The Six Five team discusses Google Cloud Next 2024 event.
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Transcript:
Patrick Moorhead: Google Cloud Next 2024. What happened? I think it was a whole lot of AI.
Daniel Newman: Gosh, so much AI. Look, I want to talk about the macro of Google. So you and I have both said this. Look, Google’s had this weird stop start across their early launches and they’ve sort of had a couple fumble the ball moments. Now I want to give them a lot of credit because they had to fumble the ball moment in almost all of their first three launches in some way, shape, or form. And yet at the same time at Google Cloud Next, they absolutely came out and reiterated that their provenance in AI is so deep. I mean, the world’s most prolific and important data set by volume is with Google.
What does that create? It creates a massive opportunity to build AI capabilities in their cloud and of course across their entire ecosystem. Let’s be very, very clear what Google is able to do because it has itself as customer zero is build some pretty incredible AI capabilities in its cloud to be able to support its solutions, whether that’s going to be YouTube, whether that’s going to be search, the next generation of search, generative search. And so having the chance to go there, listen to customers, listen to executives, listen to Thomas Kurian, what blew me away. So first general compute offering Axion of course was this star of our show because Pat, we get more media inquiry I think, than anyone on the planet when it comes to chips.
But again, chips is the foundation. Silicon is what enables all this cool software. So all this generative capability, all the stuff we saw with workspace, with vids, with meet capabilities, of course Vertex for enterprise, for applications, what these companies are doing, it all starts with chips. Google has basically finally, or they’re moving away from kind of homegrown silicon just for their own purposes to now democratizing and offering homegrown silicon at scale. Who’s done this before? Well, they’re following the footsteps of AWS. They’re following the footsteps of Microsoft, which on the whole compute thing, the TPU though, of course, which they offered a new, their next generation clusters and of course, what is it v5, Pat? I’m trying to remember just because I’m talking so fast.
Patrick Moorhead: V5p.
Daniel Newman: V5p. You and I tweeted this. Probably our most successful tweet of the whole event. Dude, they trained their entire Gemini Pro model, their Gemini stack, all on their own hardware. Now, I don’t know about you, but I’ve been told many times by that same Twitterati group that also told me Intel’s going away, that the only way to train an LLM is on NVIDIA. And it turns out that Google has built full stack from hardware to software for LLMs all on their own engine, on their own silicon. Now Pat, this is really interesting and important because two things. One is they’re doing it for themselves, but now they’re democratizing it for customers. So customers are going to be able to use Vertex, democratize all the models. They by the way, don’t have use Google. This was a really important reiteration that they made is you can use them, but you can use Anthropic, you can use Hugging Face.
It’s a use your own adventure when it comes to LLM, but you can run it on TPU. I think this also reiterates my point about Intel and kind of the, everyone in the world is collaborating with NVIDIA like Google, but also has a very strong hedge to NVIDIA because they know they need it. But Google’s done an amazing job. They’ve built a very powerful accelerator, XPU, and of course they’re offering all the merchant’s silicon. We can talk about this all day. My last thing is Workspace. Pretty cool stuff coming out of Workspace Pat. I saw some of the capabilities with vids. I saw some of the next generation capabilities with Meet. I saw some of the next things in terms of document management and file management and generative content creation. Pretty blown away by what it can do.
Now, I’m going to tell you what I told Google’s executives about this is there’s still this little bit of a gap between these technologies and reality for me. Sometimes you see it like a vids Pat, like you and I, like all the hard work we do to put together a video for an interview or a customer, a reel, and they made it look like this can all be done in two seconds by uploading some stuff to photos and some stuff in G Drive and Docs. I’m really eager to see how well this stuff works. But the next like too long, don’t read, just read me, is that it’s changing quickly. This probably doesn’t work exactly as I need it to today, Pat, but in the next year or two years, so much of the sludge of work that we do with stuff, if we use Workspace, it’s becoming really competitive.
And by the way, when you see what they’re doing with Workspace and how much it could help an enterprise potentially like a co-pilot can, you also go, I get why they might want to try to buy HubSpot. And I know that’s kind of a rumor. We didn’t have a segment on that here, but that rumor has continued to persist throughout this week. Google has the best data. Google has the full stack from your enterprise data now. They can take your customer data, your advertising, your behavioral data, and then they can put it into a CRM. Is Google going to be the next big enterprise software play? It gets kind of interesting, but they’re doing some really cool things with generative AI. Great week.
Patrick Moorhead: Yeah, you pretty much talked about everything, Dan.
Daniel Newman: Did I?
Patrick Moorhead: I’m going to try to do some gap fills here. No, we all do that. We go back and forth. So one of my big takeaways, and again, I’m not always the sharpest knife in the drawer here, is this ability for agents or co-pilots to rewrite the enterprise SaaS market. If you have access to the data, and again, Apple didn’t say, sorry, Google didn’t say it out loud, but when you look at employee agent, well that could be a human resource agent. It could be a marketing demand gen agent, it could be a marketing brand agent. Right. Why do I need these CRM tools? Again, this is why it’s imperative that all the SaaS companies jump on this and integrate it quickly. I thought that was pretty interesting.
I continue to be impressed by Vertex AI. It seems easy, and again, I’m not a data scientist or a computer science person, but when I look at, I think communication is half the battle and it looks like the simplest end-to-end platform that I’ve seen, and them adding RAG capabilities to that and getting a little bit more respect for how enterprises use some of their data to do that. And by the way, even leveraging Google search in addition I think is pretty powerful. Like you, I was impressed that TPU v5 did all of the Gemini training that was out there. I got a lot of good shame out there on X saying,-
Daniel Newman: Your tweet was epic.
Patrick Moorhead: It said, “Hey, idiot. They already told people that.” Well, maybe I missed it. And you know what? Based on the hundreds of thousands of people who saw interest in that, I wasn’t the only one who missed that.
Daniel Newman: I did.
Patrick Moorhead: Yeah. Workspace, I got to tell you the RAG capabilities that they put in there, the ability to plop in 100 resumes, do compare and contrast, put in four proposals for, I don’t know, video production companies and to be able to create a grid and compare and contrast them. I couldn’t help but to think, gosh, is this a risk to Office? I went in and played with Copilot. Heck, Dan, I’ve got the highest grade Copilot I can get for Microsoft and I can’t even dump in files in like, I can even a GPT-4, or GPT-4 Turbo and competition is good baby.
Daniel Newman: Vertex is solid though, the Vertex workspace, like they’re off to a good start.
Patrick Moorhead: Yeah, for sure. I got to talk about Axion. Right. Three to four internal organizations using this general purpose CPU already. Google has the largest data state on the planet. They don’t get a lot of credit and Google Cloud folks don’t like to talk about that, which always kind of stymies me. The one thing I still think Google missed big and a lot of hyperscalers are whiffing on this is how do I leverage on-prem data? And the answers seem to be, oh, just take it to the cloud. I’ve talked to a total of zero enterprises that are interested in wholesaling, moving their data up to the cloud. So come on folks, let’s do this. I want to see a Google distributed cloud solution with infrastructure from the players who are in there to be able to do that, right?
And AWS is doing kind of a local zone strategy more than they are on their distributed cloud, but it’s still very frustrating to me. The final comment that I think Google needs to work on, and then you brought this out there, is the stumbles that they had with three of their launches, and I brought this on Yahoo Finance. That bleeds over to the commercial market. And I think that Google needs to do some hard work on proving that their AI is industrial strength, not necessarily coming up with odd images and some odd results. Anyways, I’ll leave it at that. Woo. Big event, baby.
Daniel Newman: There was some oxygen there because I kind of just breezed past Axion, I mean with Neoverse two and how it’s designed and how is it differentiated, and I think we need to come back and do a segment on this one because we should do something Pat in the future that kind of looks at Graviton Azure’s offering and of course what Google’s building to kind of talk a differentiation and what’s really the value prop because everyone got this kind of more performant, more sustainable, but I feel like it’s the same slide. What really is different and when do companies pick these things versus the merchant stuff that is still so pervasive?
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
