The Race to Put AI Agents Everywhere

0:00Today on the AI Daily Brief, the race to productize agents and make them enterprise grade is on. Before that, in the headlines, NVIDIA's CEO says the company is on track for a trillion dollars in revenue. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, 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 slash ai daily brief, or you can subscribe on Apple Podcasts. To learn about sponsoring the show, send us a note at sponsors at aidailybrief.ai.

0:42We'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.

1:21At every GTC, Jensen's keynote, which is planned but not fully scripted, is the big event. This one was no exception. It was two and a half hours long, totally jam-packed with big announcements. We got confirmation of the new GroK-powered server focused on inference. The new rack-mounted system will combine 256 GroK chips with 72 NVIDIA Rubin GPUs, delivering 35 times 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 DLSS5, the technology combines traditional graphics with an AI filter to create stable photorealistic graphics.

1:59Being 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 two years.

2:31It'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 two financial years at$500 billion apiece, meaning it's not so much a material change. Bloomberg analyst Kunjan Sobani 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.

3:03In 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, Demand 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 ComputeNet. Next up, if it is NVIDIA's world, one of the new players in it is, of course, the NeoClouds.

3:34On that front, Meta has signed a$27 billion deal with Nebius. Nebius, which is similar to CoreWeave and NScale, operates smaller AI data centers than their hyperscaler counterparts. This often includes differentiated chips or full-stack support for model training or specialized inference. Nebius' new deal with Meta spans five years, and this is in addition to a$3 billion deal signed by Meta in November. Nebius plans to deploy NVIDIA's new Verorubin chips on Meta's behalf. The chips are expected to be available in the second half of this year, with Nebius powering on the new cluster early next year.

4:04Now, 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.

4:36AI infrastructure continues to scale up at a massive pace and the neoclouds seem to be getting their slice of the action. One area of infrastructure build-out 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 Aveline, Texas.

5:05That 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 Khati will oversee the division, which consists of three 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.

5:42Reporting 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.

6:13Responding 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 three senior researchers left the Quenn team. The departures included technical lead Justin Lin, who was credited with shepherding Quen from its first training run to becoming one of the most popular open-source models.

6:48Speculation 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 Quen models. According to a memo cited by Bloomberg, the restructuring is now complete. The Quen 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.

7:18Create 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 close 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.

7:56The speed boost is arguably a bigger deal, with the model optimized for running open-claw-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.

8:26Writes 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. Corinne on X wrote, ZAI has been the loudest open-source voice in AI for two 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.

9:00Nathan 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 slash not changing slash weak. Definitely a trend worth watching, but for now, that is going to do it for the headlines. Next up, the main episode.

9:21Why 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 slash AIDB. That's recall.ai slash AIDB.

10:24the first voice agent to be certified against AIUC1 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 11 labs 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 & Pencils, a company that is growing fast.

11:00Their 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.

11:31They 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 slash careers. That's robotsandpencils.com slash careers. You've tried in-IDE co-pilots. 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.

12:06While co-pilots 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 co-pilots. 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.

12:35Welcome back to the AI Daily Brief. We're coming up on the end of Q1, and as part of that, I've been working on a big quarter two 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.

13:23Part 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 open claw 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 slob.

13:58After 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.

14:34And 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, Hermes, Moltis, 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.

15:06In 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 sub-agents to get things done and build things that people want.

15:39In 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.

16:10The 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 Clawification 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.

16:46This 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.

17:20They 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.

17:54For 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, CloudCode, 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.

18:25By 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.

18:56Out 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 8pm, 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 Lemon did write of a good experience that he recently had through Adaptive.

19:36The 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 Adaptive was, analyze YouTube videos about AI and clawed workflows from the last 24 hours that have at least 10 ,000 views, pull the full transcripts, extract the top three most tactical and actionable workflows, and send me a daily email report every morning.

20:04The third and maybe biggest open claw and agent related announcement yesterday, however, came from NVIDIA. The context for that quote we heard at the beginning about every company needing an open claw 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.

20:41The 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. NemoClaw is model and hardware agnostic, and allows users to choose between cloud and local models. Encapsulating this whole shift, Jensen Huang said, OpenClaw 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.

21:13It 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.

21:44Tristan Rhodes writes, I've been avoiding OpenClaw and waiting for it to mature. There have been countless variation in 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 Su 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. ClawCamp 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.

22:24EnterpriseClaw, meanwhile, is a managed six-week executive sprint that's meant to help individual enterprise leaders and teams from enterprises get that same sort of learning but but in a much more in-depth and supported way. Now, as part of Enterprise Claw, we gave people the choice to either use OpenClaw or do a generic version of agent team building using Claw 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.