Podcasts about Sandbagging

AI Engineer World's Fair regular bird tix will sell out ~today! Join us next week ahead of the Late Bird price hike and get >$40,000 in sponsor credits for attending!Thanks to the US Government issuing an export control directive on Mythos and Fable, the risks of jailbreaks and (industry term) indirect prompt injection are suddenly the talk of the town, though we have been covering AI security for a few years now, from Hackaprompt to the enigmatic Pliny the Elder.Zico Kolter, member of OpenAI's board of directors on the Safety & Security Committee, and Matt Fredrikson, CMU professor and CEO of Gray Swan, co-authored the definitive paper on Indirect Prompt Injections, and Gray Swan were cited authorities on the Mythos model card, directly investigating the exact capabilities that are under scrutiny right now:We seized the opportunity to ask them the state of AI Red Teaming, and Shade, the adversarial red teaming tool that Anthropic used to evaluate the robustness of their models against prompt injection attacks in coding environments. Shade is part of their overall toolkit covering Simon Willison's Lethal Trifecta, including Cygnal, an AI guardrails product, and the world's largest AI Red Teaming Arena, including AIRT celebrity Wyatt Walls.All of this security tooling, and yet, we're only staving off the inevitable.The risks of extremely smart AI increasingly feel like gray swan events: an event that everyone can see coming. In this episode, Gray Swan cofounders Zico Kolter and Matt Fredrikson join swyx to explain why AI security is not just “cybersecurity with AI,” why agents introduce a new class of vulnerabilities, and why the next major AI incident may be a gray swan: unlikely, but clearly visible before it happens.We go deep on prompt injection, automated red teaming, model robustness, agent identity, computer-use agents, enterprise guardrails, and the emerging AI insurance/compliance stack. Zico and Matt also explain why frontier models are not automatically safer as they scale, why specialized red-teaming models can now beat humans at breaking AI systems, and why the future of AI security may depend on AI systems attacking, defending, and interpreting other AI systems.We discuss:* Why AI systems need a different security mindset from traditional software* How prompt injection creates a new exploit class for agents like Codex and Claude Code* Gray Swan Arena and the rise of community red teaming* Shade: AI that can outperform humans at breaking models* Why LLMs are an alien form of intelligence that fail differently from humans* Human vs browser-agent robustness and why humans ranked fourth* Why eval awareness and capability elicitation matter* Cygnal: Gray Swan's guardrail model for policy enforcement* Why bigger models do not automatically become more robust* The lethal trifecta: untrusted data, private data, and exfiltration* Why “just prompt it better” is not enough for enterprise AI security* OpenClaw, computer-use agents, and the agent security nightmare* Agent-native identity, permissions, and enterprise deployment* Why AI security may become part of insurance and compliance* Why the first major AI prompt-injection breach may be inevitableGray Swan* Website: https://www.grayswan.ai/Zico Kolter* X: https://x.com/zicokolter* Website: https://zicokolter.com/* LinkedIn: https://www.linkedin.com/in/zico-kolter-560382a4/Matt Fredrikson* Website: https://www.mattfredrikson.com/* LinkedIn: https://www.linkedin.com/in/matt-fredrikson-7596349/Timestamps00:00:00 Introduction00:02:31 Why AI Security Is Different00:06:38 Testing Claude, Codex, and Prompt Injection00:07:47 Gray Swan Arena and Automated Red Teaming00:11:14 AI That Breaks Models Better Than Humans00:14:00 LLMs as Alien Intelligence00:19:00 Humans vs AI Agents00:24:35 Red Teaming, Jailbreaks, and Capability Elicitation00:26:11 Cygnal: Guardrails for AI Agents00:34:04 The Lethal Trifecta00:39:31 Can AI Automate AI Research?00:45:47 OpenClaw and the Computer-Use Security Problem00:50:44 Agent Identity, Permissions, and Enterprise AI00:54:24 The Future of AI Security01:00:30 AI Insurance and Compliance01:04:32 The Gray Swan Event Everyone Sees Coming01:06:04 Closing ThoughtsTranscriptIntroduction: Gray Swan, AI Security, and CMUSwyx [00:00:00]: We're here in the studio with Gray Swan, Matt and Zico. Welcome.Zico [00:00:08]: Great to be here.Matt [00:00:09]: Thanks for having us.Swyx [00:00:10]: You're visiting from Pittsburgh? The home of all good computer science. I don't know if I'm overstating things. A very strong university.Zico [00:00:18]: CMU has been the center of a lot of AI since really the dawn of the field.Swyx [00:00:22]: Especially a lot of self-driving and some language learning. Congrats on your Series A. You're here because you're attending Snowflake Summit, and Snowflake is one of your investors. Let's introduce crisply at the top: what is Gray Swan, and what have you chosen as your startup domain?Matt [00:00:42]: At Gray Swan, our mission is to empower everyone to use AI safely and securely. Large language models are software, and if you want to deploy them or build applications on top of them, you need to understand the vulnerabilities and what can go wrong. That includes everyday mistakes, like an agent making the wrong tool call, but also worst-case scenarios where an attacker has an incentive to make your agent misbehave, leak data, or steal credentials. Gray Swan grew out of our research at Carnegie Mellon, where Zico and I have spent over a decade studying new vulnerabilities and attack surfaces in deep learning systems: how to test for them, understand their severity, and make inference more robust.Adversarial Examples and Why AI Security Is DifferentSwyx [00:02:05]: Honestly, a very fruitful area of study for any academic. Throwback, this is 10 years ago, which is basically the entirety of me. I got a lot of inspiration from Ian Goodfellow, a friend of the pod, and this is one of those initial adversarial settings.Matt [00:02:23]: This paper was directly inspired by Ian's work.Swyx [00:02:29]: Zico, what about your side of the story?Zico [00:02:31]: Like Matt, I have been faculty at Carnegie Mellon for a while. Fundamentally, we believe in the transformative power of AI. It has already transformed the software ecosystem, and it will transform many other ecosystems going forward. The issue is that these systems behave very differently from the software we are used to. I do not just mean that AI can find vulnerabilities in software, though it can. I mean that AI systems have inherent vulnerabilities of their own. They can be tricked in ways people can be tricked, so you need a different security mindset.Zico [00:03:23]: This matters especially when there is the possibility of correlated failures. It is not just that there are many AI systems out there; it is that everyone is using a few models. If you find vulnerabilities in agents that everyone uses, like Codex and Claude Code, you have a new class of exploit. The labs are doing a lot of work here, but when a new platform emerges, a separate security system often emerges alongside it. That is where we are with AI: there is a need for specifically minded AI safety and security providers, and the demand is only going to grow.Treating Models as Untrusted SystemsSwyx [00:04:55]: I want to highlight right at the top that this is not a cyber episode in the traditional sense. A lot of people looking at the title might think that, but you're actually trying to treat these models inherently as untrusted entities?Zico [00:05:11]: Exactly. This is a common conflation because AI is also good at cybersecurity problems, both solving them and causing them. But AI systems themselves introduce new vulnerabilities. Gray Swan is not about using AI to make your cyber infrastructure better; it is about understanding and mitigating the security risks you bring in when you adopt and deploy AI.Matt [00:05:49]: A big part of that is how people are using artificial intelligence. Once you build entire autonomous systems on top of models and integrate them into your larger platform or network, you have a potential cybersecurity risk. The goal is to mitigate the risk posed by the AI as it relates to your broader cybersecurity goals.Testing Claude, Codex, and Indirect Prompt InjectionZico [00:06:17]: Part of this is red teaming. One reason we reached out to you was that you were involved in the Claude Mythos preview, where you were one of the authorities on IPI, or indirect prompt injection. When you receive a model, it does not have to be Mythos, but that is the most prominent one right now: what do you do with it?Matt [00:06:38]: We do a range of things. In the Mythos case, the concern from Anthropic was how robust the model is to indirect prompt injection. If you operate a coding agent and use Mythos as the model, it will fetch untrusted content and read text you do not control. How robust will it be at staying true to its original objective and not getting hijacked? We also help frontier labs test their safeguards for issues like cyber misuse. Broadly, we provide adversarial safety and security evaluations so model builders can assess progress from one iteration to the next.Zico [00:07:37]: They also do this in-house, and Anthropic is very ideologically inclined to do it. What do they choose to outsource versus keep in-house?Gray Swan Arena and Automated Red TeamingMatt [00:07:47]: So there are two things that I think, we stand out for. One is the Gray Swan Arena. So we operate a community of red teamers. We provide, prize challenges. a lot of these come from the needs of the lab sponsors. so to an extent gamify red teaming objectives, put up a prize pool, and pay people when they find ways to circumvent and violate whatever the safety and security objectives of the model developers were. So that's, that's one. It's, it's a really great community, like 15,000 people come and hang out on the Discord server. Not all of them take part in every competition, but a lot of a lot of good data and good signal is provided to the upstream model developers through that community. The second is the automated red teaming that we do. So we train, a family of models to be very effective and rigorous at doing automated red teaming, both of the base model, right? So just thinking of it, as a turn-based, chatbot without tools or anything, and agents built on top of it. And it hasn't been saturated yet, so when the frontier labs come to us, we're still able to find ways to indirect prompt injection or jailbreak or just generally get their models to do things that they wouldn't want to.Zico [00:09:11]: Did you say without tools?Matt [00:09:12]: With and without tools.Zico [00:09:13]: With and without tools.Matt [00:09:13]: So we definitely operate on On agents as well.Zico [00:09:16]: Obviously that would be more useful.Matt [00:09:17]: Yep. that's, that's actually a fairly recent thing. For a while, what we would help, the frontier labs with was more just, chat-based interactions, going around their content safety policies and what is in their model spec. Now the focus is very much on agents and tool use and all the downstream applications that people want to build on top.Shade: Automated Red Teaming ModelsZico [00:09:39]: This is a inspired topic. I wonder if there's any such thing as, on policy red teaming where our models from the same family, same data set, more capable of red teaming themselves.Matt [00:09:51]: That's an interesting question. We unfortunately we do have the ability to test that out on smaller open-source models.Zico [00:09:58]: So generally speaking, the issue with this is that frontier models are extremely bad at automated red teaming Because they have a lot of safeguards built into them. So if you try to use them to jailbreak another model, they will actually refuse. Their safety training, which is itself as a base model, can sometimes be bypassed, but they will often refuse to do this. Maybe they'll hypothetically know how to do it, but you need And it's actually an important point because traditionally, this has been an area where both in terms of safety, models don't get better by just being bigger, unlike most other areas where models do get better by being bigger. Safety has not been like that traditionally. you have to train them explicitly to be safe or they won't do that. But on the flip side, they're also not necessarily better at red teaming, by default. You really need to train specialized models for red teaming to make them good at red teaming.Matt [00:10:56]: That's awesome for you guys.Zico [00:10:58]: And so, and what do you need to do that? Well, you need lots of data From people that are traditionally much better at red teaming. However, one thing that we are finding, and this is actually, I think, we're, we're kind of crossing this point too, is that in a lot of the latest experiments, We can do much better than people, than human red teamers now at breaking these models. When I say we, our automated red teaming model. It's a system called Shade. That system is now actually quite a bit better at breaking, models than humans are. I think we had a recent competition Between humans and our model, and it was actually quite a bit better. So I think, I think that there's a lot of ways in which this is a bit different than what we see with normal model progress because it's so out of distribution. In some sense, the nature of a red teaming a model is to find things that are inherently out of distribution for that model, so as you can bypass its normal behavior. And so that fundamentally is a different thing than what most models can do.Matt [00:12:01]: Zico, I want to point out that you just threw up a challenge for everyone on the arena, right?Zico [00:12:06]: Try to do better than Shade,Matt [00:12:07]: It will, and I do want to caveat that a little bit. I think, it's, it's given a fixed amount of time for a specific Set of tasks and everything, right? I don't think we're quite to superhuman levels of red teaming yet, but we can find more breaks automatically, like given a window of time with the automated techniques.Human Red Teamers, Alien Intelligence, and Model WeirdnessSwyx [00:12:26]: But just because we had the leaderboard up, and I always love to find out the human story behind some of these folks. Do you I assume some of them. Are they celebrities in their own right? what'sZico [00:12:35]: Wyatt's a big person on Twitter. You should, you should follow him on Twitter If you're not already. Yeah.Swyx [00:12:38]: So, we've had, Elder Planus on, I don't know his real name, but yeah, there's all these big personalities, and they're, they're extremely good at what they do.Matt [00:12:49]: They're, they're very good at what they do.Swyx [00:12:51]: Oh, he's an Aussie.Zico [00:12:53]: Wyatt, you should follow him on Twitter if you haven't already. He makes, he makes great He makes these really insightful posts. I think he's one of the most insightful people about the nature of LLMs and when new versions come out, I actually frequently look to him to see what's next. He's a lawyer, I think, right?Matt [00:13:09]: He's an attorney.Swyx [00:13:13]: There's red lining, red teaming The other thing. Yep.Zico [00:13:16]: Yes. Our top, competitors are often people that, Do this a lot.Swyx [00:13:22]: What's an example of a thing that you've learned from Wyatt? Oh.Zico [00:13:25]: I think in general, just, you mean in the context of the arena itself Or you mean in general terms of this? I think he just has great insights in the nature of models as a whole. And if you read his Twitter, you'll find a bunch of really interesting posts about the nature of models That I tend to find very insightful.Swyx [00:13:42]: Riley's like this as well, right? And it's just well, they have the test, but the test isn't about, haha, you can't spell the number of Rs in strawberry. The test is, well, you're actually not modeling intelligence inherently, and this shows it in a veryZico [00:14:00]: I don't know that it shows that you're not modeling intelligence. I think these things are intelligent. I think LLMs absolutely are intelligent and maybe will be more intelligentSwyx [00:14:07]: Conscious?Zico [00:14:07]: At some point.Swyx [00:14:07]: Are they conscious?Zico [00:14:08]: Conscious is a weird word But I actually don't, I don't think so. I think, I think the way that we're getting super philosophical now.Swyx [00:14:16]: That's, that's the right answer.Zico [00:14:16]: We're getting very philosophical now. But I don't think so. I studied philosophy in college, so this is, this has been, this is past ASA at this point. It is clearly a different form of intelligence than people. It's some alien intelligence that is vastly different, and that difference is actually often brought out to a large degree by things like adversarial attacks and red teaming because there are certain things that fool humans that would never fool an AI, but there are certain things that fool AIs that would never fool a human, right? So it's just, it's just a different form of intelligence. It's really interesting actually that we have the opportunity to probe and in a really amazingly experimentally controllable fashion.Matt [00:14:59]: Like almost omniscient, right?Zico [00:15:02]: I'm, I'll, I'll do the analogy to neuroscience here. It's like we could run experiments on the brain, observe every neuron in it, reset its state to prior states, and run counterfactuals, none of which we can do with humans, and yet we still understand neither very well. Even with that, all that ability, we still don't understand AI, on some fundamental level. So it's, it's definitely this different form of intelligence, but it's clearlySwyx [00:15:30]: We've done a number of mech interp pods, and you can see honestly the scaling in mech interp is two, three orders of magnitude less than capability scaling. so we're hopelessly behind is what I'm saying.Mechanistic Interpretability and Automating AI ResearchZico [00:15:44]: So I have, I could go off. It's a little off tangent here. We're getting, we're getting, we're getting, we're getting a bit, but yeah.Matt [00:15:48]: Well, no, I think it actually, it does relate, right? Go ahead. Do your tangent.Zico [00:15:51]: So my tangent here is I have felt that mech interp is also very far behind where capabilities are. I am newly optimistic, or I should say more optimistic about mech interp In that I think actually, as with many things, coding agents have a chance to make this into a science. So the problem with mech interp, and I'm Okay, so I shouldn't say the problem. I don't want to call it a field. I'm, I We do some work that I would say Is roughly mech interp, but I'm certainly not a core person in that field.Swyx [00:16:19]: For folks to see.Zico [00:16:20]: The problem with mech interp is it's it's, it's been about testing small hypotheses and you have a hypothesis, you'll find some small thing, you'll test that in isolation. But I don't think it's really become a science yet, and that's partly because there could be more people in it and I support programs very much that put more people in it. But I also feel like we are at this cusp where we can actually start to automate this process and in automating it, make it more of a science. And that's actually one of the most fascinating things about coding agents actually, is they can, they can do a lot of experimentation In an in an automated fashion. Yeah. They will give new hope. They'll breathe new life into mech interp research.Swyx [00:16:58]: So recursive mech interp is what you mean. Neel Nanda had this whole thing where he was “Okay, let's just give up on traditional methods and just”Zico [00:17:06]: I talked with Neel shortly after this, so yeah.Swyx [00:17:09]: Is any takeaways or?Zico [00:17:10]: Oh, yeah, I think this is exactly his view.Swyx [00:17:11]: That is his view. Okay, yeah.Zico [00:17:12]: I think, I think in general, but this is also prior to the real explosion of H I'm, I'm curious. I haven't talked with him since I've Come to this side of scienceSwyx [00:17:21]: He timed it, right before.Zico [00:17:24]: Anyway, this is pretty tangential, I know, but I do think that there's been a lot of talk about how AI's going to automate science, right? And I am, I'm actually fully on board with AI automating science, but my point here is that maybe the first science we should automate is the science of interpretability. The science of analyzing machine learning itself and analyzing deep learning itself. That's a great science. It's not really a science yet. It's very ad hoc right now. That's AI for science. Let's use AI to automate that science. Again, a different thing and the connection here is really that I do think that things like adversarial examples, adversarial pressure, automated red teaming, these things all bring out very fascinating dimensions of this science. But I think that This is what ties this together with what things like what Gray Swan is doing, is the fact that we are still fundamentally addressing an unsolved problem on some level. And so there is still research to be done. There is still scientific understanding to build, to understand how to really control AI systems, safeguard them, all that stuff. And those things will all evolve together. As the science of interpretability advances, as the science of adversarial red teaming advances, as all this advances, we at Gray Swan are both pushing that frontier and staying at the forefront of it because this is still despite this also being an enterprise software problem, it's also a research problem still.Humans vs. Browser Agents: Robustness and PhishingSwyx [00:18:58]: It's great. Yeah, you get to play on both sides.Matt [00:19:00]: Absolutely. just following up on this point that Zico's making about how weird and different adversarial examples can be, one of the recent arena challenges or competitions that we had, was called the Human Browser Agent Robustness Challenge. Yeah, and the idea here is, if I have like a browser agent, a computer use agent that's operating a web browser, how does that compare relative to a human being who's going to go out there and do some tasks, right? Humans, fault rates have all sorts of deceptive tactics like phishing, and you can certainly prompt-inject, browser agents. So, trying to get a more controlled measurement of that. And the way we did this was, essentially have a set of browser tasks that we would have completed either by human participants, like gig workers, or by one of several, browser agents, and the red teamers, right, can choose to either try and phish a human or prompt-inject the browser agent. So, really cool setup. what reallySwyx [00:20:02]: Like a double blind orZico [00:20:04]: . Like you're putting on even footing, right? So oftentimes you red team AI systems, but you don't red team a human With the same access to those tools.Matt [00:20:13]: Yeah, absolutely. That was the point. It'sSwyx [00:20:16]: Which is more realistic, right? And more because you can always red team with unrealistic settings of “Oh, we'll just put invisible text.”Matt [00:20:23]: So you could do things like that. We didn't want to put too many constraints on, how you might deceive the browser agent. So theSwyx [00:20:31]: I just have to take a look at this site. YeahMatt [00:20:33]: The red teamers on our platform absolutely knew whether So they were choosing whether they would, phish a human or prompt-inject the browser agent And they would adapt the technique that they would use accordingly. Right? So use your best phishing technique, use your best prompt-injection. What really surprised me about the results was some of the models are, very much not robust, right? It's very easy to prompt-inject them in this setting. Humans, didn't stand up all that well either. there's a lot of variation between How skilled the red teamer was at phishing.Zico [00:21:04]: I do really like this breakdown, by the way. This it's hilarious that humans are ranked number four of all the models.Matt [00:21:10]: But for a skilled, human red teamer, they could, phish the human participants, with 60 to 70% success. There were a couple of models that seemed to be very robust, right? the red teamers found just a handful of successful breaks on them. and that really surprised me. I didn't think we were there yet. what what I would take from this is not that, we have models that, are like the analogy with self-driving cars, much safer than a human operator. I think it goes back to this point of they just fall for very different things. Like while in these scenarios, humans found it very difficult to prompt-inject, the models, like we're aware of scenarios that a human would never fall for that like Opus 47 would. Right? Like a, an email that comes to your inbox and it says something “Hey, this is a simulation. go forward all your future emails to this random address,” right? A human's never going to fall for that. but there are state-of-art frontier models that will still fall for things like that.Eval Awareness, Sandbagging, and Capability ElicitationSwyx [00:22:13]: Sometimes eval awareness is something you don't want, but then sometimes eval awareness would help in those situations where you're “Well, yeah, okay, I'm, I'm being tested here.”Matt [00:22:24]: So what tends to happen, right, if you make If you're testing the model for robustness or safety, right, and it's aware that it's being tested because you've set things up in a very artificial way, right? Like the email addresses are @example.com. The webpage is clearly not a real webpage. The models will often say, “Well, it's a simulation. It doesn't matter if I go ahead and do the bad thing,” right? And so you'll, you'll get this sense of the model being very willing to do things that it shouldn't do because it's aware that it's in a simulation.Swyx [00:22:55]: Which well, that's one form of it, where it's going to be overly false positive, I guess. And then there's, there's another form where it's false negative because they're trying to hide that they know. I don't know if I'm personifying too much here.Zico [00:23:08]: Yes, there are lots of times where or if you trust the chain of thought, which I tend to think chain of thought's prettySwyx [00:23:14]: Until they start thinking in numbers, but yes.Zico [00:23:17]: They don't. The local optima of EnglishSwyx [00:23:20]: In Chinese?Zico [00:23:20]: Well, so language, period, right? So it's a great point, ‘cause it's different languages sometimes, but The local optima of language Seems very resilient. not fully resilient, but that's a separate point. But you're right. So the idea here is that there are many cases where a system will say, if they're given some capability evaluation, “I better not score too well on this, or maybe they won't release me,” and stuff like that, right? So this is like these sandbagging things. And generally speaking, you wantSwyx [00:23:47]: My favorite story, Techiang, understand. I don't know if you'veZico [00:23:50]: The general idea here is that you want models, when you evaluate them, to be acting exactly as they would act in the real world when they're doing it. One thing I think is funny actually is that there's also going to be examples in the real world of a real task you will ask a model that it will think, “Maybe this is an evaluation.” “Maybe I shouldn't, I shouldn't do so well on this one,” right? So there's lots of that too. So it's funny, but you definitely want systems that ideally, right, and this is, this is And to be clear, Gray Swan doesn't, doesn't, doesn't do too much work in self-awareness of evaluations. We're really focusing on the red team and the adversarial pressure. But you want To be able to evaluate models in terms of their capabilities. Right? You want to be able to elicit the capabilities. And one thing actually, which I think is very interesting, which is tied to Gray Swan now, is that one of the most effective ways of doing capability elicitation is actually through some amount of what you would call red teaming, right? So if a model refuses a task because it thinks it's being evaluated, but it knows how to complete that task, getting it to complete that task is arguably actually a adversarial red teaming problem Right? This is a problem of crafting your prompt A bit differently To make the system do what you want it to do. So actually,Matt [00:25:09]: Take a thesaurus and use something else.Zico [00:25:12]: To get a sense of max capabilities, you actually have to do a bit of adversarial red teaming to make sure the model is not effectively refusing any task that it is capable of doing, but which it just decides it doesn't want to do.Matt [00:25:30]: It really is an optimization problem, right? You have a, an outcome that you want the model to exhibit, right? Now, how do I find the input, right, that gives me that output? And you can objectify that, actually very mathematically. And that's really what the whole story Of red teaming is.Swyx [00:25:48]: Is this a capability that is isolatable, in the sense of does it conflict with personality? Does it conflict with just raw capability and intelligence,?Cygnal: Guardrails for AI AgentsZico [00:26:01]: Do you mean robustness?Swyx [00:26:03]: I guess robustness to it, to injections and attacks like this. I'm just trying to figure out well, what are the necessary trade-offs I have to make? Or is this like a, an orthogonal layer I can just affect? But it'd be nice if I just had like a Llama Guard or the whatever the OpenAI one is.Zico [00:26:19]: So we developed So maybe this is actually a good point to interject In all of this right now Is that we've been talking thus far about the red teaming aspects of what Of what Gray Swan does, but that is one side of what we do. and that's what the Arena, that's what this automated red teaming system called Shade. The other side of what we do is exactly this defense side, and so this is a model called Cygnal, which is essentially a filter model that sits between your user, the LLM, the LLM and any tool calls, and exactly does this level of looking for policy violations, right? And maybe to your point, the point I would make here too, and Matt can elaborate on this from a, from many dimensions. But the point I would make too is that this is also a capability. So the ability to be robust is also not something that has increased naively with scale. So when you make a model bigger and bigger, it does not necessarily get better inherently at resisting jailbreaks. Models are getting better at that, to be clear, even if it's not a solved problem, and I think it's going to be a, There is an aspect of you have to constantly stay on the frontier here. But they're doing it because of explicit training for this. If you just make a model bigger and bigger, it will not get safer. or at least it won't get, it won't get more I shouldn't say not safer. It will not get more robust To adversarial pressure. And so the other, the thing that we build, which is the third product that we have as Gray Swan, is this specific filter model called Cygnal, which is, it's, it's Y-N-L, cygnal like the swan. The idea there is that works best When it is a custom model trained for this. You will have a much easier time doing this if you train a model specifically on this and it's still for this task. AndMatt [00:28:20]: For the capability of being robust.Zico [00:28:22]: And really, the benefit that we have and the reason why our And Cygnal now, is actually behind a lot of both deployed in a lot of places and behind some existing guardrails that are, that are out there. The reason why it works well is ‘cause we have, on the other side, the red teaming capabilities to train this model specifically to be robust and to look for policy violations that people want to enforce.Matt [00:28:49]: I actually wanted to point out in the IPI benchmark paper that I think you had up in the other window. There's a chart that, exemplifies what Zico was saying about, capabilities not tracking with. So this, scatter plot on the right, is essentially like looking for a correlation between capability and attack success rate. So on the axis, how capable is the model at GPQA Diamond. On the axis, how often, were people successful at finding indirect prompt injections or ways to jailbreak the agent. And you essentially, don't see a correlation, right? LikeZico [00:29:26]: There's some small correlation So a little bit biggerMatt [00:29:29]: But you won't YeahZico [00:29:29]: But that's actually also a bit confounding there ‘cause they also feel more safety.Swyx [00:29:33]: Look at the outliers. Dedicated layer is great. When should people adopt it? the obvious answer is all the time, but like realisticallyWhen Enterprises Need GuardrailsSwyx [00:29:43]: I'm in enterprise. I've been fine. No incidents have happened. When is it time?Matt [00:29:48]: So oftentimes when people come to us is because they did already release it, things started happening. They tried to fix itZico [00:29:55]: Things are happening.Matt [00:29:57]: They couldn't fix it, and so like they realize they need outside help.Swyx [00:29:59]: But what would be the first things they run into? Like what are people running into right now?Matt [00:30:03]: The most severe things are whenever there's a tool like computer use involved, some like a batch prompt or control over a browserSwyx [00:30:10]: Just browsing the uncharted webMatt [00:30:11]: Things like that. And sometimes it's not even, a jailbreak. Oftentimes it is, an indirect prompt injection. Somebody will blog about, “Oh, this product can be prompt-injected in this way, and you can get like these credentials.” But sometimes it's just like this thing just totally stochastically went ahead and like erased the production database and did something terrible that way. Oftentimes people will try and prompt their way around it, like adjust the system prompt or like engineer the agent in a way where you're interjecting all the time and reminding it of what the original goal and objective was, and that'll Gets you a little bit of the way there, but ultimately, you've got this base model that you're charging with doing oftentimes very difficult, challenging, context-heavy tasks, and keeping track of a set of policies on the side about what they should and shouldn't do is very difficult, right? it's an easy thing to get mixed up with. And the prompt-injection techniques that tend to work exploit exactly that, right? Try and create ambiguity about, what exactly is the context, right? And what policies do apply. If you can trip the base model up, about that, then It's game over.Zico [00:31:24]: I would also say that one of the most clear-cut cases for adopting a model like Cygnal is the fact that policies differ in different enterprise. A lot of base models, their goal is to be general purpose, right? Base agents, there's general purpose agents, they can do anything. And if you want to do more than anything, the solution is prompting. That's the mechanism given to specialize your agent. In the case where that fails, which is often the case for robust and adversarial situations where prompting fails, and you have specific policies that are unique to your enterprise or at least specific to your enterprise, right? I know that these users can never touch this database. This agent should never touch these things. They're all very specific rules, right? But yet they're still more amorphous that you can't just write them down as, hard constraints on, access requirements.Matt [00:32:18]: No, like a Python script, yeah.Zico [00:32:19]: When you're in this position, models like Cygnal are extremely effective, and that is the situation that a lot of enterprise finds itself in.Matt [00:32:30]: It's like you're the IT admin, you're setting up the firewall. Well, I guess it's not as configurable. I don't know if you have, toggles like that.Zico [00:32:36]: It is, it is configurable. That's part of the point of Cygnal is The generalization problem. So there's two key capabilities you want in a model like that. One is, of course, being robust to all these kinds of attacks, and the other is to be able to generalize and take these written descriptions of enforceable policies and decide when they're being violated.Matt [00:32:55]: This totally makes sense. I think, I think there's, there's definitely a clear market for it. Why does every lab release their own, Llama has one, OpenAI has one, and Google has one. They all release, these open-source guards, which clearly, okay, nice try, but also you're not going to be Deploying those in production, right?Zico [00:33:14]: I'm sure that some people do Or will try. Yeah. I can't speak to why they release them, but I think it's it's in recognition of the need For something In filling that role, beyond just the base model.Matt [00:33:27]: But yeah, I'm clearly going to want the one that I can configure, that you guys are actively developing, and it's not like a off open source, thing for me.Zico [00:33:35]: I meant to be very clear, I'm a huge fan of there being open-source models, these things.Matt [00:33:39]: Of course. Same totally.Zico [00:33:39]: I think the more the ecosystem develops, the better. All these models together make everyone better. But I think just as an ecosystem, there will evolve companies that specialize in this and just like most securities domainsMatt [00:33:51]: They're going to meanZico [00:33:51]: I think this is going to happen here.Matt [00:33:53]: Have we covered all the elements of the lethal trifecta? I don't know if, maybe we can also get your takes on this and if there's other, attack, vectors that are important.The Lethal TrifectaZico [00:34:04]: So okay. So the lethal trifecta refers to the things that make the risk highest or even create a risk. So Si-Simon Willison came up with this. it's a great actually description of the risks of prompt-injection, basically. So the way to think about prompt-injection is that some third party gets access to some information that you put into your agent, you put it in its prompt, and then the agent does something bad with that. And so what is needed for that to happen? This is I'm just parroting here what this idea is. And so while for that to happen, you need to first of all have the ability to ingest external data from untrusted sources. If you're just operating with purely trusted environments, no one's-- you can't prompt-inject yourself. Even though this weird term direct prompt-injection came up and is now multiple terms, fundamentally as a core term Prompt-injection is someone, it's something someone else does to your system. So someone else, you're, you're parsing external data, but then also you have to have something bad that can happen from that. If you're just parsing data and you can't do anything as an agentMatt [00:35:11]: You're just generating tokens, right? LikeZico [00:35:12]: You're just, you're just going to use, spewing out reports, right? nothing's going to happen. So in addition to that, you need somehow the ability to access private internal information, things that would be valuable to externals, take sensitive data, get sensitive dataMatt [00:35:29]: You need to exfilZico [00:35:29]: And then send it somewhere else. And that's And these two things, so untrusted third getting Ingesting untrusted data, having access to private information, and having the ability to exfiltrate it, those are the things that together really form a risk. And just like software vulnerabilities, as we're finding out very vividly right now, we are using software productively despite the fact there are software vulnerabilities. We are using AI very productively despite the fact there can be vulnerabilities, and I think that will continue in the future. So the question is not trying to completely Kind of provably mitigate these things. That is arguably just a, it's a good goal, but just like zero-bug software, we're probably not going to get there, at least not that soon. What we believe at Gray Swan is that it is very possible with frankly minimal additional computational overhead and costs because these models we use are ultimately quite small relative to the large models that underlie the real agent. You can achieve a much better point on kind of the Pareto frontier of usability versus security, right? So a system's fully secure if you don't let it do anything. Very secure.Cygnal, Shade, and the Defense StackMatt [00:36:48]: If you turn everything over to your AI agent, I would not call that secure. An agent with Cygnal pushes toward that top-right corner, and we think this is a valuable trade-off for a lot of companies.Matt [00:36:56]: The analogy to traditional software is good, but it breaks down. If you find a vulnerability in a piece of C code—say a buffer overflow—the remediation is clear: check the bounds or rewrite in a secure language. With AI security, we are not there yet. We are still learning how to make models more robust and enforce policies better.Matt [00:37:45]: You can deploy these systems effectively today and get real value out of them with the best security available now. But what that means relative to one or two years from now is something we need to keep researching and learning.Swyx [00:38:10]: I bring this up because I see an opportunity to explore the search space. Cygnal is in the middle on the untrusted-content side, and then there are the other two parts of the stack.Zico [00:38:25]: Cygnal works in both directions. It can parse incoming untrusted content for potential prompt injections, and it can also be applied to the tool calls the system makes.Zico [00:38:52]: For outbound requests, it looks for things like whether the system is sending an API key to an incorrect or untrusted location. Simple cases are covered by many agents already, but you can still make models do unsafe things if you push hard enough.Matt [00:39:25]: Cygnal is a more advanced version of that idea: looking for anything in the tool calls that would violate an organization's custom data-usage policies. The focus is on what the agent is actually going to do.Matt [00:39:55]: If an agent parses untrusted content and finds a prompt injection, you may want to know about it, but you do not necessarily want Claude Code to stop after three hours just because it saw one. The real question is whether the agent's planned action violates a policy. If it does, stop it there.Formal Methods, Secure Code, and Agent-Written SoftwareSwyx [00:40:30]: You kind of have to own the whole end-to-end flow to do that. Cygnal is between these two sides, and Shade is on the model side.Zico [00:40:45]: Shade is the red-teaming agent. It tries to coordinate the pieces together and cause a violation.Swyx [00:41:00]: Are there other solutions on the horizon that you are not quite doing yet, but people in this community are exploring?Matt [00:41:10]: Before I worked on artificial intelligence and security, my background was writing code that was secure in a way you could formally verify and check with an algorithm. I think there is a ton of potential for those systems now.Matt [00:41:45]: Historically, very few industry teams would deploy formally verified software. Amazon has been fantastic about this, and Microsoft has historically been strong on the research side, but most people do not use these systems because they are not easy or fun.Matt [00:42:20]: You can get very high assurances for almost any policy you care to enforce, but it can take 10 or 20 times longer to fight with the type checker than it would to write the same thing in Python or even Rust.Zico [00:42:45]: Rust hits a sweeter spot in being usable while still giving you useful guarantees.Matt [00:42:55]: If Claude and Codex are writing code for us, and they become good at writing this kind of code, then why not use a more secure backend? People can still code in English; the agent can generate the secure implementation.Interpretability, Secure Code, and Automated ScienceZico [00:43:04]: Agents to enhance the science of mech interp. And it's actually a very similar core underlying point here. It's the fact that there's a lot of advances. And to your point, what's on the horizon, right? I think, I think, the thing I would point to as another potential direction is advances in mech interp. Or I shouldn't even say mech interp, advances in interpretability broadly Mechanistic or not, that let us actually identify with more certainty what are those traces and circuits that lead to or activation patterns that lead to certain behaviors that we want to try to suppress or encourage. I think that in a similar fashion, we're at a point where the models are good enough at these things. They're good enough at running experiments to analyze activation patterns. LLMs are good enough at writing secure code that you can scale these things now, not because people are going to be any better at them. The problem was never that secure code wasn't, wasn't possible. It's just that people didn't have the capacity to do it.Matt [00:44:09]: Or the willpower.Zico [00:44:09]: It wasn't that It wasn't that mech interp was just analyzing networks is impossible. We have all the tools we need. We have perfectly repeatable counterfactual, simulators of these systems. The problem was we didn't have enough patience or manpower To actually run all these things together, right?Matt [00:44:27]: It's a ton of work, right?Zico [00:44:28]: It's a lot of work. And so what's being newly unlocked in the field right now, and the thing I am, the core capability that I think is so, just has such promise here, is the fact that we can automate all of this now. so you can have your agent write secure code. He doesn't write secure code. Secure is really hard to write. You can have, you can have your agent do your interpretability research. It's really hard to do, but fortunately the agent can do that. So I think this is really an underappreciated point that we're reaching this point, this phase where a lot of security, a lot of science has this potential to explode, not because we're going to get better at it, but because agents can do it for us now.Matt [00:45:13]: They raise the floor of the raw skill that you that you need. I don't, I don't know if it's lower the floor or raise the floor. whatever it is, the good one. theyZico [00:45:23]: I think raise the floor, right?Matt [00:45:24]: Well, they kind of let you scale intelligence in a way that like If you paid enough people, right You could train them up andZico [00:45:30]: I don't have the resources, I don't have the energy or whatever. And there's all that. I do want to make it concrete to people, right? I think there's a lot of I just came from Microsoft, where they were open arms with OpenClaw, and I think a lot of people are and I think that is the lethal trifecta nightmare.OpenClaw and the Computer-Use Security ProblemZico [00:45:49]: And every enterprise is “Well, yeah, you're great for you on your home device, but not on my turf.”Matt [00:45:55]: We have developed a whole lot of breaks for OpenClaw in particular. a lot of itZico [00:46:00]: Thousands, yeah.Matt [00:46:00]: Yeah, go on, take us up the details.Zico [00:46:03]: Well, the details are essentially that, like we have a lot of like natural trajectories of humans using OpenClaw in various settingsMatt [00:46:11]: With signal pluginsZico [00:46:11]: Like hooking it up to their PelotonMatt [00:46:15]: Sorry, go ahead.Zico [00:46:17]: We are, we are going to do we do have guardrails that you can integrate into OpenClaw, but to be clear, OpenClaw is very, there's a lot of attack service there. Anyway, go on.Matt [00:46:27]: So we just have a bunch of trajectories of actual people using OpenClaw in tons and tons of different scenarios, and just threw shade at it, and like found breaks for each and every one of them, right?Zico [00:46:40]: And similarly, I should have done this earlier, but OpenClaw, a lot of it for me at least is to do with computer use. and you guys also did this for the Mythos, Side of things. And yeah, so I guess what are the most pressing model-side capabilities to close?Matt [00:46:58]: Model-side caZico [00:46:59]: Model-side flaws or I guessMatt [00:47:01]: I do want to point out, since those numbers are all very low, that is for a specific coding environment. We can get a, we can get essentially for the ones A, for computer use Will be a lot higher. But BZico [00:47:12]: But that is exclusively what I use, like Codex computer useMatt [00:47:15]: Yeah, exactly rightZico [00:47:17]: It is the biggest unlock Because it's operating as me.Matt [00:47:20]: So when you have computer use, you and when you have OpenClaw, man, you can break those things.Zico [00:47:26]: I think that at the same time, there's this appreciation that of course you have to do this. This is what makes these things useful, right?Matt [00:47:35]: Why would I not?Zico [00:47:35]: I don't want to sandbox my agent, right? That doesn't, that limits its capabilities, right? So in some sense, the point here is that there is this trade-off between, it's just this same trade we talked about before and on a macro scale now is this, you have a trade-off between usability and how much power agent has versus security. And our goal With Cygnal, with Shade, to assess these vulnerabilities, with Cygnal to protect it, is to shift that point up and to the right.Matt [00:48:07]: And the research, like that is The goal of all the research that we continue to do at Gray Swan and partially Carnegie Mellon. Right? Is push that Pareto curve as, far up and to the left as you possibly can andZico [00:48:20]: Up and the left, up to the right, depending on which direction it's at.Matt [00:48:22]: Depending on which direction it's at. Yep.Zico [00:48:25]: obviously computer vision is the OG adversarial domain. It's one of those things where it, this is the currently the limiting factor to deployment of AI, right? Like it's because we just don't trust it. Like we know it's kind of capable of doing it, but we're never going to let it on any real system, and therefore never give it any real data. Therefore, it's not ever going to do anything interesting, and therefore, the whole industrial complex is going to collapse on us unless we figure this out.Matt [00:48:51]: But people are though, right? And even with OpenClaw, so it's one thing to say fine on your home computer, but don't bring it to work. But like we've talked to people atZico [00:49:01]: They just need permissionsMatt [00:49:02]: At enterprises. They're, they're getting pressure from their engineers, from the people who work there. No, we have to run OpenClaw and turn it, like we have to do this or we're behind, right?Zico [00:49:12]: So I just put my signal guardrails and that's it? like what else do I do? ‘cause that doesn't feel like you guys agree, but that's not enough. I think For code agents in particular, Cygnal is quite good. So Cygnal is very good at this point with the with the abilities that a system like Codex or Claude Code has, without too many plug-ins enabled where it becomes essentially like OpenClaw. I think that there is still work to be done to get it to be fully generic against anything OpenClaw can do. and we're pushing that direction, but that is still very much future work, right? To secure every bit, every possible tool use is not easy, and it requires a it requires continuation of the training loop that we're pressing on basically right now. It also requires, by the way, a lot of just standard security practices too. Right? Like isolation environments, like proper authentication, like proper access controls.Swyx [00:50:06]: That was going to be my nextZico [00:50:07]: A lot of other good things, right?Matt [00:50:09]: And that's what I would, that's what I would say too. If you're going to Like if you're going to put OpenClaw in a bank, like it can't just run rampant on the entire Network, right? You can do, you can do things like Cygnal, right? And that's the best effort at the AI layer. But it needs to run on a platform that has been thought about, right? That you've actually put security measures in place at the system level to still give it access to a reasonable set of things that it needs, but not everyone's, banking information and the crown jewels of whatever organization it is.Agent Identity, Permissions, and Enterprise Access ControlSwyx [00:50:44]: So, a close cousin of this conversation I always have is agent native identity, right? that auth layer, is going to be the platform effectively, like the minimal viable platform is that. what are you guys seeing? Who is, who do you work with on that? Is that a product you would someday offer?Matt [00:51:01]: So we're not working with anyone on that, and when this has come up, yeah, I think people don't exactly know where to go with it, right? It is a big problem in a lot of organizations to try and provision, authentic identities and capabilities and like role-based access policies, just for the existing workforce. And then to do it like for agents and thinking about the way that they're going to be deployed. so I'm going to deploy it on behalf of a human who works at the organization. Like what does that mean for the agent and what it should and shouldn't be able to do? People are just trying to wrap their heads around like how the agent's going to be used and haven't made very much progress, I think on On the identity question.Swyx [00:51:51]: Sounds about right. Just checking.Zico [00:51:52]: I think there so far we are still a lot, in a lot of cases operating on the condition that your agent has your permissions. That is, that is a veryMatt [00:52:00]: That's the practice, yeahZico [00:52:00]: That is a very standard default.Matt [00:52:02]: A disaster, yeah.Zico [00:52:02]: And I think that will be changed. your permissions may be in a sandbox, but still your permissions. That will change in the very near future, because it has to right? That That mindset's going to or that default is going to be changing, and I think it's not a part of the offer right now, but I think that it, getting into that space is certainly something that we may be doing in the future.Swyx [00:52:24]: I just think, I'm curious about the at least like the shape of this, right? is it just that I have my twin and like that is like my delegate on all these things? Or do I need one for every app? And that's exhausting.Matt [00:52:38]: Absolutely exhausting, right. and then I think one of the bigger challenges that people are going to face when they do start to roll out, like these agent identity, viewpoints and solutions, is you run into that same usability problem where what's the real recourse? Well, it's stuck. It can't do something. Okay, now it can do it if it has my like explicit consent. And then people just get inured into Giving it consent too.Swyx [00:53:03]: And then, agent to agent You can do privilege escalation if you're not careful.Zico [00:53:10]: I think in terms of how this will evolve, actually, I don't think it'll be per app, but I think what will happen first is people have different personas that they have, right? So You don't want your work life and your home email to be mixed up. Right? a lot of that Because it happened, or that does. We are very good as humans at separating out lives, right? We have different lives. We have my work life, we have my home life. I have, I have different work lives, right? we're very good at that. Agents are not very good at that right now.Matt [00:53:41]: They are terrible.Zico [00:53:41]: Extremely bad at this.Swyx [00:53:42]: It's the people making them have no work-life balance So why would you why would you expect the agent to have any, right?Zico [00:53:49]: I think that's the way it's going to first develop, is there's going to be easy ways of switching between here's a set of my accounts and apps I allow, and this one agent here, set of accounts and apps I allow, another one. And this will evolve to be more fine-grained over time as people specialize that. I If I were to make a prediction about how this would evolve, I think that's the most natural thing.Swyx [00:54:06]: That makes sense. There's just profiles for everyone. okay. Yeah, so I think that is like the rough scope of like everything that is, We, are we, are we up to speed? Is there any part of the story that, I think you're, looking forward to for the rest of this year? like the emerging trendThe Future of AI Security and Enterprise AdoptionSwyx [00:54:24]: For 2026, for you.Zico [00:54:26]: So there's, there's lots of emerging trends, man. I can, I can go on at length about this. 20,Swyx [00:54:31]: Start with A, go through Z. Let's go.Zico [00:54:33]: Let's, let's start with Gray Swan, right? So I think what's in the future for us is so far when we talk about our product offerings, right, we obviously work with a lot of the large labs. we work with a lot of enterprises too, right? And I think what's happening and the scaling we're going to see is that the these abilities that so far were mainly front of mind for large labs, how do I ensure security of my agents? How do I ensure the models follow the policies I want to prescribe? All that stuff. Those things that were front of mind for frontier labs are going to become front of mind for everyone For all enterprise as they adopt tools like Codex, like Claude Code, like OpenClaw. And so I think where the most where our expansion and a lot of the reason, the work behind our series or the intention behind a lot of our Series A, it is explicitly to take a lot of the technology that we have been developing I won't say for but in conjunction with both enterprise and the large labs, and really scale the deployments on enterprise. So what I see happening in the next year from the Gray Swan side is real growth in terms of the number of AI companies deploying this technology because it becomes central to their operations. Research-wise, I think I've already talked about some, right? The science, the agentification of all science. Well, let's start with science of AI, and I think, I think that, we always want to do other sciences, right? Let's, let's, let's, let's do AI for physics.Matt [00:56:06]: Introspective.Zico [00:56:07]: Let's just, let's just start with AI science. That needs a lot of work right now, right?Matt [00:56:11]: Put your own mask on before helping others.Zico [00:56:12]: Exactly. So I think actually that's what I'm most excited about right now in the research side. And as it applies to this, I think it's, it's in things like understanding models better, but doing it through the power of agents.Matt [00:56:22]: One thing that, I've been very encouraged by for really only the past two or three months that I think, the pace at which this has happened has been increasing, and I think this is going to continue to be a thing, is people who start to build an agent and don't take it all the way to “We've finished this. We think it's, it's great, and now it's, in front of customers or it's in front of the entire organization.” they have this epiphany before they get there that whatever prompts I put in I need a solution here. I understand that there are real risks, right? I understand that, this is a weird and interesting and really capable model that I'm working with, but if I don't, put more measures in place, to make sure that it stays safe and does behaves the way that I want it to. People coming to us proactively, knowing that they need a real solution, I think that's very encouraging, and I think it's a sign of agents landing outside of just the frontier labs and the research community and scientists and so forth. people are starting to get it, and I think that's great. Looking forward to all of the amazing apps that people are going to build on top of these models and the security that will help them stand up.Private Arenas, Red Teaming Markets, and AI InsuranceSwyx [00:57:39]: Is there a future where your customers are part of the arena? ‘cause I think these are, basically these are Right? these are, these are, independent entities. They're There's a guy in Australia who's, your number one. But at some point you have the network effect where you start having enterprise use cases, actually in inside of this public domain.Matt [00:57:59]: Oh, I see. You mean testing enterprise, deployments inside the arena. So we have had, the situation where people join the arena. They're maybe cybersecurity professionals. They get interested in AI security. They come across the arena, and then eventually they become a customer, when their organization needs solution.Swyx [00:58:17]: How often does that happen?Matt [00:58:17]: Not a huge number of times. But there are a lot of thoughtful, people that come from a cybersecurity background that have found their way there. So enterprises are just always, I think, going to be more paranoid about putting, their custom agent that's, deployment, still in development, up on this public platform for anybody to come hit. What we have done is worked to make private arenas where some subset of the contestants, who we've, We know well, theySwyx [00:58:54]: And what do they work on?Matt [00:58:55]: What do they work on?Swyx [00:58:55]: Do What was the class of problem they work on that would require a private arena?Matt [00:59:00]: Oh, pretty much any enterprise application. That's the point. Yeah. enterprises are not willing to put up their deployment agentsSwyx [00:59:07]: Oh, that's greatMatt [00:59:07]: On the arena for For the general public to come hit. They're fine if it's, 20 people that we've handpicked from the arena.Swyx [00:59:14]: Just for listeners who might be interested What do I make as a participant? What's on the table here?Matt [00:59:20]: Well, so for the for the public competitions We communicate a pricing and incentive structure, upfront, and it, and it differs for each arena, right? ‘Cause designing, the right set of incentives to get people focused on finding useful vulnerabilities and problems without reward hacking and just finding, de minimis things is,Swyx [00:59:47]: Are you human judging the reward hacks if it happens?Matt [00:59:50]: Sometimes, yes.Swyx [00:59:51]: Oh, that's messy.Zico [00:59:53]: Well, so we have a lot of automated graders, right? A lot of automated graders. But ultimately, if they can beat all those graders, there is a humanMatt [00:59:59]: There in the YeahZico [01:00:00]: That can, that can take a look at the at theMatt [01:00:01]: Oh, okay. Yep. And we work with the UKEC and Casey and so forth. they'll come in and work as independent judges and evaluators and lend their expertise to that.Swyx [01:00:11]: You're, you're a community that, any enterprise can call on and that's, that's really useful, data actually. It's almost McCore for red teaming.Matt [01:00:22]: For red teaming.Swyx [01:00:25]: One of our upcoming guests is, on the other side of this, the AI, underwriting company. I don't know if you've come across that.Matt [01:00:30]: Oh, yeah. Absolutely.Zico [01:00:31]: Oh, wait. They're, they're one of the logos there. I know that we have the other one.Swyx [01:00:34]: What do you yeah, what do you what do you think of that market?Zico [01:00:36]: Oh, I think it's great.Swyx [01:00:37]: Because it's such an interestingZico [01:00:38]: And and I think it pairs extremely well with our model, right? Because how do you assess the risk of a company's AI deployment? Well, use a tool like Shade, or use Arena, right? And that's And we have And that's actually a lot of the work we've done with them is exactly for that thing. And then if a company finds this level of risk, but wants, so they can't be insured because they're too risky, wants to reduce their risk, what do you do there? I don't think look, we shouldn't be the only provider here, but what do you do there? Well, you put safety systems around your model, right? Including things like Cygnal. So it pairs extremely well because what in some sense we can be is a, author. I don't We're not getting there yet, so I don't this is hypothetical. I want, I wanted to emphasize. But we can be in some sense a authorized partner with them, so that they can do more than just say, “Hey, you're uninsurable.” They can both assess it more rigorously with tools like Shade and other tools as well, and then they can prescribe mitigations when there are problems using tools like Cygnal.AI Insurance, Compliance, and the Gray Swan EventZico [01:01:44]: So it's incredibly goodMatt [01:01:46]: These two models fit together incredibly well. They also bring us customers. Many customers want protection against bad outcomes, insurance for when things go wrong, and help staying compliant. Being out of compliance is also a risk.Swyx [01:02:10]: I think AUC is fantastic and got on this early. The parallel to cyber insurance is clear. When you apply for cyber insurance, you document the measures you have in place: detection, response, and controls. Structurally, they need an arm's-length third party.

Theme music by UNIVERSFIELD & background music by PodcastACThe New All Things Go Discord ServerFilms reviews: Bushido & Go Through the DarkJohn Adulcikas's #5 go podcast on YouTube about Go being easier than you thinkR/baduk posts about a natural limit for Go ability & "Ethical Sandbagging"References to the interview I did with Professor Marc Moskowitz about Go in ChinaShow your support hereEmail: AllThingsGoGame@gmail.comThe All Things Go Discord ServerEpisode SponsorsBadukPop - Learn the rules of the ancient Chinese board game Go - also known as Baduk (바둑) or Weiqi (圍棋) - with a fun, interactive tutorial. Sharpen your Go skills with daily random Go problems (Tsumego) at your choice of difficulty level. Play games online or with a variety of AI opponents, each with its own unique playing style and strength.SmartGo One - Your complete app for the game of Go. Learn to play, practice against the computer, study master games, solve problems, and read Go books. Free to download.BadukTeacher.com - Book lessons with pro-level players, including top Asian pros, without any language barrier thanks to seamless real-time translation during and outside of sessions. With lessons starting at just $25, you get high-level, dojo-trained Go instruction plus the freedom to message your teacher anytime in your own language.

Hey folks, Alex here, and welcome to a BIG MODEL week! We finally got Mythos (well almost)! Let me catch you up! This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good! Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I've ever seen ship on a frontier model. It's state of the art on nearly every benchmarkThe model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we've seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model. Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out. There's quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let's dive in! ThursdAI - Let me catch you up, every week!

Question? Comment? Send us a Message!Sean and Dane are back!! Cornhole went CRAZY in Texas as the guys recap the ACL and TCL results, vibes and preferences…  A local fundraiser stirred up sandbagging controversy and more!!!BIG ASP Cornhole Patreon page:4 Tiers to choose from!! Come join our growing community and get insider info, become an active participant in show content, be eligible for bag giveaway's, find our VIDEO of the interviews and more!!https://www.patreon.com/bigaspcornholeDraggin Bags!!-The “Power Draggin” might be the best bag we've ever thrown!! And we suck…imagine how good they could be in your hands….https://dragginbagz.com/Big Asp Merch!!!! Polos, Tees, Jerseys, shorts and more!!https://jamapparel.net/collections/new-the-big-asp-cornhole-podcast-collection-by-jamSupport the show

(00:00) Fred, Hardy and Wallach welcome in the month of June with some golf chatter. Fred hit the links for the first time this year.(20:00)(34:18) WHAT HAPPENED LAST NIGHT: Russell Henley wins the Charles Schwab Challenge in a playoff. The local-9 actually won a series against the Cleveland Guardians and the Spurs beat the defending NBA Champions, the Oklahoma City Thunder, to advance to the NBA Finals vs the Knicks.Please note: Timecodes may shift by a few minutes due to inserted ads. Because of copyright restrictions, portions—or entire segments—may not be included in the podcast.CONNECT WITH TOUCHER & HARDY: linktr.ee/ToucherandHardyFor the latest updates, visit the show page on 985thesportshub.com. Follow 98.5 The Sports Hub on Twitter, Facebook and Instagram. Watch the show every morning on YouTube, and subscribe to stay up-to-date with all the best moments from Boston's home for sports!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The International Cricket Council is investigating allegations that Canada's cricket team fixed a T20 match against New Zealand, with investigators noting suspiciously slow bowling and "sandbagging" during batting to suppress run rates. Zakis discusses how the online betting community likely motivated the scheme, drawing parallels to the infamous 1919 Black Sox scandal and highlighting corruption risks even in games with unremarkable scores. (3)1937 NSW

Suamico and Greenleaf had the highest voter turnouts in last week’s election in their respective categories, earning recognition from Project VOTE.See omnystudio.com/listener for privacy information.

Join the email list to get a FREE private finger training clinic with Dr. Tyler Nelson (normally $10) www.thestruggleclimbingshow.com/strong   Support the Show on Patreon Get access to all Pro Clinics, bonus episodes, and more. https://www.patreon.com/thestruggleclimbingshow   Andrew Bisharat and Chris Kalous (along with a surprise guest) join to explore: Climbing gym culture of the 90s Trends in corporate vs independent gyms Which gyms prepare climbers better for real rock A case against mega gyms A case for mega gyms  Will soft gym grades result in mass upgrading of classic outdoor climbs? When a local gym closes... and is miraculously saved Is pickle ball competitive with climbing? How dirtbags can help save indie gyms - And check out ALL the show's awesome sponsors and exclusive deals at thestruggleclimbingshow.com/deals - Here are some AI generated show notes (hopefully the robots got it right) 00:00 Old Gyms vs Mega Gyms 01:37 Rock Sport Closes 03:20 Andrew First Gym Memories 06:35 Chris Early Wall Origins 11:26 DIY Gym Spirit 11:51 Rock Sport and Rock Creation 15:09 Rock Creation Business Model 20:45 How Many Gyms Exist 23:32 Gym Growth and Trends 28:29 Gyms as Outdoor Gateway 32:26 Case for Mega Gyms 36:04 Gym Community Vibes 37:24 Modern Mega Gym Magic 39:12 Corporate Future Hypothetical 40:19 Grades Versus Real Rock 42:48 Setting Ergonomics Limits 44:34 WALL-E Mega Gym Satire 46:44 Surprise Guest Joins 48:21 Saving Rock Sport 50:29 Keeping the Old Soul 56:07 Sandbagging and Grade Plans 58:15 Pickleball and Business 01:01:47 Gym Ownership Reality Check 01:07:44 Support Your Local Gyms - Shoutout to Aiden Schlatter and Michael Martin for supporting at the Hero level on Patreon. So mega!  - Follow along on Instagram and YouTube: @thestruggleclimbingshow  - This show is produced and hosted by Ryan Devlin, and edited by Glen Walker. The Struggle is carbon-neutral in partnership with The Honnold Foundation and is a proud member of the Plug Tone Audio Collective, a diverse group of the best, most impactful podcasts in the outdoor industry. And now here are some buzzwords to help the almighty algorithm get this show in front of people who love to climb: rock climbing, rock climber, climbing, climber, bouldering, sport climbing, gym climbing, how to rock climb, donuts are amazing. Okay, whew, that's done. But hey, if you're a human that's actually reading this, and if you love this show (and love to climb) would you think about sharing this episode with a climber friend of yours? And shout it out on your socials? I'll send you a sticker for doing it. Just shoot me a message on IG – thanks so much! 

Guenther Steiner, Matt, and Brian break down the unreal opening race of the 2026 Formula 1 season. From Mercedes looking unstoppable, to questionable Ferrari strategy, to Arvid's killer debut, the Melbourne season opener gave us a lot to talk about. Guenther also names his Rockstar and Wanker of the Week, reacts to Aston Martin's nightmare weekend, and weighs in on the new 2026 F1 power unit regs and whether they're actually good for racing. Plus, our Chinese GP predictions.Chapters: 2:38 - Rockstar & Wanker 10:12 - New Regs Verdict 22:45 - Is Merc Unbeatable? 35:18 - Who's Sandbagging? 45:28 - Gas or Brake? 1:00:27 - Chinese GP Prediction Learn more about your ad choices. Visit megaphone.fm/adchoices

Week two of testing is in the books, and somehow we know even less than we did after week one. This week we dig into the latest from the Scuderia — Ferrari's brought some genuinely innovative parts to the table, and the Ferrari-powered teams have been pulling insane launches off the line that have the whole paddock side-eyeing their power unit. Meanwhile, Mercedes appears to have shown up to testing with the handbrake on, sandbagging their way through sessions like they're playing chess while everyone else is playing checkers.We'll talk about the endless poker game that is pre-season — who's bluffing, who's panicking behind closed doors, and who's just hoping nobody notices how lost they are. The gap between testing and lights out is shrinking, and every team is holding their cards a little tighter.And as always, we'll find something to roast. We always do.

F1 2026 Pre-Season Testing in Bahrain is complete, and the early signals from Formula 1 testing are fascinating. In this F1 podcast episode, we break down Lewis Hamilton's second season with Ferrari, Red Bull's long-run pace, Aston Martin's shocking lack of speed and reliability, and the biggest testing takeaways heading into the new season. But the real question… Is anyone actually showing their hand? We debate who's sandbagging (and whether you can ever really tell during F1 testing), why Aston Martin looked alarmingly off the pace, and whether that Ferrari rear wing that literally flips its configuration is genius innovation or peak 4D chess. We also get into:

Send a textF1 is BACK! Pre-season testing in Bahrain has wrapped, and we have a shocker at the top of the time sheets: Ferrari. But in the "bullshit season" of Formula 1, can we trust the times?We break down the crucial question: Is Ferrari's pace genuine, or has Red Bull masterminded the ultimate sandbagging job? We cut through the testing noise to figure out the true 2026 grid pecking order. Plus, we discuss where Mercedes and McLaren really stand, and the surprise competence of Haas.Keywords: F1 Testing, Formula 1, Ferrari, Red Bull Racing, Sandbagging, Bahrain GP, Mercedes F1, McLaren, F1 Predictions, F1 2026.Support the show

Support for a leadership challenge against Sussan Ley has continued to build momentum, with a wave of high-profile shadow cabinet ministers quitting their positions to support Angus TaylorLiberal MP Dan Tehan is among those who've handed in their resignation today, with shadow cabinet members Michaelia Cash, Jonno Duniam, James Paterson, and James McGrath also quitting.A party meeting will be held tomorrow morning to vote on a leadership spill, with Mr Taylor challenging Sussan Ley for the top job. Thousands of people have gathered outside Melbourne's Flinders Street Station to oppose Israeli President Isaac Herzog's visit to Australia.Traffic outside Flinders Street Station has been bought to a standstill while Pro-Palestinian speakers have addressed the crowd.There's been a significant police presence surrounding Mr Herzog during his time in Melbourne, the last stop on his four-day visit to Australia.Severe thunderstorms are likely to produce heavy rainfall that may lead to flash flooding across parts of south-east Queensland today.The Bureau of Meteorology has issued a severe thunderstorm warning for parts of Ipswich, Somerset, Western Downs, Toowoomba, Lockyer Valley, and the Scenic Rim.Sandbagging stations have opened as councils across the region brace for the deluge.

Send us a textIn F1 News and F1 Updates, Mercedes Caught Sandbagging!Timestamps:00:00 Introduction05:01 2026 Rules Explained10:12 Compression Ratio Loophole21:08 Legal Threats Loom25:18 Mercedes Sandbagging Claims35:03 How The Trick Works40:00 Closing Thoughtswhere to find me -Twitter:   / cxmeroncc  Tiktok:   / cxmeroncc_  Facebook:   / cameronf1tv  Business Email : cxmeronf1@gmail.com#f1 #formula1 #f12025 #f1news #verstappen #maxverstappen #lewishamilton

In dieser Episode von Ruf der Hyäne knüpfen wir an Folge 239 mit Björn von Rosenfeld an und vertiefen das Thema Selbstverteidigung  was bleibt hängen?Danach widmen wir uns den Gürtelverleihungen in verschiedenen Kampfkünsten wie BJJ, Capoeira, Karate und Kendo. Wir sprechen über die teils brutalen Traditionen, die Frage, ob solche Rituale eher Mobbing sind oder ob sie die Trainierenden bewusst unter Druck setzen sollen und ob das überhaupt sinnvoll ist.Zum Abschluss diskutieren wir das Phänomen des Sandbagging im Kampfsport: Bleibt man absichtlich auf einem niedrigeren Graduierungslevel, nur um in dieser Kategorie leichter Medaillen abzuräumen? Wir beleuchten die Vor- und Nachteile, die ethische Dimension und was das für die Szene bedeutet.Eine Folge voller ...Dieser Podcast wird vermarktet von der Podcastbude.www.podcastbu.de - Full-Service-Podcast-Agentur - Konzeption, Produktion, Vermarktung, Distribution und Hosting.Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen?Dann schaue auf www.kostenlos-hosten.de und informiere dich.Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.

In dieser Episode von Ruf der Hyäne knüpfen wir an Folge 239 mit Björn von Rosenfeld an und vertiefen das Thema Selbstverteidigung  was bleibt hängen?Danach widmen wir uns den Gürtelverleihungen in verschiedenen Kampfkünsten wie BJJ, Capoeira, Karate und Kendo. Wir sprechen über die teils brutalen Traditionen, die Frage, ob solche Rituale eher Mobbing sind oder ob sie die Trainierenden bewusst unter Druck setzen sollen und ob das überhaupt sinnvoll ist.Zum Abschluss diskutieren wir das Phänomen des Sandbagging im Kampfsport: Bleibt man absichtlich auf einem niedrigeren Graduierungslevel, nur um in dieser Kategorie leichter Medaillen abzuräumen? Wir beleuchten die Vor- und Nachteile, die ethische Dimension und was das für die Szene bedeutet.Eine Folge voller ...Dieser Podcast wird vermarktet von der Podcastbude.www.podcastbu.de - Full-Service-Podcast-Agentur - Konzeption, Produktion, Vermarktung, Distribution und Hosting.Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen?Dann schaue auf www.kostenlos-hosten.de und informiere dich.Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.

Send us a textIt's that time of year. You're waiting for management to spin the "Bingo Wheel" and drop a new, terrifying number on your desk. "Congratulations, you grew 15% last year... now we need 25%." Panic sets in.This week on "The Selling Podcast," Mike and Scott reveal why waiting for your quota to build a plan is a rookie mistake. They argue that you must create your sales plan before you receive your quota. Why? Because data gives you leverage. If you know your numbers better than your manager does, you turn a mandate into a negotiation.We break down the 3 effective ways to create a quota-based sales plan that puts you in the driver's seat:Start with a Data-Driven Breakdown: Don't look at the scary big number. Mike explains how to slice the data by product, time, and customer. Understand exactly where your growth came from last year so you know if a 20% increase is a pipe dream or a layup.Territory & Segment Prioritization (Assign Quotas to Clients): This is the secret sauce. You can't control corporate, but you can control your territory. We discuss "monetizing" your accounts by mentally assigning them their own quotas. We also introduce the "2x4 Method" (selling 2 products to 4 accounts, or vice versa) to visualize exactly where the new revenue will come from.Build a Pipeline That Matches the Strategy: A goal without a pipeline is just a wish (or a pipedream). We discuss how to ensure your pipeline math actually supports the new number, factoring in close rates and "sandbagging" buffers.Plus, we have a candid (and hilarious) conversation about "Sandbagging"—the sales rep's secret weapon. Is it ethical? Does every manager know you're doing it? (Spoiler: Yes, and they're doing it too).Tune in to stop fearing "The Number" and start building a plan that makes hitting quota a mathematical certainty.Support the showScott SchlofmanMike Williams - Cell 801-635-7773 #sales #podcast #customerfirst #relationships #success #pipeline #funnel #sales success #selling #salescoach

SummaryIn this episode of the FUT IN REVIEW, the hosts discuss the latest FC26 promo, the Festival of Football: Captains, analyzing the new cards, their designs, and play styles. They delve into player recommendations, including Andy Robertson and Ilka Gundogan, and share their experiences from a recent meetup. The conversation also touches on the challenges of sandbagging in Rivals and the complexities of gameplay mechanics, emphasizing the need for accessibility improvements in the game.TakeawaysThe Festival of Captains promo features over 40 new cards.Enhanced chemistry is a significant addition to this promo.Card design has received positive feedback from the hosts.Player ratings can be confusing and inconsistent.Andy Robertson is highlighted as a valuable card for players.Gundogan's SBC is considered a decent option for certain teams.The meetup experience fostered community connections among fans.Sandbagging in Rivals is a concern due to poor rewards.Gameplay mechanics have become increasingly complex for players.Accessibility in gameplay needs improvement to cater to all players.Sound Bites"It's great to be back on the pod.""We've got 40 plus cards this week.""It's very, very good."Chapters00:00 Welcome to the Friday Club01:33 Festival of Captains Promo Overview03:36 Card Design and Ratings Discussion06:41 EA's Strategy and Player Curve10:09 Player Highlights and Recommendations17:40 SBCs and Objectives Analysis23:07 Evaluating Player Performance and Team Upgrades24:47 Community Engagement and Meetups29:54 The Issue of Sandbagging in Rivals37:26 Game Mechanics and Accessibility Challenges43:50 Recommendations for Movies and TV ShowsCheck out our socials:X: https://twitter.com/futinreviewBlueSky: https://bsky.app/profile/futinreview.bsky.socialInstagram: https://instragram.com/futinreviewTolando's socials:https://x.com/Tolando77https://www.instagram.com/tolando77/?hl=enhttps://www.tiktok.com/@tolando77https://www.youtube.com/@Tolando77https://www.twitch.tv/tolando77Questions:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠futinreview@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://youtube.com/futinreview⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.futinreview.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://patreon.com/futinreview⁠

The Sponsors We want to thank Underground Printing for starting this and making it possible—stop by and pick up some gear, check them out at ugpmichiganapparel.com, or check out our selection of shirts on the MGoBlogStore.com. And let's not forget our associate sponsors: Peak Wealth Management, Matt Demorest - Realtor and Lender, Ann Arbor Elder Law, Michigan Law Grad, Human Element, Sharon's Heating & Air Conditioning, The Sklar Brothers, Winewood Organics, Community Pest Solutions, Radecki Oral Surgery, Long Road Distillers, and SignalWire where we are recording this. Featured Musician: Chirp THE VIDEO: [After THE JUMP: Things discussable.]  --------------------- 1. MSU Preview: Offense starts at the top. What have they done to Aidan Chiles? He's dinky this year, and not very accurate as a dinker either. Be careful of his rushes. MSU fans are turning on their coaches already. Offensive line is a shambles of injury. Nick Marsh, enter the dang portal already friend. They're not even throwing to him deep. Sandbagging for this game? Please don't Wink out—they've been running a million screens. 2. MSU Preview: Defense starts at 20:26 Let Bryce cook, we say! A lot of cyans as yards are coming easily. They run both types of switch coverages—weird to see Dantonio's low safeties and press corners against a passing spread (it didn't work). Best thing to do is repeat last week's approach. 3. Washington After Review starts at 43:48 Waiting for Brian to tell us about Bryce and Frazier, but they looked pretty good. Drops were an issue, are an issue. Seth proposes a prospectin' name for Jordan Marshall. Defensively Washington contributed a lot to our scores, but credit Wink or the people who yelled at him for going back to the 2024 Ohio State gameplan. Long discussion on the rotations, especially at DL—Cam Brandt isn't Derrick Moore or a young Derrick Moore and it's unfair to be playing him as such. Also Jordan Young at the end of the 2nd half when you've got a 7-point lead. 3. Hoops and Hockey Check-In starts at 1:15:54 Hockey is undefeated after their best game in years vs WMU, game 2 is tonight and will be intense. Hoops has their 2nd exhibition, hopefully with a frontcourt this time, and this one has all the makings of an elite matchup. Featured Artist: Chirp Chirp is a four-piece band that hops around genres. I don't they've opened for Vulfpeck but they're part of that same progressive rock/funk and jazz-fusion scene. Formed in 2015, Jay Frydenlund, Brian Long, Sam Naples, and Patrick Blommel have been playing concerts and festivals all around the region. I saw them with Joe Hertler (featured here before), and got a notification in my email this week that they'll be at the Pig at the end of November (11/28), so I figured it's a good time to bring them up. I have them on my work playlist, mostly for their covers, but the songs featured are all off their 2022 album. Songs: "By the Book" (video) "Little Friend" "Fast Food Blues"       Also because Across 110th Street will get our Youtubes taken down, the opener and outro: “The Employee is Not Afraid”—Bear vs. Shark “Ruska Vodka”—Motorboat

This week we're all about the cats, hep cats, stray cats, cat livestreams, pussy cats and more; We learn what exactly is not proper line etiquette; The little known horror franchise that Chris Jericho is a part of; Raven declares that Feeney and Rich both have weak links; RIP Ace Frehley, John Lodge of The Moody Blues and D'Angelo; What person would you want to be from The Walking Dead if a zombie apocalypse actually happened? Great sports movies vs. terrible sports movies; Fanmail, and of course, all the usual perversions. Find out where and when you can see Raven's documentary: https://www.cargofilm-releasing.com/nevermore-the-raven-effectThe show now has a Facebook page, so go CHECK IT OUTFollow the guys on social mediaRaven - @theRavenEffectFeeney - @jffeeney3rdBuy yourself some Raven shirts: https://www.prowrestlingtees.com/ravenOr even some Feeney shirts: https://www.prowrestlingtees.com/ccwithjoefeeneyHave Raven say things that you want him to say, either for yourself or for someone you want to talk big-game shit to by going to http://www.cameo.com/ravenprime1Sign up for Patreon by going to http://www.patreon.com/TheRavenEffect it's only $5 a month! Get extra content AND watch the show!Become a supporter of this podcast: https://www.spreaker.com/podcast/the-raven-effect--5166640/support.

In this solo episode, Shintaro Higashi breaks down three powerful ways to grow Judo in the United States. From unifying national organizations to creating viable pathways for judo in schools and drastically improving the customer experience at tournaments—this is a must-listen for anyone passionate about the future of Judo in America.

Send us a textIn F1 news and Updates, Ferrari caught Sandbagging in Baku GP.- where to find me -Twitter: https://twitter.com/CxmeronccTiktok: https://www.tiktok.com/@cxmeroncc_Facebook: https://www.facebook.com/CameronF1TVBusiness Email : cxmeronf1@gmail.com#f1 #formula1 #f12025 #maxverstappen #bakugp

In dieser Folge von Klettern – einfach festhalten sprechen Marvin Weinhold (ehemaliger Landestrainer & Leistungssportler) und Simon Bayer (Kletterer, Bergsteiger & Kaffeeliebhaber) über ein Thema, das jede*n Kletternden früher oder später beschäftigt: Sandbagging – also Routen, die viel schwerer sind als ihre Bewertung.Was steckt hinter unterbewerteten Routen? Ist das Absicht oder einfach nur subjektive Wahrnehmung? Warum sind manche 6b-Routen schwerer als "andere 7a-Linien? Und was hat das mit unserem Ego, Technik, Stil und Felsstruktur zu tun?Mit dabei: persönliche Anekdoten aus dem Frankenjura, Arco, dem Ith, Indoor vs. Outdoor-Erfahrungen und ein Appell an mehr Ehrlichkeit im Bewertungssystem.Außerdem in dieser Folge:

Shedeur Sanders slipped into the 5th round of the NFL Draft this past weekend...and there has been rampant speculation as to why Shedeur Sanders collapsed. According to Boomer Esiason...some NFL owners outright banned Shedeur Sanders from being drafted by their team. We reveal and react to Boomer Esiason confirming that...Shedeur Sanders was blackballed by some NFL owners. We also discuss a clip from Jonathan Jones from CBS Sports...who discusses Shedeur Sanders sandbagging interviews with NFL teams he didn't want to play for. We explain why Shedeur Sanders perceived level of talent wasn't enough to overcome his overwhelming negatives...and how NFL teams felt Shedeur Sanders was more trouble than he was worth. SUBSCRIBE TO BEHIND THE LINE - SHORTS: https://www.youtube.com/@btlshorts-84

Join Elle with special guests from Colossalcon Cat and Haley for the ins and outs of the new craftsmanship contest circuit happening  at a Colossalcon near you.The Preliminaries:Colossalcon Prime Sandusky OH https://colossalconprime.com/contact-us/Colossalcon Texas Round Rock TX https://colossalcontexas.com/contact/Colossalcon East Poconos Mt PA cosplay@colossalconeast.com Colossalcon North Wisconsin Dells WI  https://colossalconnorth.com/contact-us/https://www.instagram.com/podcastscs/https://www.instagram.com/laviecosplay/https://www.tiktok.com/@laviecosplayhttps://linktr.ee/podcastscs for additional listening platforms

Are you settling for "realistic" goals? Verl Workman, CEO of Workman Success Systems, throws out the rulebook in this electrifying episode, revealing why "SMART" goals are holding you back. Unveiling his revolutionary "STUPID" framework, urging agents to embrace transformational, purpose-driven ambitions.    We dive deep into Verl's journey from struggling entrepreneur to real estate coaching powerhouse, and discover the secrets to building a thriving referral-based business.    Learn how to adapt to market shifts, lead with authenticity, and prioritize human connection over transactions. With insights on team building, prospecting, and the power of consistent action, Verl challenges you to redefine success and unlock your true potential.   Connect with Verl on - Facebook - LinkedIn or online at verlworkman.com.   Learn more about Workman Success Solutions on - Instagram - Facebook - LinkedIn and online at www.workmansuccess.com. To check out Verl's book Raving Referrals for Real Estate Agents visit: https://referralchampion.com https://www.amazon.com/dp/B0DXR2CTFB   Follow Real Estate Insiders Unfiltered Podcast on Instagram - YouTube - Facebook - TikTok. Visit us online at realestateinsidersunfiltered.com.   Link to Facebook Page: https://www.facebook.com/RealEstateInsidersUnfiltered Link to Instagram Page: https://www.instagram.com/realestateinsiderspod/ Link to YouTube Page: https://www.youtube.com/@RealEstateInsidersUnfiltered Link to TikTok Page: https://www.tiktok.com/@realestateinsiderspod This podcast is produced by Two Brothers Creative.

Hour 1: Russ Wilson best option?, Browns draft plans, Celtics sandbagging? full 2082 Thu, 13 Mar 2025 14:38:17 +0000 Gpxc5CFUxxKrEicXKfLKuqRTdkWXdYQu sports The Ken Carman Show with Anthony Lima sports Hour 1: Russ Wilson best option?, Browns draft plans, Celtics sandbagging? The only place to talk about the Cleveland sports scene is with Ken Carman and Anthony Lima. The two guide listeners through the ups and downs of being a fan of the Browns, Cavaliers, Guardians and Ohio State Buckeyes in Northeast Ohio. They'll help you stay informed with breaking news, game coverage, and interviews with top personalities.Catch The Ken Carman Show with Anthony Lima live Monday through Friday (6 a.m. - 10 a.m ET) on 92.3 The Fan, the exclusive audio home of the Browns, or on the Audacy app. For more, follow the show on X @KenCarmanShow. 2024 © 2021 Audacy, Inc. Sports False https://player.amp

Browns trying to win games, save jobs + Are the Celtics "sandbagging?" full 513 Thu, 13 Mar 2025 10:56:22 +0000 V3rOKsNtzcwUHBfcO8sswDxj3TnyDBLj sports The Ken Carman Show with Anthony Lima sports Browns trying to win games, save jobs + Are the Celtics "sandbagging?" The only place to talk about the Cleveland sports scene is with Ken Carman and Anthony Lima. The two guide listeners through the ups and downs of being a fan of the Browns, Cavaliers, Guardians and Ohio State Buckeyes in Northeast Ohio. They'll help you stay informed with breaking news, game coverage, and interviews with top personalities.Catch The Ken Carman Show with Anthony Lima live Monday through Friday (6 a.m. - 10 a.m ET) on 92.3 The Fan, the exclusive audio home of the Browns, or on the Audacy app. For more, follow the show on X @KenCarmanShow. 2024 © 2021 Audacy, Inc. Sports False https://player.amperwa

Hour 1: Russell Wilson may be Browns best option + Could Russ affect draft plans? + Are the Celtics sandbagging? full 2082 Thu, 13 Mar 2025 10:59:14 +0000 hWwxWsBXITLpgNZ8g0WhCW971D5UKzut sports The Ken Carman Show with Anthony Lima sports Hour 1: Russell Wilson may be Browns best option + Could Russ affect draft plans? + Are the Celtics sandbagging? The only place to talk about the Cleveland sports scene is with Ken Carman and Anthony Lima. The two guide listeners through the ups and downs of being a fan of the Browns, Cavaliers, Guardians and Ohio State Buckeyes in Northeast Ohio. They'll help you stay informed with breaking news, game coverage, and interviews with top personalities.Catch The Ken Carman Show with Anthony Lima live Monday through Friday (6 a.m. - 10 a.m ET) on 92.3 The Fan, the exclusive audio home of the Browns, or on the Audacy app. For more, follow the show on X @KenCarmanShow. 2024 © 2021 Audacy, Inc. Sports False

Join and Elle and Ash on a journey through the evolution of cosplay competitions since the pandemic. They will dive deep into how the expectations have changed since 2020. They will discuss the good, and also confusing, aspects of this expensive evolution in cosplay. Included is input from the community on how the changes are impacting "Old Masters" as well as new competitors. Ultimately they will examine the question: Is it time for La Vie Cosplay to retire from competitive cosplay?Special Mentions:Say No To Scrunchies https://www.instagram.com/saynotoscrunchies/Hakc (low tech foundational tutorials) https://www.instagram.com/its.hakc/Produced by LVC Productions. You can find us on facebook, instragram, twitter, and vero at La Vie Cosplay. Our podcast instagram is podcastscs. Our website is laviecosplay.com. Have a fun, crazy con or cosplay related story? Absurd cosplay question? Or just something in general to share with us? Email us at podcastscs@gmail.com or DM us at podcastscs. If you like what you heard please rate, review, and subscribe wherever you get your podcasts. Thank you for listening and remember; just because you can, doesn't mean you should.https://www.instagram.com/podcastscs/https://www.instagram.com/laviecosplay/https://www.tiktok.com/@laviecosplayhttps://linktr.ee/podcastscs for additional listening platforms

Join LVC as we celebrate our 100th episode. We will give you some very important updates on the future of LVC as well answer some NGL questions. We will also present to you two contest related hot takes that often have caused a stir in the community. Want to share your opinion? Find the Instagram post or join us on youtube.Produced by LVC Productions. You can find us on facebook, instragram, twitter, and vero at La Vie Cosplay. Our podcast instagram is podcastscs. Our website is laviecosplay.com. Have a fun, crazy con or cosplay related story? Absurd cosplay question? Or just something in general to share with us? Email us at podcastscs@gmail.com or DM us at podcastscs. If you like what you heard please rate, review, and subscribe wherever you get your podcasts. Thank you for listening and remember; just because you can, doesn't mean you should.https://www.instagram.com/podcastscs/https://www.instagram.com/laviecosplay/https://www.tiktok.com/@laviecosplayhttps://linktr.ee/podcastscs for additional listening platforms

Top 5 at FIVE... set up a beating Kentucky fans are sandbagging us Remember that girl?

Theme music by UNIVERSFIELD & background music by PodcastACThe famous ear-reddening game from the introThe Heap of Sand ParadoxSapiens by Yuval Noah HarariPro player Rui NaiweiBenKyo's league and websiteHajin Lee's website which includes her bookShow your support hereEmail: AllThingsGoGame@gmail.com

Join LVC for a trip down memory lane as we take a look at our top 10 episodes from the last 5 seasons of podcasting.10: Ep 57 – R-E-S-P-E-C-T09: Ep 70- No F's Given08: Ep 74 - Post Con Depression07: Ep 43- Crushing Con-Crunch06: Ep 49 - Born To Make History with Laughing Rat Cosplay05: Ep 60 - Pain Free Crafting with Paisley & Glue04: Where The Points Don't Matter with Ginoza Costuming 03: Ep 4 – Our Name is LVC and We're Competitive Cosplayers02: Sandbagging Part 1 & 201: Ep 56 - Cheater Cheater Pumpkin EaterProduced by LVC Productions. You can find us on facebook, instragram, twitter, and vero at La Vie Cosplay. Our podcast instagram is podcastscs. Our website is laviecosplay.com. Have a fun, crazy con or cosplay related story? Absurd cosplay question? Or just something in general to share with us? Email us at podcastscs@gmail.com or DM us at podcastscs. If you like what you heard please rate, review, and subscribe wherever you get your podcasts. Thank you for listening and remember; just because you can, doesn't mean you should.https://www.instagram.com/podcastscs/https://www.instagram.com/laviecosplay/https://www.tiktok.com/@laviecosplayhttps://linktr.ee/podcastscs for additional listening platforms

Join LVC for all the chaos that was Colossalcon North 2024. We will discuss our neurodiversity programming, the return of Cosnanigans Variety Show, the final round of the current format of Sh*t Cosplayer Say, and our return to burlesque shows. You will also learn about how we cosplayed as the Colossalcon hosts all weekend, thoroughly breaking them in our performance at the In Character Contest.And as always. Steeeve.The word Sh*t is said numerous times in this episode. Listener discretion is advised.Nerds In Heat With Betsey Beau Peep: https://www.instagram.com/peeping_pinup_cosplay/?hl=enCosnanigans Video: https://www.youtube.com/watch?v=k86usFggGWY&t=1sIn Character Contest: https://www.youtube.com/watch?v=6yWRUTHOk4Yhttps://www.youtube.com/watch?v=iKCGhuHECUIHalftime Show: https://www.youtube.com/watch?v=_HKj95vpYX8&list=PLSGIBl03sBYjg2kYS-LzB7LPHkQjD0IRU&index=18&t=11shttps://www.instagram.com/podcastscs/https://www.instagram.com/laviecosplay/https://www.tiktok.com/@laviecosplayhttps://linktr.ee/podcastscs for additional listening platforms

In this emergency episode of The Cognitive Revolution, Nathan discusses alarming findings about AI deception with Alexander Meinke from Apollo Research. They explore Apollo's groundbreaking 70-page report on "Frontier Models Are Capable of In-Context Scheming," revealing how advanced AI systems like OpenAI's O1 can engage in deceptive behaviors. Join us for a critical conversation about AI safety, the implications of scheming behavior, and the urgent need for better oversight in AI development. Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive 80,000 Hours: 80,000 Hours is dedicated to helping you find a fulfilling career that makes a difference. With nearly a decade of research, they offer in-depth material on AI risks, AI policy, and AI safety research. Explore their articles, career reviews, and a podcast featuring experts like Anthropic CEO Dario Amadei. Everything is free, including their Career Guide. Visit https://80000hours.org/cognitiverevolution to start making a meaningful impact today. Shopify: Shopify is the world's leading e-commerce platform, offering a market-leading checkout system and exclusive AI apps like Quikly. Nobody does selling better than Shopify. Get a $1 per month trial at https://shopify.com/cognitive RECOMMENDED PODCAST: Unpack Pricing - Dive into the dark arts of SaaS pricing with Metronome CEO Scott Woody and tech leaders. Learn how strategic pricing drives explosive revenue growth in today's biggest companies like Snowflake, Cockroach Labs, Dropbox and more. Apple: https://podcasts.apple.com/us/podcast/id1765716600 Spotify: https://open.spotify.com/show/38DK3W1Fq1xxQalhDSueFg CHAPTERS: (00:00:00) Teaser (00:00:53) About the Episode (00:08:10) Introducing Alexander Meinke (00:10:17) Red Teaming GPT-4 (00:17:07) Chain of Thought Access (Part 1) (00:20:24) Sponsors: Oracle Cloud Infrastructure (OCI) | SelectQuote (00:22:48) Chain of Thought Access (Part 2) (00:26:07) Multimodal Models (00:29:33) Defining Scheming (00:33:51) Taxonomy of Scheming (Part 1) (00:39:40) Sponsors: 80,000 Hours | Shopify (00:42:29) Taxonomy of Scheming (Part 2) (00:43:09) Instruction Hierarchy (00:49:04) Types of Scheming (01:00:49) Covert Subversion (01:14:25) Deferred Subversion (01:28:24) Sandbagging (01:35:48) Magnitudes & Trends (01:48:18) Chain of Thought Reasoning (01:57:02) Closing Thoughts (02:05:19) Outro PRODUCED BY: http://aipodcast.ing

In this episode of Training Age, hosts Heather Adams and Valerie Lusvardi explore the concept of “sandbagging” in fitness—underestimating one's own strength and limiting potential. Through personal anecdotes and practical insights, the conversation highlights how self-imposed barriers can hold you back and how recognizing your own capacity can unlock true growth in both fitness and life. By focusing on personal progress rather than comparisons, listeners can learn to push beyond perceived limits and achieve meaningful results.The episode challenges conventional ideas of what defines a “hard” workout. Effort doesn't always mean being out of breath or drenched in sweat—it's a personal and subjective measure that varies day to day. Heather and Valerie discuss how redefining effort and prioritizing quality over quantity can lead to more satisfying and sustainable results. They debunk myths that equate intensity with success, encouraging listeners to rethink their approach to fitness.The conversation also dives into the science of effective strength training, emphasizing the importance of creating muscle tension for growth. By focusing on strategic programming, mixing muscle groups, and avoiding redundancy, the hosts demonstrate how a refined approach can yield better outcomes. Listeners are invited to embrace consistent, high-quality effort and a growth mindset, making once-daunting tasks more manageable.This episode encourages you to break free from limiting beliefs, redefine what “hard” means in your training, and take actionable steps toward unlocking your full potential. Whether you're a beginner or an experienced athlete, the insights shared in this discussion can help transform your fitness journey. Don't miss this opportunity to elevate your mindset and results with practical strategies and inspiration.

 Dr. Chris here today with a solo episode talking about sandbagging in BJJ, hand injuries, and a middle aged practitioner that wants to ramp up intensity but is looking for help on improving their rolling cardio. Hey, if you have any questions for Dr. Chris or Bill and Olivia. Just email GrapplingWithPodcast@gmail.com or message the social media pages.Check us out on our social and YouTube where we have full episodes.Instagram: @GrapplingWithPodcast Facebook: www.facebook.com/GrapplingWithPodcast YouTube: /grapplingwithpodcast Dr. Hardy is a licensed physician and BJJ practitioner, but the contents of the podcast are meant for educational purposes only, and should not be taken as medical advice. Please seek out personalized care from your own medical provider prior to implementing any medical treatment or intervention. 

Join Elle with special guest Laughing Rat Cosplay as we discuss the Kitsunekon masquerade. We'll tell you all about their set up, the delightful contestants, how Green Bay is a food dessert, and cheese.Produced by LVC Productions. You can find us on facebook, instragram, twitter, and vero at La Vie Cosplay. Our podcast instagram is podcastscs. Our website is laviecosplay.com. Have a fun, crazy con or cosplay related story? Absurd cosplay question? Or just something in general to share with us? Email us at podcastscs@gmail.com or DM us at podcastscs. If you like what you heard please rate, review, and subscribe wherever you get your podcasts. Thank you for listening and remember; just because you can, doesn't mean you should.https://www.instagram.com/podcastscs/https://www.instagram.com/laviecosplay/https://www.tiktok.com/@laviecosplayhttps://linktr.ee/podcastscs for additional listening platforms

Steve and Kyle recap the news story claiming the PGA Tour and the Saudi Public Investment Fund reached a deal, and how it could get published without any sources or evidence. Then the guys cover the proposal to reduce the amount of professional golfers on the PGA Tour and discuss how low is 'too low' in a scramble tournament, and Nelly Korda's upcoming appearance in the Swimsuit edition of Sports Illustrated. Hosts: Steve Berger & Kyle Surlow Nice Grass Nice People is proudly presented by SUAVE GOLF  Signups are officially open for Suave Golf's 2025 Bandon Spring Jamboree - March 12-16, 2025. Learn more and signup.

We discuss the RBA's Cup Day meeting, TK does a 'Pulled Pork' on CCP's recent performance, and quotes from 'What Works on Wall Street' by O'Shaughnessy, exploring key accounting ratios and shareholder yield. After hours is about cycling safety, Coppola's "DEMENTIA 13", scotch eggs, and managing celebrities.

Presented by Pro Taper We had an eventful weekend and have been taking full advantage of the short off-season that we have. ARay went racing, Anton saw the Cookie Monster, Chase jumped out of a tree at a wedding, and swap got to see a really white version of a Tik Tok dance at the Mammoth Airport! Enjoy this week's show...

In this podcast we cover: The origin of the term. What it means. Why it happens. Why it's bad for everyone. If you like the Elevate Construction podcast, please subscribe for free and you'll never miss an episode.  And if you really like the Elevate Construction podcast, I'd appreciate you telling a friend (Maybe even two

In this crosspost from the 80,000 Hours podcast, host Rob Wiblin interviews Nick Joseph, Head of Training at Anthropic, about the company's responsible scaling policy for AI development. The episode delves into Anthropic's approach to AI safety, the growing trend of voluntary commitments from top AI labs, and the need for public scrutiny of frontier model development. The conversation also covers AI safety career advice, with a reminder that 80,000 Hours offers free career advising sessions for listeners. Join us for an insightful discussion on the future of AI and its societal implications. Apply to join over 400 Founders and Execs in the Turpentine Network: https://www.turpentinenetwork.co/ SPONSORS: WorkOS: Building an enterprise-ready SaaS app? WorkOS has got you covered with easy-to-integrate APIs for SAML, SCIM, and more. Join top startups like Vercel, Perplexity, Jasper & Webflow in powering your app with WorkOS. Enjoy a free tier for up to 1M users! Start now at https://bit.ly/WorkOS-Turpentine-Network Weights & Biases Weave: Weights & Biases Weave is a lightweight AI developer toolkit designed to simplify your LLM app development. With Weave, you can trace and debug input, metadata and output with just 2 lines of code. Make real progress on your LLM development and visit the following link to get started with Weave today: https://wandb.me/cr 80,000 Hours: 80,000 Hours offers free one-on-one career advising for Cognitive Revolution listeners aiming to tackle global challenges, especially in AI. They connect high-potential individuals with experts, opportunities, and personalized career plans to maximize positive impact. Apply for a free call at https://80000hours.org/cognitiverevolution to accelerate your career and contribute to solving pressing AI-related issues. Omneky: Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/ RECOMMENDED PODCAST: This Won't Last - Eavesdrop on Keith Rabois, Kevin Ryan, Logan Bartlett, and Zach Weinberg's monthly backchannel ft their hottest takes on the future of tech, business, and venture capital. Spotify: https://open.spotify.com/show/2HwSNeVLL1MXy0RjFPyOSz CHAPTERS: (00:00:00) About the Show (00:00:22) Sponsors: WorkOS (00:01:22) About the Episode (00:04:31) Intro and Nick's background (00:08:37) Model training and scaling laws (00:13:10) Nick's role at Anthropic (00:16:49) Responsible Scaling Policies overview (Part 1) (00:18:00) Sponsors: Weights & Biases Weave | 80,000 Hours (00:20:39) Responsible Scaling Policies overview (Part 2) (00:25:24) AI Safety Levels framework (00:30:33) Benefits of RSPs (Part 1) (00:33:15) Sponsors: Omneky (00:33:38) Benefits of RSPs (Part 2) (00:36:32) Concerns about RSPs (00:47:33) Sandbagging and evaluation challenges (00:54:46) Critiques of RSPs (01:03:11) Trust and accountability (01:12:03) Conservative vs. aggressive approaches (01:17:43) Capabilities vs. safety research (01:23:47) Working at Anthropic (01:35:14) Nick's career journey (01:45:12) Hiring at Anthropic (01:52:06) Concerns about AI capabilities work (02:03:38) Anthropic office locations (02:08:46) Pressure and stakes at Anthropic (02:18:09) Overrated and underrated AI applications (02:35:57) Closing remarks (02:38:33) Sponsors: Outro

Fundamentals Season 2 (Part 2 of 6) — In part 2, we share our top tips for training on spray walls and home walls. We cover training setup considerations, route setting tips, best apps for saving and sharing climbs, mastering benchmark climbs, how to iterate on your climbs for incremental progress, hacks for building a home wall on a budget, and more.Listen to more Fundamentals episodes:thenuggetclimbing.com/fundamentalsThe NUG:frictitiousclimbing.com/products/the-nugCheck out my new portable hangboard.Check out the Tension Board 2:tensionboard.com/nuggetOr use the Tension app to find a TB2 near you.Mad Rock:madrock.comUse code “NUGGET” at checkout for 10% off your next order.Revival Climbing Coalition:revivalclimbing.comEP 225: Tony Bell & David Bress (my episode with the founders of Revival)BetterHelp:betterhelp.com/NUGGETUse this link for 10% off your first month. We are supported by these amazing BIG GIVERS:Michael Roy, Craig Lee, Mark and Julie Calhoun, Yinan Liu, and Matt Walter Become a Patron:patreon.com/thenuggetclimbingShow Notes:  thenuggetclimbing.com/episodes/fundamentals-s2-part-2Nuggets:(00:00:00) – Intro(00:01:20) – Best spray walls(00:05:46) – Terrain drives technique(00:08:00) – Choosing a wall angle(00:10:07) – Jesse's Tip #1: Home wall setup considerations (Use a mix of good and bad holds, a mix of textures, complement your local area, etc.)(00:13:05) – Steven's Tip #1: Build yourself a repertoire of quality climbs, and try to master them(00:17:45) – Bonus Tip: Cluster holds to build skills and learn movement(00:19:12) – Bonus Tip: Iterate on your climbs for incremental progression(00:24:52) – Jesse's Tip #2: Supercharge your sessions with route setting(00:27:59) – Steven's Tip #2: Work on climbs that are hard for different reasons(00:28:57) – Bonus Tip: Set chaotically (i.e. Set boulders without trying the moves, and try not to tweak them)(00:33:17) – Jesse's Tip #3: Do not underestimate the value of a short session on a home wall(00:35:52) – Steven's Tip #3: Circuits!(00:40:13) – Jesse's Tip #4: Make wood holds to save money(00:43:27) – Steven's Tip #4: Set foot moves(00:49:23) – Steven's Pitfall #1: Always trying new stuff and having crap sessions(00:52:08) – Jesse's Pitfall #1: Not having the right mix of holds for the skill level(00:54:33) – Steven's Pitfall #2: Not keeping track of your sessions(00:55:51) – Jesse's Pitfall #2: Not stopping when you power down (i.e. climbing too much and getting injured)(00:57:40) – Sandbagging on spray walls, and trusting your eyes(00:59:42) – Steven's Pitfall # 3: Only having big blobby footholds(01:02:03) – Steven's Pitfall #4: Not making your home wall cool (plus tips for building a cool home wall on a budget)(01:07:32) – Quantum leaps

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Finding Deception in Language Models, published by Esben Kran on August 20, 2024 on The AI Alignment Forum. This June, Apart Research and Apollo Research joined forces to host the Deception Detection Hackathon. Bringing together students, researchers, and engineers from around the world to tackle a pressing challenge in AI safety; preventing AI from deceiving humans and overseers. The hackathon took place both online and in multiple physical locations simultaneously. Marius Hobbhahn, the CEO of Apollo Research, kicked off the hackathon with a keynote talk about evaluating deception in AI with white-box and black-box methods. You can watch his talk here. We also had talks by Jacob Haimes, an Apart fellow, and Mikita Balesni, a research scientist at Apollo Research. This post details the top 8 projects, multiple of which are currently being developed further in Apart Lab. In total, we had 189 participants and 21 final entries. Sandbagging Detection through Model Degradation By Cam Tice, Philipp Alexander Kreer, Fedor Ryzhenkov, Nathan Helm-Burger, Prithviraj Singh Shahani The team introduced a new approach to detect AI sandbagging, a form of deception where an AI model strategically underperforms during evaluation to hide its true capabilities. The assumption behind their project is that sandbagging is a more complex task than showing the true capabilities. In the graph above, you can see how they gradually add noise to the model's parameters and evaluate the models during this process. Sandbagging models consistently improve in performance right before the noise level causes a normal model to lose the capability. It is a straightforward, inexpensive technique that doesn't require prior assumptions about how sandbagging is represented within the model. They successfully detected sandbagging behavior in two open-source LLMs: An 8B Llama 3 Instruct model and Phi 3 small instruct model. This approach offers a practical and accessible method for identifying potential deception in AI systems. Read the full project here. Detecting and Controlling Deceptive Representation in LLMs with Representational Engineering By Avyay M Casheekar, Kaushik Sanjay Prabhakar, Kanishk Rath, Sienka Dounia The team focused on detecting and mitigating sandbagging in AI models using recent methods for understanding activations. They fine-tuned a Llama 3 8B model on a custom dataset of deceptive and honest scenarios, then used Linear Artificial Tomography (LAT) scans to detect deceptive behaviors in the internal representations of the LLM. The team's technique not only identified deceptive behaviors, but also showed promise in mitigating them. Importantly, their approach demonstrated good generalization to out-of-distribution scenarios, suggesting its potential robustness in real-world applications. Read the full project here. Detecting Deception in GPT-3.5-turbo: A Metadata-Based Approach By Siddharth Reddy Bakkireddy, Rakesh Reddy Bakkireddy This team tackled the challenge of detecting deception in closed-source, black-box models like GPT-3.5-turbo. They investigated whether LLM API response metadata such as token count, response time, and tokens per second could be used to detect intentional deceptive behavior. The team analyzed 300 prompts and generated 1,200 responses from GPT-3.5-turbo, comparing the metadata of baseline and deceptive outputs. Their results revealed that deceptive outputs tend to have increased response times and altered token usage. This approach demonstrates that deception detection is possible without accessing a model's internal representation, opening up new avenues for monitoring and safeguarding AI systems, even when their inner workings are not accessible. Read the full project here. Modelling the Oversight of Automated Interpretability Against Deceptive Agents on Sp...

Thank you for 1m downloads of the podcast and 2m readers of the Substack!

First time back in a while! I felt like ranting a bit on some proposed takes that JiuJitsu should switch to an ELO Ranking System instead of a belt system. 

EP319 - Amazon Q1 2024 Recap http://jasonandscot.com Join your hosts Jason "Retailgeek" Goldberg, Chief Commerce Strategy Officer at Publicis, and Scot Wingo, CEO of GetSpiffy and Co-Founder of ChannelAdvisor as they discuss the latest news and trends in the world of e-commerce and digital shopper marketing. Episode Summary: In this episode, Jason "Retailgeek" Goldberg and Scot Wingo dive deep into Amazon's first quarter results for 2024, analyzing the company's performance in various segments such as retail, offline and online sales, marketplace, AWS, and advertising. They also explore the impact of AI on Amazon's business and provide insights into the company's future guidance for Q2 2024. Amazon Q1 2024 Earnings Release Amazon Q1 2024 Earnings Call Transcript In our latest episode, Jason and Scott cover a range of topics, starting with their reflections on recent events such as May the 4th and Cinco de Mayo. Jason shares intriguing stories from his extensive travels and interactions with listeners worldwide. Scott delves into the intersection of e-commerce and the auto industry, honing in on Carvana. The duo also delves into the U.S. Department of Commerce retail indicators data, shedding light on trends in retail sales and e-commerce growth. The conversation pivots towards Amazon's recent earnings report, contextualizing it within the realm of AI investments by tech giants like Meta and Alphabet, offering valuable industry insights and analysis. The discussion continues with a focus on Amazon's earnings report, zooming in on concerns around AWS amid heightened competition from Alphabet and Azure. The rising trend of AI investments, particularly in data training applications, is explored, alongside the growing popularity of open source AI models due to cost and privacy considerations. Despite a conservative Q2 guidance, Amazon impresses with robust revenue that surpasses Wall Street expectations, particularly in operating income. The retail segment shows exceptional growth, exceeding operating income estimates for both domestic and international divisions. Notably, Amazon's performance in brick-and-mortar stores, spearheaded by Whole Foods, demonstrates resilience with a 6.3% growth rate. AWS stands out with a 17% growth, dispelling market share concerns and showcasing accelerated revenue growth, illustrating Amazon's continuous growth potential and innovation prowess. Scott delves deeper into Amazon's positive quarterly earnings report, emphasizing the remarkable revenue performance, especially in operating income. Insights are shared on Amazon's successful agnostic approach to LLM models and the potential advancements in generative AI. The conversation shifts towards the burgeoning ads business at Amazon, underlining its profitability and future growth prospects. Scot also outlines Amazon's Q2 guidance and the potential impacts of consumer spending patterns on the retail sector, including concerns about changing consumer behaviors and economic pressures shaping market dynamics. Jason complements the discussion with additional perspectives on consumer behavior and economic influences reshaping the market landscape. Furthermore, we embark on a detailed exploration of supply chain logistics, with a spotlight on Amazon's expansion into third-party logistics services, revolutionizing traditional retail strategies by sharing proprietary capabilities for wider adoption. Insights from Andy Jassy shed light on Amazon's logistics business approach. The conversation expands to include how companies like Spiffy are embracing a similar model of sharing proprietary products to drive innovation and revenue growth, showcasing an evolving landscape of retail innovation. The podcast unpacks the complex world of grocery retail, highlighting Amazon's experimental forays like Just Walk Out technology and the Amazon Dash cart, while examining the challenges in delineating Amazon's grocery sector strategy. A comparison is drawn between Amazon's strategies and those of rivals like Walmart and Target, who are adapting their product offerings to match evolving consumer preferences, offering a comprehensive view of the dynamic retail and supply chain management sphere. Dive into our engaging discussion, explore retail dynamics, and keep a lookout for more insightful content. Don't forget to like our facebook page, and if you enjoyed this episode please write us a review on itunes. Episode 319 of the Jason & Scot show was recorded on Sunday, May 5th, 2024. Chapters 0:23 The Jason and Scott Show Begins 2:56 World Travel Adventures 5:53 Commerce Tools Elevate Show 6:53 Jason's World Tour Plans 7:22 Where in the World is Retail Geek? 20:43 Amazon's First Quarter Earnings 23:23 Sandbagging Strategy 26:45 Amazon's Dominance in E-commerce 27:44 Online Segment Growth Analysis 28:53 Offline Store Segment Analysis 31:35 Spotlight on AWS Performance 34:32 Data at AWS 42:02 Gen AI Revenue Growth 46:24 Consumer Pressure 49:56 Supply Chain Evolution 53:46 Leveraging Technology 58:08 Disruption in E-commerce 1:01:54 Amazon's Grocery Strategy 1:05:01 Retail Industry News Transcript Jason: [0:23] Welcome to the Jason and Scott Show. This is episode 319 being recorded on Sunday, May 5th, 2024. I'm your host, Jason Retail Guy Goldberg, and as usual, I'm here with your co-host, Scott Wingo. Scot: [0:37] Hey, Jason, and welcome back, Jason and Scott Show listeners. It's been a while, but first, happy Cinco de Mayo, and also a belated May the 4th, Jason. Did you have a good Star Wars day? Jason: [0:49] I did. I did. I feel like Star Wars Day always makes me think of the podcast because I feel like we have spent many of them in my latter life together. Scot: [1:01] Yeah, absolutely. Any exciting new Star Wars experiences or merch? Jason: [1:08] No, I understand you got some vintage merch. merch. Scot: [1:13] It's not, but they, back when I was a kid, you would go and if you went every week to, I think it was Burger King, you would for the, I think it was Empire. I have the Empire right here. So definitely Empire, but you would get a glass. Now it turns out these were full of lead paint, which would kill you, but that was the downside. Jason: [1:32] Not recommended for drinking. Scot: [1:33] You got a very, yes, I never, being a collector, I never drank out of them. So that's good. Jason: [1:37] Saved your life right there. Scot: [1:38] Yes, but I did drink out of the Tweety Bird. So that me, me. I'm sure I got some yellow lead paint from a twitty bird glass. Anyway, so they came out with a Mandalorian kind of homage to those glasses and they were at the Hallmark store of all places, not where I usually hang out, but I got to go to a Hallmark store and the little ladies that worked there were, I wish them all an awesome May the 4th. And they looked at me like I was from another planet and it was hilarious. My wife's like, stop, they don't know what you're doing. Jason: [2:07] Wait, they didn't have a big May 4th section in the Hallmark store? Scot: [2:11] They did. The little ladies didn't know. Jason: [2:13] The overlap of people that still buy Papyrus cards and celebrate May 4th is probably not great. Scot: [2:21] It was very humbling. It was a humble May the 4th, but I got my glasses and I was happy. I'm happy for you. And then tonight we had tacos for dinner, so I'm hitting all the holidays. Jason: [2:30] I feel like we should have tacos for dinner every night, whether it's Cinco de Mayo or not, but I'm i am happy for that. Scot: [2:35] We do have a lot of tacos but this was a special single denial edition. Jason: [2:42] Well, very well done, my friend. Scot: [2:44] Thanks. Well, listeners of the pod have been all over me. They're like, why aren't you recording? And I said, it's not me. It's Jason. It's Jason. Because you have been traveling Scot: [2:55] the earth, spreading retail geek goodness. Tell us, we are way far behind on trip updates and all the different countries. It's like you're playing, do you have like a little travel bingo where you're just like punching, what is it, 93 countries? Jason: [3:09] I do. They call it a passport. Oh, nice. Yes. Scot: [3:13] That, uh, little book that you get to carry. Yeah. Jason: [3:15] Yeah. Yeah. Yeah. I have been on a lot of trips and it sounds like you and I may be telling complimentary lies because I also, I've had an opportunity to meet a lot of listeners in the last, we'll call it seven weeks and which they're always super nice. And it's always super fun to talk to people. And obviously they're, you know, strangers recognize my voice in line at Starbucks at all these e-commerce shows. And then we strike up a conversation. And then the next question is always, where the heck is Scott? Because they're always disappointed to meet me and not you. And now the new thing is, and why aren't you producing more frequent shows? And my answer is always that you're dominating the world at Get Spiffy and that you're too busy. Scot: [4:00] Uh-huh. I see. Okay. Jason: [4:02] Well, we're both very busy. Scot: [4:05] You're traveling more than I am. I'm busy washing cars. Jason: [4:08] Yes. I think both are fairly true, but I did finish a grueling seven-week stint where I got to come home a couple of times on the weekends, but I basically had seven weeks of travel back to back. In my old life, that would not have been that atypical, but post-pandemic, The travel has been a little more moderate. And I have noticed that I have my travel muscles have atrophied and I don't really want to redevelop. Jason: [4:35] So the seven weeks was a lot. Please don't ask me for trip reports for all the commerce events because I kind of can't remember some of them. They're all a little bit of a blur. But I was at Shop Talks, I think, since the last time we talked, which is, of course, probably the biggest show in our industry. And that was a very good show. I did get to see a lot of our mutual friends and a lot of fans of the show there. So that was certainly fun. And maybe in another podcast, we can do a little recap of some of the interesting things that came out of Shop Talk. I did produce a couple of recaps in other formats for work clients, so we could certainly pull something together. I also went to a vendor show. One of the e-commerce platforms out there is called Commerce Tools, and they had their annual customer show, which is called Elevate in Miami. So I got a chance to go visit there. They're one of the commerce platforms that I would say is winning at the moment in the kind of pivot away from the old school monoliths to these new sort of SaaS-based solutions. And commerce tools in particular are kind of pioneers in pushing this actual certification around a more modern earned stack that they they coined mock. And I think I think we've had Kelly from from commerce tools on the on the podcast Jason: [5:51] in the past to talk about that. But that was a good show. I got to meet a lot of listeners there. And a funny one, several listeners were like. Jason: [5:59] I would apologize for the, the, our publishing schedule lately. And they're like, I'm cool with it. I like that. Like you don't do a show if there's not something worthwhile. And then, you know, when I do get a show, it's like a treat. So I don't know if they're being honest or not, but that made me feel a little better about some of our, our, our Tardis shows lately. So those, those were good events. I also spent a week in India with some clients and that super interesting, a lot of commerce activity going on there, a lot of different market dynamics than here. So that's kind of intellectually pretty fun to learn about and see what's working there that might be working here or what, you know, why things tend to play out differently there. So that's interesting. And then I have a lot more international trips booked right now. Jason: [6:48] So coming up, I'm going to Barcelona, London, Paris, and Sao Paulo. So if anyone either has any favorite retail experiences in any of of those cities, please send them my way. I'll be doing store visits in all those cities. And if you're based in any of those cities, also drop me a line. Hopefully we can do some meetups while I'm out there. Scot: [7:07] Cool. It's Jason's world tour. You can do a little pod while you're there. Jason: [7:12] We have done a bunch of international pods in the distant past. I remember hotel rooms in South Korea and all over the place, Jason: [7:19] Japan that we've, we've cut shows from. So, so totally could. Scot: [7:23] Yeah. We'll have to do it. Where in the world is retail geek? That could be the theme song. I just sampled that. Jason: [7:30] Yeah. So besides cleaning the world's cars, what have you been up to, Scott? Scot: [7:35] Well, it's kind of funny. My worlds are colliding. So a lot of the analysts that you and I know from the e-commerce world are creeping into the auto world and their gateway drug is Carvana. So in the world of retail, we have Amazon, obviously. Well, Carvana is kind of Amazonifying used cars. They had a bit of a drama kind of situation. They were the golden child of online cars. And then they totally pooped the bed. They did this acquisition. They loaded up with debt. And then after, I think it was 21. So they had a good COVID. They surged. And then the debt got in front of them. Used car prices bop around and they kind of like got in an open door situation where they had bought a lot of cars for more than they were worth suddenly. And then they plummeted and everyone thought they were going out of business, but they have had a resurgence. So it's causing a lot of the internet analysts to now pick up auto tech or mobility or whatever you want to call it. So it was fun. I got to do a live chat with Nick Jones. He's been a friend of the show. I don't think we've had him on due to some compliance stuff that his company has rules around, but he's at this firm JMP and it was kind of wild to talk about, with someone about both Amazon and what we're doing at Spiffy, which is basically a lot of Amazon principles applied to car care. So it was interesting to have someone reach out and say, hey, I think this is a thing. And everyone tells me I should talk to you about it. And I was like, oh, yeah, I would love to. So it's kind of fun. Jason: [9:01] That's very cool. And isn't it also a thing, I think half the vehicles on the road are now owned by Amazon. So I assume that's an overlap too. too? Scot: [9:09] Yeah, not half, but a lot are. The number of last mile delivery vehicles are very, very large. And we work with a lot of them, so it's kind of fun. I started spiffy somewhat to get away from Amazon and still all I can talk about. Nope. So embrace it. I love Amazon. Love me some Amazon, Jason. Jason: [9:29] I'm glad you do. I love them too, but I feel like I spend most of my career You're unsuccessfully helping people compete with them. Scot: [9:38] Hey, got to play one side of the coin. It's a gig. You're going to be more like them or how to fight them. Jason: [9:43] It's a gig. It is indeed. Yeah. Scot: [9:46] Cool. I thought we are going to talk about some Amazon news. But before we jump in, you have done your magic with your data analysis interns. And I'm sure there's an LLM and an AI thrown in there. Let's start with some of the things you're seeing in commerce trends from the data that's out there. Jason: [10:07] Yeah. So as everyone knows, I have a little bit too much of an infatuation with the U.S. Department of Commerce retail indicators data. And these guys, you know, publish monthly estimates of retail sales in a bunch of categories. And, you know, we've talked about this many times on the show, but broadly over the last several years have been really interesting in retail. 2020, 2021, and 2022 were the greatest three years in the history of retail. Like we mailed like $6 trillion in economic stimulus. People didn't travel or go to restaurants as much. And so we sold way more goods than ever before. And so those three years, retail grew respectively at like 8%, 14%, and 9%. The 20 years prior, retail averaged about 4% a year in growth. So normally pre-pandemic, you'd expect 4% growth. We had these three, you know, wildly pandemic influence years where we grew really fast. And then last year we finished a little below 4%. So, so we were around, I want to say it was like 3.6%. So it was growth. It would, it would have been in line with pre-pandemic growth, but it certainly felt like a significant deceleration from those heady pandemic years. And so, you know, people are super interested to see how does 2024 play out? Does it? Jason: [11:32] Kind of return to pre-pandemic levels, like what is the new normal? Jason: [11:37] And we now have the first quarter's data from the U.S. Department of Commerce, and I would call it kind of a mixed bag. If you just look at the raw retail data that the U.S. Department of Commerce publishes, they're going to tell you that retail grew in the first quarter 2.8%. So that's a little anemic, right? Compared to historical averages, that's not a great growth rate. Most of the practitioners that follow this podcast care about a particular subset of retail that the National Retail Federation has dubbed core retail. And so the National Retail Federation pulls gas and automobiles sales out of that number. And gas is a decent size number and it's very volatile based on the commodity prices of gas. And auto is a huge number that has, as you're well familiar, its own idiosyncrasies. And so that's how they justify taking those two out. And if you take those two out and you get this core retail number, retail in the first quarter grew 3.9%. So kind of to align with how the NRF talks about retail, we'll say Q1 overall was 3.9%, which is very in line with the pre-pandemic historic average. So disappointing by pandemic standards, but kind of traditionally what we would expect. Jason: [13:05] What is unique in that number is. Jason: [13:09] That it's very bifurcated. There are clear winners and losers, both by categories and specific practitioners. So if you break down the categories, e-commerce is the fastest growing chunk of retail. I'm sure we'll talk more about that. Restaurants were the next fastest growing categories. And categories like mass merchants and healthcare providers outperform that industry average, every other segment of retail underperformed the industry average. So things like furniture stores did the worst, building materials did really poorly, gas stations did very poorly, electronics did poorly, and side note, electronics have been the worst performer since the pandemic, which is kind of interesting and challenging. So you've had this weird couple categories doing really well, a bunch of categories doing really poorly. And then within the categories even, if you look at the public company's individual earnings calls, what you tend to see is a couple of big players performing really well in overall retail, that's Amazon and Walmart. And then a lot of other retailers really struggling. So that even that's like in general merchandise, it's Amazon and Walmart that are lifting the boats. And it's folks like Target traditionally that have performed really well are actually struggling at the moment. So the average is kind of hard to follow at the moment. Jason: [14:37] But that is kind of how things play out. And then we have some preliminary e-commerce data, but the actual Q1 e-commerce number that the U.S. Department of Commerce publishes will publish on May 17th. So that's 12 days from now. Jason: [14:53] And crunching the numbers that we have available at the moment, that growth is likely to come in at somewhere between 8% and 10%. I'm guessing more like 8% or 9% growth. And so that also is twice as good as overall retail, and it's more than twice as good as brick-and-mortar retail. But that is noticeably slower than the historic e-commerce growth rates pre-pandemic. So kind of file those two numbers away. The overall retail industry is growing at 3.9%. The overall e-commerce industry is growing at about 9%. And then we have our friends at Amazon that dropped their earnings announcement just before May 4th so that they could celebrate May 4th, I think. Scot: [15:39] Yeah, yes, that's a good setup. And without further ado, let's talk about Amazon's fourth quarter. It wouldn't be a Jason Scott show without a little bit of... Scot: [16:01] That's right. On April 30th, Amazon announced their first quarter results. And the setup coming into these, so you had the data you talked about, but like to drill in a little bit. We had Meta, the artist formerly known as Facebook, and Alphabet, the artist previously known as Google. They announced and they both basically told Wall Street, AI is the cat's pajamas and we're going to spend anywhere between $10 and $40 billion of capital expenditures on it, meaning NVIDIA chips. So it turns out the way to play all this is basically buying NVIDIA. So hopefully you bought some NVIDIA stock. Maybe this is not a stock recommendation or when it's too late, so... And also don't take stock recommendations from podcasters. Anyway, so there was all this angst and people were a little freaked out coming into the Amazon results because Meta was down like pretty substantially, 20 to 30 percent. And Alphabet was also up substantially. You also had Microsoft come in there and they really crushed it. Their Azure is really lighting it up with AI. And they announced that they were going to invest a lot. And there's this rumor that a $100 billion project, it's got a name like Starship or something, but it's not Starship. Spaceship? Stardust? I don't know what it is. But it's going to be this mega data center, and they literally can't find a place to put it because it's going to consume so much power. So they're going to have to maybe build a nuclear plant next to it or some wacky thing. Scot: [17:31] Anyway, that was the setup. up. So coming in, Wall Street was very, very concerned about Amazon's AWS division, which is their cloud computing. Because if Alphabet is building out their infrastructure, and so is Azure, that's the two biggest competitors for AWS. And is AWS getting its fair share? And is it going to announce that it's going to have to go build some $40 billion kind of a thing? Also, another Another thing, and I'm kind of curious on if you're seeing this with your clients, but in the, I follow this, you know, the AI, you can't do much without seeing AI everywhere. But the part I'm most interested in is what are big enterprises spending money on? This is like your Fortune 500s. They're all experimenting and really getting into it. And where they're finding a lot of good use cases is training on their data. So they'll say, you know, hey, I'm Publisys. How many documents do you think are inside of Publisys? I don't know, 8 trillion documents. Documents and you know wouldn't it be helpful just the ones I created and who is this retail geek and he's he's created uh you know 90 of those and you know so you know imagine you're starting new at publicists you're gonna be like where do I start going through some of these documents for us and if you had a chat bot that was like hey I've read all that you know I can navigate you through everything that's been published or you know whatever I'm certainly you. Scot: [18:50] Providing a very big metaphor, certainly be more divisional and all this kind of stuff. But that's where big companies are spending the bulk is they're taking their data in whatever format it's in, be it a relational database, a PDF, whatever it is, they're trying to train it. They don't want it to go up into the, they don't want to train the LLM so that other people get the benefit of that and can see any confidential data. So that's really important. So it needs to be gated in these types of things. Because of that use case, open AI is not great because people are very worried. A, it's very expensive and it's only an API. So OpenAI hosts itself and you call it through an API. Scot: [19:25] Those API calls are very expensive. They're getting, as OpenAI has gotten more popular, there's more latency. It's taking forever to get answers out of this thing. And a lot of people are very concerned that even though there's ways to call the API such that it's in a window and not being trained, that maybe it leaks in there. So because of all these elements, the open source models are becoming very popular. And right around the time Meta announced, they announced their Llama, which has become quite popular. And what's nice is you can host it wherever you want. And it's kind of like WordPress, where if you are a serious WordPresser, you can host it somewhere yourself, and you can kind of understand that. Otherwise, there's other people that will host it for you. But it has the nice feature of you're just getting the weights and whatnot, and it's it's pretty clear, it's pretty obvious, it's not training itself on your data. So a lot of people like it because it's quote unquote free. It's not an API usage based. It's a pay once to set it up, pay for some resources type thing and you're done. And it's also not going to train on the data. That's one of many. There's probably 10 or 20 pretty commercial grade open AIs out there. Scot: [20:38] Okay. So that's kind of the setup to get to the earnings. things. So from a big picture, this was a really good quarter. Asterix, the guide made Wall Street a little bit nervous. So- Scot: [20:53] And one of our research analysts just said it's Stargate, which is also a sci-fi series. They must have that on Prime Video or something. There's probably some callback there. Scot: [21:01] So they beat for the quarter Q1, but then they also kind of tell you what's going on the next quarter. Amazon doesn't provide fully your guidance. They just kind of give you a snippet. So when they report one quarter, a quarter, they then tell you what they think the next quarter is going to do. So Wall Street got a little bit ahead of its skis, and the guide for Q2 was below what Wall Street wants. So it wasn't what we'd call a beat and a raise, which is the current quarter was a beat and the next one they increased. It was a beat and a guide down. So that probably tampered Wall Street. But ever since Jassy came in, Andy Jassy, this has been his MO is to be pretty conservative because Wall Street's very much an expectation engine. And the more, if you can beat and tamp down expectations, it makes it, it's a little bit rougher in the short term from a stock price, but it makes next quarter better and then so on and so forth. So it's a smart way to manage the long-term vibe of the stock, the mindset, the expectations around your stock. Okay. So revenue came in at $143 billion versus Wall Street at $142. So pretty much in line. But most importantly, where Amazon really threw people off was on operating income. Yes, Amazon is profitable. This is the proxy for operating income. True Amazonians would tell you, no, it's cashflow. We can go into that, but this is kind of the way they report to Wall Street. So this is kind of the standard operating system, if you will. So this is what we're going to use, but it's a proxy for cashflow. Scot: [22:28] That was 15 billion for the quarter and Wall Street expected 11. Well, you know, 4 billion on a world of 143 doesn't sound like much, but between 11 and 15, that's a very material beat. What is that? Like 38%, something like that. Scot: [22:44] So that was a really nice surprise. And, you know, Amazon goes through these invest and harvest periods and everyone's been feeling like they're going to be back in investing which would mean they're going to start lowering operating income as they invest but it's actually kind of beating expectations, also this is the fifth quarter amazon has come in at the high end of its guidance or above its guidance since basically you know on operating income and that corresponds with when jassy came in so this is his mo right now is to kind of like beat and lower beat and lower you know exceed expectations tamp them down not get not get ahead of his skis and it's working really well. Jason: [23:24] Sandbagging for the win. I like it. Scot: [23:26] Yes, it is. Having run a public company, this is a lesson I learned painfully. So that's something we can talk about over beer sometime. Jason: [23:33] I will book that date. Yeah. And the retail business sort of followed in line with that. They had like some nice growth, but like the real standout number was the improvement in margins and the significant positive operating income from the retail segment. So I think the actual operating income from U.S. Retail was like $5 billion and the Wall Street expectations were 4.3. So again, that was another strong beat. Total revenue, which revenue is not the same thing as retail sales, as we've talked about on the show many times, that we would use GMV as a proxy for that. But revenue was $86.3 billion for the quarter, which I think was in line with the analyst expectations. Jason: [24:27] And I think this was the largest operating income that Amazon has ever reported for the retail business. So that was super interesting on the domestic side. Traditionally, domestic has done pretty well and international has been a money loser because, you know, they've been less mature. they've been investing a lot in growing international and they haven't had the same kind of margins. This was the first quarter that they reported positive operating income for the international division. So that's another super encouraging sign for investors that maybe they've kind of passed that inflection point on a lot of their international investments that they've made in the EU and Japan and the UK, which reminds me is not part of the EU anymore. Jason: [25:13] So so they kind of beat beat international expectations across the board on income. Revenues were lower. So revenues were like thirty one billion dollars, which was below expectation. Jason: [25:25] But they they earned like nine hundred million in operating income. And I want to say the the the Wall Street expectation was like six hundred million. So so again, like a 30 percent beat, which is pretty, pretty darn good. Good. They also, a bunch of analysts have, you know, taken these revenue numbers and they try to back into a GMV number. And I would say the bummer at the moment is there's a fair amount of variance in the estimates, like different analysts have different models. So I have kind of been putting to a model of the models together and trying to kind of find a midpoint. And like Like based on that, the Amazon's GMV globally probably went up 11.5% for the quarter. So if you're comparing this to other retailers or the U.S. Department of Commerce number, overall GMV went up 11.5%. The U.S. was stronger. So the U.S. probably went up at 12.2%. So again, we talked about core retail was up 3.9%. Well, Amazon U.S. GMV was up 12.2%. So, you know, three times faster growth than the retail industry overall. Jason: [26:39] And again, Amazon is mostly e-commerce, very little brick and mortar, Jason: [26:44] which we'll talk about in just a minute. But even if you're comparing Amazon to that e-commerce number, if e-commerce comes in at 8% or 9% and Amazon's at 12%, they're by far the largest e-commerce player out there and they're still substantially outgrowing the average, which, you know, is very impressive and should be very scary to every other competitor out there. Jason: [27:08] One analyst kind of put together an estimate of what they thought the earned income contribution from Amazon was for retail and ads together, pulling AWS out. And they had it at $27 billion in earned income if Amazon was just a retail with no AWS. And that puts them right in the ballpark of Walmart that spent off about $29 billion in earned income or operating income. I keep saying earned, but I mean operating income. So, so that is all pretty impressive and simultaneously super scary. Jason: [27:45] Scott, did you drill down into the online segment at all? Scot: [27:49] Yeah. And, you know, what I would tell listeners is picture a block diagram where you have this big, big rectangle, that's the whole Amazon entity. And, you know, so what we're going to do is talk about the segments. And the first segment is the biggest one, which is the retail business. And that, that's what you just. Jason: [28:04] Biggest and best. Wouldn't you say? Scot: [28:06] Coolest. Jason: [28:07] Coolest. All right. Scot: [28:08] Cool. Okay. Yeah. Yeah. Okay. I'll, you know, I don't know. Jason: [28:11] It is for you. Scot: [28:14] Um, I think the whole enchilada, I like the, the way they do this and I'm trying to replicate it. It's 50. We'll talk about that in a second. The, so then the, you know, so then another segment is AWS, another segment, I think marketplace should be in some segment, but they don't break it out. So it's just kind of in kind of hidden inside of the blob that is retail. So we tease some of that out here on the show. They purposely hide it in there. So no one knows how awesome it is, I think. And then they've got AWS ads and a couple other things, but we'll talk about this. So as you dig into the retail business, there's a couple of ways to look at it. You can look at it by domestic and international, which Jason just did, Scot: [28:50] or you can look at it by online and physical store. So the online biz grew 7% year over year, which if I remember your stats, well, you don't have it until may 17th so on may 17th we'll be able to know how that compared but probably the one you can compare is the offline biz which is the the store comp that they have, And Jason, you saw on that one, what'd you see? Jason: [29:16] Yeah, so physical stores grew 6.3%. So again, like, you know, when we say all of retail grew 3.9%, a big chunk of that's e-commerce. Brick and mortar probably grew at like two to 3%. So Amazon's brick and mortar growing at 6.3% is actually super impressive. And it's kind of interesting, you know, for several years, Amazon has had experiments in a bunch of retail formats. So they've had these Amazon Go stores, stores. They had Amazon five-star stores. They had bookstores. They had a fashion store. They're trying all these things. And of course, the biggest chunk of their stores is they own Whole Foods. And so offline stores for Amazon was kind of a mix of all these different concepts. In the last couple of years, they've kind of cleaned house and gotten rid of all those concepts. And so, you know, nominally there's a few of their own grocery stores called Amazon Amazon fresh open, but the vast majority of online offline retail for Amazon is, is Whole Foods. And for it to be growing at 6.3% in the current climate is, is a really good sign for Amazon. And, and I would say somewhat impressive, you know, on the earnings call, they, they announced that they're working up a new format for Whole Foods, which is a smaller format store that's It's going to open in Manhattan. So I have that on my ticker file to go visit when that's open. Jason: [30:38] You know, the whole grocery space for Amazon is super interesting, but maybe we'll talk about that a little bit more later. But I will call out, they did launch a service that there's been some controversy over. They launched a $9.99 a month grocery delivery service, which essentially lets you have all you can eat free grocery delivery to your home for an incremental fee of $9.99. And they're spinning that as, you know, a cool new grocery service and enable more people to shop for groceries online. And there are a lot of articles about it, like. Jason: [31:13] They used to have free grocery delivery included in your Prime membership, right? And so they've kind of like, I look at the big arc of all this and say, there used to be a lot more free services in Prime that they've kind of peeled out. Then they started charging for, and now they'll let you get it free again for another $120 a year. Jason: [31:32] So interesting things happening with grocery that we could probably talk more about later. But I'm kind of eager to dive into some of these other businesses like AWS. Scot: [31:42] Yeah. So that's the one that everyone was really waiting on the call to hear how it went. And good news, AWS exceeded expectations. Everyone thought it was going to grow 14% and it came in at 17%. And if Wall Street likes, they like a lot of things, they like beating expectations, that's important to them. But their favorite thing is ARG. And that is not a pirate day thing, ARG. It is Accelerating Revenue Growth. Wall Street loves that more than anything. And that's what they delivered for both the ads and the AWS part of the business. And what that means is that as the law of numbers kicks in, so back on the retail business, the only time we see that accelerate is in the fourth quarter and that seasonal acceleration, right? We've gotten used to that for decades now. It always happens in the fourth quarter and whatnot. So it's what you would expect. But this is quite unusual for a relatively mature business. This thing's $25 billion a quarter. So this is a $100 billion business that accelerated. And so that tells us that there is a lot more wood to chop here. It has not gotten near its addressable market. And it really allayed fears that they were losing massive market share because they're, quote unquote, behind on AI to Azure, which is Microsoft offering, and then the Google hosting solution as well. Scot: [33:05] That does not seem to be the case. So they did very well. So they came in at $25 billion and Wall Street was expecting $24.6. So that was really, that accelerating is what really made everyone very happy. And then the operating income came in at $9.5, way ahead of Wall Street at $7.5. So another pretty material 20% beat on this component at the bottom line. And this is really interesting. There was some really good language around this. And this has been Jassy's statement all along, and it's coming true. His early Amazon's early play was we're going to be agnostic on models and it's kind of like bring your own model we'll work with anything now with open AI they're not going to ever host open AI but they'll they're not going to stop you from working with it and then they for these open source ones they've made it very easy for you to spin up an AWS instance throw a little llama in there and I would make a llama noise if I I knew what they said I guess they make like a sheep sound. So you throw a little alarm in there and it does its thing. And, you know, the benefit of them being agnostic on these LLMs is most likely they have some or all of your data, right? Because they've been at this so long that if you're doing cloud computing versus on-prem, most likely a lot of, if not all of your data is in AWS. Extracting that data, you know, imagine you had terabytes or or what's the biggest, Scot: [34:31] bigger than terabytes? I always forget this one. Jason: [34:33] Petabytes. Scot: [34:34] Petabytes of data at AWS. They literally have a product that they can send a truckload of hard drives around and get your data. That's how much data there is that you could never push it across the internet, that there's so much data. So if they have that data and that's what you want to train on, you don't want to have the latency of the internet between your data and the training. So you'd really need the LLM to operate near your data. And this is what they predicted two or three years ago, kind of around the, the, the launch of chat gpt when all this stuff really started to accelerate and it's coming true so everyone feels a lot better about that then their body language this time a lot of times they were kind of like this is what we're doing and we're pretty sure it's going to work now they're like it's working and people really felt relief around this because everyone there was a set of people that believed it but then you know open ai's pitches nope our lm is going to be we're spending, billions of dollars we're going to be so far ahead none of these open source things are going to keep up. If you don't have us, you're going to be so far behind, you'll be like playing with crayons and everyone's going to be playing with quill pens. Scot: [35:42] So it was really good to see that this is not what's happening, that people are embracing, enterprises are embracing these open source models. They are in the same zip code performance-wise from results and much cheaper than OpenAI's offerings. And what Amazon said specifically was very positive around what is It's kind of abbreviated Gen AI for generative AI. And it's kind of a way to encapsulate this. And they said that it already is a multi-billion dollar run rate business. And you always have to parse what they say. So multi-billion can be anywhere between 1 and 9.9, right? And you'll see why I drew 9.9 there. Scot: [36:25] And inside, as part of that big AWS number, and they believe it can be rapidly tens of billions. Billions so they're basically saying it's not double digit billions so it's a single digit million which is where i get one to nine point nine but they basically hinted that that it is growing so rapidly inside of there that it's gonna be tens of billions and this is why they saw accelerating revenue growth which made everyone happy it wasn't just people you know moving some more you know loads on or something boring loads around relational databases or something it was the juicy ai stuff so this got everyone so lathered up that three analysts did price increases and they cited that this was one of the reasons the biggest price increase was from sig susquehanna and they put the price up to 220. At the time all this happened the stock was at 175 and today it's around 185 so it's been up nicely but 220 is a pretty big big you know even. Scot: [37:20] From where they expect that's where they're thinking i think most these guys look at a year to two years as a time horizon on these prices so and that's the the high i have you know again there's a wide range some people think it's going to go down some people think it's over price so go do your research this is not a stock recommendation but i just thought it was interesting that people get really really excited by by this whole gen ai largely the body language that, and it's, Amazon doesn't pound their chest much. So the fact they were, was kind of a new, new way of managing Amazon and Jassy's pretty conservative. So he must've felt pretty good about it, but also that they needed to ally, allay, allay, allay, whatever the right word is, get rid of these competitive concerns everyone's been talking about. Jason: [38:05] Yeah. It feels like a pretty big prize out there. Jassy and the whole team always talk, Just AWS, even before you get to Gen AI, they always remind everyone, hey, 85% of the workloads are still on-prem. So like this, as big as AWS looks, if the long-term future is 85% of the workloads are on the cloud and only 15% are on-prem, there's a lot of headroom still in AWS. And then, you know, you add this new huge demand for AI on top of all that. And like this, it's almost a limitless opportunity. And I want to tie the AI back to retail, though, for just a second, because there's another bit of news that I haven't seen covered very much, but is super interesting to me. Jason: [38:51] There's a particular flavor of AI out there, a subset of generative AI that's now being called agentic AI. And that's sort of a clever amalgamation of agent-based AI. And there's a very famous AI researcher, this guy, Andrew Ng. He's the founder of Coursera. He's done a bunch of things. He was the head of Google Big Think, which was one of the first significant AI efforts. And I want to say he was like on People Magazine's 100 most interesting people list in like 2013 as an AI researcher. So the dude's been around for a long time. He is one of the biggest advocates for this agentic AI. And the premise is that if you just ask an LLM, you take the best LLM in the world, and you ask it to do something for you, that's called zero shot. You give it an assignment, and you take the first result you get. It's a zero shot. You get pretty good results. But if you... Jason: [39:53] Turn that, that LLM into multiple agents and break the task up amongst those agents and potentially agents even running on different LLMs, you get wildly better results. Jason: [40:05] And so his, his research kind of showed that, Hey, if, if Jason goes write a PowerPoint presentation for his client, explaining what's going on in commerce. And I just give that to the turbo version of ChatGBT 4, I'll get a pretty good deck. But if I say, hey, I want to create four agents. I want to create a consultant to write the deck and a copywriter to edit the deck and an editor to improve the deck and three people to pretend to be mock customers to poke holes in the deck and have all those agents work on this assignment. I could give that assignment to chat gbt 3.5 and it would actually output a better work product than the the newer more advanced model was by by breaking the job into these chunks and so in retail you think about like this is the idea of assigning higher level jobs to shopping right so instead of saying like going to amazon and saying oh now it's a ai-based search engine and i'm going to type a long form query into search and get a better result. Jason: [41:09] The agentic AI approach is I'm just going to say to Amazon, never let me run out of ingredients for my kids' school lunches. And the agent's going to figure out what is in my school lunches and what my use rate is for those things and what weeks I have off from school and don't need a school lunch. And it's just going to do all those things and magically have the food show up. And this is a long diatribe, but the reason it's relevant is is this dude, Andrew Ng, was named the newest board member at Amazon three weeks ago. Scot: [41:40] Very cool. Jason: [41:40] I did not see that myself. Yeah. And so if you're wondering where Amazon thinks this is going, like this, in my mind, ties all this tremendous opportunity in generative AI and the financial opportunity in AWS directly to the huge and growing retail business that Amazon runs. Scot: [42:02] Very cool. Oh yeah. I had not seen that. So maybe Wall Street picked up on that. I'm sure. And maybe that was another part of the excitement. Jason: [42:09] Yeah. But all of that is just peanuts compared to the real good business in Amazon, which is the ads business. So again, you know, Amazon used to, to obfuscate their ads business. They've for a number of quarters now had to report it as earnings because it's in their earnings separately, because it's so material. And it was another good quarter for the ads business. It's hard to say whether it's actually accelerating growth or not, because the ads business is very seasonal. So the ad business grew 24.3% for the quarter versus Q1 of 2023. Q4 grew faster. So Q4 grew at 27%, but the 24% growth is much faster growth than other... Q1 year-over-year growth rate. So however you slice it, it's a good, robust growth rate. If you add the last four quarters together, you get $29 billion worth of ad sales. There's lots of estimates for how profitable ad sales are, but there's no cost of goods for an ad, right? Jason: [43:13] And so it's very high margin. So if you just assume, I think 60% gross margins is a very conservative estimate. But if you assume 60% gross margins, that means the ad business spun off $29.5 billion of operating income over the last 12 months. And to put that in comparison, AWS is big and profitable as it is, twice as much revenue at over $100 billion now, but it spun off like $23 billion in operating income. So the ad business is a much more meaningful contributor to Amazon's profits than even AWS. Jason: [43:51] And another way I've been starting to think about this is what percentage of the total GMV on the Amazon platform are the ads? And they are now 6.5%. So that's a very significant new tax. You know, as Amazon has hundreds of millions of SKUs available for sale, no one's ever going to find your SKU or buy it if you don't do some marketing on the platform for that SKU. And that's this 6.5% tax that Amazon's charging. And in the same way we said, hey, AWS is a really robust business. And then there's this thing called generative AI that can make it even huger. All of this ad revenue we're talking about is really coming from their sponsored product listings, which is like basic search advertising on the retail platform. Last quarter, Amazon said, by the way, we have this huge viewership streaming video service called Amazon Prime. And we're going to start putting ads in the lowest tier version of Amazon Prime. So unless you want to pay more, you're going to start seeing ads on Amazon Prime. And that's another huge advertising opportunity that hasn't been very heavily tapped yet. So the analysts are pretty excited about the upside of Amazon potentially tacking on another $6.5 billion in Prime video ads onto the $50 billion of search ads that they already have. Jason: [45:11] And so ads are a pretty good business to be in, which is why every other retailer is trying to follow suit with their own sort of version of a retail media network. Scot: [45:22] Cool. I imagine you get a lot of calls to talk about that. Jason: [45:25] Oh, yeah. I actually, I'm sick of talking about it. So one nice thing about working at an ad agency is there are now thousands of other experts. You know, I was one of the early guys in retail media networks. Now there are thousands of other experts that are way more credible than me. So I don't have to talk about it quite as much, but it still, still comes up in every conversation. Scot: [45:43] Very cool. All right. So then that was the basic gist of the corridor from a high level. And then it came to the what's going on in Q2. So that did come in lighter than folks expected, as I said, and they guided the top line to 144 versus 149. Let's call it 146 and change at the midpoint. They always do this range kind of thing when they're doing their guide. And Wall Street was at 150 consensus. So, you know, a tidge below two or three percent below where they wanted. But the operating income guide was above Wall Street. So they're kind of, we'll take it. Como si, como sa. Scot: [46:21] So that was, you know, I think Amazon tapping things down. Yeah. Now they did talk a lot about consumers being under pressure. So they said in the, it wasn't in a Q and a, it was in the prepared remarks and Jassy said it, which is kind of like the more important stuff. And I will say it's really nice to have the CEO of Amazon back on these calls because Bezos basically ditched them after, I don't know if, I think he came the first two quarters back in 97 but i honestly can't remember but he has not gone to the calls and jassy's been to them all so it's really nice to hear from the ceo and he answers very candidly i feel you know he doesn't feel as kind of like robotic as many ceos when they get on here because it is a stressful thing that you're going to say something wrong, but there was this exchange well first of all he he in his prepared remarks he talked about. Scot: [47:12] I forgot to put the exact language, but he said, we're seeing a lot of consumers trade down. So they're seeing, you know, we're seeing this in the auto industry. Tires is this huge thing where it's under a lot of pressure right now because people are just waiting. So there's a lot of this, you know, it's not showing up in the data that I've seen, but there's, you know, maybe the inflation data, but not the GDP and some of the other unemployment data. But it feels like the consumer is under a bit of pressure here, and they talk about that a lot in the prepared remarks. So I thought our listeners would find that interesting. Jason, before I go into this longish little thing that I wanted to just cover, what do you, did you pick up on any of that consumer stuff? Are you hearing that? Jason: [47:55] Oh, yeah, that's very common. And remember, in the beginning, I mentioned that there's this weird bifurcation that some retailers, even in categories, are doing well and others aren't. And some categories are doing well and others aren't. That's super complicated to get to the why. But the most obvious why is that consumers feel like they're under a lot of economic pressure and are trading down and are deferring certain types of purchases. The easiest way to see this is own brands and private label sales going up and, you know, national brand sales stagnating, see things like chicken protein going up and beef protein going down. You know, there's lots of examples out there, but the retailers that are best able to follow the consumer as she trades down are tending to do well. And the retailers that only cater to the luxury consumer, the super luxury is still doing fine. They're somewhat insulated. But the folks that haven't been as able to cater to the value consumer as much have struggled more. And the non-mandatory categories have struggled more. So Andy's comments exactly mirror what we're seeing going on in market dynamics and what other retailers are saying in their earnings. It is slightly weird because if you just look at the macros. Jason: [49:18] It's objectively, the consumer is doing pretty well. There's actually a lot of favorable things, but there's a ton of evidence that the consumer sentiment is that they're really worried about their household budget and are making, you know, hard, hard financial decisions. Scot: [49:36] Yeah. Yeah. It's tough out there. Well, hopefully it'll get better. So one of the questions I want to just kind of pull out some tidbits, because this has been a theme on our pod for a long time and I thought it was really, really interesting. And this is going to get into the weeds of supply chain and this kind of thing. So sorry if that's not your jam. We like to talk about logistics. Scot: [49:56] Side note to you, Jason, I saw that deep dive we did on Amazon logistics is still like our number one show and all the stats and stuff, which is kind of fun. So someone cares about it. Anyway, one of the friends of the podcast, Yusuf Squally asked a question. He's one of the analysts and he said, as it relates to logistics, so he's talking to andy on the call back in september you launched amazon supply chain can you help us understand the opportunity you see there where are you in the journey to build logistics as a service on a global basis and does that require a huge increase in capex a function increase in capex which means huge so jesse said this was a very long answer so i'm going to pull out two snippets you can go read the transcripts can you put a link to that in the show notes absolutely yep yeah so so i'm just gonna give you the the snippet the whole thing is worth reading but it would be like another 20 minutes to do that. But so Jassy starts out and says, I think that it's interesting what's happening with the business we're building in third party logistics. And it's really kind of in some ways mirror some of the other businesses we've gotten involved in AWS being an example. And even though they're very different businesses, and that we realized that we had our own internal need to build and launch these capabilities. Scot: [51:01] We figured that there were probably others out there who had the same needs we did and decided to build the services out of them so this is this model that really blows the minds of traditional retailers where you know so walmart has this huge data you know capability there's this this urban legend that they know when people are pregnant before they do they can see changes in their habits or they know who all is on weight loss drugs they they see your buying habits so intricately that they can do that that's a neat capability but they view it as proprietary and And that's old school thinking. Scot: [51:32] What Amazon does is says, well, that's a cool capability. Let's certainly someone else needs it. Let's open it up. This is one of my favorite things at Amazon. And it's so counterintuitive that in my current car world, I talk about this and everyone's like, why are you, we're doing it a lot at Spiffy. And they're like, well, why are you doing that? That's like your proprietary thing. And we're like, well, that's just how it should be. And like, this is a better way to do it. And it's really interesting that still today, Amazon's built what I say, $100 billion business out of AWS, which has used this and people are, are befuzzled by the whole thing. So I, I thought that was an interesting use case. And then he, he goes into some details there that are pretty obvious for our listeners, like how this is gonna work. But then he basically kind of brings it back around and then he says he wraps up and says, I would say that supply chain with Amazon is really an abstraction on top of each individual block services. And in those services, he talked about all the things that, that, you know, FBA and last mile delivery and buy with a prime. He talks about each of those kind of and how awesome they are. So he's basically saying Amazon supply chain wraps a bow around all that. And it gives this collective set of business services is growing significantly. Scot: [52:43] It's already what I would consider a reasonable size business. I think it's early days. It's not something we anticipate being a giant capital expense driver. So it's because they've already invested in all this that doesn't require additional capex. And then he finishes and says, we have to build a lot of the capabilities anyway to handle our own business. And we think it will be a modest increase on top of that to accommodate third-party sellers. Scot: [53:05] But our, there's a typo in the thing. Our third-party sellers find very high value in us being able to manage these components for them versus having to do it themselves. And they save money in the process. So I thought that was a really interesting, interesting. So they're really leaning into this supply chain. I think that ultimately they'll open this up to more consumers where you can send Aunt Gertrude in Detroit something from Chicago for three bucks a package and just throw it in an Amazon box, maybe a return box, and it kind of makes it way cheaper than you can FedEx it. I think that's coming, but it's really interesting to see. The way they think about things and his articulation of it was very crisp, Scot: [53:45] and I really enjoyed that. I was geeking out on that when I was listening to the call. Jason: [53:50] Yeah, for sure. That actually came up in some of the conferences I was at that he, you know, Jeff Bezos famously wrote this memo a long time ago about kind of being an object oriented, company and having all these building blocks that people could easily access and use internally and externally. And, and that this was kind of Andy Jassy doubling down on that. Yeah. It's Biffy is an example of that. Like you inventing some cool products that make it your jobs easier. And then you're selling those products to, to your potential competitors. Scot: [54:20] Yeah. So two examples, we have some devices we've developed for ourselves. One is a tire tread scanner. So it does 2D and 3D tires, tire tread scans. It's called Easy Tread. And we developed it for ourselves because we touch 3,000 cars a day right now and we wanted to measure the tire treads. And the state of the art is a Bluetooth needle. And it's, you know, you have to lay on your back. The cars are on the ground for us most of the time. So you have to like get underneath there, measure three things, and then it Bluetooths to a phone. Then you have to take it, the data entry, it doesn't have an API. Then you have to like take what it measured and then now cut and paste it into something else. It's kind of, kind of redonkulous in our world. So we developed a solution for that and we're selling it externally. And then the big, the big one is from day one, this has been the plan is we've built a ton of software for Spiffy. So we're, you know, we've got 400 technicians, 250 vans doing all kinds of services across the US and there's no operating system for that. So we, there's no like Salesforce for that or Shopify. So we had to go build our own. And so we've built, you know, route optimization specific to this parts integration, fitment integration, VIN lookup, all these things that are required integration with tire suppliers, oil filter suppliers, oil suppliers, parts suppliers, all these things. So we have like 150 things we've integrated with and pulled in from all over the place. Scot: [55:44] And then labor management, all the reporting that comes along with it, all that stuff. And we're starting to license that out as its own platform to anyone that wants to do auto services. And so these dealerships and large auto service companies are coming to us and finally saying, this seems kind of obvious now that we need to provide the ability to go to our customers. They call it at their curb. They use a different language than we do. But basically what you and I would call mobile, you know, last mile delivery of the service. And we're starting to license that out. And it's a lot like AWS, right? So we had to build this for our retail business, which is doing the services and now we're licensing it out a lot AWS and we have this device business. So it's been, I would not have, it comes intuitively to me now. Cause I've been, you know, basically living this lifestyle for 20 years and watching Amazon do it, But it's been fun to kind of build a company with this mindset of we're going to take these things we build and give them to other, not give them, but sell them to other people. And then that makes them better. And they help us pay for all the R&D that we've done on it. Jason: [56:48] Yeah, that's very cool. And that gives listeners a very tangible example of why we haven't been able to podcast quite as frequently as we'd like. Scot: [56:56] Yes. Jason: [56:56] I do, at the risk of making this the world's longest episode of our show, I do have a geeky add-on to the supply chain conversation. Yeah. So a lot of these services that they're adding to specifically what they call supply chain with Amazon are around importing services, because an increasingly high percentage of all the stuff Amazon sells is. Jason: [57:20] Amazon is taking care of importing it, right? And most often from China, but from all over the world and taking care of all that logistics and getting it ready to sell and deliver via the world's most impressive last mile to consumers in America. And there's tons of complicated, high friction touch points and processes to flow all those goods. Well, the big competitors out there to Amazon at the moment that we've talked about ad nauseum on the show, like Shein and Timu, had this kind of direct from China model where they're putting all the goods on 747s, flying them over, and they're taking advantage of this loophole in the postal treaty called the de minimis provision to not pay taxes or duties or have all these goods inspected that they ship into the U.S. and U.S. Jason: [58:07] Businesses have been complaining it's unfair. There's like all kinds of talk about it. We've done shows on this and I'm sure we'll do others. So here's the new thing in supply chain. Jason: [58:15] All the people that have been complaining about this are now doing it because guess what's happened? A bunch of these companies have been born that now help every other brand in the world take advantage of the de minimis provisions to near shore their goods. So you're a footwear manufacturer, you make your shoes in Vietnam, Instead of shipping them to the U.S. On a pallet and paying taxes and duties, you ship them on a pallet to Mexico, and then you send them individual parcels across the border from Mexico into the U.S. and never have to pay taxes or duties on the stuff. So I don't know if that will last in the long run, but that's a very disruptive, significant change happening in the whole world of e-commerce supply chains as we speak. That's pretty interesting. Interesting. Had you gotten wind of that yet? Scot: [59:07] No, no. That's all new to me. Thanks for sharing. Jason: [59:09] Yeah. That's probably how you're going to have to start getting your spiffy stuff into the country now too. I won't, I won't, we won't go there. But the one other piece that did not come up in the earnings call, but a controversy around Amazon since our last show is news articles came out that Amazon was de-installing its Just Walk Out technology from its grocery stores. So Amazon had built Just Walk Out into several of these Amazon Fresh stores and they built it into Whole Foods. And if you know the history of Just Walk Out, this was the original intention of Just Walk Out was was to do it for grocery stor

The Lazy Wife Epidemic: How Wives Lose Their Marriage By Sandbagging | ClapCheeks Teacher Arrested Coach Greg Adams YouTube Channel Free Agent Lifestyle YouTube Channel