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IBM and Grock are announcing a strategic partnership to give clients ultra high speed, low latency AI capabilities via Grock's inference technology. For more and how this partnership is going to provide greater access to the full potential of enterprise AI, We're joined by Rob Thomas, Senior Vice president of Software and Chief Commercial Officer at IBM, and Jonathan Ross, CEO and founder of GROC and Jonathan, I want to start
with you. You know, the way that I look at this is it's a very interesting go to market channel for you, a sales channel. Think about all of the clients that IBM has and how you've tried to grow the company. Explain how people will access LPUS through this or through the cloud matrix.
Absolutely, it's an extraordinary opportunity for both of us. IBM is going to have their sellers sell Grock SKEW and so now you'll be able to directly access our speed. The advantage which is that we offer you could think of it a little bit like offering broadband in the era where dial up wasn't fully rolled out and people were still trying to connect to the internet. Our lpus are just significantly faster, but we also keep the cost down.
Just imagine if you were to offer broadband and you charged more per bit of data that was sent over the line, it would be on economical Broadband increases a demand. With agentic use cases, it's particularly important to reduce the speed. You don't want to ask a question, wait ten minutes later and come back. You'd rather get the answer under a minute.
Rob Under this arrangement with Jonathan, does IBM make any sort of financial investment into GROC or is there some kind of sales or revenue split. Explain the economics of this deal for you guys.
Big pictures.
We have a lot of momentum in AI with Watson X, as we said on our earnings last quarter seven and a half billion dollars as a book of business, and we're trying to solve the client problem of how do.
They deploy AI faster.
So this partnership is all about what Jonathan said, which is five x performance at twenty percent of the cost. We've seen it with Watson X running on GROC and so we will be distributing GROC as part of our go to market and there's a revenue share as part of that. We are really excited because we've seen clients already getting an impact to how they're deploying AI because of the integration of our technology together.
Let's talk about that, Rob a little bit more. Because you're the man who's in charge of the software business, you're also really responsible for the world revenue and profitability of your company. So help us understand why grow was the obvious choice. How is it helping your clients get outs as faster? On the inference side of.
Things, we looked at every possibility in the market, and the clients are looking for significant performance, so some of that changes how your call center operates or how you're supply chain runs, and then you combine that with a fraction of the cost.
Suddenly the economics makes sense.
AI does have a cost problem, and we think this breaks through that. And IBM we've said we're going to drive four and a half billion of productivity by the end of this year. That's another example of AI truly having an impact. And the number one question I get from clients now is how are you doing that at IBM and can you help us do that? And we think the combination of IBM and GROC can make this a reality for any company.
Well, let's dig into that a little bit now with you, Jonathan, because the integration with what's the next orchestraate? What does that look like on your side? How does that happen and happen seamlessly?
So the wantsonex API is available for anyone to use today, It'll be invisible to most users. It'll simply work. We have a compatible API and this is something we've been working on. We will also work on some lower level integrations with VLM, which is a technology that IBM is very deeply involved in. But it should just be transparent. You should just get more speed. Just imagine one day you come home, you had dialogue and now you have broadband in a cost less.
Rob where's the demand coming from on your side, like IBM, Granite or some other agentic workload that they want to run using the GROC lpus. Are these public sector names? Are they private sector SMEs? I'm trying to understand who you're serving with it.
As often happens, I would say financial services have been early adopters. But the thing that has changed in the market in the last six months is everything is moving to multimodel. We have IBM models that we open source, which are the Granite models. We announced the partnership with Anthropic. We have a partnership with Mistraw and Lama, just to name a few. What is incredible about what Jonathan and team have built is any model can run and get instant improvement run.
The LP used from ROCK.
So I think this is a combination of a multimodel world accelerating inference with ROCK.
I think this is a great combination.
Jonathan, does this capacityority exist or are you supply constraints still? You've got to go out and build it either in Saudi Finland here in the States.
So the entire world is supply constrained, and I would actually expect that to continue for at least the next five to ten years when it comes to AI. Our advantage is that we have a supply chain that actually ramps much faster, so customers will be able to come to IBM, put in an order and we will be able to fulfill that faster than you would be able
to with other technologies. But the supply constraints that are real, and this is another reason to start working with IBM sooner the sooner you get access to that capacity, the sooner you're going to have it. I can't tell you how many startups come to us and other companies come to us and they are looking for capacity, because some of them are actually growing twenty or even thirty percent per week or per month, which is an astronomical growth rate.
But by approaching us early, we can build to your needs.
You were just mentioning Rob about all the partnerships you have when it comes to llms and the offerings that you're intertwining within yours. Will you go to others to ensure that inference is as fast as possible or is it this exclusive with GROC.
We are open to working with anybody in the ecosystem of AI around what we're doing specifically on the acceleration with GROC. We want to lean into this partnership. That's why this is the one that we've announced today, because we have confidence working together with GROC. As Jonathan mentioned, we're also enabling some of the lower level technologies and open source like VLM. So this is the right place
to be when it comes to inference. But when you think broadly about what's happened in AI, we have many companies working with us on agents. Last week we announced SMP Global is now running on Watsonnext Orchestrate as an example.
So we're always open to new partnerships.
And let's just talk about Jonathan the go to market strategy here of teaming with the age old Juggernau that is IBN, that has so many deep relationships across global enterprises. But is that how you're going to work this going forward? It is teaming up with companies that have those legacy relationships, or do you still go out there and win the business yourself.
So I would say this is a peanut butter and jelly sort of relationship in the sense that oftentimes when we meet with sea level executives, those sea level executives turn to their tech teams and ask them to evaluate GROC. And I've been in meetings where the CTO did that and the response from the person is I already use GROC. It's my default for everything. So we already have the bottoms up. We have two point three million developers already
building on us. For comparison, open Aie has four million now going to those deep relationships from IBM, and the fact that IBM is a trusted partner who's been delivering for decades. You put those two together and that's an amazing go to market motion.
Wow, it's been great having you both on to talk about the go to market strategy. Jonathan Ross, CEO Grock, of course, Rob Thomas of Senior Vice president of Software over at IBM. We thank you both very much.
Indeed,