The Race to Put AI Agents Everywhere

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, AI UC, robots and pencils and blitzie. To get an ad-free version of the show, go to patreon.com/aideallybrief or you can subscribe

out on podcasts to learn about sponsoring the show, send us a note at [email protected]. But 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 agent madness.ai, submissions close very soon so I encourage you to check it out. Now 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 two and a half hours long, totally jam packed with big announcements. We got confirmation of the new GROC powered server focused on inference.

The new Rackmounted system will combine 256 GROC 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 JNI system that can enhance video game graphics on the fly. While DLSS5, the technology combines traditional graphics with an AI filter to create stable

photo realistic 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 entrance into the open claw category which will cover in the main episode, but ultimately, while the keynote had many big moments. None grabbed headlines like Jensen's massive revenue forecast. In the 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. 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 two financial years at 500 billion a piece, meaning it's not so much material change. Bloomberg analyst Kunjan Sabani wrote, "Be 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 is now signal 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 Arab co-following slightly short, if Huang's forecast is correct, it will be completely unparalleled growth for a company of anywhere near-in-video size. Working on the event Josh Cale wrote, "The man doubled his demand forecast to a trillion

dollars, announced data centers in space, and closed the show with a robots singing country music." This is Nvidia's world. Everyone else is just renting computer net. 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 Nebias, Nebias, which is similar to core weave and end-scale, operate smaller AI data centers than their hyperscaler counterparts. This often includes differentiated chips or full-stack support for model training or specialized inference. Nebias is new deal with Meta spans five years, and this is in addition to a $3 billion

deal signed by Meta in November. Nebias 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 Nebias powering on the new cluster early next year. Now while it's possible that Meta is turning to Nebias 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.

Nebias 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 Neoclouds seem to be

getting their slice of the action. One area of infrastructure build up that has been a little bit shall we say, beleaguered, is the Open AI 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,

site in Avaline, Texas.

“That reporting also suggested that the Stargate name would be attached to all data centers”

operated by Open AI 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 Katty 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 Open AI'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 Open AI is less concerned about ownership of data centers and more willing to lease in order to scale up compute. With the suit of 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.”

Let's fund for Open AI's that they just got sued by Addictionary, Incyclopedia Britannica and their subsidiary Maryam Webster have sued Open AI for use of their dictionaries and Incyclopedias in training data. Further Britannica claims that ChatGPT has cannibalized their web traffic by producing content that substitutes or competes, responding to the lawsuit in Open AI's spokesperson said,

"Our model's empower innovation and are trained on publicly available data and grounded and 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 stories that Ali Baba has restructured their AI organization in a shifted

seems to maximize profits. Rumors were swirling earlier this month that a big move was in the works as three senior researchers left the QUEN team. The departures included technical lead just in Lynn, who is credited with Shepard and Quen from its first training run to becoming one of the most popular Open Source models.

Speculation at the time was that Ali Baba 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. Quen research team has been folded into a new division that also includes consumer-facing

apps and AI-related products like the quarks, marklasses. The new division is called the Ali Baba 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 "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 start-up Z.A.I. has released a faster cheaper version of their leading model, but they are "keeping it close source." The new model is called GLM5 Turbo, an offer similar performance to GPD5.2 at a cost that's closer to Gemini 3 Flash.

The speed boost is arguably a bigger deal, with the model optimized for running open-class style tasks like tool use and long-chain execution. Z.A.I. said the model would be released as close 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 the 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. This venture beat, that would not mark the end of open-source A.I. from Chinese labs, but it could mean their most strategically important agent-focused offerings appear first behind-close 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.”

Koran on X-Route Z.A.I. has been the loudest open-source voice in A.I. for two years. They just release their first close 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 Z.A.I. 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/notchanging/week." Definitely a trend worth watching, but for now, that is going to do it for the headlines. Next up, the main episode.

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It's called AIUC-1, and it builds 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 11 labs, 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 11 labs can point to a third-party certification and say our agents are secure, safe, and verified, that changes the conversation.

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Here coming up on the end of Q1 and as part of that, I've been working on a big quarter two state-of-A-I report. As you might expect, maybe the key story of Q1 was open-cloth, not even just because of open-cloth itself, but because of what it represented.

“I think you can look at open-cloth as the instantiation of the new capabilities set that”

shifted around the end of last year, and which is really come to the fore this year. It's what I called on yesterday's episode AI 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 agent 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 in video CEO Jensen Wong's speech. At their annual GTC event yesterday, where Jensen said explicitly, "Every software company

in the world needs to have an open-cloth strategy, and where he began to show off their enterprise-grade version of the software." Now even before this, the qualification of the world was well underway. Kevin Symbak from Delphi Labs recently wrote a post about all of the different variations and competitors, and started by claiming that open-cloth opened the door.

Kevin writes, "Before open-cloth, agents were mostly technical experiments that produced nothing more than timeline slot. After open-cloth and with the advent of Opus 45 and 46, agents became accessible, just

to telegram message away, always on, actually doing helpful things, and kick-starting a new

generation of digital opportunities." Open-cloth 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 has sold on open-cloth itself, and there has been a mad race to build or update alternatives.

A bunch of them, like nanobot, zero-cloth, pico-cloth, or nanoclaw, are all attempts to reduce the overall complexity down to some specific useful feature set, and then there's others like Open-Fang Hermes, Multice, and Ironcloth that are all trying to bring security to it through self-hosting. Yet, if that represents one end of the spectrum of the qualification of AI, on the other hand,

you have a huge number of companies, some that were AI native, some that weren't AI native,

In other words, agents that are deeply integrated and integrated, 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 Open-cloth, 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 Proplexity Computer. Proplexity Computer is a very full-throated, reimagining of Proplexity 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. In the couple weeks since Proplexity 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 MacMany part

of the theme with their launch of personal computer, which they call an always-on local

merge with Proplexity Computer that works for you 24/7. Getting philosophical, Proplexity CEO, Arvin Shrinivas, 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 Arvin is arguing is that the full potential of agent 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 qualification 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. With 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 and swift entirely on your computer with no code written

manually, combining with existing connectors to create simulated workflows and creating local routines with personal projects, agents, and schedule tasks. In the blog post, without naming open-cloth, they acknowledged 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 G writes, "Claude code, co-work, open-cloth, 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.

“At a 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, any time 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 all I learned did write of a good experience that he recently had through Adaptive. 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 Adaptive was, analyzed 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 three most tactical and actionable workflows, and send me a daily email report every morning.

from Nvidia. The context for that quote we heard at the beginning about every company needing an open-clod strategy was the setup for gents and introducing Nemo-clod. Now, functionally, this is not actually a stand-alone agent, but rather a software toolkit built on top of the open-clod project.

Open-clod creator Peter Steinberger wrote yesterday, "Bin so much fun cooking open-shell and Nemo-clod with the Nvidia folks, huge step toward secure agents who can trust."

So what this is is basically an approach that adds privacy and security to open-clod

instances by giving them an isolated sandbox to work in. The agent can still access resources as necessary, but the Nemo-clod stack formalizes access control. Specifically, it integrates into policy-based security and other guard rails to theoretically allow it to operate safely within enterprises.

Nemo-clod is model and hardware agnostic and allows users to choose between cloud and local models. In capsulating this whole shift, Gents and Huang said, "Open-clod 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 on to 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 Symbac again writes, "Exit it to dig into Nemo-clod, have spent a good bit of my career in enterprise, I've been pretty vocal about Open-clod 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 Open-clod waiting for it to mature. There have been countless variation in forks along the way, but Nvidia's the most valuable company in the history of the world.

“Does that mean Nemo-clod becomes the dominant variation of Open-clod?”

Ericsson wrote an entire ex article called "Invidea 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 Open-clod related things going on right now.

Clock camp is an open-free self-directed program that walks people step by step through setting up their own Open-clod 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. Enterprise-clod meanwhile is a managed six-week executive sprint that's meant to help individual

enterprise leaders and teams from enterprises, you get that same sort of learning but in a much more in-depth and supported way. Now as part of Enterprise-clod we gave people the choice to either use Open-clod or do a generic version of agent team building using cloud code, codex, cursor etc. And interestingly it's about half-and-half in terms of who wanted to learn on Open-clod

versus who wanted to use other systems, meaning that even in the pre Enterprise-grade Open-clod

“world there is still demand for figuring out how to use this platform, which I think”

is certainly validation of everything that Jen's in a saying. Now Robert Scobel had an interesting note from the Nvidia GTC Expo Hall that was actually more about Open AI than it was about in video. He writes visiting the Expo Hall shows you why Open AI is changing strategy. All the big booths are Enterprise.

The biggest news here is how Nvidia is bringing Open-clod to the Enterprise, which brings us to another important story from yesterday. The Wall Street Journal reports that Open AI is done with side quests and will refocus on nailing a corp business which is now more than ever refocused on enterprise encoding. The journal reporting states that CEO of applications, VGCMO, has delivered a wake-up call

within the company, pointing out that there do everything strategy has reduced their lead on a competition. CMO 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 I've device just to name a few.

As basically everyone on AI Twitter has done, the journal compared that approach to anthropics

very narrow strategy, built around agent decoding and the way that that expands into broader sets of knowledge work for the Enterprise. Now it's not new that Open AI 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 CMO, the code read from last year is not over. Last week she told staff, "We are very much acting as if it's a code read." 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 Open AI developer's 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 as work unfolds." LLM Junkie and Will writes, "In the next Codex update, multi agents will get a massive

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 a hundred 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. I may know all the Pietro went through some use cases for this subagent system, things like a code review where he argues you could have one agent per concern, test coverage with one subagent writing tests and other checking edge cases and other validating, etc.

And it's clear that even though the foot is still firmly on the gas, the shift in Open AI strategy seems to be bearing some fruit.

“Open AI president Greg Brockman wrote yesterday, "GPD5.4 has ramps 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, enriching an annualized run rate of 1 billion

in net new revenue." Sam Altman showed a chart of codex usage, being very aggressively up into the right, adding the codex team or hardcore builders and it really comes through in what they create. No surprise, all the hardcore builders I now have switched to codex. Responding to the news about Open AI shifting focus, doing on x-rites, I actually thought

“Open AI were already doing a good job focusing on coding?”

Codex is amazing for coding.

One area where they absolutely fail is UI. GPT5.4 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 Cloud for it to copy exactly, although my experience with codex when it comes to actually building has been really good. Something 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, or 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.com. For now, that is going to do it for today's AI Daily Brief.

I appreciate you listening or watching as always, and until next time, peace.