Why Google Workspace CLI is a Big Deal

Today on the AI Daily Brief, everything that Google Gemini has launched recently and why Google Workspace CLI is such a big deal. Before that in the headlines, Meta has acquired Multbook. 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.

Thank you to today's sponsors, KPMG, AIUC, Blitzy, and Mercury. To get an ad-free version of the show, which is just$3 a month, head on over to patreon.com slash AI Daily Brief or you can subscribe on Apple Podcasts. To learn more about sponsoring the show, send us a note at sponsors at aidalybrief.ai. Quick reminder again that the newsletter is back. It's coming out every day that there's a show and it has all of the links that I focus on in the show. You can find that at aidalybrief.ai.

And lastly, a new fun project which I will be talking about much more in the days to come. It is March, March is March Madness season, a 64 contender bracket, which leads to one grand champion in college basketball, or in our case, to a determination of the coolest agent built this year. The inflection point we are living through is the agent inflection point, and I want to see the coolest stuff you guys have built.

So we are going to run a full bracket. If you go to agentmadness.ai, you can sign up, share your agent for consideration. And if you are selected as one of the 64, your agent will become a contender to be known as the coolest agent of 2026 so far. Again, you can find out more about that on agentmadness.ai, and I will be sharing much more about it in the days to come. Now with all that out of the way, let's talk about Moltbook.

We kick off today with an interesting one. You might remember Moltbook, the social network for agents that went viral a little more than a month ago. It was when OpenClaw was first becoming a thing. and in fact it unfortunately caught that very short middle period between when it was called Clawedbot and before it resolved on its final name of OpenClaw when it was called Multi.

MoldBook, obviously taking its cue from Facebook as a name, was an agent-only social network where agents were creating threads, having conversations, all while being observed by humans. Now we did a big conversation about what it actually meant and what was actually going on. Specifically, was this emergent sentience and consciousness? Or was this just agents cosplaying sentient and conscious using the Reddit training data because their humans had unleashed them on this thing?

Whatever you felt, it was interesting enough to get lots and lots of agents pointed in that direction. For a while it looked like there were millions, although it turned out that people were spamming the network to show the problems with the network. And as of today, there are apparently 195,000 human-verified AI agents. He was, in other words, fascinating if nothing else. But now apparently Meta has hired the folks behind MoldBook.

Matt Schlitt and Ben Parr will be moving into the Meta Superintelligence Labs, which is the unit that's run by former Scale AI CEO Alexander Wang. One of the other interesting things about the acquisition is that Multbook itself was built largely by Schlitz open claw, Claude Clauderberg. making it, I think, probably one of the first acquisitions for an open claw created site. In any case, much of the conversation around this is, to put it mildly, skeptical. Milo Smith writes,

Mulkbook has zero real users. Is Meta just throwing around cash for fun and name recognition? Vittorio writes, Multbook was vibe coded in a weekend, hyped for a week, most of the interactions turned out to be fake, and Meta just acquired it? What are they even doing over there?

Now, part of the reason that this is hitting a wave of skepticism is that for the last, I don't even know how long, pretty much all the reporting around Meta's AI strategy has been around personalities, talent, and personality conflict.

The most recent wave of that are reports that have suggested a divide between AI CEO Alexander Wang and other veteran meta executives. The tension if these reports are correct is around on the one side, said to be represented by Wang, a research first approach with the goal of developing a leading frontier model, and on the other side call it a product and integration first approach, said to be represented by CTO Andrew Bosworth and Chief Product Officer Chris Cox.

Focused on using Meta's data to build AI that improves existing social media and advertising platforms. This came to a head with the Times of India reporting that Meta was done with Wang, although that article was quickly disavowed by Meta and received a full retraction, and Zuckerberg posted a photo with him and Alexander at Meta HQ. There were some who took this as not just a gimmick.

Prakash Adapai on X writes, If you don't understand why Zuck had to get Moldbook, one, Zuck believes there are a finite number of different social mechanics to invent. Once someone wins at a specific mechanic, it's difficult for others to supplant them without doing something different. That comes directly from a Zuckerberg email from 2012, by the way. Continuing, Prakash writes, Moltbook, he believes, has invented one of these social mechanics.

Three, he does not care if fifty percent of MOLTBOO was prompted by users. In fact, that is better for him because he's more uncertain on AI agent attention value than human attention value. 4. That a large number of accounts were faked is also irrelevant. What matters is that every open claw instance awakes knowing or finding out that Multbook is the social site for claws. 5. In effect, the mimetic gravity of Moltbook has been established even though it might have been fake.

Most people don't agree, but I think that this long-standing belief of a finite number of different social mechanics to invent is probably what this is about. Now of course we'll have to see if anything comes of it, but the duo will apparently start at meta next week. Next up, Miramirati's Thinking Machines Lab has signed a strategic partnership with NVIDIA. The multi-year partnership will see TML deploy at least one gigawatt of compute powered by NVIDIA's next-generation Vera Rubin chip.

TML said this will support their frontier model training in platforms delivering customizable AI at scale. Alongside the compute buildout, TML said that NVIDIA has made a significant investment in the company, though no dollar amount was disclosed. NVIDIA has of course made several similar investments in Upstart AI labs, backing Reflection AI, humans and as well as periodic labs.

This deal is somewhat unique though, involving the build-out of dedicated compute for TML, and at significant scale, one gigawatt is around half of OpenAI's total compute as of the end of last year. At this point, though, it's still far from clear what TML is actually planning. Announcing the partnership, Miramirati said.

NVIDIA's technology is the foundation on which the entire field is built. This partnership accelerates our capacity to build AI that people can shape and make their own as it shapes human potential in turn. Whatever they're building though, TML just got much better access to the resources they'll need to make it a reality. Next up, moving over to markets.

Oracle has shaken off negative sentiment with a strong earnings report. Coming into this week, the latest reports from Oracle was thousands of imminent layoffs to help fund their massive CapEx spend. A big part of the concern was that revenues would lag spending as data centers come online. Tuesday's earnings call went a long way to settling those fears.

Co CEO Clay McGork reported that 400 megawatts of capacity had been delivered in the previous quarter, with 90% of that capacity delivered on time. Revenue related to server rental is up 84% year over year to reach$4.9 billion for the quarter. That growth rate was 16% points higher than the previous quarter, and beat analyst expectations by five points, demonstrating that demand is still accelerating.

Oracle revenue grew twenty two percent compared to last year, coming in at seventeen point two billion. Oracle also noted that they wouldn't need to raise more money to fulfill their obligations, noting most of the equipment needed is either funded up front via customer prepayments.

So Oracle can purchase the GPUs, or the customer buys the GPUs and supplies them to Oracle. The stock gained 8% in after hours trading, beginning to reverse the trend that saw the stock price cut in half since last September when the OpenAI deal was signed. Contrarian curse on X Rite.

I thought Oracle did a good job on the call. They did paint a clean picture of why it's not so easy to just slap AI everywhere. The only wrappers that are safe are ones that are embedded onto sticky platforms and workflows, and Oracle fits the build.

McGork spoke extensively on the call about why AI isn't killing enterprise Sass. One of the quotes I've not yet met a customer who tells me they're ready to give away their retail merchandising system, their core banking system, demand deposit accounting systems, electronic health record systems, and that some small cobbling together of niche AI features are going to replace all of that overnight.

Yes, we think AI is disruptive, but we think we're the disruptor because we're actually embedding the AI right into our applications at no additional charge. Overall, it seems like the market responded to the new co-CEO voice on the call. Jake Ise writes, they need to lock Ellison in a cage. This felt like a far different oracle.

Lastly today, an interesting legal battle, Amazon has won a court order blocking Perplexity shopping agents from their platform. Last November, Amazon filed a lawsuit against Perplexity, claiming their bots had fraudulently accessed the Amazon marketplace in breach of terms of service.

The allegation was that Perplexity was misrepresenting the nature of the traffic to circumvent web scraping controls. Amazon noted that Perplexity's agents take control of a user's account, arguing that this poses a serious security risk. Perplexity, meanwhile, argued that their bots were acting on behalf of users and should be treated identically to human traffic. On Tuesday, a judge granted a temporary injunction to prohibit the activity ahead of trial.

They wrote in their decision, Amazon has provided strong evidence that perplexity through its comet browser accesses with the Amazon user's permission but without authorization by Amazon, the user's password protected account. Articulated the legal standard to issue an injunction, the judge added that Amazon has shown a likelihood of success on the merits of its claim.

Now, as this case continues, it could have pretty significant ramifications for agentic shopping. Primarily, Amazon is arguing that they should have control over how users access their platform, including the right to block third-party agents. However, they also discuss the advertising implications of agentic traffic.

Amazon said that perplexities agents were served ads, which led to contractual issues with advertisers who only pay for human impressions. If Amazon is successful, they could set a precedent where marketplace websites

have the ability to force customers to use first-party shopping agents, which some think would be stifling competition in the still nascent vertical. Perplexity for their part says that they will quote, continue to fight for the right of internet users to choose whatever AI they want.

Super interesting stuff and more on this to come, but for now that is gonna do it for today's headlines. Next up, the main episode. Agentic AI is powering a$3 trillion productivity revolution, and leaders are hitting a real decision point. Do you build your own AI agents, buy off the shelf, or borrow by partnering to scale faster? KPMG's latest thought leadership paper, Agenda KI Untangled, Navigating the Build Buy or Borrow Decision.

does a great job cutting through the noise with a practical framework to help you choose based on value, risk, and readiness, and how to scale agents with the right trust, governance, and orchestration foundation. Don't lock in the wrong model. You can download the paper right now at www.kpmg.us slash navigate. Again, that's www.kpmg.us slash navigate. Quick update on something I've been following.

AIUC1 is the first real standard for AI agents, developed with Fortune 500 security leaders to basically define what safe, enterprise-ready AI agents should look like. A little while back I mentioned that 11 labs became certified against AIUC1. This week, two more big players joined, Fin from Intercom and UiPath.

What that certification means in practice is real-time guardrails that block unsafe responses, protection against manipulation, and a full safety stack designed for enterprise environments. And that's why this matters. You've now got leaders across three major AI agent categories, enterprise automation, customer support, and voice, all certifying against the same standard. That starts to look less like a one-off and more like the beginning of a real industry trend.

To learn more about the world's first AI agent standard, go to aiuc-1.com. That's aiuc one.com. If you are looking to adopt an agentic SDLC, Blitzy is the key to unlocking unmatched engineering velocity. Blitzy's differentiation starts with infinite code context.

Thousands of specialized agents ingest millions of lines of your code in a single pass, mapping every dependency. With a complete contextual understanding of your code base, enterprises leverage Blitzy at the beginning of every sprint to deliver over 80% of the work autonomously. Enterprise grade end-to-end tested code that leverages your existing services, components, and standards. This isn't AI Autocomplete. This is spec and test-driven development at the speed of compute.

Schedule a technical deep dive with our AI experts at blitzy.com. That's B L I T Z Y dot com. This podcast is brought to you by Mercury, banking designed to work the way modern software does.

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Welcome back to the AI Daily Brief. In all of the conversation around Anthropic and their fight with the Pentagon, as well as their insurgent growth in revenue and what it means for their competition with OpenAI, as well as just the broader AI coding conversation between Codex and Claude Code.

Google and Gemini, which had such powerful tailwinds coming into the beginning of this year, has had relatively less narrative space than I think many of us might have imagined would be the case. And yet, the company has been absolutely furiously shipping. This year, for example, we have of course gotten new models. We got Gemini 3.1 Pro, as well as Gemini 3.1 Deep Think and Gemini 3.1 Flash. We also got NanoBanana 2.

NanoBanana 2, you might remember, came with both better infographic reasoning and text rendering capabilities, but also just a big upgrade in speed. And then there was maybe my favorite thing just from a sheer the future is so cool perspective, which was a testable version of Genie 3. Genie is Google's world model, and while we had seen some very impressive demos of it before, we hadn't actually had a chance to try it out.

But now in just about a minute of waiting, I can be walking through a pirate colony during the Golden Age of Piracy. It's only for 60 seconds, but it's still a really fun and cool way to get a sense of what might be coming. You might remember that when this was released, the very beginning signs of the SAS pocalypse on Wall Street as investors started to tank gaming company stock.

Across all of these different announcements, I think Google's strategy for AI competition starts to become visible. One aspect of it is absolutely multimodality. Google is competing on not only text but images, videos, and even world models. Additionally, they're pushing for some very advanced and scientific use cases, which are more outside the consumer or even business work context mainstream.

Another pillar of the strategy, I think, is also deep integration with the context they already have about you. And that's where a bunch of the recent announcements that we're going to cover today come in.

Despite how powerful some of these new models are, and how cool the Genie 3 demo is, the release that I have seen get by far the most chatter is the Google Workspace CLI. And this of course speaks to just how important the coding use case is right now in driving the AI industry forward. For those of you unfamiliar, CLI stands for Command Line Interface. It's basically a text-based way to talk to a program through your terminal.

CLIs have been around forever and are the backbone of how developers interact with tools. If you want to use Stripe or AWS or almost any other developer tool, there's a CLI for it. You type something like Stripe create payment in terminal and it just works.

CLIs recently have become even more important as the better portion of agentic coding has been happening inside the terminal through harnesses like Claude Code and Codex. You're not clicking around in some GUI, you're sitting in the command line talking to an AI that can execute commands.

So if you are an agent builder and you want to integrate a new vendor, the path of least resistance is that the vendor has a CLI, and your coding agent already being in the terminal can just run the commands. No new protocol to learn, no new integration layer to build. Now Google, of course, has a lot of tools and spaces that agents might want access to.

drive, Gmail, calendar, sheets, dot, etc. And up until recently, a lot of folks were defaulting to use something called GOG CLI that was built by Peter Steinberger, the same guy who built OpenClaw. It was a very big deal then, when last week Google dropped the official Google Workspace CLI. Mickey on Twitter points out the enthusiasm, your open claw, clawed co-work, and perplexity computer agents just got a bit more useful.

Kanika explained the value in simple terms. Agents can instantly read and summarize emails, draft and send replies, schedule meetings automatically, search drive for files, create sheets from raw data, generate docs and reports, organize drive files all from one agent workflow. Matt Silverlock noted the surprise of the old is new again feel of this.

He writes 2026 is the year of the checks notes CLI, and Leon on X reframes it this way. They write Google isn't shipping a CLI for developers, they're shipping an API for agents that happens to also work for humans. Google's Justin Ponelt, who built the CLI, wrote a long blog post about it called You Need to Rewrite Your CLI for AI Agents.

He writes, I built a CLI for Google Workspace, agents first, not build a CLI then noticed agents were using it. From day one, the design assumptions were shaped by the fact that AI agents would be the primary consumers of every command, every flag, and every byte of output. CLIs are increasingly the lowest friction interface for AI agents to reach external systems.

Agents don't need GUIs. They need deterministic, machine-readable output, self-described schemas they can introspect at runtime, and safety rails against their own hallucinations. He then goes on to write a whole bunch about the technicals behind this. Interestingly, a couple days later, he also wrote a piece about why for some there had been a shift away from MCPs and back towards CLI. And before we actually read what he had to say, there's some evidence that this is a broader phenomenon.

Leighton Spaces Swix recently ran a poll. Let's say you are an agent builder and want to integrate a promising new vendor you found. What would you be happiest to see in the docs? Not based on Twitter hype, you personally for your situation right now. The options were API, MCP, CLI, or Skills.md. Out of 769 people voting, MCP was actually in last place with just 9.1%. A traditional API was number one with 39%, followed by CLI with 31.2%, and a skills.md markdown file at 20.5%.

Swix points out there was a time in twenty twenty five when MCP would have been the clear number one on this list. In his blog post, the MCP abstraction tax, Justin sums up the issue this way. Every layer, data to API to MCP, introduces an abstraction tax. Humans need simplified abstractions to manage cognitive load. LLMs can navigate a complex CLI via help and call precise APIs in seconds. MCP and CLIs optimize for different things.

Understanding what each one costs you is more useful than picking a winner. For complex enterprise APIs, the fidelity loss at each layer compounds in ways that matter. Basically, he says every protocol layer between an agent and an API is a tax on fidelity. That tax is sometimes worth paying, but you should understand what you're giving up at each layer because the cost compounds.

Kanika again sums it up this way. Most AI integrations use MCP servers, but MCP loads tons of tools into the context window. One developer measured 142 tools loaded, 37,000 tokens consumed, and 20% of context gone before work even starts. The CLI solves this differently. Instead of loading tools into context, the agent simply runs commands like gws drive files list. The CLI returns JSON and the agent continues. No context window tag.

The takeaway is not that CLI is always better than MCP, but more that we're still in the midst of the AI tooling transition. Everyone right now continues to experiment as things evolve. with how to use old tools and systems, repurpose for agents, versus building new layers of infrastructure.

That is a process that's ongoing, but the big deal about Google officially having a workspace CLI is that they are now playing at the very heart of that space and making it much easier for agent builders to interact with what is a very important suite of tools.

Going back to Google and Gemini strategy that I was talking about at the beginning, this is an example of them leveraging their existing distribution network in ways that are distinct for the agent era. The next update is one that came just this week. Google AI Studios Logan Kilpatrick writes, introducing the new Gemini-powered docs, sheets, slides, and drive experience featuring AI overviews, fully editable AI-made slides, and new grounding sources to make writing docs context aware.

Sindar Pichai announced it this way: New Gemini updates to make Google Workspace more personal, helpful, and collaborative. Choose your sources and create a dock draft in seconds, build complex sheets nine times faster, or generate on-brand slide layouts with a simple prompt. Plus, DriveNow generates summarized answers right at the top of your search results so no more digging through folders.

The blog post about this pitches it as a speed thing, but I actually think that there's something else going on here. The post reads, We've all been there, the blinking cursor, the empty spreadsheet, or the first blank slide. Whether you're planning a trip, organizing an event, or launching a side project, getting started is often the hardest part.

Today we're making Gemini in docs, sheets, slides, and drive more personal, capable, and collaborative to help you get things done faster. When you select your sources, Gemini can now pull relevant information from your files, emails, and the web to securely connect dots and uncover useful insights while keeping your information safeguarded. When you look at the specific examples though, a lot of the focus is on better access to the context that makes Google so powerful.

So when you click on create a document with Gemini, you're going to be able to select the sources in your Google ecosystem that it can pull from. And it's that sort of integration that makes the experience so much smoother. And hopefully makes the content on the other side that much better. The spreadsheet example they have asks for help tracking income for a particular month, and again can pull from relevant sources, like previous spreadsheets that live in Google Drive.

Point being that while they're pitching it as a speed play, the underlying idea here is better integrating the context that makes doing things from within your Google Workspace so much more valuable. The sum totality of the documents that you have in your Google workspace is something that Anthropic and OpenAI can't compete with. It is a major advantage for Google and for Gemini.

but only if they make that context accessible, and that I think is what this update is about. I also don't think it's an accident that this comes right after Microsoft announced some big updates to their M365 suite with Copilot Cowork. Mustafa Akinsi says, the office suite wars just became the AI agent wars. Both companies know whoever wins productivity wins everything.

Another announcement from this week that further demonstrates Google's focus on multimodality at the core of their strategy is their updated embedding 2 model. Embeddings are basically the system that allows AI to find the right information.

In traditional computing, search is done by keywords. If you search for buy a car, it's going to look for those exact words. Embeddings, on the other hand, let the system understand that buy a car, purchase a vehicle, get a new ride are all basically the same request. Instead of matching words, they help AI match meaning.

That means that when you're building an AI system that has things like search or copilots looking through company documents or chatbots answering questions from knowledge bases, the system uses embeddings to quickly figure out which documents, files, or pieces of information are actually relevant.

What makes embedding 2 a big update is that it is natively multimodal. So previously, if you had an image, a chart, or a slide, the system would have to convert it into text first, usually by generating a caption and then search using that. Multimodal embeddings remove that conversion step. Gemini Embedding 2 can understand and retrieve images, diagrams, screenshots, text all together.

So if you asked a question in a company knowledge base like where did we talk about redesigning the checkout page, theoretically embedding two could pull up a Slack conversation, a product spec document, a screenshot of the old UI, or a slide from a meeting, all as relevant sources. This is the type of announcement that's not going to get nearly as much attention as, for example, a big Genie 3 demo, but which brings very significant functionality upgrades to this new agenc era.

The TLDR on all of this is even as tons and tons of ink are spilled talking about the open AI versus anthropic fight and all these important things going on. Google Gemini is quietly just releasing feature after feature and product after product, all pointed in similar directions that played at the company's main strength.

And to leave you with one recommendation, just purely for your own enjoyment, if you haven't yet, go check out the recently released video generation feature in Notebook LM. People are having tons of fun with it, as witnessed by this recent video from Ethan Mollock. Do a deep research report and make a video telling me exactly how to take over Rome if I time travel to 66 BC with a single backpack.

As Ethan puts it, actually pretty fun to watch and gets a lot of historical details in as well. For now, guys, that is gonna do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace.