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 a trillion dollars 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.

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When 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 agentmanus.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 two and a half hours long, totally jammed packed with big announcements.

We got confirmation of the new GROC powered server focused on inference.

The new rack mounted system will combine 256 GROC chips with 72 Nvidia Ruben 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 GNI 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 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 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.

Lay 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 one 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 a material change.

Bloomberg analyst Kunjian Subhani wrote,

the update should ease fears of a pullback in 2027 as Ruben 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 in 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 Neo Clouds.

On that front, Meta has signed a 27 billion dollar deal with Nebius.

Nebius, which is similar to CoreWeave and Nscale, operates smaller AI data centers than their hyper-scaler 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 Vera Rubin 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.

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 Neo Clouds 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 Neo Clouds 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 built up 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 Avaline, 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 executives Sachin Kati 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.

Reporting also confirms that OpenAI is less concerned about ownership of data centers and more willing to lease in order to scale up compute.

With the suit to 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's that they just got sued by a dictionary.

Encyclopedia Britannica and their subsidiary Miriam Webster have sued OpenAI for use of their dictionaries and encyclopedias in training data.

Further Britannica claims that chat GPT has cannibalized their web traffic by producing content that substitutes or competes.

Responding to the lawsuit in 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 OpenSource 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 quen team.

The departures included technical lead Justin Lin, who is credited with shepherding quen from its first training run to becoming one of the most popular open source models.

The 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 OpenSource 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.

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 close source.

The new model is called GLM5 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 open-cloth-style tasks like tool use and long chain execution.

ZAI 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.

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.

Karan on X wrote,

ZAI has been the loudest open-source voice in AI for two years.

They just released 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 ZAI chose to release the model with is a glowing lobster writing a horse.

Nathan Lambert, who just wrote an interesting essay on this topic, wrote,

were in the era when the cost of building LLM's 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.

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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 quarter two state of AI 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 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 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 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 claw-focation of the world was well underway.

Kevin Simbaek 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-slop.

After open-cloth and with the advent of Opus 45 and 46, agents became accessible, just a 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 is 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 Openfang Hermes, Multus, 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 claw-focation of AI, on the other hand, you have a huge number of companies, some that were AI-natives, some that weren't AI-natives, offering upward or effectively their own versions of Open-cloth.

In other words, agents that are deeply integrated and integrate a bull, 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 Mac many part of the theme, with their launch of personal computer, which they call an always-on local merge with Proplexity Computer that works for your 24-7.

Getting philosophical, Proplexity CEO Arvind 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 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 qualification of everything category that were announced just yesterday.

Menace, 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 Manace 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 and 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 schedule 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 Manace 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 Manace'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 Manace 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, cloud code, co-work, OpenClaw, Codex and Manace 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 web page. 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 8 p.m., 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 Ola Lemon 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 and the rough.

The prompt he gave Adaptive 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 three most tactical and actionable workflows, and send me a daily email report every morning.

The third and maybe biggest open-cloth agent related announcement yesterday, however, came from Nvidia.

The context for that quote we heard at the beginning about every company needing an open-cloth 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 open-cloth project.

Open-cloth creator Peter Steinberger wrote yesterday, been so much fun cooking open-shell in 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 open-cloth 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.

NemoClaw is model and hardware agnostic and allows users to choose between cloud and local models.

In encapsulating this whole shift, Jensen Huang said, Open-cloth 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 Symbach again writes,

excited to dig into NemoClaw, have spent a good bit of my career in enterprise, I've been pretty vocal about Open-cloth 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-cloth 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 Open-cloth?

Ericsu 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 Open-cloth related things going on right now.

Cloth-camp is an open free self-directed program that walks people step by step through setting up their own Open-cloth

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-cloth 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 in a much more in-depth and supported way.

Now as part of Enterprise-cloth we gave people the choice to either use Open-cloth or do a generic version of agent team building using Cloth Code, Codex, Cursor, etc.

And interestingly it's about half and half in terms of who wanted to learn on Open-cloth versus who wanted to use other systems.

Meaning that even in the pre-enterprise-grade Open-cloth world there is still demand for figuring out how to use this platform, which I think is certainly validation of everything that Genshin is saying.

Now Robert Scobal had an interesting note from the NVIDIA GTC Expo Hall that was actually more about Open-AI than it was about NVIDIA.

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-cloth 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 core business which is now more than ever refocused on enterprise encoding.

The journal reporting states that CEO of applications Fiji Simo has delivered a wake-up call within the company pointing out that they 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 Anthropics' 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 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 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 adds 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 sub agents into Codex.

The Open-AI developers account rights, 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 unfold.

LLM junk EM will rights.

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 sub agents.

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.

A manual, the Petro went through some use cases for the sub agent system, things like code review where he argues you could have one agent per concern, test coverage with one sub agent writing tests and other checking edge cases and another 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, 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 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 know have switched to Codex.

Responding to the news about Open-AI shifting focus, Dwayne on X writes, 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. 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 Clawed 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 Open-Claw,

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 Open-Claw.

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.

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