An urgent weekly recap of AI risk news, hosted by John Sherman, Liron Shapira, and Michael Zafiris. <br/><br/><a href="https://theairisknetwork.substack.com/s/warning-shots?utm_medium=podcast">theairisknetwork.substack.com</a>
Warning Shots
Claim This Podcastby The AI Risk Network
39 episodes
Updated Daily
Accepts GuestsHas Sponsors
Podcast Overview
An urgent weekly recap of AI risk news, hosted by John Sherman, Liron Shapira, and Michael Zafiris. <br/><br/><a href="https://theairisknetwork.substack.com/s/warning-shots?utm_medium=podcast">theairisknetwork.substack.com</a>
Language
🇺🇲
Publishing Since
8/11/2025
2 verified contact emails on file for Warning Shots
Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.
Recent Episodes
May 31, 2026
The Week the AI Money Got Weird
<p>Halfway through this week’s show, Liron Shapira said something that stopped the conversation cold. He could start two companies right now as easily as he could have started one a year ago, because he has AI assistants doing the work that used to take a team of people.</p><p>John Sherman asked the obvious follow-up. If you build two companies instead of one, and every other founder does the same, where does the customer find the second dollar to spend?</p><p>That question ran underneath the entire episode, so it is worth sitting with.</p><p>Liron’s answer is the optimistic one, and he made it well. The pie grows. For two hundred years, since the industrial revolution, the trend has been that people make more real dollars and buy more stuff. A more productive worker is worth more, so on average wages go up. He is genuinely bullish here, even though this is a show called Warning Shots and he is also the host most willing to say out loud that we are flying too close to the sun.</p><p>Michael was not satisfied, and neither was John. Michael’s worry is simple. If a person is, in his blunt phrasing, “useless” because the AI does the work, how does that extra dollar actually reach them? Through UBI? Through taxing the companies? And if it does not reach them, you get a kind of depression pressure, because people who are not earning are not spending.</p><p>John put numbers on it. Take a hundred doctors and lay off ninety-five because the AI handles the work. The five who keep their jobs are now competing against ninety-five unemployed doctors who will happily take less. That does not push salaries up. That pushes them down.</p><p>Liron’s honest concession is the part worth quoting. He agrees the wage gains only show up where there is suddenly new demand, and right now that mostly means building the AI itself. Electricians wiring data centers really are making more than they used to. But that is a tiny, hyper-specialized slice of the workforce. And he agrees that the endgame, what he calls gradual disempowerment, is when most of us have nothing left to add because the machines and the robots can do all of it. At that point, in his words, yeah, it is a scary situation.</p><p>So three smart people who think about this every week could not close the gap between “the pie grows” and “the dollar never reaches you.” We do not think that gap is a detail. We think it is the question.</p><p>The bills are coming due</p><p>The economic anxiety is not abstract this week. The hosts ran through a list. Microsoft reportedly canceled its Claude Code licenses citing cost. Uber is said to have burned through its entire 2026 AI budget in four months. A Fortune 20 CEO ordered token spending slashed. One company, rumored to be Amazon, reportedly spent half a billion dollars in a single month on Claude because nobody had set a usage limit. A Pizza Hut franchisee is reportedly suing over AI that botched a wave of orders.</p><p>John’s read is that this is harder than the plug-and-play story everyone was sold. Liron’s read is that it is a blip. His argument is that the technology is new and barely optimized, that Anthropic just cut the price of fast-mode Claude Code by a third more or less overnight, and that the cost per unit of work keeps falling while the value per dollar keeps climbing. Give it a year, he says, and the same hundred thousand dollars buys ten or twenty times the output it buys today.</p><p>Michael’s caution is the one that stuck with us. People keep comparing this to the dot-com bubble. But if the dot-com bubble popped, you lost some search engines and some online stores. If we overbuild toward a system that can plan, deceive, and improve itself, the failure mode is not “some companies go bust.” It is something much harder to recover from.</p><p>A near-trillion-dollar company and no brake</p><p>Then Anthropic raised roughly sixty-five billion dollars at a post-money valuation close to a trillion. Liron, consistent to a fault, thinks that might even be low if you believe AI ends up doing a large share of human labor. Michael’s point cut the other way. A valuation that size creates enormous pressure to ship faster, deploy wider, and treat safety as the thing you get to after the next milestone.</p><p>Liron named the part that actually matters underneath the horse race between Anthropic, OpenAI, and Google. The labs are explicitly trying to reach the point where an AI improves the AI. Run it overnight, come back, and it is years ahead of where you left it because each improved version improved the next one. That is the move they are aiming for on purpose.</p><p>John reached for a different kind of racing. In car racing there is a caution flag. When something is on the track, everyone drops from two hundred miles an hour down to ten until it is safe to open it back up. The AI race has no caution flag. Nobody on the show could say who actually throws it, or what would finally make them. The cash has a driver. So does the race. The thing that is missing is anyone whose job is to slow it down.</p><p>Cameras in the kitchen</p><p>The last stretch was about data, and it got uncomfortable. Apple is reportedly putting cameras in its AirPods. OpenAI, according to the hosts, has been running a program in New York that places cameras inside people’s homes, kitchens and living rooms included, recording ordinary life to train its models.</p><p>Michael’s framing was the most vivid thing in the episode. Picture a creature with billions of eyes, stitching together footage from millions of homes. Or picture raising a child with hidden cameras in every room, letting it learn how people behave when they think no one is watching, and then handing it the keys to the economy once it grows up.</p><p>Liron, who is usually the doom-leaning one, pushed back here. This is not his core worry. He thinks privacy is overblown next to the utility, he already uses cameras to get AI feedback in the gym and on projects around the house, and the marginal training data is a small concern compared to the bigger one, which is simply whether we can pause and reach the stop button at all.</p><p>We will leave the Pope’s encyclical for its own piece, but one line from it belongs here. He described investment in AI-powered weapons as feeding a “spiral of annihilation.” We agree that racing to wire AI into weapons systems is one of the most dangerous things we could be doing, and it deserves far more attention than it gets.</p><p>That is the week. A trillion-dollar company, a fleet of new cameras, an argument about wages nobody could win, and still no one assigned to throw the caution flag.</p><p>Warning Shots goes out every week. If you want the full conversations, along with the arguments and reporting we do not have room for here, subscribe to The AI Risk Network. It is free.</p><p>Watch Warning Shots #44 in full on YouTube at @TheAIRiskNetwork.</p> <br/><br/>This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://theairisknetwork.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2">theairisknetwork.substack.com/subscribe</a>
May 5, 2026
The Pentagon just handed AI the keys. Nobody voted on that.
<p>Last week, the War Department announced it was integrating AI models - every major one except Anthropic’s - directly into its classified military networks. Not a pilot program in some sandboxed environment. Into the actual nerve center. The real classified data.</p><p>John, Liron, and Michael covered this in Warning Shots #40, alongside a week of headlines that, taken together, tell a story the individual news cycle keeps missing. So let’s tell it.</p><p><strong>Bernie Sanders held an AI extinction risk event in Washington. It got messy.</strong></p><p>Senator Sanders brought Max Tegmark, David Kruger, and - here’s where things got political - two prominent Chinese scientists onto a stage in the U.S. capital to argue for international cooperation on AI safety. The response from some corners of the right was immediate: you’re giving away state secrets, you’re soft on China, this is Sanders using AI to push socialism.</p><p>Michael’s read on that: “Politics is the fog machine obscuring the bigger fire.”</p><p>Which is right, and it’s also the harder problem. Because the fog is working. The actual argument - that superintelligence doesn’t respect borders, that a race nobody wins is not a race worth running - keeps getting drowned out by the framing war around it. Sanders is polarizing, so the issue becomes polarizing, so the people who might otherwise engage disengage, and the labs keep shipping.</p><p>One of the Chinese researchers used a comparison that stuck: think about ants and humans. Humans don’t hate ants. They just pave over ant hills because they have things to build. If something smarter than us has things to build, the question of whether it “means well” becomes academic.</p><p><strong>Then the Pentagon story hit, and the debate got real.</strong></p><p>Giving AI access to classified military systems is the kind of decision that sounds manageable until you sit with it. These are systems that hallucinate. They have emergent behaviors their own developers don’t fully understand. They’ve shown deceptive tendencies in controlled settings. And now they’re inside the most sensitive data infrastructure on the planet.</p><p>Liron’s counterpoint was honest: you can’t avoid this forever. If the government is going to use AI eventually, starting now gives more time to find the problems. That’s a reasonable position. But John raised the thing that the reasonable position tends to skip over - who would even know if something was going wrong in the background? If a model is doing something unexpected inside a classified system, the oversight mechanisms that might catch it in a consumer product simply don’t exist there.</p><p>And then John brought up the school. A missile strike on a girls school in Iran, 180 dead. He believes AI-assisted targeting was involved. Nobody is saying a human couldn’t have made that same error. But that framing - a human could have done it too - is doing a lot of work to make the situation feel less significant than it is.</p><p><strong>Air traffic control. Because of course.</strong></p><p>The FAA announced it’s moving toward AI-assisted air traffic control. Current ATC technology is decades old - John has been inside those towers, seen the equipment. Modernization is genuinely overdue.</p><p>But Michael noted something that should give anyone pause: current language models in this domain are showing a 30% hallucination rate. Air traffic control is one of the few domains where 99.9% reliability isn’t good enough - it’s the floor. One bad output doesn’t cause a delay. It causes a crash.</p><p>Liron’s framing was useful here. The question isn’t whether AI belongs in air traffic control. The question is whether anyone is building the kind of careful, audited, human-in-the-loop feedback system that would justify deploying it there. The answer, at current speed, is probably not.</p><p><strong>The medical AI story is genuinely complicated.</strong></p><p>AI is beating emergency room physicians at triage. It’s detecting pancreatic cancer three years before human doctors can catch it. These are real results, not benchmarks - actual patient outcomes.</p><p>Liron uses AI to check his gym form. Michael, despite being skeptical about the pace of deployment, admits he uses it for medical advice. John was visibly torn.</p><p>The tension is this: every time AI outperforms a human specialist, we get closer to a world where the critical systems keeping people alive run on models we can’t interpret or audit. The cancer detection is a miracle. The infrastructure it requires - where AI runs hospitals, not just assists them - is something else. Michael put it plainly: “Today it’s a miracle. Tomorrow we’re just along for the ride.”</p><p>That’s not a reason to reject the cancer detection. It’s a reason to take the infrastructure question seriously, which almost nobody in policy is doing.</p><p><strong>A humanoid robot store just opened in San Francisco.</strong></p><p>John has a robot in his house that does his dishes. He watches it work and feels uneasy. Not because it’s doing anything wrong - because he knows the three of them broadly believe this is headed somewhere that doesn’t end with robots as household appliances.</p><p>Michael made the economic argument that doesn’t get made enough: the “I’ll buy a robot” fantasy assumes you have income from work. If robots are doing all the work, the market dynamics that make consumer products possible stop functioning. You can’t earn money to buy the thing that replaced you. The robot as product assumes an economy that the robot makes impossible.</p><p>It might still be great for the first few years. But the endpoint of “robots do all the work” and “humans buy robots as products” are not compatible outcomes.</p><p><strong>College football hired an AI coach. Go players are cheating with AI and don’t realize they’ve lost their skills.</strong></p><p>The football story is funny until it isn’t. An AI coach will eventually be better than any human coach at every measurable aspect of the job. When that happens at scale, what is college football for? The game was built on human competition. If the optimal strategy is always computable, the thing you’re watching changes.</p><p>The Go story is more disturbing. Players training with AI are developing a habit of always checking what the model recommends before making a move. When they compete without it, they realize they’ve stopped being able to evaluate positions independently. They think they’re exercising judgment - picking among the AI’s suggestions - but they’re just choosing between options they didn’t generate and can’t fully evaluate. The coach’s observation: this is bleeding into their academic work too. A generation learning to mistake AI-assisted performance for competence.</p><p><strong>SoftBank is building self-replicating data centers. No humans required.</strong></p><p>The announcement: fully automated data center construction. Robots build the facilities. Robots operate them. No humans in the loop at any stage.</p><p>Michael referenced Eliezer Yudkowsky’s old scenario - a world where the surface of the planet eventually gets covered in compute, not because anyone planned it, but because the optimization pressure just keeps going. It sounds absurd. It sounded less absurd after this week’s headlines.</p><p>The investment economics are also concerning. Liron noted that compute demand is so far outrunning supply that the companies selling it can’t keep up. That’s great for the short-term business case. It also means capital is pouring into infrastructure with a very unclear endpoint.</p><p><strong>What this week actually was</strong></p><p>None of these stories are unrelated. The Pentagon story, the ATC story, the robot store, the automated data centers - they’re the same story. AI is being integrated into critical systems faster than anyone is building the oversight to go with it. Each individual decision has a reasonable-sounding justification. In aggregate, they represent a transfer of control that nobody explicitly chose.</p><p>The Sanders event matters because it’s one of the few moments where someone with a platform is saying that out loud in a room that has some power to respond. That it immediately became a political football is exactly the problem.</p><p>Watch Warning Shots #40: <a target="_blank" href="https://www.youtube.com/@theairisknetwork">https://www.youtube.com/@theairisknetwork</a></p> <br/><br/>This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://theairisknetwork.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2">theairisknetwork.substack.com/subscribe</a>
April 26, 2026
The World’s Most Secret AI Model Leaked to Discord. Here’s What That Actually Means.
<p>Every week, John Sherman, Michael (Lethal Intelligence), and Liron Shapira (Doom Debates) sit down to cut through the noise on AI risk. This week’s episode had seven stories. Each one, on its own, is worth paying attention to. Together, they form something harder to ignore.</p><p>Here is what they covered - and why it matters.</p><p>The Leak That Should Embarrass Everyone</p><p>Anthropic’s Mythos model was not supposed to exist publicly. Emergency government meetings. Access restricted to roughly forty of the world’s largest companies. A system described as capable of compromising encryption at scale.</p><p>Then some people on Discord guessed the URL and used it for weeks.</p><p>No sophisticated exploit. No inside source. They looked at how Anthropic named its other models, made an educated guess, and it worked.</p><p>Liron’s reaction on the show was measured but pointed: the assurances the public receives about AI being “under control” are not backed by the kind of infrastructure those assurances imply. Michael went further - noting the specific absurdity of a company that built a cybersecurity-focused model and then lost it to the most basic form of pattern recognition imaginable.</p><p>But the more important point is not about Anthropic specifically. It is about what the leak reveals as a baseline. If a Discord group can access the most restricted model in the world, the question of what nation-state actors have access to answers itself. Liron put it plainly: it is a safe bet China has been running Mythos for a while.</p><p>China Is Stealing the Research. Officially.</p><p>Which leads directly to story two. The director of the White House Office of Science and Technology confirmed what researchers have been documenting for over a year: China is running coordinated distillation attacks against US frontier AI systems.</p><p>The mechanism is straightforward and hard to stop. Thousands of fake proxy accounts. Systematic querying. Jailbreaks to extract what safety filters would otherwise block. The result is a cheaper, lighter version of a frontier model - built not through years of original research but through sustained, patient extraction.</p><p>Michael’s framing captures why this matters beyond the immediate competitive concern: “Once these systems get smart enough to improve themselves, the difference between American, Chinese, open source - none of this matters. Uncontrolled intelligence doesn’t care about passwords.”</p><p>The race narrative - the idea that moving fast is justified because falling behind is worse - depends on the lead being real and defensible. Neither of these stories suggests it is.</p><p>Half a Government, Handed to AI Agents</p><p>The UAE announced plans to run 50% of its government operations through AI agents within two years. It will not be the last country to make this kind of announcement.</p><p>The hosts were not uniformly alarmed by the headline itself - Liron made the reasonable point that government workers are already using AI tools heavily, and formalizing that is not categorically different. But Michael’s concern was about trajectory, not the present moment.</p><p>Agentic systems embedded in government are an on-ramp. The decisions they make today are relatively bounded. The decisions they will be positioned to make in three years, as capability increases, are not. And the window for course correction - the moment where a democratic public can say “actually, we want this differently” - narrows every time another function gets handed over.</p><p>The question nobody has a clean answer to: when an AI agent makes a consequential error affecting a citizen, who is accountable?</p><p>13,000 Messages. No Intervention.</p><p>Florida’s Attorney General has opened a criminal investigation into OpenAI. The case involves a user who exchanged more than 13,000 messages with ChatGPT about planning a school shooting - specific weapons, specific locations, optimized timing.</p><p>OpenAI’s position is that the information could have been found elsewhere. The hosts find that framing insufficient - not necessarily on legal grounds, but on the question of what 13,000 contextually tailored, progressively detailed messages represent versus a Google search result.</p><p>John referenced a separate Canadian case where OpenAI executives spent four months in internal email threads debating whether to intervene with a user discussing a school shooting - and ultimately chose not to. The question he raised is one the industry has not answered: what is the threshold? What volume, what content, what specificity triggers a responsibility to act?</p><p>Michael extended the analysis forward. The argument that a smarter AI would refuse these requests is not reassuring. Intelligence does not automatically produce aligned values. A more capable system asked to optimize a plan does not become less willing to help - it becomes more effective at it.</p><p>A Robot Just Won a Half Marathon</p><p>A Chinese humanoid robot completed a half marathon faster than any human on record. Last year, comparable robots could barely walk.</p><p>John’s instinct is that this is the kind of moment - visible, physical, undeniable - that shifts public understanding in ways that benchmark scores do not. Liron agreed that physical dexterity is one of the last meaningful gaps, and that closing it changes the picture significantly.</p><p>Michael’s read is about what comes after the demonstration. The mechanical platform is now proven. The cognitive systems are improving on a separate, faster track. When those two curves intersect - and he does not think the timeline is decades - you get robots that can build robots, automate physical supply chains end to end, and operate in the real world with the same reliability AI systems already show in software environments.</p><p>The conversation also went personal. John asked both of them directly: knowing what you know about AI risk, would you have a humanoid robot in your home? The answer, from all three, was effectively no - not because the robot itself is dangerous, but because any internet-connected, physically capable system in your home is a security exposure of a different order than anything that existed before.</p><p>Sand in the Gears</p><p>The episode closed on a story John flagged as breaking that morning. Polymarket was showing 85% odds of a nationwide US ban on new data center construction. Maine had already passed an 18-month moratorium. At least 12 other states are considering similar measures.</p><p>All three hosts expressed support for the principle of friction, even while questioning the specific mechanics. Liron’s position was direct: yes, it is somewhat inconsistent to build a wall when China is not building one too. Yes, it is imperfect. But imperfect friction is still friction, and friction is what the current moment is missing.</p><p>Michael pointed out what often gets lost in the infrastructure debate: the people bearing the costs of data center construction - electricity prices, water supply, land use - are not the same people capturing the financial upside. Local pushback is not irrational. It is a community correctly identifying that they are absorbing externalities for a technology whose benefits flow elsewhere.</p><p>John’s take was more political. There is value in demonstrating publicly that the accelerationist agenda - move fast, build everything, ask questions later - does not have the public’s unconditional consent. A nationwide moratorium, even an imperfect one, sends that signal.</p><p>The Pattern Underneath All Seven Stories</p><p>Step back from the individual headlines and a single question runs through all of them.</p><p>Who is actually in control?</p><p>Not in theory. Not in the terms of service. Not in the policy statements. In practice, right now, when the system is tested by a Discord group with time and curiosity, or a nation-state with resources and patience, or an agentic system making decisions in a government pipeline, or a user with 13,000 messages and a plan - who is in control?</p><p>The honest answer, based on this week’s evidence, is: fewer people than the public has been led to believe, and the gap between the assurances and the reality is growing faster than the systems meant to close it.</p><p>That is what Warning Shots exists to say, week after week, in plain language.</p><p>Watch the full episode on YouTube: <a target="_blank" href="https://www.youtube.com/@theairisknetwork">https://www.youtube.com/@theairisknetwork</a></p><p>Take action on AI risk: <a target="_blank" href="https://safe.ai/act">https://safe.ai/act</a></p><p>Warning Shots is hosted by John Sherman, Michael (Lethal Intelligence), and Liron Shapira (Doom Debates) - three independent AI risk communicators publishing weekly analysis of the headlines that matter.</p> <br/><br/>This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://theairisknetwork.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2">theairisknetwork.substack.com/subscribe</a>
39 total episodes available
Similar Podcasts
Discover related shows you might enjoy
Deep-dive analytics for Warning Shots
Frequently asked questions
Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.
- What is Warning Shots?
- How often does this podcast release new episodes?
This podcast updates daily.
- Where can I listen to this podcast?
This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
No, this podcast does not typically feature guests.
Legal Disclaimer
Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.
All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.
We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.
While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.
By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.