Anthropic's $40 Trillion AI Ambition and the Race for Control

One of the most anticipated headlines of the year, Anthropic's IPO filing, offers a revealing look at the business of AI from a $40 trillion view of knowledge work to rapidly advancing cyber capabilities. Meanwhile, both Anthropic and OpenAI are looking to deepen their hold on how the enterprise works, and Google is having a bit of a TPU moment. What should enterprise leaders make of those moves and what decisions do they change? As always, we're joined by Mike Taylor to help make sense of all of that.

It's Saturday, October 31st, and this is the AI Proven Ground podcast from Worldwide Technology. Let's jump in the Mike. And you know, I want to start with what it tells us about the company's ambitions versus the ambitions of its customers. Anthropic estimates that knowledge workers generated more than 40 trillion, that's a trillion with a T in economic value globally last year.

And the Frontier Model Provider thinks AI will expand that massive output in the coming years, and this is where it gets important. They want to participate in that growth. Growth, excuse me. There's an interesting article from the Wall Street Journal that really articulates how they arrived at that figure and how the calculus behind total addressable market, you know, has really evolved over the years.

But their math does lead to like a much bigger conversation, I think. Most businesses probably start with a budget for AI tools and subscriptions, but increasingly the pitch is about changing the cost of an entire business process or doing work the company couldn't afford to do before. In other words, businesses bring its people, data, and its knowledge of the business to making AI useful. But according to Anthropic, it sounds like AI suppliers are now pursuing a share of that output as well.

That was really long-winded. So I'm going to get to you now. How should leaders actually be thinking about how much of the resulting value of AI belongs to the technology provider and how much of it should be retained by the business? Yeah, I it one, it it validates a huge market that we can go after here together.

And those efficiencies, I think, are in large part still to be recognized within our enterprise customers. I think the the consumer technology team moving from you know things that we're playing around with into what we're calling kind of workforce to applied AI solutions within our customers. I don't know, you know, uh 40 trillion is the right number, but but it's massive. And I think very little of it is going to be derived from kind of borrowing or stealing from other markets, if that makes sense.

I think we're talking about you know real productivity gains on top of things that we're already already using. We've proven out, I think the SAS pocalypse or the collapse of those things is is not necessarily, you know, completely replaced with or or taken over by this $4 trillion market that they're talking about. And then the the the robotics layer of that or the physical AI layer of it and superintelligence related to that, I think is is another piece of it. So I think about this the $40 trillion market as a function of market value that they're creating, not just for the you know, for the the models and and frontier providers, but what that's going to mean to ultimately, I think the the cash flow and productivity of businesses as they get more efficient, to me, is is the most interesting part.

And you know, we've seen announcements, whether it was going back, and these are just things out there in the public domain, but companies that are leaning into this in terms of investment, you see Eli Lilly, what they're doing with with AI and what they're doing in conjunction with NVIDIA and Jensen's support. You see what that's done to you know their market valuation just as one little example. And I think you could start to wrap your head around that this is tens of trillions of dollars of opportunity.

How those pieces come together, I think this is a good framework, probably for us to pressure test over the next, you know, three to five years in terms of exactly how it shakes out. But you have enough examples to point to here that that it doesn't seem irresponsible to think that we're tens of trillions of dollars that that are out there to be chased after. Yeah, and you know, these very large numbers are not alone with just anthropic. I mean, certainly the the the most recent uh string of earnings reports that we've seen come in from the likes of Microsoft, from the likes of Alphabet.

I mean, these are solid double-digit growth figures here, and yet the market is still a little bit jittery in tell it in terms of how it reacts. Is that mostly from the you know, the infrastructure investment side of things, or or how do you make sense of that? Yeah, I you know, we were we were talking this week. I think, you know, we're we're in the spend money to make money phase, right?

And you know, a bit of this in in a supply-constrained market, the commitments that companies are having to make to get access to power and shell are are scary, I guess, you know, as you think about it from a market perspective. The the part that that I balance in that though is that yes, while these these companies were, you know, their their stock prices were impacted this week, it has far more to do with how the investment side of their business is impacting their cash flow side.

But you know, I again as an optimist, I look at this and say, look, you know, record growth within Microsoft and and Alphabet. Amazon, it's hard to say record growth, just you know, in in terms of the overall business, but AWS itself, including these these offerings, was up, I think, 39 or 40 percent. So if you looked at it, you said, okay, cash flow and and the the impact of cash flow based on debt that they're that they're creating and that they're gonna have to service into the future is gonna require that they continue to grow at these sorts of rates or more.

And I do think it is a little bit of a you buy these assets, you secure these assets, and anthropic is an example of this. You can support, you know, their their growth quarter over quarter is is you know 400, 500 percent. And you start to think if these traditional cloud providers had access to the power and shell and some of the infrastructure that they had out there, they have an opportunity to do the same. Now, they don't have the model platform and some of the other pieces that are out there, but as open weights become more important as the ability to develop new models, and as we look at this space between GPU as a s as a as a service and traditional SaaS, I think we're gonna have a lot of room for very industry and function-specific models that are gonna get created.

And the Microsoft's Alphabets, Amazons of the world, along with some of those NeoCloud providers, are going to profit from that. They're going to be able to host it based on being able to lean in first on the PowerShell commitments to run the infrastructure. So, yeah, I get why the hit is there. We talked about this even as it related, you know, a few weeks ago here to Core Weave.

This is for me far more of a valuation pause, perhaps, than it is an indication that fundamentally the business model here is broken and that we're in some, you know, some massive, massive bubble. Yeah. Yeah, well, certainly in any case, Anthropic's economic ambition, you know, it's starting to take shape along with others. Anthropic's IPO set for mid to late November.

Meanwhile, another interesting part of that filing shows how these providers intend to become more involved in the work that we're all doing itself. Anthropic described an increasingly integrated business between the models, the development platform, and applications like Cloud Code or Cowork. I mean, effectively, Anthropic is saying it's providing both the intelligence and more of the tools employees use to get work done. Now, that can make AI easier to deploy, certainly, but it also makes the business relationship with Anthropic deeper and potentially harder to unwind than simply switching models.

You know, meanwhile, on a similar track, Oracle is giving us another example. Its newly announced AI workspace is designed to let employees describe a business goal inside of ChatGPT, which brings together connected Oracle applications, data, and tools to help accomplish all that. Like the anthropic integrations, this could save employees from navigating a lot of separate systems. But as I already mentioned, it also means our relationship with an AI provider extends well beyond which model answers our questions.

Now, optionality and flexibility have been well-used keywords in our discussions that we've had over the last few weeks. Where does optionality matter most as these platforms take on more of the work and how do you preserve it without giving up any of the benefits of that integration? Yeah, I think we're, you know, we we've seen this with other SaaS providers. I think ServiceNow, Salesforce, and you know, now Oracle in that front, they're gonna have to place some bets as they are with frontier model providers that are going to create uh frontier-like experiences within their SaaS or ERP or regardless of where they're hosted applications.

And so to me, this is an indication of progress. It's an indication that, you know, building a true frontier model uh versus uh you know hosting systems that provide core value like SaaS and Oracle and others do, they've gotta they've gotta place their bets there. Oracle's building out massive infrastructure to support these capabilities. There's a two-sided economy here, I think, in in all cases with these providers where they're not only consuming those services, but they're building out infrastructure at scale that's going to participate in the in the development of the models, you know, as well, that that that's happening there.

And I I I think the bigger question for uh for us and for our customers are you you have ERP systems, you have data sets that you have out there, and they live in all sorts of different platforms. Architecturally, you're going to have AI capabilities that reside natively within applications. And you also, though, need to be thinking about and architecting for AI that's going to persist and utilize data across disparate data sets, systems that maybe aren't naturally connected into a business process flow within your organization, because you want to gain insights.

You want to create an omni-channel experience for your customer. You want to take customers in a maybe a commercial banking sense, that you also want to be consumer banking customers. And so figuring out how you connect those things, I think, is going to be the real opportunity beyond what's going to be embedded into Oracle's Fusion, Net Suite, and other database products that they offer out there. So lean into that.

I think it's good good for all of us that they're investing in that, but also understand, I think there's going to have to be a bigger picture and a broader architecture to get to where where we're starting to aspire to think about how we run and operate our businesses. Yeah. Keeping with Oracle is a little bit of the pull-through here. I mean, these platform decisions, they sit on top of another set of choices.

One of them being where does the computing actually happen and what does it cost? Oracle's announcements this week extended into that layer. They announced plans with Google to bring Google's TPUs, those are their purpose-built AI chips, to Oracle cloud infrastructure beginning next year in 2027. Now, TPUs have been used internally by Google for a long time, and some Google Cloud customers have had been using them uh for over the last several years.

But this deal would make Oracle, you know, one of the first hyperscalers outside of Google itself to offer them. The information, you know, meanwhile, reports that OpenAI plans to buy TPUs, while Enthropic has disclosed substantial commitments to TPUs as well. For enterprise leaders, Mike, I think you know, there are two ways we could think about this directly, if if you're operating your own models or through the price, performance, and availability of the AI services that you know we might be buying.

Are these choices something that business leaders need to be evaluating directly or something that we should expect an AI provider to manage for us? I I think we're gonna have we're gonna have circumstances where that we'll be using these, you know, at the end of the day, we'll be using these models in in some of these clouds, and we're not even gonna know what they're running on, and frankly, we're not gonna care. Our costs will be a function of, you know, the cost of the model and the cost of the infrastructure that's there.

And and, you know, TPUs are, I think, you know, Google's been very open about this. Their entire Gemini suite runs on it. They have the opportunity between DeepMind and the model development and then the hardware orchestration, figure out how these things operate and function you know, in a in sort of a harmonized fashion between the software and the hardware that they're running on. The the piece with the TPUs, I think Cerebrus is in this boat, you know, NVIDIA is leaning into this as well.

Is we're gonna have high performance architecture consumed within these data centers, whether they're ours or they're hosted or they're you know, NEOs, whatever. But I think more so we're we're seeing these use cases evolve to where we we want or need that compute to be closer to the consumer or the point of data generation, for lack of a better term. And all of these, you know, you know, TPU solutions, the cerebrus stuff that's out there and the investments that NVIDIA is making are gonna give us optionality in conjunction with, but also outside of you know the physical AI and robotic circumstances to where I I think you know it it's reasonable to believe you're going to have some form of high performance architecture running inside of a branch, an office, a uh a clinic or surgical hospital.

You know, like that that that to me makes a ton of sense, and these are going to be options to be able to do it. I personally feel like you know, Google's delay in bringing the TPUs to market was more just the excessive demand that they had internally to build out Gemini and and all the capabilities that have gone into their consumer and office productivity products, that they maybe they're at a point now where they're like, okay, we can share some of this with other people because it's it's it's being adopted at a very fast rate now that it's available in the you know out to to portions of the consumer and enterprise markets.

Yeah, quick follow-up in that regard. I mean, over the last few weeks, you've you've alluded to a good foundational mindset, which is to choose infrastructure around the work, evaluate it in your own environment, and expect the right deployment mix to change as adoption grows. I mean, more options give customers more ways to match the infrastructure to whatever workloads they're running, but they can also introduce another decision into an AI journey that's already complicated.

I mean, some companies are still figuring out where AI delivers value, if at all, others are running enough of it that performance and cost are becoming much bigger considerations. If an organization came up to you today, Mike, and asked whether they should be looking at TPUs, what would you need to understand about their business or their infrastructure or their workloads before you could give them some type of useful answer? I I think it's a couple things. I think the first is right now, you know, what what are the use cases that you're trying to take on would be a big one.

The second would be, you know, the the TPUs, the the products, you know, AMD and and NVIDIA, and you know, they they all have products now that are more call them inference focused. And also I would say, you know, facilities and infrastructure friendlier, if that makes sense. And so, you know, if if it's taking the the big data center providers two years to get a generator, it's probably gonna take you know, me as an enterprise or or even some of the Fortune 500 as an enterprise, it's gonna take you two years to do that.

And then you're also fighting the supply-demand curve with companies where their their business is actually providing that power and shell. And so, how do you compete? So what we see in most of our our our use cases uh in customers, about 80% of their demand today is more in the inference side versus training. That doesn't mean that won't change, but you know, as you think about providing inference, you have a lot more options there in terms of the you know intensity by which you had.

And some of the questions will come down would would you rather have no inference or would you rather have some inference and then figure out how you sort of retro fit that into data center facilities and spaces that you have, limiting your capital exposure to do so, and now offering, call it on-premise, sovereign to you, your country, your organization capabilities. And and I think that's that's where a lot of this stuff is going to be headed over the next six to twelve months.

The gap between the frontier models and the open weight models, we talked about that continuing to close. We just released this week, I was on with our teams this week on harnesses that we built to try to test these models out against different use cases from development to sentiment analysis to language processing and other capabilities, where our teams are pretty impressed with with how all models are performing, but particularly just the the the distance closure between between the open and the club.

So all those things to me lead to on-premise in some way, shape, or form, not everything, won't be everything. And and I think all of these provide those options. Yeah. Well, I mean, in any case, these infrastructure decisions, they're going to depend on where a customer is today and what they're ready to take on next.

But I mean, important to note that time is not always a luxury that we have in hand, and nowhere is that tension more palpable than in cyber. Back to the Anthropic S1. Enthropic said Claude's success rate on exploit bench. That's a test of its ability to exploit software vulnerabilities, rose from 40% to 91% in roughly four months.

I mean, fair to note, you know, we already know that you know newer models are better at exploiting weaknesses than earlier models. We've talked about automating and patching and compressing maintenance windows probably on every single episode that we've had here together. I suppose we'll keep hammering that message home until it's less of an urgency. But I want to bring up an interesting wrinkle in this that I heard from Nikesh Aurora, the Palo Alto Network CEO, who was on the all-in podcast earlier this week.

Nikesh, who who by the way will be with us next week when we're at the World Wide Technology Championship, uh, he'll be a speaker at our business innovation summit. So shameless plug, go uh and watch that on the golf channel the tournament and won't be able to have the innovation summit. We'll release those videos at a future date. But he was discussing the recent PHP security incident.

He described a customer that could patch some websites while others were running software so old that fixing them required taking them down and rebuilding them all together. And he warned that incidents like this could become increasingly frequent, putting more pressure on organizations that already struggle to remediate them. Now, Nikesh is describing a different problem than simply, you know, the patch faster mentality. He's saying some systems may need to be, you know, rebuilt from the ground up.

How do you decide, Mike, or how should organizations decide which of those systems can be protected, you know, temporarily even, and which can we can no longer you know wait. We may have to just rebuild or get a replacement. Yeah, I uh he he did a great job, first of all. And I think it it was a good balance of people, process, and technology, and you know, and what they're what they're talking about.

Because we can instrument these harnesses and even helping some of our our customers respond to this PHP issue that that they've been dealing with. The first barrier we run run into is we can run all these scans and figure out, okay, where does this vulnerability pr exist? What we then run into is who who actually owns that system. You know, it is it something, you know, we we had one example we were working on with a customer and we we found a a whole host of things that we we think weren't actually their devices.

They were just someone who had sort of attached to their architecture and and done some registration in other countries around things. So it's we can we can run these things, do these patches, but uh things like who owns the system, who has the you know, the credentials, where are they stored? Are they in our secrets password management? Are they off in some other area?

And so the hygiene that goes into this, I I cannot underscore how good, strong process hygiene just feeds into machine speed like capabilities. Because you can push the button proliferate that out, but you gotta have your secrets management taken care of, you gotta know who owns these applications, and you're pressed a bit here where if you can't figure those things out quickly, then you're faced with a decision of do you turn it off? You know, do you do you just have to to quarantine it, turn it off, and and see if somebody says something, which is a worse spot to be in.

So I I think that's where we are, and and the you know, the the PHP is an example of that. We had a couple other zero days that came out this week as well on on perimeter-based technology providers that what that's the coaching we're given right now. You know, if you had to do this tomorrow, could you actually get in touch with or find the people who understand what this system is doing and be able to verify that it's functioning properly after we patch it and reboot it? Excellent.

And for those that are interested in what Nikesh has to say at the Business Innovation Summit, we'll be sure to uh release at least a portion of that session that should be uh fantastic. We'll release it here on the AI Proving Ground podcast. Uh Mike, I know we're up on time here, uh, so I did want to be able to let you go. Wish you a happy Halloween.

Are you a full-size candy bar uh giver outer or what's your what's your strategy? Yeah, you know, we we do all right in the neighborhood. Yeah, yeah, we do all right. Uh we we've got uh I'm a big arm and joy fan.

Nobody seems to like those, so this is the best time of year. Yeah, no, I'll bring all mine to the office and hand them over to you because those things are disgusting. But Mike, appreciate the time, and uh, we'll see you next week.