources. The speaker claims that authorized users will be able to “replay the movements” of every government official and their immediate family. The transcript further asserts that the system is “no difference” than current Flock Safety capture systems. It also states that information will be stored securely and only authorized users will have access, and that data will be stored for thirty days. The speaker says local businesses will be invited to join a network so government officials can continue being protected while shopping, eating, or traveling throughout the city. They then address concerns that the approach sounds invasive, including worries about abuse, unauthorized access, and tracking innocent people. The transcript says the concerns mirror what citizens have, referencing “eight hundred and sixty-four” people (or a similar number). It claims the system will be defended on the idea that “if you’re doing nothing wrong, you got nothing to hide,” and that there is “no expectation of privacy in public,” so tracking and uploading movements online is framed as acceptable. The transcript emphasizes that authorized users will have access and that only thirty days of data will be stored, arguing it is “no different from Flock.” The speaker then shifts to a privacy-focused objection, saying privacy should not stop being important when the word “safety” is used, and arguing that surveillance of elected officials and their families should not be accepted “for ourselves.” The transcript states: “this is satire,” adds “I wouldn’t never do this to you. Could I? Probably. But I wouldn’t, because it’s wrong,” and continues that the speaker is “innocent” and does not do anything wrong. They say their movements are tracked and uploaded every day without permission and request that tracking be stopped. The speaker asks for data collection to be put behind a warrant, “just like my phone records.” They end by saying they hope everyone has been having a great summer.

Correct. I am now about to launch Gideon, America's first ever AI threat detection platform built specifically for law enforcement. It scrapes the Internet twenty four seven using an Israeli grade ontology to pull specific threat language and then routes it to local law enforcement. It's a twenty four seven detective. It never sleeps, and it's going to get us in front of these attacks. Would it have picked up on this, do you think? 100%. Percent. I wish this pro I wish my program would already be up. We're not launching until next week. I've got a dozen agencies on board, Trace. I just onloaded a major Northeast, agency with over 2,700 sworn. This is America's early warning system.

Correct. I am now about to launch Gideon, America's first ever AI threat detection platform built specifically for law enforcement. It scrapes the Internet twenty four seven using an Israeli grade ontology to pull specific threat language and then routes it to local law enforcement. It's a 20 fourseven detective. It never sleeps, and it's going to get us in front of these attacks. Would it have picked up on this, do you think? 100%. I wish this pro I wish my program would already be up. We're not launching until next week. I've got a dozen agencies on board, Trace. I just onloaded a major Northeast agency with over 2,700 sworn. This is America's early warning system.

The police will be on their best behavior because we re we're we're constantly recording, watching, and recording everything that's going on. Citizens will be on their best behavior because we're constantly recording and reporting everything that's going on. And it's unimpeachable. The cars have cameras on them. So if that altercation had occurred, that occurred in Memphis, the chief of police would be immediately notified. In other words, every police officer is going to be supervised at all times. We have you know, same thing. We have drones. A drone goes out there. I get there way faster than a police car. There's no reason for, by the way, high speed chases. You shouldn't have high speed chases between cars. You just have a drone follow the car. I mean, it's very, very simple. And then new generation generation of autonomous drones.

Speaker 0 breaks a little bit of news on your program, Jesse. He reports that 'our partners that do sort of geotagging with devices, they told us that they tracked over 277,000 devices in the vicinity of State Farm Stadium in Glendale, Arizona.' He adds, 'Wow. 277,000. That's unbelievable.' He concludes, 'Gives you an idea of the scale of humanity out there.' These statements illustrate the large number of devices detected near a major venue, highlighting the scale of activity in the area during events. The segment emphasizes the reach of geotagging data and public disclosure in reporting near real-time device counts. It conveys a perspective on how many devices can be tracked in a single location.