The AI Daily Brief: Artificial Intelligence News and Analysis - Why Google Workspace CLI is a Big Deal

Starting point is 00:00:00 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. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, AI, UC, 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 AIDailybrief.aI.

Starting point is 00:00:41 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 AIDailydief. And lastly, a new fun project which I will be talking about much more in the days to come. It is March, March's 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. You can sign up, share your agent for consideration, and if you are selected as one of the

Starting point is 00:01:21 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 agent madness.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 MaltBook. We kick off the day with an interesting one. You might remember MoldBook, the social network for agents that went viral a little more than a month ago. It was when OpenClawe was first becoming a thing. And in fact, it unfortunately caught that very short middle period between when it was called Claudebot and before it resolved on its final name of OpenClaw when it was called Maltie. Mold Book, obviously taking its cue from Facebook as a name, was an agent-only social network

Starting point is 00:02:03 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 cosplays sentient and conscious using their 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

Starting point is 00:02:35 195,000 human verified AI agents. It was, in other words, fascinating if nothing else. But now, apparently, Meta has hired the folks behind Multbook. Matt Schlett 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 Openclaw Claude Clotterberg, making it, I think, probably one of the first acquisitions for an OpenClaw created site. In any case, much of the conversation around this is, to put it mildly skeptical. Milo Smith writes, Malthbook has zero real users. Is Meta just throwing around cash for fun and name recognition?

Starting point is 00:03:15 Vitorio writes, Moldtbook 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 conflicts. 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,

Starting point is 00:03:51 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 to savowed 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 Aida Pi on X writes,

Starting point is 00:04:21 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, Moldt book, he believes, has invented one of these social mechanics. Three, he does not care if 50% of Moldt book was prompted by users.

Starting point is 00:04:46 In fact, that is better for him because he's more uncertain on AI agent attention value than human attention value. Four, 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 Moldbook is the social site for claws. Five, in effect, the memetic gravity of Maltbook has been established even though it might have been faked. Most people don't agree, but I think that this longstanding 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 apparently start at meta next week. Next up, Miramaradi's Thinking Machines Lab,

Starting point is 00:05:19 has signed a strategic partnership with NVIDIA. The multi-year partnership will see TML deploy at least one gigawad of compute, powered by NVIDIA's next generation Vera Rubin chips. TML said this will support their frontier model training and 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. Invidia has, of course, made several similar investments in Upstart AI labs,

Starting point is 00:05:43 backing reflection AI, humans, and as well as periodic labs. This deal is somewhat unique, though, involving the buildout 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, Miramoradi 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.

Starting point is 00:06:12 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 McGorick 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 percentage points higher

Starting point is 00:06:51 than the previous quarter and beat analyst expectations by five points, demonstrating that demand is still accelerating. Oracle revenue grew 22% compared to last year coming in at 17.2 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% and after-hours trading, beginning to reverse the trend that saw the stock price cut in half since last September when the Open AI deal was signed. Contrary and curse on X writes, I thought Oracle did a good job on the call. They did paint a clean picture of why it's not so

Starting point is 00:07:27 easy to just slap AI everywhere. The only rappers that are safe are ones that are embedded onto sticky platforms and workflows, and Oracle fits the bill. McGorick spoke extensively on the call about why AI isn't killing enterprise SaaS. 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 sub-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

Starting point is 00:08:01 market responded to the new co-CEO voice on the call. Jake Eyes 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 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

Starting point is 00:08:42 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 comment 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 discussed the advertising implications of agentic traffic. Amazon said that

Starting point is 00:09:20 Perplexity's 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 stillness and vertical. Perplexity for their part says that they will, 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 going to 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

Starting point is 00:09:58 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, Agendic AI untangled, navigating the build buy-or-borrow decision. It is 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.org.comg dot us slash navigate. Quick update on something I've been following.

Starting point is 00:10:33 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, Finn 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 and 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

Starting point is 00:11:08 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-1.com. If you're looking to adopt an agenic SDLC, Blitzy is the key to unlocking unmatched engineering velocity. Blitzie'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 Blitsey 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

Starting point is 00:11:49 development at the speed of compute. Schedule a technical deep dive with our AI experts at blitzie.com. That's B-L-I-TZY.com. This podcast is brought to you by Mercury, banking designed to work the way modern software does. One thing I've always found weird as a founder is that almost every tool you use to run a company is modern. Your analytics tools, your email tools, your AI tools, they all feel like software built in, you know, the last decade. Then you go to banking and suddenly it feels like you've time traveled back to the 70s. That's why I use Mercury. It's business banking that actually works like the rest of the tools founders rely on.

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Starting point is 00:12:53 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 ClaudeCode, Google and Gemini, which had such powerful tailwinds coming into the beginning of this year, has had relatively less narrative space that 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. Nano Banana 2, you might remember, came with both better infographic reasoning and text rendering capabilities, but also just a big upgrade in speed.

Starting point is 00:13:47 And then there was maybe my favorite thing just from a sheer, The Future is So Cool Perspective, which was a testable version. version of Jeannie 3. Jeannie 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 SaaSpocalypse on Wall Street, as investors started to tank gaming company stocks. Across all of these different

Starting point is 00:14:22 I think Google's strategy for AI competition starts to become visible. One aspect of it is absolutely multi-modality. 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.

Starting point is 00:14:55 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,

Starting point is 00:15:11 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.

Starting point is 00:15:29 CLIs recently have become even more important as the better portion of agentic coding has been happening inside the terminal through harnesses like ClaudeCode 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.

Starting point is 00:15:56 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 Gogcili 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 OpenClaw, Claude Co-work, and Perplexity computer agents just got

Starting point is 00:16:22 a bit more useful. Conica 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, organized 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.

Starting point is 00:16:49 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 Ponealt 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 bite of output. CILIs 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

Starting point is 00:17:27 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 CLIs. And before we actually read what he had to say, there's some evidence that this is a broader phenomenon. Latent's space's 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.

Starting point is 00:18:01 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 1 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 2025 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.

Starting point is 00:18:38 LLMs can navigate a complex CLI via help and call precise APIs in seconds. MCP and CLIs optimized 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. Conica again sums it up this way.

Starting point is 00:19:05 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 tax. 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.

Starting point is 00:19:36 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's 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.

Starting point is 00:20:10 Google AI Studios, Logan Kilpatrick writes, introducing the new Gemini powered docks, sheets, slides, and drive experience featuring AI overviews, fully editable AI-made slides and new grounding sources to make writing docs context aware. Sundarpe Chai 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, Drive Now generates summarized answers right at the top of your search results,

Starting point is 00:20:39 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 docks, sheets, slides, and drive more personal, capable, and collaborative

Starting point is 00:21:00 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 created document with Gemini, you're going to be able to select the sources

Starting point is 00:21:24 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.

Starting point is 00:21:44 point being that while they're pitching it as a speedplay, 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 co-pilot co-work. Mustafa Akinsi says, The Office Suite Wars just became the AI agent wars.

Starting point is 00:22:23 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.

Starting point is 00:22:53 That means that when you're building an AI system that has things like search or co-pilots 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.

Starting point is 00:23:19 Multimodal embeddings remove that conversion step. Gemini Embedding 2 can understand and retrieve images, diagrams, screenshots, text altogether. So if you asked a question, in a company knowledge base like, where did we talk about redesigning the checkout page, theoretically Embedding 2 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

Starting point is 00:23:48 to this new agentic era. The TLDR on all of this is even as tons and tons of ink or spill talking about the OpenAI 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 play to the company's main strengths. 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 L.M. People are having tons of fun with it, as witnessed by this recent video from Ethan Malik, do a deep research report and make a video telling me exactly how to take over Rome

Starting point is 00:24:24 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 going to do it for today's AXX. Daily Brief. Appreciate you listening or watching as always and until next time, peace.

The AI Daily Brief: Artificial Intelligence News and Analysis - Why Google Workspace CLI is a Big Deal

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