A Guy Used AI to Cure His Dog's Cancer*

Today on the AI Daily Brief: all about that guy who used AI to cure his dog's cancer and what it says about the discourse in AI's second moment. Before that, in the headlines, a preview of NVIDIA's GTC. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Blitzy, AIUC, and PromptQL. To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts. To learn about sponsoring the show, send us a note at sponsors@aidailybrief.ai. And while you are at aidailybrief.ai, you can find out all about all the various things going on in this ecosystem. The big one this week is of course Agent Madness. It's a March Madness style bracket where we will be having live human and agentic voting on the coolest things that you have vibecoded and built this year. In addition to bragging rights, I will feature these agents on the show. So if you are interested in that, check out agentmadness.ai. Currently, submissions are slated to close on March 18th. That is Wednesday of this week. So again, get on over to agentmadness.ai. It is a big week for NVIDIA as their GTC developer conference kicks off in San Jose. CEO Jensen Huang was scheduled to deliver his keynote on Monday morning. So we'll likely know more by the time this episode goes out. In the lead-up to the event, much of the speculation was around a new chip system developed in collaboration with Groq. That is G-R-O-Q, not G-R-O-K. Groq with a Q is the one that is not an Elon Musk company. Nvidia acquired the chipmaking startup in December and are expected to announce the first collaborative product this week. The information described the new product as integrating Groq's language processing chips into Nvidia's rackscale servers. If that's the case, this will be Nvidia's first attempt to directly address inference demand. Until now, Nvidia's chips have been world-leading in AI training but haven't been particularly focused on efficient inference. That's where Groq steps in, delivering a chip tailored exclusively to inference workloads. Nvidia is expected to announce OpenAI as a buyer of the new chip. Sources said that production has been ramping up at Samsung's chip foundry and mass production is expected to begin in the second half of the year. Notably, this will be the first time Nvidia has manufactured an AI chip outside of TSMC, potentially diversifying supply chains out of Taiwan. The new servers also use Intel CPUs rather than Nvidia CPUs, according to sources, which suggests that Nvidia's chips don't integrate well with Groq chips at this stage. The sources added that multiple generations of hardware are being planned, with the potential to build Groq's technology into Nvidia's Fineman GPUs, which are the next generation following Rubin later this year. Outside of product releases, Nvidia's Neo Cloud partners are stepping up operations. The Information reports that Nscale is in negotiations to acquire a huge data center site in West Virginia. The site has cleared regulatory hurdles and is targeting 2 gigawatts of capacity by 2027. Now the deal is a little unusual for a Neo Cloud provider, which have typically rented data centers in the past. It would also immediately make UK-based Nscale a major player in the US market as they move towards an IPO. New documents surfaced by The Information said that the acquisition would triple Enscale's revenue projections to $30 billion for 2027. They are reportedly in talks to rent the capacity to ByteDance, but could also rent their servers back to Nvidia. Writes Moor Insights Strategy CEO and Chief Analyst Patrick Moorhead, Nvidia is no longer a chip company. As GTC 2026 opens, the company plans to present itself as a full-stack, heterogeneous AI infrastructure platform spanning training, prefill, decode, inference, and agent orchestration. Next up, while many software CEOs have been downplaying the AI disruption risks to their company this year, SEC filings are telling a different story. So far this year, 27 firms have listed AI agents as a material risk to their business model, up from just 7 this time last year. The list of companies warning about agents includes Figma, Workday, and HubSpot, whose CEOs have all recently dismissed concerns. During their most recent earnings call, Figma CEO Dylan Field said, "I think it is the case that humans will continue to use software, and increasingly agents will too, and I'm excited about that." However, he added, "I think right now, if you're willing to hand off mission-critical work to agents and just let them do it unsupervised, you're a very brave person." Meanwhile, Figma's 10-K filing released on the same day acknowledged that Agentic AI may "change how people access and interact with digital products in ways that reduce reliance on traditional software applications." Now keep in mind, SEC filings should not be taken too literally. Companies are required to discuss any material risk to their business, which often leads to disclosures of fanciful or unlikely risks. Still, while individual disclosures don't tell us all that much, the volume is another signal that we've moved past the tipping point on agents. The idea that agents were capable of disrupting SaaS barely registered in the first half of last year, and yet disclosure volume rapidly increased in the second half and in the beginning of this year as the technology became more viable. If nothing else, the shift means software executives are taking the threat of disruption more seriously, or at least their legal departments are. Next up, ByteDance has paused the global launch of their cutting-edge video model due to copyright disputes. The Information reports the global release of SeedDance 2.0 has been mothballed due to a series of copyright disputes with Hollywood studios. SeedDance 2.0 was released in China last month, gathering a huge online reaction. You might recall this viral clip with Tom Cruise and Brad Pitt in a fistfight, which demonstrated incredibly high-fidelity replication of real-world actors. The new model led to outrage in Hollywood, with companies including Disney, Warner Bros., Paramount, and Netflix sending cease and desist notices to ByteDance. Motion Picture Association CEO Charles Rivkin said in a statement at the time, "Seeddance 2.0 has engaged in unauthorized use of U.S. copyrighted works on a massive scale." ByteDance had planned to make the model available globally in mid-March. The plan included API access through their cloud platform Byte+, as well as a new consumer app designed for a foreign audience. Those plans are now reportedly on hold. Chinese users, meanwhile, are reporting the model is far more tightly controlled than it was at launch—to the point of rejecting prompts with no relation to copyrighted content. Enterprise customers have complained that model access is limited to Chinese companies with no intention of distributing content internationally—one source said they've been unable to negotiate terms without committing to spending around $1.5 million on the model. Interestingly, it seems like the major holdup is not so much about implementing guardrails, but instead about refining them so that they don't block too much unrelated content. We've seen this with OpenAI's release of Sora too as well—while it is relatively straightforward to block copyrighted content, doing so without frustrating the user with too many refused prompts is a much more difficult engineering problem. And speaking of difficult engineering problems, a new AI startup led by former Anthropic founders is raising money to push the frontier of AI-enhanced scientific research. The new company, called Mirandyl, is in talks to raise $175 million at a billion-dollar valuation. And if successful, the round would make Mirandyl the latest AI startup to establish unicorn status in their seed round. The company is led by former Anthropic researchers Bedim Nishabur and Harsh Mehta, who spent their time at Anthropic working on things like long-horizon scientific reasoning with AI agents and automated AI research. Both founders also have experience at Google. Now, exactly what the company plans to do is not known yet, Sources say the new company aims to conduct AI-enhanced scientific research in fields including biology and materials science. This area of AI research is quickly gathering interest and investment dollars, as multiple NIAL Labs focus on AI for science. I would expect this to be a trend that continues throughout the year. Speaking of Google, Google Maps is getting an AI twist with a new conversational interface. The new feature, called Ask Maps, allows users to tap into a Gemini-powered chatbot to help them navigate the world. The feature is designed to answer questions about landmarks and help schedule travel. Google gave small practical examples like being able to ask for a nearby location to charge a phone or find a public tennis court with lights for an evening match. The feature can also help with trip planning, with Google offering the example of building a multi-stop trip to the Grand Canyon. Writes Google: Previously, finding this information meant lots of research and sifting through reviews, but now you can just tap the "Ask Maps" button and get your questions answered conversationally—and with a customized map to help you visualize your options. The feature integrates with Gemini's memory, so if you ask Maps for a restaurant recommendation, it can tap into what Gemini already knows about your preferences. Google is also leveraging Gemini to launch a new visualization mode for navigation in Maps. The update adds a 3D view that depicts buildings, overpasses, and surrounding terrain. Once again, Google flexing its multimodality and the integration of its entire ecosystem. Lastly today, sort of a bridge topic to our main episode: ServiceNow CEO Bill McDermott has warned that AI could send unemployment soaring above 30% for young professionals. In an interview with CNBC, McDermott said that unemployment for college graduates could, quote, easily go into the mid-30s in the next couple of years. So much of the work is going to be done by agents, he continued, so it's going to be challenging for young people to differentiate themselves in the corporate environment. Now, according to data from the Federal Reserve, unemployment for recent college graduates currently stands at 5.6%, which is far lower than the 7.8% unemployment rate for young people without a college degree. However, 42.5% of college graduates are classified as underemployed, meaning they don't have enough work or are working in roles that don't require a college degree. This is the highest level of underemployment for college grads since 2020. Computer science majors have among the highest unemployment rates at 7%, but their underemployment rate is relatively low at 19.1% compared to other majors. Now, just why this type of discourse is so potent right now is in fact the topic of our main episode, so with that, we will close the headlines and move on over to the main. Agentic AI is powering a $3 trillion productivity revolution, and leaders are hitting a real decision point. Do you build your own AI agents, buy off the shelf, or borrow by partnering to scale faster? KPMG's latest thought leadership paper, Agentic AI Untangled: Navigating the Build, Buy, or Borrow Decision, does a great job cutting through the noise with a practical framework to help you choose based on value, risk, and readiness. And how to scale agents with the right trust, governance, and orchestration foundation. Don't lock in the wrong model. You can download the paper right now at www.kpmg.us/navigate. Again, that's www.kpmg.us/navigate. With the emergence of AI code generation in 2022, NVIDIA Master Inventor and Harvard engineer Sid Pareshi took a contrarian stance: inference-time compute and agent orchestration, not pre-training, would be the key to unlocking high-quality AI-driven software development in the enterprise. He believed the real breakthrough wasn't in how fast AI could generate code, but in how deeply it could reason to build enterprise-grade applications. While the rest of the world focused on copilots, he architected something fundamentally different. Blitzy, the first autonomous software development platform leveraging thousands of agents that is purpose-built for enterprise-scale codebases. Fortune 500 leaders are unlocking 5x engineering velocity and delivering months of engineering work in a matter of days with Blitzy. Transform the way you develop software. Discover how at blitzy.com. That's blitzy.com. There's a new standard that I think is going to matter a lot for the enterprise AI agent space. It's called AIUC1, and it bills itself as the world's first AI agent standard. It's designed to cover all the core enterprise risks, things like data and privacy, security, safety, reliability, accountability, and societal impact, all verified by a trusted third party. One of the reasons it's on my radar is that ElevenLabs, who you've heard me talk about before and is just an absolute juggernaut right now, just became the first voice agent to be certified against AIUC1. AIUC 1 and is launching a first-of-its-kind insurable AI agent. What that means in practice is real-time guardrails that block unsafe responses and protect against manipulation, plus a full safety stack. This is the kind of thing that unlocks enterprise adoption. When a company building on ElevenLabs can point to a third-party certification and say our agents are secure, safe, and verified, that changes the conversation. Go to aiuc.com to learn about the world's first standard for AI agents. That's aiuc.com. If you're an operator, your day is a nonstop stream of decisions, and most of them require you to look at the data. You don't need another dashboard. You need answers you can trust fast, but the bottleneck is always the same. The data isn't ready. It's scattered. It's messy. Definitions aren't clear. You're waiting on your data team or waiting on domain experts for clarification and confirmation. That's the bottleneck today's sponsor PromptQL is built to break. PromptQL is a trusted AI analyst for high-frequency decision-making. It connects across warehouses, databases, SaaS, and internal APIs. No massive data prep or centralization required. It's built for multiplayer input. Teammates can jump into a thread, correct assumptions and nuance, flag edge cases. PromptQL turns everyday conversations into a shared context. And if something is ambiguous, it doesn't guess. It escalates to the right expert, captures the correct logic, and gets it right next time. That's how it delivers trust and accuracy. Over time, PromptQL specializes to your business, like that veteran employee who just knows things. From simple what-is questions to complex what-if scenarios, you can model impact and stress test decisions before you commit, all through a simple natural language prompt. PromptQL, the trusted AI analyst for teams with shared context and messy data. Welcome back to the AI Daily Brief. Today's episode is nominally about this guy who used AI to cure his dog's cancer, or at least that's what everyone was talking about online. But more broadly, it's about the state of the AI discourse. And I think that the starting question that we need to ask, taking a big step back from all of the headlines, is what the heck is going on right now? The AI discourse out there is absolutely frenetic right now. You've got Bernie Sanders dropping 9-minute-long videos about X-risk, CEOs like Bill McDermott from ServiceNow dropping insanely terrifying statistics all over the mainstream media. In this case, a casual prediction that AI is going to cause recent college graduate unemployment over 30%. Every time a poll comes out in America, it shows just increasingly negative sentiment around AI, which who knows, maybe has something to do with all these media outlets publishing these scary predictions. But then on the flip side, you've got normal people who haven't coded before managing teams of a dozen agents or more doing all of this work that was never possible for them before. The divergence, in other words, between mainstream perception and actual capability has never been higher. And yet both of them are in this incredibly heightened state. So what is going on? The short of it is, and this is a concept that I imagine we'll end up exploring a lot in the near term, I think that we are in AI's second moment. Obviously in this case, I'm using AI as shorthand for generative AI. And the first moment was the ChatGPT moment at the end of 2022, beginning of 2023. This moment was the Claude Code, Opus 4.5, Codex 5.2, etc. moment. And if you want to be really reductive about it, it's the AI moment and the agents moment. At the beginning of the month, Ethan Malik tweeted, from an AI user perspective, the 4 big leaps so far in ability. 1, GPT-3.5, ChatGPT, November 2022. 2, GPT-4, spring 2023. 3, Reasoners starts with 0.1 preview, but the real deal was 0.3, spring 2025. 4. Workable agentic systems, hardest plus good reasoner models, December 2025. But really, I think his first two and his second two were all part of one thing. And remember, in and around the first time, we also got some really heightened frenetic discourse. You might remember in May of 2023, which was the second month of this show, when Time Magazine dropped an issue called The End of Humanity, a special report on how real is the risk. So the point that I'm making is that if this really is AI's second moment, it makes sense that the cloud of dust being kicked up around it is proportionally bigger and more heightened and more dramatic than even the important conversations we've had in between these two moments. And to some extent, I think part of what we're experiencing is just a resurfacing of everything that came up in the wake of the first moment with some key differences now. The first difference is that there's obviously been a huge increase in capabilities. ChatGPT with 3.5 was amazing. You combine that with some of the image generation capabilities of the models that were coming out around then, and people who were trying these tools absolutely felt like wizards. You didn't really have to convince most people. If they tried these tools, they realized that something big was changing. And yet, even in those early days, there was still this idea of something even bigger. The first episode that I ever had go viral, at least in the terms of a show like this on YouTube, was about an early prototype agent. We had experiments like AutoGPT and BabyAGI and GPT-Engineer, which would form the seeds that would go on to be Lovable. And so 2 years later, as agents really come online, that big increase in capabilities has, I think, proportionally heightened the discourse once again. A second big change between the first moment and the second moment is that there are now many more people in the conversation. Around the ChatGPT moment, these tools were some of the fastest growing we'd ever seen. Remember, ChatGPT got its first 100 million users in its first 5 weeks. Beating the previous record of 8 months for TikTok. But now we have literally billions of people using these tools every week. Even people who don't like the tools are using the tools. So there are just far more people in the conversation. A third difference between the first moment and the second moment is higher economic stakes. And in this case, I'm not even really talking about theoretical future job displacement things. I'm talking about right here and right now. Wall Street's interaction with SaaS companies, AI infrastructure build-out deals, and the private financing thereof. Valuations for private companies that are building AI, etc., etc., etc. Anthropic wasn't even a blip on the radar to most people then, and now it's at a $19 billion run rate, taking down industries every time it announces a new feature. A fourth key difference between AI's first moment and second moment has nothing to do with AI itself, but has to do with the evolution of the market between 2022 and 2026. AI is now useful as a corporate fall guy, specifically in the context of companies trying to undo overhiring in the post-COVID period. Investor Chamath Palihapitiya writes, "What if AI doesn't need to show an immediate ROI, but instead is the plausible deniability companies use to RIF 50% of the workforce they already knew did nothing?" Number 5, no matter what you think of the politics of the moment, I think it's fairly inarguable that finally, as a difference between the first and second moment, this is happening in the context of generally increased political volatility. In other words, AI isn't the only thing happening in the world, It's now interacting with things like war in Iran. There is a last difference which I could point out, is that we've now had 3.5 years of the AI industry doing a completely awful job of explaining itself and talking about the future in any way that's going to be even remotely resonant to the average person. Not Boring's Packy McCormick recently tweeted, AI is very weird for me because normally I'd be the guy who'd argue that it's crazy we're not more excited about this miracle technology, but I completely get the negative sentiment. AI companies have clearly botched telling the story. That's a big piece of this. Telling people, we built this thing that is definitely going to take your job, and hopefully we can figure out how to give you handouts or something on the other side, or come up with even better jobs or whatever, say thank you, is clearly terrible messaging. Anyways, it's a much longer tweet, but I think that the incredibly poor messaging from the AI industry is absolutely another thing that has changed between the first and the second moment. Not that there was good messaging around that first moment, mind you. There just hadn't been as much time for us to shoot ourselves in the foot over and over yet. The point of this is right now, everything around the AI discourse is incredibly heightened. The whole conversation is at an 11 all the time, and basically has been since we all returned to work at the beginning of 2026. There were two conversations that really demonstrated this this weekend. The first was around a weekend project from developer Andrej Karpathy that became an absolute firestorm. At 5 PM Eastern time on Saturday night, KaitoNX tweeted, "5 minutes ago, Andrej Karpathy just dropped karpathy/jobs. He scraped every job in the US economy, 342 occupations from BLS, scored each one's AI exposure 0 to 10 using an LLM, and visualized it as a treemap. If your whole job happens on a screen, you're cooked. Average score across all jobs is 5.3 out of 10. Software devs, 8 to 9. Roofers, 0 to 1." "Medical transcriptionists 10/10. Skull emoji." It pointed to this link, karpethi.ai/jobs, which is the full chart. Instantly, Twitter was flooded with takes like this one from Tuki: "Siren emoji, do you understand what Karpethi just did? He didn't write an opinion piece. He scraped every single job in America, ran it through AI, and scored how replaceable you are. On a scale of 1 to 10. Not a prediction, a diagnosis." Accountants scored 9, paralegals 9, copywriters cooked, radiologists reading scans, the AI already does it faster. The only jobs that scored low are the ones that require you to physically touch something. In 2015, learn to code was the answer to everything. In 2025, code writes itself. The people who listen are now the most replaceable generation in history. I guess your degree didn't prepare you for a career. Even people who aren't usually schlock merchants like that started to veer into this same sort of sensationalist territory. Chubby @chimonismus writes, "Karpathy is by no means interested in hyper-exaggeration. Using AI, he concluded that out of 143 million people working in the US, approximately 57 million are at high to very high risk of their jobs being negatively impacted by AI. That's almost 40%! Let that sink in and consider what it means." Now at this point if you listen frequently, you're probably waiting for the "yes, but where's the nuance here?" Well, first of all, If you go actually read the page that Karpathy posted, which I don't think most of the people who were tweeting about it did, he has a very important caveat on digital AI exposure scores. He writes, these are rough LLM estimates, not rigorous predictions. A high score does not predict the job will disappear. Software developers score 9 out of 10 because AI is transforming their work, but demand for software could easily grow as each developer becomes more productive. The score does not account for demand elasticity, latent demand, regulatory barriers, or social preferences for human workers. Many high-exposure jobs will be reshaped, not replaced. Indeed, Karpathy himself was frustrated by the response. When someone on that original tweet from Kaito said, "I can't find it," Andrej responded, "This was a Saturday morning 2-hour vibe-coded project inspired by a book I'm reading. I thought the code and data might be helpful to others to explore the BLS dataset visually, or color it in different ways or with different prompts or add their own visualizations. It's been wildly misinterpreted, which I should have anticipated even despite the README doc, so I took it down." In another tweet, he wrote, "The 'exposure' was scored by an LLM based on how digital the job is. This has no bearing on what actually happens to these occupations, which has to do with demand elasticity and a lot more. People are sensationalizing the visualization tool and putting words in my mouth." Now, there was some interesting nuanced conversation about this. The Update newsletter's Stefan Schubert wrote, "Many seem to take this as a reason to believe that the overall pace of automation will be high, but I don't think that makes any sense." Even more to the point, and more insistently phrased, was Chicago Booth economist Alex Emas who wrote, "Exposure does not mean threat of displacement. It can literally mean the opposite. AI-exposed jobs may increase hiring and attract higher wages. It all depends on a) elasticity of consumer demand and b) number of AI-exposed tasks in a job." Anthropic's Peter McCrory added, "I agree strongly with Alex here. And my read is that Claude usage patterns clearly point toward uneven labor market implications." Our recently introduced observed exposure measure aims to identify cases where exposure is more likely to transform into actual displacement, i.e., Claude is used in automated ways for work-related purposes on tasks that are conceptually feasible for LLMs. But no exposure measure is perfect or has monotone predictions. And even when much of a job is automated, the remaining bottleneck tasks may ultimately increase demand for complementary human skills even among highly exposed roles. Toronto economist Kevin Bryan, said, "I bet $1,000 that from now to 2030, most quote unquote susceptible jobs see increased share of labor. In the model these types of charts are based on, it is explicitly not AI can substitute, but AI is related. AI is a complement too. Who doesn't want to code right now, for instance?" And I think that's all true, and obviously we will continue to discuss the real no BS labor market implications of AI, but the point is relative to our larger conversation, this frenetic tone to the discourse, Not helping this was the fact that literally within 1 minute of Kaito posting that thing about Karpathy's research, the Kobayashi letter posted, "Breaking: Meta is planning sweeping layoffs that could affect 20% or more of the company." Like I said, right now the conversation goes to 11. But it wasn't just the negative side of AI that was at 11. Google DeepMind's Seb Krier shared an article link from The Australian that went hyper-viral with nearly 13 million views. Vittorio summed it up this way: This is actually insane. Be tech guy in Australia. Adopt cancer-riddled rescue dog, months to live. Pay $3,000 to sequence her tumor DNA. Feed it to ChatGPT and AlphaFold. Zero background in biology. Identify mutated proteins, match them to drug targets. Design a custom mRNA cancer vaccine from scratch. Genomics professor is gobsmacked that some puppy lover did this on his own. Need ethics approval to administer it. Red tape takes longer than designing the vaccine. 3 months, finally approved. Drive 10 hours to get Rosie her first injection, tumor halves, coat gets glossy again, dog is alive and happy. Professor, if we can do this for a dog, why aren't we rolling this out to humans? One man with a chatbot and $3,000 just outperformed the entire pharmaceutical discovery pipeline. We are going to cure so many diseases. I don't think people realize how good things are going to get. So here's the story. Australian entrepreneur Paul Coeningham has a dog named Rosie. In 2024, Rosie was diagnosed with cancer that ended up being non-responsive to chemotherapy or surgery. The tumors just kept growing. When Paul turned to ChatGPT for help, it suggested that he should get Rosie's DNA sequenced and then use Google DeepMind's AlphaFold to look for mutations that could be a target for immunotherapy. When a drugmaker wouldn't provide an off-the-shelf immunotherapy treatment, Coiningham turned to Pally Thordarson, the director of the RNA Institute at the University of New South Wales. Thordarson used Rosie's DNA to develop a bespoke mRNA vaccine in less than 2 months. He told the press, "This is the first time a personalized cancer vaccine has been designed for a dog. This is still at the frontier of where cancer immunotherapeutics are, and ultimately we're going to use this for helping humans. What Rosie is teaching us is that personalized medicine can be very effective and done in a time-sensitive manner with mRNA technology." Now, as you can tell, there is a lot more to this process than simply prompting ChatGPT to cure cancer.— and indeed, even the treatment itself wasn't entirely successful. Yes, some of Rosie's tumors have shrunk, but it would be certainly going too far to call it a cancer cure. On top of that, it's arguably a story about how revolutionary the Nobel Prize-winning AlphaFold model is rather than a story about ChatGPT. Palle Thordarson ended up turning to X to explain some nuances of the story. The nuances include: the fact that this was less about a cure and more about buying time, the fact that it's difficult to estimate the real costs, as lots of people donated time and resources to this, A third nuance is that regulation of vet research and treatment is obviously quite different than human health. But ultimately, Pally says, in the human health space, Rosie's story demonstrates that we can democratize the process of designing cancer vaccine. While genomic analysis and RNA production will continue to be specialized, they could turn into pure service provision, especially as automation increases. This then begs the question, do we need to overhaul the regulatory regimes with this in mind? And can we ensure equitable access? Now, of course, there were tons of people who were skeptical on spec when they saw the story, even before all that nuance was shared. And what's more, unsurprisingly, I personally find it a little bit refreshing to have people excited about the positive disruptive potential of AI than to just be constantly looking at the negative. But the point is that these are still two sides of the same coin. We are in the midst of the transition into AI's second moment. And for a little while, until we all get used to the new paradigm that we're living in, It's gonna be weird. All I can promise is that if you hang out around here, you will feel at least slightly less like you're taking crazy pills. For now, that is gonna do it for today's AI Daily Brief. I appreciate you listening or watching, as always. Until next time, peace.