In Defense of Tokenmaxxing

**Nathaniel Whittemore** (0:00)
Today on the AI Daily Brief, a defense of token maxing, the controversial practice of incentivizing employees to spend as many AI tokens as they can.
Before that in the headlines, Google starts dropping announcements ahead of IO., including the new Gemini Intelligence. 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, registrations are live for Enterprise Claw Cohort 3 You can get a link from the AI Daily Brief site or at enterpriseclaw.ai. And second, as I mentioned yesterday, I am hiring a growth engineer for the podcast and podcast ecosystem. All of these crazy things we do like the context portfolio builder and these free education programs, that's the type of stuff you're going to get to build as a growth engineer. Your job will be to expand not only the audience of the podcast, but the impact of the audience. You can find information about that at jobs.aidalyebrief.ai.
We are actively recruiting. I am looking to hire fast. And it is a full-time role. And with that, let's get to the headlines.
We got a lot today, so let's cook.
First up, Google has announced a new agentic suite for Android users called Gemini Intelligence. Framing their vision, Google wrote, As Android transitions from an operating system into an intelligence system, your devices are becoming even more helpful with upgrades that will save you time. Now, you might be thinking to yourself, Wait, isn't Google IO just around the corner? Why are we getting announcements now? What we've actually seen is for the last couple of years, where there's so much that Google has to announce at their big IO event that they actually start dropping things the week before. I will say when they're introducing an entire new agentic suite in advance of the event, you gotta wonder what's gonna come at that event. But in any case, Gemini Intelligence will include a major upgrade to the Gemini Assistant, allowing it to handle more complex tasks and multi-step processes, as well as a new feature called Personal Intelligence, which is Google's AI memory system. Gemini Intelligence will roll out to the latest generation of Google and Samsung headsets over the summer and will become available on smartwatches, glasses and laptops later this year.
Speaking of laptops, also on Tuesday Google unveiled the Google Book, a new iteration of the Chromebook designed with AI in mind. The device will now run on a mix of Android and Chrome OS, allowing handset features to be easily migrated across. The Google Book will have a built-in AI Assistant built on the same Gemini Intelligence stack with a bunch of new modes of interaction. For example, instead of learning a new hotkey to summon the Gemini Assistant, users can simply jiggle the mouse and Gemini will pop up. DeepMind actually showed a research demo of where the concept of an AI enhanced mouse pointer is going. The demo showed a user gesturing with the mouse while giving voice instructions, asking the AI to do things like add these two ingredients to my shopping list without naming them. This seems directly related to the conversation we were having yesterday about interaction models where the next generation of AI interfaces is about having them interact more like we do rather than us having to learn how to interface with them. Now there is some competitive weirdness here, given that Google is powering the new Siri, but it also named its toolset Gemini Intelligence after Apple named its Apple Intelligence. This frenemy era is frankly very hard to keep a handle on.
Google is also the latest company taking a closer look at orbital data centers. The Wall Street Journal reports that Google is in talks with SpaceX to launch data centers into space. Sources said that those exploratory talks are also being held with other rocket companies, with Google planning to have their first prototypes in orbit by next year. While to some orbital data centers seemed like a fantastical sci-fi concept, there has been a major groundswell over recent months. As part of their recent deal with SpaceX, Anthropic seemed to express a genuine interest in orbital data centers as a way to get around land permitting issues. There's also been a huge surge in new startups pursuing the idea. This week, for example, Robinhood co-founder Bajubat unveiled his new startup, Space Cowboy Corp, and announced fundraising at $2 billion.
In an accompanying article, The Wall Street Journal asked whether data centers in space were just a pipe dream or the next big thing in AI, noting that even NVIDIA is getting on board, recently posting a job ad for an orbital data center system architect. This I think is going to continue to be a growing theme. But before we leave Google, there is one additional story on that front. Just a day after OpenAI confirmed their consulting plans, and a week after Anthropic announced their new initiative, Google is apparently jumping on the AI consulting bandwagon as well, with a new plan to hire hundreds of forward deployed engineers. The new group will be housed within Google Cloud as part of the go-to-market team. Google Cloud CEO Thomas Kurian wrote in a LinkedIn post, While having FTEs is not new for Google Cloud, the demand from customers and partners for Google Enterprise AI products and Google engineers to help them embrace agent development is growing very rapidly. In a separate post, Chief Revenue Officer Matt Renner noted that the way AI services are sold looks very different to traditional cloud services. He wrote that adding hundreds of FTEs would help Google show up for our customers with more technical resources versus just the notion of salespeople. Google is also apparently taking the fight directly to Anthropic and OpenAI with their own private equity partnerships. The Information reported that Google is in talks with Blackstone, KKR, and QT to deploy Google's AI products throughout their portfolio companies. The very clear takeaway is that the AI race is no longer just about model performance benchmarks but has a major new dimension in model deployment.