The Race to Put AI Agents Everywhere

Today on the AI Daily Brief: The race to productize agents and make them enterprise-grade is on. Before that in the headlines: NVIDIA CEO says the company is on track for $1 trillion in revenue. 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: Recall.ai, AIUC, Robots and Pencils, and Blitzy. 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. We're going to dive right in, but one quick reminder, Agent Madness submissions are live right now. This is our bracket voting competition to find the coolest agents that people in this community have built. If you want a chance to have your agent featured on the show, go to agentmadness.ai. Submissions close very soon, so I encourage you to check it out. Now with that out of the way, let's talk about a trillion bucks in revenue. I'm old enough to remember when a trillion-dollar market cap was a big deal. And now here we are, AI is booming, and Nvidia CEO Jensen Huang has kicked off the company's annual GTC conference with a massive prediction that the company will see a trillion dollars in revenue between now and 2027. At every GTC, Jensen's keynote, which is planned but not fully scripted, is the big event. This one was no exception. It was 2.5 hours long, totally jam-packed with big announcements. We got confirmation of the new Groq-powered server focused on inference. The new rack-mounted system will combine 256 Groq chips with 72 Nvidia Rubin GPUs, delivering 35x the inference efficiency of current generation Blackwell chips, with the system expected to ship in the second half of this year. Jensen also unveiled a new gen AI system that can enhance video game graphics on the fly. Called DLSS 5, the technology combines traditional graphics with an AI filter to create stable photorealistic graphics. Being able to produce this effect at runtime on consumer hardware is a big breakthrough that could significantly change the way video games are made. For my claw fans out there, there is a new entrant into the open claw category which we'll cover in the main episode. But ultimately, while the keynote had many big moments, none grabbed headlines like Jensen's massive revenue forecast. Late last year, Huang said that he expects $500 billion in sales in 2026. On Monday, he doubled the forecast to a trillion, stating, "I believe that computing demand has increased by 1 million times in the last 2 years. It's the feeling that we all have. It's the feeling every startup has." Now, some tried to downplay the forecast, noting that it merely combines 2 financial years at $500 billion apiece, meaning it's not so much a material change. Bloomberg analyst Kunjan Sobhani wrote, the update should ease fears of a pullback in 2027 as Rubin enters the cycle, although it may also reset market expectations higher and raise the bar again. This feels to me to be slightly missing the bigger picture. Jensen has now signaled that Nvidia can see enough demand to drive $500 billion in annual sales. This would more than double revenue from the past year. In fact, the list of companies with a half a trillion in annual sales is just Walmart and Amazon, with Saudi Aramco falling slightly short. If Huang's forecast is correct, it will be completely unparalleled growth for a company of anywhere near Nvidia's size. Remarking on the event, Josh Kale wrote, "The man doubled his demand forecast to a trillion dollars, announced data centers in space, and closed the show with a robot singing country music. This is Nvidia's world. Everyone else is just renting compute in it." Next up, if it is Nvidia's world, one of the new players in it is of course the neoclouds. On that front, Meta has signed a $27 billion deal with Nebulas. Nebbius, which is similar to CoreWeave and N-Scale, operates smaller AI data centers than their hyperscaler counterparts. This often includes differentiated chips or full-stack support for model training or specialized inference. Nebbius's new deal with Meta spans 5 years, and this is in addition to a $3 billion deal signed by Meta in November. Nebbius plans to deploy Nvidia's new Vera Rubin chips on Meta's behalf. The chips are expected to be available in the second half of this year, with Nebbius powering on the new cluster early next year. Now, while it's possible that Meta is turning to Nebius for specialized data center management, the simpler explanation is just that the entire industry is capacity constrained right now and that Meta, like all the other AI labs, is gobbling up all the available data centers they can get their hands on. That includes partnering with the neoclouds to take any capacity they can offer. The deal though also represents a phase shift for the smaller end of the data center industry. Nebius is one of the larger neoclouds, yet they only had a little over a billion dollars in revenue last year. Meaning for my math friends out there, this is an order of magnitude larger than all the business they've done so far. AI infrastructure continues to scale up at a massive pace and the Neo Clouds seem to be getting their slice of the action. One area of infrastructure buildout that has been a little bit, shall we say, beleaguered is the OpenAI Stargate effort. The company has now appointed new leaders to oversee their revamped and restructured Stargate. Now over the last couple of months, we've heard all sorts of things about Stargate. We learned that the joint venture with Oracle and SoftBank never really got off the ground. And more recently that OpenAI was walking away from expansion plans at the flagship site in Abilene, Texas. That reporting also suggested that the Stargate name would be attached to all data centers operated by OpenAI rather than only their own site developments. Now the information reports that the structure of the new-look Stargate division has been put in place. Former Intel executive Sachin Katti will oversee the division, which consists of 3 distinct teams. One team will work on technical data center design, another on commercial partnerships with various cloud providers and chip manufacturers, and the third will be responsible for on-the-ground management of facilities. Previously, OpenAI's infrastructure teams were organized by project rather than role and reported up to President Greg Brockman, meaning this restructuring could represent a more specialized and dedicated in-house team being put in place. Reporting also confirms that OpenAI is less concerned about ownership of data centers and more willing to lease in order to scale up compute. This would comport with basically everything else we're seeing in the industry, where all of the fancy and fiddly efforts are kind of flowing by the wayside in order to just get access to as much compute as possible. Less fun for OpenAI is that they just got sued by a dictionary. Encyclopedia Britannica and their subsidiary Merriam-Webster have sued OpenAI for use of their dictionaries and encyclopedias in training data. Further, Britannica claims that ChatGPT has cannibalized their web traffic by producing content that substitutes or competes. Responding to the lawsuit, an OpenAI spokesperson said, "Our models empower innovation and are trained on publicly available data and grounded in fair use." Now for our last topic today, it's actually two stories that both seem to point in a similar direction, which is a change in how open source AI gets developed. The first story is that Alibaba has restructured their AI organization in a shift, it seems, to maximize profits. Rumors were swirling earlier this month that a big move was in the works as 3 senior researchers left the Qwen team. The departures included technical lead Justin Lin, who is credited with shepherding Qwen from its first training run to becoming one of the most popular open source models. Speculation at the time was that Alibaba was shifting focus from pure research to driving AI-related revenue through their first-party API. Some wondered if this shift would herald the end of open-source Qwen models. According to a memo cited by Bloomberg, the restructuring is now complete. The Qwen research team has been folded into a new division that also includes consumer-facing apps and AI-related products like the Quark smart glasses. The new division is called the Alibaba Token Hub and will be directly led by CEO Eddie Wu. Wu wrote in the memo, ATH is built around a single organizing mission. Create tokens, deliver tokens, and apply tokens. I will lead ATH directly with a mandate to drive strategic coordination across our AI businesses, embed AI deeply into how we work, and preserve the agility that lets us move fast. Bloomberg writes that the restructuring, quote, signals the company's clear emphasis on monetizing AI. The division's name is a direct reference to the units of computing that companies charge users. Meanwhile, another Chinese startup, Z.AI, has released a faster, cheaper version of their leading model, but they are keeping it closed source. The new model is called GLM-5 Turbo and offers similar performance to GPT-5.2 at a cost that's closer to Gemini 3 Flash. The speed boost is arguably a bigger deal, with the model optimized for running OpenClaw-style tasks like tool use and long-chain execution. ZAI said the model would be released as closed source but that its capabilities would be folded into future open-source releases. VentureBeat wrote that the decision is emblematic of a broader shift in the Chinese market. They suggest that Chinese labs are adopting an approach where lightweight open-source models are used to boost distribution and generate goodwill among developers, while more powerful models are delivered as proprietary systems aimed at generating enterprise sales. Writes VentureBeat, that would not mark the end of open-source AI from Chinese labs, but it could mean their most strategically important agent-focused offerings appear first behind closed access, even if some of their underlying advances later make their way into open releases. This, I think, is a trend that is worth keeping an eye on. Karin on X wrote, ZAI has been the loudest open source voice in AI for 2 years. They just released their first closed source model. That one decision tells you more about where the industry is heading than any benchmark. By the way, for those of you who are just listening and not watching, the picture that ZAI chose to release the model with is a glowing lobster riding a horse. Nathan Lambert, who just wrote an interesting essay on this topic, wrote, we're in the era when the cost of building LLMs is skyrocketing and the why for releasing them openly is static/not changing/weak. Definitely a trend worth watching, but for now, that is going to do it for the headlines. Next up, the main episode. Why is there always a meeting bot in your Zoom call? Blame Recall.ai. Recall.ai powers the meeting bots and desktop recording apps behind products like Cluely, HubSpot, and ClickUp. They handle the hard infrastructure work capturing clean recordings, transcripts, and metadata across Zoom, Google Meet, Microsoft Teams, in-person meetings, and more. So developers don't have to build it themselves. If you're building a meeting note-taker or anything involving conversational data, Recall.ai is the API for meeting recording. Get started today with $100 in free credits at recall.ai/aodb. That's recall.ai/aodb. There's a new standard that I think is going to matter a lot for the enterprise AI agent space. It's called AIUC-1, 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 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. Today's episode is brought to you by Robots and Pencils, a company that is growing fast. Their work as a high-growth AWS and Databricks partner means that they're looking for elite talent ready to create real impact at velocity. Their teams are made up of AI-native engineers, strategists, and designers who love solving hard problems and pushing how AI shows up in real products. They move quickly using RoboWorks, their agentic acceleration platform, so teams can deliver meaningful outcomes in weeks, not months. They don't build big teams, they build high-impact, nimble ones. The people there are wicked smart with patents, published research, and work that's helped shape entire categories. They work in velocity pods and studios that stay focused and move with intent. If you're ready for career-defining work with peers who challenge you and have your back, Robots and Pencils is the place. Explore open roles at robotsandpencils.com/careers. That's robotsandpencils.com/careers. You've tried in-IDE copilots. They're fast, but they only see local silos of your code. Leverage these tools across a large enterprise codebase and they quickly become less effective. The fundamental constraint? Context. Blitzy solves this with infinite code context. Understanding your codebase down to the line-level dependency across millions of lines of code. While copilots help developers write code faster, Blitzy orchestrates thousands of agents that reason across your full codebase. Allow Blitzy to do the heavy lifting, delivering over 80% of every sprint autonomously with rigorously validated code. Blitzy provides a granular list of the remaining work for humans to complete with their copilots. Tackle feature additions, large-scale refactors, legacy modernization, greenfield initiatives, all 5x faster. See the Blitzy difference at blitzy.com. That's B-L-I-T-Z-Y dot com. Welcome back to the AI Daily Brief. We are coming up on the end of Q1, and as part of that, I've been working on a big Q2 State of AI report. As you might expect, maybe the key story of Q1 was OpenClaw, not even just because of OpenClaw itself, but because of what it represented. I think you can look at OpenClaw as the instantiation of the new capability set that shifted around the end of last year. And which has really come to the fore this year. It's what I called on yesterday's episode AI's second moment and refers to this idea that agents are actually at this point viable and that people are in the midst of a million experiments right now, giving agents systems access, building new types of systems to have agents interact, and especially, and as we'll talk about today, solving some of the key challenges of agents to make sure that they can diffuse across the entire business world. Part of the specific catalyst for today's show is NVIDIA CEO Jensen Huang's speech at their annual GTC event yesterday, where Jensen said explicitly every software company in the world needs to have an OpenClaw strategy, and where he began to show off their enterprise-grade version of the software. Now, even before this, the clawfication of the world was well underway. Kevin Simbach from Delphi Labs recently wrote a post about all of the different variations and competitors and started by claiming that OpenClaw opened the door. Kevin writes: Before OpenClaw, agents were mostly technical experiments that produced nothing more than timeline slop. After OpenClaw and with the advent of Opus 4.5 and 4.6, agents became accessible, just a Telegram message away, always on, actually doing helpful things, and kickstarting a new generation of digital opportunities. OpenClaw quickly proved two things at once: people don't want AI chat, they want to get work done, and giving an LLM broad access to your machine and/or personal info is both insanely useful and mildly terrifying. So, as he writes, the last month has been a weird kind of Darwinism, with builders shipping faster than slot posters, security people screaming into the void, and a growing cohort of people saying, oh crap, this is actually going to rewrite how software and digital businesses work. And yet, as Kevin acknowledges, not everyone is sold on OpenClaw itself, and there has been a mad race to build or update alternatives. A bunch of them, like Nanobot, ZeroClaw, PicoClaw, or NanoClaw, are all attempts to reduce the overall complexity down to some specific useful feature set. And then there's others like OpenFang, Moltus, and Ironclaw that are all trying to bring security to it through self-hosting. Yet if that represents one end of the spectrum of the clawfication of AI, on the other hand, you have a huge number of companies, some that were AI native, some that weren't AI native, offering up what are effectively their own versions of OpenClaw. In other words, agents that are deeply integrated and integratable with some key set of systems and personal context. At the end of February, Notion introduced custom agents, which have a lot of features in common with OpenClaw, and also all of the context that comes from integration with Notion, where many companies are running all of their information. And of course, we also got Perplexity Computer. Perplexity Computer is a very full-throated reimagining of Perplexity from the ground up into a complete problem solution design system capable of spinning up complex systems of agents and subagents to get things done and build things that people want. In the couple weeks since Perplexity released Computer, they've also released Computer for Enterprise, which can operate from within Slack and which also has direct connections, they claim, to more than 400 applications. And they also even got on the Mac mini part of the theme with their launch of Personal Computer, which they call an always-on local merge with Perplexity Computer that works for you 24/7. Getting philosophical, Perplexity CEO Aravind Srinivas wrote a long post about why the AI is the computer. In it, he argues AI models are becoming so capable that the products built around them have been bottlenecked for showing their true potential. The chat UI is good for answers and agents are good for individual tasks. Meanwhile, the UI for entire workflows has always been the computer. Effectively, what Arvind is arguing is that the full potential of agentic systems requires the complete canvas of what your computer offers, bridging from local files to cloud systems and beyond. Which brings us to the not one, not two, not even really three, but closer to three and a half new entrants into this "Cloudification of Everything" category that were announced just yesterday. Manus, which was purchased by Meta in December, was one of the early leaders throughout 2025 in general-purpose agents. This week they announced a new Manus desktop app, the key feature of which they called My Computer. Very much picking up on the new design pattern, they write, it's your AI agent now on your local machine. The use cases they point to include organizing thousands of unsorted photos, renaming hundreds of invoices, building desktop apps in Swift entirely on your computer with no code written manually, combining with existing connectors to create seamless automated workflows, and creating local routines with personal projects, agents, and scheduled tasks. In the blog post, without naming OpenClaw, they acknowledge the realization of the need to be able to bridge from cloud to local. They write, the cloud sandbox has served Manus well. Inside an isolated, secure environment, it has everything an AI agent needs: networking, a command line, a file system, and a browser. This is the foundation of Manus's power as a general AI agent, always online and always ready to work. However, there has always been a fundamental limitation. Your most important work happens on your own computer. Your project files, development environments, and essential applications all reside locally, not in the cloud. My Computer then is a way to close that gap. Now one interesting thing about the Manus announcement is that they're thinking a little bit ahead in terms of the specific opportunities that come with desktop. For example, doing something that I haven't seen from a lot of the other competitors, they're actually pushing the idea of building fully working Mac apps, not just cloud-based applications that other people would use. Cedric Chi writes, Cloud Code, Cowork, OpenClaw, Codex, and Manus all seem to be converging on the same idea. The agent lives on your machine. The second related announcement yesterday came from Adaptive. They wrote, introducing Adaptive Computer. We put AI inside of an always-on personal computer that it uses to get work done, schedule agents, create software, automate anything. By the end of this year, they write, AI agents will use more software than humans do. You won't be the one clicking the button or browsing the webpage. Your agent will. That requires a new kind of computer. We built one. "Most business software," they continue, "has the same problem. Someone has to sit there and operate it, moving data, updating records, filling out forms. That someone is usually you." The example they gave, interestingly, is the real-world business example of a hardware store owner who has 47 new products in a spreadsheet and needs them to get added to Square. Adaptive says drag the file into Adaptive, tell it what you want, and it handles the rest. Out of scope of this particular show, but I think it's super interesting that you're seeing these very bleeding-edge tech companies trying to appeal to the hardware store owner use case. They then go on to pitch their secret sauce, which they call encoded memory. They write, what makes Adaptive different is what happens after. It encodes what it learned, how Square works, how your catalog is organized, and how you prefer things to be done. So the next week when you ask for a daily sales report at 8 PM, it builds the agent, schedules it, pulls from Square data that it already knows. Now, anytime there's a new launch, it tends to be pretty hard to get good signal from Twitter at this point because so much of the discourse is either AI bots or undisclosed paid tweets. But Ole Lyman did write of a good experience that he recently had through Adaptiv. The example he gave was automating YouTube AI research. Basically, his argument is that YouTube has a ton of really great videos on in-depth AI systems that are extremely up to date and current with the moment, but there is a ton to filter through that makes it hard to sit around and browse to get the diamonds in the rough. The prompt he gave Adaptiv was analyze YouTube videos about AI and cloud workflows from the last 24 hours that have at least 10,000 views pull the full transcripts, extract the top 3 most tactical and actionable workflows, and send me a daily email report every morning. The third and maybe biggest OpenClaw and agent-related announcement yesterday, however, came from NVIDIA. The context for that quote we heard at the beginning about every company needing an OpenClaw strategy was the setup for Jensen introducing NemoClaw. Now functionally, this is not actually a standalone agent, but rather a software toolkit built on top of the OpenClaw project. OpenClaw creator Peter Steinberger wrote yesterday, "Been so much fun cooking OpenShell and NemoClaw with the NVIDIA folks. Huge step towards secure agents you can trust." So what this is is basically an approach that adds privacy and security to OpenClaw instances by giving them an isolated sandbox to work in. The agent can still access resources as necessary, but the NemoClaw stack formalizes access control. Specifically, it integrates into policy-based security and other guardrails to theoretically allow it to operate safely within enterprises. Nemo Claw is model and hardware agnostic and allows users to choose between cloud and local models. Encapsulating this whole shift, Jensen Huang said, Open Claw gave the industry exactly what it needed at exactly the time, just as Linux gave the industry exactly what it needed at exactly the time, just as Kubernetes showed up at exactly the right time, just as HTML showed up. It made it possible for the entire industry to grab onto this open source stack and go do something with it. Now what's been interesting about the response is that for most, although not for all, this hasn't been a jump the shark or jump the lobster moment. Instead, people have been pretty enthusiastic about what NVIDIA is trying to do. Kevin Simbach again writes, excited to dig into NemoClaw. Have spent a good bit of my career in enterprise. I've been pretty vocal about OpenClaw not being enterprise ready, but the concept of an agentic workforce is a killer and enterprises are going to want it. So this may be what really kicks it off. Tristan Rhodes writes, I've been avoiding OpenClaw and waiting for it to mature. There have been countless variations and forks along the way, but NVIDIA is the most valuable company in the history of the world. Does that mean NemoClaw becomes the dominant variation of OpenClaw? Eric Hsu wrote an entire X article called NVIDIA Just Solved the One Problem Blocking AI Agents, of course all about the security concerns. Now one thing I will say that's been interesting from our own experience— regular listeners know we have two different OpenClaw-related things going on right now. Claw Camp is an open, free, self-directed program that walks people step-by-step through setting up their own OpenClaw and giving them access to a community of other builders who can help them along the way that at this point more than 7,000 people have signed up to participate in. EnterpriseClaw meanwhile is a managed 6-week executive sprint that's meant to help individual enterprise leaders and teams from enterprises get that same sort of learning but in a much more in-depth and supported way. Now as part of EnterpriseClaw, we gave people the choice to either use OpenClaw or do a generic version of agent team building using Claude Code, Codex, Cursor, et cetera. And interestingly, it's about half and half in terms of who wanted to learn on OpenClaw versus who wanted to use other systems. Meaning that even in the pre-enterprise-grade OpenClaw world, there is still demand for figuring out how to use this platform, which I think is certainly validation of everything that Jensen is saying. Now, Robert Scoble had an interesting note from the NVIDIA GTC Expo Hall that was actually more about OpenAI than it was about NVIDIA. He writes, visiting the Expo Hall shows you why OpenAI is changing strategy. All the big booths are enterprise. The biggest news here is how NVIDIA is bringing OpenClaw to the enterprise. Which brings us to another important story from yesterday. The Wall Street Journal reports that OpenAI is done with side quests and will refocus on nailing a core business which is now more than ever refocused on enterprise and coding. The Journal reporting states that CEO of Applications Fiji Simo has delivered a wake-up call within the company, pointing out that their do-everything strategy has reduced their lead on the competition. Simo told staff last week, "We cannot miss this moment because we are distracted by side quests. We really have to nail productivity in general and particularly productivity on the business front." Now, this is of course a big shift away from Sam Altman's traditional management approach, which he described as betting on a series of startups within the company. That led to a fairly dizzying array of product bets, including the Sora app, the Atlas browser, and the yet-to-be-revealed Johnny Ive device, just to name a few. As basically everyone on AI Twitter has done, The journal compared that approach to Anthropic's very narrow strategy built around agentic coding and the way that that expands into broader sets of knowledge work for the enterprise. Now, it's not new that OpenAI has decided to refocus efforts on similar themes. That's been the big story since GPT-5 was released and Codex came out, but there clearly seems to be a new urgency. Interestingly, according to Simo, the code red from last year is not over. Last week, she told staff, we are very much acting as if it's a code red. And while a lot of people are speculating around what might get the axe because of that, —for example, the much-maligned ads approach—every day it seems we get some new announcement around Codex and their larger coding suite. The most recent, and the one that we got yesterday and that I think is coherent with all of these qualification themes, is the native integration of subagents into Codex. The OpenAI developers account writes, "You can accelerate your workflow by spinning up specialized agents to keep your main context window clean, tackle different parts of a task in parallel, steer individual agents' work unfolds." LLM Junkie and Will writes, in the next Codex update, multi-agents will get a massive flexibility upgrade. Hey Codex, when you implement this plan, I want you to delegate all of the lower complexity tasks to GPT-5.3 Spark subagents. Instead of needing to create 100 different custom agent roles for different situations, you can just prompt your agent to spawn whatever model or reasoning level you want with only natural language. Emmanuel DiPietro went through some use cases for the subagent system, things like code review where he argues you could have one agent per concern. Test coverage with one sub-agent writing tests, another checking edge cases, and another validating, etc., etc. And it's clear that even though the foot is still firmly on the gas, the shift in OpenAI's strategy seems to be bearing some fruit. OpenAI President Greg Brockman wrote yesterday, GPT-5.4 has ramped faster than any other model we've launched in the API. Within a week of launch, 5 trillion tokens per day, handling more volume than our entire API one year ago, and reaching an annualized run rate of $1 billion in net new revenue. Sam Altman showed a chart of Codex usage being very aggressively up and to the right, adding the Codex team are hardcore builders and it really comes through in what they create. No surprise all the hardcore builders I know have switched to Codex. Responding to the news about OpenAI shifting focus, Dwayne on X writes, I actually thought OpenAI were already doing a good job focusing on coding. Codex is amazing for coding. One area where they absolutely fail is UI. GPT-5.4 can't design to save its life even if you have super detailed skill to guide it. It has zero taste. And for what it's worth, I talked about this on my operator show, this has very much been my experience to the point where I can't just give Codex guidelines, I literally have to give it the actual design files from Claude for it to copy exactly. Although my experience with Codex when it comes to actually building has been really good. Summing all this up, if Q1 was a realization that agents are here and a mass wide-scale experimentation with the form factors and design patterns introduced by OpenClaw, Q2 is set up to be an absolute sprint to productize those agents and get them ready for broader diffusion, especially within the enterprise. One thing that I will be watching closely is how much old patterns of productization, where conventional wisdom was all about simplifying things for wider audiences, still hold given that the breakout was this incredibly complex system in OpenClaw. I'm not sure I know where the right complexity band is going to be, or if it's going to be a spectrum of different types of complexity for different users. But I can guarantee that just about everything that can be tried will be tried in the quarter to come. For now, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. And until next time, peace.