Last year, by the way, we did $2.6 billion, up quarter-to-quarter in revenue. That $17.3 billion number in that ballpark, you’re not too far off, is about equivalent to last year. It’s about $2.6 billion. But I would tell you one thing underneath that, Toni, is we’ve seen a very different dramatic change in the currency FX U.S. dollar strengthening. To tune about $300 million, if you go look at the math, back into ’19, 2020, 2021. So, underneath it, pretty consistent quarter-to-quarter performances last year, which by the way, I would highlight, was the peak of our ELA cycle last year and it was our strong first fourth quarter in a z16 environment. We’re going to match that even taking into account the FX headwinds overall that we called out going forward.
One last thing that I’ll put and then I’ll get into the color is, on a profit basis, although you didn’t ask that, historically, profit, we generate somewhere, if I remember correctly studying over the weekend, about $1.3 billion quarter-to-quarter. You look at our operating pre-tax margin, which we recommitted 0.5 point for the year, that puts you in a profit range of, I don’t know, $1.7 billion, $1.8 billion quarter-to-quarter. So, you see the fundamental operating leverage of what’s happening to our business. So, you put all that together, we still believe we have a very confident year at that 3% to 5%, by the way, led by Software, delivering 3 points of that IBM growth. Consistent, I would say, very good performance competitively in Consulting, driving about 2 points of IBM’s growth.
Infrastructure around the product cycle downside, we said beginning of the year and we’re consistent, it’s about 1 point hurt to IBM. And then, you got the divestiture impact of about 0.5 point. You put all that together, I think that’s a pretty good year overall and pretty much on top of our model.
Patricia Murphy: Okay. Sheila, let’s go to the next question, please.
Operator: Next we will hear from Matt Swanson with RBC Capital Markets. Your line is open.
Matt Swanson: Yeah, thank you for taking my question. It was great to hear about some of the quantifiable success that you’ve seen in generative AI so far. But maybe flipping that a little bit, whether it’s through early consulting engagements or customer conversations, where are you seeing some of the choke points that might hold up people from deploying as fast as they want? And whether it’s through foundation models or some of your other tools around the governance side, kind of how that’s developing your R&D to develop solutions for those problems?
Arvind Krishna: Yeah, thanks. Let me try to address your question here. Look, I think the concerns that people have are coming around, are these models accurate? How accurate are they? Do they result in things that may cause long-term liability? People are worried about the conversation because they hear and they read that whether it’s artists, whether it’s authors, whether it’s code writers are suing some of the producers of large language models. You alluded to governance. That comes more around life cycle and how do you carry it out over the long term. Coupled with this are some people’s concerns. If you add their own private data into a model, now what happens to the model? Where is it protected? Where does it stay? Do others learn from it?
So, if you begin to unpack all of that, we begin to say, all right, for models that are IBM produced, we will give you indemnification, meaning, we are confident in our ability to stand by the data we have used to train, what is being used to be output, and we’ll stand behind the same indemnification as we provide for all enterprise software. As you would expect, that’s hard for us to do for open source models. But we believe that that takes care and that is why we are so excited about code and customer service to start with, because that’s where we believe that people can benefit from this indemnification. Talking about the governance and the life cycle side, people are also worried about some of the long-term deployment, because an enterprise may well deploy a model for five or 10 years.
So, the governance tools that allow you to keep a lineage of the data, used to train a model, and then, for those adding private data, we give them a commitment that data stays with them and the refinements of that model go nowhere else, helps to mitigate some of the fears and uncertainty around those issues. I do believe that this is going to play out well over the next few years, but I also want to point out there are many cases where people are not worried about some of these risks. If you’re giving a quick website response, you could well use a model that may have some of these risks, because the danger in using any of those other areas is small. So that is how it’s playing out right now. I expect to see a lot of deployment in 2024 going into full production across the world of whether you use the word large language models or foundation models or generative AI.
Patricia Murphy: Great. Thank you, Matt. Let’s go to the next question, please.
Operator: Our next question comes from Ben Reitzes with Melius Research. Your line is open.
Ben Reitzes: Hey, thanks a lot. I appreciate it. My question is on Consulting. I was very surprised to see 32% bookings growth after 24% the prior quarter, 7% the prior quarter. It’s really diverging from your perceived rival in consulting [Accenture] (ph). And I was just wondering what the reasoning you would say is behind that. And then, Jim, with regard to those bookings, what does that do for your revenue visibility for Consulting, not only for the fourth quarter but for early next year? I would think the good book-to-bill might make you feel pretty confident about that 6% to 8% and continuing. Thanks a lot.
Jim Kavanaugh: Ben, thanks very much for the question. As I said earlier, pleased with the team overall in Consulting, not only on the signings and booking, but our revenue is well positioned for that 6% to 8%, which gave us confidence to reiterate that. But also the fundamentals of that business, right, we talked a lot about this over the last handful of years. We’re getting good operating leverage in that business, good cash contribution. So, all in all, pretty pleased overall. When you look at the bookings overall, I mean, let’s just put some statistics. Let me talk about kind of headwind, tailwind overall, because I think what you’re trying to get at is the confidence level Arvind and I and the team have about this book of business going forward into fourth quarter and into ’24.
We’re still seeing very good demand overall in areas around digital transformation, application modernization, and where there’s real technology value, productivity, cost efficiency, quick payback. Is there pressure on discretionary-based activity and that like? Absolutely. But you know what, we’ve seen that all year long. We haven’t seen really any substantive change in the macro and the client buying behaviors overall. But that has enabled us to grow 30%-plus. By the way, it’s strongest bookings quarter we had quite a period of time and now we’ve got a trailing-12 month book-to-bill of 1.16 I think the strongest in about two-and-a-half years overall. Now, underneath that, what’s going on? One, we’ve opened up IBM to strategic partnerships.
We’re seeing great ecosystem velocity and strategic partnership growth. Signings were about 50% year-over-year. Hyperscalers underneath that, 2x signings. Our hybrid cloud and application modernization, Red Hat, we signed north of $1 billion alone in the quarter. Now that’s $10 billion inception to date. We got acquisitions that are now accretive and scaling nicely. So, I think from that aspect, you look at the top-end growth, we feel pretty confident about the 6% to 8% for this year, and we feel confident about the value and the growth contribution of the growth factor of consulting in our book of business heading into 2024. Now, with that said, headwinds wise, yeah, listen to every other services consulting company, there are macroeconomic challenges, without a doubt, uncertainty, but we got to go out and compete every single day here.
